This article constructs a corporate social responsibility indicator system and conducts regression analysis on the relationship between food and beverage enterprises fulfilling social responsibility and enhancing brand value. Research has found that food and beverage companies have a significant positive impact on brand value enhancement when fulfilling social responsibilities to shareholders, suppliers, and governments; The fulfillment of social responsibility towards employees, creditors, and consumers does not have a significant impact on brand value enhancement; The overall completion of social responsibility by food and beverage enterprises has a significant positive impact on enhancing brand value. Among them, the feedback of employees, creditors, and consumers on the fulfillment of corporate social responsibility is not easy to fully measure, and they belong to anonymous beneficiaries, whose impact on brand value also shows a hidden nature. The social responsibilities undertaken by different types of enterprises vary greatly. Therefore, this article introduces the moderating variable of enterprise type to conduct heterogeneity analysis and verify that enterprise type plays a positive promoting role in the relationship between food and beverage enterprises fulfilling social responsibility and enhancing brand value.
{"title":"The Relationship between Social Responsibility and Brand Value of Chinese Food and Beverage Enterprises in the Context of High-Quality Development","authors":"Anning Ye, Min Zhang","doi":"10.54097/fcis.v6i2.05","DOIUrl":"https://doi.org/10.54097/fcis.v6i2.05","url":null,"abstract":"This article constructs a corporate social responsibility indicator system and conducts regression analysis on the relationship between food and beverage enterprises fulfilling social responsibility and enhancing brand value. Research has found that food and beverage companies have a significant positive impact on brand value enhancement when fulfilling social responsibilities to shareholders, suppliers, and governments; The fulfillment of social responsibility towards employees, creditors, and consumers does not have a significant impact on brand value enhancement; The overall completion of social responsibility by food and beverage enterprises has a significant positive impact on enhancing brand value. Among them, the feedback of employees, creditors, and consumers on the fulfillment of corporate social responsibility is not easy to fully measure, and they belong to anonymous beneficiaries, whose impact on brand value also shows a hidden nature. The social responsibilities undertaken by different types of enterprises vary greatly. Therefore, this article introduces the moderating variable of enterprise type to conduct heterogeneity analysis and verify that enterprise type plays a positive promoting role in the relationship between food and beverage enterprises fulfilling social responsibility and enhancing brand value.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"186 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138997190","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}
Jiale Fu, Hongyan Li, Yanxia Zhao, Run Zhang, Hejing Zhang, Taotao Pang
[Purpose] Based on the text mining method, this study analyzes the job demand for domestic e-commerce industry positions in the Internet-oriented recruitment data, promotes the matching of e-commerce positions and talents, and promotes the good construction of the employment environment in the domestic e-commerce industry.[Method] Use the LDA(Latent dirichlet allocation) topic model for job competency requirements to calculate the similarity of the word segmentation results for professional talent requirements, determine the optimal number of topics, and output the visualization results of the LDA topic model.[Conclusion] In eastern China, there is a high demand for e-commerce jobs, and the majority of these positions only call for 1-3 years of experience. The basic requirement for the majority of jobs is a college degree. The Director of Operations and Director of Network Operations focus on e-commerce operations and management, the Network Promotion Specialist focuses on e-commerce advertising and product promotion, the Data Analyst and Market Analyst focus on data analysis and market research, and the Sales Representative focuses on sales. There is a correlation between the salary of the positions and education, experience and region; the higher the education, the more experience and the higher the level of regional development, the higher the salary level.
