Pub Date : 2021-08-01DOI: 10.1109/ISCEIC53685.2021.00094
Tao Li, Fei Shen
Based on multithreading, a compact temperature control system with high accuracy and low latency is designed in this paper. The system aims to provide some small-sized precision instruments with a constant temperature working environment. In this paper, there are some novelty designs such as voltage regulation circuit and structure of software. The experimental results show that the system designed in this paper can enter the stabilization stage quickly in 400 seconds, and the variance is less than 0.005, and the extreme range do not exceed 0.375 degrees Celsius at stabilization phase. This system can basically meet the special needs of miniaturization with high precision and low latency, and is expected to be widely used in the actual engineering or scientific research fields.
{"title":"High-precision Compact Intelligent Temperature Control System Based on Multithreading","authors":"Tao Li, Fei Shen","doi":"10.1109/ISCEIC53685.2021.00094","DOIUrl":"https://doi.org/10.1109/ISCEIC53685.2021.00094","url":null,"abstract":"Based on multithreading, a compact temperature control system with high accuracy and low latency is designed in this paper. The system aims to provide some small-sized precision instruments with a constant temperature working environment. In this paper, there are some novelty designs such as voltage regulation circuit and structure of software. The experimental results show that the system designed in this paper can enter the stabilization stage quickly in 400 seconds, and the variance is less than 0.005, and the extreme range do not exceed 0.375 degrees Celsius at stabilization phase. This system can basically meet the special needs of miniaturization with high precision and low latency, and is expected to be widely used in the actual engineering or scientific research fields.","PeriodicalId":342968,"journal":{"name":"2021 2nd International Symposium on Computer Engineering and Intelligent Communications (ISCEIC)","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133904289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-01DOI: 10.1109/ISCEIC53685.2021.00030
Jinbo Shen, Hu Chen
Computed Tomography (CT) is widely used in medicine, which has an irreplaceable role compared with other medical imaging methods because of its fast imaging speed, low cost and good imaging effect on bone and lung. But X-rays are harmful to the human body. In order to reduce the harm caused by the process of obtaining CT, low-dose CT is gaining popularity in recent years. Guided by deep learning, low-does CT denoising is successful using artificial neural network. This paper will use the convolutional neural network (CNN), combined the attention block and perceptual loss, to achieve excellent low-does CT denoising performance while preserving more details. Experimental results show that our method achieves good results at different noise levels.
{"title":"CT Denoising by Multi-feature Concat Residual Network with Cross-domain Attention Blcok","authors":"Jinbo Shen, Hu Chen","doi":"10.1109/ISCEIC53685.2021.00030","DOIUrl":"https://doi.org/10.1109/ISCEIC53685.2021.00030","url":null,"abstract":"Computed Tomography (CT) is widely used in medicine, which has an irreplaceable role compared with other medical imaging methods because of its fast imaging speed, low cost and good imaging effect on bone and lung. But X-rays are harmful to the human body. In order to reduce the harm caused by the process of obtaining CT, low-dose CT is gaining popularity in recent years. Guided by deep learning, low-does CT denoising is successful using artificial neural network. This paper will use the convolutional neural network (CNN), combined the attention block and perceptual loss, to achieve excellent low-does CT denoising performance while preserving more details. Experimental results show that our method achieves good results at different noise levels.","PeriodicalId":342968,"journal":{"name":"2021 2nd International Symposium on Computer Engineering and Intelligent Communications (ISCEIC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133813277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-01DOI: 10.1109/ISCEIC53685.2021.00024
Liwen Wang, Yicheng Yan, Wei Guo
Class imbalanced data streams often have unbalanced sample distribution and a certain type of sample size is too small, which will lead to over fitting phenomenon due to insufficient sample learning, and most current classifiers have problems such as model instability. Therefore, choosing the online sequential extreme learning machine (OSELM) as the basic theoretical model, and combining the AdaBoost ensemble learning ideas and cost-sensitive strategies, an ensemble online weighted sequential extreme learning machine algorithm (ABC-OSELM) is proposed. Firstly, in order to solve the problem that the minority classes are easily misclassified due to class imbalance, the OSELM algorithm based on cost-sensitive learning (C-OSELM) is proposed, which improves the misclassification by assigning different penalty parameters to various samples, it can effectively alleviate the phenomenon of excessive deviation of the decision-making surface. On this basis, in order to further improve the classification accuracy and stability of the algorithm, combining C-OSELM with ensemble learning ideas, an ensemble C-OSELM algorithm based on AdaBoost (ABC-OSELM) is proposed. By adopting a homogeneous integration strategy, iteratively adjust the weight of the base classifier to generate a more stable strong classifier. Finally, the effectiveness and feasibility of the ABC-OSELM algorithm are verified through 15 class II imbalanced datasets.
