bluuebunny/crossref_metadata_2025_split huggingface dataset:
Study of Modular Multilevel Converter in AC/DC Hybrid Distribution Network
Authors: Lei Hou , Huizhuo Ma , Yang Liu , Yusen Zhang , Yang Su , Zhen Sun 
Published: August 2021

Abstract
The AC/DC hybrid power system plays an important role in the intelligent distribution network with the advantages of large power supply radius and capacity, low line cost and convenient distributed access. In this paper, a modular multilevel converter based on AC-DC side of AC/DC hybrid distribution network is exploited. An improved MPC sub-module capacitor voltage balance control method is proposed to reduce the calculation burden of MPC. The cost function is defined for phase currents, circulating currents, and sub-modules capacitor voltages. The weight factors of the cost function are eliminated, and the sub-module capacitor voltage control algorithm is improved to reduce the computational complexity of MPC. Not all cost functions for possible switching states combinations of MMC are calculated using improved method. The effectiveness of proposed improved method is evaluated based on 11 levels MMC system using Matlab/Simulink.
A New Construction of Command System under the Active-rush Repair Mode of Distribution Network
Authors: Zhengyang Yuan , Bin Song 
Published: August 2021

Abstract
With the improvement of socio-economic level and people’s material living standard, the distribution network changes from the traditional passive repair to the active-rush repair mode in order to improve the efficiency of emergency repair. This paper proposes to construct a new command system under the active-rush repair mode of distribution network in order to improve the efficiency of emergency repair. First of all, through information sharing and data fusion between different departments, we can effectively clear the obstacles and strengthen the efficiency of multi-department cooperative office. Then, with the help of highly covered distribution automation system, the monitoring and automatic evaluation of distribution equipment are realized and the original construction of the command system has been constructed.
Research on the Hydropower Generation Optimal Scheduling of Larger-Scale Cascade Hydropower Stations on Jinsha River and Lancang River
Authors: Si Dajun , Wang Yuan , Liang Shuhao , Wu Yaowu , Zhang Wangxi , Huang Shiru , Wang Bingran 
Published: August 2021

Abstract
There are some characteristics, such as multi-dimensional, stochastic inflow, non-uniform service and scheduling subjects, in optimal scheduling of power generation. The problem is also subjected to hydrological and meteorological conditions, operation control, water demand, and loads of the power grid. Aiming at the electric power and energy balance problem in the high hydropower system, this paper uses the typical annual load curve to analyse the electricity power and energy balance, so as to better consider the influence of seasonal differences in hydrological conditions on the storage capacity of adjustable hydropower stations and the start-up of thermal power stations, and establish a mid-long term model of hydropower generation optimal scheduling of large-scale cascade hydropower stations. Taking the cascade hydropower stations in the Jinsha River and Lancang River as examples for simulation analysis, and using GUROBI to solve the model, the correctness and effectiveness of the proposed optimal scheduling model are verified.
Research on Output Scenarios of Offshore Wind Power Considering Characteristics of Marine Weather
Authors: Chen Honglin , Chen Xing , Yu Hao , Lou Suhua , Lin Yong , Xu Liang 
Published: August 2021

Abstract
Accurate modeling of stochastic wind power output is one of the key links in the optimal operation of power systems with large-scale wind power. According to the output characteristics of offshore wind farms, a data-driven method that models offshore wind power output scenarios is proposed in this paper. This method takes full account of the marine climate characteristics to study the offshore wind farm output data seasonally and identifies the main climatic elements affecting the output of offshore wind farms through correlation analysis. Meanwhile, the evaluation index of output characteristics is established. In order to reflect the influence of marine climate on the output of offshore wind farms, the climatic factors are used as an indicator to distinguish different weather. Accordingly, the output scenarios of offshore wind farms in extreme and non-extreme weather are determined, and the corresponding probability calculation method is proposed.
Optimal Capacity Planning of a Bundled Wind-Photovoltaic-Thermal Generation System Considering Carbon Emission Trading
Authors: Ouyang Litao , Fan Yue , Lou Suhua , Zhang Yuanxin , Bai Chongtao , Yu Haiyang 
Published: August 2021

Abstract
In view of the situation of the bundled wind-photovoltaic-thermal generation system (BWPTGS) in China’s new energy bases and the demand of carbon emissions reduction in generation system, this paper constructs a capacity planning method for wind-photovoltaic-thermal bundling power supply considering the life cycle carbon emissions. Based on the concept of low-carbon economy, this paper introduces carbon emission trading (CET) method into capacity planning of BWPTGS. Then, this paper calculates the carbon emissions cost of the generation system under the carbon emissions trading mechanism. With the objective of minimizing the comprehensive cost of generation system taking into account the cost of CET, the paper constructs an optimal generation system planning model for new energy bases considering carbon emission trading. The optimization model is applied to a BWPTGS for a new energy base in north-western China. Case studies are conducted to demonstrate the effectiveness of this proposed model and method.
Optimal Configuration of Energy Storage System Capacity in PV-integrated EV Charging Station Based on NSGA-III
Authors: Shi Shanshan , Zhang Yu , Fu Zhangjie , Fang Chen , Wang Yufei , Zhao Luyi 
Published: August 2021

