Increasing productivity without jeopardizing the network’s operation, consumers’ experience, and the safety and integrity of procedures, is a major goal for all utilities. Scheduling of work has a direct impact on productivity, especially in the case of utilities that cover a wide geographical area using a limited number of employees. In the case of power utilities, however, scheduling has to consider, apart from the location of issues, the type and technical characteristics of each issue as well as its priority has to be considered in order to produce an optimum schedule. This paper focuses on the impact on productivity of a geospatial ticket management system considering the experience from such a system applied on network studies performed by HEDNO, the Greek Distribution Network Operator, in Patras Area. The term "network studies" is used to describe the study of all expansion and alteration works, ranging from a single Low Voltage (LV) pole installation to major Medium Voltage (MV) network rearrangements/expansion, and includes visits and measurements on the actual location as well as in-door calculations. In Patras Area, the local HEDNO division implemented in 2021-2022 a geospatial ticket managing system, based on available network data and custom Google Maps, aiming to increase its productivity by optimizing the scheduling process. Initial results published in February 2022, showed a significant productivity increase (up to 42%). However, the initial results considered a time span of only one month and thus could easily be misleading. This paper revisits the issue considering a larger time span (more than two years) that should provide more trustworthy results. It also briefly presents the latest updates and improvements made to the system. Results show that the increase in the number of studies and their predicted costs are similar to the initial results, with the increase in productivity being around 41%. In September 2022, HEDNO set the very ambitious goal of significantly increasing the overall production of network studies (more than double in terms of predicted costs) and the use of such a system can provide valuable help towards achieving this goal.
{"title":"Investigating the Effect on Productivity of a Geospatial Ticket Management System for Power Distribution Network Studies","authors":"Dionisios Pylarinos","doi":"10.48084/etasr.6202","DOIUrl":"https://doi.org/10.48084/etasr.6202","url":null,"abstract":"Increasing productivity without jeopardizing the network’s operation, consumers’ experience, and the safety and integrity of procedures, is a major goal for all utilities. Scheduling of work has a direct impact on productivity, especially in the case of utilities that cover a wide geographical area using a limited number of employees. In the case of power utilities, however, scheduling has to consider, apart from the location of issues, the type and technical characteristics of each issue as well as its priority has to be considered in order to produce an optimum schedule. This paper focuses on the impact on productivity of a geospatial ticket management system considering the experience from such a system applied on network studies performed by HEDNO, the Greek Distribution Network Operator, in Patras Area. The term \"network studies\" is used to describe the study of all expansion and alteration works, ranging from a single Low Voltage (LV) pole installation to major Medium Voltage (MV) network rearrangements/expansion, and includes visits and measurements on the actual location as well as in-door calculations. In Patras Area, the local HEDNO division implemented in 2021-2022 a geospatial ticket managing system, based on available network data and custom Google Maps, aiming to increase its productivity by optimizing the scheduling process. Initial results published in February 2022, showed a significant productivity increase (up to 42%). However, the initial results considered a time span of only one month and thus could easily be misleading. This paper revisits the issue considering a larger time span (more than two years) that should provide more trustworthy results. It also briefly presents the latest updates and improvements made to the system. Results show that the increase in the number of studies and their predicted costs are similar to the initial results, with the increase in productivity being around 41%. In September 2022, HEDNO set the very ambitious goal of significantly increasing the overall production of network studies (more than double in terms of predicted costs) and the use of such a system can provide valuable help towards achieving this goal.","PeriodicalId":11826,"journal":{"name":"Engineering, Technology & Applied Science Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135917885","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}
Fahad Alotaibi, Arafat Al-Dhaqm, Yasser D. Al-Otaibi
Although there is a considerable amount of studies in drone forensics that describe numerous practical and technical perspectives, there is a lack of a comprehensive investigation framework. This study used design science research methodology to design a conceptual model for the comprehensive investigation of Unmanned Aerial Vehicles (UAVs) under forensic conditions. This model can identify, capture, preserve, analyze, and document UAV incidents. The proposed model consists of four stages: preparation, data collection, analysis, and documentation. In the preparation stage, data are collected and analyzed about UAV-related resources, including the origin and model of the aircraft, any software or hardware installed onboard, and the legal framework and regulations in place. The data collection stage involves the completion of the collection process, where participants gather parts of the UAV and the data needed, such as the flight controller, flight log, and memory cards. The analysis stage involves analyzing the collected evidence. Lastly, the documentation stage involves documenting relevant evidence, analysis results, and any conclusions derived. This model provides a comprehensive process to forensically investigate UAV incidents and provides an efficient and effective approach to the analysis of UAV evidence, ensuring that evidence was collected and analyzed according to accepted forensic techniques. The proposed model can be applied to any UAV type and legal framework.
