A polynomial time algorithm for GapCVPP in l1 norm

This paper concerns the hardness of approximating the closest vector in a lattice with preprocessing in l1norm,and gives a polynomial time algorithm for GapCVPPγin l1norm with gapγ=O(n/log n).The gap is smaller than that obtained by simply generalizing the approach given by Aharonov and Regev.The main technical ingredient used in this paper is the discrete Laplace distribution on lattices which may be of independent interest.     

Using Genetic Algorithm to Support Artificial Neural Network for Intrusion Detection System

The following methods detect the attacks intrusion detection system： ANN （artificial neural network） for recognition and GA （genetic algorithm） for optimization of ANN results. We use KDD-CUP dataset to obtain the results, which shows around 0.9998 accuracy of applied methods in detecting the threads. ANN with GA requires 18 features.     

Performance Evaluation of Lateration,KNN and Artificial Neural Networks Techniques Applied to Real Indoor and Outdoor Location in WSN

WSN （wireless sensors networks） is a promising research area which has been in center of attention in many applications on telecommunications. Despite of many existing applications, a bunch of algorithms have been created or applied to solve different issues surrounding WSN. An attractive subtopic on research area is Localization Algorithms. Due to the countless applications, it is almost impossible to list all the algorithms applicable to solve sensor＇s location problem in WSN over distinguished parameters associated to diverse environments. This paper evaluates a geometrical algorithm, an instance based algorithm and a function approximator algorithm, having the RSS/ （received signal strength indicator） as metric to estimate planar coordinates in an indoor and outdoor environment using a WSN based on IRIS mote. The analysis of the WSN is constructed over statistical data obtained from empirical experiments and the observed characteristics of the algorithms. We also estimate the performance for different parameters configurations applied to the algorithms for both indoor and outdoor environment. Also, some comments about the tradeoff between the accuracy and the performance of the algorithms are made objecting to. Also, some objections about the tradeoff between the accuracy and the performance of the algorithms are made when relevant.     

A Privacy Taxonomy for the Management of Ubiquitous Environments

Pervasive and ubiquitous environments must handle the detection and management of users, devices and services, while guaranteeing the privacy of both the users and the environment itself. Current techniques for handling privacy found in the literature treating the subject in various ways, while concentrating on the device management, communication protocols, user profiles and environmental access. This paper examines a control model for privacy in pervasive environments from the perspective of the environment. A prototype was devised and tested to validate the generic model of privacy which was also used to compare taxonomic concepts in the literature. Moreover, the prototype was devised and tested to validate the generic model of privacy for control and manage various users, devices and environments and so on. The prototype was based on Percontrol （a system for pervasive user management）, which was only intended to identify users using Wi-Fi, and now it is capable of managing temperature, luminosity and other preferences, measured by a WSN （wireless sensor network） embedded to Percontrol, and the data treatment is done by an ANN （artificial neural network）. Results confirmed the viability of device detection with Wi-Fi, Bluetooth and RFID （radio frequency identification） for an increases slight of the latency in registering new devices on the system.     

Face Orientation Normalization Using Eye Positions

Despite the fact that progress in face recognition algorithms over the last decades has been made, changing lighting conditions and different face orientation still remain as a challenging problem. A standard face recognition system identifies the person by comparing the input picture against pictures of all faces in a database and finding the best match. Usually face matching is carried out in two steps： during the first step detection of a face is done by finding exact position of it in a complex background （various lightning condition）, and in the second step face identification is performed using gathered databases. In reality detected faces can appear in different position and they can be rotated, so these disturbances reduce quality of the recognition algorithms dramatically. In this paper to increase the identification accuracy we propose original geometric normalization of the face, based on extracted facial feature position such as eyes. For the eyes localization lbllowing methods has been used： color based method, mean eye template and SVM （Support Vector Machine） technique. Experimental investigation has shown that the best results for eye center detection can be achieved using SVM technique. The recognition rate increases statistically by 28% using face orientation normalization based on the eyes position.     

