In the field of underwater target recognition, forward-looking sonar images are widely applied in underwater rescue operations. The emergence of object detection technologies powered by deep learning has significantly enhanced the ability to recognize underwater targets. In object detection, the neck network, serving as a critical intermediary component, plays a vital role. However, traditional Feature Pyramid Networks (FPN) have two main problems: 1) During the feature fusion process, FPN does not modify the importance of features across various levels, resulting in imbalanced features at different scales and loss of scale information. 2) Lack of effective information transmission between features of different scales. In this article, we propose a novel neck network architecture, Multi Scale Selective Fusion with Dense Connectivity Network (MSSF-DCNet), which encompasses two components to tackle the previously mentioned challenges. The first one is the Multi Scale Selection Module, which effectively balances the weights of features at different levels during the feature fusion process by calculating and weighting weights for different scales, better preserving scale information. The second one is the Cross Scale Dense Connection module, which exchanges information between different feature layer levels. The model is capable of capturing global context information at every layer. thereby improving the detection capability of the neck network. By replacing the FPN with MSSF-DCNet in the Faster R-CNN framework, our model achieves an increase in Average Precision (AP) by 1.2, 4.0, and 2.6 points using MobileNet-v2, ResNet50, and SwinTransformer backbones, respectively. Furthermore, when employing ResNet50 as the backbone, MSSF-DCNet enhances the RetinaNet by 3.4 AP and ATSS by 4.1 AP. At the same time, we compared different neck networks with MSSF-DCNet on the Faster R-CNN baseline network, and MSSF-DCNet achieved the best performance in all metrics.
{"title":"MSSF-DCNet: multi-scale selective fusion with dense connectivity network for sonar image object detection","authors":"Yu Dong, Jianlei Zhang, Chun-yan Zhang","doi":"10.1117/12.3032084","DOIUrl":"https://doi.org/10.1117/12.3032084","url":null,"abstract":"In the field of underwater target recognition, forward-looking sonar images are widely applied in underwater rescue operations. The emergence of object detection technologies powered by deep learning has significantly enhanced the ability to recognize underwater targets. In object detection, the neck network, serving as a critical intermediary component, plays a vital role. However, traditional Feature Pyramid Networks (FPN) have two main problems: 1) During the feature fusion process, FPN does not modify the importance of features across various levels, resulting in imbalanced features at different scales and loss of scale information. 2) Lack of effective information transmission between features of different scales. In this article, we propose a novel neck network architecture, Multi Scale Selective Fusion with Dense Connectivity Network (MSSF-DCNet), which encompasses two components to tackle the previously mentioned challenges. The first one is the Multi Scale Selection Module, which effectively balances the weights of features at different levels during the feature fusion process by calculating and weighting weights for different scales, better preserving scale information. The second one is the Cross Scale Dense Connection module, which exchanges information between different feature layer levels. The model is capable of capturing global context information at every layer. thereby improving the detection capability of the neck network. By replacing the FPN with MSSF-DCNet in the Faster R-CNN framework, our model achieves an increase in Average Precision (AP) by 1.2, 4.0, and 2.6 points using MobileNet-v2, ResNet50, and SwinTransformer backbones, respectively. Furthermore, when employing ResNet50 as the backbone, MSSF-DCNet enhances the RetinaNet by 3.4 AP and ATSS by 4.1 AP. At the same time, we compared different neck networks with MSSF-DCNet on the Faster R-CNN baseline network, and MSSF-DCNet achieved the best performance in all metrics.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141368946","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 article takes the National College Student Smart Car Competition as the background and designs and produces an electromagnetic tracking car based on the STC32 chip. On the basis of not violating the competition rules, in order to improve the running stability and speed of the car, the hardware uses a DRV8701E driver board to drive the motor and a four-inductor arrangement to collect the inductance signal; the tracking algorithm uses a ratio algorithm and Fuzzy PID handles electromagnetic values. The experimental results show that the car has good tracking effect and strong anti-interference ability, and can successfully complete various requirements in smart car competitions.
