bluuebunny/crossref_metadata_2025 huggingface dataset:
Improving the cam profile of the spring operating mechanism of vacuum circuit breaker based on energy matching
Authors: Li Fuqiang , Wang Shufen , Li Yuguang , Sun Zhenzhen
Published: August 2021

High-voltage circuit breakers are important protection and control equipment in power systems. In order to understand the mechanical characteristics of vacuum circuit breaker, the mathematical relationship between the released energy of closing spring, the stored energy of opening spring and overtravel spring and the rotation angle of output crank arm is studied. The characteristic curve between the released energy of closing spring and the rotation angle of output crank arm and the characteristic curve between the stored energy of opening spring and overtravel spring and the rotation angle of output crank arm are drawn by MATLAB. In order to improve the matching of energy release and energy storage characteristic curves, a method of improving cam profile is proposed. The dynamics simulation model of spring operating mechanism of high voltage circuit breaker is established and the original cam and adjusted cam are imported into the dynamics simulation model of mechanism. The simulation results show that the adjusted cam improves the closing speed of vacuum circuit breaker. This helps to reduce the stress on the parts of the vacuum circuit breaker and increase the service life of the vacuum circuit breaker.
A double closed loop control algorithm based on current mode in full bridge circuit
Authors: Xu Chengbo , Chen Jing , Li Zhihua , Xiao Chun
Published: August 2021

In order to improve the problems of large output current and voltage ripples and slow system response when the full-bridge circuit is working. This paper proposes a double closed-loop control algorithm based on current mode. Use fuzzy controller to optimize the parameters of voltage outer loop PID controller. The genetic controller is used to optimize the parameters of the current inner loop PID controller. After the dual closed-loop structure of the current control mode is optimized for the traditional PID controller, the overshoot is reduced, the system response speed is accelerated and the current ripple and voltage ripple are also greatly reduced. The simulation verifies the effectiveness of the designed double closed-loop control algorithm.
Kinematics analysis of transmission mechanism of electronic jacquard machine based on virtual prototype and vector method
Authors: Guo Shuai , Zhang Chi , Wu Xiaoguang , Zhang Chengjun
Published: August 2021

Aiming at the problem that the electronic transmission mechanism of the electronic jacquard machine is difficult to design, a kinematic analysis of the mechanical transmission mechanism of the electronic jacquard machine is proposed to clarify the kinematic performance of the electronic transmission mechanism. Based on the vector method, the kinematics mathematical model of the mechanical transmission mechanism of the electronic jacquard machine was established, and the matrix of the displacement, velocity and acceleration curve of the pulling blade was obtained. After the parameters are selected, the correctness of the mathematical model is verified through virtual prototype modeling and simulation. Through simulation analysis and verification, it is found that the numerical calculation results of the solution matrix are consistent with the simulation results of the virtual prototype model, and the peak error does not exceed 0.42%, which provides a certain theoretical basis for the study of electronic jacquard transmission mechanism.
Application of smart substation site management system based on 3D digitization
Authors: Xiong Zaibao , Gan Xing , Li Yunwei , Ding Ding , Geng Xiaohong , Gao Yu
Published: August 2021

In order to standardize the construction of smart construction site of substation, meet the requirements of observable, measurable and controllable whole process of project technology, safety, quality, progress and procurement, and realize the goal of lean management and control, this paper expounds the concept of smart construction site from four aspects: Framework Construction of smart construction site, platform and module construction of smart construction site, mechanism construction and pilot application. This paper puts forward an overall scheme of smart substation construction site based on 5g technology, which combines big data, artificial intelligence, state perception and video recognition technology to solve the multi-dimensional management requirements of safety, quality and progress during construction. The intelligent site visualization platform is established and integrated with 3D model to realize 3D visualization display of site information.
Research on factors influencing the positioning accuracy of four-quadrant detector
Authors: Li Dongkang , Zhang Yanmei
Published: August 2021

