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This article presents a model predictive control (MPC) coupled with an artificial potential field (APF) to resolve the trajectory tracking while considering the obstacle avoidance. In this article, the obstacle avoidance problem is solved by a local path planning based on the artificial potential field by constructing a virtual goal. A virtual goal is generated to produce an attractive force to guide the mobile robot to a collision-free space. The planned path is controlled by a proportional–integral–derivative (PID) controller to avoid collision. After arriving at the virtual goal, an off-line explicit MPC is calculated to obtain the optimal control inputs to track the reference trajectory. The simulation results show that the proposed method can be applied to control the mobile robot in the environment with one obstacle.

In recent years, the news industry, digital rights management, digital advertising, content production and social media are facing inevitable problems, such as fake news spreading, press freedom conflicts, advertising fraud, difficulties in digital rights management, defects in content production mechanisms, and rumor spreading aggravated by social media. Fortunately, the development of blockchain technology, characterized by decentralization, security, and tamper-proof, offers a brand-new perspective on the above problems. Existing research and projects on this topic firmly believe in the new opportunities for the media industry brought by blockchain technology. This study mainly focuses on the following parts: (1) Describe current difficulties faced by the media; (2) Review the relevant research and projects on blockchain applications in the media industry, and discuss the possibility of developing “Blockchain + Media”; (3) Summarize the shortcomings and challenges of blockchain application in media. Through investigation, it is found that blockchain has great potential for media development, but current studies on this topic are still in early-stage, and the results need to be observed.

In order to extract as much power as possible from the costly PV power system, the maximum power point tracking (MPPT) is necessary. It is well known that the output characteristics of PV array are nonlinear, the MPPT control scheme at same irradiation is easy because of there is only one maximum power point (MPP). However, the P–V output curves of the PV array display the multiple peaks with several local peaks (LPs) and one global peak (GP) at partial shading. Rapid fluctuation of the shading and improper physical constraint make the MPPT method difficult. Generally, there will exist several peaks due to the passing cloud, shading of buildings, dust, and excreta of passing birds. In this paper, a novel forecasting technology using the law of conservation of energy is proposed to roughly track the GP of a PV array which located in the adjacent area of accurate GP. The proposed strategy is easy to combine with the conventional MPPT method and rapidly gain the actual GP. Study results and experiment tests demonstrate that the proposed algorithm can perform on a real PV power system under uniform or complex irradiation conditions.

Numerical simulation has always been the most important method for predicting tropical cyclone activity and has also been widely used in tropical cyclone climate research in recent years. However, the ability of numerical models to simulate tropical cyclone activities on a climate scale is still uncertain. Therefore, it is necessary to strengthen the modeling of the physical process of the model and strengthen the understanding of the impact of simulated tropical cyclone activities. In this article, time information is used to study the activities of tropical cyclones. Since time information is everywhere, a large amount of time information is now generated. This paper proposes a Spark-based auxiliary catalog system for managing large amounts of time data in a distributed environment. It uses a local index structure to significantly improve the query efficiency of temporal operations. Using this technology to explore tropical cyclones in the sea can improve the accuracy of tropical cyclone activity simulation and further the research on tropical cyclone climatology. In this article, we also apply tense big data to business English cross-cultural communication and use tense big data to manage a large amount of information that exists in business English cross-cultural communication, so that researchers can use relevant information to find problems in cross-cultural communication in the company language, and then study these problems. From the perspective of cross-cultural communication theory, this helps to enhance students’ phonological awareness and improve their use of business English. This paper uses a large amount of time data as the research basis and applies it to tropical cyclones at sea and business English to promote the vigorous development of cross-cultural communication.

