Pub Date : 2022-02-24DOI: 10.1109/icaeee54957.2022.9836342
Shah Alam, Mahfuzulhoq Chowdhury, S. Rasel
Over the years, the Post-office plays an excellent role to raise the standard of people's life by connecting people through the postal package delivery services along with other socio-economic services. With the progress of the digital age, nowadays peoples want more faster and reliable postal services. To cope up with people's expectations, Bangladeshi post-offices require user demand and verification-aware digital mobile applications for the postal services. At present, the existing work on Bangladeshi postal services do not present any intelligent digital mail mobile application by considering user demand-aware postal package delivery, login/signup system for users and officials, package tracking and receiver verification by using QR code, different bill payment option, nearby post-office suggestion, complaint regarding service delivery, help, and customer notification regarding the package delivery at the same time. To overcome the existing issues, this paper presents a user demand and verification-aware 'digital mail’ mobile application featuring flexible parcel delivery for the Bangladeshi post offices. The proposed mobile application offers several facilities for postal services like user verification, login, demand-aware package submission, payment, package notification, and delivery, parcel tracking, user feedback, and help features. The user feedback results with almost 85% users satisfaction indicate the necessity of the proposed system.
{"title":"Digital Mail: An User Demand and Verification-aware Mobile Application Featuring Parcel Delivery for the Bangladeshi Postal Services","authors":"Shah Alam, Mahfuzulhoq Chowdhury, S. Rasel","doi":"10.1109/icaeee54957.2022.9836342","DOIUrl":"https://doi.org/10.1109/icaeee54957.2022.9836342","url":null,"abstract":"Over the years, the Post-office plays an excellent role to raise the standard of people's life by connecting people through the postal package delivery services along with other socio-economic services. With the progress of the digital age, nowadays peoples want more faster and reliable postal services. To cope up with people's expectations, Bangladeshi post-offices require user demand and verification-aware digital mobile applications for the postal services. At present, the existing work on Bangladeshi postal services do not present any intelligent digital mail mobile application by considering user demand-aware postal package delivery, login/signup system for users and officials, package tracking and receiver verification by using QR code, different bill payment option, nearby post-office suggestion, complaint regarding service delivery, help, and customer notification regarding the package delivery at the same time. To overcome the existing issues, this paper presents a user demand and verification-aware 'digital mail’ mobile application featuring flexible parcel delivery for the Bangladeshi post offices. The proposed mobile application offers several facilities for postal services like user verification, login, demand-aware package submission, payment, package notification, and delivery, parcel tracking, user feedback, and help features. The user feedback results with almost 85% users satisfaction indicate the necessity of the proposed system.","PeriodicalId":383872,"journal":{"name":"2022 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126764615","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}
Pub Date : 2022-02-24DOI: 10.1109/icaeee54957.2022.9836361
Khandakar Rabbi Ahmed, Md. Alomgir Hossain, A. Akter, Lamia Akthar
This research focuses on secure automating the level crossing and tracking the train using a microcontroller and a train detection system. An important issue in Bangladesh's railway transportation is the level crossings bar controlling system. The bars are presently manually adjusted in Bangladeshi railways. As soon as the train arrives, the bar lineman will know. On the basis of that data, they will close and re-open the A lineman's irresponsibility is a big issue. An automated railway gate control system can prevent this by automatically closing or opening the bar as a train arrives or departs. In normal operation, the sensors close the bars when a train is spotted and open them after it departs. Trains, however, will be GPS-tracked. The passengers and railroad authorities could track it. Each train has its own code. With the code, passengers can communicate with the train tracking system. In order to avoid collisions, the authority and train driver might be alerted.
