Earticle

Memory-based CF algorithms have the weakness of low real-time ability and scalability. For these issues, a SVD-based K-means clustering CF algorithm is proposed. Traditional clustering-based CF algorithms have low recommendation precision because of data sparsity. So we first fill the missing ratings by SVD prediction, and then implement k-means clustering in the filled matix. This algorithm overcomse the data sparsity issue via SVD and keep the advantage of clustering, such as good real-time ability and scalability. Experiments results show that this algorithm outperforms Pearson CF, svd CF and k-means CF.

The dynamics of a first order delay system is highly nonlinear, time variant, uncertain and coupling effects. The main objectives to control of first order delay system are time response and acceleration measurements. The problem of acceleration measurements can be reduced, based on design sensor-less Proportional-Integral-Derivative (PID) filter controller in this research. Assuming unstructured uncertainties and structure uncertainties can be defined into one term and considered as an uncertainty and external disturbance, the problem of computation burden and large number of parameters can be solved to some extent. To solve the uncertainties acceleration measurements play an important role. In order to design sensor-less PID filter controller, an accurate PD surface and the derivative of PD surface plays important role. To design an accurate PD surface, stable and tuning surface slope is needed to form the structure of main PID controller. In this algorithm, the derivative of PD surface computes the second derivation of error. Regarding to this method, the challenge of system uncertainties and time response have been solved based on sensor-less acceleration linear filter controller. As this point if s = K1e + e + K2Σe is chosen as desired surface, if the dynamic of first order delay is derived to surface then the linearization can be realized. Because, when the system dynamic is on the surface is used the derivative of surface S = K1e + e + K2e is equal to the zero that is a decoupled and linearized closed-loop systems dynamics. Linearization and decoupling by the above method can be obtained in spite of the quality of the first order delay dynamic model.

The paper firstly describes some typical manual and automatic semantic annotation, then analyses strategy of semantic annotation based on ontology. Secondly, this paper proposes semantic hierarchical structure of fuzzy ontology based on Variable Precision Rough Set and concept lattice in order to make up the shortage of traditional semantic annotation methods. Novel algorithm of semantic annotation based on fuzzy ontology for domain Webpage is presented in the paper by comparing ontology data of extractive webpage. Finally, experiments show that this presented algorithm is better than the traditional method in semantic annotation rate of support and recall.

Context information in smart space are complex and uncertain, it increases the difficulty of information prediction. In order to increase the accuracy of information prediction, a new method is proposed in this paper. Firstly, first-order predicate logic is used to describe the context information and Markov logic network is used to achieve the unification of the first-order logic and probabilistic graphical models. Secondly Markov logic network model is established based on some particular scenes which were predicted by neural network, the purpose of this model is to realize the prediction of device information which is valuable for the task planning by relevant complex context information. At last, simulation test is done using the real data collected from our smart space laboratory, the experiment results show that method is effective.

Nowadays, machine learning and deep learning has been becoming popular and useful in applying to resolve people’s problem. Specially, in HCI (Human Computer Interaction) field like robots or automatic game programs. Go-Game (the game of Go) is still a challenge in coding to get the wisest moves each turn to achieve the winner at the end of a game. In our work, we suggest the next move based on Convolutional Neural Networks (CNNs) and make evaluations and comparisons to gamers separate in 3 ranks (levels). We train 5-layers CNNs by supervised learning from a database of human games using the board-states. The network suggests the move of the selected player and the others player can be helped or not- depend on playing option. The program can also play the game automatically without human interactions during all the game progress (Machine-Machine game). In the other way, our program can interact with a human-player and accept move commands from player (Human-Machine or Human-Human). This technique allows Go-game program play the game without searching as traditional program but trained by convolutional neural networks. In our tests, we separate in 3 levels and use totally 598,472 board-states for training data. Our main aim is to help people who are the newbie in playing Go-game. With this technique, we expected that we can apply to develop AI programs and devices with more and more effects and higher performance.

In computer aided diagnosis (CAD) process, one of the most challenging problems is data sparsity, which leads to the diagnosis results are not reliable. This paper proposes a clustering collaborative filtering based algorithm to solve the problem of data sparsity. In this paper, we use k-means clustering algorithm to cluster the same type of patients, and then adopt collaborative filtering method to fill the missing data values for each cluster, in this way to reduce the complexity of similarity calculation of collaborative filtering. The proposed method makes full use of the information-sharing mechanism of "similar patient population" to predict and fill the missing values. A hepatitis dataset is used for evaluating the performance of the algorithm. Results indicate that the proposed algorithm has better performance for medical record data sparsity problem.

