nils-herrmann/clean_scirepeval huggingface dataset:
HSI color model based lane-marking detection

Lane-marking detection is one of the major concerned topics in the field of driving safety and intelligent vehicle. In this paper, a new method using HSI color model for lane-marking detection, HSILMD, is proposed. In HSILMD, full color images are converted into HSI color representation, within the region of interest (ROI) aiming to detect road surface on host vehicle, the difference of intensity distribution of a row of pixels within ROI is recorded and clustered with fuzzy c-means algorithm. Thresholds of intensity and saturation are selected accordingly. With simple thresholds and operations, lane markings on various road scene images are detected. Results are compared with the same scheme using RGB color model and a different scheme. Robustness of this reduced computation consumption system is observed
Adaptive integral-type sliding mode control for a class of uncertain nonlinear systems

An integral-type sliding mode control method for a class of uncertain nonlinear systems in parameter feedback form is developed. The design procedure can be divided into two steps. Firstly, design a nonlinear integral-type sliding manifold for the uncertain system such that not only the sliding motion can occur on that manifold, but also the system's motion along the manifold is asymptotically stable. The integral-type sliding surface is constructed by adding an integral term into the linear sliding surface. The additional integral in the proposed sliding surface provides one more degree of freedom; Secondly, based on the Lyapunov stability theorem, design a control law, such that the system's states are driven towards the manifold and stay there for all future time, regardless of uncertainties.
Multi-swarm particle swarm optimization for payment scheduling

The payment scheduling negotiation problem with multi-mode and resource constraints (MRCPSNP) is a practical extension to the resource-constrained project scheduling problem (RCPSP). It considers the interests of both the client and the contractor, who negotiate with each other to maximize their own benefits. Since the interests of the client and contractor are conflicting and the two roles have special scheduling requirements, it is difficult to solve this problem. This paper addresses MRCPSNP and adopts the net present value (NPV) of discounted cash flows to measure the potential value. To simulate the negotiation process, we adopt a co-evolution strategy and make the two parts negotiate with each other to maximize their own NPV. To specify special requirements of the two roles, a multi-level region of interest strategy is proposed. The co-evolution strategy and the multi-level region of interest strategy are integrated into a particle swarm optimization (PSO) algorithm, forming a multi-swarm particle swarm optimization (MPSO) approach. Experimental comparisons with the other two methods on 30 project instances demonstrate that the proposed approach is very effective and promising.
Optimal Stochastic Disturbance Rejection Design for Discrete-time Systems with Time-delay

Optimal control for discrete-time systems with time-delay affected by additive stochastic disturbances is considered. By introducing a sensitivity parameter, we transform the original optimal disturbance rejection control (ODRC) problem into a series of two-point boundary value (TPBV) problems without time-delay and time-advance terms. The approximate ODRC law consists of analytic state feedback and feedforward terms and a compensation term in a series form. The compensation term can be obtained with an iterated formula of the adjoint vectors. Physical realization of the approximate ODRC law is solved by using a state estimator. Simulation results show the approach in this paper is more robust with respect to exogenous persistent disturbances.
A survey of security middleware

Nowadays, with the awareness of information security, the security middleware technology is spread among an expanding number of people. On one hand, the security middleware can provide users with simple, understandable and safe information security technologies. On the other hand, it can exert positive effect on setting up a series of standardized and common interface for manufactures, so that the products from different vendors can be used compatibly. The paper firstly briefly introduced the structure of security middleware system, and on this basis, prospected some valuable applications. In addition, summarized and illustrated the hot spots in detail, including the implementation methods of parallel security middleware and vulnerability monitoring middleware. Finally, according to these significant applications, proposed some meaningful prospects related to research directions.
The MARS-Based Research about Reliability Judgement of Members of Supply Chain Alliance

Supply chain alliance (SCA) is becoming an important origin for enterprises to win competitive advantages.How to judge reliability of members in SCA is an essential problem for SCA, and is also one of the key problems for members to keep long-term collaboration. The paper constructs a model to judge the members'reliability by multivariate adaptive regression splines (MARS) for the first time. MARS has the attributes of objectivity, effectivity and robust, and it could avoid dependency on subjective factors to some extent, which contributes to a scientific, integrated judgement system,and thus helps the SCA operate steadily and effectively.
A novel approach to ML DOA estimation based on eigenfiltering and stochastic search

