This paper presents the development and simulation of photovoltaic (PV), wind turbine and battery energy storage system (BESS) based microgrid in a Mongolian case. Although many standalone solar and wind microgrids are installed in Mongolia, they are not operating at total capacity and reliably due to a lack of control and proper use. The microgrid system operates in autonomous mode to serve the loads. To effectively control the microgrid voltage and frequency and achieve smoother power flow control between the generation and consumption, voltage–frequency (V/F) control based on the fuzzy logic controller (FLC) is proposed. Even though there are sudden load variations in the system and fluctuations in PV output power, the microgrid voltage and frequency are effectively maintained within limits by the proposed FLC. A fuzzy logic controller is used for an off-grid operated Microgrid constituted by the solar system, wind system and battery. The PV, wind turbine and BESS based microgrid system are simulated using Matlab/Simulink.
{"title":"Realization of Fuzzy Logic Controller in Microgrid for Mongolian case","authors":"Zagdkhorol Bayasgalan, Munkhtuya Erdenebat","doi":"10.14464/ess.v9i1.511","DOIUrl":"https://doi.org/10.14464/ess.v9i1.511","url":null,"abstract":"\u0000 \u0000 \u0000This paper presents the development and simulation of photovoltaic (PV), wind turbine and battery energy storage system (BESS) based microgrid in a Mongolian case. Although many standalone solar and wind microgrids are installed in Mongolia, they are not operating at total capacity and reliably due to a lack of control and proper use. The microgrid system operates in autonomous mode to serve the loads. To effectively control the microgrid voltage and frequency and achieve smoother power flow control between the generation and consumption, voltage–frequency (V/F) control based on the fuzzy logic controller (FLC) is proposed. Even though there are sudden load variations in the system and fluctuations in PV output power, the microgrid voltage and frequency are effectively maintained within limits by the proposed FLC. A fuzzy logic controller is used for an off-grid operated Microgrid constituted by the solar system, wind system and battery. The PV, wind turbine and BESS based microgrid system are simulated using Matlab/Simulink. \u0000 \u0000 \u0000","PeriodicalId":322203,"journal":{"name":"Embedded Selforganising Systems","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132762452","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 third thermal power plant is one of the largest in Mongolia, generating about 30% of Mongolia's electricity and more than 60% of Ulaanbaatar's thermal energy. Although Mongolia's electricity consumption has grown steadily, no new power plants have been built to meet this increased demand. This increase in load and the intermittent characteristic of renewable energy have adversely affected thermal power plants' sustainability. Therefore, to study how the static and dynamic transition process of the power plant is affecting the operation, the 110kV 35kV 10kV 6kV general circuit scheme was fully modelled on Powerfactory software to analyse the performance of the load flow, load loss, voltage level, determination of the loading condition, balanced and unbalanced short circuit calculation results are demonstrated.
{"title":"Load Flow, Load Loss and Short Circuit Analysis of The Third Thermal Power Plant’s Electrical Supply System","authors":"Battulga Munkhbaatar, Perenlei Khurelbaatar, Otgonjargal Purevsuren, Narangarav Ulzii, Byambajav Munkhbayar","doi":"10.14464/ess.v9i1.508","DOIUrl":"https://doi.org/10.14464/ess.v9i1.508","url":null,"abstract":"The third thermal power plant is one of the largest in Mongolia, generating about 30% of Mongolia's electricity and more than 60% of Ulaanbaatar's thermal energy. Although Mongolia's electricity consumption has grown steadily, no new power plants have been built to meet this increased demand. This increase in load and the intermittent characteristic of renewable energy have adversely affected thermal power plants' sustainability. Therefore, to study how the static and dynamic transition process of the power plant is affecting the operation, the 110kV 35kV 10kV 6kV general circuit scheme was fully modelled on Powerfactory software to analyse the performance of the load flow, load loss, voltage level, determination of the loading condition, balanced and unbalanced short circuit calculation results are demonstrated.","PeriodicalId":322203,"journal":{"name":"Embedded Selforganising Systems","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134643140","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}
Battulga Munkhbaatar, Bold S., Bat-Erdene B., Tuvshinzaya G., Zagdkhorol B.
