Proceedings of 2017 Chinese Intelligent Automation Conference

The cross-eye dynamic jamming based on power regulation is simple and the accuracy requirement of the jamming amplitude is reduced, which achieves ideal jamming effect. The principle of cross-eye is introduced in this paper. According to the implementation process of cross-eye dynamic jamming, a design method of cross-eye dynamic jamming simulation system is proposed, and its composition and work flow are described in detail.

The real-time data exchange technology is the key to the multi-node docking and multi-type data interaction of the maneuverable command automation system, which is the necessary way to realize the information sharing and efficient use. This technology can solve the problem that multiple transmission modes coexist and the data can not exchange, etc., in the interconnection process between the system and different hardware platform, network platform, database system. Based on the requirements analysis, a real-time data exchange software using dynamic multi-thread programming and multicast technique, is designed and realized. The software supports the status real-time display. It realizes the protocol conversion and data recording under different systems. It also can complete speed change recapitulation of the original data and data analysis, which achieves good results.

In this paper, an active fault tolerant attitude control system is provided for a flexible spacecraft to accommodate the unknown sensor faults. Firstly, a model-based fault estimation technique developed into the process behavior by using a virtual filter and an adaptive observer, and then estimating the amplitude magnitude for the faulty sensor in the presence of system disturbance and parameter uncertainty. Next, reconfigurable attitude controller is developed by combining both integral sliding mode control and linear matrix inequality technique. Meanwhile, the stability of the closed loop attitude systems under the designed active fault tolerant control (FTC) scheme is analyzed by utilizing Lyapunov approach. Finally, the effectiveness of the proposed fault tolerant scheme has justified by simulation result on a flexible spacecraft attitude control systems with a time varying sensor fault.

This paper presents a method that the algorithm of calculating the electric field combining with vector electric field multi-electrode gradient information is optimized by using the compression-aware greedy reconstruction algorithm, which can effectively identify the electric field gradient information and improve the accuracy rate of the voltage level detection judgment, through the research on the calculation method of the electric field gradient information of the electrical equipment in the substation. This method can accurately calculate the electric field distribution and voltage level around the high voltage conductor by analyzing the electric field information around the charged body. The distribution of the field strength at one interval of the charged carrier is obtained by tested in complex power frequency electric field environment of the 110 kV substation equipment. The results show that the method can effectively improve the detection capability of the voltage level of the charged carrier and the response speed of the electric field around the charged body, and improve the safety of the staff working in the complex electric field.

Passivity-Based Control (PBC) strategy, a new nonlinear method based on Euler-Lagrange (EL) model, of Split-Capacitor-type Shunt Active Power Filter (2C SAPF) is proposed in this paper. The strategy can effectively maintain the stability of the DC bus voltage and compensate for current harmonics, reactive power, unbalanced and neutral current without processing the positive and negative sequence components of all harmonics. In addition, it has a faster response and better compensating effect than conventional linear and nonlinear strategy. The proposed strategy is validated by simulations.

The evolution of cooperation among intelligent agents is a fundamental issue in multi-agent systems. It is well accepted that the individual strategy-updating rules play a significant role in the cooperation dynamics on graphs. The imitation mechanisms account for a large proportion of these rules, in which an individual will choose a neighbor with higher payoff and follows its strategy. In this paper, we propose a strategy-updating rule based on incremental learning process for continuous prisoner’s dilemma game. Under our strategy-updating rule, each individual refreshes its decision according to original strategy (self-opinion) and new strategy learnt from one of neighbors (social-opinion). The simulation results show the incremental learning rule can enhance cooperation dramatically when individual has higher resistance to imitate others or lower payoff sensitivity. We also find that the incremental learning rule has greater influence when individual obtains fewer information of neighbors’ payoff. The reason behind the phenomena is also given. Our results may shed some light on how cooperative strategies are actually adopted and spread in spatial network.

In this paper, stabilization in accelerated time with continuous state feedback control is considered for mechanical systems. A general approach to construct Lyapunov functions that can be used to show rapidly exponential stability of the feedback controlled systems is developed. Furthermore, the continuous feedback control does not lead to chattering phenomena in the presence of measurement noise. The rapidly exponentially stabilization scheme given here generalizes this prior research to multiple degree-of-freedom mechanical comparison is carried out through numerical simulations on two classical nonlinear systems that are representative of a broad class of mechanical systems.

To solve the problem of automatic recognition in the presence of significant geometric and topological variations of 3D shape components, a semantic relation driven SVM-based function recognition method is proposed. Firstly, the shape segmentation scheme based on approximate convexity decomposition is proposed to decompose the shape into shape components with different semantics. Secondly, a functional semantic similarity method based on component context relations is presented to qualitatively measure semantic relations between the obtained shape components. Finally, the SVM classifier with functional semantic similarity as kernel function is constructed to achieve the task of shape recognition. Experimental results show that the proposed method could improve the accuracy of function recognition of 3D shapes, especially for shapes with large-scale deformation.

Base on the analysis of the maximum Lyapunov exponent that can be used to characterize the system dynamic status and the irregularity degree, and according to the nonlinear dynamic characteristics on surface profile of shearing marks, the analysis method on characteristics of shearing marks based on Maximum Lyapunov Exponent is proposed. The collected surface profile curve of shearing marks is treated as the time series. The time series are reconstructed by phase space reconstruction theory. In order to make the reconstructed phase space fully reflect the system dynamic characteristics, the determination problem of time delay and embedding dimension are discussed. On above basis, the maximum Lyapunov exponent is calculated, and the reciprocal of the largest Lyapunov exponent is defined as the quantitative index of the mark surface, it can be seen that the maximum Lyapunov mxponent and the quantitative index of the surface profile curve with different shear marks is obviously different according to the analysis and calculation of the maximum Lyapunov exponent of the surface profile curve of the shearing marks. It is proved that the quantitative index is an effective parameter to characterize the surface profile of different shear marks. The characteristics of marks can be extracted and recognized effectively. Therefore the theoretical basis and technical method are provided for studying the surface characteristics of shearing marks.

In this paper, we improve an underactuated finger mechanism, design a robot palm, and a three finger robot hand with the finger designed. Solidworks simulation is used to verify the rationality of the design. The parts of the hand are modified for 3D printing, and the prototype of hand is produced by 3D printing which can reduce the production process with low cost, designing flexibility and other advantages. Finally, we make some grasping experiments for the prototype. The results show that the robot can grasp the objects with different sizes, and verify the rationality of the design and the feasibility of making the robot hand by 3D printing.

