nils-herrmann/clean_scirepeval huggingface dataset:
Design and test of MEMS gyroscope control system based on LMSD

In order to improve the performance of MEMS gyroscopes, the least mean square demodulation (LMSD) algorithm is utilized in MEMS gyroscope control system. By using Matlab tool, the simulation result shows that the LMSD algorithm has better noise suppression performance than multiplicative demodulation (MD). LMSD algorithm is carried out in field-programmed-gate-array (FPGA) and experimental results are in good agreement with the simulation. Combining with automatic gain control (AGC) and phase-locked loop (PLL) technology, the amplitude variance and phase variance of drive mode are respectively 62ppm and 0.001°. The nonlinearity of scale factor based on LMSD algorithm is 0.068%. Moreover, the gyroscope using LMSD exhibits a bias stability (BS) of 1.8°/h and an angle random walk (ARW) of 0.031°/Vh, better than the BS 6.7°/h and ARW 0.114°/Vh using MD. The experiment proves the feasibility and effectiveness of LMSD algorithm in the digital control system of MEMS gyroscope.
Combining beamforming and transmit diversity for closed-loop downlink transmission

Multiantenna technique is an effective means for improving performance of the downlink transmission. Both beamforming (BF) and transmit diversity (TD) which exploit multiple antennas are attractive candidates for increasing channel capacity. In this paper, an adaptive downlink transmission approach combining beamforming with transmit diversity in the closed-loop mode is presented, and the transmitter can adaptively change the transmission mode between two methods according to actual channel states. The first method combines BF with space-time block coding (STBC), while the second one combines BF, STBC and selection transmit diversity (STD). Both of them exploit the feedback information from the mobile terminal to aid the downlink transmission. Simulation results in terms of bit error rate (BER) performance are presented, which show the high performance over conventional STBC scheme.
A study of half-duplex two-way decode-and-forward relay transmission with asymmetric traffic using superposition coding

In this paper, we study the outage performance of a two-way relay transmission, where each terminal has a single antenna and operates in a half-duplex manner, especially, a decode-and-forward (DF) relay is employed to help the bidirectional communications between two user terminals and superposition coding is applied at the relay to re-encode the successfully decoded messages. Unlike existing works, we consider a more general setup that the two user terminals have different target rates (i.e., asymmetric two-way relay transmission). First, an achievable rate region of the network scenario under consideration is characterized followed by a definition of the overall system outage probability. Then, the investigated network scenario is evaluated in terms of outage probability, after which the corresponding closed-form expressions are formulated over Rayleigh fading channels. Next, to maximize the energy efficiency, optimum power allocation between the superposed signals at the relay are examined. As a result, two different policies implementing fixed and variable scaling coefficients of superposition coding are proposed. Then, simulation experiments are performed and performance comparisons are conducted. The provided results validate the accuracy of our derived expressions and the efficiency of our propositions.
Analysis and application of Wireshark in TCP/IP protocol teaching

TCP/IP is widely employed to interconnect computing facilities in today's network environments, and it is the most important content of networking elements study. This paper introduces the functions and characteristics of Wireshark, and expounds its analysis and application in TCP/IP protocol teaching by some specific examples. The practice shows that good teaching result will be received with the aid of Wireshark and it is worth popularization and application.
Demonstration of GaN Current Aperture Vertical Electron Transistors With Aperture Region Formed by Ion Implantation

