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This article investigates finite-time stabilization of competitive neural networks with discrete time-varying delays (DCNNs). By virtue of comparison strategies and inequality techniques, finite-time stabilization of the underlying DCNNs is analyzed by designing a discontinuous state feedback controller, which simplifies the controller design and proof processes of some existing results. Meanwhile, global exponential stabilization of the DCNNs is provided under a continuous state feedback controller. In addition, global exponential stability of the DCNNs is shown as an M-matrix, which contains some published outcomes as special cases. Finally, three examples are given to illuminate the validity of the theories.

This article investigates the global stability of bidirectional associative memory neural networks with discrete and distributed time-varying delays (DBAMNNs). By employing the comparison strategy and inequality techniques, global asymptotic stability (GAS) and global exponential stability (GES) of the underlying DBAMNNs are of concern in terms of p -norm ( p ≥ 2 ). Meanwhile, GES of the addressed DBAMNNs is also analyzed in terms of 1-norm. When distributed time delay is neglected, the GES of the corresponding bidirectional associative memory neural networks is presented as an M -matrix, which includes certain existing outcomes as special cases. Two examples are finally provided to substantiate the validity of theories.

This article investigates global exponential stabilization (GES) of Takagi-Sugeno (T-S) fuzzy memristive neural networks with multiple time-varying delays (DFMNNs) via intermittent control strategy. By resorting to differential inclusion theory, comparison means, and inequality techniques, some results are developed to ensure GES of the underlying DFMNNs via a fuzzy intermittent state feedback control law within the sense of Filippov. The outcome is generalized to GES of FMNNs with infinite distributed time delays. Additionally, the global exponential stability of FMNNs with discrete time-varying delays is explored in terms of 1-norm. The derived conditions herein contain certain existing ones as special cases. Finally, three examples are presented to illuminate the validness of the outcomes.

This article investigates the global exponential stabilization (GES) of inertial memristive neural networks with discrete and distributed time-varying delays (DIMNNs). By introducing the inertial term into memristive neural networks (MNNs), DIMNNs are formulated as the second-order differential equations with discontinuous right-hand sides. Via a variable transformation, the initial DIMNNs are rewritten as the first-order differential equations. By exploiting the theories of differential inclusion, inequality techniques, and the comparison strategy, the $p$ th moment GES ( $p\geq 1$ ) of the addressed DIMNNs is presented in terms of algebraic inequalities within the sense of Filippov, which enriches and extends some published results. In addition, the global exponential stability of MNNs is also performed in the form of an M-matrix, which contains some existing ones as special cases. Finally, two simulations are carried out to validate the correctness of the theories, and an application is developed in pseudorandom number generation.

This article investigates finite-time synchronization of neural networks (NNs) with infinite discrete time-varying delays and discontinuous activations (DDNNs). By virtue of theory of differential inclusions, comparison strategies, and inequality techniques, finite-time synchronization of the underlying DDNNs can be developed via a discontinuous state feedback control law, and the synchronous settling time can be estimated. The delayed state feedback controller and finite-time stability theorem are not employed during the analysis. As a special case, finite-time synchronization of NNs with bounded delays and discontinuous activations is given. Finally, two examples are provided to illustrate the validity of the theories.

This paper focuses on Lagrange exponential stability and finite-time stabilization of Takagi-Sugeno (T-S) fuzzy memristive neural networks with discrete and distributed time-varying delays (DFMNNs). By resorting to theories of differential inclusions and the comparison strategy, an algebraic condition is developed to confirm Lagrange exponential stability of the underlying DFMNNs in Filippov's sense, and the exponentially attractive set is estimated. When external input is not considered, global exponential stability of DFMNNs is derived directly, which includes some existing ones as special cases. Furthermore, finite-time stabilization of the addressed DFMNNs is analyzed by exploiting inequality techniques and the comparison approach via designing a nonlinear state feedback controller. The boundedness assumption of activation functions is removed herein. Finally, two simulations are presented to demonstrate the validness of the outcomes, and an application is performed in pseudorandom number generation.

