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Über dieses Buch

This two-volume set (CCIS 848 and CCIS 849) constitutes the thoroughly refereed proceedings of the 5th International Conference Geo-Spatial Knowledge and Intelligence, GSKI 2017, held in Chiang Mai, Thailand, in December 2018.The 142 full papers presented were carefully reviewed and selected from 579 submissions. They are organized in topical sections on smart city in resource management and sustainable ecosystem; spatial data acquisition through RS and GIS in resource management and sustainable ecosystem; ecological and environmental data processing and management; advanced geospatial model and analysis for understanding ecological and environmental process; applications of geo-informatics in resource management and sustainable ecosystem.

Inhaltsverzeichnis

Frontmatter

Advanced Geospatial Model and Analysis for Understanding Ecological and Environmental Process

Frontmatter

Feature Point Detection and Target Tracking Based on SIFT and KLT

The Scale Invariant Feature Transform, SIFT, has good ability to detect very stable feature points. But at present, there are very little researches on SIFT in our country, and most of them are concentrated in the areas of Image Registration and Image Stitching. In this paper, SIFT and KLT will be combined for feature points detection and tracking. First, SIFT algorithms is used to detect stable feature points, and then the KLT method is used to track the feature points. The experimental results show that the new method provides a good method in the field of feature points detection and tracking.

Huajing Zheng, Changchang Chen

Research on the Handwriting Character Recognition Technology Based on the Image Statistical Characteristics

The research on the image pattern recognition has always been a hot topic. In this paper, the automatic identification technology of image is studied, the research contents include image preprocessing, image feature extraction and image content identification. BP neural network is used for the research on the image content identification. The processing methods in this article include the following steps, first, the pre-processing of the image. Including image de-noising and feature extraction; second, training the BP neural network with the processed handwriting character image; third, the recognition test of the unknown handwritten character. 95% recognition accuracy is realized, and the research has some practical application value.

Yongfeng Sun, Zhonghua Guo, Weijiang Qiu

A Listwise Approach for Learning to Rank Based on Query Normalization Network

Learning to rank is one of the hotspots in the intersection between information retrieval and machine learning. In the traditional listwise approach for learning to rank based on the neural network, the model predicts the score of each document independently, which cannot reflect the link between those documents associated with the same query. To solve the problem, this paper proposes a new ranking neural network model called Query Normalization Network (QNN). In QNN, normalization is added as a part of the original neural network model to perform the normalization operation for each query sample collection. Through this operation, the prediction scores of documents returned by the same query are also associated with each other. Then, this paper proposes a listwise approach called Optimizing Normalized Discounted Cumulative Gain (NDCG) Query Normalization Network (OptNDCGQNN) which based on QNN and directly optimize the evaluation measure NDCG. OptNDCGQNN use QNN as model and Stochastic Gradient Descent (SGD) as optimization algorithm to optimize an upper bound function of the original loss function, which directly defined according to the evaluation measure NDCG. Experimental results show that OptNDCGQNN has better ranking performance than other traditional ranking algorithms. It also show that when the amount of training data is large enough, OptNDCGQNN can enhance the ranking performance by training deep neural network.

Chongchong Zhu, Fusheng Jin, Yan Li, Tu Peng

Soft Frequency Reuse Scheme with Maximum Energy Efficiency in Power Telecommunication Networks

This paper, we investigates the energy efficiency optimization problem in SFR-based cellular networks. To the end, we uses divide-and-conquer method to improve network energy efficiency. We build an optimization model with an objective function denoting energy efficiency of networks, which is a fractional program and very hard to be solved directly. To solve the model, we transform the objective function into another form. Then we utilize the Lagrange function and dual function to obtain the optimal energy efficiency with the gradient method updating the transmitting power allocation. Finally, we make a numerical simulation to validate the algorithm proposed. The simulation results show that the performance of our method is feasible.

Lina Cao, Daosheng Li, Fei Xia, Xiaobo Huang, Siwen Zhao, Shuang Liu

Mining High Utility Co-location Patterns Based on Importance of Spatial Region

Co-location pattern mining aims at finding the subsets of spatial features whose instances are frequently located together in geographic space. Most studies mainly focus on whether spatial feature instances are frequently located together. However, the utilities of spatial instances in different space regions are different. Based on the importance of spatial a region, the utility value of the region is determined, and then a utility participation index of co-location patterns as a new interestingness measure is defined. We present a basic high utility co-location pattern mining algorithm. To reduce the computational cost, an improved mining algorithm with pruning strategy is developed by cutting down the search space. The experiments on synthetic and real world datasets show that the proposed methods are effective and efficient.

Jiasong Zhao, Lizhen Wang, Peizhong Yang, Hongmei Chen

Analyzing Community Structure Based on Topology Potential over Complex Network System

Community structure is one of complex network properties which reveals the main organizing proposition in most real-world complex networks. The special interests are groups of vertices within the intense edges or connections that are not only overlapping, but also change over-time. In this paper, we present the overview of structured complex network properties that affect the process of discovering community structure. Topology potential of nodes in complex network is also described. Topology potential is a measurement method to investigate the interaction among community members. From the recent literatures, the community structure discovered by topology potential needs to be improved in term of performance and accuracy in order to obtain more meaningful results.

Kanokwan Malang, Shuliang Wang, Tianru Dai

Static Detection Method for C/C++ Memory Defects Based on Triad Memory Model

The improper use of pointers in C/C++ programming language brings about a lot of memory-related issues. In this paper, causes of four kinds of memory defects are analyzed and summarized. Besides, a novel triad memory model has been proposed. Based on the model and the variable life cycle methodology, an approach for inner-procedure and inter-procedure detection has been presented too. Eventually, the prototype CAnalyzer is implemented on the basis of Clang static analyzer. Experiment results show that CAnalyzer can effectively detect the four types of memory defects.

Yuxia Wang, Fusheng Jin, Xiangyu Han, Runan Wang

An Immune Neural Network Model for Aeroengine Performance Monitoring

In this paper, an aeroengine performance monitoring and fault detection model, based on immune neural network, is put forward. By combining artificial immune system recognition mechanism with artificial neural network, the deviation degree of aeroengine performance (abnormal degree) can be determined, and the monitoring of performance trend can be achieved. With this method, the overall performance change of aeroengine can be reflected sensitively and accurately, the abnormity recognition rate of aeroengine performance can be enhanced, and potential early engine fault can be detected to prevent further development. This method is proved effective through the monitoring of a certain type of turbofan aeroengine.

Wei Wang, Shengli Hou, Jing Guo

Based on AHP and Minimum Spanning Tree of Fuzzy Clustering Analysis of Spatial Sequence Arrangement of Old Dismantling Area

This paper discusses the application of AHP, minimum spanning tree and fuzzy cluster analysis in the spatial arrangement of the old dismantling area. A fuzzy clustering analysis method based on minimum spanning tree for spatial sequence arrangement of old dismantling area is proposed. An accurate method is provided for spatial arrangement of old districts. Take lion town in Chengde city of Hebei Province as an example. Establishing suitability evaluation index system of old dismantles area. The weight of each evaluation index is established by using AHP. In view of the characteristics of the spatial sequence of the old dismantling area, the old dismantling project area was established. The minimum spanning tree is used to do the fuzzy clustering to arrange the space of different old demolition projects. The reliability analysis shows that the fuzzy clustering analysis based on minimum spanning tree is a reliable method for the spatial arrangement of the old dismantling area.