{"title":"Design and implementation of an electromagnetic tracing intelligent vehicle based on STC32","authors":"Zhaocheng Zhang, Mo Shen, Hongtao Guo","doi":"10.1117/12.3032092","DOIUrl":"https://doi.org/10.1117/12.3032092","url":null,"abstract":"This article takes the National College Student Smart Car Competition as the background and designs and produces an electromagnetic tracking car based on the STC32 chip. On the basis of not violating the competition rules, in order to improve the running stability and speed of the car, the hardware uses a DRV8701E driver board to drive the motor and a four-inductor arrangement to collect the inductance signal; the tracking algorithm uses a ratio algorithm and Fuzzy PID handles electromagnetic values. The experimental results show that the car has good tracking effect and strong anti-interference ability, and can successfully complete various requirements in smart car competitions.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141368520","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}
Zeya Li, Yang Liu, Longqing Gong, Danni Xu, Jinfeng Tang
In response to the growing demands of real-time application scenarios, TSN has emerged as a crucial solution. However, the implementation of a credit-based shaper to ensure low-priority traffic transmission may unintentionally result in burst traffic generation. This could lead to storage overflow and potential data loss, posing a significant challenge to the efficient operation of TSN networks. To address this issue, we propose the ICBS algorithm, an enhancement of the original CBS mechanism, which preserves the fundamental principles of preventing low-priority starvation while mitigating the risk of burst traffic generation. The ICBS algorithm demonstrates enhanced fine-grained operation of data frames by refining the credit calculation method, effectively minimizing the occurrence of bursts in AVB traffic. Furthermore, we have devised an ICBS adaptation evaluation algorithm to assess the rationality of pre-scheduling results for AVB, ensuring optimal resource allocation. The simulation results demonstrate that the proposed ICBS algorithm effectively achieves its objective of low-priority traffic transmission with extra worst-case delay cost, making it a highly suitable substitute for existing TSN shaper solutions. The ICBS algorithm not only enhances the efficiency and reliability of TSN networks but also paves the way for future advancements in real-time application scenarios.
{"title":"An improved credit-based shaper for TSN","authors":"Zeya Li, Yang Liu, Longqing Gong, Danni Xu, Jinfeng Tang","doi":"10.1117/12.3031997","DOIUrl":"https://doi.org/10.1117/12.3031997","url":null,"abstract":"In response to the growing demands of real-time application scenarios, TSN has emerged as a crucial solution. However, the implementation of a credit-based shaper to ensure low-priority traffic transmission may unintentionally result in burst traffic generation. This could lead to storage overflow and potential data loss, posing a significant challenge to the efficient operation of TSN networks. To address this issue, we propose the ICBS algorithm, an enhancement of the original CBS mechanism, which preserves the fundamental principles of preventing low-priority starvation while mitigating the risk of burst traffic generation. The ICBS algorithm demonstrates enhanced fine-grained operation of data frames by refining the credit calculation method, effectively minimizing the occurrence of bursts in AVB traffic. Furthermore, we have devised an ICBS adaptation evaluation algorithm to assess the rationality of pre-scheduling results for AVB, ensuring optimal resource allocation. The simulation results demonstrate that the proposed ICBS algorithm effectively achieves its objective of low-priority traffic transmission with extra worst-case delay cost, making it a highly suitable substitute for existing TSN shaper solutions. The ICBS algorithm not only enhances the efficiency and reliability of TSN networks but also paves the way for future advancements in real-time application scenarios.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141369406","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}
As a critical stage in modern Very-Large-Scale Integrated (VLSI) design, placement plays a crucial role in positioning numerous circuit modules of varying sizes onto a 2D chip canvas to achieve optimal performance. In recent years, applying machine learning methods to placement has emerged as a promising solution for significantly enhancing efficiency and achieving superior results. However, machine learning-driven methods are still in their early stages, facing challenges such as exploration and convergence difficulties. In addition, it is challenging to integrate netlist data with the placement information. This paper proposes a novel approach leveraging deep reinforcement learning to address these challenges. First, a multi-layer chip canvas state representation method is proposed to tackle the challenges of storing and using the placement information. Additionally, a graph neural network is used to assist in generating placement information. Second, this paper proposed a semi-shared policy and value network, and to accommodate the scale and complexity of chip placement, a residual-like neural network is proposed. Third, extensive experiments on eight circuits of public benchmarks show that GoPlace achieves 10% ~ 25% wirelength reduction compared to other reinforcement learning-based methods, lowest congestion, and guarantees zero overlap.
