Pub Date : 2022-10-21DOI: 10.1109/INMIC56986.2022.9972927
Danyal, Usman Azmat
Human Activity tracking is the process of detection and understanding of the human activity. It can be done by analyzing human motion behavior data extracted from different smartphone-embedded sensors. Recognizing human activity has become widely popular and particularly attracted many researchers in different industries. Activity recognition has become increasingly important in many areas, especially for the recognition of fitness, sports, and health monitoring. This paper propose a robust model that is trained and tested on remotely extracted data from the smartphone-embedded inertial sensor. Initially, the system clean the input data and then performs windowing and segmentation. After pre-processing, a number of features are extracted. Further, the Lukasiewicz similarity measure (LS) based features selection is used to reduce the features set by removing the least important features. In the next step, the Yeo-Johnson power transformation method is utilized to optimize the selected features. The optimized features set is then forwarded to the multi-class support vector machines (SVM) classifier. The system was designed and experimented with over a well-known dataset named WISDM. The presented model performed well by achieving a mean accuracy rate of 94%.
{"title":"Human Activity Recognition via Smartphone Embedded Sensor using Multi-Class SVM","authors":"Danyal, Usman Azmat","doi":"10.1109/INMIC56986.2022.9972927","DOIUrl":"https://doi.org/10.1109/INMIC56986.2022.9972927","url":null,"abstract":"Human Activity tracking is the process of detection and understanding of the human activity. It can be done by analyzing human motion behavior data extracted from different smartphone-embedded sensors. Recognizing human activity has become widely popular and particularly attracted many researchers in different industries. Activity recognition has become increasingly important in many areas, especially for the recognition of fitness, sports, and health monitoring. This paper propose a robust model that is trained and tested on remotely extracted data from the smartphone-embedded inertial sensor. Initially, the system clean the input data and then performs windowing and segmentation. After pre-processing, a number of features are extracted. Further, the Lukasiewicz similarity measure (LS) based features selection is used to reduce the features set by removing the least important features. In the next step, the Yeo-Johnson power transformation method is utilized to optimize the selected features. The optimized features set is then forwarded to the multi-class support vector machines (SVM) classifier. The system was designed and experimented with over a well-known dataset named WISDM. The presented model performed well by achieving a mean accuracy rate of 94%.","PeriodicalId":404424,"journal":{"name":"2022 24th International Multitopic Conference (INMIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128981881","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 : 2022-10-21DOI: 10.1109/INMIC56986.2022.9972954
Q. Ain, Sohail Iqbal
Electrical load forecasting is an integral part of power system planning, operation, and control. Accurate load forecasting is beneficial for making various operational decisions such as energy generation, reliability analysis, and dispatch scheduling of generated energy. However, short-term load fore-casting is difficult due to the complexity posed by the nature of load time series as it expresses multiple seasonality and nonlinear trend. In this paper, we propose an extension of a novel hybrid hierarchical deep learning-based forecast model which incorporates multiple seasonality. The original groundbreaking hybrid forecasting model is developed by Smyl. The model presented in this paper is based on a dilated recurrent neural network with a context vector by integrating exponential smoothing (EScdRNN). Exponential smoothing performs the adaptive time series processing whereas dilated recurrent neural network using context vector helps in cross-learning. This helps in the selection of useful input information which leads to improved accuracy. The results of the proposed methodology are compared with different statistical machine learning methods which show the potential of our proposed approach in terms of increased accuracy.
