2023 International Conference for Advancement in Technology (ICONAT)最新文献

{"title":"Battery Energy Storage Train Scheduling in Power System Considering Renewable Power Generation","authors":"Komal Mohan Todakar, P. P. Gupta, V. Kalkhambkar","doi":"10.1109/ICONAT57137.2023.10080735","DOIUrl":"https://doi.org/10.1109/ICONAT57137.2023.10080735","url":null,"abstract":"Uncertain nature of renewable power sources (RES) like wind generation presents a significant issue for system operators. To reduce the negative effects of network congestion on the power system, battery energy storage (BES) Trains offer a potential way to deliver the energy produced by RES to the load center. The effects of stochastic scheduling of BES trains for railway transportation networks with uncertain wind power generation are evaluated in this paper. Using Autoregressive Integrated Moving Average (ARIMA) models, the uncertainties related to wind power for scenario generations are considered. Also, the vehicle routing problem related to the railway transportation system is solved using the time-space network model. As a case study, the BES Train integrated six-bus system with a three-station and three-line railway network is investigated. Simulation results evaluate the impact of BES Train, wind uncertainty, BES Train charging/discharging schedule, wind curtailment and computational time. In addition, BES Train can economically reduce network congestion and decreases operational cost.","PeriodicalId":250587,"journal":{"name":"2023 International Conference for Advancement in Technology (ICONAT)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123306369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimum Tilt-Pitch Analysis and Design of 25kW Grid Connected Solar PV System using PVSyst Software","authors":"Tanuj Monu, S. Vadhera","doi":"10.1109/ICONAT57137.2023.10080333","DOIUrl":"https://doi.org/10.1109/ICONAT57137.2023.10080333","url":null,"abstract":"Electricity generation through solar PV system is one of the significant contributors in the renewable energy. Solar PV can be considered as a viable option to meet the increasing demand. The efficiency of solar PV system depends on location as well as on angle at which solar panel gets installed. This paper describes the tilt-pitch analysis of 25kW grid connected solar PV system for location of Kurukshetra, India using PVSyst software. The data required for this study is taken from the meteonorm site. This software demonstrates total energy produced and system losses. This software also provides various user optimization tools to analyze the system. The paper deals with the various factor like system design, performance analysis and losses with help of various charts and tables. The performance ratio of the system calculated over the year is found to be 0.752 which shows that solar energy can be a good source of energy generation to meet the increasing daily demand.","PeriodicalId":250587,"journal":{"name":"2023 International Conference for Advancement in Technology (ICONAT)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121903015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Evaluation of Techniques for Controlling and Monitoring Greenhouse","authors":"Megha Ahirwar, Ram Niwash Mahia","doi":"10.1109/ICONAT57137.2023.10080566","DOIUrl":"https://doi.org/10.1109/ICONAT57137.2023.10080566","url":null,"abstract":"Climatic conditions have a significant impact on greenhouse crop yield. A greenhouse is an enclosed structure that offers plants excellent climate control to manage their growth, hence reducing production costs and increasing agricultural yields. The primary focus of this analysis is on enhancing current greenhouse environmental management systems. In order to guarantee the health of greenhouse crops and achieve near-optimal conditions for plant growth, producing more goods with fewer resources, critical environmental factors are proposed to be controlled namely temperature and relative humidity. The intended output is prompted by a variety of control algorithms such as conventional control, modern control, intelligent control [13], hybrid control etc.","PeriodicalId":250587,"journal":{"name":"2023 International Conference for Advancement in Technology (ICONAT)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124017502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sentiment Analysis of Online Customer Reviews for Handicraft Product using Machine Learning: A Case of Flipkart","authors":"Venkata Chaitanya Kanakamedala, Sonal Hukampal Singh, Rishitha Talasani","doi":"10.1109/ICONAT57137.2023.10080169","DOIUrl":"https://doi.org/10.1109/ICONAT57137.2023.10080169","url":null,"abstract":"Online customer reviews have been recognised as a vital source of market information related to customer preferences and customer experience. In the e-commerce era, online review content helps customers for developing purchase intention and purchase decision. However, revealing meaningful insights from the large volume of online reviews is the major challenge faced by majority of customers. Hence, extracting the online customer reviews and analyzing such online database are crucial pace for developing understanding of customer preferences and customer experience. Analysing online customer review also helps entrepreneurs to develop new products and understand their product in the customer preference perspective. The online customer comments can be segregated into three classes such as positive, negative and neutral. Employing classifiers give signals to the