Earticle

The high complexity of the supply chain network and the characteristics of products made supply chain management of agricultural products were more susceptible to the risks emergence of loss. Therefore, it is required to develop a mechanism for price negotiation which distributes the risks fairly for each stakeholder in the supply chain. In addition it is necessary to identify and evaluate supply chain risks in order to avoid continuing problems that can occur at any point in the supply chain network. The objectives of this study were to describe the model of identification and evaluation for corn supply chain risk, to formulate a fair pricing mechanism for corn supply chain using risk balancing model. Risk identification was conducted using fuzzy Analytical Hierarchy Process (AHP) approach and risk evaluation was done by using fuzzy logic with data input form the opinion of several experts maize supply chain. A fairly pricing model at farmer level was developed by using stakeholder dialogue approach based on a balanced fuzzy risk utility preference that was faced by all stages of the supply chain. In addition, fuzzy risk utility optimization was used to get a consensus of the supply chain stakeholder dialogue, where basic risk utility function was derived using fuzzy regression approach. Risk mitigation for each stage of supply chain was developed using fuzzy inferences based on the risk that has been evaluated. Based on the verification results, the model could identify the level of risks for each party of the supply chain and the action that must be taken for minimizing its impacts using appropriate strategies. The model could shift the risks from the farmer to the other parties to determine the fair benefit distribution on the price negotiation.

In a video surveillance network, it is always required to track and recognize people when they move through the environment. This paper presents a novel re-identification method for multiple-people using feature selection with sparsity. By using the multiple-shot approach, each of appearance models is created in this method. The human body is divided into five parts form which the features of color, height, gradient were extracted respectively. Our appearance model is represented by linear regression method. Experimental results show that our appearance model is robust and attain a high precision rate and processing performance.

In recent years, the trustworthiness of the software has become the focus of software quality. The software trustworthiness analysis and measurement has become a hot issue. Because of the great influence of software environment dynamic openness and uncertainty to the trustworthiness of the software, this paper attempts to examine the changes of software running environment, consider the impact on the software trustworthiness, and build the software trustworthiness evaluation (STE) model with subjective logic. The model can be real-time control the software running state and evaluate the software trustworthiness dynamically. In addition, on the setting of weight aspect, a subjective group preference weight setting algorithm is designed. Simulation results show that the model is reasonable and effective can be more accurate to evaluate the software trustworthiness.

Design a robust artificial intelligent nonlinear controller for second order nonlinear uncertain dynamical systems is one of the most important challenging works. This paper focuses on the design of a robust chattering free mathematical model-base artificial intelligence (fuzzy inference system) variable structure controller (MFVSC) for highly nonlinear dynamic continuum robot manipulator, in presence of uncertainties. In order to provide high performance nonlinear methodology, variable structure controller is selected. Pure variable structure controller can be used to control of partly known nonlinear dynamic parameters of continuum robot manipulator. In order to reduce/eliminate the chattering, this research is used the artificial intelligence (fuzzy logic) theory. The results demonstrate that the model base fuzzy variable structure controller with switching function is a model-based controllers which works well in certain and partly uncertain system. Lyapunov stability is proved in mathematical model-based fuzzy variable structure controller with switching (sign) function. This controller has acceptable performance in presence of uncertainty (e.g., overshoot=1%, rise time=0.9 second, steady state error = 1.6e-8 and RMS error=4.8e-8).

In this paper, we introduced a robust approach to induction motor control combining fuzzy logic and variable structure with a sliding mode control. Fuzzy tuning schemes are employed to improve control performance as well as to reduce chattering in the sliding mode. This combination ensures system high performance and fast dynamic response with no overshoot. Becoming a very robust, insensitive to process parameters variation and external disturbances.

