Abstract:K-nearest neighbor classifier (kNN) is a simple and effective non-parametric classification algorithm. The major drawbacks of the kNN include parameters to be determined manually, its low efficiency in testing phase and suffered effect of “curse of dimensionality”. An efficient method is proposed that constructing a new kNN classifier and Naive Bayes combination classifier. K-means clustering is used to build an optimal set of cluster prototype, reducing storage space while improving the classification efficiency and determining automatically parameters. Using three types of overlapping analysis strategies and fuzzy norms measure are to relieve impacts of “curse of dimensionality”. Experimental results on both synthetic and real-world data sets show that the new classifier has good classification efficiency and classification accuracy.

Abstract:Thanks to the development of virtualization technology, the private and public cloud data center has more and more appeared in enterprises, schools and research institutions. Relative to the physical machine, the virtual machine has better mobility,scalability,and relatively cheap to buy and maintenance, so more and more small and medium-sized entrepreneurs tend to buy virtual machine deployment services. So for cloud service providers, how to allocate physical resources to the VMs in one cloud environment and maximize the utilization of hardware resources pool meeting SLA is becoming more and more important. In this paper, we analyzed the key parameters that affect the VMs' application performance, we used a method of SVD + non-linear model in the application performance modeling and a good results were obtained in the experiment that makes the average prediction error can reach about 12%.

Abstract:Public network protocol is generally used in network, but some special users, with special consideration, do not trust the safety of the public protocol. It needs customized proprietary protocols, to meet the needs of security and particularity. A proprietary protocols is designed based on CIPSO standard transformation, to realize the existing normal communication under the network environment, and meet other specific information requirements according to the security level to control the data flow. To avoid conflicts due to the similarity by the protocol, the concept of anti-conflict flag is proposed and the anti-conflict negotiation mechanism is designed. With the method of transplantation LWIP stack, the proprietary protocol is implemented.

Abstract:Hidden Markov Model (HMM) is one of the simple but effective models for DNA sequence modeling, and the first-order HMM has been popularly used in practice. However, due to the non-aftereffect property, a first-order HMM cannot describe the dependencies between adjacent bases. This generally results in the loss of useful statistics in sequences. In this paper, based on the analysis of the specific biological structure for DNA sequences, a second-order HMM for DNA sequence classification is proposed. The new model inherits the advantages of the first-order model, while fully expresses the biological statistics contained in the DNA sequences, which makes the model more meaningful in biology. Based on the new model, a new Bayesian method is proposed for DNA sequence classification, which is experimentally evaluated on the real DNA sequences. The experimental results show that the new method is able to obtain high classification accuracy, as the structure information hidden in bases of DNA sequences can be captured more adequately by the new second-order HMM.

Abstract:Aiming to the dynamics and nonlinearities of stock price, a stock price prediction model that based on support vector regression (SVR) with parameters optimized by improved genetic algorithm (GA) was proposed. First, the wavelet was used to de-noise the samples of stock price. Then the SVR model whose parameters were optimized by improved GA was utilized to predict and assess the data de-noised by wavelet. The result demonstrated that the improved wavelet-GA-SVR model has good prediction effect, and it is significant to the study of the prediction of stock price.

Abstract:Integrating massive information on the Web accurately and effectively is the important basis of developing analytic applications, such as Web information dynamic aggregation tools, market information analysis tools, public opinion analysis tools, and business intelligence tools, etc. To solve the problem that different presentations refer to the same entity during the integrating process, this paper proposes an algorithm to recognize the synonymous entities by using the snippets from the search engine and a frame of Web information integration based on synonymous entities recognition. The experimental results on hospital information integration testing data sets show that the proposed method outperforms the synonymous entities recognition based on VarientDice, VarientCosine, VarientJaccard and VarientOverlap.

Abstract:Visual simulation of material supply for large ships could visually simulate the handling process, calculate the handling time, find the bottleneck and further optimize the handling process. Firstly, the supply type and handling process of large ships are qualitatively analyzed. After that, the visual simulation system structure based on Flexsim of material supply on large ships is proposed. The simulation model is designed and the key technologies used in the simulation platform are discussed. With a specific example, several simulation experiments are done through the simulation platform. The results show that visual simulation is very useful for the design and operation management of material supply handling system in a large ship.

