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Teachers and students' performances are of great importance in education. However, how to evaluate teachers' works and students' academic levels are extremely difficult and complex because it contains various effects of weights that should be used to assess the achievements. Also, education workers often find it difficult to manipulate the large-scale data while evaluating the education works and students' performances. To address this problem, we used machine learning techniques to develop two groups of models for evaluating teachers and students' performances respectively. Using artificial neural networks (ANNs) can ensure the accuracy and fairness of the evaluation works. Our results successfully proved that general regression neural network (GRNN) model can effectively generate the robust responses to analyze different independent variables and give out correct results to distinguish different achievements done by teachers and students.

Seaports are among the vital requirements of a country’s trade. The service capacity of a seaport is important to maintain seaport operations. Determining the number of the berthing places is a major decision for planning a seaport by considering the future port capacity. Queuing theory is used to determine the optimum berth number in a seaport. In this study, optimum number of berths of Izmir seaport in Turkey was determined by implementing the queuing theory. Likewise, the ship traffic was predicted using two forecasting models: Linear Regression Analysis and Artificial Neural Networks. Model results were candidates of input variables for the queueing model for estimating the feasible berth numbers between 2015 and 2023. For 2015, feasible number of berthing places was found as 14 and for 2023 it was found as 16 by the queuing theory.

In order to solve problems of poor real-time capability, poor reliability and low fault tolerance for data transmission in embedded serial communication, a serial communication protocol based on finite state machine (FSM) was designed and implemented, and was applied in communications of network management. The packet format and the communication control mechanism of the serial communication protocol are described, and complete framework for the application is given. Process of receiving and transmitting FSM models in serial communication are abstracted, combined with the interrupt service routine. In practical application, the serial communication program can meet the functional and performance requirements of serial communication between embedded devices and network management system (NMS).

Intelligent test sheet generation is a multi-objective constrained optimization problem. Genetic algorithm based on groups search strategy can provide a better solution for multi-objective optimization. Traditional genetic algorithm in test sheet generation process has many drawbacks, such as poor convergence, low fitness and high exposure times. To solve these problems, this paper proposes an adaptive cellular genetic algorithm based on selection strategy. Selection strategy can adaptively determine candidate test items set and the conceptual granularities according to the desired concept scope. Then, a new cellular population is formed by candidate test items. After evolution by the rule, genetic algorithms are executed. The experimental results show that the proposed algorithm gets rid of tests that do not meet the requirements which can reduce knowledge related errors, lower the exposure of tests, and increase the possibility of escape from local optima. In general, the algorithm proposed in this paper effectively improves the convergence speed as well as generates test papers more in line with people's demands.

This paper presents a new technique for marker selection called marker selection using skeletonization. Markers are the most reliable pixels that represent a particular class. Marker selection using skeletonization is further analysed to do classification of hyperspectral image with very low training samples, as low as one pixel per class. Both spatial and spectral information are used to improve the final classification accuracy. An Extended Morphological Profile with duality is used to extract spatial information. Furthermore, it is shown that by using the spatial and spectral information with non- parametric supervised feature extraction methods, better classification accuracy can be achieved even when very low training samples are available. The classification maps will be shown and discussed for very low training sample analysis using marker selection by skeletonization technique.

In missile guidance systems, classical proportional navigation law has been used most widely. But with the increasing of the maneuverability of the targets, the classical proportional navigation guidance law has been unable to meet the requirements of people on the ballistic parameters. So this paper will further study the algorithm error of guidance law. Based on the previous theoretical results and the working principle of aircraft in the homing guidance system, this paper studies a guidance trajectory guidance principle which based on the ratio, and has carried on the analysis and the simulation theory. And simulation accuracy analysis has been carried on with practical examples. The results show that the method is put forward to effectively reduce the guided ballistic calculation method of error.

It doesn’t matter whether its coin, banknotes or even gold the usual currency in circulation in our communities. But have we ever thought of using virtual currency in the real world, with a higher value to that of gold. Bitcoin have caused waves across the globe, it is believed to be the most valuable currency in the future. This article describes the sensation caused by the global virtual currency Bitcoin, the rise resulting in principle and practical instruction on how to get Bitcoin currently in use.