{"title":"Ensemble online weighted sequential extreme learning machine for class imbalanced data streams","authors":"Liwen Wang, Yicheng Yan, Wei Guo","doi":"10.1109/ISCEIC53685.2021.00024","DOIUrl":"https://doi.org/10.1109/ISCEIC53685.2021.00024","url":null,"abstract":"Class imbalanced data streams often have unbalanced sample distribution and a certain type of sample size is too small, which will lead to over fitting phenomenon due to insufficient sample learning, and most current classifiers have problems such as model instability. Therefore, choosing the online sequential extreme learning machine (OSELM) as the basic theoretical model, and combining the AdaBoost ensemble learning ideas and cost-sensitive strategies, an ensemble online weighted sequential extreme learning machine algorithm (ABC-OSELM) is proposed. Firstly, in order to solve the problem that the minority classes are easily misclassified due to class imbalance, the OSELM algorithm based on cost-sensitive learning (C-OSELM) is proposed, which improves the misclassification by assigning different penalty parameters to various samples, it can effectively alleviate the phenomenon of excessive deviation of the decision-making surface. On this basis, in order to further improve the classification accuracy and stability of the algorithm, combining C-OSELM with ensemble learning ideas, an ensemble C-OSELM algorithm based on AdaBoost (ABC-OSELM) is proposed. By adopting a homogeneous integration strategy, iteratively adjust the weight of the base classifier to generate a more stable strong classifier. Finally, the effectiveness and feasibility of the ABC-OSELM algorithm are verified through 15 class II imbalanced datasets.","PeriodicalId":342968,"journal":{"name":"2021 2nd International Symposium on Computer Engineering and Intelligent Communications (ISCEIC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115516264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-01DOI: 10.1109/ISCEIC53685.2021.00028
Qian Wang
Target detection as a part of computer vision occupies an important position in the field of recognition. It has seen significant improvements in algorithm performance at every stage. You only look Once (YOLO), for example, seems to have the greatest advantage as a target detection model. It is clear that it only needs to be viewed once to identify the class and location of objects in an image. As YOLO continues to improve, it exhibits even faster and more accurate recognition. This paper discusses the features and advantages shown by the different target detection algorithms at each stage. From the analysis results, YOLO shows more advantages in object detection. YOLO detection is fast and can process streaming video in real-time. Also, the number of false background detections is less than half compared to other algorithms while showing good generalization.
{"title":"Analysis of target detection algorithms at different stages","authors":"Qian Wang","doi":"10.1109/ISCEIC53685.2021.00028","DOIUrl":"https://doi.org/10.1109/ISCEIC53685.2021.00028","url":null,"abstract":"Target detection as a part of computer vision occupies an important position in the field of recognition. It has seen significant improvements in algorithm performance at every stage. You only look Once (YOLO), for example, seems to have the greatest advantage as a target detection model. It is clear that it only needs to be viewed once to identify the class and location of objects in an image. As YOLO continues to improve, it exhibits even faster and more accurate recognition. This paper discusses the features and advantages shown by the different target detection algorithms at each stage. From the analysis results, YOLO shows more advantages in object detection. YOLO detection is fast and can process streaming video in real-time. Also, the number of false background detections is less than half compared to other algorithms while showing good generalization.","PeriodicalId":342968,"journal":{"name":"2021 2nd International Symposium on Computer Engineering and Intelligent Communications (ISCEIC)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116572061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-01DOI: 10.1109/isceic53685.2021.00001
{"title":"[Title page i]","authors":"","doi":"10.1109/isceic53685.2021.00001","DOIUrl":"https://doi.org/10.1109/isceic53685.2021.00001","url":null,"abstract":"","PeriodicalId":342968,"journal":{"name":"2021 2nd International Symposium on Computer Engineering and Intelligent Communications (ISCEIC)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123986267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-01DOI: 10.1109/ISCEIC53685.2021.00020
Xuyi Chen, Xiaoning Jiang
With the rapid development of the energy industry, the scale of gas stations continues to expand, and gas station equipment has become sophisticated and complex. Once the gas station equipment fails, it will affect the service of the gas station and even cause serious safety accidents. Aiming at the complex and dangerous environment of gas stations, a gas station IoT data collection method based on MQTT is proposed. Gas station equipment is connected to the cloud through the IoT technology, the system collects and analyzes data, visualizes the analysis results, and finally saves the data in the database. Through this data collection method, coupled with the improvement of gas station data observability, gas station fault detection and maintenance will be more efficient.
