Earticle

According to the traffic light recognition problem of intelligent vehicle, this paper proposes a traffic light recognition algorithm based on compressive tracking. First, the candidate regions of traffic light are extracted. Second, after extracting the HOG feature of candidate regions, using the machine learning for classification and recognition, compressive tracking algorithm is used to track lights that have been identified a automatically. This algorithm combines feature recognition and tracking identification of traffic light and traffic light changes in scale and color can be identified normally. The algorithm proposed in this paper has been tested on actual road in intelligent vehicle; traffic light recognition effect is good.

In this paper, based on Magnetotactic Bacteria Optimization Algorithm(MBOA), magnetotactic bacterium multi-objective optimization algorithm (MBMOA) is proposed for solving multi-objective optimization problems(MOPs). Magnetotactic bacterium optimization algorithm is a novel random research algorithm which simulates the process of magnetotactic bacteria (MTB) producing magnetosomes(MTS) to regulate cell moment and makes the magnetostatic energy reach the minimum .The algorithm MBOA proposed three operators named by MTS producing, MTS amplification and MTS replacement by imitating the development process of magnetosomes, the adjustment process of magnetosomes moment and the replacement process of magnetosome with worse moment. In MBMOA, MBOA is applied to produce the next population, while non-dominated feasible solutions gained by MBOA are conserved in the archive, then the evaluation method of SPEA2 is adopted to update the archive, at the last through benchmark functions test and classic algorithm comparison, the simulation results show that the MBMOA is feasible and effective for solving multi-objective optimization problems.

Software testing can be stated as the process of validating and verifying that a computer program, application and product [1]. Software testing can also provide an objective, independent view of the software to allow the business to appreciate and understand the risks of software implementation. Software testing, depending on the testing method employed, can be implemented at any time in the development process. Traditionally most of the test effort occurs after the requirements have been defined and the coding process has been completed. Testing can never completely identify all the defects within software. A primary purpose of testing is to detect software failures so that defects may be discovered and corrected. Testing cannot establish that a product functions properly under all conditions but can only establish that it does not function properly under specific conditions. There are many approaches to software testing. Reviews, walkthroughs, or inspections are referred to as static testing, whereas actually executing programmed code with a given set of test cases is referred to as dynamic testing. Regression testing is an important but expensive software maintenance activity performed with the aim of providing confidence in modified software. Regression test selection techniques reduce the cost of regression testing by selecting test cases for a modified program from a previously existing test suite. Regression testing is done every time when a program is modified to ensure that the modifications do not introduce new bugs into previously validated code. Regressions Testing can be done by collectively perform Regression Test Selection, Test Minimization and Test Case Priotrization Technique. An important research problem, in this context, is the selection of a relevant subset of test cases from the initial test suite. Regression test selection (RTS) techniques minimize both the regression testing time and effort. Regression test selection (RTS) techniques select a subset of valid test cases from an initial test suite (T) to test that the affected but unmodified parts of a program continue to work correctly. Use of an effective regression test selection technique can help to reduce the testing costs in environments in which a program undergoes frequent modifications. D is a new programming language. This is an object-oriented, imperative, multi-paradigm system programming language. Regression testing on D programming language still untouched by researchers. Our research attempts to bridge this gap by introducing a techniques to revalidate D programs. A framework is proposed which automates both the regression test selection and regression testing processes for D programming language. As part of this approach, special consideration is given to the analysis of the source code of D language. In our approach system dependence graph representation will be used for regression test selection for analyzing and comparing the code changes of original and modified program. First we construct a system dependence graph of the original program from the source code. When some modification is executed in a program, the constructed graph is updated to reflect the changes. Our approach in addition to capturing control and data dependencies represents the dependencies arising from object-relations. The test cases that exercise the affected model elements in the program model are selected for regression testing. Empirical studies carried out by us show that our technique selects on an average of 26.36. % more fault-revealing test cases compared to a UML based technique while incurring about 37.34% increase in regression test suite size.