Accelerator has been widely applied to high energy, low energy physics, Medical environmental and military fields, etc. The magnet power supply is one of the most important parts of the accelerator. While the magnet power supply of accelerator is taken as the control object, and the design of LQR controller of the magnet power supply based on PSO has been proposed. At the beginning, the state-space model of the magnet power supply has been established by modern control theories. Then, the PSO (Particle Swarm Optimization) algorithm has been used for the weight matrices optimization of LQR controller in order to ensure that the magnet power supply can provide a magnetic field quickly which the accelerator required. The experimental results indicate that the method that proposed in the paper can meet the requirements of fast output response of the system and each control index of the LQR controller is obviously superior to that by the traditional method.

The ability of storage and energy consumption are the core problem faced by the data centers. The shared MDS storage system takes obvious advantage of large-scale , especially PB-level scale storage systems in solving the problem of efficiency and energy consumption.So designed the optimized architecture with hot-spot buffer, and put forward a new method of performance evaluation for the system. We also analyzed the energy consumption in parallelized data requests in the system. We find out that this architecture improved the throughout in the request of chunk data, and reduced the energy consumption. The serving efficiency and performance of the system are also improved significantly.

The intent of the paper is to establish the energy relationship on adjacent graphs of LB-fractal constructed iteratively through an iterated function system containing five contraction maps. The harmonic matrices on the LB-fractals are obtained through harmonic extension. The normal derivatives are also found for the same.

With the rise in population and increase in older people Wireless Body Sensor Networks can prove to be beneficial in providing medical service to people who require continuous monitoring and care. Development in technologies for Wireless Communication has led to sensor nodes which can be worn over the body, implanted and embedded over the body. These are small sized devices which can perform processing on the signals sensed from the human body and then communicate it to required destination where this data can be used for various research purposes or other diagnosis. In the past much of the research work concerned with Body Area Networks deals with the designing of Sensor nodes, miniaturizing the nodes, various protocols related to communication and routing. This paper gives an overview of Wireless Body Sensor Networks, devices used, its architecture, protocol stack, issues, topology, WBSN standard, challenges. Some protocols and security schemes for Wireless Body Sensor Networks have also been discussed.

Combinatorial testing can effectively detect the faults triggered by interactions among the various factors in software system. Harmony Search Algorithm (HS), which is a new optimization algorithm and has been widely applied in the fields of portfolio design, repeatedly adjusts the solution variables in harmony memory to reach the optimum. In order to improve the convergence speed of HS, we propose an improved HS algorithm (IHS) and uses one-test-at-a-time strategy to generate a set of optimum initial solutions in IHS. To avoid the algorithm falling into local optima, we dynamically adjust the values of HMCR and PAR in the new algorithm. Compared to some existing algorithms and tools, the improved harmony search algorithm performs more stably and efficiently in generating optimum combinatorial test cases.

Spectrum sensing is one of the vital functions, plays an important role in cognitive radio networks (CRN). This paper discuss to enrich the sensing performance of propose estimated signal to noise ratio (SNR) based detector. Estimated SNR detector consists two detectors, energy detector (ED) & ED with ADT detector, out of these two detectors only one will perform sensing operation at a time. Which detector will be work, selection will be done on condition between estimated SNR value (Se) and threshold (γ). Results show that proposed ESNR_ADT scheme outperforms the cyclostationary based sensing method by 30.5 % at – 10 dB SNR. It is also shown that the proposed scheme takes smaller detection time period as compare to cyclostationary detection in the order of 5.2 ms at - 20 dB SNR. Furthermore, the scheme was associated with cooperative spectrum sensing (CSS) where each CRs work together in order to take final decision. Finally, it was analyzed that proposed ESNR_ADT sensing scheme with CSS improves detection performance in the order of 0.9 at low SNR value of – 18.5 dB approximately.

With development of 3D scanner, it becomes more convenient to access point data. However, for processing the large-scale point cloud, it raised a new challenge for computer graphics. This paper places an emphasis on the point data own characteristics, and then the point data have been divided into certain point sets by clustering algorithm, that is will be divided into different clusters. In order to suit for the point data organization or space division, the clustering algorithm would be improved. This paper provided a new Kmeans algorithm with density constraints. Before processing the point cloud by Kmeans algorithm with density constraints, the density of the point cloud have been defined in this paper, the density of the point cloud can be used for quantification of the convergence. Finally, the Kmeans algorithm with density constraints is verified by the experiment results. Our experiment showed that the improved Kmeans can reduce the processing time, especially, As the increase of the value of K, that is number of cluster, the calculating time of the clustering algorithm can be decreased greatly. In addition, with the increases of the the scale of data size, the stability of the improved Kmeans algorithm has been verified.

