nils-herrmann/clean_scirepeval huggingface dataset:
Research on Current Commutation Measures for Hybrid DC Circuit Breakers

The hybrid direct-current circuit breaker (DCCB) consists of a mechanical switch (MS) branch and a static DCCB branch. Once a short-circuit fault is detected, the current should commutate from the MS branch to the static DCCB branch first. In this paper, two current commutation measures have been investigated. A hybrid DCCB prototype with current commutated by the arc voltage of the SF6 switch is developed. Based on the prototype, current commutation and interruption tests have been carried out. The results show the current of 3.4 kA can be commutated within 2.4 ms, and be interrupted within 6 ms. Then, a current commutation drive circuit (CCDC) is proposed as the other current commutation measure. Equivalent current commutation tests have been carried out. The results show with the CCDC, both positive current and negative current can be commutated within tens of microseconds. Using a CCDC in series with a mechanical vacuum switch as the MS branch, a 44 kV/3.4 kA hybrid DCCB prototype is developed. Based on the prototype, current commutation and interruption tests are carried out. According to the results, the current of 3.4 kA can be commutated within 100 μs, and be interrupted within 2 ms.
Research on fuzzy rules extraction of futures trading based on MapReduce

Due to the limitation of memory, time complexity and data complexity, common fuzzy rule extraction algorithms can only handle small or medium data sets. Parallel computing is one of the effective tools to deal with big data. This paper mainly proposes a parallel processing method to present how to extract fuzzy if-then rules from futures trading data in the programming model MapReduce. First, the proposed method combines K-means fusion FCM clustering algorithm to generate reasonable fuzzy sets based on the processed futures data. Then, the fuzzy rules with antecedent and consequent are extracted based on these fuzzy sets. Finally, by calculating the support degree of each rule, we can obtain the meaningful fuzzy rules used for futures trading. In the experimental studies, the feasibility and validity of the extracted meaningful fuzzy rules are verified by predicting the futures price trend.
Shot Noise in NbN/AlN/NbN Superconducting Tunneling Junctions

With sensitivity approaching the quantum limit, superconductor-insulator-superconductor (SIS) mixers play an important role in radio astronomy and atmospheric science at millimeter and submillimeter wavelengths. As one of the intrinsic noise sources in superconducting tunneling junctions, shot noise is still not well understood, particularly for those of relatively high energy gap (e.g., NbN/AlN/NbN). In this paper, we mainly study the multiple Andreev reflection (MAR) enhanced shot noise of two different NbN SIS junctions (i.e., junction array and long junction) as well as their temperature dependence. Barrier strength Z is taken into consideration for theoretical analysis of effective charge of the MAR effect based on the Blonder-Tinkham-Klapwijk (BTK) theory with the Andreev clusters method. It has been found that the effective charge of the MAR effect is inversely proportional to temperature, the barrier strength Z and transparency T are proportional to temperature. Detailed measurement results and analysis will be presented.
Coupled Chaotic Tent Map Lattices System with Uniform Distribution

A coupled chaotic map lattices system with uniform distribution (CML-UD) consisting of tent maps is presented based on the security from the viewpoint of cryptography. The system inherited the coupled diffusion and parallel iteration mechanism of coupled map lattices (CML). Through the dual non-linear effect of the rolled-out and folded-over of local lattices tent map and modular algorithms, CML-UD allows the system to enter into an ergodic state, and to rapidly generate uniform distributed multi-dimensional pseudo-random sequences concurrently. The experimental results show that, the time sequences generated by the system has the same differential distribution character with the real random sequence of which each element has equal chance to appear, and with this property, the system effectively restrains the short-period phenomenon which is easy to occur in digital chaotic system. In addition, it had many special properties such as zero correlation in total field, uniform invariable distribution and the maximum Lyapunov exponent is much bigger and steady. All of the properties suggest that the CML-UD possesses the potential application in encryption.
Optimized GPU Framework for Ultrasound Color Flow Imaging

Abstract- A GPU framework for ultrasound color flow imaging (CFI) based on auto-correlation is presented. The parallel CFI processing framework implementation is mainly based on CUDA performance features, such as the memory selection strategy, applicable thread structure and high-throughput bandwidth. Parallel convolution algorithm and multi-channel championship algorithm are proposed. This CFI method achieves a frame rate of 300 fps from the Doppler signal, in which the number of scan lines is 44, the number of samples along the axial line is 510 and ensemble size is 16.
Fuzzy control based fusion scheme for side information generation in multiview distributed video coding systems

In multiview distributed video coding (DVC) systems, both temporal and inter-view correlations can be exploited through a fusion scheme to generate a better side information (SI) in order to improve the rate distortion (R-D) performance. However, many fusion methods available can not always achieve considerable performance for the influence of some subjective factors. In this paper, we present a new fusion scheme for side information generation based on fuzzy control. Experimental results show that our scheme has good performance in a variety of test conditions.
Evaluation on Competitiveness of Modern Service Industry Based on Catastrophe Theory

