bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessment of Optimizers impact on Image Recognition with Convolutional Neural Network to Adversarial Datasets
Authors: Vidushi  , Agarwal Manisha , Rajak Akash , Shrivastava Ajay Kumar
Published: August 2021

In Artificial Intelligence, the machine modeling technique means to behave in the manner of human reflects indistinguishable. To automatizes the development of rational model for data evaluation, machine learning mechanism of artificial intelligence, is used. Deep learning is the machine learning discipline, having objective to imbide the system and to discover pattern from input. In pattern recognition, deep learning has paramount importance and different advance powerful model’s architecture. The most effective, vital, and influential innovation in computer vision discipline is one of the architectures of deep learning called convolutional neural network. In this neural network various optimizers can be used for model molding into its appropriate form by weights futzing. Aiming to overcome the problem in getting the optimized result, the research used various algorithms of weight optimization. The article elaborates convolutional network concept as well as the idea behind the use of optimizers. Furthermore, the detailed study of optimizers is also presented in this paper. Along with it, experimental comparison and result of different learning paradigm optimizers is shown in the document. Considering the different image datasets, including MNIST, and CIFAR 10 dataset, the accuracies of convolutional model with different optimizers are verified.
Efficient Construction of Successive Cancellation Decoding of Polar Codes using Logistic regression Algorithm
Authors: Albert raj A. , Judson D. , Beno A.
Published: August 2021

In recent days, polar codes have attracted the attention of Industry and Academia. It is one of a linear block error correction codes. Polar codes are one of the capacity achieving algorithms over a wide range of channels, making them very attractive from a theoretical perspective. Polar codes are shown to be instances of concatenated codes. It has been shown that the effect of a polar code can be enhanced by showing the multistage decoding algorithm with log-likelihood based Successive List decoding (SCL). However, SCL decoders are no longer optimal in terms of frame error rate. It is believed that the machine learning based algorithm can improve the FER for the decoded bit. Thus, the logistic regression algorithm is proposed in this paper. The proposed method offers an optimized solution to the decoded bits. The simulation result shows that with sufficient training, this method can provide a lower frame error rate for different code lengths.
Privacy-preserving Search over Encrypted Personal Health Record in Multi-Source Cloud
Authors: Hema Latha M , Suhasini Snigdha A , Vijaya Lakshmi B , Padmavathi K , Satish M
Published: August 2021

The biggest domain which needs a cloud platform in this smart world is the medical domain, where hospitals have a huge amount of data regarding the patient’s records. When these personal health records are stored in the cloud, the system needs some encryption mechanism to secure the records. In this paper, an enhanced Attribute-Based Encryption (ABE). The health records of the patient are decrypted with the help of private keys. In this model, the patient doesn’t need any certificate for authorization, so the model uses the details of the patients as attributes, and these details are encrypted. A threshold value is defined as t, if the number of encrypted attributes is matched with the threshold value then only the data owner can decrypt the data. A hierarchical mechanism helps the model to have smooth access to the data. The traditional algorithms are good at handling a small amount of data but this algorithm provides security as well as scalable property. The enhanced mechanism includes the unsupervised machine learning approach to label the patients based on their disease. In the cloud, a group of clusters are formed to separate the patients and allocate the doctors based on their disease treatment. The process of labeling is major drawback in case of attribute based encryption mechanism.
Compressing Biosignal for Diagnosing Chronic Diseases
Authors: Ithayarani P. , Jainul Fathima A.
Published: August 2021

Electrocardiogram, electromyogram, electroencephalogram are the foremost required vital signs for diagnosing chronic diseases like sleep disorder, mood disorder, epilepsy etc., which demands long-term monitoring. A sensor based wearable system which is enabled with internet technology, supports the continuous recordings of these vital signs without troubling the patient’s daily activities. And the wearable hub is responsible for collecting the readings of bio signals from multiple micro-sensor nodes deployed around the body which creates the short range of communication and forward to the observer. These continuous monitoring increases the signal transmission cost and declines the battery life of wearables. So, the observed multiple bio signals can be compressed jointly than individually before sending, at an edge level. This paper proposes transfer learning based multimodal convolutional denoising autoencoder to perform multimodal compression and to reconstruct the data from its latent representation. Transfer learning helps the system to reuse the learned weights which may reconstruct the data with better quality score than by randomly initialized weights. The proposed work achieves compression ratio of 128 and it is proved that multimodal compression is better than unimodal compression in case of consuming multiple sensors. And the experimental result proves that the computation cost is low in multimodal compression than in unimodal compression.
Bottle Line Detection using Digital Image Processing with Machine Learning
Authors: Anush Ch , Yashwanth K. , Shashank S. , Venkat Reddy M. , Kumar Ashwani
Published: August 2021