Allocation of optimum active power is a backbone of power system generation planning and its high impact contribution is the need of current electrical utilities and power engineers need to browse this area in short and long term planning scenarios. Power demand requirements mapped to economic feasible solutions matching voltage profile, power demand, minimization of losses, voltage stability and improve the capacity of the system is the need of the hour. Modern techniques based on evolutionary computing, artificial intelligence, search method find their objectives in the area of economic load dispatch planning to reach global optimal solution for this multi-decision, multi-objective combinatorial problem subjected to different constraints. Many algorithms suffer from global convergence problem. To vanish this drawback, neuro inspired genetic hybrid algorithm (NIGHA) has been proposed in this paper to solve economic dispatch problem. Unlike other algorithms, NIGHA utilizes the weights of Neural Network to explore information and knowledge to train GA parameters to search for feasible region where optimal global solution converges. The suggested technique is tested on IEEE 25 bus system. Test results are compared with other techniques presented in literature. Proposed technique has outperformed other methods in terms of cost, computation time.

In mobile network, the mobile user has the strict requirement for the performance of accessing the information. In order to provide the personalized service for mobile user timely and accurately, an online learning model of mobile user preference based on context quantification is proposed. In the model, a context quantification method is proposed, which can enhance the accuracy of learned mobile user preference; and the sliding window and the online extreme leaning machine (O-ELM) are introduced to realize the online learning. Firstly, it needs to judge whether the mobile user preference is affected by the context through analyzing the mobile user behaviors. Secondly, the context is quantified according to the context relevancy and the context similarity. And then, the sliding window is employed to select the samples that need to be learned when updating the mobile user preference. Finally, O-ELM is employed to learn the mobile user preference. The experimental results show that the proposed method surpasses the existing methods in the performance.

Network Security is a supreme worry today for all computer users and organizations. It is the center of focus in all large and small networks. Extensively used internet protocols do not have promising mechanisms for protection against malicious activities. So the network attackers manage to shape and modify these protocols to launch various kinds of attacks on the legitimate clients. These attacks can have threatening effects on personal as well as professional aspects of clients. This paper proposes a fuzzy logic system to analyse and examine the susceptance of network clients towards various types of precarious attacks. The factors affecting the susceptibility have been carefully shortlisted by an extensive survey into journals, magazines and various research papers by leading authors in this field.

Complicated data structure can handicap the integration of systems in enterprises and universities. It is important to find an efficient way to deal with the mass documents of these heterogeneous and distributed systems to be integrated. In this paper, a three layered data scheme is introduced for data recommendation in software engineering process inspired by spreading activation theory. Metadata can be more efficiently managed with this scheme in integration and analysis.

Motion estimation (ME) and motion compensation (MC) are the key elements for frame rate up-conversion (FRUC) system. Fast and accurate motion estimation is the premise of high quality motion compensation. Unlike conventional unidirectional motion estimation which brings holes, overlaps and blocking artifacts, the bi-directional motion estimation does not produce any overlapped pixel or hole in the interpolated frames. As a result, the bi-directional motion estimation has better performance than conventional unidirectional motion estimation. This paper presents an efficient FPGA architecture targeting bi-directional motion estimation hardware implementation. This proposed architecture can achieve real-time processing for 1280x720@60Hz under 200MHz operating frequency. The design is described in Verilog HDL, verified in Virtex5 FPGA platform. Experimental results show that the proposed architecture has high performance and low cost for bi-directional motion estimation algorithm. This architecture can be used for video post-processing system.

Due to the direct utilization of thermal energy and possibility of using renewable energy resources, the interest of using desiccant wheels for air-conditioning application is increasing rapidly. The thermally driven desiccant cooling systems are environmental friendly and has a great potential to reduce the peak electricity demand. During the experiments the inlet and outlet parameters in the dehumidifier can be easily measured. However, in theoretical analysis it is difficult to predict the relationship between these parameters because of the complexity of combined heat and mass transfer process. In this paper a statistical analysis using Minitab software is used to predict the functional relationships between the input and output parameters in the dehumidifier. The factorial analysis is first carried out to eliminate the factors which does not affect the response, significantly. Then, the regression analysis is carried out to analyze the effect of significant operating variables (independent) on the moisture absorption rate in the dehumidifier and to develop a relationship between dependent and independent variables. Regression analysis shows that the inlet air humidity ratio, ratio of mass flow rates, and regeneration temperature cause significant variation (P<0.01) in the absorption rate.