Edge computing is a promising paradigm to provide computing capabilities in close proximity to mobile devices in fifth-generation (5G) networks. However, most wireless sensor devices connected to the 5G network have limited battery life, and how to effectively reduce energy consumption and extend the network life cycle has become one of the hot problems in current research. Due to this motivation, an improved Stable Election Protocol (SEP), named Lightweight in Edge Computing-SEP (LEC-SEP) is proposed. LEC-SEP algorithm considers the heterogeneity of the initial energy of the nodes and the cluster head election is determined by the probability that the relative level of the initial energy and the residual energy. According to the influence of the number of cluster heads, the optimal clustering number is calculated to balance the network traffic. At the same time, the location of the base station is redefined to facilitate adding the edge server, which can store the data aggregated and fused by base station, providing powerful and real-time storage and computing power to effectively offload the pressure of the central cloud. The simulation results show that the energy consumption is well distributed in the proposed algorithm, and LEC-SEP algorithm achieves a longer stabilization period in the network than other typical clustering algorithms. The network life of LEC-SEP improved by 8.17% and 20.34% in comparison with the P-SEP algorithm and the IDEEC algorithm respectively.

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Smart devices are emerging objects with computing capacity and sensing function, which play a vital role in the intelligent society. There is a growing body of literature that recognizes the importance of smart devices. This study uses the literature research method to discuss related researches on the usage of smart devices. The results show the overview of smart devices and some research issues, focusing on the three key themes: usage behavior, usage motivation, and influence to present the current state of user researches. The main challenges in this field are identified as the digital divide, digital assets & privacy protection, development challenges of human survival. Finally, some broader perspectives from future tendencies are explored.1

The high penetration distributed generation (DG) access to the distribution network caused the distribution network protection to be extremely complicated. Based on the analysis of the impact of distributed power grid connection on the fault current of the distribution network, a protection scheme based on operating conditions was proposed. In the scheme, some signals including voltage, current and frequency which collected during the island detection process of the distributed photovoltaic distribution network are used to determine whether the grid-connected distributed photovoltaic power generation system is in the state of equal power or under power or over power through the principle of power conservation. Further, according to the magnitude of the sudden change in the voltage and current on the grid-connected side and the output power, voltage, and current of the photovoltaic array side, a determination is made as to whether a fault and a fault occurrence area occur. The solution requires low accuracy of voltage, current, frequency and other information, and is simple and reliable, and does not require the high communication channels and speeds. Various data are collected during the island detection process, which saves sensors and is good economy for the distribution network.

Parameter optimization of a hydrological model is intrinsically a high dimensional, nonlinear, multivariable, combinatorial optimization problem which involves a set of different objectives. Currently, the assessment of optimization results for the hydrological model is usually made through calculations and comparisons of objective function values of simulated and observed variables. Thus, the proper selection of objective functions’ combination for model parameter optimization has an important impact on the hydrological forecasting. There exist various objective functions, and how to analyze and evaluate the objective function combinations for selecting the optimal parameters has not been studied in depth. Therefore, to select the proper objective function combination which can balance the trade-off among various design objectives and achieve the overall best benefit, a simple and convenient framework for the comparison of the influence of different objective function combinations on the optimization results is urgently needed. In this paper, various objective functions related to parameters optimization of hydrological models were collected from the literature and constructed to nine combinations. Then, a selection and evaluation framework of objective functions is proposed for hydrological model parameter optimization, in which a multiobjective artificial bee colony algorithm named RMOABC is employed to optimize the hydrological model and obtain the Pareto optimal solutions. The parameter optimization problem of the Xinanjiang hydrological model was taken as the application case for long-term runoff prediction in the Heihe River basin. Finally, the technique for order preference by similarity to ideal solution (TOPSIS) based on the entropy theory is adapted to sort the Pareto optimal solutions to compare these combinations of objective functions and obtain the comprehensive optimal objective functions’ combination. The experiments results demonstrate that the combination 2 of objective functions can provide more comprehensive and reliable dominant options (i.e., parameter sets) for practical hydrological forecasting in the study area. The entropy-based method has been proved that it is effective to analyze and evaluate the performance of different combinations of objective functions and can provide more comprehensive and impersonal decision support for hydrological forecasting.