In order to analyze the positioning accuracy of the four-quadrant detector, this paper deeply studies the positioning principle of the four-quadrant detector, establishes the mathematical model of azimuth standard deviation based on Gaussian facula, and deeply analyzes the influence of each factor on the positioning accuracy from the three influencing factors of facula position, facula radius and signal-to-noise ratio. The results show that the positioning accuracy can be effectively improved by improving the signal-to-noise ratio, using smaller light spot and making the light spot as close to the center of the detector as possible.
Research on application of tower sharing in overhead transmission line in China
Authors: Zhang Zhuoqun , Tao Hanxun , Wen Weibin , Tian Weiguang , Liu Quan
Published: August 2021

As a social public infrastructure, overhead transmission lines were mainly used to transmit high load electrical energy and communication information. It was an important lifeline project, and had the characteristics of extensive spatial distribution, large number, and safe operation and so on. Transmission lines had a certain technical reserve and good feasibility in shared tower technology, in line with the communication and sensing industry of shared application and development. The results demonstrated that shared transmission line can support the application of different OPGW cables, acre station and micro base station for multiple vendors, and different sensing devices. It was showed very good economic benefit and social benefit in different shared application scenarios.
Research on DC side power decoupling control of photovoltaic inverters
Authors: Zhu Longji , Wang Shuying
Published: August 2021

Inverter is a vital component in photovoltaic power generation system, and it is related to the performance and efficiency of photovoltaic power generation. When the inverter is connected to the grid, the instantaneous power on the DC side and the AC side is unbalanced, and the instantaneous power pulsation of double frequency will be generated on the DC side, which directly affects the current stability and power utilization rate of the inverter on the DC side. This paper proposes a Power Decoupling Circuit (PDC) based on a single-phase photovoltaic inverter. This circuit uses a closed-loop feedforward power decoupling control strategy to compensate for the unbalanced pulsating power on both sides of the inverter. Eliminate low-frequency harmonics on the DC side, achieve the purpose of power decoupling, stabilize the DC side voltage of the photovoltaic inverter, and improve the performance and efficiency of photovoltaic power generation.
Analysis and evaluation of short-term wind power interval forecast error based on K-means clustering
Authors: Li Songshan , Ma Zhicheng , Zhang Xiaoying , Wang Kun , Zhou Qiang , Zhang Jinping , Chen Wei
Published: August 2021

The intermittency, volatility and randomness of wind power will inevitably affect the accuracy of wind power prediction. In order to improve the accuracy of wind power interval prediction and evaluation, a method of wind power interval prediction and evaluation based on K-means clustering is proposed. First, use K-means clustering to classify wind power prediction error data; second, combine the climate type to more accurately divide and process historical data, and apply multiple linear regression models to establish preliminary prediction models corresponding to each subset; Finally, according to the distribution characteristics of forecast errors, the results of wind power forecasting are analyzed and evaluated with interval coverage and interval average bandwidth as indicators. Comparing the K-means clustering algorithm with the BP neural network algorithm, the results show that the method proposed in this paper can better capture the characteristics of wind power error data, and can obtain more accurate wind power interval prediction and evaluation results.
Study of overtaking method of intelligent vehicle under vehicle road coordination
Authors: Wang Yifei , Yin Kaiyang
Published: August 2021

Aiming at the safe overtaking in the autonomous driving of the intelligent vehicle, the overtaking method of intelligent vehicle under vehicle road coordination is advanced. First, according to the current vehicle movement state and the surrounding environment, the overtaking behaviour decision is proposed. Second, with the support of vehicle-road collaboration technology, the principle of the overtaking method is analysed. The position and speed of the associated vehicles within the real-time range are collected with the Road Side Unit (RSU). The RSU communicates with the autonomous vehicle in two-way, and vehicle selects overtaking behaviour. Finally, the validity of this method is verified by the cases, the overtaking method includes overtaking in the borrowing lane, the extended overtaking lane and the emergency lane. The research results of this study not only provide a basis for the method of the safe overtaking in the process of the intelligent vehicle autonomous driving, but also lay a foundation for the intelligent transportation system (ITS).
Quality spectra fluctuation modeling for manufacturing process based on deep transfer learning
Authors: Hu Sheng , Li Zhe , Zhang Shoujing
Published: August 2021