In today’s world, the web has dramatically changed the way that people express their opinions. People use the internet to express their opinion, attitude, feeling and emotion about films, goods, news etc. It is challenging to automatically classify mass subjectivity comments into different sentiment orientation categories (e.g. positive/negative). Furthermore, the ambiguity and randomness, which are existed in natural language, lead to lower classification accuracy in text sentiment classification. In this paper, we propose a novel chinese text sentiment classification algorithm based on mixed cloud vector model clustering and kernel fisher discriminant. In this algorithm, we firstly analysis the role of cloud model theory in conversion between qualitative concept and quantitative values, and explore a mixed feature cloud model (MFCM) based on cloud model to represent a single document. In MFCM, both effect of different part-of- speech features and ambiguity of sentiment tendency are considered. And then, documents are clustered according to their similarity between MFCM. Finally, kernel fisher discriminant (KFD) is adopted as the classifier to judge views. The experimental results demonstrate that our proposed method outperforms traditional approaches.

Cognitive radio (CR) can improve the utilization of the spectrum by making use of licensed spectrum in an opportunistic manner. However, the security aspects of cognitive radio networks have garnered little attention. In this paper, we identify a threat to cognitive radio networks, which we call the spectrum sensing data falsification (SSDF) attack. SSDF attack can hugely degrade the achievable detection accuracy. To counter this threat, we proposed a new method to confront the SSDF attacks by excluding malicious users is cognitive radio. In detail, the proposed scheme defense the SSDF attacks by calculating and updating the credit value of the Secondary Users (SUs), malicious users are excluded to avoid the attacks affect in cooperative spectrum sensing. Simulations results show that both the detection probability and the false alarm probability are significantly improved compared to the case when all users are by default trusted to be normal users.

Power system under heavily loaded conditions is at high risks of consequent voltage instability problem. Voltage deviation minimization is the reliable indicators of voltage security of power networks. The aim is to minimize the voltage deviation and installation cost under increasing load condition in power system network. GA is used to solve the optimization problem in this paper which is one of the heuristic methods. Genetic Algorithm optimizes the location and size of the SVC. The effectiveness of the proposed work is tested in IEEE-30 Bus test system. It has also been observed that the proposed algorithm can be applied to larger systems and do not suffer with computational difficulties. The obtained results show that the allocation of SVC device with the proposed method considerably enhanced the voltage stability and reduce installation cost.

In view of the operating mode of wireless sensor networks, the node initial probability and channel listening, a distributed clustering algorithm DCOS based on the optimal search model is proposed, along with the corresponding mathematical optimization model. The goal of model optimization is to find a feasible allocation scheme for search resource so that the probability of successful communication reaches its maximum under the constrained condition for search resource. Clustering algorithm is designed to balance the network energy consumption, thereby maximizing network lifetime. In the clustering algorithm, the cluster head is generated in the comprehensive consideration of the minimum number of nodes in the overlay network, and the node residual energy and the number of neighboring nodes, during the operation of the network, setting the maximum wait time for cluster heads may avoid the their energy consumption. Analysis and simulation results show that, compared to several important clustering algorithms, the performance of DCOS is proven to be superior in balancing node energy consumption and prolonging the network lifetime.

Based on redefining the concept of technological convergence, this paper analyzes market demand of convergent products according to the characteristics of technological convergence in IT industry. We define market power as the function of product price and the improvement of convergent product’s performance. We also assume the original technology is symbiotic. Based on the above analysis, the paper modifies the Bass Model and the hypotheses of related models, builds a demand growth model of technological convergent products, and uses this model to analyze and estimate the demand growing of converged products. At last, we discuss the conditions that technological convergence will be successful.

First order delay system (FODS) is in class of nonlinear systems. In these systems design control algorithms are very important. In this research nonlinear terms of incremental Proportional Integral Derivative (PID) algorithm is used to nonlinear model-free integrate large amounts of control methodology in a single methodology. This work, proposes a developed method to design nonlinear based PID controller. In this methodology nonlinear model-free sliding mode algorithm help incremental PID to estimate and linearization of first order delay system. According to this research, the controller robustness improved based on nonlinear term of sliding mode algorithm and the chattering is reduced/eliminate based on PID incremental method.

Feature extraction is a key issue for 3d model retrieval. A novel architecture to extract the curve-skeleton of 3d model is introduced. The algorithm firstly calculates the 3D vector fields of models represented by discrete unit, and then extracts the hierarchical curve-skeleton based on topological features of the critical curve and critical points of the vector fields. The similarity among 3D curve-skeletons is measured by using an improved Earth Mover's Distance (EMD) algorithm. The curve-skeleton extracted with this novel algorithm can be used to categorize models and implement global matching and partial matching.