nils-herrmann/clean_scirepeval huggingface dataset:
Design of Supervisory System Based on CAN Bus for Wind Power Plant

The design of the real-time state supervisory system based on the CAN (controller area network) bus for wind power plants is presented, including the network structure of the whole system, the design of hardware and software. The supervisory system has four layers, namely, the data acquisition layer, the data processing layer, the data transport layer and the user layer. The electricity data that are processed by the industrial computers are transmitted by the CAN bus to top-layer computer to be managed and displayed. This system has been put into operation in a wind power plant in Guangdong and achieved favorable effects.
Tilting alignment for electron tomography based on local cross-correlation and adaptive searching

Understanding three-dimensional (3D) structure of components ranging from molecules to cells can help to understand the biological function of viruses. Accurate image alignment is needed for computing 3D reconstruction from transmission electron microscope tilt series. In this paper, an image alignment method for electron tomography is presented which is based on local cross-correlation and adaptive searching. Using several feature regions to compute cross-correlation instead of whole image can overcome the noise-sensitivity of the cross-correlation method. And the adaptive searching can improve the accuracy of alignment in effective time.
Multi-object Tracking Based on Region Corresponding and Improved Color-Histogram Matching

This paper presents a tracking algorithm based on secondary tracking strategies. First, region corresponding is used as the primary strategy. Unlike existing methods, no prediction is required in the proposed algorithm, which means high definition and low computation. Second, to deal with the corresponding problem caused by possible interaction among multiple moving objects that region corresponding approach cannot solve, further moving objects' distinguishing is carried out by the application of color-histogram matching. In order to improve the accuracy of color-histogram matching, we apply a new approach for similarity evaluation of color histograms. An intelligent surveillance system is developed to test the performance of our algorithm. Results show that the algorithm is efficient in computation and robust in various tracking tasks, and has the advantages of tracking multi- object accurately, even the shape, velocity and motion directions of objects change largely and rulelessly.
Studies on stepping characteristics of traveling-wave ultrasonic motor

This paper presents analysis on speed regulation and stepping characteristics of Shinsei USR60 ring-type traveling-wave ultrasonic motors (USM) based on experimental results, and discusses the influence of controlling method and load variation on stepping control. From the experiments the relationship between the variables is revealed, and improvements to the stepping control are proposed. The improvement focuses on correcting the stepping angle deviation with an empirical equation obtained by performing the least square curve fitting on the results from the USM characterizing experiments. The improved controller is able to perform equal distance stepping when the ring-type traveling-wave USM is operating with loads. The proposed stepping control method is capable of realize open-loop positioning control of ring-type traveling-wave USM without position sensors with precision reaching 0.005°.
Information sharing based on social network construction under dynamic alliance environment: A simulation study

This paper examined the social network construction mode based on connected network component under dynamic alliance environment. We assume that firms in one industry participant in alliances dynamically and simulation results show how the alliance network factors influence the social network information sharing performance. Four propositions in a long-term and no extreme value context are built: first, the probability an incumbent is chosen to become a member of a new alliance has a inverted U-shape effect on information sharing performance; second, the probability that the alliance being assembled will include a previous collaborator of an incumbent on the alliance has a inverted U-shape effect on information sharing performance; third, the initiate alliance size has a positive effect on information sharing performance; fourth, the max time during which a firm is not in any alliance has a positive effect on information sharing performance.
A Research on EOG Feature Parameters Extraction Based on Linear Predictive Coding Model

EOG is a very effective eye movement recording method, which is not only noninvasive, but also can record any eye movements' information. In order to extract some features come form these information contained in EOG automatically, this paper presents a novel EOG feature parameters extraction algorithm. The proposed algorithm includes three main parts. The first is endpoint detection unit which detects the startpoint and endpoint of EOG signals. The second is a preprocessor which consists of band-pass filter, frame blocking procedure and windowing step. The third is feature parameters extracting part which extracts EOG feature by a linear predictive coding model and converts LPC parameters to LPC cepstral (LPCC) coefficients. Finally, the paper gives experimental results.
Adaptive arrays for increased performance in mobile communications

Adaptive antenna arrays are applied at the base station to improve performance of a mobile communication system operating in a flat slowly fading Rayleigh channel. Analytical expressions are derived for the average probability of error of the sample matrix inversion (SMI) and an eigenanalysis-based method for the case of a single co-channel interference. It is shown that the eigenanalysis-method is superior to the SMI method when the array covariance matrix is estimated from small data sets. The analysis is supported by numerical results obtained from simulations.
Crowd Target Extraction and Density Analysis Based on FTLE and GLCM