In this paper, we report the successful demonstration of current aperture vertical electron transistors (CAVETs) obtained by using a novel implantation-based compensation method to achieve a conductive aperture. This innovation leads to the first demonstration of “regrowth-free” CAVETs. Two gallium nitride (GaN) CAVETs were fabricated using the ion-implantation-compensated aperture regions, both with Mg-doped p-GaN as current-blocking layers (CBLs). The aperture regions were formed by implanting Si into the p-GaN CBL. In one of the CAVET samples, the Si-implantation-based aperture was formed prior to the regrowth of AlGaN/GaN layers on top. The other CAVET sample was subjected to aperture formation via Si implantation after all the device layers were grown. An ion-implantation scheme using multiple-energy levels was designed to realize a 250-nm Si box profile with a total dose of <inline-formula> <tex-math notation="LaTeX">$\textsf {4.2} \times \textsf {10}^{\textsf {15}}$ </tex-math></inline-formula> cm<sup>−2</sup>, converting a 250-nm p-GaN (Mg: <inline-formula> <tex-math notation="LaTeX">$8\times 10^{\textsf {19}}$ </tex-math></inline-formula> cm<sup>−2</sup>) to conductive (n-type) GaN successfully. This novel fabrication method enables the use of Mg-doped CBLs without conventional etch and regrowth steps. Moreover, the proposed scheme can ultimately lead to regrowth-free CAVETs.
A Study of T–S Model-Based SMC Scheme With Application to Robot Control

In light of the remarkable benefits and numerous applications of the Takagi-Sugeno (T-S) fuzzy system modeling method and the sliding mode control (SMC) technique, this paper aims to study the design of robust controllers for a set of second-order systems using a combination of these two approaches. The combined scheme is shown to have the merits of both approaches. It alleviates not only the online computational burden by using the T-S fuzzy system model to approximate the original nonlinear one (since most of the system parameters of the T-S model can be computed offline) but also preserves the advantages of rapid response and robustness characteristic of the classic SMC schemes. Moreover, the combined scheme does not need to online compute any nonlinear term of the original dynamics, and the increase in the number of fuzzy rules does not create extra online computational burdens for the scheme. The proposed analytical results are also applied to the control of a two-link robot manipulator and compared with the results using classic SMC design. Simulation results demonstrate the benefits of the proposed scheme.
An Improved Data Anomaly Detection Method Based on Isolation Forest

An improved data anomaly detection method SA-iForest is proposed to solve the problem of low accuracy, poor execution efficiency and generalization ability of data anomalies detection algorithm based on isolated forest. Based on the idea of selective integration, the precision and the difference value are taken as the criterion, and the simulated annealing algorithm is used to select the isolation tree with high abnormality detection and differentity to optimize the forest. At the same time, the excess detection precision is small and the difference is small Isolation tree improves the forest construction process of isolated forests, which improves the efficiency of the algorithm and improves the efficiency of the algorithm. The method of data anomaly detection based on SA-iForest is compared with the traditional Isolation Forest algorithm and LOF algorithm, and the accuracy and efficiency of the data are verified by the standard simulation data set. There is a significant improvement.
Formalization of multi-resolution modeling based on dynamic structure DEVS

So far there exists no general, common specification for multi-resolution modeling (MRM) to represent models consistent with each other. To address this problem, a new formalism, multi-resolution DEVS (MR-DEVS) is put forward. The definition of multi-resolution entity (MRE), which is the basic object MRM deals with and base for MR-DEVS, is given. Then, MR-DEVS, combining and extending multiple popular existing dynamic structure DEVS formalisms, is elaborated in detail. MR-DEVS is capable of capturing all key characteristics of MRM essentially, including adapting to resolutions changing automatically by reflection, describing consistency mappings and modeling causation by consistency mapping functions and modeling emergence by functions of coupling among sub-entities constituting the same MRE. Any other existing formalism can't do these all.
A new dual-core Permanent Magnet Synchronous Motor Servo System

A new Permanent Magnet Synchronous Motor (PMSM) Servo System is designed based on the dual-core composed of digital signal processor (DSP) and field programmable gate array (FPGA). The dual-core combined of DSP and FPGA achieves the control algorithm by assigning the tasks. The design of external circuit using FPGA simplifies the system structure and enhances the stability. DSP program with multi-interrupt drives the IPM module through pulse-width modulation (PWM) signals. The improved control methods of the three loops of PMSM servo system are achieved in interrupt program: the self-tuning control based on resistance and inductance observer is used in current loop, which ameliorate the following accuracy of current. In order to reduce the response overshoot, the speed is controlled by the integral-separated and limit-weaken PID controller. The feed-forward is added to the proportional position control to reduce the dynamic response error. The application on CNC lathe verifies the feasibility of the design of dual-core system and the control methods of the three loops.
A Feasible Architecture for ARM-Based Microserver Systems Considering Energy Efficiency

