nils-herrmann/clean_scirepeval huggingface dataset:
Nonlinear interval regression analysis based on spline fuzzy model with interval coefficients

Interval regression analysis is an effective method of handling uncertain and imprecise data. In this paper, a kind of spline fuzzy model is designed for modeling interval data. Spline function is chosen as the membership function, and the coefficients of this model are taken as interval-valued numbers. A target function based on approximation errors and specificity is proposed to improve the quality of estimated interval data. Accordingly, gradient descent algorithm is employed to tune these interval weights. Some numerical simulations are carried out to validate the effectiveness of the proposed spline interval regression model.
High data rate wireless system

We investigate the adaptive modulation and coding (AMC) scheme in a high data rate (HDR) system. The soft demodulation and turbo decoder techniques are proposed. Based on the forward link of the HDR, we perform simulations to evaluate the AMC scheme. We have also designed the transmitter and receiver using the multiple antenna diversity techniques STS (space time spreading) and BLAST (Bell Labs layered space-time). These two techniques can improve the system's spectral efficiency greatly. For BLAST, we choose the ZF-BLAST algorithm as detector. The simulation results show that they require lower SNR than conventional antenna, and this can equally translate into supporting higher data rates in the HDR.
Interactive Signal Control for Over-saturated Arterial Intersections Using Fuzzy Logic

On an arterial, over-saturated intersections raise the risk of congestion. Once congestion happens, it may spread to neighboring intersections along the road. This paper proposes an interactive fuzzy logic signal controller utilizing neighboring traffic information to avoid congestion under over-saturated condition. The fuzzy signal controller decides to extend, early cut or terminate a signal phase according to evaluation of traffic condition. The structure and method of fuzzy logic controller is stated. A simulation taken under Paramics is presented. Comparison with traditional vehicle-actuated method and local fuzzy control for signal intersection is also made. Results show the effectiveness of the proposed method.
Decentralized H∞ state feedback control for a class of switched linear interconnected systems

The problem of decentralized H∞ state feedback control for a class of large-scale systems made of switched linear systems is addressed. Using convex combinations approach, decentralized switching strategy and decentralized control laws are presented based on linear matrix inequalities. These switching strategy and control laws guarantee the global asymptotical stability of the controlled systems in the absence of disturbances. Moreover, a given disturbance attenuation level in the H∞ sense can also be satisfied when there are disturbances. Finally, a numerical simulation is given, which illustrates the effectiveness of the design methods.
Groupwise Bayesian dimension reduction

Nearly all existing estimations of the central subspace in regression take the frequentist approach. However, when the predictors fall naturally into a number of groups, these frequentist methods treat all predictors indiscriminately and can result in loss of the group-specific relation between the response and the predictors. In this article, we propose a Bayesian solution for dimension reduction which incorporates such group knowledge. We place a prior whose variance is constrained to the form of a direct sum on the central subspace and directly model the response density in terms of the sufficient predictors using a finite mixture model. This approach is computationally efficient and offers a unified framework to handle categorical predictors, missing predictors, and Bayesian variable selection. We illustrate the method using both a simulation study and an analysis of a temperature data set.
Phoneme variation based synthesized speech discrimination for speaker verification

How to discriminate the synthesized speech from the natural speech for speaker verification is addressed in this paper. With the development of HMM-based speech synthesis, it is easy to obtain high quality synthesized speech which sounds like target speaker, the robustness of synthesized speech become important for speaker verification. In this paper, a method based on the phoneme variation is proposed to discriminate synthesized speech from natural speech, which could be used as front-end module to detect the synthesized speech for speaker verification system. The experimental results show the effectiveness of the proposed method.
Robust Sound Event Classification Using Deep Neural Networks

The automatic recognition of sound events by computers is an important aspect of emerging applications such as automated surveillance, machine hearing and auditory scene understanding. Recent advances in machine learning, as well as in computational models of the human auditory system, have contributed to advances in this increasingly popular research field. Robust sound event classification, the ability to recognise sounds under real-world noisy conditions, is an especially challenging task. Classification methods translated from the speech recognition domain, using features such as mel-frequency cepstral coefficients, have been shown to perform reasonably well for the sound event classification task, although spectrogram-based or auditory image analysis techniques reportedly achieve superior performance in noise. This paper outlines a sound event classification framework that compares auditory image front end features with spectrogram image-based front end features, using support vector machine and deep neural network classifiers. Performance is evaluated on a standard robust classification task in different levels of corrupting noise, and with several system enhancements, and shown to compare very well with current state-of-the-art classification techniques.
Improved delay-dependent stability criteria for networked control systems