The paper proposes a novel approach that a new likelihood function is derived from observation data after filtered with eigenfilters, and hybrid gravitational search algorithm (H-GSA) optimization is collaboratively applied to maximum likelihood (ML) estimation of the direction of arrival (DOA) parameters of multiple signals impinging on a sensor array. This method prevents the ML estimation performances from deteriorating severely where the angular separation between signal sources is small and the SNR / sample size are low. Simultaneously due to the use of H-GSA, we make direct maximization of likelihood realistic in practice. In order to examine the performances of the proposed method, four kinds of situations are designed. Simulation results indicate that the proposed method offers significant performance enhancement at low signal to noise ratios, and hybrid GSA stochastic search technique is therefore efficient and reliable.
Regularized Robust Estimation of Mean and Covariance Matrix Under Heavy-Tailed Distributions

In this paper, the joint mean-covariance estimation problem is considered under the scenario that the number of samples is small relative to the problem dimension. The samples are assumed drawn independently from a heavy-tailed distribution of the elliptical family, which can model scenarios where the commonly adopted Gaussian assumption is violated either because of the data generating process or the contamination of outliers. Under the assumption that prior knowledge of the mean and covariance matrix is available, we propose a regularized estimator defined as the minimizer of a penalized loss function, which combines the prior information and the information provided by the samples. The loss function is chosen to be the negative log-likelihood function of the Cauchy distribution as a conservative representative of heavy-tailed distributions, and the penalty term is constructed with the prior being its global minimizer. The resulting regularized estimator shrinks the mean and the covariance matrix to the prior target. The existence and uniqueness of the estimator for finite samples are established under certain regularity conditions. Numerical algorithms are derived for the estimator based on the majorization-minimization framework with guaranteed convergence and simulation results demonstrate that the proposed estimator achieves better estimation accuracy compared to the benchmark estimators.
Comparison between speaker dependent mode and speaker independent mode for voice recognition

The purpose of this study was to compare two available modes for voice recognition. The speaker dependent mode allowed the speaker to record his/her own voice while the speaker independent mode had basic words already recorded and stored on the microcontroller. The study was focused on evaluating the sensitivity of the system while varying the volume and distance of the speaker, as well as the word used. The overall effectiveness was evaluated based on this criteria. The system was developed using Sensory Inc. technology (VR Stamp™).
A Secure and Efficient E-Cheque Protocol from Chameleon Hash Function

The electronic cheque (E-cheque), a mode of electronic payments, makes it possible for the customers to remotely pay their products through the Internet. However, because of the openness of the network, the E-cheque is confronted with many secure threats when transferring via this public communication channel. Moreover, some resource limited devices such as smart phones are used in the electronic commerce as the Internet-enabled terminals. Thus, a secure and efficient E-cheque system is of critical importance that the customer can exchange with the merchants. In this paper, we propose an E-cheque protocol based on the chameleon hash function. In our protocol, if the customer generates a dud E-cheque, anyone is able to find a collusion of the chameleon hash and the secret key of the customer is disclosed. We further prove the security of the protocol against the malicious customer in the random oracle model. Compared with the previous E-cheque schemes, our protocol has the advantages of low computation and communication overheads.
Ontology based user personalization mechanism in meta search engine

Ontology as a concept and model description way, is widely used in various fields, and achieved good effect. Ontology based on intelligent Agent how to realize its definition of the concept in Meta search engine, help system realize the personalized, still need further research. This paper put forward a meta search engine personalized mechanism based on ontology with the use of the Agent technology, this mechanism through mining user behavior of characteristics with Agent for information retrieval, combining ontology technology set up and update the user general view, can well reflect user current interest and to the user's interest to the renewal of the dynamic. Based on ontology users can also according to the user's query scene reasoning and used in filter search results, can make the search results to meet user needs, has the personalized. Finally, in the intelligent Meta search engine prototype platform of the related experimental study, results verified the feasibility of personalized mechanism, and reached a certain degree of intelligent effect.
Reason Analysis On the Faults of Electric Equipments Owing to Harmonic

Through the analysis and measurements of power quality on distribution line of 10 kV and 0.38 kV, it was approved that harmonic voltage and harmonic current can be generated while the steelworks was running. Because of harmonic affect, the capacitive voltage-rise was taken place, the longer of the transmission line the higher of the capacitive voltage-rise, meanwhile, a lot of electric equipments were damaged
A review of rehabilitation robot