This paper proposes an improved Harmony Search algorithm, and gives the definition of the Global Sharing Factor of the Harmony Search (HS) algorithm. In the definition, the number of creations of the HS algorithm is applied to the sharing factor and calculated. In this algorithm, the natural harmony encoding method is used to encode the initial harmony, and the total path length of all vehicles is taken as the optimization objective function. A new harmony generation strategy is proposed as follows: each tone component in an evolution is calculated separately using the new learning strategy and update strategy. In the calculation process, the tone component is judged by whether it needs to be adjusted according to the adjustment strategy. In this way, the problems of singularity and randomness of the new harmony generation strategy of basic HS are solved to improve the diversity of algorithm solutions. Then, a new Harmony Search method with Global Sharing Factor based on natural number coding and decoding for the Vehicle Routing Problem (GSF-HS-VRP) is proposed. The improved Global Sharing Factor-Harmony Search-Vehicle Routing Problem (GSF-HS-VRP) algorithm is applied to capacity-limited vehicle path optimization problems compared with the HS, Improved Harmony Search (IHS), Global-best Harmony Search (GHS), and Self-adaptive Global Best Harmony Search (SGHS) algorithms. The small-scale data and Solomon examples were adopted as the experimental data. Compared with the other four algorithms, the GSF-HS-VRP algorithm has the shortest running time, more rapid convergence speed, and higher efficiency. In the multi-vehicle test, with the increase of the number of vehicles, the optimized path of the vehicle is more satisfied in relation to the actual needs of customers. The results showed that this method could effectively improve the optimization performance of the capacity-limited vehicle routing problem.

In the era of multi-screen, users acquire information and entertainment through smartphones, personal computer (PC), tablets and other devices. At present, most of the advertisers distribute the advertisements (ads) to multiple devices in order to cover a larger area of users and achieve better effect, however they ignore the differences in user behavior at different devices. This study explored the different avoidance strategies of users when they faced video ads on PC and mobile phones, which were influenced by both technical and content factors. The purpose of this study was to probe the impact of different devices and advertisement length (ad length) on users' advertising avoidance behavior, and to provide insightful and targeted suggestions for multi-screen advertising placement. This study found that device and ad length were interrelated, and affected users' advertising avoidance behavior together. This study proposed that there were differences in the users' functional positioning of the phone and PC, which may lead to different behaviors of users at different devices. The study suggested that future researchers could investigate the psychological time differences of different device users, and consider the possible impact of multi-device cognitive load on user duration judgment.

In order to improve the effectiveness of intrusion detection, an intrusion detection method of the Internet of Things (IoT) is proposed by suppressed fuzzy clustering (SFC) algorithm and principal component analysis (PCA) algorithm. In this method, the data are classified into high-risk data and low-risk data at first, which are detected by high frequency and low frequency, respectively. At the same time, the self-adjustment of the detection frequency is carried out according to the suppressed fuzzy clustering algorithm and the principal component analysis algorithm. Finally, the key factors influencing the algorithm are analyzed deeply by simulation experiment. The results shows that, compared to traditional method, this method has better adaptability.

Parameter optimization methods for hydrological models have an important impact for the hydrological forecasting. To achieve the parameters’ optimization and calibration for the distributed, conceptual watershed Xinanjiang model effectively and accurately, a multi-objective Artificial Bee Colony algorithm named RMOABC which adopts the mechanisms of regulation operator and Adaptive Grid is introduced in the paper. In the evolution of the algorithm, the regulation operator mechanism can balance the weights of local search and global search, and the Adaptive Grid mechanism is utilized to evaluate and maintain the Pareto solutions in the external archive. In the experiments, three commonly used multi-objective optimization algorithms, the NSGA-II, the e-MOEA and the SMPSO, with the RMOABC algorithm were applied in Heihe River Basin, and the parameter optimization problem of Xinanjiang hydrological model was taken as the application case for long-term runoff prediction to validate and compare their performance. The experiments results showed the RMOABC algorithm can provide more comprehensive and reliable parameters sets for practical hydrological forecasting in the study area with lower execution time.