Image processing is often used in industrial applications. The automatic visual inspection of a product before it is packaged and dispatched to the client is one of the most common uses for image processing. Using these types of systems in production facilities helps the avoidance of situations where a faulty or sub-standard product is shipped to the customer. The quantity of the fluid in each bottle differs with the accuracy level of the machine and furthermore due to mistakes done by employee during the underlying arrangement. Inappropriate filling during a bottle leads the makers to dispose of whole bunches of an item resulting in huge capital loss. A Economical mechanized review structure for inline packaging which detects oddities has been examined. The system are ending up being to be assign with checking the degree of the fluid filled inside each bottle as the container goes through the assessment framework introduced round the current packaging line. It will help to keep up the product quality and quantity as well and furthermore maintains the accuracy of the filling. The bit of the space between the outside of the fluid inside the bottle and container will be used to observe the differences like underfilling and overfilling. The proposed work is accomplished using image processing techniques in Machine Learning using python platform. Here we use ANN algorithm. Process of detecting the liquid level in bottle as follow as a group of pictures were given, taken under close to consistent lighting conditions in the manufacturing plant. These arrangement of pictures contains both underfilled and overfilled bottles which the packaging organization expects that you should use to identify certain issues as a component of the filling, covering and naming tasks before the final packing stage. The distance for each dataset must be same. We need to provide threshold value to detect if the bottle is overfilled or underfilled.
Improvisation of RSA Algorithm in Respect to Time and Security with the Proposed (AEA) Algorithm
Authors: Taneja A , Shukla R K , Shukla R S
Published: August 2021

The security of digital information has become increasingly vital in the digital world, which is currently evolving and changing at such a quick speed. With the rapid growth of hidden contact and communication around the world, the need for communication security is becoming increasingly important. The main motive of cloud service providers is to collect the information and details of the material uploaded on their cloud servers. One of the major concern is security of the contents uploaded on the cloud servers. Although, they challenge about the security provided by them to the individuals but they are the owner of servers and the information on it. The same has been observed when one of our server crashed and we consulted about the incident with the customer care department of our CSP. We use to back up our university servers backup on cloud for which we pay some amount as decided by our management as well as CSP. The aim of this research is that the cloud owners cannot have access to data of the users. I proposed a new robust encryption technique in this paper to improve the security of the key generating algorithm. To achieve this, an algorithm has been implemented which performs much better than renowned algorithm i.e. RSA algorithm. This algorithm achieves better response time than RSA algorithm which is described and proved in this paper. This paper uses ASCII and EBCDIC codes while encryption and decryption.
Intelligent System for Skin Disease Prediction using Machine Learning
Authors: Elngar Ahmed A. , Kumar Rishabh , Hayat Amber , Churi Prathamesh
Published: August 2021

Skin is an extraordinary human structure. It frequently suffered from many known and unknown disease. Therefore, diagnosis of human skin diseases is the most uncertain and complicated branch of science. It has been observed that most of the cases remain unnoticed because of the lack of better medical infrastructure and facilities. This paper is devoted to solve this challenge. Therefore, this paper effectively proposed (CNN-SVM-MAA) system which combines Convolutional Neural Network with Support Vector Machine classifier to develop a Mobile Android Application. Thus, to evaluate the performance of the proposed system several experiments are conducted on our dataset. This dataset consists around 3000 images which collected from a lot of sources like Beni-Suef University Hospital, Cairo University Hospital and various websites as well to be more accurate and realistic. A comparative study of applying different Feature extraction algorithms with different classifiers was accomplished. The results obtained showed the adequacy of the proposed (CNN-SVM-MAA) system how many skin diseases images have been detected from skin disease dataset. Which lead to detect skin disease and provide the user with the disease name and treatment related prescription with high accuracy.
II International scientific and practical conference “Information technologies and intelligent decision making systems” (ITIDMS-II-2021)
Authors: Gibadullin Arthur
Published: August 2021

Within the framework of the presented material, the results of the conducted II International scientific and practical conference “Information technologies and intelligent decision making systems” (ITIDMS-II-2021).
System analysis methodology for decision making in the design problems of new generation nuclear reactors
Authors: Okunev V S
Published: August 2021