This paper is concerned with robust H∞ control of uncertain networked control systems with interval time-varying delay and packet dropout. For less conservative transformation, we propose a new integral inequality by dividing time interval into two subintervals and defining an appropriate scalar instead of introducing free weighting matrix. Based on this integral inequality, a new delay-dependent stability criteria for the systems are obtained by solving a convex domain. Moreover, we may obtain the feasible solutions by solving a minimization problem in terms of linear matrix inequalities. Finally numerical examples are used to illustrate the effectiveness and validity of the proposed methods.
Generic Carrier-Based Core Model for Undoped Four-Terminal Double-Gate MOSFETs Valid for Symmetric, Asymmetric, and Independent-Gate-Operation Modes

A generic carrier-based core model for undoped four-terminal double-gate (DG) MOSFETs has been developed and is presented in this paper. The model is valid for symmetric, asymmetric, and independent-gate-operation modes. Based on the exact solution of the 1-D Poisson's equation in a general DG MOSFET configuration, a rigorous derivation of the drain-current equations from the Pao-Sah's double integral has been performed. By using the channel carriers as the intermediate variable, a very compact analytical drain-current expression can be obtained. The model is extensively verified by comparisons with a 2-D numerical simulator under a large number of biasing conditions. The concise mathematical formulation allows the unification of various DG models into a carrier-based core model for a compact DG MOSFET model development.
MyBSP: An Iterative Processing Framework Based on the Cloud Platform for Graph Data

Massive cloud-based data-intensive applications (e.g., iterative MapReduce-based) could involve graph data processing. How to effectively analyze and process large-scale graph data is an unsolved challenging problem. We present a parallel computation framework, named MyBSP, which is inspired by Google's Pregel system. MyBSP supports and implements the Bulk Synchronous Parallel (BSP) programming model, and introduces a module of parallel execution unit to achieve iterative processing, which avoids the restart cost of computation jobs, and therefore reduces the I/O overhead (e.g., network communication and disk access). Furthermore, we implement the MyBSP-based PageRank algorithm. Some experiments are conducted to evaluate and compare the performance and scalability of our MyBSP prototype system with MapReduce model. The experimental results show that the speedup in MyBSP compared to MapReduce is about 3.5X for the small-size graph dataset. Meanwhile, the performance improvement of MyBSP also outperforms MapReduce a factor of 2.1 when processing the large-scale dataset.
Interface State Calculation of the Wafer-Bonded Ge/Si Single-Photon Avalanche Photodiode in Geiger Mode

More and more attention has been paid to the heterogeneous semiconductor device based on the wafer-bonding method due to its high-quality wafer-bonded active layer. In this paper, we first theoretically study the dependence of the single-photon properties, including the single-photon detection efficiency (SPDE), dark count rate (DCR), and afterpulsing probability (AP), of the wafer-bonded Ge/Si single-photon avalanche photodiode (SPAD) on the interface state at the Ge/Si (hydrophilic reaction) and Si-Si (hydrophobic reaction or surface-activated method) wafer-bonded interface based on the Poisson statistics. It is found that the interface state density and the energy level position of the interface state significantly affect the linear-mode electric field distribution (avalanche probability), 3-dB bandwidth (dark carriers), gain (quantum efficiency), and effective transit time (trapped carriers), which in turn affect the DCR and the SPDE. Furthermore, the dependence of the AP on the avalanche charge and the interface recombination rate is clarified as well. It is expected that this paper may give guidance for the fabrication of high-performance SPAD.
Model predictive control for unconstrained polytopic systems under try-once-discard protocol

This paper is concerned with the model predictive control (MPC) problem for a class of unconstrained polytopic systems under the try-once-discard (TOD) protocol. Under the TOD protocol, only the sensor gaining the highest priority is allowed to transmit the data to the controller at each time instant. In this case, a switched system related to the activated sensor is constructed and, based on which, a “min-max” problem is formulated to facilitate the design of the MPC-based controllers. Furthermore, by taking the influence of the TOD protocol into consideration, an online minimization problem on certain upper bound of the objective function is put forward in order to acquire locally optimal control performance under the TOD protocol. Finally, a simulation example is used to illustrate the effectiveness of the proposed technique.
Fractional order Liu-system synchronization and its application in multimedia security

This paper proposed a method to achieve the projective synchronization of fractional order Liu-system. Based on constructing an approximation integer order system for the fractional order Liu-system, a control algorithm is proposed to improve on the projective synchronization in the new integer order systems. The synchronization method is adopted to realize the generalized synchronization of 2.7 order Liu-system, and the simulation result verify the effectiveness. This control scheme can be used in some multimedia security and information transmission based on Internet.
A graphical user interface (GUI) with FDTD for electromagnetic scattering from a dielectric sphere

