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RBFNN Based Demand Forecasting for Next Generation Ethernet Passive Optical Network 基于RBFNN的下一代以太网无源光网络需求预测
2023 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2023-09-06 DOI: 10.1109/TENSYMP55890.2023.10223635
Sabbir Ahmmed, Sujit Basu, Pallab K. Choudhury
{"title":"RBFNN Based Demand Forecasting for Next Generation Ethernet Passive Optical Network","authors":"Sabbir Ahmmed, Sujit Basu, Pallab K. Choudhury","doi":"10.1109/TENSYMP55890.2023.10223635","DOIUrl":"https://doi.org/10.1109/TENSYMP55890.2023.10223635","url":null,"abstract":"The rapid growth of internet users and bandwidth-intensive applications have led to increasing demand for efficient data transmission. Optical fiber transmission has become essential in network architecture and has adopted deep learning based intelligence to categorize complex internet traffic with a focus on bandwidth prediction. To improve the performance of the Next-Generation Ethernet Passive Optical Network, the proposed scheme uses a deep learning-based Radial Basis Function Neural Network (RBFNN) termed RBFNN-DBA model to track user's demand and predict their bandwidth needs before receiving a request from the optical network unit. By reducing the sole dependency on the traditional Request-Grant mechanism, the RBFNN-DBA model leads to assure a better quality of service metrics. The effectiveness of the RBFNN-DBA model is evaluated by comparing it to the existing long short-term memory model based on the grant-to-reporting ratio, end-to-end delay, and fairness. The results show that the proposed model outperformed all metrics.","PeriodicalId":314726,"journal":{"name":"2023 IEEE Region 10 Symposium (TENSYMP)","volume":"155 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123090829","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}
引用次数: 0
Color Attribute Compression for Block Based Representation of Point Cloud 基于块的点云表示的颜色属性压缩
2023 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2023-09-06 DOI: 10.1109/TENSYMP55890.2023.10223641
H. Kimata
{"title":"Color Attribute Compression for Block Based Representation of Point Cloud","authors":"H. Kimata","doi":"10.1109/TENSYMP55890.2023.10223641","DOIUrl":"https://doi.org/10.1109/TENSYMP55890.2023.10223641","url":null,"abstract":"Compression of point cloud obtained by sensing real-world objects with LiDAR or RGBD sensors has been studied. Block-based geometry compression methods using deep learning have been presented, however, less studies have been reported on compression of attribute information such as colors. In this paper, an efficient encoding of color attribute information is proposed for block-based geometry compression, which has an advantage that parts of point cloud are processed in parallel. The proposed method encodes color information as an image projected onto a surface, block by block, in order to achieve better subjective quality of the rendered image. A deep learning-based image compression method for the projected image is also studied. The overall efficiency is discussed in this paper.","PeriodicalId":314726,"journal":{"name":"2023 IEEE Region 10 Symposium (TENSYMP)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122833987","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}
引用次数: 0
Feasibility Study and Selection of Open-Source Flight Controller for UAV Applications 开源无人机飞行控制器的可行性研究与选择
2023 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2023-09-06 DOI: 10.1109/TENSYMP55890.2023.10223640
Satyadev P.V.L.N, K. B, UshaRani Nelakuditi, Venkata Subbarao. P
{"title":"Feasibility Study and Selection of Open-Source Flight Controller for UAV Applications","authors":"Satyadev P.V.L.N, K. B, UshaRani Nelakuditi, Venkata Subbarao. P","doi":"10.1109/TENSYMP55890.2023.10223640","DOIUrl":"https://doi.org/10.1109/TENSYMP55890.2023.10223640","url":null,"abstract":"Today, vertical takeoff multi-rotor drones are vital in many fields, including medical, delivery of commodities, agriculture, security, and surveillance. The drone's brain, or flight controller, handles all other drone activities in addition to stabilizing the drone. Flight controllers come in a variety of sizes and sophistications. Changes in gains or PID coefficients can be made with the help of flight controller configuration, which results in quick locked-in responses. This paper focused on the selection and evaluation of flight controllers suitable for a range of applications like delivery, photography and pesticide spraying based on their technical characteristics and sophistication.","PeriodicalId":314726,"journal":{"name":"2023 IEEE Region 10 Symposium (TENSYMP)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129812169","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}
