Pub Date : 2023-07-27DOI: 10.1109/ICSSE58758.2023.10227189
Hoang Vu Dao, K. Ahn
In this paper, a novel states and disturbances observer is designed to simultaneously observe both lumped uncertainties/disturbances and unmeasurable joint velocities of robot manipulators. The proposed observer inherits the advantages of the previous nonlinear observer with high estimation accuracy and a simple structure compared to other observers. However, to increase the steady-state estimation accuracy without deteriorating the transient response, a time-varying bandwidth mechanism is proposed which adjusts the observer bandwidth according to the output estimation error. The stability of the proposed observer is proved based on Lyapunov theory. Simulation results validate the performance of the proposed method.
{"title":"Nonlinear Observer Design with Time-varying Bandwidth for Robot Manipulators","authors":"Hoang Vu Dao, K. Ahn","doi":"10.1109/ICSSE58758.2023.10227189","DOIUrl":"https://doi.org/10.1109/ICSSE58758.2023.10227189","url":null,"abstract":"In this paper, a novel states and disturbances observer is designed to simultaneously observe both lumped uncertainties/disturbances and unmeasurable joint velocities of robot manipulators. The proposed observer inherits the advantages of the previous nonlinear observer with high estimation accuracy and a simple structure compared to other observers. However, to increase the steady-state estimation accuracy without deteriorating the transient response, a time-varying bandwidth mechanism is proposed which adjusts the observer bandwidth according to the output estimation error. The stability of the proposed observer is proved based on Lyapunov theory. Simulation results validate the performance of the proposed method.","PeriodicalId":280745,"journal":{"name":"2023 International Conference on System Science and Engineering (ICSSE)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129051246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-27DOI: 10.1109/ICSSE58758.2023.10227147
Truong Hoang Bao Huy, D. Vo, H. Nguyen, Phuoc Hoa Truong, K. Dang, K. H. Truong
The widespread implementation of renewable energy sources is posing new and distinct challenges for power systems. Consequently, power system state estimation has become increasingly essential for monitoring, operating, and safeguarding modern power systems. Conventionally, physics-based models such as weighted least square or weighted least absolute value were utilized, which classically analyze a single snapshot of the systems and fail to capture the temporal connections of system states. Thus, this study exploits the potential of machine learning approaches to forecast the state values of power systems. The performance and stability of innovative machine learning methodologies are validated using the IEEE systems. The results of the simulations are encouraging, which shows the effectiveness and feasibility of the proposed machine learning methods for power system state estimation.
{"title":"Enhanced Power System State Estimation Using Machine Learning Algorithms","authors":"Truong Hoang Bao Huy, D. Vo, H. Nguyen, Phuoc Hoa Truong, K. Dang, K. H. Truong","doi":"10.1109/ICSSE58758.2023.10227147","DOIUrl":"https://doi.org/10.1109/ICSSE58758.2023.10227147","url":null,"abstract":"The widespread implementation of renewable energy sources is posing new and distinct challenges for power systems. Consequently, power system state estimation has become increasingly essential for monitoring, operating, and safeguarding modern power systems. Conventionally, physics-based models such as weighted least square or weighted least absolute value were utilized, which classically analyze a single snapshot of the systems and fail to capture the temporal connections of system states. Thus, this study exploits the potential of machine learning approaches to forecast the state values of power systems. The performance and stability of innovative machine learning methodologies are validated using the IEEE systems. The results of the simulations are encouraging, which shows the effectiveness and feasibility of the proposed machine learning methods for power system state estimation.","PeriodicalId":280745,"journal":{"name":"2023 International Conference on System Science and Engineering (ICSSE)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127757417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-27DOI: 10.1109/ICSSE58758.2023.10227152
Dao Ngoc Mai Phuong, D. T. Toan
Determination of the vibration of the structure is one of the most important operations in the health examination of a bridge. In particular, wireless IoT DAQ equipment has many advantages including tiny size, simple installation, and low inspection cost. However, such equipment system has been relatively expensive and mainly imported. The main content of this paper is to focus on the design and fabrication of a low-cost wireless DAQ device with a piezoelectric sensor of PVDF material in order to sense the vibration of the bridge structure. Additionally, the measured vibration results at the small bridge of Lam Kinh, Vietnam from the wireless DAQ are analytically compared with those obtained from the wired based-device.
