Practical Applications of Intelligent Systems

Hebei province is a populous province and a big agricultural province. Residents suffered various major natural disaster risks, therefor, major natural disasters defense and compensation are particularly important. Through randomly choosing 262 urban and rural residents in Hebei province for the survey, as well as the relevant material, the article analysed the status quo of the current major natural disasters defense and compensation. Besides, the article used the Probit model to analyze the desire and influence factors of urban and rural residents in Hebei to participate in major natural disaster insurance. Finally, relevant policy suggestions were put forward.

P2P file sharing networks usually are made up of selfish peers, who will harm the network availability greatly by their free riding in file sharing. Credit exchange is a mechanisms and credit relationships form a credit network. The constructing and routing algorithm in credit networks are proposed to prevent free riding and as far as possible to decrease the cost of the mechanism. Simulations prove the effectiveness and stability of the incentive mechanism.

With the arrival of an aging society, more and more elderly population is needed to be taken care of, and community health care system has become increasingly significant. This paper presents a solution to community health care wireless based on sensor network, and designed the wireless sensor nodes for collecting physiological data. Then through these wireless sensor nodes physiological datas achieve wireless multi-hop transmission. Wireless multi-hop implementation uses a CTP routing protocol.

In this paper, an improved Multi-Strategy routing for the UWB ad hoc network have been proposed and evaluated. In this Routing Algorithm, influence factor is redefined and improved to find the master node, GRA has been adopted to find the cluster routing, and some strategies have been given to choose the accurate routing. Different strategies can be used in different clusters. Simulation results show that the Multi-Strategy Routing Algorithm is more stable and flexible, the packet deliver ratio is higher than BSR and PFA Algorithm.

Temporal constraints are regarded as an important quality criterion for regulating services to get a higher quality of service (QoS) in the business level. Therefore, it is important for the given composition of services to verify the compatibility under temporal constraints. According to the actual need, in this paper the conception of probability based timed compatibility based on normal distribution is firstly present, by which the state of timed composition compatibility can be quantitatively distinguished. Then, we present an automatically checking algorithm, which can guide the users to compose service or adjust the temporal constraints. In the end, we validate our method by a real example of e-commerce scenario.

With the advent of mobile networks and mobile devices,mobile payment has been attractive to many ecommerce users. However, due to mobility of users, payment services and mobile devices must be adapted to the new environment. In this paper, a personal assistant agent is presented for automatic and intelligent payment and a model is implemented for inference and making appropriate decisions. To evaluate the suggested framework, context data were collected from content providers and a mobile network operator. The obtained results confirm the efficiency of the method.

Currently online audio/video applications gradually expanding into the mobile world, Source Specific Multicast (SSM) will globally disseminate to many users of multicast infrastructure and services. Tree morphing protocol is adaptive to SSM source mobility, but extra-encapsulated data lead to network overload and non-optimal routing algorithm to delay. In this paper we present and discuss an improved protocol. We use network simulation tool NS-2 to complete the improved protocol, and compare with tree morphing protocol, convergence time of our method is shorter.

The research of a comprehensive security system of the high speed rail is extremely important. It has been the primacy in countries having high speed rail or willing to have it. Raw data used for analyzing security features is usually in binary format and its organization is complicated, so unpacking original high speed rail data is particularly important. In terms of unpacking process, we must ensure the accuracy and real-time property. MapReduce technique has gained a lot of attention from the scientific community for its applicability in large parallel data analysis. To process huge volumes of high speed rail data, this paper presents a parallel method based on MapReduce. The experimental results demonstrate that the proposed parallel method may efficiently unpack the large high speed rail datasets.

Although domestic and international children-oriented Human-Computer Interaction (HCI) researches, based on a particular technology, have made a lot of success, there is no a unified structure for HCI. In this paper, we put forward WisPad (a structure for children-oriented HCI) based on children-oriented HCI design principles. WisPad integrates the tangible user interface, gesture interaction, Avatar-based HCI, high-level semantics interactive technology and realizes the natural interaction between children and computer. Then we develop a children’s video game (iNature) to verify the structure.

Nowadays, people expect robots to perceive environments in a concept-oriented way, and how to teach robots some abstract concepts becomes a new problem. Aiming at teaching robots abstract place concepts, a method using a sketch map is proposed. The method is based on a prototype mechanism, in which feature objects and qualitative spatial relationships among them are taken into account simultaneously. Two primitives used on a sketch map are defined: one is the closed curve which represents an object, and the other is the arrow which is used to assist to get the orientation of a certain object. A concept prototype is gotten through analyzing primitives on a sketch map. The analysis is divided into two parts: getting information about objects and getting the scores of spatial relationships according to certain usual relationship attributes. Finally, the method is verified by simulation, and place concept teaching can be reliably realized.

The development of mobile phone is rampant and abundant since ever. Its utilities expand in a huge leap to be the most indispensable personal device. This research aims at converting 19k Wind website, our prior research achievement, to a mobile phone interface, and exploring the principle of cell-phone web design. However, there are many constrains in mobile web design, such as small dimension for less functional keys, small display screen for less information and navigation space, small capacity for less micro-processing and memory volume. This research compared 12 large company websites and analyzed the proper web design principle to redesign the 19K Wind website. Six factors were emanated: visual image, visual intriguing, easy to read, easy to use, flat structure, and relevant information.

The paper investigates observational research on users navigating through multimedia and online resources. The audit trails of these users have been used to create a series of navigational patterns and graphs of tool usage. From this research, the need for a set of navigational tools or a toolbox of tools has been identified. This paper then discusses these tools and looks at researchers who have been developing or using tools. A new project called Arch uses these tools to enable exploration of an existing Arts and Humanities resource. Demonstrators for this project will be exhibited during the presentation. These tools could be generic and multipurpose and produced as a tool kit. The concept of this toolkit is that it would be available to users across a range of resources and prevent the need to re-learn new skills to use similar tools.