In order to study the cross-type rigid steel frame node hysteretic behavior of components, the axial compression ratio, H type steel column web thickness, girder section and stiffener as design parameters, 8 steel frame cross-type rigid joints and a T-type rigid joint are designed. Based on simplified mechanical model and constitutive relations of material, using ABAQUS software the corresponding finite element model is established and the hysteretic performance analysis is carried out. Through comparison between given T-type node test results and analysis results, both are in good agreement, thus the rationality of the finite element model established in this paper is verified. Simulation analysis of cross-type node is developed in further, load -displacement hysteretic curve of nodes, skeleton curve, envelope figure and the stress nephogram are extracted and comparative analysis is conducted, influence regularity of four parameters on the seismic behavior of the joints is obtained. The corresponding design suggestion for the axial compression ratio and column web and flange thickness ratio is proposed, design limit value of axial compression ratio of column should be taken as 0.5 and limit value of column web and flange thickness ratio is taken as 0.75,and these can provide reference for actual engineering design.

Large perceptual class motor load is the main production equipment of metallurgical enterprise, although has taken independent secondary substation power supply mode, but due to the influence of the impact load, Often appears on the bus voltage drop and low power factor and harmonic big problems, , this article is based on SVC compensation control mechanism, the sectional control mode, the principle of reactive power compensation and harmonic suppression methods are analyzed, , and designed a set of reactive power compensation and harmonic suppression function of comprehensive compensation device, it has been verified by actual operation, high reactive power compensation and harmonic suppression effect, and effectively suppress the busbar voltage drop and improving the quality of the power supply, created the direct economic benefits for the implementation of the enterprise.

According to this research, a new intelligent-based parallel gravity controller is proposed for industrial robot arm. All model-base conventional controllers are related to dynamic model of systems especially in uncertain systems. To solve this challenge intelligent theory and partly linear methodology are play important role. Fuzzy logic theory is used to estimate the nonlinear dynamic part of robot manipulator and to improve the fuzzy performance PD gravity is used. To estimate the 2 DOF robot manipulator system’s dynamic, proportional plus modified derivative with 7 rules Mamdani inference system is design and applied to modified PD gravity methodology. The proportional coefficient of controller is tuned by new methodology in limitation uncertainties. The results demonstrate that the proposed controller is a partly model-free controllers which works well in certain and partly uncertain system.

Based on the analysis on the basic principles and characteristics of the existing multi-objective genetic algorithm (MOGA), an improved multi-objective GA with elites maintain is put forward based on non-dominated sorting genetic algorithm (NSGA). NSGA-II algorithm theory and parallel hybrid evolutionary theory is described in detail. The design principle, process and detailed implementations of the improved MOGA are given. IMNSGA-II algorithm and NSGA-II algorithm are applied to test the performance of the two algorithms for different test function, experiments of example are preformed. Experimental results show that the improved MOGA achieved the optimal between the convergence and diversity.

The focus of this paper is on the development and high precision robust control of an electro-mechanical robot manipulator that serves as a sensing and motion system for hybrid testing. The originality of the design is inspired from the Stewart Platform mechanism for a parallel axis configuration and a two-degree-of-freedom (2-DoF) moving platform. This design resulted in strongly non-linear and coupled dynamics as well as an inertial moving platform that attracted model-based control strategies. A novel non-linear control technique based on differential-geometric flatness was selected to meet the multiple simultaneous specification control of linearization, decoupling and asymptotic tracking. Pole placement was used to achieve a stable tracking, while the fuzzy-logic added intelligence to the control system through an automatic tuning of the pole placement coefficients. Simulation results demonstrated the validity of the fuzzy-flatness control with asymptotic and stable tracking at different frequency inputs. For the experimental implementation, the real-time constraint was further imposed and the actuators time-delay was compensated for using a forward prediction algorithm based on a fourth-order polynomial extrapolation. This compensation demonstrated a well synchronized control signal at different excitation conditions. Moreover, the non-linear flatness control was systematically assessed for the experimental validation and its implementation was made accessible for future validation and perspectives. This current research has contributed to the rapprochement of three important autonomous domains, namely: Parallel Manipulators, Hybrid Testing and Automatic Control. In addition, it has inspired many research perspectives for robust non-linear control and multi-frequency substructuring.