基于人工鱼群优化支持向量机水文预报系统模型*

在深入分析比较各种水文预报方法的基础上,利用人工鱼群算法对支持向量机训练算法进行了改进,提出了基于人工鱼群优化的支持向量机算法。实验结果表明,基于人工鱼群优化的训练算法的训练速度优于标准的支持向量机的训练速度,能够为水文预报提供更快捷的技术支持。     

Metamodeling development for reliability-based design optimization of automotive body structure

Metamodels are commonly used in reliability-based design optimization (RBDO) due to the enormously expensive computation cost of numerical simulations. However, for large-scale design optimization of automotive body structure, with the increasing number of design variable and enhanced nonlinearity degree of structural performance, polynomial response surface which is commonly used for vehicle design optimization often suffers exponentially increased computation burden and serious loss of approximation accuracy. In this paper, support vector regression, along with other four complex metamodeling techniques including moving least square, artificial neural network, radial basis function and Kriging, is investigated for approximating frontal crashworthiness performance which is one of the most highly nonlinear performances. It aims at testing support vector regression and providing advanced metamodeling technique for RBDO of automotive body structure. Approximation results are compared in both accuracy and computational efficiency. Based on the frontal crashworthiness example, it is found that support vector regression and moving least square are preferable techniques to approximate structural performances with good accuracy. But support vector regression is recommended for its computational efficiency and better approximation potential. Moreover, the ensemble of support vector regression, moving least square, Kriging and artificial neural network is an effective alternative and is proved, in the RBDO example for the lightweight design of front body structure, to outperform any other single metamodel. The remarkable predominance indicates that the ensemble of support vector regression, moving least square, Kriging and artificial neural network holds great potential in approximating highly nonlinear performances for RBDO of automotive body structure.     

A soft computing approach to projecting locational marginal price

The increased deregulation of electricity markets in most nations of the world in recent years has made it imperative that electricity utilities design accurate and efficient mechanisms for determining locational marginal price (LMP) in power systems. This paper presents a comparison of two soft computing-based schemes: Artificial neural networks and support vector machines for the projection of LMP. Our system has useful power system parameters as inputs and the LMP as output. Experimental results obtained suggest that although both methods give highly accurate results, support vector machines slightly outperform artificial neural networks and do so with manageable computational time costs.     

An adaptive switching scheme for iterative computing in the cloud ,

Delta-based accumulative iterative computation （DAIC） model is currently proposed to support iterative algorithms in a synchronous or an asynchronous way. However, both the synchronous DAIC model and the asynchronous DAIC model only satisfy some given conditions, respectively, and perform poorly under other conditions either for high synchronization cost or for many redundant activations. As a result, the whole performance of both DAIC models suffers from the serious network jitter and load jitter caused by multi- tenancy in the cloud. In this paper, we develop a system, namely Hyblter, to guarantee the performance of iterative algorithms under different conditions. Through an adaptive execution model selection scheme, it can efficiently switch between synchronous and asynchronous DAIC model in order to be adapted to different conditions, always getting the best performance in the cloud. Experimental results show that our approach can improve the performance of current solutions up to 39.0%.     

Reference direction based immune clone algorithm for many-objective optimization

In this paper, a new preference multi-objective optimization algorithm called immune clone algorithm based on reference direction method （RD-ICA） is proposed for solving many-objective optimization problems. First, an intelligent recombination operator, which performs well on the functions comprising many parameters, is introduced into an immune clone algorithm so as to explore the potentially excellent gene segments of all individuals in the antibody pop- ulation. Second, a reference direction method, a very strict ranking based on the desire of decision makers （DMs）, is used to guide selection and clone of the active population. Then a light beam search （LBS） is borrowed to pick out a small set of individuals filling the external population. The proposed method has been extensively compared with other recently proposed evolutionary multi-objective optimization （EMO） approaches over DTLZ problems with from 4 to 100 objectives. Experimental results indicate RD-ICA can achieve competitive results.     

