Pub Date : 2023-05-01DOI: 10.1109/REEDCON57544.2023.10151021
Mir Tabish Altaf, H. Fatima, M. Jamil
The degradation of batteries is so harsh due to the rapid charging and discharging cycles which are associated with the quick discharge of the battery and the effect on the battery performance due to high peak current. When the battery is unable to support the load for the required time, forces the user to use oversized energy storage systems, which can supply the amount of energy required but the constraints on the size, weight, and capital must be satisfied. This study shows the effect of peak current on the performance of the Battery, and how to prevent such reduction in time by properly handling the usage of the energy storage systems. This study contains a simulated MATLAB model, and a comparison is made on different peak current levels, keeping the parameters like ambient temperature and current rate fixed. From the analysis, the loss in the battery support time with the peak of the discharge current is obtained.
{"title":"Effect of peak current on battery performance","authors":"Mir Tabish Altaf, H. Fatima, M. Jamil","doi":"10.1109/REEDCON57544.2023.10151021","DOIUrl":"https://doi.org/10.1109/REEDCON57544.2023.10151021","url":null,"abstract":"The degradation of batteries is so harsh due to the rapid charging and discharging cycles which are associated with the quick discharge of the battery and the effect on the battery performance due to high peak current. When the battery is unable to support the load for the required time, forces the user to use oversized energy storage systems, which can supply the amount of energy required but the constraints on the size, weight, and capital must be satisfied. This study shows the effect of peak current on the performance of the Battery, and how to prevent such reduction in time by properly handling the usage of the energy storage systems. This study contains a simulated MATLAB model, and a comparison is made on different peak current levels, keeping the parameters like ambient temperature and current rate fixed. From the analysis, the loss in the battery support time with the peak of the discharge current is obtained.","PeriodicalId":429116,"journal":{"name":"2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125958423","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 : 2023-05-01DOI: 10.1109/REEDCON57544.2023.10151392
Kanwarpartap Singh Gill, Rupesh Gupta
Kidney disease is a serious public health issue that is spreading around the world. According to estimates, 10% of people globally suffer from chronic kidney disease, which is one of the main causes of mortality and disability. Hence, for early identification, prevention, and disease treatment, precise prediction of renal disease is crucial. Overall, renal disease prediction is important for research because it can improve patient outcomes, tailor care, and lead to the creation of fresh preventative and therapeutic approaches. In order to forecast renal illness for this study's GridSearchCV with 10-fold cross-validation, we must first import the required libraries and load the dataset. Secondly, dividing the dataset into features and labels to prepare it for modelling. We created a pipeline that comprises preprocessing procedures and a machine learning algorithm after dividing the data into training and testing sets. Then, using 10-fold cross-validation, fit the GridSearchCV object to the training data after establishing the hyperparameters to search over and using it. Lastly, we forecasted renal illness on the test set using the best estimator discovered by GridSearchCV, and assessed the model's performance using measures like accuracy, precision, and recall.