Abstract
In order to improve the revenue of PV-integrated EV charging station and reduce the peak-to-valley load difference, the capacity of the energy storage system of PV-integrated EV charging station is optimally configured considering the interests of both the charging station operator and the distribution grid. Firstly, for the uncertainty of charging load, the day-ahead load charging demand is calculated using Monte Carlo method with random sampling. Then, considering the economic indicators such as initial investment cost, operation and maintenance cost, interaction cost with the grid and technical indicators on the grid side, the optimal configuration model of energy storage capacity is established with the objectives of maximizing the revenue of charging stations and minimizing the peak-to-valley difference rate, and the NSGA-III algorithm is used to solve the model. Finally, the effectiveness of the proposed multi-objective optimization model is verified, three schemes with peak-to-valley difference rates of 30%, 45%, and 60% were selected to complete the optimal configuration of energy storage capacity, the economy and reliability of the system are improved on the basis of meeting the load demand, and the overall performance of the charging station is optimized.
A Data-driven Approach for Charging Characteristic Parameter Identification Method of Electric Vehicles
Authors: Gao Zhenyu , Chen Zhonghua , Hu Qian , Xue Hua , Ling Chen , Zhang Xiaowen 
Published: August 2021

Abstract
In order to accurately identify new energy electric vehicles charging behaviour characteristic parameter, the theory of consistency is put forward based on the k-means clustering method. The complex coupling network including consistency control is introduced into the data clustering analysis to accurately describe the consistency characteristics of the electric vehicles charging load data in different periods, and the dissimilarity measure with the state update of the adjacent cluster data is used to quickly calculate the initial cluster center. The k-means method is used to quickly identify the expected value of EV initial charging time in typical scenes, and to accurately extract the probability distribution function of EV charging probability and charging initial time. Combined with a practical case, it is verified that the proposed method has the advantages of simple calculation, fast clustering.
Design of LLC Resonant Type Full-Bridge DC-DC Converter
Authors: Lei Hou , Lida Niu , Xiangxin Nie , Huizhuo Ma 
Published: August 2021

Abstract
In order to develop and utilize renewable energy reasonably, the problems such as the increasingly intensive power load, insufficient power supply capacity and short power supply radius in major cities should be overcome. In this paper, a LLC resonant DC-DC converter is proposed for low voltage distribution network. Firstly, the energy transmission model and mathematical model of DC-DC converter under different modes are analysed. Secondly, the parameters of the resonator are designed. Finally, the simulation platform is built, and the simulation and experiment of DC-DC converter under different working conditions are carried out to verify the correctness of the proposed scheme.
Computational Fluid Dynamics (CFD) Analysis of Two Types of Greenhouse in Humid Climates in Optimizing Air Flow Distribution for Organic Food Production in Times of Pandemic
Authors: Valentin Nilson , Maldonado Edwin , Prieto Erick Arthur , Chomba Nelson Omar , Chauca Mario 
Published: August 2021

Abstract
In a greenhouse we find multiple variables that have great impacts on crops, for this a CFD model is built to simulate microclimate distribution and obtain optimal climate control for the growth of the crop. Analyzes of solar heat gains, heat losses, and temperature and air distributions for each special moment provide a good guide for product selection. In the CFD simulation the greenhouse volume and wind speed will be obtained for an optimal temperature distribution. The time required for maintenance, the need for sensor calibration, natural deterioration, and unexpected failures will have to be taken into account. The analyzes show a comparison between various variations of the air flow velocity in 0.5 m / s, 1 m / s and 1.5 m / s in two different models of greenhouses, which we will obtain the optimal model for the development and elaboration of a greenhouse. Demonstrating the efficiency of CFD models for the design, simulation and application of greenhouses.
Research on Admittance Control Method of Industrial Robot Based on SMC Strategy
Authors: Wang Jun , Liu Yan 
Published: August 2021

