nils-herrmann/clean_scirepeval huggingface dataset:
Intelligent attitude control of space target based on characteristic model

With the purpose of assisting space robots to make on-orbit experiments, a space target is constructed. In order to realize good performance of attitude control for the space target, this paper proposes a characteristic modeling method. In this method, the space target, as a high-order plant, is described by a slowly time-varying difference equation. Corresponding to this method, a parameter estimation algorithm is given. Based on the characteristic model, an intelligent attitude controller for the space target is derived. The controller includes four components such as maintaining/tracking, feedback, logic integral and logic differential control law. The effectiveness of the proposed modeling and control method is verified through the experiments on a test-bed.
Research on comprehensive assessment of ecological security in Hebei Mountains

Ecological Security Assessment is a new field in Sustainable Development study. In order to curb the deterioration of the ecological environment, we should conduct timely researches on the comprehensive evaluation of ecological security, which is recognized as the most powerful method to protect the regional economy effectively. Based on the sequence data of 1987, 2000 and 2005, the study takes Hebei Mountains, which is under-developed and has frangible ecological system, as a research case. In the light of Remote Sensing (RS), Geographic Information System (GIS) and Pressure Feedback Control (PFC) model, the evaluation systems were established. Ecological security conditions of Hebei Mountains were evaluated by synthetic index. The regional differences and differentiation rules of ecological security were revealed to provide proof of further proposed not only to meet the needs of decision-making but also maintain control measures of regional future development and to promote regional natural, economic, social sustainable development harmoniously.
Electronic circuits automatic design algorithm

For the evolutionary electronic circuit design, the representation of the circuit is important, because the representation of the circuit may affected the significance solution circuit or the optimize solution, and also should speeds up the convergence speed of the algorithm search. The hardware representation methods mainly include binary bit string representation and Cartesian Genetic Programming representation. In this paper, we use a hybrid representation method-tree representation and Cartesian Genetic Programming. Based on this representation, we designed the evolutionary algorithm for the electronic circuit design. The experiment results showed our algorithm have higher successful rate compared with the traditional method.
Intra-conversation intra-speaker variability compensation for speaker clustering

Recently, the speaker clustering approach exploiting the intra-conversation variability in the total variability space has shown promising performance. However, there exists the variability in different segments of the same speaker within a conversation, termed as intra-conversation intra-speaker variability, which may scatter the distribution of the corresponding i-vector based representation of short speech segment, and degrades the clustering performance. To address this issue, we propose a new speaker clustering approach based on an extended total variability factor analysis. In our proposed method, the intra-conversation total variability space is divided into the inter-speaker and intra-speaker variability space. And by explicitly compensating the intra-conversation intra-speaker variability, the short speech segments would be represented more accurately. To evaluate the effectiveness of the proposed method, we conduct extensive experiments on NIST SRE 2008 summed channel telephone dataset. The experimental results show that the proposed method clearly outperforms the other state-of-the-art speaker clustering techniques in terms of clustering error rate.
Eigenvalue-based approach to global consensus of nonlinear multi-agent systems

This paper investigates the global consensus of asymmetrically coupled multi-agent systems with nonlinear dynamics. By employing a Lyapunov function, a consensus criterion is presented by checking an inequality involving the smallest eigenvalue except zero of a redefined symmetric matrix associated with the asymmetric Laplacian matrix to guarantee the global consensus of the considered multi-agent systems. In particular, we show that the presented criterion is equivalent to the result by defining a generalized algebraic connectivity [21] corresponding to the Laplacian matrix. Numerical simulations are carried out to demonstrate the effectiveness of the proposed method.
Performance evaluation of deep bottleneck features for spoken language identification

Our previous work has shown that Deep Bottleneck Features (DBF), generated from a well-trained Deep Neural Network (DNN), can provide high performance Language Identification (LID) when Total Variability (TV) modelling is used for a back-end. This may largely be attributed to the powerful capability of the DNN for finding a frame-level representation which is robust to variances caused by different speakers, channels and background noise. However the DBF in the previous work were extracted from a DNN that was trained using a large ASR dataset. Optimal LID DBF parameters may differ from those that are known to be optimal for ASR. Thus this paper focuses on investigating different DBF extractors, input layer window sizes and dimensionality, and bottleneck layer location. Additionally, principal component analysis (PCA) is used to decorrelate the DBF. Experiments, based on the Gaussian Mixture Model-Universal Background Model (GMM-UBM) operating on the NIST LRE 2009 database, are conducted to evaluate the system. Results allow comparison between different DBF extractor parameters, as well as demonstrating that LID based on DBF can significantly outperform the conventional shift delta cepstral (SDC) features.
ASIC: Supervised Multi-class Classification using Adaptive Selection of Information Components