Detecting and tracking up to 8-9 targets is already an arduous task for traditional methods. So it is even more challenging to deal with crowd objects. In this paper, segmentation and density analysis for video crowd flow is realized by a chaotic dynamics based approach. Crowd motion system is treated as a chaotic dynamic system, and crowd targets are treated as particles. As a result, Finite Time Lyapunov Exponent (FTLE) field is obtained. After morphological processing, motion region extraction and classification, texture information can be got from Gray Level Co-occurrence Matrix (GLCM). In GLCM analysis, density information can be achieved. Experimental results show the effectiveness of the proposed approach. Keywordscrowd object detection; finite time Lyapunov exponent; gray level co-occurrence matrix
Heterogeneous Multi-Sensor Data Fusion with Multi-Class Support Vector Machines: Creating Network Security Situation Awareness

Multi-sensor data fusion and network situation awareness are emerging technique in the field of network security and they help administrators to be aware of the actual security situation of their networks. This paper mainly focuses on heterogeneous multi-sensor data fusion and situation awareness. We adopted Snort and NetFlow collector as two sensors to gather real network traffic and fused them use multi-class support vector machines that could solve a multi class problem. In order to avoid dimension disaster, we employed an effective feature reduction approach to decrease the dimension of input vector and the computation time of support vector machines that improved fusion performance and real time characteristic. Our framework is proved to be feasible and effective and has better performance than neural network through a series of experiments that using real network traffic.
Evolutionary Game Analysis of the Reverse Supply Chain Based on the Government Subsidy Mechanism

The evolutionary game model of cooperation behavior of manufacturers and collectors in reverse supply chain is established based on the government subsidy mechanism. Relevant conclusions are proved and verified by means of numerical simulation. Two necessary and sufficient conditions for the game members' cooperation are obtained. The one that cooperation can bring more than un-cooperation of profits. The other one is that the subsidies given by government are not less than manufacturers' cooperation costs. On this basis, the greater the government subsidizes the higher risk preference the enterprises tend to cooperate.
Experimental Study on Low NOx Emission Using Blast Furnace Gas Reburning and Industrial Application in Stoker Boiler

To get the modification method of blast furnace gas reburning technology on one 75t/h stoker boiler, the effect of some key factors such as stoichiometric coefficient, residence time, temperature and heat ratio input on NOx formation regulation was studied on a one-dimensional test system with fuel reburning. The results showed that prolonging the reburn zone residence time can help to lower the NOx emission and the best time was 0.8s. There existed an optimum value 1.0 for reburn zone stoichiometric coefficient. The NOx reduction efficient increased with the increase of the reburn heat input and the reburn zone temperature. The use of blast furnace gas reburning technology has been successfully implemented during the modification of stoker boiler, the NOx reduction rate was over 50%.
Cryptanalysis and Improvement of Two Blind Proxy Signature Schemes

Cryptanalysis of two blind proxy signatures is made, which shows that neither of them satisfy unforgeability of proxy signature. Corresponding forgery attack is presented. Using the forgery attack, a dishonest original signer can forge the proxy key and generate valid blind proxy signatures. According to security drawback of the two existed signature schemes, two improved blind proxy signature scheme are proposed. Security analysis shows that these two improved schemes can resist the original signerpsilas forgery attack and also satisfy other properties of blind proxy signature scheme.
The Relationship between the Advertising Investment and R&D Competition Strategy for New Product

Using a standard model of dynamic R&D competition in patent races, and performing a comparative-static analysis on the firm's profit-maximizing R&D strategy, this paper examines R&D rival and advertising rivalry in the R&D project. The conclusion is that an aggressive advertising by the rival results in the firm scaling back its R&D investment and reducing its own advertising activities. On the other hand, greater R&D spending by the rival also lowers own R&D expenditures and advertising efforts, especially when the probability of own R&D success is low. Finally, we also have two corollaries and. a practical example
Value evaluation of enterprise information based on grid-fuzzy borda number analytical method

