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Research on the closed-loop supply chain of intelligent products considering government subsidies in the context of the internet of things

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Discover Internet of Things 2025年第1期5卷 1-20页

作者： Zhao, Jinghua Cui, Shaoyun Xu, Zheng School of Business University of Shanghai for Science and Technology Shanghai China School of Computer and Information Engineering Institute for Artificial Intelligence Shanghai Polytechnic University Shanghai201209 China

The rapid development of Internet of Things technology and the continuous improvement of consumer demand have spawned the emergence of intelligent products based on Internet of Things technology. The existing research on the closed-loop supply chain of intelligent products under the background of the Internet of Things still lacks consideration of different subsidy objects and recyclers. Therefore, for different subsidy objects in the three-stage closed-loop supply chain of intelligent products, a pricing decision model involving manufacturers, recyclers, retailers and consumers under the minimum standard supervision of recycling technology is constructed, and different recyclers are distinguished according to the characteristics of intelligent products. By comparing the profits of each subject in different models, the optimal model of government subsidy is obtained. It is found that under the four modes, subsidizing recyclers without self-recovery technology makes the total profit of the supply chain reach the optimal solution;capital recyclers can obtain the largest number of old intelligent product recycling by using self-recovery technology;when subsidizing manufacturers, the total profit and product recovery rate of the supply chain are at the bottom. In addition, recycling subsidies, subsidy impact coefficients and technical standards will affect the best profits of enterprises. Through the analysis of the closed-loop supply chain of intelligent products, the research provides theoretical support and decision-making basis for the supply chain members to formulate relevant strategies and the government to take relevant measures to promote resource utilization and environmental protection. © The Author(s) 2025.

关键词： Smart manufacturing

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Leveraging machine learning for intelligent agriculture

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Discover Internet of Things 2025年第1期5卷 1-21页

作者： Sowmya, B.J. Meeradevi, A.K. Supreeth, S. Pradeep Kumar, D. Ravi Kumar, B.N. Rohith, S. Mishra, Divyansh Koushik, Abhishek Patil, Ankit U. Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru560054 India Department of Artificial Intelligence and Machine Learning M S Ramaiah Institute of Technology Bengaluru560054 India School of Computer Science and Engineering REVA University Bengaluru560064 India Department of Computer Science Ramaiah Institute of Technology Bengaluru560054 India Department of Information Science and Engineering BMS Institute of Technology & Management Bengaluru560119 India Department of Electronics and Engineering Nagarjuna College of Engineering and Technology Bengaluru562164 India

Agriculture, the backbone of many economies, faces challenges like lack of information, outdated practices, and limited access to technology, hindering farmer productivity. This work proposes a user-friendly, multilingual platform leveraging Generative AI to address farmers' diverse needs. The platform encompasses various features to enhance agricultural practices. An LLM-powered Government Scheme Advisor functions as a multilingual chatbot offering intelligent guidance on government agricultural schemes and subsidies. The Disease Detection module utilizes AI technology for real-time identification and treatment recommendations, minimizing crop diseases and yield losses. The Soil Testing Centre feature locates nearby soil testing centers, providing essential information based on geographical data to assist farmers in optimizing soil quality. A Crop Recommendation feature employs Machine Learning algorithms to offer personalized crop recommendations, considering various factors and aiding informed decision-making. The Crop Planning Tool, with its intuitive user interface, simplifies planning planting schedules and managing resources. Additionally, the platform includes an MSP Center Locator to find nearby Minimum Support Price (MSP) centers based on location. By integrating these innovative solutions, this platform bridges the gap between conventional agricultural techniques and contemporary technology, equipping farmers with the resources and expertise essential for advancing productivity and sustainability. Multilingual support ensures accessibility for a wider audience, breaking down language barriers and promoting inclusivity in the agricultural sector. This work proposes an innovative, multilingual platform powered by Generative AI to address these issues. Key features include an LLM-driven chatbot for government scheme guidance, AI-based real-time disease detection, and location-based tools for soil testing and MSP center identification. Additionally, the platf