In this paper, a supervised multi-class classification approach called Adaptive Selection of Information Components (ASIC) is presented. ASIC has the facilities to (i) handle both numerical and nominal features in a data set, (ii) pre-process the training data set to accentuate the spatial differences among the classes in the training data set to reduce further computational load requirements, and (iii) conduct supervised classification with the C-RSPM (Collateral Representative Subspace Projection Modeling) approach. Experimental results on a variety of data sets have shown that the proposed ASIC approach outperforms other well-known supervised classification methods such as C4.5, KNN, SVM, MLP, BN, RF, Logistic, and C-RSPM, with higher classification accuracy, lower training and classification times, and reduced memory storage and processing power requirements.
WaveDec: A Wavelet Approach to Identify Both Shared and Individual Patterns of Copy-Number Variations

Copy-number variations (CNVs) are associated with complex diseases and particular tumor types. Array-based comparative genomic hybridization (aCGH) is a common approach for the detection of CNVs. Traditional CNV detection methods for multiple aCGH samples mainly use batch samples to find common variations, not accounting for the individual characteristics of each sample. Accurately differentiating both the commonly shared and the individual CNV patterns is pivotal to identify cell populations, or to distinguish cell growth (as in cancer) from invasion of new cells. Our preliminary results have now demonstrated that both the shared and individual CNV patterns have distinctive characteristics after wavelet transform.  To exploit these characteristics, we propose to formulate a quadratic data-separation problem within the wavelet space to discriminate the shared and individual CNVs from raw data. We have elaborated a numerical solution and shown that the solution can be obtained by solving decoupled subproblems. By this approach, computational costs can be limited, enabling efficient application in the analysis of large sequencing datasets.  The advantages of our proposed method, called WaveDec, have been demonstrated by comparison with popular CNV-detection methods using synthetic and empirical aCGH data. The performance of WaveDec was further validated by experiments with single-cell-sequencing data.   WaveDec can successfully differentiate shared and individual patterns, and performs well even in data contaminated with high levels of noise.  Both the shared and individual patterns can be uniquely characterized as well as effectively decomposed within the wavelet space.
Designing Electronic Circuits by Means of Gene Expression Programming

This work investigates the application of gene expression programming (GEP) in the field of evolutionary electronics. GEP is a genetic algorithm as it uses populations of individuals, selects them according to fitness, and introduces genetic variation using one or more genetic operators. We propose the new means for designing electronic circuits and introduce the encoding of the circuit as a chromosome, the genetic operators and the fitness function. For the case studies this means has proved to be efficient, experiments show that we have better results
Evaluating the Contributions to Knowledge of the Staff Based on Performance Evaluation System for Project Management Enterprise

This paper investigates how to establish a performance evaluation system mainly based on the knowledge contribution of the staff in the knowledge management information platform of project management enterprise. First, the necessity of creating knowledge-based enterprise was analyzed and many existing problems about how to value contribution to the knowledge of the staff were discussed in the current enterprise. Secondly, a performance evaluation system, which take the advantage of the existing information platform and used analytic hierarchy process and Delphi methods to value the knowledge contribution of the staff, was presented and established. Finally, it was completed with corresponding incentive measures. The investigation proves that the system was beneficial to effectively execute human resource management and knowledge management, and to enhance the enterprise's core competitiveness and sustainable development capability.
Optimal torque control constraint rigid spacecraft attitude maneuver with constraints

In this paper, the existed problems about optimal torque control of rigid spacecraft attitude maneuver with constraints are discussed. The pseudospectral method is adopted to solve the problem which has torque constraint or state constraints, and the simulation analysis on the problem in the case of unconstrained or the torque constraint or state constraints is made. The simulation results show that this problem can be effectively solved by the pseudospectral method.
Micro-satellite attitude determination algorithm based on adaptive UKF

This paper addresses the problem of Micro-satellite attitude determination via adaptive unscented kalman filter (UKF). First, mathematical model for satellite attitude determination system is established based on bi-Vector method; then an adaptive UKF is constructed to achieve satellite attitude determination. Compared with UKF-based approach, the present method has an advantage to deal with the occurrence of external disturbance and the noise statistics. In adaptive UKF design, the adaptive factors are introduced to real-time adjust covariance matrix and gain matrix such that the present filter has self-adaptive ability. Finally, simulation results on a satellite example are provided to verify the effectiveness of the proposed attitude determination technique.
Design of Routing Protocol and Node Structure in Wireless Sensor Network Based on Improved Ant Colony Optimization Algorithm

