nils-herrmann/clean_scirepeval huggingface dataset:
Microcontroller based switch inverter for controlling assistive technology devices

A practical device for switching needs has been constructed to take advantage of limited index finger abduction in a person with paralysis. The device utilizes a microcontroller to handle mechanical switch debouncing, convert normally closed switch to normally open switch, turn on an audible output for cueing, control an external device, and be tolerant of spasms. A simple time window technique is developed to prevent involuntary switching due to spasms. The switch activates a relay controlled, isolated output for interfacing with any external assistive technology device that can be operated with a single switch.
Machine learning inspired energy-efficient hybrid precoding for mmWave massive MIMO systems

Hybrid precoding is a promising technique for mmWave massive MIMO systems, as it can considerably reduce the number of required radio-frequency (RF) chains without obvious performance loss. However, most of the existing hybrid precoding schemes require a complicated phase shifter network, which still involves high energy consumption. In this paper, we propose an energy-efficient hybrid precoding architecture, where the analog part is realized by a small number of switches and inverters instead of a large number of high-resolution phase shifters. Our analysis proves that the performance gap between the proposed hybrid precoding architecture and the traditional one is small and keeps constant when the number of antennas goes to infinity. Then, inspired by the cross-entropy (CE) optimization developed in machine learning, we propose an adaptive CE (ACE)-based hybrid precoding scheme for this new architecture. It aims to adaptively update the probability distributions of the elements in hybrid precoder by minimizing the CE, which can generate a solution close to the optimal one with a sufficiently high probability. Simulation results verify that our scheme can achieve the near-optimal sum-rate performance and much higher energy efficiency than traditional schemes.
Cluster Distance Factor Searching by Particle Swarm Optimization for Self-Growing Radial Basis Function Neural Network

A very important step for the RBF network training is to decide a proper number of hidden nodes. This paper proposes a PSO-based algorithm for searching optimal cluster distance factor. Thus, the self-growing RBF network training algorithm can be realized by employing the optimal cluster distance factor to create hidden neurons automatically from the input data set. Two experiments that include function approximation and chaotic time series prediction are used to compare our proposed method with other related approaches. The PSO-based RBF network exhibits better generalization performance and shorter training time.
Leakage detection and location for long range oil pipeline using negative pressure wave technique

Leakage accidents in oil transmission pipelines not only cause loss of material, but also bring about hidden troubles for people's property and the environment. Pipeline leakage accidents occur frequently in recent years, particularly in those developing countries, and most of which were done destructively. Negative pressure wave technique is an effective method for paroxysmal oil leakage detection and location. However, it is difficult to distinguish sources which led to the oil pressure drop. In order to solve the problem, wavelet transform algorithm was adopted to define inflexion of the negative pressure wave when it propagates along the pipe, and wavelet threshold denoise technique was used to separate the characteristic inflexion of negative pressure wave when calculating the leaking position. A new pipeline detection and location system on the basis of that was developed. The monitoring system acquires operational parameters of the pipeline from the existing SCADA system, which was installed on most oil transmission pipeline to monitor operational parameters for long range crude oil or product oil pipeline. Thus, the cost and complexity of the system was greatly reduced. The earliest leak detection and location system which installed on a crude oil transmission pipeline in the east of China 8 years ago is still working well. It responses to leakage in 2 minutes and location accuracy is lower than 2% of the whole length of the pipeline between two stations.
Hysteresis Compensation Control Algorithm for the Giant Magnetostrictive Actuators

When the giant magnetostrictive actuator (GMA) is used in the control system, the hysteresis of the GMA is particularly significant and causes undesired effect. Therefore, no matter what model of the actuator is employed, the efficient procedures for hysteresis and nonlinear compensation are required. Here a hysteresis compensation control strategy is proposed. To reduce model mismatch effect and unmodeled dynamics, a model estimator using a dynamic recurrent neural network (DRNN) is first constructed to obtain dynamic hysteresis characteristics of the GMA. Then a feedforward compensator using another DRNN learns and obtains inverse hysteresis characteristics of the GMA according to dynamic characteristics from the estimator. The system has been approximately linearized through compensation. Tracking the errors for random multisine wave reference signals, simulation results validate the excellent performances of the proposed strategy
Cross-Project and Within-Project Semisupervised Software Defect Prediction: A Unified Approach

