Earticle

In allusion to problems of higher complexity in simulation of coastal waves, a dynamic model construction method of local coastal waves is proposed. This method divides period of motion of coastal waves into four phases. The solitary wave solution of KDV EQUATION is improved and optimized based on oceanographic classical theory of ocean. The method completely demonstrates period of motion of coastal waves and their forms including breaking of waves, surge of broken waves, and rollback of waves combining with particle system method. At last visualization rendering simulation of coastal waves is realized by using OPENGL. The simulation results show that the model constructed can demonstrate basic motion features of coastal waves in small scenarios.

This paper analyzes the revenue of a microgrid employing the lightweight vehicle-to-grid energy trade coordinator, focusing on such parameters as seasonal rates, number of participating electric vehicles, stay time, and amount to sell. The heuristic method cuts down the vast search space for the complex optimization problem by by iteratively matching the time slot having the least available electricity. The analysis result, obtained from a real-life demand pattern, reveals that on the winter rate, 100 EVs can sell all of their energy, while for the 200-EV case, they can sell and double the revenue in 12 out of 20 days. Next, on the summer rate, in which the peak rate interval is extended, the revenue increases by 1.5 times. At the same time, the 200-EV case sometimes brings less benefit than the 100-EV case for the given parameter set. As the target brokering system responsively finds a near-optimal solution until a certian bound, it can be further scalable with a module-based distributed brokering mechanism.

Now, most of the instrument has become more precise, light-weight and portable so that the influence of the natural vibration and environmental noise confirms growing role in the instrument performance. The vibration source and the correspondent vibration isolation technologies were analyzed in the paper to provide theoretical reference for the vibration of the laser interference absolute gravimeter and theoretical guidance for the application of absolute gravimeter to the aviation and navigation.

To overcome the problem in recognition of retina texture with noise, an improved multi scale retina texture recognition algorithm is proposed based on fusion area threshold. Integrated with regional threshold filtering, it is useful to suppress the effect of non-uniform blocks for texture recognition. Also, the nonlinear multi-scale transform can analyze the geometric space coefficient of retinal vessels with multi direction invariant features. The maximum likelihood local mean standard deviation analysis is used for texture parameters estimation and recognition. Accurate identification of texture feature is obtained with the noise reduced greatly. The simulation results show that the algorithm can well characterize the retinal vascular texture, with good performance in different texture feature recognition. With good robustness, the recognition accuracy is improved also.

Development of Internet-of-Services will be hampered by heterogeneous Internet-of-Things infrastructures, such as inconsistency in communicating with participating objects, connectivity between them, topology definition & data transfer, access via cloud computing for data storage etc. Our proposed solutions are applicable to a random topology scenario that allow establishing of multi-operational sensor networks out of single networks and/or single service networks with the participation of multiple networks; thus allowing virtual links to be created and resources to be shared. The designed layers are context-aware, application-oriented, and capable of representing physical objects to a management system, along with discovery of services. The reliability issue is addressed by deploying IETF supported IEEE 802.15.4 network model for low-rate wireless personal networks. Flow-sensor succeeded better results in comparison to the typical - sensor from reachability, throughput, energy consumption and diversity gain viewpoint and through allowing the multicast groups into maximum number, performances can be improved.

The food quality and safety condition are the important symbols of the national economic development level and the quality of people's lives. Ecological agricultural park strictly controls seedling, producing, processing, storing and distributing links to provide the market with high-quality traceable food. Websites are important ways of the brand marketing of ecological agriculture park. The usability of the websites is directly linked to the benefit of park. This paper establishes the three levels GAHP (Group-Analytic Hierarchy Process) model that consists of index layer, criterion layer and target layer, and the synthetic weight of each index were determined by the four experts. Taking four ecological agriculture park websites as the evaluation cases and selecting 30 users to rate, the comprehensive score of the four websites was obtained after weighting. GAHP method plays collective intelligence, having a relatively high application value for the optimum selection and optimization of the ecological agriculture park website.