The value evaluation of enterprise information is one of important parts of enterprise information construction. The paper firstly summaries the current circumstance of value evaluation of enterprise information from China; then, a set of evaluation system of enterprise information is constructed on the basis of referring to the booklet <<Indicates System of China's Enterprise Information>> published by Ministry of Industry and Information Technologies of the Republic of China at year 2002; In the section three, the model of value evaluation of enterprise information, which is on the basis of grid-fuzzy borda number analytical method, is built, and we give the detailed computing steps; Finally, the effectiveness of this method is verified by an example.
Innovating to Create Value for the Mass Customers in Developing Countries: New Dimensions of Secondary Innovation

How to create value for the mass customers through innovation-whether technological innovation or business model innovation-has attracted increasing research attention from the field of strategy and innovation management, such as Christensen's theory of disruptive innovation. Many local enterprises in China, like UTStarcom, have achieved great market success through business model innovation to provide cheap but good enough products or services that ordinary Chinese citizens can afford, rather than expensive ones with excessive functions. This exploratory work attempts to explore new potential source of latecomer advantage implied in the phenomena of overshooting, described by Christensen, and extends the theory of secondary innovation with new business model dimensions. A business model framework, comprising of value proposition and value network, is proposed to explain the process how a latecomer creates and realizes the economic value latent in the acquired technology for the mass customers in developing countries. Secondary innovation, in terms of value creation, can be conceived as an iterative process initiated by the discovery of a new business model for a technology-based acquisition which leads to development, production, and marketing tasks striving for the secondary commercial success of the acquired technology within a new value network especially in developing countries
An adaptive zero-phase digital filter based on phase compensation

General difference filter can not only bring phase-shifting, but also induce the wave aberration. A new zero-phase algorithm is proposed. It utilizes cross power spectrum estimation to get the information about the signal's frequency value and phase shift and designs a phase corrector to complete the compensation of the filtered signal's phase and zero-phase filtering. This method has the advantages of easy implementation, high precision, good real-time performance, etc.. The results of MATLAB simulation show that the method has a reliable phase compensation performance.
Modeling and error analysis of Time-Driven Activity-Based Costing

According to the fundamental principles of Time-Driven Activity-Based Costing, this paper sets up a mathematical model to solve problems on the cost of cost objects and analyzes errors occurred during the implementation process of TDABC. By taking a case, it demonstrates the applications of this model and the effects on cost account by errors. The emphasis is laid on the demonstration of data source in the model's applications.
Learning pairwise SVM on deep features for ear recognition

Recently, deep features extracted from Convolutional Neural Networks (CNNs) have been widely adopted in various applications, such as face recognition. Compared with the handcrafted descriptors, deep features have more powerful representation ability which can lead to better performance. Effective feature representations play an important role in ear recognition. While deep features have not been applied to represent the ear images. In this paper, we propose to extract deep features of ear images based on VGG-M Net for solving the ear recognition problem. And due to the lack of training images per person, we propose to use the pairwise SVM for classification firstly. For computational efficiency, Principal Component Analysis (PCA) is exploited to reduce the dimension before classification. Finally, we evaluate our approach on two public ear databases: USTB I and USTB II. The experimental results achieve a promising recognition rate and show superior performance compared with the state-of-the-art methods.
Crowd density estimation: An improved approach

Crowd density estimation is important in crowd analysis and texture analysis is an efficient method to estimate crowd density, this paper proposes an improved estimation approach based on texture analysis. First, background is removed by using a combination of optical flow and background subtract method. Then according to texture analysis, a set of new feature is extracted from foreground image. Finally, a self-organizing map neural network is used for classifying different crowds. Some experimental results show compared to former crowd estimation methods, the proposed approach can carry out the estimation more accurately, the rate of true classification is 86.3% on a data set of 600 images.
An excitation signal design for identification with the amplitude constraints

This paper proposes an optimal excitation signal design approach with amplitude constraints for open-loop system identification. For the convenience of imposing the amplitude constraints, generalized binary noise (GBN) is adopted as the excitation signal. Then based on the asymptotic principles of prediction error method (PEM), excitation signal spectral density and the upper bound of identified relative frequency-domain deviation are obtained via constructing and solving a set of linear matrix inequalities (LMIs). Owing to the statistical property of the obtained model, a probability constraint can be imposed to improve the model accuracy for robust control application. Simulation results demonstrate the advantage and effectiveness of the proposed approach.
L-shaped segmentations in motion-compensated prediction of H.264