Baganuur Southeast Electricity Distribution Network 15 kV electrical transmission line interruptions research was conducted, and a factor analysis was performed to determine the conditions of the line outage. Due to the neutral grounding of the 15 kV electrical transmission line and the relatively small distance between the line wires and crossbar, there is a high incidence of grounding during the landing and flight of birds, which hurts the reliable operation and ecology of the line. Therefore, the structure of the poles changed, and the results are reflected.
{"title":"Results From Changes in The Pole of 15 kV Power Transmission Lines","authors":"Battulga Munkhbaatar, Bold S., Bat-Erdene B., Tuvshinzaya G., Zagdkhorol B.","doi":"10.14464/ess.v9i1.509","DOIUrl":"https://doi.org/10.14464/ess.v9i1.509","url":null,"abstract":"Baganuur Southeast Electricity Distribution Network 15 kV electrical transmission line interruptions research was conducted, and a factor analysis was performed to determine the conditions of the line outage. Due to the neutral grounding of the 15 kV electrical transmission line and the relatively small distance between the line wires and crossbar, there is a high incidence of grounding during the landing and flight of birds, which hurts the reliable operation and ecology of the line. Therefore, the structure of the poles changed, and the results are reflected.","PeriodicalId":322203,"journal":{"name":"Embedded Selforganising Systems","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127094057","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 editorial introduces the issue of 2022 for Embedded Selforganising Systems (ESS) journal. This issue focuses on a discussion about Advances in Smart technologies and Applications in different areas of engineering solutions.
{"title":"Advances in Smart technologies and Applications","authors":"Zagdkhorol Bayasgalan, Bat-Erdene Byambasuren","doi":"10.14464/ess.v9i1.510","DOIUrl":"https://doi.org/10.14464/ess.v9i1.510","url":null,"abstract":"This editorial introduces the issue of 2022 for Embedded Selforganising Systems (ESS) journal. This issue focuses on a discussion about Advances in Smart technologies and Applications in different areas of engineering solutions.","PeriodicalId":322203,"journal":{"name":"Embedded Selforganising Systems","volume":"480 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123397333","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}
Vision Based systems have become an integral part when it comes to autonomous driving. The autonomous industry has seen a made large progress in the perception of environment as a result of the improvements done towards vision based systems. As the industry moves up the ladder of automation, safety features are coming more and more into the focus. Different safety measurements have to be taken into consideration based on different driving situations. One of the major concerns of the highest level of autonomy is to obtain the ability of understanding both internal and external situations. Most of the research made on vision based systems are focused on image processing and artificial intelligence systems like machine learning and deep learning. Due to the current generation of technology being the generation of “Connected World”, there is no lack of data any more. As a result of the introduction of internet of things, most of these connected devices are able to share and transfer data. Vision based techniques are techniques that are hugely depended on these vision based data.
{"title":"Towards Autonomous Driving Using Vision Based Intelligent Systems","authors":"J. Nine","doi":"10.14464/ess.v8i2.496","DOIUrl":"https://doi.org/10.14464/ess.v8i2.496","url":null,"abstract":"Vision Based systems have become an integral part when it comes to autonomous driving. The autonomous industry has seen a made large progress in the perception of environment as a result of the improvements done towards vision based systems. As the industry moves up the ladder of automation, safety features are coming more and more into the focus. Different safety measurements have to be taken into consideration based on different driving situations. One of the major concerns of the highest level of autonomy is to obtain the ability of understanding both internal and external situations. Most of the research made on vision based systems are focused on image processing and artificial intelligence systems like machine learning and deep learning. Due to the current generation of technology being the generation of “Connected World”, there is no lack of data any more. As a result of the introduction of internet of things, most of these connected devices are able to share and transfer data. Vision based techniques are techniques that are hugely depended on these vision based data.","PeriodicalId":322203,"journal":{"name":"Embedded Selforganising Systems","volume":"193 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115645651","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}
Vehicle detection is one of the primal challenges of modern driver-assistance systems owing to the numerous factors, for instance, complicated surroundings, diverse types of vehicles with varied appearance and magnitude, low-resolution videos, fast-moving vehicles. It is utilized for multitudinous applications including traffic surveillance and collision prevention. This paper suggests a Vehicle Detection algorithm developed on Image Processing and Machine Learning. The presented algorithm is predicated on a Support Vector Machine(SVM) Classifier which employs feature vectors extracted via Histogram of Gradients(HOG) approach conducted on a semi-real time basis. A comparison study is presented stating the performance metrics of the algorithm on different datasets.
