bluuebunny/crossref_metadata_2025 huggingface dataset:
Magnetically induced elastic deformations in model systems of magnetic gels and elastomers containing particles of mixed size
Authors: Fischer Lukas , Menzel Andreas M
Published: October 2020

Soft elastic composite materials can serve as actuators when they transform changes in external fields into mechanical deformation. Here, we theoretically address the corresponding deformational behavior in model systems of magnetic gels and elastomers exposed to external magnetic fields. In reality, such materials consist of magnetizable colloidal particles in a soft polymeric matrix. Since many practical realizations of such materials involve particulate inclusions of polydisperse size distributions, we concentrate on the effect that mixed particle sizes have on the overall deformational response. To perform a systematic study, our focus is on binary size distributions. We systematically vary the fraction of larger particles relative to smaller ones and characterize the resulting magnetostrictive behavior. The consequences for systems of various different spatial particle arrangements and different degrees of compressibility of the elastic matrix are evaluated. In parts, we observe a qualitative change in the overall response for selected systems of mixed particle sizes. Specifically, overall changes in volume and relative elongations or contractions in response to an induced magnetization can be reversed into the opposite types of behavior. Our results should apply to the characteristics of other soft elastic composite materials like electrorheological gels and elastomers when exposed to external electric fields as well. Overall, we hope to stimulate corresponding experimental realizations and the further investigation on the purposeful use of mixed particle sizes as a means to design tailored requested material behavior.
Nitrogen infusion R&D at DESY a case study on cavity cut-outs
Authors: Wenskat Marc , Bate Christopher , Dangwal Pandey Arti , Jeromin Arno , Keller Thomas F , Knobloch Jens , Köszegi Julia , Kramer Felix , Kugeler Oliver , Kulkarni Satish , Reschke Detlef , Schaffran Jörn , Dalla Lana Semione Guilherme , Sievers Sven , Steder Lea , Stierle Andreas , Walker Nicholas
Published: October 2020

A first series of nitrogen infusion runs of 1.3 GHz single-cell cavities at DESY resulted in an unexpected and severe deterioration observed during the vertical cold test. To investigate the origin of the deterioration, one of the cavities underwent extensive radio-frequency measurements and a temperature- and magnetic field-mapping was performed in collaboration with the Helmholtz-Zentrum Berlin. After combining all results, regions of interests were identified and cut-out of the cavity. Subsequent surface analysis techniques (EBSD, PALS, PIXE, SEM/EDX, SIMS, XPS) were applied in order to identify the microscopic origin of the deterioration and especially the differences between hot and cold spots as well as quench spots. An excess of niobium carbides, reducing the thermal conductivity, was identified as the probable cause for the deterioration, and the size- and density-distributions were observed to be crucial for the resulting performance reduction. The origin for the local differences in the niobium carbide formation between hot and cold spots is an effect of preexisting variations of the crystal structure.
Evidence for Radio Loud to Radio Quiet Evolution from Red and Blue Quasars
Authors: Garofalo David , Bishop Katie
Published: October 2020

Recent work on red and blue quasi-stellar objects (QSOs) has identified peculiar number distributions as a function of radio-loudness that we explore and attempt to explain from the perspective of a picture in which a subset of the population of active galaxies evolves from the radio-loud to the radio-quiet state. Because the time evolution is slowed down by an order of magnitude or more for the radio-quiet phase, the numbers of red and blue QSOs approach each other at the extreme end of the radio-quiet range of radio-loudness with larger numbers. The rapid time evolution of most radio-loud phases, instead, makes the numbers similar but lower at the far radio-loud end. At the midpoint of radio-loudness, instead, the differences between red and blue QSOs experience their largest values which results from accretion rapidly spinning black holes down but subsequently spinning them up more slowly. Recovering these basic features of the observations is evidence for rapid evolution away from radio-loudness and slow evolution in radio-quiet states.
Statistical potentials for 3D structure evaluation: From proteins to RNAs*
Authors: Tan Ya-Lan , Feng Chen-Jie , Wang Xunxun , Zhang Wenbing , Tan Zhi-Jie
Published: October 2020

Structure evaluation is critical to in silico 3-dimensional structure predictions for biomacromolecules such as proteins and RNAs. For proteins, structure evaluation has been paid attention over three decades along with protein folding problem, and statistical potentials have been shown to be effective and efficient in protein structure prediction and evaluation. In recent two decades, RNA folding problem has attracted much attention and several statistical potentials have been developed for RNA structure evaluation, partially with the aid of the progress in protein structure prediction. In this review, we will firstly give a brief overview on the existing statistical potentials for protein structure evaluation. Afterwards, we will introduce the recently developed statistical potentials for RNA structure evaluation. Finally, we will emphasize the perspective on developing new statistical potentials for RNAs in the near future.
Magnetic properties and promising cryogenic magneto-caloric performances of Gd20Ho20Tm20Cu20Ni20 amorphous ribbons*
Authors: Zhang Yikun , Wu Bingbing , Guo Dan , Wang Jiang , Ren Zhongming
Published: October 2020