The main goal of the article is to summarize the results of many years of work on the creation of a unified methodology for designing new generation nuclear reactors that meet the requirements for generating electricity on a given scale, economic efficiency, inherent safety, and fuel supply. The methodology is based on the methods of systems analysis and operations research. Accidents with unacceptable releases of radioactive substances outside the NPP in new generation reactors can be eliminated by using core materials with specified (obtained within the same methodology) properties. The research methods are based on solving mathematical programming problems in different formulations, solving a number of multicriteria problems, using the non-formalized theory of conflict and informal procedures. As a result of the research, decision-making algorithms have been proposed; software has been developed and modernized to solve the problem of designing nuclear reactors. The practical significance of research in substantiating the fundamental possibility of creating safe nuclear power based on high-power reactors with lead cooling, cermet fuel and innovative structural materials.
Big Data technology: assessing the quality of the educational environment
Authors: Denisova O A
Published: August 2021

The paper shows the prospects for the development of digital technologies of Big Data, which are proposed to be used as a means of qualitative assessment of education, as well as its effectiveness. The use of intelligent systems that work at the level of the best teacher in the future will be available in any educational organization. This will make it possible to overcome the “educational inequality” and reduce educational barriers for people with disabilities. If the methodology of traditional teaching is created based on the personal experience of the teacher, then the methodology based on big data analysis technologies is the result of the experience of a huge number of users. With the help of big data, you can create methods that are adapted to a large number of students; personalize content; choose learning modes, that is, learning becomes adaptive and personality-oriented. The educational program is transformed from the traditional approved text format to the format of personalized online content, which dynamically changes depending on requests. In addition, approaches to monitoring and evaluating both the educational process itself and the results of education are changing. As a result of the application of Big Data technologies (multidimensional statistical analysis), new methods of forecasting are proposed for use, when a combination of known data predicts the desired unknown; structure identification and clustering; network analysis. Big data is a huge volume of information, big data analysis methods are the systematization of this information, but they can never replace the teacher who gives his own and “reads” the emotions of the student.
Study on the Effect of Cement Sheath on the Stress of Gas Storage Well
Authors: Song Chengli , Liu Xinbao , Li Guangshan , Wang Shuai
Published: August 2021

Compressed natural gas (CNG) has been widely used as an automotive fuel in china, which the service security of CNG storage well (CSW), the main storage equipment in Chinese filling station, is becoming more and more prominent. In order to analyze the stress of CSW and its influencing factors, the mechanical model of CSW + cement sheath + stratum (CCS) processed by both elastic mechanics and finite element method was studied in this work. Using the most common well of Φ177.8× 10.36mm as a calculation case to obtain the analytic solutions and numerical solutions of CCS. The results indicated that the analytic solutions and numerical solutions are very close with relative deviation less than 3%, which verified their reliability each other. The calculation case can prove that the stress of CSW reduced evidently due to the effect of well cementation, which equivalent to the value of circular and axial stress of CSW strengthens to 18% and 20%. And when increasing the elastic modulus of cement sheath, the stress of CSW decreased that the support and potentiation to CSW by cement sheath becomes stronger.
Examinations of Errors in a D/Pd Gas-loading System when Using a Heat-flow Calorimeter
Authors: Wang Xingye , Shen Bingjun , Jin Lihong , Zhou Dan , Tian Jian
Published: August 2021

Measurement of a reaction between deuterium (hydrogen) gas and materials using a calorimeter which is calibrated by nitrogen will show erroneous excess heat power production due to the structure of the reaction chamber and the difference of thermal conductivity of the gases used in the calibration and triggering experiments, which leads to the energy generated in the chamber of the calorimeter has not been totally measured. The error is not obvious at low applied power, and it increases significantly with the increase of applied power. This insidious error is discussed by using a stable heat-flow (Seebeck) calorimeter and procedures to reduce error are put forward. And the authenticity of Rossi’s patent is questioned.
Transient Hydrodynamic Analysis of Ball Mill Multi-Pocket Pivoted Pad Hydro-static Bearing
Authors: Li Miaomiao , Ma Xizhi
Published: August 2021

Huge ball mill bearings experience a transient period when they are fed with raw material or their basis is impacted. The behavior of such bearings during the transient period is significant to the safety of the machine. In the present paper, the step load is used to simulate this process. Transient hydrodynamic lubricating models of a multi-pocket pivoted pad hydro-static bearing used in ball mill are established. The hydrodynamic and squeeze effects are take into account, and the models are solved using nonlinear numerical methods. The journal center trajectory, time response of the pad tilting, oil pressure in the pockets, and minimum pad film thickness during the transient period are presented. The oil flow rate evolutions out of the main pockets in all pads are exhibited. The effects of step load on bearing performance are discussed. It was found that the decrease in minimum film thickness can be quite large under the step load impact, which may lead to the damage of the bearing.
Design of Intelligent Robot Flexible Sorting Center
Authors: Ran Wenxue , Bai Haixia , Ma Ruijin
Published: August 2021