{"title":"Enhancing Short-Term Load Forecasting using ES-dRNN with Context Vector","authors":"Q. Ain, Sohail Iqbal","doi":"10.1109/INMIC56986.2022.9972954","DOIUrl":"https://doi.org/10.1109/INMIC56986.2022.9972954","url":null,"abstract":"Electrical load forecasting is an integral part of power system planning, operation, and control. Accurate load forecasting is beneficial for making various operational decisions such as energy generation, reliability analysis, and dispatch scheduling of generated energy. However, short-term load fore-casting is difficult due to the complexity posed by the nature of load time series as it expresses multiple seasonality and nonlinear trend. In this paper, we propose an extension of a novel hybrid hierarchical deep learning-based forecast model which incorporates multiple seasonality. The original groundbreaking hybrid forecasting model is developed by Smyl. The model presented in this paper is based on a dilated recurrent neural network with a context vector by integrating exponential smoothing (EScdRNN). Exponential smoothing performs the adaptive time series processing whereas dilated recurrent neural network using context vector helps in cross-learning. This helps in the selection of useful input information which leads to improved accuracy. The results of the proposed methodology are compared with different statistical machine learning methods which show the potential of our proposed approach in terms of increased accuracy.","PeriodicalId":404424,"journal":{"name":"2022 24th International Multitopic Conference (INMIC)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127666544","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}
Diabetes is one of the world's most serious public health problems. It has a direct impact on an individual's quality of life, as well as the necessity for ongoing medical care and financial implications. Therefore, it is critical to maintaining a healthy blood glucose level. In the developing countries diabetic patients are at high risk of amputation due to improper oxygen supply to distal body parts. The objective of this study is to design a “diabetes-centered cost-effective therapeutic device” based on the principle of reflexology. The proposed device aims to monitor the patient's health condition, avoid distal part amputation through improved oxygen saturation in the blood by providing vibrational therapy, and lessen the dependency of patients on medication. The scope of this study is to propose the design and approach by using established principles of reflexology and prior to patient application clinical trials will be required for modifications.
{"title":"Acupressure based Therapeutic Smart Shoe for Developing Countries","authors":"Syed Muhammad Razi Kazim Naqvi, Asma Iqbal, Sumbala Ameen, Namra Afzal","doi":"10.1109/INMIC56986.2022.9972978","DOIUrl":"https://doi.org/10.1109/INMIC56986.2022.9972978","url":null,"abstract":"Diabetes is one of the world's most serious public health problems. It has a direct impact on an individual's quality of life, as well as the necessity for ongoing medical care and financial implications. Therefore, it is critical to maintaining a healthy blood glucose level. In the developing countries diabetic patients are at high risk of amputation due to improper oxygen supply to distal body parts. The objective of this study is to design a “diabetes-centered cost-effective therapeutic device” based on the principle of reflexology. The proposed device aims to monitor the patient's health condition, avoid distal part amputation through improved oxygen saturation in the blood by providing vibrational therapy, and lessen the dependency of patients on medication. The scope of this study is to propose the design and approach by using established principles of reflexology and prior to patient application clinical trials will be required for modifications.","PeriodicalId":404424,"journal":{"name":"2022 24th International Multitopic Conference (INMIC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129856472","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 : 2022-10-21DOI: 10.1109/INMIC56986.2022.9972945
Syed Muhammad Faraz Ali, M. Tahir, A. B. Khalid
Polyps segmentation is one of the key medical challenges in the gastrointestinal (GI) tract. Polyps segmentation provides the early-stage diagnosis of polyps which may lead to colon cancer in the GI tract. Deep learning models such as U-Net can segment polyps with good performance. But individual deep learning models may suffer from generalization problems. Deep ensemble learning combines the power of both deep and ensemble learning so that the final combined model has better generalization ability. In this paper, a bagging based U-Net architecture (BaggedUNet) is proposed to improve the polyps segmentation in GI-Tract. Our proposed BaggedUNet model trains several lighter U-Net architectures. Decisions from various models are then combined using majority voting. The proposed method is compared with recent deep learning architectures: U-Net and ResUNet++. The evaluation of models is performed using quantitative metrics including Dice coefficient and mean Intersection over Union (mIoU). The proposed BaggedUNet architecture was able to achieve 3 %-9 % improvement on different evaluation metrics on two publicly available datasets for polyps segmentation.