Based on the analysis of the needs of constructing virtual exhibition, a virtual exhibition platform architecture is proposed that substitutes convention and exhibition industry in reality with the Application of Virtual Reality Technology. As an example, “2011 automobile virtual exhibition system was established with 3D modeling technology, Vray advanced material baking technology, and character control technology,etc, which composited automotive multimedia information display and achieve the desired results. The construction method of automobile virtual exhibition system, provide an effective way for realization of virtual display.

This paper presented an effective method to recognize diving posture in diving competition videos, which was composed of object segmentation and feature extraction. In the first stage, Lucas_kanade optical flow method is used to estimate the global motion and the object area. Then we use the skin color distribution characteristics in YCbCr space to detect accurately the athletes’ skin color. Next, projection method is used to eliminate noise and segment object. In extracting features stage, we extract color, aspect ratio, area proportion and SIFT features. These features are extracted to recognize every kind of diving posture by support vector machine. The experimental results show that this method for recognizing diving posture has good recognition performance and robustness.

While enjoying the convenience of email communications, many users have also experienced annoying email spam. Even if the current spam detecting approaches have gained a competitive edge against text-based email spam, they still face the challenge arising from image-based spam (image spam in short). Image spam normally includes embedded images that contain the spam messages in binary format rather than text format and cost more storage and bandwidth resources. In this paper, we propose a hybrid image spam filtering framework to detect spam images based on both extracted text and image features. Our experimental results show that our approach achieves significant improvement in detection accuracy as compared with other methods that simply use text or image features, and works robustly in an environment with either complex background or compression artifact.

Conventional PI controller is usually found to provide poor performances for nonlinear systems. In this paper, an auto-tuning PI scheme is presented for the speed control of a permanent magnet synchronous motor drive. The aim of this study is to obtain a controller that provides a fast and smooth dynamic response under both moment of inertia change and load disturbance. To improve the transient response, the proportional and integral gains of the proposed controller are continuously modified based on the current process trend. To illustrate the performance of the proposed controller, the simulation is presented separately for the auto-tuning PI controller (ATPIC) and classical PI controller. The results are compared with each other and discussed in detail.

Optimal launch mission planning is one of the most important engineering fields where optimization tools and optimal control theory have found routine application. Optimal control theory is critical for launch mission to meet mission requirement such as minimum time, minimum fuel requirement, minimizing undershooting and so on. Compared to traditional off-line launch mission planning, on-line launch mission planning could generate guidance plan with respect to the current situation of the vehicles to enhance the guidance precision greatly and reduce the risks resulted by unpredictable events, while the core issue of on-board launch mission planning is how to solve the corresponding Two-Point Boundary-Value Problem (TPBVP) quickly, efficiently and precisely. We intend to introduce a stable algorithm here with a verified convergent ability to handle optimal launch mission planning problems with relatively little time consumption.

The supply system in urban mass transit provides power for vehicles. The quick and accurate fault positioning after its failure is worth researching and key for the reference of maintenance personnel, to minimize the duration of power failure. Based on Object-oriented Petri nets (OOPN), this paper proposes Object-oriented Colored Petri nets (OOCPN), which introduces member variables and member methods into OOPN. With OOCPN, the conventional CPN model for topology analysis is modified into an OOCPN model, through the solution of which a gross fault region is located, together with possible miss-trips and error-trips. The result of OOCPN model is then reasoned backwards with protection and action information from human-machine conversation for final fault node and further breaker analysis. The method proposed takes into consideration the peculiarity of protection configuration in supply system, and is subjected to both theoretical analysis and an example case, which proves it practical and efficient.

This design of the demodulation system of the wavelength information of the FBG strain sensor was based on data acquisition system of DSP and FPGA, which can realize real-time detection and analysis of the multi-point strain. FBG strain sensor is an optical passive instrument. Through the demodulation analysis of the wavelength drift, the variation of corresponding strain can be calculated. This design includes: the demodulation principle of FBG strain sensor, the components of demodulation system, hardware design and analysis of experimental data.

In this paper, a Negative Selection Algorithm (NSA)-based motor fault diagnosis scheme is proposed. The hierarchical fault diagnosis scheme takes advantage of the feature signals of the healthy motors so as to generate the NSA detectors, and uses the analysis of the activated detectors for fault diagnosis. It can not only efficiently detect incipient motor faults but also correctly identify the corresponding fault types. Our method has been investigated using one practical motor fault diagnosis example.

With the higher quality standard from industries, the need for steel surface quality control has been greatly increased. The detection and recognition of steel surface defect is a critical issue for the quality control process. Among the techniques applied to tackle the problem, machine vision based approaches have advantages due to its flexibility, accuracy, and economy. This paper describes a real-time steel inspection system, which investigated the usage of support vector machine (SVM) and multiple kernel learning (MKL) method. Based on the preliminary experimental results, the proposed method demonstrates the efficiency in detection and recognition steel surface detects. It is shown that the advanced classification methods such as SVM and MKL are applicable for the real-time steel surface inspection system.

Knowing the subcellular locations of proteins is an important step in understanding the protein functions. The experimental methods for determining the protein subcellular localization are usually costly and time consuming. Thus, in the last decade, many computational methods were introduced to determine the protein subcellular location

in sillico

. Although the physicochemical properties of membrane proteins and globular proteins are different, most of these methods treated the globular proteins and membrane proteins equally. Thus, developing method for determining the subcellular locations for them separately would be necessary. We proposed an algorithm called Weighted Gene Ontology Scores (WGOS) to predict the protein subcellular location of membrane proteins. By comparing the prediction performance of WGOS to MemLoci, which is the only existing method for predicting membrane protein localizations, WGOS performed significantly better than the MemLoci, indicating WGOS might be able to provide better results in practical applications.

Transaction cost plays an important role in the return on investment of the stock market. Market impact cost, one of the important components of transaction cost, is analyzed in this paper. A prediction model of market impact cost is proposed which can improve trading strategies, reduce total transaction costs, and boost investors’ returns. It is verified that the cost prediction model has better performance in both simulation environments and practical applications.

Combined with the biometric identification technology and digital watermarking technology, a digital watermarking algorithm based on iris features is proposed. A secret key is used to generate the pseudorandom sequence which would have the cryptography in the sense of security. Through the two-scale wavelet transform and the combination of singular value decomposition, the robustness of the watermark can be improved. The experimental result shows that the watermarking algorithm is fit for iris feature and has strong robustness in noise resistance, geometric transform, compression and other aspects.