For verifying the design rationality and properties of a Linkage Underactuated Finger, we use Solidworks to simulate the grasp operation of the finger with different situations, which can be used to analyze the range of grasping, the underactuated characteristic, the uniformity of motion and the mechanical property of the finger. The finger mechanism contains springs that gives adaptive grasp capability for different objects. The results show the curve of angle, moment and contact force. We can get the results that the finger can grasp the cylinders whose diameter can vary from 0.106 to 0.851 respecting to the finger length and can produce 5 times of hand grasping force by small electromotor. Through the simulations, the effectiveness and performance of the finger is analyzed and verified.

In this paper, we propose a new trust model called EnhanEigen. This model uses the comprehensive trust to evaluate the peers and enhanced probabilistic peer selection algorithm to filter malicious peers and select the peers with high trust value. This comprehensive trust is composed of local trust value, global trust value, malicious percent (MP) and feedback consistency percent (FCP). MP and FCP are important parameters to help filter malicious peers. In experiments, good peers, malicious and malign peers, feedback cheating peers are used to validate the performance of the new trust model in peer to peer file sharing environment. Experiment show that the new trust model can greatly reduce the false feedbacks adopted by the network, has the shorter algorithm execution time and has a higher success rate of transactions. The new model can distinguish false feedbacks to resist the cooperative attacks from malicious peers and feedback cheating peers effectively.

In order to solve the problem that it is difficult to measure the polymerization conversion of synthetic rubber effectively, a soft sensor model based on Adaptive Network-based Fuzzy Inference System is proposed. The closed-loop feedback optimization system model is introduced to improve the convergence speed and accuracy of the model. The simulation results of styrene butadiene rubber device of a plant based on field data show that the soft measurement model can realize the on-line prediction of the conversion, which can meet the production requirements of the industrial field, and is of great significance for realizing the optimal operation of the device.

Strapdown inertial navigation system (SINS)/celestial navigation system (CNS) integrated navigation is widely used to achieve autonomous navigation for plane. The accurate initial attitude estimation of the SINS and the precise calibration of the Inertial Measurement Unit (IMU) installing errors have a significant impact on the overall navigation accuracy. Installation errors of the IMU in SINS causes the existence of an additional interference acceleration in accelerator output and gyros drift in gyro output, which can lead to navigation errors. To solve this problem, a new initial attitude estimation and installation errors calibration of the IMU by the SINS/CNS integration method is presented, in which the initial attitude and installation errors are estimated by aiding the SINS with celestial measurements. In this method, the SINS error equation in the navigation frame is used as state model, and the horizontal velocity errors, starlight vector and outputs of IMU are chosen as measurements. The simulations show that the maximum error in attitude is 6.18″ and the maximum estimation errors of installing errors is 2″, which demonstrates the feasibility and effectiveness of this method.

Systematic production order planning is the core task of steelmaking production. Its implementation has the following benefits: from the perspective of business, improving the management level of business, achieving the global optimization and strengthening the competitiveness; from the perspective of productivity, improving the direct rate of HCR and HDR, reducing consumption of energy, enhancing production efficiency, lower costs, improving the efficiency, and reducing the charge of waste products. In this paper, a two-stages mathematical model from scheduling the monthly plan to the order period plan is built up and an improved heuristic algorithm for steel production plan is proposed. Numerical results for the optimize plan is tested based on one of the largest steelmaking company in China. The application example shows this optimize scheduling method is useful, especially for the complex scheduling problem of large-scaled steel plant.

In this paper, a dynamic surface control (DSC) based adaptive fuzzy backstepping method is proposed for AUV (autonomous underwater vehicle) systems. The DSC is utilized to solve the “explosion of complexity” of traditional backstepping, the fuzzy logic systems(FLSs) are used to approximate unknown nonlinear function of AUV systems and the adaptive backstepping is employed to design controllers, and Matlab is used to conduct the simulation. The proposed control method can achieve position tracking effectively. The simulation results show that the adaptive fuzzy controller can overcome the influences of parameter uncertainties and load disturbance as well as achieve a good control effect on AUV system. This study has lots of practical application value.

This paper investigates the self-organized task allocation behaviors in swarm systems by means of evolutionary game theory. A group of E-puck robots are employed to study the potential factors influencing the strategy choices of tasks that require different costs. Endowing the cooperation and defection strategy to robots, we find possible approaches to promote cooperation among selfish robots without explicit communication or cooperation mechanisms. Irrespective of the global information and centralized control, the proposed method is related with the strategy evolution adopted by the robots performing the tasks. Results are presented for a system of physical robots capable of moving and collectively form a specified spatial pattern. The contribution is that evolutionary game theory offers a new approach to environment-specific task modelling in collective robots.

Since the conventional Duffing circuit is limited to low frequency signals detection, this paper proposes a new type of Duffing circuit for high frequency signals, which effectively avoids the instantaneous saturation problem. It also gives an operational block diagram and the detailed design of the main unit circuit. By adjusting the parameters of several elements, the circuit can realize the wide frequency detection from low to high frequency. Simulation results prove that the circuit is a chaotic system and has the extreme sensitivity to the initial condition.

Canonical Correlation Analysis (CCA) is the root method in the area of multi-view representation learning, but this method does not utilize the class information of the samples. Discriminative Canonical Correlation Analysis (DCCA) is developed based on CCA which takes the class information into consideration. However, DCCA is unable to take advantages of numerous unsupervised data and may perform poorly in real-world problems. Thus, we propose a method called Manifold Regularized Discriminative Canonical Correlation Analysis for Semi-supervised Data (MRDCCA). Our algorithm can not only use labeled samples to preserve the discriminant structure, but also estimate the intrinsic geometric manifold structure of data with both labeled and unlabeled samples by introducing the Laplacian regularization terms. Experimental results on Multiple Features database and face databases show the proposed approach can provide a better recognition performance.

A dual stage control system for inertially stabilized platform can be represented by a multi-input multi-output (MIMO) system which often consists of a gyro-stabilized platforms as the coarse stage and an additional servo mechanism in the imaging optical path as the fine stage. This paper presents a robust MIMO controller, which focused on the systematic design method not only for the fine stage but also for the coarse stage. And by considering the obvious difference of the model uncertainties and bandwidths between the two stages, the frequency separation design can be obtained by the well-defined weighting functions. The proposed controller can achieve both good tracking performance and overall system stability. Simulations indicate that the proposed controller can effectively improve the steady state tracking performance and overall system stability.