In this paper, we propose the architecture of the ARM-based server system and physically build the ARM-based server cluster board (SCB). Based on a real implementation, the efficiency of the microserver is evaluated to verify the benefits of the proposed scheme. In experiments, every SCB is equipped with four server-grade ARM quad-core processors for enterprise-class applications. The major difference between our work and other studies is that this paper tests the performance of the server-grade processors, whereas previous works use the cluster system formed by the embedded processors. The experiment results show that the SCB-based systems perform with lower power-consumption than the x86 systems. Since various types of cluster systems can be built with the SCB through either the PCI Express (PCIe) interface or gigabit Ethernet interface, the proposed design of the ARM server board and the systems can, therefore, be extensively applied. This physical implementation and measurement show that the proposed architecture can work well. The proposed energy-saving (i.e., green) server designs with low-power processors are suitable for relative applications in the coming era of Industry 4.0 and the Internet of Things.
Data Normalization for Diabetes II Metabonomics Analysis

Variable normalization alters the structure of data, thus affecting the outcome of multivariant analysis and calibration. The aim of this study was to evaluate the effect of two normalization methods, 1-norm and 2-norm, on the outcome of classification of 1H nuclear magnetic resonance (NMR) metabonomic profiling, and to identify the characteristic metabolites that are responsible for the discrimination of plasma samples of diabetes II and healthy volunteers. The 1H NMR spectra of human plasma from 14 healthy volunteers and 14 diabetes II volunteers were analyzed using principal component analysis (PCA) with the two normalization methods. The loading plots obtained by PCA using 1-norm and 2-norm were compared and same metabolites were identified that are responsible for the observed separations, with 2-norm method has better classification effect. These results demonstrated that NMR-based metabonomics analysis is capable of identifying the metabolites that are important for discriminating individuals of diabetes II from individuals of healthy.
A novel design of variable stiffness linkage with distributed leaf springs

Compliance is an important property in machine, but mechanism to realize compliance is not adequate to the demand. In this paper, the variable stiffness linkage (VSL) was designed to achieve safety assurance, stability improvement and energy conservation in robots and mechanical applications. Stiffness change could be realized by changing the position of the slider on each leaf spring. In order to save the energy from holding the specific stiffness, a screw-slider-linkage-slider mechanism was applied. Design principle and mechanical realization of VSL were presented. And then an effective stiffness model was designed. Finally the simulation was performed, which proved the effectiveness of VSL.
Development of a Nonresonant Piezoelectric Motor With Nanometer Resolution Driving Ability

A nonresonant-type piezoelectric motor with a precise driving ability was proposed. The operating principle of the proposed motor is different from the previous nonresonant piezoelectric motors using either the clamping and feeding mechanism (inchworm mechanism) or the inertia drive mechanism. An oblique linear motion formed by the hybrid of two bending motions of a sandwich transducer was used to push the runner step-by-step. Two square-wave voltages were applied to the horizontal and vertical PZT elements to obtain the desired oblique linear motion. The mechanism of the proposed piezoelectric motor was illustrated in detail. Then, transient analyses were performed by ANSYS software to simulate the motion trajectory and to find the response characteristics of the motor. Finally, a prototype was fabricated to verify the mechanism and to test the mechanical output characteristics of the proposed motor. Under the input square-wave voltages of 500 V$_{\text{p-p}}$ , the prototype achieved a step displacement of 5.96 μm, a maximum no-load velocity of 59.64 μm/s, and a maximum thrust of 30 N. This paper provides a new mechanism for the design of a nonresonant piezoelectric motor with long stroke and precise driving ability.
Improving Web Image Search by Bag-Based Reranking