The magnetic cooling utilizing magneto-caloric effect is recognized as promising energy efficiency and environmentally friendly technology. Here we report a systematical study on the microstructures, magnetic properties and cryogenic magneto-caloric performances of the Gd20Ho20Tm20Cu20Ni20 amorphous ribbons. It is found that the ribbons reveal a second-order phase transition and are accompanied by a table-shaped magneto-caloric effect. The calculated magnetic-entropy-change maximum |ΔS
M|, temperature averaged entropy change (i.e., TEC(10)), and refrigerant capacity reach 13.9 J/kg⋅K, 13.84 J/kg⋅K and 740 J/kg with magnetic field change of 0–7 T, respectively, indicating that the present Gd20Ho20Tm20Cu20Ni20 amorphous ribbons are good candidates for magnetic cooling.
A first-principles study on zigzag phosphorene nanoribbons terminated by transition metal atoms*
Authors: Yang Shuai , Wang Zhiyong , Dai Xueqiong , Xiao Jianrong , Long Mengqiu
Published: October 2020

We have investigated the electronic and magnetic properties of zigzag phosphorene nanoribbons (ZPNRs) with transition metal (TM) passivated atoms, it can be found that the ZPNRs with TM passivated atoms exhibit different magnetisms except for the Ni-passivated system. Meanwhile, the results show that the magnetic moments of ZPNRs with TM passivated atoms are larger than that of ZPNRs with other passivated non-metals/groups. Interestingly, it can be found that Fe-passivated ZPNR exhibits magnetic semiconducting character, which provides the possbility for the application of phosphorene in information storage. For Mn-passivated ZPNRs, it exhibits the half-metallicity. These results may be useful for potential applications of phosphorene in electronic and high-performance spintronic devices.
Glass formation and physical properties of Sb2S3–CuI chalcogenide system*
Authors: Ye Qilin , Chen Dan , Lin Changgui
Published: October 2020

Novel chalcogenide glasses of pseudo-binary (100 – x)Sb2S3–xCuI systems were synthesized by traditional melt-quenching method. The glass-forming region of Sb2S3-CuI system was determined ranging from x = 30 mol% to 40 mol%. CuI acts as a non-bridging modifier to form appropriate amount of [SbSI] structural units for improving the glass-forming ability of Sb2S3. Particularly, as-prepared glassy sample is able to transmit light beyond 14 μm, which is the wider transparency region than most sulfide glasses. Their physical properties, including Vickers hardness (H
v), density (ρ), and ionic conductivity (σ) were characterized and analyzed with the compositional-dependent Raman spectra. These experimental results would provide useful knowledge for the development of novel multi-spectral optical materials and glassy electrolytes.
Solution to the Sudakov suppressed Balitsky-Kovchegov equation and its application to HERA data *
Authors: Xiang Wen-Chang , Wang Meng-Liang , Cai Yan-Bing , Zhou Dai-Cui
Published: October 2020

We analytically solve the Sudakov suppressed Balitsky-Kovchegov evolution equation with fixed and running coupling constants in the saturation region. The analytic solution of the S-matrix shows that the

rapidity dependence of the solution with the fixed coupling constant is replaced by the

dependence in the smallest dipole running coupling case, as opposed to obeying the law found in our previous publication, where all the solutions of the next-to-leading order evolution equations comply with

rapidity dependence once the QCD coupling is switched from the fixed coupling to the smallest dipole running coupling prescription. This finding indicates that the corrections of the sub-leading double logarithms in the Sudakov suppressed evolution equation are significant, which compensate for a part of the evolution decrease of the dipole amplitude introduced by the running coupling effect. To test the analytic findings, we calculate the numerical solutions of the Sudakov suppressed evolution equation, and the numerical results confirm the analytic outcomes. Moreover, we use the numerical solutions of the evolution equationto fit the HERA data. This demonstrates that the Sudakov suppressed evolution equation can achieve a good quality fit to the data.
The noise and impedance of microelectrodes
Authors: Mierzejewski Michael , Steins Helen , Kshirsagar Pranoti , Jones Peter D
Published: October 2020

Objective. While the positive correlation between impedance and noise of microelectrodes is well known, their quantitative relationship is too rarely described. Knowledge of this relationship provides useful information for both microsystems engineers and electrophysiologists. Approach. We discuss the physical basis of noise in recordings with microelectrodes, and compare measurements of impedance spectra to noise of microelectrodes. Main results. Microelectrode recordings intrinsically include thermal noise,