Aiming at the attitude cooperative control of fractionated spacecraft with uncertain moment of inertia and external disturbance torques, this paper proposes a distributed adaptive attitude cooperative controller according to graph theory and consistency theory. By using the controller, the cooperative tracking of modular spacecraft attitude to desired attitude is realized. Firstly, the modified Rodriguez parameter and Euler equation of motion are utilized to describe the attitude. Then, a directed graph is applied to describe the communication topology among modular spacecrafts, and the uncertainty of the moment of inertia of modular spacecraft is comprehensively represented by one parameter, followed by the online evaluation using the designed adaptive law. Meanwhile, compensation term is designed in the controller to offset the influence of the disturbance torques. The final simulation results verify the effectiveness of this controller.

For the sensorless control of permanent magnet synchronous motor (PMSM) drive system, it is necessary to realize the motor speed and position estimation. In order to overcome the serious chattering problem caused by the discontinuous control, on the basis of analyzing the principle of the traditional sliding mode observer, the Sigmoid function is introduced to the traditional sliding mode observer, and a new type of sliding mode observer is designed and its stability is proved. The simulation test results show that the novel sliding mode observer based on Sigmoid function eliminates the serious problem of chattering existing in traditional sliding mode observer, reduce the filter links and phase compensation link, and it can realize the precise estimation of the speed and position of the PMSM. At the same time the proposed method is effective for sensorless control of permanent magnet synchronous motor.

When the three-axis turntable runs at a low speed condition, the friction torque has great influence on the control performance and leads to bad tracking results. The paper puts forward the variable universe fuzzy control method based on fuzzy inference. The turntable variable universe fuzzy controller has been de-signed. It uses fuzzy inference to substitute function model design scaling factor which changes the universe of inputs and output of the fuzzy controller according to the inputs and output value. By comparing the low speed performance characteristics of the turntable system simulation under three different control strategies which are PID, Fuzzy PID and Variable Universe Fuzzy, it verifies the variable universe fuzzy control can effectively improve the control system performance, and has a strong adaptability.

How to improve the accuracy of industrial process fault recognition and the efficiency of algorithm training has been the focus and hotspot in fault diagnosis field. In this paper, deep learning is introduced into this field, and a fault diagnosis method (DE-DBN) is proposed by combining DE algorithm and DBN. First of all, we have established a DBN model, which can extract the effective features from the massive fault data and realize the Tennessee-Eastman (TE) process fault diagnosis; Then a set of hyper-parameters of the DBN model are learned by the DE algorithm, which is used for hyper-parameter initialization of DBN; At last, during the adjustment of DBN network weights, the weights are updated by DE algorithm using random deviation perturbation, which makes the optimized DBN network get better fault diagnosis effect. After a lot of experiments in TE process and compared with other commonly used methods, the result shows that DE-DBN method can effectively diagnose and recognize multiple faults from the original signal, and have high accuracy and efficiency of fault diagnosis.

Droplet micro-fluidization is an important branch of microfluidic chip field, and it has been widely used and studied because of its many unique advantages. The application of droplet-based system has a requirement that the dimension, speed, frequent of droplet should be controlled in accuracy because of require of experimental conditions and droplet detection is an urgent need. This paper based on the overview of existing droplet detection methods, droplet frequency detection based on image processing. The purpose is to design a microfluidic system which is portable, convenient detection, accuracy control. The advantage of droplet detection method is no need to add markers, high sensitivity, suitable for field testing.

Depending on the working principle of the robot in the laboratory, this paper explores the joint control of two-degree-of-freedom robot. The dynamics equations are established by Lagrange function. Then, it is not difficult to get the dynamic model of the robot from the dynamic equations. The control of the robot is achieved by the combination control of the position control of the robot itself and the current control of the permanent magnet synchronous motor (PMSM) which is motivated by the servo control system. The control position of the robot is achieved by using the adaptive controller and the control of the motor is completed by the sliding controller. By utilizing the aforesaid control strategies, it can get the servo control model of the robot. The simulation arises out of the simulation module in MATLAB show that the whole system can realize a superior tracking control of the robot, which proves the feasibility of the control methods.

Multi-cluster tools are widely used in semiconductor manufacturing. Recently, semiconductor manufacturing industry shows to reduce the wafer lot size, down to just a few, even being only 5–8 wafers. In such cases, it is impossible to apply a cyclic scheduling method since there is no cyclic action sequence. Therefore, this paper mainly focuses on the application of noncyclic scheduling with a unified description for different problems which contains serial flow, parallel flow and re-entrant flow. Moreover, a reachability graph model is developed based on the unified description, and then a dynamic decision method is used to search the optimal solution in the reachability graph. A series of experiments are conducted, and it shows that the proposed method is effective for noncyclic scheduling of multi-cluster tools.

Aiming at (SEMG) of gait recognition, multi-channel SEMG presents a adopts the wavelet packet transform (WPT) and learning vector quantization (LVQ) algorithm of neural network classifier. The SEMG signal based on the entropy criterion of optimal wavelet BaoJi decomposed each node decomposition coefficients, calculate the coefficient of each node corresponding sub band signal energy, after normalization processing characteristic vector input, LVQ neural network to achieve the gait recognition based on SEMG. Experimental results show that take two SEMG signals, the classifier can effectively identify upstairs, downstairs, uphill and downhill four action pattern, the recognition rate of 96%, can be reliably applied to the control of the prosthetic.

Computer go game is one of the most challenging research branches in the field of artificial intelligence and cognitive science. The success of AlphaGo has received worldwide attention on deep learning and computer go. In this paper, we present the survey of methods and algorithms for computer go game searching and situation evaluation according to the discussed literature in different development stages. This paper also gives the promising future research on the computer go.

In this paper, the additive kernel partial least square (AKPLS) is proposed for on-line monitoring of batch processes. The proposed AKPLS is a special case of kernel partial least square method which inherits advantages of partial least square and considers the nonlinear relationships among monitoring variables. Moreover, the monitoring statistics can be estimated only with incomplete data because the additive kernel can be decomposed into the sum of kernels in different time slices, which is suitable for the online-monitoring application of batch processes. Finally, the effectiveness of proposed AKPLS is verified by experiments on the fed-batch penicillin fermentation process.

In this paper, the disadvantages of the traditional artificial potential field method are analyzed when it applies to the mobile robot path planning. The improved artificial potential field method is put forward, and the problems in APF are overcome. By adding the relative distance between the robot and the goal into the function of the repulsive potential field, the GNRON problem is solved. And the method that sets the intermediate target point in path planning is proposed to solve the local minimum problem. On the basis of the improved artificial potential field method, the A* algorithm is used to get the required intermediate targets and the global optimization path are obtained. The mobile robot can find a more optimal and collision-free path in the indoor environment. The simulation result proves the efficient and flexibility of our new method.