Playing the role of removing temporal correlation in video signals, motion-compensated prediction aims at the ideal prediction and eliminates the compensation. However, the block-based video coding infrastructure at present was not competent in exactly present the various shapes of moving objects in actual video. Contrast to that, an exact segmentation of coding area according to the actual moving objects in video is also not easy to achieve and will introduce significant computational complexity into the video coding scheme. Concerned with this conflict, this paper proposes a new set of segmentations of macroblocks, in which one macroblock splits into one L-shaped and one square segment. Experiments of this named "L-shaped segmentations" technique show that it can outperform the H.264 P-picture coding by 0.11 dB.
Grid Information Service Architecture in ChinaGrid

Grid information service, a key component of grid, implements the management of grid system and its resources. This paper presents Grid Information Service Architecture (GISA2.0) that developed for ChinaGrid Support Platform (CGSP2.0). GISA2.0 is a service-oriented hierarchy management model, which supports unified management of all grid resources. It implements a hierarchical monitoring model, which collects information effectively with low system intrusion and discovery of state change in time. Domain autonomy and grid information security are emphasized, and secure global information sharing is achieved with a standard domain exchange mechanism
A new framework to extract heart rate information from photoplethysmographic (PPG) signals with strong motion artifacts

Photoplethysmography (PPG) is a kind of physiological information that can be easily disturbed by motion artifacts. Therefore,this paper paid attention to the extraction of information from PPG with motion artifacts. In the paper, a new framework that can extract heart rate (HR) information from PPG signals with severe motion artifacts is proposed, using Singular Spectrum Analysis, Real-time Clustering, Frequency Points Selection and Prediction and Multiple-way Selection as the key points.
Stochastic Game Theoretic Method of Quantification for Network Situational Awareness

Stochastic game theory is proposed to apply to the research on network situational awareness (NSA),and then a novel quantification method of NSA based on analysis of network service states is proposed. In this paper, network offense-defense game model including both the attacker and the defender in it is constructed; and game mathematical modeling of network situation is realized; the payoff of the both sides in the game is quantified according to the specific application, and the game situations is obtained by analyzing network service state. Finally, simulation experiments are done to validate the Nash equilibrium's existing, at which point both of the sides get a balance between their payoffs and by which the current stable network security state is reflected, finally the quantification of NSA is achieved.
Thermal Characterization of Both Bare Die and Overmolded 2.5-D Packages on Through Silicon Interposers

The next generation of heterogeneous integration requires 2.5-D packages on through silicon interposer (TSI) as enabling technology for less signal delay, faster speed, and more functionality. In the meantime, the introduction of multiple chips on interposer tends to increase the heat density with added interconnect complexity, which requires systematic thermal analysis and characterization. In this paper, thermal characterization of 2.5-D packages on TSI is reported in both bare-die package and overmolded package formats. The test vehicle consists of two dummy chips and thermal test die assembled on the same interposer of 18 mm × 18 mm × 0.1 mm through the flip chip bumping and joining process. A thermal test chip of 5.08 mm × 5.08 mm is built in with heaters and diodes for thermal characterization. Thermal measurements are conducted for thermal resistances from junction to the ambient, from junction to the board, and from junction to top casing. Measurement accuracy is improved through distributed through silicon via network, multidie temperature monitoring and uncertainty analysis and minimization. It is found that the overmolded package has lower thermal resistances than the bare die package. In addition, the thermal resistance from the junction to the casing is also characterized with a liquid-cooled minichannel cold plate as heat sink, indicating the vast difference between bare die package and molded package. Besides experimental measurements, thermal simulation models under different boundary conditions are established, respectively, to compare with the measurements. Good agreements are generally achieved between simulation and measurements. Further simulation is also conducted to examine the effects of overmold thickness and power dissipation from the multichips module on the interposer.
Data-Driven Modeling and Predictive Control for Boiler–Turbine Unit

This paper develops a novel data-driven modeling strategy and predictive controller for boiler-turbine unit using subspace identification and multimodel method. To deal with the nonlinear behavior of boiler-turbine unit, the system is divided into a number of local regions following the analysis of the nonlinearity distribution along the operation range, and then the corresponding measurement data are organized to identify the local models through the subspace method. By transforming local models into the same basis, the resulting multimodel system (MMS) is shown to represent the boiler-turbine unit very closely, and thus, used in designing a multimodel-based model predictive control (MMPC). As an alternative approach, a data-driven direct predictive controller (DDPC) is developed by utilizing the intermediate subspace matrices as local predictors. Online update of the predictor is also implemented on the multimodel structure to make the controller responsive to plant behavior variations. Simulation results demonstrate the feasibility and effectiveness of the proposed approach.