bluuebunny/crossref_metadata_2025 huggingface dataset:
Design of Atmospheric Environmental Health Monitoring System Based on Spatial Bayesian Network
Authors: Lu Xia , Chu Jiangzhou , Zhu Wei
Published: November 2021

With the rapid development of our country’s economy and industry, atmospheric environmental issues have gradually attracted more and more people’s attention. In recent years, the vigorous development of the Internet of Things technology has made it possible to “Internet of Everything”. At present, the common traditional atmospheric environment monitoring systems on the market generally have shortcomings such as single function, large system measurement error, inability to collect data for a long time, large system power consumption, and inability to view data after being far away from the device. As Bayesian network theory is more and more applied to the modeling of monitoring events, more and more learning optimization methods about Bayesian network have been proposed and become a research hotspot. The purpose of this paper is to study the design of atmospheric environmental health monitoring system based on spatial Bayesian network. This paper studies the intelligent anomaly detection technology based on Bayesian network, the purpose is to use the intelligent anomaly detection technology to predict unknown behaviors that affect the health of the atmospheric environment, improve the learning ability of the detection system, and strengthen the active defense capability of the atmospheric environmental health monitoring system; Secondly, this article proposes an improved spatial Bayesian network model based on the shortcomings of the spatial Bayesian network. Finally, in view of some of the shortcomings of the detection system, this paper proposes a system model that combines anomaly detection with other detection mechanisms, which can significantly improve the accuracy of detection and reduce the false alarm rate of the atmospheric environmental health monitoring system. This paper aims at the above shortcomings and at the same time integrates the current situation of the atmospheric environment monitoring system, analyzes the possible trends of the atmospheric environment monitoring system, and combines the advantages of the spatial Bayesian network to address temperature, humidity, carbon monoxide concentration, and suspended particle concentration. For the main atmospheric environment indicators, a multi-functional, low measurement error, low power consumption, and low-cost atmospheric environment monitoring system is designed. Experimental research results show that: in terms of concentration, nitric oxide and nitrogen dioxide are the highest, at 58% and 68%, respectively, and they are also the largest in the air, accounting for a total of 70%. All in all, the monitoring system needs to monitor even more types of gases listed above, so that the feedback of the atmospheric environment health status is more accurate.
RETRACTED: Intelligent Control Optimization of Remote Transmission Quality of Communication Signals Based on 5G Network
Authors: Zhang Nannan
Published: November 2021

With the rapid development of the information age, mobile communication networks have gradually entered people’s lives. The emergence of 5G networks has accelerated the convenience of people’s communication and contact, and has become an advanced information technology that has been widely used in various fields. Although the emergence of 5G networks is beneficial, the remote transmission quality of communication signals has also caused many problems. The optimization of remote transmission quality can effectively improve the operational stability of the communication network. There are many ways to optimize the transmission quality of communication signals. For example, when signal interference needs to be reduced, calculate the average value of signals in various places, set the interference reduction threshold function, and complete the remote transmission quality optimization of communication signals. The existing method is to use microblogs with hard and soft thresholds to decompose the signal quality through multiple scales to provide interference reduction thresholds, but ignore the calculation of the average value of the subband signals, so the signal transmission quality is optimized The effect is not satisfactory. It is recommended to reduce the interference in the network communication process based on the anti-window. First, the input communication signal is Pulley converted, the frequency domain interference detection is performed on the communication signal, a low-pass prototype filter is formed, the average value of the signal in each place is calculated, the interference reduction threshold is set, and the network communication process is completed. The weakening of interference realizes the optimization of transmission quality. This article conducts research and discussion on the optimization of the long-distance transmission quality of the communication signal of the 5G network. Experimental research results show that by demonstrating the optimization of the quality of the 5G communication signal transmission process, these demonstrations can ensure the quality of the communication signal, and people’s demand for the network is already indispensable.
Computer-assisted Music Composition Algorithm Design Dependent on Interactive Genetic Algorithm with Interval Fitness
Authors: Guo Qili
Published: November 2021

Computer-assisted music composition refers to computer-assisted music composition with the participation of people. However, there are problems such as style and expression. In this paper, a computer-assisted music composition algorithm based on the interactive genetic algorithm with interval fitness is proposed. A new music prediction model is established by integrating melody units and rhythms into traditional models with only notes or rhythms as units. Moreover, the generated music phrases are optimized by the interactive genetic algorithmphrase. The simulation results suggest that the proposed algorithm can generate music phrases quickly with a certain melody logic that conforms to the personal demand of users using a small data set.
Application of Machine Learning Algorithm Based on Neural Network Technology
Authors: Lai Yiqiang
Published: November 2021

