Earticle

Localization is used in location-aware applications such as navigation, autonomous robotic movement, and asset tracking to position a moving object on a coordinate system. In this paper, a Nonlinear Programming algorithm is proposed based on RSSI and improved DV-Hop algorithm, called NPRDV-Hop. The algorithm makes four major contributions to the localization problem in the wireless sensor networks (WSNs). Firstly, a hop distance is improved, called Hop Distance. This scheme could assure that the most nodes receive the Hop Distance from beacon node who has the least hops between them. This practical localization scheme is relatively high accuracy and low cost for WSNs. Secondly, Heron's formula is introduced as objective function. Thirdly, Gauss distribution is introduced to select RSSI so that the error of distance is little. Lastly, the general problem is considered by the nonlinear programming to solve for the locations of the sensors. Simulation results show that the proposed method can improve location accuracy and coverage without increasing hardware cost of sensor node. The performance of this algorithm is superior to the original DV-Hop algorithm.

IEEE 802.11p defines an international standard for wireless access in vehicular environments. It is normally designed for operating at high user mobility. Thus, decreasing the signal bandwidth to 10 MHz in IEEE 802.11p makes the communication more efficient for the vehicular channel. Furthermore, the Orthogonal Frequency Division Multiplexing (OFDM) methodology is commonly used to mitigate the Inter-Symbol Interference (ISI) occurred over a wireless channel. However, the OFDM suffers from certain drawbacks including higher system complexity due to the generation of the peak to average ratio (PAPR). The objective of this study is to reduce the high PAPR resulted from adapting the OFDM system to vehicular networks. In this paper, we propose a novel precoding Zadoff-Chu Sequence (ZCS) combined with the Clipping technique for PAPR reduction in OFDM based on the Discrete Cosine Transform (DCT). The proposed technique which is based on precoding the constellation symbols of the vehicular channels with DCT succeeds to decrease the PAPR ratio up to 4dB for 64 subcarriers.

The optimal discrete signal are widely used in spread spectrum communication systems, sonar, navigation, code division multiple access systems and other fields. The perfect sequence pair is an extension of the single best signal, so its application is more widely. This paper puts forward new methods of constructing the binary sequence pair of three values and quaternion sequence pair of three values according to the problem of constructing the sequence pairs of three values. This method is that it uses N long cyclotomic pseudo-random sequence and its shift sequences pair to do series connection to constitute a 4 N long binary sequence pair of three values. New sequence pairs of three values is obtained by this method proved to have good balance. The application steps of this method are given and shall be verified by some examples.

Semantic similarity is a core technique of many NLP research fields. However, state-of-the-art metrics for semantic similarity computation often operate at different levels, e.g., words or sentences. In this paper, semantic similarity computation metrics are firstly introduced and the quality is measured in order to determine their advantages and limitations; then a new semantic similarity metric based on multi-features fusion is proposed. Distributed representations of words are used for alignment-based disambiguation operation, and Wikipedia tags are used to enhance the performance of our approach. The proposed metric is unsupervised, and can be applied at different levels e.g., single words or entire documents. The metric is evaluated on both English and Chinese datasets, it is shown that the precision and recall scores are higher than metrics which simply using knowledge base or distributed representation of words.

In fraud detection paradigms, the role of predictor variables cannot be overemphasized particularly when analytical tools – such as statistical, machine learning and artificial intelligent tools are employed. These variables or attributes are used to organize records of data in database tables. The combination of the values of these attributes usually affects which class of a target variable a record or an observation would belong. In this paper, we propose an algorithm and employ spreadsheet ‘count’ ‘count if’ and ‘filtering’ functionalities (techniques) to take toll on how the individual attribute may affect the prediction of the class of an observation in an insurance dataset of 5000 observations. The analysis showed that indeed, the individual predictor attribute affects the outcome of the target variable (legal or fraudulent) differently.