{"title":"Research on Mining Talent Demand for E-commerce Majors based on LDA Topic Model","authors":"Jiale Fu, Hongyan Li, Yanxia Zhao, Run Zhang, Hejing Zhang, Taotao Pang","doi":"10.54097/fcis.v6i2.09","DOIUrl":"https://doi.org/10.54097/fcis.v6i2.09","url":null,"abstract":"[Purpose] Based on the text mining method, this study analyzes the job demand for domestic e-commerce industry positions in the Internet-oriented recruitment data, promotes the matching of e-commerce positions and talents, and promotes the good construction of the employment environment in the domestic e-commerce industry.[Method] Use the LDA(Latent dirichlet allocation) topic model for job competency requirements to calculate the similarity of the word segmentation results for professional talent requirements, determine the optimal number of topics, and output the visualization results of the LDA topic model.[Conclusion] In eastern China, there is a high demand for e-commerce jobs, and the majority of these positions only call for 1-3 years of experience. The basic requirement for the majority of jobs is a college degree. The Director of Operations and Director of Network Operations focus on e-commerce operations and management, the Network Promotion Specialist focuses on e-commerce advertising and product promotion, the Data Analyst and Market Analyst focus on data analysis and market research, and the Sales Representative focuses on sales. There is a correlation between the salary of the positions and education, experience and region; the higher the education, the more experience and the higher the level of regional development, the higher the salary level.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"15 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139001294","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}
It is urgent for the manufacturing industry to transform its development mode and achieve intelligent transformation. With the increasingly fierce global market competition, relying solely on first-class product quality can no longer guarantee a long-term competitive advantage for enterprises. Therefore, this article conducts research on the collaborative optimization of supply chain intelligent management and industrial AI(Artificial Intelligence). By timely displaying the quality status of each link, intelligent management of goods is achieved, strengthening control and tracking of product quality, greatly improving the efficiency of the quality management system, and ensuring that enterprises can provide high-quality products as much as possible. Incorporate supplier production flexibility, continuous research and development capabilities, and information technology into the criteria for selecting suppliers, seek higher quality suppliers, and establish strategic partnerships with suppliers. The research in this article is beneficial for improving the production and manufacturing efficiency of enterprises, and is an important theoretical exploration in the development process of intelligent manufacturing.
{"title":"Collaborative Optimization of Supply Chain Intelligent Management and Industrial Artificial Intelligence","authors":"Yuzhou Zhang","doi":"10.54097/fcis.v6i2.04","DOIUrl":"https://doi.org/10.54097/fcis.v6i2.04","url":null,"abstract":"It is urgent for the manufacturing industry to transform its development mode and achieve intelligent transformation. With the increasingly fierce global market competition, relying solely on first-class product quality can no longer guarantee a long-term competitive advantage for enterprises. Therefore, this article conducts research on the collaborative optimization of supply chain intelligent management and industrial AI(Artificial Intelligence). By timely displaying the quality status of each link, intelligent management of goods is achieved, strengthening control and tracking of product quality, greatly improving the efficiency of the quality management system, and ensuring that enterprises can provide high-quality products as much as possible. Incorporate supplier production flexibility, continuous research and development capabilities, and information technology into the criteria for selecting suppliers, seek higher quality suppliers, and establish strategic partnerships with suppliers. The research in this article is beneficial for improving the production and manufacturing efficiency of enterprises, and is an important theoretical exploration in the development process of intelligent manufacturing.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"102 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138998635","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 rapid development of information technology, non-contact sensing technology has shown great potential in the field of real-time emotional monitoring and feedback. The purpose of this study is to deeply explore the application of this technology in improving the intelligence of human-computer interaction and realizing personalized service. By synthesizing the experimental results and related literature, a series of important research findings have been formed. First of all, we found that non-contact sensing technology effectively improved the objectivity of emotion monitoring. Secondly, the introduction of real-time feedback mechanism has significantly improved the user experience. However, non-contact sensing technology still faces some challenges in practical application, including privacy issues, cross-cultural adaptability, environmental interference and so on. To solve these problems, technological innovation, the establishment and standardization of privacy policies are needed to ensure the sustainable application of technology in a wider range of fields. This study emphasizes the importance and application prospect of non-contact sensing technology in real-time emotional monitoring and feedback. Future research should focus on the further innovation of technology, the improvement of privacy protection mechanism and the deepening of interdisciplinary cooperation, so as to promote the wider application of this technology in the field of human-computer interaction.