Purpose–This research aims to supplement or replace the existing embedded OS with architecture of semi-network OS, so as to form a co-mobile computing system and to create a new type of networked mobile devices.Method–Co-mobile computing system can only be realized by combining network resources with local resources, then this combination of resources is used to promote intelligent mobile devices to integrate into modern network environment more quickly and comprehensively, and then intelligent mobile devices become real networked one.Result–Under co-mobile computing system, networked mobile devices can not only safely use local resources to maintain the stability and reliability of devices operation, which is mainly attributed to the base portion of semi-network OS(similar to existing embedded OS), but also make full use of network resources to remedy the defect of existing embedded OS,which is mainly attributed to the expanded portion of semi-network OS. Conclusion–The concept of co-mobile computing mentioned here is defined as a data computing system specially designed for networked mobile devices, which is based on semi-network resources and formed by the merging of multiple networked mobile devices with different embedded OS and functions, now, the semi-network OS architecture will pave a new way for advancement of co-mobile computing system.The semi-network OS refers to that a complete operating system is divided into an expanded portion and a base portion,where the expanded portion of OS is stored on network server, which is ready to be downloaded at any time (similar to a supplement to embedded OS), and the base portion of OS is stored in OSPU located on local platform(similar to embedded OS); wherein the OSPU is a key hardware component in semi-network OS architecture and is movably installed on different local platforms by users.

This paper investigates the stability and stabilization of Takagi–Sugeno (T–S) fuzzy systems with discrete and distributed time-varying delays. First, $p$ th moment global exponential stability ( $p\geq 1$ ) of the addressed delayed fuzzy systems is considered by virtue of the comparison approach and inequality techniques. The developed algebraic criteria include some existing outcomes as special cases. Second, global exponential stabilization of the underlying delayed fuzzy systems is performed under a fuzzy state feedback controller. Third, considering that only a few studies have been concerned with finite-time stabilization of T–S fuzzy systems, by employing the comparison strategy and a nonlinear controller, finite-time stabilization of the nominated delayed fuzzy systems is presented. The result obtained herein establishes a general theoretical framework to analyze the finite-time behavior of delayed T–S fuzzy systems. Finally, simulation examples are conducted to illustrate the validity of the results.

This paper is concerned with exponential stabilization for a class of Takagi-Sugeno fuzzy memristive neural networks (FMNNs) with unbounded discrete and distributed time-varying delays. Under the framework of Filippov solutions, algebraic criteria are established to guarantee exponential stabilization of the addressed FMNNs with hybrid unbounded time delays via designing a fuzzy state feedback controller by exploiting inequality techniques, calculus theorems, and theories of fuzzy sets. The obtained results in this paper enhance and generalize some existing ones. Meanwhile, a general theoretical framework is proposed to investigate the dynamical behaviors of various neural networks with mixed infinite time delays. Finally, two simulation examples are performed to illustrate the validity of the derived outcomes.

Task assignment is a critical part of human-robot collaboration. The traditional task assignment methods in the intuitionistic fuzzy (IF) environment only consider two options, i.e., the task is executed by humans or machines. However, some tasks may need to be completed by humans and machines working together, such as mecha. Therefore, we propose a human-robot task assignment method under the IF environment based on three-way decision (3WD) theory in this paper. By extending the traditional two-way assignment mode into the three-way one, our method can flexibly deal with the concerned situations. At first, the appropriateness of assigning each task to the machine is calculated by IF TOPSIS. When ranking the IF evaluation values, the decision maker's attitude toward uncertainty is considered. Then the appropriateness served as conditional probability and a cost-sensitive IF 3WD model is constructed to complete the assignment. Furthermore, we construct algorithms to deal with dynamic situations. Finally, a numerical example illustrates the feasibility of the proposed method.

Recommendation system is a popular research field. In the age of information explosion, a reliable recommendation system is necessary for users. There are a certain number of approaches to do recommendation work. Reinforcement learning is one of the methods used in recommendation system. In this paper, we use reinforcement learning to recommend items to target users, and achieved a rather good result. To give a better user experience, we have added explanations for recommended items. The explanation is realized by Knowledge Graph. We use TransE to embed target users and items, and it helps manage the information of users and items. Our method KGDQN combines Knowledge Graph and reinforcement learning, which can decide the proper recommendation items, and find the reasoning paths from target users to recommended items. Redundant edges are pruned and the DQN model renders a reward function which gives back the result of recommended items, and the explanation paths of the recommendation. Experiments are conducted on Amazon datasets which show the superior performance of KGDQN