{"title":"Research on Data Collection and Observability of Gas Station in Cloud Environment","authors":"Xuyi Chen, Xiaoning Jiang","doi":"10.1109/ISCEIC53685.2021.00020","DOIUrl":"https://doi.org/10.1109/ISCEIC53685.2021.00020","url":null,"abstract":"With the rapid development of the energy industry, the scale of gas stations continues to expand, and gas station equipment has become sophisticated and complex. Once the gas station equipment fails, it will affect the service of the gas station and even cause serious safety accidents. Aiming at the complex and dangerous environment of gas stations, a gas station IoT data collection method based on MQTT is proposed. Gas station equipment is connected to the cloud through the IoT technology, the system collects and analyzes data, visualizes the analysis results, and finally saves the data in the database. Through this data collection method, coupled with the improvement of gas station data observability, gas station fault detection and maintenance will be more efficient.","PeriodicalId":342968,"journal":{"name":"2021 2nd International Symposium on Computer Engineering and Intelligent Communications (ISCEIC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131960090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-01DOI: 10.1109/ISCEIC53685.2021.00071
Jian Li, Haibin Liu, Wentao Zhang, Lu Li, Wenyue Wang
The rapid development of UAV has brought great convenience to various application fields. In the meanwhile, its extensive utilization has also resulted in many problems such as public safety hazards, personal security threats and personal privacy violations. UAV is difficult to capture in real-time because of its small scale and complex flight environment. In order to solve the above problems from the perspective of security protection, a low-cost UAV detection, distance measure and protection scheme are proposed based on deep learning in this paper. The influences of different loss functions and thresholds are studied on the detection accuracy of YOLOv4 to improve the detection performance of YOLOv4 on UAV. At the same time, in order to achieve more effective prevention and control of UAV, the monocular ranging method based on PnP is introduced to get the distance between camera and UAV. Finally, the study is applied in the real-world scene, and a good target detection and ranging effect have been achieved so that the proposed model is verified in the feasibility and effectiveness.
{"title":"The UAV Detection and Ranging Based on YOLOv4","authors":"Jian Li, Haibin Liu, Wentao Zhang, Lu Li, Wenyue Wang","doi":"10.1109/ISCEIC53685.2021.00071","DOIUrl":"https://doi.org/10.1109/ISCEIC53685.2021.00071","url":null,"abstract":"The rapid development of UAV has brought great convenience to various application fields. In the meanwhile, its extensive utilization has also resulted in many problems such as public safety hazards, personal security threats and personal privacy violations. UAV is difficult to capture in real-time because of its small scale and complex flight environment. In order to solve the above problems from the perspective of security protection, a low-cost UAV detection, distance measure and protection scheme are proposed based on deep learning in this paper. The influences of different loss functions and thresholds are studied on the detection accuracy of YOLOv4 to improve the detection performance of YOLOv4 on UAV. At the same time, in order to achieve more effective prevention and control of UAV, the monocular ranging method based on PnP is introduced to get the distance between camera and UAV. Finally, the study is applied in the real-world scene, and a good target detection and ranging effect have been achieved so that the proposed model is verified in the feasibility and effectiveness.","PeriodicalId":342968,"journal":{"name":"2021 2nd International Symposium on Computer Engineering and Intelligent Communications (ISCEIC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132049230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-01DOI: 10.1109/ISCEIC53685.2021.00037
P. Dai, Jun Tang, Jiangnan Yuan, Yue Yu
Recently, pedestrian detection has been an important issue in the field of computer vision. To solve the problem of large computation and poor real-time performance in pedestrian detection scene of original histogram of oriented gradients (HOG) algorithm, this paper presents a simplified HOG feature extraction algorithm and an efficient architecture in field programmable gate array (FPGA). This simplified algorithm and Support vector machine (SVM) classifier are successfully implemented on Xilinx Zynq FPGA by using parallelism and pipeline technology. In the feature extraction step, the dimension of HOG feature is reduced by changing the strides of the sliding block, and the complexity of this algorithm and the utilization of hardware resources are reduced. The result shows that this proposed algorithm can achieve 86% true positive rate and 88% precision rate in training stage on INRIA and MIT datasets. The FPGA implementation with pipeline technical and parallel circuit architecture can achieve real-time detect and the simplified algorithm can greatly reduce the utilization of FPGA resources.
