2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)最新文献_第9页

Output Voltage Stability of a DC-DC Buck Converter via an Improved Backstepping Method 基于改进反步法的DC-DC降压变换器输出电压稳定性研究

2023 International Conference on Electrical, Computer and Communication Engineering (ECCE) Pub Date : 2023-02-23 DOI: 10.1109/ECCE57851.2023.10101543

Md. Zubaer Alam, T. K. Roy, Subarto Kumar Ghosh, N. Mohammad, L. C. Paul

引用次数: 0

Estimation of Soil Moisture with Meteorological Variables in Supervised Machine Learning Models 基于监督机器学习模型的气象变量土壤湿度估计

2023 International Conference on Electrical, Computer and Communication Engineering (ECCE) Pub Date : 2023-02-23 DOI: 10.1109/ECCE57851.2023.10101650

M. Hussain, N. Sharmin, Sumayea Binte Shafiul

{"title":"Estimation of Soil Moisture with Meteorological Variables in Supervised Machine Learning Models","authors":"M. Hussain, N. Sharmin, Sumayea Binte Shafiul","doi":"10.1109/ECCE57851.2023.10101650","DOIUrl":"https://doi.org/10.1109/ECCE57851.2023.10101650","url":null,"abstract":"Water cycles, climate-related hazards, and agroirrigation are strongly controlled by soil moisture (SM) content. For water resource management, prediction is a key to mitigate and regulate expected economic losses and property damages. This paper compares two supervised machine learning (ML) techniques: support vector regression (SVR) and random forest (RF) to predict SM. In RStudio, various meteorological variables: temperature, relative humidity, wind speed, and rainfall are trained to estimate SM. For eight divisions, SM and weather variables are obtained from the National Aeronautics and Space Administration (NASA) Prediction of Worldwide Energy Resources (POWER). The experiments include daily observations for 39 (1982 to 2020) to develop SVR and RF models. To estimate SM from the predictive model, datasets from diverse regions: Rajshahi, Mymensingh, Chittagong, and Sylhet are utilized in training (60%) and Rangpur, Barisal, Khulna, and Dhaka are segregated for validation (40%) resulting in accuracy of 88 to 95.8%. This model further is applied to forecast daily SM for each city including two districts (Bogra and Jessore) and found slightly higher model performance for SVR (90.7%) than RF (90.1%) on average (Year: 2021). For agricultural, industrial and urban water supplies as well as drought, landslides, and river erosions can be mitigated by an accurate estimation of soil moisture. The investigations encourage for providing SM budget to public with supervised ML techniques mostly among data-sparse regions.","PeriodicalId":131537,"journal":{"name":"2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114192270","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

Fuzzy Logic-based Soft Starter for Controlling Starting Parameters of Induction Motor 基于模糊逻辑的感应电机软起动器起动参数控制

2023 International Conference on Electrical, Computer and Communication Engineering (ECCE) Pub Date : 2023-02-23 DOI: 10.1109/ECCE57851.2023.10101647

Sharith Dhar, Md. Saiful Islam

{"title":"Fuzzy Logic-based Soft Starter for Controlling Starting Parameters of Induction Motor","authors":"Sharith Dhar, Md. Saiful Islam","doi":"10.1109/ECCE57851.2023.10101647","DOIUrl":"https://doi.org/10.1109/ECCE57851.2023.10101647","url":null,"abstract":"The industrial revolution has increased the use of induction motors enormously. Today's soft starter is used for controlling starting current, acceleration torque, and acceleration time of the induction motor. Intelligent techniques are used in soft starters for controlling starting parameters of the induction motor smoothly. But developed intelligent algorithm based soft starter takes more acceleration time, and due to this induction motor can not accelerate the load properly during the starting period. To solve this problem fuzzy logic-based soft starter is proposed in this paper. This proposed starting technique reaches the target through its instinctive decision making capability. The fuzzy logic controller takes stator phase current and torque from the three phase induction motor (IM) and gives firing angles to the thyristor unit in the soft starter by using the Mamdani fuzzy inference system and the mean of maximum method in defuzzification. The proposed technique accelerates the IM with the load smoothly by decreasing acceleration time. The proposed intelligent soft starter reduces the starting current of IM with a Direct on line (DOL) starting technique by more than 10% at the constant load and also provides proper acceleration torque. The proposed soft starter provides a better response compared with another method.","PeriodicalId":131537,"journal":{"name":"2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122145469","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