This study is to explore a newly configured data management system of Internet of Things and other intelligent technologies in ubiquitous computing based on semi-network OS architecture. Modern Internet of Things and other intelligent technologies have increasingly shown a trend of diversification and ubiquitous computing, and the usual mobile devices characterized by “single-task”, “stable”, “oriented”, and “immutable” are also bound to face the pressure and motion of reform and innovation. The advancement of merger of single-task mobile devices will be unstoppable, and the operating system rightfully needs to be divided into “base portion” and “expanded portion”, which just fits the concept of semi-network operating system architecture; the “base portion” is similar to existing “embedded operating system”, and the “expanded portion” is network resources. Today, the merger of single-task mobile devices and the formation of multi-task mobile devices have become general trend, and architecture of semi-network operating system can lay a new foundation for this wave of merger. The multi-task mobile device formed by merger of single-task mobile devices based on architecture of semi-network operating system has its special computing system: co-mobile computing system, where including a data management system. Multi-task mobile devices form a so-called co-mobile computing system, and also form a newly configured secure data management system, which is like a universal key to form a new working system for opening a lot of unspecified locks based on new principle and security mechanism.

Collaboration is complex. To solve a problem occurring in collaboration, using computers, we must first define and specify the problem. This paper presents a challenging problem in collaboration, called group role assignment with cooperation and conflict factors (GRACCFs). This problem’s solution aims at creating a high-performance group by role assignment with consideration of cooperation and conflicts between agents. The contribution of this paper is the formalization of the proposed problem, a confirmation of the complexity of the problem, a practical solution that uses the IBM ILOG CPLEX optimization package (ILOG), a verification of the benefits of solving the GRACCF problem by simulations and a practical way of collecting the required factors to support decision makers in solving such a problem within a real-world scenario. Experiments are used to verify the efficiency of the proposed ILOG solution.

In order to improve the intelligent traffic management and dispatching capabilities, it is required to optimize the design of intelligent acquisition of dynamic traffic data and optimize the processing of traffic data information by dynamically mining traffic data. Therefore, an intelligent acquisition algorithm of dynamic traffic data based on Internet of Things is proposed in this paper. In this algorithm, the distributed wireless sensor networking is used to construct an Internet of Things model of traffic data acquisition and optimize deployment and design of Internet of Things nodes for data acquisition; the adaptive weighted algorithm is used to process the fusion of dynamic traffic data to extract spectral characteristic quantity of dynamic traffic data, and the spectrum analysis method is used to perform anomaly detection to dynamic traffic data; then the detection results are conducted with fuzzy clustering, so as to realize intelligent acquisition of dynamic traffic data in Internet of Things environment. The simulation results show that in acquisition of dynamic traffic data, this method has high acquisition accuracy under different SNR. In this paper, when the SNR is 4 dB, the acquisition accuracy is 100, while the traditional method is up to 16 dB, the acquisition accuracy is 100. The proposed method has the advantages of high accuracy, good data recall, strong anti-interference ability during acquisition and good self-adaptability.

Decision-making processes of Air Traffic Management (ATM) need to organize information from different data sources. For example, reroute requires four-dimensional (4D) trajectory and weather information. Future ATM Decision-Support Systems (DSS) are expected to perform trustworthy complex decision-making automatically. This paper proposes an approach to DSS in ATM based on an ontology including concepts and instances of trajectories and meteorology. Temporal and spatial relationships are then set in this ontology. Reasoning rules are also build to represent knowledge in DSS. A case study shows a reroute scenario during a thunderstorm. In this scenario, information of the flight and the weather is combined to support decision-making by the proposed approach.

This paper researches the problem of adaptive fuzzy tracking control for stochastic nonlinear multi-agent systems subject to asymmetric hysteretic quantizer. We apply the fuzzy logic system to estimate the unknown nonlinear functions and stochastic disturbance term. Based on the sector bound property, we introduce a nonlinearity decomposition method of asymmetric hysteresis quantizer. According to the backstepping technique, a new adaptive fuzzy control protocol and adaptive law are developed. By using stochastic Lyapunov stability theory, it is testified that all the followers’ outputs converge to the neighborhood of the leader output. The simulation example is proposed to verify the availability of the control strategy.

This study proposes an improved version of the Firefly Algorithm (FA) for scheduling feature selection optimization in data-driven job shop scheduling system. The proposed FA variant employs a dynamic step strategy with the elite individual dipartition in firefly population to improve the optimization ability of FA. Selecting essential schedule feature data from production system attributes data based on various production requirements to construct data-driven job shop scheduling system is a critical issue because of the existence of irrelevant and redundant attributes in job shop production system, by selecting the important attributes as schedule features, robust performance can be expected in data-driven job shop scheduling system. A wrapped scheduling feature selection approach based on the proposed FA variant and extreme learning machine (ELM) is presented for ELM-based job shop scheduling system. The feasibility and effectiveness of the proposed scheduling feature selection approach have been verified via a practical job shop scheduling case.