This paper studies Uyghur single text summarization and proposes some of new or improved approaches in the aspects of keyword extraction and evaluation, sentence selection and redundancy removal, also in readability improvement and so on. Proposes an improved frequent pattern-growth approach to extract the semantic strings which perfect both on its semantics and structural integrity, to evaluate this strings uses multi-feature fusion approach and select most important ones as keywords to describe the text theme effectively. In the aspect of sentence similarity and redundancy removal, proposes the idea of theme including degree, so as to effectively remove the redundant sentences and improves the summary quality significantly. Also introduces sentence alignment between the texts that after being stemming and original text, so as to solve the problems that summary naturalness, coherence and comprehensibility decline and other issues caused by stemming process.

Based on the concept of outcome-based maintenance, the outcome-based contractor equipment support is proposed as the logistical support strategy in the military industry. The outcome-based contractor equipment support is focus on the combat efficiency rather than on the performance of the equipments. Choosing the right contractor is a very important part in the process of its implementation. In fact, the contractor selection is a multi-criteria decision-making problem, which is largely dependent on the uncertainty and ambiguity of the decision-makers. This paper presents a systematic decision-making process of the outcome-based contractor selection, which employs fuzzy AHP to determine the importance weights of each index and adopts fuzzy TOPSIS to obtain the performance ratings of the alternative contractors. Finally, the validity of this method is verified by a specific case.

The melting of the sea ice in the Arctic area increases the possibility of exploiting the Northern Sea Route (NSR)by economic method. Compared to the traditional routes, navigating through the Northern Sea Route (NSR) will save much distance and time from Asia to Europe, but the ice condition along the NSR is a key obstacle to most vessels. To ensure the safety of the navigation, ice breaker assistance service becomes crucial. This paper introduces the procedure of icebreaker assistance service firstly, and then a static scheduling model is presented to illustrate icebreaker assistance service. Considering real operation, a dynamic model is proposed, and First Come First Serve (FCFS) principle is implemented into models. The experiment results show that this model is beneficial to control the average waiting time, while it may leads to wait too long for some vessels. And the average fuel cost of each trip remains stable even increasing service request.

This paper proposes an Adaptive Cuckoo Search Algorithm (ACSA) for solving economic emission dispatch (EELD) problem with quadratic fuel function. The ACSA is developed by performing two adaptive updated step size parameters on conventional CSA in aim to enhance the convergence speed and quality solution of the conventional CSA. In addition to minimizing electricity generation fuel cost, emission released into the air from thermal plants is also another main objective needs to be minimized. In order to test the performance of the proposed ACSA two systems including a three unit system with one load case and a six unit system with three load cases are employed. The obtained result by the ACSA compared to that from other methods has revealed that the proposed ACSA is a very promising meata-heuristic algorithm for solving economic emission load dispatch problem.

A number of multi-phase motors can be series- connected and driven by a single inverter via the appropriate phase transformation rules, and all motors in series can be independently controlled. In this paper, the working principle of symmetrical Six-phase PMSM series connecting three-phase PMSM is proposed, a novel SVPWM method to achieve the decoupled control is presented. The implementing method of SVPWM is presented. In Matlab/simulink, the motor operating conditions，via id=0 vector control strategy, of variable load and speed are analyzed; the feasibility of the proposed SVPWM control strategy is verified.

This dissertation studies the wind shear field, modeling and simulation of wind shear radar echo. Firstly it introduces the concept of wind shear, classification, causes and its impact on aviation safety of flight. Then it generally introduces wind shear detection technology and signal processing methods. It finishes computer simulations and gets an echo signal velocity spectrum. Finally it develops a software for wind shear echo model simulation. The computer simulation results show that the velocity spectrum data of echo model coincided with the echo model of theoretical analysis. The wind shear echo signals can be simulated based on the simulation software. It can be simple and practical applied to tests of wind shear radar target detection performance.

This research contains the simulation and designing of Quadcopter using Adaptive Neuro Fuzzy Controller to control the altitude of quadcopter and obstacle detection. Now a day’s advancement in technology has made it possible to develop low power and lightweight with accurate sensors which are used with controllers for controlling, which have high processing power but small power consumption. This has been allowed for the development of complex and difficult control systems that can be implemented onboard UAV. With this combination of high precision and light weight, real-time onboard navigation or guidance and autonomous flights are now practical. This research work used a Fuzzy controller to control the pitch angle of quadcopter and avoiding obstacles. The fuzzy controller receives the sensory data and adjust the pitch accordingly until unless it finds the clear path. For detecting obstacles the IR sensors are used. For designing a fuzzy inference system used Sugeno model and used mat lab commands to design ANFIS as we have another method for designing by using a Simulink as well. ANFIS designed is based on kinematics and dynamics equation of quadcopter that will be able to control the pitch of quadcopter. Simulations results in mat lab show that by using ANFIS the performance of Quadcopter will be improved significantly.