Juanmin Cui, Wenguang Ji, Yang Jae Lee

An Improved Method on the Wave Height of Ocean Surface Based on X-Band Radars

It contains plenty of ocean wave and sea surface current information in the sea clutter images formed by X-band marine radar’s echo. Applying the method to calculate the significant wave height from the SAR imagery, which supposes the significant wave height in linear relation with the square of the signal-to-noise ratio of radar images, the significant wave height has been obtained from estimating the images of X-band radar. The experimental data were analyzed in the Small Mai-island sea area. Firstly comparing the effect of filtering direct current versus estimating result, deriving the significant wave height estimated by counting the signal-to-noise ratio after filtering direct current which is match better; then according to wave height measured by wave buoy, analyzing low and high wave height to do linear fit and gain calibration coefficient separately, the significant wave height evaluated is all the more precise.

Yi Wang, Mingyuan He, Haiyang Zhang, Jingjing Ge

Short-Term Operation Optimization of Cascade Hydropower Reservoirs with Linear Functional Analysis

In the operation optimization of cascade hydropower reservoirs, upstream reservoir outflow is usually taken as downstream reservoir inflow, which influences generation scheme accuracy when flow time-lag cannot be neglected. In this study, based on linear space mapping and bounded linear functional theory, the relationship between upstream reservoir outflow and downstream reservoir inflow is quantized. Their corresponding map functions and functional operators are proposed considering river channel storage capacity. Additionally, a short-term generation optimization model of cascade hydropower reservoirs considering flow time-lag is established, with no impact on the objective function and water balance. Furthermore, to verify the feasibility and effectiveness of the model, it is applied to the short-term generation optimization of Jinping-Guandi cascade hydropower reservoirs in Yalong River. Results tally more with the actual operation process compared with the original scheme. This work can increase generation scheduling accuracy and provide reference for short-term operation of cascade hydropower reservoirs.

Yanke Zhang, Jinjun You, Changming Ji, Jiajie Wu

Digging More in Neural World: An Efficient Approach for Hyperspectral Image Classification Using Convolutional Neural Network

Classification of hyperspectral images (HSI) can benefit from deep learning models with deep architecture in remote sensing. In this letter, a novel method based on Convolutional Neural Network (CNN) is proposed for the classification of hyperspectral images. Due to using more spatio-spectral features for the classification of hyperspectral images, the proposed method outperforms the existing state-of-the-art classification techniques. Our proposed method first reduces the dimension of hyperspectral images using Principle component analysis (PCA). The spatial and spectral features are then exploited by a fixed size convolutional filter to generate the combine spatio-spectral feature maps. Finally, these feature maps are fed into a Multi-Layer Perceptron (MLP) classifier that predicts the class of the pixel vector. To validate the effectiveness of our proposed method, computer simulations are conducted using three datasets namely Indian Pines, Salinas and Pavia University and comparisons with existing techniques are made.

Adnan Iltaf, Matee Ullah, Junling Shen, Zebin Wu, Chuancai Liu, Zeeshan Ahmad

An Intelligent Cartographic Generalization Algorithm Selecting Mode Used in Multi-scale Spatial Data Updating Process

In multi-scale spatial data updating process, cartographic features vary dramatically with the scales evolution. So, it is the critical step to select suitable cartographic generalization algorithm which can perfectly fulfill the scale-transformation task. This problem is also a main obstacle in the way of automatic spatial data updating. Through deeply studying the flows of multi-scale spatial data updating process, an intelligent cartographic generalization algorithm selecting mode is proposed. Firstly cartographic generalization algorithm base, knowledge base and case base is built in this mode. Secondly, based on the step of resolving the cartographic generalization process into segments, a self-adaption cartographic generalization algorithm selecting architecture is constructed. Thirdly, an intelligent cartographic generalization algorithm selecting and using flow is established and put into effect. Overall, this mode provides a new idea to solve the automatic problem of multi-scale spatial data updating.

Junkui Xu, Dong Li, Longfei Cui, Xing Zhang

A Cross-National Analysis of the Correlated Network Structure of Marine Transportation in the Indian Ocean Rim Association

Taking the countries within Indian Ocean Rim Association as the research objects, this paper analyzes the network pattern of shipping alliance consisting of 21 member countries from 2006 to 2016 through the Circos Graphical Model. The results indicate that the countries with strong position in the shipping field spread out dense network, especially playing a boosting role to lead those with relative low position. The association of maritime transportation in the alliance is transformed from marine correlation between close range and small scope group to the regional broad trading association. The gap of maritime transportation between different countries has gradually narrowed, and the marine transport network with uniform and dense coverage has gradually formed. All these suggest that the countries of the vulnerable group in IOR-ARC are supposed to transform the old preference of internal shipping in close and small group into long-distance association. In the contemporary days, it is necessary for low-level nations to improve the construction level of port facilities while high-level countries should make full usage of the “Network Sprawl” opportunities to strengthen the tightness of connectivity.

Shuguang Liu, Xiaoxin Yang, Han Zhang

A Software Reliability Combination Model Based on Genetic Optimization BP Neural Network

The software reliability model is the basis for the quantitative analysis and prediction of software reliability. In recent years, neural networks due to its generalization and learning ability have been widely applied in the field of software reliability modeling. However, the slow convergence and local minimum of neural networks may cause unsuccessful prediction. Therefore, this paper presents a software reliability combination model based on genetic optimization BP neural network. This model uses three classical software reliability models as the input of BP neural network, and then uses the genetic algorithm optimization to automatically configure and optimize the weight and the thresholds. The results of experiments show that the model proposed has better fitting effect and prediction ability than other similar models.

Runan Wang, Fusheng Jin, Li Yang, Xiangyu Han

Practical Experience of the Use of RGB Camera Images in UAV for the Generation of 3D Images in the Accurate Detection Distance of Vegetation Risk in Right-of-Way Transmission Line

The Brazilian National Interconnected System has an extensive power transmission grid, where the lines are mostly located far from the urban centers and in difficult access areas. This makes inspection and maintenance more expensive and complex. In this sense, it is important to monitor and apply technological innovations in terms of equipment (sensors, cameras), telecommunication systems and vehicles, which aim at reducing costs, reducing environmental impacts and increasing reliability. This work will investigate the use of 3D modeling of a training transmission line stretch using RGB images captured using sensor coupled in unmanned aerial vehicle. It is a first investigative step for the specific purpose of detecting, with high accuracy, the vegetation along the right-of-way. Exemplifications and conclusions are presented at the end of the paper.

Mauricio G. M. Jardini, Augustinho José Menin Simões, José Antonio Jardini, Jose Mauricio Scovino de Souza, Ferdinando Crispino

An Exploratory Study and Application of Data Mining: Railway Alarm Data

The railway industry generates large data but there are few researches on railway data analysis. The paper presented an exploratory study and application of data mining from railway alarm data. The railway alarm data is analyzed to find the correlation between alarm items and between railway bureaus when alarm occurred and predict the alarm occurring. The paper proposed an alternative measurement mode with three values: support, Kulc and balance to mine the correlation from alarm data analysis, and the results finally indicated the very possibility of associated railway bureaus.

Yichuan Yang, Hanning Yuan, Dapeng Li, Tianyun Shi, Wen Cheng

Research on Smooth Switching Technology of UAV Complex Flight Control Laws

In order to solve the switching problem between multiple complex control laws during the autonomous flight of Unmanned Aerial Vehicle (UAV), a smooth switching method through series recurrence is proposed to obtain the integrator initial value. Firstly, all links of complex control laws are transformed formally and split into the basic units consisted of the proportion and integral. Secondly, all integrator initial values of complex control laws are deduced in turn to realize the aim of surface smooth switching, which realizes the smooth switching between the different control laws. Simulation verifies that the smooth switching method of multiple complex flight control laws is effective.

