bluuebunny/crossref_metadata_2025 huggingface dataset:
A Learning Design Analysis of the Pre-service Teachers’ Mathematics Pedagogical Content Knowledge
Authors: Fiangga S , Khabibah S , Amin S M , Ekawati R
Published: May 2021

A number of international assessments result in Indonesian students’ low achievement in mathematics. One substantial factor that affects to the inadequate achievement is the quality of the mathematics teachers. In 2007, teacher certification was launched by the government concerning the improvement program for teachers’ qualifications. Nevertheless, the program still struggling to foster the teachers’ quality. One fundamental aspect that constitutes to the teachers’ qualification is a Pedagogical Content Knowledge (PCK). This study aims to analyze the pre-service teacher PCK from the designed learning materials. This content analysis uses the learning materials which are the final product developed by the pre-service mathematics teachers. The framework from both Maher et.al. and Chick et.al. are used for analyzing the Clearly PCK categorization. Document analysis was used to obtain the data. The result suggests that pre-service teachers lack the ability to consider the students thinking and their misconception to develop effective teaching strategies.
Development of Learning Tools Using Remote IoT Labs with Blended Learning Method in The Department of Engineering Education
Authors: Purnamawati  , Mangesa R T , Ruslan  , Idhar
Published: May 2021

Learning is a process that involves educators, students, teaching materials, learning resources, learning media, and methods. The conventional method, whose main source of knowledge is only from educators, is considered to be less effective if it is the only source of knowledge transfer to students. The boring learning process, the media used in Digital Electronics courses is a simulator, the features presented in the simulator are less interesting. So it was appointed a research study on the development of the IoT Remote Lab learning tool with Blended Learning Digital Electronics Subjects in accordance with the Industrial Revolution era 4.0. This study aims to determine the stages and feasibility of developing the IoT Remote Lab learning tool with a valid, practical, and effective Blended Learning method in the Department of Electronics Education Faculty of Engineering Universitas Negeri Makassar (FT-UNM). The research method used by Research and Development (R&D) with the 4D development model (1), (2) planning, (3) development, and (4) distribution. The results of this study indicate that the Remote Lab IoT learning tool with the Blended Learning method has a very valid category to use. Got a response from students who said it was very practical in its use and the results of student test scores after implementation had increased. It was concluded that the data stated that the Remote Lab IoT learning device with the Blended Learning method that had been developed was declared valid/feasible to use as well as practical and efficient in its use.
Using HTML-Based Worksheet to Support Students in Active Mathematics Learning
Authors: Nasrullah  , Alimuddin  , Talib Ahmad
Published: May 2021

This developmental research aims to create an HTML-BW application. This technology is an alternative that serves to support teacher teaching activities and student learning. By utilizing this technology, the stimulus and response principles will work interactively. There are five students from SMA Unismuh Makassar getting engaged to be observed during the process of research. In terms of research instruments and data collection, this study applies three kinds of research instruments, such as HTML-Based worksheets, observation sheets, and response sheets. To support the interactive principle to be achieved, this application was built with due observance of guidelines, learning support, and values. Then so that it can be seen this research asks users to respond to directing user understanding, good learning media, media that supported learning activities, media that supported interests, and including modern media. The results of their opinion poll showed that this device was dominantly responded positively for the five things. Overall, it shows that HTML-BW is very supportive of active learning because it utilizes the stimulus-respond principle in its use. In other words, HTML-BW will direct teachers and students to engage in interactive activities.
Analysis of the Weakness to The Implementation of Online Learning During the Covid-19 Pandemic In the City of Surabaya, Indonesia
Authors: Raharjo  , Suparmi M
Published: May 2021

The aim this research to get an information of weakness/obstacles faced by the Education Office of Surabaya, in implementing online learning programs in Pandemic Covid 19 era. The data source is the result of stuffing questionnaire by Principal and Teachers of Elementary and Junior High School in the city of Surabaya, which consists of 37 782 teachers. Data obtained from the results of this study concluded that the implementation of the online learning is still not optimal and the need to increase both from the aspect of quantity and quality.
The development of integrated textbook cooperative learning model to improve learning outcomes and practice literation skills
Authors: Indana S , Susantini E , Bashri A
Published: May 2021

In facing the challenges of education in the 21st century, education is expected to lead students to learn cooperatively (learning to live together) by using literacy skills in order to solve problems. The purpose of this study is to produce an integrated textbook of cooperative learning models to practice literacy skills. The biology material used in this textbook is virus material in X grade. The development of this textbook implements the 4-D model development steps. Textbook implementation in class using the Posttest-Only Control Increased student learning outcomes data were obtained through daily test score, while the literacy skills data were obtained through the assignments score that practiced literacy skills. The results of implementation on classes that use integrated textbooks of cooperative learning models on virus topics show the Daily test score and assignments score are better when compared to the control class. The average value of student assignments in the class using teaching material is 76.88 while the value of students in the class without using teaching material integration of innovative learning models is 72.73. It can be concluded that the integrated textbook of cooperative learning models developed can improve student learning outcomes and literacy skills.
The Effectiveness of The Animation Video Learning Earth’s Layer Media to Improve Students’ Concept Understanding
Authors: Rosdiana L , Ulya R M
Published: May 2021