When there exist not enough historical defect data for building an accurate prediction model, semisupervised defect prediction (SSDP) and cross-project defect prediction (CPDP) are two feasible solutions. Existing CPDP methods assume that the available source data are well labeled. However, due to expensive human efforts for labeling a large amount of defect data, usually, we can only utilize the suitable unlabeled source data. We call CPDP in this scenario as cross-project semisupervised defect prediction (CSDP). Although some within-project semisupervised defect prediction (WSDP) methods have been developed in recent years, there still exists much room for improvement on prediction performance. In this paper, we aim to provide a unified and effective solution for both CSDP and WSDP problems. We introduce the semisupervised dictionary learning technique and propose a cost-sensitive kernelized semisupervised dictionary learning (CKSDL) approach. CKSDL can make full use of the limited labeled defect data and a large amount of unlabeled data in the kernel space. In addition, CKSDL considers the misclassification costs in the dictionary learning process. Extensive experiments on 16 projects indicate that CKSDL outperforms state-of-the-art WSDP methods, using unlabeled cross-project defect data can help improve the WSDP performance, and CKSDL generally obtains significantly better prediction performance than related SSDP methods in the CSDP scenario.
Comprehensive Evaluation of Sewage Source Heat Pump System Applied in Shanghai

This paper takes an office building in Shanghai as an example and calculates the energy consumption index, economic index and environmental index of its sewage source heat pump system, fuel boiler & water chillers system and fuel boiler & sewage source heat pump system. Then entropy weight analysis hierarchy process was used to make a comprehensive evaluation of these three cold, heat source programs. The research results show that the overall performance of sewage source heat pump system is better than the other two cold, heat plans. On this basis, the paper further calculates the application scope of sewage source heat pump system. The energy-saving environmental benefits of sewage source heat pump system are much more obvious, when the distance from the sewage plant to the building does not exceed this range.
Fast extraction method of auditory brainstem response based on wavelet transformation

In this paper, the principle, process and experimental analysis for the single extraction of auditory brainstem potential by wavelet transformation are discussed. The results indicate that, by a wavelet filter obtained from correlation analysis between results of wavelet transformation and traditional superposing and averaging method, the auditory brainstem response signal can be extracted from the signal containing spontaneous brain wave interference based on only a single measurement of the signal. As comparison to traditional superposing and averaging method, the wavelet transformation analysis method presented in this paper can provide much more distinct waveforms and shorten the time of whole detection by considerably reducing the number of tests.
Structure and Magnetoelasticity of R(Zn1xFex) (R=Tb, Dy) Polycrystalline Alloys

This article report on the effect of substituting other transition metals on the magnetostriction and Curie temperature of R(Zn<sub>1-x</sub>Fe<sub>x</sub>) (R=Tb, Dy) polycrystalline alloys. The X-ray diffraction analysis confirmed that the DyZn<sub>1-x</sub>Fe<sub>x</sub> alloys consisted of nearly a single Dy(Zn,Fe) phase with the CsCl-type cubic structure in the range 0<x<0.15. When x>0.15, they consist of a Dy(Zn,Fe) phase with a small amount of Dy-rich second phase. For TbZn<sub>1-x</sub>Fe<sub>x</sub> alloys, a similar result is been obtained.
Lane departure warning system based on dynamic vanishing point adjustment

A fast lane departure warning system which realized in embedded system is presented in this paper. Based on small process area of image and Nearest Near Peak (NNP), the lane-marking detection and tracking process can extract lane-marking pattern fast. Further, the dynamic vanishing point is employed to fine-tuning both of sides for lane-marking results. Finally, the departure alarm is active when the vehicle departs from its lane. In experiment, several senses are considered to challenge the proposed system, the results show that it has well correct rate of warning alarm, and robust enough to perform in real driving environment.
Research on derivative events for Chinese natural disasters based on the Apriori improved model

The article based on the Apriori improved model, combined with the related theory of natural disasters derivative events, analyzes the information data of the natural disaster in the last three years in China, and finally confirms 10 representative derivative events of natural disasters. Through analyzing and sorting, this paper checks the results based on minimum support and minimum confidence, and ultimately determines the relationship between different derivative events. By discussing the items that rank ahead with the high confidence in associated results of the 2-items, 3-items and 4-items, respectively, it could be confirmed that the correctness of association results, and reflected the efficiency of the Apriori improved algorithm. Therefore, individuals can use the results to forecast the possibility of related derivative events after natural disasters occurring, with the ultimate goal of improving the prediction system.
Activity Analyzer for Guided Independent Living Environments (AAGILE)

The AAGILE is a device designed to reinforce a healthy daily regimen for those who choose to live independently, especially those with diminishing cognitive or physical functions. The device does so by setting goals, providing personalized interactive reminder messages, and monitoring daily activity in 5-minute increments. The purpose of this study is to minimize the overall device size and footprint of the motherboard, further develop the functions of the AAGILE and test the overall health benefits with respect to different physical and cognitive limitations and in comparison to different commercially available monitoring devices.
Infinite-time linear quadratic differential games for stochastic system with Markov jumps and multiplicative noise