Xianwei Hao, Aiqun Xiao, Duo Li, Ying Wang

Study on the Spatial and Temporal Pattern of Qinghai Lake Area in the Past 50 Years

Lakes are not only the essential water resources for life, but also the sensitive indicators of climate change. The change of Lake area has a direct impact on the regulation of ecological environment. The area changes of shoreline were analyzed by using Multi-source Satellite Data and water identification model of Qinghai Lake in the past 50 years, the main conclusions are as follows: (1) The monitoring accuracy of Qinghai Lake area can significantly be improved by using multi-source high satellite data; (2) The experience threshold model based on the combination multi band with single band has a higher recognition accuracy of water when monitoring the area of the Qinghai Lake; (3) In recent 50 years, although the Qinghai Lake area of the stage showed an decreasing trend, since the beginning of twenty-first Century, the Qinghai Lake area increased, especially significantly in 2010, the Qinghai Lake area has been close to in 1995. But overall, still showed a decreasing trend; The analysis of area change in dry and wet season showed that the Qinghai Lake area were increased (R2 = 0.805 and 0.861, P < 0.01) in April and September from 2001 to 2015, since 2005, increasing trend is more obvious; What it is clear changes by the use of environmental mitigation satellite data in Qinghai Tibet Plateau the changes can be used to fine the monitoring area of Qinghai Lake monthly and seasonal occasion. (4) In the past 50 years, Qinghai Lake shoreline change has a large difference between North and south. Qinghai Lake on the west coast of Shi Nai Hai - Heather skin, Quanji river north of Hukou near the east coast of Salix River into the lake and the island Haiyan Bay, lake shoreline retreat is most obvious.

Baokang Liu, Yu’e Du, Weiguo He, Shuiqiang Duan, Tiangang Liang

An Algebraic Multigrid Preconditioner Based on Aggregation from Top to Bottom

In aggregation based algebraic multigrids, the current schemes are to construct the grid hierarchy from bottom to top, where several nodes on the finer level are clustered into a node on the coarser level step by step. Therefore this kind of scheme is mainly based on local information. In this paper, we present a new aggregation scheme, where the grid hierarchy is formed from top to bottom in a natural way. The adjacent graph of the original coefficient matrix is partitioned first, and then each part is recursively partitioned until some limitations are met for a certain level. Then the grid hierarchy is formed based on the global information, which is completely different from the classical ones. When partitioning graphs, any kind of method can be used, including those based on coordinate information and those based on the element of the matrix only, such as the methods provided in the software package METIS. Finally, the new scheme is validated from the solution of some discrete two-dimensional systems with preconditioned conjugate gradient iterations.

Jianping Wu, Fukang Yin, Jun Peng, Jinhui Yang

COKES: Continuous Top-k Keyword Search in Relational Databases

Keyword search in relational databases has been widely studied in recent years. Most of existing methods focus on answering snapshot keyword queries in static databases. However, in practice, relational databases are always being updated continually. Reevaluating a keyword query using existing methods after the database is updated is prohibitively expensive. In this paper we describe the COKES system, which keeps the set of answers whose upper bounds of future relevance scores are larger than a threshold for top-k answers maintenance. Experimental results show that the proposed method is efficient in answering continuous top-k keyword queries in relational databases.

Yanwei Xu, Yicheng Yang

Core Competencies Keywords Discovering Algorithm for Employment Advertisements

As librarianship evolves, it is important to understand the changes taken place in its core competencies. One good way to do this is to analyze job advertisements (ads) for professional librarian positions. Most related works are based on manual method; the semi-automatic framework requires a classifier consisting of manual rulesets as input. In this paper, a framework and a multi-label short text clustering algorithm, ICNTC, are proposed to automatically identify core competencies from job ads. Data from the American Library Association (ALA) Joblist from 2009 through 2014 is used to validate the method. The analysis of experiment results shows that the method may identify most of core competencies, with a good performance in evaluating the frequency of each competencies. The accuracy of keyword extraction on ALA dataset is 89 ± 1.3%.

Xiaoping Du, Lelai Deng, Xingzhi Zhang, Qinghong Yang

A Clothing Image Retrieval System Based on Improved Itti Model

Aiming at the problems of Itti visual attention model like inadequate feature extraction, complex feature synthesis process and feature incompatible with existing retrieval system, a better Itti model is proposed to improve the low-level visual features, image segmentation and interesting area in this paper. And then the improved Itti visual attention model is introduce to content based Clothing image retrieval system, the experimental results show our system has obvious advance on the accuracy of retrieval effect than the existing similar system.

Yuping Hu, Chunmei Wang, Hang Xiao, Sen Zhang

Study on a Kind of War Zone Equipment Material’s Urgency Transportation Problem for Multi-requirement Points

Equipment material’s transportation is one of the most important part in the theater of equipment support work, which decides whether the whole theater’s equipment support could be fast and efficient to carry out and successfully completed. Considering the complexity, rapidity and danger of the equipment transportation in the wartime situation, two optimization models of emergency transportation of multi-demand and multi-cargo equipment under different time factors are established. The mathematical model is deduced, and the analytic algorithm is given for the two models. The correctness of the model and the validity of the algorithm are illustrated by concrete examples.

Peng Dong, Peng Yu, Kewen Wang, Gongda Yan

A New Algorithm for Classification Based on Multi-classifiers Learning

Quality and quantity are the two key factors to influence the accuracy of classification. In order to improve the classification accuracy, in this paper, we propose a new algorithm, called CMCM (Classification based on Multiple Classifier Models), which consists of two classification models. In Model1, we mainly focus on the improvement of quality, thus the best attribute value from both the items and their complements in the training set is selected as the first item of a classification rule. While in Model2, quantity is taken into consideration, so it constructs two candidate sets and uses the one-versus-many strategy to generate several rules at one time. The experiment results show that: (1) Model1 can extract sufficient high quality rules and achieve high classification accuracy. (2) Model2 can extract sufficient information and achieve high classification accuracy. (3) CMCM can achieve higher classification accuracy compare with traditional classification.

Yifeng Zheng, Guohe Li, Wenjie Zhang

An Information Distance Metric Preserving Projection Algorithm

This paper proposes a novel dimensionality reduction algorithm. The algorithm, coined information distance metric preserving projection (IDPP), aims to identify the complicated intrinsic property of high dimensional space. IDPP employed geodesic information distance to evaluate the relationship between each pair-wise data points. It yielded a distance preserving projection to map sample data from high dimensional observation space to low dimensional feature one. IDPP preserves intrinsic structure of high dimensional space globally. It possesses explicit projection formula which makes it easily to be used for new sample data. Unsupervised and supervised approaches constructed on the basis of IDPP was evaluated on financial data. Experimental results show that trustworthiness of IDPP is almost the same as ISOMAP, and it performs much better than the rival algorithms.

Xiaoming Bai, Chengzhang Wang

Bug Patterns Detection from Android Apps

Android has become the most popular OS because of its user-friendly environment, free-ware licensing and thousands of available applications. It is an open source for contributors and developers. The challenging problem in Android apps is to handle the bugs those are generated because of code segment (code constructs) written by developers to fix the reported bug. so code change management is also as critical task, as bug tracking. We have investigated all available previous history of Android bug reports and code changes to identify bug introducing changes. Apply the chi square test to observe the buggy construct. This study will help the reviewers, contributors, developers and quality assurance testers to concentrate and take special care while making or accepting changes to those constructs where it is most likely to induce a bug, which will lead to improve the quality of services provided by Android platform, and ultimately will get more satisfied user.

Waheed Yousuf Ramay, Arslan Akbar, Muhammad Sajjad

An Improved PHD Filter Based on Dynamic Programming

Traditional PHD filter for detecting and tracking weak targets does not work well in the case of low detection probability. In this paper, an improvement of PHD filtering based on dynamic programming is proposed. The method takes advantage of the correlation among the multi-frame data. The result of dynamic programming is applied to PHD filter for getting stable detecting and tracking effect. Monte Carlo simulation results show that the improved method is superior to the PHD filter under low detection probability.