{"title":"Chronic Kidney Disease Detection Using GridSearchCV Cross Validation Method","authors":"Kanwarpartap Singh Gill, Rupesh Gupta","doi":"10.1109/REEDCON57544.2023.10151392","DOIUrl":"https://doi.org/10.1109/REEDCON57544.2023.10151392","url":null,"abstract":"Kidney disease is a serious public health issue that is spreading around the world. According to estimates, 10% of people globally suffer from chronic kidney disease, which is one of the main causes of mortality and disability. Hence, for early identification, prevention, and disease treatment, precise prediction of renal disease is crucial. Overall, renal disease prediction is important for research because it can improve patient outcomes, tailor care, and lead to the creation of fresh preventative and therapeutic approaches. In order to forecast renal illness for this study's GridSearchCV with 10-fold cross-validation, we must first import the required libraries and load the dataset. Secondly, dividing the dataset into features and labels to prepare it for modelling. We created a pipeline that comprises preprocessing procedures and a machine learning algorithm after dividing the data into training and testing sets. Then, using 10-fold cross-validation, fit the GridSearchCV object to the training data after establishing the hyperparameters to search over and using it. Lastly, we forecasted renal illness on the test set using the best estimator discovered by GridSearchCV, and assessed the model's performance using measures like accuracy, precision, and recall.","PeriodicalId":429116,"journal":{"name":"2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127538697","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 : 2023-05-01DOI: 10.1109/REEDCON57544.2023.10150567
Paulami Purkayastha, M. Choudhry, Manjeet Kumar
This Image-Dehazing paper proposes to combine the Multi-Scale Fusion technique with the Retinex Algorithm. The paper proposes to extract reflectance matrices and incorporate them into the multi-scale fusion algorithm. The technique proposed aims to reduce the halo effect observed in image-dehazing applications and related works for heavily hazy images. Moreover, an improvement in the quality of the output using the proposed novel algorithm is observed. Quantitative, as well as a visual display of results, using the DENSE HAZE dataset, give an accurate interpretation of the effectiveness of the proposed work. The best value of Structural Similarity Index (SSIM) obtained is 0.9128 which shows a 62% increase in image quality as compared to average SSIM values of previously known methods. The Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) show improvement by 78% (TT Playroom) and 95% (Castle) respectively. To allow analysis with regards to pixel compression that may have resulted during the process, two No Reference Image Quality Metrics have been also computed.
{"title":"A Retinex Prior to Multi-Scale Fusion for Single Image Dehazing","authors":"Paulami Purkayastha, M. Choudhry, Manjeet Kumar","doi":"10.1109/REEDCON57544.2023.10150567","DOIUrl":"https://doi.org/10.1109/REEDCON57544.2023.10150567","url":null,"abstract":"This Image-Dehazing paper proposes to combine the Multi-Scale Fusion technique with the Retinex Algorithm. The paper proposes to extract reflectance matrices and incorporate them into the multi-scale fusion algorithm. The technique proposed aims to reduce the halo effect observed in image-dehazing applications and related works for heavily hazy images. Moreover, an improvement in the quality of the output using the proposed novel algorithm is observed. Quantitative, as well as a visual display of results, using the DENSE HAZE dataset, give an accurate interpretation of the effectiveness of the proposed work. The best value of Structural Similarity Index (SSIM) obtained is 0.9128 which shows a 62% increase in image quality as compared to average SSIM values of previously known methods. The Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) show improvement by 78% (TT Playroom) and 95% (Castle) respectively. To allow analysis with regards to pixel compression that may have resulted during the process, two No Reference Image Quality Metrics have been also computed.","PeriodicalId":429116,"journal":{"name":"2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133786327","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 : 2023-05-01DOI: 10.1109/REEDCON57544.2023.10151029
Rudresh Pillai, N. Sharma, Rupesh Gupta
Skin cancer is a lethal condition that, if not detected in its early stages, becomes more difficult to cure and can have fatal outcomes. Thus, skin cancer must be diagnosed accurately, precisely, and as early as possible so that it doesn't progress into further stages. Traditional methods for diagnosing skin cancer involve numerous tests and consultations with dermatologist experts. Because many kinds of skin cancer might seem similar, especially in their early stages, correct skin cancer detection can be challenging, even for dermatologist experts. This paper proposed a Convolutional Neural Network (CNN) for diagnosing and stratifying skin cancer into seven classes. The proposed CNN model consists of 26 layers. The images utilized for training and testing the model were obtained from the HAM10000 dataset, which was then augmented using various techniques and then classified by the proposed CNN model into seven labeled classes, including AKIEC, BCC, BKL, DF, MEL, NV, and VASC. The presented CNN model was shown to have a high accuracy of 99.94%, outperforming state-of-the-art algorithms for accurately diagnosing and categorizing skin cancer. This paper aims to prevent premature mortality, provide health in resource-constrained settings, and seek patients' healthy lives, which can be done through an accurate and early-stage skin cancer diagnosis.