This paper discusses the linear quadratic (LQ) differential games for stochastic systems with Markov jumps and multiplicative noise in infinite-time case. We introduce the definitions of exact detectability and stochastic detectability and the connection between them, which have close relation to Lyapunov equation. Based on Lyapunov equation, we obtain four-coupled algebraic Riccati equations (AREs), which are essential on finding the optimal strategies (Nash equilibrium strategies) and the optimal cost values for infinite stochastic differential games with Markov jumps. In addition, we also propose the PBH criterions of exact detectability for stochastic systems with Markov jumps.
Identification of Splice Sites Based on Discrete Wavelet Transform and Support Vector Machine

With the imminent completion of the Human Genome Project and the fast increase of many complete genomes of prokaryotes and eukaryotes, the task of organizing and understanding the generated sequence and structural data becomes more and more pressing and demands better and efficient analysis algorithms. Computational gene identification is an issue of obvious importance as a tool of identifying biologically relevant features that often cannot be found by traditional sequence database searching technique. It bases on the accurate gene structure and developing accurate splice sites identification algorithms plays an important role in the identification of gene structure in eukaryote organisms. In this paper, we proposed a novel approach and developed an accurate splice sites identification algorithm based on discrete wavelet transform learned by support vector machine, which can achieve a superior performance for the prediction of splice sites, without any explicit use of local features. What's more we bring forward a new coding method for DNA sequence, so that the sequence data can be dealt with as image processing. The results show that our splice sites identification method is much better than or similar to other methods that are developed based on explicit statistical features, despite that it is so easy and simple. So this method could be applied in other signal detection problems for a try.
Adaptive segmentation of coronary angiograms

The problem of unsupervised segmentation of coronary angiograms is investigated. An algorithm which uses an iterative line search procedure to adapt the segmentation thresholds is proposed. This algorithm is parallel in nature, and could be implemented on a disturbed computer architecture. Based on the angiogram's histogram, two thresholds are obtained which classify the pixels into three types: artery, background, and unclassified, The threshold adaptation is an iterative process. A heuristic line search is conducted throughout the neighborhoods of the unclassified pixels. The results of the search are used to adapt the thresholds. The process is continued until all pixels are classified as either artery or background. The algorithm was implemented on an IBM PC/AT-based imaging system and tested with coronary arteriogram images. Preliminary results demonstrated the algorithm's usefulness in enhancing the arterial structure, even under low signal-to-noise ratios. Furthermore, the segmentation was achieved within a few iterations.<<ETX>>
Improved adaptive backstepping sliding mode control for multi-machine system with TCSC

For the multi-machine power system with TCSC, the TCSC controller is designed by using the improved backstepping method, adaptive control and sliding mode control under the condition of the uncertainty of damping coefficient and external interference. In the design process, the k-class function is introduced to improve the backstepping method. For the external interference, we use the adaptive control to design the adaptive estimation law for the upper bound of the interference, and add to the sliding mode term to suppress the interference. Finally, the simulation results show that the TCSC controller can quickly suppress the oscillation and ensure the transient stability of the multi-machine power system.
Graph theory based decentralized H∞ filtering for large-scale T-S fuzzy systems

This paper is concerned with the problem of decentralized H∞ filtering for large-scale T-S fuzzy systems, where the interconnections are nonlinear and with known weights. The algebraic graph theory is used to construct a new Lyapunov function such that the effects of the nonlinear interconnections can be included in some constants related with the interconnected weights. As a result, a novel decentralized filtering scheme is derived, and the corresponding filter synthesis conditions are given in terms of the solutions of a set of linear matrix inequalities (LMIs). In contrast to the conventional filtering schemes, where the interconnections are assumed to be local linear, the proposed decentralized filter design conditions are based on fewer fuzzy rules and less computational burden. Moreover, a unified filter design framework is derived for the large-scale T-S fuzzy systems with or without time-delayed nonlinear interconnections. Finally, a simulation example is provided to illustrate the effectiveness and advantages of the theoretical results.
Design of honeycomb core docking mechanism