Abstract:How to solve the problem with humanization design and how to meet the constraints of more efficient arrangement system are the difficult points of educational administration. According to some colleges' course arrangement problem, this paper puts forward a college course arrangement method based on space model. First, we compare and analyze the advantages and disadvantages of the present ways of course arrangements. Then, it combines the college's requirement of actual course arrangement, builds up the space model and restrict model of course arrangement so as to decrease the complexity of course arrangement. At last, it optimizes the course arrangement method based on genetic algorithm and puts forward a solution project of course arrangement problem. The experimental results show that this course arrangement system has solved this college's course arrangement problem better and also provide experience for other similar problems.

Abstract:This paper designs a remote boiler control system based on Ayla cloud platform, which can control the boiler remotely through the Android APP by accessing the Ayla Cloud Platform. Intermediate equipment, which connected with Ayla cloud platform and the gas boiler, was developed by Ayla embedded modules, which regard WM-N-BM-09A piece WiFi chips and STM32F100BV master chip as the core. The boiler establishes the communication channel with Ayla Cloud Platform, using the wireless router. The boiler operational status information and the alarm data were pushed to the APP in real time via the active pushing mechanism of Ayla Cloud Platform. Test results show that remote control system project, which was based on Ayla Cloud Platform, is low in cost and easy to realize. Also, the project can be applied to other remote intelligent control systems, providing a low cost and highly reliable solution for remote control of smart devices.

Abstract:With the rapid development of economy and the improvement of living standards, people's demands for health are increasing, and the field of health consumer has also risen gradually. In this case, how to seek a kind of health management mode which is dynamic, convenient and simple in the fast-paced way of life has become the focus of widespread attention. This paper proposes an Android-based health information management system, combining mobile terminals and the cloud technology which has powerful computing and storage capacity. In order to achieve systematic, scientific and friendly health information management, this system shows the user's significant indicators of body movements in real time, presents a variety of health information graphically and digitally, and maximally reduces the use-cost from the users' perspective.

Abstract:According to the need of the current rapid development of hydrological monitoring system, this paper puts forward a complete and feasible hydrological monitoring system solution based on Internet of things technology. In this solution, all kinds of new technology are used, including the Zigbee wireless sensor network technology, embedded technology, GPRS mobile communication technology, the Beidou satellite communication technology and B/S structure method and etc. The scheme is stable and reliable in practical all-weather environment, which can acquire data in real-time, and the maximum delay is not more than 13 seconds, so it is an ideal solution.

Abstract:The communication module of subway electrical cabinet logic tester is researched and designed in this paper. The communication module of subway cabinet logic tester contains two parts: the communication between system layer and management layer, the communication between management layer and testing layer. In the cabinet logic tester, the communication between system layer and management layer has large amount of data and high frequency, so CAN bus is used, at the same time, the communication between management layer and testing layer doesn't have the problem of distance and has small amount of data, so RS485 is used. On the basis of hardware design, communication software is designed. According to site commissioning, the communication module designed enables reliable communication between systems.

Abstract:This paper proposes a feedback model for virtual laboratory based on components. Because of the feature of virtual laboratory based on components, we transform the experimental procedure and device parameters into specific strings firstly. At the same time, the experimental reference library including standard experimental procedure strings has been established. Then, the experimental procedure strings of users actually are built and the standard experimental procedure strings of the experimental reference library including execute the pattern matching. According to the results based on the pattern matching, experimental guidance will be given to the users. This model meets the needs of learners for evaluation and guidance in correcting during virtual experiment, and also provides a solution idea for improving learner's experience.

Abstract:In the existing Cigarette market Supervision mechanism, between the Tobacco Monopoly Bureau, Administration for Industry and Commerce, Public Security Bureau cooperative information mainly relies on manual interaction. This model is simple and the efficiency is low. Cigarette market supervision data of each department of huge is difficult. This paper presents a large data environment, various departments of the data together, achieves the technical scheme of law enforcement information sharing, business collaboration, the regulatory process optimization. The experiments prove that the scheme is effective and improves the work efficiency of the cigarette market law enforcement cooperation.