A fuzzy control scheme is proposed for the system of filling and draining off water of the rubber dam（FDWRD） which is a typical nonlinear, multi- variables, time- varying and strong coupling control system base on traditional PID control or model-based control methods can not meet its constant flow sufficient drainage control requirements, during to it couldn‘t be established accurate mathematical model. In a practical project, a fuzzy PD controller is designed for the constant current control of filling and draining off water of the rubber dam, combining with the traditional PID algorithm which is widely used in industrial process control, and achieved good control effect. This paper elaborates the control system of filling and draining off water of the rubber dam and the implementation process of the fuzzy PD controller, and then giving the actual control curve of constant current. Practice proves that the proposed control scheme is efficient and the system possesses good static, dynamic performance and certain robustness.

The personalized recommendation systems could better improve the personalized service for network user and alleviate the problem of information overload in the Internet. As we all know, the key point of being a successful recommendation system is the performance of recommendation algorithm. When scholars put forward some new recommendation algorithms, they claim that the new algorithms have been improved in some respects, better than previous algorithm. So we need some evaluation metrics to evaluate the algorithm performance. Due to the scholar didn’t fully understand the evaluation mechanism of recommendation algorithms. They mainly emphasized some specific evaluation metrics like Accuracy, Diversity. What’s more, the academia did not establish a complete and unified assessment of recommendation algorithms evaluation system which is credibility to do the work of recommendation evaluation. So how to do this work objective and reasonable is still a challengeable task. In this article, we discussed the present evaluation metrics with its respective advantages and disadvantages. Then, we put forward to use the Matthews Correlation Coefficient to evaluate the recommendation algorithm’s performance. All this based on an open source projects called mahout which provides a rich set of components to construct the classic recommendation algorithm. The results of the experiments show that the applicability of Matthews correlation coefficient in the relative evaluation work of recommendation algorithm.

This paper expands a Multi Input Multi Output (MIMO) fuzzy baseline control (FBC) which controller coefficient is off-line tuned by gradient descent algorithm. The main goal is to adjust the optimal value for fuel ratio (FR) in motor engine. The fuzzy inference system in proposed methodology is works based on Mamdani-Lyapunov fuzzy inference system (FIS). To reduce dependence on the gain updating factor coefficients of the fuzzy methodology, PID baseline method is introduced. This new method provides an optimal setting for other factors which created by PID baseline method. The gradient descent methodology is off-line tune all coefficients of baseline fuzzy based on mathematical optimization methodology. Simulation results signify good performance of fuel ratio in presence of different torque load and external disturbance.

Due to the significant advances of digital photography and the availability of many powerful photo editing tools, it becomes easier to create forgery images by non-professional users. Median filtering that is usually applied to erase the forensically significant fingerprints has recently received increased attention. In this paper, we present an effective blind forensic algorithm to detect the median filtering manipulation. First, the median filtered residual (MFR) is generated by computing the difference between a testing image and a median filtered version of itself. Then, three feature sets including histogram, autocorrelation and gradient are extracted from the median filter residual. Last, those features are fetched into support vector machine (SVM) for training and classification. Our experimental results demonstrate that our proposed forensic method achieves not only better detection rate but also lower computational complexity compared with other existing median filtering detection methods. Our proposed forensic method also can locate local median filtering of image effectively.