{"title":"A Secure Automated Level Crossing and Train Detection System for Bangladesh Railway","authors":"Khandakar Rabbi Ahmed, Md. Alomgir Hossain, A. Akter, Lamia Akthar","doi":"10.1109/icaeee54957.2022.9836361","DOIUrl":"https://doi.org/10.1109/icaeee54957.2022.9836361","url":null,"abstract":"This research focuses on secure automating the level crossing and tracking the train using a microcontroller and a train detection system. An important issue in Bangladesh's railway transportation is the level crossings bar controlling system. The bars are presently manually adjusted in Bangladeshi railways. As soon as the train arrives, the bar lineman will know. On the basis of that data, they will close and re-open the A lineman's irresponsibility is a big issue. An automated railway gate control system can prevent this by automatically closing or opening the bar as a train arrives or departs. In normal operation, the sensors close the bars when a train is spotted and open them after it departs. Trains, however, will be GPS-tracked. The passengers and railroad authorities could track it. Each train has its own code. With the code, passengers can communicate with the train tracking system. In order to avoid collisions, the authority and train driver might be alerted.","PeriodicalId":383872,"journal":{"name":"2022 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126127616","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}
Pub Date : 2022-02-24DOI: 10.1109/icaeee54957.2022.9836425
Bristy Chanda, H. Nyeem
Automatic Hand Gesture Recognition is a key requirement for variety of applications, including translation of Sign Language, Human-Computer Interaction (HCI) and, ubiquitous vision-based systems. Due to the lighting variance and complicated background in the input image set of gestures, meeting this criterion remains a challenge. This paper introduces semantic segmentation to deep learning-based hand gesture recognition system for sign language translation. Building on the U - Net architecture, the proposed model obtains the semantically segmented mask of the input image, which is then fed to convolutional neural networks (CNNs) for multiclass classification. The proposed model is trained and tested for four different depths of the CNN architectures followed by the comparison with some pre-trained CNN architectures such as Inception V3, VGG16, VGG19, ResNet50. The proposed model is evaluated on National University of Singapore (NUS) hand posture dataset II (subset A), which contains 2000 images in 10 classes. A significant recognition rate of 97.15 % is achieved for the proposed model outperforming a set of prominent models and demonstrating its promises for sign language translation.
{"title":"Automatic Hand Gesture Recognition with Semantic Segmentation and Deep Learning","authors":"Bristy Chanda, H. Nyeem","doi":"10.1109/icaeee54957.2022.9836425","DOIUrl":"https://doi.org/10.1109/icaeee54957.2022.9836425","url":null,"abstract":"Automatic Hand Gesture Recognition is a key requirement for variety of applications, including translation of Sign Language, Human-Computer Interaction (HCI) and, ubiquitous vision-based systems. Due to the lighting variance and complicated background in the input image set of gestures, meeting this criterion remains a challenge. This paper introduces semantic segmentation to deep learning-based hand gesture recognition system for sign language translation. Building on the U - Net architecture, the proposed model obtains the semantically segmented mask of the input image, which is then fed to convolutional neural networks (CNNs) for multiclass classification. The proposed model is trained and tested for four different depths of the CNN architectures followed by the comparison with some pre-trained CNN architectures such as Inception V3, VGG16, VGG19, ResNet50. The proposed model is evaluated on National University of Singapore (NUS) hand posture dataset II (subset A), which contains 2000 images in 10 classes. A significant recognition rate of 97.15 % is achieved for the proposed model outperforming a set of prominent models and demonstrating its promises for sign language translation.","PeriodicalId":383872,"journal":{"name":"2022 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129187166","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}
Pub Date : 2022-02-24DOI: 10.1109/icaeee54957.2022.9836373
Ruma, H. Hosano, T. Sakugawa, H. Akiyama
High voltage pulsed electric discharge in water is an effective method for generation of enormous chemical active species and reactive radicals. Discharge propagation in gas bubbling water influence the discharge characteristics and the production of chemical active species in water. A magnetic pulsed compression (MPC) pulse power generator with 0.5 J/pulse, 0-30kV is employed to generate discharge in water under both with and without gas bubbling condition. The main objective of this research is to investigate the effect of gas bubbling on the physical characteristics of discharge and to measure H2O2 as an indicator of chemical species formation in water. Depending on the bubbles propagation, discharge characteristics changes from streamer to arc in gas bubbling water, where only streamer discharge propagates in water without gas bubbling. The concentration of H2O2 is higher by the discharge in the presence of gas bubbling than without gas bubbling in water.