Abstract:With the development of educatinal technology, more and more people use PPT documents in their learning process. By constructing a concept map for PPT doc uments, learners can quickly and comprehensively understand all the knowledge points of the PPT documents, being beneficial for the learners to speed up the learning and collect his learning behaviors. In this paper, we proposed an algorithm using Microsoft Office programming technology, text mining technology and social network analysis technology to automatically extract concept terminologies and the relationships between them from the PPT documents to construct concept map. The experimental results show that the psoposed algorithm can effectively calculate the importance of each concept terminology. And all the concept terminologies extracted by the proposed algorithm have a certain accuracy, the more important the concept terminology, the higher the accuracy.

Abstract:Compared with the existing ontology construction methods, the approach of building oilfield domain ontology based on Thesaurus is proposed, which can alleviate the amount of work involved in building ontology, and solve a series of problems in the process of transformation from the thesauri to the ontology, such as the selecting of some uncertainty concept, the representation of some uncertainty relationships among words in thesaurus and the refinement of those relationships. This paper proposes a series of principles which describe the refinement of “USE、UF、NT、BT、TT、RT” five relationships in thesaurus, and presents how to use OWL language to describe the thesaurus and several relationships among words, so as to realize the OWL description of the Thesaurus.

Abstract:Fuzzy c-means clustering is an effective method used in image segmentation, but it is corrupted by noise easily because of ignoring spatial contextual information and structure information.A neighbourhood weighted fuzzy c-means clustering method based on DCT subspace is proposed. This papper first applies the discrete cosine transform (DCT) on image patches combined with the idea of partitioning, it establishes a similarity measure model based on image pacthes and local information. Then defines the neighbourhood-weighted distance to replace the Euclidean distance in the objective function. Finally, applied this method to synthetic image with different noises, real-world images, as well as magnetic resonance images. The experimental results show that the proposed algorithm can obtain more precise segmentation results and has the stronger anti-noise property.

Abstract:The standard genetic algorithm was used to solve the existing optimization model of material handling sequence problem under the parallel process with multi handling tools. It had slow convergence speed and being easily trapped in local optimal. The improved genetic algorithm of the model is proposed in this paper, where elitism strategy instead of traditional roulette wheel selection method is adopted and the adaptive strategy is applied to design cross operator and mutation operator. As an example, a specific material handling sequence optimization problem is solved by the improved genetic algorithm. The results show that the improved genetic algorithm has faster convergence speed and better solution than the standard genetic algorithm.

Abstract:With the surge in sequence data of biological sequence database, developing a algorithm which has the high biology sensitivity and efficiency is very urgent. Based on the deep analysis on the Needleman-Wunsch and Smith-Waterman Algorithm of bio-sequence alignment, the author enhances the Smith-Waterman algorithm as well as proves its accuracy through a series of experiments in this paper. Comparing between the Smith-Waterman algorithm and the improved one, the author analyzes the performance of the two algorithms. Experimental results show that the newly improved algorithm can optimize the number of local optimal solutions for pairwise sequence, reduce the complexity in time and space of bio-sequence alignment algorithms, and increase the scores and accuracy of sequence alignments.

Abstract:Based on feature point, image matching has been widely applied in image registration, object recognition and tracking field. Now, two phase feature point matching (i.e., first coarse matching, then precise matching) is the most commonly used method. However, the two phase matching exists two issues, on the one hand, the impact of coarse matching for precise matching is irreversible, that is, the results of coarse matching will determine the optimal precision of precise matching. On the other hand, the post knowledge which can be obtained from precise matching cannot be regarded as feedback information to coarse matching, which can revise mismatching. Hence, the paper proposes a new feature point matching algorithm which is based on iterative correction. In the algorithm, post knowledge of precise matching is regarded as feedback information to coarse matching. The coarse matching can obtain more correct matching pairs and decrease missing correct matching pairs. After much iterations, better matching can be obtained. Experiments show that the proposed algorithm can extract more matching pairs than traditional two phase method and improves the matching stability.

Abstract:To solve the problem of the inefficient identification and the large amount of data transmission, an enhanced group-based anti-collision algorithm based on partial responses for RFID tag was proposed. The algorithm used the grouping strategy and partial response mode combines. The tags in each group were identified by reader in turn, it could reduce the probability of collision and the number of the identified tags. The partial response mechanism was used to identify all tags in each group, it reduce the data traffic effectively. The simulation results show that, compared with several other algorithms, the proposed algorithm has the advantage of efficient identification and a small amount of data exchange.