{"title":"A Conceptual Digital Forensic Investigation Model Applicable to the Drone Forensics Field","authors":"Fahad Alotaibi, Arafat Al-Dhaqm, Yasser D. Al-Otaibi","doi":"10.48084/etasr.6195","DOIUrl":"https://doi.org/10.48084/etasr.6195","url":null,"abstract":"Although there is a considerable amount of studies in drone forensics that describe numerous practical and technical perspectives, there is a lack of a comprehensive investigation framework. This study used design science research methodology to design a conceptual model for the comprehensive investigation of Unmanned Aerial Vehicles (UAVs) under forensic conditions. This model can identify, capture, preserve, analyze, and document UAV incidents. The proposed model consists of four stages: preparation, data collection, analysis, and documentation. In the preparation stage, data are collected and analyzed about UAV-related resources, including the origin and model of the aircraft, any software or hardware installed onboard, and the legal framework and regulations in place. The data collection stage involves the completion of the collection process, where participants gather parts of the UAV and the data needed, such as the flight controller, flight log, and memory cards. The analysis stage involves analyzing the collected evidence. Lastly, the documentation stage involves documenting relevant evidence, analysis results, and any conclusions derived. This model provides a comprehensive process to forensically investigate UAV incidents and provides an efficient and effective approach to the analysis of UAV evidence, ensuring that evidence was collected and analyzed according to accepted forensic techniques. The proposed model can be applied to any UAV type and legal framework.","PeriodicalId":11826,"journal":{"name":"Engineering, Technology & Applied Science Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135918035","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}
Daily peak load forecasting is critical for energy providers to meet the loads of grid-connected consumers. This study proposed a Seasonal Trend decomposition using Loess combined with Long Short-Term Memory (STL-LTSM) method and compared its performance on peak forecasting of electrical energy demand with Convolutional Neural Network and LSTM (CNN-LSTM), Wavenet, and the classic approaches Artificial Neural Network (ANN) and LSTM. The study evaluated the models using demand data from the power system in Tien Giang province, Vietnam, from 2020 to 2022, considering historical demand, holidays, and weather variables as input characteristics. The results showed that the proposed STL-LSTM model can predict future demand with lower Base Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Therefore, the proposed method can help energy suppliers make smart decisions and plan for future demand.
{"title":"Application of Seasonal Trend Decomposition using Loess and Long Short-Term Memory in Peak Load Forecasting Model in Tien Giang","authors":"Ngoc-Hung Duong, Minh-Tam Nguyen, Thanh-Hoan Nguyen, Thanh-Phong Tran","doi":"10.48084/etasr.6181","DOIUrl":"https://doi.org/10.48084/etasr.6181","url":null,"abstract":"Daily peak load forecasting is critical for energy providers to meet the loads of grid-connected consumers. This study proposed a Seasonal Trend decomposition using Loess combined with Long Short-Term Memory (STL-LTSM) method and compared its performance on peak forecasting of electrical energy demand with Convolutional Neural Network and LSTM (CNN-LSTM), Wavenet, and the classic approaches Artificial Neural Network (ANN) and LSTM. The study evaluated the models using demand data from the power system in Tien Giang province, Vietnam, from 2020 to 2022, considering historical demand, holidays, and weather variables as input characteristics. The results showed that the proposed STL-LSTM model can predict future demand with lower Base Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Therefore, the proposed method can help energy suppliers make smart decisions and plan for future demand.","PeriodicalId":11826,"journal":{"name":"Engineering, Technology & Applied Science Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135918170","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}
This study presents a correction approach that can capture the discontinuities in the bending moment and shear force in the dynamic analysis of beam-like structures traveled by a moving vehicle. The proposed approach was based on the Dynamic Modal Acceleration Method (DyMAM) to correct the dynamic response of the supporting structure with a reduced number of vibration modes. The use of a two-axle vehicle model was adopted to consider the pitching effect in the presence of surface irregularity and damping. The interacting forces between the beam and vehicle were filtered to avoid undesirable high-frequency contributions. Subsequently, a new formulation for the entire vehicle-beam system was obtained. The corresponding equation was solved using the Newmark numerical scheme to obtain the system responses in each time step. A numerical example was illustrated, showing that the proposed method was in close agreement with previous correction solutions in the vehicle-beam interaction analysis.