Given a textual query in traditional text-based image retrieval (TBIR), relevant images are to be reranked using visual features after the initial text-based search. In this paper, we propose a new bag-based reranking framework for large-scale TBIR. Specifically, we first cluster relevant images using both textual and visual features. By treating each cluster as a “bag” and the images in the bag as “instances,” we formulate this problem as a multi-instance (MI) learning problem. MI learning methods such as mi-SVM can be readily incorporated into our bag-based reranking framework. Observing that at least a certain portion of a positive bag is of positive instances while a negative bag might also contain positive instances, we further use a more suitable generalized MI (GMI) setting for this application. To address the ambiguities on the instance labels in the positive and negative bags under this GMI setting, we develop a new method referred to as GMI-SVM to enhance retrieval performance by propagating the labels from the bag level to the instance level. To acquire bag annotations for (G)MI learning, we propose a bag ranking method to rank all the bags according to the defined bag ranking score. The top ranked bags are used as pseudopositive training bags, while pseudonegative training bags can be obtained by randomly sampling a few irrelevant images that are not associated with the textual query. Comprehensive experiments on the challenging real-world data set NUS-WIDE demonstrate our framework with automatic bag annotation can achieve the best performances compared with existing image reranking methods. Our experiments also demonstrate that GMI-SVM can achieve better performances when using the manually labeled training bags obtained from relevance feedback.
Life cycle vibration analysis based on EMD of rolling element bearing under ALT by constant stress

Bearing is one of critical rotation components in mechanical systems. Many grievous accidents of mechanical systems are chiefly caused by bearing failure. So it's significant meaningful for practical application to research failure feature detection and residual life prediction of key rotation components such as bearings. This paper has designed experimental scheme of Accelerated Life Test of rolling element bearings and gained all vibration signals of bearings through their total life, from normal to fault. Then we extracted failure feature from those signals and analyzed its evolving rule in the bearing life cycle based on Empirical Mode Decomposition(EMD). Finally, we have analyzed bearing failure feature in each stage through its life cycle by comparing Frequency Spectrums with Hilbert Marginal Spectrums.
Design of IoT-based energy efficiency management system for building ceramics production line

This paper analyses the current situation of Chinese building ceramics production lines. For most of the current production lines, the overall equipment efficiency is low, resulting in low production and inefficient usage of energy. Moreover, the traditional manual meter reading in most of the production lines is consuming and it is hard to judge whether the data stored in the database is accurate or not. On the other hand, this paper analyses the development of the Internet of Things (IoT) and proposes an Energy Efficiency Management System (EEMS) for building ceramics production line based on IoT. The required relevant technologies including cloud computing, MQ Telemetry Transport (MQTT), Tornado and RESTful API, are introduced. At the end of the paper, the design of EEMS is presented. Built on the cloud computing centre, the EEMS, with Role-based Access Control (RBAC) and the database interface RESTful API, collects equipment data using MQTT protocol, implements the Tornado web server framework to provide web services and uses web front-end technologies including HTML, CSS and JAVASCRIPT to build web applications. The implement of proposed system can effectively improve the energy efficiency of production lines and the competitiveness of building ceramics enterprises.
Inductance-Emulating Control for DFIG-Based Wind Turbine to Ride-Through Grid Faults

For doubly fed induction generator (DFIG)-based wind turbines, the rotor side of DFIG is prone to suffering from overcurrent during grid faults, due to large electromotive force (EMF) induced in the rotor circuit. To solve this problem, this paper proposes an inductance-emulating control strategy for DFIG-based wind turbine to suppress the postfault rotor current, thereby enhancing its low-voltage ride through capability. Under the proposed control strategy, once the grid fault is detected, the rotor side converter (RSC) is controlled to emulate an inductance. Furthermore, with proper inductance value, both the required rotor voltage and postfault rotor current can be reduced within the permissible ranges of RSC, thus the controllability of control system can be maintained during transient process. Moreover, the oscillation of electromagnetic torque can be effectively suppressed during transient state of both grid fault and fault recovery. Finally, the simulation and experimental results are presented to demonstrate the effectiveness of the proposed method.
A single-stage forward inverter with high frequency isolation for grid-connected application