Science learning of the earth’s layers subject is not optimal. One of the factors causing the less optimal learning is the lack of media that support this subject. Students want a learning media that can motivate them to learn and also make it easier for them to understand the subject matter. This study aims to analyze the effectiveness of instructional video animation media on the earth’s layers subject in terms of the results of understanding the concepts of junior high school students. This study used a research design “One group pretest-posttest design”. The subjects of this study were 28 students of class VII-D SMP Negeri 3 Sidoarjo. The data collection technique used the test method. The conceptual understanding indicator used is to give examples, categorize, compare and explain. The results of this study are that the indicators give an example of obtaining an n-gain of 0.84 in the high category, the indicators categorizing obtaining an n-gain of 0.67 in the moderate category, the indicators compare getting an n-gain of 0.69 with the medium category and the indicator. explained that obtaining an n-gain of 0.68 in the medium category. The indicator compares getting the lowest posttest score and the indicator gives an example of getting the highest posttest score. So it can be concluded that the animated video media as a learning medium on earth layer material is very effective in increasing students’ understanding of concepts 0.69 in the medium category and the indicator explains that it gets an n-gain of 0.68 in the moderate category. The indicator compares getting the lowest posttest score and the indicator gives an example of getting the highest posttest score. So it can be concluded that the animated video media as a learning medium of earth’s layers subject is very effective in increasing students’ understanding of concepts 0.69 in the medium category and the indicator explains that it gets an n-gain of 0.68 in the moderate category. The indicator compares getting the lowest posttest score and the indicator gives an example of getting the highest posttest score. So it can be concluded that the animated video media as a learning medium of earth’s layers subject is very effective in increasing students’ understanding of concepts.
Characteristics and practicality of problem-based project learning to improve process and product performances of prospective physics teachers
Authors: Dwikoranto  , Munasir  , Setiani Rahyu , Suyidno  , Rohman A , Suprianto  , Zainudin
Published: May 2021

This study aims to describe the characteristics and practicality of a problem-based project learning model to improve process performance and product performance of prospective physics teachers. The problem-based project learning (PBPL) model uses the design of the Plomp. The trial subjects are the PBPL model and the Unesa laboratory physics course students. The trial used a one-group pretest-posttest design. The data were collected using theoretical and empirical study sheets, observation sheets for the implementation of the Lecture Program Unit (LPU) PBPL Model, an instrument for student learning material readability assessment sheets, an instrument for Student Activity Sheet (SAS) readability assessment sheets, an instrument for observing field constraints during learning process. The data were analyzed descriptively by calculating the percentage and proportion then looking at the category group. Data analysis used quantitative descriptive statistics. The results showed: 1) The characteristics of the developed model have: syntax consisting of 5 steps, supported by a solid theoretical foundation, specific learning environment, and specific learning objectives. 2) The model developed is included in the practical category in terms of: (a) The implementation of the model with implemented criteria; (b) Students are active in learning physics using the PBPL model; (c) small and insurmountable learning constraints. It is concluded that the PBPL model developed has certain characteristics and is practical to improve process performance and product performance of prospective teachers.
Google Classroom as a Distance Learning Tool during a Pandemic
Authors: Maman  , Baharun Hasan , Witarsa Ramadhan , Ainin Dewi Tumatul , Hodaili Zafrul , Mushorfan  , Wiranata Majid Afnani
Published: May 2021

This research was conducted to analyze the assessment strategy using Google Classroom at Nurul Jadid University, Paiton, Probolinggo, East Java, Indonesia. Learning through Google Classrooms is an alternative in the distance learning system during the Covid-19 pandemic. Data collection techniques are carried out through interviews, observation and documentation. The data analysis was carried out in a circular manner, starting from presenting the data, reducing data to drawing conclusions. It uses a case study type qualitative approach. The results of this study indicate that the Google Class room based assessment strategy is carried out through; assessment of student discipline, assessment of student performance / portfolio assessment, self-assessment and assessment through forms.
Intelligent defaulter Prediction using Data Science Process
Authors: Raviteja Chilagani , Santosh D Teja
Published: May 2021