For the problems of the complicated installation and removal of dynamometer card detection equipment, high cost, not convenient for real-time detection, prone to failure and maintenance workload, a kind of low cost wireless dynamometer is developed. With the implement of wireless transmission gateway, comprehensive monitoring software is programmed. This system consists of a pumping dynamometer, gateway, monitoring software and indicator diagram measuring algorithm. Finally, the wireless dynamometer was installed at RenQiu oil field, and the data shows that the system works well and can be used for the long-term working and operating state of pumping oil field monitoring.

Agent evaluation is an important topic in collaborative systems. Agent evaluation is to accurately assess the ability of the agent to complete a task. The ability of the agent is demonstrated by the result of the task, and the ability changes over time. Thus, this paper studies the evaluation of agent in collaborative systems. Considering the independent and interaction abilities of an agent for a role, as well as the historical and current evaluation value, we propose a trust-based evaluation approach that promotes the effectiveness of evaluation. The experimental results show that this approach can obtain more accurate results when the agent capability changes fast and there are only a few known evaluation values.

Collaborative Decision Making (CDM) has been adopted by major airlines in most countries to improve Air Traffic Flow Management (ATFM). The CDM introduces significant benefits to Ground Delay Programs (GDP), and one is to reduce the overall delay costs by allocating and reallocating slots to flights. This paper focuses on the slot reallocation problem, and begins by modeling this problem with environments-classes, agents, roles, groups, and objects (E-CARGO) model. Then a qualification matrix expressing the profit of assigning each flight to each slot is obtained by integrating all the roles’ requirements. An approach using group role assignment (GRA) is proposed to maximize the total profit according to the qualification matrix. Experiment results proves the effectiveness and efficiency of the proposed approach.

With the development of high-performance fighters, the function of pilot helmets is also constantly upgrading, and helmet-mounted display (HMD) has become a future development trend. To solve the inter-restriction between the eye position control and the pressure comfort under high overloads of pilot helmets, this paper optimizes the helmet design by building a head-helmet finite element model: (1) Under a 10 G overload, the deformation of helmet soft liner should not exceed 2 mm; and (2) The combination of liner materials used in various areas should satisfy the requirements of most comfortable head pressure distribution (Pfront: Ptop: Pside: Pback = 2:2:1:3). The study on 46 different combinations of materials finds that the helmet can meet both the eye position maintenance requirement under overloads and the requirement of comfortable pressure distribution when linear elastic materials with Young's modulus of 1000 pa, 55000 pa and 65000 pa are used for the side, top and front parts of the head, and nonlinear elastic hyperfoam material with two-fold stress variation is used for the head back area.

Adaptive collaboration (AC) is essential for group performance optimization in collaborative systems. This paper begins by introducing AC within the context of solving a real-world problem. Next, AC problems are formalized based on the environment-class, agent, role, group, and object (E-CARGO) model. Three algorithms are proposed for solving AC problems. They are based on three different scenarios: 1) the current group state (GS); 2) the GS after a specific period; and 3) the GS throughout the collaboration. More complex AC problems and their solutions are then investigated. Derived from the above-mentioned algorithms, two additional algorithms are presented. They consider reassignment costs. Experiments are developed to analyze the performance of each proposed algorithm. Results indicate that the proposed algorithms perform better than static collaboration where there are no reassignment costs. If reassignment costs exist, we also provide a way of determining whether to adopt an AC approach. This paper provides insights into the AC process and its effectiveness in various scenarios.

This paper discusses impulsive synchronization of stochastic reaction–diffusion neural networks with Dirichlet boundary conditions and hybrid time delays. By virtue of inequality techniques, theories of stochastic analysis, linear matrix inequalities, and the contradiction method, sufficient criteria are proposed to ensure exponential synchronization of the addressed stochastic reaction–diffusion neural networks with mixed time delays via a designed impulsive controller. Compared with some recent studies, the neural network models herein are more general, some restrictions are relaxed, and the obtained conditions enhance and generalize some published ones. Finally, two numerical simulations are performed to substantiate the validity and merits of the developed theoretical analysis.