Study on Robot Path Planning Based on an Improved Artificial Potential Field Method

The method of artificial potential field has obvious advantages among the robot path planning methods including simple structure, small amount of calculation and relatively mature in theory. This paper puts forward the ＂Integral method＂ focusing on solving the problem of local minimization. The method analyses the distribution of obstructions in a given environment and regards adjacent obstacles as a whole, By changing the parameters of the repulsive force field, robots can quickly get out of the minimum point and move to the target point. This paper uses the Simurosot platform to carry on the simulation experiment on the improved artificial potential field method, which projects a feasible path successfully and verifies this method.     

A Non-heuristic Approach to the General Two Water Jugs Problem

The two water jugs problem is a famous problem in recreational mathematics, problem-solving, artificial intelligence, neuroscience, computer programming and cognitive psychology. The methods of solutions are usually based on heuristics or search methods such as BFS （breadth first search） or DFS （depth first search）, which could be time and memory consuming. In this paper, we present a non-heuristic approach to solve this problem, which can be modeled by the Diophantine equation mx＋ ny - d, where m, n denote the capacities of the jugs and d denotes the amount of water to be determined, with 0 〈 m 〈 n and 0 〈 d 〈 n. By simple additions and subtractions only, the special solutions （x, y） can be found very easily by using the non-Heuristic approach, which correspond to the number of times of the water jugs being fully filled in the whole water pouring process. Also, a simple formula for determining an upper bound on the total number of pouring steps involved is derived, namely 2（m ＋ n - 2）, based on the method of linear congruence. Due to its simplicity and novelty, this approach is suitable for either hand calculation or computer programming. Some illustrative examples are provided.     

Fabrication of strongly adherent platinum black coatings on microelectrodes array

Platinum black coating can effectively improve the performance of MEAs(microelectrodes array)in neural signal transduction,though its lack of adhesion strength and durability tampers its usage in long term experiments.Here a new method of composite electrodeposition provides highly adhesive platinum black coating that enables MEAs for a month’s long task and repeatable utilization.The new method was compared with present techniques on multiple aspects,e.g.actual surface area,surface morphology,interfacial impedance,durability and real application tests.Results show that the new composite coating provides greatly improved durability without compromising its performances.Neural cells were cultured on these MEAs for 40 days in vitro and spontaneous action potentials with high signal/noise ratio were recorded.Theoretical model and simulation provided preliminary understanding on the mechanism of this strengthened platinum black coating.     

Multiple Chaos Generator by Neural-Network-Differential-Equation for Intelligent Fish-Catching

Continuous catching and releasing experiment of several fishes make the fishes find some escaping strategies such as staying stationary at corner of the pool. To make fish-catching robot intelligent more than fishes＇ adapting and escaping abilities from chasing net attached at robot＇s hand, we thought something that goes beyond the fishes＇ adapting intelligence will be required. Here we propose a chaos-generator comprising NNDE （neural-network-differential-equation） and an evolving mechanism to have the NNDE generate chaotic trajectories as many as possible. We believe that the fish could not be adaptive enough to escape from chasing net with chaos motions that have much different chaos, since unpredictable chaotic motions of net may go beyond the fishes＇ adapting abilities to the net motions. In this report we introduce the chaos generating system by NNDE, which can produce many kinds of chaos theoretically, and then analyze the chaos with Lyapunov number, Poincare return map and initial value sensitivity.     

Support vector design of the microstrip lines

In this article, the support vector regression is adapted to the analysis and synthesis of microstrip lines on all isotropic/anisotropic dielectric materials, which is a novel technique based on the rigorous mathematical fundamentals and the most competitive technique to the popular artificial neural networks (ANN). In this design process, accuracy, computational efficiency and number of support vectors are investigated in detail and the support vector regression performance is compared with an ANN performance. It can be concluded that the ANN may be replaced by the support vector machines in the regression applications because of its higher approximation capability and much faster convergence rate with the sparse solution technique. Synthesis is achieved by utilizing the analysis black‐box bidirectionally by reverse training. Furthermore, by using the adaptive step size, a much faster convergence rate is obtained in the reverse training. Besides, design of microstrip lines on the most commonly used isotropic/anisotropic dielectric materials are given as the worked examples. © 2008 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2008.     