{"title":"An Improved Correction Technique for the Prediction of the Dynamic Response of a Beam under a Moving Vehicle","authors":"Duy Hung Nguyen, Nguyen Dang Diem, Thi Kieu Pham","doi":"10.48084/etasr.6129","DOIUrl":"https://doi.org/10.48084/etasr.6129","url":null,"abstract":"This study presents a correction approach that can capture the discontinuities in the bending moment and shear force in the dynamic analysis of beam-like structures traveled by a moving vehicle. The proposed approach was based on the Dynamic Modal Acceleration Method (DyMAM) to correct the dynamic response of the supporting structure with a reduced number of vibration modes. The use of a two-axle vehicle model was adopted to consider the pitching effect in the presence of surface irregularity and damping. The interacting forces between the beam and vehicle were filtered to avoid undesirable high-frequency contributions. Subsequently, a new formulation for the entire vehicle-beam system was obtained. The corresponding equation was solved using the Newmark numerical scheme to obtain the system responses in each time step. A numerical example was illustrated, showing that the proposed method was in close agreement with previous correction solutions in the vehicle-beam interaction analysis.","PeriodicalId":11826,"journal":{"name":"Engineering, Technology & Applied Science Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135918189","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}
A Laced Reinforced Concrete (LRC) structural element comprises continuously inclined shear reinforcement in the form of lacing that connects the longitudinal reinforcements on both faces of the structural element. This study conducted a theoretical investigation of LRC deep beams to predict their behavior after exposure to fire and high temperatures. Four simply supported reinforced concrete beams of 1500 mm, 200 mm, and 240 mm length, width, and depth, respectively, were considered. The specimens were identical in terms of compressive strength ( 40 MPa) and steel reinforcement details. The same laced steel reinforcement ratio of 0.0035 was used. Three specimens were burned at variable durations and steady-state temperatures (one hour at 500 °C and 600 °C, and two hours at 500 °C). The flexural behavior of the simply supported deep beams, subjected to the two concentric loads in the middle third of the beam, was investigated with ABAQUS software. The results showed that the laced reinforcement with an inclination of 45˚ improved the structural behavior of the deep beams, and the lacing resisted failure and extended the life of the model. The optimal structural response was observed for the specimens. The laced reinforcement improved the failure mode and converted it from shear to flexure-shear failure. The parametric study showed that the lacing bars remarkably improved the strength of the deep beams and they were not affected more by the steady-state temperature and duration. Furthermore, a greater increase in load-carrying capacity was associated with an increase in the flexural diameter of approximately 12 and 16 mm by approximately 24.77% and 87.61%, respectively, compared to the reference LRC deep beams.
{"title":"The Experimental and Theoretical Effect of Fire on the Structural Behavior of Laced Reinforced Concrete Deep Beams","authors":"Abbas Kareem, Shatha D. Mohammed","doi":"10.48084/etasr.6272","DOIUrl":"https://doi.org/10.48084/etasr.6272","url":null,"abstract":"A Laced Reinforced Concrete (LRC) structural element comprises continuously inclined shear reinforcement in the form of lacing that connects the longitudinal reinforcements on both faces of the structural element. This study conducted a theoretical investigation of LRC deep beams to predict their behavior after exposure to fire and high temperatures. Four simply supported reinforced concrete beams of 1500 mm, 200 mm, and 240 mm length, width, and depth, respectively, were considered. The specimens were identical in terms of compressive strength ( 40 MPa) and steel reinforcement details. The same laced steel reinforcement ratio of 0.0035 was used. Three specimens were burned at variable durations and steady-state temperatures (one hour at 500 °C and 600 °C, and two hours at 500 °C). The flexural behavior of the simply supported deep beams, subjected to the two concentric loads in the middle third of the beam, was investigated with ABAQUS software. The results showed that the laced reinforcement with an inclination of 45˚ improved the structural behavior of the deep beams, and the lacing resisted failure and extended the life of the model. The optimal structural response was observed for the specimens. The laced reinforcement improved the failure mode and converted it from shear to flexure-shear failure. The parametric study showed that the lacing bars remarkably improved the strength of the deep beams and they were not affected more by the steady-state temperature and duration. Furthermore, a greater increase in load-carrying capacity was associated with an increase in the flexural diameter of approximately 12 and 16 mm by approximately 24.77% and 87.61%, respectively, compared to the reference LRC deep beams.","PeriodicalId":11826,"journal":{"name":"Engineering, Technology & Applied Science Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135918320","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}
Rotary machinery plays an important role in industry. Combined faults can be observed in rotating machinery, making fault classification difficult. In this paper, the Minutiae algorithm is used to classify the faults from the frequency domain of a particular fault. This paper provides a fault classification technique based on image processing for fault analysis of rotating machinery, recognizing function extraction automatically. Minutiae algorithm, a rising method within the discipline of image processing for characteristic extraction, is utilized in this paper to classify specific faults from the converted recurrence plot. The results reveal the effectiveness of the proposed method, providing a rather powerful tool for fault diagnosis of rotating machinery. The proposed model achieved an accuracy of 100% for combined faults, 98.33% for loosened faults, and 95% for unbalanced faults proving its applicability.