{"title":"BaggedUNet: Deep Machine Vision approach for Polyps Segmentation in Gastrointestinal Tract","authors":"Syed Muhammad Faraz Ali, M. Tahir, A. B. Khalid","doi":"10.1109/INMIC56986.2022.9972945","DOIUrl":"https://doi.org/10.1109/INMIC56986.2022.9972945","url":null,"abstract":"Polyps segmentation is one of the key medical challenges in the gastrointestinal (GI) tract. Polyps segmentation provides the early-stage diagnosis of polyps which may lead to colon cancer in the GI tract. Deep learning models such as U-Net can segment polyps with good performance. But individual deep learning models may suffer from generalization problems. Deep ensemble learning combines the power of both deep and ensemble learning so that the final combined model has better generalization ability. In this paper, a bagging based U-Net architecture (BaggedUNet) is proposed to improve the polyps segmentation in GI-Tract. Our proposed BaggedUNet model trains several lighter U-Net architectures. Decisions from various models are then combined using majority voting. The proposed method is compared with recent deep learning architectures: U-Net and ResUNet++. The evaluation of models is performed using quantitative metrics including Dice coefficient and mean Intersection over Union (mIoU). The proposed BaggedUNet architecture was able to achieve 3 %-9 % improvement on different evaluation metrics on two publicly available datasets for polyps segmentation.","PeriodicalId":404424,"journal":{"name":"2022 24th International Multitopic Conference (INMIC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132074933","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 : 2022-10-21DOI: 10.1109/INMIC56986.2022.9972924
Usman Tahir, Amanullah Yasin, Ahmad Jalal
The Artificial Agent's ability to enhance knowledge incrementally for new data is challenging in class incremental learning because of catastrophic forgetting in which new classes make the trained model quickly forget old classes knowledge. Knowledge distilling techniques and keeping subset of data from the old classes have been proposed to revamp models to accommodate new classes. These techniques allow models to sustain their knowledge without forgetting everything they already know but somewhat alleviate the catastrophic forgetting problem. In this study we propose class incremental learning using bi-distillation (CILBD) method that effectively learn not only the classes of the new data but also previously learned classes. The proposed architecture uses knowledge distillation in such a way that the student model directly learns knowledge from two teacher model and thus alleviate the forgetting of the old class. Our experiments on the iCIFAR-100 dataset showed that the proposed method is more accurate at classifying, forgets less, and works better than state-of-the-art methods.
{"title":"Class Incremental Learning for Visual Task using Knowledge Distillation","authors":"Usman Tahir, Amanullah Yasin, Ahmad Jalal","doi":"10.1109/INMIC56986.2022.9972924","DOIUrl":"https://doi.org/10.1109/INMIC56986.2022.9972924","url":null,"abstract":"The Artificial Agent's ability to enhance knowledge incrementally for new data is challenging in class incremental learning because of catastrophic forgetting in which new classes make the trained model quickly forget old classes knowledge. Knowledge distilling techniques and keeping subset of data from the old classes have been proposed to revamp models to accommodate new classes. These techniques allow models to sustain their knowledge without forgetting everything they already know but somewhat alleviate the catastrophic forgetting problem. In this study we propose class incremental learning using bi-distillation (CILBD) method that effectively learn not only the classes of the new data but also previously learned classes. The proposed architecture uses knowledge distillation in such a way that the student model directly learns knowledge from two teacher model and thus alleviate the forgetting of the old class. Our experiments on the iCIFAR-100 dataset showed that the proposed method is more accurate at classifying, forgets less, and works better than state-of-the-art methods.","PeriodicalId":404424,"journal":{"name":"2022 24th International Multitopic Conference (INMIC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125339395","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 : 2022-10-21DOI: 10.1109/INMIC56986.2022.9972956
R. Tahir, M. Naeem
Social media platforms like Twitter, Facebook, Instagram, etc., are considered a common source of extracting information about individuals, such as their needs, interests, and opinions. Our major contribution in this paper is to identify user interests and desires related to the fashion industry in Pakistan. Since people in Pakistan mostly write tweets and reviews in Roman Urdu, the dataset we focused on in this research was comprised of Roman Urdu Tweets and Google Map reviews. From the literature, we observed that not much effort has been done on Roman Urdu tweets and reviews because of its being a low resource language. In terms of methodology, we applied LDA, LSA, and BERT for topic modeling; Vadar combined with TextBlob and DistilBert for sentiment analysis; and K-Means for identifying user clusters with similar interests. In our experiments, we used 15000 tweets and 6000 Google reviews. We were able to create five distinct clusters for each brand. These clusters were further used to track the users based on their interests. We evaluated the performance of our approach and validated it empirically based on Cohen's Kappa score, and achieved a score of 0.45 that shows moderate agreement between human and machine.
