bluuebunny/crossref_metadata_2025_split huggingface dataset:
Lithium-ion Batteries RUL Prediction Based on Temporal Pattern Attention
Authors: Qin Haochen , Fan Xuexin , Fan Yaxiang , Wang Ruitian , Tian Fengyuan 
Published: August 2022

Abstract
Accurate prediction of battery remaining useful life (RUL) under various operating conditions is essential for battery management systems to evaluate battery reliability, reduce the risk of battery usage and provide a rationale for battery maintenance. However, RUL prediction is a challenging problem since battery degradation is a nonlinear process and is influenced by external factors. In order to improve the prediction speed and accuracy, the research proposes a new Li-ion batteries RUL prediction method based on temporal pattern attention-based, which can take into account the influence of different variables for prediction. To model time-invariant patterns across multiple time steps, it combines a gated recurrent unit (GRU), a convolutional neural network, and an attention mechanism. Battery capacity, impedance and temperature are taken as input to train the model. Experiments are validated on public datasets and the results are compared with state of art methods. The experimental results show that the proposed method achieves the lowest MAE with 8.99, which proves the effectiveness of the method.
VSC-HVDC Incorporated Corrective Security-Constrained Optimal Power Flow
Authors: Fu Cong , Zhong Yashan , Zhang Pingping , He Xiangzhen , Yang Yun , Zuo Jian 
Published: August 2022

Abstract
With the rapid development and diverse engineering applications of VSC-HVDC technology, development in power industry has put forward higher economic and security requirements for the security-constrained optimal power flow. Under these circumstances, this paper proposes a security-constrained optimal power flow model for hybrid AC/DC power grids that incorporates the VSC-HVDC corrective control. The linear relationship between the pre-contingency state power flow and post-contingency state power flow is established based on the shift factor and the reliability constraints are described as the linear inequality constraints of the post-contingency state active power flow which is expressed by the pre-contingency state branch power flow. With the inherent ability of the VSC-HVDC converter station (short as VSC station) to rapidly regulate active power taken into account, this paper utilizes the linear relationship between the active power adjustment of the VSC station and the power flows of transmission lines and electrical equipment and corrects the reliability constraints with the sensitivity coefficient. Hence the correction of the post-contingency state power flow is reflected in the constraints. By solving the proposed model with the primal-dual interior-point method, the pre-contingency state optimal security operating point that considers the corrective capability of the VSC stations is obtained. Finally, the correctness and validity of the proposed model is verified based on a VSC-HVDC modified IEEE 14-bus system.
Design and Implementation of AGV Scheduling System based on Spring
Authors: Zhao Ruihang , Zhang Wei 
Published: August 2022

Abstract
This paper designs and implements an AGV scheduling application based on spring framework. This application consists of four modules, intelligent scheduling module, traffic control module, equipment management module and communication protocol module. Through interaction with AGV vehicle-mounted applications, the intelligent scheduling module realizes the function of automatic task allocation and vehicle body accepting tasks autonomously, which solves the problem of low efficiency of existing applications. Traffic control can avoid congestion by announcing node, path and area possession, which solves the problem of congestion that is easy to occur in multi-vehicle operation. The equipment management module realizes the management function of charging station, temporary parking point and other equipment; Communication protocol module to solve the vehicle, equipment, applications between the information encryption, protocol definition and other functions.
Redundancy Strategy of Cascaded Energy Storage System Based on Model Prediction Control
Authors: Yang Bo , Tang Wencheng 
Published: August 2022

Abstract
The cascaded energy storage system is difficult to achieve global optimal control, because its controlled object includes not only external variables such as output power, voltage and current, but also various internal variables such as power module capacitor voltage and battery cluster charge state. Redundancy strategy is proposed in this paper based on model prediction control. Firstly, the predictive model of the system in discrete domain is constructed, and the fast model predictive control method of the cascaded energy storage system is studied to realize the global optimal control and improve the dynamic response performance of the cascaded energy storage system. Secondly, in order to realize the continuous and reliable operation of the cascaded energy storage system under the fault state, the redundancy control method of the cascaded energy storage system is studied. Finally, the validity and effectiveness have been testified by relevant simulations.
Study of Electromagnetic Characteristics of Silicon Steel Sheet and Transformer Vibration Under Different Tension/Compression Stress
Authors: Houguang Chu , Longfei Li , Lei Liu , Fei Zhang , Sirui Zhu , Libing Zhou , Zhaoguang Du , Jintao Wen 
Published: August 2022