In order to make up for the shortcomings of traditional robot sorting flexibility and limited operation efficiency, the concept of intelligent robot flexible sorting center is proposed in this paper. We firstly conceive its characteristics, then construct its structure, which consists of a robot with changeable claws, a claw storage, an automatic setback stereoscopic warehousing system, an order goods conveying system, a visual recognition information system. Thus make the intelligent sorting intensive system with more functions enables the robot to adapt to a variety of picking methods, showing higher flexibility, higher order sorting quality, higher equipment utilization rate, relatively stable sorting capacity and great adaptability, its application scope can be continuously expanded to meet the needs of more diversified goods.
The Control System of Mountainous Multi-function Miniature Pile Drill WZFT
Authors: Liu Ruijun , Hou Zhongwei , Liu Quan , Ding Xiaowu
Published: August 2021

With the development of the power industry in China, in order to promote the further efficient and comprehensive development of the power industry in China, it is necessary to achieve precise mechanical automation in power engineering to meet the needs of the power industry in the new era, so that the level of automation of power machinery and equipment demand is higher and higher. Especially in the narrow, special field, mountain construction surface drilling operations of manpower and time cost of the project are heavier, to develop advanced first-class related drilling equipment is necessary, so as to improve the power industry punch into miniature pile operation automation level and efficiency, realize the equipment localization, To meet the requirements of high efficiency in the construction of power engineering, reduce the safety risk of construction personnel, greatly reduce the cost of the project, prevent soil erosion and protect the environment. The control system designed in this paper is to complete the power industry special multi-functional drilling equipment, to achieve the construction of drilling equipment used to drill into miniature piles, mainly through the open CAN bus control, and produced by Jiangxi Dongrui Machinery Co., Ltd., to achieve the control of high real-time and intelligence.
Research of Sponge City Landscape Planning Based on Landscape Security Pattern
Authors: Shi Yu , Li Jun , Shi Tiemao , Xiu Daixi , Chu Yaqi
Published: August 2021

With the rapid urbanization and increasing urban impervious areas in China, the construction of the sponge city has gradually turned to the urban ecological space planning. Based on the theory of landscape safety pattern, the construction of sponge city should improve the rainwater management system from multiple scales and enhance the functional of urban landscape space. Firstly, the paper describes the ecological basement elements of the sponge urban landscape safety pattern, as well as the steps and technical methods of sponge urban landscape planning. Secondly, taking Shenfu New City as an example to elaborate the method of sponge urban landscape planning from multiple scales, the landscape ecological planning. The approach is introduced into the theoretical method of sponge city construction, and the feasibility of the study is illustrated by verificationof model, which provides new ideas and methods for sponge urban landscape planning.
Research on Classification and Hierarchical Management of Industrial Data Based on Entropy Method-TOPSIS Theory
Authors: Xu Jian , Qiang Ren , Zhou Ziqi , Luo Wei , Yuan Qinglin , Cai He
Published: August 2021

In the era of accelerating digitalization, digital infrastructure is one of the most popular topics today, and digital infrastructure dominated by the industrial sector has become the vanguard of development. In response to this, industrial companies and platform companies attach importance to industrial data. Increasingly, the classification and hierarchical management of industrial data has become an indispensable procedure for the effective processing and safety management of industrial data. This article classifies the vertical data of each domain from the general process of industrial production, namely R&D, production, operation and maintenance, management and other classification domains, and according to the industrial production, self-economic benefits and social benefits brought by industrial data. Potential impact on industrial data can be classified by horizontal data classification of primary data, secondary data, and tertiary data from data features such as data importance, tamper-proof, and anti-corruption, and use entropy weight method to calculate the weight of each indicator feature and evaluate the data importance, and finally apply TOPSIS theory for data grading.
Convergence application of handle platform for industrial data classification and grading and 5G technology
Authors: Liu Jinquan , Huang Ying , Qiang Ren , Cai He , Yuan Qinglin
Published: August 2021

