Pub Date : 2021-06-12DOI: 10.1109/ICECCE52056.2021.9514104
Serigne Diouf, I. Gueye, A. Kebe, Moustapha Diop
This paper proposes a comparative study of conventional proportional-integral-derivative (PID) and fuzzy logic controllers for the temperature control of an electric oven. This study gives an overview of conventional PID and fuzzy logic controllers to investigate their applicability on the temperature control of an electric oven. This study is all the more important as the PID controller is mainly used for a system control when the mathematical representation is known; contrary to the fuzzy logic control concept, which is more and more used in applications and does not require a model. To compare the performances of the two controllers, a reference temperature of 100° is fixed for an electric oven of dimensions (Length: 150 cm, Width: 131 cm, Depth: 150 cm) with a frontal opening. The simulation results performed under Matlab-Simulink were compared in terms of control performance, including steady state error, response time and system stability. The evaluation shows that the fuzzy logic controller provides the best performance.
{"title":"Comparative study of conventional PID and fuzzy logic controllers applied to an electric oven","authors":"Serigne Diouf, I. Gueye, A. Kebe, Moustapha Diop","doi":"10.1109/ICECCE52056.2021.9514104","DOIUrl":"https://doi.org/10.1109/ICECCE52056.2021.9514104","url":null,"abstract":"This paper proposes a comparative study of conventional proportional-integral-derivative (PID) and fuzzy logic controllers for the temperature control of an electric oven. This study gives an overview of conventional PID and fuzzy logic controllers to investigate their applicability on the temperature control of an electric oven. This study is all the more important as the PID controller is mainly used for a system control when the mathematical representation is known; contrary to the fuzzy logic control concept, which is more and more used in applications and does not require a model. To compare the performances of the two controllers, a reference temperature of 100° is fixed for an electric oven of dimensions (Length: 150 cm, Width: 131 cm, Depth: 150 cm) with a frontal opening. The simulation results performed under Matlab-Simulink were compared in terms of control performance, including steady state error, response time and system stability. The evaluation shows that the fuzzy logic controller provides the best performance.","PeriodicalId":302947,"journal":{"name":"2021 International Conference on Electrical, Communication, and Computer Engineering (ICECCE)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123063570","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 : 2021-06-12DOI: 10.1109/ICECCE52056.2021.9514154
Fehmida Usmani, I. Khan, M. Siddiqui, Mahnoor Khan, Muhamamd Bilal, M. U. Masood, Arsalan Ahmad, M. Shahzad, V. Curri
The rapid increase in bandwidth-driven applications has resulted in exponential internet traffic growth, especially in the backbone networks. To address this growth of internet traffic, operators always demand the total capacity utilization of underlying infrastructure. In this perspective, precise estimation of the quality of transmission (QoT) of the lightpaths (LPs) is vital for reducing the margins provisioned by uncertainty in network equipment's working point. This article proposes and compares several data-driven Machine learning (ML) based models to estimate QoT of unestablished LP before its deployment in the future deploying network. The proposed models are cross-trained on the data acquired from an already established LP of an entirely different in-service network. The metric considered to evaluate the QoT of LP is the Generalized Signal-to-Noise Ratio (GSNR). The dataset is generated synthetically using well tested GNPy simulation tool. Promising results are achieved to reduce the GSNR uncertainty and, consequently, the provisioning margin.
{"title":"Evaluating Cross- feature Trained Machine Learning Models for Estimating QoT of Unestablished Lightpaths","authors":"Fehmida Usmani, I. Khan, M. Siddiqui, Mahnoor Khan, Muhamamd Bilal, M. U. Masood, Arsalan Ahmad, M. Shahzad, V. Curri","doi":"10.1109/ICECCE52056.2021.9514154","DOIUrl":"https://doi.org/10.1109/ICECCE52056.2021.9514154","url":null,"abstract":"The rapid increase in bandwidth-driven applications has resulted in exponential internet traffic growth, especially in the backbone networks. To address this growth of internet traffic, operators always demand the total capacity utilization of underlying infrastructure. In this perspective, precise estimation of the quality of transmission (QoT) of the lightpaths (LPs) is vital for reducing the margins provisioned by uncertainty in network equipment's working point. This article proposes and compares several data-driven Machine learning (ML) based models to estimate QoT of unestablished LP before its deployment in the future deploying network. The proposed models are cross-trained on the data acquired from an already established LP of an entirely different in-service network. The metric considered to evaluate the QoT of LP is the Generalized Signal-to-Noise Ratio (GSNR). The dataset is generated synthetically using well tested GNPy simulation tool. Promising results are achieved to reduce the GSNR uncertainty and, consequently, the provisioning margin.","PeriodicalId":302947,"journal":{"name":"2021 International Conference on Electrical, Communication, and Computer Engineering (ICECCE)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121891723","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 : 2021-06-12DOI: 10.1109/ICECCE52056.2021.9514122
Abdulbari Ali Mohamed Frei, M. Güneser
In this paper an optimal integration of Distributed Energy Resource (DER) such as Photo-Voltaic Generation System (PVGS), Wind Turbine Generation System (WTGS), and Electric Vehicles (EVs) in supply network simultaneously implemented for motive of abatement of overall power loss, overall cost and emanations dispatched through the thermal generators. To accomplish these planned purposes and profits, we designed a multi-objective function. For optimization of the cost we used artificial bee colony algorithm.