Parameter optimization and calibration play a crucial role in the overall performance of hydrological models and the quality of hydrologic forecast results. The hydrological model is characterized by high complexity, a large number of parameters, high dimensionality and a large amount of data processing. Therefore, there are many computationally intensive tasks in model parameter optimization that require a large CPU processing time. To improve the optimization precision and performance for parameters optimization of the Xinanjiang model, a parallel Multi-core Parallel Artificial Bee Colony algorithm (MPABC) was proposed based on the hybrid hierarchical model and Fork/Join framework. The algorithm is to introduce the multi-populations’ parallel operation to guarantee the population’s diversity, improve the global convergence ability and avoid falling into the local optimum. And also in order to divide the complex computing task into several independent parallel sub-tasks on different cores, so as to take all the performance advantages of multi-core CPU. The experiment is divided into two parts. In the first part, the performance of the original serial ABC algorithm and the MPABC algorithm is analyzed and compared based on four benchmark objective functions. The results show that the MPABC algorithm can achieve a speedup of 3.795 and an efficiency of 94.87% in solving complex problems. The MPABC algorithm could greatly improve the optimization efficiency. The second part is to select the Nash–Sutcliffe coefficient as the objective function and apply the MPABC and PPSO and PgGA algorithms to optimize the Xinanjiang hydrological model in the Heihe River Basin. The results showed that the MPABC algorithm can make full use of multi-core resources, improve the solution’s quality and efficiency, and have the advantages of low parallel cost and simple realizing process. Thus, the MPABC algorithm is an effective and feasible method to solve the hydrological model parameters’ optimization problem, and can provide a reliable parameter decision support for practical applications of hydrological forecasting.

The Failure Detection algorithm based on Fuzzy Neural Network (FDD-FNN) method is proposed for effective detection of Wireless Sensor Network (WSN) system failures. In this method, the failure detection method for WSN and the minimum deviation optimization model based on the principle component analysis-Bayes discriminant analysis (PCA-BDA) and feature information entropy are proposed, input layer, fuzzy layer, fuzzy rule layer and ambiguity layer are designed, and the algorithm processes is introduced. Finally, the effects of key factors of FDD-FNN were investigated using simulations and its performance was compared with those of conventional algorithms. The results indicated excellent adaptability of FDD-FNN.

Cholera, as an endemic disease around the world, has generated great threat to human society and caused enormous morbidity and mortality with weak surveillance system. In this paper, we propose a mathematical model to describe the transmission of Cholera. Moreover, basic reproduction number and the global dynamics of the dynamical model are obtained. Then we apply our model to characterize the transmission process of Cholera in China. It was found that, in order to avoid its outbreak in China, it may be better to increase immunization coverage rate and make effort to improve environmental management especially for drinking water. Our results may provide some new insights for elimination of Cholera.

In this paper, we introduce NeuralClassifier, a toolkit for neural hierarchical multi-label text classification. NeuralClassifier is designed for quick implementation of neural models for hierarchical multi-label classification task, which is more challenging and common in real-world scenarios. A salient feature is that NeuralClassifier currently provides a variety of text encoders, such as FastText, TextCNN, TextRNN, RCNN, VDCNN, DPCNN, DRNN, AttentiveConvNet and Transformer encoder, etc. It also supports other text classification scenarios, including binary-class and multi-class classification. Built on PyTorch, the core operations are calculated in batch, making the toolkit efficient with the acceleration of GPU. Experiments show that models built in our toolkit achieve comparable performance with reported results in the literature.

The study seeks to establish a measurement system that uses physiological indicators (LLE and ANB) in the teaching process to validate the learning efficiency and the educational physiological indicators. Through experimental, the study collected pulse-wave data of 60 sophomores before and after the class of Fundamentals of Computer Programming to get the big data about the largest Lyapunov exponent (LLE) and the autonomic nervous balance (ANB). By the global analysis and the comparative analysis, the study found that study pressure has no significant effect on LLE, but it will increase ANB, and the lack of interest and fatigue will lead to the reduction of ANB.