Meng Fang, Wenguang Wang, Dong Cao, Yan Zuo

Type Analysis and Automatic Static Detection of Infeasible Paths

Infeasible paths are a common type of defect in software testing, which can cause failure of software system and lead to problems about software reliability and safety. In this paper, infeasible paths are divided into three types, which are control infeasible paths, logic infeasible paths, constraint infeasible paths. For each type, details and examples are given to find out the defects of infeasible paths during software testing. In order to improve the detection efficiency, automatic static detection method is given based on three types of infeasible paths. The experimental results show that the proposed method can detect the infeasible paths of code accurately and effectively.

Fuping Zeng, Wenjing Liu, Xiaodong Gou

A New Perspective on Evaluation Software of Contribution Rate for Weapon Equipment System

In view of the difficulty of quantitative evaluation of the contribution rate for each member in the equipment system, the challenges of the rate assessment for system contribution were analyzed in detail, and the basic concepts and main contents of the system contribution rate assessment were expounded. In the context of system confrontation, the main key technologies were analyzed. A new perspective of quantitative assessment of system contribution rate was put forward from the perspectives of capability, mission, structure and evolution. In terms of evaluating the role and contribution rate of equipment in the operational system, the method has achieved relatively good results. The results provide a new solution for the research on the equipment system and the contribution rate assessment.

Huadong Yang, Fang Liu, Yongdun Yan

Research on Sentiment Analysis of Online Public Opinion Based on Semantic

In this paper, we combine the traditional analysis method based on sentiment dictionary and two kinds of text sentiment based on semantic pattern. We then propose an improved text sentiment analysis technology, including constructing an emotional dictionary, and designing 4 kinds of calculation rules based on dependency syntax and 3 kinds of calculation rules based on complex sentences. Finally, we construct the emotional semantic relation tree to calculate the value of text sentiment. Experimental results show that the accuracy rate, recall rate and F-measure of our method are significantly better than traditional algorithms.

Zhengtao Jiang, Lu Liu

A New Method of Dish Innovation Based on User Preference Multi-objective Optimization Genetic Algorithm

With the improvement of living level, people put forward new requirements for the diversification of diet and greater demand for new dishes. However, it is hard to make food collocation to meet specific requirement, since there are too many foodstuffs, while their nutrition ingredients and incompatibility are not well known to the ordinary people. To solve this problem, food collocation and dish creation to meet the user’s requirement or preference are studied in the paper. First, the data of food composition are collected, the different food guides are referenced and the food component incompatibility is studied. Second, a food nutrition evaluation model is constructed and an improved non-dominated sorting genetic algorithm is proposed. A probability operator is introduced, by analyzing the existing recipes, to control the number of foodstuffs of a dish. A strategy to model user preference is also proposed and the non-dominated solutions are filtered by using the preference model. Third, the experiments are carried out and the experiment results show that the proposed algorithm and nutrition evaluation model can meet the requirements of user preferred dish creation and multi-objective optimization, and has better convergence speed than the original algorithm.

Zijie Mei, Yinghua Zhou

Algorithm for Calculating the Fractal Dimension of Internet AS-Level Topology

A box-covering algorithm to calculate the fractal dimension of Internet topology at AS-level was introduced. The algorithm first selects some nodes that have big degree and put them into different boxes, and then uses the node as seed in each box to cover the network. The purpose is to ensure that the boxes to cover the network are as little as possible. By analyzing a large number of the actual measurement data of AS-level topology, we found the relationship between the number of the nodes that were first selected as seeds and the size of the network. The number of the boxes to cover the network obtained by this algorithm is very close to the minimum number of boxes needed to cover the entire network. The results show that the algorithm can get the near-optimal solutions to cover the Internet network at AS-level without an exhaustive search, and thus effectively saves the time for calculating the fractal dimension of Internet topology at AS-level.

Jun Zhang, Hai Zhao, Wenbo Qi

An Improved GPSR Routing Algorithm Based on Vehicle Trajectory Mining

The taxi GPS trace data has great potential value for the development of intelligent transportation. By analyzing the data, the social attributes of vehicles can be found, and the excavated information could play a guiding role in VANETs routing designing. In this paper, an improved GPSR Routing Algorithm based on Vehicle Trajectory Mining algorithm is proposed. The algorithm can effectively improve the routing performance by eliminating the unreliable forwarding node and improving the perimeter forwarding strategy in the GPSR algorithm by comparing the social attributes of these nodes. The simulation experiment shows that our algorithm can improve the packets delivery ratio and reduce the average end-to-end delay.

Peng Zhou, Xiaoqiang Xiao, Wanbin Zhang, Weixun Ning

Design and Implementation of a Self-powered Sensor Network Node

For the applications of Wireless Sensor Networks (WSN) randomly deployed in the complicated environment, the most important challenge is to design sensor networks that require long system lifetime and some metrics for the Quality of Service (QoS). Therefore, in this paper, we proposed the self-powered WSN node system based on MC9S12XS128 and LTC6803-4, designed the hardware circuits like charging and discharging of lithium ion battery pack, temperature measure, voltage acquisition and equilibrium control, compiled the corresponding underlying software, compared the accuracy of the sampling values and measured values of battery voltage, charging and discharging current, battery temperature, and tested the effects of equilibrium control. The test results showed that the designed photovoltaic charging management system was safe and reliable, which could provide stable energy sources for WSN node in future.

Jun Jiao, Moshi Wang, Lichuan Gu

Mining Association Rules from Multidimensional Transformer Defect Records

There are various types of transformer device defects and the formation reasons are complex. Exploring the influencing factors and occurrence of transformer devices defects is a focus in the field of power transmission and transformation devices state inspection and evaluation. This paper proposes an analysis method, multidimensional FP-Growth algorithm (MDFPG) to mine association rules from multidimensional transformer defect records. The method combines records from different system of power grid to construct multidimensional records first. Then, the records are preprocessed and encoded into single dimension form. The MDFPG method speeds up the mining process by adding a pruning step. Experiments show that MDFPG method has a better performance than FP-Growth algorithm on large data sets. Some conclusions can be learned from the experiment result, which has a certain value for making equipment maintenance plans and exploring defect occurrence regularity.

Yi Yang, Yujie Geng, Yi Ju, Xuan Zhao, Danfeng Yan

A Modeling Algorithm to Network Flows in OTN Based on E1 Business

Recently, Optical Transport Networks (OTN) have been extensively deployed and applied in communication networks. Compared with traditional transport networks, OTN can provide much larger traffic transport ability. However, the properties and characteristics of network flows in OTN are not deeply studied and this is still a larger gap between theory analysis and practical applications. This paper studies the modeling problem of network flows in OTN. We propose a Walsh transform-based modeling method to describe end-to-end traffic amount of network flows in OTN. Firstly, the end-to-end traffic is denoted as a independent identically distributed random time-varying series. Then the Walsh transform theory is used to characterize the end-to-end traffic of network flows. By calculating the corresponding parameters, the proposed model is build correctly. Simulation results show that our approach is feasible and effective.

Fei Xia, Fanbo Meng, Zongze Xia, Xiaobo Huang, Li Song

Computing Offloading to Save Energy Under Time Constraint Among Mobile Devices

The recent advancement in wireless communication has motivated increasing number of mobile applications, including computing-intensive tasks. However, it takes resource-limited mobile devices a lot of energy to execute these tasks. Computing offloading is helpful in the scenario, where mobile device offloads part of the task to available devices. In this paper, we propose an algorithm AOA (Alternately Optimizing Algorithm) to alternatively optimize task and power allocation in order to achieve the minimum system energy consumption under given time constraint. KM (Kuhn-Munkres) algorithm in graph theory is adopted to get the optimal task assignment. And we get the optimal solution for power allocation via mathematical derivation. Simulations have shown that the proposed algorithm can give a global optimal task and power allocation solution.