{"title":"Optimal Accommodation of DERs in Practical Radial Distribution Feeder for Techno-Economic with Artificial Bee Colony Algorithm","authors":"Abdulbari Ali Mohamed Frei, M. Güneser","doi":"10.1109/ICECCE52056.2021.9514122","DOIUrl":"https://doi.org/10.1109/ICECCE52056.2021.9514122","url":null,"abstract":"In this paper an optimal integration of Distributed Energy Resource (DER) such as Photo-Voltaic Generation System (PVGS), Wind Turbine Generation System (WTGS), and Electric Vehicles (EVs) in supply network simultaneously implemented for motive of abatement of overall power loss, overall cost and emanations dispatched through the thermal generators. To accomplish these planned purposes and profits, we designed a multi-objective function. For optimization of the cost we used artificial bee colony algorithm.","PeriodicalId":302947,"journal":{"name":"2021 International Conference on Electrical, Communication, and Computer Engineering (ICECCE)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123861812","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 : 2021-06-12DOI: 10.1109/ICECCE52056.2021.9514086
Z. Soltani, Kresten Kjaer Soerensen, J. Leth, Jan Dimon Bendtsen
Supermarket refrigeration systems represent an important type of energy demanding appliances, which is in such widespread use that any development in the associated technology can have a huge impact on general health and global warming. Using automatic fault detection and diagnosis may for instance improve energy efficiency and reduce food waste as well as reduce expenses for the supermarket owners. In this paper, three model-free classification algorithms are tested on faulty/non-faulty data obtained from an actual refrigeration system. It is found that support vector machines (SVM) are able to classify fan faults in a real refrigeration system with near-100% classification accuracy, independent of the number of input variables. The classification performance and robustness against an unseen operation mode, low-resolution data, noisy data, and data of different operating points is tested for three different classifier configurations. The results show Principle Component Analysis (PCA)-SVM is highly robust to different operating points, disturbances, and gives the best computational efficiency, as it is able to reduce the feature space to only two dimensions. It is concluded that while all of the examined methods are insensitive to noise, and effective in terms of detecting faults from relatively small amounts of data, overall, PCA -SVM is slightly more computationally efficient.
{"title":"Robustness analysis of PCA-SVM model used for fault detection in supermarket refrigeration systems *","authors":"Z. Soltani, Kresten Kjaer Soerensen, J. Leth, Jan Dimon Bendtsen","doi":"10.1109/ICECCE52056.2021.9514086","DOIUrl":"https://doi.org/10.1109/ICECCE52056.2021.9514086","url":null,"abstract":"Supermarket refrigeration systems represent an important type of energy demanding appliances, which is in such widespread use that any development in the associated technology can have a huge impact on general health and global warming. Using automatic fault detection and diagnosis may for instance improve energy efficiency and reduce food waste as well as reduce expenses for the supermarket owners. In this paper, three model-free classification algorithms are tested on faulty/non-faulty data obtained from an actual refrigeration system. It is found that support vector machines (SVM) are able to classify fan faults in a real refrigeration system with near-100% classification accuracy, independent of the number of input variables. The classification performance and robustness against an unseen operation mode, low-resolution data, noisy data, and data of different operating points is tested for three different classifier configurations. The results show Principle Component Analysis (PCA)-SVM is highly robust to different operating points, disturbances, and gives the best computational efficiency, as it is able to reduce the feature space to only two dimensions. It is concluded that while all of the examined methods are insensitive to noise, and effective in terms of detecting faults from relatively small amounts of data, overall, PCA -SVM is slightly more computationally efficient.","PeriodicalId":302947,"journal":{"name":"2021 International Conference on Electrical, Communication, and Computer Engineering (ICECCE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124266883","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 : 2021-06-12DOI: 10.1109/ICECCE52056.2021.9514218
Ömer Yiğit Astepe, Ali Seymen Alkara
In Tiipras oil refineries, an average of 100 thousand maintenance requests are created annually for more than 140 thousand pieces of equipment. These requests are prioritized manually by chief experts with over 25 years of experience and classified as urgent or planned. If maintenance requests that need to be solved urgently in the refining industry are mislabeled and delayed, they may cause process upsets leading to health & safety hazards, environment problems or big asset damage. To minimize this risk, we think that supporting the decision mechanism with algorithms and cross checking/replacing human decisions by using today's AI technologies is the right approach that reduces the possibility of human error. In this study, our main goal is to automate maintenance prioritization process with supervised and unsupervised ML algorithms, deploy an AI system and achieve high accuracy. Our study was carried out basically in 4 main steps: • Exploratory Data Analysis • Clustering - Feature Addition - Feature Selection • Model Selection and Results • Additional Studies With this study, we aim to explain our AI study, share our experience with other partners that have similar needs and provide them an effective tool and systematic approach about management of transition from human to machine with a real industry case. We believe that the transfer of priority selection process from human to algorithms ensure consistent decisions, reduce costs and tolerate experience losses.