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, with the real component of impedance integrated over the recording frequency band. Impedance spectroscopy allows the quantitative prediction of thermal noise. Optimization of microelectrode noise should also consider the contribution of amplifier noise. These measures enable a quantitative evaluation of microelectrodes’ recording quality which is more informative than common but limited comparisons based on the impedance magnitude at 1 kHz. Significance. Improved understanding of the origin of microelectrode noise will support efforts to produce smaller yet low noise microelectrodes, capable of recording from higher numbers of neurons. This tutorial is relevant for single microelectrodes, tetrodes, neural probes and microelectrode arrays, whether used in vitro or in vivo.
Design and Development of 220kV SF6 Gas Insulated GIS Actual Testing Platform
Authors: Zhang Shiling
Published: October 2020

To simulate the performance parameters of SF6 gas insulated AC GIS equipment under normal and failure condition in the laboratory environment, and the development trend of characteristic parameters such as partial discharge and SF6 gas decomposition can be monitored periodically and quantitatively, the 220kV SF6 gas insulated GIS type test platform is designed and developed. The test platform is in accordance with GIS specifications used in substation, including gas insulated circuit breaker and other typical GIS equipment. The external voltage is realized by the series resonant circuit. The paper starts from the design of the GIS bus and the solid insulation structure, and the three-dimensional E-field distribution characteristics of the solid insulator are simulated and analyzed. The hardware installation of the partial discharge measurement and SF6 gas decomposition monitoring on GIS model test platform are discussed. Through design and development of 220kV SF6 gas insulated GIS type-test platform, the feasibility of the structure design of GIS bus and the solid insulation are verified, it provides theoretical and practical basis for the operation and maintenance of SF6 gas insulated GIS equipment.
Natural scene text detection based on YOLO V2 network model
Authors: Haifeng Dong , Siqi Han
Published: October 2020

As an important part in the field, the natural scene text detection has been widely applied in visual navigation system, content-based image and video retrieval, instant translation system and so on. In this paper, we introduce several object detection network based on deep learning, and apply the YOLO v2 into natural scene text detection, changing the multi objects detection problems into the two classification problems. The main works in the paper include the following: prepare the datasets; we train the YOLO v2 with the optimum parameters, carry out the regression analysis of the coordinate parameters and categories of bounding boxes, obtain the detection result; according to different detection models, the detection results of different datasets are compared and analyzed, YOLO V2 model detection speed 0.105s/image has certain advantages.
The LSTM Neural Network Based on Memristor
Authors: Chu Ziqi , Xu Hui , Liu Haijun
Published: October 2020

The recurrent neural network adds the concept of time series on the basis of the traditional multi-layer feedforward neural networks, provides the memory function, and makes the network show good modeling ability on time-series data. Therefore, this paper proposes a LSTM (Long Short-Term Memory) neural network based on memristor. It establishes a discrete weighted LSTM network model by simplifying the traditional recurrent neural network, and uses memristor arrays on the premise of ensuring recognition performance. We realize the function of weight matrix to improve the structure of LSTM neural network, and finally carry out simulation research on the proposed neural network. And due to the volatility and yield of memristors, this paper also demonstrates and analyzes the impact of these two characteristics on network performance, and the performance level of the LSTM neural network based on memristor is verified under the existing preparation level. Experiments on the TIMIT speech database show that the proposed neural network in this paper has good accuracy and its speech recognition performance is superior.
Design and Implementation of Automatic Code Generation Method Based on Model Driven
Authors: Chen Hailang
Published: October 2020

With the continuous development of software development technology, people have summarized more and more repetitive work in the process of software development. These repetitive tasks increase the developer’s workload of repetitive coding, extend the market demand response time, and then increase development costs. At the same time, in the team development process, due to the uneven code writing ability among the team members, the coding style is not uniform, and the quality and maintainability of the code cannot be guaranteed. The article builds a software development model from the perspective of public functions such as software addition, deletion, modification, and query. On this basis, a code automatic generation engine based on Rete algorithm is designed to realize rapid software code generation. Application practice shows that this method can quickly generate codes with uniform coding style, and improve software development efficiency and code quality.
Construction of Blended ESP Teaching Model and Performance Evaluation System in the Context of “Internet +”
Authors: Ran Zhao
Published: October 2020

The “Internet +” era presents new possibilities and new requirements for ESP education. Online teaching under the background of the “Internet +” is to enable learners to make efficient use of fragmented time. The process and requirement of blended teaching is to break through the ideology of “learning only in the classroom”, so that students can spend time in learning even after class. There is a problem of overload of information in online platform teaching, which puts a certain burden on students’ knowledge construction, aggravates the cognitive load of students, and the lacks of teachers’ summarization and guidance. The article is to explore how to build a blended ESP learning model and corresponding performance evaluation system to make ESP learning in the “Internet +” context more systematic and efficient.
Research on Container Throughput Forecast Based on ARIMA-BP Neural Network
Authors: Zhang Yifei , Fu Yuhui , Li Genghua
Published: October 2020