As times become increasingly digitized and the importance of IoT_(Internet of Things) technology still growing, the number of IP-based IoT devices is also sharply increasing. This has the advantage of improving the quality of life because by increasing the numbers of IoT devices, but IP-related security threats are also increasing together while the system for preventing these threats is insufficient. In this paper, we study the IP exposure notification system for IoT devices using IP search engine ‘Shodan’. First, we study the keywords to search the IP address of IoT devices. After that, we output the result file about those keywords using Shodan API. Finally, we develop a web-based IP exposure notification system for IoT devices using the output file and Google Map API. In the future, we can develop our system combining this study and the IP exposure threat level. It can also prevent the threat of countries or public institutions.

Among the nutrients in milk, protein content is a core indicator for measuring the quality of milk. Therefore, to explore a rapid detection method for milk protein content and develop rapid detection equipment for protein ingredient can provide reference for quality analysis and quality control of the production process. In this paper, different dairy protein detection methods are analyzed, the working principles of Micro-Kjeldahl method are studied, and a new type of milk protein content detector is designed based on the Micro-Kjeldahl nitrogen determination method. As for this instrument, LPC2214 microcontroller with ARM structure is taken as the control core, with the functions such as adding certain volume of lye, adding certain volume of boric acid absorption liquid, automatically generating steam required, automatically titrating hydrochloric acid solution, accurately determining the endpoint, conveniently setting parameters and so on, which realizes the digital and intelligent design in determining the protein content in dairy products. Experimental results show that the system is stable, with broad application prospects.

With the rapid development and improvement of public life and social economy, the whole society has entered into the age of information, which can use common computer assisted control system more and more widely. For the demand of the sports management with efficient and safe operation, the wireless sports management monitoring management system of a sports management is designed based on MCP2515/CAN bus in this paper. In order to monitor the operation state information of the sports management real-time, the system monitor front-end is designed by using the platform with Cortex A8 and WinCE6. MCP2515 driver and Wireless communication message is designed by using Modbus/TCP protocol and the remote sports management monitoring and unified management of the sports managements are realized by the two-way communication of GPRS and remote management center. The stable and reliable of the system is proved by the field operation and the system has good application prospect.

For user’s web accessibility, Responsive Web Design (RWD) is the preferred approach to web design layout aimed at avoiding eye strain to the user due to small screen size. As the user’s visual convenience, the PC browser provides magnification of the web page. An effort to enhance the web accessibility has become more important due to the wide range of ages and the increase in number of web users. Presbyopia relates to loss of the elasticity of the lens of the eye, occurring in the middle age and starting from 40. Because of the aging of existing web users, CSS is developed for personal services in accordance with the emergence of a new silver generation who is positive in social participation. Personality diversity of the web screen has also been implemented by utilizing login subsequent age and personal information, we describe its effectiveness.

According to the present medical monitoring system still exist the problems such as low accuracy of the condition judgment and the less range of data transmission, a kind of lung sounds signal separation model of medical monitoring is put forward based on wireless sensor network. First, using the optimization strategy of the flying speed and the effect between particles to two-way optimization for particle swarm optimization algorithm (PSOA), and then applied it to the blind source separation of lung sounds signal, in order to improve the precision of the blind source separation of lung sound signals, then carried on the optimization of artificial fish behavior through tabu search, did coverage optimization for wireless sensor network by using the improved algorithm, to expand the scope of wireless data transmission. As the simulation experiments results showed that, the proposed lung sounds signal separation model of medical monitoring based on wireless sensor network had good accuracy and large range of data transmission, and deserved to be popularized and used.

How to extract the robust discrimination features is the key of face recognition (FR). Local binary pattern is one of the most widely used feature extracting method in FR for its comprehensive representation of the visual content of face image. However, the feature vector extracted by LBP is usually very high-dimensional and maybe contains information redundancy. To deal with the drawback of LBP, a novel nonlinear version of LBP is presented. The main idea is firstly all the feature vectors extracted by LBP are mapped into a feature space by a nonlinear mapping, and then the mapped features are expressed using the corresponding projection vectors. Lastly, FR is performed based on the projection vectors. Compared with LBP, the new method has two advantages. Firstly, it can capture the nonlinear information of the feature vector extracted by LBP. Secondly, it avoids the complex expression of the nonlinear mapping. The experimental results on two public standard visual face datasets demonstrate the proposed method is superior to LBP in recognition accuracy while its computational complexity is considerably reduced.