Enhancing competitiveness of modern service industry is great important to promote economic and social development. So it is necessary to evaluate the competitiveness of city modern service industry. After introducing catastrophe theory, we elaborate the evaluation approach based on the catastrophe theory and make use it to evaluate competitiveness of modern service industry. At last, we do an empirical analysis of competitiveness of modern service industry by using actual data of modern service industry of Guangzhou. The empirical results show that the evaluation method is reasonable, and it provides a new way for evaluation on competitiveness of modern service industry.
The digital control for two-transistor forward converter based on capacitor charge balance

In this paper, a novel digital control algorithm is proposed to achieve an excellent dynamic performance for two-transistor forward converter. By using the charge balance control(CBC), the optimal transient time and the suitable duty cycle series can be predicted, then better transient performance can be obtained. A comparison of dynamic performances between CBC and conventional voltage mode control is performed in Matlab/Simulink, the results demonstrate the superiority of proposed algorithm.
Stability analysis for neural networks with time-varying delay

This paper studies the problem of stability analysis for neural networks (NNs) with a time-varying delay. The activation functions are assumed to be neither monotonic, nor differentiable, nor bounded. By defining a more general type of Lyapunov functionals, some new less conservative delay-dependent stability criteria are obtained and shown in terms of linear matrix inequalities (LMIs). Since less variables are involved, the computational complexity of the new conditions is reduced. Numerical examples are given to illustrate the effectiveness and the benefits of the proposed method.
An approach of chaotic generalized synchronization with the space transformation

In terms of the space transformation theory, a new approach of generalized chaos synchronization is developed in the paper. It realizes generalized chaos synchronization by introducing space transformation into GS, in which the new approach takes use of filtering transformation to construct two isomorphic systems to actualize generalized synchronization. Furthermore, the new approach breaks the normal chaotic response system and explores a new approach by using space theory and its transformation. Additionally, its validity and feasibility are proved by simulation between Lorenz chaotic system and Rossler chaotic system with Kalman filtering respectively.
Study on event correlation analysis in evidence chain structure

Computer forensics has limitations in representation formalism of the electronic evidence and data missing. A method in construction of electronic evidence chain was proposed on the basis of the study and analysis of event correlation, and it makes use of Bayesian network inference algorithm, which analysis of causal relationship of the events to deal with the missing data. This method to consider the interaction between evidence events and sequence relationship, it realizes formalization of the electronic evidence and reduces data redundancy in evidence analysis, which strengthens the pertinence of data process and evidence analysis, forensics system becomes more perfect.
An Application Specific Instruction Set Processor optimized for FFT

This paper presents an Application Specific Instruction Set Processor (ASIP) pruned for high-throughput and variable-length Fast Fourier Transform (FFT), which is a key component of various Orthogonal Frequency Division Multiplexing (OFDM)-based wireless communication standards. The ASIP executes dedicated FFT instructions to process two radix-4 or four radix-2 butterfly operations every clock cycle. Furthermore, a shuffle-embedded register file and a programmable memory access coprocessor are employed to tackle the memory access bottleneck and reduce power consumption. The implementation results show that our ASIP requires only 892 clock cycles for a 1024-point FFT, which outperforms TI TMS320C64x DSP and Tensilica ConnX ASIP by 6.74X and 2.03X, respectively. A test chip of the proposed ASIP was fabricated using CMOS 65nm process with the core area of 1.9mm2. It consumes 85mW when it runs at the maximum frequency of 150MHz.
A Fuzzy Multi-attribute Group Decision Making Model and Its Application to Selection of Air Conditioning Cold/Heat Source

Aiming at complexity, fuzziness and cognition limitation on selection of air conditioning cold/heat source, using for reference triangle fuzzy number computation mentality, a multi-attribute group decision making model based on GIOWA is proposed to use. Through applying in the selection of air conditioning cold/heat source, research results indicates that it may solve effectively multi-attribute decision making question presented by linguistic form, simultaneously, simple computation, clear mentality and satisfying outcomes.
Effect of Malware Spreading on Propagation of Cascading Failure in Cyber-Coupled Power Systems

In this paper, we consider the failure propagation in a power system which is coupled with a cyber network. We identify the ratio of the infection rate of malware to the tripping rate of elements in the power network, defined as cyber-physical propagation ratio (CPPR), as a crucial parameter, and show that CPPR affects the propagation pattern. When CPPR is very small, the overloading effect in the power system dominates, and the failure propagation profile shows a typical "jump" or step pattern. Moreover, as CPPR increases, the step magnitude rapidly reduces and becomes less noticeable. Furthermore, we develop a simple approach to distinguish various propagation patterns for different values of CPPR. Results show that CPPR effectively characterizes the significance of cyber coupling and hence the relative impact of malicious attack from the cyber network, and its value determines how cascading failure propagates in a cyber-coupled power network.
Event-based model-free adaptive control for discrete-time non-linear processes