引用次数: 0
Optimal Energy Procurement Strategy for DISCOM: A Case Study DISCOM的最佳能源采购策略:一个案例研究
2023 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2023-09-06 DOI: 10.1109/TENSYMP55890.2023.10223484
Yash Vardhan Omar, Souvik Bera, N. Pindoriya
{"title":"Optimal Energy Procurement Strategy for DISCOM: A Case Study","authors":"Yash Vardhan Omar, Souvik Bera, N. Pindoriya","doi":"10.1109/TENSYMP55890.2023.10223484","DOIUrl":"https://doi.org/10.1109/TENSYMP55890.2023.10223484","url":null,"abstract":"The Electricity Distribution Companies (DISCOMs) in India are subjected to considerable Unscheduled Interchange (UI) penalties based on the deviation between the actual and scheduled power drawn from the grid to strictly maintain the nominal operating conditions in the system according to the Deviation Settlement Mechanism (DSM) guidelines. Therefore, it becomes essential to procure power optimally while incurring minimum penalty due to UI for the better financial viability of the DISCOMs. This study proposes a framework for the optimal procurement of power considering multiple resources while minimising UI for a grid-connected microgrid with non-shiftable or non-curtailable loads. The term virtual load is referred to account for the variations in demand, which represent the UI for each 15-minutes time interval. The problem statement is a nonlinear problem (NLP) formulated in GAMS software and solved using the Solving Constraint Integer Programs (SCIP) solver. Further, a real case study of Gujarat International Finance-Tec City Power Company Limited (GIFT PCL), which supplies power to GIFT City infrastructure, is considered in this study to validate the proposed model.","PeriodicalId":314726,"journal":{"name":"2023 IEEE Region 10 Symposium (TENSYMP)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125701276","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}
引用次数: 0
Incremental Conductance Based Model Predictive Control Algorithm for Solar PV Module for Tracking of MPP 基于增量电导的太阳能光伏组件MPP跟踪模型预测控制算法
2023 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2023-09-06 DOI: 10.1109/TENSYMP55890.2023.10223623
Srishti, D. P. Samajdar, P. Padhy
{"title":"Incremental Conductance Based Model Predictive Control Algorithm for Solar PV Module for Tracking of MPP","authors":"Srishti, D. P. Samajdar, P. Padhy","doi":"10.1109/TENSYMP55890.2023.10223623","DOIUrl":"https://doi.org/10.1109/TENSYMP55890.2023.10223623","url":null,"abstract":"The great advances in Photovoltaic (PV) efficiency and performance are valuable only if they are coupled with effective mechanisms for tracking and controlling the operating conditions of the PV system to ensure that it operates at or near the Maximum Power Point (MPP). Only then can the system produce the maximum amount of power for a given set of conditions, maximizing the benefits of the advances in PV technology. In this paper, an Incremental Conductance (InC) based Model Predictive Control (MPC) algorithm is used to track the MPP. To assess its effectiveness the performance of an INC-based MPC algorithm in the photovoltaics field has been evaluated against two of the most used algorithms, (Perturb and Observe) P&O and INC. And also compared with PI-based MPP since it is one of the recommended controllers in industrial applications. This comparative analysis has been done for a sudden change in temperature and irradiance. The complete setup has been done in MATLAB/SIMULINK set including DC-DC Boost converter solar PV Module and algorithms. DC-DC Boost converter is used for getting higher value in the output. The overall efficiency is higher in INC-based MPC and less fluctuation is obtained at MPP.","PeriodicalId":314726,"journal":{"name":"2023 IEEE Region 10 Symposium (TENSYMP)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134069378","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}
引用次数: 0
Personality Prediction Based on Contextual Feature Embedding SBERT 基于上下文特征嵌入的SBERT人格预测
2023 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2023-09-06 DOI: 10.1109/TENSYMP55890.2023.10223609
Md. Ali Akber, Tahira Ferdousi, Rasel Ahmed, Risha Asfara, Raqeebir Rab