{"title":"Predicting “Maintenance Priority” with AI","authors":"Ömer Yiğit Astepe, Ali Seymen Alkara","doi":"10.1109/ICECCE52056.2021.9514218","DOIUrl":"https://doi.org/10.1109/ICECCE52056.2021.9514218","url":null,"abstract":"In Tiipras oil refineries, an average of 100 thousand maintenance requests are created annually for more than 140 thousand pieces of equipment. These requests are prioritized manually by chief experts with over 25 years of experience and classified as urgent or planned. If maintenance requests that need to be solved urgently in the refining industry are mislabeled and delayed, they may cause process upsets leading to health & safety hazards, environment problems or big asset damage. To minimize this risk, we think that supporting the decision mechanism with algorithms and cross checking/replacing human decisions by using today's AI technologies is the right approach that reduces the possibility of human error. In this study, our main goal is to automate maintenance prioritization process with supervised and unsupervised ML algorithms, deploy an AI system and achieve high accuracy. Our study was carried out basically in 4 main steps: • Exploratory Data Analysis • Clustering - Feature Addition - Feature Selection • Model Selection and Results • Additional Studies With this study, we aim to explain our AI study, share our experience with other partners that have similar needs and provide them an effective tool and systematic approach about management of transition from human to machine with a real industry case. We believe that the transfer of priority selection process from human to algorithms ensure consistent decisions, reduce costs and tolerate experience losses.","PeriodicalId":302947,"journal":{"name":"2021 International Conference on Electrical, Communication, and Computer Engineering (ICECCE)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125546847","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 : 2021-06-12DOI: 10.1109/ICECCE52056.2021.9514243
Mohd Zainal Bin Nurdin, Z. Yusoff, M. Roslee, S. Hashim, Azah Syafiah Mohd Marzuki
Totem pole class B envelope tracking supply modulator is generally consisting of two single ended transistors operating in anti-phase. In this paper, the totem pole class B supply modulator design consists of two transformers that are used to split the input signal and to combine the output signal. The totem pole consists of two N-channel BJTs connected where the output is taken to the second transformer. The result shows that the designed supply modulator is able to operate in wide bandwidth from 100MHz to 1 GHz. This supply modulator works as hybrid supply modulator which the DC signal will be combined with the high frequency signal at the transformer.
