nils-herrmann/clean_scirepeval huggingface dataset:
Research on Neural Network Based on the Improved Adaptive Genetic Algorithm

the adaptive genetic algorithm (AGA), this paper presents an improved adaptive genetic algorithm ( IAGA) which can optimize the weights and thresholds of the neural network. A stock Prediction system based on neural networks and Fuzzy theory is designed. According to the analysis of the history data of the stock, the system predicts this stock's market trend of the following days and makes the decision support for the investors on the stock's market. The experimental results show that the proposed approach has high accuracy, strong stability and improved confidence. Neural network has very strong learning ability, and has been applied widely in artificial intelligence, pattern recognition, and automatic control and etc. Back propagation (BP) algorithm is currently one of the most widely used and mature neural network models. But due to the gradient search techniques adopted in BP algorithm, there is bound to slow convergence, local minimum and so on. The design of the structure of BP neural network is primarily based on the experience of designers. Experiments are repeated in large sample space to select parameters of the structure. There is no theoretical guidance and their weights are usually determined by the gradient method. Thus, it is often difficult to find the optimal network structure and weights through repetitious experiments. But the genetic algorithm (GA) is a optimal search method based on natural selection and principles of genetics, with the characteristic of parallel computing. It can improve the calculation speed and achieve the global optimization. Therefore, the proposal of genetic algorithms to optimize the BP network weights provides a new way for neural network design and training. In this paper, we mainly focus on the improved adaptive genetic algorithm (IAGA) and apply it on stock prediction to analyze its performance. II. GA-BP ALGORITHM BP algorithm has the characteristic of precise optimization, while the genetic algorithm has a strong global search capability and good performance of global optimization. Therefore GA and BP network are combined. GA is adopted to optimize in training; and when the search rang becomes narrow, the BP network is applied for accurate optimizing. It can achieve the purpose of optimizing quickly and efficiently. In this paper, we first determine the threshold and weight values of the BP network, which are used to encode the genes of GA. The population is initialed and then be handled with selection, crossover and mutation operation. The elite individuals are selected to evolve, until the algebra of evolution reaches preset parameters or the individual of the largest fitness is found. The optimal individual, namely, a set of neural network weight and threshold values, are then passed to the BP neural network, which is retrained by the historical data. Finally, the neural network is used to generate predictive values. The flow chart of the algorithm is shown in
Novel Dual-Output Step Up and Down Switched Capacitor DC/DC Converter

A novel dual-output switched capacitor (SC) DC/DC converter with simultaneous step-up and step- down output voltages was proposed. As well as featuring all merits of classical charge pumps, it features lower-cost and higher-efficiency by using charge capacitor sharing and multiple gain pair techniques. A set of analytical equations were derived to describe its performances and optimize its design. The converter was designed and simulated in 0.35 mum CMOS process with Cadencereg SPECTRE and the simulation results were well consistent with analytical predictions. The errors between them were less than 0.01%-0.33%.
Structured discriminative concept factorization for data representation

Concept factorization has attracted much attention in the past few years. To consider the manifold structure embedded in data, the graph regularizer is incorporated into model of concept factorization. However, a single graph cannot effectively model the intrinsic structure information of data. To solve this problem, a novel method, called Structured Discriminative Concept Factorization (SDCF), is proposed to explore the intrinsic structure information of data. Specifically, the proposed SDCF method incorporates both the local affinity and the distant repulsion constraints into the model of CF. Moreover, an efficient optimization scheme based on multiple update algorithm for the proposed SDCF method is developed. Experimental results on benchmark datasets have validated the effectiveness of the proposed method.
Dynamical complexity of a typical nonlinear system

The dynamical complexity of a typical nonlinear system will be clarified in this paper. These stochastic symbolic sequences bear three features. The distribution of frequency, inter-occurrence times, the first passage time and the r-th passage time are discussed carefully. Especially, one introduces ordinal passage time and gives certain distribution functions in this typical nonlinear system. Finally, the visitation density function is obtained by Frobenius-Perron(F-P) operator P.
Diagnosis of Breast Cancer Tumor Based on PCA and Fuzzy Support Vector Machine Classifier

In this paper we propose an efficient algorithm based on principal component analysis (PCA) and fuzzy support vector machine (SVM) for the diagnosis of breast cancer tumor. First, PCA algorithm is implemented to project high-dimensional breast tumor data into much lower dimensional space, then the processed data are classified by a fuzzy SVM classifier. Experimental and analytical results show that in the diagnosis of breast cancer tumor the proposed method can greatly speed up the training and testing of the classifier, get high testing correct rate and pick out untypical cases to be reexamined by experienced doctors, superior to the traditional rigid margin SVM classifier.
Multiclass Least Squares Auto-Correlation Wavelet Support Vector Machines