Abstract
In order to study the electromagnetic and vibration performance of transformer under different tensile and compressive stresses. This paper firstly studies the magnetic properties of core silicon steel sheets for transformers under different tensile and compressive stresses, the B-H curves, B-P curves, and permeability curves of silicon steel sheets under different tensile and compressive stresses are given. Secondly, based on the test results of magnetic characteristics of the silicon steel sheet, the electromagnetic performance and vibration of a 3kVA transformer are simulated and analyzed. In the simulation process, the influence of different tensile and compressive stresses on the performance of the silicon steel sheet core of the transformer is considered. It provides a new idea for the electromagnetic-vibration analysis of transformers.
Development and Application of Infrared Video Intelligent Diagnosis Device for Substation Equipment Based on Edge Calculation
Authors: Liu Guangwei , Mu Ka , Liu Boyan , Ma Xinsheng , Hao Zhen , Ma Jixian 
Published: August 2022

Abstract
In recent years, infrared detection technology is widely used in the field of state detection of power equipment. It can timely and effectively find equipment defects and avoid the further development of equipment defects. However, the conventional manual inspection has some problems, such as heavy workload, long time-consuming and low efficiency. Based on the research of infrared image intelligent detection technology of substation equipment based on deep learning algorithm and digital image lossless compression technology, a set of infrared video intelligent diagnosis device of substation equipment based on edge calculation is developed and applied in practice. The device realizes local real-time rapid processing and timely diagnosis of equipment defects, and the accuracy of defect identification is about 94.44%, which greatly improves the safe and stable operation level and power supply reliability of substation equipment.
Resilience Assessment of An Expressway Microgrid Considering Network Reconfiguration
Authors: Cheng Boyuan , Tian Xinshou , Jia Limin 
Published: August 2022

Abstract
With the development of distributed generation, energy supply to the transportation industry through microgrids has become a reality. However, in recent years, affected by environmental changes, extreme weather has occurred frequently. Due to the simple topology, the safe operation of microgrids has been threatened. Therefore, it is necessary to analysis and assess the resilience of microgrids. At the same time, due to the high penetration rate of distributed generation, the microgrid can be reconfigured during extreme disasters to form distributed generation islands to ensure the power supply of local loads. This paper first introduces the resilience curve and resilience index of the expressway microgrid, and then proposes a mathematical model for network reconfiguration to improve the resilience of the microgrid. Finally, the resilience assessment method proposed in this paper is used to assess the resilience of an expressway microgrid under extreme storm weather, then, the reconfigured system is obtained by solving the model, which verifies the effectiveness of this method in improving the resilience.
One-Shot Face Recognition through a Region Inception ResNet with Modified Triplet Loss
Authors: Sun Bo , Luo Ao , Rong Bojie , He Jun , Yu Lejun 
Published: August 2022

Abstract
Face recognition has become a convenient method for identity verification, especially the one-shot task has much practical value. Some former works have achieved considerable results. However, they perform ill under the situation of COVID-19 with more and more people wearing a mask. This requires the extracted face features distinguishable and robust enough for classifying different masked people. It is difficult for the one-shot task with only one sample for training. To solve this problem, we designed a network called Region Inception ResNet with Modified Triplet Loss, which generates robust features. It keeps high accuracy even under masked condition. The networks are trained on the datasets of CASIA-Webface and Ms-Celeb-1M, tested on LFW (Labelled Faces in the Wild). Experiments in Section 6 show the effectiveness of our method.
Lossless Compression and Optimization Method of Data Flow Efficiency of Infrared Image Depth Learning Model of Substation Equipment
Authors: Liu Guangwei , Hao Zhen , Niu Zheng , Mu Ka , Ma Jixian , Zhang Wenzhe 
Published: August 2022

Abstract
Infrared detection of substation equipment has become one of the important means for live detection of power grid equipment. The insulation fault and equipment defect of electrical equipment in operation can be found by using infrared thermal imaging technology. Infrared image deep learning model usually needs a very large computational cost and storage space, which greatly limits the application of deep learning model in embedded terminal. In order to apply the deep learning model well in embedded devices with limited resources, this paper proposes a lossless compression method of infrared image deep learning model of substation equipment, which can reduce the size of the deep learning network model by 35 to 49 times without loss of recognition accuracy. A hybrid sparse matrix storage format based on recursion and a cache blocking method based on multi-core/many-core processors are proposed to optimize the data flow efficiency between processors.
Image Point Set Registration: Phased Gaussian Mixture Model
Authors: Hu Xin , Qin Hao 
Published: August 2022