The machine learning classifiers employed are not good enough to clearly classify the loan defaulters. In order to alleviate this problem, the data science process is adopted. During the machine learning phase, the random forest classifier with tuned hyperparameters is trained on the loan lending dataset for obtaining the bank loan defaulter model. This classifier is learned by using a smaller number of features as predictor variables obtained after the Exploratory Data Analysis phase. The ability of the model to correctly classify the unseen loan applicant’s data is evaluated in terms of the classifier accuracy and attending the false positives during the model diagnosis from the confusion matrix which unattended will prove fatal for the loan lending banks. The conclusion drawn from this analysis is that the performance metric of the classifier namely the classifier accuracy for Random Forest has outperformed the state-ofart statistical classifiers.
Review on Radiation Hardness Assurance by Design, Process and NextGen Devices
Authors: Rao Ch S V Maruthi , Chavan Ameet
Published: May 2021

Aggressive scaling, shrinking device size and exponential rise in device densityover the past two decades has contributed significantly in improving the power and performance metrics of Integrated Circuit (IC) technology. For specific applications like Space electronics, Implanted Biomedical, Sensor devices etc. drastic scaling has resulted in increasing the susceptibility of the nanoscale circuits to radiation effects. The objective of the paper is to provide an overview of the radiation hardening techniques at various levels of IC design and development, i.e. at Device Structure-Fabrication process (Radiation Hardening by Process) and Circuit and layout design (Radiation Hardening by Design), to mitigate the effects of radiation and improve reliability. The work also presents comparison of next generation device structure that utilize Carbon Nano Tubes (CNTs) and Semiconductor NanoWires (GAA-Si, GaAs, InN) against traditional MOSFET based device structures for operation under the radiation environment.
Patang Abhidhani - Convolution Neural Network based Butterfly Research Survey
Authors: Priya R L , Mate Yash , Tirodkar Gaurav , Somai Neelam , Patil Gayatri , Bhopale Nikhil
Published: May 2021

There has been a great loss of biodiversity worldwide and efforts have to be taken in order to restore it. To restore biodiversity, it’s crucial to know the cause for its decline and the role played by the insects in it. In India, butterflies are not given much importance in the conservation species. Since there are no names for butterflies in regional languages, the only access for comprehensive information for people’s awareness is really nothing. This paper aims at providing details of research done on different butterfly species, their existence once in different parts of the world, and technologies employed for solving the problems like identification of different species, distribution, restoration, and rejuvenation of diversity.
Classification of abnormalities in breast ultrasound images using ANN, FIS and ANFIS classifier: A comparison
Authors: Precious J Glory , Selvan Shirley , Avudaiammal R
Published: May 2021

When diagnosed early, cancer can be cured. The leading cause of death for women is breast cancer, the most common form of cancer in women. However, there are several ways to think about breast cancer, the US is widely used as it is not aggressive and painless. The appearance of dots on ultrasound images reduces image clarity, affecting image quality. Dots are drawn to accurately define distorted images. This is accompanied by the separation of the wound using a level setting method, after which the features of the lesions are removed. Only the most distinct traits are considered. Using the division of ANN, FIS and ANFIS, with a accuracy of 92.3 percent, 88 percent and 96 percent, the inconsistencies in the 41 images of Breast Ultrasound are identified and classified.
An Intelligent System for Waste Materials Segregation Using IoT and Deep Learning
Authors: Azhaguramyaa V R , Janet J , Narayanan Vijay Varshini Lakshmi , Sabari R S , Santhosh Kumar K
Published: May 2021

Disposing of Garbage is still a hurdle in today’s world and not many waste materials are disposed of correctly as per the requisite procedure. They are rather dumped in landfills and other sources like water bodies causing Pollution. The proposed system amalgamates the Internet of Things with Deep Learning and Image Processing which replaces the conventional waste management system with a smart segregation process. The proposed model consists of a Raspberry Pi camera to scan and detect the object. This detected object is then exported to the Deep learning Model as the input using the ‘Histogram of Oriented Gradients’ algorithm. This is invariable to the geometrical and photometrical changes of the object. The image classification is accomplished using the combination of Faster Regions with the CNN algorithm (RCNN) with contextual information and incremental learning which improves the detection performance under special and extreme conditions, accurately classifying the objects into bio and non-biodegradable waste. Using the Faster R-CNN algorithm, explicit information can be obtained from the different layers of the neural network of Faster R-CNN, thereby proving that the proposed system has a higher accuracy rate.
Convoluted fashion classifier prediction using Neural Networks
Authors: Kumar T Rajesh , Kishor S , Kishore R U , Kumar C Naveen
Published: May 2021