{"title":"Proposed Convolution Neural Network for Skin Cancer Diagnosis and Classification","authors":"Rudresh Pillai, N. Sharma, Rupesh Gupta","doi":"10.1109/REEDCON57544.2023.10151029","DOIUrl":"https://doi.org/10.1109/REEDCON57544.2023.10151029","url":null,"abstract":"Skin cancer is a lethal condition that, if not detected in its early stages, becomes more difficult to cure and can have fatal outcomes. Thus, skin cancer must be diagnosed accurately, precisely, and as early as possible so that it doesn't progress into further stages. Traditional methods for diagnosing skin cancer involve numerous tests and consultations with dermatologist experts. Because many kinds of skin cancer might seem similar, especially in their early stages, correct skin cancer detection can be challenging, even for dermatologist experts. This paper proposed a Convolutional Neural Network (CNN) for diagnosing and stratifying skin cancer into seven classes. The proposed CNN model consists of 26 layers. The images utilized for training and testing the model were obtained from the HAM10000 dataset, which was then augmented using various techniques and then classified by the proposed CNN model into seven labeled classes, including AKIEC, BCC, BKL, DF, MEL, NV, and VASC. The presented CNN model was shown to have a high accuracy of 99.94%, outperforming state-of-the-art algorithms for accurately diagnosing and categorizing skin cancer. This paper aims to prevent premature mortality, provide health in resource-constrained settings, and seek patients' healthy lives, which can be done through an accurate and early-stage skin cancer diagnosis.","PeriodicalId":429116,"journal":{"name":"2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133993879","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 : 2023-05-01DOI: 10.1109/REEDCON57544.2023.10151069
S. Sarwar, S. Jabin
Cone-beam computed tomography (CBCT) is a popular imaging modality in dentistry for diagnosing and planning treatment for a variety of oral diseases with the ability to produce detailed, three-dimensional images of the teeth, jawbones, and surrounding structures. CBCT imaging has emerged as an essential diagnostic tool in dentistry. CBCT imaging has seen significant improvements in terms of its diagnostic value, as well as its accuracy and efficiency, with the most recent development of artificial intelligence (AI) techniques. This paper reviews recent AI trends and practices in dental CBCT imaging. AI has been used for lesion detection, malocclusion classification, measurement of buccal bone thickness, and classification and segmentation of teeth, alveolar bones, mandibles, landmarks, contours, and pharyngeal airways using CBCT images. Mainly machine learning algorithms, deep learning algorithms, and super-resolution techniques are used for these tasks. This review focuses on the potential of AI techniques to transform CBCT imaging in dentistry, which would improve both diagnosis and treatment planning. Finally, we discuss the challenges and limitations of artificial intelligence in dentistry and CBCT imaging.
{"title":"AI Techniques for Cone Beam Computed Tomography in Dentistry: Trends and Practices","authors":"S. Sarwar, S. Jabin","doi":"10.1109/REEDCON57544.2023.10151069","DOIUrl":"https://doi.org/10.1109/REEDCON57544.2023.10151069","url":null,"abstract":"Cone-beam computed tomography (CBCT) is a popular imaging modality in dentistry for diagnosing and planning treatment for a variety of oral diseases with the ability to produce detailed, three-dimensional images of the teeth, jawbones, and surrounding structures. CBCT imaging has emerged as an essential diagnostic tool in dentistry. CBCT imaging has seen significant improvements in terms of its diagnostic value, as well as its accuracy and efficiency, with the most recent development of artificial intelligence (AI) techniques. This paper reviews recent AI trends and practices in dental CBCT imaging. AI has been used for lesion detection, malocclusion classification, measurement of buccal bone thickness, and classification and segmentation of teeth, alveolar bones, mandibles, landmarks, contours, and pharyngeal airways using CBCT images. Mainly machine learning algorithms, deep learning algorithms, and super-resolution techniques are used for these tasks. This review focuses on the potential of AI techniques to transform CBCT imaging in dentistry, which would improve both diagnosis and treatment planning. Finally, we discuss the challenges and limitations of artificial intelligence in dentistry and CBCT imaging.","PeriodicalId":429116,"journal":{"name":"2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131641248","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 : 2023-05-01DOI: 10.1109/REEDCON57544.2023.10151398