Xiaomin Zhou, Yong Zhang, Tengteng Ma

A New Weighted Connection-Least Load Balancing Algorithm Based on Delay Optimization Strategy

The load balancing problem of edge computing networks is researched in this paper. Edge nodes can process information collaboratively, which may reduce the workload of the cloud data centers, and improve the quality of experience of users. A new weight connection-least load balancing algorithm based on delay optimization strategy with the user time constraint is proposed. A new weight setting method of server is put forward to measure the performance of servers, which can adjust the data forwarding times of each edge node as soon as possible. Experimental results show that our method can improve the performance of edge computing networks significantly.

Guangshun Li, Heng Ding, Junhua Wu, Shuzhen Xu

An Extensible PNT Simulation Verification Platform Based on Deep Learning Algorithm

In this paper, a simulation platform for multi-source PNT (Positioning, Navigation and timing) means is designed. The platform, which adopts a distributed simulation architecture to transmit data through the network, provides a test environment for multi-source PNT users and performs performance evaluation. Through in-depth learning of the output data of various PNT tools collected by different types of users in different scenarios, the platform can achieve parameter settings of multi-source PNT based on user characteristics. Seen from the test results can give the simulation platform, weight value will affect the comprehensive results, deep learning can make the weight value distribution is more close to the human judgment through a large amount of data to judge the artificial experience, so as to realize the autonomous PNT multi-source fusion.

Shuangna Zhang, Li Tian, Fuzhan Yue

A Binary Translation Backend Registers Allocation Algorithm Based on Priority

As most binary translation systems don’t consider the difference of register requirements of basic blocks, which brings redundant memory access instructions caused by unnecessary registers overflow. To solve this problem, a binary translation backend registers allocation algorithm based on priority (BTBRAP) is proposed. Firstly, local global register is allocated statically to reduce the global register maintenance overhead, according to the statistical features of registers on the source platform. Then, the number of every register requested in basic blocks is determined, according to the relationship between intermediate representation and the source platform registers. So the priority of registers allocation is obtained. Conclusively, allocate the registers dynamically based on the priority to reduce the registers overflow. As the test results of nbench, representative recursive programs and SPEC2006 show, the algorithm effectively reduces the redundant memory access of local code, and improves the program performance with an average increase of 7.94%.

Jun Wang, Jianmin Pang, Liguo Fu, Zheng Shan, Feng Yue, Jiahao Zhang

Applications of Geo-Informatics in Resource Management and Sustainable Ecosystem

Frontmatter

A New Information Publishing System for Mobile Terminal by Location-Based Services Based on IoT

In this paper, a new information publishing system based on Internet of things is proposed. It mainly consists of three parts: the screen side, the service side and the client side. And all of them connect each other by network. The screen side has realized the internet of screens in which geographically dispersed independent screens are connected to the internet by the customized set-top boxes. The server cluster provides high performance application services and manages the promptly increasing multimedia publishing information to users on the service side. Meanwhile MQTT protocol is deployed to implement a lightweight broker-based publish and subscribe messaging mechanism in our constrained environments to solve the bandwidth bottleneck. On the client side, the mobile terminal user can release information with dedicated software by location-based services. The paper has realized a prototype SzIoScreen, and give some related test results.

Li Zhu, Guoguang Ma

An Improved Spatial-Temporal Interpolation and Its Application in the Oceanic Observations

An improved spatial-temporal interpolation method, which can eliminate suspicious interpolated values effectively and consider both direction anisotropy and angle-based weight, is proposed to overcome the insufficiency of the traditional spatial-temporal interpolation. By using the traditional and improved spatial-temporal interpolation respectively, different weekly products of ocean temperature fields from surface to 2000 m are reconstructed in the Pacific Ocean for interpolated test based on the Argo observations, and then the reconstructed products are validated in comparison with other gridded product and in-situ observations. The results show that the error of reconstructed data by the improved method is smaller than that of the traditional method apparently, and the improved interpolation can eliminate the suspicious estimated data effectively and improve the interpolated results greatly. The improved interpolation method is very effective to interpolate scattered data onto regular grids for data reconstruction.

Huizan Wang, Ren Zhang, Hengqian Yan, Shuliang Wang, Lei Liu

The Spatial-Temporal Simulation of Mankind’s Expansion on the Tibetan Plateau During Last Deglaciation-Middle Holocene

Attributed by vast territory, extremely harsh physical environment, and comparatively integrated geographical unit, the Qinghai-Tibet Plateau has become a valuable site to investigate adaptation regime of prehistoric human to extreme environment. Which is currently focused on single dependent site in most studies, where a integrated research that coves complete scope the plateau is needed. to better understand expansion logic of prehistoric human moving towards the Plateau. This paper build a comprehensive index to indicate the characteristics of natural environment by using GIS software, which is composited with elevation, vegetable type, level of river system, and accumulated temperature of 0 °C etc., combined with the archaeological ages of 69 gathered microlithic sites, followed by environmental adaptation and spreading hypothesis, namely the doctrine of the time of human expansion was broadly consistent in the regions where the natural environments were similar. We simulated the spatial and temporal process of prehistoric human’s migration and expansion on the plateau during Last Deglaciation- Middle Holocene. The results of our study indicated that during the Last Glacial Maximum(24–16 ka B.P., LGM), human activities were very weak, which is mostly likely distributed in the Huang River Valley area of northeastern margin of Tibet Plateau and Yarlung Zangbo River valley of southern Tibet, where elevation was 1,640 m on average; During 15–13 ka B.P., the microlithic hunter-gatherer activities became strong, which had expanded to Qinghai Lake-Gonghe basin of the northeast of Tibet Plateau, and Hengduan Mountains valleys in the east, where elevation was 2,800 m on average. The area of activity region accounted for 5.5% in the Tibet Plateau. Also, during the periods of 13–11 ka B.P., prehistoric human expanded towards higher zones along the valleys of Yellow River, Ya-lung River, Yangtze River and Yarlung Zangbo River, where elevation was 3,658 m on average, the increased expansion area accounted for 11.4% in the Tibet Plateau. The expansion was relatively obvious; During 11–9 ka B.P., with the rapid improvement of environmental conditions, hunter-gatherers expanded to the principal part of the Tibet Plateau. The elevation of expansion area was 3,971 m on average. The increased expansion area accounted for 11.5% in the Tibet Plateau. During 9–7 ka B.P., the human expansion speed was the fastest. During the periods, the expansion area was wide, and the area was maximum in the period. Human activities rapidly expanded to the hinterland of the Tibet Plateau, including the northern Tibet Plateau, the source regions of the Yangtze and Yellow Rivers and Kunlun Mountains. Many regions among them were depopulated zones nowadays. But the locations where some microlithic sites were found, The increased expansion area accounted for 52.2%, in the Tibet Plateau during 9–7 ka B.P. The elevation of expansion area was 4,700 m on average during the periods. And the expansion and occupation of prehistoric human towards the Tibet Plateau was basically completed by then. But regions (accounting for 19% of the plateau) with extreme environmental conditions, such as cold mountain area, desert plateau in Northern Tibet, salt desert in Qaidam Basin had not been occupied. Migration and expansion of prehistoric human towards Tibet Plateau in prehistoric period occurred multiple times. The human expansion during 15–7 ka B.P. was oriented by hunter-gatherers, which happened from the west to the east, from low to high, from the margin to the principal part of Tibet Plateau. The environmental evolution acted as important driver of hunter-gatherers expansion towards the Tibet Plateau during the Last Deglaciation- Middle Holocene. Yellow River and Yangtze River valleys were important passages of prehistoric human expansion towards the Tibet Plateau.

