bluuebunny/crossref_metadata_2025_split huggingface dataset:
Submodelling method for modelling and simulation of high-density electronic assemblies
Authors: Kong Lingsong , Yao Quanbin , Lv Xiaorui , Zhao Wenzhong 
Published: September 2020

Abstract
In order to enable the computation of fatigue life prediction of high-density electronic assemblies (solder joints > 4000), a modified submodelling approach was developed to integrate the detailed interconnections into equivalent layers with consistent mechanical parameters and thus reduce the complexity of modeling and simulation. The approach incorporates the uniform equivalent interconnect layers into the global model and simulate the general system response to thermo-mechanical loads. Displacements calculated from this first simulation are fed into a local model with more details of worst-case solder joints, in order to obtain local indicator of fatigue life. In this paper, referred to mechanical experimental methodology, a FEA process was used to extract the mechanical parameters of the equivalent layer. The validity of the modified submodelling approach was verified on fatigue life prediction of a high-density flip-chip package under thermal cycling.
Development and application of online load measurement system for hanger and support
Authors: Wan Yu , Liu Xubi , Jin Chuanling , Yin Jiyang 
Published: September 2020

Abstract
It was difficult to monitor the load and evaluate the performance of the hanger and support of power plant. The online load measurement system for hanger and support was developed and some experiments had been carried out. Compared with the offline bench performance testing machine, the experimental results showed that the average measurement error of the online measurement system was less than 1%. It can effectively measure the performance parameters of the hanger, conveniently and reliably monitor the real-time load of the hanger, which is important for the safe operation of the hanger and the steam/water pipeline in the power plant.
Analysis of pipeline fatigue performance under random vibration load
Authors: Jin Chuanling , Wan Yu , Yang Qingxu , Xu Jiaju 
Published: September 2020

Abstract
As one of the most important equipments in power plant, the mechanical properties of steam pipe were analysed in this paper. Random vibration theory and Miner linear cumulative damage theory as well as frequency-domain fatigue analysis method were adopted, modal analysis and fatigue performance analysis of the in-service steam pipe were carried out with ANSYS, calculated first ten natural frequencies and cumulative damage under random excitation use Steinberg three-interval method. Results show that no fatigue failure will occur under this excitation load, the random vibration frequency-domain fatigue analysis method can effectively evaluate the fatigue performance of steam pipe, and provide a reference for the pipeline random vibration fatigue analysis.
Hybrid energy storage system of storage battery / super capacitor for mine electric locomotive
Authors: Zhu Longji , Wang Xinrui 
Published: September 2020

Abstract
At present, mining electric locomotive with lead-acid battery energy storage, when accelerating or braking, the battery bank (BT bank) in a short period of time is difficult to discharge large power and absorb feedback power, which affects the running efficiency and cruise mileage of electric locomotive. In view of the above problems, the hybrid energy storage system of storage battery and super capacitor is applied to the motor of mine car, and the complementary power distribution scheme of static power output by storage battery and dynamic power output by super capacitor is proposed. In order to realize the power sharing of the two energy storage devices, a bi-directional three-level DC/DC converter is proposed to realize the current and power control of the SC bank. The analysis and experiment show that the HESS can not only output large current and improve the acceleration performance of electric locomotive, but also can save electric energy and improve the running efficiency and cruise mileage of electric locomotive.
Bypassing-contravariant mode switching method for UPS in economic mode based on DSP program
Authors: Li Dawei , Zhang Qiaochu , Liu Dan , Li Yue , Ren Yi , Zhuang Qiming 
Published: September 2020

Abstract
Uninterrupted power supply has the highest efficiency and reliability under economic mode. When the grid power quality is qualified, the economic model UPS uses the grid power supply to supply the load directly, which achieves the highest system efficiency. However, when the voltage is too high, too low or the frequency is not normal, the large circulation will cause device damage or UPS failure, which cannot meet the requirements of load on power quality. To solve this problem, this paper proposes a switching control method of UPS system from bypass power supply state to inverter power supply state in economic mode based on DSP program. The method analyses the theoretical basis and switching time sequence of over-high detection of mains power in detail, and proves the effectiveness of the proposed method through experiments.
State estimation of lithium-ion battery based on Gaussian process regression
Authors: Li Jiabo , Jiao Shengjie , Ye Min , Wei Meng 
Published: September 2020

Abstract
The state of charge (SOC) of lithium-ion battery is one of the important parameters of battery management system (BMS). Accurate estimation of SOC can improve the safety of battery. Therefore, in order to improve the accuracy of SOC estimation, a new method based on Gaussian process regression (GPR) is proposed. The SOC estimated value at the previous time is used as the feedback vector, together with the current and voltage measured at the current time are used as the input vectors of the model to estimate the SOC at the current time. The experimental results show that the error of the proposed method is controlled within 2%, which verifies the effectiveness of the proposed method.
Analysis of contact resistance between fitting terminals
Authors: Fu Jianghao , Zhao Chongyang , Wang Wenhao , Zhang Wei 
Published: September 2020