{"title":"Effect of Gas Bubbling on the Physical and Chemical Activity of High Voltage Discharge Plasma in Water","authors":"Ruma, H. Hosano, T. Sakugawa, H. Akiyama","doi":"10.1109/icaeee54957.2022.9836373","DOIUrl":"https://doi.org/10.1109/icaeee54957.2022.9836373","url":null,"abstract":"High voltage pulsed electric discharge in water is an effective method for generation of enormous chemical active species and reactive radicals. Discharge propagation in gas bubbling water influence the discharge characteristics and the production of chemical active species in water. A magnetic pulsed compression (MPC) pulse power generator with 0.5 J/pulse, 0-30kV is employed to generate discharge in water under both with and without gas bubbling condition. The main objective of this research is to investigate the effect of gas bubbling on the physical characteristics of discharge and to measure H2O2 as an indicator of chemical species formation in water. Depending on the bubbles propagation, discharge characteristics changes from streamer to arc in gas bubbling water, where only streamer discharge propagates in water without gas bubbling. The concentration of H2O2 is higher by the discharge in the presence of gas bubbling than without gas bubbling in water.","PeriodicalId":383872,"journal":{"name":"2022 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123422446","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}
Pub Date : 2022-02-24DOI: 10.1109/icaeee54957.2022.9836422
Md. Shohidul Islam Polash, Shazzad Hossen, Rahmatul Kabir Rasel Sarker, Md. Atik Bhuiyan, A. Taher
Stomach Cancer is a strange development of cells that starts in the stomach. It can be called gastric cancer and can influence any stomach piece. All over the universe, malignant stomach development is the fifth -driving sort of disease and the third driving justification for death from threat. After being determined to have malignant growth, the doctor determines the patient's chances of survival and how long they can survive. The doctor usually estimates lifespan from his previous patient seeing experience; in some cases, estimation is wrong. But with the assistance of machine learning, it is possible to make this assumption very accurately. Typically individuals tackle these issues as regression issues. We have shown how the arrangement is conceivable with multiclass grouping. Moreover, the SEER data set guides us in our outing. Our created model can predict the sur-vival period of Stomach cancer patients. Exceptionally affected characteristics from SEER helped in the ML approaches. These high features feed to eight different classification algorithms: Extra tree, Random Forest, Bagging, Gradient Boost, LightGBM, XGBoost Decision tree, and HGB. The Extra Tree Classifier can predict the survival time with 97.27 % accuracy. These models will revolutionize the medical management of doctors.
胃癌是一种奇怪的细胞发展，始于胃。它可被称为胃癌，可影响胃的任何一块。在整个宇宙中，恶性胃发育是第五种驱动疾病，也是第三种驱动死亡的理由。在被确定患有恶性肿瘤后，医生决定病人的生存机会和他们能活多久。医生通常根据他以前看病人的经验来估计病人的寿命;在某些情况下，估计是错误的。但在机器学习的帮助下，可以非常准确地做出这个假设。通常，个人将这些问题视为回归问题。我们已经展示了多类分组是如何安排的。此外，SEER数据集指导我们的郊游。我们建立的模型可以预测胃癌患者的生存期。来自SEER的异常影响特征有助于ML方法。这些高特征提供给八种不同的分类算法:Extra tree, Random Forest, Bagging, Gradient Boost, LightGBM, XGBoost Decision tree和HGB。Extra Tree Classifier预测存活时间的准确率为97.27%。这些模式将彻底改变医生的医疗管理。
{"title":"Functionality Testing of Machine Learning Algorithms to Anticipate Life Expectancy of Stomach Cancer Patients","authors":"Md. Shohidul Islam Polash, Shazzad Hossen, Rahmatul Kabir Rasel Sarker, Md. Atik Bhuiyan, A. Taher","doi":"10.1109/icaeee54957.2022.9836422","DOIUrl":"https://doi.org/10.1109/icaeee54957.2022.9836422","url":null,"abstract":"Stomach Cancer is a strange development of cells that starts in the stomach. It can be called gastric cancer and can influence any stomach piece. All over the universe, malignant stomach development is the fifth -driving sort of disease and the third driving justification for death from threat. After being determined to have malignant growth, the doctor determines the patient's chances of survival and how long they can survive. The doctor usually estimates lifespan from his previous patient seeing experience; in some cases, estimation is wrong. But with the assistance of machine learning, it is possible to make this assumption very accurately. Typically individuals tackle these issues as regression issues. We have shown how the arrangement is conceivable with multiclass grouping. Moreover, the SEER data set guides us in our outing. Our created model can predict the sur-vival period of Stomach cancer patients. Exceptionally affected characteristics from SEER helped in the ML approaches. These high features feed to eight different classification algorithms: Extra tree, Random Forest, Bagging, Gradient Boost, LightGBM, XGBoost Decision tree, and HGB. The Extra Tree Classifier can predict the survival time with 97.27 % accuracy. These models will revolutionize the medical management of doctors.","PeriodicalId":383872,"journal":{"name":"2022 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125789060","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}