Neural networks have strong characteristics for processing data and information. At the same time, the current computer technology is also very advanced, and many kinds of very powerful information technologies have been developed under the promotion and promotion of modern science and technology. Therefore, relevant personnel will carry out advanced technology and neural network structure methods. Fusion, and then an artificial neural network was established on this basis. In a broad sense, machine learning refers to how to enable a machine to acquire relevant knowledge through autonomous learning, and the purpose is to enable the machine to have relevant skills similar to what people need to acquire knowledge. The research in this article aims to explore the machine learning algorithms based on neural network technology, and through literature research methods, case analysis methods, etc., to have an in-depth understanding of machine learning algorithms in neural network technology, and then through the analysis of machine learning algorithms in neural network technology The learning advantage and its influencing factors are designed based on the machine learning algorithm in the neural network and experimented. Experimental results show that LSTM performs well in replication tasks, and the performance of LSTM even far exceeds that of NTM, but the performance of LSTM in addition and multiplication tasks is much lower than that of NTM. Although the accuracy of NTM on the test set is higher than that of LSTM and RNN, the performance of the model is still relatively poor.
RETRACTED: Research on Urban Electric Vehicle Public Charging Network Based on 5G and Big Data
Authors: Yao Weijing , Zhang Cheng , Deng Guoru , Ke Wangsong , Zhang Dai , Li Lei
Published: November 2021

Under the pressure of energy and environmental protection, we will promote the technological progress and demonstration of electric vehicles, and the construction of charging facilities will continue. Charging facilities planning and orderly charging, as two major research directions of electric vehicle infrastructure, are of great significance for the future development of electric vehicles. The optimal charging of electric vehicles can effectively improve the safe and economic operation ability of distribution network, which is of great significance to its safe operation. Therefore, this paper proposes the outsourcing test experiment and processing of urban electric vehicle public charging network based on 5G and big data. In this paper, through the analysis of the development status of urban electric vehicles, this paper proposes to optimize the charging mode of electric vehicles by combining the charging network forward and backward algorithm. In the outsourcing test experiment, the electrical safety test shows that when the current reaches 1.1-37.1kw: 5000A, when the power factor is 0.8 ∼ 0.9, when the short-circuit current impact is tolerated, the connection device will not affect the breaking operation by contact fusion welding, and the insulation protection will not be invalid. Through investigation and analysis, the satisfaction degree of electric vehicle optimization algorithm is increasing year by year. Through the analysis of the test results, the research in this paper has achieved ideal results and made a contribution to the research of urban electric vehicle public charging network.
Mobile Image Multi-label Recognition Algorithm Based on PaddlePaddle Platform
Authors: Chen Yuanyi
Published: November 2021

As one of the core algorithms of machine vision, the mobile image multi-label recognition algorithm has received extensive attention from researchers in recent years and has been widely used in cutting-edge fields such as deep learning framework paddlepaddle platform, video surveillance, intelligent robots, and unmanned aerial vehicles. However, the existing recognition algorithms are not completely satisfied with the practical application in life and production. Due to the complexity of the platform environment, they can often only propose specific solutions based on existing problems, and there is no universal algorithm that is suitable for all kinds of Complex environment. The purpose of this paper is to study the multi-label recognition algorithm of moving images based on PaddlePaddle platform. This research mainly analyzes and researches the mobile image multi-tag space deployment plan and the multi-tag recognition algorithm, and further improves the tag reading rate and recognition reliability of the mobile image on the PaddlePaddle platform. This research first analyzes several key factors that affect the performance of UHF recognition system, considers the improvement plan of PaddlePaddle platform’s mobile image multi-tag recognition algorithm from the two aspects of space diversity and frequency diversity, and finally determines the multiple The label space diversity scheme, and the introduction of a multi-label optimization recognition algorithm to improve the recognition efficiency of the PaddlePaddle platform’s mobile image multi-label. Experimental data shows that the reading rate can reach 0.907 when identifying 300 tags in the experiment, and when the number of tags is greater than 300, the reading rate is close to 1, which verifies that the algorithm proposed in this paper is used in the multi-tag recognition of moving images on the PaddlePaddle platform.
Internet of Things and Intelligent Transportation System
Authors: Zhang Dandan , Tan Panjing
Published: November 2021