Tianyun Xue, Changjun Xu, Sunmei Jin

Remote Environmental Information Real-Time Monitoring and Processing System of Cow Barn

Hot and humid weather in southern China lead the modern dairy farming into trouble. If the temperature, humidity and illumination of the cow house, and the noxious gas including carbon dioxide, ammonia and hydrogen sulfide from the breaths of the cows and decomposition of the organic can be monitored in the real time, the trouble because of the weather in the modern dairy farming can be solved. So this paper focuses on the wireless network, though applying the MSP430F149 and Nrf905 chips as the wireless network module, and LTM8901 as the sensing devices, the real-time monitor and processing system for the cow house can be established to solve the trouble of modern cow farming in the south of China. This system has low operating cost and stable property so that it is extremely fit for the hilly terrain in the south of China. Meanwhile, it can be also used in monitoring environmental pollution in the real time.

Faquan Yang, Chunsheng Zhang, Ling Yang

The Application of Big Data Technology in Competitive Sports Research

With the coming of the big data era, a new proposition that how the competitive sports system should conform to the times development is put forward. Based on the analysis of the importance of big data technology in competitive sports, this paper puts forward a competitive sports analysis framework based on big data analysis. Opportunities and challenges faced by competitive sports in the age of big data are illustrated with concrete examples.

Xiaobing Du

UMine: Study on Prevalent Co-locations Mining from Uncertain Data Sets

We can collect a large amount of spatial data by utilizing sensor positioning technology and wearable devices. However, most of the acquired data are uncertain because of the gaps in data collection or to maintain subject privacy. Thus, we investigate co-location pattern mining problem in the context of uncertain data. The prevalent co-location pattern under uncertain environments has two different definitions. The first definition, referred as the expected prevalent co-location, employs the expected interest degree of co-location to measure whether this pattern is frequent. The second definition, referred as the probabilistic prevalent co-location, uses the probabilistic formulations to measure frequency. Here a novel system called UMine is proposed to compare this two different definitions with a user-friendly interface. The core of a system such as this is the mining algorithm, and UMine is integrated with the expected mining method, probabilistic mining method, and approximate mining method. In this paper, the system is introduced in detail, and the comparison between these two types of definitions is implemented. The experimental results show that the difference between these two definitions’ result sets changes as the threshold changes. By flexibly adjusting the parameters, users can observe interesting patterns in the data. In addition, the demonstration provides data generation and preprocessing function while showing its practicality for either real-world or synthetic data sets. The study can also provide support for the further uncertain Co-location patterns mining research.

Pingping Wu, Lizhen Wang, Wenjing Yang, Zhulin Su

Research for Distributed and Multitasking Collaborative Three-Dimensional Virtual Scene Simulation

Currently most research of virtual scene simulation focus on the establishment algorithm of three-dimensional terrain model in the scene, but rarely consider the communication mechanism, multi-thread synchronization, dynamic loading issues of the distributed three dimensional virtual scenes. In this paper, the simulation engine based on vega prime is achieved by multi-threading technology, and communication protocol is implemented by winsocket technology, then the network architecture with high cohesion and low coupling control/running terminals are built. Combined with dynamic loading model reuse technology, three-dimensional virtual scene system based on distributed network communication is achieved. Based on the algorithm interface reserved in the system, the multitasking cooperative swarm intelligent pathfinding algorithms can be integrated. Then the communication model, multi-thread synchronization, dynamic loading and pathfinding problems are solved in the distributed multitasks three-dimensional visual scene system. The simulation results show that network data communication and the routing algorithm simulation are realized in three-dimensional virtual environment, and the effectiveness and feasibility of the algorithm can be verified, thereby the cost and risk of late operation are reduced.

Jing Zhou

Comparisons of Features for Chinese Word Segmentation

Machine learning based approach is the most important one among Chinese word segmentation, and feature selection is very crucial to it. This paper overviews feature sets used in a few machine learning based approaches for Chinese word segmentation closed task. The comparison of these feature sets is made on the SIGHAN corpora and the same machine learning framework – maximum entropy model. Based on this model, two new efficiency measures are presented, i.e. the numbers of unique events and predicates. The experimental results for the impacts of feature sets on effectiveness and efficiency are shown, according to which the suggestion for feature selection is presented when building machine learning based Chinese word segmentation system.

Xiaofeng Liu

Forecasting of Roof Temperature in a Grey Prediction Model with Optimal Fractional Order Accumulating Operator

Building roof temperatures mainly affected by solar radiation. With the solar radiation intensity changing, the change of roof temperature also occurs constantly. It has an uncertainty to a high degree, so the grey system can be combined with data analysis for researches. Based on the classical NDGM model, this paper introduced the fractional order $$ NDGM^{{\tfrac{q}{p}}} $$ N D G M q p model to study the important properties of the model and used the PSO particle swarm optimization algorithm to optimize the fractional order. Finally, the two representative measuring points, namely, the maximum solar radiation and the second solar radiation points, were taken as the experimental objects. The experimental results show that the mean absolute percentage error (MAPE) of the optimized fractional order $$ NDGM^{{\tfrac{q}{p}}} $$ N D G M q p model for the roof temperature is several percentage points higher than that of the classical GM, DGM and NDGM models, as well as the minimum error of the model can reach 2.4247%.

Yuan Zhang, Xiaoyong Peng, Wei Hu

Wa Language Syllable Classification Using Support Multi-kernel Vector Machine Optimized by Immune Genetic Algorithm

A novel Wa syllable classification method based on multi-kernel support vector machine (MKSVM) optimized by immune genetic algorithm (IGA) is proposed in this paper. First, use vowel main body extension (VMBE) to extract the first dynamic characteristic parameter, pitch frequency. Then, use adaptive variational mode decomposition (AVMD) to extract the second dynamic characteristic parameter, formant frequency. Next, extract the mean values, standard errors, minima and maxima from the pitch frequency sequence and the first three formant frequency sequences respectively. Again, the feature sets with the mean values, standard errors, minima, maxima and label information, are inputted to IGA optimized MKSVM for analysis mode identification. Theoretical analysis demonstrates that MKSVM can approximate any multivariable function. The global optimal parameter vector of MKSVM can be rapidly identified by IGA parameter optimization. The experimental result of Wa syllable classification shows that, the proposed method significantly increases the accuracy of syllable classification and enhances the generalization of its application, and that, therefore, is feasible and effective on Wa language syllable classification.

Meijun Fu, Wenlin Pan, Hua Yang, Huazhen Dong

A Novel Method for Detecting the Degree of Fatigue Using Mobile Camera

This paper presented a novel method for detecting human fatigue using mobile camera and cloud techniques. Photoplethysmography technique and detrended fluctuation analysis (DFA) method are used to fatigue detection. The experimental results confirm the correctness of the proposed method. The proposed method has realistic significance.

Qing Yu, Ludi Wang, Ying Xing, Xiaoguang Zhou, Wei Zhou

WPNet: Wallpaper Recommendation with Deep Convolutional Neural Networks

The recommendation quality of new users plays an increasingly important role in recommender systems. Collaborative Filtering cannot handle the cold-start problem, while the content-based approach sometimes can achieve recommendation with new items. To recommend in the wallpaper field, this paper proposes a content-based recommender system and extracts the features of wallpaper via the deep learning approach. The first part of the recommendation model is the convolution layers, and the model takes the output of full connection layer as features to employ. In order to improve the scalability, the model adopts deep neural network as non-linear dimension reduction method to reduce the image features. Taking the recommended results into account, this paper compares the feature similarities of user images and those in the image library. Finally, the model sorts them via cosine similarity, and presents the recommendation results using Top-K list. In the experiment, our model is trained with selected wallpapers on MIRFLICKR dataset, and uses VGG on ImageNet for feature extraction. The experimental results indicate that WPNet will have higher hit rates with different K if the image division of some wallpapers can be improved, and achieve a better performance in less time under the recommendations of new items.

