Pub Date : 2022-12-02DOI: 10.1109/ETECTE55893.2022.10007247
Saman Khan, Nimra Latif, Huzafa Adnan, I. Khosa
Agriculture is the backbone of Pakistan economy. According to an estimate, 38% of total labor is connected with agriculture. The quality of agricultural yield is ensured employing multiple procedures including soil preparation, proper cultivation, fertilization, and applying pesticide spray to avoid contamination. The last step involves manual visual inspection of the crop to detect any infection which is a lengthy procedure as well as tiring. To facilitate the farmer, we propose a computer vision-based automatic health assessment system for crops mounted on a drone. For evaluation of the system, we opted for two of the major crops of Pakistan: potato and cotton where the experiment is performed in the field for real time testing. The purpose build single board computer Jetson Nano developed by Nvidia Inc. is used for real time processing. The developed system showed 99% accuracy overall for both the crops.
{"title":"A Portable Real Time Health Inspection system for Cotton and Potato Crop using Drone","authors":"Saman Khan, Nimra Latif, Huzafa Adnan, I. Khosa","doi":"10.1109/ETECTE55893.2022.10007247","DOIUrl":"https://doi.org/10.1109/ETECTE55893.2022.10007247","url":null,"abstract":"Agriculture is the backbone of Pakistan economy. According to an estimate, 38% of total labor is connected with agriculture. The quality of agricultural yield is ensured employing multiple procedures including soil preparation, proper cultivation, fertilization, and applying pesticide spray to avoid contamination. The last step involves manual visual inspection of the crop to detect any infection which is a lengthy procedure as well as tiring. To facilitate the farmer, we propose a computer vision-based automatic health assessment system for crops mounted on a drone. For evaluation of the system, we opted for two of the major crops of Pakistan: potato and cotton where the experiment is performed in the field for real time testing. The purpose build single board computer Jetson Nano developed by Nvidia Inc. is used for real time processing. The developed system showed 99% accuracy overall for both the crops.","PeriodicalId":131572,"journal":{"name":"2022 International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128688845","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 : 2022-12-02DOI: 10.1109/ETECTE55893.2022.10007235
Mehwish Moiz, M. Akmal, Muhammad Shakeel Ishtiaq, Usman Javed
Rice leaves may suffer serious impacts such as low production or yield of the respective products if necessary precautions are not taken. Therefore, to ensure the healthy and normal growth of the rice plants, early diagnosis of any disease and application of the necessary therapy to the damaged plants are paramount. Because manual disease diagnosis requires a lot of time and effort, an effective automated method is required for early disease diagnosis. As a result, this study presents a deep learning-based solution to the aforementioned issue for the automated detection of three plant diseases: leaf smut, bacterial leaf blight, and brown spot that frequently affect rice plants. The transfer learning with VGGNet convolutional neural network (CNN), which was pre-trained on a sizable Imagenet dataset, was used in this study to effectively classify the illnesses of rice leaves. A number of cutting-edge classifiers, including Support Vector Machine (SVM), k-nearest neighbour (kNN), Convolutional Neural Network (CNN), Random Forest, and Decision Tree, are used to compare the performance of the proposed framework. The results demonstrate that the proposed CNN-transfer learning framework outperforms other classifiers with a mean accuracy of 97.22% in the 5-fold cross-validation.