As for Software-Defined Networking (SDN), an additional management plane may possibly be of great significance besides the control plane and the data plane. However, the implementation of this management plane is not yet mature. Thus in this case, study on the management plane is of great importance for management issues of SDN. This paper then aims to promote the implementation of the management plane for SDN, by studying the SDN management roles, proposing the framework of the management plane for SDN, discussing the SDN management operations, and utilizing the queuing theory for SDN management. Simulation results validate that, the proposed queuing model for the management plane of SDN prospects a feasible way to understand the queuing principles between management applications and the SDN manager.

We present an automatic summarization of Arabic texts. In this context, two methods for automatic summarization of Arabic documents are proposed and comparatively analyzed. The first method is based on rhetorical structure theory RST that uses linguistic knowledge. The second method is based on digital learning theory that relies on mathematical rules. Both methods are implemented using the Arabic summarization RST ASRST and Arabic summarization digital learning ASDL systems. Thereafter, results obtained by the two systems are presented and a comparative study between the obtained results is conducted to highlight the advantages and limitations of each method. Evaluation results showed that the numerical approach has better performance than the symbolic approach, particularly when the texts are short. By contrast, the symbolic approach provides better result than the numerical approach when the texts are long.

Due to the insufficient of the fuzzy neural network in solving complex problems, an improved ant colony optimization(ACO) algorithm is introduced into the fuzzy neural network in order to propose a novel hybrid optimization(APEACOFNN) algorithm in this paper. In the APEACOFNN algorithm, the self-adaptive pheromone evaporation factor strategy is used to dynamically adjust the pheromone evaporation factor on searching route in order to gradually lessen the amount of information between the optimal path and the worst path, and realize the full searching optimization for decision variable space. Then an improved ACO(APEACO) algorithm is obtained. Aiming at the parameters optimization problem of fuzzy neural network, the proposed APEACO algorithm is used to comprehensively optimize and select the parameters of fuzzy neural network in order to propose a novel hybrid optimization (APEACOFNN) algorithm. Finally, in order to test the effectiveness of the APEACOFNN algorithm, five UCI data sets are selected. The experimental results show that the proposed APEACOFNN algorithm takes the faster approximation objectives and higher solving accuracy.

Service quality prediction plays a very important role in many fields related of service-oriented systems. Now, there are large numbers of approaches to predict the quality of service-oriented systems. However, it is impossible to apply one method to all prediction tasks without distinction. In reverse, unclearly to recognize the category of the prediction tasks will result in losing in all kinds of prediction approaches. In order to discover which methods are suitable for their corresponding service quality prediction scenarios, it is necessary to classify the prediction tasks and to classify the prediction approaches clearly. In this paper, a classification framework for predicting tasks for QoS of service-oriented systems is defined, four types of the prediction technologies are mainly introduced, and the research findings of quality prediction approaches for service-oriented system in literature using those four types of prediction technologies are listed and the increasing use of the method based on probabilistic model checking are specially concentrated on, and finally the result of this survey are given and the open research questions about predicting service quality by probabilistic model checking method are discussed.

Shrimp farming is one of the most important aqua cultural activities, but farmers are facing many serious problems like shrimp diseases partly due to problems with the shrimp having to cope with poor water quality during cultivation. After the long survey with the farmers in different regions about their practices in farming methods, the current paper provides some solutions to ensure our product Auto Switch Aqua Feeder 1(ASAF1) with new technologies replace with automation of the farming methods with their traditional practicing methods unchanged, and designed a user interface convenient and compactable for farming. User interface design, and timer controls are implemented on dsPIC30F5011 for automation of feeding methods and substantially reducing the labor cost and improve the quality of commodity.

In recent years, with the rapid increase of office buildings and the requirements to improve the office environment, office building energy consumption has increased significantly. That has brought great pressure to the user, environment and city power supply and demand. The electric energy consumption of office building conservation is the most important way to reduce the energy consumption. Simulation is a major method to understand about office building power consumption. At present domestic and foreign computer software simulation technology cannot work without a lot of professional parameters. And the obtained results is so professional that cannot give the management decision auxiliary. This paper presents a holistic statistical distribution instead of complex thermodynamic iterative computation model, and the analysis method. Using the mode "facing to the whole" for analysis of power energy consumption of office buildings, without more intricate parameters, and can be iterative adjusted according to the difference between measured data and the prediction.