In investment process, investors usually have the characteristic of loss aversion. This paper discusses the portfolio selection problem with loss aversion under uncertain environment. Return rates are described as uncertain variables; uncertain measure is used to measure the uncertainty. The optimal model of maximizing expected utility based on loss aversion is established. When return rates are special uncertain variables, the model can be transformed to the crisp one, for generic uncertain return rates, hybrid intelligence algorithm integrating genetic algorithm and 99-method is designed to solve the model. Finally, numerical example is given to illustrate feasibility and validity of this method.

Identifying design patterns from source code is one of the most promising methods for improving software maintainability, reusing experience and facilitating software refactoring In the process of design pattern application, different methods of instantiation usually lead to generation of pattern variants. The detection of these variants from source code is a key point and a challenge of reverse engineering. In this paper, we propose an approach of detecting design pattern variants from source code based on constraints. More specifically, we first propose the method to describe the design pattern variants based on constraints, input the constraint conditions of design patterns into the library of pattern features, conduct static analysis on source code including analysis of control flow and data flow to obtain representations of pattern participants in the source code, conduct matching with the predicate matching tool, and then merge the instances by clustering analysis and obtain the final collection of pattern instances. Finally, a tool of extracting design pattern variants from source code is implemented and the feasibility of our approach is verified through the results of running it on three open source software.

Robotic surgery, computer-assisted surgery, and robotically-assisted surgery are terms for technological developments that use robotic systems to aid in surgical procedures. Robotically-assisted surgery was developed to overcome the limitations of minimally-invasive surgery and to enhance the capabilities of surgeons performing open surgery. In the case of robotically-assisted minimally-invasive surgery, instead of directly moving the instruments, the surgeon uses one of two methods to control the instruments; either a direct telemanipulator or through computer control. A telemanipulator is a remote manipulator that allows the surgeon to perform the normal movements associated with the surgery whilst the robotic arms carry out those movements using end-effectors and manipulators to perform the actual surgery on the patient. In computer-controlled systems the surgeon uses a computer to control the robotic arms and its end-effectors, though these systems can also still use telemanipulators for their input. One advantage of using the computerised method is that the surgeon does not have to be present, but can be anywhere in the world, leading to the possibility for remote surgery. The multi degrees of freedom actuator is an important joint, which has attracted worldwide developing interests for its medical, industry and aerospace applications. This paper addresses the problem of trajectory tracking of three dimensions joint in the presence of model uncertainties and external disturbances. An adaptive fuzzy sliding mode controller (AFLSMC) is proposed to steer a three dimension joint along a desired trajectory precisely. First, the dynamics model of a three dimension joint is formulated and the trajectory tracking problem is described. Second, a sliding mode controller (SMC) is designed to track a time-varying trajectory. The fuzzy logic system (FLS) is employed to approximate the uncertain model of the three dimension joint, with the tracking error and its derivatives and the commanded trajectory and its derivatives as FLS inputs and the approximation of the uncertain model as FLS output. And a fuzzy logic system is also adopted to attenuate the chattering results from the SMC. The control gains are tuned synchronously with the sliding surface according to fuzzy rules, with switching sliding surface as fuzzy logic inputs and control gains as fuzzy logic outputs. The stability and convergence of the closed-loop controller is proven using the Lyapunov stability theorem. Finally, the effectiveness and robustness of the proposed controller are demonstrated via simulation results. Contrasting simulation results indicate that the AFLSMC attenuates the chattering effectively and has better performance against the SMC.

In this paper, we investigate the constructions of generalized bent Boolean functions defined on with values in Z4. We first present a construction of generalized bent Boolean functions defined on with values in Z4. The main technique is to utilize bent functions to derive generalized bent functions on odd number of variables. In addition, by using Boolean permutations, we provide a specific method to construct generalized bent functions on odd number of variables.