This paper gives an overall review of research status in rehabilitation robot technology. In order to study the status of rehabilitation robot technology, they are divided into two categories. One of them is auxiliary and replacement robots and another is training and therapeutic robots. The fixed and mobile robots, intelligent artificial limbs and supporting tools of the auxiliary and replacement robot are introduced in detail. Their characteristics of light structure and fast response are elaborated and research status and existing problems with setting are indicated, then the function of rehabilitation robots is pointed out. The upper and limb rehabilitation robots of training and therapeutic robot are introduced and this kind of robots help the patient carry on the rehabilitation training to show their strong recovery function and achieve a function from replace to recovery. At the end of the paper, issues in the field of rehabilitation robotics research and their development in the future are discussed.
Low-Complexity Nonlinear Analysis of Synchrophasor Measurements for Events Detection and Localization

This paper is concerned with the computational efficient nonlinear analysis of phasor measurement unit data and presents a Nyström principal components analysis-based algorithm for events detection and localization in an electrical power system. Based on the properly chosen sample subset of every moving window data, the Nyström approximation is carried out to obtain the principal eigenvalues and related eigenvectors of a mapped kernel matrix. Then, the <inline-formula> <tex-math notation="LaTeX">$T^{2}$ </tex-math></inline-formula> statistic is constructed to detect the abnormal states of an electrical power system. In addition, the contribution of each variable to the <inline-formula> <tex-math notation="LaTeX">$T^{2}$ </tex-math></inline-formula> statistic is derived to determine the location of the fault bus. Compared with the previous works, the novel version proposed in this paper efficiently reduces the computational burden, and accurately locates the fault bus. Computer simulations using both realistic data, collected from the China power system, and simulated voltage and phase-angle data, validate the reliability of the proposed algorithm.
Construction of Traditional Chinese Medicine Database based on ISIS Base

This article describes the construction of a Traditional Chinese Medicine Database based on ISIS Base. It offers not only basic compound properties such as English name and synonyms, physical properties, bioactivity data, formula, molecular weight, CAS Registry Number and 2D structure of the molecule but also detailed information on their natural sources, including Latin name, English name, pinyin name, used part, indication, family, and curative effect. The schema design and data transformation based on XML are described. And the constructed TCM database is also introduced. The plant information in database is helpful for understanding the mechanism of the Traditional Chinese Medicine and relationship between Traditional Chinese Medicine and western medicine.
PSO-based learning rate adjustment for blind source separation

Blind source separation (BSS) is a technique for recovering a set of source signals without a priori information on the transformation matrix or the probability distributions of the source signals. In the previous works of BSS, the choice of the learning rate would reflect a trade-off between the stability and the speed of convergence. In this paper, we adapted the particle swarm optimization (PSO) technique to find suitable learning rates for each signal in each time slot. Experiments employing four mixed source signals were separated by our work and compared with other related approaches. The proposed approach exhibited rapid convergence and made the independent component analysis (ICA) algorithms become more efficient and stable than other related approaches.
Adequate determination of a band of wavelet threshold for noise cancellation using particle swarm optimization

Noise reduction problem is addressed by this study. Recently, wavelet thresholding has become popular and has gotten much attention among a number of de-noisy approaches. The most of threshold determination are developed from universal method proposed by Donoho. But, some shortcomings of the determination are caused from several incorrectly estimated factors and the lack of adaptability for whole frequency. By the reason, this paper replaces a universal threshold by multi-thresholds for matching the coefficients of each wavelet segment, and then the band of threshold will be fined by particle swarm optimization (PSO). Because original signals and noise are mutually independent, an objective function of PSO is created to evaluate the second order correlation and high order correlation. In order to confirm the validity and efficiency of the proposed algorithm, several simulations which include four benchmarks with high or low noise degree are designed. Moreover, the performance of proposed algorithm will have compared with that of other existing algorithms.
Exact observability/exact detectability and spectrum assignment of stochastic systems

In the present paper, we study the exact observability and exact detectability for stochastic Itô systems, which have state-dependent noises in both state equation and measurement equation. Some results for the exact observability and exact detectability are obtained, respectively. We also consider the problem of spectrum assignment and give a necessary and sufficient condition on ensuring all the spectra in a specified disk. Finally, the relationship between the solutions of generalized Lyapunov equation (GLE) and generalized algebraic Riccati equation (GARE) is considered.
Vision-based fall detection system for improving safety of elderly people