关键词： Smart agriculture

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Enhancing Data Forwarding Efficiency in SIoT with Multidimensional Social Relations

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computers, Materials & Continua 2024年第1期78卷 1095-1113页

作者： Fang Xu Songhao Jiang Yi Ma Manzoor Ahmed Zenggang Xiong Yuanlin Lyu School of Computer and Information Science Hubei Engineering UniversityXiaogan432000China School of Computer Science and Information Engineering Hubei UniversityWuhan430062China Artificial Intelligence Industrial Technology Research Institute Hubei Engineering UniversityXiaogan432000China

Effective data communication is a crucial aspect of the Social Internet of Things(SIoT)and continues to be a significant research *** paper proposes a data forwarding algorithm based on Multidimensional Social Relations(MSRR)in SIoT to solve this *** proposed algorithm separates message forwarding into intra-and cross-community forwarding by analyzing interest traits and social connections among *** new metrics are defined:the intensity of node social relationships,node activity,and community *** the community,messages are sent by determining which node is most similar to the sender by weighing the strength of social connections and node *** a node performs cross-community forwarding,the message is forwarded to the most reasonable relay community by measuring the node activity and the connection between *** proposed algorithm was compared to three existing routing algorithms in simulation *** indicate that the proposed algorithmsubstantially improves message delivery efficiency while lessening network overhead and enhancing connectivity and coordination in the SIoT context.

关键词： SIoT data forwarding social attributes social relations community

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STPM: Spatial-Temporal Token Pruning and Merging for Complex Activity Recognition

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IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 5407-5418页

作者： Su, Yumeng Zhang, Jiachao Yan, Rui Li, Pengpeng Xie, Guo-Sen Shu, Xiangbo Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China Artificial Intelligence Industrial Technology Research Institute Nanjing Institute of Technology Nanjing211167 China Nanjing University Department of Computer Science and Technology Nanjing210023 China

Lightweight video representation techniques have advanced significantly for simple activity recognition, but they still encounter several issues when applied to complex activity recognition: 1) The presence of numerous individuals and varying spatial positions makes it difficult for traditional token pruning methods to maintain accuracy. 2) Simply discarding entire frames may result in the loss of crucial clues. 3) To maintain parallel computing, applying the same pruning rate to every frame leads to significant redundancy in frames with low information content. To this end, we propose a lightweight and novel Spatial-Temporal Token Pruning and Merging (STPM) framework, specifically designed for complex action videos where human actors occupy a small spatial resolution within video frames. Our framework considers two critical factors: semantic importance and spatial-temporal redundancy, to further reduce overhead. For semantic importance, STPM captures class-specific attention scores by learning multiple class tokens within the transformer to guide token pruning. For spatial-temporal redundancy, STPM employs an anchor graph and temporal attention to perform spatial and temporal token merging, preserving appearance and temporal cues while eliminating semantic duplication and redundancy. We conduct extensive experiments on JRDB-PAR primarily using recently introduced video transformer backbones, e.g., MViT and ViT. Our framework achieves similar results while requiring 40% less computation. © 1991-2012 IEEE.

关键词： Economic and social effects

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Hybrid millimeter wave heterogeneous networks with spatially correlated user equipment

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Digital Communications and Networks 2024年第4期10卷 904-917页

作者： Arif Ullah Ziaul Haq Abbas Ghulam Abbas Fazal Muhammad Jae-Mo Kang Department of Computer Engineering College of IT ConvergenceChosun UniversityGwangju61452South Korea Faculty of Electrical Engineering GIK Institute of Engineering Sciences and TechnologyTopi23640Pakistan Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and TechnologyTopi23640Pakistan Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan Department of Artificial Intelligence Kyungpook National UniversityDaegu41566South Korea