The Korean government implemented 'The Horse Industry Promotion Act' in 2011 in hopes of boosting rural economy. Since then a number of horse-riding facilities, parks, training schools and academies have mushroomed throughout the nation. Along with such rapidly changing circumstances, the huge efforts to develop horse industry as a means of cultural resource have been exerted in the recent years. In this regard, this study aims to introduce recent propensity regarding horse industry and find the direction of the future. Also, it will focus how Korea's horse industry has evolved.

This paper focuses on the application of BP neural network, taking an evaluation system of English autonomous learning ability as an example. Based on the full analysis the inscape of the system, the recorded data that is related with Web server log in a process of students' online learning has been collected and analyzed by using data mining technology. With BP neural network pattern, an evaluation model of autonomous learning ability based on BP neural network has been founded on the data collected. The experimental results show that the evaluation results of model are in general accord with those of experts: the highest accuracy was 100%, the lowest is 91.7%, and the average is 94.6%; therefore, it is feasible and effective to apply the BP neural network to the evaluation of autonomous learning ability.

Mobile cloud computing (MCC) is a relatively new concept that leverages the combination of cloud technology, mobile computing, and wireless networking to enrich the usability experiences of mobile users. Many field of application such as mobile health, mobile learning, mobile commerce and mobile entertainment are now taking advantage of MCC technologies. Since MCC is new, there is need to advance research in MCC in order to deepen practice. Currently, what exist are mostly descriptive literature reviews in the area of MCC. In this paper, a systematic literature review (SLR), which offers a structured, methodical, and rigorous approach to the understanding of the trend of research in MCC, and the least and most researched issue is presented. The objective of the study is to provide a credible intellectual guide for upcoming researchers in MCC to help them identify areas in MCC research where they can make the most impact. The SLR was limited to peer-reviewed conference papers and journal articles published from 2002 to 2014. The study reveals that privacy, security and trust in MCC are the least researched, whereas issues of architecture, context awareness and data management have been averagely researched, while issues on operations, end users, service and applications have received a lot of attention in the literature. Keywords: Mobile Cloud Computing, systematic literature

The basic ant colony optimization (ACO) algorithm takes on a longer computing time in the search process and is prone to fall into local optimal solutions, an improved ACO (CEULACO) algorithm is proposed in this paper. In the CEULAC algorithm, the direction guidance and real variable function are used to initialize pheromone concentration according to the path information of undirected graph. The pheromone dynamic evaporation rate strategy is proposed to control the pheromone evaporation in order to increase the global search capability and accelerate the convergence speed. An adaptive dynamic factor is introduced into pheromone updating rule to control the updating proportion of pheromone concentration with optimal solution in single iteration. And the local search strategy is used to improve the quality of the solution and select the current optimal path for global updating the pheromone in order to save some computing time and not reduce the searching efficiency. Some traveling salesman problems are selected to test the performance of the CEULACO algorithm. The simulation experiments show that the improved ACO algorithm can effectively improve the accuracy and the quality of solutions, and distinctly speed up the convergence speed and computing time.

The construction of enterprise information is facing more or less heterogeneous system integration problems in the deepening process. On the basis of analyzing the advantages of service oriented architecture (SOA) and Web service technology in realizing heterogeneous system integration, a fast and flexible enterprise information system integration scheme based on combining SOA and Web service technology is proposed to solve these problems of poor information sharing capability and business adaptability. This integration scheme uses the advantages of self described and contained application module and platform independence and programming language independence to implement the complex aggregation. The SOA is used to implement the standard and loosely coupled application architecture by using the characteristics of Web service. So this integration system provides a great solution to messaging communication with different enterprises or inter-enterprise. And it can effectively reduce the maintenance cost, shorten the adjustment time, improve the execution efficiency and make the enterprise with more competitive.