Recognition of human movements is very useful for several applications, such as smart rooms, interactive virtual reality systems, human detection and environment modeling. The objective of this work focuses on the detection and classification of falls based on variations in human silhouette shape, a key challenge in computer vision. Falls are a major health concern, specifically for the elderly. In this study, the detection is achieved with a multivariate exponentially weighted moving average (MEWMA) monitoring scheme, which is effective in detecting falls because it is sensitive to small changes. Unfortunately, an MEWMA statistic fails to differentiate real falls from some fall-like gestures. To remedy this limitation, a classification stage based on a support vector machine (SVM) is applied on detected sequences. To validate this methodology, two fall detection datasets have been tested: the University of Rzeszow fall detection dataset (URFD) and the fall detection dataset (FDD). The results of the MEWMA-based SVM are compared with three other classifiers: neural network (NN), naïve Bayes and K-nearest neighbor (KNN). These results show the capability of the developed strategy to distinguish fall events, suggesting that it can raise an early alert in the fall incidents.
A decentralized technique based protection and control scheme for distribution network with high DER penetration

This paper presents a decentralized technique based protection and control scheme for the active radial distribution network. A partitioning method for radial distribution network with multi-distributed energy resources is developed considering island operation possibility of DER, hence, the active distribution network is partitioned into zones with DER and zones without DER. Directional node configuring method is proposed to identify the positional relation of circuit breakers (CBs) on boundaries of zones. After configuring each intelligent feeder terminal units (IFTUs) with the directional node relationship information, the connection relationship between zones can be obtained automatically. So that only by communicating with its neighbors, the IFTU can decide which CBs should be opened when a fault occurs. Peer to peer communication technique is established for interacting fault measurement information among the adjacent IFTUs, fast and accurate faulted zone location and isolation can be achieved. Meanwhile, seamless islanding of the zone integrating DERs can be realized to keep supplying power to the islanded feeder continuously after distribution network splitting. Moreover, power balance control for intentional islanding is discussed to ensure the islanding stable. Finally, results of case studies demonstrate the feasibility and effectiveness of the proposed scheme.
Forward vehicle detection system based on lane-marking tracking with fuzzy adjustable vanishing point mechanism

Based on lane-marking tracking with fuzzy adjustable vanishing point mechanism, this paper presents robustness forward vehicle detection system. Compared to most of the detection systems with a large curvature of road trend, which are not effective for the routes to detection and marking. Therefore, follow the current image frame, the proposed system calculate the error between lane detection point and regressed lane-marking lines, and then fuzzy inference system derive and update the proper vanishing point in next image frame. The algorithm proposed in this paper can be carried out for different road situations and adaptive tuning of the car camera has been successfully tested and proven for the highway to the robustness of the system.
Fast Convergence Outer Loop Link Adaptation with Infrequent Updates in Steady State

In wireless communications, link adaptation is used to select a suitable modulation and coding scheme. The purpose of link adaptation is to adapt to varying channel and interference conditions and to aim for a specified block error rate or to maximize the throughput. In support of link adaptation, there will be estimates of signal to interference ratios, path gain or transmit powers. These estimates can contain systematic and random errors, that may affect the performance of link adaptation. To correct for such errors, there is an outer loop performing link adaptation, usually based on feedback of the bit error rate. We investigate commonly used outer loop link adaptation algorithms and propose a new scheme based on sequential hypothesis testing. The new scheme is shown to converge faster at initialization and after disturbances and to have good performance in steady state.
A wide area protection scheme for smart distribution network with DG

This paper proposes a wide area protection scheme for smart distribution network with distributed generation (DG). After the division of distribution network, the splitting equipments (SEs) and advanced feeder terminal units (AFTUs) are installed at the borders of the zones which contain DGs and the AFTUs are installed at the borders of the zones which contain no DGs. AFTU detects the local realtime information and exchanges information with the adjacent AFTU, and then the fault zone is located. When the SE satisfies the splitting condition, the splitting point separates and the zone becomes an intentional island. Intelligent electronic devices (IEDs) are installed in the fault zone. The IED exchanges information with the adjacent one, then the fault section is located and isolated. The intentional islanding scheme and the control strategy of re-closure and reconnection of island are designed. The case analysis demonstrates the feasibility and effectiveness of the scheme.
Intelligent Model Selection for Refrigerating Compressors

The traditional methods used to select refrigerating compressors have errors, such as consulting values from charts or estimating values subjectively. It leads to selecting the models of compressors inaccurately. During the course of model selection for compressor, too many parameters are concerned, which leads to a large amount of work and calculation. Method of minimum squares can be used to imitate the performance curves of compressor. It makes selecting compressor without chart and only concerned with condensation temperature and evaporation temperature, which greatly improves the speed and quality of selecting compressor.