Earticle

The characteristics of power ternary polymer Li-ion batteries are closely connected to ambient temperature. The capacity characteristic, resistance and state of charge-open circuit voltage (SOC-OCV) curve are important parameters to represent the performance of power batteries and to determine battery management system (BMS) design. The experiments at different ambient temperatures are carried out and the laws between temperature and capacity, resistance and OCV are studied. The capacity drops sharply under low temperature, and increases with a relatively slower rate than under low temperature when the temperature goes up. Polarization and ohmic resistances during charge and discharge process decrease when the temperature rises, and the change rate of ohmic resistance is higher than that of the polarization resistance. Moreover, the change of ohmic resistance under low temperature is more significant than under high temperature. With the decrease of temperature, the SOC-OCV curve moves down, but generally, the curve is affected only slightly by the temperature.

Advances in the development of electric vehicles along with policy incentives will see a wider uptake of this technology in the transport sector in the coming years. However, the widespread adoption of electric vehicles will add a substantial energy load to power grids. As a result, many technical problems related to the impact of this technology on the power grid need to be addressed, especially the management and allocation of the energy to the plug-in hybrid electric vehicles (PHEVs), electric vehicles (PEVs). In this paper, we formulated the optimal power allocation to PHEVs/PEVs for the PHEVs/PEVs charging stations scheduling problem as a nonlinear resource allocation continuous problem. We used pegging algorithm to solve the optimal power allocation to the PHEVs/PEVs. A mathematical framework for the objective function (i.e., minimizing the average depth of discharge (DoD) at the next time step) was also given. The authors characterized the performance of optimal power allocation to PHEVs/PEVs problem and pegging algorithm using MATLAB simulation, and compared it with other charging methods.

Sphere decoding is a very powerful algorithm in searching the optimal solution of multiple input and multiple output systems. However, it cannot perform in parallel directly. Sphere decoding can be depicted as searching in a tree. In this paper, we propose a parallel mode of the sphere decoding algorithm. We proposed that the searching tree can be partitioned into several sub-trees. The searching is divided into two stages. In the first stage, the partial Euclidean distances of sub-tree root nodes are calculated. In the second stage, several sub-trees perform their searching simultaneously. The Euclidean distance of the early finished sub-tree helps to reduce the calculation in the later finished sub-trees search. Simulation results sho

Classic SURF algorithm may lead to matching failure, low recall because of incorrect main direction when constructing feature points describing operator. To solve it, A Method using auxiliary direction to improve SURF recall is put forward. The improved algorithm first select out auxiliary direction which is similar to main direction in characteristics, then generate new operator for describing the auxiliary direction characteristic. When matching, the improved algorithm adopts stricter nearest neighbor proportion inhibition. Experimental results show that feature point recall increase about 6% compared with the classical SURF while maintaining the precision.

Recent development of robot technology is revolutionizing the medical field. The concept of using robot assistance in medical surgery has been receiving more and more recognition throughout the world. Robot-assisted surgery has the advantage of reducing surgeons' hand tremor, decreasing post-operative complications, reducing patients' pains, and increasing operation dexterity inside the patients' body. Robotic assistants have been broadly used in many medical fields such as orthopedics, neurology, urology and cardiology, and robot assisted surgery is keeping expanding its influences in more general medical field. This research study aims at utilizing advanced robotics manipulator technologies to help surgeons perform delicate procedures associated with surgery. The Four-axis Virtual Robot arm (FVR) is a MATLAB-based computer program, which can be used to simulate the functions of a real robotic manipulator in terms of design parameters, movement and control. It has been designed with adjustable kinematic parameters to mimic a 4-axis articulate robotic manipulator with revolute joints having 4 degrees of freedom. The FVR can be manipulated using direct kinematics to change the spatial orientation of virtual objects in three dimensions. Picking and placing of virtual objects can be done by using the virtual proximity sensors and virtual touch sensors incorporated in to the jaw design of the FVR. Furthermore, it can be trained to perform a sequence of movements repeatedly, to simulate the function of a real surgical robotic manipulator. All steps to modeling are discussed in this research. Proportional-Integral-Derivative control technique is used to control of FVR.

In order to promote the development of natural gas Combined Cooling Heating and Power (CCHP) project in China, it is critical for us to comprehensively evaluate the social benefits. This paper establishes a comprehensive evaluation index system which contains the natural environmental benefit, and thethethe social economy benefits. Moreover, a comprehensive evaluation model based on AHP-formula method and GCDM is proposed to evaluate the social benefits of the natural gas CCHP project. At last, an empirical analysis of a project in city A is presented, which shows that the social benefits of this project is “very good”, and the feasibility of this model is proved.