Test functions play an important role in validating and comparing the performance of optimization algorithms. The test functions should have some diverse properties, which can be useful in testing of any new algorithm. The efficiency, reliability and validation of optimization algorithms can be done by using a set of standard benchmarks or test functions. For any new optimization, it is necessary to validate its performance and compare it with other existing algorithms using a good set of test functions. Optimization problems are widely used in various fields of science and technology. Sometimes such problems can be very complex. Particle Swarm Optimization is a stochastic algorithm used for solving such optimization problems. This paper transplants some of the test functions which can be used to test the performance of Particle Swarm Optimization (PSO) algorithm, in order to improve its performance and have better results. Different test functions can be used for different types of problems. These test functions have a specific range and values, which can be applied in different situations. These functions, when applied to the PSO algorithm, can give the better comparison of results. The test functions that have been the most commonly adopted to assess performance of PSO-based algorithms and details of each of them are provided, such as the search range, the position of their known optima, and other relevant properties.

The purpose of this paper is to deal with the data in large document databases, analyzes the research trend of scientific papers and research hot spots. There is practical significance to promote the development of science and technology in China in order to provide effective methods and tools for the researchers.

In this paper, a differential evolution (DE) algorithm combined with Lévy flight is proposed to solve the reliability redundancy allocation problems. The Lévy flight is incorporated to enhance the ability of global search of differential evolution algorithm. DE is used for local search mainly. The method considers the trade-off of the diversification and the intensification simultaneously. Experimental results for three benchmark problems demonstrate that the proposed algorithm is more effective for solving the reliability redundancy allocation problems.

A mobile agent is self-governing software that has one or more goals and can migrate from one node to another in a network. Mobile agent has received pervasive interest in both research and academia in recent years because of its support for asynchronous and disconnected operation capability in distributed system Along with these affirmative sides, the full scale adoption of mobile agent has been delayed by several fault tolerance complexities in untrustworthy network environments such as Internet. This paper proposed a framework to provide fault tolerant execution to mobile agents. Key concepts used to accomplish this goal include the transaction manager concept, checkpointing, timeout mechanism. Agent saves partial results at home server. The framework can tolerate agent failure, server failure and communication delay. It has been implemented as an add-on to mobile agent platform called Aglets. Experiments have been conducted to test the feasibility and performance of the proposal.

Outlier detection and clustering technologies are an important branch of data mining, such as combining the two technologies can improve the mining significance. In this paper, both clustering and outlier detection can be the starting point, proposed a DBSCAN-LOF algorithm is the core idea is to use k_ neighbors thought, DBSCAN redefine the core of the object, making the only non-core objects LOF The operation, thereby reducing the original LOF algorithm is computing the number of global objects, and makes no DBSCAN algorithm input parameters Eps. Real and simulated data sets by experimental results confirm that the algorithm to improve the operating efficiency and the LOF algorithm DBSCAN clustering effect, and while producing clustering and outlier detection results.

In nature, the organisms have a limited lifespan and they grow older with time. Aging is an essential process which leads to the maintenance of species diversity in environment. Every group of species is lead by a leader. As the lifespan of every organism is limited, at a certain point of its life time, the organism deteriorates and become inefficient to lead its group. In this situation, a new leader is found who can efficiently lead its group. The lifespan of the leader and its leading power is checked, if it is not efficient enough, a new challenger is found to lead the group. This aging mechanism is applied to the stochastic process of Particle Swarm Optimization(PSO), in order to remove the limitations that existed in PSO such as: it gets stuck in local optima and the algorithm converges pre-maturely. When aging leader algorithm is applied to PSO, these limitations are removed in an efficient manner. This paper presents some issues that occur while designing and implementing a variant of PSO (Particle Swarm Optimization) i.e. ALC-PSO (PSO with Aging Leader and Challengers) which can highly improve the performance of PSO by applying the process of aging to the members of the swarm , bringing its members to the best position.

Affecting the effects of MCT evaluation of spatial cognition has a lot of factors. In problems of shape discriminate, when using a simulation model of MCT instead of perspective MCT model accuracy improved. In problems of quantity discriminate, by comparing the simulation model of MCT and perspective model of MCT, correct answer no bigger difference. Produced with the help of computer simulation model can help students to comprehend, but three-dimensional space cognitive enhancement is based on the experience of students as the main body understanding and the result of rational thinking.