A single-stage inverter based on bi-directional forward converter with high frequency isolation for grid-connected application is proposed. The inverter is comprised of two improved bi-directional forward dc-dc converters, which are input-parallel and output-series (IPOS) connected. Each bidirectional dc-dc converter has two secondary rectifiers in series and the voltage stress on the freewheeling switches is halved. The operational principle of the inverter is given with transformer reset and voltage stress analyzed in details. Experimental results are presented to verify the validity and feasibility of the proposed single-stage inverter.
Finite element analysis and vibration control of the Substation Charged Maintenance Robot

Substation insulator strings which exposed to the outdoor environment need to be cleaned the industrial contamination and natural contamination regularly in order to prevent the flashover phenomenon that occurs in rain and fog and other inclement weather because of the accumulation of contamination and then cause power outages and other incidents. The Substation Charged Maintenance Robot is used for cleaning the substation equipment in 110kV-220kV AIS Substation. The vibration characteristics of the robot's chassis and operating platform cannot be ignored to ensure the accuracy of the job. Statics simulation analysis and modal analysis are conducted according to the job characteristics of the robot. Stress distribution of operating platform and low-order vibration frequencies and mode shapes of telescopic arms are obtained and then vibration control schemes are proposed based on the simulation results.
Industrial wireless deterministic communication based on WLAN: Design, implementation and analysis

In this article, it presents the status of wireless communication being applied in industrial environment, and points out its deterministic communication issue existed in control network. According to this issue, a kind of industrial wireless deterministic communication mechanism and method based on WLAN is put forward. Afterwards, the main part is to describe the process of design, implementation and test programs. Depending on test results, some analysis is set out to indicate that the parameters performance of IWLAN with deterministic scheduling mechanism is better than commercial WLAN. At the end of this article, simulation result is supplied as the reference of actual IWLAN parameters performance.
An improved residual LSTM architecture for acoustic modeling

Long Short-Term Memory (LSTM) is the primary recurrent neural networks architecture for acoustic modeling in automatic speech recognition systems. Residual learning is an efficient method to help neural networks converge easier and faster. In this paper, we propose several types of residual LSTM methods for our acoustic modeling. Our experiments indicate that, compared with classic LSTM, our architecture shows more than 8% relative reduction in Phone Error Rate (PER) on TIMIT tasks. At the same time, our residual fast LSTM approach shows 4% relative reduction in PER on the same task. Besides, we find that all this architecture could have good results on THCHS-30, Librispeech and Switchboard corpora.
GoFuzzKegg: Mapping Genes to KEGG Pathways Using an Ontological Fuzzy Rule System

In this paper we present a method for finding the main pathways represented in a set of genes (say obtained from a microarray experiment). The method is based on a fuzzy mapping between genes represented as sets of gene ontology terms and KEGG pathways using a new type of fuzzy rule system called ontological fuzzy rule system (OFRS). As opposed to a crisp mapping, the fuzzy mapping produces a nonzero value even if the gene name is not explicitly listed in a given KEGG pathway. An OFRS is a fuzzy rule system in which the rule memberships are obtained using similarity measures between objects computed based on the gene ontology (GO) annotations. To test our approach, we randomly selected without replacement 10 sets of Arabidopsis thaliana genes from KEGG (each set had 15 genes from 3 different pathways) and tried to predict the pathways they were selected from. Our method was able to find, 90% of the right pathways with a 65% false alarm rate at a p-value of 0.01. The high false alarm rate is due in part to the experimental setting. In a pilot dataset of 526 Arabidopsis thaliana genes we identified 8 clusters which proved to be linked to important pathways such as ATP synthesis and transcription factor