It is difficult to characterize and monitor the quality fluctuation caused by multi-correlation parameters in manufacturing process. Motivated by the powerful ability of digital images to characterize process states, this paper presents a quality spectra fluctuation modeling method based on deep transfer learning. Firstly, through the multi-parameter correlation of spectra pixels, the quality spectra is constructed to characterize quality fluctuation. Then, a deep residual network transfer learning model is used to identify the types of quality fluctuation. Finally, the effectiveness analysis of proposed model is demonstrated by the Tennessee Eastman process.
Analysis and application of manufacturing data driven by digital twins
Authors: Gao Xiyu , Liu Peng , Zhang Qixun , Gao Dawei , Huang Xin
Published: August 2021

This article introduces the importance of data processing to the production process and the application of data in the digital twin workshop. Then the overall process of data processing is introduced. First, several typical data acquisition methods are listed, feed the collected data to the digital twin model, and then extract the model data for integration and convert it into the same XML text format. Perform data cleaning and data analysis on it, and then use the DBN in deep learning to extract and classify the data to complete the real-time matching and transmission of production information to employees. Finally, the future development direction of the data analysis will be briefly summarized.
Research on evaluation of intensive economic benefits of equipment manufacturing enterprises
Authors: Wu Huiyuan
Published: August 2021

Intensive economic benefit is the result that the total factor productivity is improved by improving the utilization efficiency and combination quality of asset elements and human elements, and the increased output exceeds the total input of asset elements and human elements. In order to analytical study of smart manufacturing level and intensive economic benefit, by taking Liaoning Equipment Manufacturing Enterprise as the research object this paper establishes production function model and total factor productivity model of relationship between input and output of Liaoning Equipment Manufacturing Enterprise. Meanwhile by establishment grows model of total factor productivity it realizes measure of smart manufacturing level and intensive economic benefit of Liaoning Equipment Manufacturing Enterprise. Final by contrastive analysis total factor productivity and increase economic efficiency of contribution rate of equipment manufacturing enterprise this paper puts forward feasible countermeasures and suggestions to increase total factor productivity and realize intensive management of Liaoning Equipment Manufacturing Enterprise.
Research on wind turbine system reliability modeling and preventive maintenance policy considering performance degradation and shock
Authors: Li Haiyan , Li Yaping , Chen Zhen
Published: August 2021

With the development of the wind turbine industry, the operation and maintenance of wind turbines has gradually become one of the main concerns of the wind turbine industry. In view of the wind turbine system’s vulnerability to external environment, this paper considers the performance degradation and external shocks of the unit to establish a reliability model and develop preventive maintenance policy. First, Markov chain is used for degradation modeling, and unit is divided into different health states; Homogeneous Poisson process is used for shock modeling, assuming that the shock is cumulative. The impact of shocks on unit degradation was described as the unit in different states had different state transition matrices, and then establish the state reliability. According to the state probability of the unit, formulate the corresponding preventive maintenance policy. Finally, a generator in a wind turbine system is taken as an example, the influence of external shocks on unit performance degradation is explained. Meanwhile, the preventive maintenance strategy proposed in this paper is compared with the maintenance policy with fixed maintenance threshold, and the advantages of the reliability model and maintenance strategy proposed in this paper are illustrated.
Integrated modelling of automobile maintenance expert system based on knowledge graph
Authors: Liu Gen , Hong Ge , Huang Mengdie , Xia Tangbin , Chen Zheng
Published: August 2021

Using online information resources to build knowledge bases to provide knowledge answering services would help auto companies or third-party platforms to gain competitive advantages. Therefore, a construction plan of automobile maintenance expert system based on knowledge graph was proposed by integrated modelling. In terms of the entity recognition algorithm, the BM LSTM (Boyer-Moore Long Short-Term Memory) algorithm was proposed by integrating hidden Markov model, Conditional Random Field (CRF), Bi-directional Long Short-Term Memory (BiLSTM), BiLSTM-CRF and Lattice LSTM, which improved the accuracy index F1-score. In terms of the text quality evaluation algorithm, a secondary text quality evaluation system was designed. It evaluated the matching quality of the problem based on the word toolkit Synonyms and Levenshtein Distance algorithm. And it evaluated the quality of the answer text based on the TF-IDF (Term Frequency-Inverse Document Frequency) similarity algorithm and centered on completeness, accuracy, reliability, and argument strength. Finally, experiments are carried out on the proposed model and algorithm to prove its effectiveness.
Prospects for Power Systems with High Proportion of Renewable Energy Considering the Coordination of Source-grid
Authors: Ling Xu , Yu Haiyang , Wang Yuan , Wu Yaowu , Liang Shuhao , Hu Zhigang
Published: August 2021