{"title":"GoPlace: chip placement like playing go","authors":"Jianguo Hu, Shengzhi Shen, Yanyu Ding, Yuhe Wang, Jiakai Pan, Wenjun Huang, Deming Wang","doi":"10.1117/12.3032070","DOIUrl":"https://doi.org/10.1117/12.3032070","url":null,"abstract":"As a critical stage in modern Very-Large-Scale Integrated (VLSI) design, placement plays a crucial role in positioning numerous circuit modules of varying sizes onto a 2D chip canvas to achieve optimal performance. In recent years, applying machine learning methods to placement has emerged as a promising solution for significantly enhancing efficiency and achieving superior results. However, machine learning-driven methods are still in their early stages, facing challenges such as exploration and convergence difficulties. In addition, it is challenging to integrate netlist data with the placement information. This paper proposes a novel approach leveraging deep reinforcement learning to address these challenges. First, a multi-layer chip canvas state representation method is proposed to tackle the challenges of storing and using the placement information. Additionally, a graph neural network is used to assist in generating placement information. Second, this paper proposed a semi-shared policy and value network, and to accommodate the scale and complexity of chip placement, a residual-like neural network is proposed. Third, extensive experiments on eight circuits of public benchmarks show that GoPlace achieves 10% ~ 25% wirelength reduction compared to other reinforcement learning-based methods, lowest congestion, and guarantees zero overlap.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141370280","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 low-complexity FMCW-SAR motion target imaging scheme has been proposed. This scheme consists of an FMCWSAR system and a moving object detection method. The FMCW-SAR system uses an equivalent virtual array to increase the number of transceiver antenna pairs, thereby improving radar azimuth resolution and the use of a PLL structure to improve signal linearity in FMCW radar. This hardware design improves radar imaging performance while reducing complexity. The motion target detection method controls the virtual array components to monitor the motion targets in real-time during the process of motion target monitoring. In the subsequent signal processing, signal interpolation is used to fill the signal used for imaging processing. The experiment shows that this method can effectively and accurately image the detected targets and has good time resource utilization.
{"title":"A low-complexity FMCW-SAR imaging system and moving target detection method","authors":"Chao Wang, Donghao Feng, Peiyuan Guo, Wuliang Chang, Jiachang Guo, Pengsong Duan, Yangjie Cao","doi":"10.1117/12.3031896","DOIUrl":"https://doi.org/10.1117/12.3031896","url":null,"abstract":"A low-complexity FMCW-SAR motion target imaging scheme has been proposed. This scheme consists of an FMCWSAR system and a moving object detection method. The FMCW-SAR system uses an equivalent virtual array to increase the number of transceiver antenna pairs, thereby improving radar azimuth resolution and the use of a PLL structure to improve signal linearity in FMCW radar. This hardware design improves radar imaging performance while reducing complexity. The motion target detection method controls the virtual array components to monitor the motion targets in real-time during the process of motion target monitoring. In the subsequent signal processing, signal interpolation is used to fill the signal used for imaging processing. The experiment shows that this method can effectively and accurately image the detected targets and has good time resource utilization.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141368970","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}
To address the current issue of slow face detection and the low accuracy of single-feature fatigue detection in drivers, we first introduce a lightweight Retinaface network. This is achieved by replacing the backbone of the Retinaface network with Ghostnet, which accelerates face detection while improving accuracy. We then proceed to locate facial key features. Following this, a comprehensive SSD network is employed for the identification of the driver's ocular and oral conditions. By combining the MAR (Mouth Aspect Ratio) and EAR (Eye Aspect Ratio) values with fatigue detection thresholds, we ultimately determine the driver's condition. The experimental findings reveal that the enhanced Retinaface algorithm surpasses the original Retinaface approach, exhibiting an average accuracy improvement of 2.64%. The final fatigue detection, based on multiple features, achieves an average correctness rate of over 90%.
