nils-herrmann/clean_scirepeval huggingface dataset:
Integration of System-Dynamics, Aspect-Programming, and Object-Orientation in System Information Modeling

Contemporary information modeling of enterprise systems only focuses on the technical aspect of the systems, though it is known that they are social-technical (socio-tech) systems in essence. In fact, there are many lessons that can be learned from failures in the management of enterprise systems, which range from a small one (e.g., failure to install a printer driver) to a large one (e.g., nuclear power plant post-accident management). This paper, therefore, proposes that the enterprise system should be viewed as a socio-tech system. The paper presents a novel integrated approach to information modeling of socio-tech enterprise systems. In particular, the approach integrates object-orientation, systems-dynamics (as a means to represent high-level dynamics), and aspect-programming. The paper discusses an example to illustrate how the proposed approach works.
A study of evaluating indexes for the reliability of equipment support network

The evaluating indexes for the reliability of equipment support network plays an important role in the study of equipment support networking under the information conditions, and the evaluating indexes are the precondition and foundation for testing the reliability of equipment support network. The paper firstly defines equipment support network and the reliability of the network, and then makes the principles in building up index system by analyzing the factors influencing the reliability for equipment support network. As the basis, this paper builds up the evaluating index system of the reliability of equipment support network in the three aspects of invulnerability, survivability and availability, and defines the meaning of the indexes in order to provide decision makers a reference for implementing effectively evaluation of equipment support network reliability.
A New Iterative Multiuser Detection Algorithm for MIMO CDMA System

The paper presents a new iterative receiver algorithm which is proposed for the high order modulation system. It has low complexity and good performance. Firstly, the algorithm uses zero forcing multiuser detection (MUD) algorithm. Then it is performed by a soft-input soft-output (SISO) multiuser detector and a bank of SISO channel decoders, through an iterative process. And the SISO multiuser detector adopts a novel soft interface cancellation method based on a linear minimum mean-squared error (LMMSE) filter. This makes the detector overcome the limitation of binary phase shift keying (BPSK) while the complexity is polynomial in terms of codes to be detected in the system. Simulation results show that, the performance of the proposed iterative receiver can approach that of single code maximal likelihood detection (MLD) only after two iterations.
Robust control of spacecraft eigenaxis maneuver via integral sliding mode

The control problem of spacecraft eigenaxis maneuver in the presence of parametric uncertainty and external disturbance is addressed in this paper. Modified Rodrigues parameters (MRPs) are utilized as the attitude parameters for the non-redundancy. An eigenaxis maneuver control law with feedback and feed-forward terms is presented for the nominal system. The transient response of close loop system can be adjusted according to an approximate second order system. To improve the robustness, integral sliding mode (ISM) technique is adopted to reject undesirable effects and track nominal trajectory. The resulting controller can perform the eigenaxis maneuver in the presence of parametric uncertainty and external disturbance while possess good system performance. The effectiveness of the proposed method is demonstrated by simulation results.
Robotic cognitive behavior control based on biology-inspired Episodic memory

This paper proposes a framework called Episodic memory-driving Markov decision processes (EM-MDPs) for incremental self-learning of robotic experience and cognitive behavior control under uncertainty. The framework simulates the organization process of episodic memory by introducing the neuron stimulation mechanism. Firstly, episode model is built, and the activation and stimulation mechanism of state neurons is proposed based on cognitive neuroscience. Secondly, episodic self-learning is also proposed by utilizing sparse distributed memory (SDM) through Hebbian rules, to realize memory real-time storage, incremental accumulation and integration. Finally, a robotic cognitive behavior control approach is established. Neuron synaptic potential is introduced for event localization. Robot can evaluate the past events sequence, predict the current state and plan the desired behavior. Two main challenges in robot behavior control under uncertainty are addressed in the paper: high computational complexity and perceptual aliasing. The proposed system is evaluated in several real life environments for mobile robot. The applicability and the usefulness of the developed method are validated by the results obtained.
The Development of Fuzzy Rough Sets with the Use of Structures and Algebras of Axiomatic Fuzzy Sets