Various numerical techniques have been developed for modeling electromagnetic field propagation in conducting and dielectric media. The validity of these techniques is usually verified by comparison to the numerical solution of canonical or non-canonical problems of well-known solutions. MATLAB language in complex numerical analysis, matrix computation, digital signal processing has obvious advantages, so in the production needs of large amounts of data for various operations and processing software, in the GUI using MATLAB language programming is a good choice. This paper presents a software package designed as an educational and validation tool for the purpose of verifying the availability of Finite-Difference Time-Domain (FDTD) and visualizing the physical phenomena of electromagnetic scattering from a dielectric sphere. A friendly graphical user interface (GUI) is constructed to calculate and display the near and far fields due to an excitation from a plane wave using the FDTD method.
Three-tiered sensor networks architecture for traffic information monitoring and processing

With the advent and applications of sensor networks, pervasive traffic information can be gathered rapidly and collaboratively, which can upgrade traditional traffic monitoring system and enable many unprecedented services for travelers. In this paper, three-tiered sensor network architecture is proposed to approach the novel traffic information service system that would dramatically improve the ways and qualities of traffic information collection. A brief analysis of the important requirements of system, as well as the key issues that are faced in the design process, is described. The detailed strategies at each level, from the network architecture, to the sensor unit configuration and software deployment, to the operation of system, are explained. The implementation of a pilot platform is discussed in the end.
A Two-Stage Stochastic Programming Approach Considering Risk Level for Distribution Networks Operation With Wind Power

Wind power is a challenge for active operation of distribution networks because of its stochastic nature. In this paper, a two-stage stochastic-programming-based optimal power flow model is proposed for the short-term operation (e.g., 1 h ahead) of distribution networks with wind power. In this model, the wind power is dispatched in the first stage, and then, in the second stage, not only the additional cost caused by the difference between dispatched wind power and actual wind power is considered into the recourse cost but also the operational risk is introduced to characterize the system security level. Both the number and severities of violations and near violations can be reflected by the risk index. Thus, the impacts of the wind power dispatch decision on the system operation regarding the realizations of wind power can be comprehensively evaluated. The 69-bus system is used to verify the effectiveness of the proposed approach. Simulation results show that the proposed approach can give a much lower risk level with a small increase in operational cost.
Architecture and simulation of sensor network system for urban surveillance

This paper proposes a novel urban surveillance system architecture based on the concept of sensor network. Firstly, this paper describes the system briefly and presents the architecture of the overall system. The function of each part and information flow diagram are also presented. Furthermore, ARM chip is introduced to develop a sensor node and wireless communication module is integrated into this device. In order to forecast and analyze the properties of the communication system, a simulation study using a practical road network is presented. The simulation results show that the system proposed in this paper is valid and applicable. A conclusion is drawn in the final part and some theoretical and technical problems are also discussed as future key works
Synchronization via sampled-data output-feedback for a class of chaotic systems

This paper considers the problem of using discrete-time output-feedback to synchronize the slave chaotic system with the master chaotic system. The problem is of practical importance since in practice not all the states are measurable and the measurement is transmitted as a sampled signal. In this paper, sampled-data output-feedback is used to control the slave system and a sampling period is carefully determined such that the slave system states will asymptotically converge to the master system states. The effectiveness of the proposed approach is also illustrated by the simulation for a chaotic Chua oscillator.
Image classification by exploiting the spatial context information

Finding the effective image representation is an important problem for classification. Previous approaches have demonstrated the utility of the bag-of-feature (BoF) models. These methods are interesting due to the computational efficiency and conceptual simplicity. However, it is achieved by discarding the spatial context information. Furthermore, it may suffer from the quantization error introduced by the hard quantization of local features. To address these issues, we proposed an effective image representation that exploits the spatial context information. Specifically, the visual codebook is constructed on the pair-wise descriptors lied in spatial neighborhoods which can capture the near-context information, and the spatial pyramid structure is further combined to capture the far-context information. Then for image classification, an effective soft quantization method is proposed, which can accurately represent the original features by the regression of neighboring visual words. To evaluate the effectiveness of the proposed method, we compared it with existing BoF representations on benchmark datasets including Scenes-15 and Caltech 101 in image classification. The experimental results demonstrate the superiority of the proposed method compared with state-of-the-art methods.
Evaluating Soil Organic Matter with visible spectroscopy