Abstract:For increasing serious phishing attacks, machine-learning method is proposed to detect phishing webs. Firstly, sensitive features are extracted from the URL, then, using AdaBoost algorithm to get the trained classifier, and then the classifier is used to detect unknown URLs. Finally, considering of non-equilibrium problems of AdaBoost, the paper puts forward the improved learning algorithm called AdaCostBoost, which contains computation of cost factors. According to the experiment result, the proposed phishing detection method has better detection performance.

Abstract:In the motion orientation estimation based on MEMS sensor technology, gyroscope signal drift error and gravity superimposed with linear acceleration are the two major reasons affecting the accuracy of estimation. In practice, static compensation and filter technology are commonly used to reduce the orientation estimation error. This paper designs a novel double stage extend Kalman Filter performed on self-developed inertial measurement unit. Above all, we construct adaptive acceleration error covariance matrix to eliminate the linear acceleration in quaternion-based orientation estimation model. Then, in order to correct the drift error produced by gyroscope, the multi-sensor data fusion technology is adopted to fuse the data. Experiment result indicates that the performance of our algorithm is in accordance with the motion capture system Xsens approbated widely. It proves that the algorithm can meet the application requirements effectively.

Abstract:This paper presents a new scoring method for the translation in CET-4 and CET-6 based on improved PSO-BP neural network. We get the text feature values by using the algorithm of BLEU and SVD, together with the scores the teacher have scored, are gathered as input set. We use it to train the improved PSO-BP neural network, which can be reversely used to predict the translation score. This paper improves the PSO-BP neural network by the calculation of the inertia weight and the adaptive value function. We use Matlab to make simulation, the result shows that, in the translation scoring, the use of improved PSO-BP neural network is better than using multiple linear regression to obtain better correlation, and the Pearson correlation coefficient of artificial scoring average increased by 12%.

Abstract:In cloud storage centers, replica of file may be lost because of the failure of nodes, which will affect the reliability of system, as well as the efficiency of file concurrent access. There are some deficiencies in the default replica copy algorithm in Hadoop, such as a concentration of data transfer process on a few DataNodes, load imbalance, low disk I/O throughput. To address this issue, this paper proposes a rapid replica copy algorithm based on popularity in Hadoop. It handles the popular block firstly, and chooses source and destination DataNodes properly. The simulation results show that the proposed algorithm improves the disk I/O throughput, load balance, and reduces average service response time significantly.

Abstract:Aiming at the high undetected rate and false detection rate, and other less which are existed in the AdaBoost algorithm based on Haar feature for face detection, the expanded categories of Haar features are added in this paper, and it can effectively reduce the erroneous judgement caused by the approximation of the gray value between the eyebrows and eyes. At the same time, the real-time of algorithm is improved by removing some features having bad effect for face detection. The cascade classifier constituting of Haar feature and AdaBoost algorithm is analyzed in depth. Finally, the experimental results verify the feasibility of the improved algorithm.

Abstract:To improve the optimizing accuracy, and solve the problem of falling into local optima and the lower rate of convergence in cooperative particles swarm optimization, an improved cooperative particles swarm optimization algorithm is proposed. The proposed approach combines the strong global search ability of genetic algorithm and the excellent local search ability of extreme optimization algorithm. Firstly, an improved strategy is presented for particle swarm optimization. Then, the genetic algorithm is used to increase the diversity and optimal benign of the particles. After a certain iterations intervals, extreme optimization is adopted to accelerate the convergence. The experimental results show that the proposed approach can improve the optimal performance, escape from local optima, and enhance the rate of convergence.

Abstract:This paper presents a LSSIM algorithm for image registration. First extract the feature points from images by classic corner detection method; Then use phase correlation method to estimate the overlapping area between the two images; Then transform the neighborhood area of feature point with Laplace; Last, establish the matching relationship between the two images by the improved SSIM (structural similarity) method. Results show that the LSSIM method can complete a good job of feature point registration. The matching point pairs are not only adequate but also accurate and at the same time have certain robustness to brightness difference, so it can ensure the accuracy of image registration.