A new approach of conditions on for s-sparse recovery δ2s（φ）

In this paper, we provide a unified expression to obtain the conditions on the restricted isometry constant δ2s（φ）. These conditions cover the important results proposed by Candes et al. and each of them is a sufficient condition for sparse signal recovery. In the noiseless case, when δ2s（φ） satisfies any one of these conditions, the s-sparse signal can be exactly recovered via （11） constrained minimization.     

基于Logistic回归、ANN、SVM的乳腺癌复发影响因素研究

为找出乳腺癌复发的影响因素,并比较人工神经网络（ANN）型、支持向量机型（SVM）和logistic回归型在乳腺癌复发中的预测效能.本文结合南斯拉夫卢布尔雅那大学医疗中心乳腺癌肿瘤研究所的277例数据,对乳腺癌复发的影响因素进行研究.分别采用了logistic回归、人工神经网络和支持向量机方法来建立乳腺癌复发的预测模型,并对这三种分析方法进行了理论方法和预测效能的比较.结果发现,肿瘤大小、有无结节冒、肿瘤恶性程度（P<0.05）是乳腺癌术后复发的主要影响因素,而在不同的预测方法中相对于logistic回归模型,支持向量机和人工神经网络具有更好的预测效能,其中支持向量机的预测效能最好.     

Sequence memory based on an oscillatory neural network

In the brain,the discrete elements in a temporal order is encoded as a sequence memory.At the neural level,the reproducible sequence order of neural activity is very crucial for many cases.In this paper,a mechanism for oscillation in the network has been proposed to realize the sequence memory.The mechanism for oscillation in the network that cooperates with hetero-association can help the network oscillate between the stored patterns,leading to the sequence memory.Due to the oscillatory mechanism,the firing history will not be sampled,the stability of the sequence is increased,and the evolvement of neurons’states only depends on the current states.The simulation results show that neural network can effectively achieve sequence memory with our proposed model.     

Relevance vector machines approach for long-term flow prediction

Over the past years, some artificial intelligence techniques like artificial neural networks have been widely used in the hydrological modeling studies. In spite of their some advantages, these techniques have some drawbacks including possibility of getting trapped in local minima, overtraining and subjectivity in the determining of model parameters. In the last few years, a new alternative kernel-based technique called a support vector machines (SVM) has been found to be popular in modeling studies due to its advantages over popular artificial intelligence techniques. In addition, the relevance vector machines (RVM) approach has been proposed to recast the main ideas behind SVM in a Bayesian context. The main purpose of this study is to examine the applicability and capability of the RVM on long-term flow prediction and to compare its performance with feed forward neural networks, SVM, and multiple linear regression models. Meteorological data (rainfall and temperature) and lagged data of rainfall were used in modeling application. Some mostly used statistical performance evaluation measures were considered to evaluate models. According to evaluations, RVM method provided an improvement in model performance as compared to other employed methods. In addition, it is an alternative way to popular soft computing methods for long-term flow prediction providing at least comparable efficiency.     

The Recognition of Fault Type of Transmission Line Based on Wavelet Transmission and FNN

The article firstly utilizes wavelet transform to do denoising, filtering and other preprocessing to the information collected and realizing the purification of three-phase component and zero sequence current component which used for determining the ground fault, then using the effective combination between fuzzy set theory and neural network to build five-layer fuzzy neural network, taking T-S fuzzy reasoning model as an interference layer in network, then regarding the purified and optimized fault characteristic quantity preprocessed by wavelet transform as the input of fuzzy neural network, regarding fault type as output to train and learn the fuzzy neural network to further realize the way of recognition of fault type. Finally, simulation testing shows this way can effectively realize the recognition of fault type, strongly enhancing the recognition rate of fault types.