In view of the combination of sage-husa and strong track filtering (STF) algorithm still existing negative definite noise variance, failing to estimate process and measurement noise simultaneously and STF depending on the measurements excessively, proposed a novel approach to solve the problems stated above. In a newly scalar sequence processing way, discussed and modified the convergence criterion. When in its convergence, only updated measurements noise variance under the control of a improved forgetting factor; when in its divergence, updated priori estimate error covariance and measurements noise variance simultaneously to make innovation sequence orthogonal and have same order of magnitude. The experiments results illustrated that compared with the conventional one, the proposed method could hold the noise variance positive and came to convergence more quickly with higher accuracy. Although the noise variance increased sharply when in the state of divergence, it met our design objective.

The speed-up gearbox is one of key components in the large-sized wind turbine. During the operation, some faults often cause long maintenance downtime and higher cost. In this investigation, the gearbox faults were diagnosed by Bayesian Networks method. Based on the analysis of fault factors, the different signal features of fault diagnosis were confirmed. According to Bayesian Networks theory, the fault model of speed-up gearbox was established. The probability of sub-node was obtained by the conditional probability relationship of different nodes. Using the conditional independence of each node, and simplifying the probability distribution, the fault probability was counted out. Finally, the availability of Bayesian Networks method is proved by a calculation case on the test-platform. The study shows that the method can improve the fault diagnosis and operation level of the large-sized wind turbine when be used to judge the fault position in the gearbox.

The problem of point selection is very important for system testability design. In this paper, a method based on Clonal Selection Algorithm (CSA) and multi-signal model is proposed to select the optimum test set of complex system. The problem of test selection is transformed into an integer programming problem through building multi-signal model. Then, the CSA-based method is used to search the optimal test set for system. The method can not only avoid the local optimization and premature convergence, but also improve the searching efficiency. The experimental results indicate that the proposed method is easier to find the optimum test sets with high effectiveness and acceptable time consumption.

In order to solve the problems such as blindfold search, slower convergence, and sometimes unsuccessfully search in the present genetic algorithms used for intelligent test paper generating, this paper introduces an intelligent test paper generating algorithm based on chapter ratio score and release strategy. The algorithm transforms test paper generating problem into the question of finding a suitable solution of multivariate equations group with multiple conditions. It initially determines the numbers of those pending small questions of various question types in each chapter according to its ratio score. If the number determined by the proportion can’t meet the requirements, then firstly adjust the numbers of various types of questions to be selected into paper within the chapter while maintaining the same chapter ratio score. If the number can’t be adjusted within the chapter, then make other adjustable chapters release its part share of scarce type of questions for the chapter. Eventually it will find scheme for generating test paper to meet ratio score of each chapter and strictly to meet the types of questions and the number of small questions of various types. Theoretical analysis and experimental results show that the algorithm can quickly and efficiently find a reasonable solution for generating test paper compared with the current genetic algorithms.

Many invasions against severs of campus-networks occurred in recent years. Such attacks obtained unauthorized privileges or promoted authority levels by exploiting vulnerabilities in servers’ operating system or software applications. We studied this kind of attacks and proposed an automated mining and assessing method by using FTP server vulnerabilities as an example. Our proposed method provides a general approach to discover safety vulnerabilities in server applications. It covers solutions in details from creating and sending abnormal data, to monitoring and fault isolation. The method can be easily port to other internet-facing server applications.

Traditional aircraft sensor system requires a lot of cables and volume for each sensor’s connection. It can not meet the rapid development of modern information technology requirements of aircraft. Future aircraft sensor system requires less cables and more speed. This paper proposed a new design of digital aircraft sensor bus. It combines the I2C with SOC technology. It’s also designed to be a modular structure in order to increase or decrease additional functional module when additional features in need. In this paper, a fully functional prototype of aircraft sensor bus and an upper PC system was designed. Corresponding test software based on LabWindows\CVI is written in order to actually test the efficiency of the prototype. The following test result verified the original design.

This paper proposes a reinforcement learning based tag recommendation algorithm to deal with the data sparseness that affects the performance stability of collaborative filtering algorithms. Our algorithm integrates user tags into traditional collaborative filtering algorithms and attaching importance to user interest shift in the process of user interest learning process. Empirical Cases of comparing with traditional collaborative filtering algorithms indicate that our recommend algorithm exhibits better performance competition.

Increasingly, customers use the internet as a vehicle for purchasing things. While customer value measurement is an important element in customer relationship management. Based on analyzing some typical models of customer value, the paper explores the advantages and limitations of current models and methods on customer value evaluation, and it focuses on the online customers’ behavior and develops a model of online customer value. The framework of the model is consistent of historical value, current value and the future potential value of online customers.

In any organization, because of the importance of management responsibility and its effect on efficiency improvement, the selection of the appropriate person as a manager is one of the important decision making subjects. This paper proposes a new fuzzy multiple attribute-based decision support system (DSS) for choosing suitable managers as such a selection may involve both quantitative and qualitative assessment attributes. There are many fuzzy ranking methods available to solve multi-attribute decision making (MADM) problems. Some are more suitable than other for particular decision problems. The proposed DSS has ability to choose the most appropriate fuzzy ranking method for solving given MADM problem, based on the type of attributes and the size of the problem, considering the least computation and time consumption for ranking alternatives. A DSS software prototype has been developed on the basis of the proposed DSS which can be applied for solving every FMADM problem which needs to rank some alternatives according to some attributes.

This paper presents the modules and features of an e-learning system which can automatically assessing the answers of elementary algebra proving problems. The system translates the answer given by a student, which is written in Chinese, into its corresponding formal proof, which is the semantic of the original sentences in the answer, and then automatically verifies the correctness of the formal proof.