In order to improve the accuracy of the container throughput, the paper proposed a prediction method based on ARIMA-BP neural network for the container throughput, and compared with the combined prediction method based on ARIMA-BP neural network, from the perspective of simple weighting and residual optimization. It is applied to the container throughput prediction of the Qingdao port statistics for a total of 24 quarters from 2014-2019. The results show that the prediction accuracy of the combination prediction method based on residual optimization was the highest. Compared with other prediction models, the evaluation indexes RMSE(Root Mean Square Error), MAE(Mean Absolute Error), and MAPE(Mean Absolute Percentage Error) were 15.95, 13.31 and 2.52% respectively and the prediction accuracy based on the BP neural network was lowest. The prediction method proposed in this paper for container throughput can provide guidance for the related personnel.
An Interpretable Framework for Stock Trend Forecasting
Authors: Wang Lewen , Ye Zuoxian
Published: October 2020

Stock trend forecasting plays a critical role when investing in the stock market. Comparing with traditional technical analysis and fundamental analysis, deep learning models own better forecasting performance. However, the poor interpretability of deep learning models brings lots of limitations to its practical application since the lack of interpretability increases the investment risk. In this paper, we propose a graph-based framework, which owns good interpretability while maintaining forecasting performance. Specifically, the framework explains the stock returns by dividing the returns into multiple parts: 1) the part related to individual stock; 2) the part related to company business; 3) the part related to the corresponding industry; 4) the part associated with the whole market. Extensive experiments on real-world Chinese stock market data have demonstrated the effectiveness of our proposed framework for stock trend forecasting. Afterward, we try to illustrate the interpretability of our framework.
Reactive power optimization of distributed photovoltaic access distribution network based on improved multi-objective particle swarm optimization
Authors: Xiaomeng Wu , Mingyue Yang
Published: October 2020

With the widespread development of distributed power generation technology, photovoltaic power generation has been widely used. Aiming at the past, the reactive power optimization algorithm with distributed photovoltaic access distribution network needs to convert the multi-objective problem into a single-objective solution process where the weight setting is not objective. This paper proposes a multi-objective optimization algorithm based on improved particle swarm optimization algorithm. The multi-objective model based on Pareto optimal solution is used to obtain the Pareto optimal solution set, and the optimal solution is selected from it. At the same time, the algorithm is improved, the external file is used to update the optimal position of the particle, the population diversity is increased to avoid premature problems, and the performance of the algorithm is improved by analyzing the test function. Through modeling, the influence of distributed photovoltaic power generation on the power grid is studied, and simulation is performed in the IEEE-33 node system. The simulation results prove the feasibility of the improved algorithm in this paper.
Research on the stiffness of Grillage Method in Virtual Cross Beam based on Fuzzy recognition
Authors: Sun Lixiang
Published: October 2020

The stiffness of virtual cross beam is an important parameter which affects the calculation results when using beam grillage method to analyze the stress of bridge structure. In order to obtain a reasonable value of the stiffness about the virtual cross beam, it is necessary to make comparative analysis. On the basis of the comprehensive comparative analysis of the stress and deflection of the control section in hollow slab bridge and simply supported beam bridge with twin-box single cross section, the value range of the virtual cross beam stiffness is obtained. Based on the theory of fuzzy mathematics, a fuzzy recognition model for the stiffness of the virtual beam was proposed. By calculating the relative membership degree vector of the stiffness in each virtual beam, the length and width of the cross section of the virtual beam is obtained to be 0.7 times height of the beam. In order to further verify the reasonable of the value, the stress and deflection values of the control section have been compared. These values are taken from the single-box and double-chamber plexiglass box girder model and highway single-box three-chamber box beam. The compared results show that the solution of the grillage model with the stiffness of the virtual beam in this paper is in good agreement with shell solutions and solid solutions.
Leukemic cell detection model based on deep learning
Authors: Chen Hongyi
Published: October 2020

This project examines the feasibility of building a deep learning model that can accurately distinguish leukemic cells from normal cells, as well as the viability of analyzing the black box model so as to explain it in human terms. An accurate and explainable model can be deployed in areas previously inaccessible to the limited existing methods of detecting leukemia in its early stages. It is acknowledged that this is a very difficult process, as cells from individuals vary and cancerous cells are highly morphologically similar to normal cells.

There are two major steps to this project. First, we built the cell classification model by supervised training on a convolutional neural network with provided labeled training data. Many parameters had to be adjusted to yield better results when tested on separate testing data. Second, we created a program designed to help explain the neural network, and the model is inserted into the program and tested on multiple examples. We examined the results, which highlighted regions the algorithm deems important in making its decision, and attempted to explain the algorithm as much as possible.