Lacking labeled samples is an important bottleneck in the development of novelty detection in practical industrial applications. To solve this problem, this paper proposes a novel novelty detection method called active learning-based support vector data description (ALSVDD). Here, we combine the uncertainty information and the importance of each sample to guide the selection process of active learning. In addition, we propose a simple recursive sequential minimal optimization (SMO) strategy to solve the ALSVDD optimization problem. Finally, the experiments carried out on the UCI data sets prove the effectiveness of the proposed method.

Driven by the positive policies, the number of electric passenger vehicle in China is increasing rapidly. However, due to limited driving range, long charging time, and slow development of the charging facilities, there is a serious “mileage anxiety” and “charge anxiety” resulting in the driver. In order to solve the “mileage anxiety”, this paper adopts the driving data of electric passenger vehicle (EPV) of Jianghuai IEV5 to establish the range model based on SOC (State of Charge) under different seasons. As the driving data contains a large number of outliers, in order to reduce the interference of the outliers and effectively exploit the useful information contained in the outliers, the robust regression analysis (RRA) is introduced for the first time to establish the SOC-based range regression model based on the data-driven modeling.

The fault of the wind turbine pitch system is an important factor that causes the wind turbine to stop. In order to improve the accuracy of fault prediction, an intelligent algorithm for fault prediction of turbine pitch system based on Least Squares Support Vector Machines (LS-SVM) parameter optimization is proposed. Firstly, the data of SCADA system are analyzed, and four kinds of parameters, which are closely related to the turbine pitch system fault, are selected as the input of the model, and introduced the minimum output coding (MOC) to construct multiple classifications LS-SVM to realize multi-class classification of pitch fault. Secondly, the algorithm of particle swarm optimization is implemented to select the optimal feature parameters for the multi-class LS-SVM classifiers, and the classification accuracy of the PSO is taken as the fitness function value of the PSO. Finally, the model is applied to a wind farm 1.5 MW turbine. For comparison purpose, three widely used pitch fault prediction methods such as the BP neural network and standard support vector machines are utilized. The results show the proposed approach has a better performance both in training and testing accuracies, and provides an effective method for turbine fault identification and analysis.

This paper investigates the observability of multi-agent systems with time delay and time-varying topology. By using the property of structural controllability as well as the relationship between the second-smallest eigenvalue of Laplacian matrix and the control protocol, a control strategy is designed to preserve the controllability of the multi-agent systems. In addition, the results show that the observability of multi-agent systems with time delay and time-varying topology is consistent with that of the corresponding multi-agent systems with no time delay. Finally, some examples are presented to demonstrate the effectiveness of the theoretical results.

Security is a key problem to restrict the promotion of cloud computing, access control has important effect on security of cloud data for the distributed and dynamic characteristics of cloud computing. In order to realize the fine grained access control in cloud computing environment, the sensitivity classification strategy of objects and the authorization rules between the object owner and the cloud server have been studied. And then the classified-task-role access control model(CT-RBAC) according to T-RBAC model has been designed to get better efficiency and fine grain.

This paper proposes a novel fractional-order sliding mode control technique to synchronize two fractional-order gyroscope systems. It is assumed that all parameters of both master and slave system are fully unknown in advance. Moreover, the effect of input nonlinearity is taken into account. In order to deal with those unknown parameters, some adaption laws are given. On the basis of sliding mode control (SMC) technique and finite-time theory, a robust controller is designed to synchronize two fractional-order gyroscope systems in a finite time. Finally, a simulation example is presented to verify the effectiveness and applicability of the proposed finite-time SMC scheme.

In the ultra-tightly integrated navigation system, inertial navigation aids to carrier loops can improve the tracking speed and accuracy of GPS receivers effectively. Carrier loop using second-order Frequency-locked Loop (FLL) to aid third-order Phase-locked Loop (PLL) is under-researched in ultra-tightly INS. This paper analyses the basic structure of the carrier loops and derives the error expression of FLL-assisted PLL with inertial navigation aids theoretically. Analysis results and simulations represent that the better tracking efficiency is obtained by FLL-assisted PLL, while its phase vibration error is not rebounded when the loop bandwidth increases to a certain extent.

A real-time localization method based on monocular is proposed for unmanned aerial vehicles (UAVs) navigation in the indoor environment. ORB features are used to speed up the feature detection and feature matching is made more accurate by applying random sampling consensus strategies. We take advantage of g2o (General Graphic Optimization) to realize the bundle adjustment and thus improve the precision of motion estimation. To reduce the drift of estimated trajectory to a certain extent, a map based algorithm is adopted instead of the traditional pairwise visual odometry. Finally, the effectiveness of the algorithm is verified by the dataset, and several experiments have been performed in indoor environment to evaluate the performance of the algorithm.

Scene parsing is the task of labeling every pixel in an image with its semantic category. This paper presents an approach that combines the convolution neural network with per-exemplar detectors for scene parsing. We use a convolution neural network to learn the image global features. Compared to most standard feature extraction approaches, the convolution neural network features can describe images better. Our system has the following steps. First, we use the global features to find the retrieval set which is similar to query images from training dataset. Then, we use local features to compute the likelihood scores of superpixels in the query image, which combined the per-exemplar detectors and Support Vector Machine (SVM) for classification. Finally, our system integrates multiple cues into a Markov Random Field (MRF) framework. We evaluate our system on two challenging datasets. The experimental results show that our method can achieve good performance.

This paper developed a temperature and humidity monitoring system for a plant growth cabinet based on the embedded technology.The monitoring system took a STM32 microprocessor as its core controller, and the accurate acquisition, display and control of environmental factors (including temperature, humidity) in the plant growth cabinet were achieved by the monitoring system. In order to solve the problem of low accuracy of such monitoring system, the means and variances of the temperature and humidity data after fusion were obtained from a batch estimation fusion method based on average values. Then an adaptive weighted fusion was applied on each layer of sensor data in accordance with the optimal weight distribution principle to obtain accurate temperature and humidity values. The testing results shows that the system is quite stable and reliable, which can monitor and display the real-time temperature and humidity existed in the plant growth cabinet accurately and satisfy the needs for control.