Abstract
In order to reduce the influence of the through-current heating between hardware terminal and the electrical equipment terminal on the performance of the hardware, this paper studies and analyzes the composition of the contact resistance of the hardware and the factors that affect the resistance value, and proposes a method to reduce the contact resistance. Under the condition that the current is constant, it can reduce the heat between the terminal blocks and provide a reference for the subsequent design of the metal terminal block.
The effect of data sampling time on the accuracy of maximum output power of a thermoelectric module
Authors: Gao Junling , Feng Bo , Xi’an Fan , Zhang Shimin 
Published: September 2020

Abstract
The instantaneous short-circuit current and open-circuit voltage of TEM (Thermoelectric Module) can be used to quickly estimate its maximum output power (P

omax). Because of their instantaneous samples, the sampling time has a great influence on the accuracy of the estimated result. In this paper, the influence mechanism of sampling time is analysed systematically and a module of TEG1-127-2.8-1.2-250 is taken as a sample for verification. By means of either steady open-circuit/instantaneous short-circuit at mode A or steady short-circuit/instantaneous open-circuit at mode B, P

omax can be estimated quickly. At single mode A or B, the shorter the sampling time is, the greater the relative error of the estimation of P

omax has. This error varies with different temperature of hot plate, but the variation trend with sampling time is consistent. The valuations at model A and B are larger and smaller than the accurate values, respectively. When the sampling time is 600 to 1000ms, the error is the smallest (less than 3%). The accuracy of P

omax is significantly improved by using the average results of mode A and B. In this way, the sampling time has little influence on the estimation error, and the shortest sampling time corresponds to the highest estimation accuracy.
Diagnosis of partial discharge defects of 35kV cable joint based on high frequency and oscillating wave detection technology
Authors: Li Xu , Yu Yang , Kan Yongming , Zhu Mingzheng , Fang Shengchen 
Published: September 2020

Abstract
Combining with a cable test case, this paper introduces the process of locating and diagnosing a partial discharge defect in the middle joint of 35kV cable by using high frequency and oscillating wave detection technology, and analyses the characteristics of partial discharge signal, and puts forward some suggestions for the later test.
A study on negative oxygen ions shunting based on direct current pulsating co-frequency coupled electric fields method
Authors: Ji Dafeng , Gong Jianhao 
Published: September 2020

Abstract
This paper aims to explore the feasibility and stability of negative oxygen ions (NOI) shunting based on direct current (DC) pulsating co-frequency coupled electric field method, and the safeness under a high anion concentration. Negative oxygen ions were obtained under corona electric discharge from high negative voltage, a shunting method which was employed to detach the negative ions and neutral gas was executed with an uncoupled and a coupled electric field. In coupled field, the negative ions were exported with the same electric field derived from the ion emission polar as a guiding polar, while neutral gas was exported from another outlet; In uncoupled electric field, the purity of NOI from guiding polar powered separately is employed as a control group. Safeness test was approached with 2 rats under a normal cage system. The purity(separate) ratio of NOI between the guide outlet and the neutral gas outlet is 8.5, while the separate ratio is 163636.4 in the coupled electric field respectively. The purity of NOI is 18 million per cm3 in coupled electric field and 9.8 million per cm3 in uncoupled electric field. The purity of NOI is 180 million/cm3 under a voltage of -26 kV. The purity and separate ratio of NOI could be improved significantly after using the coupled electric field compared to uncoupled electric field.
Design and implementation of smart flying ball target based on APP
Authors: Zhou Yuying , Sun Chuanxing , Fang Yuan , Wang Ding 
Published: September 2020

Abstract
Home entertainment smart products have gradually become a hot topic. At present, home entertainment smart products are relatively simple to implement. They mainly use hardware or software methods to control and transmit information through Bluetooth or Zigbee. In order to solve the shortcomings of home entertainment intelligent products that lack intelligence and a single design method in the current market, this article uses embedded technology and Wi-Fi communication technology to design an intelligent flying ball target control system based on Android APP. Based on the hardware platform, the filtering algorithm and scoring algorithm of the flying target are studied. The flying target software system based on μC / GUI and μC / OS-III is written. The Android operating system is designed to connect to the flying ball target APP.
Discrete optimization model for permutation flow shop under time-of-use electricity tariffs
Authors: Wang Fang , Qin Pan , You Jing 
Published: September 2020