Though far from perfect, the results are intriguing. Our algorithm had an accuracy of 68% on the testing data set. This is clearly suboptimal; however, this is also significantly higher than random results, demonstrating that our algorithm learnt useful processes and that this research direction is worth exploring further. The main difficulty is the aforementioned similarity between leukemic and normal cells; thus, the training process must be complex and fine-tuned to yield good results. After analyzing the results from the explanation program, we determined that our algorithm focuses on the contour of cells in its decision-making process, especially unsmooth or protruded sections. To conclude, our algorithm is far from the level required for practical applications; however, we demonstrated that such algorithms are possible and can be explained.
Research on Digital, Networked and Intelligent Manufacturing of Modern Ship
Authors: Li Hao
Published: October 2020

The development of ship intelligent manufacturing is divided into three stages of digital, networked and intelligent manufacturing. In the digital manufacturing stage, shipbuilding enterprises strengthen the relationship between manufacturing and design to improve the efficiency of manufacturing. In the networked manufacturing stage, shipbuilding enterprises use the internet of things technology to improve the controllability in the production process and effectively enhance the coordination and cooperation between machines and persons. In the intelligent manufacturing stage, shipbuilding enterprises let the new generation of artificial intelligence technology run through the design, management, service and production, greatly improving the flexibility of manufacturing. This paper studies the application model of industrial design system in ship design, the application model of internet of things technology in the production control of shipbuilding enterprises, and the application model of new generation artificial intelligence technology in intelligent decision support, which makes a preliminary exploration for the intelligent development of shipbuilding enterprises.
Application of analytic hierarchy process in performance appraisal system
Authors: Shi Gao-Yang , Wang Xiao-Yan
Published: October 2020

Based on the KPI and BSC assessment method commonly used in management, combined with the analytic hierarchy process in mathematics, the core algorithm of the assessment system is updated, the weight of each assessment element is scientifically measured, and the requirement of fairness and justice in the assessment function of the system is guaranteed, which provides scientific basis for the final performance appraisal results. Zero client B/S architecture is adopted, and UML modeling is used for system analysis. Modeling methods such as timing diagrams are used in the system design, and combined with JavaWeb technology to complete the construction of system pages and interaction with the server. It realizes the design and development of the performance appraisal system, and makes human resource management more scientific and convenient.
An evaluation method of government micro-blogging service quality based on user behavior analysis
Authors: Wang Suozhu , Guo Tianhao , Gao LinQi
Published: October 2020

As a new channel and way for the government to provide public services, how to accurately evaluate the service quality is the key to its healthy and rapid development. The government micro-blogging service quality evaluation index system is constructed and the calculation methods of the evaluation indexes are given in this paper. It contains four dimensions, namely, government micro-blogging construction quality, government micro-blogging functional service, user interaction behaviour and benefit-cost. On this basis, an evaluation process and method of government micro-blogging service quality that integrates subjective and objective indexes is proposed based on the idea of fuzzy comprehensive evaluation in this paper.
The Research on Effectiveness Evaluation of Mobile Broadband Wireless Communication System
Authors: Gao Lijuan , Chen Long , Yu Fengkun
Published: October 2020

It analyzes the classical methods of the effectiveness evaluation based on the research of the steps and principle of system effectiveness evaluation. It builds the indicator system from three aspects, such as: basic communication ability, communication reliability and interconnection ability, based on the characteristics and applications of wireless communication system. Then it evaluates the effectiveness of wireless communication by ADC method which can provide some suggestions for the system development.
Research on clustering method of government micro-blogging user group based on user preference
Authors: Guo Tianhao , Wang Suozhu
Published: October 2020

The goal of government micro-blogging is to provide the public with faster government services. Therefore, it is of great significance to study the characteristics of users and cluster analysis to provide personalized services and microblog operation management. A user preference measurement method based on the usage behavior data of users’ retweeting, commenting, and praising on the government micro-blogging platform is proposed and a user group fuzzy clustering method based on user preferences is given in this paper. An example is given to illustrate the effectiveness and feasibility of the method proposed in this paper.
Study on Residential Smart Lighting under the Perspective of Light and Space
Authors: Song Jia , Wang Zun , Zou Nianyu , Wang Zhisen
Published: October 2020

Focusing on the current residential lighting, it does not meet the basic lighting function, and ignores the residents’ visual and psychological acceptance. Its insufficient brightness severely violate people’s lighting life. In response to this problem, this article discusses intelligent lighting theory around the internal atmosphere of the theme from the perspective of light and space, with communities and people as the main body. The idea that lighting should be centered on people was proposed. The relevant evaluation model was used to provide data support by this paper for strengthening internal functions from the perspective of community space, the improvement of the internal atmosphere of the community was completed and the conflict between the lighting and people’s emotions were eventually eased.
Determination of the width of Haihua Island branch channel based on simulation test
Authors: Xu Yisen , Wang Fengwu , Yin Zhongxun , Liu Sihang
Published: October 2020