In this paper,we analyze a hybrid Heterogeneous Cellular Network(HCNet)framework by deploying millimeter Wave(mmWave)small cells with coexisting traditional sub-6GHz macro cells to achieve improved coverage and high data *** consider randomly-deployed macro base stations throughout the network whereas mmWave Small Base Stations(SBSs)are deployed in the areas with high User Equipment(UE)*** user centric deployment of mmWave SBSs inevitably incurs correlation between UE and *** a realistic scenario where the UEs are distributed according to Poisson cluster process and directional beamforming with line-of-sight and non-line-of-sight transmissions is adopted for mmWave *** using tools from stochastic geometry,we develop an analytical framework to analyze various performance metrics in the downlink hybrid HCNets under biased received power *** UE clustering we considered Thomas cluster process and derive expressions for the association probability,coverage probability,area spectral efficiency,and energy *** also provide Monte Carlo simulation results to validate the accuracy of the derived ***,we analyze the impact of mmWave operating frequency,antenna gain,small cell biasing,and BSs density to get useful engineering insights into the performance of hybrid mmWave *** results show that network performance is significantly improved by deploying millimeter wave SBS instead of microwave BS in hot spots.

关键词： Downlink cell association Heterogeneous cellular networks Integrated sub-6GHz and mmWave networks Millimeter wave communications Poisson cluster process

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Remote Sensing Object Detection Based on Improved Detection Transformer 3

Remote Sensing Object Detection Based on Improved Detection ...

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3rd International Conference on Cloud Computing, Big Data Application and Software engineering, CBASE 2024

作者： Hu, Yuanzhong Hu, Po Wang, Haihui Wang, Yuzhuo School of Computer Science and Engineering School of Artificial Intelligence Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798331506582

Nowadays, the accuracy of remote sensing image target recognition has greatly improved. It enables humans to efficiently monitor crop growth, track climate changes and natural disasters, and even provide critical strategic intelligence for national defense. However, due to the challenges posed by the large number of objects and the immense size of remote sensing images, there is still considerable room for improvement in recognition accuracy and efficiency. Detection Transformer (DETR) framework utilizes the Transformer architecture, change the object detection task to a set prediction problem, thus simplifying the traditional post-processing workflow. Through its attention mechanisms, DETR effectively captures object features in intricate scenes by modeling global contextual information. Nonetheless, there is still potential for improvement in extracting fine-grained details. In this study, three modifications are proposed to the DETR framework to boost remote sensing object detection accuracy. First, a multi-scale feature extraction approach is employed to capture deeper and more detailed image features. Second, a bidirectional learning path is introduced to enhance the contextual relationships between image features. Experimental results demonstrate that our proposed method has achieved strong competitiveness compared to other approaches © 2024 IEEE.

关键词： Optical remote sensing

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Decoding Sentiments: A Comprehensive Exploration of Real-Time Emotion Recognition from Text Through Deep Learning 10

Decoding Sentiments: A Comprehensive Exploration of Real-Tim...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Sowmya, G. Akhilesh, R. Rohan, Aurangabadkar Rao, Garikapati Kausthub Nayak, Atul Kumar Midhilesh, Koppolu Computer Science Engineering Artificial intelligence and Machine learning Mlr Institute of Technology Hyderabad India

ISBN: (纸本)9798350384369

Emotional state of a person can be found out through various modalities such as using facial expressions, voice of the person, text and so on. Our study is solely based on the emotion extracted through the text. Past research focused on categorizing texts into sentiments like positive, negative, or neutral, our novel approach shifts to predicting specific emotional states like angry, sad or happy. Our study covers the previous researches done in this field which are ranging from deep learning models, natural language processing, and machine learning models. Recurrent neural networks and its variants are mainly studied for recognizing the emotion in text. The integration of emotion detection in the customer feedback enhances organizational insights, enabling proactive measures to elevate customer satisfaction. © 2024 IEEE.