{"title":"Fault Diagnosis of Rotating Machinery based on the Minutiae Algorithm","authors":"Shyam Mogal, Sudhanshu Deshmukh, Sopan Talekar","doi":"10.48084/etasr.6175","DOIUrl":"https://doi.org/10.48084/etasr.6175","url":null,"abstract":"Rotary machinery plays an important role in industry. Combined faults can be observed in rotating machinery, making fault classification difficult. In this paper, the Minutiae algorithm is used to classify the faults from the frequency domain of a particular fault. This paper provides a fault classification technique based on image processing for fault analysis of rotating machinery, recognizing function extraction automatically. Minutiae algorithm, a rising method within the discipline of image processing for characteristic extraction, is utilized in this paper to classify specific faults from the converted recurrence plot. The results reveal the effectiveness of the proposed method, providing a rather powerful tool for fault diagnosis of rotating machinery. The proposed model achieved an accuracy of 100% for combined faults, 98.33% for loosened faults, and 95% for unbalanced faults proving its applicability.","PeriodicalId":11826,"journal":{"name":"Engineering, Technology & Applied Science Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135918327","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}
Daniel Marasescu, Mihaela Greti Manea, Paul Burlacu, Andreea Codrina Tanase
In the current geo-strategic context, the North Atlantic Treaty Organization (NATO) and the International Maritime Organization (IMO) impose increasingly high standards for seafarers' preparation in the ship's vitality training centers (elaborate constructions, designed and equipped specifically for training personnel in scenarios involving flooding and fires that may occur on board ships). The buildings housing these training areas experience considerable mechanical and thermal stress, which, over time, affects their structural integrity. Therefore, repairs and modernization work become necessary. This paper provides a case study on the application of technologies for terrestrial laser scanning of buildings, with a focus on the vitality training polygon. The originality of the approach comes from achieving "as-built" documentation for the vitality polygon using 3D laser scanning technology. This includes both three-dimensional modeling based on digital information from the scanning process, as well as structural analysis using finite element techniques for the buildings where crew training takes place. The study also analyzes the distribution of total deformations and stresses in the walls of exercise compartments for flooding and water fight scenarios. Moreover, temperature distribution in the walls and interior atmosphere of these compartments is examined for fire scenarios and firefighting.