Hang Yu, Quan Cheng, Jiejing Shao, Boyang Yu, Guangli Li, Shuai Lü

Equipment Maintenance Support Decision Method Research Based on Big Data

Facing huge amounts of data in the field of equipment maintenance support, this paper studies the technique of equipment maintenance support data analysis and decision, formed information analysis technology and relevant methods for stages of Data acquisition, data integrate, theme data, online analysis and data mining, which is theory and practice basis of equipment maintenance support data analysis and decision support method research; It puts forward the trinity system framework for the technology of data storage, analysis and show. Through constructing safe and reliable storage environment, designing intelligent and efficient analysis algorithm, provide intuitive display form, it provides technical support for scientific decision based on the data.

Ziqiang Wang, Yuanzhou Li

Research on a New Density Clustering Algorithm Based on MapReduce

The empirical solution parameters for the Density-Based Spatial Clustering of Applications with Noise(DBSCAN) resulted in poor Clustering effect and low execution efficiency, An adaptive DBSCAN algorithm based on genetic algorithm and MapReduce programming framework is proposed. The genetic algorithm (minPts) and scanning radius size (Eps) optimized intensive interval threshold, at the same time, combined with the similarities and differences of data sets using the Hadoop cluster parallel computing ability of two specifications, the data is reasonable of serialization, finally realizes the adaptive parallel clustering efficiently. Experimental results show that the improved algorithm (GA) - DBSCANMR when dealing with the data set of magnitude 3 times execution efficiency is improved DBSCAN algorithm, clustering quality improved by 10%, and this trend increases as the amount of data, provides a more precise threshold DBSCAN algorithm to determine the implementation of the method.

Yun Wu, Zhixiong Zhang

Bounded Correctness Checking for Extended CTL Properties with Past Operators

Bounded semantics of a certain temporal logic is a basis to develop bounded model checking algorithms for finding errors in system designs. Traditional temporal logics are used in formal verification to predicate about the future evolutions of dynamic systems, both with a linear time model or a branching time model. This paper we further study bounded semantics of extended CTL formulas with past operators (PeCTL) which not only allows some sort of fairness but also could reason about the past behaviors of the systems being verified. Since QBF (quantified boolean formula) is exponentially more succinct in expressing specifications, we thus develop a QBF encoding of PeCTL from the proposed bounded semantics. Finally, we present a bounded correctness checking algorithm for PeCTL formulas and apply the bounded semantics of PeCTL to derive a QBF-based characterization of PeCTL properties.

Fei Pu

A Cloud Based Three Layer Key Management Scheme for VANET

A Cloud Based VANET (Vehicular Ad hoc Network) is a convenient vehicular communication network which is commonly susceptible to various attacks. Many key management schemes for VANETs are presented to solve various security problems. Threshold secret sharing, ECC and Bilinear Pairing computation is efficient approach for the key management design. And PKI is also excellent key management method. In this paper, the above approaches are adopted to construct three layer key management schemes for VANET in order to realize secure communication. After constructing the security structure, we carefully analyse the security performance and efficiency of the scheme in detail. In the end, the conclusion is drawn.

Wanan Xiong, Bin Tang

An Evaluation Method Based on Co-word Clustering Analysis – Case Study of Internet + Innovation and Entrepreneurship Economy

Co-word clustering analysis can be used to discover new trends of socio-economic behavior. In this paper, several key words for youth innovation and entrepreneurship under the internet crowdfunding were extracted through literature search. Based on the Co-word clustering analysis, combining with the test of algorithm validation parameters, the results showed that market guidance, continuous innovation and profitability were the key factors for the success of the project.

Yunjie Ji, Yao Jiang, Ling He

An Empirical Case of Applying MFA on Company Level

Material flows analysis (MFA) is used regularly in tracing and estimating domestic and international natural resource’s usage. This article examines the use of MFA to model and manage the use of natural resources within a large Chinese chemical company. This company, situated within the China-ecology industry zone, provides an excellent case of how MFA can be used on the level of an individual company to analyze natural resources’ flows for an improved understanding and management of the economical, environmental and socio-economic consequences of industrial production processes. Its deployment further supports efficient production investment policies. The case study supports the assumption that MFA is particularly suited on a company level because the required data can more easily by collected and the effect of an MFA application can easily be weighted and calculated. MFA can help a company to leverage its resources and its investment decisions. Due to the fact that the MFA method considers all possible materials related to the transformation processes, (including easy-omitted materials such as steam which has no direct monetary value), MFA offers data on environmental emission factors that can be incorporated into green GNP accounts.

Lina Wang, Koen Milis

PAPR Reduction Using Interleavers with Downward Compatibility in OFDM Systems

In this paper, we propose a new PAPR reduction method using interleaving technique without data rate loss. The main idea is to set up all possibility of interleaving to get the best reduction of PAPR and to transmit the resulting information via null-subcarriers (NS) available in OFDM (Orthogonal Frequency Division Multiplexing) standards. Thus, we develop a new coding of interleaver key based on mapping symbols at the transmitter and a robust decoding procedure at the receiver. Simulation results in the context of WLAN IEEE 802.11a standard show an improvement of PAPR reduction about 5,2 dB, with the same performance in BER (Bit Error Rate), while respecting the communication criteria (Data Rate and spectrum specification).

Y. Aimer, B. S. Bouazza, S. Bachir, C. Duvanaud, K. Nouri, C. Perrine

Design and Implementation of Wireless Invoice Intelligent Terminal Based on ARM

In this paper, in order to solve the complexity and limitations of the current invoice issued, it designs and implements a wireless invoice intelligent terminal based on ARM [1]. The terminal is based on ARM embedded platform and the hardware part of the terminal is composed of main control module, memory module, LED liquid crystal display module and GPRS communication module. The software part of the terminal mainly includes the bottom driver design of Win CE system, the design of application software interface and the design of GPRS dial-up program. The invoice can be printed conveniently by using the wireless data interaction between software and tax administration information system. The system has been tested, which has the advantages of stable operation, friendly interface, convenient operation, low cost and easy popularization.

Yuexia Zhang, Shuang Chen, Yijun Jia

The Design and Implementation of Swarm-Robot Communication Analysis Tool

With the rapid development of artificial intelligence and automation technology, robots have been widely applied in various fields. Compared with the single robot case, swarm robots have more strength in executing tasks by cooperation. However, multi-robot cooperation needs high-quality communication, which is also concentrated by this paper. In order to analyze the communication behavior of the swarm robots in the process of moving, this paper designs and implements a swarm-robot communication analysis (SRCA) tool in the Robot Operating System (ROS) software framework. This tool selects the packet error rate (PER) as the communication quality metric, simulates communication channels and packet loss, and provides visualization and playback capabilities, which are the functions that is not provided by existing ROS simulator. Then we simulate an outdoor formation application which involves 10 quadrotors, and verify the effectiveness of our tool in three different scenarios.

Yanqi Zhang, Bo Zhang, Xiaodong Yi

The Research and Implementation of the Fine-Grained Implicit Authentication Framework for Android

Nowadays, in order to protect sensitive information in Android apps, plenty of identity authentication techniques were developed, including the password, graphical-password, fingerprinting, etc. Unfortunately, these schemes have many disadvantages. For example, the graphical-password could be reproduced by the trace on the screen. Different from the explicit authentication above, the implicit authentication scheme silently collects user behavior patterns for authentication, without the actions like inputting password. In this paper, firstly, we realized a more fine-grained implicit authentication scheme for the first time, which has refined the unit for authentication from App-level to Activity-level. Secondly, we improved the feature extraction and applied the classification algorithm called SVDD. Thirdly, we developed a no-buried-point library to enhance the usability. Finally, we recruited 21 volunteers for experiments. The experimental results reveal that the accuracy of proposed scheme can double the previous work and the no-buried-point feature can greatly improve the efficiency of app development.