new customers regarding for the particular product. This paper classifies the online review dataset into the positive, neutral and negative based on the frequency of the words associated with respective sentiments. The bag-of-word is constructed using online customer review from Flipkart. This paper employed classifier algorithms such as logistic regression, K nearest neighbours, Multilayer perceptron and Support vector analysis to segregate the online comments as positive, neutral and negative online reviews. In the current research, 28995 online customer reviews for handicraft products are extracted from Flipkart using Python. This research aims to understand the perception of the customers for handicraft products in e-commerce platform. This paper employed machine learning algorithms such as logistic regression, KNN, Multilayer perceptron (MLP) and Support Vector Machine (SVM) and simulated by using Python. Accuracy of LR, KNN, MLP and SVM is also estimated using TF-IDF and Count Vectorizer. With respect to TF-IDF and count vectorizer, the accuracy of the SVM is the higher than the KNN, MLP and LR.","PeriodicalId":250587,"journal":{"name":"2023 International Conference for Advancement in Technology (ICONAT)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126309578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"User Recognition Via Facial Parameters With Occlusion Using CNN","authors":"Mahadeo D. Narlawar, R. D. J. D Pete","doi":"10.1109/ICONAT57137.2023.10080688","DOIUrl":"https://doi.org/10.1109/ICONAT57137.2023.10080688","url":null,"abstract":"The prime essence of occlusion based face detection has been derived across the world with a simple agenda to tackle the rise of frauds that security reinforcement or surveillance techniques provide concrete solution with bio metric database. Though the prime solution is by bio metric authentication via smart phones through facial recognition of users; still the misuse is a challenge as the user’s face is often partially covered or occluded. Therefore, face detection or rather authentication even when occluded has become very important to prevent cyber frauds. Traditional approaches in terms of facial bio metric authentication typically have comprised of image processing or machine learning steps: feature extraction, pooling, segmentation, flattening and recognition. The researchers of this paper propose a facial occlusion detection network utilizing Convolutional Neural Networks (CNN). The result showed that accuracy in our developed model is in the range of 88 to 91 percent which in competence with the reported algorithms till occlusion of face in terms of percentage is restricted up to 50 percent of the focus area and 40 percent of the complete face.","PeriodicalId":250587,"journal":{"name":"2023 International Conference for Advancement in Technology (ICONAT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129673571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Aviation Industry Recommender System(AIRS) using K-nearest Neighbour and Cosine Similarity","authors":"Z. Mundargi, Shubham Mulay, Dnyaneshwari Navale, Vishwam Talnikar, Akhilesh Nawale, Vishal Sonkusale","doi":"10.1109/ICONAT57137.2023.10080009","DOIUrl":"https://doi.org/10.1109/ICONAT57137.2023.10080009","url":null,"abstract":"The Aviation Industry has flourished recently with a big boom in tourism.However, tourists are confused about selecting the best airline amongst available options. The existing systems just compare the price and analyze past data related to it and predict accordingly. We focus on calculative analysis of multivariate data derived through survey for recommendation. Our approach is to develop a system which will recommend an airline which is supreme amongst everyone.The broad criteria for selection is based on the quality of services, network & passengers per aircraft and factor of safety of the aircraft. With the help of k-nearest neighbor as the main algorithm, assisting with directional features provided by cosine similarity model makes our system robust in predicting output. The architecture also possesses calculations using pivot matrix along with a random number generating method recommending the best airline for given input. Label encoding technique is implemented in preprocessing to resolve and rectify error of conversion of deferring input data types. This paper covers fundamental application of KNN as well as cosine similarity in an intuitive way.","PeriodicalId":250587,"journal":{"name":"2023 International Conference for Advancement in Technology (ICONAT)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129545396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Surrogate-Assisted Multi-objective Genetic Fuzzy Associative Classification by Multiple Granularity Measures","authors":"A. K. Behera, Satchidananda Dehuri, Ashish Ghosh","doi":"10.1109/ICONAT57137.2023.10080059","DOIUrl":"https://doi.org/10.1109/ICONAT57137.2023.10080059","url":null,"abstract":"This paper presents a new surrogate-assisted multi-objective genetic fuzzy associative classification model by learning multiple granularities. The specific method is the hybridization of multi-objective genetic algorithms (MOGAs), radial basis function neural networks (RBFNs), and rough set. We show that our approach requires only a few numbers of fitness evaluations compared to the methods proposed in [34] without compromising to maintain an average classification ability in almost all the datasets considered in this work for evaluation of the model.","PeriodicalId":250587,"journal":{"name":"2023 