Abstract
The introduction of probability model into point set matching has achieved great success. Because the image point set has more outliers and the image has complex transformation, the traditional Gaussian Mixture Model (GMM) does not perform well in image point set matching. In this paper, we propose a Phased Gaussian Mixture Model (PGMM) for point set registration between images. Different from the existing registration algorithms based on GMM, PGMM uses Locality Sensitive Hashing to distinguish the feature reliability of feature points. The initial transformation relation is obtained in the segment with high feature reliability and vector constraint is applied to other segments. The average Hamming distance is used to give different weights to each Gaussian component, which is related to the characteristics between data points and model points. The proposed method has good performance in the case of large image transformation scale.
A Water Gauge Scale Capturing Method in Tidal Well Based on Image Recognition
Authors: Guo Hai , Wang Yanyan , Zhang Yifei 
Published: August 2022

Abstract
Tide level observation data is an important technical support data for the delimitation of warning tide level and disaster prevention. The performance of the tidal well is tested by regularly checking the external water gauge, so as to maintain the tidal follow-up and wave dissipation of the tidal well. The check of water gauge inside and outside the well shall be carried out once a month. It is time-consuming and labour consuming. The paper proposes a real-time borehole water gauge capturing algorithm in tidal well based on image recognition. Experiments have proved that the method can recognize the water gauge scale effectively. The comparison experiments with the other standard measurement method are done. It can be seen from the comparison chart that the error between the water gauge scale and the tide level value is ± 1cm, which meets the national standard.
Engineering naturally-derived human connective tissues for clinical applications using a serum-free production system
Authors: Safoine Meryem , Côté Alexandra , Leloup Romane , Hayward Cindy Jean , Plourde Campagna Marc-André , Ruel Jean , Fradette Julie 
Published: August 2022

Abstract
The increasing need for tissue substitutes in reconstructive surgery spurs the development of engineering methods suited for clinical applications. Cell culture and tissue production traditionally require the use of fetal bovine serum (FBS) which is associated with various complications especially from a translational perspective. Using the self-assembly approach of tissue engineering, we hypothesized that all important parameters of tissue reconstruction can be maintained in a production system devoid of FBS from cell extraction to tissue reconstruction. We studied two commercially available serum-free medium (SFM) and xenogen-free serum-free medium (XSFM) for their impact on tissue reconstruction using human adipose-derived stem/stromal cells (ASCs) in comparison to serum-containing medium. Both media allowed higher ASC proliferation rates in primary cultures over five passages compared with 10% FBS supplemented medium while maintaining high expression of mesenchymal cell markers. For both media, we evaluated extracellular matrix production and deposition necessary to engineer manipulatable tissues using the self-assembly approach. Tissues produced in SFM exhibited a significantly increased thickness (up to 6.8-fold) compared with XSFM and FBS-containing medium. A detailed characterization of tissues produced under SFM conditions showed a substantial 50% reduction of production time without compromising key tissue features such as thickness, mechanical resistance and pro-angiogenic secretory capacities (plasminogen activator inhibitor 1, hepatocyte growth factor, vascular endothelial growth factor, angiopoietin-1) when compared to tissues produced in the control FBS-containing medium. Furthermore, we compared ASCs to the frequently used human dermal fibroblasts (DFs) in the SFM culture system. ASC-derived tissues displayed a 2.4-fold increased thickness compared to their DFs counterparts. In summary, we developed all-natural human substitutes using a production system compatible with clinical requirements. Under culture conditions devoid of bovine serum, the resulting engineered tissues displayed similar and even superior structural and functional properties over the classic FBS-containing culture conditions with a considerable 50% shortening of production time.
Watersaving Cleaning Processing of Sheep Wool and Eco-friendly Extraction of Lanolin
Authors: Wang He , Couture Sylvain , Bédard Jérôme 
Published: August 2022

Abstract
Sheep wool, an important natural raw material for textile industry. This article propose a watersaving sheep wool cleaning processing. For each kilogram of wool, containing about 35% impurities, the water loss in washing is as low as 2.5 liters. Wool washing sewage with high content of COD (chemical oxygen demand), up to 20-30 g/L, is treated by neutral detergent and then a solid hydroxide (Calcium Hydroxide or Alum) with cationic polyacrylamide (CPAM) in order to have a fast flocculation of solid waste. The COD content in wastewater can be as low as 352 mg/L. On the other hand, solid matter contains a large amount of high-value chemical oils, collectively known as lanolin, by extraction of solid matter in hot alcoholic solution (85% Ethanol,15% methanol) at 60 degrees Celsius. About 40 g of lanolin crude product is obtained.
Energy Efficient Operation Optimization of Building Air-conditioners via Simulator-assisted Asynchronous Reinforcement Learning
Authors: Zhang Wenhao , Zhang Zhiang 
Published: August 2022