Umair Yousuf, Sambhavi, Abdul Haq Nalband, Mohammed Riyaz Ahmed
Next-generation wireless networks’ attractive use cases call for more extensive coverage and highly dependable connectivity. A promising candidate that considerably helps to fulfil these requirements is beamforming. In massive Multiple-Input-Multiple-Output (MIMO) systems, the conventional digital beamforming method results in significant costs and hardware complexity. By using fewer RF chains than the conventional digital beamforming method, hybrid beamforming lowers the hardware needed. However, due to the restrictions on hardware consumption, it is difficult to arrive at the open optimal solution for joint optimization problems. We suggest a hybrid beamformer that learns to maximize spectral efficiency using deep learning as its foundation. To achieve the optimal beamforming weights, the channel state information (CSI) is supplied into the deep learning model. Both perfect and imperfect CSI are used to validate the proposed hybrid beamforming scheme. Simulation results reveal that the proposed method outperforms the current statistical approaches while lowering cost and hardware complexity. It is also more robust to poor CSI.
{"title":"Deep Learning Framework for Spectral Efficient Intelligent Hybrid Beamforming","authors":"Umair Yousuf, Sambhavi, Abdul Haq Nalband, Mohammed Riyaz Ahmed","doi":"10.1109/REEDCON57544.2023.10151398","DOIUrl":"https://doi.org/10.1109/REEDCON57544.2023.10151398","url":null,"abstract":"Next-generation wireless networks’ attractive use cases call for more extensive coverage and highly dependable connectivity. A promising candidate that considerably helps to fulfil these requirements is beamforming. In massive Multiple-Input-Multiple-Output (MIMO) systems, the conventional digital beamforming method results in significant costs and hardware complexity. By using fewer RF chains than the conventional digital beamforming method, hybrid beamforming lowers the hardware needed. However, due to the restrictions on hardware consumption, it is difficult to arrive at the open optimal solution for joint optimization problems. We suggest a hybrid beamformer that learns to maximize spectral efficiency using deep learning as its foundation. To achieve the optimal beamforming weights, the channel state information (CSI) is supplied into the deep learning model. Both perfect and imperfect CSI are used to validate the proposed hybrid beamforming scheme. Simulation results reveal that the proposed method outperforms the current statistical approaches while lowering cost and hardware complexity. It is also more robust to poor CSI.","PeriodicalId":429116,"journal":{"name":"2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132149747","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 : 2023-05-01DOI: 10.1109/REEDCON57544.2023.10151241
Uzma Salmaz, T. Islam
Capacitive sensors are easy to fabricate, compact in size, economical, and quite efficient in the detection of impurities and adulteration in fluids (like milk, fruit, vegetable juices, etc.) and other consumable food items. The change in the dielectric constant of consumable fluids due to the mixing of adulterants and preservatives can be utilized to detect the extent of adulteration and hence its quality. The change in a dielectric property of a fluid can be reflected in terms of the change in capacitance values utilizing these highly economical and compact capacitive sensors. In this work, a simulation of parallel plate capacitive sensors of different sizes and configurations using copper material is done. Also, cross capacitive sensor with brass material is simulated in various shapes of electrodes with different dimensions. The shift in the base value of capacitances due to a milk sample drop is acquired for the sensors to comment on their sensitivity. It is found that for the cross-capacitive sensor if the dimension is close to the size of the drop the maximum change in capacitance is obtained. For the parallel plate sensor by changing the size of the electrodes the base value and sensitivity change to an extent till the optimum value of dimensions for which sensitivity is maximum.