Abstract:To maximize the optimization of engineering resource in the engineering application area, this thesis analyses the theoretical foundation and mode theorem of genetic algorithms and multi objective optimization by simulating genetic algorithms into natural evolution to get the optimal solution. It also discusses the advantage of using genetic algorithm to solve the problems in multi objective optimization engineering resources and applies the multi objective genetic algorithms into the configuration of specific engineering resources. The result of simulation and optimization shows genetic algorithms are advanced, reliable and optimal in the configuration process of engineering resources.

Abstract:Based on the analysis of GCC's structure, the porting and optimization of GCC on C4350AL is presented. GCC's x86 backend is extended for the compiler's recognition of C4350AL. According to the properties of C4350AL's architecture, a processor pipeline model description is built. The model's effect is tested on SPEC2006 benchmark. The experiment results show that this model has improved GCC's performance on C4350AL.

Abstract:Nowadays, the present incremental database association rule mining is an important area as data mining, has been widely used in education, medical, health and other fields, so it has become the most active data min are change and unknown. If use the Apriori algorithm to calculate, on the one hand it is difficult to achieve good results, on the other hand, a great impact on support changes we can not determine the support change. So with the mechanism of qualitative attribute theory and attribute computing network boundary learning algorithm with IUBM algorithm, we propose an algorithm for mining association rules based on incremental qualitative attribute. For example, in order to score crossed the enrollment system, qualitative datum point, it may completely change the life of a student's life. With the experiments show that, this algorithm reduces the redundant rules generated in the incremental mining association rules in large-scale data processing, at the same time the mining efficiency has been greatly improved. Apply the researches to prediction of College Students' employment for graduates to understand the learning situation of application is very meaningful.

Abstract:The traditional target tracking algorithms based on compressive sensing uses color video sequence. It is easily affected by rotation, occlusion and illumination. Thus it has poor robustness in complex environment. In order to obtain stable tracking results, this paper proposed a novel face detection and tracking algorithm using compressive sensing based on depth information. Firstly, we identify the position of face in the initial depth map automatically using the improved centroids segmentation algorithm. Secondly, we calculate the mean curvature graph with the depth map. Thirdly, the compression features are extracted based on the mean curvature graph. Finally, by compression and dimension reduction, the feature template is updated and the best postion of face is estimated in the neighborhood space based on the mean curvature graph. The experiment results demonstrate that by combining the depth information of human face and the compressive sensing feature, the proposed algorithm has better robustness dealing with problems of rotation, illumination and occlusion.

Abstract:With the constant accumulation of data, there is much higher desire for processing and analysis power to handle these data. Since the traditional k-Nearest Neighbor (kNN) query algorithm is easy to cause load imbalance on account of the regular partitioning method and its current platform is single process or single machine platform which cannot obtain high enough overall performance today, an irregular partitioning method based kNN algorithm is presented and being executed on the distributed parallel computing model which positioning to process large scale datasets in a distributed parallel way— MapReduce in this paper. Experimental results and analysis show that the irregular partitioning method based kNN algorithm can realize much significant operational efficiencies and support efficient query of big data much better.

Abstract:As a new wireless network technology, Wireless Mesh Network is applied increasingly widespread. The paper presents a new combining algorithm based on the characteristics of ant colony algorithm and immune algorithm to deal with the problem of the wireless mesh network's QoS routing. Through enhancing the contrast of solution and adjusting parameter Q dynamically in the process of search for the solution, the algorithm not only speeds up the convergence, but prevents the algorithm into a local optimum. It also proposes an immune variation solve the QoS routing problem by the priori knowledge, and this variation improves the algorithm global performance effectively.

Abstract:It is more difficult that cognizance and punishment of organized crime groups with development of the Internet. According to the characteristics of the organized crime groups, we are using co-offending networks analysis methods and data mining techniques to identifying organized crime structures and their constituent entities. An novel algorithm for mining organized crime groups is proposed. The goal of our work is to improve the efficiency of the organized crime detection for extracting information from large real-life crime datasets to obtain evidence of the organized crime group. Experimental results show that the algorithm of time performance is superior compared with other existing algorithms.