The hybrid technique of Swine Influenza Model Based Optimization (SIMBO) and Genetic Algorithm (GA) for designing linear phase FIR low pass filter has been presented in this paper. The major difficulties using SIMBO algorithm in designing filter was premature convergence and unacceptable computational cost. To address this problem, a hybrid SIMBO-GA is proposed where GA is used to help SIMBO escape from local optima and prevent premature convergence. DEPSO, GLPSO DVN are adopted for comparison. In contrast with aforementioned algorithms it has been divulged that hybrid SIMBO-GA seems to be promising tool for optimum FIR filter design.

In this paper, a novel negative selection algorithm for recognition problems was given. Compared with the traditional negative selection algorithm, a co-stimulation signal was added to start the detectors, which a key factor in immune response. Co-stimulation signal was calculated by the techniques of the statistics and the sliding window, which not only reduced time complexity of algorithm but also improved accuracy of the algorithm. Entropy was adopted to evaluate the density of detectors for optimizing the coverage of nonself area. Experiment results proved high accuracy and efficiency of the proposed algorithm.

With the development of deep learning, it has achieved impressive results in feature extraction field. This paper drives research in feature extraction based on deep learning. First, this paper gives a brief introduction on the world's research status on deep learning and principle of Restricted Boltzmann machine (RBM). Then this paper conducts reducing experiment based on RBM for handwritten digits. According to the analysis based on the results of the experiments, this paper tries to get a proper dimension which handwritten digits reduced to achieve better performance. Finally, this paper finds that it reach the goal when handwritten digits is reduced to half dimensional raw digits. This is an important foundation of deep learning layering and offers help to researchers in feature extraction based on deep learning.

A novel approach for the efficient weighted association rule mining proposed in this present paper. The proposed approach reducts the transactional dataset (weighted) by utilizing the power of Rough Set theory. Furthermore, proposed approach acquires the benefit for weighted measures (w-support, w-confidence) for obtaining the most profitable weighted frequent itemsets and the Genetic Algorithm for the extracting the desired set of optimized weighted association rules. Experimental analysis of proposed approach has been done and observed that the approach works well and will be helpful in situation when there is a requirement for the consideration of extracting the best weighted association rules in decision-making process.

Most traditional code clone detections have less accurate results because they ignore the structure of the program itself, and some of them really think about it by creating a complex syntax tree but leading to a high time complexity. Confronting such situation, this paper proposes an efficient metric vector-based code clone detection method using function-calling tree. Considering the two program code to be detected, feature vectors in all defined functions of the two different code are extracted first. Then, two function-calling trees are created according to the function-calling process and node matches each other between two trees, at the same time, the matching similarities are calculated. Finally, by using the bottom-up approach and combining similarity values of all child nodes, the detection can get the similarity of the two program code to be detected. Our experiment selects a set of typical code sample to measure and the results demonstrate that, compared the famous JPlag system, it shows better detection effect.

The mobile public e-Government cloud services platform solution will encompass new technologies, and through the Big Data concept for data collection and exchange it will enable innovative channels of communication and collaboration among different players. The major benefit of the cloud-based platform is the efficient execution of heavy computation algorithms in the cloud simply by using Big Data storage and processing platforms. This new approach will address the mismatch between labour demand and supply to make the services of the public employment agencies more efficient, more effective, user-friendly and personalized. The main focus is given on the positive impact of e-services for employability prospects of young unemployed people, those with disabilities, and older workers. The platform will facilitate the matching of people for job openings, and more specifically, the job search by people with special skill-profiles, including special needs or time requirements. It will provide a networking platform and information services to address socio-economic side-effects of labour market exclusion. The main purpose of the model of mobile public e-Government cloud services platform is to provide an integrated environment where public institutions will receive complete data analysis.

IRIS flower data is a class of multi variable data set, which is widely applied in data classification. This paper aims at the parameter optimization problem of least squares support vector machine (LS-SVM) in data classification, an improved particle swarm optimization(IMPSO) algorithm is introduced into the LS-SVM model for improving the learning performance and generalization ability of LS-SVM model. A new data classification method based on IMPSO algorithm and LS-SVM (IMPSO-LS-SVM) model is proposed. First, the numbers of current iteration and population are added into the control strategy of adaptive adjustment inertia weight in order to improve the performance of inertia weight of PSO algorithm. Then the IMPSO algorithm is used to search the optimal combination values of the parameters of kernel function for obtaining the IMPSO-LS-SVM. Finally, the training samples are used to comprehensively train the IMPSO-LS-SVM, and the best large-scale data classification model is constructed. The IRIS flower data is used to validate the effectiveness of the IMPSO-LS-SVM model. The result indicates that the IMPSO algorithm can effectively search the optimal combination values of the parameters, and the proposed data classification model has better generalization performance, faster training speed and higher classification precision.