{"title":"Design and Simulation of Capacitive Sensors of Various Geometries for Drop based Quality Analysis of Beverages","authors":"Uzma Salmaz, T. Islam","doi":"10.1109/REEDCON57544.2023.10151241","DOIUrl":"https://doi.org/10.1109/REEDCON57544.2023.10151241","url":null,"abstract":"Capacitive sensors are easy to fabricate, compact in size, economical, and quite efficient in the detection of impurities and adulteration in fluids (like milk, fruit, vegetable juices, etc.) and other consumable food items. The change in the dielectric constant of consumable fluids due to the mixing of adulterants and preservatives can be utilized to detect the extent of adulteration and hence its quality. The change in a dielectric property of a fluid can be reflected in terms of the change in capacitance values utilizing these highly economical and compact capacitive sensors. In this work, a simulation of parallel plate capacitive sensors of different sizes and configurations using copper material is done. Also, cross capacitive sensor with brass material is simulated in various shapes of electrodes with different dimensions. The shift in the base value of capacitances due to a milk sample drop is acquired for the sensors to comment on their sensitivity. It is found that for the cross-capacitive sensor if the dimension is close to the size of the drop the maximum change in capacitance is obtained. For the parallel plate sensor by changing the size of the electrodes the base value and sensitivity change to an extent till the optimum value of dimensions for which sensitivity is maximum.","PeriodicalId":429116,"journal":{"name":"2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133291270","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}
The Advanced Intensive Care Unit (ICU) system brings new opportunities for monitoring and providing patient care. It requires techniques to integrate data from multiple sources in a streamlined manner, to provide a comprehensive view of the patient’s condition. The Digital ICU architecture is designed to enable the integration of patient data from multiple sources, to provide an intelligent patient monitoring platform and enhance patient care outcomes. The Digital ICU system is used for monitoring critically ill patients and ambit its advantages in overcoming the limitations of traditional monitoring systems. Finally, the paper proposes new implementations to enable the interoperability of digital ICU systems. We envision that the proposed Digital ICU system will improve clinical efficiency, provides remote monitoring, a real-time dashboard and alerts, enhance patient outcomes, and provide a new dimension to patient care in a faster manner. The proposed system will increase the efficiency of the nurses and helps in effective look of the admitted patients.
{"title":"Advanced ICU Patient Monitoring With Sensor Integration, IV Detection WITH Canny Edge Detection and ECG Monitoring With Live Feed","authors":"Kothapally Aditya Reddy, Suggu Pavan, Kiran Mannem, Bindhu Priya","doi":"10.1109/REEDCON57544.2023.10151176","DOIUrl":"https://doi.org/10.1109/REEDCON57544.2023.10151176","url":null,"abstract":"The Advanced Intensive Care Unit (ICU) system brings new opportunities for monitoring and providing patient care. It requires techniques to integrate data from multiple sources in a streamlined manner, to provide a comprehensive view of the patient’s condition. The Digital ICU architecture is designed to enable the integration of patient data from multiple sources, to provide an intelligent patient monitoring platform and enhance patient care outcomes. The Digital ICU system is used for monitoring critically ill patients and ambit its advantages in overcoming the limitations of traditional monitoring systems. Finally, the paper proposes new implementations to enable the interoperability of digital ICU systems. We envision that the proposed Digital ICU system will improve clinical efficiency, provides remote monitoring, a real-time dashboard and alerts, enhance patient outcomes, and provide a new dimension to patient care in a faster manner. The proposed system will increase the efficiency of the nurses and helps in effective look of the admitted patients.","PeriodicalId":429116,"journal":{"name":"2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON)","volume":"118 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113996639","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 : 2023-05-01DOI: 10.1109/REEDCON57544.2023.10150710
Salman Rafi, Md.Khursheed Alam, M. Jalil, S. Kirmani
The partial shading (PS) is a phenomenon that affects PV panels frequently. Many Photo Voltaic (PV) array reconfiguration techniques have advanced recently, making significant progress in providing solutions to partial shading. But the majority of the currently used techniques still fall short of achieving efficient shade dispersion and power enhancement. This paper presents the study of Improved Sudoku technique and also provides a comparative analysis with other techniques. This Improved Sudoku Technique is a puzzle-based technique where the sum of numbers in each row and column is constant, and each row and column contains numbers from 1 to 9. The main objective of this paper is to improve the power and dispersion of the standard two shade patterns. Additionally, a comparison with other renowned techniques like Honey-comb (HC) and Total Cross Tied (TCT) is conducted to demonstrate its outstanding performance. In terms of power enhancement and fill factor (FF), the developed method offers extremely satisfactory performance in every area. To further demonstrate the adaptability of the suggested methodology, a more thorough analysis based on additional performance metrics such as maximum power GMPP(Pmax), fill factor (FF) along with mismatch losses (ML) and execution ratio (ER) is carried out.
