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Student to business(Abbr. S2B) e-commerce network platform is designed to display the college students’ creative works, and build joint channel for the creative talents of college and small and medium-sized enterprises requiring innovative resources. Firstly, this paper analyses the targets and features of S2B e-commerce platform. Secondly, the user requirements-oriented explicit functional demands are listed from two aspects of the individual users and enterprise users through investigation. In addition, implicit functional demand is obtained based on ontology. According to the method of ontology mapping, the function description by natural language is mapped to semantic elements and demand items, then the relationship of demand items coming into semantic structures. According to the method of ontology inference, the semantic structure is mapped to domain ontology of S2B e-commerce network platform, as well as the implicit functional requirements of platform are digging out. As a result, the whole functional structure tree of S2B e-commerce network platform is constructed. Finally, the detail functions of the platform are described by UML modeling tool. Through the platform the college students’ creative works are integrated and flow to meet the innovative demand of enterprises. S2B e-commerce model can make the creative resources be fully used and put forward the innovative and creative industry developing well.

Many of linear control applications require real-time operation; higher density programmable logic devices such as field programmable gate array (FPGA) can be used to integrate large amounts of logic in a single IC. This work, proposes a developed method to design PD controller (PDC) with optimal- gains using FPGA. The method used to design PD controller is to design it as digital design Proportional and Derivative controller in parallel through the summer. The proposed design is 32-bits FPGA-based controller (32PDC), which uses 32-bits for each input/output variable. The single joint of robot is used to test the controller in simulation environments, using VHDL code for the purpose of simulation in Xilinx. The same design is coded in MATLAB environment (MPDC) in order to make a comparison with the proposed FPGA-based design. PDC needs 16 clock cycles to complete one action with maximum frequency of 108.5 MHz. 32PDC is able to produce an output in 13.24 MHz with the robot system. Therefore, the proposed controller will be able to control a wide range of the systems with high sampling rate and 75.545 ns delays.

In recent years, huge amounts of nonperforming loans (NPLs) of commercial banks have become one of the biggest obstacles constraining reform and development in Chinese commercial banks. Finding a way to control the banks’ NPLs is a core issue that it continues to be explored and researched in the finance. In this paper, PCA and relief algorithm in data mining methods were adopted to extract and analyze NPLs characteristics in commercial banks through contrasting the performing and nonperforming loans records, based on the predecessors’ literatures. In this paper, a bank's loans data with 96 features and 10415 samples is collected. At last, we construct nonperforming loans of commercial banks classification model. Our research is very important for capturing warning signal timely, detection of NPLs and sound operation of commercial banks.

Parents of infants in NICU are facing challenges in the lack of information or in understanding it, in addition to their negative feeling in this stressful experience. One way to support parents is to develop an automatic system that generates a clear and concise summary about infant progress. In order for this system to function properly there is a need for ontology. This project present a simple ontology contains some main concepts in NICU domain. This ontology was developed through a Protégé version 3.4.2 in seven steps that used to create the RDF and OWL. The ontology mainly includes 10 classes, 17 data properties, 8 object properties, some individuals and cardinality restrictions on relationships between classes, and other descriptions. At the end, the author found the ontology consistent after reasoning then executed four different SPARQL query and its result were correct. This ontology can be developed and used in another application around the NICU domain.

The teaching quality assessment of physical education for the undergraduates has a very important role for the development of the college physical education teaching. In the age of information, we can improve effectively the speed and accuracy of the assessment through using rationally the computer technology. The AHP method and the DEA method are two kinds of evaluation methods which are used widely by the scholars. However, whether we use AHP or DEA, there are some defects on the using and scope. In order to overcome these shortcomings, we propose the RAHP/DEA method. This method combines the advantages of the RAHP method and the DEA methods. And we enlarge the range of use. Finally, in the computer simulation experiment, we adopt the method to evaluate the teaching quality of college physical education. The experimental results verify the rationality of the project evaluation and the decision making process. This shows that the evaluation system and the evaluation method meet the decision making ideas and the practical requirements.

Over the past several years electronic commerce (e-commerce) has changed the business transaction way, and online service quality has a significant influence on many important aspects of e-commerce. However, there are few quantitative studies on the investigation of the online service quality evaluation focused on the market in China, which is one of the developing countries with the highest online population growth. This paper is an attempt to identify the main factors affecting the online service satisfaction of the e-commerce websites in China, and Fuzzy TOPSIS is employed to evaluate the e-service quality through internet for the online consumers. The results could be the guideline for the e-commerce companies in terms of improving their online services.