As the core of the new monitoring system, the Internet of Things realizes the integration of wireless sensor networks and traditional communication networks, and provides a platform for remote management and monitoring of the underlying equipment. The intelligent transportation system framework built on this basis combines intelligent transportation technology and the organic combination of vehicle management technologies is conducive to the safety, speed and reliability of vehicle transportation, and plays an important role in further reducing transportation costs. Based on this, this article launched the research on the Internet of Things and intelligent transportation systems. This article first summarizes the concepts of the Internet of Things, intelligent transportation and wireless sensor network technology and the current research status at home and abroad. By analyzing and comparing the performance and characteristics of various communication methods, embedded core microprocessors and embedded operating systems, this paper proposes an overall design scheme of the Internet of Things transportation system based on embedded technology. This paper analyzes the path planning problems in the application of the transportation system, combining the shortest path algorithm simulation results and the actual characteristics of the transportation network, and proposes a simulation data fitting method based on two network parameters and Bellman-Ford, Dijkstra, and Floyd algorithms. The route optimization scheme, and the above-mentioned design scheme was implemented in the transportation system, and the scheme verification was carried out. Finally, this article describes in detail the overall debugging process and operating results of the transportation system, thereby fully verifying the feasibility and correctness of the design and implementation methods of the intelligent transportation system based on the Internet of Things. The research results show that when the INF-PROPORTION is small, the Dijkstra algorithm is better than the Bellman-Ford algorithm. When INF-PROPORTION=0.3, the two algorithms T overlap. Since then, the advantages of the Bellman-Ford algorithm gradually appear, but it is approaching in INF-PROPORTION. At 1 o’clock, the Dijkstra algorithm has a sharp decrease, which is again smaller than the Bellman-Ford algorithm. The second loop condition in the main loop of Dijkstra’s algorithm cannot be satisfied, resulting in a decrease in T.
The Application Status and Development Prospects of Artificial Intelligence and Industrial Robots in the Manufacturing Industry
Authors: Zhu Yurong , Zhang Kui
Published: November 2021

China has achieved quite good results in the research and application of information technology and other fields. The continuous advancement of technologies such as AI, 5G communications, and cloud computing have played a key role in the development of intelligence, convenience, and innovation in various industries in China. The application of AI as a technical means in the Internet industry has been relatively mature and has achieved relatively good results. However, AI has not yet achieved satisfactory results in the development and upgrading of the manufacturing industry. As the most critical industry in the real economy, the manufacturing industry has played a decisive role in the sustainable development of China’s real economy. Therefore, it is necessary to promote the deep integration of AI technology and manufacturing and to further advance the process of intelligent manufacturing. Accordingly, this article discusses the application status and development prospects of AI and industrial robots in the manufacturing industry.
Design and Research of Virtual Reality Course Management System Based on Artificial Intelligence
Authors: Zou Zibo
Published: November 2021

Today, artificial intelligence and virtual reality are rapidly developing as a new technology, and major universities and training institutions have opened relevant courses one after another. However, the traditional course management system cannot meet the needs of virtual reality technology. Therefore, the virtual reality course management system based on artificial intelligence has become an important technological breakthrough to solve this problem. The purpose of this article is to study the design of a virtual reality course management system based on artificial intelligence. This article first, on the basis of fully understanding the core concepts of virtual reality, transforms the curriculum management needs into actual functions for design, and designs the three key functional modules of the design and research of the virtual reality curriculum management system based on artificial intelligence in detail. At the same time, the software architecture and database of the system are rationally designed, the system is designed and explained, and the key points in the main functional modules are explained in more detail. Finally, combined with the artificially designed virtual reality curriculum management system based on artificial intelligence, the application technology, education application and application experience analysis are carried out through questionnaire survey. The results of the questionnaire show that 47% of students are “very willing” to join the immersive virtual reality classroom teaching, and 40% of students choose “more willing”, indicating that students are more willing to accept the management system model.
Automatic Course Scheduling System in Universities Based on Hybrid Genetic-Ant Colony Algorithm
Authors: Duan Yanbei , Lu Wenjie
Published: November 2021

Scheduling is the daily work of the Ministry of Education in Colleges and universities. In the past ten years, the scale of our colleges and universities has expanded rapidly, but the teaching resources are relatively limited. Many schools are facing the problem of insufficient classroom resources and teachers resources. The current way of organizing courses is increasingly difficult to make full use of existing resources to solve the changing needs and inefficiencies, which need to be improved urgently. This paper applies the hybrid Genetic-Ant algorithm to the automatic course scheduling system in Colleges and universities, and uses the cross-function to design and build the automatic course scheduling system in Colleges and universities. And select a college’s course scheduling system from this city for research, and use the Genetic-Ant hybrid algorithm to improve the original system to form a new system, called the original system A, and called the improved new system B, to compare the operation time and system suitability of the two systems. The results show that the fitness of system B is better than that of system A. When the scheduling unit is 100, the fitness of system A is 181, and system B is 203. When the scheduling unit is 400, the fitness of system B is 14 higher than that of system A. When the scheduling unit is 800, the fitness of system B is 64 higher than that of system A. Thus, the hybrid algorithm of genetic ant colony can improve the rationality of the curriculum.
Application of Three-Dimensional Animation in Mechanical Control Mechanism
Authors: Shao Dong
Published: November 2021