{"title":"Dataset Evaluation for Multi Vehicle Detection using Vision Based Techniques","authors":"J. Nine, Aarti Kishor Anapunje","doi":"10.14464/ess.v8i2.492","DOIUrl":"https://doi.org/10.14464/ess.v8i2.492","url":null,"abstract":"Vehicle detection is one of the primal challenges of modern driver-assistance systems owing to the numerous factors, for instance, complicated surroundings, diverse types of vehicles with varied appearance and magnitude, low-resolution videos, fast-moving vehicles. It is utilized for multitudinous applications including traffic surveillance and collision prevention. This paper suggests a Vehicle Detection algorithm developed on Image Processing and Machine Learning. The presented algorithm is predicated on a Support Vector Machine(SVM) Classifier which employs feature vectors extracted via Histogram of Gradients(HOG) approach conducted on a semi-real time basis. A comparison study is presented stating the performance metrics of the algorithm on different datasets.","PeriodicalId":322203,"journal":{"name":"Embedded Selforganising Systems","volume":"292 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132374089","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}
Traffic light detection and back-light recognition are essential research topics in the area of intelligent vehicles because they avoid vehicle collision and provide driver safety. Improved detection and semantic clarity may aid in the prevention of traffic accidents by self-driving cars at crowded junctions, thus improving overall driving safety. Complex traffic situations, on the other hand, make it more difficult for algorithms to identify and recognize objects. The latest state-of-the-art algorithms based on Deep Learning and Computer Vision are successfully addressing the majority of real-time problems for autonomous driving, such as detecting traffic signals, traffic signs, and pedestrians. We propose a combination of deep learning and image processing methods while using the MobileNetSSD (deep neural network architecture) model with transfer learning for real-time detection and identification of traffic lights and back-light. This inference model is obtained from frameworks such as Tensor-Flow and Tensor-Flow Lite which is trained on the COCO data. This study investigates the feasibility of executing object detection on the Raspberry Pi 3B+, a widely used embedded computing board. The algorithm’s performance is measured in terms of frames per second (FPS), accuracy, and inference time.
{"title":"Traffic Light and Back-light Recognition using Deep Learning and Image Processing with Raspberry Pi","authors":"J. Nine, R. Mathavan","doi":"10.14464/ess.v8i2.490","DOIUrl":"https://doi.org/10.14464/ess.v8i2.490","url":null,"abstract":"Traffic light detection and back-light recognition are essential research topics in the area of intelligent vehicles because they avoid vehicle collision and provide driver safety. Improved detection and semantic clarity may aid in the prevention of traffic accidents by self-driving cars at crowded junctions, thus improving overall driving safety. Complex traffic situations, on the other hand, make it more difficult for algorithms to identify and recognize objects. The latest state-of-the-art algorithms based on Deep Learning and Computer Vision are successfully addressing the majority of real-time problems for autonomous driving, such as detecting traffic signals, traffic signs, and pedestrians. We propose a combination of deep learning and image processing methods while using the MobileNetSSD (deep neural network architecture) model with transfer learning for real-time detection and identification of traffic lights and back-light. This inference model is obtained from frameworks such as Tensor-Flow and Tensor-Flow Lite which is trained on the COCO data. This study investigates the feasibility of executing object detection on the Raspberry Pi 3B+, a widely used embedded computing board. The algorithm’s performance is measured in terms of frames per second (FPS), accuracy, and inference time.","PeriodicalId":322203,"journal":{"name":"Embedded Selforganising Systems","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115844953","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 sleeping driver is potentially more likely to cause an accident than the person who speeds up since the driver is the victim of sleepiness. Automobile industry researchers, including manufacturers, seek to solve this issue with various technical solutions that can avoid such a situation. This paper proposes an implementation of a lightweight method to detect driver's sleepiness using facial landmarks and head pose estimation based on neural network methodologies on a mobile device. We try to improve the accurateness by using face images that the camera detects and passes to CNN to identify sleepiness. Firstly, applied a behavioral landmark's sleepiness detection process. Then, an integrated Head Pose Estimation technique will strengthen the system's reliability. The preliminary findings of the tests demonstrate that with real-time capability, more than 86% identification accuracy can be reached in several real-world scenarios for all classes, including with glasses, without glasses, and light-dark background. This work aims to classify drowsiness, warn, and inform drivers, helping them to stop falling asleep at the wheel. The integrated CNN-based method is used to create a high accuracy and simple-to-use real-time driver drowsiness monitoring framework for embedded devices and Android phones