Debris accumulation in the bottom and side gap leads to a poor machining stability and low machining efficiency of electrical discharge machining (EDM). Thus, an understanding on the mechanisms of debris distribution and exclusion of flow field in the discharge gap during EDM process should be investigated. However, these mechanisms have not been fully understood because debris distribution and exclusion process in the bottom and side gap is difficult to simulate and observe. This paper suggests a two-dimensional model of flow field with kerosene and debris phases for the bottom and side gap in ultrasonic assisted EDM process by computational fluid dynamics. The mechanism of debris distribution and exclusion in the discharge gap during ultrasonic vibration cycle was researched through the model. The effect of amplitude and frequency of ultrasonic vibration of the tool on debris distribution and exclusion during electrical discharge machining of whole array was investigated through numerical simulation. It is shown that ultrasonic vibration of the tool has significantly influence on the debris exclusion from discharge gap. In addition, the debris distribution and exclusion becomes strong with the increase of the amplitude and frequency of ultrasonic vibration of the tool.

In this paper, estimation of transition activity in multi coding technique with the help of hamming code estimator is done and power consumption is reduced using clock gating technique. The VLSI technology allows thousands of transistors in a single chip. In order to integrate more functions in a single chip and to improve their performance, the feature size of the transistors are continue to shrink. This results in maximum power dissipation of the circuit followed by heat removal and cooling of the circuit. The charging and discharging of the internal node capacitance due to transition activity highly contributes to dynamic power dissipation. Mainly the power dissipation occurs due to the transition activity and this can be estimated using hamming code estimator. Coded data is given to the hamming code estimator from each coding type. Estimated code is compared with the help of comparator, this result in analysis of the minimum transition activity for the code in appropriate coding technique. Also clock gating is used to minimize the unnecessary power consumed in the idle state.

Most of existed action recognition methods based on spatio-temporal descriptors have ignored their spatial distribution information. However the spatial distribution information usually is very useful to improve the discriminative ability of the motion representation. An improved spatio-temporal is proposed in this paper by combining local spatio-temporal feature and global positional distribution information (FEA) of interest points. Furthermore, in order to improve the classifier’s performance, an Adaboost-SVM method is utilized to recognize the human actions by using the proposed motion descriptor. The proposed recognition method is tested on the public dataset of KTH. The test results verified the proposed representation and recognition method can more accurately describe and recognize the human motion.

Performance of SACOCDMA-FSO system using MD codes at 10 Gbps is evaluated. MD code has become an area of interest in optical communication because of its ability to support many users at higher data rates, flexibility to select weight parameters. The proposed system was compared with SACOCDMA-FSO System employing RD codes in different conditions. The simulative results demonstrated that optical system based on MD codes is better than the system employing RD codes.

In order to overcome the drawbacks of standard shuffled frog leaping algorithm that converges slowly at the last stage and easily falls into local minima, this paper proposed two-phases learning shuffled frog leaping algorithm. The modified algorithm added the elite Gaussian learning strategy in the global information exchange phase, updated frog leaping rule and added the learning capability that the worst frog of current swarm learned from the best frog of other swarm. The learning capability of two-stage on the one hand increased the search range, on the other hand enhanced the diversity of population. Experiments were conducted on 13 classical benchmark functions, the simulation results demonstrated that the proposed approach improved the convergence rate and solution accuracy, when compared with common swarm intelligence algorithm and the latest improved shuffled frog leaping algorithm.

Patents, as technological innovation achievements and sign, are potential and intangible assets of enterprises and can enhance their core competitiveness. How to obtain useful information and knowledge from a large number of patents data, have become an urgent problem. In order to satisfy the actual needs of enterprises for patent information, the paper proposed a frame of patent information analyzing and predicting system based on data mining. We designed the function structure of the system which consists of patent data preprocessing, patent data mining and patent mining results visualization. We also introduced the implementation method of each part in the system. By using the designed patent information mining and predicting system, enterprises can automatically gained the required patent information and improve their core competitiveness.