In order to ensure the safe navigation of ships, it is necessary to provide ships with sufficient fairway width for navigation. We selected a suitable ship model, extracted the ship test track zone through simulation test, selected the track point along the edge of the track zone, and integrated it. According to the distribution of edge track points, the channel should be appropriately widened so that most of the track points are within the new channel. The width of the channel widened by the edge track point has a good consistency with the calculation results of the “General Design Specification for Seaports”, indicating that it is feasible to determine the channel width using this method.
Fighting COVID-19 with Fever Screening, Face Recognition and Tracing
Authors: Tan Weijun , Liu Jingfeng , Zhuo Yue , Yao Qi , Chen Xing , Wang Wei , Liu Rushuai , Fu Yu
Published: October 2020

Since the outbreak of COVID-19 corona virus in late 2019, it has become a tremendous threat to the whole world. Driven by the mission to save lives, we develop a fever screening and tracing system which can detect patients with fever symptom, and identify the patients using face recognition. In addition, our big data AI platform enables the tracing of the patients possible. A real-time alert sent to the personnel on duty on a web or mobile app activates the action to trace the patient and the close contacts providing an effective means to control the spread of the virus.
Load stability analysis of Kazakhstan photovoltaic power system based on data visualization
Authors: Wang Liang , Zhang Chen
Published: October 2020

Traditional methods can not track the load migration of Kazakhstan photovoltaic power system dynamically, and the analysis results have limitations. Therefore, this paper introduces the data visualization method to analyze the load stability of Kazakhstan photovoltaic power system. The simulation model of photovoltaic power system is built to set the model parameters. The system stability node is obtained by using the principle of power flow calculation. The load stability analysis of the system is realized by controlling the output impedance of boost converter. In order to verify the effectiveness of the proposed method, a comparative experiment was designed. The results show that this method can control the operation state of Kazakhstan photovoltaic power system, and has a good effect on the load stability analysis of the system.
Dynamic characteristics analysis of time-delay fractional order dynamic system
Authors: Dong Jun , Xiao Yao , Ma Hu , Zhang Guangjun
Published: October 2020

In this paper, a kind of time-delay fractional order dynamic system is studied. The classical integral sliding mode control method is used to design a single controller to control the time-delay fractional order Liu system from chaos to fixed point, and the single controller is further designed to control the time-delay fractional order Liu system from chaotic attractor to limit loop, so the chaos control of time-delay fractional order nonlinear dynamic system is achieved. On that basis, the dynamic characteristics of the time-varying delayed fractional-order Lorenz system are analyzed by the simulation, and the chaotic phenomena of the time-varying delayed fractional-order Lorenz system are verified under the provided time delay and order.
Production and logistics optimization of packing workshop in sugar enterprises
Authors: Xu Tianxing
Published: October 2020

In the increasingly fierce market competition, sugar enterprises often face the problems of insufficient production capacity and low logistics efficiency. Based on the balance theory of production line, this paper studies the packaging production in the packaging workshop of sugar enterprises, determines the bottle-neck of production in the packaging workshop, and puts forward the corresponding optimization scheme to improve the production process and increase the production efficiency, reduce the probability of material accumulation in production line, so as to improve the production level and logistics level of enterprises, so that enterprises get better production efficiency.
H∞ Control Design of Power System Based on Generalized Model
Authors: Zhao Lina , Qiu Zhanzhi , Xu Zhichao
Published: October 2020

The generalized model power system is a complex interconnected system with complex structure and many external disturbance factors, which makes the stability of the system difficult to control. In order to guarantee the stability of the generalized model power system under the condition of external disturbance, more and more effective stability controller design methods are sought. Based on ricarty method and LMI method, the design conditions of H ∞ controller for the generalized power system composed of N subsystems are studied. At the same time, it also provides an effective way and method for analyzing the stability of similar systems.
Accuracy of range estimation of pure electric vehicles under compound conditions comparative analysis
Authors: Dai Tianlu , Zhang Zipeng , Wang Feifei , Sun Long , Wang Rui
Published: October 2020

In high and low temperature environments, especially in high-speed scenarios, the estimation accuracy of the range of electric vehicles is a pain point which is more concerned by consumers. Based on the China light-duty vehicle test cycle for passenger car, this paper deeply studies the estimation accuracy of the range of electric vehicles under different ambient temperature conditions. Further, a complex test cycle including long-distance in high-speed driving conditions is constructed, and the influence rule of the complex test cycle and high-speed vehicle speed on the estimation accuracy of cruising range is compared and analyzed through actual vehicle tests.
The Design of Correlation-aware Service Composition System
Authors: Guo Hua , Shu Min
Published: October 2020

A correlation-aware service composition system is designed in this paper. The involving key modules in this system are introduced including service description, service request submission, service composition construction, service composition instantiation, service composition optimization, and service correlation extraction. The feasibility of the system is verified and analyzed based on the example of “online purchasing automobiles parts”.
A flour impurity detection system based on image processing
Authors: Zhao Jimin , Xue Chenchen , Xue Chenchen
Published: October 2020