{"title":"Classification of Rice Leaves Diseases by Deep CNN-Transfer Learning Approach for Improved Rice Agriculture","authors":"Mehwish Moiz, M. Akmal, Muhammad Shakeel Ishtiaq, Usman Javed","doi":"10.1109/ETECTE55893.2022.10007235","DOIUrl":"https://doi.org/10.1109/ETECTE55893.2022.10007235","url":null,"abstract":"Rice leaves may suffer serious impacts such as low production or yield of the respective products if necessary precautions are not taken. Therefore, to ensure the healthy and normal growth of the rice plants, early diagnosis of any disease and application of the necessary therapy to the damaged plants are paramount. Because manual disease diagnosis requires a lot of time and effort, an effective automated method is required for early disease diagnosis. As a result, this study presents a deep learning-based solution to the aforementioned issue for the automated detection of three plant diseases: leaf smut, bacterial leaf blight, and brown spot that frequently affect rice plants. The transfer learning with VGGNet convolutional neural network (CNN), which was pre-trained on a sizable Imagenet dataset, was used in this study to effectively classify the illnesses of rice leaves. A number of cutting-edge classifiers, including Support Vector Machine (SVM), k-nearest neighbour (kNN), Convolutional Neural Network (CNN), Random Forest, and Decision Tree, are used to compare the performance of the proposed framework. The results demonstrate that the proposed CNN-transfer learning framework outperforms other classifiers with a mean accuracy of 97.22% in the 5-fold cross-validation.","PeriodicalId":131572,"journal":{"name":"2022 International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126820608","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 : 2022-12-02DOI: 10.1109/ETECTE55893.2022.10007120
Tanzeel Zaidi, Samia Aziz, Muhammad Usman, Awais Azam, Adeel Ashraf Cheema, S. Ajmal
Delay constraints and energy consumption are becoming a barrier to running complex applications on mobile devices due to the rise of latency-sensitive applications. One of the fundamental technologies of Mobile Edge Computing (MEC), which compensates for the limitations of mobile devices in terms of storage space, computing power, and battery efficiency, is computation offloading. The computation offloading methods in MEC networks are now the subject of extensive study for both the industry and academia, using a variety of valuable techniques and methodologies. This paper provides a detailed analysis of computing task offloading in a MEC environment. This study focuses on essential challenges related to numerous offloading goals, such as delay time, energy consumption reduction, income maximization, and system utility enhancement.
{"title":"Edge Computing and Computational Task Offloading Analysis – A review study","authors":"Tanzeel Zaidi, Samia Aziz, Muhammad Usman, Awais Azam, Adeel Ashraf Cheema, S. Ajmal","doi":"10.1109/ETECTE55893.2022.10007120","DOIUrl":"https://doi.org/10.1109/ETECTE55893.2022.10007120","url":null,"abstract":"Delay constraints and energy consumption are becoming a barrier to running complex applications on mobile devices due to the rise of latency-sensitive applications. One of the fundamental technologies of Mobile Edge Computing (MEC), which compensates for the limitations of mobile devices in terms of storage space, computing power, and battery efficiency, is computation offloading. The computation offloading methods in MEC networks are now the subject of extensive study for both the industry and academia, using a variety of valuable techniques and methodologies. This paper provides a detailed analysis of computing task offloading in a MEC environment. This study focuses on essential challenges related to numerous offloading goals, such as delay time, energy consumption reduction, income maximization, and system utility enhancement.","PeriodicalId":131572,"journal":{"name":"2022 International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130465474","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 : 2022-12-02DOI: 10.1109/ETECTE55893.2022.10007300
Hassam Uddin Abro, Zafi Sherhan Shah, H. Abbasi
The COVID-19 pandemic continues to negatively impact people's mental health worldwide. Due to the rise in unemployment, loss of income, and lack of social interaction, people are now more likely to feel lonely, go on fewer outings, and dread the unexpected nature of viral transmission. Meanwhile, Public Health authorities are interested in monitoring people's mental and emotional well-being. In this paper, natural language processing is used to analyze human sentiments concerning the COVID-19 pandemic that has been dangerously affecting individuals' mental and physical well-being for more than two years now. Even though several waves of COVID-19 have passed, of which the first and third waves i.e., the initial pandemic period from 20th March 2020 and the rise of the Delta variant from January 2020 had the most impact on the mental health of individuals, this is further evident by the results of this paper. This research focuses on how severely this virus has affected people's mental health and emotions. After processing the data i.e., cleaning, formatting, and removing irregularities from the data, feature engineering models are applied to acquire the results. The results through VADER (valence-aware dictionary and sentiment reasoning) indicate an increase in overall negative sentiments between two mentioned periods. Additionally, the NRC-EIL (National Research Council of Canada - Emotion Intensity Lexicon) analysis showed that “fear” and “sadness” occurred during those times.