In order to improve the global solving ability and convergence speed, avoid falling into local optimal solution, the basic ant colony optimization (ACO) algorithm is improved to propose an efficient and intelligent ant colony optimization (IMVPACO)algorithm. In the IMVPACO algorithm, the updating rules and adaptive adjustment strategy of pheromones are modify in order to better reflect the quality of the solution based on the increment of pheromone. The dynamic evaporation factor strategy is used to achieve the better balance between the solving efficiency and solving quality, and effectively avoid falling into local optimum for quickening the convergence speed. The movement rules of the ants are modify to make it adaptable for large-scale problem solving, optimize the path and improve search efficiency. A boundary symmetric mutation strategy is used to obtain the symmetric mutation for iteration results, which not only strengthens the mutation efficiency, but also improves the mutation quality. Finally, the proposed IMVPACO algorithm is applied in solving the traveling salesman problem. The simulation experiments show that the proposed IMVPACO algorithm can obtain very good results in finding optimal solution. And It takes on better global search ability and convergence performance than other traditional methods.

This paper describes the steps involved in the tip control and automation of a hydraulic boom and knuckle crane which is used to position loads with accuracy and repeatability in shortest possible time. The crane is required to perform erection and installation of a modular bridge consisting of number of identical modules which have to be picked up or raised, moved along a predefined path and lowered at a drop point sequentially by the crane. Automation and tip control of the crane will reduce the burden on the operator and also save time. The requirement of raising and lowering of the loads require synchronised operation of two joints of the crane. For this purpose two different algorithms were derived, and the one which was feasible with the crane was used in the actual trials. For modelling and simulation of the crane, the kinematic equations of the crane were developed in MATLAB & the hydraulic circuit simulation & 3D Model of the crane simulation was developed in ITI_SimulationX. Actual system trials were done using dummy loads with the crane. The sensors to obtain feedback of joint angles were retrofitted on the existing crane structure. In place of the manual joystick controller, a new MMI based control system based on PLC was designed. The adapted crane can be operated manually by the individual valve joysticks as well as in automatic mode by the MMI and PLC based controller. The positions of the crane are taught to the controller using ‘teach’ mode in the controller. The controller calculates the new positions based on the kinematic equations and gives command to the valves to position the load accordingly.

With the fast increasing of Natural Science Foundation of China (NSFC) supported projects, management of project relevant achievements such as publication, awards, books, and patents is becoming more and more important. In this paper, we make a comprehensive explanation of our NSFC project oriented information sharing system, which has been developed in the last 10 years. Some key system functions such as project based searching, achievement based searching, statistical displaying, user feedback etc. are analyzed in detail with illustrative figures and tables. We first propose our overall architecture for OA library. Then, we present detailed design from upper layer OA webpage with 3-level display hierarchy to the middle layer some key function modules. Next, bottom layer raw data acquisition module and other important system function modules like interface module, people management and security modules are explained with illustrative figures and table.

The Telecommunication laboratory plays an important role in carrying out research in the different fields like Telecommunication, Information Technology, Wireless Sensor Networks, Mobile Networks and many other fields. Every Engineering University has a setup of laboratories for students particularly for Ph.D. scholars to work on the performance analysis of different Telecommunication Networks including WLANs, 3G/4G, and Long Term Evolution (LTE). The laboratories help students to have hand on practice on the theoretical concepts they have learned during the teachings at the university. The technical subjects have a practical part also which boosts the knowledge of students and learning of new ideas. The Telecommunication and Engineering laboratories are equipped with different electronic equipment’s like digital trainers, simulators etc. and some additional supportive devices like computers, air conditioners, projectors, and large screens, with power backup facility that creates the perfect environment for experimentation. The setup of Telecommunication and Engineering laboratories cost huge amount, required to purchase equipment, and maintain the equipment. In any working environment risk factor is involved. To handle and avoid risks there must be risk management policy to tackle with accidents and other damages during working in the laboratory, may it be human or equipment at risk. In this paper, we have proposed a risk management policy for the Telecommunication and Engineering laboratories, which can be generalized for similar type of laboratories in engineering fields of studies.

The cancer is one of the diseases of serious threat to mankind's health and life nowadays. Resistance to radiation and chemotherapy is a significant obstacle to the treatment of advanced cancer. Gene therapy is a new therapeutic tool for the treatment of diverse types of diseases, including cancer, congenital genetic, infectious diseases. It is well known that the somatic mutation is an important factor that might lead to cancer development. It is difficult to distinguish driver mutations from passenger mutations because of mutational heterogeneity, which is the key to solve and deal with the problem of cancer treatment. In this study, we find an efficient way Multi-Population Genetic Algorithm to solve the maximum weight submatrix problem which is designed to find important mutated driver genes in cancer, then makes the factors comparison of the methods on the simulated and several real mutation datasets. The results show that MPGA performs more efficiently, In addition, MPGA is a most robust method among these approaches, and these important pathways in these cancers are successfully rediscovered. The approach achieved what we expect.

