scholarly journals Research on Security Access of Power Internet of Things Terminals Based on Edge Computing Technology

2021 ◽  
Vol 2108 (1) ◽  
pp. 012014
Author(s):  
Yong Tang ◽  
Linghao Zhang ◽  
Juling Zhang ◽  
Siyu Xiang ◽  
He Cai
Keyword(s):  
Internet Of Things ◽  
Abnormal Behavior ◽  
Detection Methods ◽  
Communication Security ◽  
Stable Operation ◽  
Computing Technology ◽  
Security Authentication ◽  
Abnormal Behavior Detection ◽  
And Control ◽  
Terminal Equipment

Abstract In view of the current lack of unified security authentication and control for the power Internet of Things terminal equipment, at the perception level of the power Internet of Things, the perception layer terminal access control, front-end authentication technology realization and terminal equipment abnormal behavior detection methods are proposed. This method enhances the communication security between power equipment and edge nodes, and ensures the safe and stable operation of the power Internet of Things.

scholarly journals A Novel Detection Framework for Detecting Abnormal Human Behavior

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Chengfei Wu ◽  
Zixuan Cheng
Keyword(s):  
Human Behavior ◽  
Public Safety ◽  
Space Time ◽  
Abnormal Behavior ◽  
Detection Methods ◽  
Time Block ◽  
Safety Issues ◽  
Behavior Detection ◽  
Abnormal Behavior Detection ◽  
Abnormal Behaviors

Public safety issues have always been the focus of widespread concern of people from all walks of life. With the development of video detection technology, the detection of abnormal human behavior in videos has become the key to preventing public safety issues. Particularly, in student groups, the detection of abnormal human behavior is very important. Most existing abnormal human behavior detection algorithms are aimed at outdoor activity detection, and the indoor detection effects of these algorithms are not ideal. Students spend most of their time indoors, and modern classrooms are mostly equipped with monitoring equipment. This study focuses on the detection of abnormal behaviors of indoor humans and uses a new abnormal behavior detection framework to realize the detection of abnormal behaviors of indoor personnel. First, a background modeling method based on a Gaussian mixture model is used to segment the background image of each image frame in the video. Second, block processing is performed on the image after segmenting the background to obtain the space-time block of each frame of the image, and this block is used as the basic representation of the detection object. Third, the foreground image features of each space-time block are extracted. Fourth, fuzzy C-means clustering (FCM) is used to detect outliers in the data sample. The contribution of this paper is (1) the use of an abnormal human behavior detection framework that is effective indoors. Compared with the existing abnormal human behavior detection methods, the detection framework in this paper has a little difference in terms of its outdoor detection effects. (2) Compared with other detection methods, the detection framework used in this paper has a better detection effect for abnormal human behavior indoors, and the detection performance is greatly improved. (3) The detection framework used in this paper is easy to implement and has low time complexity. Through the experimental results obtained on public and manually created data sets, it can be demonstrated that the performance of the detection framework used in this paper is similar to those of the compared methods in outdoor detection scenarios. It has a strong advantage in terms of indoor detection. In summary, the proposed detection framework has a good practical application value.

scholarly journals Abnormal Access Behavior Detection of Ideological and Political MOOCs in Colleges and Universities

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Ni Hong ◽  
Xuefeng Wang ◽  
Zhonghua Wang
Keyword(s):  
Moral Education ◽  
Access Network ◽  
Colleges And Universities ◽  
Abnormal Behavior ◽  
Detection Methods ◽  
Network Behavior ◽  
Behavior Detection ◽  
Detection Model ◽  
Abnormal Behavior Detection ◽  
Effectiveness And Efficiency

In many colleges and universities, MOOCs have been applied in many courses, including ideological and political course, which is very important for college students’ ideological and moral education. Ideological and political MOOCs break the limitations of time and space, and students can conveniently and quickly learn ideological and political courses through the network. However, due to the openness of MOOCs, there may be some abnormal access behaviors, affecting the normal process of MOOCs. Therefore, in this paper, we propose a detection method of abnormal access behavior of ideological and political MOOCs in colleges and universities. Based on deep learning, the network behavior detection model is established to distinguish whether the network behavior is normal, so as to detect the abnormal access network behavior. In order to prove the effectiveness and efficiency of the proposed algorithm, the algorithm is compared with the other two network abnormal behavior detection methods, and the results prove that the proposed method can effectively detect the abnormal access behavior in ideological and political MOOCs.