{"title":"A Machine Learning based Approach to Identify User Interests from Social Data","authors":"R. Tahir, M. Naeem","doi":"10.1109/INMIC56986.2022.9972956","DOIUrl":"https://doi.org/10.1109/INMIC56986.2022.9972956","url":null,"abstract":"Social media platforms like Twitter, Facebook, Instagram, etc., are considered a common source of extracting information about individuals, such as their needs, interests, and opinions. Our major contribution in this paper is to identify user interests and desires related to the fashion industry in Pakistan. Since people in Pakistan mostly write tweets and reviews in Roman Urdu, the dataset we focused on in this research was comprised of Roman Urdu Tweets and Google Map reviews. From the literature, we observed that not much effort has been done on Roman Urdu tweets and reviews because of its being a low resource language. In terms of methodology, we applied LDA, LSA, and BERT for topic modeling; Vadar combined with TextBlob and DistilBert for sentiment analysis; and K-Means for identifying user clusters with similar interests. In our experiments, we used 15000 tweets and 6000 Google reviews. We were able to create five distinct clusters for each brand. These clusters were further used to track the users based on their interests. We evaluated the performance of our approach and validated it empirically based on Cohen's Kappa score, and achieved a score of 0.45 that shows moderate agreement between human and machine.","PeriodicalId":404424,"journal":{"name":"2022 24th International Multitopic Conference (INMIC)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126404144","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 : 2022-10-21DOI: 10.1109/INMIC56986.2022.9972969
Ghiayas Tahir, Arshad Hassan
In this paper, design of a passive power divider using Groove Gap Waveguide (GGW) is presented for mm-wave frequency spectrum (28 GHz sub band) for 5G and beyond applications. A one-to-two way power divider is initially designed by modifying T-junction with three matching pins. Subsequently, designed T-junction is scaled for one-to-four way passive power divider. Simulation results are quite promising and reflection coefficient less than 20 dB is achieved for frequency spectrum from 24.592 GHz to 34.259 GHz for one-to-two way power divider, and 26.265 GHz to 32.981 GHz for one-to-four way power divider. Dimensions of GGW are selected as per standardized rectangular waveguide for integration and practical utilization for 5G applications at 28 GHz.
{"title":"Design of Power Divider for Groove Gap Waveguide at Millimeter Wave Spectrum","authors":"Ghiayas Tahir, Arshad Hassan","doi":"10.1109/INMIC56986.2022.9972969","DOIUrl":"https://doi.org/10.1109/INMIC56986.2022.9972969","url":null,"abstract":"In this paper, design of a passive power divider using Groove Gap Waveguide (GGW) is presented for mm-wave frequency spectrum (28 GHz sub band) for 5G and beyond applications. A one-to-two way power divider is initially designed by modifying T-junction with three matching pins. Subsequently, designed T-junction is scaled for one-to-four way passive power divider. Simulation results are quite promising and reflection coefficient less than 20 dB is achieved for frequency spectrum from 24.592 GHz to 34.259 GHz for one-to-two way power divider, and 26.265 GHz to 32.981 GHz for one-to-four way power divider. Dimensions of GGW are selected as per standardized rectangular waveguide for integration and practical utilization for 5G applications at 28 GHz.","PeriodicalId":404424,"journal":{"name":"2022 24th International Multitopic Conference (INMIC)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133470330","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 : 2022-10-21DOI: 10.1109/INMIC56986.2022.9972971
Chaudry Bilal Ahmad Khan
The purpose of this study is to investigate the moderating effect of individual entrepreneurial orientation between the three factors of the theory of planned behavior and entrepreneurial intention. A sample of 307 students was analyzed using PLS-SEM. The results revealed that individual entrepreneurial orientation has a significant positive moderating effect on the relationship between attitude towards behavior and entrepreneurial intention, it has a significant reverse effect on the relationship between perceived control behavior and entrepreneurial intention, and no significant effect on the relationship between subjective norm and entrepreneurial intention. The study concludes that although students' individual entrepreneurial orientation significantly enhances the attitude toward starting a new venture, it also reduces the belief to control the newly started venture. The results also show that students without any support from family or friends, feel vulnerable to the challenges to be faced in the market.