With the continuous upgrading of the national industrial Internet, the traditional application-centric data organization method has gradually been unable to meet the requirements of large-scale interoperability and sharing of industrial data and data security in the era of big data. With the help of the classification and grading of industrial data, cross-regional, cross-departmental, and cross-domain information query and sharing can be realized at the macro level; at the micro level, product information can be traced through identification analysis, and information can be achieved safely and controllably through its internal security management mechanism. In the field of industrial Internet, network equipment is usually required to have a large-scale connection expansion capability; in some fields such as telemedicine and industrial control, it requires the communication process to have low latency and high reliability capabilities; and in some areas such as autonomous driving and ultra-high-definition live transmission, the communication process requires the ability to transmit large-bandwidth data. Compared with 4G technology, 5G communication technology, its massive machine communication capabilities and high reliability and low latency characteristics make it a reliable technical guarantee for the Internet of Everything at this stage. Therefore, it is urgent to realize the integration of 5G technology and industrial data classification and grading technology. Based on the in-depth interpretation of the characteristics of 5G technology and the detailed description of the handle system, this paper proposes a method of fusion of the handle platform and 5G technology, laying the cornerstone for the effective implementation of industrial data classification and grading.
Architecture design of wireless access system in power grid application scenario based on 5th Generation Mobile Communication Technology small base station
Authors: Bai Jie , Xie Shimulin , Zhang Xiang , Fang Shuang , Li Zhirong , Nian Weifan
Published: August 2021

The use of digital radio station and GPRS public network is affected by false data, which leads to the small coverage radius of access network and the throughput is inconsistent with the ideal situation. In order to solve these problems, the architecture design of wireless access system in the application scenario of power grid based on 5G small base station is proposed. FPGA channel encoding and decoding is adopted to design base band processing unit, which is helpful to deploy the platform with other network virtualization functions. Using switch, we can get useful information through monitoring module, scheduling module and information description module. The abnormal data are detected by using RF processing unit. In view of the situation of the data produced by the switch having state mutation, the reliability factor is introduced to monitor the false data in real time. The operation status of open architecture wireless access network is analyzed, and the application scenario of power grid is determined. Thus, the non-interference wireless access network is deployed. The experimental results show that the coverage radius of the architecture is 15cm, the static scene throughput range is 26-33KB, and the dynamic scene throughput range is 45-59KB, which has good access effect.
Research on Power Flow Calculation of Distribution Network with Distributed Power Based on Optimal Multiplier
Authors: Zhang Xiaopeng
Published: August 2021

With a large number of distributed power connected to the grid, there are many new node types in the distribution network. The traditional power flow calculation method cannot be applied to the distribution network with multiple distributed generations. Three kinds of distributed power grid model such as synchronous machine, asynchronous machine and inverter are established, according to the characteristics of different distributed power. The P0 node model is proposed for nodes with reactive power output as active power output and terminal voltage function. To improve the condition number of Jacobian matrix included P0 node model, the optimal multiplier is introduced to correct Jacobian matrix. Through the simulation of a variety of operation mode in certain district grid, the results show that the introduction of distributed generation has effects on the process of power flow calculation. The optimal multiplier can effectively improve the ill-posed problem of power flow caused by P0 node model, and increase the fast convergence speed of power flow calculation.
Error Analysis of Directional Coupler for Lower Hybrid Wave Power Measurement
Authors: Ma Wendong , Zhao Lianmin , Wang Mao , Ding Bojiang , Liu Fukun , Shan Jiafang
Published: August 2021

As one of the important parameters for lower hybrid wave (LHW) systems with 24 4.6 GHz/250 kW continuous-wave klystron amplifiers, the microwave power needs to be accurately acquired in real time for the experimental advanced superconducting tokamak (EAST) experiment. A new 4.6 GHz/250 kW directional coupler was developed for power measurements in an LHW system. The design principle and characteristic parameters of the coupler are discussed based on transmission line theory and the system requirements. The calculations and simulations indicate that the measurement error of the incident power is less than 5% when the isolation is greater than 25 dB. The directional coupler was also applied to the 4.6 GHz LHW system. The results show that the device can meet the requirements of LHW system operations. Comprehensive Research Facility for Fusion Technology(CRAFT)is being built. This work is beneficial to the design of power measurement system for LHW system.
Research on system software reliability based on service component architecture
Authors: WenQinq Li , Chenguan Xu , Yao Rao , Liangliang Zhu , Yongxian Yi
Published: August 2021

Software reliability is an important index to evaluate system quality. Aiming at the web configuration system of service component architecture, this paper proposes a service process-oriented software reliability model, which analyses the service reliability from the whole service life cycle, and finally gives the comprehensive reliability calculation method of software. Using the method proposed in this paper, the service composition can be optimized in the system design phase, and the reliability of the whole software system can be improved.
Analysis of Port Container Sea-Rail Intermodal Transportation System
Authors: Chen Haizhou , Zhang Yi
Published: August 2021

With the continuous deepening of the China-Europe freight train, the interconnection of countries and regions along the “Belt and Road” has been accelerated, and railway transportation has become an important carrier and transportation backbone linking China-Europe road logistics, effectively promoting the new development of the geo-economics of China and Europe. The container sea-rail intermodal transportation system has developed rapidly. Based on the composition of the existing port container sea-rail intermodal transportation system, this paper use system analysis to describe its various subsystems, and analyzes the port container sea-rail intermodal transportation system composed of the external structure and the internal structure.
Partition Optimal Band Selection Method for Hyperspectral Image
Authors: Pan Yuetao , Xing Shishuai , Liu Danfeng
Published: August 2021