{"title":"Design Improvement for a Maritime Training Polygon using 3D Terrestrial Laser Scanning Technology","authors":"Daniel Marasescu, Mihaela Greti Manea, Paul Burlacu, Andreea Codrina Tanase","doi":"10.48084/etasr.6306","DOIUrl":"https://doi.org/10.48084/etasr.6306","url":null,"abstract":"In the current geo-strategic context, the North Atlantic Treaty Organization (NATO) and the International Maritime Organization (IMO) impose increasingly high standards for seafarers' preparation in the ship's vitality training centers (elaborate constructions, designed and equipped specifically for training personnel in scenarios involving flooding and fires that may occur on board ships). The buildings housing these training areas experience considerable mechanical and thermal stress, which, over time, affects their structural integrity. Therefore, repairs and modernization work become necessary. This paper provides a case study on the application of technologies for terrestrial laser scanning of buildings, with a focus on the vitality training polygon. The originality of the approach comes from achieving \"as-built\" documentation for the vitality polygon using 3D laser scanning technology. This includes both three-dimensional modeling based on digital information from the scanning process, as well as structural analysis using finite element techniques for the buildings where crew training takes place. The study also analyzes the distribution of total deformations and stresses in the walls of exercise compartments for flooding and water fight scenarios. Moreover, temperature distribution in the walls and interior atmosphere of these compartments is examined for fire scenarios and firefighting.","PeriodicalId":11826,"journal":{"name":"Engineering, Technology & Applied Science Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135918625","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}
Mohamed Laissy, Zuhair Al-Turkmani, Omar Atarji, Abdaulaziz Saud
Seismic analysis is considered as an important aspect of the design of high-rise buildings, particularly in earthquake prone areas. The structural system choice can have a considerable impact on the building seismic response. The goal of this study is to compare the seismic behavior of multiple slab systems used in a multi-story building in Saudi Arabia's Madinah region. This study's goal is to determine the most effective and efficient slab system performance in a seismic zone. The ETABS V20.3 program was used in this work to model and assess the seismic response of three different types of slab systems: flat, solid, and hollow blocks slab types. Many earthquake aspects, including story displacement, base shear, story drifts, column forces, and bending moments, are estimated for each system. The study examines and assesses each system's seismic response, and the conclusions are given and discussed. According to the findings, the choice of slab system has a considerable impact on the seismic reaction of the building. The hollow block system has the least base shear value and bending moments, while the flat slab system has the greatest. The values in the solid slab system are in the middle. In terms of story displacement and column forces, the study additionally indicates that the hollow block type system performs effectively in terms of story drifts, however, the solid slab system outperforms the others. The study's findings can assist designers and engineers to determine the best slab system for multistory buildings in seismic-prone areas by providing important insight and suggestions.
{"title":"A Comparative Study of the Seismic Response of Different Concrete Slab Systems for a Multistory Building in Madinah","authors":"Mohamed Laissy, Zuhair Al-Turkmani, Omar Atarji, Abdaulaziz Saud","doi":"10.48084/etasr.6047","DOIUrl":"https://doi.org/10.48084/etasr.6047","url":null,"abstract":"Seismic analysis is considered as an important aspect of the design of high-rise buildings, particularly in earthquake prone areas. The structural system choice can have a considerable impact on the building seismic response. The goal of this study is to compare the seismic behavior of multiple slab systems used in a multi-story building in Saudi Arabia's Madinah region. This study's goal is to determine the most effective and efficient slab system performance in a seismic zone. The ETABS V20.3 program was used in this work to model and assess the seismic response of three different types of slab systems: flat, solid, and hollow blocks slab types. Many earthquake aspects, including story displacement, base shear, story drifts, column forces, and bending moments, are estimated for each system. The study examines and assesses each system's seismic response, and the conclusions are given and discussed. According to the findings, the choice of slab system has a considerable impact on the seismic reaction of the building. The hollow block system has the least base shear value and bending moments, while the flat slab system has the greatest. The values in the solid slab system are in the middle. In terms of story displacement and column forces, the study additionally indicates that the hollow block type system performs effectively in terms of story drifts, however, the solid slab system outperforms the others. The study's findings can assist designers and engineers to determine the best slab system for multistory buildings in seismic-prone areas by providing important insight and suggestions.","PeriodicalId":11826,"journal":{"name":"Engineering, Technology & Applied Science Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135918634","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 machining ability and accuracy of a machine are determined by parameters such as the stiffness and load capacity of its spindle unit. In addition, the effectiveness and technique of lubrication and cooling can significantly affect the operational characteristics of the machine spindle. The current study investigated the effects of two different lubrication methods, grease and air-oil mixture, on the temperature which is generated at the spindle bearings of a Computer Numerical Control (CNC) machine. The temperature distribution and rise rate of the bearings were measured using a thermal imaging camera and thermocouples. The results indicated that the air-oil mixture method was more effective in dissipating heat and reducing the temperature of the bearings than the grease method, due to the direct cooling provided by the air-oil mixture to the bearing balls, resulting in improved lubrication efficiency and heat exchange with the environment. Compared to the grease lubrication method, the temperature of the bearings was lower by 7°C to 9°C depending on the position of the bearing on the CNC spindle. Therefore, it is recommended to use the air-oil mixture lubrication method, especially for high-speed processing on CNC machines. However, the discharge of oil particles from the ventilation system should be carefully controlled. Overall, the findings offer valuable insights into optimizing lubrication methods for CNC machines to enhance processing quality and reduce the impact of temperature on the bearing performance.