{"title":"High Bandwidth Hybrid Supply Modulation Using Totem Pole Configuration for Envelope Tracking Applications","authors":"Mohd Zainal Bin Nurdin, Z. Yusoff, M. Roslee, S. Hashim, Azah Syafiah Mohd Marzuki","doi":"10.1109/ICECCE52056.2021.9514243","DOIUrl":"https://doi.org/10.1109/ICECCE52056.2021.9514243","url":null,"abstract":"Totem pole class B envelope tracking supply modulator is generally consisting of two single ended transistors operating in anti-phase. In this paper, the totem pole class B supply modulator design consists of two transformers that are used to split the input signal and to combine the output signal. The totem pole consists of two N-channel BJTs connected where the output is taken to the second transformer. The result shows that the designed supply modulator is able to operate in wide bandwidth from 100MHz to 1 GHz. This supply modulator works as hybrid supply modulator which the DC signal will be combined with the high frequency signal at the transformer.","PeriodicalId":302947,"journal":{"name":"2021 International Conference on Electrical, Communication, and Computer Engineering (ICECCE)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126218829","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 : 2021-06-12DOI: 10.1109/ICECCE52056.2021.9514095
M. Domb, G. Leshem
Free-space communication is a leading component in global communications. Its advantages relate to a broader signal spread, no wiring, and ease of engagement. However, satellite communication links suffer from arbitrary weather phenomena such as clouds, rain, snow, fog, and dust. Therefore, satellites commonly use redundant signal strength to ensure constant and continuous signal transmission, resulting in excess energy consumption, challenging the limited power capacity generated by solar energy or the fixed amount of fuel. This research proposes a Machine Learning [ML]-based model that provides a time-dependent prediction of the expected attenuation level due to rain and fog. Based on the predicted attenuation level, we calibrate the communication signal strength to save energy. We used collected data from the Genesis LEO satellite and corresponding simulated data in the range of 2.4GHz to 72GHz. We then executed the ML system, and after several adjustments for the frequencies up to 48GHz, we reached a very narrow gap between the predicted and actual attenuation levels. However, in the 72GHz frequency, we got a partial correlation.
{"title":"Rain Attenuation Prediction for 2.4-72GHz using LTSM, an artificial recurrent neural network technology","authors":"M. Domb, G. Leshem","doi":"10.1109/ICECCE52056.2021.9514095","DOIUrl":"https://doi.org/10.1109/ICECCE52056.2021.9514095","url":null,"abstract":"Free-space communication is a leading component in global communications. Its advantages relate to a broader signal spread, no wiring, and ease of engagement. However, satellite communication links suffer from arbitrary weather phenomena such as clouds, rain, snow, fog, and dust. Therefore, satellites commonly use redundant signal strength to ensure constant and continuous signal transmission, resulting in excess energy consumption, challenging the limited power capacity generated by solar energy or the fixed amount of fuel. This research proposes a Machine Learning [ML]-based model that provides a time-dependent prediction of the expected attenuation level due to rain and fog. Based on the predicted attenuation level, we calibrate the communication signal strength to save energy. We used collected data from the Genesis LEO satellite and corresponding simulated data in the range of 2.4GHz to 72GHz. We then executed the ML system, and after several adjustments for the frequencies up to 48GHz, we reached a very narrow gap between the predicted and actual attenuation levels. However, in the 72GHz frequency, we got a partial correlation.","PeriodicalId":302947,"journal":{"name":"2021 International Conference on Electrical, Communication, and Computer Engineering (ICECCE)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126273020","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 : 2021-06-12DOI: 10.1109/ICECCE52056.2021.9514205
Jiayi Fan, Yongkeun Lee
Torque sharing function (TSF) method is widely used in switched reluctance motor (SRM) drive to reduce the torque ripple. Besides maintaining the torque control performance, the copper loss reduction should also be considered while determining the TSF profiles. In this paper, an improved TSF method modified from the previous method recently done by the other research group is proposed focusing on the optimal allocation of torque component in the commutation phases and reduction of copper loss. Based on the torque generating nature of SRM, the commutation region is suggested to be divided into two regions where the incoming phase and outgoing phase have different torque generating capacity. The commutation phase with higher rate of change of inductance with respect to the rotor position is preferred to mainly contribute to the torque production while the other phase is penalized to have reduced current. Thus, the total copper loss can be reduced. Simulation is carried out in MATLAB/Simulink environment and the simulation results show that the modified TSF with region division has a lower copper loss compared to the previous method done by the other research group.