{"title":"Analysis Of COVID-19 Effects On Wellbeing - Study Of Reddit Posts Using Natural Language Processing Techniques","authors":"Hassam Uddin Abro, Zafi Sherhan Shah, H. Abbasi","doi":"10.1109/ETECTE55893.2022.10007300","DOIUrl":"https://doi.org/10.1109/ETECTE55893.2022.10007300","url":null,"abstract":"The COVID-19 pandemic continues to negatively impact people's mental health worldwide. Due to the rise in unemployment, loss of income, and lack of social interaction, people are now more likely to feel lonely, go on fewer outings, and dread the unexpected nature of viral transmission. Meanwhile, Public Health authorities are interested in monitoring people's mental and emotional well-being. In this paper, natural language processing is used to analyze human sentiments concerning the COVID-19 pandemic that has been dangerously affecting individuals' mental and physical well-being for more than two years now. Even though several waves of COVID-19 have passed, of which the first and third waves i.e., the initial pandemic period from 20th March 2020 and the rise of the Delta variant from January 2020 had the most impact on the mental health of individuals, this is further evident by the results of this paper. This research focuses on how severely this virus has affected people's mental health and emotions. After processing the data i.e., cleaning, formatting, and removing irregularities from the data, feature engineering models are applied to acquire the results. The results through VADER (valence-aware dictionary and sentiment reasoning) indicate an increase in overall negative sentiments between two mentioned periods. Additionally, the NRC-EIL (National Research Council of Canada - Emotion Intensity Lexicon) analysis showed that “fear” and “sadness” occurred during those times.","PeriodicalId":131572,"journal":{"name":"2022 International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121041466","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 : 2022-12-02DOI: 10.1109/ETECTE55893.2022.10007122
Sameen Fatima, Muhammad Aun, Shafiq Hussain, Badar ul Din, Waseem Sajjad, Nimra Shahzadi, Ramsha Jameel
The medical industry is growing significantly along with emerging technologies like IoT., The fundamental idea behind integrating IoT into healthcare facilities is to enable it remotely accessible. The contact between patient and his doc tor is easy as well detection of disease. The purpose of this research is to provide a secure healthcare framework. This framework used blockchain technique to encrypt the patients' data on cloud servers. The blockchain is adopted in the healthcare industry since data is vulnerable to attacks and patients' record is having sensitive information that should be secure from attacks. For the last few years, different blockchain models have been put forth in the health system. However, these models' decentralized nature-a hallmark of blockchain technology-has led to the emergence of an issue known as illness overlaps as the chains expands. This problem is addressed using a blockchain, which has the ability to add a new block of data in the chain whenever there is an updating in patients' record. Additionally, multilayer blockchain approach is considered to secure the sensitive patient's data. This strategy protects data as well as maintain trust for the user. In this framework, AES 128-bit key is generated which is then provides support to SHA-256. Data is arranged into different blocks and then each block is encrypted to prevent unauthorized access. A system is created to demonstrate how the suggested approach is applied in the real healthcare sector. The efficiency of the proposed framework is compared with the RSA and DSA that are the two widely used encryption methods. Such algorithms, which are essential to blockchain technology and are used in the proposed work, are later evaluated by comparing their performances to one another. According on research findings, the framework has the shortest encryption and decryption time, making its efficiency more reliable and effective. Additionally, it is more durable and effective.