Abstract:Considering the contradiction of real-time and accuracy in existing shadow detection method, this paper presents a new shadow detection method, the method combines color feature and texture feature by weight fusion. Firstly, we use HSV color information to extract the suspected shadow points. Secondly, we calculate the shadow brightness membership according to the brightness ratio, then judge the points with high brightness membership as real shadow points, so we can reduce the calculation of texture detection. For those suspected shadow points with low brightness membership, we extract the CS-LBP texture of these points, because CS-LBP is highly efficient. Via matching texture, we calculate the texture membership according to the level of similarity of textures and distribution of shadow. At last, considering the fact that texture change with the brightness, we put forward the method of feature texture membership by weight fusion, and this weight self-adapts to the brightness ratio. Experiment results show that, the proposed method has a good real-time performance, it can remove the self-shadows, and performs better accuracy in segmentation.With using membership, the proposed method is more robust to noise and illumination changes.

Abstract:Subcutaneous interstitial fluid continues to be the preferred site for glucose sensing due to its easy access and lower risk of infection than that of the blood stream. But changes in subcutaneous interstitial fluid glucose are delayed with respect to changes in blood glucose. Besides, the sampling signals are inevitably influenced by noise in the measurement process. For the reasons above, a neural network soft-sensing method based on wavelet denoising is put forward to accurately predict blood glucose levels. In this method, some auxiliary variables associated with blood glucose are denoised and then used to train the neural network to establish the blood glucose soft-sensing model. The methodology is tested using the simulation data of NO.1 and NO.2 adult. Testing result shows that the blood glucose values obtained by this model has smaller root mean square error, better signal-to-noise ratio, and smaller measurement delay than subcutaneous interstitial fluid glucose values.

Abstract:In actual visual servoing systems, image processing itself and image information transmission from a camera to an image processing device can cause time delay in the acquirement of visual information. This paper proposes a visual tracking control method with time-delay compensation. Real-time prediction of image feature information of an end-effector is achieved by on-line polynomial fitting of each element of the image Jacobian matrix, which reduces estimation error on image features. Also, a control scheme with time-delay compensation is designed. Simulation experiments of tracking a moving object verify effectiveness of the proposed method.

Abstract:GUI-based analysis and testing methods of Android applications have become a research focus in recent years. Automation and high GUI coverage could improve the efficiency and effectiveness of most methods. However, previous work is insufficient to meet the requirements of automation and high GUI coverage. In this paper, we propose an automatic method of traverseing the GUIs in Android applications whose source codes are not available. The main idea of the method is to simulate user actions to explore the GUIs of an app automatically. The work tackles some key issues mainly in three aspects: extraction and process of UI elements, simulation of user actions, algorithm design and model building of GUI traversal. The result of experiments shows that the method is effective to traverse the GUIs of an application with a high GUI coverage. Furthermore, this method could assist other researches, such as program security analysis, GUI testing.

Abstract:When supervised classification of hyperspectral images, the traditional supervised learning method for hyperspectral data classification needs to obtain enough samples marked as training samples, which can effectively avoid Hughes effects. Hyperspectral data under actual conditions with more bands and relatively small training set a challenge to the traditional remote sensing image classification. Therefore, this paper presents an approach based on a weighted combination of features and characteristics of hyperspectral image classification algorithm for texture analysis more difficult reality, the use of a first-order statistical characteristics describe the image histogram texture features within class scatter matrix by inverse matrix method as a feature weighting matrix structure combined kernel function hyperspectral spectral characteristics and spatial characteristics integrate, while taking advantage of features to improve the small weighted training samples for supervised classification accuracy. Experimental results show that the method proposed in this paper for a small sample of hyperspectral data classification with good results.

Abstract:Network is the foundation of hospital informationization construction. With the informatization construction is becoming more and more mature, Each of the local medical organization pay more attention on all kinds of application systems running on the network infrastructure, so the focus of purchase has gradually begun to turn to management direction of application system. This topic which is JUSHA Medical Company according to the needs and the characteristics of the hospital, develops and designs a network management system. The project uses JAVA technology to program the system, realized the network communication, medical display consistency verification of medical monitor, remote management and remote calibration, save the test report, asset management and other functions. During the design, implementation and testing of the project, many practical problems has been researched, improved and solved, which let network management system more reliable, high-speed, safety.