With the development of the times and the advancement of science and technology, the development of 3D animation has gradually been applied in various fields. In the field of mechanical control, three-dimensional animation has gradually become a very important application. In the actual application process, the use of three-dimensional animation effects cannot be applied in the field of mechanical control only by imagination. It must be applied in practice from the perspective of mechanical control. Analyze from its own stability and external factors to realize its application value. The purpose of this paper is to study the application of 3D animation in mechanical control mechanism. This article will take mechanical control as an entry point to discuss the application of three-dimensional animation in its mechanism. The research is carried out from two aspects. The first is the application of the robot arm. Based on the kinematic path planning of the shortest time priority strategy, the shorter the motion time of the robot arm, the faster the speed, and the vibration is more difficult to avoid. This is the vibration of the flexible arm. Improve its work efficiency, lay the foundation for more complex path planning problems; create conditions for lighter and faster robotic arm applications. The second is the application of the mechanical foot. The proposed ankle joint control strategy based on the lateral and forward cycle matching can be combined with the forward plane walking control strategy under the stable initial gait of the robot to realize the humanlike virtual prototype of the biped robot. Dynamic walking for analysis. Experimental investigations have shown that in the process of normal walking, there is a dynamic swing process and a static support process. The swing process refers to the lifting of the foot to move forward or backward, and the supporting process refers to the immobilization of one foot to the other. One fulcrum on each foot allows it to move forwards and backwards. Among them, the dynamic swing process accounts for 40% of the whole process, and the static support process accounts for 60%. In general, based on these data, the three-dimensional robotic arm and three-dimensional robotic foot can be better studied.
Influence of Installation Mode of Sensing Board on Accuracy of Smart Voltage Measurement in Switch Cabinets
Authors: Huang Jie , Niu Xiaochen , Zhou Zihan
Published: November 2021

Based on the working principle of capacitor voltage transformer, this paper analyzes the influence of the installation mode of induced voltage board in the switchgear on the spatial stray capacitance by taking the switchgear, which is widely used as the carrier. Ansys finite element analysis software is used to establish the models of the voltage sensing board under different installation modes, and the influence of environmental factors such as the position, structure and temperature of the voltage sensing board on the stray capacitance between the bus bar and the sensing board is analyzed; The three-dimensional model of electric field shield plate structure is established for simulation. The installation mode of the voltage sensing board and the influence of the shielding board on the stray capacitance are revealed.
Camellia Oleifera Harvester Based on Model Processing
Authors: Kang Di , Fan Youhua , Chen Zejun , Ma Fang , Peng Shaofeng , YongWang
Published: November 2021

Aiming at the problems of high leakage rate and high damage rate in mechanized picking of Camellia oleifera fruit, a branch shaking type picking machine was designed. According to the working principle of picking, the structure design of key components was completed, and the orthogonal test of three factors and three levels was designed. The results showed that the best combination of operating parameters was: picking time 45s, vibration frequency 8Hz, amplitude 5cm of picking head. The field test of Camellia oleifera fruit picking machine was carried out, and the picking rate was 91.2%, and the damage rate of flower bud was 18.2%.
Influence of the welding parameters on the weld metal mechanical heterogeneity of EP517 (Fe12Cr2NiMoWVNb) steel and 36NKhTYu (Fe36Ni12Cr3TiAl) alloy dissimilar welded joints
Authors: Borodavkina K T , Terentyev E V , Sliva A P , Marchenkov A Yu , Zhmurko I E , Santalova Yu V , Kozyrev K M
Published: November 2021

The article presents the results of assessing the effect of the welding speed and the displacement of the electron beam relative to the joint on the mechanical heterogeneity of the weld metal of dissimilar welded joints of EP517 (Fe12Cr2NiMoWVNb) steel and 36NKhTYu (Fe36Ni12Cr3TiAl) alloy. Aging curves are plotted for the weld metal of welded joints made at electron beam welding (EBW) speeds of 30 m/h and 120 m/h, as well as for the weld metal of the welded joint made at a speed of 30 m/h with various electron beam displacements. An assessment of the change in the mechanical heterogeneity of the weld metal was carried out by the change in the standard deviation of the hardness values, and metallographic studies were also carried out. It was found that a decrease in the EBW speed leads to a decrease in the standard deviation of the results of measuring the hardness of the weld metal after aging from 45 to 14 HV5 or from 18% to 6%. It was also found that an increase in the displacement of the electron beam to alloy 36NKhTYu (Fe36Ni12Cr3TiAl) to 60% leads to an increase in the hardness of the weld metal from 225 to 305 HV5 (by 35%).
Synthesis of spherical zinc sulfide nanoparticles produced by femtosecond laser ablation and deposited on a silicon substrate under the action of an electrostatic field
Authors: Chernikov A S , Kochuev D A , Voznesenskaya A A , Egorova A V , Khorkov K S
Published: November 2021