{"title":"Modelling And Simulation of PV Array Reconfiguration Techniques to Optimize the Power","authors":"Salman Rafi, Md.Khursheed Alam, M. Jalil, S. Kirmani","doi":"10.1109/REEDCON57544.2023.10150710","DOIUrl":"https://doi.org/10.1109/REEDCON57544.2023.10150710","url":null,"abstract":"The partial shading (PS) is a phenomenon that affects PV panels frequently. Many Photo Voltaic (PV) array reconfiguration techniques have advanced recently, making significant progress in providing solutions to partial shading. But the majority of the currently used techniques still fall short of achieving efficient shade dispersion and power enhancement. This paper presents the study of Improved Sudoku technique and also provides a comparative analysis with other techniques. This Improved Sudoku Technique is a puzzle-based technique where the sum of numbers in each row and column is constant, and each row and column contains numbers from 1 to 9. The main objective of this paper is to improve the power and dispersion of the standard two shade patterns. Additionally, a comparison with other renowned techniques like Honey-comb (HC) and Total Cross Tied (TCT) is conducted to demonstrate its outstanding performance. In terms of power enhancement and fill factor (FF), the developed method offers extremely satisfactory performance in every area. To further demonstrate the adaptability of the suggested methodology, a more thorough analysis based on additional performance metrics such as maximum power GMPP(Pmax), fill factor (FF) along with mismatch losses (ML) and execution ratio (ER) is carried out.","PeriodicalId":429116,"journal":{"name":"2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129110966","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 : 2023-05-01DOI: 10.1109/REEDCON57544.2023.10150829
Mohd Mustafa Khan, Anwar Sadat, E. Khan
The adulteration of olive oil is one of the widely existing oil quality problem. A technique known as Dielectric Spectroscopy is used in this paper to determine adulteration in olive oil with soya and mustard oil. The Dielectric parameter that is dielectric constant of the oil samples were investigated within the frequency range of 100Hz-1MHz. As the soya and mustard oil concentrations increases over the measured frequency range, the dielectric constant of olive oil adulterated with other oils increases as well. The findings from this proposed study could be utilised to distinguish between olive oil that has been contaminated with other vegetable oils at levels ranging from 0% to 50%.
{"title":"Determination of olive oil adulteration using Dielectric Spectroscopy","authors":"Mohd Mustafa Khan, Anwar Sadat, E. Khan","doi":"10.1109/REEDCON57544.2023.10150829","DOIUrl":"https://doi.org/10.1109/REEDCON57544.2023.10150829","url":null,"abstract":"The adulteration of olive oil is one of the widely existing oil quality problem. A technique known as Dielectric Spectroscopy is used in this paper to determine adulteration in olive oil with soya and mustard oil. The Dielectric parameter that is dielectric constant of the oil samples were investigated within the frequency range of 100Hz-1MHz. As the soya and mustard oil concentrations increases over the measured frequency range, the dielectric constant of olive oil adulterated with other oils increases as well. The findings from this proposed study could be utilised to distinguish between olive oil that has been contaminated with other vegetable oils at levels ranging from 0% to 50%.","PeriodicalId":429116,"journal":{"name":"2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116416967","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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