Wireless sensor network is composed of many wireless sensor nodes with the same or different functions. A typical sensor node consists of four parts: sensor unit, information processing unit, wireless communication unit and energy supply unit. In this paper, the existing ant colony algorithm is analyzed, and an improved ant colony optimization algorithm is proposed. The paper presents design of routing protocol and node structure in wireless sensor network based on improved ant colony optimization algorithm. The experimental results show that the proposed method is effective.
Simulation Study of Mixed Traffic in China--a Practice in Beijing

Traffic congestion on urban roads is forcing city authorities to look at innovative transportation solutions. Traffic simulation and other advanced technologies have been used to provide solutions for maximizing the capacity of urban transportation networks and to evaluate and optimize traffic management and signal settings. The authors have investigated about 10 urban signalized intersections to evaluate the adopted traffic organization and signal settings in Beijing. One of these intersections was taken as example to illustrate how to assess the effectiveness of ameliorative measures using the microscopic simulation model.
Study and application on aided innovation design method based on semantic analysis and TRIZ theory

For the situation that complexity of the design method, high demand of knowledge for the designers in the conceptual design process, and which is difficult to realized, this paper presents the solving process of innovative problems based on the TRIZ theory. Ontology and knowledge database is constructed based on TRIZ. The application-oriented knowledge database is constructed, and the description of innovative problems are associated with knowledge. The innovative design method is developed for designers to achieve the relevant knowledge. And the aided innovation system is developed based on semantic analysis technology, which can automatically recognize the description of the problem. The feasibility and effectiveness of the innovative design method and the aided innovation system is verified through the case of innovative design of the coupling diaphragm component.
A hybrid technique for acoustical property of induction cookers

Based on the finite volume method (FVM), large-eddy simulation (LES) and Ffowcs Williams-Hawkings (FWH) analogy algorithm, a new hybrid technique for acoustical property simulation of induction cookers, so-called FVM-LES-FWH method, is developed in this paper. The FVM, LES and FWH are used to establish numerical equation, compute sound sources, and predict far-field sounds, respectively. To verify the FVM-LES-FWH approach, the simulated results are compared with experimental data. The results suggest that the hybrid FVM-LES-FWH method is accurate and effective for acoustical analysis of the induction cookers. In view of the applications, the newly proposed method can be extended to other fields for solving aeroacoustic problems in engineering.
Apache spark based urban load data analysis and forecasting technology research

Considering the complexity of data analysis caused by increasing urban load data, an Apache Spark based urban load analysis and forecasting technology is proposed in this paper. Firstly, the large-scale data processing platform is designed. Secondly, statistics of urban load data are processed by transformation and action in the form of resilient distributed datasets (RDDs). Thirdly, load forecasting model based on Dynamic Bayesian Network (DBN) is built for short-term urban load forecasting. GraphX is used for graph-parallel computation. Finally, the result of load analysis is presented and forecasting results of DBN are compared with other models. The comparison outcome shows that the DBN model implemented in Spark has better accuracy in load forecasting.
The Application of Case Teaching Combined with Task-Driven Teaching Method in Obstetric Nursing Teaching

The aim of the article is to explore whether case teaching combined with task-driven teaching is better than the traditional methods in obstetric nursing teaching. Methods 118 nursing students were divided randomly into experimental and control group, 59 student nurses were selected as the experimental group, using teaching method combined with task-driven, and other 59 student nurses were selected as the control group, by using traditional teaching methods. Each student was evaluated by theory, operational skills and clinical practice ability.  The excellent and good rate was 81.4% (48/59) in the observation group and 55.9% (33/59) in the control group, the difference was statistically significant (P
Electrothermal modelling of novel through-silicon carbon nanotube bundle vias (TS-CNTBV)

In this paper, we at first propose an almost-carbon interconnects structure consisting of through-silicon carbon nanotube bundle vias (TS-CNTBV), graphene-based transmission lines (GTL) and metal bumps. In order to characterize its electrical performance, an effective complex conductivity of the CNT bundle is introduced and studied for different temperatures and CNT radii. Further, the TS-CNTBV transmission characteristics are investigated based on its distributed transmission line model, and in particular, effects of metallic bump attached to the TS-CNTBV are considered and treated in an appropriate way.
Assessmentof the effect of common cause failure and diversity on diagnostic coverage of a self-checking pair