Flour plays an important role in People’s Daily consumption, and the content of impurities in flour indicates the quality of flour. At present, most domestic factories are using magnifying glass and other simple tools for impurity detection. This method is troublesome and does not meet the requirement of precision. This paper designs an automatic impurity detection system based on image processing, which not only improves the detection efficiency, but also greatly improves the detection accuracy. The basic process of this system is to grayscale the image obtained by photographing, then carry out local entropy transformation, and then map to form entropy image. Finally, the impurity detection is completed after image filtering, image segmentation and edge detection.
Strength Analysis of Torsion Beam of a MPV vehicle Based on ADAMS
Authors: Ren Nver , Meng Qingting , Mei Junyu
Published: October 2020

This paper is based on HyperMesh and MotionView to establish a flexible body file of the rear torsion beam of a MPV vehicle, and then according to the vehicle parameters, bushing, shock absorber and spring and other parts properties and hard point information, the flexible file of torsion beam is input into ADAMS to establish the vehicle dynamic suspension model after rigid and flexible coupling; according to the load transfer, the ADAMS software is used to calculate the force at each connecting point of torsion beam under different working conditions, and the force at each point is added to the finite element model of torsion beam to analyze the stress distribution of torsion beam under each working condition. According to the post-processing cloud diagram, the risk points of torsion beam under various working conditions are found, which provides the direction and train of thought for the structural optimization of torsion beam in the later stage.
Clothing image classification based on random erasing and residual network
Authors: Gao Zongyan , Han Lixin
Published: October 2020

The traditional clothing classification method mainly consists of manually extracting obvious features such as color, texture and edge of the image. These artificial feature extraction methods are cumbersome and feature recognition rate is not high. In recent years, Deep Residual Network (ResNet) has been widely used in various fields by increasing network depth to obtain higher recognition accuracy. In this paper, the ResNet model is applied to the classification of clothing images, and on this basis, its data pooling layer is improved so that it can learn more rich features of image data. Clothing images are easy to be deformed and occluded. In this paper, a random erasing data enhancement algorithm is used to integrate and improve the model to improve the generalization ability of the ResNet model to such data. The final experimental results show that the classification accuracy of the improved residual model on clothing data in this paper is improved by 2.43%. At the same time, after integrating the random erasure data enhancement algorithm, the generalization ability of the model has been further improved.
Polygonal inclusions with nonuniform eigenstrains in an isotropic half plane
Authors: Yonggang Lee , Bin Xu , Wennan Zou
Published: October 2020

Polygonal inclusion problem in an isotropic half plane is investigated in this paper. The eigenstrains prescribed in the inclusion are assumed to be characterized by polynomials of arbitrary order in the Cartesian coordinate system. Based on a novel superposition method, the solution of the inclusion problem in a half plane is decomposed into two subproblems: the inclusion problem in a full plane and the auxiliary boundary problem in the half plane. Furthermore, the Kolosov-Muskhelishvili (K-M) potentials for the full plane and the auxiliary potentials for the half plane along with their derivatives are expressed into two sets of basic functions, which involve the boundary integrals of the inclusion domain. For polygonal inclusions, exact explicit expressions for both basic functions are explicitly derived, which leads to those for the induced displacement, strain and stress fields.
Numerical research of the effects of fouling on the performance of marine propellers
Authors: Zhang Tao , Yu Sheng-Xue
Published: October 2020

As it is known, propeller has been most widely-used ship thruster, propeller blade surfaces are of polished metal and have no antifouling provision, which makes them vulnerable to fouling. Under the effects of fouling, the propulsion efficiency of propellers decreases and fuel consumption of ships increases, thus greenhouse gas emissions increase, although the effects are rarely studied. The present work aims at quantifying the effects and providing a deep insight into the physical mechanism by means of Computational Fluid Dynamics (CFD) and the surface panel method. The simulation employs the SST k-ω turbulence model and is carried on NSRDC-NACA-66-mod blade section which is widely applied to marine propeller. Barnacles are selected as the study subject from the fouling community and are directly modeled at geometry level. With Propeller 4383 being studied, the induced velocity field under several advance coefficients through the surface panel method, hence the incoming velocity and effective attack angle of the blade sections at each radius are obtained. Then the lift and drag force of each blade section under fouling conditions are got through the CFD numerical method, and then the propeller open water performance parameters under fouling conditions are obtained through blade elements theory, such as the thrust coefficient TK, torque coefficient QK and open water efficiency η0. The results shows that the fouling has a serious adverse effect on the propeller performance parameters, and the serious calcareous fouling leads TK goes down by 36.1%, QK increases by 25.7% and η0 reduces by 49.2%.
Generation of high-energy-density collimated electron-positron beam by a linear polarized laser-plasma interaction from a cone
Authors: Shan Fei , Ma Yanyun , Yang Xiaohu , Zhang Guobo , Tian Lichao , Jiang Jing , Li Xiaoxiao , Ma Long , Yuan Yun , Han Peng , Cui Ye
Published: October 2020