Exhaust contaminant of gasoline vehicles is a crucial aspect to measure the vehicle performances and the air pollutions. According to the feature of vehicles, the emission of exhaust contamination of a vehicle is different as time goes by, which shows an increase tendency in most of the cases. Measuring the changes of a vehicle's exhaust contaminant emission is of great importance in the field of vehicle engineering. However, it is hard to determine and find out the regulations of the emission, needing a long time for regular determination and advanced relevant machines. In this article, we aim at providing two novel methods for the prediction of exhaust contaminant of gasoline vehicles, using grey model GM (1,1) and artificial neural networks (ANNs) models respectively. Results show that both the GM (1,1) model and ANN models are comparatively precise for the prediction. The GM (1,1) model can quickly obtain the predicted values of exhaust contaminant, but it is less precise than ANN models. However, ANN models need more time for the training process, compared to GM (1,1) mo

Multiplier is the major component for processing of large amount of data in DSP applications. Using different recoding schemes in Fused Add-Multiply (FAM) design for the reduction of power and look up tables. The performances of 8-bit, 12-bit & 16-bit signed multipliers were designed and obtained results are tabulated using Efficient Modified Booth Recoding (EMBR) techniques, which can be used for low power applications.

Cloud resource managers face many problems, such as the dynamic changes of incoming load and demand elasticity of resources. From the aspect of elastic configuration management technology of virtual resources, this paper focus on how to provide quick and reliable cloud resources for users. Virtual machine resources automatic configuration management technology is proposed in this paper, the reverse reinforcement learning technique is introduced into cloud virtual resource management, configuration management process of the virtual machine is modeled as a Markov decision model. According to the running state of the application system and the dynamic changes of the input load, this technology can make an automatic decision to add or remove a number of virtual machines. Experimental results show that this technology can complete the tasks of automating configuration of virtual resource management according to the changing load, respond to end user's in a timely manner, and ensure the SLA requirements of cloud users.

Metamaterial is the arrangement of "artificial" elements in a periodic manner providing unusual electromagnetic properties. This unusual property has made it an area of interest for last few decades. It has wide applications in antennas. Gain, directivity, bandwidth, efficiency, and many other parameters of microstrip patch antenna can be improved using metamaterials. In this review paper, we first overview the metamaterials, its types and then the application of metamaterials in Microstrip patch antennas over the last 13-15 years.

In this paper, a six-dimensional Lie algebra is first introduced, whose corresponding loop algebra is constructed, for which an isospectral problem is established. By zero curvature equations, we obtain the nonlinear integrable couplings of the Kaup-Newell (KN) hierarchy.

The estimation of probability density function (pdf) by the nonparametric kernel methods requires a reliable estimate of the bandwidth. There have been several studies on how to efficiently estimate this parameter. In this work, we propose a new optimization method of the smoothing parameter of the variable kernel estimator (VKE) based on the statistical properties of the probability distributions of random variables. In this setting, we show how to use the maximum entropy principle for estimating the smoothing parameter. The optimized estimator is after used in building the Bayesian classifier. In the same setting, the estimated probability density function is called optimal in the sense of having a minimum error rate of classifying data. Finally, a practical implementation with the aid of a dataset of DNA microarray is used to illustrate the behavior of the optimization technique.

In order to improve supply chain’s operation efficiency, shorten delivery time and decrease distribution costs, two-stage cross docking scheduling problem under direct shipment mode was studied in this paper. Taking into consideration the influence of numbers of vehicles in distribution center on cross docking problem, three models were established based on different assumptions including only one vehicle in distribution center, many vehicles in distribution center and the location of distribution center to be determined, and the objective was to minimize transportation time. Hybrid particle swarm optimization was proposed to solve the model on the basis of PSO and GA. The algorithm introduced clone selection operator to make particles multiply and mutate by calculating the affinity between individuals so that the best individual can be reserved and the poor can be improved. Clone operator, crossover operator, antibody reorganization operator and mutation operator were designed to improve the performance of the algorithm. Computational experiments showed that the hybrid particle swarm optimization algorithm has faster convergence speed and better solution precision compared with other algorithms. The result of the present work implied that the model in this paper was accord with the reality, and it was effective and feasible.

This paper put forward constrained epistemic action model logic (EAML), which extended the action modal logic and reason epistemic default logic in the dynamic epistemic theory framework. In this paper, the constrained epistemic default logic and action model logic was combined to model the dynamic cognitive update process by introducing a cognitive operator. As constrained epistemic default logic restricts the current possible epistemic state and action model logic can describe epistemic actions, the new proposed logic can better portray the cognitive operations and the restriction of the corresponding accessibility relations. The description of the logic framework was given in this study, and the related theorems were proved to show the interpretation of the constrained epistemic action model logic.