In this paper we consider the synthesis of spherical zinc sulfide (ZnS) nanoparticles (NPs) deposited on a silicon substrate by femtosecond laser ablation of ceramic target under the action of an electrostatic field in argon gas atmosphere. The use of an electrostatic field in the process of ablative synthesis of NPs allows, in addition to capturing particles, to carry out predicted deposition of nanomaterials on the substrate, while carrying out the ablation products from the region of laser beam propagation. The morphology and structural characteristics of synthesized ZnS NPs were investigated using scanning electron microscopy and X-ray diffraction analysis. The size distribution of ZnS NPs were studied using dynamic light scattering technique. Obtained NPs have a spherical shape and are characterized by a hexagonal phase of wurtzite ZnS.
ON IMPROVING THE METHOD OF ELECTRON-BEAM DEPOSITION
Authors: Rozhkov Konstantin A. , Starikov Sergey S. , Varushkin Stepan V. , Trushnikov Dmitry N. , Zubko Irina A.
Published: November 2021

The paper deals with improvement of the electron-beam additive forming of metal products using a vertically fed filler wire in vacuum with two electron beams as a heating source. We compared the importance of the power of the heat source required for fusing the layers with each other and the calculated power of the heat source required to melt the filler wire and the surface of the product. Within the experimental conditions of the multilayer electron beam deposition using side wire feeding, the electron beam power of 2.4 kW was required to ensure fusion without the defect formation between the layers during the deposition of Ti-6Al-4V titanium alloy. At the same time, approximate calculations of the minimum power of the heat source required to melt the filler wire and the surface of the product showed a level of 730 W.
Evolution of porosity depending on SLM mode and subsequent HIP processing
Authors: Voznesenskaya A A , Zhdanov A V , Raznoschikov A S
Published: November 2021

In this work, we obtained the results of studying the effect of pore formation on the energy density deposited in the SLM process, as well as the effect of post-processing on the change in the nature of the pores. To study the porosity formed during the manufacture of parts by the SLM method, groups of primitive specimens were prepared and subjected to HIP processing. Pore formation was investigated by two methods: by optical microscopy and by weighing samples with subsequent calculations of density and porosity values. During the study of the initial samples, 4 types of pores were identified. Pore formation was investigated before and after HIP treatment. The dependence of pore formation on energy density is obtained.
Investigation of the effect of HIP processing on the physical and mechanical characteristics of parts obtained by the SLM method
Authors: Raznoschikov A S , Voznesenskaya A A , Kochuev D A , Gerke M N , Chkalov R V
Published: November 2021

In this paper presents the results of studying the powder material AISI 321. Selective laser melting (SLM) of the samples was carried out in modes with a change in the radiation power. The subsequent processing of the samples by the method of hot isostatic pressing (HIP) was carried out. The roughness of the surfaces and the microhardness of the samples before and after the HIP were studied.
The Sparse Learning of The Support Vector Machine
Authors: Cui Lipeng , Shen Jie , Yao Song
Published: November 2021

The sparse model plays an important role in many aeras, such as in the machine learning, image processing and signal processing. The sparse model has the ability of variable selection, so they can solve the over-fitting problem. The sparse model can be introduced into the field of support vector machine in order to get classification of the labels and sparsity of the variables simultaneously. This paper summarizes various sparse support vector machines. Finally, we revealed the research directions of the sparse support vector machines in the future.
UAV Assisted Wireless Network
Authors: Teng Sihao
Published: November 2021

With the increasing demand of social network service, the unmanned aerial vehicle has been used as a base station to assist terrestrial base station to improve wireless network performance. UAV base station provides high efficiency and wider data transmitting range due to the small size and flexibility of UAV. However, UAV wireless network faces few challenges. Energy efficiency is hard to achieve due to small battery capacity. The base station performance is also very important. It can be determined by aircraft’s flying stability, the performance of air to ground communication and the limitation of wireless coverage of UAV. In order to achieve optimal UAV deployment, improving deployment delay, communication coverage and UAV number limitation are important. Trajectory optimizing problems also need to be considered. This article analyzes UAV assisted wireless networks through investigating UAV energy efficiency, UAV aided network performance, optimal deployment methods and flight trajectory. It is shown that energy efficiency can be optimized by applying LoS based channel in UAV trajectory planning. And inequality iteration algorithm proposed by former researchers is used to determine optimal flight trajectory. This method is efficient because of cellular network’s interference-free ability. As for performance, channel selection methods are used to reduce overflow rate and boost data-received size. These methods are analyzed and proved to be effective for improving UAV aided wireless network performance.
Realization of combinatorial optimization of small-scale S-box
Authors: Huang Yating , Mao Ming , Li Yanjun , Zhang Weiguo
Published: November 2021