Media multitasking can be used as the scale to indicate the behavior of digital media. Using multitasking scale can quantify media multitasking capabilities. The study is devoted to analyzing the impact of media multitasking on emoticons usage. Firstly, 400 participants were invited to completed the experiment of media multitasking. According to their numbers, the top 27% of the participants with higher numbers were selected as high-media-multitasking group and the lower 27% of the numbers were selected as low-media-multitasking group. Then, using questionnaires to investigate emoticons usage of the two groups. The results show that media multitasking has a significant impact on emoticons usage. Group with a high number of media multitasking use emoticons more frequently, use emoticons on multiple devices, use more categories of emoticons, and updating emoticons more frequently.

For the purpose of optimizing the energy consumption of urban rail transit, a train model that can produce accurate speed profile is badly in need. In this paper, a multi-particle train model is derived and proposed for the energy-efficient train speed profile optimization. Instead of treating a moving train as a mass point, the proposed multi-particle train model takes the train length factor into account. In comparison to the results obtained with mass-point train model, the differences of the multi-particle train model are evaluated in a real-world case study based on the data of Shanghai Metro Line 7.

The artificial bee colony (ABC) algorithm has become one of the popular optimization metaheuristics and has been proven to perform better than many state-of-the-art algorithms for dealing with complex multiobjective optimization problems. However, the multiobjective artificial bee colony (MOABC) algorithm has not been integrated into the common multiobjective optimization frameworks which provide the integrated environments for understanding, reusing, implementation, and comparison of multiobjective algorithms. Therefore, a unified, flexible, configurable, and user-friendly MOABC algorithm framework is presented which combines a multiobjective ABC algorithm named RMOABC and the multiobjective evolution algorithms (MOEA) framework in this paper. The multiobjective optimization framework aims at the development, experimentation, and study of metaheuristics for solving multiobjective optimization problems. The framework was tested on the Walking Fish Group test suite, and a many-objective water resource planning problem was utilized for verification and application. The experiment’s results showed the framework can deal with practical multiobjective optimization problems more effectively and flexibly, can provide comprehensive and reliable parameters sets, and can complete reference, comparison, and analysis tasks among multiple optimization algorithms.

Aiming at the low recognition effect of apple images captured in a natural scene, and the problem that the OTSU algorithm has a single threshold, lack of adaptability, easily caused noise interference, and over-segmentation, an apple image recognition multi-objective method based on the adaptive harmony search algorithm with simulation and creation is proposed in this paper. The new adaptive harmony search algorithm with simulation and creation expands the search space to maintain the diversity of the solution and accelerates the convergence of the algorithm. In the search process, the harmony tone simulation operator is used to make each harmony tone evolve towards the optimal harmony individual direction to ensure the global search ability of the algorithm. Despite no improvement in the evolution, the harmony tone creation operator is used to make each harmony tone to stay away from the current optimal harmony individual for extending the search space to maintain the diversity of solutions. The adaptive factor of the harmony tone was used to restrain random searching of the two operators to accelerate the convergence ability of the algorithm. The multi-objective optimization recognition method transforms the apple image recognition problem collected in the natural scene into a multi-objective optimization problem, and uses the new adaptive harmony search algorithm with simulation and creation as the image threshold search strategy. The maximum class variance and maximum entropy are chosen as the objective functions of the multi-objective optimization problem. Compared with HS, HIS, GHS, and SGHS algorithms, the experimental results showed that the improved algorithm has higher a convergence speed and accuracy, and maintains optimal performance in high-dimensional, large-scale harmony memory. The proposed multi-objective optimization recognition method obtains a set of non-dominated threshold solution sets, which is more flexible than the OTSU algorithm in the opportunity of threshold selection. The selected threshold has better adaptive characteristics and has good image segmentation results.

This paper analyzes an agency model of crisis bargaining where two states have private information about war payoffs. In the model, two leaders bargain on behalf of their own states. Importantly, owing to political bias and audience costs, a leader’s war payoff and peace payoff differ from those of her state at large. I establish general results about leaders’ bargaining strategies and the possibility of peaceful resolution. By examining incentive compatibility constraints, I show that in any equilibrium that has zero probability of costly war, a leader’s payoff net of audience costs cannot vary with their private information. After that, I identify the size of resource necessary to appease both states. If this necessary condition holds, which is affected by political bias, there exist properly specified audience costs that guarantee peaceful bargaining outcomes.