The conventional way of choosing a fashion catalogue has a lot of drawbacks and is yet to be resolved. Struck in a dilemma of what to choose and what not to, not wearing fashion items that are appropriate, and lured by terms that are misleading such as 50 percent off limited period offer, when in reality the prize was never higher in the first place. These are the flaws that go under the radar for humans, but that is not the case with machines. The proposed system when given an image of a fashion item, by the user makes use of a Convolution Neural Network to find the type of item, then suggest items based on occasion. The system also gives you the prize of that particular item over a period of time in various e commerce sites, and if there is any chance of drop in prize in the future. This eliminates the human part of errors while choosing a fashion catalogue and makes everything automated. The next advantage of our site is to resell our old clothes at best prize so our used clothes are not wasted and it can be used by others.
Yolov3 Supervised Machine Learning Framework for Real-Time Object Detection and Localization
Authors: Srithar S , Priyadharsini M , Margret Sharmila F , Rajan R
Published: May 2021

Nowadays to detect and classify the objects from the sequence of image frames various machine learning models are used. The performance of the object recognition model is purely depending on the number of trained images. The image processing and pattern extraction focus on object recognition, localization, and classification. To classify the objects from the external events or to detect multiply objects from an image the confident level of the trained weights should be maximum. The existing object detection methods look at the specific region to detect and classify the objects in an image. The proposed object detection system takes an improved YOLOv3 model for object classification. The improved model will look at the entire image to detect and recognize the objects. The YOLOv3 model uses the neural network on the image which split the image into a region and map the confidence probability. The proposed model will detect multiple objects by exploiting the contextual information using a single CNN. The model can process 45 frames in a second and it is suitable for object detection in real-time.
AI enabled smart surveillance system
Authors: Keerthana T , Kaviya K , Priya S Deepthi , Kumar A Suresh
Published: May 2021

The conventional household door locking system has lot of drawbacks and it is still yet not resolved. Most of the security systems so far in our markets includes video surveillance or vigilance system. In order to improve security level facial recognition and object detection technique using CNN algorithms can be used which is also provides remote proctoring facilities to owners. The proposed system detects the object and identifies the anomalous activity near the door by applying Convolutional Neural Network. Electric door lock solenoid is used to unlock the door. An ultrasonic sensor is utilized to measure the distance between a person and door through the facial recognition when it reaches a certain threshold value that has been kept to detect the person reaching the doors and it tries to capture the human image only if it is mismatched from database. When a stranger try to access the door an alert message might be triggered to registered mobile number and he proprietor would be able to control the door locking system and inspect the image of person which has been mailed.
Detect fake identities using improved Machine Learning Algorithm
Authors: Suganya R , Muthulakshmi S , Venmuhilan B , Varun Kumar K , Vignesh G
Published: May 2021

Nowadays, Social networks are controlling the world. Number of users using the social media platforms are continuously increasing every year. The main advantage of Online social media is anyone from anywhere in the world can connect to any users easily and able to communicate with them. This paved the way of malicious attacks like fake identities and spams. The Survey of Facebook and Twitter states that the actual users present in their platforms is lesser than the number of accounts created. It means there is an increase in fake profiles over the year. With these accounts, their creators can influence the users and distribute false information. They exploit the users in order to achieve their hateful aims, for example, creating hateful posts/tweets. The increasing number of fake accounts in social media platforms is becoming a major risk for the internet users. So, there is a need for identifying these fake identities. The existing machine learning algorithms are SVM linear, naïve bayes, random forest and Decision tree algorithm which are used to detect fake identities provides less accuracy and has become inefficient. We implement Ada-boosting algorithm with SVM to detect and identify bots. The proposed model is Ada-boost SVM classifier provides higher accuracy in detecting the fake accounts and show better results in the detection of identity bots.
Automated Railway gate control system using Arduino and Ultrasonic sensors
Authors: Ilampiray P , Deepak K , Santhosh M G Deepak , kishore S
Published: May 2021

In a country like India, which has a population of about 1.39 Billion depends on its Transportation for its daily living. Transportation plays a main role in India’s Economic Development and more than 35% of the lives of people in India depends on its transportation.

Railways is the most popular and highly used transportation in India. It is the most effective mode of transportation not only in India, but also all over the World. As like its usage it also leads to a high number of accidents. Like road accidents, there are more accidents happening at railway crossings due to the unmanned level crossings and the carelessness of the road users. According to the National Crime Records Bureau [NCRB], the rate of level crossing accidents is 20% up in 2019 than 2018. However there will be an increase in the percentage of accidents every year but the percentage in the last two years has increased drastically. There were 1788 level crossing accidents in 2019, which include 1762 deaths. “Necessity is the mother of Invention” as like this saying the is a simple system, which automatically closes the level crossing gates during the train’s arrival and then the gates are opened automatically when the train passes by. In our system we have used ultrasonic sensors for detecting the departure and arrival of the trains. As soon as the sensor senses the train’s arrival it sends a message to the Arduino which will switch on the buzzer, so that the road users will know that the train is nearer, and after that the servo motor which is attached to the gates will close them and it will be opened after the train passes by. And then the buzzer will also be turned off. This is automated, highly effective and cheap. Our system will eliminate the manpower used at the level crossings.
The Face Mask Detection Technology for Image Analysis in the Covid-19 Surveillance System
Authors: Hussain G K Jakir , Priya R , Rajarajeswari S , Prasanth P , Niyazuddeen N
Published: May 2021