The Job-Shop Scheduling Problem (JSSP) is a well-known and one of the challenging combinatorial optimization problems and falls in the NP-complete problem class. This paper presents an algorithm based on integrating Genetic Algorithms and Simulated Annealing methods to solve the Job Shop Scheduling problem. The procedure is an approximation algorithm for the optimization problem i.e. obtaining the minimum makespan in a job shop. The proposed algorithm is based on Genetic algorithm and simulated annealing. SA is an iterative well known improvement to combinatorial optimization problems. The procedure considers the acceptance of cost-increasing solutions with a nonzero probability to overcome the local minima. The problem studied in this research paper moves around the allocation of different operation to the machine and sequencing of those operations under some specific sequence constraint.

This article examines navigation of a flying robot inside a building environment in three dimensional spaces in which the size and location of some obstacles are not determined and other obstacles and target can be moving. This article suggests a new method by combining Q-learning algorithm and Monte Carlo algorithm on optimal navigation by the flying robot. The rewards are intended to be maximized when the robot flies in the right route; moreover, the maximum performance power would be measured according to the future predictions and the well-doing of that action would be also measured. Here, this method has been implemented with Webots simulator, and simulated data are analyzed by MATLAB. The simulation results show that control of the policy obtained from Q-learning and Monte Carlo methods is more efficient compared to traditional methods in controlling flying robot navigation.

Agriculture enterprise management’ comprehensive quality evaluation, is the important content for transformation of agricultural and talent cultivation. This paper through the expert investigation method, a wide range of questionnaire investigation, establishing agriculture enterprise management’ comprehensive quality evaluation system, and using the method of fuzzy mathematics evaluation for agriculture enterprise management’ quality, working for the professional information construction personnel training to provide feedback.

Due to the normal forecasting methods for subgrade settlement using observation data have different applicability and disadvantages, The Combined forecasting model is put forward based on support vector machine (SVM) and real-coded quantum evolutionary algorithm (RQEA) in this paper. Its core is that, according to the basic settlement law of subgrade and characteristics of settlement curve, the growth curve which has S-type characteristic are chosen as single forecasting model, then support vector machine is used to combine the predicting results of each single forecasting model, at the same time, RQEA is adopted to optimize support vector machine parameter to improve the SVM’s performance. The analytical result of engineering practice indicates that the proposed combined forecasting model of subgrade settlement base on SVM and RQEA can not only improve the predicting accuracy, but also reduce the predicting risk, and can meet engineering demand.

Genomic repositories gradually increase individual and reference sequences, which shares long identical and near-identical strings of nucleotides. In this paper a lossless DNA data compression technique called Optimized Base Repeat Length DNA Compression (OBRLDNAComp) has been proposed, based upon redundancy of DNA sequences. For easy storage, retrieval time reducing and to find similarity within and between sequences compression is mandatory. OBRLDNAComp searches long identical and near-identical strings of nucleotides which are overlooked by other DNA specific compression algorithms. This technique is an optimal solution of longest possible exact repeat benefits towards compression ratio. It scans a sequence horizontally from left to right to find statistic of repeats then follow substitution technique to compress those repeats. The algorithm is straightforward and does not need any external reference file; it scans the individual file for compression and decompression. The achieved compression ratio 1.673 bpb outperforms many non-reference based compression methods.

Developing computer software system is a crucial part of the intelligent design. In order to solve problems presented in software system quality analysis, this paper proposes a quality analysis model for computer software system based on fuzzy information content. In this model, fuzzy information content of quality analysis indicators is defined according to information axiom, and a software system quality analysis index system is constructed with the basic layer, the support layer and the application layer considered. With the index system, fuzzy information content can be computed and analyzed to obtain fuzzy information content of computer software system. This facilitates the quantitative analysis on the quality of the software system. Finally, an engineering case study is introduced to explain how the model works and proves efficacy and feasibility of the model.

In this paper we presented an architecture and basic learning process underlying in fuzzy inference system and adaptive neuro fuzzy inference system which is a hybrid network implemented in framework of adaptive network. In real world computing environment, soft computing techniques including neural network, fuzzy logic algorithms have been widely used to derive an actual decision using given input or output data attributes, ANFIS can construct mapping based on both human knowledge and hybrid learning algorithms. This study involves study of ANFIS strategy ANFIS strategy is employed to model nonlinear functions, to control one of the most important parameters of the induction machine and predict a chaotic time series, all yielding more effective, faster response or settling times.