It is well known that the fixed pitch vertical axis wind turbine (FP-VAWT) has some disadvantages such as the low start-up torque and inefficient output efficiency. In this paper, the variable pitch vertical axis wind turbine (VP-VAWT) is analyzed to improve the output characteristics of FP-VAWT by discussing the force of the six blade H type vertical axis wind turbine (VAWT) under the stationary and rotating conditions using built the H-type VAWT model. First, the force of single blade at variable pitch and fixed pitch is analyzed, respectively. Then, the resultant force of six blades at different pitch is gained. Finally, a variable pitch control method based on a six blade H type VP-VAWT is proposed, moreover, the technical analysis and simulation results validate that the variable pitch method can improve the start-up torque of VAWT, and increase the utilization efficiency of wind energy, and reduce the blade oscillation, as comparable with that of FP-VAWT.

To achieve effective and accurate optimization for multi-objective optimization problems, a multi-objective artificial bee colony algorithm with regulation operators (RMOABC) inspired by the intelligent foraging behavior of honey bees was proposed in this paper. The proposed algorithm utilizes the Pareto dominance theory and takes advantage of adaptive grid and regulation operator mechanisms. The adaptive grid technique is used to adaptively assess the Pareto front maintained in an external archive and the regulation operator is used to balance the weights of the local search and the global search in the evolution of the algorithm. The performance of RMOABC was evaluated in comparison with other nature inspired algorithms includes NSGA-II and MOEA/D. The experiments results demonstrated that the RMOABC approach has better accuracy and minimal execution time.

There are lots of computationally intensive tasks in optimization process of Artificial Bee Colony (ABC) algorithm, which requires large CPU processing time. To improve optimization precision and performance of the ABC algorithm, a parallel Multi-cores Parallel ABC algorithm (MPABC) was proposed based on the Fork/Join framework. The algorithm is to introduce the multi-populations’ parallel operation to guarantee population’s diversity, improve the global convergence ability and avoid falling into the local optimum. The performance of the original serial ABC algorithm and the MPABC algorithm was analyzed and compared based on four benchmark objective functions. The results show that the MPABC algorithm can achieve the speedup of 3.795 and the efficiency of 94.87% in solving complex problems. It can make full use of multi-core resources, improve the solution’s quality and efficiency, and have the advantages of low parallel cost and simple realizing process.

The sunbeam can be converted into electricity via photovoltaic solar cells, and sun trackers are such devices for efficiency improvement. In this paper, different types of sun-tracking systems are reviewed, such as fixed tilt-angle, dual-axis, single-axis with East-West direction, and single-axis with North-South direction. The captured irradiation using different sun trackers are compared, and different influence factors are analyzed to gain the optimal orientation of the sun, such as time error, latitude, azimuth angle and tilt-angle of a photovoltaic module, reflectivity, and composite transparent coefficient. These results are very important to improve the tracking efficiency of the sun tracker.

There are lots of speed monitoring data in the commercial vehicle monitoring system, and the accuracy of the data depends on accuracy of the analysis for driving behavior characteristics. In this paper, the driving behavior characteristics for commercial vehicles is analyzed so that six common types of errors for vehicle monitoring data are obtained, such as false date for time information, outlying high/low speed values, zero-speed signal drift, false zero-speed records, outlying acceleration/deceleration values, noise jamming data. Then data cleaning rules are made. Finally, we compare data cleaning effect through typical data source case. Results show that the data cleaning method is used for fitting the data, and correct the error signal from the vehicle driving curves more correspond with the real-world driving and road conditions.