In the modern business, a vast amount of data has been accumulated, but mass data has not been fully utilized. Therefore, it is a vital issue for the business and IT sector to explore these data with new technology, which is useful to support better business decision-making and help enterprises to increase profit and market share. BI (Business Intelligence) technologies emerge as the times require, and ETL (Extract, Transform and Load) which realizes the technical service and decision-making support plays an important part role in BI project. In this paper, the main characteristics, advantages and disadvantages in existing ETL methods are analyzed, and some factors affecting the performance of ETL are also summarized. Furthermore, an ETL approach which combines ETL tools and SQL coding was proposed and implemented based on EL-T (Extract, Load and Transform) framework. The practice and experiment results show that the proposed approach has better efficiency and applicability than other existing ETL methods.

In this paper, center conditions and bifurcation of limit cycles at the nilpotent critical point in a class of quartic polynomial differential system are investigated. With the help of computer algebra system MATHEMATICA, the first 8 quasi-Lyapunov constants are deduced. As a result, sufficient and necessary conditions in order to have a center are obtained. The fact that there exist 8 small amplitude limit cycles created from the three order nilpotent critical point is also proved. Henceforth we give a lower bound of cyclicity of three-order nilpotent critical point for quartic Lyapunov systems.

The Interactive Digital Television (iDTV) has facilitated and expanded the communication and interaction in activities of knowledge acquisitions, entertainment and recreation in Distance Learning field. This new way of teaching and learning has been called t-Learning. In this context, the Learning Objects (LOs) have an important role to assist in the electronic courses’ development. Due the fast progress of e-Learning, some efforts to standardization have appeared in order to enable the reusability of educational contents and interoperability among systems, and one of these standards is the Sharable Content Object Reference Model (SCORM). Therefore, the main goal of this work is to present an extension of SCORM aiming to adapt it in order to improve the search and navigation of LOs with educational content for t-Learning. That will be done through an authoring tool named T-SCORM ADAPTER, which will be able to apply this extension in a fast and efficient way.

In Geographic Information System (GIS) secondary development, problems appeared when the traditional MapObjects method was adopted. To solve it, possible solutions were discussed and studied by means of the introduction of a common image file and combined layers display method. Not only was Mapobjects layer management Capacity enhanced, but the problem of leaked out the key information was worked out. The method proposed by the study will provide a new solution for the GIS secondary development based on MapObjects.

With the development of e-commerce technologies, more and more sellers choose to open online shops in the e-commerce platforms due to the advantages of saving costs and spaces, and more and more buyers choose to do online shopping with the advantages of saving time and money, as well as the convenience. On one hand, this kind of web based trading greatly facilitates both sellers and buyers; on the other hand, it makes more technical demands on the e-commerce platforms providers, such as the automatic detection of the prohibited goods, especially with Chinese names or descriptions due to the ambiguity nature of Chinese language. In this paper, we propose a novel idea for addressing this problem by web based aliases extracting. The experiment results illustrate the effectiveness and feasibility of our method.

This paper aims at analyzing the dynamic technological transition of a

Naional Innovation System

(subsequently abbreviated as NIS) through a theoretical model of complex system, presented in a non-linear difference equation. This paper shall also specify the way through which a hypothetical technological paradigm may be explored and the forces that shape the economical, social and institutional spheres.Putting together the results presented by the analysis of the complexity with the characteristics proposed by the evolutionist theory related to technologic dynamics inside the NIS, it is possible to systemize in a better way the results of the variation in the time of the economical changes presented.

Real-time data warehouse as an extension of traditional data warehouse, it is effectively shortening the delay of information and providing timely and accurate decision support to decision makers. The ETL processing is the core technology of data warehouse, especially in real-time data warehouse. This paper is analyzing the current mainstream technology for real-time ETL. We propose a way to capture changed data which is based on log analysis and log listening. This method provides an effective solution for the huge amount of data, which greatly improve system performance.

DNA barcoding technology, which uses of one or a few DNA fragments for rapid identification of species, is a research focus on biodiversity information studies. In this paper, a data analysis toolbox, iDNABar, is designed and implemented to support species identification with all phases of the analytical flow, form specimen collection to tightly validated barcode library for any DNA fragment. The method of identification is proposed; key elements to management and sharing are also presented, including the user security, statistical functions and application interfaces. Meanwhile, the applications of the implementation are discussed to drive the species identification cloud’s development. (For download: http://www.darwintree.cn/tools.htm)

Regression testing is to check whether a new software version fails to preserve specification properties that its previous versions have preserved. It is needed when new version has been filed for fixing a previously reported bug. We present artificial intelligence (AI) techniques to enhance the accuracy of test selection in such a setting. The core technique of our regression testing is an adaptive fitness function that learns how to select test cases based on their similarity to the bug-revealing test cases. The evaluation of similarity between two test cases is in turn based on feature variables extracted with the LCS (longest common subsequences) algorithm. We implemented our techniques with a symbolic simulator of the models of the software under test. We also show how to take the capabilities of the simulator into consideration to learn for better testing performance. Experiment report shows the potentials of applying AI techniques to software verification.

In the current century, the highly developed transportation system can not only boost the economy, but also greatly accelerate the spreading of epidemics. While some epidemic diseases may infect quite a number of people ahead of our awareness, the health care resources such as vaccines and the medical staff are usually locally or even globally insufficient. In this research, with the network of major aviation routes as an example, we present a method to determine the optimal locations to allocate the medical service in order to minimize the impact of the infectious disease with limited resources. Specifically, we demonstrate that when the medical resources are insufficient, we should concentrate our efforts on the travelers with the objective of effectively controlling the spreading rate of the epidemic diseases.

The applications of two typical swarm intelligence algorithms in the optimization of the reentry trajectory for the hypersonic gliding vehicles are discussed in our paper. A trajectory optimization strategy based on the swarm intelligence algorithms is presented. Firstly, a penalty function is constructed to deal with the inequality constraint functions. Secondly, the attack angle commands of the vehicles are considered as the parameters to be optimized, and on the basis of the mathematical model of the hypersonic vehicles, the optimal law of the attack angle is considered as the input for the trajectory optimization. Finally, two kinds of swarm intelligence algorithms are applied to handle this difficult optimization problem. Numerical simulations have demonstrated the effectiveness of the swarm intelligence algorithms for the complex and large-scale trajectory optimization problem. Our work lays a solid basis for the schematic trajectory design of the hypersonic gliding vehicles.