We are living in a hypercompetitive market. Therefore, companies running online shopping malls must muster their wisdom to make online shopping websites one of their strategic assets. To meet this objective, companies should understand online consumers’ cognitive styles, and then design their online websites according to the styles. In this sense, we focus on two cognitive styles such as systematic type and intuitive type. The former means that consumers may pay more attention to reasonable statements about target product before making decisions, while the latter indicates that consumers may react more sensitively to intuitive information such as images and abstract conjecture. For the sake of novel approach to conducting experiments, we adopted an eye-tracking approach which we believe is more reliable in providing relevant information capable of answering our research questions. For the utilitarian product like notebook computer, systematic type users may show higher TFD (Total Fixation Duration) and FC (Fixation Count) to consumer reviews than intuitive type users. Also, intuitive type users may be more sensitive to special purchase opportunity-giving events information with higher TFD and FC than systematic type users. Through our experiments with an eye-tracking method, we could conclude that a strategic redesign of the online shopping websites is necessary where consumers' cognitive styles need to be significantly considered.

In the basketball segmentation in long shots, Gauss filter is adopted to smooth noise in the image firstly. Secondly, the background is separated by the difference of inter-frame and the connected regions are labeled. Thirdly, a strategy is designed to identify basketball by the characteristic of basketball. Finally, the edge deviation is revised and the optimal result is obtained using improved Snakes.

Short text is a popular text form, which is widely used in real-time network news, short commentary, micro-blog and many other fields. With the development of the application such as QQ, mobile phone text messages and movie websites, the size of data is also becoming larger and larger. Most data is useless for us while other data is significant for us. Therefore, it is necessary for us to extract the useful short text from the big data. However, there are many problems with the short text classification, such as fewer features, irregularity and so on. To solve these problems, we should pretreat the short text set first, and then choose the significant features. This paper use semi-supervised learning method and SVM classifier to improve the traditional methods and it can classify a large number of short texts to mining the useful massage from the short text. The experimental results in this paper also show a good promotion.

In this paper two famous and commonly used feature mining non-parametric supervised feature extraction techniques (NSFETs) called Non-parametric Weighted Feature Extraction (NWFE) and Decision Boundary Feature Extraction (DBFE) are analyzed to see their efficiency in the presence of noise. In particularly these feature extraction techniques are used in classification as they give better classification accuracy. This study reveals that NSFETs are very sensitive to noise because of which the number of features increases and we get low classification accuracy. In order to see the behavior of NSFETs, spatial and spectral information from hyperspectral image classification is used. The experimental results show that in the presence of noise, spectral information is much more effected than the spatial information when features are extracted using the NSFETs. It is also examined that NWFE is more affected by noise than DBFE. The linear filtering technique is used just before the classifier in order to mitigate the noise effects in NSFETs. Using linear filtering just before the classifier does improve the final classification accuracy but with high number of spatial and spectral features. This does not satisfy the one of the main purpose of feature extraction and that is feature reduction.

Human body detection in video surveillance is an important research point, which has been devoted for many years by researchers. But it can’t detect human body with the feature of HoG and classifier of SVM. This paper proposes an improvement measure to the traditional algorithm of human body detection in order to detect human body in video surveillance in real time. Through the Integral images to speed up the computation of features of HoG and HoD under the condition of Kinect, It can’t detect human body in video surveillance in real time. As human body detection using HoG features can’t meet the real-time requirement in the current time. This paper abandon the time-consuming processing of computing features of HoG with three line interpolation and Gauss filter in terms of traditional algorithm of human body detection, and This paper introduces the notion of Integral images to speed up the computation of the features of HoG and HoD with the help of Kinect. As a result, Our improved algorithm can get a better time performance than ever, which can get the average detection rate of 30~40 FPS, current performance getting promotion greatly than ever, under the condition of CPU 3.10Ghz, RAM 4 GB, 640*480 detection window.