This study focuses on a new improvement for the success of ecological sanitation project by using urine diverting dehydrating toilets (UDDTs). This concept links human waste to food security, recognizing the value of treated human waste for fertilizer. This shows that the UDDT is an efficient way to prevent the mixing of urine and feces by providing an area for these separate waste products to be treated. The level of acceptance toward UDDTs concept is acceptable to the people in San Fernando, La Union since they have no existing sewer and drainage system especially on the area of the bay side residents.

A novel continuous camera calibration algorithm is proposed, which can address the calibration for non-goal area of soccer video. By considering the fact that the position of main camera is stable, a novel continuous camera calibration algorithm is proposed for soccer video analysis. There are two stages for calibration: the first stage addresses the calibration only for goal area and the position of camera is computed and recorded, and then the second stage can address calibration for any area including non-goal area where the camera position is given at first stage. Thus, the proposed algorithm can address the calibration for non-goal area of soccer video, which cannot be addressed by traditional calibration algorithms since the inadequate of calibration objects.

At present, we level science award according to a system of norms, e.g., innovation, practicability, academic value and social value etc. However, science achievement is the combined event with multiple properties and characteristics of uncertainty. It is vital to give an objective assessment conforming to the characteristics of multiple properties about science achievement, and it is one of ensuring the accuracy of the science award judgment. Therefore, in the paper system of norms is showed by interval-valued fuzzy number and sets, and we present an interval number to scores of experts. And further study develops function for judgment the similar degree of two interval-valued fuzzy numbers and method to order interval-valued fuzzy numbers in order to make science award judgment reasonable.

This paper introduces a research aiming at the development of a new approach to mastering industrial risks and prevent accident scenario. Starting from modeling and analyzing accidental process to understand the causes of accidents using quantitative risk assessment and reliability, they are essential issues in modern safety to make reliable decision, and it is new approach used for risk management to identify accident scenarios that may occur at their facilities which are sources of damage. Risk assessment of safety instrumented systems is approaches designed primarily to reduce the existing risk inherent in engineering system to a level considered tolerable and maintain it over time. In this study, the reliability of quantitative risk assessment using fuzzy sets based on event tree analysis and layer of protection analysis is the model proposed to deal with inaccuracy and uncertainty of data, The model proposed to determine the severity of the scenario and determine the safety integrity level SIL using Fuzzy Sets theory. The results which have got by this model is more motivate to deal with uncertainty of which considered as complementary for logical and arithmetic computation. As the accident is a chain of failure events, each related to its (causal) event or events, the early detection and diagnosis of faults in processes is very important, we use Fault Tree Analysis to show the possible malfunctions by enumerating the suspect components and their respective failure modes. Fault diagnosis when error occurs is performed by engineers and analysts performing extensive examination of all data feedback from the system and decisions are made based on threshold values by using fault trees. Since those decision-making tasks are safety critical and must be done promptly, the engineers who manually analyze the data are facing time challenge. To automate this process, we present in this approach to use decision trees to capture the contents of fault trees and detect faults by running the telemetry data through the decision trees in real time. Decision trees are the binary trees built from data samples and can classify the objects into different classes, the decision trees can classify different fault events or normal events. Given a set of data samples, decision trees can be built and trained, and then by running the new data through the trees, classification and prediction can be made.

Communication-Based Train Control (CBTC) system is an automated train control system using bidirectional train-ground communications to ensure the safe operation of rail vehicles. The interference from Wi-Fi signals to the CBTC signals has significant impacts on the train control performance. Most of previous works use data from simulation platform. This paper proposes a method to test the Wi-Fi interference to the Communication Based Train Control(CBTC) systems. The test is made mainly based on real subway, and supplemented by laboratory simulation platform. The collected data is to be compared and analyzed. The test result shows that the signal transmission mode of waveguide is the most reliable, followed by the leaky cable. The transmission mode of free wave has the worst anti-interference capability to Wi-Fi signals. This is because that both the waveguide and leaky cable transmission have a better encapsulation compared with the free space transmission. Also applying for specific bands can effectively reduce the interference transferred from Wi-Fi signals to the CBTC.

The strategies of the welding seam dynamic passing control and feasible loose edge rolling technology are put forward. In the process of cold mill welding, by dynamic regulation roll-gap, bending force and stand speed. The control strategy of reducing tensions between stands and strip edge is achieved. At the same time, the decoupling control strategy of plate shape and thickness can reduce the influence of roll-gap on plate shape and of bending force and stand speed on the steel thickness. Simulation analysis and practical application show that the technology effectively reduces the occurrence of steel strip fracture of tandem cold mill units.