{"title":"Drowsiness Classification for Internal Driving Situation Awareness on Mobile Platform","authors":"J. Nine, Naeem Ahmed, R. Mathavan","doi":"10.14464/ess.v8i2.491","DOIUrl":"https://doi.org/10.14464/ess.v8i2.491","url":null,"abstract":"the sleeping driver is potentially more likely to cause an accident than the person who speeds up since the driver is the victim of sleepiness. Automobile industry researchers, including manufacturers, seek to solve this issue with various technical solutions that can avoid such a situation. This paper proposes an implementation of a lightweight method to detect driver's sleepiness using facial landmarks and head pose estimation based on neural network methodologies on a mobile device. We try to improve the accurateness by using face images that the camera detects and passes to CNN to identify sleepiness. Firstly, applied a behavioral landmark's sleepiness detection process. Then, an integrated Head Pose Estimation technique will strengthen the system's reliability. The preliminary findings of the tests demonstrate that with real-time capability, more than 86% identification accuracy can be reached in several real-world scenarios for all classes, including with glasses, without glasses, and light-dark background. This work aims to classify drowsiness, warn, and inform drivers, helping them to stop falling asleep at the wheel. The integrated CNN-based method is used to create a high accuracy and simple-to-use real-time driver drowsiness monitoring framework for embedded devices and Android phones","PeriodicalId":322203,"journal":{"name":"Embedded Selforganising Systems","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134473296","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}
Automotive technologies are ever-increasinglybecoming digital. Highly autonomous driving together withdigital E/E control mechanisms include thousands of softwareapplications which are called as software components.Together with the industry requirements, and rigoroussoftware development processes, mapping of components as asoftware pool becomes very difficult. This article analyses anddiscusses the integration possibilities of machine learningapproaches to our previously introduced concept of mappingof software components through a common software pool
{"title":"Analysis of Machine Learning Approach for the mode model in SWC Mapping in Automotive Systems","authors":"Owes Khan, Geri Shahini, W. Hardt","doi":"10.14464/ess.v7i2.473","DOIUrl":"https://doi.org/10.14464/ess.v7i2.473","url":null,"abstract":"Automotive technologies are ever-increasinglybecoming digital. Highly autonomous driving together withdigital E/E control mechanisms include thousands of softwareapplications which are called as software components.Together with the industry requirements, and rigoroussoftware development processes, mapping of components as asoftware pool becomes very difficult. This article analyses anddiscusses the integration possibilities of machine learningapproaches to our previously introduced concept of mappingof software components through a common software pool","PeriodicalId":322203,"journal":{"name":"Embedded Selforganising Systems","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123851540","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 article considers a classification for validation and quality assessment of the user interface (UI) from the point of view of the main aspects of design and its application in the development of web-applications. The problem with inaccurately crafted user interface requirements is relevant and as a result, developers often have to redesign the interface and architecture of the application. The article analyzes the role and place of UI in the architecture of client-server applications, analyzes aspects of UI design, on the basis of which the classification is formed. The classification is used to analyze UI design oversights of the developed web-applications for BPMS “Fireproof Corporation” company. Based on the results of UI validation, a set of typical UI design oversights has been added.
{"title":"Classification for Quality Assessment of the User Interface and its Application in the Development of Web-applications","authors":"N. Gervas, Evgeny L. Romanov, W. Hardt","doi":"10.14464/ess.v8i1.481","DOIUrl":"https://doi.org/10.14464/ess.v8i1.481","url":null,"abstract":"The article considers a classification for validation and quality assessment of the user interface (UI) from the point of view of the main aspects of design and its application in the development of web-applications. The problem with inaccurately crafted user interface requirements is relevant and as a result, developers often have to redesign the interface and architecture of the application. The article analyzes the role and place of UI in the architecture of client-server applications, analyzes aspects of UI design, on the basis of which the classification is formed. The classification is used to analyze UI design oversights of the developed web-applications for BPMS “Fireproof Corporation” company. Based on the results of UI validation, a set of typical UI design oversights has been added.","PeriodicalId":322203,"journal":{"name":"Embedded Selforganising Systems","volume":"302 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115238857","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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