{"title":"Research on the Application of Non-contact Sensing Technology in Real-time Emotional Monitoring and Feedback","authors":"Lehan Zhang","doi":"10.54097/fcis.v6i2.02","DOIUrl":"https://doi.org/10.54097/fcis.v6i2.02","url":null,"abstract":"With the rapid development of information technology, non-contact sensing technology has shown great potential in the field of real-time emotional monitoring and feedback. The purpose of this study is to deeply explore the application of this technology in improving the intelligence of human-computer interaction and realizing personalized service. By synthesizing the experimental results and related literature, a series of important research findings have been formed. First of all, we found that non-contact sensing technology effectively improved the objectivity of emotion monitoring. Secondly, the introduction of real-time feedback mechanism has significantly improved the user experience. However, non-contact sensing technology still faces some challenges in practical application, including privacy issues, cross-cultural adaptability, environmental interference and so on. To solve these problems, technological innovation, the establishment and standardization of privacy policies are needed to ensure the sustainable application of technology in a wider range of fields. This study emphasizes the importance and application prospect of non-contact sensing technology in real-time emotional monitoring and feedback. Future research should focus on the further innovation of technology, the improvement of privacy protection mechanism and the deepening of interdisciplinary cooperation, so as to promote the wider application of this technology in the field of human-computer interaction.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"19 S27","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138999064","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 introduces the current application status and development trends of the Internet of Things and artificial intelligence technology in smart logistics, analyzes how the Internet of Things and artificial intelligence technology work together to achieve efficient operation of smart logistics systems, as well as the challenges and opportunities they face. This article believes that the Internet of Things and artificial intelligence technology are the core driving forces of smart logistics. They can achieve informatization, automation, and intelligent processing in various aspects of logistics, improve logistics efficiency, reduce logistics costs, and promote green and sustainable development of logistics.
{"title":"The Collaborative Application of Internet of Things and Artificial Intelligence in Smart Logistics","authors":"Xiangpeng Liu","doi":"10.54097/fcis.v6i2.08","DOIUrl":"https://doi.org/10.54097/fcis.v6i2.08","url":null,"abstract":"This article introduces the current application status and development trends of the Internet of Things and artificial intelligence technology in smart logistics, analyzes how the Internet of Things and artificial intelligence technology work together to achieve efficient operation of smart logistics systems, as well as the challenges and opportunities they face. This article believes that the Internet of Things and artificial intelligence technology are the core driving forces of smart logistics. They can achieve informatization, automation, and intelligent processing in various aspects of logistics, improve logistics efficiency, reduce logistics costs, and promote green and sustainable development of logistics.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"117 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138999424","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}
Chang-Yi Liu, Xiangyang Zhou, Jun Li, Chuantao Ran
This study provides an improved YOLOv8-based printed circuit board (PCB) defect identification method to address the current challenges associated with PCB defect detection, including the detection of small targets, low accuracy, and other related concerns. The YOLOv8 model serves as the foundational framework, and in order to enhance detection speed, the YOLOv8s model is selected due to its reduced parameter count. However, feature extraction becomes challenging for small target defects; to address this, the CA attention mechanism is implemented, which is more attuned to target feature information and aids in feature extraction. As indicated by the experimental findings, the enhanced YOLOv8s-CA algorithm model has the following characteristics: a footprint of 5.79 MB, a mean average precision (mAP) of 90.4 percent, an increase of 6.6 percent over the initial network, and a parameter count augmentation of merely 0.007M. Consequently, this model finds utility in compact industrial inspection apparatus and possesses a wide range of potential applications.