The notion of a rough set was originally proposed by Pawlak underwent a number of extensions and generalizations. Dubois and Prade (1990) introduced fuzzy rough sets which involve the use of rough sets and fuzzy sets within a single framework. Radzikowska and Kerre (2002) proposed a broad family of fuzzy rough sets, referred to as (phi, t)-fuzzy rough sets which are determined by some implication operator (implicator) phi and a certain t-norm. In order to describe the linguistically represented concepts coming from data available in some information system, the concept of fuzzy rough sets are redefined and further studied in the setting of the axiomatic fuzzy set (AFS) theory. Compared with the (phi, t)-fuzzy rough sets, the advantages of AFS fuzzy rough sets are twofold. They can be directly applied to data analysis present in any information system without resorting to the details concerning the choice of the implication phi, t-norm and a similarity relation S. Furthermore such rough approximations of fuzzy concepts come with a well-defined semantics and therefore offer a sound interpretation. Some examples are included to illustrate the effectiveness of the proposed construct. It is shown that the AFS fuzzy rough sets provide a far higher flexibility and effectiveness in comparison with rough sets and some of their generalizations.
A fast method for particulate microflows

Modeling of active control of microparticles is important in a number of different microfluidic applications, including bioflows and self-assembled structures. We present here a fast method for simulating the dynamics of many particles in complex microgeometries. The method is based on a spatial distribution of finite force multipoles and requires much less resolution than full direct numerical simulations. The numerical formulation is summarized, and examples are given for Stokes flow and low Reynolds number flow in smooth and rough microchannels. Comparisons made with full direct numerical simulations and experiments validate the accuracy and efficiency of the proposed approach.
Microseismic location method using Ω penalty function based ant colony optimization

The development of seismic monitoring brings hope to the prediction of the rockburst and other seismic hazards, where the determination of the potential hypocenter is the key point. Here the equations are listed according to the measured data of sensors, and the seismic location is solved from the equations. The accuracy of location is closely related to the solve method. In this paper, a microseismic location method using Ω penalty function based on ant colony optimization is proposed. Experimental results show that the presented method gets a more accurately prediction in the microseismic source location.
Noise reduction for ultrasonic elastography using transmit-side frequency compounding: a preliminary study

Ultrasonic elastography is an imaging technique providing information about the relative stiffness of biological tissues. In general, elastography suffers from noise artifacts, which degrade lesion detectability and increase the likelihood of misdiagnosis. This paper proposes a method called transmit- side frequency compounding for elastography (TSFC). Beamforming is modified to transmit frames with N alternating center frequencies. Pairs of frames with the same center frequency are used to calculate sub-elastograms that are then averaged to produce one compounded elastogram. Simulation results based on an uniformly elastic tissue model demonstrate the decorrelation among sub-elastograms and the improvement in elastographic signal-to-noise ratio (SNRe) achieved by compounding sub-elastograms. An elastic phantom experiment further validates the noise reduction obtained by the proposed technique.
A novel flexible load coordination control based on distributed generation consumption in the active distribution network

With the reform of electrical market, the application of demand-side management or demand response has been widely researched. The large elasticity of electricity price enables a more flexible control strategy in load-side. In this paper, the basic features of flexible loads including continuous and discrete loads or interruptible or notinterruptible loads will be analysed and modelled. Besides, all constrains concerning the behaviour of power consumption have been brought up. Next, an optimal control strategy oriented to minimum the energy expenditure of a typical home would be established and elaborated. Finally, a study case would be applied to validate the efficiency of the theory. The newly proposed method will be in the end validated by the on-site project of the National High-tech R&D Program (863 Program) in Guizhou Province, China.
AST-based multi-language plagiarism detection method

To detect plagiarism on programming course, the plagiarism detection method based on Abstract Syntax Tree (AST) is proposed. First, we parse source codes into the corresponding AST by syntax analyzer and a biology sequence matching algorithm is used to calculate the similarities of programs. Second, the AST features of similar parts of the programs are extracted and then space vectors of the features are obtained. Finally, we find “copy cluster” by clustering the vectors. Experimental results show that this method has a good effect on the detection of plagiarism and can also find the “copy cluster” accurately.
Battle damage level prediction on Fuzzy theory and Bayesian method

In the actual war, battle damage assessment and prediction problem is an important research topic. Grasping the enemy's information is one of the basis on our forces' war resource allocation. The main problem of this paper is battle damage level prediction, and the model of target damage level prediction based on Fuzzy theory and Bayesian method is established. First, Fuzzy theory and Bayesian method is described. Then, the conception of damage reflect factor is presented and Fuzzy theory and Bayesian method are adopted in the model. Finally, example calculations are given to demonstrating the effectiveness and accuracy of this method. The method of target damage level prediction based on Fuzzy theory and Bayesian method is given according to the comprehensive analysis of the target information.
Atmospheric profiling synthetic observation system at THz bands