Abstract
Aiming at the problem of compliance control of industrial robot, in order to overcome the influence of unfavorable factors such as nonlinearity and strong coupling of industrial robot system on robot compliance control, so as to improve robot control accuracy and avoid damage to robot or workpiece caused by environmental contact force. Therefore, a robot admittance control method based on sliding mode control (SMC) strategy is proposed in this paper. The dynamic model of the robot with parameter uncertainty is established, and the exponential reaching law sliding mode control method is introduced into the robot admittance controller to improve the flexibility of the robot. Finally, the six-axis robot is simulated by the simulation platform to verify the feasibility and effectiveness of the proposed method.
A Realization of Two-Level Scheduling in Embedded Operating System
Authors: Zeceng Song , Weizhong Qian 
Published: August 2021

Abstract
In embedded operating systems, how to ensure thread tasks execute in order of priority is an important issue. Considering that thread may have child threads, and child threads are scheduled by their parent thread, so they also need to be executed accroding to their priorities. For such two-level scheduling issue, this paper proposes a new scheduling policy: parent thread uses the table-driven scheduling policy, and child thread uses the fixed priority scheduling policy to ensure the orderly execution of both parent and child threads. In order to cooperate with the implementation of two-level scheduling, this paper also realizes the creation of child threads in an embedded operating system by imitating Linux thread replication, connects the parent and child threads through pointers, and realizes a priority queue of child threads in their parent thread. In addition, by saving the ID of current executing child thread into the stack of corresponding parent thread, this paper ensures that parent thread can recover its last executing child thread after several switches of parent threads. Several experiments on 2440 single core development board showed that method proposed by this paper could realize the creation of sub threads and two-level thread scheduling in embedded operating system.
Intelligent Sensor for Thermal Process Control using Convolutional Neural Network
Authors: Rendon Angelica Viviana , Martinez Fredy Hernan 
Published: August 2021

Abstract
A thermal combustion process involves three variables: the fuel (which oxidizes and gives off heat), the comburent (which accelerates combustion), and the heat source. The proportion of these three variables determines the behavior of the process, and in the case of industrial production, their control is fundamental to guarantee continuity, quantity, and quality of the product. In the case of carbonization of plant material, the control of oxygen is decisive to guarantee these production parameters. However, the measurement in real-time becomes a complex problem due to the process temperatures, the requirements of precision and accuracy of the measurement, and the characteristics of the combustion furnace that normally must keep the material in motion. This research proposes an intelligent sensor that allows its remote use and guarantees the constant and safe monitoring of the variable, as well as its conditioning and communication. The sensor is composed of a digital camera aligned with the flame capable of capturing video frames continuously and safely. These digital images are processed by a categorization module previously trained with a convolutional neural network, and the result is transmitted to the control unit. In tests on a real furnace, high performance and reliable operation sufficient for industrial implementation were proved.
Multi-Layer Semantic Middleware for Cross-Domain Internet of Things
Authors: Qiao Wenjing , Zhao Shengjie , Deng Hao 
Published: August 2021

Abstract
Heterogeneous access is an issue caused by different standards of devices and systems in the Internet of Things(IoT), which makes cross-domain devices difficult to connect and communicate with each other. In this paper, to solve the interoperability problem for cross-domain IoT, a multi-layer semantic middleware framework using a WoT-based knowledge graph is proposed. Firstly, the knowledge graphs of sub-domains are built respectively, and then integrate them through a middleware framework based on WoT. We evaluated the effectiveness of this proposed approach through a cross-domain Smart Building project. We find that our approach can effectively enable cross-domain IoT interoperability.
A Survey on the Security of PUFs
Authors: Su Shuqin , Zhu Min , Wang Hanning , Yang Bohan , Liu Leibo 
Published: August 2021

Abstract
Physical Unclonable Function (PUF) is an essential security block in security systems, which can be used for authentication and key generation. Its security is related to the security of the entire security system, thus it has received widespread attention and it is necessary for designers and users to know the security threats and solutions for PUFs before working on PUFs. The paper mainly introduces and summarizes a variety of side-channel attacks, machine learning attacks, and corresponding countermeasures for PUFs. Side-channel attacks are usually combined with machine learning attacks to improve the effectiveness of attacks. Attackers can use machine learning methods to achieve mathematical modeling of PUFs and most of the existing PUFs are vulnerable to machine learning attacks.
Analyzing Wuhan’s Strategies for Controlling Covid-19 Epidemic
Authors: Wang Yuanxiu 
Published: August 2021