{"title":"PCB Board Defect Detection Method based on Improved YOLOv8","authors":"Chang-Yi Liu, Xiangyang Zhou, Jun Li, Chuantao Ran","doi":"10.54097/fcis.v6i2.01","DOIUrl":"https://doi.org/10.54097/fcis.v6i2.01","url":null,"abstract":"This study provides an improved YOLOv8-based printed circuit board (PCB) defect identification method to address the current challenges associated with PCB defect detection, including the detection of small targets, low accuracy, and other related concerns. The YOLOv8 model serves as the foundational framework, and in order to enhance detection speed, the YOLOv8s model is selected due to its reduced parameter count. However, feature extraction becomes challenging for small target defects; to address this, the CA attention mechanism is implemented, which is more attuned to target feature information and aids in feature extraction. As indicated by the experimental findings, the enhanced YOLOv8s-CA algorithm model has the following characteristics: a footprint of 5.79 MB, a mean average precision (mAP) of 90.4 percent, an increase of 6.6 percent over the initial network, and a parameter count augmentation of merely 0.007M. Consequently, this model finds utility in compact industrial inspection apparatus and possesses a wide range of potential applications.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"36 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138997856","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 paper provides an in-depth study on the challenges of vegetable merchandising in fresh produce supermarkets, aiming to provide a comprehensive set of management strategies to optimize supermarket operations. First, the sales volume and sales of six types of vegetables were analyzed by descriptive statistics and the cyclical trend was explored by time series processing; second, good correlations between edibles and aquatic roots and tubers as well as edibles and eggplants were found by plotting correlation matrices and heat maps of Spearman's coefficients. Next, this paper analyzed the relationship between cost-plus pricing and total sales and predicted the total replenishment and pricing of vegetables in the coming week using an LSTM time series forecasting model and evaluated the model performance using root mean square error (RMSE). Finally, a Gaussian regression model was used to predict a small sample of data to develop an optimal replenishment volume and pricing strategy for the superstore, which maximized the superstore's revenue. The results of the study show that the inventory management efficiency of fresh supermarkets can be effectively improved by these methods.
{"title":"Research on Establishing Inbound Strategies for Supermarkets based on LSTM and Gaussian Process Regression Modeling","authors":"Wei Weng, Yifu Lin, Jiawei Wu","doi":"10.54097/fcis.v6i2.10","DOIUrl":"https://doi.org/10.54097/fcis.v6i2.10","url":null,"abstract":" This paper provides an in-depth study on the challenges of vegetable merchandising in fresh produce supermarkets, aiming to provide a comprehensive set of management strategies to optimize supermarket operations. First, the sales volume and sales of six types of vegetables were analyzed by descriptive statistics and the cyclical trend was explored by time series processing; second, good correlations between edibles and aquatic roots and tubers as well as edibles and eggplants were found by plotting correlation matrices and heat maps of Spearman's coefficients. Next, this paper analyzed the relationship between cost-plus pricing and total sales and predicted the total replenishment and pricing of vegetables in the coming week using an LSTM time series forecasting model and evaluated the model performance using root mean square error (RMSE). Finally, a Gaussian regression model was used to predict a small sample of data to develop an optimal replenishment volume and pricing strategy for the superstore, which maximized the superstore's revenue. The results of the study show that the inventory management efficiency of fresh supermarkets can be effectively improved by these methods.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"16 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139000821","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 rapid development of deep learning technology, the demand for computing resources is increasing, and the accelerated optimization of hardware on artificial intelligence (AI) chip has become one of the key ways to solve this challenge. This paper aims to explore the hardware acceleration optimization strategy of deep learning model on AI chip to improve the training and inference performance of the model. In this paper, the method and practice of optimizing deep learning model on AI chip are deeply analyzed by comprehensively considering the hardware characteristics such as parallel processing ability, energy-efficient computing, neural network accelerator, flexibility and programmability, high integration and heterogeneous computing structure. By designing and implementing an efficient convolution accelerator, the computational efficiency of the model is improved. The introduction of energy-efficient computing effectively reduces energy consumption, which provides feasibility for the practical application of mobile devices and embedded systems. At the same time, the optimization design of neural network accelerator becomes the core of hardware acceleration, and deep learning calculation such as convolution and matrix operation are accelerated through special hardware structure, which provides strong support for the real-time performance of the model. By analyzing the actual application cases of hardware accelerated optimization in different application scenarios, this paper highlights the key role of hardware accelerated optimization in improving the performance of deep learning model. Hardware accelerated optimization not only improves the computing efficiency, but also provides efficient and intelligent computing support for AI applications in different fields.