In this paper, we introduce a THz dual-band Superconductor-Insulator-Superconductor (SIS) heterodyne radiometer which is under development for the atmospheric profiling synthetic observation system (APSOS) project. This THz radiometer is intended to have a durable and compact design to meet the challenging requirements of remote operation at Tibetan Plateau. The radiometer as well as its major components such as antenna tipping, quasi-optics, cryogenics, SIS mixers and FFTS backend is discussed thoroughly. Some scientific simulation focusing on the atmospheric profiling components at THz bands is also investigated.
Convolutional Neural Network-Based Block Up-Sampling for Intra Frame Coding

Inspired by the recent advances of image super-resolution using convolutional neural network (CNN), we propose a CNN-based block up-sampling scheme for intra frame coding. A block can be down-sampled before being compressed by normal intra coding, and then up-sampled to its original resolution. Different from previous studies on down/up-sampling-based coding, the up-sampling methods in our scheme have been designed by training CNN instead of hand-crafted. We explore a new CNN structure for up-sampling, which features deconvolution of feature maps, multi-scale fusion, and residue learning, making the network both compact and efficient. We also design different networks for the up-sampling of luma and chroma components, respectively, where the chroma up-sampling CNN utilizes the luma information to boost its performance. In addition, we design a two-stage up-sampling process, the first stage being within the block-by-block coding loop, and the second stage being performed on the entire frame, so as to refine block boundaries. We also empirically study how to set the coding parameters of down-sampled blocks for pursuing the frame-level rate-distortion optimization. Our proposed scheme is implemented into the high-efficiency video coding (HEVC) reference software, and a comprehensive set of experiments have been performed to evaluate our methods. Experimental results show that our scheme achieves significant bits saving compared with the HEVC anchor, especially at low bit rates, leading to on average 5.5% BD-rate reduction on common test sequences and on average 9.0% BD-rate reduction on ultrahigh definition test sequences.
A novel voltage controlled temperature compensated crystal oscillator for eliminating the trim effect

A novel scheme of voltage controlled temperature compensated crystal oscillator (VCTCXO) which could realize frequency adjustment and temperature compensation simultaneously is presented. The scheme is based on the binary function V = F(T, ft) which could be obtained from the temperature experiments, where ft is the target output frequency (controlled and adjusted by external control voltage); T is the ambient temperature and V is the compensating voltage. Through extending the two-dimensional temperature-frequency characteristics curve to three-dimensional surface and using a microcontroller to works out compensating voltages according to the above binary function, this new approach provides the possibility for eliminating the adverse influence of trim effect on traditional temperature compensated crystal oscillators (TCXOs).
An FPGA-Based Optical IOH Architecture for Embedded System

Data traffic keeps increasing dramatically in the connected world and it pushes hard on evolution of IO technology. As technology progress, a simple and high-speed unified interconnect becomes practical to support many different protocols. Light Peak (LPK) is a high-bandwidth, inexpensive optical interconnect technology developed by Intel for this purpose. Also, to meet versatile and ever changing I/O needs, an adaptive IOH (I/O Hub) architecture could provide satisfying flexibility. In this paper, we present a high bandwidth and flexible IOH architecture for embedded system that is implemented on FPGA. The platform has 2x 10Gbps optical LPK links, and several SATA, Ethernet and Display Port interfaces. In addition to hardware, we present a software stack framework that provides a unified interface for protocols mapping, replacing numerous individual hardware controller, by utilizing LPK's versatile host controller. Thus it reduces the cost and development effort, and enhances the IOH flexibility. We also describe a configurable hardware protocol adapter in device side, which can be directly connected to any device. IO meter and Iperf show performance of SATA, Ethernet on this FPGA based IOH is competitive with the native ones.
Active distribution network layered and distributed control strategy & implementation

With the high penetration of distributed energy resource (DER) and energy storage system, the real-time performance of centralized management is really poor. In this paper, a multi-layered model is proposed to implement the control of active distribution network (ADN) in different time scales. The indicator of feeder control error (FCE) is used to represent deviation between real-time operating status and global optimized status. According to the layered and distributed control strategy, the regional controller model is established as a host of autonomous region. The constant feeder exchange power mode, regional autonomous control mode and regional cooperative control mode are put forward to adapt to different occasions. The effectiveness and reliability of ADN layered and distributed control strategy is verified by theoretical analysis and simulation.
Distributed adaptive attitude synchronization of multiple spacecraft with external disturbances

This paper considers the distributed attitude synchronization problem for multiple spacecraft with external disturbances. Distributed discontinuous adaptive controller is designed based on the relative attitudes and angular velocities of neighboring spacecraft and attitude synchronization can be reached for any communication graph containing a directed spanning tree. To tackle the chattering effect caused by the discontinuous controller, a continuous controller is further proposed, under which both the synchronization errors and the adaptive gains are ultimately bounded. Extensions to the case with a leader-follower communication graph with the leader as the spanning tree's root are further studied.
A Business Semantic-Oriented Workflow Description Method