scholarly journals Automatic Plants Watering System for Small Garden

2021 ◽  
Vol 15 (02) ◽  
pp. 200
Author(s):  
Maria Beata Inka Astutiningtyas ◽  
Monika Margi Nugraheni ◽  
Suyoto Suyoto
Keyword(s):  
Internet Of Things ◽  
Rapid Development ◽  
The Internet ◽  
Electronic Control ◽  
Computing Technology ◽  
New Era ◽  
Processing Data ◽  
The World ◽  
And Control ◽  
The Internet Of Things

Background: Automation is starting to dominate the world today. We are enter-ing a new era of computing technology, the Internet of Things (IoT), which is experiencing rapid development. IoT is a worldwide neural network in the cloud that connects a mixture of things, aiming to maximise the benefits of internet con-nectivity in transferring and processing data. Using IoT, one can monitor and control a device remotely with a computer or smartphone. IoT can apply in vari-ous fields, one of which is the smart garden. Objective: This research aims to design an automatic plant's watering system used to small gardens in houses. Smart Garden is an electronic control and garden monitoring system for the pro-cess of watering plants so that it can help people care for plants. Method: This paper presents a design of the Internet of Things for small gardens inside houses using Wireless networks and sensors.  In automatic watering plants, information about soil moisture needed for plants. Sensors are devices used for smart agricul-ture. Arduino Uno will control all system operations as monitoring the plant wa-tering system. Result: The result of this paper is a Plants Watering System De-sign for Small Garden at Homes.

Detecting Abnormal Behavior of an IoT Device in the Network Based on a Traffic Model

Telecom IT ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 50-55
Author(s):  
D. Saharov ◽  
D. Kozlov
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Mobile Networks ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Abnormal Behavior ◽  
Traffic Model ◽  
The Internet ◽  
Wide Spread ◽  
The Internet Of Things

The article deals with the СoAP Protocol that regulates the transmission and reception of information traf-fic by terminal devices in IoT networks. The article describes a model for detecting abnormal traffic in 5G/IoT networks using machine learning algorithms, as well as the main methods for solving this prob-lem. The relevance of the article is due to the wide spread of the Internet of things and the upcoming update of mobile networks to the 5g generation.

scholarly journals A Conceptual Model of Measurement Uncertainty in IoT Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1827
Author(s):  
Piotr Cofta ◽  
Kostas Karatzas ◽  
Cezary Orłowski
Keyword(s):  
Sensor Networks ◽  
Internet Of Things ◽  
Reference Model ◽  
Primary Objective ◽  
Theory And Practice ◽  
Fitness For Purpose ◽  
Industrial Practice ◽  
Control Networks ◽  
And Control ◽  
Measurement And Control

The growing popularity of inexpensive IoT (Internet of Things) sensor networks makes their uncertainty an important aspect of their adoption. The uncertainty determines their fitness for purpose, their perceived quality and the usefulness of information they provide. Nevertheless, neither the theory nor the industrial practice of uncertainty offer a coherent answer on how to address uncertainty of networks of this type and their components. The primary objective of this paper is to facilitate the discussion of what progress should be made regarding the theory and the practice of uncertainty of IoT sensor networks to satisfy current needs. This paper provides a structured overview of uncertainty, specifically focusing on IoT sensor networks. It positions IoT sensor networks as contrasted with professional measurement and control networks and presents their conceptual sociotechnical reference model. The reference model advises on the taxonomy of uncertainty proposed in this paper that demonstrates semantic differences between various views on uncertainty. This model also allows for identifying key challenges that should be addressed to improve the theory and practice of uncertainty in IoT sensor networks.

scholarly journals Examination of Abnormal Behavior Detection Based on Improved YOLOv3

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 197
Author(s):  
Meng-ting Fang ◽  
Zhong-ju Chen ◽  
Krzysztof Przystupa ◽  
Tao Li ◽  
Michal Majka ◽  
...  
Keyword(s):  
Detection Method ◽  
Abnormal Behavior ◽  
Detection Accuracy ◽  
Average Precision ◽  
Research Results ◽  
Behavior Detection ◽  
Abnormal Behavior Detection ◽  
The Mean ◽  
Examination Room ◽  
Detection Speed