{"title":"An IoT DAQ with Piezoelectric Sensor for Bridge Structure Vibration Measurement","authors":"Dao Ngoc Mai Phuong, D. T. Toan","doi":"10.1109/ICSSE58758.2023.10227152","DOIUrl":"https://doi.org/10.1109/ICSSE58758.2023.10227152","url":null,"abstract":"Determination of the vibration of the structure is one of the most important operations in the health examination of a bridge. In particular, wireless IoT DAQ equipment has many advantages including tiny size, simple installation, and low inspection cost. However, such equipment system has been relatively expensive and mainly imported. The main content of this paper is to focus on the design and fabrication of a low-cost wireless DAQ device with a piezoelectric sensor of PVDF material in order to sense the vibration of the bridge structure. Additionally, the measured vibration results at the small bridge of Lam Kinh, Vietnam from the wireless DAQ are analytically compared with those obtained from the wired based-device.","PeriodicalId":280745,"journal":{"name":"2023 International Conference on System Science and Engineering (ICSSE)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124510966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-27DOI: 10.1109/ICSSE58758.2023.10227172
Dang Le Binh, H. Minh, Quynh Ngo Diem, Duy Tran Ngoc Bao
The development of extractive text summarization by the support of deep learning makes a great chance for more and more methods proposed. However, with legal text, this seems to be a great challenge. Apart from the quite large number of researches on general text summarization, there are still few on the legal text summarization. The main problem may due to the complicated structures with long length, specialized vocabulary of each sentences in a legal document. To be specific, unlike general text, legal text requires a document format containing redundant formal sentences, while the main idea is just in a few sentences but widely distributed, not just in a single or few sentences. Moreover, it is also usually structured as an imperative clause, not just a normal statement. Especially with Vietnamese language, this topic seems to be entirely new with the researchers. In this paper, we will use a framework using a pretrained model and a multi-layer classification approach with different ranking methods. We will also compare different pre-trained model versions on the Vietnamese legal text dataset in order to find the best way for the summarizing task.
{"title":"An Extraction-based Approach for Vietnamese Legal Text Summarization","authors":"Dang Le Binh, H. Minh, Quynh Ngo Diem, Duy Tran Ngoc Bao","doi":"10.1109/ICSSE58758.2023.10227172","DOIUrl":"https://doi.org/10.1109/ICSSE58758.2023.10227172","url":null,"abstract":"The development of extractive text summarization by the support of deep learning makes a great chance for more and more methods proposed. However, with legal text, this seems to be a great challenge. Apart from the quite large number of researches on general text summarization, there are still few on the legal text summarization. The main problem may due to the complicated structures with long length, specialized vocabulary of each sentences in a legal document. To be specific, unlike general text, legal text requires a document format containing redundant formal sentences, while the main idea is just in a few sentences but widely distributed, not just in a single or few sentences. Moreover, it is also usually structured as an imperative clause, not just a normal statement. Especially with Vietnamese language, this topic seems to be entirely new with the researchers. In this paper, we will use a framework using a pretrained model and a multi-layer classification approach with different ranking methods. We will also compare different pre-trained model versions on the Vietnamese legal text dataset in order to find the best way for the summarizing task.","PeriodicalId":280745,"journal":{"name":"2023 International Conference on System Science and Engineering (ICSSE)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120966964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-27DOI: 10.1109/ICSSE58758.2023.10227155
Van-Khoa Pham
In artificial neural network applications, convolutional neural networks (CNNs), compared to conventional fully connected networks, significantly reduce the number of trained synaptic weights by stacking many convolution layers sequentially. In addition, CNNs outperform a fully-connected approach in terms of accuracy. However, these advantages only come for a fee because sharing trained weights results in many computation-intensive operations. With practical applications using resource-constraint hardware to process large-scale input images, these layers consume much more computing time as well as power because of utilizing massive complexity hardware and a large memory footprint. To deal with the challenge, an alternative approach using the in-DRAM processing concept is proposed in this study to avoid the multiplier operation. The design was tested with the GTSRB dataset to verify the recognition performance of the trained neural network. In comparison to the conventional combination of main memory with processing chips on Von-Neumann computer architectures, the simulation results indicate that the proposed circuit can achieve a competitive performance and significantly reduce the number of computation cycles as well.