Abstract
The time-of-use electricity tariffs (TOU) which is widely used at home and abroad make the timeline for scheduling to have the attribute of different electricity tariffs. In order to solve the difficulty of coordination between electricity price segmentation and product processing time, this paper proposes a discrete optimization model, which uses central moment method modelling to ensure the continuity of job-processing and the uniqueness of processing time points. A discrete optimization model with maximum completion time and total electricity cost is constructed. According to the instance of the processing workshop and the characteristics of GAMS software, a virtual small-scale case of detection is constructed. By setting 11 groups of experiments with different weights, the total electricity cost is reduced by 30% compared with the traditional scheduling scheme after the electricity cost optimization is considered to avoid the peak arrangement of the electricity price. Although the maximum completion time is increased by nearly 30% on the surface, the downtime of staggering the peak is removed, the actual processing time is 42 minutes, and the total completion time is only increased by 5%.
Automatic grinding particle size control technology and equipment
Authors: Li Zhijing , Wang Yuxuan , Jin Zhongwu , Zhou Yinjun 
Published: September 2020

Abstract
The article provides an automatic particle size control technology, which uses advanced particle analysis research technology and modern motor variable frequency control and computer software technology. It can quantify and control the particle size of all products within the grinding capacity of the high-pressure micro-mill. This technology can be widely applied to model sand processing in river engineering model tests, which can greatly improve the processing accuracy, work efficiency, environmental protection performance and reduce energy consumption of ordinary micro-powder mills.
Research and application of the conveyor self-advancing tail without repeated support
Authors: Ma Lianwei 
Published: September 2020

Abstract
Aiming at the current situation of conveyor self-advancing tail process of the traditional heading face in the coal mine, such as “retreating machine-manually pulling--manually adjusting the deviation-ground anchoring”, in order to realize efficient and rapid excavation, a new type of conveyor self-advancing tail used in mining face suitable for crushing roof was developed. The design of the endless force, the stepping distance, the conveying capacity, the adapting belt speed and the idler type of the designed conveyor self-advancing tail were carried out. Based on the virtual prototype technology the intermediate frame of the tail and the dynamic frame were established and the model’s structural strength were checked. The correctness of the design of the whole machine was verified by the actual application of the tail in the coal mine. The successful development of conveyor self-advancing tail without repeated struts effectively solves the problem that the tail of the coal mining face can not be self-moving or needs to be support self-moving, which greatly improves the efficiency of the tail movement of the driving face. At the same time, it also provides the possibility to upgrade the automation of the face.
Fashion Color Verification System Based on Similarity Search Algorithm
Authors: Xu Chengrui , Tian Yujing , Zheng Zeyu , Li Ge , Shang Wenxiang 
Published: September 2020

Abstract
As the times progresses and the technology rapidly develops, it has become a trend to make use of artificial intelligence to accurately predict the trend of fashion color. In facing up with the demand of enterprises and design developers’ verification on the accuracy of color decision-making, a set of fashion color verification system based on similarity search algorithm is proposed on basis of the technical feasibility of ‘searching image by color’ and the prediction method of the fashion color in this paper. Through the reasonable construction of database and analysis framework, the known color is introduced into the system. Not only can the masonry layout of color image under the selected labels be gained, but also the visual report on the analysis of the color trend can be gained, in which the verification and correction of the color can be achieved. By comparing this set of system with the analysis process of the traditional artificial color trend, it’s concluded that the system can effectively improve the accuracy of predication and the efficiency of the verification.
New Approaches and Tools for Ship Detection in Optical Satellite Imagery
Authors: Walter Avila Cordova Aaron , Condori Quispe William , Jorge Cuba Inca Remy , Nina Choquehuayta Wilder , Castro Gutierrez Eveling 
Published: September 2020

Abstract
Ship detection using optical satellite images is a very important task for the field of maritime security, either in search of lost ships or in maritime control of a commercial or military type. Added to this are the advances in the field of Computer Vision, especially in the use of models based on Artificial Intelligence, which allow the construction of robust and more precise detection systems. However, geographic scenarios, typical of a satellite image, limit the development of this type of system since they require the availability of a large number of images in different scenarios. In this paper, a new approach to Ship Detection is proposed using two new data sets labeled with horizontal bounding boxes (HBB). Likewise, a new labeling tool (DATATOOL) is presented that allows better organization and distribution of data. The new data sets, Peruvian Ship Dataset (PSDS) and Mini Ship Dataset (MSDS), have been generated from optical satellite images obtained from different sources. PSDS is created from 22 satellite images of PERUSAT-1 with 0.7m spatial resolution, giving a total of 1310 images. MSDS has been generated using Google Earth satellite images, generating 2993 images of 900x900 pixels. Ships are found both at sea or inshore. Finally, results of the tests using Deep Learning Algorithms such as YOLT and YOLOv4 are presented, following the approach and the proposed tools. Resource and source code available at https://gitlab.com/williamccondori/datatool
CRWMS: Bipartite Network Embedding based on Constrained Random Walk and Mixed-Skipgram
Authors: Ye Yaling , Ma Lina , Zhang Xia , Ni Qixuan , Wang Yuyao , Bu Zhan 
Published: September 2020