juktoMala: A Handwritten Bengali Consonant Conjuncts Dataset for Optical Character Recognition Using BiT-based M-ResNet-101x3 Architecture juktoMala:基于位的M-ResNet-101x3架构的用于光学字符识别的手写孟加拉辅音连词数据集

2023 International Conference on Electrical, Computer and Communication Engineering (ECCE) Pub Date : 2023-02-23 DOI: 10.1109/ECCE57851.2023.10101581

M. Hasan, Md. Ali Hossain, Azmain Yakin Srizon, Abu Sayeed

引用次数: 0

94.5 GHz Dual-loop Optoelectronic Oscillator 94.5 GHz双环光电振荡器

2023 International Conference on Electrical, Computer and Communication Engineering (ECCE) Pub Date : 2023-02-23 DOI: 10.1109/ECCE57851.2023.10101595

G. Hasanuzzaman, S. Iezekiel, A. Kanno

引用次数: 0

Brain Tumor Classification Using Watershed Segmentation with ANN Classifier 基于神经网络分类器分水岭分割的脑肿瘤分类

2023 International Conference on Electrical, Computer and Communication Engineering (ECCE) Pub Date : 2023-02-23 DOI: 10.1109/ECCE57851.2023.10101528

F. Chowdhury, Tania Noor, Md. Saiful Islam, Md Khorshed Alam

引用次数: 0

Efficient Hardware and Software Co-design for EEG Signal Classification based on Extreme Learning Machine 基于极限学习机的高效脑电信号分类软硬件协同设计

2023 International Conference on Electrical, Computer and Communication Engineering (ECCE) Pub Date : 2023-02-23 DOI: 10.1109/ECCE57851.2023.10101619

Songyang Lyu, M. Chowdhury, Ray C. C. Cheung

{"title":"Efficient Hardware and Software Co-design for EEG Signal Classification based on Extreme Learning Machine","authors":"Songyang Lyu, M. Chowdhury, Ray C. C. Cheung","doi":"10.1109/ECCE57851.2023.10101619","DOIUrl":"https://doi.org/10.1109/ECCE57851.2023.10101619","url":null,"abstract":"ElectroEncephaloGram (EEG) is associated with multiple functions, including communications with neurons, organic monitoring, and interactions with external stimuli. By decoding EEG signals, certain human activities such as sleeping, brain diseases, motor imagery, movement of limbs, and others can be observed and controlled through the brain-computer interface (BCI). Therefore, it is vital to efficiently process EEG signals with a robust and accurate system to build BCI systems with powerful applications. However, as a weak biosignal, EEG demands a fast-reaction system signal processing with high accuracy and sensitivity. In this work, a hardware/software co-design network based on Extreme Learning Machine (ELM) is introduced for the classification of certain actions, motor imagery of the human brain. This system is based on an optimized Hierarchical Extreme Learning Machine (H-ELM) on the software layer. The proposed method has advantages over previous designs with an accuracy of 90.3%. It also improves the training speed by around 25X compared to conventional methods. The software model is also translated into efficient FPGA hardware design to maintain high computation efficiency and reduce power consumption for biomedical applications.","PeriodicalId":131537,"journal":{"name":"2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127990144","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: 2

Seagull Optimization Algorithm for Solving Economic Load Dispatch Problem 求解经济负荷调度问题的海鸥优化算法

2023 International Conference on Electrical, Computer and Communication Engineering (ECCE) Pub Date : 2023-02-23 DOI: 10.1109/ECCE57851.2023.10101516