{"title":"Copper Loss Reduction of Torque Sharing Function in Switched Reluctance Motor by Division of Commutation Region","authors":"Jiayi Fan, Yongkeun Lee","doi":"10.1109/ICECCE52056.2021.9514205","DOIUrl":"https://doi.org/10.1109/ICECCE52056.2021.9514205","url":null,"abstract":"Torque sharing function (TSF) method is widely used in switched reluctance motor (SRM) drive to reduce the torque ripple. Besides maintaining the torque control performance, the copper loss reduction should also be considered while determining the TSF profiles. In this paper, an improved TSF method modified from the previous method recently done by the other research group is proposed focusing on the optimal allocation of torque component in the commutation phases and reduction of copper loss. Based on the torque generating nature of SRM, the commutation region is suggested to be divided into two regions where the incoming phase and outgoing phase have different torque generating capacity. The commutation phase with higher rate of change of inductance with respect to the rotor position is preferred to mainly contribute to the torque production while the other phase is penalized to have reduced current. Thus, the total copper loss can be reduced. Simulation is carried out in MATLAB/Simulink environment and the simulation results show that the modified TSF with region division has a lower copper loss compared to the previous method done by the other research group.","PeriodicalId":302947,"journal":{"name":"2021 International Conference on Electrical, Communication, and Computer Engineering (ICECCE)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130277048","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 : 2021-06-12DOI: 10.1109/ICECCE52056.2021.9514162
Horacio M. Frene, C. D. Arrojo, R. Dias, J. C. Scaramutti
This work presents a mathematical and parametric analysis of physical and electrical variables involved in electromechanical forces due to three-phase short-circuit currents. Focus is on three-phase currents since they usually cause higher stress on electrical power equipment. Since, in the authors' opinion, a fully practical understanding of IEC 60865-1 Standard [2] is not straightforward, electromagnetic force parameters are analyzed, evaluated, and compared aiming to relate the mentioned phenomenon to the standard. Graphical material is included to make the topic clear. Future papers will be focus on tests at a testing facility.
{"title":"Mechanical stresses of electromagnetic origin. Effects produced by three-phase short-circuit currents on a rigid busbar system","authors":"Horacio M. Frene, C. D. Arrojo, R. Dias, J. C. Scaramutti","doi":"10.1109/ICECCE52056.2021.9514162","DOIUrl":"https://doi.org/10.1109/ICECCE52056.2021.9514162","url":null,"abstract":"This work presents a mathematical and parametric analysis of physical and electrical variables involved in electromechanical forces due to three-phase short-circuit currents. Focus is on three-phase currents since they usually cause higher stress on electrical power equipment. Since, in the authors' opinion, a fully practical understanding of IEC 60865-1 Standard [2] is not straightforward, electromagnetic force parameters are analyzed, evaluated, and compared aiming to relate the mentioned phenomenon to the standard. Graphical material is included to make the topic clear. Future papers will be focus on tests at a testing facility.","PeriodicalId":302947,"journal":{"name":"2021 International Conference on Electrical, Communication, and Computer Engineering (ICECCE)","volume":"133 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130911495","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 : 2021-06-12DOI: 10.1109/ICECCE52056.2021.9514144
M. Ferdous, Sk. Md. Masudul Ahsan
Construction sites are the most unsafe and risky places where thousands of workers are injured and die every year throughout the world. Some protective gear like hardhat can protect personnel from unexpected accidents. Administrators need to confirm all personnel put on hardhat on their heads during working time. However, it is inefficient and time-consuming to monitor this task manually. Hence, an automatic system may give convenience to detect personnel whether they wearing hardhat or not when they are on duty. RatinaNet is used to detect and localize the hardhat/head of personnel into the construction site. ResNet50+Feature Pyramid Network (FPN) is used as the backbone of the architecture, a classification and a regression sun-module are used to classifying objects and localizing bounding box around the object. A robust semantical description is achieved using both top-down pathways and lateral connections. Hardhats or heads are detected on a multiscale using the bottom-up and top-down modules. Experimental analysis on a dataset using RatinaNet produces a prominent result that may be usable in real-time applications.
{"title":"Multi-Scale Safety Hardhat Wearing Detection using Deep Learning: A Top-Down and Bottom-Up Module","authors":"M. Ferdous, Sk. Md. Masudul Ahsan","doi":"10.1109/ICECCE52056.2021.9514144","DOIUrl":"https://doi.org/10.1109/ICECCE52056.2021.9514144","url":null,"abstract":"Construction sites are the most unsafe and risky places where thousands of workers are injured and die every year throughout the world. Some protective gear like hardhat can protect personnel from unexpected accidents. Administrators need to confirm all personnel put on hardhat on their heads during working time. However, it is inefficient and time-consuming to monitor this task manually. Hence, an automatic system may give convenience to detect personnel whether they wearing hardhat or not when they are on duty. RatinaNet is used to detect and localize the hardhat/head of personnel into the construction site. ResNet50+Feature Pyramid Network (FPN) is used as the backbone of the architecture, a classification and a regression sun-module are used to classifying objects and localizing bounding box around the object. A robust semantical description is achieved using both top-down pathways and lateral connections. Hardhats or heads are detected on a multiscale using the bottom-up and top-down modules. Experimental analysis on a dataset using RatinaNet produces a prominent result that may be usable in real-time applications.","PeriodicalId":302947,"journal":{"name":"2021 International Conference on Electrical, Communication, and Computer Engineering (ICECCE)","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121251389","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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