This paper investigates the synchronization issue of coupled reaction–diffusion neural networks with directed topology via an adaptive approach. Due to the complexity of the network structure and the presence of space variables, it is difficult to design proper adaptive strategies on coupling weights to accomplish the synchronous goal. Under the assumptions of two kinds of special network structures, that is, directed spanning path and directed spanning tree, some novel edge-based adaptive laws, which utilized the local information of node dynamics fully are designed on the coupling weights for reaching synchronization. By constructing appropriate energy function, and utilizing some analytical techniques, several sufficient conditions are given. Finally, some simulation examples are given to verify the effectiveness of the obtained theoretical results.

In order to meet the computing speed required by 4G wireless communications, and to provide the different data processing widths required by different algorithms, an SIMD (Single Instruction Multiple Data) core has been designed. The ISA (Instruction Set Architecture) and main components of the SIMD core are discussed focus on how the SIMD core can be configured. Finally, the simulation result of the multiplication of two 8*8 matrices is presented to show the execution of instructions in the proposed SIMD core, and the result verifies the correctness of the SIMD core design.

With the widespread popularity of wireless mobile devices, the demands of emerging applications have been proposed, such as video telephony and HD video play. The next generation of wireless mobile computing devices needs to have higher data transfer rate, more complex algorithms, as well as low power consumption. This paper gives out a variable-width SIMD processor architectures controller and data transfer module designing method. The controller designing focused on controlling the program flow and initializing the data transfer module. By simulating the architecture on Xilinx ISE Design Suite, the fundamental modules have been designed and tested. The results of simulation verify the correctness of the controller and data transfer module design.

As using digital algorithms alone will lead to lower gray level while the use of simu-lation algorithms alone is difficult to achieve fine-tuning or the circuit is too complex,a method of combining digital and analog is proposed to achieve AM-OLED white balance by using analog simulation and digital adjustment to make up for these shortcomings.Analog simulation is determined by the drive voltage according to the three primary colors.Digital adjustment using LM optimized BP learning algorithm fits the curve of the transmission characteristics of the OLED.Compared with traditional BP algorithms,learning speed of this algorithm is improved,the convergence of the network is accelerated,and it avoids a system into a local minimum.The simulation results and the display results show that this method achieves the white balance of the image.

The k-nearest neighbour (kNN) rule, which naturally handles the possible non-linearity of data, is introduced to solve the fault detection problem of gas sensor arrays. In traditional fault detection methods based on the kNN rule, the detection process of each new test sample involves all samples in the entire training sample set. Therefore, these methods can be computation intensive in monitoring processes with a large volume of variables and training samples and may be impossible for real-time monitoring. To address this problem, a novel clustering-kNN rule is presented. The landmark-based spectral clustering (LSC) algorithm, which has low computational complexity, is employed to divide the entire training sample set into several clusters. Further, the kNN rule is only conducted in the cluster that is nearest to the test sample; thus, the efficiency of the fault detection methods can be enhanced by reducing the number of training samples involved in the detection process of each test sample. The performance of the proposed clustering-kNN rule is fully verified in numerical simulations with both linear and non-linear models and a real gas sensor array experimental system with different kinds of faults. The results of simulations and experiments demonstrate that the clustering-kNN rule can greatly enhance both the accuracy and efficiency of fault detection methods and provide an excellent solution to reliable and real-time monitoring of gas sensor arrays.

A display driving module based on the Field Programmable Gate Array(FPGA) with LCD driver ICs was designed by the research of the 17.8 cm(7 in) AMOLED.The program is designed with Verilog HDL.By studying the characteristics of OLED display,the LCD driver ICs is chosen to fit it.A method connecting screen with ICs is present.Through using double data-driver and changing the polarity of the ICs,the problem that dot inversion of LCD Driver ICs is not matching AMOLED is resolved,and the brightness is doubled.For the data driver IC can only receive 6 bit/pixel data,but lost 2 bit data,this paper introduced the digital halftone technique.Using dynamic Bayer dithering algorithm on the low 2-bit input data to compare,and the treatment results is fed back to the high 6-bit data.Thus the output image quality is improved.

The traditional K-means clustering algorithm prematurely plunges into a local optimum because of sensitive selection of the initial cluster center. Hierarchical clustering algorithm can be used to generate the initial cluster center of K-means clustering algorithm. The geometric features of input data can achieve a good distribution by means of pretreatment and feature extraction and selection. In the learning of fuzzy neural network, Java language is used to write source code of the algorithm. The experimental results show that new algorithm has improved the clustering quality effectively.