{"title":"in-Memory Processing to Accelerate Convolutional Neural Networks","authors":"Van-Khoa Pham","doi":"10.1109/ICSSE58758.2023.10227155","DOIUrl":"https://doi.org/10.1109/ICSSE58758.2023.10227155","url":null,"abstract":"In artificial neural network applications, convolutional neural networks (CNNs), compared to conventional fully connected networks, significantly reduce the number of trained synaptic weights by stacking many convolution layers sequentially. In addition, CNNs outperform a fully-connected approach in terms of accuracy. However, these advantages only come for a fee because sharing trained weights results in many computation-intensive operations. With practical applications using resource-constraint hardware to process large-scale input images, these layers consume much more computing time as well as power because of utilizing massive complexity hardware and a large memory footprint. To deal with the challenge, an alternative approach using the in-DRAM processing concept is proposed in this study to avoid the multiplier operation. The design was tested with the GTSRB dataset to verify the recognition performance of the trained neural network. In comparison to the conventional combination of main memory with processing chips on Von-Neumann computer architectures, the simulation results indicate that the proposed circuit can achieve a competitive performance and significantly reduce the number of computation cycles as well.","PeriodicalId":280745,"journal":{"name":"2023 International Conference on System Science and Engineering (ICSSE)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128433173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-27DOI: 10.1109/ICSSE58758.2023.10227251
Le Tien Thanh, Le Hoang Lam, Thanh Nha Nguyen, D. Tran
To address the issue of plastic waste, a robot using deep learning technology for visual recognition to classify plastic waste has been developed. This system includes a 3DOF robot arm, a conveyor, a camera, an electrical cabinet, and a computer. The object detection component of the system is designed using transfer learning with a pre-trained YOLOv5 model to ensure the system operates in real time. Selecting the best model by evaluating and comparing the results of models trained using labeling by bounding box and polygon methods. Then, the real-world coordinates for the origin of the robot arm are determined by utilizing matrices obtained from MATLAB through chessboard images. The computer processes the data and transmits commands to the robot arm system and conveyor, which is controlled by a PLC and 3 different Servo Drivers, for object sorting on the conveyor. The best-performing model has a Precision of 92.1% and a Recall of 87.3%, and the success rate of picking up an object is 91.5%. While the experimental results indicate complete stability in inter-device connectivity, implementing it would necessitate hardware improvements to leverage its potential.
{"title":"Designing of A Plastic Garbage Robot With Vision-Based Deep Learning Applications","authors":"Le Tien Thanh, Le Hoang Lam, Thanh Nha Nguyen, D. Tran","doi":"10.1109/ICSSE58758.2023.10227251","DOIUrl":"https://doi.org/10.1109/ICSSE58758.2023.10227251","url":null,"abstract":"To address the issue of plastic waste, a robot using deep learning technology for visual recognition to classify plastic waste has been developed. This system includes a 3DOF robot arm, a conveyor, a camera, an electrical cabinet, and a computer. The object detection component of the system is designed using transfer learning with a pre-trained YOLOv5 model to ensure the system operates in real time. Selecting the best model by evaluating and comparing the results of models trained using labeling by bounding box and polygon methods. Then, the real-world coordinates for the origin of the robot arm are determined by utilizing matrices obtained from MATLAB through chessboard images. The computer processes the data and transmits commands to the robot arm system and conveyor, which is controlled by a PLC and 3 different Servo Drivers, for object sorting on the conveyor. The best-performing model has a Precision of 92.1% and a Recall of 87.3%, and the success rate of picking up an object is 91.5%. While the experimental results indicate complete stability in inter-device connectivity, implementing it would necessitate hardware improvements to leverage its potential.","PeriodicalId":280745,"journal":{"name":"2023 International Conference on System Science and Engineering (ICSSE)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127262224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-27DOI: 10.1109/ICSSE58758.2023.10227149
An-Nhuan Le, Dinh-Tuyen Nguyen, Q. Phan, Phuoc Hoa Truong, Minh Duc Pham, Chan Viet Nguyen
The double step-down or also known as the series-capacitor (SC) converter, is attractive due to its high step-down conversion ratio and inherent current balancing. To have a higher step-down function and reduce the output current ripple, the multi-phase SC was proposed. However, as the number of phases is increased, the operation range of the converter is reduced by n (n is the number of phases). This paper proposes a 4-phase floating buck (4P-FB) converter based on SC structure to achieve a high conversion ratio while keeping the wide operation range. Moreover, the proposed converter has low input current ripple and natural current balancing for all four phases without feedback control. In this paper, a 1.25-kW 4P-FB converter is simulated to validate the performance of the proposed structure.