{"title":"A Hardware-Efficient HOG-SVM Algorithm and its FPGA Implementation","authors":"P. Dai, Jun Tang, Jiangnan Yuan, Yue Yu","doi":"10.1109/ISCEIC53685.2021.00037","DOIUrl":"https://doi.org/10.1109/ISCEIC53685.2021.00037","url":null,"abstract":"Recently, pedestrian detection has been an important issue in the field of computer vision. To solve the problem of large computation and poor real-time performance in pedestrian detection scene of original histogram of oriented gradients (HOG) algorithm, this paper presents a simplified HOG feature extraction algorithm and an efficient architecture in field programmable gate array (FPGA). This simplified algorithm and Support vector machine (SVM) classifier are successfully implemented on Xilinx Zynq FPGA by using parallelism and pipeline technology. In the feature extraction step, the dimension of HOG feature is reduced by changing the strides of the sliding block, and the complexity of this algorithm and the utilization of hardware resources are reduced. The result shows that this proposed algorithm can achieve 86% true positive rate and 88% precision rate in training stage on INRIA and MIT datasets. The FPGA implementation with pipeline technical and parallel circuit architecture can achieve real-time detect and the simplified algorithm can greatly reduce the utilization of FPGA resources.","PeriodicalId":342968,"journal":{"name":"2021 2nd International Symposium on Computer Engineering and Intelligent Communications (ISCEIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130183347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-01DOI: 10.1109/ISCEIC53685.2021.00092
Bin Yang, Yongtang Zhang
This paper proposes a reliable LEACH protocol based on the packet loss rate measurement design based on the LEACH protocol. First, assume that the wireless link channel is unreliable, and there is a certain probability that data packets will be lost. In order to estimate the packet loss rate of the node, in each round of data transmission, the current statistics of the packet loss rate of the target node will be dynamically updated through an iterative formula. On this basis, a trust model for neighbor nodes is established. Under this condition, the packet loss rate of the selected head node is introduced and the distance factor is combined as the basis for determining the selection of the cluster head. In the end, the overall successful transmission rate of network data can be increased, and the additional energy consumed by nodes due to data packet retransmission can be reduced, so as to achieve the purpose of network energy saving and improving data transmission efficiency.
{"title":"Design and Performance Analysis of TCP Enhanced Accelerator in IP Network","authors":"Bin Yang, Yongtang Zhang","doi":"10.1109/ISCEIC53685.2021.00092","DOIUrl":"https://doi.org/10.1109/ISCEIC53685.2021.00092","url":null,"abstract":"This paper proposes a reliable LEACH protocol based on the packet loss rate measurement design based on the LEACH protocol. First, assume that the wireless link channel is unreliable, and there is a certain probability that data packets will be lost. In order to estimate the packet loss rate of the node, in each round of data transmission, the current statistics of the packet loss rate of the target node will be dynamically updated through an iterative formula. On this basis, a trust model for neighbor nodes is established. Under this condition, the packet loss rate of the selected head node is introduced and the distance factor is combined as the basis for determining the selection of the cluster head. In the end, the overall successful transmission rate of network data can be increased, and the additional energy consumed by nodes due to data packet retransmission can be reduced, so as to achieve the purpose of network energy saving and improving data transmission efficiency.","PeriodicalId":342968,"journal":{"name":"2021 2nd International Symposium on Computer Engineering and Intelligent Communications (ISCEIC)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129948239","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 reliability of electric power communication system plays an important role in the safe and stable operation of power system, it is not only the basis to ensure the power system safe, stable, efficient, and the inevitable requirement of market of electric power enterprises, therefore, research on the reliability of electric power communication system has more practical significance. Factors that affect the reliability of electric power communication system operation are analyzed, discussed the method of reliability of electric power communication system, laid the foundation for the future development of the electric power communication system.
{"title":"Research on Reliability of Electric Power Communication System","authors":"Hongyu Gao, Tongjun Jiang, Lei Qiao, Zhuoyue Li, Xuxia Zhang, Xueran Zhang","doi":"10.1109/ISCEIC53685.2021.00061","DOIUrl":"https://doi.org/10.1109/ISCEIC53685.2021.00061","url":null,"abstract":"The reliability of electric power communication system plays an important role in the safe and stable operation of power system, it is not only the basis to ensure the power system safe, stable, efficient, and the inevitable requirement of market of electric power enterprises, therefore, research on the reliability of electric power communication system has more practical significance. Factors that affect the reliability of electric power communication system operation are analyzed, discussed the method of reliability of electric power communication system, laid the foundation for the future development of the electric power communication system.","PeriodicalId":342968,"journal":{"name":"2021 2nd International Symposium on Computer Engineering and Intelligent Communications (ISCEIC)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126333710","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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