Examination is a way to select talents, and a perfect invigilation strategy can improve the fairness of the examination. To realize the automatic detection of abnormal behavior in the examination room, the method based on the improved YOLOv3 (The third version of the You Only Look Once algorithm) algorithm is proposed. The YOLOv3 algorithm is improved by using the K-Means algorithm, GIoUloss, focal loss, and Darknet32. In addition, the frame-alternate dual-thread method is used to optimize the detection process. The research results show that the improved YOLOv3 algorithm can improve both the detection accuracy and detection speed. The frame-alternate dual-thread method can greatly increase the detection speed. The mean Average Precision (mAP) of the improved YOLOv3 algorithm on the test set reached 88.53%, and the detection speed reached 42 Frames Per Second (FPS) in the frame-alternate dual-thread detection method. The research results provide a certain reference for automated invigilation.

Automated sequencing batch bioreactor under extreme peaks of 4-chlorophenol

2003 ◽  
Vol 47 (10) ◽  
pp. 175-181 ◽  
Author(s):  
G. Buitrón ◽  
M.-E. Schoeb ◽  
J. Moreno
Keyword(s):  
Dissolved Oxygen ◽  
Metabolic Activity ◽  
Automated System ◽  
Stable Operation ◽  
High Concentration ◽  
Batch Bioreactor ◽  
On Line ◽  
The Moment ◽  
And Control ◽  
System Operating

The operation of a sequencing batch bioreactor is evaluated when high concentration peaks of a toxic compound (4-chlorophenol, 4CP) are introduced into the reactor. A control strategy based on the dissolved oxygen concentration, measured on line, is utilized. To detect the end of the reaction period, the automated system search for the moment when the dissolved oxygen has passed by a minimum, as a consequence of the metabolic activity of the microorganisms and right after to a maximum due to the saturation of the water (similar to the self-cycling fermentation, SCF, strategy). The dissolved oxygen signal was sent to a personal computer via data acquisition and control using MATLAB and the SIMULINK package. The system operating under the automated strategy presented a stable operation when the acclimated microorganisms (to an initial concentration of 350 mg 4CP/L), were exposed to a punctual concentration peaks of 600 mg 4CP/L. The 4CP concentrations peaks superior or equals to 1,050 mg/L only disturbed the system from a short to a medium term (one month). The 1,400 mg/L peak caused a shutdown in the metabolic activity of the microorganisms that led to the reactor failure. The biomass acclimated with the SCF strategy can partially support the variations of the toxic influent since, at the moment in which the influent become inhibitory, there is a failure of the system.

scholarly journals Genetic and Pathogenic Characterization of QX(GI-19)-Recombinant Infectious Bronchitis Viruses in South Korea

Viruses ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1163
Author(s):  
So-Youn Youn ◽  
Ji-Youn Lee ◽  
You-Chan Bae ◽  
Yong-Kuk Kwon ◽  
Hye-Ryoung Kim
Keyword(s):  
Phylogenetic Analysis ◽  
Disease Prevention ◽  
South Korea ◽  
Prevention And Control ◽  
Genetic Recombination ◽  
Detection Methods ◽  
Infectious Bronchitis ◽  
Reference Sequences ◽  
And Control

Infectious bronchitis viruses (IBVs) are evolving continuously via genetic drift and genetic recombination, making disease prevention and control difficult. In this study, we undertook genetic and pathogenic characterization of recombinant IBVs isolated from chickens in South Korea between 2003 and 2019. Phylogenetic analysis showed that 46 IBV isolates belonged to GI-19, which includes nephropathogenic IBVs. Ten isolates formed a new cluster, the genomic sequences of which were different from those of reference sequences. Recombination events in the S1 gene were identified, with putative parental strains identified as QX-like, KM91-like, and GI-15. Recombination detection methods identified three patterns (rGI-19-I, rGI-19-II, and rGI-19-III). To better understand the pathogenicity of recombinant IBVs, we compared the pathogenicity of GI-19 with that of the rGI-19s. The results suggest that rGI-19s may be more likely to cause trachea infections than GI-19, whereas rGI-19s were less pathogenic in the kidney. Additionally, the pathogenicity of rGI-19s varied according to the genotype of the major parent. These results indicate that genetic recombination between heterologous strains belonging to different genotypes has occurred, resulting in the emergence of new recombinant IBVs in South Korea.

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