Docking mechanism was low degree of automation and behindhand in product line of honeycomb paper processing, so pneumatic control technology and structure of new docking mechanism had been designed based on simulation technique. First, the structure and working principle of docking mechanism were presented. Then, the motor process of docking mechanism was analysed used in the software of ADAMS, its pneumatic loop and electric loop was designed and optimized by way of the software of FluidSIM. At last, the docking mechanism had been effectively controlled and used in line of production. Now, the verification results showed that the machine had raised degree of automation, working efficiency and machining accuracy.
Unicode-proof code injection attack on Windows CE — A novel approach of evading intrusion detection system for mobile network

Code injection attack is a major way of spreading malware on network. The key section of code injection attack is a small piece of code, called shellcode, which performs unauthorized operations when it is injected into software as part of valid data. On Windows CE, input data are often encoded using Unicode before being processed. In such cases, shellcode should be built in a way that bypasses such encoding; that is, it should be Unicode-proof. Unicode-proof shellcode also has great advantage of evading instruction detection system. However, it is quite difficult to build Unicode-proof shellcode for the ARM architecture, on which most embedded devices are developed, because the subset of instructions that can be used to write Unicode-proof shellcode is very limited. Moreover, the instruction cache in the ARM processor restricts the application of self-modifying code, which is frequently used in shellcode writing. This novel research proposes an approach to building ARM Unicode-proof shellcode on Windows CE under these constraints. The approach applies to all versions of ARM processors and Windows CE, including systems evolved from Windows CE, such as Windows Mobile and Windows Phone. The shellcode is tested on three currently available devices.
The Research of a Double Objective Model of Loans Portfolio Optimization Based on PSO

The paper establishes a double objective decision-making model of the commercial bank's loans portfolio optimization, its objective functions are income of loans portfolio and CVaR, meanwhile, considering the relationship between the accumulative and incremental loans. It full controls all risk for the commercial bank's loans portfolio. In order to solve this model that is a doubleobjective 0-1 programming question, we present an adaptive particle swarm optimization (APSO) algorithm. It is shown with the numerical result that this algorithm is effective for solving commercial bank's loans portfolio decision-making problem. The algorithm can solve the middle or large-scale question and the given model is reasonable. KeywordsLoans portfolio optimization; double-objective 0-1 programming; particle swarm optimization algorithm; Conditional Value-at-Risk (CVaR)
Enhanced Anomaly Detection Via PLS Regression Models and Information Entropy Theory

Accurate and effective fault detection and diagnosis of modern engineering systems is crucial for ensuring reliability, safety and maintaining the desired product quality. In this work, we propose an innovative method for detecting small faults in the highly correlated multivariate data. The developed method utilizes partial least square (PLS) method as a modelling framework, and the symmetrized Kullback-Leibler divergence (KLD) as a monitoring index, where it is used to quantify the dissimilarity between probability distributions of current PLS-based residual and reference one obtained using fault-free data. The performance of the PLS-based KLD fault detection algorithm is illustrated and compared to the conventional PLS-based fault detection methods. Using synthetic data, we have demonstrated the greater sensitivity and effectiveness of the developed method over the conventional methods, especially when data are highly correlated and small faults are of interest.
An improved flush material belt weigh feeder system via fuzzy logic controller and adaptive neural networks

The Flush Material Belt Weigh Feeder (FMBWF) has used in many material handling plants. The stability and the performance of the layer control system will affect the quality of the production. In general, the behavior of the flush material on the BWF is non-linear, time-lag, and disturbance character. The layer of the flush material on the belt is hard to be stably controlled especially the occurrence of unstable situation in flush material or the prefeeder feeding rate and the variation of the set point value of the FMBWF. This paper focuses on the performance improvement of the FMBWF via adopted a fuzzy controller under the situation of the Flow Rate Set Value (FRSV) variation. The proposed fuzzy controller is utilized instead of the original feed forward compensation function to deal with the problems of the temporary unstable situation. This paper offers the simulation result comparison of the set point value variations between the BWF for the original and the proposed control system. The simulation results are distinct better than the original control system. The influence of the variations of the set point value can be successfully evaluated by the Fuzzy controller and adjusted the Proportional Actuator immediately. Finally the proposed control system remains convergence with smooth and stable. Therefore, the accuracy, stability and the performance of the control system are improved.
Analysis on the Developing Trend of China's Banking Industry

The financial system of the world's main developed countries is mixed management, which is the trend of the international financial development. China will thoroughly open its financial market till December, 2006. China's banking industry will face a lot of great shocks coming from foreign banks which engage in mixed management. In this financial globalization time, China's banking industry must go with the mixed management trend to improve its competitiveness. This article first introduces the history of mixed management in the world's main developed countries, then analyzes China banking industry's history, its status and the reasons why mixed management is the inevitable choice of China's banking industry, and at last, the author brings forward relative suggestions to mixed management of China's banking industry