PurposeThe purpose of this paper is to explore concrete design methodology which can improve production technology.Design/methodology/approachAn application of advanced process control (APC) on distributed control systems (DCS) was designed by defining the project target, scope, and control strategy. Calculation is based on heat and mass conservation, such as RPID=FC 203×900×(DC 241 PV‐400)×1.06/500 DC 241 PV; CATRATE=LEVELRATE×0.22×3.14/4. The quality controller writes the control target on the density controller, which writes the initial value or output value onto the DCS.FindingsThe technique has dramatically increased the control quality of the DCS system: production capacity of the equipment has been improved and the aim of fecundity with little input is realized. The transit time of the product name is shortened and the excess stock is reduced.Originality/valueThe paper is aimed at operational researches and engineers.

The harsh natural environment of cold and arid regions results in difficult to obtain information and severely restricts the development of Geoscience. To build up networks of field observation instruments in cold and arid regions, and promote the overall automation and real-time of field observation data transfer, the instrument networking node and gateway node were designed in this paper. The designed nodes have the properties of smaller size, lower power consumption, and could accomplish the tasks of data collection and transmission, and construct observation instruments networking in cold and arid regions. To achieve the standardization control of data acquisition device, a standardized, integrated, and transparent commands set was proposed for data loggers which can reduce the complexity of software development and improve system's universality and portability.

The output characteristics of multiple photovoltaic (PV) arrays at partial shading are characterized by multiple steps and peaks. This makes that the maximum power point tracking (MPPT) of a large scale PV system becomes a difficult task. The conventional MPPT control method was unable to track the maximum power point (MPP) under random partial shading conditions, making the output efficiency of the PV system is low. To overcome this difficulty, in this paper, an improved MPPT control method with better performance based on the genetic algorithm (GA) and adaptive particle swarm optimization (APSO) algorithm is proposed to solve the random partial shading problem. The proposed genetic algorithm adaptive particle swarm optimization (GAAPSO) method conveniently can be used in the real-time MPPT control strategy for large scale PV system, and the implementation of the collect circuit is easy to gain the global peak of multiple PV arrays, thereby resulting in lower cost, higher overall efficiency. The proposed GAAPSO method has been experimentally validated by using several illustrative examples. Simulations and experimental results demonstrate that the GAAPSO method provides effective, fast, and perfect tracking.

Webpages tampering attack has become one of the most serious hazards to network security. Security measures of Website system were limited to install network layer protection devices, thus they could not provide effective monitoring and protection for illegal page tampering attack which making use of vulnerabilities of operating system. In this paper, we analyzed the research development of domestic and international Webpages tamper-resistant technologies, presented Website tamper-resistant technology system architecture of combining the third-generation anti-tampering technology with hardware device. We also established an integrated anti-tampering system that could actively detect attacks and dynamically defend tampering attack for Websites. Test results showed that the system could effectively prevent Webpages to be tampered, and could recovery real time in the event of tampering and alert administrators. It was proved that the deployment of this system would not result in a performance loss of Web server environment.

The enterprise management diagnosis is always subject to the human factors. Therefore, the Multi-Agent System is used to substitute for the artificial diagnosis in order to improve diagnostic accuracy and efficiency. This paper introduces the construction process of the enterprise management diagnosis ontologies and puts forward the Jadex transformation model and the specific transformation method which enables the extended Jadex to support the use of ontology to describe the beliefs. Thus the ontology in Multi-Agent System can be used to describe objects and the common understandings of the relationships. It can promote communication and collaboration efficiency between multi-agents. Meanwhile, it can also improve understanding of domain knowledge. At last, through the ontology-based enterprise diagnostic concept, we establish the multi-agent diagnosis system of enterprise management, and the experiments demonstrate that the ontology-based multi-agent diagnostic system is feasible and efficient for the enterprise management diagnosis.

Interpersonal trust relationship is an important dimension of interpersonal relationships. With new introduced plots of literature, we can evaluate the environment of characters and predict plot development to some extent. This paper proposes a representation of interpersonal trust relationship based on the fuzzy set theory in the restricted domain of A Dream of Red Mansions. Interpersonal trust degrees are obtained by the comprehensive evaluation based on the fuzzy analytic hierarchy process. This paper proposes a thought that solves the divergences of domain experts, and proposes an approach that establishes the initial trust degree between characters. Based on above, this paper analyzes the trust bias and overall trust degree between characters and mines the relationship between characters and content of the whole trust network of characters. The experiments show that the model is efficient in reflecting the interpersonal trust relationship of A Dream of Red Mansions. The interpersonal trust relationship of A Dream of Red Mansions is modeled and analyzed by the fuzzy set theory, which provides a novel thought on mathematical study of interpersonal trust relationship in literary works.