In the past year, the northbound interface application has become a hot topic in the Software-Defined Network technology. It offers an opening method and makes people able to develop own business application upper the SDN. In this paper, we propose and evaluate a visual plane based on android phone application to present an explicit watch of the state and working performance of the Openflow-based underlying network topology in Software-Defined Network. The impacts of the designing architecture are evaluated in the form of software simulation and actual operating effects of the android application that we have designed. Through the interface opened by the floodlight controller to the upper business application, we are aimed at making it able to call the underlying network resources and inquiry its status information conveniently. So this paper wants to achieve the unified dispatching by elaborated methods and transplants the idea to the android application. By the way of the software programming, we could make the android phone to probe the southbound network resources through the northbound interface whose connotation we will expand. Between the northbound interface and the android application, we introduce a data center and select the mysql database to implement its function. Through the technology framework we built, we are able to use an android phone to access the status of the underlying devices and monitor the software defined network better aiming at providing better service. And we judge its function from the perspective of overall feasibility and stability not only through the system performance but also sound experiment effects. Finally, we can use any android phone to view about the topology of the network effectively and simply.

Flexible Alternating Current Transmission Systems (FACTS) represents a vast development in the area of power system operation and control. As we know that under heavily loaded conditions our power system is at high risks of consequent voltage instability problem. This paper gives an overview about application of series connected Flexible alternating current transmission system (FACTS) for improvement of power system performance like transfer stability, secure voltage profile and reduce the system losses etc. FACTS devices require huge capital investment. Therefore, heuristic techniques are used for optimal location and sizing of series FACTS controllers like Genetic Algorithm (GA), Particle Swarm Optimization (PSO) etc. These techniques are used to solve the optimization problem. This paper gives details of optimal placement and sizing of FACTS devices based on different evolutionary techniques which is used for minimization of transmission loss, enhancement of stability of power system. In this study one of the FACTS devices is used as a scheme for enhancement of power system stability.Proper installation of FACTS devices also results in significant reduction of transmission loss. In this review,TCSC is selected as the compensation device.

Analysis and clustering of very large scale data set has been a complex problem. It becomes increasingly difficult to compute the results in a reasonable amount of time as data amount increases and with its feature dimensions. The GPU (graphics processing unit) has been a point of attraction in a last few years for its ability to compute highly-parallel and semi-parallel problems way faster than any traditional sequential processor. This paper explores the capability of GPU with MapReduce Model. This highly scalable model for distributed programming can be scaled upto thousands of machines. This was developed by Google’s developers Jeffrey Dean and Sanjay Ghemawat and has been implemented in many programming languages and frameworks like Apache Hadoop, Hive, and Pig etc. For this paper we’ll mainly focus on Hadoop framework. First two sections present the introduction and background. The working mechanism of this combination has been shown in section 3. Then further we explore frameworks present to implement MapReduce on GPU. In section 5, a comparative experiment was performed on GPU and CPU, both implementing MapReduce Model. The paper ends conclusion.

The grey model theory is widely used in many field of investigation and of course includes inbound tourism demand. The original GM (1, 1) model couldn’t have accuracy prediction in some situation. Thus, an optimized GM (1, 1) model is proposed in this paper. Aiming at the deficiencies of the model, the improvements of GM (1, 1,) are include initial sequence, background value and parameter optimization. At last, the optimized GM (1, 1) model is used to predict inbound tourism demand of China and the results show that the proposed model is better than original GM (1, 1) model and time series model on prediction accuracy.

The main focus of this paper is to design a Artificial Neural Network Modal to identify specific binary pattern. This pattern will work at binary level to recognize that the received data is authentic at low level, through this work we are able to design a specific binary pattern and it would apply directly to the input layer to check accuracy in data at binary mean at low level. In this paper we have four bit data and user may arrange it in his own specific pattern, then ANN Modal will train for the given pattern then it will apply to check the pattern is found and data is authentic.

A novel hybrid clamped dual-PWM three-level converter topology is proposed for induction motor drives in this paper. The switching states of hybrid clamp three-level converters increase to sixty-four from twenty-seven switching states of diode clamp three-level converters. In order to realize optimization of its redundant voltage space vectors by detecting voltage of clamp capacitor and difference of capacitor voltage in DC side, Generating an optimized switching pattern, The hybrid clamped three-level converter increases the voltage levels number, reducing the harmonics associated to the commutation frequency and limiting the dv/dt by all the switches . It can quickly balance the DC voltage, Realized system of 4-Quardant Running. the control circuit and main circuit was designed with DSP and CPLD, experimentation results proved it is very effective and practicability.