{"title":"A Secure BlockChain Framework for IoT Healthcare","authors":"Sameen Fatima, Muhammad Aun, Shafiq Hussain, Badar ul Din, Waseem Sajjad, Nimra Shahzadi, Ramsha Jameel","doi":"10.1109/ETECTE55893.2022.10007122","DOIUrl":"https://doi.org/10.1109/ETECTE55893.2022.10007122","url":null,"abstract":"The medical industry is growing significantly along with emerging technologies like IoT., The fundamental idea behind integrating IoT into healthcare facilities is to enable it remotely accessible. The contact between patient and his doc tor is easy as well detection of disease. The purpose of this research is to provide a secure healthcare framework. This framework used blockchain technique to encrypt the patients' data on cloud servers. The blockchain is adopted in the healthcare industry since data is vulnerable to attacks and patients' record is having sensitive information that should be secure from attacks. For the last few years, different blockchain models have been put forth in the health system. However, these models' decentralized nature-a hallmark of blockchain technology-has led to the emergence of an issue known as illness overlaps as the chains expands. This problem is addressed using a blockchain, which has the ability to add a new block of data in the chain whenever there is an updating in patients' record. Additionally, multilayer blockchain approach is considered to secure the sensitive patient's data. This strategy protects data as well as maintain trust for the user. In this framework, AES 128-bit key is generated which is then provides support to SHA-256. Data is arranged into different blocks and then each block is encrypted to prevent unauthorized access. A system is created to demonstrate how the suggested approach is applied in the real healthcare sector. The efficiency of the proposed framework is compared with the RSA and DSA that are the two widely used encryption methods. Such algorithms, which are essential to blockchain technology and are used in the proposed work, are later evaluated by comparing their performances to one another. According on research findings, the framework has the shortest encryption and decryption time, making its efficiency more reliable and effective. Additionally, it is more durable and effective.","PeriodicalId":131572,"journal":{"name":"2022 International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121812713","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 : 2022-12-02DOI: 10.1109/ETECTE55893.2022.10007347
Junaid Imtiaz, Talha Shakil, Arsalan Ansari
Energy efficient communication systems are of great importance for both device designers and customers. Higher energy consumption decreases the overall energy efficiency of the systems. Green powered cognitive radio (CR) attributes a promising approach in effective utilization of the radio spectrum. Green powered CR is capable of liberating the wireless network from spectral and energy constraints. This paper proposes a cooperative approach based on energy consumed by circuit-aware cognitive nodes(CN) for a green-powered cognitive scheduler (CS). Energy efficiency (EE) and throughput of the system are derived mathematically for energy consumed involvement (ECI) based cooperation. Simulation results show that the proposed approach increases EE by $+40%$ compared to conventional approach when reporting loss is considered.
{"title":"Energy Consumed Involvement for Circuit-Aware Cognitive Nodes in A Green Powered Cognitive Scheduler","authors":"Junaid Imtiaz, Talha Shakil, Arsalan Ansari","doi":"10.1109/ETECTE55893.2022.10007347","DOIUrl":"https://doi.org/10.1109/ETECTE55893.2022.10007347","url":null,"abstract":"Energy efficient communication systems are of great importance for both device designers and customers. Higher energy consumption decreases the overall energy efficiency of the systems. Green powered cognitive radio (CR) attributes a promising approach in effective utilization of the radio spectrum. Green powered CR is capable of liberating the wireless network from spectral and energy constraints. This paper proposes a cooperative approach based on energy consumed by circuit-aware cognitive nodes(CN) for a green-powered cognitive scheduler (CS). Energy efficiency (EE) and throughput of the system are derived mathematically for energy consumed involvement (ECI) based cooperation. Simulation results show that the proposed approach increases EE by $+40%$ compared to conventional approach when reporting loss is considered.","PeriodicalId":131572,"journal":{"name":"2022 International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124778842","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 : 2022-12-02DOI: 10.1109/ETECTE55893.2022.10007264
Sameen Fatima, Shafiq Hussain, Nimra Shahzadi, Badar ul Din, W. Sajjad, Yasir Saleem, Muhammad Aun
Internet of Things (IoT) plays a vital role in the growth of medical healthcare industry. Therefore, to secure the medical data as well as privacy of users is of major concern. In this paper a hybrid novel encryption technique is used to secure the medical data in IoT infrastructure. In this paper, hybrid encryption of Elliptic Curve Cryptography (ECC), Serpent and Advanced Encryption Standard (AES) is used to improve the security measures as well as data integrity of medical healthcare data. Security Analysis as well as performance comparisons are illustrated in this paper to prove the efficiency of proposed method. It is clearly shown in results that the proposed hybrid encryption method is more efficient than the state-of-art method.