Face mask recognition has been growing rapidly after corona insistent last years for its multiple uses in the areas of Law Enforcement Security purposes and other commercial uses Face appears spreading others to corona a novel approach to perform face new line detection and face mask recognition is proposed. The proposed system to classify face mask detection using COVID-19 precaution both in images and videos using convolution neural network. Extensive experimentation on the datasets and the performance evaluation of the proposed methods are exhibited. Further, we made a successful attempt to preserve inter and intra class variations of face mask detection using symbolic approach. We studied the different classifiers like Support Vector Machine and a Symbolic Classifier. The project is developed as a prototype to monitor temperature measurement and to detect mask for the people. The first method is performed using temperature sensor used to detect the present temperature of the body and automatically spray the sanitizer. In the second method, the work is designed to provide a safety system for the people in order to avoid COVID-19. We proposed continuous monitoring of the people conditions and store the people’s data in the server using the Deep learning concept. In order to investigate the performance the proposed method an extensive experimentation is conducted on 50 various Image dataset. We conducted experimentation under varying of training and testing percentage for 10 random trails. From the results we could observe that, the results obtained for symbolic approach is better than the conventional approach.
A Smart Helmet for the Mining Industry using LoRaWAN
Authors: Pradeepkumar G , Rahul S Sanjay , Sudharsanaa N , Suvetha S , Ponnusamy Dineshkumar
Published: May 2021

Air Quality becomes an important factor in mining areas where the health condition of the workers is prominently considered. The composition of many toxic gases under the mining area causes many fatalities that keep on increasing day by day. The Total Volatile Organic Compounds (TVOC) and Carbon-di-oxide creating a significant role in the health system of the workers. The unstable levels of these TVOC’s cause many health issues like nausea, emesis, fatigue, epitasis and dyspnoea. Exceeding the standard levels of these toxic compounds in the air causes many problems like severe breathing trouble and headache. In this proposed work, Smart Helmet has been developed for mining workers using LoRaWAN to overcome the above mentioned problems. This device helps to alert user on the air quality. When the air quality changes, the device inform the user, so that they can take necessary actions. The effectiveness and performance of this smart helmet using LoRaWAN can be analyzed with the help of experimental results.
Mixture Weibull probabilistic model in Wind Turbine Power Analysis
Authors: Indhumathy D , Narmatha D , Meenambika K
Published: May 2021

Wind energy is renewable and environment friendly. It is pollution free source of energy when compared with fossil fuels that pollute the lower layer of atmosphere. A new method depending on the parameters of three parameter mixture Weibull distribution is presented in this paper for calculating the output energy. Parameter estimation is much critical for the application of statistical model and is a challenging problem precisely for a Weibull distribution with more than two parameters. A study on estimation of parameters using maximum likelihood method is discussed. An expression to estimate the Capacity factor is formulated and the average power produced from a turbine is computed using the three parameter mixture Weibull distribution.
Intelligent Analysis for Drowsiness Alert using Conventional Neural Networks
Authors: Jeyabharathi D , Jeevanantham K , Kavinmukhil M , Mohamed Quanith hasan J B
Published: May 2021

When a job is assigned to drivers who travel through the roads and highways riding a CAB or NATIONAL PERMIT TRUCKS face the risk of tiredness especially during night travels and early morning. This project is to develop a driver drowsiness detection system by using Deep learning. It is known that a driver is under drowsiness influences by looking at the eyes for a moment of duration. Based on the previous research, there is none added tome counter that may exclude driver drowsiness from other activities of eyelid movement. The result can be accurate because histogram analysis analyzed the whole image upto a certain duration giving alarm with battery connection to be disabled for mentioned span of time. Therefore, he can start the vehicle only when the driver completes his rest time.
Ensemble Modeling on Job Scam Detection
Authors: Asmitha Shree R , Nirmala D , Sweatha S , Sneha S
Published: May 2021