{"title":"Analyzing the Effects of Lubrication Techniques on CNC Spindle Bearing Heat: An Experimental Investigation","authors":"Duc-Do Le, Tuan-Anh Bui","doi":"10.48084/etasr.6146","DOIUrl":"https://doi.org/10.48084/etasr.6146","url":null,"abstract":"The machining ability and accuracy of a machine are determined by parameters such as the stiffness and load capacity of its spindle unit. In addition, the effectiveness and technique of lubrication and cooling can significantly affect the operational characteristics of the machine spindle. The current study investigated the effects of two different lubrication methods, grease and air-oil mixture, on the temperature which is generated at the spindle bearings of a Computer Numerical Control (CNC) machine. The temperature distribution and rise rate of the bearings were measured using a thermal imaging camera and thermocouples. The results indicated that the air-oil mixture method was more effective in dissipating heat and reducing the temperature of the bearings than the grease method, due to the direct cooling provided by the air-oil mixture to the bearing balls, resulting in improved lubrication efficiency and heat exchange with the environment. Compared to the grease lubrication method, the temperature of the bearings was lower by 7°C to 9°C depending on the position of the bearing on the CNC spindle. Therefore, it is recommended to use the air-oil mixture lubrication method, especially for high-speed processing on CNC machines. However, the discharge of oil particles from the ventilation system should be carefully controlled. Overall, the findings offer valuable insights into optimizing lubrication methods for CNC machines to enhance processing quality and reduce the impact of temperature on the bearing performance.","PeriodicalId":11826,"journal":{"name":"Engineering, Technology & Applied Science Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135918001","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}
Surveillance videos are recordings captured by video recording devices for monitoring and securing an area or property. These videos are frequently used in applications, involving law enforcement, security systems, retail analytics, and traffic monitoring. Surveillance videos can provide valuable visual information for analyzing patterns, identifying individuals or objects of interest, and detecting and investigating incidents. Object detection and classification on video surveillance involves the usage of computer vision techniques to identify and categorize objects within the video footage. Object detection algorithms are employed to locate and identify objects within each frame. These algorithms use various techniques, namely bounding box regression, Convolutional Neural Networks (CNNs), and feature extraction to detect objects of interest. This study presents the Mayfly Optimization with Deep Learning-based Robust Object Detection and Classification (MFODL-RODC) method on surveillance videos. The main aim of the MFODL-RODC technique lies in the accurate classification and recognition of objects in surveillance videos. To accomplish this, the MFODL-RODC method follows a two-step process, consisting of object detection and object classification. The MFODL-RODC method uses the EfficientDet object detector for the object detection process. Besides, the classification of detected objects takes place using the Variational Autoencoder (VAE) model. The MFO algorithm is employed to enrich the performance of the VAE model. The simulation examination of the MFODL-RODC technique is performed on benchmark datasets. The extensive results accentuated the improved performance of the MFODL-RODC method over other existing algorithms with an output of 98.89%.
{"title":"Mayfly Optimization with Deep Learning-based Robust Object Detection and Classification on Surveillance Videos","authors":"Venkatesan Saikrishnan, Mani Karthikeyan","doi":"10.48084/etasr.6231","DOIUrl":"https://doi.org/10.48084/etasr.6231","url":null,"abstract":"Surveillance videos are recordings captured by video recording devices for monitoring and securing an area or property. These videos are frequently used in applications, involving law enforcement, security systems, retail analytics, and traffic monitoring. Surveillance videos can provide valuable visual information for analyzing patterns, identifying individuals or objects of interest, and detecting and investigating incidents. Object detection and classification on video surveillance involves the usage of computer vision techniques to identify and categorize objects within the video footage. Object detection algorithms are employed to locate and identify objects within each frame. These algorithms use various techniques, namely bounding box regression, Convolutional Neural Networks (CNNs), and feature extraction to detect objects of interest. This study presents the Mayfly Optimization with Deep Learning-based Robust Object Detection and Classification (MFODL-RODC) method on surveillance videos. The main aim of the MFODL-RODC technique lies in the accurate classification and recognition of objects in surveillance videos. To accomplish this, the MFODL-RODC method follows a two-step process, consisting of object detection and object classification. The MFODL-RODC method uses the EfficientDet object detector for the object detection process. Besides, the classification of detected objects takes place using the Variational Autoencoder (VAE) model. The MFO algorithm is employed to enrich the performance of the VAE model. The simulation examination of the MFODL-RODC technique is performed on benchmark datasets. The extensive results accentuated the improved performance of the MFODL-RODC method over other existing algorithms with an output of 98.89%.","PeriodicalId":11826,"journal":{"name":"Engineering, Technology & Applied Science Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135918147","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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