{"title":"Hardware Accelerated Optimization of Deep Learning Model on Artificial Intelligence Chip","authors":"Zhimei Chen","doi":"10.54097/fcis.v6i2.03","DOIUrl":"https://doi.org/10.54097/fcis.v6i2.03","url":null,"abstract":"With the rapid development of deep learning technology, the demand for computing resources is increasing, and the accelerated optimization of hardware on artificial intelligence (AI) chip has become one of the key ways to solve this challenge. This paper aims to explore the hardware acceleration optimization strategy of deep learning model on AI chip to improve the training and inference performance of the model. In this paper, the method and practice of optimizing deep learning model on AI chip are deeply analyzed by comprehensively considering the hardware characteristics such as parallel processing ability, energy-efficient computing, neural network accelerator, flexibility and programmability, high integration and heterogeneous computing structure. By designing and implementing an efficient convolution accelerator, the computational efficiency of the model is improved. The introduction of energy-efficient computing effectively reduces energy consumption, which provides feasibility for the practical application of mobile devices and embedded systems. At the same time, the optimization design of neural network accelerator becomes the core of hardware acceleration, and deep learning calculation such as convolution and matrix operation are accelerated through special hardware structure, which provides strong support for the real-time performance of the model. By analyzing the actual application cases of hardware accelerated optimization in different application scenarios, this paper highlights the key role of hardware accelerated optimization in improving the performance of deep learning model. Hardware accelerated optimization not only improves the computing efficiency, but also provides efficient and intelligent computing support for AI applications in different fields.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"1 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139000072","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}
UNet and its various variants are commonly used methods in medical image segmentation tasks; however, many network parameters, complex calculations, and slow usage are problems that need to be overcome. These problems hinder the specific application of fast image segmentation in real-time tasks. At the same time, the lesion area has problems such as small size, irregular shape, and blurred edges, which makes the network feature extraction difficult and the segmentation accuracy needs to be improved. At the same time, medical image segmentation provides a variety of effective methods for the accuracy and robustness of organ segmentation, lesion detection, and classification. Medical images have fixed structures, simple semantics, and diverse details, so integrating rich multi-scale features can improve segmentation accuracy. Given that the density of diseased tissue may be comparable to that of surrounding normal tissue, both global and local information are crucial to segmentation results. To this end, we propose an image segmentation method (SC -UNe X t) based on edge feature extraction and multi-scale feature fusion of convolutional multi-layer perceptron (MLP). The network is a deeply supervised encoder-decoder network, in which the encoder and decoder pass through a series of nested, multiple jump paths to reduce the semantic gap between the feature maps of the encoder and decoder sub-networks.; Multi - scale feature fusion is introduced based on the UNe Finally, we evaluate our model approach on the LIDC dataset public dataset. Experiments have proven the effectiveness of this method. Our model's similarity coefficient and intersection ratio reached 86.44% and 90.86% respectively. Compared with UNet and UNe X t, the network proposed in this article has improved in accuracy, intersection ratio of real values and predicted values, similarity coefficient, and segmentation effect.