The new advancement of online enrollment and occupation enlistment systems has made another media for web fraudsters. The improvement of the update of ongoing innovation has restricted a few organizations to move into electronic board structures. The benefits of such electronic edges are liberal. From one perspective, they are the most ideal approach to lead numerous organizations’ applicants, and afterward once more, the contenders while following a task will be more involved. Community with bogus motives research these structures for sensitive information to be optimized by techniques for fake counteractive action advancements. In this article we are utilizing open informational indexes and AI computations to arrange phony or real posts. The target of this audit is to fuse both content, put together information and meta-information with respect to the situations in the work. The informational collection can be utilized to make characterization models that can acclimate themselves with the deceptive arrangements of duties. Gathering information models utilizing request computations like Logistic Regression, K Nearest Neighbors and RandomForest.
Deep learning methods to analyse and detect the presence of COVID-19 using X-rays
Authors: Selvy P Tamije , Sai Kumar S K Lalith , Thirumalraj R , Vimal G
Published: May 2021

The COVID-19 was discovered in December 2019 in China’s Wuhan. and resulted in a major outbreak in several cities in China and spread globally, continuing to have a devastating influence on the world wide population’s health and well-being. The virus affects the respiratory system and it is transmitted through close contact of individuals. Efficient screening of infected patients is a crucial step in standing up to COVID-19. As the virus affects the respiratory system, images of chest X-rays are analyzed using deep learning techniques for early detection of COVID-19. The existing deep learning models identify COVID-19 with an accuracy of 79%. The proposed model focuses on detecting COVID-19 in an effective manner, the model includes the following phases: Preprocessing the image using data augmentation and infusing the trained model with different Convolutional Neural Network architectures. The proposed model uses Residual Neural Network architecture (ResNet-152v2), NASNetLarge, and Visual Geometry Group architecture (VGG16), a Convolutional Neural Network (CNN) which are faster when compared to the already existing systems and have an accuracy of 87.50%, 87.50% and 82% respectively.
Web Intelligence Based Flexi Vehicle Insurance Application
Authors: Selvy P Tamije , Akash S , Gobalakrishnasridhar T , Hariharan T
Published: May 2021

Computerization is currently a key element in everybody’s life – individuals, businesses, travel sectors, medical services and many more. As the world is pushing ahead, the web and innovation have been the implications that these areas use to contact individuals and get benefit from the brand or administration they advance. The private protection area is perceived as one of the quickest developing ventures. This quick development has powered unfathomable changes over the previous decade. These days, most valuable resource protective items exist, such as homes, vehicles, gems, life, etc. All insurance agencies are at the forefront in taking leading tasks, measures and numerical models in order to increase benefits by adapting customer claims. Customary techniques based only on human-in-the-loop models are extremely tedious and incorrect. In this paper we are establishing a safe and computerised protection framework that reduces human communication, ensures safeguards, alerts and illuminates hazardous customers, distinguishes misleading cases and reduces money-related unhappiness. The proposed Framework for Vehicle Protection Strategies is a single-page online application that aims to establish an entirely practical, free framework to monitor vehicle insurance records. It is designed to provide such insurance agencies with an online stage for exact preparation, coordinated information, production recovery and record capacity.
A survey on intrusion detection system and prerequisite demands in IoT networks
Authors: Aravamudhan Parthiban , Kanimozhi T
Published: May 2021

Over the past few years the development in the mobile industry and development of internet, network for all, 4G, 5G etc. enabled the ordinary people as well as the elite people to depend upon mobile networks for regular business developments, entertainment, medical and educational needs. Almost all areas of development depend on the so called improvement of the mobile network. As the advantages and flexibility increases, the consumers entering by new registration increase widely and service requirement of existing consumers increase massively. It is mandatory to provide high level of security and dual privacy protection to the users sharing the large set of information through the cloud. The massive crowd sensing is important for any kind of network security system to ensure the detection of any miscellaneous activity entering the network grid. The study is focused on gathering various literature evidences on demand for intrusion detection system, analyzing the pitfalls in current models and creating an idea that would be helpful for us to proceed further with the research on intrusion detection system implementations and innovating a novel methodology that improvise from the present system. The future enhancement and interpretations on solutions would be discussed too.
Survey on video compression techniques for efficient transmission
Authors: Sathiya Sivam B , Sumithra M G , Sreelatha P
Published: May 2021

Nowadays, due to the rapid growth of information technology, data representation has to be performed in several ways. Data file includes text, images, audio, video, and animations, which are large and require lots of space in the hard disk. The Video file consists of sequence of images to be framed in single entity. Image Compression is the effective way to reduce the storage space and speedup the transmission. The Video transmission incurs higher bandwidth requirements. It is necessary to transfer the high quality images to the user devices without loss and latency. This gives encouragement to the researchers to find acceptable compression algorithms. Even though many compression schemes exist, there will be the need for fast compression algorithms which produce acceptable quality images or video with minimum size. This paper presents a survey of various research articles about the image or video compression techniques.
Distributed Trust Evaluation protocol with Privacy Protection for Inter-cloud
Authors: Rajkumar M , Aransha R , Deepa M , Harini P , Iswarya B
Published: May 2021