Hyperspectral data has the characteristics of large number of bands, narrow band width and large amount of data, which brings great difficulties to the further interpretation of the image. The partition optimal band selection(POBS) is proposed to select a certain number of bands according to the actual needs. The weight calculation formula of the band includes three parameters: standard deviation, information entropy and correlation coefficient, so it can be guaranteed that the selected band subset contains a large amount of information and low correlation. In addition, the whole band space is evenly divided into several band subspaces, and then the band with the largest weight is selected in each band subspace. The bands can be more dispersed through the uniform partition of the band space. It can ensure that the bands in the band subset are more representative. The classification test is carried out on two hyperspectral datasets by KNN method, and the classification results show that POBS selects a large amount of band information, and the divisibility of the ground object is good.
A Non-intrusive Load monitoring Algorithm Based on Seq2point
Authors: Zhang Zhen , Ma Junqiang , Li Menghou , Li Haofang
Published: August 2021

Aiming at the problems of long training time and poor decomposition performance of neural network in the application of Sequence-to-point deep learning method in the field of non-intrusive load monitoring, a non-intrusive load monitoring optimization model based on Seq2point was proposed. In this model, the training speed of the neural network is optimized by the forward gated recurrent unite, and the decomposition performance is improved by the median filter and the standardized data pre-treatment method. Experimental results on public data set AMPDS2 show that this algorithm can effectively improve the accuracy of load decomposition and shorten the training time of network.
Development and application of infrared imaging check and evaluation system
Authors: Fucu Huang , Dongpeng Sui , Ye Zhao , Chang Liu , Zhinan Guo , Yanan Wang
Published: August 2021

According to the parameters of various kinds of infrared imaging equipment was performance full automatic detection and evaluation of research in this paper. The evaluation system will to accuracy, slit function (SRF), minimum resolvable temperature difference (MRTD), minimum detectable temperature difference (MDTD) and noise equivalent temperature difference (NETD) and other parameters as the basis, the establishment of infrared imaging evaluation system database, to convergence of digital interface to realize automatic detection and evaluation of infrared imaging device, solving the technical problem of performance evaluation of grid infrared imaging equipment, ensure the accuracy of condition based maintenance for power transmission system.
Consumer-Transformer Relationship Identification Based on Two-scale similarity and SC Algorithm
Authors: Lai Xuanping , Liu Siyangjie , Cao Min , Nie Yongjie , Zhao Tengfei
Published: August 2021

In view of the chaotic relationship between user-transformer in the current low-voltage stations, manual identification consumes a long time period, low identification accuracy, and high input system costs. This paper proposes a method for identifying household change relations in low-voltage stations based on dual-scale similarity and spectral clustering algorithm. First, according to the different similarities of user voltage curves in different stations, consider Euclidean distance and cosine distance to establish a two-scale spectral clustering similarity matrix from the distance and shape of the voltage curve. Then based on the two-scale similarity matrix, perform spectral clustering on the voltage curves of the same station area to complete the clustering of users in the same station area. Examples show that the method proposed in the article has high recognition accuracy, low input cost, and has good application effects.
SSD Model Selection Method Based on Machine Learning Algorithm
Authors: Yang Bing , Yang Hong , Zhang Chengmei , Wang Yajie , Hao Miao
Published: August 2021

The chemical pollutants produced by human activities often lead to the risk of ecological species diversity destruction by affecting the physiological activities of organisms in nature. Ecological risk assessment (ERA) can be used to evaluate the degree of adverse effects of external factors on the ecological environment, provide the basis for taking effective ecological protection measures and formulating reasonable environmental policies, and also an important means to understand the possible adverse effects of ecological health and pollutants on the ecological environment. The species sensitivity distribution (SSD) method is a widely used evaluation method, and its core step is to select the appropriate species toxicity data for curve fitting. In this paper, the basic concept of ecological risk assessment is briefly introduced, and the basic principle and implementation steps of species sensitivity distribution method are described in detail. Aiming at the problem of SSD model selection in water environment, machine learning algorithm is introduced, and corresponding neural network is constructed. Taking the root mean square (RMSE) and sum of squared error (SSE) were as the index, the optimal SSD model suitable for water environment is determined, The SSD model selection method based on machine learning algorithm is obtained. At last, the application of machine learning algorithm in SSD model selection is prospected, and the shortcomings of the existing research and the future research direction are pointed out.
Dam Deformation Prediction Based on Modified Grey Wolf Optimization Algorithm and Support Vector Machine
Authors: Mingjun Li , junxing Wang , Jiangyang Pan , Cheng Peng , Songzhang Li
Published: August 2021