This method describes a new technology for combinatorial logic optimization in detail, which can combine multiple criteria to reduce the hardware implementation area of the S-box. This technique can be achieved in two steps. The first is to optimize the non-linear part of the S-box. According to the optimization criterion of multiplication complexity, the quality of the S-box nonlinear part optimization can be judged, and the realization of the S-box with the smallest multiplication complexity can be obtained. The second step is to optimize the linear part of the S-box, and optimize on the basis of the results of the first step, focusing on reducing the number of XOR gates, and the optimization is performed through a heuristic-based algorithm. The above combinatorial logic optimization technology can be applied to any small S-box (5 × 5 and below). Finally, the S-box of PRESENT algorithm and CTC2 algorithm are used as examples to illustrate the optimization effect, and the optimal realization under the minimum AND gate condition is obtained.
Triplet Siamese Network Model for Lithium-ion Battery Defects Classification Using Few-shot Learning Approach
Authors: Wu Ke , Tan Jie
Published: November 2021

In this paper, we propose a triplet siamese model for lithium-ion battery defects classification. It is a difficult task to detect the surface defects of lithium-ion batteries with stainless steel surface. The lack of three-dimensional information and the lack of marker datasets due to reflections prevent two-dimensional computer vision detection methods from meeting classification needs. In this work, the multiple exposure structured light method is utilized to obtain the three-dimensional shape of a lithium-ion battery with a stainless steel surface. The defect point cloud with three-dimensional information is obtained by this method, and then the 3D information of the defect point cloud is converted into grayscale information, and the grayscale image is used as the target domain data of the triplet siamese network. The public dataset MiniImageNet is utilized as the training data of the triplet siamese network model. The accuracies of the experimental results are 88.9%, 95.6%, and 97.8% for 1-shot, 5-shot, and 10-shot respectively. This result proves that our method can be used for lithium-ion battery defect detection.
Change detection based on dimension reduction SLIC and image matching for remote sensing images
Authors: Chang Zhenliang , Yang Xiaogang , Lu Ruitao , Zhuang Hao , Huang Pan
Published: November 2021

The detection accuracy of traditional change detection algorithms is seriously affected by the low accuracy and high rate of omission, the radiometric correction accuracy, and the classification threshold for difference image. A change detection method based on image segmentation and image matching was proposed for remote sensing images. In this method, super-pixel-based dimension reduction SLIC image segmentation algorithm and SURF algorithms were used. The homogeneous region was used as the segmentation standard, and the homogeneity method was proposed to suppress the impact of inconsistent image segmentation on the change detection results. The experimental results show that this method improves the accuracy of remote sensing image change detection, has good robustness to the problem of redundant data, significantly reduces the error detection rate of image change detection, and can effectively accelerate the speed of change detection.
Research on Service Value Chain Model Based on Technology Service Platform
Authors: Chen Xiang , Huang Gaijuan , Zhang Yangsen , Li Jianlong
Published: November 2021

For the technology service platform, serving users with science and technology resources is the purpose of platform construction, and the quality of the service value chain affects the operation of the technology service platform. Aiming at the immature status of the research on the service value chain model of the technology service platform, this paper proposes a set of construction ideas based on the service value chain model of the technology service platform. The model takes the technology service platform as the central node, and at the same time integrates users, third-party technology service platforms, service resource providers in various fields and other participant nodes, and links the interests of all parties through a dynamic chain with value flow as the core. Each participant node cooperates with each other to form a complex, cross-domain service value chain model. In order to ensure the service quality of the platform, a feedback mechanism that reflects the attitude of users using scientific and technological service resources is introduced into the service value chain model. By taking the manufacturing of vertical elevators as an example, the operation of the service value chain is simulated, and the usability of the service value chain model is verified.
Research on Model Compression for Embedded Platform through Quantization and Pruning
Authors: Hu Xiao , Wen Hao
Published: November 2021

So far, artificial intelligence has gone through decades of development. Although artificial intelligence technology is not yet mature, it has already been applied in many walks of life. With the explosion of IoT technology in 2019, artificial intelligence has ushered in a new climax. It can be said that the development of IoT technology has led to the development of artificial intelligence once again. But the traditional deep learning model is very complex and redundant. The hardware environment of IoT can not afford the time and resources cost by the model which runs on the GPU originally, so model compression without decreasing accuracy rate so much is applicable in this situation. In this paper, we experimented with using two tricks for model compression: Pruning and Quantization. By utilizing these methods, we got a remarkable improvement in model simplification while retaining a relatively close accuracy.
Attention Based Spatial-Temporal Graph Convolutional Networks for Short-term Load Forecasting
Authors: Liu Rong , Chen Luan
Published: November 2021