{"title":"Moderating Effect of Entrepreneurial Orientation on Attitude Towards Intent to Start a New Venture","authors":"Chaudry Bilal Ahmad Khan","doi":"10.1109/INMIC56986.2022.9972971","DOIUrl":"https://doi.org/10.1109/INMIC56986.2022.9972971","url":null,"abstract":"The purpose of this study is to investigate the moderating effect of individual entrepreneurial orientation between the three factors of the theory of planned behavior and entrepreneurial intention. A sample of 307 students was analyzed using PLS-SEM. The results revealed that individual entrepreneurial orientation has a significant positive moderating effect on the relationship between attitude towards behavior and entrepreneurial intention, it has a significant reverse effect on the relationship between perceived control behavior and entrepreneurial intention, and no significant effect on the relationship between subjective norm and entrepreneurial intention. The study concludes that although students' individual entrepreneurial orientation significantly enhances the attitude toward starting a new venture, it also reduces the belief to control the newly started venture. The results also show that students without any support from family or friends, feel vulnerable to the challenges to be faced in the market.","PeriodicalId":404424,"journal":{"name":"2022 24th International Multitopic Conference (INMIC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117306499","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 : 2022-10-21DOI: 10.1109/INMIC56986.2022.9972886
A. Khan, S. Siddiqui
Emerging applications of Wireless Sensor Networks (WSN) often rely on generation and collection of heterogenous data. In various application scenarios of WSN such as health care and surveillance, the data needs to be prioritized for timely actions. This paper offers a comparison of two MAC schemes Priority-MAC and FROG-MAC that have been developed to deal with heterogeneous traffic in WSN; Priority-MAC was designed based on differentiated TDMA slot allocation for traffic of different priorities, whereas FROG-MAC proposed to transmit traffic of low priority in fragments in order to earlier transmit the high priority data. Traffic of two priorities (urgent and normal) has been used for simulations over 30 nodes in a single-hop environment. Maximum and average delay of both protocols have been compared. It has been found that FROG-MAC serves better as compared to Priority-MAC due to no requirement of waiting for complete transmission of low priority packets. Furthermore, the influence of varying network and fragment size over delay of urgent traffic in FROG-MAC has also been studied
{"title":"Performance Evaluation of Priority Mechanisms for Industrial Wireless Sensor Networks","authors":"A. Khan, S. Siddiqui","doi":"10.1109/INMIC56986.2022.9972886","DOIUrl":"https://doi.org/10.1109/INMIC56986.2022.9972886","url":null,"abstract":"Emerging applications of Wireless Sensor Networks (WSN) often rely on generation and collection of heterogenous data. In various application scenarios of WSN such as health care and surveillance, the data needs to be prioritized for timely actions. This paper offers a comparison of two MAC schemes Priority-MAC and FROG-MAC that have been developed to deal with heterogeneous traffic in WSN; Priority-MAC was designed based on differentiated TDMA slot allocation for traffic of different priorities, whereas FROG-MAC proposed to transmit traffic of low priority in fragments in order to earlier transmit the high priority data. Traffic of two priorities (urgent and normal) has been used for simulations over 30 nodes in a single-hop environment. Maximum and average delay of both protocols have been compared. It has been found that FROG-MAC serves better as compared to Priority-MAC due to no requirement of waiting for complete transmission of low priority packets. Furthermore, the influence of varying network and fragment size over delay of urgent traffic in FROG-MAC has also been studied","PeriodicalId":404424,"journal":{"name":"2022 24th International Multitopic Conference (INMIC)","volume":"172 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117317432","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 : 2022-10-21DOI: 10.1109/INMIC56986.2022.9972951
Rabia Fazal Dad, S. Saleem
Because of increasing energy demand and environmental concerns, use of renewable energy resources has increased during the past two decades. Wind, solar, hydro power, biomass, and hydrogen fuel cells are some common renewable energy resources. Due to their complementary nature and inherit intermittency, renewable energy resources are often combined along with a battery backup to form an off-grid or grid-connected hybrid system, also known as renewable micro grid. Due to the cost and reliability concerns, proper sizing of such system is very crucial at the design stage. This paper reviews various optimization techniques for the optimal sizing of a renewable micro grid. Moreover, based on this review a hybrid strategy that combines various optimization techniques is recommended to optimally size and increase overall efficiency of a renewable micro grid.
{"title":"A Comparative Study of Various Optimization Techniques to Size a Hybrid Renewable Energy System","authors":"Rabia Fazal Dad, S. Saleem","doi":"10.1109/INMIC56986.2022.9972951","DOIUrl":"https://doi.org/10.1109/INMIC56986.2022.9972951","url":null,"abstract":"Because of increasing energy demand and environmental concerns, use of renewable energy resources has increased during the past two decades. Wind, solar, hydro power, biomass, and hydrogen fuel cells are some common renewable energy resources. Due to their complementary nature and inherit intermittency, renewable energy resources are often combined along with a battery backup to form an off-grid or grid-connected hybrid system, also known as renewable micro grid. Due to the cost and reliability concerns, proper sizing of such system is very crucial at the design stage. This paper reviews various optimization techniques for the optimal sizing of a renewable micro grid. Moreover, based on this review a hybrid strategy that combines various optimization techniques is recommended to optimally size and increase overall efficiency of a renewable micro grid.","PeriodicalId":404424,"journal":{"name":"2022 24th International Multitopic Conference (INMIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129214734","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：