Software testing consumes 50% of the total expenditure done on the overall software. Cost of the testing phase is generally high due to heavy manual intervention. Many steps have been made in literature to reduce the cost of the phase by replacing existing manual work with automatic process. For example, manual test case generation is replaced by automatic test generation. A number of search methods exist in literature to automate test generation process in which Genetic Algorithm based approaches are very popular. A number of papers have been proposed in literature to show the use of GA to generate suitable test data for given software for desired adequacy criteria. Although GA is very effective in searching good test cases yet it has some inherent weaknesses. For example application of Simple Genetic algorithm may generate redundant test data in upcoming generation. This requires additional effort and expenditure without adding any value to the desired objective. This problem can be eliminated if non-revisiting GA is used in place of simple GA. GA also use fitness of various test cases to judge when to finished searching. At present this fitness evaluation is done by a human being hence it is a costly affair. This work can be automated by replacing a human being with a neural network. This paper proposes a combination of neural network and genetic algorithm to overcome the existing problems of automatic test data generation for white box testing.

Computer-aided design (CAD) and computer-aided manufacturing (CAM) play an important role in substantial productivity for manufacturing companies. However, the combined utilization of CAD and CAM systems decreases the performance of softwares. When CAD and CAM systems are handled separately the related processes are executed in a more flexible way. That being the case, the selection of CAD system among available alternatives can be regarded as a critical decision making problem, since it lead s to not only time and cost effective, but also convenient design processes. In this paper, a fuzzy information axiom based decision making approach is proposed to select the CAD system which provides highest level of satisfaction with respect to the characteristics of design processes of companies. The proposed approach is applied to CAD system selection of a real life mould production system.

Total Quality Management (TQM) is a management philosophy, with a quality focus, based on the participation of all organization members and has an aim of long-run success through customer satisfaction. On the other hand, performance of a company is measured as to the extent that the company achieves its corporate strategy and goals. In this study, based on the quality frameworks and current TQM literature, a performance measurement model for TQM is proposed and importance of the criteria is determined by using Fuzzy Analytical Network Process (FANP).

Due to the miniature underwater vehicle’s navigation and positioning accuracy is susceptible to the influences of environment and sensor. This paper puts adaptive-filtering algorithm forward to process the sensor information. Meanwhile, modeling the system in consideration of the environmental factors, which enhances the system’s adaptive ability and ensures the underwater vehicle’s intelligentialize and high precision. The simulation test shows that, after adopts the adaptive-filtering algorithm, the underwarter vehicle’s navigation and positioning precision improves significantly. Compared with general filtering algorithms, their calculated amounts are similar, but adaptive filtering has stronger engineering properties.

Regression testing is for retesting modified software to ensure that changes are correct and do not adversely affect other parts of the software. It is usually One extremely hard for engineers to figure out how a change in software will echo in other parts of the software. We propose a framework that combines machine learning and program slicing for test cases prioritization to solve the problem. We developed a library, called Intelligent Test Oracle Library (InTOL), for the instrumentation of a system under test (SUT) to generate test traces. The program slices serve to indicate the relevance of test cases to regression testing of software modifications. Then we relate the slicing information with the test traces. We use Artificial Neural Network (ANN) as our underlying technology for machine learning. In the training phase, we first use ANN to learn the count of times that a program segment will be visited in the execution of a test case. We then use the count estimation for the program segments as a part of our feature vector for a test input and feed the vector to another ANN for test prioritization of the test input. We experiment with two benchmark programs of Software-artifact Infrastructure Repository. Our experiment data shows a good fault-detection ability.

In our previous work, we have created an ontology for describing both the structure and the content of on-line analytical processing (OLAP) multidimensional cubes using the Resource Description Framework (RDF) XML sublanguage. In the present paper, we describe how we have mapped our ontology onto a multidimensional database schema which can effectively be used to define the data access layer for Microsofts Business Intelligence Semantic Model (BISM). This mapping facilitates so-called self-service business intelligence by allowing immediate end-user access to the multidimensional data, from desktop applications such as spreadsheet pivot tables, without the need for any special technical expertise to guide the transformation process.

Risk management is the identification, assessment, and prioritization of risks followed by coordinated and economical application of resources to minimize, monitor, and control the probability and/or impact of unfortunate events. In the last decade risk management has become a vital part of supply chain management. The risk sources of supply chain are identified in five areas namely: transport/distribution, manufacturing, order cycle, warehousing, and procurement. The aim of the study is to build a supply chain risk measurement system using Fuzzy Inference Systems (FIS).

In consideration of the common sensor failures in aero-engine control system, a new approach is proposed using partition flight envelope and two-stage empirical modeling strategy based on neutral network in this paper. Clusters of input parameter data are formed using GMMs and neutral network status observers are designed and trained for every flight envelope partition, so the destination of the whole flight envelope aero-engine sensor failure diagnosis can be realized in real-time and on-board. Digital simulation results show that this method is very practical, effective and feasible.

This paper presents a novel design and analysis of a reconfigurable parallel kinematic manipulator that can be applied to a machine tool. The proposed manipulator has three Degrees of Freedom (DOFs), including the rotations of a moving platform about the x and y axes and a translation of this platform along the z axis. A reconfigurable driving mechanism is introduced into this new parallel manipulator in order to adapt the actuator vector so that the various configurations can be applied for different task. Constraint analysis is investigated, inverse kinematic problem is solved and Jacobian analysis is conducted. Finally, the dynamic simulations are performed using Adams/View to verify dynamic performance.

Software Product Line (SPL) Engineering analyzes and applies various existing software modules from different systems in a domain for software mass customization. Feature models are often used to represent all of the product configurations in an SPL in terms of features. A main challenge in SPLs is how to select a good feature set to achieve customer requirements, subject to the constraints of feature model as well as the resource constraints. To solve it, this paper presents an approach to formalize the feature selection problem using 0-1 programming. Moreover we document the problem formalization in canonical form. Our approach makes it possible that most existing techniques for 0-1 programming can be used directly to deal with the feature selection problem in SPLs, which provides a new perspective to solve the problem and improve the efficiency.