User motion recognition through image recognition has attracted considerable attention with the development of the Kinect senor by Microsoft. The Kinect exhibits excellent motion recognition performance at a small expense via a motion tracking method utilizing an RGB-D camera. Further, the Kinect offers the advantage that applications can be easily written for it using the Kinect SDK. However, the Kinect also suffers from the disadvantage that it is unable to recognize an image “shielded” by an object because of the application of the vision recognition method by the camera. Consequently, it is difficult to obtain accurate data concerning user movement. Studies on observing an object from various directions with multiple Kinects are being conducted to compensate for this problem, and it is possible to acquire the movement data for only one user in these studies. This study implements the human body trace system for multiple users. We suggest a method to compensate for the location of the user and motion data that cannot be recognized by one Kinect using data acquired by multiple Kinects.

With the progress of society signal measurement and analysis have more widely range of applications, but the spectrum analyzer was generally expensive to make it difficult for popularization and application. In this paper, a design methodology of low cost, stable reliable spectrum analyzer based on the idea of Software Defined Radio (SDR) is presented, which consists of three functional units. The STM32 is the core of spectrum analyzer and is the key base to signal acquisition and analysis of the system. This paper designs the detailed hardware circuit and the software and optimizes the display program to obtain a higher refresh rate. Through testing the design is stable and reliable, simple and practical, and suitable for use in scientific research and industry production.

Video contents may adopt various conditions for one may access a system and this system may adopt pay-per-view. This scrambling method is used in commercial way to protect copyright. As image and video contents are widely released, content protection becomes an important issue. In this paper, we present a scrambling method which uses magnitude and phase decomposition. A given image is transformed into spectral domain to obtain magnitude and phase components. The magnitude component is transferred to unauthorized individual. However phase component is selectively distributed to protect contents because phase component contains important information. Simulation results show that the proposed method gives reliable performance.

In order to provide a scientific decisions for prospect new oil formation and reduce production costs, this paper proposed reservoir identification model based on Smap-ED. Using the actual seismic attribute data as the research object, the principles are analyzed to explore the reservoir recognition model , Through the experimental analysis of model of effectiveness evaluation indexes , the five models of applicability and effectiveness was comparative study from the relative index, external index and running time. The experimental results show model based on Smap-ED of seismic attribute data clustering effect is better than model based on S-Map and the running time of algorithm close to model based on SOM, this model can provide more effective support for scientific decision-making.

Resistance welding is the most commonly implemented method to join steel sheets in the automobile industry. To increase the efficiency and to maintain or reduce the weight of vehicles, advanced high-strength steel (AHSS) has been developed as a build material for vehicular structures. This paper aims to exploit the ability of prediction for nugget sizes in resistance spot welding by using support vector regression (SVR) and artificial neural network (ANN) model. In this study, the nugget size will be predicted according to parameters in resistance spot welding, such as welding current, welding time and welding force, etc. using machine learning methods. In addition to considering important process parameters for resistance spot welding, some design parameters, such as the thickness of the materials and the coating, are also considered. As the experimental results, SVR shows better performance in the prediction of nugget size when compared to ANN.

To obtain a panoramic image by using ordinary cameras instead of an expensive panoramic camera, various researches are being studied. We usually use Stitching of the images using a homography matrix that has been evaluated by the method based on feature points such as SURF to obtain a panoramic image. However, the above method has some problems that it need a lot of time to extract feature points and feature points are obtained differently depending on various external factors. Proposed method is stitching the images using spatial transformation from two cameras at the same time. We expect to be able to apply proposed method to various electronic devices to obtain a panoramic image easily.

According to the standard ant colony algorithm in Internet of things (IOT) medical data transmission path selection, there are the problems of searching time is not long, easy to fall into partial optimization. This paper puts forward a kind of IOT medical data transmission model based on quantum pheromone updating and cluster heads choose optimization ant colony algorithm. First of all, using quantum bit probability amplitude encodes information of each path element, using quantum revolving doors and the route of the ants to update pheromone, then the ant search behavior is concentrated near the optimal solution, and the way of searching and restriction mechanism of avoiding precocious pheromone are combined, and finally optimizing the cluster heads of IOT medical data transmission model with the improved algorithm. The simulation experiments show that, through the pheromone update optimization of standard ant colony algorithm, and after the cluster heads optimization of IOT medical data transmission model, improved ant colony algorithm is more robust compared with the standard algorithm.