This paper studies consensus of heterogeneous multi-agent systems composed of first-order and second-order dynamic based on event-triggered mechanism. Firstly, based on event-triggered control protocols are designed for both leaderless and leader-follower heterogeneous systems. By using Lyapunov theory and bilinear matrix inequalities (BMIs), the sufficient conditions are obtained to guarantee the consensus of heterogeneous multi-agent systems. Meanwhile, the Zeno behavior is excluded and a positive lower triggered bound be found between two consecutive actuation updates. Finally, numerical examples are presented to demonstrate the effectiveness of the proposed event-triggered consensus control.

The wireless sensor network is applied widely in many fields to monitor the service condition of the devices. However, due to the limit energy of the sensor nodes, the lifetime of the wireless sensor network becomes the vital factor to guarantee the stability and reliability of the system. In this paper, the cluster-based method is adopted to minimize the energy consumption of all the nodes and balance the energy consumption among all sensors to prolong the lifetime of the sensor network to the extreme. First, we calculate the optimal number of the clusters, ensuring the minimizing of the total energy consumption; and then we optimal the clusters based on K-means ++, guaranteeing the nodes close to each other are divided into the same cluster and decrease the energy consumption among the Non-Cluster Head nodes and Cluster-Head node. The valid of the strategy is verified in the following simulation, and the lifetime of the sensor network is increased noticeably.

In this paper, the issue of the static output feedback guaranteed cost reliable controllers investigated for uncertain linear systems with time-delay and actuator failures. By employing Lyapunov stability theory, Linear Matrix Inequality (LMI) and fault treatment method. Firstly, a static output feedback guaranteed cost controller is designed when there is no fault in the system. Then it is found that the system is unstable if the actuator fault occurs under the same controller. Therefore, for the same fault model, the static output feedback reliable controller is redesigned to make the system remain asymptotically stable and satisfy original performance index no matter the actuator fails or not. Finally, the simulation on the motion system of the linearized unmanned aerial vehicle and state response curve were made with MATLAB, and the simulation results show the feasibility and effectiveness of the proposed control method.

EPnP algorithm as a camera pose estimation algorithm has attracted much attention in recent years for its low computational complexity. However, since it is an analytical pose estimation algorithm, the algorithm is not robust to image noise. For this reason, in the paper, an iterative version of EPnP algorithm, called the IEPnP algorithm, is proposed. The basic idea of EPnP algorithm, such as virtual control points, is reserved, while the computational process are simplified. Simulations under different image noise levels are conducted, the results show that the proposed algorithm is more robust than the original algorithm to image noise.

Dynamic performance is an important indicator of system testing and plays a key role in analyzing the performance of the system under test. SPHS is used to test the dynamic performance of the system. The test time is greatly shortened and the excitation is more stable and equality. The frequency characteristic of the system is obtained by the Mirror mapping, and the dynamic performance of the system is judged. It can also effectively avoid the problem of digital ill posed by traditional least square method. The simulation results show that the proposed method has high precision and is suitable for the rapid test of dynamic performance of the system.

Active disturbance rejection control (ADRC) is a new type of control technology. It regards unknown disturbances and system’s uncertainties as total disturbance, which can be estimated and compensated in real time, so as to guarantee desired performance. Although bandwidth parameterization approach is effective, the performance may be not optimal. For getting a simple, effective and optimal parameter tuning approach, a new tuning approach based on time-multiplied absolute-value of error (ITAE) optimal index and bandwidth parameterization approach is proposed for second-order ADRC. Time domain and frequency domain responses and ITAE indexes have been compared between bandwidth parameterization approach and the approach proposed in this paper. Simulation results confirm the proposed tuning approach.

Twin podded propulsion is a new type of ship electric propulsion. Equivalent rudder angle analysis method was used to establish a relationship between ship speed and steering angle of POD propeller and the conventional rudder angle, and the maneuverability of twin podded ship was analyzed in different working conditions. RBF-ADRC (active disturbance rejection control) integrated controller is designed for twin podded ship according to its special structure, and the control effect was compared and analyzed with RBF-PID controller through MATLAB.

Skeleton-based human action recognition has recently drawn many researchers’ attention with the prevalence of Microsoft Kinect sensors. In this paper, a method of action recognition using the skeletal features is proposed to improve the classification performance and reduce the training time to a large extent. We first select several key frames in each action sequence which can represent the corresponding action significantly, and extract meaningful joint-based and body part-based features of key frames. Then, we use extreme learning machine (ELM) algorithm to achieve action recognition. We compare the proposed approach with other state-of-the-art methods on a large public dataset to evaluate the performance. The experimental results indicate that our approach can achieve a good recognition performance with extremely fast training speed, while applying the proposed method to the online action recognition system.

Adaptive dynamic programming (ADP) as an effective way for data optimization has been a long-term focus in the intelligent learning and control field while achieving many successful applications. However, improving the computational performance of ADP still remains a challenging problem in dealing with some complex systems. In response to this issue, we propose a novel ADP control scheme through the integration of a tunable node radial basis function (RBF) into the action neural network (NN) which is one of the most important parts in ADP. Through the use of the tunable RBF with fast learning speed and low computational cost, the computational efforts of ADP could be improved. The simulation tests on a complex control system are conducted, and the results show that our proposed scheme is effective while achieving a satisfactory robustness.

Hidden danger prediction plays an important role in safety production and safety supervision. To improve the hidden danger prediction accuracy of tertiary industries in some small-medium cities, this paper utilizes extreme learning machine (ELM) algorithm to study the impact of relevant management index on the trend of hidden danger, and conduct hidden danger prediction. ELM is a novel learning algorithm for single hidden layer feedforward neural network (NN) with fast learning speed and good generalization performance. We use the nationwide enterprise hidden danger data to conduct the prediction experiment, and the comparisons between traditional NN learning method and ELM demonstrate the effectiveness and superiority of our method.

In order to perfect the domain ontology construction process and construct more effective domain ontology to provide semantic support for multiple domain applications, a domain ontology construction method DOCM is proposed. In view of the shortcomings of existing methods, this method divides the ontology construction process into ontology requirements analysis, domain knowledge analysis, ontology establishment, ontology evaluation, and ontology modification effort assessment. And a three-dimensional modification assessment method is proposed to evaluate the modification effort when the ontology is used or modified. In this assessment method the changes of the number of concepts, properties and the semantic relationships in the ontology are all considered. Therefore, Based on the assessment method, the ontology owner can track the ontology development process and grasp the effort for ontology modification and use. And according to the evaluate results users can determine whether modifying the ontology directly or extracting the needed ontology elements from the ontology before it is used.