To predict the load of the power system with a known network structure, this paper proposes a novel attention based spatial-temporal graph convolutional network (ASTGCN) model to predict the node load in the power grid. The experimental results show the good performance of ASTGCN.
Research on an invasive and low-frequency power consumption data acquisition method
Authors: Sun Weijia , Wang Hui , Liu Yongfeng
Published: November 2021

Aiming at the large-scale, compatibility and reliability problems faced by the data acquisition engineering for power consumption in the industrial field, for reducing the complexity and cost of the data acquisition engineering, and improving the quality, maintainability, scalability and manageability of the data acquisition, an engineering-oriented, intrusive, low-frequency data acquisition scheme for power consumption is proposed. The scheme leverages multi-agent networking technology to solve the large-scale problem, utilizes the communication driver of the dynamic loading adapter mode to tackle the compatibility trouble. Furthermore, the local storage, breakpoint retransmission, flow control, automatic recovery, automatic connection, and time synchronization are combined to solve the reliability issue. Engineering tests show that the proposed scheme possesses the significant backward compatibility characteristics, which can effectively reduce the complexity and cost of data acquisition engineering for power consumption, and significantly improve the quality, maintainability, scalability and manageability of data acquisition.
Power analysis of smart home appliances based on SSA-TCN
Authors: Tang Chao , Tang Yong , Liang Huihui , Zhang Linghao , Xiang Siyu
Published: November 2021

The popularity of smart home equipment has led to higher requirements for equipment automation operation and maintenance. However, the energy consumption status and hidden faults of household equipment cannot be controlled in time only by using traditional monitoring methods. Therefore, this paper proposes a methods of power analysis for smart home appliances based on SSA-TCN using the energy consumption data of smart home appliances. The effective information of the data is extracted through the SSA singular spectrum analysis method, and the data sequence is input into the sequential convolutional network for judgment, so that the energy consumption status and working status of the equipment is obtained. The actual data is used as the training set and the test set to verify the recognition rate of the model. The experimental results show that the recognition rate of the method is about 82%, which provides an effective way for equipment automation and intelligent operation and maintenance.
Analysis and Application of intelligent Power Control System Cyber Security Situation Awareness Based on Wavelet Neural Network
Authors: Zhao Jingcheng , Li Xiaomeng , Cao Yaofu , Liu Junwen , Yan Junlu , Li Chengwei
Published: November 2021

In recent years, international industrial control network security incidents have occurred frequently. As a core component of the industrial control field, intelligent power control systems are increasingly threatened by external network attacks. Based on the current research status of power industrial control network security, closely combining the development of active monitoring and defense technology in the public network field and the problems encountered by network security operators in actual work, this paper uses data mining methods to study the power control system network security situation awareness technology. Combing operational data collection and integrated processing, situation index screening and extraction, we use wavelet neural network analysis method to train the sampled data set, and finally calculate the true value of the network security status through deep intelligent learning. Finally, we conclude that the artificial intelligence algorithm based on wavelet neural network can be used for power control system network security situation awareness. In actual work, it can predict the situation value for a period of time in the future and assist network security personnel in judgment and decision-making.
Feature Extraction and Clustering of High Dimensional Electromagnetic Interference Signals Based on Multidimensional Scaling and SOM Network
Authors: Li Hongyi , Xu Tianshi , Lian Haoran , Chen Wei , Zhao Di
Published: November 2021

With the wide application of communication technology, the threat of interference is becoming more and more serious. These disturbances usually appear as higher-dimensional vectors, and the nuances between the different vectors are indistinguishable to humans. A great deal of research has been done on how to deal with this interference in an automated way. But most of the computation time in these studies was spent on the training and computation of the convolution layer. As the electromagnetic equipment tends to be a large inheritance system, the electromagnetic signal also presents higher dimensional characteristics. Then the convolutional neural network represented must use larger and more convolutional layers, so the time cost is very high. Therefore, feature extraction of the original signal is carried out before clustering. After adjusting the selection of features, the algorithm performs well on our dataset.
Design and implementation of endogenous security container based on union file system
Authors: Xing Fukang , Zhang Zheng , Ma Bolin , Li Bingzheng
Published: November 2021

In order to solve the increasing attacks on container file system and the IO errors of containers in big data processing scenarios in cloud computing environment, a scheme based on the idea of heterogeneous redundancy in endogenous security and transformation of container union file system was proposed to improve the security and fault tolerance of containers. Based on the above scheme, experiments are carried out on Docker, the most popular container technology, and OverlayFS, the most representative union file system. The experimental results show that this scheme can improve the security and fault tolerance of containers on the premise of ensuring availability, and realize the endogenous security of containers.
Design of low emittance booster ring magnets for Iranian Light Source Facility[This paper is supported by Iranian Light Source Facility.]
Authors: Saeidi F. , Yousefnejad S. , Hashemi M.
Published: November 2021