In this study, a novel event-based model-free adaptive control (MFAC) algorithm for discrete-time non-linear systems is presented. Different from the traditional MFAC scheme which calculates the control signal at fixed sampling instants, an event-based sampling scheme is given to calculate the new control signal only when the input/output (I/O) data sufficiently changes. The event-triggered MFAC can obviously reduce the computational load and network communication. The closed-loop system is proven to be ultimately bounded by using the Lyapunov technique. Finally, the simulation examples indicate the effectiveness and applicability of the proposed event-trigger model-free adaptive control algorithm.
Nonlinear Difference Imaging Approach to Three-Dimensional Electrical Impedance Tomography in the Presence of Geometric Modeling Errors

To evaluate the recently proposed nonlinear difference imaging approach to electrical impedance tomography (EIT) in realistic 3-D geometries.  In this paper, the feasibility of nonlinear difference approach-based EIT is tested using simulation studies in 3-D geometries of thorax and larynx, and with an experimental study of a thorax-shaped water tank. All test cases exhibit severe modeling errors due to uncertainty in the boundary shape of the body.  In all test cases, the conductivity change reconstructed with nonlinear difference imaging outperforms the conventional reconstructions qualitatively and quantitatively.  The results demonstrate that the nonlinear difference reconstructions tolerate geometrical modeling errors at least to the same extent as the conventional linear approach and produce quantitatively more accurate information on the conductivity change.  Physiological processes that produce changes in the electrical conductivity of the body can be monitored with difference imaging based on EIT. The wide popularity of linearized difference imaging in EIT is mainly based on its good tolerance for the ubiquitous modeling errors, which are predominantly caused by inexact knowledge of the body geometry. However, the linearized difference imaging produces only qualitative information on the conductivity change, and the feasibility of the estimates also depends on the selection of the linearization point which ideally should be equal to the conductivity of the initial state. Based on the findings of this paper, these problems can be avoided by nonlinear difference imaging, and potentially the approach can enable quantitative imaging of conductivity change in medical applications.
Application of Combinatorial Probabilistic Neural Network in Fault Diagnosis of Power Transformer

Probabilistic Neural Network (PNN) overcame the shortcomings of entrapment in local optimum, slow convergence rate which was in BP algorithm. With enough training samples, PNN obtained the optimal result of Bayesian rules. Because of the fast training rate, the training samples can be added into PNN at any time. So, PNN is fit to diagnose the fault of power transformer and has auto-adaptability. In order to improve the classification accuracy, the conception of combination is introduced into PNN. The fault diagnosis of power transformer is consisted of 4 Probability neural networks in this paper. PNN1 is used to classify the normal and fault. PNN2 is used to classify the heat fault and partial discharge (PD) fault. PNN3 is used to classify the general overheating fault and severe overheating fault. PNN4 is used to classify the partial discharge fault, and energy sparking or arcing fault. The example shows that the effect of combinatorial PNN is a good classifier in the fault diagnosis of power transformer. The combinatorial PNN has better diagnosis accuracy than BPNN and FUZZY algorithm.
Boundary exploration algorithm of disaster environment of coal mine based on multi-UAVs

This paper proposes a boundary exploration algorithm of multiple unmanned aerial vehicles (multi-UAVs) under the disaster environment of coal mine. Firstly, the paper analyzed characteristics and disadvantages of the boundary exploration algorithm based on the utility value. Secondly, the angle factor in the process of navigation is taken into consideration and the dispersion function is introduced. As a result, a novel improved boundary exploration algorithm thus is proposed. Finally, the detection effect is compared between the two algorithm by simulation experiment. The simulation results shows that the improved boundary exploration algorithm can shorten the detection time and reduce the phenomenon of repeated coverage and crowding. Besides, the energy required for UAVs is reduced by about 13.5%.
An improved RM algorithm for preventing streaming media tasks from starvation

A streaming media system is a soft real-time system according to streaming media service features. Some soft real-time tasks and best-effort tasks will be starved when using traditional RM algorithm to schedule streaming media services. Aiming at this problem, an improved RM algorithm with elastic priority is proposed in this paper. The algorithm can prevent periodic streaming media tasks from starvation, improving QoS of streaming media system. Some simulation experiments are given in the paper, and these experiments illustrate that the algorithm can reach our anticipative target.
Application of Kernel C-Means Clustering and Dempster-Shafer Theory of evidence in power transformers fault diagnosis