Abstract
Because of the high infectiousness of COVID-19, the paper divided infected people into two groups, the confirmed cases to be treated and isolated in hospitals and unconfirmed carriers in free environment. First, a COVID-19 transmission model was built based on the classification of infected population, and a sensitivity analysis algorithm was constructed to optimize unknown parameters in the model, such as the probabilities of transmission and the diagnosis rate. Second, the transmission model and an optimization procedure were used to simulate the COVID-19 epidemic in Wuhan and Diamond Princess Cruise, and the simulation results were compared with actual data reported by governments. Finally, Wuhan’s strategies for controlling COVID-19 epidemic at different stages were analyzed through the COVID-19 transmission model. The results showed: only isolating and treating the confirmed patients suffering severe symptoms could not effectively inhibit the rapid spread of COVID-19; isolating all the confirmed patients could reduce the infected population over 30 times; besides isolating all the confirmed patients, the city-wide lockdowns and fast test methods could dramatically contain the spread of the epidemic, including decreasing the cumulative infected population and shortening the period of epidemic; compared with Wuhan’s control strategies, the protection and isolation measures of Diamond Princess Cruise could not effectively inhibit the spread of COVID-19.
Rating Prediction for Mobile Applications via Collective Matrix Factorization Considering App Categories
Authors: Gnotthivongsa Nalinsak , Huang Dong Jun 
Published: August 2021

Abstract
The rapid development of Internet and mobile communication, smart phone, tablet computer and other mobile devices become widespread, they have become an indispensable part of life, changing the human lifestyle, way of working and learning style, it brings great convenience and fun to us. This paper mainly discusses the status of personalized recommendation system, mobile application and analyse the main problems of recommendation system. This research proposes a rising mobile application domain, which uses web crawling technique to get explicit feedback from a real-world mobile application market. The explicit feedback is the users’ ratings data on Apps and the corresponding category information. To establish a mobile application recommendation model based on user interest and categorization information and propose a novel collective matrix factorization algorithm. Through the crawled data, the improved algorithm, and the traditional collaborative. The traditional mobile application recommendation systems are mostly based on keyword search, popularity, download usage and categories, thus they do not make personalized recommendation to users, and invalid to find applications that users are interested in. Through the user’s historical behaviour, a personalized recommendation system can be established to recommend the applications that fit user’s interests effectively. This paper is based on the collective matrix factorization using user interest such as user’s ratings and category information, designs a personalized recommendation system for mobile applications by using the relationship between them.
Faster Detection Method of Driver Smoking Based on Decomposed YOLOv5
Authors: Shi Fangfei , Zhou Hui , Ye Chunyang , Mai Jianbin 
Published: August 2021

Abstract
Aiming at the problem of large computational resource consumption in the existing object detection network for driver smoking detection, a decomposing YOLOv5 network (Dec-YOLOv5) is proposed for optimization. This method uses singular value decomposition (SVD) to split the standard convolution in the pre-trained YOLOv5 network into two simpler convolution operations to reduce the computational cost. The optimized network generated after decomposition does not need to be retrained, which can reduce the number of parameters and calculations while maintaining the detection accuracy of the pre-trained model. The experimental results show that the detection time of Dec-YOLOv5 is only 80% of the original YOLOv5 when the overall detection accuracy reaches 93.5%. At the same time, compared with the current mainstream object detection model, the Dec-YOLOv5 network has a better ability to express the characteristics of the driver’s cigarettes, and the detection accuracy is higher.
Research on Plant Growth State Classification Based on CNN- LSTM
Authors: Tian Liguo , Sun Yu , Li Meng , Wang Yuesong , Liu Jinqi , Liu Chuang 
Published: August 2021

Abstract
The plant electrical signal is a physiological signal that reflects the growth state of plants affected by the external environment. Online monitoring of plant growth states is realized by studying the electrical signal changes of plants in different growth states. In this paper, a Convolutional Neural Network(CNN) based and Convolutional Neural Network and Long Short-Term Memory Neural Network(CNN-LSTM) based classification model of plant growth state is built to realize feature extraction and training and classification studies of Aloe Vera electrical signals in different growth states. The short-time Fourier transform (STFT) is used to convert the de-noised aloe electrical signal into a signal energy map, which is used as the input of the classification model, and the different growth states of the aloe are used as the output of the classifier. It is concluded that the CNN-LSTM neural network model has high accuracy in the classification of aloe electrical signals in different growth states when training, and the plant electrical signals can be used as an effective evaluation index for plant growth state detection.
Adaptive Learning Image Tracking Algorithm Based on Characteristic Fusion
Authors: Feng Yu , Deng Chenlong , Wu Liyang , Wang Xingdi , Xu Jing , Liu Lei 
Published: August 2021

Abstract
To solve the problem of model free real-time image tracking, this paper proposes an image tracking algorithm based on characteristic fusion to adaptively learn the historical characteristics of the target(CFAL). In this algorithm, the color feature is redesigned by using the idea of hog to achieve the feature consistency; the particle filter is combined with the reality constraint to improve the matching efficiency. Finally, the algorithm is programmed and tested in reality, animation and video scenes. The experimental results show that adaptive feature learning and weight adjustment can improve the tracking effect and CFAL can track the target stably and reliably under the environment of attitude change and partial occlusion.