The main object of workflow description is to map business requirements quickly and accurately mapped to business process. An approach of business semantic-oriented workflow description method is proposed aiming at the current workflow description method lack of resource allocation means oriented business staff. It provides a description way that business staff familiar with through which business elements can be polymerized in business-level. The approach reflects the of business staff as the core of business description with the features of simple, convenient plug and highly abstract. Experiments show that it can effectively reduce the required workflow description time.
Analysis on the characteristics of radiation emission of EMU

The radiation emission of trains must be concerned to ensure its safe operation. In this paper, the radiation emission of EMU under different working conditions is explored. The radiation emission is tested when EMU passes through the test point by the speed of 50 kilometers per hour in accordance with the standard of the 3-level traction and 3-level braking. Test result shows that radiation emission exceeded the limit at frequency 9kHz-11MHz. Radiation emission of other frequency range is qualified. Then the traction system of a certain type of EMU is analyzed to find the cause of exceeding radiation. From the test results, it can be judged that the source of the disturbance is traction converter. The traction system is introduced to explore the reason of exceeding radiation emission. Some way towards the traction converter is suggested to suppress the radiation emission, and showed the suppressing effect.
Filter-based compounded delay estimation with application to strain imaging

Ultrasonic wave interference produces local fluctuations in both the envelope, known as speckle, and phase of echoes. Furthermore, such fluctuations are correlated in space, and subsequent motion estimation from the envelope and/or phase signal produces patterned, correlated errors. Compounding, or combining information from multiple decorrelated looks, reduces such effects. We propose using a filter bank to create multiple looks to produce a compounded motion estimate. In particular, filtering in the lateral direction is shown to preserve delay estimation accuracy in the filtered sub-bands while creating decorrelation between sub-bands at the expense of some lateral resolution. For Gaussian apodization, we explicitly compute the induced signal decorrelation produced by Gabor filters. Furthermore, it is shown that lateral filtering is approximately equivalent to steering, in which filtered sub-bands correspond to signals extracted from shifted sub-apertures. Field II simulation of a point spread function verifies this claim. We use phase zero and its variants as displacement estimators for our compounded result. A simplified deformation model is used to provide computer simulations of deforming an elastic phantom. Simulations demonstrate root mean square error (RMSE) reduction in both displacement and strain of the compounded result over conventional and its laterally blurred versions. Then we apply the methods to experimental data using a commercial elastic phantom, demonstrating an improvement in strain SNR.
A parallel algorithm for side information generation in Distributed Video Coding

Most of the reported Distributed Video Coding (DVC) schemes have a high time-delay in decoder which hinders its practical application in real-time systems. In this paper, we propose a new DVC decoder architecture based on MPI clusters and an efficient parallel algorithm for generation of the Side Information (SI) based on the structure. In order to achieve a balanced workload among the processors and minimize the overheads of inter-process interaction, we adopt a task-pool model to implement dynamic mapping of tasks onto processors. In addition, the proposed method also uses a threshold-based approach to attain good trade-off between decoding time and video quality. We analyze the effect of computational granularity and the number of processors on performance through defining a performance function. The experimental results on multicore PCs show that a near-linear speedup can be obtained by using the proposed parallel SI generating algorithm.
Tag quality improvement for social images

Online social media sharing web sites like Flickr allow users to manually annotate images with tags, which can facilitate image search and organization. However, the tags provided by users are often imprecise and incomplete, which severely limits the application of tags to image search and browse. In this paper, we propose a scheme to improve poorly annotated tags associated with social images. Two properties are exploited and integrated in an unified optimization framework: (1) consistency between visual and semantic similarities, where the semantic similarity is estimated using tags; (2) compatibility of tags before and after improvement, since the initial user provided tags carry valuable information. An iterative bound optimization method is derived to solve the optimization problem. Experimental results on Flickr dataset show that the proposed method can significantly improve the quality of tags.
Development and Implementation of Intelligent System for Gas Insulated Switchgear

A set of online monitoring and control system for Gas Insulated Switchgear (GIS) has been developed and implemented in this paper. Most main mechanical and electrical parameters of GIS can be monitored and phase controlled operation can be realized by this system. Therefore, the intelligentization of GIS can be achieved. Optical Ethernet, embedded technologies and modular design method have been adopted in system design and development, which make the system meet the requirements of IEC61850¸ the new generation communication standard for substation automation. Furthermore, EMC technologies have been applied and the system has passed the fourth rigorous level EMC test, which is the most strictest one for substation automation device. The monitoring system has been practically applied in a 110kV substation.