The establishing of an accurate and reliable dam deformation prediction model is an important content of dam safety evaluation. To this end, a dam deformation prediction method based on support vector machine (SVM) with optimal parameters selected by improved grey wolf optimization algorithm (IGWO) is proposed by introducing the crossover and mutation operators of the differential evolution algorithm into the GWO algorithm. The initial population is enriched by the differential evolution algorithm, an IGWO is proposed, and the penalty factor and kernel function of SVM are optimized by the IGWO algorithm, then a dam deformation prediction model based on the SVM-IGWO algorithm is established. The results will be compared with those of the SVM and SVM-GWO models through the measured data of Xiluodu super high arch dam. The research shows that the proposed SVM-IGWO model can effectively improve the accuracy of dam deformation prediction.
Design of analog electromagnetic launcher based on STM32
Authors: Zhang Xu , Shao Huazhuang , Zhang Guoyun , Zheng Zimeng
Published: August 2021

In order to improve the accuracy of the electromagnetic launching system, this paper designs a simulated electromagnetic launching device based on STM32 MCU, which takes the steering gear pylons as the motion system, the solenoid coil as the launching system, and the capacitor as the energy storage element. After collecting data, the microcontroller controls the deflection of the pylon steering gear to adjust the launching Angle or the control transformer to adjust the launching voltage, and then controls the charging or discharging of the capacitor to provide energy for the launching device to launch the projectile. Two modes, automatic and manual, are designed. The former takes OpenMV as the target identification and ranging device, and transmits data to SCM through serial communication. The latter can be displayed on the OLED screen by entering the target position through a manual key. The launcher simulates the real electromagnetic launcher and realizes the automatic and accurate launch of the projectile.
Research on a Costmap that can Change the Turning Path of Mobile Robot
Authors: Wen Hao , Yang Jiamin
Published: August 2021

In the field of mobile robots based on ROS (Robot Operating System) platforms, A*, Dijkstra, and BFS (Breadth First Search) are widely used to obtain the shortest path. This approach, however, often results in paths that are close to the obstacles located in corners. In practical applications, cumulative positioning errors in the navigation process are produced by aging components, skidding wheels and so on. In addition, the local path planning of the robot does not completely follow the global path planning, so the robot may easily cross the inflation of the costmap and collide with obstacles when turning. This paper proposes a method to change the turning path of the robot by adding virtual obstacles to the obstacles in map corners. A global path is generated by the path planning algorithm along the virtual obstacle so that the robot can travel along a smooth track that is far from the obstacle when turning. The proposed algorithm ensures that the robot remains far from physical obstacles and avoids collisions.
Research ON Satellites Fault Diagnosis Method Based On Artificial Intelligence
Authors: Song Kezhen , Zhao Chen , Liu Jie , Zhang Hao , Li Zhiyu
Published: August 2021

In order to enable satellites to use fault diagnosis, fault prediction, failure prediction and other data models to determine the health status of satellites autonomously, According to the statistics of satellite orbit faults, this paper gives an artificial intelligence-based satellite fault diagnosis method and compares the advantages and disadvantages of three different satellite fault diagnosis methods to ensure that when a satellite fault occurs, it can implement autonomous reconfiguration, redundancy switching, autonomous mode switching or downgrade use when its own conditions allow The study ensures that when a satellite failure occurs, it can implement autonomous reconfiguration, redundancy switching, autonomous mode switching or downgrade use when its own conditions allow, ensuring the safety of satellite operation in orbit. Through the analysis of the application of typical fault diagnosis methods for a satellite control subsystem, it can be seen that artificial intelligence-based satellite fault diagnosis methods have gradually replaced traditional methods and become the core key technology for the future application of autonomous satellite health management system in orbit, which is also one of the core functions that future software-defined satellites should have.
Design and Implementation of a New-energy Self-purification Device for Domestic Sewage
Authors: Jie Shen , haichen Yu
Published: August 2021

The self-purification MBR system for domestic sewage is commonly used in living areas, scenic spots, and industrial sites, for remote areas and other regions where energy supply is inconvenient. The new energy system provides energy to the sewage treatment system, and achieves remote monitoring, which is of substantial significance for saving energy to reduce pollution. In this paper, three modes of power generation, namely, photovoltaic, wind and temperature difference, are employed for domestic sewage purification systems, and a reasonable energy supply and energy control strategy is proposed according to the conventional water treatment; and then a new-energy self-purification system for domestic sewage is designed and experimented with supplying and consuming quantity, tracking the system-consumed energy and water quality data for one year, so as to present that the equipment has good application prospects.
Real-Time Safety Monitoring of Charging Pile Based on 5G and Cloud Computing
Authors: Xuefei Chang , Rongqing Yi , Chao Zhou , Lizhuo Liu
Published: August 2021