Pub Date : 2022-02-24DOI: 10.1109/icaeee54957.2022.9836471
Md. Rejwanul Hossain, M. Arefin
Point-of-interest (POI) recommendation system is popularly used in location based social networks where the goal is to recommend interesting unvisited locations to users. The sequential nature of check-ins hindered many researchers to apply Recurrent Neural Network (RNN) based models for this task. However, most of the models consider only historical check-ins of the user for generating recommendations and fail to incorporate information about current location and time which plays an important role. For reducing data sparsity in spatial dimension, many models use hierarchical gridding of the map which can not reflect spatial distance properly between neighboring grids. Besides, most of the existing models ignored the impact of weather condition when generating recommendation. Keeping these limitations in mind, in this paper we present a framework for point-of-interest recommendation that can model sequential nature of check-ins using Long Short-Term Memory (LSTM) network. We incorporate current spatiotemporal information with weather condition that can provide better personalized recommendation. Instead of hierarchical gridding, we perform linear interpolation for smooth representation of distance between two locations. Extensive experiments on two real world dataset shows that our proposed method surpasses existing state-of-the art methods by 16-18%.
{"title":"Developing a Framework for Next Point-of-interest Recommendation from Spatiotemporal Data","authors":"Md. Rejwanul Hossain, M. Arefin","doi":"10.1109/icaeee54957.2022.9836471","DOIUrl":"https://doi.org/10.1109/icaeee54957.2022.9836471","url":null,"abstract":"Point-of-interest (POI) recommendation system is popularly used in location based social networks where the goal is to recommend interesting unvisited locations to users. The sequential nature of check-ins hindered many researchers to apply Recurrent Neural Network (RNN) based models for this task. However, most of the models consider only historical check-ins of the user for generating recommendations and fail to incorporate information about current location and time which plays an important role. For reducing data sparsity in spatial dimension, many models use hierarchical gridding of the map which can not reflect spatial distance properly between neighboring grids. Besides, most of the existing models ignored the impact of weather condition when generating recommendation. Keeping these limitations in mind, in this paper we present a framework for point-of-interest recommendation that can model sequential nature of check-ins using Long Short-Term Memory (LSTM) network. We incorporate current spatiotemporal information with weather condition that can provide better personalized recommendation. Instead of hierarchical gridding, we perform linear interpolation for smooth representation of distance between two locations. Extensive experiments on two real world dataset shows that our proposed method surpasses existing state-of-the art methods by 16-18%.","PeriodicalId":383872,"journal":{"name":"2022 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125900930","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}
Pub Date : 2022-02-24DOI: 10.1109/icaeee54957.2022.9836364
Merajur Rahman Mollah, Muhammad Asad Rahman, Md. Shohanur Rahman Shohan
A multi-band Sierpinski carpet fractal antenna with a modified ground plane is designed. Fractal shapes are applied on the both sides of the antenna to achieve multi-band characteristics. Sierpinski carpet fractal with iteration-3 is applied to the rectangular-shaped radiating patch. Here, the novelty of the proposed design is the modified ground plane. The ground is modified through the same fractal shape of the patch (i.e., Sierpinski here) up to 2nd iteration as defected ground structure (DGS) on a partial ground to get more resonant bands over the range of 4 GHz to 12 GHz. Moreover, partial ground helps to get better input impedance matching at the resonance frequencies. The overall dimension of the proposed structure is 45 mm x 60 mm x 1.60 mm. The proposed antenna operates at six resonant frequencies (6 GHz, 6.42 GHz, 7.09 GHz, 7.63 GHz, 9.15 GHz, and 10.11 GHz) over the range of 4 to 12 GHz with good impedance matching, good gain and efficiency. The design is suitable for different applications of C- and X-bands.