Iranian Light Source Facility (ILSF) is a 3 GeV synchrotron
radiation laboratory in the detailed design stage. The main building
site is being excavated in Qazvin, 150 km from Tehran. The Booster
ring of this facility with a circumference of 504 m and a low
emittance of 3.5 nm rad accelerates electrons from 150 MeV to
3 GeV. Using POISSON and OPERA codes, we designed the ILSF
booster ring magnets including dipoles, quadrupoles, sextupoles, and
separate correctors. Cooling and electrical calculations are done,
and ramping effects are considered. The ILSF has also been
attempting to practice mechanical designs and to build prototype
magnets at this stage too.
The RabGAPs EPI64A and EPI64B regulate the apical structure of epithelial cells†
Authors: Miller Matthew R. , McDermitt David J. , Sauvanet Cecile , Lombardo Andrew T. , Zaman, Riasat , Bretscher Anthony
Published: November 2021

RabGAPs EPI64A and EPI64B specifically localize to and regulate the morphology of the apical domain of polarized epithelial cells.
Lula’s Prison Letters and the Brazilian Presidential Papers: Archives, Readings, and Uses
Authors: Heymann Luciana , Moreli Alexandre
Published: November 2021

For five hundred eighty days, between 2018 and 2019, former Brazilian President Luiz Inácio Lula da Silva remained imprisoned in the city of Curitiba, about two hundred miles South of São Paulo. During that period, more than twenty thousand letters were sent to him from all regions of the country—the result of a campaign initiated by the Workers’ Party and rapidly disseminated through social media—correspondence now archived at the Lula Institute, in the city of São Paulo, as part of the Lula Presidential Archives. This article surveys the status of private presidential archives in Brazil, probing the nature of “prison letters” and their uses in campaign actions for Lula’s release, with special attention to the circulation of documents in digital format on websites and social media. The authors had access to the documentation cited in this piece during the mobilization for the Lula Livre movements. Finally, the article contributes to the study of political correspondence and analyzes the challenges posed to historical research by the digital circulation of documents.
Clinical pharmacist involvement in expanding naloxone distribution in a veteran population
Authors: McQuillan Amanda
Published: November 2021

Purpose

To describe the process used in a clinical pharmacist specialist (CPS)–led Opioid Overdose Education and Naloxone Distribution (OEND) program initiative to increase naloxone distribution to veterans at high risk for overdose via provider education and identification of barriers to naloxone distribution.

Drug overdose is the leading cause of accidental death in the United States. One step toward counteracting the epidemic includes expanding access to and use of naloxone. The Veterans Health Administration has developed initiatives to target veterans at risk for opioid overdose, such as the Veterans Affairs (VA) OEND program. Pharmacists can play a unique role in OEND by both prescribing naloxone and educating patients and providers on risk mitigation strategies. Through provider education, patient education, and facility-wide initiatives, naloxone prescribing was increased by 9-fold from August 2016 to August 2018. In addition, the number of new naloxone prescribers increased by almost 7-fold during the intervention period. Naloxone distribution to high-risk groups drastically increased across all target groups.

Conclusion

CPS involvement in promoting OEND at VAPHS drastically increased rates of prescribing of naloxone kits to veterans at risk for opioid overdose. This initiative showed that a CPS can play multiple roles in supporting OEND outreach at a large healthcare setting.
META-DDIE: predicting drug–drug interaction events with few-shot learning
Authors: Deng Yifan , Qiu Yang , Xu Xinran , Liu Shichao , Zhang Zhongfei , Zhu Shanfeng , Zhang Wen
Published: November 2021

Drug–drug interactions (DDIs) are one of the major concerns in pharmaceutical research, and a number of computational methods have been developed to predict whether two drugs interact or not. Recently, more attention has been paid to events caused by the DDIs, which is more useful for investigating the mechanism hidden behind the combined drug usage or adverse reactions. However, some rare events may only have few examples, hindering them from being precisely predicted. To address the above issues, we present a few-shot computational method named META-DDIE, which consists of a representation module and a comparing module, to predict DDI events. We collect drug chemical structures and DDIs from DrugBank, and categorize DDI events into hundreds of types using a standard pipeline. META-DDIE uses the structures of drugs as input and learns the interpretable representations of DDIs through the representation module. Then, the model uses the comparing module to predict whether two representations are similar, and finally predicts DDI events with few labeled examples. In the computational experiments, META-DDIE outperforms several baseline methods and especially enhances the predictive capability for rare events. Moreover, META-DDIE helps to identify the key factors that may cause DDI events and reveal the relationship among different events.