International Conference for Advancement in Technology (ICONAT)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125021133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Engagement in Video Graphic Online Learning Using the Emotional Dimensions in the Learning Context","authors":"B. Baranidharan, Harsh Bhandari, Avi Tewari, Ishan Sachadeva, Abhinav","doi":"10.1109/ICONAT57137.2023.10080769","DOIUrl":"https://doi.org/10.1109/ICONAT57137.2023.10080769","url":null,"abstract":"The COVID-19 pandemic has become the prime reason for organizations across the world to shift their entire workforce onto virtual platforms. One of the major drawbacks of these virtual platforms is that it lacks a real-time metric which could be used to detect whether a person is attentive during the lectures and meetings or not. This was most evident in the case of educational institutions, where students would often fail to pay attention to the content that was being taught by teachers and professors at home. With this research work, our aim is to create a solution for this problem with the help of AI-FER (Artificial Intelligence Facial Emotion Recognition). For this, we have proposed our own Convolutional Neural Network model achieving an overall accuracy of 59.03%. We have also used several pre-trained models available in Google’s Tensorflow library like DenseNET and VGG.","PeriodicalId":250587,"journal":{"name":"2023 International Conference for Advancement in Technology (ICONAT)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121225468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of Portable Tethered Vertical Profiler for Underwater Monitoring","authors":"Steevan Loyd Sequeira, E. Manish, Rakshith, Pruthviraj Umesh, K. Gangadharan","doi":"10.1109/ICONAT57137.2023.10080657","DOIUrl":"https://doi.org/10.1109/ICONAT57137.2023.10080657","url":null,"abstract":"Human activities and industrial waste flow to water bodies will contaminate the coastal areas, leading to changes in the underwater ecosystem and affecting aquatic habitats. Understanding these ecosystems requires quick data collection, systematic process monitoring, and coastal resource preservation through proper decision-making. We can understand the variation of water properties with depth by deploying sensors from the surface, one of the most popular methods of gathering ocean data. However, high-resolution oceanographic data collection and long-term human observations are risky and expensive. Although some autonomous profiling systems address these issues, there is still scope for developing practical, affordable, and accurate data-providing systems. This paper discusses an open-source, economical, easily portable, propeller-driven underwater Vertical Profiler controlled by a joystick located in Ground Control System (GCS) using a Neutrally buoyant tether cable. Data from sensors, like temperature, pressure, and depth, are collected. The paper also discusses functionality and sensor tests conducted on the developed vehicle at a controlled environment/test site. The developed Profiler can perform basic manoeuvres and collect water quality data upstream of vented dams and rivers in and around the Dakshina Kannada district of Karnataka, India.","PeriodicalId":250587,"journal":{"name":"2023 International Conference for Advancement in Technology (ICONAT)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116513823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cost-Effective and Retrofit Solution Control Algorithm of Renewable Fed DC Microgrid System","authors":"B. R, Susan Thambi, K. P.","doi":"10.1109/ICONAT57137.2023.10080690","DOIUrl":"https://doi.org/10.1109/ICONAT57137.2023.10080690","url":null,"abstract":"DC microgrids (MG) provide efficient integration of renewable resources, storage capabilities, and load distribution into the electric power system, which makes them superior to AC MGs. Different power sources such as wind turbines, distributed energy resources (DER), photovoltaic (PV), and battery energy storage systems are considered to supply the load demand. In the present environment, when enormous amounts of renewable energy are being injected into the electrical system oscillations caused by load or source transients and energy management should be ministered appropriately, as per global standards. Supercapacitors are efficient energy storage devices that can function as ESS in a droop-regulated DC microgrid in order to compensate for the ups and downs in the DC bus voltage brought on by the load demand and DER integrations. The control schemes, droop control, master-slave control, and other distributed and hierarchical control techniques, have several advantages and disadvantages with regard to cost-effectiveness, complexity, and resilience. A novel microgrid control technique is proposed as part of this work, where a piecewise droop control mechanism is introduced in order to utilize the surge power handling capability of supercapacitors to address the aforementioned voltage swings. The proposed method is evolved by combining features of the conventional droop method and the DC bus signaling mechanism. Simulation studies performed with the proposed control indicate predominant improvement in the system characteristics, whose results are presented in the later sections.","PeriodicalId":250587,"journal":{"name":"2023 International Conference for Advancement in Technology (ICONAT)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126855654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}