Collaborative Filtering (CF) assists Recommender Systems (RSs) in recommending products or services that they are likely to be of interest to users. Various CF schemes have been proposed, but most of them are seriously limited by a cold-start problem which refers to a situation that RSs are incapable of drawing recommendations for new items, new users or both. Moreover,insignificant ratings whose values are less than the corresponding average ratings adversely affect recommendations.In this paper, we propose a Credible and cO-clustering filterBot for cold-stArt recommendations (COBA). It filtersinsignificant ratings by introducing rating confidence level, which substantially reduces the dimensionality of the item-user matrix. To overcome data sparsity, COBA co-clusters items and users, and smoothes ratings within every user cluster. Finally, it predicts user preference byfusing recommendations from item and user clusters. Our experiments show that COBA solves the cold-start problem regarding recommendation accuracy and scalability.

Focusing on the future development, the latest research progress of U.S. military about pint-sized fire control system of UAV is introduced. By analyzing the UAV fire control system elements under the two operating procedures, we explored and definated the key technologys and its application characteristics about information fusion,artificial intelligence,data chain and intelligent sensor etc.offered the research priorities of autonomous system, and at last we made out the road for development about the two kinds of system.

Automatic speech recognition (ASR) often fails in acoustically noisy environments. Aimed to improve speech recognition scores of an ASR in a real-life like acoustical environment, a speech pre-processing system is proposed in this paper, which consists of several stages: First, a convolutive blind source separation (BSS) is applied to the spectrogram of the signals that are pre-processed by binaural Wiener filtering (BWF). Secondly, the target speech is detected by an ASR system recognition rate based on a Hidden Markov Model (HMM). To evaluate the performance of the proposed algorithm, the signal-to-interference ratio (SIR), the improvement signal-to-noise ratio (ISNR) and the speech recognition rates of the output signals were calculated using the signal corpus of the CHiME database. The results show an improvement in SIR and ISNR, but no obvious improvement of speech recognition scores. Improvements for future research are suggested.

The pinyin tagging system has a wide range of applications. The tagging accuracy is lower because of Chinese polyphone problems. The Chinese multi-tone word ratio is far less than the polyphone, so the tagging approach of word segmentation can improve tagging accuracy. The tagging accuracy is improved by 10.8%. The pinyin dictionary mechanism is studied in the word segmentation process to improve the tagging speed.

Safety Culture

describes how safety issues are managed within an enterprise. In this paper we describe a prototype for safety culture assessment using

Belief Degree Fuzzy Cognitive Maps

. Our approach discusses how to assess safety culture within an organization, and find out the main factors that need to be changed to reach a strong safety culture. Fuzzy Cognitive Maps have been used in many application fields were decision making process is related with uncertainty, vague and incomplete information. Belief Degree Distributed Fuzzy Cognitive Maps provide more flexibility and freedom for experts to give their assessments by different data structures.

With the development of microelectronic technology, the quantity of electronic devices in aircraft is expanding at an exponential rate. But the traditional control buses on aircraft are not portable or scalable. It is an important problem to modern aircraft design to connect numbers of digital electronic devices within one bus. In this paper, we propose a new design of aircraft control bus which has characteristics like high-speed, small-size, highly-reliability, more-scalable etc. The following test of the prototype validates the design. This new aircraft control bus is very suitable for aircraft systems which require compact size, flexible configurations and highly reliability.

PPI protocol is a communication protocol specially designed for S7-200 system by Siemens. PPI protocol makes read and write operations directly to the PLC without having to program, but the PPI protocol is not opened by Siemens. Refer to IEC 61158-4-3 standard and define the communication process and messages basic format of PPI protocol. By the way of intercepting messages, intercept PPI protocol messages between STEP 7-Micro/WIN and S7-200 PLC communication. According to the intercepted messages, refer Profibus fieldbus data link protocol format and analyze the data mixed storage messages format of PPI protocol and the result can be used in communication between upper computer, field device and PLC.

Aiming at the problem of traditional Smith predictive control depending on the accurate mathematic model, a kind of intelligent control method for large time-delay system based on fuzzy strtegy is proposed and the intelligent self-adaptive-correcting controller that is suitable to the large time-delay system is designed. Dynamic and static performance, robust performance, and anti-interference performance of the intelligent contol system are discussed. The simulation results show that the self-adaptive-correcting characteristics of this intellingent control method is superior to those traditional control amd the quality characteristics and robustness of the system are improved.

Vessel’s degaussing system plays an important role in improving the performance of the magnetic protection. However, during ship sailing, magnetometer will be influenced by a variety of interfering magnetic fields, which will seriously affect the accuracy of degaussing current control. To solve this problem, this paper proposes a degaussing current controller based on magnetometer. With analyzing and processing the real-time data of the magnetic field, the instrument eliminates the impact of the interfering magnetic fields on the degaussing current control. The result of practical application shows that the instrument can detect the continuously changing magnetic fields and accurately control the degaussing current to achieve a good degaussing performance.

This paper describes an urban traffic control system which aims at contributing to a more efficient traffic management system in the cities of Brazil. It uses fuzzy sets, case-based reasoning, and genetic algorithms to handle dynamic and unpredictable traffic scenarios, as well as uncertain, incomplete, and inconsistent information. The system is composed by one supervisor and several controller agents, which cooperate with each other to improve the system’s results through Artificial Intelligence Techniques.

This paper focuses on the problem of guaranteed cost control of polynomial nonlinear uncertain systems with time-delay. Both the state and control uncertainties, which are unknown but bounded by some continuous nonlinear functions, are taken into consideration. Based on the tenets of sate-dependent polynomial Lyapunov functions, we developed a state feedback controller to obtain an upper bound for the guaranteed and optimal cost by using the Sum of Squares technique. Finally, numerical results were presented to demonstrate the effectiveness of the proposed approach.