The optimal generation dispatch problem with only one fuel option for each generating unit has been solved for many recent years. However, it is more realistic to represent the fuel cost function for each fossil fired plant as a segmented piece-wise quadratic functions. This is because of development of technology in thermal plants to reach maximum fuel save. Those units are faced with the difficulty of determining which the most economical fuel to burn is. This paper presents two effective methods for solving economic load dispatch problem with multiple fuel options. An advantage of the methods is to formulate Lagrange mathematical function easily based on the Lagrange multiplier theory. The proposed methods are tested on one test system consisting of ten generating units with various load demands and compared to other methods. The simulation results show that the methods are very efficient for the optimal generation dispatch problem with multiple fuel options

The block compressed sensing has brought forth the problem that the reconstructed image is of lower quality compared with that of the compressed sensing. A new method is proposed in this paper, named as Block Compressed Sensing of Self-adaptive Measurement and Combinatorial Optimization, which capably solves the problem. According to different sparsity of each image block, we firstly measure the blocks by using different projections; then, we choose measurement with the optimal reconstruction as the final measurement. Eventually, reconstruct the original image using the optimal measurement we got. The proposed method outperforms the compressed sensing in terms of real-time and better reconstruction quality is achieved than the block compressed sensing. Our experimental results verify the superiority of the proposed method.

The cloud service is a new type service way to provide the users in distributed environment, but the service model, service package and the knowledge searching methods trouble it popularizing. Aiming at the problems, a cloud service model based on indirect addressing is proposed in the paper. It analyzed the new problems serving in cloud firstly, then comes out a recourse and service invoking model based on indirect addressing, gave out the service actors of the model in detail, studied the package method of the cloud interfaces, produce the knowledge step in the indirect addressing mode of resources distribution. Proposed a knowledge discovery strategy based on Chord ring to resolve the knowledge retrieval problem and introduced the Chord ring in detail. The case studies proved the method with some advantages, and it proposes a new way to resolve the service problem in cloud.

Due to continuously increasing demand of wireless communication technology, the problem of spectrum shortage arises. Cognitive radio technology enables efficient utilization of the existing wireless spectrum resources. CRN utilizes spectrum band by allowing secondary users to use the spectrum opportunistically, by changing their transmission parameters. We need to analyze the performance of a wireless network. To analyze the performance of a wireless network, game theory is used due to its ability to model individual, independent decision makers. In this paper we model how game theory can be used to allocate power levels to different CR nodes in a CRN at physical layer to ensure a certain QoS.

The joint replenishment problem with deterministic resource restriction is considered. We present a differential evolution (DE) algorithm that uses indirect grouping strategy to solve constrained joint replenishment. The procedure and structure of the DE algorithm is proposed. Extensive computational experiments are performed to compare the performances of the DE algorithm with results of genetic algorithm (GA) and heuristic algorithm CRAND. The experimental results indicate that the DE algorithm performs relative to CRAND and superior to GA.

The present paper, we applied combined neural network, fuzzy logic and genetics algorithm approach to multi criteria decision-making in different areas. The paper begins with introduction and literature review followed by some fundamental of fuzzy set theory, neural network and genetics algorithm and methodology to apply them in multi criteria decision-making, which provides a better level of satisfaction to obtain the better decision.

A coarse-to-fine traffic sign classification algorithm is proposed. The task for traffic sign classification is to analyze the detected regions and determine the class of the sign in the region. By analyzing existing traffic sign classification algorithms, the major problem affecting the classification accuracy is pointed out. Based on this analysis, a coarse-to-fine classification algorithm is proposed. The algorithm first classifies traffic signs into several super classes, then performs class-specific shape adjustment, and finally gets the fine classification result. Experimental results show that the proposed algorithm outperforms other existing algorithms in classification accuracy, and is robust to many adverse situations.