Today, an effective website design has emerged as a thing of main concern for online shopping businesses and thus the companies involved in the online shopping businesses are aspiring for well-designed websites. In the past, several methods have been reported but they proved unproductive as they failed to handle the decision making involved in the process to evaluate the customer needs efficiently. Thus to cope up with the issue, the paper considers QFD approach as it a customer driven process that strongly assists in guiding the design decisions. The purpose of this paper is to explore the issues that have resulted in the decrease of satisfaction rate of users, analyze the problems with the methods used and propose effective solution through the literature review. The contribution of this paper is manifold. First, it explores QFD at length and evaluates its applicability for effective design of online shopping websites. This helps users understand the decision making capabilities of QFD. Second, it highlights the limitations of QFD approach in handling the vague and imprecise data that arise due to the inherent crisp approach and subjectivity involved in the QFD approach. Finally, the paper proposes fuzzy integration with QFD to deal with the uncertainty and vagueness involved in the QFD process for improved decision making and more precise results leading to effective design of online shopping websites. The literature review is based on information from various research papers, tools and web sources.

DRAM system has been more and more critical on modern multi-core architecture where the Moore’s law has been made effect on increasing the number of cores integrated in a processor chip. The performance of DRAM system is usually measured in term of bandwidth and latency, which are regarded as inherently depending on Row Buffer Hit Rate (RBHR) according to previous studies. In this paper, we find that Memory Level Parallelism (MLP) exhibits a stronger correlation with the performance of DRAM system on multi-core/many-core architecture than RBHR, and promoting MLP significantly improves DRAM system performance. In order to exploit the MLP, we have evaluated various approaches including multi-bank, multi-row-buffers, multi-memory-controllers and the obsolete Virtual Channel Memory (VCM). The experimental results show that VCM is a better alternative to traditional DRAM chip on multicore/many-core architecture than the other three approaches because VCM has almost all the advantages of the others: 1) it can improve homogeneous workloads’ IPC by 2.21X on a 16-core system with 32 virtual channels due to leveraging unexploited MLP. 2) It can also promote Quality-of-Service (QoS) of DRAM system by removing unfairness while memory controllers serve memory requests. 3) It can save energy and has low area costs. Unfortunately, VCM, which was proposed in the late 1990s, faded away before multi-core/manycore became dominated. Therefore, we suggest memory chip vendors reconsider the VCM technology for multi-core architecture.

With the development of information and communications technology (ICT), this new technology has brought new challenges and opportunities to enterprises. Human resource outsourcing platform is a system framework based on internet, and it has facilitated the expansion of enterprises by forming and supporting the organization structure. In this paper, we proposed a human resource system based on internet collaborative, and analyze the human resource outsourcing strategy by using decision-making model. The result shows that the evaluation index system of HRO model can be divided into five parts as strategic factor, management factor, cost factor, technical factor and service factor. Also, in the three outsourcing service model, the recruitment outsourcing has the highest score as 7.6.It shows that recruitment outsourcing can improve the work efficiency in

Today, with the globalization and the competitive element, there are great developments in commerce and banking management. Therefore traditional methods change to the modern ones. Undoubtedly innovative organizations need to be equipped with the advanced information technology and administrative skills. This paper present the quality of electronic services in the improvement of customer relationship in the branches of Khorasan Razavi Melli Bank. 150 questionnaires are distributed among the customers that use electronic services. Data is analyzed by correlation coefficient, regression and SPSS. Results indicate the effect of electronic services on the customer satisfaction, reduction of customer costs and increase security for customers of Melli Bank. At the end some ideas recommend to these branches.

Synergetic neural network (SNN) is a top-down network to explain the phase transition and self-organization in non-equilibrium system. The network parameters have a crucial impact on the recognition performance of synergetic neural network. At present, there is no good way to control and adjust the network parameters. To solve these problems, an improved parameters optimization algorithm based on differential evolution algorithm is proposed and implemented in this paper. There are two main works in this paper. Firstly, a semantic analysis model based on synergetic neural network is presented. Secondly, differential evolution algorithm is used to search the global optimum of network parameters in the corresponding parameter space. The experiments showed that the optimization algorithm can improve the synergetic recognition performance.