In order to deal with the uncertainty of evidences mostly existing in pineapple disease identification system, a reasoning model based on evidence credibility factor was established. The uncertainty reasoning method is discussed,including: uncertain representation of knowledge, uncertain representation of rules, uncertain representation of multi-evidences and update of reasoning rules. The reasoning can fully reflect the uncertainty in disease identification and reduce the influence of subjective factors on the accuracy of the system.

To conquer the bottleneck problems existing in the traditional rule-based reasoning diseases diagnosis system, such as low reasoning efficiency and lack of flexibility, etc.. It researched the integrated case-based reasoning (CBR) and rule-based reasoning (RBR) technology, and put forward a litchi diseases diagnosis expert system (LDDES) with integrated reasoning method. The method use data mining and knowledge obtaining technology to establish knowledge base and case library. It adopt rules to instruct the retrieval and matching for CBR, and use association rule and decision trees algorithm to calculate case similarity.The experiment shows that the method can increase the system's flexibility and reasoning ability, and improve the accuracy of litchi diseases diagnosis.

Classical approaches to implement Grid resource information service (GRIS) are either centralized or hierarchical. It will result in problems of scalability and single point of failure as the scale of Grid systems rapidly increases. To address scalability and reliability of Grid Resource Information Service, we proposed a distributed resource information service model that extends the information service architecture of GT4 based on superpeer model which is one of the P2P techniques. In the results of our simulation experiments, it observed that this model has high scalability and robustness. Future we will implement it in our MICE-G Grid system.

For the heterogeneous and dynamic nature of grid environments, the ability to accurately and timely predict future capabilities of resources is very important. Autoregressive models are appropriate and much less expensive for predicting host load, but Autoregressive modeling includes a model identification procedure, that is, it is necessary to choose the order that best describes the host load variety. In this paper four of suggested criteria to determine the optimal order of AR models have been evaluated: The final prediction error (FPE), Akaike's information criterion (AIC), minimum description length (MDL) and the Bayesian information criterion (BIC). We evaluated these criteria on four of long, fine grain load traces from a variety of real machines, and our experimental results demonstrate that BIC criteria has the best determination of the optimal order than others and the optimal orders of AR models should be different in heterogeneous machines for load prediction.

Field stations in cold and arid regions are all located in harsh natural environment that resulting in difficult to obtain information and severely restricting the study of Geosciences. To build up networks of field observation instruments in cold and arid regions, and promote the overall level of automation and real-time of field observation data transfer and data quality, a new routing protocol named clustering protocol of sub-area and backup cluster-head (LEACH-ABC) for observation instruments networking was proposed-based low energy adaptive clustering hierarchy (LEACH) protocol in this paper. The protocol mainly improved the various aspects of LEACH protocol such as clustering approach, cluster head (CH) selection, communication after clustering. We use the Matlab software for simulation environment to analyse the lifecycle, network throughput and energy consumption of LEACH and LEACH-ABC routing algorithm. Experiments results show that LEACH-ABC algorithm can effectively improve energy efficiency and extend life cycle of observation instruments network.

First, a novel current calculated (CC) principle is discussed in the study of the characteristics of the photovoltaic (PV) array under partial shading irradiation conditions. In this paper, the particle swarm optimization maximum power point tracking (MPPT) control method integrates with the proposed current calculated method to realize the rapid and accurate tracking efficiency for PV power under partial shading conditions. The proposed CC method is used as a tool to study the effects of partial shading and distribution of multiple peaks. In addition, the combined MPPT method is easy to gain the global peak of multiple PV arrays at complex partial shading. The experimental and simulation results prove the availability as compared with the traditional Perturbation and Observation method.