Electron-positron plasma has attracted much attention recently due to their unique properties. In this paper, we study the acceleration of electron-positron plasma in a cone by using PIC method. The electrons and positrons will be scattered by transverse ponderomotive force during the interaction between laser and electron-positron plasma. A new scheme is proposed to obtain high-energy dense collimated electron-positron plasma by a hollow gold cone. Transverse scattered electrons and positrons are pinched in the gold cone. Both electrons and positrons can be accelerated by ponderomotive force continuously and a dense collimated electron-positron plasma is produced. The energy of electrons and positrons obtained in the cone is much higher than the upper limit of electron energy obtained by ponderomotive force acceleration. The high quality electron and positron beam has great potential value in further research.
Research on Physical Fitness Test Data Mining and Analysis Based on Apriori Algorithm
Authors: Shi Chengxiang , Liu Lei
Published: October 2020

Based on the analysis of the characteristics of Apriori algorithm, this paper uses the iterative method of layer by layer search to find out the relationship of item sets in the students’ body measurement database to form the mining model of association rules. The model maximizes the generation of candidate sets, reduces the size of frequent sets, and achieves good performance. At the same time, the average GM (1,1) model is constructed by the accumulation of the original sequence and the least square estimation of the parameters, as well as the whitening differential equation form, to predict the trend of College Students’ physique change in the future, and the research shows that the error of the model is small and the precision is high.
Soil organic carbon fractions in oil palm management systems
Authors: Handayani IP , Widiastuti H , Coyne MS , Widawati S
Published: October 2020

Conversion of deforested areas into oil palm plantation has been suggested as a means to improve soil quality and carbon sequestration capacity in forest margin areas. Soil organic carbon fractions, such as particulate organic carbon is the most sensitive indicator in improving soil organic matter and soil quality. We determined particulate organic matter - carbon (POM-C) and mineral associated carbon (MAC) in Bengkulu and West Java Provinces Indonesia. The study areas were grain crop fields, secondary forest, 5 yr-oil palm plantation, 10 yr-oil palm plantation, 15 yr-oil palm plantation and 25 yr-oil palm plantation. Soil organic carbon fraction magnitudes varied in the surface of 0 to 30 cm of grain crop fields, secondary forest and oil palm plantations. Twenty five year-oil palm plantation provided the largest total organic carbon, while 5 yr-oil palm plantation and grain crop fields contained similar amount of total organic C. Both POM-C and MAC fractions were increased in the plantation sites with oil palm trees and secondary forest compared to the grain crop fields. The plantation sites had a larger POM-C content than grain crop fields, but the increase of POM-C was limited to the surface of 0 to 10 cm of soil. Carbon stock in 0-30 cm under oil palm plantations were reduced up to 20% compared to secondary forests and 28% compared to rubber plantations in Bengkulu and West Java Provinces. On average, converting forest to plantations led to a loss of 10 Mg C/ha after about 10 years of conversion. However, the C stock in the subsoil was similar under the forest and the plantations in Bengkulu. Further, limited C input from litter would eventually cause more losses of SOC in oil palm plantations compared to rubber plantations. In conclusion, investigating the deeper profiles and soil erosion may be an important tool to unfold the trends of soil organic carbon fraction dynamics and magnitudes after the conversion of natural ecosystems to intensive plantations.
Organic carbon sequestration at gambier (Uncaria gambier L.) cultivation in sloping area under wet tropical region
Authors: Yulnafatmawita  , Maira L. , Dennico H.N.P. , Haris ZA
Published: October 2020

Organic carbon (OC) is an important factor for soil and the environment. This research aimed to identify OC sequestration under different age of gambier crops in central Gambier, Pesisir Selatan, West Sumatra. Soil samples were taken from different crop ages ( 2, 5, and 10 years old), and then under secondary forest nearby as a comparison at 0-30 cm soil depth. The results showed that, in general, soil OC content was considered low to very low either under Gambier crops (<2%), or under forest land use (2.11%). Then, the OC stock increased by increasing crop age from 2 to 10 years old. The OC stock at ten years old Gambier reached 90% of that at the forest on the top 30 cm soil depths. Rate of OC sequestration was approximately 1.03 Mg ha-1y-1and 4.88 Mg ha-1y-1, respectively, for crop age between 2 and 5 as well as between 5 and10 years old. This low soil organic carbon (SOC) content combined with fine soil particles caused the soil had medium-high soil BD, low-medium total soil porosity, rather slow-medium hydraulic conductivity rate, and unstable-rather stable soil aggregate stability. The study concludes that SOC sequestration under Gambier plantation in the sloping area was quite slow, especially during the first five years due to new crop establishment and the leaves harvested.