Inter-cloud attempts to energize resource splitting between fogs. To help cloud, a trust appraisal structure in the middle of fogs and customers mandatory. For trust growth, standard shows are customarily established on a bound together designing concentrate on solitary connection. For cloud, the status is uncommonly remarkable, scattered along with associations can single course or multiple (for example. fogs offer sorts of help to everyone). Paper presents a scattered trust development show along with security prevent for Inter-cloud. The latest responsibilities and different features are summarized under. In any case, input is gotten by homomorphism encryption with evident secret sharing. Next, oblige the exceptional thought Inter-cloud, trust growth can be driven in scattered manner is valuable regardless, As a segment of the social occasions are disengaged. Third, support revamp trust appraisal, an imaginative instrument is familiar to store contribution, so much, all might arranged deftly while getting analysis assurance. The show has been exhibited reliant on an appropriate security model. Re-enactments have been performed to show ampleness of the show. The results show the regardless, although major piece of the fogs are noxious or disengaged, by picking suitable operational limits the show can regardless maintain amazing trust appraisal and, security insurance. In distributed storage, remote knowledge trustworthiness testing is important. It can lead customers to double-check if their revaluated information is still unblemished without having to download the entire file. Customers may be required to store data on several Cloud Servers in some application scenarios.
Research on the Whole Model Splicing Problem of the Provincial and Regional Power Grid Based on Dispatching and Control Cloud
Authors: Pan Na , Wang Zheyu , Wang Shenjie , Niu Haonan , Wang Zi , Xu Zhicheng
Published: May 2021

With the increasing expansion of power grid scale and the increasing complexity of power grid structure, the integration of power network is becoming more and more obvious. The existing real-time model of power network can no longer meet the requirements of network analysis and calculation. In order to improve the accuracy of online analysis and application function of power grid, a whole model splicing method of the provincial and regional power grid is proposed, and the modeling method of boundary equipment, boundary splicing method and processing of boundary T connection lines are studied. By applying the whole model splicing scheme on Tianjin Power Grid, and validating the result, the method can improve the accuracy and rationality of the result for whole model splicing of the provincial and regional power grid, at the same time meet the requirements of various network analysis and calculation.
Study on Lightning inrush overvoltage of 500 kV GIL-GIS Hydropower Station
Authors: Ren Hongtao , Wu Yaofu
Published: May 2021

Aiming at the overvoltage of the equipment in GIL-GIS system when the tower outside the station strikes back, a calculation model based on EMTP program is proposed to analyze the influence of GIL distance length, lightning tower position and tower name on the lightning inrush overvoltage. Combined with the calculation results, it shows that the lightning inrush overvoltage level varies with GIL distance length, lightning tower position and tower name The variation law of tower nominal height can provide reference for overvoltage calculation and electromagnetic disturbance source calculation of hydropower plant.
Design and implementation of OBU terminal for Vehicle Ad-hoc Network
Authors: Wu Yan , Wang Pengkun , Sun Chao
Published: May 2021

In order to solve the traffic problems and the needs of users’ safe driving, according to the requirements of the function of the Vehicle Ad-hoc Network (VANET), this paper designs and implements the application function of VANET based on the onboard unit (OBU) terminal in Android system. The design selects ARM development board as OBU terminal and uses Android operating system to design UI interface according to functional modules to interact with users. The key issues and technologies are designed and improved, which implement the security application based on security message processing and expand the non-security application, so as to achieve the purpose of assisting users to drive safely.
Super Real Time Electromechanical Electromagnetic Hybrid Simulation Technology Architecture for Large AC / DC Power Grid
Authors: Weijie Zheng , Dechao Xu , Xing Zhang , Xujiang Chen , Jianfeng Yan , Hongwei Tang , Qing Mu , Min Liu , Feng Wang
Published: May 2021

This paper proposes a parallel algorithm of the electromagnetic electromechanical group decoupling based on the electromechanical electromagnetic dual master control, including the hybrid simulation architecture of the group decoupling including the electromechanical transient interface, the data transmission of the electromechanical electromagnetic hybrid simulation, and the corresponding network splitting method, the generation scheme of the electromagnetic subnet group and the master control scheme. The new algorithm can greatly improve the parallel computing efficiency of hybrid simulation and meet the requirements of accuracy. By the realization of the parallel algorithm of the electromagnetic electromechanical group decoupling based on the electromagnetic master control, the hybrid simulation performance of ADPSS is improved, which can support the hybrid simulation of large-scale AC and DC power grid.
Calculation and Analysis of Heat Field of Large Capacity Epoxy Pouring Dry-type Transformer Based on Fluid-solid Coupling
Authors: Wang Shuo , Xuan Yingying , Wu Peng , Yuan Bo , Zhang Shuai , Li Yan
Published: May 2021