This paper investigates adaptive generalized function projective synchronization of two colored networks in finite time. Based on the finite time synchronization control technique and Lyapunov stability theorem, sufficient conditions are derived to guarantee the realization of adaptive generalized function projective synchronization. Finally, two numerical simulations are provided to support the proposed theoretical results.

Aiming at the problem of Mars entry guidance, the all-coefficient intelligent adaptive predictor-corrector guidance method is proposed. The first order characteristic model which describes the relationship between guidance increment and error of generalized predictive range is established. The convergence of guidance method is guaranteed by the successive approximation of adaptive control, so the convergence problem of conventional predictor-corrector guidance based on iteration method is avoided effectively. The second step, the above-mentioned method is compared with robust guidance method that tracking nominal trajectory and predictor-corrector guidance method based on iteration. Finally, numerical simulation is done for the three methods. The results show that the all-coefficient adaptive predictor-corrector guidance method has higher accuracy than the other two methods. What’s more, the time is shorter than the traditional predictor-corrector guidance method, so it is more suitable for engineering application.

Encouraged by recent methods disable to achieve good tradeoff between accuracy and convergence. To close the gap, we propose to combine multi-feature based deep learning. We enable our analysis by facial recognition and comparison. We increase proportion of face and feature in an image. Firstly, we crop face,eyes, nose and mouth regions. Second, we extract features and combine them. It can be shown that it is efficient and it has capable of convergence quickly in facial recognition. Our method achieves the best performance on LWF by 97.98%. We make facial comparison by improved Siamese network. In the network, we add Spatial Transformer Networks. With improved Siamese network, it can be efficiently optimized with different perspectives and thus guarantee good robustness. Extensive experiments demonstrate that accuracy and stability improve significantly than tradition Siamese network. Furthermore, our method has good generalization. Without training again when you want to compare two images. These algorithms implanted to C# platform, we make interface of facial recognition and comparison.

Decomposition rate of raw meal is decided by decomposing furnace temperature directly, which influenced quality and energy-consumption greatly. But the set-point of decomposing furnace temperature is always given by manual mode in most of Chinese cement enterprises. So one solving scheme is put forward to give the optimal setting value of decomposing furnace temperature automatically. Firstly the model of Least square support vector machine (LS_SVM) is used to realize the online estimation of decomposition rate of raw meal. Then based on the fuzzy rules, the optimal values of decomposing furnace temperature is given, and it is modified by the current of the rotary kiln motor. The simulation results and the practical application results both showed that the system is practical and effective in a certain extent.

Safe diagnosis was viewed as the first necessary step of fault-tolerant supervision of discrete event systems (DESs), and a method of safe diagnosis for stochastic DESs was proposed by constructing safe diagnosers in the literature. However, the complexity of constructing such safe diagnosers is exponential. In this paper, we present an algorithm to perform safe diagnosis of stochastic DESs by constructing a nondeterministic automaton called the safe verifier. The necessary and sufficient condition for safe diagnosability of stochastic DESs is proposed. It is worth noting that the complexity of constructing the safe verifier is polynomial in the number of states and events of the system.

Industrial convergence is the key of the industry research at home and abroad, the integration of different industries can create new economic growth points. In the era of great health, Guizhou province put forward the concept of global tourism in a timely manner, which provides a good opportunity to develop the tourism industry. In order to promote the integration of tourism industry on the basis of the actual situation of Guizhou Province, we put forward the overall goals and problems in order to integrate with the tourism industry in one area of Guizhou province, and established the extension model. Then, we analyzed the model by using the divergent analysis, correlation analysis, conjugation analysis and contained analysis. Finally, we do the extension change and obtained some innovation strategy and solved the contradiction problem that exists in the process of the development of tourism industry integration. The method may provide some ideas to realize the intelligence, systematization and sustainable development of tourism industry in Guizhou Province.

Appropriate tracking gate selection will highly improve tracking quality. A hybrid encoding genetic algorithm is proposed to off-line optimization of maneuvering target tracking gate in clutter. Binary string and floating-point string represent shape and size of gate respectively. Hellinger distance is selected as metric for tracking performance evaluation and can be core part of the fitness function of genetic algorithm. Generally speaking, the tracking system optimization can be converted into genetic algorithm optimization, and the gate parameters can be efficiently tuned in different scenarios.

At present, the research on the theory and applications of business model often shows the experience and qualitative forms, and it may lead to the operational lack of the design and innovation of business model. Extension design is a formal modeling method to combine the qualitative form with the quantitative one, and it not only avoids the disadvantages of the most current design methods, but also easily realizes intelligent management by using computers. By combining the extension design method and the business model, we presented extension design method of business model, and proposed the community hospital as an example to show its feasibility and effectiveness.

In this paper, an adaptive sliding mode controller is designed for an active gravity offload system (AGOS). The system is consisted of a suspension structure, a buffer, a universal joint, a gantry robot, a tilt sensor, a tension sensor. The object is attached to the system through the suspension structure. The suspension structure is mounted on the buffer. The buffer is connected to the gantry robot through the universal joint and the tension sensor. The gantry robot can provide the object three dimensional space movement. The buffer and the gantry robot are utilized to simulate a variable gravity field. The suspension structure is used to guarantee the object to rotate freely. The system dynamics model is given based on Lagrange equation. Then an adaptive sliding mode controller is proposed considering the control input uncertainties, system uncertainties and external disturbances. The upper bounds of these uncertainties and disturbances are not required. The Lyapunov theory is utilized to verify the stability of the controller. Simulation results show the effectiveness of the proposed control strategy.

A novel control strategy of permanent magnet synchronous motor (PMSM) drive systems is proposed. Based on radial basis function neural network (RBFNN) and direct model reference adaptive control (MRAC), the grid-side controller is designed to regulate output direct current (DC) bus voltage. The coordination control combined sliding model control (SMC) based on signal and port-controlled Hamiltonian (PCH) control based on energy is used to motor-side and achieves the fast speed tracking control and the real-time energy optimization. The controllers based on regenerative braking ensure that the motor can run in four quadrant, energy is bidirectional flow, and the DC bus voltage is controllable. The simulation results demonstrate the validity of the strategy.

Ship is a typical multi-input and multi-output nonlinear system, which has the characteristics of large inertia and large time delay. In this paper, the mathematical model of the twin-rudder twin-propeller ship with interference under oceanic navigation conditions is established by using MMG separation model. In order to overcome the disturbances, a nonlinear iterative sliding mode (NISM) controller based on Cerebellar Model Articulation control algorithm (CMAC) is presented. And nonlinear iterative sliding mode controller is designed with strictly bounded hyperbolic tangent nonlinear function tanh(x). The stability of the algorithm is proved by the Lyapunov theory. In addition, numerical simulation of course control is carried out, and the simulation results show that the controller can get better performance.