关键词： Contrastive Learning

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Vehicle Target Detection with an Integrated Attention Mechanism 3

Vehicle Target Detection with an Integrated Attention Mechan...

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3rd International Conference on Cloud Computing, Big Data Application and Software engineering, CBASE 2024

作者： Wang, Yuzhuo Wang, Haihui Hu, Po Hu, Yuanzhong School of Computer Science and Engineering School of Artificial Intelligence Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798331506582

To address the problems of traditional vehicle detection models regarding equipment requirements, detection accuracy, and overlapping target leakage rate, this study proposes an improved vehicle target detection algorithm for YOLOv8. Firstly, we introduce EMA, an attention mechanism for cross-space learning, to improve the attention to feature information. Secondly, it combines EMA with the C2f module in the Neck network to fuse more important feature information, obtaining more accurate vehicle feature information. Finally, it uses the MPDloU loss function to speed up the model's convergence and improve detection accuracy. Experiments conducted on Pascal VOC and MS COCO datasets show that the performance of the improved algorithm proposed in this paper exceeds that of the original model. The enhanced model's mAP reaches 85.71%, 3.27% higher than the original model, improving vehicle target detection accuracy and being highly practical. © 2024 IEEE.

关键词： attention mechanism MPDIoU vehicle detection YOLOv8

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Semi-supervised Counting of Grape Berries in the field Based on Density Mutual Exclusion

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Plant Phenomics 2023年第4期5卷 825-835页

作者： Yanan Li Yuling Tang Yifei Liu Dingrun Zheng School of Computer Science and Engineering School of Artificial IntelligenceWuhan Institute of TechnologyWuhan 430205China Hubei Key Laboratory of Intelligent Robot Wuhan Institute of TechnologyWuhan 430073China

Automated counting of grape berries has become one of the most important tasks in grape yield ***,dense distribution of berries and the severe occlusion between berries bring great challenges to counting algorithm based on deep *** collection of data required for model training is also a tedious and expensive *** address these issues and cost-effectively count grape berries,a semi-supervised counting of grape berries in the field based on density mutual exclusion(CDMENet)is *** algorithm uses VGG16 as the backbone to extract image *** tasks based on density mutual exclusion are *** tasks exploit the spatial distribution pattern of grape berries in density levels to make full use of unlabeled *** addition,a density difference loss is *** feature representation is enhanced by amplifying the difference of features between different density *** experimental results on the field grape berry dataset show that CDMENet achieves less counting *** with the state of the arts,coefficient of determination(R^(2))is improved by 6.10%,and mean absolute error and root mean square error are reduced by 49.36%and 54.08%,*** code is available at.

关键词： Grape mutual backbone

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Crowd Counting Method Based on Dual-Path Multi-Feature Fusion with Transformer 6

Crowd Counting Method Based on Dual-Path Multi-Feature Fusio...

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6th International Conference on Robotics and computer Vision, ICRCV 2024

作者： Li, Lei Wu, Yuntao Yao, Xiaotong Zhang, Zhuoyu School of Computer Science and Engineering School of Artificial Intelligence Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798331527426

In dense crowd scenes, the scale of human heads varies greatly, and current visual transformer models lack learning of different scales, resulting in the model being unable to perceive the target scale changes of human heads in crowd density images, leading to a decrease in crowd counting accuracy. To address this issue, we added a Multi Path Computing Network (MPCNet) in the feature extraction stage, applying attention mechanisms at different sampling rates and levels to capture hierarchical features of the image, and fitting proximity and multi-level dependencies to effectively perceive changes in the head scale. Adding a bidirectional feature fusion module (BIFFM) to the backend network captures more complex feature dependencies by simultaneously considering two different spatial positions, effectively enhancing the interaction between feature maps of different scales and enabling the model to perform counting efficiently. Finally, this chapter conducted extensive experiments on three mainstream population counting benchmark datasets to validate the effectiveness of the method. © 2024 IEEE.

关键词： Feature extraction

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