Transformers fault diagnosis plays a vital role in running security and reliability. The detected information is collected from the disperse sensor, which is lack of the data fusion analysis and easily lead to decision error and leak. A model of oil-immersed transformer fault diagnosis based on the collaborative method of Kernel C-Means Clustering (KCM) and multi-source information data fusion is presented. The basic idea is that the trained samples are clustered first by using KCM, and then the Dempster-Shafer theory of evidence Fusion method is used to train the chosed sample, and decide the transformer fault. The result of the method shows that the above method can reduce the uncertainty efficiently and have a good performance of transformer fault diagnosis.
Impact Research of Process Parameters on Sink Index of Rapid Heat Cycle Molding Products

Taking the independently designed in-vehicle Bluetooth high glossy product as an example, in combination with the Taguchi experiment and the simulation technology, the experimental research of the impact of Rapid Heat Cycle Molding(RHCM) process on the sink mark of the product is conducted through the introduction of interference factors. The results show that the technical characteristics of high mold temperature cavity filling, pressure holding and quick cooling can effectively reduce the sink index of the RHCM molded product; the impact of the pressure factor and the temperature factor on the sink index of the RHCM molded product is more significant, while the time factor performs very slight impact, and the significance sequence of the impact of all the parameters is listed into "Packing Pressure -- Injecting Pressure -- Melt Temperature -- Mold Temperature -- Packing Time -- Cooling Time"; and compared with the common molding, the impact of pressure holding time on the sink index of the RHCM molded product is significantly increased.
Influence of Flux Diverter on Energy Storage Property of Small SMES Magnet Wound by 100-m-Class GdBCO Tape

Solenoid-type superconducting magnetic energy storage (SMES) magnets have strong anisotropic field dependence. To enhance the minimum critical current located at two end, a novel flux diverter with a raised edge is investigated in this paper. Five small solenoid magnets having different axial layers and a fixed tape usage are used to evaluate and compare the effects of the raised-edge diverters on self-inductance, minimum critical current, and maximum energy storage capacity. An energy storage parameter <inline-formula><tex-math notation="LaTeX">$K_{{{E}}}$</tex-math> </inline-formula> (J/m) obtained by dividing the maximum energy storage capacity by the tape usage, and an available energy parameter <inline-formula><tex-math notation="LaTeX">$E_{{\rm{use}}}$</tex-math></inline-formula> (J) at a fixed rated output current are introduced to evaluate the overall economical efficiency and to form some design guidelines for various small SMES magnets.
A controllable privacy protection framework in position-based routing for suspicious MANETs

The privacy protection is an important issue for suspicious MANETs, a kind of Mobile Ad-hoc Networks (MANETs) that the nodes do not trust each other. One vital problem of the privacy protection in suspicious MANETs is that the malicious nodes may abuse the privacy protection to initiate some attacks that can not be tracked. As a solution to this problem, this paper proposes a universal controllable privacy protection framework in position-based routing for suspicious MANETs. We integrate a Perturbation-based Privacy Preservation (PPP) mechanism and a Malicious Node Revocation (MNR) mechanism with the common position based routing protocols, and achieve the controllable privacy protection to nodes and the controllable security of networks. The analyses show that our framework can achieve the node control location privacy of the nodes, and can defend the malicious insider nodes and impersonation attacks with acceptable overheads in suspicious MANETs.
Fractional order model reference adaptive control for a hydraulic driven flight motion simulator

In this paper, the method of adaptive control with fractional order reference model was proposed. By introducing the fractional order calculus theory into control fields, it became much easier to promote the performance than integer order control system. Combining fractional order control theory and the traditional model reference adaptive theory, a new controller design was provided which added a fractional order reference model instead of the traditional integer order reference model. The results also are presented which indicate the validation of the modified model reference adaptive controller.
Notice of RetractionThe measure of national infrastructure project performance auditing risk's controllability: Based on Chinese experiences empirical study

The performance auditing of national infrastructure project is the important regulatory measure to improve the project's performance, and risk aversion of audit could improve the auditing's quality effectively. The paper conducted investigation in-depth based on the existing research, designed a set of measuring scale for national infrastructure project performance auditing risk's controllability, conducted the crossed empirical study taking 248 effective questionnaires as sample from government audit institutions, public accounting firms, universities, and tested the measure model of risk controllability. The results of factorial analysis showed that the risk controllability of national infrastructure project performance auditing was classified into four dimensions which were system and technology risk's controllability, operational risk's controllability, personnel quality's controllability and comprehensive risk's controllability, and they had reliability and validity. The paper discussed the influence of proportion of investment and scale of investment on auditing risk's controllability. The conclusion showed that the proportion of investment and scale of investment and auditing's risk controllability had positive correlativity.