In the future, the spatiotemporal distribution of a high proportion of renewable energy will bring unprecedented challenges to power system, and the basic form and operation of power system will have great changes. On the basis of investigating the research status in China and abroad, this paper expounds the scientific problems faced by the planning of Power Systems with High Proportion of Renewable Energy (PSHPRE) considering the coordination of source-grid, and put forward the basic ideas and framework of research from four aspects: overview of the development of PSHPRE, power planning in PSHPRE and coordinated planning of source-grid in PSHPRE. Finally, the prospects for Power Systems with High Proportion of Renewable Energy considering the coordination of source-grid are presented.
A Comparison of Top-down Load Forecasting and Bottom-up Load Forecasting in Distribution Network
Authors: Mu Zeyu , Hayinaer  , Tueraili  , Song Bin
Published: August 2021

In this paper, the electricity load of a certain region is taken as the research object. Firstly, the back propagation neural network (BP) model is used to predict the macro load of the region from top to bottom. Then, on the premise of comprehensive consideration of the region’s meteorological, economic, large user registration and other causes, the power grid of the region is divided. On this basis the differential integration moving average autoregressive (ARIMA) model is used to carry out the bottom-up micro load forecasting: First, the load forecasting of each cell is carried out, and then the prediction results are summarized in proportion to obtain the overall prediction results of the region. Through the comparative analysis of the two load forecasting results, it is found that although the two forecasting errors are fewer than 9%, the bottom-up load forecasting method which combines grid partition and fine classification has a prediction error of fewer than 5 %. The results show that the bottom-up load forecasting method is better.
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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.
A computational model of insect campaniform sensilla predicts encoding of forces during walking
Authors: Szczecinski Nicholas S , Dallmann Chris J , Quinn Roger D , Zill Sasha N
Published: August 2021

Control of forces is essential in both animals and walking machines. Insects measure forces as strains in their exoskeletons via campaniform sensilla (CS). Deformations of cuticular caps embedded in the exoskeleton excite afferents that project to the central nervous system. CS afferent firing frequency (i.e. ‘discharge’) is highly dynamic, correlating with the rate of change of the force. Discharges adapt over time to tonic forces and exhibit hysteresis during cyclic loading.

In this study we characterized a phenomenological model that predicts CS discharge, in which discharge is proportional to the instantaneous stimulus force relative to an adaptive variable. In contrast to previous studies of sensory adaptation, our model (1) is nonlinear and (2) reproduces the characteristic power-law adaptation with first order dynamics only (i.e. no ‘fractional derivatives’ are required to explain dynamics). We solve the response of the system analytically in multiple cases and use these solutions to derive the dynamics of the adaptive variable. We show that the model can reproduce responses of insect CS to many different force stimuli after being tuned to reproduce only one response, suggesting that the model captures the underlying dynamics of the system. We show that adaptation to tonic forces, rate-sensitivity, and hysteresis are different manifestations of the same underlying mechanism: the adaptive variable. We tune the model to replicate the dynamics of three different CS groups from two insects (cockroach and stick insect), demonstrating that it is generalizable. We also invert the model to estimate the stimulus force given the discharge recording from the animal. We discuss the adaptive neural and mechanical processes that the model may mimic and the model’s use for understanding the role of load feedback in insect motor control. A preliminary model and results were previously published in the proceedings of the Conference on Biohybrid and Biomimetic Systems.
Fluid–structure interaction analysis on motion control of a self-propelled flexible plate near a rigid body utilizing PD control
Authors: Luo Yang , Wright Marvin , Xiao Qing , Yue Hong , Pan Guang
Published: August 2021