It is important to consider diagnostic coverage (DC) of the automatic diagnosis (AD) scheme in course of the design of highly reliable systems in which redundancy technique is usually applied. Considering the nature of AD is to introduce some form of redundancy, its performance will be affected by common cause failure (CCF). Due to that diversity is an effective antidote for CCF, it is desirable to assess the effect of diversity and CCF on DC. Therefore, a novel method is proposed to quantitatively compare two different AD schemes which include identical redundancy and diverse redundancy based on the protection (data integrity) they provide against CCF in this paper. The AD schemes considered are implemented in the form of self-checking pairs. For the first time, the relationship among diversity, CCF rate and DC is quantified by the proposed method. A two-channel redundant system in which each redundant element employs a self-checking pair is used as an illustration case. The results confirm that the diversity technique increases the DC of the self-checking pair.
An enterprise crisis predicting system based on outlier data mining

Many factors in an enterprise are playing important roles in intense commercial competitions. Some are positive and some are negative. If dealing with these factors correctly, potential crisis will be found to avoid defeats, even bankrupt. Designing a crisis predicting system is necessary. An excellent predicting can not only predict expecting crisis and take controlling measures, but also can provide enough preparation and plan to deal with crisis smoothly. The factors are the basis data to be analyzed to support such a system and maybe they are quantitative or qualitative. In order to solve such problems as half-structured and non-structured data analysis in enterprise crisis predicting system, a predicting system based on outlier data mining is put forward. The system organization, frame construction, function and working principles are illustrated. And the working process is showed by an example of cheat predicting. The experimental results show that this method is efficient and it has wide utilization in predicting fields.
Robust sound event recognition using convolutional neural networks

Traditional sound event recognition methods based on informative front end features such as MFCC, with back end sequencing methods such as HMM, tend to perform poorly in the presence of interfering acoustic noise. Since noise corruption may be unavoidable in practical situations, it is important to develop more robust features and classifiers. Recent advances in this field use powerful machine learning techniques with high dimensional input features such as spectrograms or auditory image. These improve robustness largely thanks to the discriminative capabilities of the back end classifiers. We extend this further by proposing novel features derived from spectrogram energy triggering, allied with the powerful classification capabilities of a convolutional neural network (CNN). The proposed method demonstrates excellent performance under noise-corrupted conditions when compared against state-of-the-art approaches on standard evaluation tasks. To the author's knowledge this in the first application of CNN in this field.
A 0.6-V 10-bit 200-kS/s Fully Differential SAR ADC With Incremental Converting Algorithm for Energy Efficient Applications

This paper proposes a fully differential 10-bit energy efficient successive approximation register (SAR) analog-to-digital converter (ADC) by using incremental converting method. The voltage difference of the input between two successive samples is acquired and resolved. A judge window is properly designed, and several conversion steps of significant bits could be skipped when the voltage difference is within the preset window. Thus, the power consumptions of the digital-to-analog converter (DAC), logic circuit, and comparator are greatly saved. Moreover, the differential structure also helps to suppress common mode noise and even harmonic noise. The design is implemented with a standard 0.18-μm CMOS technology. Test results show that the power consumption of the ADC with the proposed algorithm is reduced by at least 42.8% comparing to the conventional structure. The measured DNL and INL are within 0.29 LSB and 0.80 LSB, respectively. At a 0.6-V supply and a 200-kS/s sampling rate, the ADC achieves an ENOB of 9.34 and a figure-of-merit of 8.87 fJ/conv.-step.
A simulation study on agent-network based route guidance system

This paper introduces an intelligent route guidance system based on agent-network, which can automatically guide vehicles by voice. Firstly, the paper presents the system architecture and describes the establishment method of each part. The concept of agent network is introduced to build up the system. Furthermore, a dynamic route algorithm is presented in brief. Communication system integrating the existed infrastructure is discussed and some simulation results are provided to testify the applicability of the proposed wireless data communication system. Moreover, a simulation case study using PARAMICS shows that the system proposed in this paper is feasible and practical. Finally, a conclusion is drawn and some theoretical and technical problems in this system are also presented as future key works.
Inception Single Shot MultiBox Detector for object detection

In the current object detection field, one of the fastest algorithms is the Single Shot Multi-Box Detector (SSD), which uses a single convolutional neural network to detect the object in an image. Although SSD is fast, there is a big gap compared with the state-of-the-art on mAP. In this paper, we propose a method to improve SSD algorithm to increase its classification accuracy without affecting its speed. We adopt the Inception block to replace the extra layers in SSD, and call this method Inception SSD (I-SSD). The proposed network can catch more information without increasing the complexity. In addition, we use the batch-normalization (BN) and the residual structure in our I-SSD network architecture. Besides, we propose an improved non-maximum suppression method to overcome its deficiency on the expression ability of the model. The proposed I-SSD algorithm achieves 78.6% mAP on the Pascal VOC2007 test, which outperforms SSD algorithm while maintaining its time performance. We also construct an Outdoor Object Detection (OOD) dataset to testify the effectiveness of the proposed I-SSD on the platform of unmanned vehicles.