Abstract
At present, buildings are the major energy consumers in most developed and developing countries. Energy efficiency is one of the primary objectives of today’s building projects, especially in the context of carbon-peak and carbon-neutral. In buildings, air-conditioning systems account for nearly half of total building energy consumption. Optimal control and operation can significantly improve air-conditioning’s energy efficiency but the key challenge is the complex dynamics of air-conditioning systems. This study focuses the energy efficient operation of air-conditioners in office buildings, and proposes a simulation-assisted reinforcement learning (RL) method to develop energy efficient operation strategies. This study uses a whole building energy simulation model to build the environmental simulator for the air-conditioning system, and uses asynchronous RL algorithm to learn energy efficient operation strategies. Energy saving and Setpoint Not Met is used as the optimization objective as well as the criteria for evaluating the performance of the RL operation strategies. A validation simulator with varied weather conditions is also built to validate the robustness of reinforcement learning. The results show that, compared to common rule-based control strategy, 16.1% energy saving with better thermal comfort can be achieved by the RL operation strategy. In addition, the results also show the RL operation strategy has a certain level of robustness.
Nanopore quality score resolution can be reduced with little effect on downstream analysis
Authors: Rivara-Espasandín Martín , Balestrazzi Lucía , Dufort y Álvarez Guillermo , Ochoa Idoia , Seroussi Gadiel , Smircich Pablo , Sotelo-Silveira José , Martín Álvaro 
Published: August 2022

Abstract

Motivation
The use of high precision for representing quality scores in nanopore sequencing data makes these scores hard to compress and, thus, responsible for most of the information stored in losslessly compressed FASTQ files. This motivates the investigation of the effect of quality score information loss on downstream analysis from nanopore sequencing FASTQ files.

Results
We polished de novo assemblies for a mock microbial community and a human genome, and we called variants on a human genome. We repeated these experiments using various pipelines, under various coverage level scenarios and various quality score quantizers. In all cases, we found that the quantization of quality scores causes little difference (or even sometimes improves) on the results obtained with the original (non-quantized) data. This suggests that the precision that is currently used for nanopore quality scores may be unnecessarily high, and motivates the use of lossy compression algorithms for this kind of data. Moreover, we show that even a non-specialized compressor, such as gzip, yields large storage space savings after the quantization of quality scores.

Availability and supplementary information
Quantizers are freely available for download at: https://github.com/mrivarauy/QS-Quantizer.
Social Work During COVID-19 in Slovenia: Absent, Invisible or Ignored?
Authors: Mešl Nina , Leskošek Vesna , Rape Žiberna Tamara , Kodele Tadeja 
Published: August 2022

AbstractThe article discusses the results of a research study on social work during COVID-19 in Slovenia. Governmental measures to prevent the spread of the disease have increased the need for assistance to various groups of people but at the same time limited their access to social services. The main research question was to what extent social workers and social services were able to reorganise and adapt to crisis conditions and how this relates to the role of social work in society. Data were collected using mixed methods: online survey and interviews with social workers. Content analysis and selective coding, thematic analysis and univariate descriptive statistics were used for data analysis. The analysis shows that the ability of social workers to respond to the growing and changing needs of people during the pandemic depended on the context in which they worked. Social work was perceived as more effective and responsive in social services where professional autonomy was assured and valued and where horizontal relationships existed between staff and management. The study contributed to a better understanding of the context in which the pandemic occurred and identified some structural barriers to successful social work that are internationally comparable and relevant.
Online dynamic rumour propagation model considering punishment mechanism and individual personality characteristics
Authors: Sun Chengai , Qiao Donghang , Qiu Liqing 
Published: August 2022

Abstract
In the Internet era, rumours will spread rapidly in the network and hinder the development of all aspects of society. To create a harmonious network environment, it is essential to take punitive measures against malicious rumour mongers on social platforms. Take the measure of forbidden as an example. The forbidden one may stop spreading rumours because of being punished, or he may become a disseminator again because of paranoia. Other people who know rumours may become alert and stop propagating rumours or temporarily forget rumours. And therefore, the forbidden state is added to describe the above phenomenon, and the SIFR (Ignorant–Disseminator–Forbidden–Restorer) model is proposed. Taking the vigilance and paranoia derived from punishment measures into account, the connection edges from the forbidden to the disseminator and from the disseminator to the restorer are increased in this model. And then, the stability of SIFR model is proved by using the basic regeneration number and Routh–Hurwitz stability theorem. The simulation results demonstrate that individual paranoia may do harm to the control of rumour dissemination. While the punishment mechanism, individual forgetting mechanism and vigilance can effectively curb the spread of rumours.