Dirty water is the world's biggest health risk. When water from rain roads into rivers, it picks up toxic chemicals, dirt, trash and disease-carrying organisms along the way. Many of our water resources lack basic protections, making them vulnerable to pollution from factory farms and industrial plants. Due to that, a classification model is needed to present the quality of the water environment. In this paper, the data mining techniques are used in this research by applying the classification method for water quality application. Various classifiers were studied in order to find the most accurate classifier for the dataset. This paper presents the comparison of accuracies for the five classifiers (NB, MLP, J48, SMO, and IBk) based on a 10-fold cross validation as a test method with respect to water quality from the datasets of Kinta River, Perak Malaysia. This study also explores which classifier is suitable to classify the dataset. The selected attributes used in this study were: DO Sat, DO Mgl, BOD Mgl, COD Mgl, TS Mgl, DO Index, AN Index, SS Index, Class, and Degree of pollution. The data consisted of 166 instances and obtained from the East Coast Environmental Research Institute (ESERI) of Universiti Sultan Zainal Abidin (UniSZA). The result of MLP and IBk performed better than other classifiers for Kinta River dataset because these classifiers showed the highest accuracy with the same percentage of 91.57%. In the future, we will propose the multiclassifier approach by introducing a fusion at a classification level between these classifiers to get a higher accuracy of classification.

In this paper, long term trend data from condition monitoring systems of hydropower units was used to separate to grid cells based on the water head and active power, and to build the health sample model for abnormal evaluation. For the problem of insufficient monitoring samples on non-rated operating conditions, Bootstrap method is introduced for sample expansion and confidence interval calculation, thereby to achieve limit optimize settings of health sample of hydropower unit condition data, which is more approximate to practical engineering than normal approximation method, and has achieved a good result.

Now a days, fraud is a million dollar business and every year it is increasing more and more. In a crime survey in 2009, tells that close to 30% of the company’s worldwide reported falling victim to fraud in the past year. Fraud involves individual or in a form of groups who intentionally act secretly to rob another of something of worth, for their own profit. Fraud is as against the humanity and it is unlimited variety of different forms. So in this paper, we introduce different methods and techniques to tackle the fraud and how to prevent the fraud into the organization, businesses, and hospital and insurance companies. Fraud prevention is a continuing battle. Many website owners must continually fight against many kinds of crime ranging from stealing identity theft and credit cards and many other types of fraud related to that. Internet Fraud is also very popular, where fraudster can steal the credit card number and buy thing from the different websites.

With the research in the technology of AADL, we design and realize the parser for AADL and code generation tools which transform AADL model into C source code based on Spoofax/XL. This lays a good foundation for further realizing the AADL unified modeling, verification, implementation and code generation integrated environment.

This paper aims to present a comprehensive study for various Asset Tracking technologies. Our study scales down from introduction of chief underlying tracking and localization principles to technologies and systems at higher level. At lower tier, we introduce schemes like triangulation, Time difference of Arrival (TDOA or trilateration), multilateration, Angle of Arrival (AOA), Doppler, Signal Strength (RSSI), Beam forming etc. At surfacetier, our primary focus has been laid on RFID and Bluetooth based technologies while an overview of other technologies like satellites (GPS), cellular (GSM) or data connectivity (WIFI) etc. An insight to associated schemes like Active and Passive, Indoor and Outdoor, and Behavioral Sensing augment this. Each technology is further analyzed with benefits and drawbacks for a tracking solution. After technical insight, we will focus our study on Personal Asset tracking and highlight the pros and cons of previously mentioned tracking technologies in line with five aspects; Accuracy, Budget, Energy, Host and Platform-independence. Within these five regimes, we tabulate the requirements of an affordable, efficient and practicable scheme and illustrate with current examples. We keep our focus on a common user who has a cell phone in-hand and resolve an optimal tracking solution within the resources available to him. As a consequence, we conclude Bluetooth Low Energy based tracking schemes to be optimal candidate. We foresee our endeavor to serve as a compendium for the readers who wish to get an overview of such technologies without going into discrete technical details, and propose the Bluetooth based tacking scheme as a viable and affordable solution for a common user.