In this paper, combining the auto-correlation wavelet kernel with multiclass least squares support vector machine (MLS-SVM), a novel notion of multiclass least squares support vector machine with universal auto-correlation wavelet kernels (MLS- AWSVM) is proposed. The translation invariant property of the kernel function enhances the generalization ability of the LS-SVM method and the spiral multiclass classification experimental results show some advantages of MLS-AWSVM over MLS- SVM on the classification and the generalization performance.
Automatic electroencephalogram channel selection of independent component analysis based motor imagery brain-computer interface

Independent component analysis (ICA) is widely used to separate movement-related independent components (MRICs) from multi-channel motor imagery electroencephalogram (EEG) signals. The distribution of channels is a crucial question in the design of brain-computer interface using ICA algorithm. The traditional methods on channel selection mainly use identical channels which close to the cortex area for all subjects. However, the optimal channels are subject-dependent. It is crucial to determine which channel combination should be used to reach optimal performance. In this study, based on our previous research related to the ICA-based motor imagery BCI (MIBCI) algorithm frame, we developed a new strategy to select optimal channel combination. The experiment was based on 24 runs three class motor imagery EEG datasets, in addition, the results of common spatial pattern (CSP) were compared with our channel optimization algorithm too. Experimental results revealed that higher stability and performance can be obtained after optimizing channel distribution by employed our novel channel selection strategy.
Index Reduction for Information Systems Security Risk Assessment Based on Rough Set Theory and Hierarchic Analysis

Combining the rough set theory with hierarchic analysis model in view of their characteristics and introducing the definition of importance into non-core attributes, a new data reduction algorithm is proposed using a simple discriminative matrix. The validity and completeness of the algorithm is proved, by which the problem of index reduction for information systems security risk assessment is successfully solved. The results reveal that the algorithm is able to mine the knowledge and simplify the course of risk assessment.
Lasalle's invariant principle via vector Lyapunov functions of a class of discontinuous systems

It is mainly discussed Lasalle's invariant principle for a class of nonlinear systems with discontinuous righthand sides on the basis of vector Lyapunov function in the framework of Filippov solutions. Assuming that the system is Lebesgue measurable and non-Lipschitz continuous, we extend Lasalle's invariant principle for a class of discontinuous dynamical systems by means of Filippov solutions and vector Lyapunov function which satisfies Lipschitz continuity and regular property.
Supervisory optimization of the MGT-CCHP system using model predictive control

This paper proposes a supervisory optimal control structure for the micro-gas-turbine based combined cooling heating and power (MGT-CCHP) system. In the upper layer of the structure, a dynamic optimal reference governor is developed to calculate the optimal operating points according to the given economic performance indexes, and in the lower layer of the structure, model predictive control is utilized to track the given operating points, so that a dynamic optimality can be achieved under the input-output constraints during the optimization. Moreover, a disturbance term is introduced in the model to lump the effect of unmeasured disturbances and plant behavior variations, thus, their influences on the supervisory optimization can be removed. The advantages and effectiveness of the proposed method are demonstrated through the simulations on an 80kW MGT-CCHP simulator.
Fault modeling and simulation of pure hydrogen solid oxide fuel cell system

In order to meet modeling accurately and fault simulation required, according to the working principle of Solid Oxide Fuel Cell(SOFC) Generated Elections System, dividing function module, analyzing which fault modes, building fault mathematical model and verifying model. The result shows the SOFC system fault model reflect the relation among the different module clearly and simulate fault mode effective. The model can be used in simulating, testing and fault diagnosis and so on.
Fuzzy modeling and control of boiler-turbine unit using clustering and subspace method

This paper develops a fuzzy modeling method for nonlinear boiler-turbine unit using only the input output data. The structure of the fuzzy model is determined by clustering to achieve an appropriate division of the local models. Then, the subspace identification is extended to develop local state-space models, which are dependent on the fuzzy membership functions. Owing to the advantages of the both methods, the resulting fuzzy model can represent the boiler-turbine unit very closely, and a fuzzy model predictive controller is designed based on this model. The effectiveness of the proposed fuzzy modeling method is demonstrated through the simulation.
Fault analysis and diagnosis of solid oxide fuel cell system

In order to extend the life of the Solid Oxide Fuel Cell system and maximize the performance of the stack, it is very important to study the fault diagnosis method of SOFC system. At First, this paper analyzes the phenomenon and mechanism of the blower fault and leakage fault for the SOFC system. At last, this paper introduce a method which uses the fault tree method to diagnose the external system fault (blower mechanical fault and the reformer leakage fault) for ensuring the system safe and preventing further escalation of the fault.
Client Specific Kernel Discriminant Analysis (CSKDA) Algorithm for Face Verification