{"title":"Fusing lightweight Retinaface network for fatigue driving detection","authors":"Zhiqin Wang","doi":"10.1117/12.3031940","DOIUrl":"https://doi.org/10.1117/12.3031940","url":null,"abstract":"To address the current issue of slow face detection and the low accuracy of single-feature fatigue detection in drivers, we first introduce a lightweight Retinaface network. This is achieved by replacing the backbone of the Retinaface network with Ghostnet, which accelerates face detection while improving accuracy. We then proceed to locate facial key features. Following this, a comprehensive SSD network is employed for the identification of the driver's ocular and oral conditions. By combining the MAR (Mouth Aspect Ratio) and EAR (Eye Aspect Ratio) values with fatigue detection thresholds, we ultimately determine the driver's condition. The experimental findings reveal that the enhanced Retinaface algorithm surpasses the original Retinaface approach, exhibiting an average accuracy improvement of 2.64%. The final fatigue detection, based on multiple features, achieves an average correctness rate of over 90%.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141368044","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}
Chao Wang, Xinfeng Hu, Xiaodong Yang, Wuliang Chang, Guangze Cao
Microwave radar has wide applications in the field of vital sign detection, but there are still some challenges in detecting multiple people in complex environments. For this problem, in this article, a multi-target vital signs detection system based on the fusion of radar and optical image is proposed. This system employs a fusion algorithm to enhance the speed and precision of detection. By comparing the measured data with the reference signal, it can be considered that the measured data has a certain accuracy. The average absolute error in heart rate (HR) detection was 4 beats per minute (BPM), while respiratory rate (RR) detection exhibited an error of 0.75 respirations per minute (RPM). The accuracy of heart rate detection stood at 91.61%, while respiratory rate detection accuracy attained 99.01%.
{"title":"Multi-target vital signs detection by fusing radar and optical images","authors":"Chao Wang, Xinfeng Hu, Xiaodong Yang, Wuliang Chang, Guangze Cao","doi":"10.1117/12.3032020","DOIUrl":"https://doi.org/10.1117/12.3032020","url":null,"abstract":"Microwave radar has wide applications in the field of vital sign detection, but there are still some challenges in detecting multiple people in complex environments. For this problem, in this article, a multi-target vital signs detection system based on the fusion of radar and optical image is proposed. This system employs a fusion algorithm to enhance the speed and precision of detection. By comparing the measured data with the reference signal, it can be considered that the measured data has a certain accuracy. The average absolute error in heart rate (HR) detection was 4 beats per minute (BPM), while respiratory rate (RR) detection exhibited an error of 0.75 respirations per minute (RPM). The accuracy of heart rate detection stood at 91.61%, while respiratory rate detection accuracy attained 99.01%.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141369003","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}
With the continuous promotion of national energy conservation and emission reduction, the in-depth application of information technology has gradually triggered in-depth changes in the development mode of the country, city and industry. This paper mainly starts from the status quo of national building energy consumption and related energy saving and green development plan, introduces the research status quo of building energy management, and aims at the more popular machine learning algorithm models in recent years, including Random Forest Regression Model, XGBoost Model and Stacking Multi-Algorithmic Fusion Model, and combines with CITIC Design Digital Intelligent Building System in the statistics of a certain office building with a total of 321 days of Combined with the raw data of measured energy consumption of an office building counted in the CITIC Design Digital Intelligent Building System for a total of 321 days, the prediction learning of building operation and maintenance energy consumption is carried out respectively, the prediction effects of the three prediction algorithms are compared and analyzed, and it is recommended to use the Stacking Multi-Algorithmic Fusion Model for predicting the energy consumption of building operation and maintenance and the operation and maintenance mode of building operation and maintenance energy consumption control in advance warning is proposed by combining with energy consumption prediction model.