Turing Machines are the most powerful computational machines. Turing machines are similar to algorithms, and are the theoretical basis for real computers. It is a tiresome task to build and maintain Turing Machines for all the problems. The Solution of this problem is The Universal Turing Machine (UTM) or simply a universal machine. The Formation of UTM is very difficult due to the fundamental complexities. There are so many existing tool those does not supporting formation of Universal Turing Machine that’s why it is very difficult to accomplish the task. So that JFLAP platform is used for the creation of UTM. JFLAP is wrapping up of graphical tools that are used as an assist in learning basics of formal languages and automata theory. It is software to perform experiment using formal languages topics covered Nondeterministic finite automata, L-systems, parsing, multi-tape Turing machines, different kind of grammars and Nondeterministic Pushdown automata. As well as testing and making another example for these, JFLAP provide one to research with construction proofs from one form to another, such as converting an NFA to a DFA to a minimal state DFA to a regular expression or regular grammar. A Directed graph is be used to represent a Turing Machine, uses by JFLAP. It is widely used to simulate UTM as transducer of the Turing machine and it consists of multiple inputs.

In order to successfully monitor a large-scale distributed system, it is an important issue that the monitoring function fully covers all the entities in the system. To this end, a key challenge is to efficiently transmit state information of the entities in the system. This paper solves this challenge from two aspects. First, in virtue of the idea of self-organizing networks, this paper proposes a neighborhood organization algorithm, which self-organizes the nodes into several monitoring neighborhoods based on the t distance between nodes. The second aspect focuses on message transmission. There are three common message transmission methods in network, i.e., flooding, multicast and unicast. Flooding may cause high network overhead, while unicast may pose high system delay. Based on the idea of Gossip protocol, this paper proposes a directional message dissemination algorithm (D-Gossip), which is a kind of probabilistic multicast. D-Gossip reduces message dissemination uncertainty of traditional Gossip protocols. It effectively improves the efficiency and coverage of message dissemination, while reducing redundant information in the system due to Gossip protocol. The experimental results show that the neighborhood organization algorithm and the D-Gossip can effectively solve the above challenge.

This paper discusses economical evaluation problem of implementing enterprise resource management systemby use of NPV method and options pricing theory, chooses basic index of evaluating project after implementing ERP/ERP II, analyses particular process of evaluating ERP/ERP II project, and evaluating enterprise final comprehensive benefit.

In this research, intelligent sliding mode controls are presented as robust controls for robot manipulators. The objective of the study is to design controls for robot manipulators without the knowledge of the boundary of the uncertainties by using an intelligent sliding mode control (SMC) while elucidating the robustness of the fuzzy SMC. A sliding mode control provides for unlimited accuracy in presence of bounded disturbance, although the sliding mode controller also causes chattering. Chattering is undesirable for use with actual component, since it might causes damage to them with a subsequent loss of accuracy. Such chatter is caused by overestimation of the controller gain. An intelligent sliding mode is proposed as a solution to the problems created by chattering; to illustrate, a continuum robot manipulator is simulated with an intelligent sliding mode control. The performance of intelligent gain sliding mode controller is demonstrated through the simulation results. The results of the simulations show the effectiveness for chattering mitigation by means of avoiding overestimation, and the robustness of an intelligent sliding mode control.

Opinion Target Extraction is one of the important tasks for text sentiment analysis, which has attracted much attention from many researchers. For this task, we proposed an M-Score algorithm utilized in the model which realized the domain-independent opinion target extraction function. This algorithm is derived from the Pointwise Mutual Information algorithm, but the difference is that it doesn’t need any manual seeds collection or any web searching engines, which reduces the manual participation and easy to be transplanted. This model starts with document preprocessing, effective opinion sentences extraction and candidate opinion target extraction by employing Conditional Random Fields Model with feature templates. Next, the M-Score algorithm is employed to extract seed set, and the bootstrapping approach is invoked to process the candidate opinion targets. Finally, the model uses word frequency and the Noun pruning algorithm to filter the opinion targets, and then obtains the final opinion targets for output. The experimental results show that the M-score method performs better than Pointwise Mutual Information algorithm in precision and recall.

The current advances in cloud computing layout well established research in Distributed computing, virtualization, web services, utility computing, have provided many advantages in scalability, cost and efficiency for cloud service users. These advantages are further expected to satisfy the demands of cloud users for cloud services in efficient and effective manner. This brings the issue of fault tolerance, scalability, efficiency, high availability. Central to these problems require the dynamic and efficient load balancing techniques. In this paper, we survey a special group of Swarm intelligence based load balancing algorithm and discuss the advantages and issues of these algorithms for cloud computing environment.