{"title":"Design of a Multi-band Sierpinski Carpet Fractal Antenna With Modified Ground Plane","authors":"Merajur Rahman Mollah, Muhammad Asad Rahman, Md. Shohanur Rahman Shohan","doi":"10.1109/icaeee54957.2022.9836364","DOIUrl":"https://doi.org/10.1109/icaeee54957.2022.9836364","url":null,"abstract":"A multi-band Sierpinski carpet fractal antenna with a modified ground plane is designed. Fractal shapes are applied on the both sides of the antenna to achieve multi-band characteristics. Sierpinski carpet fractal with iteration-3 is applied to the rectangular-shaped radiating patch. Here, the novelty of the proposed design is the modified ground plane. The ground is modified through the same fractal shape of the patch (i.e., Sierpinski here) up to 2nd iteration as defected ground structure (DGS) on a partial ground to get more resonant bands over the range of 4 GHz to 12 GHz. Moreover, partial ground helps to get better input impedance matching at the resonance frequencies. The overall dimension of the proposed structure is 45 mm x 60 mm x 1.60 mm. The proposed antenna operates at six resonant frequencies (6 GHz, 6.42 GHz, 7.09 GHz, 7.63 GHz, 9.15 GHz, and 10.11 GHz) over the range of 4 to 12 GHz with good impedance matching, good gain and efficiency. The design is suitable for different applications of C- and X-bands.","PeriodicalId":383872,"journal":{"name":"2022 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114260054","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}
Pub Date : 2022-02-24DOI: 10.1109/icaeee54957.2022.9836354
Zabedur Rahman, Mahfuzulhoq Chowdhury, Abu Bakkar Siddique
Car parking is one of the most significant issues in today's world. Parking cars on surrounding roads and pathways can cause unfair traffic jams and thus hampers people's daily activity. To avoid these problems, the development of a smart car parking system is a major concern for several developed countries. At present, most of the previous studies on car parking systems suffer from several limitations such as lack of security, wastage of time, huge money expenses, and lack of user interest-aware car parking system. To overcome existing challenges, this paper presents a user interest and payment-aware automated car parking system using Internet-of-things (IoT) technology. In this paper, an android application for smart car parking is developed for Bangladeshi people that allow users to choose emergency or non-emergency parking slots based on their interest and payment verification. For anti-theft purposes, this system offers an early alert and notification feature. The experimental test results by investigating several use cases depict the suitability of the proposed system.