{"title":"Research on energy management of building operation and maintenance based on multiple prediction algorithms","authors":"Ting Lei, Jingyuan Wang, Ming Jiang","doi":"10.1117/12.3031948","DOIUrl":"https://doi.org/10.1117/12.3031948","url":null,"abstract":"With the continuous promotion of national energy conservation and emission reduction, the in-depth application of information technology has gradually triggered in-depth changes in the development mode of the country, city and industry. This paper mainly starts from the status quo of national building energy consumption and related energy saving and green development plan, introduces the research status quo of building energy management, and aims at the more popular machine learning algorithm models in recent years, including Random Forest Regression Model, XGBoost Model and Stacking Multi-Algorithmic Fusion Model, and combines with CITIC Design Digital Intelligent Building System in the statistics of a certain office building with a total of 321 days of Combined with the raw data of measured energy consumption of an office building counted in the CITIC Design Digital Intelligent Building System for a total of 321 days, the prediction learning of building operation and maintenance energy consumption is carried out respectively, the prediction effects of the three prediction algorithms are compared and analyzed, and it is recommended to use the Stacking Multi-Algorithmic Fusion Model for predicting the energy consumption of building operation and maintenance and the operation and maintenance mode of building operation and maintenance energy consumption control in advance warning is proposed by combining with energy consumption prediction model.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141368452","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 emergence of industrial Internet has brought exponential growth of data, and agents may collide in the process of operation. This paper proposes and tests a path planning algorithm based on three-dimensional time window structure, which can solve avoidable and unavoidable conflicts, effectively identify and solve the conflicts of agents in time, reduce model complexity and improve computational efficiency. This paper hopes to help the rational planning and efficiency improvement of the production process.
{"title":"Multi-agent scheduling based on three-dimensional time window","authors":"Wei Wang, Xuejuan Zhang, Hairui Song, Chi Qiu","doi":"10.1117/12.3031972","DOIUrl":"https://doi.org/10.1117/12.3031972","url":null,"abstract":"The emergence of industrial Internet has brought exponential growth of data, and agents may collide in the process of operation. This paper proposes and tests a path planning algorithm based on three-dimensional time window structure, which can solve avoidable and unavoidable conflicts, effectively identify and solve the conflicts of agents in time, reduce model complexity and improve computational efficiency. This paper hopes to help the rational planning and efficiency improvement of the production process.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141370333","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}
Remote conference systems play a crucial role in modern society. In order to design a high-quality system that meets user requirements, accurate analysis and selection of user needs to derive a functional list for the system is particularly important. This study is based on the KANO model and the Better-Worse index model, utilizing a combination of qualitative and quantitative analysis methods. By collecting and analyzing user needs, the aim is to gain a deep understanding of the true requirements for remote conference systems and identify corresponding features to guide system design and development. The contribution of this study lies in providing a systematic approach for conducting requirement analysis of remote conference systems.
{"title":"Requirement analysis of remote conference system based on qualitative and quantitative analysis combination","authors":"Yiping Yan","doi":"10.1117/12.3031999","DOIUrl":"https://doi.org/10.1117/12.3031999","url":null,"abstract":"Remote conference systems play a crucial role in modern society. In order to design a high-quality system that meets user requirements, accurate analysis and selection of user needs to derive a functional list for the system is particularly important. This study is based on the KANO model and the Better-Worse index model, utilizing a combination of qualitative and quantitative analysis methods. By collecting and analyzing user needs, the aim is to gain a deep understanding of the true requirements for remote conference systems and identify corresponding features to guide system design and development. The contribution of this study lies in providing a systematic approach for conducting requirement analysis of remote conference systems.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141370579","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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