In this paper the client specific kernel discriminant analysis (CSKDA) is studied. The theory of CSKDA, which is the nonlinear model of the previously suggested model of client specific linear discriminant analysis, is proposed by using kernel technique. A new CSKDA subspace method is developed in order to reduce the computational complexity. Results of experiments conducted on the internationally recognized facial database of XM2VTS based on the Lausanne protocol show the effectiveness of the proposed methods of client specific kernel discriminant analysis
Finite Time Synchronization of Nonautonomous Multiscroll Chaotic Systems

This paper proposes a finite time synchronization scheme for nonautonomous multiscroll chaotic systems via unidirectional nonsmooth feedback control. Based on the finite time stability theory, some sufficient criteria for the finite time synchronization of the scheme are proposed and formulated in the algebraic inequalities. The settling times of the synchronization are well estimated. The effectiveness of the obtained synchronization criteria is illustrated in numerical examples.
A heuristic algorithm for mission reliability allocation of spaceflight TT&C system

The tracking, telemetry and command (TT&C) system is a phased mission system. In this paper, the mission sequential logic relations are described by sequential diagram, and mission reliability model of each phase and the reliability allocation model are built. In order to allocate its mission reliability, the main factors that should be considered in mission reliability allocation of TT&C system are introduced, and a heuristic algorithm (HA) is presented, which is based on weights. The reliability of each equipment is adjusted based on their weights, and bounded sampling method is used to predict the mission reliability after allocation is finished. Finally, with a numerical example the effectiveness of the method is discussed.
Clothing Extraction Using Region-Based Segmentation and Pixel-Level Refinement

In this paper, we demonstrate an effective method for automatic extracting clothing object from fashion photographs, an extremely challenging problem due to the non-uniform natural backgrounds, various types of apparel and different poses of human models. This method consists of three phases: (1) coarse clothing area localization by pose estimation and super pixel segmentation, (2) region-level image segmentation, (3) pixel-level refinement using spatial information and Grab cut. Experiments on a dataset with 1000 images crawled from Taobao demonstrate that the proposed method outperforms other methods, which can extract clothing from images with complex background.
Combining procrustes shape analysis and shape context descriptor for silhouette-based gait recognition

A novel and powerful gait recognition algorithm is proposed. The method of procrustes shape analysis is used to produce procrustes mean shape (PMS) as a compressed representation of gait sequence. PMS is regarded as the gait signature for classification. Quite different and novel, instead of using the procrustes mean shape distance as a similarity measure, a novel shape descriptor, shape context, to measure the similarity between two PMSs is introduced. Experimental results show that the proposed algorithm outperforms other approaches in terms of recognition accuracy.
Exploiting More Parallelism from Applications Having Generalized Reductions on GPU Architectures

Reduction is a common component of many applications, but can often be the limiting factor for parallelization. Previous reduction work has focused on detecting reduction idioms and parallelizing the reduction operation by minimizing data communications or exploiting more data locality. While these techniques can be useful, they are mostly limited to simple code structures. In this paper, we propose a method for exploiting more parallelism by isolating the reduction from users of the intermediate results. The other main contribution of our work is enabling the parallelization of more complex reduction codes, including those that involve the use of intermediate reduction results. The proposed transformations are often implemented by programmers in an ad-hoc manner, but to the best of our knowledge no previous work has been proposed to automate these transformations for many-core architectures. We show that the automatic transformations can result in significant speedup compared to the original code using two benchmark applications.
The analysis on competitive advantage of firms in the context of synergic development: based on the perspective of social capital

It is difficult for any firms to gain competitive advantage only by internal resources with the accelerated process of integration of global economy and rapid development of information technology. To win competitive advantage firms should attach more importance to build a relationship of mutual benefit with their strategy networks. Based on the perspective of social capital, it is analyzed that firms may gain advantage of efficiency, market, resource and technological innovation in the context of synergic development using the approach of field study. A negative case study is employed to illustrate that firms should pay more attention to foster and utilize social capital for improving competitive advantage. A matrix of competitive advantage -social capital is proposed to help firms build and exert social capital for competitive advantage.
Wavelet-Based Far Infrared Medical Image Compression with Histogram Shifting

A near-lossless compression algorithm for far infrared medical image is proposed based on lifting wavelet transform and histogram shifting. The algorithm can losslessly compress the data in the human body area with integrating the source data and normalizing the useless background (lossy). Experiment results show that it is an effective algorithm compared with JPEG-LS and can compress the image more than 6 times without affecting clinical diagnostics.
A Load Scheduling Strategy for Electric Vehicles Charging System