Hongbo Zhou, Yahui Yang

Fair Electronic Voting via Bitcoin Deposits

Bitcoin is the most popular decentralized digital currency now in use. Block chain is the basic technology of Bitcoin, providing a trustable ledger that can be publicly verified. Research on distributed applications based on block chain has become a new trend. We propose an electronic voting scheme based on block chain and prime numbers, which can support voting situations for multiple candidates. We design protocols for the Bitcoin voting situation, in which there are n voters and k candidates. Each voter will vote for one candidate. The proposed protocols could guarantee that the candidate who gets the majority voting wins the game and no individual voting information is disclosed. Due to the nature of the block chain, the voting results could not be tampered. It is transparent since the block chain is open to the public for verification.

Xijuan Wu, Baodian Wei, Haibo Tian, Yusong Du, Xiao Ma

Research and Development of Door Handle Test Equipment Electrical System Based on Automatic Control Technology

This paper presents an electrical control system that applied automatic control technology to the door handle test equipment. The system can realize the automation and intelligence of test process, which collect and process key data such as torque, angle, time and speed, as well as realize data input and output through friendly human-computer interaction interface. The system was able to adjust the speed of the power according to the test task, and when the door handle turns to the specified angle, it can be used to remove or reverse the power in time. This function, called delayed reset, ensured that all actions are performed in an orderly manner.

Kang Gao, Hangjian Guan, Chengyang Wei, Zhuang Ouyang, Zhijie Wang, Xiaoping Huang

Analysis and Solution of University Examination Arrangement Problems

In order to give the best examination arrangement, we first analyzed the relevant factors and set up the model. Meanwhile, we calculated the probability of conflict arising from the random arrangements. Later, in order to better select the time period of the subject arrangement and evaluate the results, we established the local conflict function and the global benefit function. In addition, we used the dye matching algorithm and the genetic algorithm to solve it. Finally, we provided ideas for solving this problem with other intelligent algorithms.

Dengyuhui Li, Yiran Su, Huizhu Dong, Zhigang Zhang, Jiaji Shen

Analyzing the Information Behavior Under the Complexity Science Management Theory

Information behavior is a complicated process. Using the method of complexity science management to study the information behavior has important theoretical significance and practical guiding value. This paper provides a new perspective for information behavior, and introduces the theory of complexity science management, method, and the basic thoughts to solve the problems. The application of complexity science management in the research of information behavior was expounded from three aspects that includes theoretical system, research methods, research process to analyze the transformation of information behavior paradigm based on complexity science management.

Rongying Zhao, Mingkun Wei, Danyang Li

Risk Explicit Interval Linear Programming Model for CCHP System Optimization Under Uncertainties

A risk explicit interval linear programming model for CCHP system optimization was proposed to provide better system cost-risk tradeoff for decision making. This method is an improved interval parameter programming, which can overcome the shortages of traditional interval parameter programming. The proposed approach can provide explicit system cost-risk tradeoff information by introducing aspiration level and system risk metric objective function. The explicit optimal strategies for decision maker with certain risk tolerance degree is more executable than the interval solutions in practice. The developed approach was applied to the CCHP system for a residential area. The results indicated that the aspiration level would have great effects on the system investment and operation decision making. For the pessimistic decision maker, the total cost would be higher with less system risk and safer system operation. For the optimistic decision maker, the total cost would be lower with higher system risk.

Ling Ji, Lucheng Huang, Xiaomin Xu

Wireless Sensor Network Localization Approach Based on Bayesian MDS

The aim of wireless sensor networks (WSNs) are to perceive, collect and process the information of sensor nodes within the coverage of the network. As a bridge between the physical world and the digital one, WSNs have widely been applied in many fields. One of the key issues for most applications is to know the location of sensor nodes. Though localization algorithms based on multidimensional scaling (MDS), which only need several anchor nodes, have been proven to be robust with respect to range-based implementations, these methods are sensitive inaccuracy range measure. This article proposes a novel localization algorithm based on Bayesian MDS, named as MDS-MAP(P, B), where P and B denote the use of patching of local maps and Bayesian MDS, respectively. Experimental results in real-world systems show that our method is more robust and efficient.

Zhongmin Pei

Empirical Study on Social Media Information Influencing Traveling Intention

Through the data and path coefficient analysis on the 509 valid samples. We develop a conceptual model to investigate the factors that may influence tourists’ travelling intention. The results show that these factors, such as perceived novelty, perceived reliability and perceived interest have an obviously positive impact on perceived enjoyment; however, perceived understand-ability has little impact on perceived enjoyment. On the other hand, perceived enjoyment, perceived trustworthiness and perceived similarity have obviously positive impacts on travelling intention.

Chunhui Huang

Evolution of Online Community Opinion Based on Opinion Dynamics

Collective opinion of online community with different age structure obviously have different mechanisms of evolution, especially for different types of opinion events. For exploring the mechanIsms and providing management strategies for government, the bounded confidence model of individual opinion is constructed according to the related research on opinion dynamics. Chinese netizens psychology-behavior characteristics are considered in simulation modeling for presenting the relation of different types of opinion events to different types of age structure of communities. Simulation results show how three types of people (i.e., young, middle-age and old people) influence the evolution of two different collective opinion (i.e., society and low, and livelihood events). The corresponding management strategies for government are then given.

Liang Yu, Donglin Chen, Bin Hu

Research on the Growth of Engineering Science and Technology Talents from the Perspective of Complex Science

With the rapid development of information technology and the strategy of “Made in China 2025”, the state has met new requirement for talents training. Under the background of “made in China 2025”, based on complex science theory, this paper established the model of “Gong” model of engineering talents. The growth of engineering talents can be divided into three stages, that is, the basic and higher education stage, the social stage and the continuing education stage. Based on the phased characteristics of talent growth, this paper studied the law of the growth of engineering talents and the path of the road and summed up the law of phased growth of engineering talent in “Gong” type, and proposed deepening the reform of continuing education to improve the training mechanism for engineering talents.

Haifeng Zhao, Weijia Jiang

Research on the Relationship Between Entrepreneurship Learning and Entrepreneurship Ability Based on Social Network

The entrepreneurship ability determines whether the entrepreneurship will be successful, the entrepreneurial learning (Project Support: Philosophy and Social Sciences Project of Heilongjiang Province (17JYC146), Heilongjiang Province Postdoctoral Fund (LBH-Z15114).) is the key element to enhance the entrepreneurial capacity, while the social network provides a lot of knowledge resources to enhance the entrepreneurial capacity, but few researches explore the action mechanism between the social network, entrepreneurial learning and entrepreneurial capacity from the entrepreneurial point of view. Based on the social network. This paper establishes the theoretical relationship model between the three aspects, and uses the structural equation analysis method to validate the theoretical hypothesis. The empirical results show that the entrepreneur can make the social network as an important platform for entrepreneurial learning, both the formal network and the informal network have a significant positive impact on the implementation of entrepreneurial learning. The entrepreneurial learning mode under the social network can be divided into three types: imitation learning, exchange learning and guidance learning, which will play a positive role in promoting the entrepreneurial capacity; entrepreneurs can choose the right way to learn entrepreneurship, and effectively enhance the entrepreneurial capacity.

Gang Hao, Qing Sun, Yingying Ding

Using C Programming in Analytic Hierarchy Process and Its Application in Decision-Making

AHP (Analytic Hierarchy Process) is one of the common methods used to deal with complex decision problems. On the basis of introducing AHP, this paper focuses on the difference of programming between C language and Matlab. According to the characteristics of open library Eigen in C language matrix programming, C language is used to realize AHP, and meanwhile the scale and complexity are greatly reduced.

Gebin Zhang, Jianmin Zhang

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