{"title":"Personality Prediction Based on Contextual Feature Embedding SBERT","authors":"Md. Ali Akber, Tahira Ferdousi, Rasel Ahmed, Risha Asfara, Raqeebir Rab","doi":"10.1109/TENSYMP55890.2023.10223609","DOIUrl":"https://doi.org/10.1109/TENSYMP55890.2023.10223609","url":null,"abstract":"Personality prediction defines an individual's interior self and provides an overview of their behavioral characteristics. Individuals can develop personally and professionally with its aid. Since its inception, the MBTI has become one of the most valuable instruments available due to its widespread application in a variety of fields. Typically, psychologists use questionnaires or conduct interviews with subjects to make predictions. However, because it is only a question-and-answer session, it is prone to error. In this paper, an implicit model is suggested in order to optimize the process using machine learning. The primary objective of this paper is to use sentence transformers to discern the context of user-written social media posts in order to automate the process. In our proposed work, various text pre-processing techniques, such as data cleansing, stopword removal, and data balancing techniques such as random oversampling, are utilized. The context of the text posts is determined using Sentence-BERT (SBERT), a pre-trained model created especially for sentence-level embeddings. Using the Myers-Briggs Type Indicator (MBTI) and a variety of machine learning techniques, such as Support Vector Machines (SVM), Logistic Regression (LR), K-Nearest Neighbors (KNN) and Random Forest (RF) Classifier, it is possible to predict people's personalities based on text. SBERT combined with the Random Forest Classifier performs outstandingly to predict the MBTI personality.","PeriodicalId":314726,"journal":{"name":"2023 IEEE Region 10 Symposium (TENSYMP)","volume":"143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134448248","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}
引用次数: 0
Graph Embedded Representation Learning in Skeleton-based Action Classification 基于骨架的动作分类中的图嵌入表示学习
2023 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2023-09-06 DOI: 10.1109/TENSYMP55890.2023.10223651
Zihan Wang, Shun Wang
{"title":"Graph Embedded Representation Learning in Skeleton-based Action Classification","authors":"Zihan Wang, Shun Wang","doi":"10.1109/TENSYMP55890.2023.10223651","DOIUrl":"https://doi.org/10.1109/TENSYMP55890.2023.10223651","url":null,"abstract":"The wide usage of GCN and GNN on the tasks of action classification tasks has made great improvement since the first proposed ST-GCN model. Plenty of works proposed methodologies based on the classification task requirements. As a result of them, we manipulated similar skeleton structures which are extracted from images and videos by analyzing intra and inter-class to represent the behaviour isomerism graphs. Our methodology manipulated the unsupervised graph embedding methodology to solve the classification downstream tasks based on the collected large-scale 2-dimensional datasets. We apply our proposed methodology on top of 4 pose estimation datasets to verify the effectiveness of the results. To solve the unsuper-vised classification problem, we are focusing on the property of skeleton data which is view-invariant through manipulating the attention-based and encoder-decoder structure to generate the corresponding embeddings and compare them through the contrastive learning methodology.","PeriodicalId":314726,"journal":{"name":"2023 IEEE Region 10 Symposium (TENSYMP)","volume":"266 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131682487","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}
引用次数: 0
Automated Rice Leaf Disease Diagnosis Using CNNs 利用cnn自动诊断水稻叶片病
2023 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2023-09-06 DOI: 10.1109/TENSYMP55890.2023.10223608
Amit Kumar, B. Bhowmik
{"title":"Automated Rice Leaf Disease Diagnosis Using CNNs","authors":"Amit Kumar, B. Bhowmik","doi":"10.1109/TENSYMP55890.2023.10223608","DOIUrl":"https://doi.org/10.1109/TENSYMP55890.2023.10223608","url":null,"abstract":"Rice is a staple food in Bharat (India) and many other parts of the world. However, the increasing demand for rice due to population growth forces various challenges, including degraded crop quality and quantity due to rice plant diseases. Diseases such as brown spots, bacterial blight, and hispa can significantly reduce farming output, thereby impacting the productivity of the agriculture sector. To address this challenge, various solutions such as Agricultural cyber-physical systems (ACPS) and precision