Inspired by a previous experimental study of fish swimming near a cylinder, we numerically investigate the swimming and station-holding behavior of a flexible plate ahead of a circular cylinder whose motion is controlled by a proportional–derivative (PD) controller. Specifically, the deformation of this two-dimensional plate is actuated by a periodically varying external force applied on the body surface, which mimics the fish muscle force to produce propulsive thrust. The actuation force amplitude is dynamically adjusted by a feedback controller to instruct the plate to swim the desired distance from an initial position to a target location and then hold the station there. Instead of directly using the instantaneous position signal, an average speed measured over one force actuation period is proposed with the inclusion of instantaneous position information to form the tracking error for the PD control. Our results show that the motion control of swimming and station holding has been achieved by this simple but effective feedback control without large overshoot when approaching the target at different flow conditions and actuation force formulas. Although the swimming distance remains the same, a plate whose initial position is closer to the cylinder requires less energy expenditure to swim to the target location and hold the station there. This is because the low-pressure zone near the trailing edge of the plate is reduced in size, which provides drag reduction, contributing to reduced swimming energy. A higher Reynolds number also leads to energy savings. Under the same control strategy, the swimming performance is more affected by the force-frequency while the phase shift of the actuation force has a less significant impact.
The role of enhanced rock weathering deployment with agriculture in limiting future warming and protecting coral reefs
Authors: Vakilifard Negar , Kantzas Euripides P , Edwards Neil R , Holden Philip B , Beerling David J
Published: August 2021

Meeting the net-zero carbon emissions commitments of major economies by mid-century requires large-scale deployment of negative emission technologies (NETs). Terrestrial enhanced rock weathering on croplands (ERW) is a NET with co-benefits for agriculture, soils and ocean acidification that creates opportunities for generating income unaffected by diminishing carbon taxes as emissions approach net-zero. Here we show that ERW deployment with croplands to deliver net 2 Gt CO2 yr−1 removal approximately doubles the probability of meeting the Paris 1.5 °C target at 2100 from 23% to 42% in a high mitigation Representative Concentration Pathway 2.6 baseline climate. Carbon removal via carbon capture and storage (CCS) at the same rate had an equivalent effect. Co-deployment of ERW and CCS tripled the chances of meeting a 1.5 °C target (from 23% to 67%), and may be sufficient to reverse about one third of the surface ocean acidification effect caused by increases in atmospheric CO2 over the past 200 years. ERW increased the percentage of coral reefs above an aragonite saturation threshold of 3.5 from 16% to 39% at 2100, higher than CCS, highlighting a co-benefit for marine calcifying ecosystems. However, the degree of ocean state recovery in our simulations is highly uncertain and ERW deployment cannot substitute for near-term rapid CO2 emissions reductions.
Machine learning to optimize climate projection over China with multi-model ensemble simulations
Authors: Li Tong , Jiang Zhihong , Treut Hervé Le , Li Laurent , Zhao Lilong , Ge Lingling
Published: August 2021

The multi-model ensemble approach is generally considered as the best way to explore the advantage and to avoid the weakness of each individual model, and ultimately to achieve the best climate projection. But the design of an optimal strategy and its practical implementation still constitutes a challenge. Here we use the random forest (RF) algorithm (from the category of machine learning) to explore the information offered by the multi-model ensemble simulations within the Coupled Model Intercomparison Project Phase 6. Our objective is to achieve a more reliable climate projection (mean climate and extremes) over China. RF is furthermore compared to two other ensemble-processing strategies of different nature, one is the basic arithmetic mean (AM), and another is the linear regression across the ensemble members. Our results indicate that RF effectively enhances the capability in capturing spatial climate characteristics. Regions with complex topography, such as the Tibetan Plateau and its periphery, show the most significant improvements. RF projects less future warming but enhanced wet conditions across China. It also produces larger spatial variability and more small-scale features. The most obvious increase of precipitation is in the northern part and the periphery of the Tibetan Plateau. The projected changes in RF for strong precipitation are almost twice higher than in AM, while in the northwestern area, weaker increases of precipitation are projected by RF, which indicates larger spatial inhomogeneity of its projection.