{"title":"4-Phase Floating Buck Converter Based on Series Capacitor Structure","authors":"An-Nhuan Le, Dinh-Tuyen Nguyen, Q. Phan, Phuoc Hoa Truong, Minh Duc Pham, Chan Viet Nguyen","doi":"10.1109/ICSSE58758.2023.10227149","DOIUrl":"https://doi.org/10.1109/ICSSE58758.2023.10227149","url":null,"abstract":"The double step-down or also known as the series-capacitor (SC) converter, is attractive due to its high step-down conversion ratio and inherent current balancing. To have a higher step-down function and reduce the output current ripple, the multi-phase SC was proposed. However, as the number of phases is increased, the operation range of the converter is reduced by n (n is the number of phases). This paper proposes a 4-phase floating buck (4P-FB) converter based on SC structure to achieve a high conversion ratio while keeping the wide operation range. Moreover, the proposed converter has low input current ripple and natural current balancing for all four phases without feedback control. In this paper, a 1.25-kW 4P-FB converter is simulated to validate the performance of the proposed structure.","PeriodicalId":280745,"journal":{"name":"2023 International Conference on System Science and Engineering (ICSSE)","volume":"5 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132124072","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}
In general, the defect inspection of a large object, such as an aircraft, a bridge, or a building, etc., must need some tools or climbing high to achieve the inspection because the object is vast and high. However, climbing high is dangerous, and relying on other tools takes time and effort. Therefore, this paper aims to establish a drone system for detecting defects in the surface of a large object. In the system, the drone can fly along the object’s exterior with the shortest path and adjust the angle of its gimbal such that the drone’s camera can inspect the defects in the object’s appearance. The shortest path is obtained from solving the Travelling Salesman Problem of the navigation points. The navigation points are built based on the normal vectors of the object’s point cloud, which is established using OpenSfMThe shortest path is obtained from solving the Travelling Salesman Problem of the navigation points. The navigation points are built based on the normal vectors of the object’s point cloud. The point cloud is created using OpenSfM (Structure from Motion). Adopting Visual Simultaneous Localization and Mapping (V-SLAM) as the drone’s position control such that it can fly stably following the shortest path composed of navigation points. After the drone collects the whole image of the object’s appearance, the network YOLOv4-P6 is used to recognizes the defects. This study finally proposed an experiment to inspect car defects and found three types of defects: paint loss, corrosion, and dent, successfully and efficiently.
一般来说，大型物体的缺陷检查，如飞机、桥梁、建筑物等，由于物体巨大、高，必须需要一些工具或爬高才能实现检查。然而，爬得高是危险的，依靠其他工具需要时间和精力。因此，本文旨在建立一种用于大型物体表面缺陷检测的无人机系统。在该系统中，无人机可以沿着物体的外部以最短路径飞行，并调整其万向架的角度，使无人机的相机可以检测物体外观的缺陷。通过求解导航点的旅行商问题得到最短路径。基于目标点云的法向量构建导航点，利用opensfm建立导航点云，通过求解导航点的旅行商问题得到导航点的最短路径。导航点是基于物体点云的法向量构建的。点云是使用OpenSfM (Structure from Motion)创建的。采用视觉同步定位与映射(V-SLAM)作为无人机的位置控制，使其能够沿着由导航点组成的最短路径稳定飞行。在无人机采集到物体外观的全图像后，利用YOLOv4-P6网络进行缺陷识别。本研究最后提出了一个检测汽车缺陷的实验，成功高效地发现了三种缺陷:油漆脱落、腐蚀、凹痕。
{"title":"Drone-Based Inspection of the Appearance Defects for a Large Object","authors":"Wenjie Wang, Xiang-Yin Dai, Chun-Yuan Cheng, Shang-Ming Ciou","doi":"10.1109/ICSSE58758.2023.10227178","DOIUrl":"https://doi.org/10.1109/ICSSE58758.2023.10227178","url":null,"abstract":"In general, the defect inspection of a large object, such as an aircraft, a bridge, or a building, etc., must need some tools or climbing high to achieve the inspection because the object is vast and high. However, climbing high is dangerous, and relying on other tools takes time and effort. Therefore, this paper aims to establish a drone system for detecting defects in the surface of a large object. In the system, the drone can fly along the object’s exterior with the shortest path and adjust the angle of its gimbal such that the drone’s camera can inspect the defects in the object’s appearance. The shortest path is obtained from solving the Travelling Salesman Problem of the navigation points. The navigation points are built based on the normal vectors of the object’s point cloud, which is established using OpenSfMThe shortest path is obtained from solving the Travelling Salesman Problem of the navigation points. The navigation points are built based on the normal vectors of the object’s point cloud. The point cloud is created using OpenSfM (Structure from Motion). Adopting Visual Simultaneous Localization and Mapping (V-SLAM) as the drone’s position control such that it can fly stably following the shortest path composed of navigation points. After the drone collects the whole image of the object’s appearance, the network YOLOv4-P6 is used to recognizes the defects. This study finally proposed an experiment to inspect car defects and found three types of defects: paint loss, corrosion, and dent, successfully and efficiently.","PeriodicalId":280745,"journal":{"name":"2023 International Conference on System Science and Engineering (ICSSE)","volume":"238 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131469198","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}