agriculture have been proposed, along with the application of deep learning techniques. This paper presents a rice leaf disease detection method using deep transfer learning. The proposed approach explores well-known pre-trained deep Convolutional Neural Network (CNN) models - VGG19, DenseNet201, InceptionV3, ResNet50, EfficientNetB3, EfficientNetB7, and XceptionNet, for image-based rice disease classification. Experimental results show that the DenseNet model by the proposed method achieved the highest classification accuracy of 98.75% when fine-tuned properly. The proposed scheme outperforms many existing approaches, delivering a superior disease control solution for rice leaf diseases.","PeriodicalId":314726,"journal":{"name":"2023 IEEE Region 10 Symposium (TENSYMP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115025627","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}
引用次数: 0
Experimental Evaluation of the Performance: A Novel Bi-Facial PV Panel 性能的实验评估:一种新型双面光伏面板
2023 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2023-09-06 DOI: 10.1109/TENSYMP55890.2023.10223485
Tanvir M. Mahim, Adnan Miah, Amin Rahim, Md Rakibul Hasan, Md. Mehedi Hasan Shawon, Tasfin Mahmud
{"title":"Experimental Evaluation of the Performance: A Novel Bi-Facial PV Panel","authors":"Tanvir M. Mahim, Adnan Miah, Amin Rahim, Md Rakibul Hasan, Md. Mehedi Hasan Shawon, Tasfin Mahmud","doi":"10.1109/TENSYMP55890.2023.10223485","DOIUrl":"https://doi.org/10.1109/TENSYMP55890.2023.10223485","url":null,"abstract":"Replacement of emission based energy production sources is vital due to the climate change effects in the world. One of the alternatives in this regard is the solar-powered PV cells. This work proposes a novel PV panel design using two mono-facial panels installed back to back to improve power efficiency at reduced space usage. A study was made with an outdoor setup during the winter season for three months, having a single-axis sun-tracking. The findings of this design suggest that the proposed panel gives 22% more power efficiency compared to the mono-facial panel of same dimension. The design was found less expensive compared to conventional bi-facial PV panels. The boost in the power efficiency of the proposed panel design is attributed to the thermal behaviour of the panel, low air pressure-humidity and surface albedo. The findings suggest white concrete surface gives optimal surface albedo for rooftop PV installation.","PeriodicalId":314726,"journal":{"name":"2023 IEEE Region 10 Symposium (TENSYMP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121206471","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}
引用次数: 1
Monitoring Sustainability Practices in Dry-Land Crops with Farm Digital Twins 利用农业数字孪生监测旱地作物的可持续性实践
2023 IEEE Region 10 Symposium (TENSYMP) Pub Date : 2023-09-06 DOI: 10.1109/TENSYMP55890.2023.10223659
R. Kulat, Ruturaj Patil, Swagatam Bose Choudhury, A. Mittal, Sanat Sarangi, Mariappan Sakkan, S. Pappula
{"title":"Monitoring Sustainability Practices in Dry-Land Crops with Farm Digital Twins","authors":"R. Kulat, Ruturaj Patil, Swagatam Bose Choudhury, A. Mittal, Sanat Sarangi, Mariappan Sakkan, S. Pappula","doi":"10.1109/TENSYMP55890.2023.10223659","DOIUrl":"https://doi.org/10.1109/TENSYMP55890.2023.10223659","url":null,"abstract":"Conservation agriculture together with organic practices could help reduce greenhouse-gas (GHG) emissions and global warming potential (GWP) to counter the adverse effects of climate change and sequester carbon in the soil improving its fertility. In light of this, there is a growing need to model and monitor farming environments to precisely address local and global concerns. We propose in this paper a farm digital twin framework that was used to monitor dry-land crops: maize, soybean, groundnut and rajma (kidney beans) to forecast and validate yields using a process-model approach while studying the overall impact on various other fronts. Deep learning models were used for early diagnosis of disease and pest conditions with imaging that drove timely operations to minimise impact on crop growth. Results indicated that application of organic manure was a key driving factor in high sequestration of carbon while no-tillage helped reduce the GWP of the farms.","PeriodicalId":314726,"journal":{"name":"2023 IEEE Region 10 Symposium (TENSYMP)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121669738","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}
引用次数: 0
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