{"title":"A Secure Framework for IoT Healthcare Data Using Hybrid Encryption","authors":"Sameen Fatima, Shafiq Hussain, Nimra Shahzadi, Badar ul Din, W. Sajjad, Yasir Saleem, Muhammad Aun","doi":"10.1109/ETECTE55893.2022.10007264","DOIUrl":"https://doi.org/10.1109/ETECTE55893.2022.10007264","url":null,"abstract":"Internet of Things (IoT) plays a vital role in the growth of medical healthcare industry. Therefore, to secure the medical data as well as privacy of users is of major concern. In this paper a hybrid novel encryption technique is used to secure the medical data in IoT infrastructure. In this paper, hybrid encryption of Elliptic Curve Cryptography (ECC), Serpent and Advanced Encryption Standard (AES) is used to improve the security measures as well as data integrity of medical healthcare data. Security Analysis as well as performance comparisons are illustrated in this paper to prove the efficiency of proposed method. It is clearly shown in results that the proposed hybrid encryption method is more efficient than the state-of-art method.","PeriodicalId":131572,"journal":{"name":"2022 International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE)","volume":"78 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129713054","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 : 2022-12-02DOI: 10.1109/ETECTE55893.2022.10007355
S. Nazir, Mohammad Kaleem
The image and video traffic on the Internet has increased due to the ease and abundance of the image capture and sharing services. However, this can sometimes result in violations of copyright and image tampering for various purposes. Finding differences between images has importance for image retrieval, aiding forensics, disease diagnosis, and environmental changes etc. Many similarity measures have been proposed that use different features extractable from the detected image. In some cases it is important to determine if the set of images differ and if so then to what extent. Digital Rights Management (DRM) has gained prominence and importance due to widespread availability of tools to modify images. Sometimes the need arises to claim the rightful ownership of an image. The blockchain through the use of hash codes can be used to determine tampering similar to its success in many industries where privacy and security of the documents is important. In this paper, we embed the image hash code in the JPEG image header. The proposed system simplifies the hashing and encryption process by processing only the image data without which a reconstruction is not possible. The results show that the image data can thus be shared without the risk of any undetected tampering or misuse.
{"title":"Embedded Hash Codes for Image Similarity Detection and Tamper Proofing","authors":"S. Nazir, Mohammad Kaleem","doi":"10.1109/ETECTE55893.2022.10007355","DOIUrl":"https://doi.org/10.1109/ETECTE55893.2022.10007355","url":null,"abstract":"The image and video traffic on the Internet has increased due to the ease and abundance of the image capture and sharing services. However, this can sometimes result in violations of copyright and image tampering for various purposes. Finding differences between images has importance for image retrieval, aiding forensics, disease diagnosis, and environmental changes etc. Many similarity measures have been proposed that use different features extractable from the detected image. In some cases it is important to determine if the set of images differ and if so then to what extent. Digital Rights Management (DRM) has gained prominence and importance due to widespread availability of tools to modify images. Sometimes the need arises to claim the rightful ownership of an image. The blockchain through the use of hash codes can be used to determine tampering similar to its success in many industries where privacy and security of the documents is important. In this paper, we embed the image hash code in the JPEG image header. The proposed system simplifies the hashing and encryption process by processing only the image data without which a reconstruction is not possible. The results show that the image data can thus be shared without the risk of any undetected tampering or misuse.","PeriodicalId":131572,"journal":{"name":"2022 International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115167548","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 : 2022-12-02DOI: 10.1109/ETECTE55893.2022.10007224
Ahsan Tanveer, Farooq Khan, Muhammad Usman, M. Irfan
The world as of today is dependent almost entirely on fossil fuel for its energy requirements. However, Fossil fuel supplies are limited and non-renewable. Therefore, it is essential to utilise readily available renewable energy sources, such as wind and solar, for a sustainable future. But because these sources are intermittent, a storage mechanism is needed to make them grid compatible. This study outlines the design of a small-scale prototype compressed air energy storage (CAES) plant that uses clean electricity from a supposed PV array or a wind farm to compress atmospheric air for storage in a subsurface tank. The stored air is fed to a generator-coupled turbine to produce electricity on as needed basis. The suggested technique, in contrast to the storage mechanisms found in literature, precludes the use of any fuel. To evaluate the performance of the proposed system, a thorough design approach, thermodynamic analysis, and selection criteria for various plant components are included. The experimental results indicated a decent efficiency of 20%, which is understandable given the plant's modest size and lack of any heat storage mechanism. Nonetheless, a number of methods have been found and are provided in order to increase the system's overall efficiency. In short, the suggested approach has proven its capacity to scale, enabling huge renewable power plants to store extra energy cheaply at the grid level without using costly pumped hydro storage or battery technology.