Intelligent charging system for electric vehicles is a new application of power grid cyber-physical system which requires higher safety and reliability. During the execution of the system, it will bring a range of safety issues because of the heavy load of charging grid in some disturbance conditions. This paper analyzes the load safety issues and presents a load scheduling strategy based on one prediction that improves the safety of the system by dispatching charging piles based on the predictive load. In addition, the authors designed the architecture of the charging pile management system to achieve scheduling and management. Meanwhile, simulation experiment about prediction and scheduling strategy is done on the basis of the above and its feasibility has been proved.
Analyzing and modeling on optimized L-ratio of evaporator section to condenser section for micro heat pipe heat sinks

This paper is focused on modeling and analysis on the determination of the optimized L-ratio, or L/sub e//L/sub c/, for the length of evaporator section to the length of condenser section of cylindrical heat pipes, for a heat sink in the horizontal position. The independent variables (for an equipped heat pipe on which all fixtures are firmed) in the function of the total thermal resistance are L/sub e/ and L/sub c/. Given the fact that this function has a local minimum between two fixed end points, the minimum extremal of the thermal resistance function can be obtained by taking the derivative of this functions and setting it to zero. Since the total length of a heat pipe, L, is constant, therefore L/sub c/ may be replaced by L-L/sub e/, then the differential equation can be solved. In this paper, a formula to analyze the optimal L/sub e//L/sub c/ ratio is established. Table 2 to table 4 show analyzed results for diameters from 4 mm to 6 mm micro heat pipes in assemblies which are currently quite popular in applications.
Effects of electromagnetic pulse on the tight junction of mice blood-testis-barrier

To investigate the changes of the mice testes and the tight junction (Tj) relative molecular and to identify the molecular mechanisms of the mice blood-testis-barrier (BTB) permea- bility increasing after the mice were exposed to electromagnetic pulse (EMP).  Observe the changes vimentin of the mice testes by laser scanning confocal technique.  After the 200 kVm-1 EMP (the accumulative pulse times was 200) exposure, the vimentin in sustentacular cells of testis became irregularly shortened and derangement, and the intensity of immuno- fluorescence also showed ununiformity compared with control.  The increasing of mice BTB permeability perhaps caused by the changing of vimentin.
Control of ultra-supercritical once-through boiler-turbine unit using MPC and ESO approaches

In order to meet the wide-range load tracking requirement and overcome unknown disturbance issues of ultra-supercritical (USC) boiler-turbine unit, an extended state observer based fuzzy model predictive controller is proposed using the techniques of fuzzy scheduling, model predictive control and extended state observer. Local state-space models are established on the basis of nonlinearity analysis. Then a fuzzy stable model predictive controller is devised on the fuzzy model using output cost function for the purpose of wide-range load tracking. An improved linear extended state observer, which can estimate plant behavior variations and unknown disturbances regardless of the direct feedthrough characteristic of the boiler-turbine unit, is synthesized with the predictive controller to enhance its disturbance rejection capability. The simulation results on a 1000MW USC boiler-turbine unit model verify the merits of the proposed strategy in achieving the satisfactory wide-range load tracking and disturbance rejection performance.
Using educational technology to support education for deaf students

With the rapid development of educational technology, It's application has been extended to education, business and other fields. For many years, the targets of educational technology mainly aimed at general education, adult education or training, The special education is the blind field of the study and practice of educational technology. As the size of special education have been expanding, our enrollment of deaf students is increasing, the quality of education is getting more and more important. In this article the author will introduce the educational technology and how it will support the education for deaf students.
Modification of the Tropospheric Refraction and Radar Target Height Determination Formula

The objective of this paper is to modify the tropospheric refraction model and radar target height determination formula (RTHDF) for wider applied range and better performance. For this purpose, the conventional refraction models and RTHDF are analyzed firstly; a novel refraction model and RTHDF using linear optimization are presented, discussed and validated. Based on this model, an iterative algorithm which provides a promising tool for accurate target height values is addressed. Compared to the performance of conventional theories', the superiority of the modified model and iterative algorithm is demonstrated using the statistical data provided by Central Radio Propagation Laboratory (CRPL), American National Bureau of Standards (ANBS). Finally, the modified RTHDF is derived and assessed by experimental results.
Trust Mechanism in Grid Based on Grey System Theory

Trust mechanism is an important precondition in realization of grid security. To improve the traditional trust evaluation models, a trust mechanism in grid based on grey system theory is put forward. In this mechanism, neighbor nodes of the resource node with higher recommendation trusts are chosen as marking nodes, detailed process for construction of recommendation trust routes is presented. and the corresponding recommendation trust is used as their own evaluation weight. Meanwhile, time decay is introduced to update the mark matrix, which provides more objective and reliable evaluation.