Fingerprint features recognition which can be used to distinguish between individuals is an intriguing study with many potential applications. In this paper, a new method for fingerprint recognition based on multifractal spectrum theory was proposed. The recognition process can be divided into the following main steps: (1) Extracting the core point in fingerprint; (2) Fragmenting the fingerprint image to get a subimage with fixed size; (3) Thinning the fingerprint image by using an improved OPTA algorithm; (4) Segmenting the curves in fingerprint image into digital straight segments with normalized straight length threshold; (5) Selecting the appropriate dividing scale to segment the processed fingerprint image; (6) Calculating and analyzing the multifractal spectrum curve - f (a) ; (7) Fitting curve equation and extracting the characteristic parameters of a - f(a); (8) Finally, the parameters matching and fingerprint feature recognition. A large number of experimental results show that our method is effective.

According to this research paper, fuzzy hype plane controller is applied to robust nonlinear controller to reduce the vibration of motor. To control of multi degree of freedom joint, nonlinear controllers are the best candidate. Sliding mode controller is one of the best choices to robust control of this nonlinear system. The sliding mode controller is used to speed up the error convergence when the error is greater than one. To reduce the error terminal sliding mode controller is recommended in this research. Fuzzy hype-plane variable sliding mode controller is adopted to guarantee the error convergence to zero in a finite time when the error is near the zero. The chattering in the conventional sliding model control systems is avoided with the employed continuous controller. To increase the system robustness in presence of uncertainty fuzzy logic controller is recommended. This technique is used to adjust the band of terminals. The simulation results show that the proposed scheme has strong robust against the uncertainties and disturbances, as well as leads to the convergence of the output to the desired value quickly and precisely than employing either sliding mode controller or terminal sliding mode controller alone.

Cat swarm optimization (CSO) is a novel meta-heuristic for evolutionary optimization algorithms based on swarm intelligence. CSO imitates the behavior of cats through two sub-modes: seeking and tracing. Previous studies have indicated that CSO algorithms outperform other well-known meta-heuristics, such as genetic algorithms and particle swarm optimization, because of complexity, sometimes the pure CSO takes a long time to converge to reach to optimal solution. For improving the convergence of CSO with better accuracy and less computational time, this study presents an improvement structure of cat swarm optimization (ICSO), capable of improving search efficiency within the problem space under the conditions of a small population size and a few iteration numbers. In this paper, an improved algorithm is presented by mixing two concepts, first concept found in parallel cat swarm optimization (PCSO) method for solving numerical optimization problems. The parallel cat swarm optimization (PCSO) method is an optimization algorithm designed to solve optimization problems Based on cats’ cooperation and competition for improving the convergence of Cat Swarm Optimization,, the second concept found in Average-Inertia Weighted CSO (AICSO) by adding a new parameter to the velocity update equation as an inertia weight and used a new form of the position update equation in the tracing mode of algorithm. The performance of ICSO is sensitive to the control parameters selection. The experimental results show that the proposed algorithm gets higher accuracy than the existing methods and requires less computational time and has much better convergence than pure CSO, and the proposed effective algorithm can provide the optimum block matching in a very short time, finding the best solution in less iteration and suitable for video tracking applications.

A hybrid intelligent optimization algorithm based on quantum particle swarm is presented to solve the problem that the local search ability of traditional SFLA is poor and converges very slowly. The particle is quantized and introduced chaos mechanism in the algorithm in order to enhance the global search ability, using the escape strategy, the group is divided into three clusters and mutation operation on the cluster within individuals, not only improves the convergence speed and ensure the performance of the algorithm. Experiments show that the improved algorithm has the characteristics of strong optimization capability and performance is improved greatly in whether comparison of the baseline function or analysis of universal database, compared with the other two algorithms have obvious advantages.