The fast developments on the computer and networking technologies have made the Internet become the largest medium of information in the word at present. Many companies hope to be able to timely and effective access to information from the Internet. Efficient webpages classification system is needed. According to the classification requirements, we use LDA-SVM model for elaborate web category classification. And we discuss the impact of topic number K in LDA to the classification. The experiments show our method is efficient.

Robust control problem of singular system with actuator saturation is studied. Stability condition for nominal system with actuator saturation is derived based on Lyapunov theory. As nonlinear uncertainty widely exists in real systems, a compensator controller and parameter adaptive law are designed for a class of singular nonlinear systems, and the linear matrix inequality condition is derived for system stability. The results of this paper could be regarded as the extension of singular system theory with actuator saturation. At the end of this article, an example is given to show the correctness and effectiveness of the proposed method.

This paper presents an adaptive tracking controller for a class of omnidirectional mobile robots with full-state constraints, model uncertainties and external disturbances. Kinematics and dynamics of three-wheel omnidirectional mobile robots are considered in the paper. And the adaptive estimation law is designed to deal with disturbances where the bounds of disturbances are unknown. Meanwhile, the control method based on barrier Lyapunov function is applied to prevent the states from violating restrictive conditions. All signals in tracking system are proved to be uniformly bounded with the proposed controller. The tracking performance will be guaranteed and the tracking errors will be sufficiently small by choosing suitable controller parameters. Simulation results validate the effectiveness and the robustness of the proposed control method.

A novel redox flow battery–single flow Zinc-Nickle battery is introduced in this paper. Based on the experimental data of battery under pulsed discharge, An improved Extreme Learning Machine(ELM) model of single-flow zinc-nickel battery which combined with equivalent circuit and Extreme Learning Machine was established. Compared with simple ELM model, the proposed model can be used to simulate the variation of single-flow zinc-nickel battery terminal voltage with higher accuracy in discharge process. Based on the model of single-flow zinc-nickel battery, the variation of battery State-of-Charge (SoC) in discharge process is calculated by using adaptive unscented kalman filter (AUKF). The experimental results show that SoC estimation value could converge to actual value nearby with fast speed by using AUKF based on proposed model, even if initial SoC error is large. Compared with unscented Kalman filter based on simple ELM model, this method has better estimation precision.

In this paper, a joint model is devised to simultaneously tackle the age invariant face recognition and age estimation. The face image is separated into two parts: the identity specific part and the age related part. Two dictionaries are introduced to encode the two parts onto two different subspaces. Label constraint terms are added to improve the discriminative capabilities of the two learned subspaces. The identity space can be used for face recognition and the age space can be used for age estimation. Extensive experiments are conducted on the FGNET dataset and MORPH dataset, illustrating a great improvement over the state-of-the-arts.

In this paper, we implement a local path planning method for quadrotor vehicle. The visual potential is utilized to guide the vehicle which is built by gradient vectors and TTC information of obstacles obtained through optical flow. Compared to traditional method, our method is more general and lower computational for calculating visual potential. Using the visual potential, the quadrotor vehicle dynamically determines the yaw angle and autonomously generates a collision free path to the destination via a PID controller operating as the dynamic control scheme without any prior knowledge or environmental maps of the workspace. The experiments carried out in virtual environments show the better feasibility of our technique for path planning.

As social networks play more and more important roles in daily life, some malicious people use them to disseminate disinformation. For mining opinion leaders, we propose a method combined with sentiment analysis to find suspicious as well as dominant users. This mining method takes the tree to interpret the users’ interaction in social network for calculating users’ influence score. Besides, a hierarchical algorithm is used for emotional analysis. In order to search for users who probably against the national security, experiments on the dataset from Sina Weibo show our procedure has an advantage over traditional methods.

Marine hydraulic steering gear is an important part of marine engine room system. Based on the analysis of the structure and mechanism of the marine hydraulic steering system, and combined with the modular modeling method, the mathematical model of marine hydraulic steering system, including system pipeline, hydraulic pump, steering gear and rudder angle controller is established. At the same time, a one-dimensional and two-dimensional cloud mathematical model, and a PD + I cloud model intelligent controller are designed and applied to control the marine hydraulic steering gear in this paper. The simulation results confirm that the cloud model based controller outperforms PID controller in terms of control stability and accuracy.

With the development of computer technologies, the demand for high quality tourism information search is getting higher, so the tourism information search system should consider more search intents of tourists. In this paper, we propose a search method of tourism information based on attraction dynamic topic distribution. First, we construct the attraction dynamic topic model. Second, we train the model with tourism comments of different time periods to get the topic distribution of attractions in time dimension. In this way, we can speculate user’s search intents by the topic distribution of the query. Finally, based on experiments on tourism comments crawled from websites, we verify the dynamic change of attraction topics and the accuracy of our proposed search method.

Motion transition of virtual human is usually used for changing from one motion to another, which is one of the most important motion editing methods for virtual human motion capture data reuse. In this paper, we present a solution to connect virtual human motion clips smoothly by using bézier quaternion curve. Four control points are used to determine the shape of the bézier quaternion curve and De Casteljau’s algorithm is for calculating interpolation points. Slerp (the abbreviation of quaternion spherical linear interpolation) is chosen as the interpolation method. As a result, realistic and real-time human motion transition is achieved, as experimentally demonstrated with the walking and running motion sequences.

Control technology is the core technology of dynamic positioning (DP) systems, and the algorithm design of a controller can affect the precision of DP systems. In this paper, an integrated controller combined RBF neural network and active disturbance rejection control (ADRC) technique is designed for a ship DP system. Compared with traditional ADRC, this method can greatly reduce the number of adjustable parameters. What’s more, Simulation results show that RBF-ADRC controller has strong robustness and adaptability, to increase the interference suppression range of ADRC controller.

Scene analysis positioning method can effectively solve the problem of high model dependence in mobile robot auditory localization, and location fingerprint database is the foundation of scene analysis method. However, for precise location by fingerprint positioning, mass reference points are always needed. A sound source localization method based on virtual reference points is proposed to meet the drawback of positioning accuracy on account of low density fingerprint, without taking up the database storage space nor increasing the matching computation. Simulation and experimental results show that the novel method can effectively reduce the number of location reference points for location accuracy needed. Compared with the conventional auditory localization method, method based on virtual reference points has better efficiency in sound source target positioning, and significantly enhance the applicability of the robot auditory system.