Based on particle swarm optimization (PSO), a new fuzzy neural network (FNN) sliding mode control (SMC) method is proposed for overhead crane. In order to ensure good dynamic performances of system, PSO algorithm is utilized to adjust adaptively controller parameters. At the same time, two FNNs are adopted to approach the uncertainties of the positioning subsystem and anti-swing subsystem. This approach could satisfy the strict specifications on the swing angle and realize trolley position control accurately. The simulation results show that good control performance is achieved, and the method can guarantee anti-swing control and accurate tracking control of trolley in considering of uncertainties and disturbances.

Intrusion detection systems (IDSs) have been widely deployed in organizations nowadays as the last defense for the network security. However, one of the big problems of these systems is that a large amount of alarms especially false alarms will be produced during the detection process, which greatly aggravates the analysis workload and reduces the effectiveness of detection. To mitigate this problem, we advocate that the construction of a false alarm filter by utilizing machine learning schemes is an effective solution. In this paper, we propose an adaptive false alarm filter aiming to filter out false alarms with the best machine learning algorithm based on distinct network contexts. In particular, we first compare with six specific machine learning schemes to illustrate their unstable performance. Then, we demonstrate the architecture of our adaptive false alarm filter. The evaluation results show that our approach is effective and encouraging in real scenarios.

For the problem of duplication of work, low efficiency, high degree of user participation when making thematic maps with computer-aided method for the same type remote sensing image, we designed a thematic map automatic production system of remote sensing image based on xml file. System makes thematic maps after getting all the required parameters of rending from xml file, achieving the purpose of making a thematic map automatically, efficiently, accurately. The experimental results show that this system has improving efficiency and saving time for users, solving the current problems about production of thematic maps.

The paper introduces the semantic Web technologies into the fields of geospatial web services to research the three questions: Establishing a service-oriented geo-ontology model(SOGM) and extending OWL-S ontology to describe the semantics of geospatial web services; Designing a four constraints matching algorithm to discovery the large size geospatial web services quickly and efficiently; Designing a heuristic algorithm basing on weighted Graph Plan to compose atomic geospatial web services exactly. An experiment on remote sensing is conducted to validate the proposed approaches. The result indicates that the proposed approaches are valid for realizing intelligent geospatial web services.

How to detect mixed or unknown network attacks is difficult in anomaly detection .Network traffic has an important feature of nonlinear dynamics, The paper proposes a new method to detect abnormal traffic by approximate entropy, one of the important dynamics parameters, the relevant parameters are tested and compared in experiments. Finally,sequence of approximate entropy generated are processed by cluster analysis to improve accuracy, the results show that the method could identify traffic with mixed or unknown attacks well. Furtherlly improvements of the method are made a discussion.

In order to improve the fleet management level and the vehicle maintenance technology level, a remote on-line diagnostic system for vehicles was putted forward and designed in this thesis. This integrated system consists of OBD, GPS, 3G Techniques and GIS platform. By this system, the vehicle running information and the positioning information were collected and sent to the enterprise monitoring center through the 3G network, so the user could acquire the operation status of vehicle in real time. The operation status of vehicle contain vehicle OBD fault information, vehicle speed, engine speed, battery voltage, coolant temperature, date and time, location and path information of the vehicles and so on. This system has a high application value, because it can make the vehicle maintenance staff and fleet manager to get the vehicle’s realtime conditions and location remotely, so that they can give a corresponding maintenance and management methods according to the information.

Information processing in wireless sensor networks, demands a theory of computation that allows the minimization of processing effort, and the distribution of this effort is relative to the whole network. When monitoring spatial phenomena, such as temperature or humidity in an indoor environment, it is obvious that some sensors might represent their neighbors well with permitted error. Recently, a new clustering algorithm, named ”affinity propagation”, is proposed. Different from the popular k-centers clustering technique, affinity propagation operates by simultaneously considering all data points as potential cluster centers (called ”exemplars”) and exchanging messages between data points until a good set of exemplars and cluster emerges. In this paper, we apply affinity propagation for choosing exemplars in wireless sensing network. However, a difference is made between the original form AP and the algorithm in our application. The AP algorithm in our experiments is exploited thoroughly, under different spatial constraints. We only consider the relationship between close neighbor nodes under a certain threshold of distance, instead of the pairwise similarities between the whole network nodes. The experiments proved that our methodology can also effectively acquire necessary information of network status. Meanwhile, the scarce resources in network (energy, etc.) can be saved in a more efficient way.

Agent technology has played an important role in distributed network application and agent communication model is an inevitable problem in multi-agent system. We propose a “Shaikh-Guide” based agent communication model (SGACM) to support asynchronous communication, and based on this communication mechanism, we present the matching algorithms to decompose the whole network service into several groups of sub-tasks. During the course of decomposition, different priorities are assigned to sub-tasks. This paper introduces a network service management scenario to support agent communication and task processing. An experiment has been done with the communication model and algorithms, the results of which demonstrate the advantage of the algorithms. As an application example, the process of network multimedia service management is presented finally.

In the era of knowledge to win, this article introduces two "knowledge distance" concepts, and analyses its adaptabilities to diagnose knowledge gap. Based on such analysis, this paper sets up a general and clear path model on making up for knowledge gap. In final, by virtue of some cases, the paper sums up such three basic security mechanisms to make up knowledge gap as: knowledge learning, knowledge innovating and knowledge sharing.

Using directional crosslinks could help significantly improving the performance of satellite network because of the high safety, low power consumption, long communication range and little self-interference of the directional crosslink. The more in-depth study on satellite network with the directional crosslink, the more urgent to study the route of the network becomes. It is necessary to formulate scheduling of links with various constraints and facts before constellation operation and deployment for narrow-beam directional crosslink in the network. This article is based on a typical Walker model to analyze the routing of the network with directional crosslink, and proposes two route schemes. Finally, these two routing schemes are compared and summarized.

Nowadays, drives that use a combination of induction motors and frequency inverters are very common, a fact due to the financial practicality and viability in purchasing and operating that system. This system modeling and simulation becomes important when it wants to evaluate the performance, to calculate and correct parameters, and it has a fundamental role in functionality and viability analysis for application of new configurations and technologies. This work is about to elaborate a simple induction motor model based in the torque versus speed characteristic, using the linearization method for application in a specific operation range to be controlled by a frequency inverter.