Dry-type transformer had the advantages of simple structure, easy maintenance, small environmental pollution, safe and reliable operation. It was widely used in power system distribution network. With the increase of urban power load year by year, large capacity dry type transformer had been highly concerned by the power industry. In this paper, the fluid-solid coupling calculation equation of a large capacity epoxy casting dry-type transformer was expounded, and a two-dimensional axisymmetric model of the transformer was established. The finite element method was used to calculate the temperature distribution and hot spot temperature with different wind speed and load rate. The results showed that when the wind speed at the transformer inlet was 0.7m/s and the load exceeded 120%, the temperature rise of the low-voltage winding reached 136.41°C, which had exceeded the temperature limit of the material. Therefore, the ventilation volume needed to be increased and the running time needed to be reduced. The research results could provide technical support for the design and safe operation of large capacity epoxy casting dry type transformer.
The Influence of Grid-connected Distributed Generation on the Power Supply Reliability of Distribution Network
Authors: Niu Haonan , Wang Shenjie , Pan Na , Peng Zhichao , Liu Xing
Published: May 2021

This paper analyzes the typical distribution network structure and the operating characteristics of distributed generation (DG). By analyzing the impact of grid-connected DG on equipment such as reclosing and spare power automatic switching, we calculate the power supply reliability indexs after the DG is connected to the grid under various operation modes such as reclosing, spare power automatic switching, de-sequencing distributed energy and islanding. Finally, the results of the reliability indexs can provide the theory and data for planning and improving the operating mode of grid-connected DG.
Vehicle and Pedestrian Detection Based on Improved YOLOv4-tiny Model
Authors: Ma Lingzhe , Chen Yu , Zhang Jilin
Published: May 2021

Target detection is the basic technology of automatic driving system. Deep learning has gradually become the mainstream target detection algorithm because of its powerful feature extraction ability and adaptive ability. How to ensure accuracy and speed is a great challenge in the field of target detection. In order to solve the problems of high miss detection rate of small target and difficult to realize embedded real-time detection in the process of complex environment detection by deep learning method, this paper adds two auxiliary remaining network blocks in the backbone network. So that the backbone network can extract the global and local features of the detected object, and carry out feature extraction based on the feature pyramid network Fusion, adding a scale to form a three scale prediction, to improve the problem of poor detection accuracy of yolov4-tiny network. The simulation results show that: Compared with yolov4-tiny, the accuracy of the improved network structure is improved by 3.3%, and the detection speed is 251 fps, which ensures the requirements of real-time detection. This algorithm has good detection effect in the case of lack of illumination and target occlusion, and its detection accuracy on the mixed data set is better than that of the contrast algorithm, which meets the real-time detection conditions and is suitable for deployment on the embedded system carried by the car.
Research on Key Technology of Full Duplex Cognitive Radio Network
Authors: Jiang Haibin , Yu Zhiyong , Yang Jian
Published: May 2021

In traditional half duplex cognitive radio networks, secondary users can only sense the spectrum or transmit at a given time. This kind of half duplex network limits the throughput of users, because the user unit can’t transmit data in spectrum sensing. However, with the development of self-interference suppression (SIS) technology, full duplex cognitive radio network allows spectrum sensing and data transmission on a given channel at the same time. Compared with traditional half duplex network, full duplex network improves throughput and reduces conflicts. In order to explore the future development direction of spectrum sensing technology, in the context of full duplex cognitive radio, a comprehensive study of radio network is carried out, including key technologies, current challenges and future research directions.
Multi-feed System Partitioning Method Based on N-1 Preconceived Fault Set and Local Expansion Theory
Authors: Ma Limin , Wen Jun , Wang Shujie , Niu Yukun
Published: May 2021

In view of the fact that the AC system with multiple LCC-HVDC feeds is prone to simultaneous commutation failure, this paper proposes the scheme of grid division for the multiple feed system to isolate the coupling between multiple HVDC and reduce the risk of occurring simultaneous commutation failure. First, N-1 scan is performed on the multi-feed system to find the set of lines that can cause simultaneous commutation failure of multiple HVDC. The line with high line load rate is removed from the line set. Then, on the basis of local expansion theory, the nodes in the system are aggregated to form the initial partition. In this process, the calculation method of voltage/reactive sensitivity matrix considering PV node and LCC-HVDC is firstly given; moreover, the corresponding indexes were calculated by the node aggregation function and the partition fitness function. Finally, the results of the partition are modified by considering the circuit set and the partition short circuit ratio, and the flow chart of the partition scheme is given. The partitioning process is validated with an IEEE 39-node system as an example. The results show that the partitioning method is reasonable and solves the problem of the failure of commutation effectively.