At present, most vehicle tracking algorithms are tested in an ideal environment (definition video, without attitude change, without occlusion). However, due to the vehicle variability (scale change, attitude change) and complexity (noise, occlusion) of the surrounding environment in real-life scenarios, there are still many difficulties for the vehicle target accurately tracking. In order to adapt to blur, occlusion, fast moving, illumination change and other complex situation in real scene, the multi-scale Retinex image enhancement algorithm and the mutual information occlusion processing algorithm are introduced into structured output SVM tracking algorithm. Experiments show that the tracking accuracy and robustness of the method are improved significantly on the premise of tracking task performance in the above case.

Future spacecraft systems will require careful co-design over both physical and cyber elements to provide a better performance for holistic system. In this paper, a neural network (NN) based self-triggered control approach is introduced for the spacecraft attitude control problem. The self-triggered control is a resource-aware strategy which allows a reduction of the computation and communication demands, while still guaranteeing desirable closed-loop behavior. The NN is used for approximation of the triggered condition. We derive the equations of attitude motion for the spacecraft, and then develop a general procedure leading to NN based self-triggered feedback control implementations on a rigid spacecraft. Finally, the efficiency and feasibility of the obtained results are illustrated by means of a numerical spacecraft example.

Currently, a mature method for Single Observer Passive Location and Tracking is purely based on azimuth. But when we want a good result of observation, the motion form of the observation platform would get limitations, and the nonlinearity between angle measurement and target motion parameters makes it difficult to locate the target purely by azimuth. As to radar systems with the ability of frequency measurement, we introduce the change rate of Doppler frequency to observation and combine it with azimuth, which can greatly improve the accuracy of targets tracking. Besides, due to the changing motion state of maneuvering targets, we may lose the targets just by using the traditional tracking methods. So, a method that adapts to the maneuvering targets should be applied into this case. Then, the intersecting multiple model (IMM) is an effective method for tracking maneuvering targets.

The paper designed a radar signal processing system in order to help the intelligent vehicle to recognize the surrounding environment. Firstly, a hardware platform was designed to filter and amplify the output signal from radar using TMS320F28335 as the main controller. The hardware platform can also supply modulation signal to the radar. Then the radar signal processing algorithm was designed to recognize the targets in front of the radar. In the algorithm, FFT and Chirp-z were used to determine the peaks’ frequency of the targets. And the CFAR algorithm was used to remove the interference of environmental factors. What’s more, the peaks match algorithm and false targets exclude algorithm were used to avoid false alarm and missing alarm. Finally, the algorithm was programmed in C language and downloaded to the hardware circuit to do some test. The test results show that the millimeter-wave radar signal processing system designed in this paper successfully realizes the calculation of the distance, velocity and direction of the targets.

Aiming at the problem of remote control for unmanned mobile robot, the architecture of monitoring system for unmanned mobile robot based on Web is designed. Which realized the real-time monitoring, stores historical data and remote control. The monitoring system is composed of front-end data acquisition with controller, data monitoring center and client. The front-end data acquisition with controller are designed by embedded platform, including ARM11S3C6410 main controller, wireless 4G DTU, gyroscope, accelerometer, GPS and speed measurement module. The main controller reads the running state information and transmits it to the data monitoring center through 4G DTU in real time. The data monitoring center receives instructions from the remote client of the Web to remotely control of the unmanned mobile robot. The data monitoring center based on B/S architecture. Finally, the client accesses the data monitoring center online through the browser to meet the needs of remote monitoring for the unmanned mobile robots.

Visual-Inertial fusing for UAVs navigation has been an attractive complementary solution with great potential. Visual approaches can provide lots of information about the environments but will fail in less-features surroundings or in fast motion. What’s more, visual sensor has no sense of gravity vector and monocular has no observability of metric scales. IMU can predict the pose of camera accurately and supply gravity vector for visual-ways localization. In this literature, we propose a novel visual-inertial tightly fusion and nonlinear optimization approach for UAVs navigation. Camera-IMU calibration is carried on for real experiment and camera-IMU states initialization is also considered as optimization can benefit from a good initial states guess. Experiments are implemented to evaluate the algorithms using EuRoC flying datasets and our own designed flying platform. Performances are compared with the state-of-the-art visual SLAM approach.

In this paper, we focus on the hybrid autogyro, which is a combination of autogyro and fixed-wing aircraft. To explore the flight characteristics and improve the control quality, a dynamics model is established, in which the rotor dynamics are modeled via an explicit mathematical form of blade element theory. Due to the complex structure of the hybrid layout UAV, the aerodynamic interference between the parts is strong, and the model has strong nonlinearity and certain uncertainty. We choose back-stepping control method to design the attitude controller. The result shows that the controller can successfully control the hybrid autogyro’s roll, pitch and yaw angles. Tracking is quick and largely satisfactory, with good stability.

This paper presents a method of vehicle lateral collision avoidance based on receding horizon strategy. In this paper, the vehicles’ position and velocity information is obtained based on the millimeter-wave radar, the global satellite positioning system (GPS) and the vehicle to vehicle communication technology. Then the method of transforming the vehicles’ position and speed to the host vehicle coordinate system is given. The trajectory prediction model and collision recognition algorithm of the host vehicle and the other vehicle are established based on the kinematic model. The receding horizon control algorithm is used to control the host vehicle speed in order to avoid the coming collision. The simulation results show that the host vehicle can achieve the goal of lateral collision avoidance by receding horizon control algorithm under the condition of constant and changed vehicle speed.

Building information modeling is the digital expression of the building, which can provide accurate information for the whole life cycle of the building. However, the problems such as poor data sharing, high hardware requirements and inflexible application services stunted the popularization of the technology. The building information model that produced in design stage is not well utilized, which causes the waste of digital assets. In order to maximize the value of digital assets, this paper puts forward a kind of building information model display technology application method which is low cost, cross platform and easy expansion.

Due to the variations of hand posture and the intricacy of environment/background, hand detection is a challenging task in human-computer and human-robot interactions. A hierarchical method is proposed in this paper to detect hand from images with cluttered background. In order to remove the most skin-like background of the image, upper body is detected from the image in the first hierarchy. Secondly, a novel approach, which combines samples threshold with experiential threshold, is proposed to detect skin/skin-like regions in images, and then skin regions are obtained according to the thresholds of area and length-width ratio of connected areas. At last, hand patches are determined by the hand model which is produced by support vector machine. The efficiency of this method is proved by corresponding experiments in hand detection in our dataset.