Abstract
A bipartite network is a basic representation model in recommendation systems, in which the explicit links (e.g., representing user-item rating information) only exist between heterogeneous nodes (e.g., users and items). Most traditional bipartite network embedding methods only consider the explicit network structure, but ignore semantic relations and rating information therein. To solve this deficiency, we designed a new bipartite network embedding approach based on the Constrained Random Walk and Mixed-Skipgram (CRWMS). Specifically, we propose a constrained random walk strategy that produces the mixed local sampling sequences, and further adopt the splitting operation to preserve both explicit and implicit structural information. Furthermore, we propose a novel and effective mixed-skipgram model to discern node representation vectors through joint training of explicit and implicit structural sequences. We compare CRWMS with five state-of-the-art network embedding methods on three real-world bipartite networks. Experiments show that our approach improves performance in edge classification, edge clustering, recommendation and link reconstruction tasks.
Bayesian Network Model Test Configuration Method based on Genetic and Binary Discrete Particle Swarm Combination Algorithm
Authors: Han Lu , Shi Xianjun , Wang Taoyu 
Published: September 2020

Abstract
Test configuration is an important part in the process of testability design. Most of the existing test configuration methods are based on multi-signal flow diagram model and adopt genetic algorithm or particle swarm optimization (pso). However, there are many problems in this solution. First of all, the multi-signal flow diagram model has poor ability to express uncertain information, low model accuracy, large deviation in the calculation of testability indicators, and the model does not have the ability to learn and update. Secondly, the efficiency of genetic algorithm is low and the computation time is long, while the binary discrete particle swarm optimization algorithm is easy to fall into the local optimal. To solve the above two problems, a test configuration method based on the hybrid algorithm of genetic -binary discrete particle swarm optimization is proposed. This method can combine the global search ability of genetic algorithm with the optimal speed of binary particle swarm optimization, and use the bayesian network model to calculate more accurate testability indexes. It is proved that the algorithm can make full use of the high-precision information provided by the model, and the calculation speed is fast.And it is not easy to fall into the local optimal solution.
Deep Pyramid Convolutional Neural Network Integrated with Self-attention Mechanism and Highway Network for Text Classification
Authors: Li Xuewei , Ning Hongyun 
Published: September 2020

Abstract
Text classification is one of the basic tasks of natural language processing. In recent years, deep learning has been widely used in text classification tasks. The representative one is the convolutional neural network. DPCNN is a deep convolutional neural network text classification model that can obtain long-distance text information, but it focuses on the extraction of global features and ignores the extraction of local features of the text. Some local feature information is very important and plays an important role in text classification tasks. Therefore, in this paper, a self-attention mechanism is introduced to extract local features of text based on the lack of extracting local features of DPCNN. In addition, although the deep convolutional neural network can extract deeper features, it is easy to cause the problem of gradient disappearance during training. Therefore, the highway network is introduced to prevent the problem of gradient disappearance caused by network training and improve the performance of the model. Experimental results show that the proposed model is better than a single DPCNN model, which further improves the accuracy of text classification tasks.
Trainable Kalman Filter Based on Recurrent Neural Network and its Application in Aviation Surveillance
Authors: Jiang Yanjun , Nong Xiangyu 
Published: September 2020

Abstract
The most commonly used algorithm for aviation surveillance is Kalman filter. The accuracy of Kalman filter is affected by the accuracy of its parameters. When the parameters may change with environment change, the accuracy of traditional linear Kalman filter will be affected, in severe cases, filtering divergence will occur. This paper proposes an aviation surveillance filtering model that treats Kalman filter as the kernel of Recurrent Neural Network, uses Back Propagation Neural Network to predict parameters of Kalman filter. Which let Kalman filter be trainable and have ability to estimate parameters dynamically. Moreover, actual radar measurement data is used for radar filtering experiment, and the experiment results show the feasibility of this model, and show that this model has better accuracy and adaptability than traditional Kalman filter.
Research on the Technology of Searching for Fashion Trend Image Based on ResNet50 Model
Authors: Tian Yujing , Zheng ZeYu , Li Ge , Xu Chengrui , Shang WenXiang , Liu Xiaogang 
Published: September 2020

Abstract
With the further development of technology, providing technical support for the forecast and development of fashion design trends through artificial intelligence has gradually become a reality. In order to enable users to obtain more accurate design trend materials in a shorter time, this paper presents a ‘searching by image’ model of clothing trends based on the ResNet50 model. Through the targeted collection of data and the rational construction of algorithms, the system can retrieve and output more image materials with relevant design features for the existing image materials for the development of design trends as well as the development and design of styles. By comparing the system with Google’s and Taobao’s ‘searching by image’ system, it is concluded that this system has high efficiency, high accuracy and high correlation.