Intermittent intensity clutter in wind profiling radar severely interferes with the identification and spectrum estimation of atmospheric echo signals. In order to remove such clutter, a method based on Fractional Fourier transform (FrFT) is presented. The proposed method makes use of energy concentration property of intermittent clutter in fractional Fourier frequency domain. Not only the intermittent intensity clutter can be removed in the fractional Fourier frequency domain by using a narrow band-stop filter, but also the atmospheric signal in the time domain can be reconstructed after removing the clutter. Computer simulations have verified the effectiveness of the method.

Innovation of computing technology is either to improve the security and performance, or to improve the convenience of user operation of system, networking and device, but sometimes it’s hard to get a very good balance between the two, in particular, the traditional localized OS and computing devices are inherently incompatible in some respects with today's network environment, therefore, although many innovations help to improve the safety, autonomy and controllability of system and device, but they are still always lacking the trustworthiness of users, one of the reasons is that their performance is lower than user's expectation, but their operation become more complicated than before. The semi-network operating system is designed according to user's actual experience, which not only utilizes the shared attributes of network to increase the flexibility of OS, but also utilizes the stability and user-autonomy attributes of local platform ensure the base functions of OS, in addition, it can greatly reduce the burden on user’s operation and maintenance of computing system, so which will be a trustworthy system architecture for users.

This paper presents a driving scheme based on FPGA for the 17.8 cm(7 in) active matrix organic light emitting display(AMOLED) using μc-Si TFT technology.This scheme is easy to be implemented.It doesn't require external cache,and consequently the difficulty of PCB layout and system control is reduced.It presents a method connecting screen with IC to resolve the problem that it is not matching between AMOLED with LCD Driver IC.Architecture of the system and operating principle of each module within the FPGA are presented in detail.And the simulation results by computer and the results tested by oscillograph on the timing and controlling signals are provided.The results indicate this design can implement the dynamic video display on AMOLED with 64 gray scales.

This paper is concerned with the finite-time robust stabilization of delayed neural networks (DNNs) in the presence of discontinuous activations and parameter uncertainties. By using the nonsmooth analysis and control theory, a delayed controller is designed to realize the finite-time robust stabilization of DNNs with discontinuous activations and parameter uncertainties, and the upper bound of the settling time functional for stabilization is estimated. Finally, two examples are provided to demonstrate the effectiveness of the theoretical results.

Conflict management is an important and challenging problem in collaboration. Conflict avoidance is one way to manage conflicts. Intuitively, we may try the best to avoid conflicts when initiating a collaborative activity, because we may save the effort of conflict resolution. However, conflict avoidance is a complex task that requires many efforts. We need to know if we could get a reward from our payment in conflict avoidance. This paper sets up a model to evaluate the collaboration performance before and after conflict avoidance; conducts experiments with simulations; finally conclude the benefits for conflict avoidance. The contribution of this paper is to assert that conflict avoidance is beneficial in most cases unless the conflicts are not severe, there are not many conflicts, and the number of roles is too small.

The social need for software continues to improve, but many software companies most be of repeating similar work, it is adverse for the development of the software industry.This paper discusses software product line combining with the framework .The purpose is to make the software can also like the factory assembly line, and make the overall structure of the software rapidly, then it can fine - tune the software that end-users need it to meet their needs.

In this paper, we investigate maximum likelihood equalization (MLE) for a large-scale antenna (LSA) system with single-carrier (SC) modulation over Rician fading channels. Orthogonal frequency division multiplexing (OFDM) is usually used to deal with frequency selectivity of wireless channels. However, for a wireless system with large-scale antennas in a Rayleigh fading channel, by combining the received signals through a matched filter (MF), the frequency selective channel can be converted into a frequency flat channel. As a result, SC modulation can be used directly with a simple one-tap equalizer. In a Rician fading channel, however, the line-of-sight (LOS) path will cause mutliuser-interference (MUI), which cannot be mitigated through MF. As a result, the simple one-tap equalizer leads to an error-floor when the signal-to-noise ratio (SNR) is large. In this paper, MLE is used to improve system performance through multiuser detection. From both theoretical analysis and simulation results, the proposed approach can eliminate the error floor and outperform existing approach.