Soil erosion is one of the most widespread ecological problems with the most serious damage worldwide. In the present study, the influence of soil erosion thickness on soybean yield of black soil in Northeast China is studied. With the pot experiment as the method, this study investigates the influence by peeling the surface black soil layer at 0, 5, 10, 20, 25, and 30 cm to simulate different soil erosion thicknesses, establishes a quantitative model of soybean yield to explore the response of soybean yield to soil erosion thickness. The 4-D-optimum orthogonal design (416-A) is adopted to conduct the coupling experiment of water and fertilizers under erosion thicknesses of 0 and 15 cm so that the coupling modes of water and fertilizers under the two soil erosion thicknesses are established. The results show that soil erosion thickness has a significant influence on soybean growth, that is, with the increase in the soil erosion thickness, soybean plant height, pods and grains per plant all reduce, and soybean yield decreases in a Z-shaped curve, while yield reduction rate increases in a S-shaped curve, respectively. Water, along with N, P, and K fertilizers, affect soybean yield, but the intensity varies with soil erosion thickness. Under the soil erosion thickness of 0 cm, the order is: W>P>N>K. While under the soil erosion thickness of 15 cm, the order is: N>W>P>K. With the increase in soil erosion thickness and the decrease in soil fertility, the effect of irrigation reduces, whereas that of fertilization increases. The research results can provide the theoretical basis for soil erosion prevention and control in the black soil region.

Circular economy as an advanced development mode for harmonizing the problems among resource, environment and economy, is destined to be the choice for keeping on developing. It is the sustainable development strategy which based on reducing, reusing, recycling principle and the recommendation of circular economy was proposed to build resource-efficient and environmental-friendly society. How to choose the evaluation index system and correctly evaluation the development trance of circular economy is treated as a decision problem. In this paper, a hybrid warning model using matter-element model, combination weight method and place value method for evaluating the development trance of circular economy is proposed. The matter-element model is used to choose the statistical index system of circular economy. The combination weight method is used to calculate the weight of indicators and the place value method is used to contribute the comprehensive evaluation and warning model of circular economy. Finally a case study demonstrates the application of the proposed model.

Development of the automation system for recognizing diseases of the infected rice is a growing research field in precision agriculture. So, the first and foremost thing we should do is to extract the disease region from rice images. The objective of this paper is to propose an image segmentation method for rice disease spots based on machine vision. The algorithm consists of two main steps: image gray-level transformation and disease region segmentation. Firstly, the color image was transformed into a gray-level image by the color indices 2G-R-B, which making an important contribution for this kind of images. Secondly, the information fusion between the self-adaptation threshold which was obtained by the mean and standard variance of the grey-scale image and the green component distribution features in color image was used to form a new segmentation standard to detect disease region. To test the accuracy and robustness of the proposed algorithm, it was tested with a broad of set of images and compared with the classical approach based on other grey-level convert methods and Otsu’s method. Test result shows that the accuracy of new algorithm appears higher and it can be applied to segment rice disease spots effectively.

In recent years, the number of users and service providers are increasing in using cloud services so the accessibility and the effective management of the required resources, irrespective of the time and place, seem to be of great importance for both sides. Improving the performance and utilization of the cloud systems are gained by the auto-scaling of the applications; this is because of the fact that, some approaches have been proposed for auto scaling. This paper seeks to checking some value, based on the learning automata, for the scalability of the web applications, which combines virtual machine clusters and the learning automata in order to provide the best possible way for the scaling up and scaling down of the virtual machines. The results of this study indicate how an increased capacity of virtual machine which have been done by the value of thresholds could effect on SLA and overhead of responding.

In the internet of things era, information technology is developing at a high speed while e-commerce technology is increasingly maturing, which builds a favorable platform for the further development of e-commerce industry. Nowadays, as a digitized business model, e-commerce has become one of the main development forms in modern commerce and trade circulation and held an important position in national economic growth by its distinct advantages, efficient, quick, easy and low-cost. On the other side, the development of commerce and trade circulation is a significant measure to promote the new-type urbanization construction. The urban construction and commercial circulation has a long-history relationship, the development of both sides are mutual precondition. The further improvement of e-commerce technologies will inevitably drive the strategic transformation and long-term development of the whole commercial circulation as well as facilitate the improvement of the entire e-commerce, yet the change will directly increase the pace of new-type urbanization construction, playing a positive role on new-type urbanization construction.

The Internet is frequently used as a medium for exchange of information and opinions, and it is imperative to conduct public opinion analysis to get people’s opinions well understood and guided. In this paper a hybrid public opinion analysis method based on improved clustering and mutual information is proposed. During feature extraction, the weights of words are modified based on Part-of-Speech Tagging to reduce the dimensions of original texts. As for clustering, a novel density peak algorithm is improved and combined with binary search algorithm to determine the cluster number K and initial centers for KMeans. Then hot words extraction, sentiment analysis and trend analysis for each cluster are processed with mutual information to mine useful knowledge to help decision-making. Extensive experiments are conducted on Hadoop, and the results show that our hybrid Public Opinion Analysis method is quite effective and has certain significance.