Cuckoo Search Algorithm (CSA), a new meta-heuristic algorithm based on natural phenomenon of Cuckoo species and Lévy flights random walk has been widely and successfully applied to several optimization problems so far. In the paper two modified versions of CSA, where new solutions are generated using two distributions including Gaussian and Cauchy distributions in addition to imposing bound by best solutions mechanism are proposed for solving economic load dispatch (ELD) problem with multiple fuel options. The advantages of CSA with Gaussian distribution (CSA-Gauss) and CSA with Cauchy distribution (CSA-Cauchy) over CSA with Lévy distribution and other meta-heuristic are fewer parameters. The proposed CSA methods are tested on two systems with several load cases and obtained results are compared to other methods. The result comparisons have shown that the proposed methods are highly effective for solving ELD problem with multiple fuel options and/nor valve point effect.

To study intelligent auxiliary drive system of port machinery, it needs to identify operational intention of quayside container crane driver. With the background of loading and unloading process of quayside container crane, upon Hidden Markov Model, double HMM model is established. The algorithm of revised Forward-Backward is applied to calculate each likelihood of HMM in operation layer, the model of the largest likelihood is selected to be the identify result of operation behavior. After combining them to constitute the observation sequence bunch, it will be sent to the intention layer of HMM to conduct the identification of operation intention of crane driver. Finally, HMM is realized by Matlab. By means of field statistics, the basic data can be determined and effectiveness is also verified. It turns out that this model can accurately identify the operational intention of quayside container crane driver, which is of great significance for studying intelligent auxiliary drive system of port machinery.

It has already been confirmed that the traffic in high-speed terrestrial network presents self-similarity, but there is little research on self-similarity of traffic in satellite network. Considering time-varying network topology and link status, this paper analyzes the aggregation and propagation of self-similar traffic between nodes in satellite network. Furthermore, a sort of special network node called ground gateway is modeled, based on which the characteristics of the output traffic that the input traffic from terrestrial network passes gateway into satellite network are analyzed. Theoretically analyses demonstrate that after aggregation and propagation between satellite nodes, traffic is still self-similar, and the self-similarity of the output traffic generated by gateway from terrestrial network to satellite network is more often than not weakened.

A single channel land surface temperature (LST) retrieval algorithm named Single Channel Water Vapor Dependent (SCWVD) method was presented for Medium Resolution Spectral Imager (MERSI) thermal infrared band aboard FengYun-3A (FY-3A) satellite. Water Vapor Content (WVC) is the only input parameter in the algorithm assuming the surface emissivity is known. NCEP reanalysis monthly mean datasets were used to develop the SCWVD algorithm. Some tests, including global numerical simulations and validations with both in-situ measurements and MODIS LST product at Lake Tahoe, USA, were carried out to evaluate the algorithm performance. The simulation results showed that the LST could be estimated by the SCWVD algorithm with the root mean square error (RMSE) less than 0.8 K. In the inversion test, MERSI Level 2 water vapor product was employed, and the MERSI band emissivity was calculated using the MODIS band 31 and 32 emissivity by an empirical expression. The results show that the difference between the retrieved MERSI LST and the in-situ measurements is less than 1 K in most situations. The comparison with the MODIS LST products (V5) shows that the RMSE is about 2.3 K.

Constraint solving is widely applied to many fields including computer aided design, 2 dimension (2D) model design and computer aided manufacturing. Geometric constraint solution is a difficult problem because there are a large number of entities and related parameters in 2D sketches. In this paper, a new method which decomposes geometric constraint relations based on entity-parameter graphs is proposed for reducing the size of constraint solution. A geometric constraint problem is decomposed into many independent sub-problems. Then, particle swarm optimization algorithm is used to solve constraint equations in each sub-problem. Solutions of all sub-problems are integrated to obtain the original problem’s solution. In experiments, the proposed method is applied to HUST-CAID system. Experimental results show that the method can effectively solve 2 dimension geometric constraints.