UNet 及其各种变体是医学图像分割任务中常用的方法，但网络参数多、计算复杂、使用速度慢是需要克服的问题。这些问题阻碍了快速图像分割在实时任务中的具体应用。同时，病变区域存在尺寸小、形状不规则、边缘模糊等问题，给网络特征提取带来困难，分割精度有待提高。同时，医学图像分割为器官分割、病变检测和分类的准确性和鲁棒性提供了多种有效方法。医学图像结构固定、语义简单、细节多样，因此整合丰富的多尺度特征可以提高分割精度。鉴于病变组织的密度可能与周围正常组织的密度相当，全局和局部信息对分割结果至关重要。为此，我们提出了一种基于边缘特征提取和卷积多层感知器（MLP）多尺度特征融合的图像分割方法（SC -UNe X t）。该网络是一个深度监督的编码器-解码器网络，其中编码器和解码器通过一系列嵌套的多重跳转路径来减少编码器和解码器子网络的特征图之间的语义差距。 最后，我们在 LIDC 数据集公共数据集上评估了我们的模型方法。实验证明了这种方法的有效性。我们模型的相似系数和交叉率分别达到了 86.44% 和 90.86%。与 UNet 和 UNe X t 相比，本文提出的网络在准确度、真实值与预测值的交集比、相似系数和分割效果方面都有所提高。
{"title":"SC-UneXt: Nested UNeXt Architecture based on Medical Image Segmentation","authors":"Lei Wen","doi":"10.54097/fcis.v6i2.07","DOIUrl":"https://doi.org/10.54097/fcis.v6i2.07","url":null,"abstract":"UNet and its various variants are commonly used methods in medical image segmentation tasks; however, many network parameters, complex calculations, and slow usage are problems that need to be overcome. These problems hinder the specific application of fast image segmentation in real-time tasks. At the same time, the lesion area has problems such as small size, irregular shape, and blurred edges, which makes the network feature extraction difficult and the segmentation accuracy needs to be improved. At the same time, medical image segmentation provides a variety of effective methods for the accuracy and robustness of organ segmentation, lesion detection, and classification. Medical images have fixed structures, simple semantics, and diverse details, so integrating rich multi-scale features can improve segmentation accuracy. Given that the density of diseased tissue may be comparable to that of surrounding normal tissue, both global and local information are crucial to segmentation results. To this end, we propose an image segmentation method (SC -UNe X t) based on edge feature extraction and multi-scale feature fusion of convolutional multi-layer perceptron (MLP). The network is a deeply supervised encoder-decoder network, in which the encoder and decoder pass through a series of nested, multiple jump paths to reduce the semantic gap between the feature maps of the encoder and decoder sub-networks.; Multi - scale feature fusion is introduced based on the UNe Finally, we evaluate our model approach on the LIDC dataset public dataset. Experiments have proven the effectiveness of this method. Our model's similarity coefficient and intersection ratio reached 86.44% and 90.86% respectively. Compared with UNet and UNe X t, the network proposed in this article has improved in accuracy, intersection ratio of real values and predicted values, similarity coefficient, and segmentation effect.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"41 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139000644","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}
Antenna technology has been continuously evolving and innovating in information transmission networks, providing essential support for information transfer and network infrastructure. This article focuses on introducing the key technologies of MIMO technology and space diversity technology, and analyzes the advantages of space diversity technology based on MIMO antenna structure. By exploring these key technologies in depth, this article helps readers better understand antenna technology and its applications.
天线技术在信息传输网络中不断发展和创新，为信息传输和网络基础设施提供了重要支持。本文重点介绍了 MIMO 技术和空间分集技术的关键技术，并分析了基于 MIMO 天线结构的空间分集技术的优势。通过对这些关键技术的深入探讨，本文有助于读者更好地理解天线技术及其应用。
{"title":"Analysis of Spatial Diversity Technology based on MIMO Antenna Structures","authors":"Yalin Wei","doi":"10.54097/fcis.v6i2.12","DOIUrl":"https://doi.org/10.54097/fcis.v6i2.12","url":null,"abstract":" Antenna technology has been continuously evolving and innovating in information transmission networks, providing essential support for information transfer and network infrastructure. This article focuses on introducing the key technologies of MIMO technology and space diversity technology, and analyzes the advantages of space diversity technology based on MIMO antenna structure. By exploring these key technologies in depth, this article helps readers better understand antenna technology and its applications.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"196 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138993837","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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