This study was conducted to develop fundamental relationships between soil organic matter and their visible reflectance spectra. The ultimate goal of this work is to develop a real-time soil property sensor for use in effective farm management. A total of 43 soil samples collected from 0-20 cm depth were used for analysis. Organic matter in soils was analyzed by conventional method in a laboratory. Visible reflectance spectra of the samples were measured by a spectrophotometer in the range of 325 to 1100 nm with a 1 nm increment. Partial least square analysis was used to build prediction models with a calibration data set of 34 randomly chosen samples. The remaining 9 samples were used to validate the models. The prediction models for measured organic matter yielded r2 values of 0.7312. This result could be useful in the development of soil property sensor for site-specific management
Application of Entropy Weight Theory in Owner's Risk Decision under EPC Model

To meet the requirements for being geared to the international standards and for self-development of an enterprise, it becomes more and more common for an enterprise to execute EPC projects. Comparing with the traditional patterns of contract, the owners will not involve in the specific process of project in EPC model, so the decision risk of the owners is increased. Through analyzing of EPC contract model, entropy weight theory is introduced into risk decision of EPC project. In the absence of index weight given by experts, multiple EPC schemes are evaluated and ranked sequence with risk level closeness degree effectively by the entropy weight model, and the optimum scheme is obtained definitely.
Study on Arbitrary Distribution in Cluster Analysis

Three clustering methods are presented and discussed by experimental analysis. The results by using three clustering methods which are partitioning methods, hierarchical methods and density-based methods visually illustrate the clustering results, in two-dimensional data sets as experimental data are used. Clearly, when the original data set is spherical shape, most of the cluster methods can get good clustering results. Partitioning methods (K-means) can't handle clusters of arbitrary shapes and different sizes, and can't handle clusters of varying densities. Hierarchical methods can identify globular clusters well whether globular clusters is in same densities or in varying densities, and this approach can handle clusters of winged shapes of well departed, but cannot handle clusters of winged-globular shapes. Based on the notions of density and density reachable, the CADD (Clustering Algorithm based on object Density and Density-reachable) can find clusters of arbitrary shapes and different sizes, and can handle clusters of varying densities.
Selection of appropriate technology based on Fuzzy Comprehensive Evaluation

Appropriate technology represents an important theory of technology and is drawing more and more attention nowadays. It is of great strategic significance for an enterprise or a country to select appropriate technology rationally and scientifically. But the previous selection of appropriate technology is based on experience only for lack of in-depth study of theory, especially quantitative study. In this paper the author thinks that Fuzzy Comprehensive Evaluation (FCE), as a feasible and effective method, can be applied into the selection of appropriate technology. After summarizing and sorting out the initial, multilevel and multioperator mathematical models of FCE, the author proposes and analyses preliminarily the index system of appropriate technology selection and its general method with FCE. He further illustrates the general process of appropriate technology selection with a practical example, in the hope of realizing the selection to be scientific, standardized and quantitative.
General algorithm for the derivation of transfer functions of multidimensional (M-D) discrete systems

A general algorithm to derive the transfer functions of M-D discrete systems has been presented. The given M-D discrete system with arbitrary order and arbitrary topology structure can be analyzed. We construct the algorithm by nodal equations, M-D DFT, M-D signal flow graph. Resorting to the algorithm, we develop computer aided analysis software for deriving the transfer function of a M-D discrete system. An example for analyzing a 2-D lattice digital filter shows our algorithm to be valid.
Passive Noise Shaping in SAR ADC With Improved Efficiency

This brief reports a passive noise-shaping (PNS) scheme for successive approximation register (SAR) analog-to-digital converter (ADC) based on the two-step integration with passive gain and comparator gain techniques. The analysis shows that the proposed method achieves a better noise-shaping (NS) efficiency than prior arts, which enhances the noise attenuation by 14 dB. A design example is provided which further adopts the delta-sampling technique to relieve the conversion efficiency loss due to the oversampling in the NS SAR ADC. The efficiency of the proposed PNS scheme and the performance of the ADC are verified by simulation achieving a 13.2 effective number of bits with a 10-b ADC architecture and eight conversion cycles for a signal bandwidth of 2 MHz sampled at 100 MS/s. The calculated Schreier figure of merit (FoM) and Walden FoM are 176.8 dB and 16 fJ/conv.-step, respectively.
Multi-Graph Semi-Supervised Learning for Video Semantic Feature Extraction

This paper proposes a video semantic feature extraction approach based on multi-graph semi-supervised learning, which aims to simultaneously deal with the insufficiency of training data and the curse of dimensionality. In contrast to traditional graph-based semi-supervised learning, which generates graph from high-dimensional low-level features, we separate the original low-level features into multiple modalities with minimum correlations, and thus multiple graphs are obtained from these modalities. This way can tackle the curse of dimensionality brought by the high-dimensional feature space. We then propose a criterion to optimally fuse these graphs based on the pairwise relationships among training samples, and implement semi-supervised learning on the fused graph. Experimental results have demonstrated the effectiveness of the proposed approach.