Abstract:The full-automatic replacing batteries robot instead of human, can automatically replace the batteries in the Electric Vehicles (EV). The positioning system, which solves the problem of the battery localization due to the EV's random position, is the key of replacing the batteries successfully for a full-automatic robot. The positioning system utilizes monocular camera to get the pose information of batteries, and achieves the goal of accurate positioning for the batteries. In this paper, we introduce the design proposal, constitution and working procedures of vision location system of the full-automatic replacing batteries robot. Experimental results validate that our proposed system works with high accuracy, robustness and safety.

Abstract:In order to obtain a more ideal network intrusion detection results, according to the network intrusion feature selection and sample selection problem, this paper proposes a network intrusion detection model based on features selecting and samples selecting. Firstly, the features of network intrusion are extracted, and normalized, and secondly kernel principal component analysis is used to select intrusion features, and the samples are selection, finally, extreme learning machine is used to set up network intrusion detection classifier, and the simulation experiments are carried out with KDD Cup99 data. The simulation results show that that the proposed model has been better network intrusion detection results, the detection rate is above 95%, the efficiency of intrusion detection can meet the requirements of network security protection.

Abstract:At present the electronic instrument almost takes serial port as the communication interface, it cannot accessing into the network, the design of an embedded serial server module is proposed. The module employs embedded chip S3C2440A as the microprocessor, takes Ethernet controller chip RTL8019AS, level conversion chip MAX3232 to convert the data, and transplants Light-weight IP Stack protocol into uC/OS-II operating system. Transparent data transmission realized between serial port and Ethernet. The experiment shows that this device runs stably and meets the need of communication in industrial environment.

Abstract:To provide convenience for teachers and students to receive and delivery express, the paper designs and implements a secure, high-concurrency management system for campus express supermarket based on PHP technology. With open source framework Thinkphp, the system adopt static pages, memcache and other technologies combined with MySQL database to support a certain amount of concurrency. The system includes query, entry, receipt, monitoring, statistical and other functions in web mode. The system not only improves the efficiency of the supermarket but also standardizes the management of express courier. System data can provide decision support for express companies.

Abstract:Roads unobstructed degree there is a big difference at different times or emergency situations, the impact on logistics costs cannot be ignored. Static vehicle route planning cannot feedback the changes of road traffic situation. Therefore, this paper introduces sub-periods of congestion coefficient, the total cost minimization as the objective function, using the population initialization algorithm with circumvention to construct improved bee colony algorithm. The experimental results show that due to using improved bee colony algorithm can be avoided road congestion, although not the shortest transport route, but it can achieve lower overall transportation costs.

Abstract:Taking FANUC 0i NC machine tool as the object of study, a depth analysis to the typical fault phenomenon and reason of the feed system is conducted. Based on the complementation of fuzzy neural network and expert system, a principle and method of fault diagnosis of NC machine tool feed system is proposed. And it gives a concrete implementation scheme based on VC.NET in this paper finally.

Abstract:To realize the parallelization of iterative problem,the author proposes a task parallelization process-oriented design method. The main idea of this method is that a single iteration process of solving problem should be designed in parallel. Then the authors apply the process-oriented thinking to the K-means clustering algorithm, use OpenMP programming model to verify the validity of the method. The analysis of the experimental results show that process-oriented task parallelism has a great advantage in efficiency compared to serial execution, and can be applied to the process of the parallel design iteration problem.

Abstract:Satellite optical communication terminal system must be real-time and reliable. For different backup and fault coverage, the paper analyzes the reliability of the system using Markov chain theory. The simulation results show that system reliability for the cold backup is higher than that for the hot backup. That can meet the requirements of reliable and real-time of the system when the fault coverage is 1.

Abstract:In general, the dimension reduction methods, such as FA and PCA, are implemented in statistical analysis software, but the data envelopment analysis is doing by DEA software. Simultaneously implementing FA-DEA and PCA-DEA all in STATA is puts forward, and an example is also listed. This approach will facilitate the process using FA-DEA and PCA-DEA in the practical application, and will provided a new solution in the STATA to design and application of data envelopment analysis.

Abstract:The paper makes an improved authentication method order by Three-Protocol of HOTP authentication method based on TOTP. The authentication method uses an authentication number threshold and a timestamp to resist brute force attacks and replay attacks, uses a random number and the MD5 encryption resist Man-in-the-Middle attack. Finally, a safe and useful Web authentication protocol is designed by PHP.