Due to the complexity of usual bridges’ indicator system, the inaccuracy and strong subjectivity of method appearance observation method, in this paper, factor weights are determined by analytic hierarchy processing. And multi-level fuzzy comprehensive evaluation is applied to evaluate the bridge structural health. On the basis of determining bridge structure indicators, an evaluation model is established. The evaluation results, which derived from evaluation model, are compared with expert evaluation results. And the compared result shows that the model can evaluate bridge structural health accurately.

Serious queue jitter and sluggish response to dynamic network traffic are problems in existing AQM algorithms.Our study has focused on the judging mechanism of link congestion and proposed an algorithm combining the queue factor and load factor.The algorithm can make adaptive adjustment to the loss probability function with instantaneous queue length and provide better performance in control.It is verified by NS simulations to enhance the responsiveness of queue and make high link utilization.It also improves the adaptability and robustness of active queue management.

This paper discusses the reasons of the difference of students’ humanistic quality in higher vocational colleges in BeiJing

.

The research isbased on Hierarchical Linear Models (HLM). And then we make a quantitative analysis of the factors from both individual level and school level which affect the humanistic quality of the higher vocational students. At last, some suggestions are proposed on how to improve the students’ humanistic quality according to the research results.

This paper firstly analyzes composed elements of the urban rail transit(URT) network, on basis of which the line and the station’s abstract models are built. An integrated impedance function model is designed to describe the network. Besides, the K-shortest path finding algorithm based on Floyd(KSPF) for path finding problem is designed. With the above model and algorithm, URT Network Digital Management System(URT_DMS) is developed. Taking Beijing URT network as an example, this paper gives out three paths between Wangjing station and Military Museum station. Compared with commercial software, the result shows the proposed model is valid.

Due to the inefficiency of ship grounding prediction in Vessel Traffic Services (VTS), this paper proposes a two-stage alert architecture for this problem. Compared with conventional method which applies the uniform process to each object, the proposed method concentrates more on the individuation. The final calculation result-GRI (grounding risk index) can accurately reflect the degree of urgency that the ship to be grounded. The simulation results demonstrates that the GRI gives expression to the risk for grounding accurately in real time along one ship’s track, shows that the method is effective and feasible.

IA-32 emulation is a good solution to software compatibility for new computer architecture. However, the address translation from guest machine to host machine is one of the most costly process in IA-32 emulation. To reduce the translation overhead, this paper presented a Pre_TLB with a prefetching window which could predict the memory access of guest machine. The prefetching window slides with the current address which causes a Pre_TLB miss. All entries in the prefetching window are translated at the same time so that several times of memory access can be reduced. Experiments showed that Pre_TLB had a good performance both on hit rate and time cost. For the program with conspicuous locality property, Pre_TLB could only obtain an improvement of 2%. However, for the program with little locality property, the hit rate improved by 9%, and the time cost reduced by 20%.

Ultrasound and SAR images are corrupted with speckle noise which is multiplicative type noise. Reduction of this noise is very essential to use these images for better information retrieval. In the literature there are many techniques available to reduce speckle noise where each one has its own advantages and disadvantages. In the current communication, a modification to some of those techniques has proposed; specifically the additive speckle noise reduction is proposed in the log domain. When an image is transformed to log domain then multiplicative speckle noise is also transformed to additive noise and this noise can be reduced using additive noise reduction models. Modified techniques have been applied on standard and ultrasonographic animal images. Experiments validates that this approach produces better results than applying these techniques directly on images with speckle noise. The quality of resultant images is measured using SNR (Signal to Noise Ratio) and PSNR (Peak Signal to Noise Ratio).

To provide high performance services not merely over the existing Internet, but also over the increasingly ubiquitous wireless networks, we present an end-to-end framework and strategies that can be used successfully in corporate networks and small portions of the Internet. Through performance analysis and relevant simulation results, this framework achieves the goal of high performance characteristics.

Science & technology project post evaluation plays a important part in improving the technology innovation capacity and management level of energy and chemical enterprises, however, it’s index systems are inadequate at present. On the basis of site visit to large domestic energy and chemical management departments, we designed and issued questionnaires, then processed the collected data by resorting to statistical analysis software spss14.0 and amos 4.0. It could be concluded that science and technology project post evaluation should include the following five aspects: direct technology output, application promotion, exterior influence, science and technology research and development ability, sustainable development.

This paper presents a numerical modeling for diffusion of nuclide in porous media. This modeling takes Darcy’s Law (hydro field) into account, and the porous media is treated as being isotropic. To simplify the complex coupled issue, we assume the temperature field is isotropic and stable and mechanical field is stable, and then we can get the governing equations of diffusion process. With FEM method, we get the result from our modeling. To validate the modeling, we use COMSOL

®

to repeat the whole process, the results from our modeling and COMSOL

®

are compared in figure. We can see that our modeling is satisfying.

Because of the development of wireless network, the error control and quality control under wireless network become an urgent problem to be resolved at present. In the paper, H.264 dynamic redundancy encoding algorithm based on the channel state and video correlation is proposed. By analyzing various mainstream redundant encoding algorithms, we adopted Pro MPEG FEC algorithms as the redundancy algorithm. In the algorithm that we proposed, redundancy bandwidth is allocated dynamically from various correlations of video content and channel condition and it can improve the utilization ratio of channel resources effectively. Thereby it can improve the quality of wireless channel video transmission. Simulation environment was built for the algorithms performance validation. The results of simulation experiments demonstrate that the algorithm can improve error recovery rate effectively under wireless network and the performance is better than the traditional channel redundancy algorithm, especially, when the network packet loss rate is changing.

This paper presents the generation of optimal trajectories by genetic algorithms (GA) for a planar robotic manipulator. The implemented GA considers a multi-objective function that minimizes the end-effector positioning error together with the joints angular displacement and it solves the inverse kinematics problem for the trajectory. Computer simulations results are presented to illustrate this implementation and show the efficiency of the used methodology producing soft trajectories with low computing cost.