In this paper, we propose an event retrieval support system that quickly finds videos in a large database based on user-entered content. The system addresses the challenges of providing fast and relevant results for a dataset of over 400 hours of videos and developing user-friendly tools. To achieve fast retrieval, we convert the videos into compact semantic features. This involves two steps: (1) Identifying keyframes that represent different content and (2) Extracting semantic features from these frames. We first use the TransNet model to find transition frames, which split the video into scenes with different content. Then we will extract the keyframes which are evenly distributed in these scenes. Finally, the CLIP model is used to extract features from these keyframes and connect them with text. This forms a compact and semantic feature database. When users search with text, we convert it into features and measure similarity with the database using cosine distance, then the most similar video is retrieved. In cases where CLIP model fails, we recommend leveraging news headlines and audio by applying Optical Character Recognition (OCR) and Automatic Speech Recognition (ASR) on videos to form a text database and comparing the entered text with this text database. Experimental results on a Vietnamese media news dataset demonstrate the effectiveness and accuracy of our method.
{"title":"Efficient Video Retrieval Method Based on Transition Detection and Video Metadata Information","authors":"Nhat-Tuong Do-Tran, Vu-Hoang Tran, Tuan-Ngoc Nguyen, Thanh-Le Nguyen","doi":"10.1109/ICSSE58758.2023.10227191","DOIUrl":"https://doi.org/10.1109/ICSSE58758.2023.10227191","url":null,"abstract":"In this paper, we propose an event retrieval support system that quickly finds videos in a large database based on user-entered content. The system addresses the challenges of providing fast and relevant results for a dataset of over 400 hours of videos and developing user-friendly tools. To achieve fast retrieval, we convert the videos into compact semantic features. This involves two steps: (1) Identifying keyframes that represent different content and (2) Extracting semantic features from these frames. We first use the TransNet model to find transition frames, which split the video into scenes with different content. Then we will extract the keyframes which are evenly distributed in these scenes. Finally, the CLIP model is used to extract features from these keyframes and connect them with text. This forms a compact and semantic feature database. When users search with text, we convert it into features and measure similarity with the database using cosine distance, then the most similar video is retrieved. In cases where CLIP model fails, we recommend leveraging news headlines and audio by applying Optical Character Recognition (OCR) and Automatic Speech Recognition (ASR) on videos to form a text database and comparing the entered text with this text database. Experimental results on a Vietnamese media news dataset demonstrate the effectiveness and accuracy of our method.","PeriodicalId":280745,"journal":{"name":"2023 International Conference on System Science and Engineering (ICSSE)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123929195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-27DOI: 10.1109/ICSSE58758.2023.10227167
N. Nguyen, D. Le, Van Duong Ngo, Van Kien Pham, K. V. Huynh
In this paper, an optimal power flow (OPF) model is developed to incorporate energy storage systems (ESSs) and renewables into power systems. ESSs are utilized for peak shaving application. The model aims at minimizing system generation cost while taking into account system limits and ESS constraints. Tests are carried out on modified IEEE 14-bus system. Simulation results show that the ESS can effectively improve system performance.
{"title":"Optimal Operation of Energy Storage Systems for Peak Load Shaving Application","authors":"N. Nguyen, D. Le, Van Duong Ngo, Van Kien Pham, K. V. Huynh","doi":"10.1109/ICSSE58758.2023.10227167","DOIUrl":"https://doi.org/10.1109/ICSSE58758.2023.10227167","url":null,"abstract":"In this paper, an optimal power flow (OPF) model is developed to incorporate energy storage systems (ESSs) and renewables into power systems. ESSs are utilized for peak shaving application. The model aims at minimizing system generation cost while taking into account system limits and ESS constraints. Tests are carried out on modified IEEE 14-bus system. Simulation results show that the ESS can effectively improve system performance.","PeriodicalId":280745,"journal":{"name":"2023 International Conference on System Science and Engineering (ICSSE)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124293854","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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