A novel fault detection, isolation, and diagnosis (FDID) strategy for self-validating multifunctional sensors is presented in this paper. The sparse non-negative matrix factorization-based method can effectively detect faults by using the squared prediction error (SPE) statistic, and the variables contribution plots based on SPE statistic can help to locate and isolate the faulty sensitive units. The complete ensemble empirical mode decomposition is employed to decompose the fault signals to a series of intrinsic mode functions (IMFs) and a residual. The sample entropy (SampEn)-weighted energy values of each IMFs and the residual are estimated to represent the characteristics of the fault signals. Multi-class support vector machine is introduced to identify the fault mode with the purpose of diagnosing status of the faulty sensitive units. The performance of the proposed strategy is compared with other fault detection strategies such as principal component analysis, independent component analysis, and fault diagnosis strategies such as empirical mode decomposition coupled with support vector machine. The proposed strategy is fully evaluated in a real self-validating multifunctional sensors experimental system, and the experimental results demonstrate that the proposed strategy provides an excellent solution to the FDID research topic of self-validating multifunctional sensors.

Space based IR system uses the information of target LOS (line of sight) for target location. Recent researches show that the measuring precision of target LOS is usually determined by measuring precision of platform’s position and attitude, and deformation of sensor etc. Most methods for improving target location precision are either through improving platform’s position and attitude measuring precision or through calib rating the whole image obtained by IR sensor. With the development of measuring technology, it is harder to make a further improvement on the measuring precision of position and attitude of the platform and the expansion of the sensor view make calibrat ion of the whole image with a larger computation cost. In this paper, a method using control points to calibrate target LOS was proposed. Based on the analysis of the imaging process of the scanning sensor of space based IR system, this paper established a modify model of target LOS based on control points, used a bias filter to estimate the bias value of sensor boresight, and finally achieved the mission of target LOS calibrat ion. Different from the traditional calibration method of remote sensing image, the proposed method only made a correct ion on the LOS of suspicious target, but not established the accurate relationship between the all pixels and their real location, and has a similar calibration performance, but more lower computational complexity.

This paper defines the production possibility set and constructs the multiple objective programming for matrix-type organization on the basis of the Charnes, Cooper and Rhodes (CCR) model. Then the efficiency score of the complex system can be calculated through solving the multiple objective programming. Compared with the traditional data envelopment analysis (DEA) model shows the new model can overcome the drawbacks of the traditional DEA model which ignore the internal structures of decision making units. Using data in input-output table, the new model is tested and the results show that the new model can also evaluate divisional efficiencies, which can help decision maker’s to detect the sub-processes needed to be evaluated. Key words: Data envelopment analysis, matrix-type organization, multiple objective program, network data envelopment analysis (DEA), subsystem efficiency, input-output table.

Swarm intelligence optimization algorithm is a heuristic search algorithm based on the swarm intelligent behavior of biology, which shows excellent performance in deal with complex optimization problems. On the basis of analyzing particle swarm optimization algorithm, ant colony algorithm and artificial bee colony algorithm, the paper presents a unified frame of swarm intelligence optimization algorithm that is helpful for improving and perfecting swarm intelligence optimization algorithm.

Travel mode choices and route choices are the core points of travel behavior analysis. Researches on integrated decision-making combining mode with route are advantageous to discover the mechanism of resident travel choices. Furthermore, there are many defects of traditional travel choice models based on Maximization Utility Theory. The choice process of uncertainty could be described more accurately by Prospect Theory. It accords with the limited rationality of human behavior. In this article, the characters of traditional travel models and the application of Prospect Theory in travel behavior analysis are summarized. Based on Prospect Function, a new travel integrated decision-making model including mode choice and route choice is set up, and also an example is put forward to prove the validity. It provides a new theoretical basis and method reference for scientific and rational decision-making of residents travel choice.

The object-oriented bayesian networks (OOBN) is prone to simulate for complex system, the model can be reused and the reasoning algorithm fully consider the encapsulation and hierarchy. Toward to the complexity of decision-making, distribution of knowledge and the hierarchy of the decision-making process, the method of unmanned combat air vehicle teams (UCAV) air-to-ground attack decision-making based on OOBN has been put forward. The modeling process base on OOBN has been given out. The method can establish the OOBN structure of complex system through analysis the causality between decision-making object and decision-making factors. It can be used to modeling and simulation for complex system after the probability parameter was given out. The application was been explained by a simulation example in the end.