The paper presents an effective method based on the Lagrange multiplier theory to solve optimal scheduling of hydrothermal power system. Optimal scheduling of hydrothermal power systems is a great important problem to electric utility systems, the main objective of the problem is to determine the generation for each plant during scheduling period of time such that the total system generation cost is minimum while satisfying the system constrains of the generating limits and available water. The problem of optimal economic operation of hydrothermal power systems with fixed head hydro plants is considered and has had many researches about this. Determining of water discharge at the first interval is performed in this paper and lead to a low number of iteration and short computation time for convergence. The proposed method is tested on one system consisting of one hydro and one thermal plant through two examples.

This paper reviews the magnetic coupling resonance wireless energy transmission from the principle, models and theories based on the development and classification of wireless energy transmission technologies. Firstly, it introduces MIT coupled mode theory and the main circuit theory models and experiments, summarizes and illustrates the characteristics of the two theories. Secondly, it introduces the worldwide research progress in this area, including the circuit theories, three-coil energy transfer systems, multi-receptor, four-coil energy transfer systems, frequency tunable resonance body, adaptive tuning and frequency division, the loss distribution, new materials, mutual inductance coupling and frequency tracking. Finally combined with the main research content of magnetically coupled resonant wireless energy transmission, it points out the existing problems and the direction for the studies of the theory and application next step.

Compared with the traditional fuel vehicle, the pure electric vehicle has excellent characteristics in the emissions and energy use. But its driving ranges are much shorter than the traditional fuel vehicle. It has become a bottleneck problem for the development of electric vehicle. It is difficult to establish the accurate model of driving ranges in the actual working condition. Its main reason is that the influence factors of electric vehicle driving ranges and driving ranges have a non-linear relationship. BP neural network can map the complex non-linear relationship and has strong non-linear fitting ability. Compared the driving ranges of fuzzy control with fuzzy PI control in pure electric vehicle with dual-energy storage system, the results of simulation experiment indicate that the fuzzy PI control can extend the driving rages of electric vehicle. The maximum value of the average error is 2.66% in BP neural network prediction.

In this paper, we propose a new model for information dissemination in mobile social networks, in which the infectious probability is defined as a function of the homogeneity and heterogeneity between nodes. Moreover, we investigate numerically the behavior of the model on a real scale-free social site with the exponent γ =1.08. We find that weak ties between nodes play an important role in information dissemination process. Specially, selecting weak ties preferentially can make information spread faster and wider, and the efficiency of diffusion will be greatly affected after removing them.

Considering that intrusion detection systems and anomaly clearly recognize malicious activity. Nowadays, data mining based intrusion detection systems, security and more rapidly detect attacks.Therefore,in this article we use a combination of k-means clustering algorithm and is used supervised support vector machine algorithm to find the best line separator. This is leading to the separation of normal and attack traffic.

The combination forecasting model IOWGA-EMD-ARMA-WNN is proposed in this paper. The randomness, periodicity and tendency of the original data are showed by EMD decomposition in EMD-ARMA model. WNN combines the advantages of wavelet analysis and BP neural network and improves the learning efficiency and forecasting accuracy. The weight of combination model is decided by forecasting precision of EMD-ARMA model and WNN model based on IOWGA method. At last, the IOWGA-EMD-ARMA-WNN model is used to forecast monthly inboard tourism demand of China and the results show that the proposed combination model has better performance on forecasting accuracy compared with the other models.

Tool wear is an inevitable and continuously aggravated process in precision hard cutting. Interaction between tool wear and dynamic characteristics in cutting system makes the machining accuracy and quality of machined surface declining, which effects dynamic characteristics of cutting system deteriorated significantly. Based on this, cutter dynamic modal parameters of a specific cutting system are measured in this paper. Cutting force and change law of acceleration power spectrum are studied by experiments. Influence mechanism of tool wear on system frequency energy distribution was obtained. The cutting system dynamic characteristics are quantified by establishing simulation model. Root locus plot of non-uniform load distribution is obtained and the minimum stiffness ratio in the root locus plot is determined. The effect of tool flank wear on cutting system stability in quantitatively stable is researched.The results can provide a theoretical basis for the selection of reasonable selection of stable precision cutting conditions in different tool wear conditions.