How to ensure the safety of charging pile including the protection of people, electric vehicles and batteries, has become the focus of social attention. This paper proposes a real-time safety monitoring scheme for the charging process based on 5G low latency and massively parallel cloud processing. The scheme is a second line of big data safety to ensure the safety of charging process. By accessing massive Internet of Things data in real time, it calculates in real time in the cloud to predict accidents, and gives early warning to the control system of the charging pile and the on-site management personnel of the charging station in time, so that they can timely intervene and control in advance, so as to avoid and reduce the occurrence of accidents.
Design of sliding mode adaptive DC-DC converter based on function approximation
Authors: Zhu Jiaxiong , Qiang JIANG , Feng CHANG , He Duyi
Published: August 2021

The output power and voltage of solar photovoltaic power generation are easily affected by climate and load changes, so it is necessary to use DC-DC converter at its output to provide stable output voltage. DC-DC converter is a time-varying nonlinear system. When the output load of the converter varies widely, the output waveform distortion and dynamic response speed of the DC-DC converter will slow down. The essence of sliding mode control is a kind of switch control method, and the DC-DC converter needs the switch of switch tube to realize voltage regulation. In this paper, a sliding mode adaptive control strategy based on orthogonal polynomial function approximation is proposed, and Laguerre polynomial is selected as the basis function of DC-DC transformation system. The output voltage error and its first derivative of the system are taken as the input of the network, and the output of the network is taken as the approximation term of the system uncertainty, and the network weight is updated according to the adaptive law. Through simulation and comparison, the adaptive controller designed in this paper is superior to the ordinary sliding mode controller in reducing chattering, adaptive load change and reducing the influence of system interference.
Security Management for Apps of Intelligent Substation Based on Blockchain Technology
Authors: liang Wang , chengwei Zhang , hanyun Chen , yangsen Li , yanguo Wang , chao Sun , liqing Ge , hua Tian , jingyu Teng , jianfeng Wang
Published: August 2021

There are many kinds of professional apps in station control system of intelligent substation. In this open and shared ecosystem, there are great security risks in app authorized installation, resource control, access control, and border protection. In this paper, a new approach of app security management based on blockchain technology has been proposed. By using the mechanism of tamper free and traceable "ledger" based on cryptography, the objective and traceable supervision of app's life cycle can be implemented through customization of blockchain's ledger. This paper provides a new solution for app security management of station control system in intelligent substation.
Research on Influencing Factors of Magnetic Shielding Effectiveness of Finite Length Cylinder
Authors: Wang Yang , Jiao Chongqing , Du Rongtian , Yang Yong , Wang Jialong
Published: August 2021

The article mainly studies the shielding effectiveness (SE) inside a cylinder with a finite-length metal shell. Since the SE inside an infinite-length cylinder is equal everywhere, this article focuses on exploring the SE of a finite-length metal shell cylinder. The article simulates the influence of the cylindrical shape factor (the ratio of the height to the inner diameter of the cylinder) on the SE at different frequencies; the influence of the frequency of the external magnetic field on the SE of the cylinder; the change of the SE on the central axis of the cylinder; the change in the SE on the central axis of the cylinder when a small hole is opened at the centre of the bottom of the cylinder; the change in the SE on the central axis of the cylinder when small holes are opened at the centre of the upper and lower surfaces of the cylinder.
Research on a new type of high-efficiency composite polymer odor bird repellent for overhead transmission lines
Authors: Xiaopo MAO , Guangyuan ZHOU , Xueming ZHOU , Chaohui LI
Published: August 2021

Aiming at the special needs of transmission lines for repelling birds, this article conducted stress experiments on various bird repellent spices, selected and compounded highly effective bird repellent spices; PVDF particles were used as the main carrier material, and dry-wet phase conversion technology was adopted. Supplemented by various functional agents, we have researched and developed a new type of high-efficiency solid bird repellent with slow release, weather resistance and long-term effect. The real-time monitoring results show that the bird repellent has a strong odor and has an obvious repellent effect on birds. It can prevent the birds from approaching the iron tower containing the bird repellent or even returning to the nest. It is environmentally friendly, efficient and long-lasting, easy to install and use, good weather resistance, etc., superior to the existing similar bird repellent products used in the power system, and meets the bird repelling requirements of the power system.