Mohammad Hanif, N. Mohammad, K. Biswas, Bijoy Harun

引用次数: 1

Segmented Nonnegative Matrix Factorization for Hyperspectral Image Classification 高光谱图像分类的分割非负矩阵分解

2023 International Conference on Electrical, Computer and Communication Engineering (ECCE) Pub Date : 2023-02-23 DOI: 10.1109/ECCE57851.2023.10101584

Md. Hasanul Bari, Tanver Ahmed, M. I. Afjal, A. M. Nitu, Md. Palash Uddin, Md Abu Marjan

{"title":"Segmented Nonnegative Matrix Factorization for Hyperspectral Image Classification","authors":"Md. Hasanul Bari, Tanver Ahmed, M. I. Afjal, A. M. Nitu, Md. Palash Uddin, Md Abu Marjan","doi":"10.1109/ECCE57851.2023.10101584","DOIUrl":"https://doi.org/10.1109/ECCE57851.2023.10101584","url":null,"abstract":"The remote sensing hyperspectral image (HSI) consists of hundreds of narrow and adjoining spectral bands. It carries a lot of significant information about the earth's objects. However, the use of all HSI bands leads to higher misclassification. Band reduction is a potential solution to resolve this issue, where feature selection and feature extraction methods are commonly accomplished for the reduction of bands. One of the most commonly used unsupervised feature extraction techniques is the Principal Component Analysis (PCA). But it fails to bring out the local intrinsic information from the HSI as it ponders only the global variation of the data. This problem can be addressed by the Segmented PCA (SPCA) which exploits both the global and local variance of the data by partitioning it into highly correlated blocks. Beside, another unsupervised feature extraction technique named Nonnegative Matrix Factorization (NMF) is also applied for HSI by approximating the data in a low-dimensional subspace. In this paper, we propose a feature extraction method, named Segmented Nonnegative Matrix Factorization (SNMF), performing NMF on the segmented strongly correlated blocks of HSI data. The efficacy of the proposed method is compared with PCA, NMF, and SPCA on the Indian Pines dataset with a support vector machine classifier. The experimental result shows that SNMF (89.00%) outperforms PCA (84.33%), NMF (85.37%), and SPCA (87.59%) over all classes' samples.","PeriodicalId":131537,"journal":{"name":"2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129425128","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

A Machine Learning and Deep Learning Based Approach to Detect Inaccurate Health Information in Bengali Language 基于机器学习和深度学习的孟加拉语不准确健康信息检测方法

2023 International Conference on Electrical, Computer and Communication Engineering (ECCE) Pub Date : 2023-02-23 DOI: 10.1109/ECCE57851.2023.10101612

Nusrat Taki, Eshatur Showan, Umratul Chowdhury, Farzana Tasnim

{"title":"A Machine Learning and Deep Learning Based Approach to Detect Inaccurate Health Information in Bengali Language","authors":"Nusrat Taki, Eshatur Showan, Umratul Chowdhury, Farzana Tasnim","doi":"10.1109/ECCE57851.2023.10101612","DOIUrl":"https://doi.org/10.1109/ECCE57851.2023.10101612","url":null,"abstract":"The spread of false health news and its dis-semination on the internet has become a major concern, due to its potential to have disastrous effects. To detect it, numerous approaches have been attempted. However, we are aware of very few studies that have sought to identify health related false information in Bangla. In this study, we have analyzed the performance of various Machine Learning and Deep Learning approaches in detecting Bangla health-related misinformation that is available online. We have created a comprehensive data repository, consisting more than 5000 data, manually annotated to two fixed categories. Several supervised machine learning classifiers and Deep Learning algorithms have been employed in this experiment to detect fake health news at the textual level. Our experiment achieves maximum accuracy of 88% in the Passive Aggressive approach and 89% in the Bi-LSTM approach. We believe that our dataset is a significant collection of health-related data in Bangla. It may open up new perspectives for the analysis of Bangla-language and health misinformation detection.","PeriodicalId":131537,"journal":{"name":"2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)","volume":"263 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133579897","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