{"title":"An User Interest and Payment-aware Automated Car Parking System for the Bangladeshi People Using Android Application","authors":"Zabedur Rahman, Mahfuzulhoq Chowdhury, Abu Bakkar Siddique","doi":"10.1109/icaeee54957.2022.9836354","DOIUrl":"https://doi.org/10.1109/icaeee54957.2022.9836354","url":null,"abstract":"Car parking is one of the most significant issues in today's world. Parking cars on surrounding roads and pathways can cause unfair traffic jams and thus hampers people's daily activity. To avoid these problems, the development of a smart car parking system is a major concern for several developed countries. At present, most of the previous studies on car parking systems suffer from several limitations such as lack of security, wastage of time, huge money expenses, and lack of user interest-aware car parking system. To overcome existing challenges, this paper presents a user interest and payment-aware automated car parking system using Internet-of-things (IoT) technology. In this paper, an android application for smart car parking is developed for Bangladeshi people that allow users to choose emergency or non-emergency parking slots based on their interest and payment verification. For anti-theft purposes, this system offers an early alert and notification feature. The experimental test results by investigating several use cases depict the suitability of the proposed system.","PeriodicalId":383872,"journal":{"name":"2022 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114347975","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}
Pub Date : 2022-02-24DOI: 10.1109/icaeee54957.2022.9836359
S. Haque, Gobinda Chandra Sarker, Kazi Md Sadat
Power generation is increasing worldwide every year to cope with ever-increasing energy demand. Therefore, a significant necessity exists for forecasting the load demand to manage and increase electricity production capacity. Short-term load forecasting (STLF) using artificial neural network has become one of the most efficient and widely popular methods. This paper proposes a hybrid network of Long Short-Term Memory (LSTM) network and Convolutional Neural Network (CNN) to predict demand for seven days into the future. The proposed CNN-LSTM method is compared with various deep learning techniques such as vanilla neural network and gated recurrent unit (GRU). Power Grid Company of Bangladesh (PGCB) has the responsibility of reliable power transmission all over the country. Each model is trained and tested on multivariate historical data collected from the daily report section of PGCB website for the Mymensingh Division in Bangladesh. Various input features such as temperature, peak generation at evening, maximum generation, month and the season of the year are used to aid the prediction. It is found that the proposed CNN-LSTM method outperforms the other models with a MAPE error rate of 2.8992%, which is less than the MAPE error of 5.5554% for demand estimation of seven days used by PGCB.
{"title":"Short-Term Electrical Load Prediction for Future Generation Using Hybrid Deep Learning Model","authors":"S. Haque, Gobinda Chandra Sarker, Kazi Md Sadat","doi":"10.1109/icaeee54957.2022.9836359","DOIUrl":"https://doi.org/10.1109/icaeee54957.2022.9836359","url":null,"abstract":"Power generation is increasing worldwide every year to cope with ever-increasing energy demand. Therefore, a significant necessity exists for forecasting the load demand to manage and increase electricity production capacity. Short-term load forecasting (STLF) using artificial neural network has become one of the most efficient and widely popular methods. This paper proposes a hybrid network of Long Short-Term Memory (LSTM) network and Convolutional Neural Network (CNN) to predict demand for seven days into the future. The proposed CNN-LSTM method is compared with various deep learning techniques such as vanilla neural network and gated recurrent unit (GRU). Power Grid Company of Bangladesh (PGCB) has the responsibility of reliable power transmission all over the country. Each model is trained and tested on multivariate historical data collected from the daily report section of PGCB website for the Mymensingh Division in Bangladesh. Various input features such as temperature, peak generation at evening, maximum generation, month and the season of the year are used to aid the prediction. It is found that the proposed CNN-LSTM method outperforms the other models with a MAPE error rate of 2.8992%, which is less than the MAPE error of 5.5554% for demand estimation of seven days used by PGCB.","PeriodicalId":383872,"journal":{"name":"2022 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130553704","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}
Pub Date : 2022-02-24DOI: 10.1109/icaeee54957.2022.9836405
Mohammad Saiful Islam, Md. Rashidul Islam, M. Shafiullah, Md. Samiul Azam
Low-frequency oscillation (LFO) is a significant problem for Multi-machine power system (MPS) networks. It makes the power system networks unstable. In this article, a new Power system stabilizer (PSS) design method is demonstrated using the Dragonfly algorithm (DA). To enhance system damping, a damping ratio-based objective function is used, and a typical lead-lag type PSS (CPSS) structure is considered. In this case, the algorithm's ability to provide the best PSS design regardless of the starting guess demonstrates its robustness. This method is tested on two separate multi-machine networks exposed to a 3-Φ fault, and compared with two well-known optimization algorithms called PSO and BSA. The optimization results show that the DA technique provides better system damping than PSO and BSA.
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