{"title":"Design & Development of a Prototype Compressed Air Energy Storage Mechanism","authors":"Ahsan Tanveer, Farooq Khan, Muhammad Usman, M. Irfan","doi":"10.1109/ETECTE55893.2022.10007224","DOIUrl":"https://doi.org/10.1109/ETECTE55893.2022.10007224","url":null,"abstract":"The world as of today is dependent almost entirely on fossil fuel for its energy requirements. However, Fossil fuel supplies are limited and non-renewable. Therefore, it is essential to utilise readily available renewable energy sources, such as wind and solar, for a sustainable future. But because these sources are intermittent, a storage mechanism is needed to make them grid compatible. This study outlines the design of a small-scale prototype compressed air energy storage (CAES) plant that uses clean electricity from a supposed PV array or a wind farm to compress atmospheric air for storage in a subsurface tank. The stored air is fed to a generator-coupled turbine to produce electricity on as needed basis. The suggested technique, in contrast to the storage mechanisms found in literature, precludes the use of any fuel. To evaluate the performance of the proposed system, a thorough design approach, thermodynamic analysis, and selection criteria for various plant components are included. The experimental results indicated a decent efficiency of 20%, which is understandable given the plant's modest size and lack of any heat storage mechanism. Nonetheless, a number of methods have been found and are provided in order to increase the system's overall efficiency. In short, the suggested approach has proven its capacity to scale, enabling huge renewable power plants to store extra energy cheaply at the grid level without using costly pumped hydro storage or battery technology.","PeriodicalId":131572,"journal":{"name":"2022 International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132202931","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 : 2022-12-02DOI: 10.1109/ETECTE55893.2022.10007403
M. Mudassir, Umair Kamal, Hasnain Aslam, Azib Gulzar
In this paper, a robust propulsion control design for a class of X-rudder Autonomous underwater vehicles (AUVs) is proposed. Two cases for nonlinear equations of motion of X-rudder AUV in heading and longitudinal planes are considered in this work. In the first case, a state feedback controller based on traditional nonlinear Sliding Mode Control (SMC) is designed which stabilizes the states of the closed-loop system and tracks a constant (desired) steering and pitch angle. Then, for the same case, in the presence of parametric variations, a robust stabilization control based on Integral Sliding Mode Control (ISMC) is evaluated. Moreover, a proper X-rudder angles allocation method is designed which converts control inputs into respective rudders commands. Furthermore, the performance of the proposed controllers is evaluated and demonstrated with the help of numerical simulations.
{"title":"Robust Propulsion Control for a Class of X-rudder Autonomous Underwater Vehicle","authors":"M. Mudassir, Umair Kamal, Hasnain Aslam, Azib Gulzar","doi":"10.1109/ETECTE55893.2022.10007403","DOIUrl":"https://doi.org/10.1109/ETECTE55893.2022.10007403","url":null,"abstract":"In this paper, a robust propulsion control design for a class of X-rudder Autonomous underwater vehicles (AUVs) is proposed. Two cases for nonlinear equations of motion of X-rudder AUV in heading and longitudinal planes are considered in this work. In the first case, a state feedback controller based on traditional nonlinear Sliding Mode Control (SMC) is designed which stabilizes the states of the closed-loop system and tracks a constant (desired) steering and pitch angle. Then, for the same case, in the presence of parametric variations, a robust stabilization control based on Integral Sliding Mode Control (ISMC) is evaluated. Moreover, a proper X-rudder angles allocation method is designed which converts control inputs into respective rudders commands. Furthermore, the performance of the proposed controllers is evaluated and demonstrated with the help of numerical simulations.","PeriodicalId":131572,"journal":{"name":"2022 International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124896234","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}
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