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VisCom-Res: An Efficient Visual Complementary E-Commerce Product Recommendations Using Attention-Enhanced Lightweight Architecture VisCom-Res:使用注意力增强轻量级架构的高效视觉互补电子商务产品推荐
IF 0.5 Q4 TELECOMMUNICATIONS Pub Date : 2026-01-16 DOI: 10.1002/itl2.70212
G. Kalyan Chakravarthi, Raghvendra Kumar, Ssvr Kumar Addagarla, Smriti Sachan, Debashis Dutta

The rapid growth of e-commerce across the world has redefined how users discover, evaluate, and purchase products especially in visually driven domains like fashion and lifestyle. While the traditional product recommendation engines mostly rely on the collaborative or textual content, and many e-commerce platforms, providing the similar visual recommendations and lacks in suggesting the complementary products. The existing CNN baseline models overlook the necessary visual features and contextual relation which are essentials for recommending complementary products. This paper introduces VisCom-Res which is a Pioneering framework using deep learning-based visual complementary product recommendation combining a customized CNN architecture, ResNet50- Efficient Ghost Attention (EGA), with Hierarchical Navigable Small World (HNSW) nearest neighbor indexing technique. The ResNet50-EGA is improved with Ghost modules and Efficient Channel Attention (ECA) to extract semantically rich and fine-grained features from fashion product images. This extracted image embedding's further indexed using HNSW to enable fast, high-precision retrieval of visually compatible items. The Proposed model evaluated on a custom curated multi-class fashion dataset sourced from various Indian e-commerce platforms like Flipkart, Myntra, and Tata CLiQ, and it outperforms conventional models such as VGG16, DenseNet121, and MobileNetV2 in terms of classification accuracy and achieves superior retrieval metrics such as Precision@10 (93.8%), Recall@10 (94.5%), NDCG@10 (0.947), and average query time of 8.2 ms. The results validate the suitability of VisCom-Res for scalable, real-time recommendation systems in visually rich e-commerce recommendation environments. Unlike conventional CNN + attention frameworks, VisCom-Res integrates Ghost feature generation with Efficient Channel Attention (ECA) within a ResNet-50 backbone, reducing redundant map computation by ≈30% while retaining complementary-aware semantics.

全球电子商务的快速发展重新定义了用户发现、评估和购买产品的方式,尤其是在时尚和生活方式等视觉驱动的领域。而传统的产品推荐引擎大多依赖于协作或文本内容,许多电子商务平台提供的是类似的视觉推荐,缺乏互补产品的建议。现有的CNN基线模型忽略了必要的视觉特征和上下文关系,这是推荐互补产品的必要条件。本文介绍了VisCom-Res,这是一个开创性的框架,它使用基于深度学习的视觉互补产品推荐,结合了定制的CNN架构,ResNet50-高效幽灵注意(EGA)和分层可导航小世界(HNSW)最近邻索引技术。ResNet50-EGA通过Ghost模块和高效通道注意(ECA)进行改进,从时尚产品图像中提取语义丰富和细粒度的特征。这种提取的图像嵌入使用HNSW进一步索引,以实现快速,高精度的检索视觉兼容的项目。该模型对来自Flipkart、Myntra和Tata CLiQ等印度电子商务平台的定制多类时尚数据集进行了评估,在分类精度方面优于VGG16、DenseNet121和MobileNetV2等传统模型,并实现了Precision@10(93.8%)、Recall@10(94.5%)、NDCG@10(0.947)等优越的检索指标,平均查询时间为8.2 ms。结果验证了VisCom-Res在视觉丰富的电子商务推荐环境中可扩展的实时推荐系统的适用性。与传统的CNN +注意力框架不同,VisCom-Res在ResNet-50骨干网中集成了Ghost特征生成和高效通道注意力(ECA),在保留互补感知语义的同时减少了约30%的冗余地图计算。
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引用次数: 0
Modular Design and Analysis of an Intelligent Storage System Based on PLC and Dual-Stepper Collaborative Control 基于PLC和双步进协同控制的智能存储系统的模块化设计与分析
IF 0.5 Q4 TELECOMMUNICATIONS Pub Date : 2026-01-15 DOI: 10.1002/itl2.70217
Shaogang Liu, Biao Si, Xin Li, Pengfei Li, Lin Jin

To address the positioning accuracy and stability requirements of material storage links at the end of flexible automated production lines in the context of manufacturing intelligent transformation, this paper designs an intelligent warehousing system based on S7-1200 PLC. The technical scheme takes PLC control as the core, integrates 3ND583 and M415B dual stepper drivers to drive a two-dimensional ball screw mechanism, and combines with a pneumatic pushing system and various sensors. Verified by joint simulation with TIA Portal PLCSIM and WinCC, the system supports manual/automatic mode switching, meeting the low-noise and high-stability storage requirements of teaching training and industrial scenarios. The innovation lies in proposing a dual stepper collaborative control algorithm, which realizes multi-axis synchronous motion control through pulse equivalent matching and speed parameter optimization. Although the system is based on the automation and information technology of Industry 4.0, its modular design, easily expandable configuration monitoring interface, and manual mode that supports manual intervention provide a good technical foundation for the transition to the ‘human-machine collaboration’ paradigm advocated by Industry 5.0.

针对制造业智能化转型背景下柔性自动化生产线末端物料存储环节的定位精度和稳定性要求,本文设计了一种基于S7-1200 PLC的智能仓储系统。该技术方案以PLC控制为核心,集成3ND583和M415B双步进驱动器驱动二维滚珠丝杠机构,并结合气动推进系统和各种传感器。通过与TIA Portal PLCSIM和WinCC的联合仿真验证,该系统支持手动/自动模式切换,满足教学培训和工业场景的低噪声、高稳定存储需求。创新之处在于提出了双步进协同控制算法,通过脉冲等效匹配和速度参数优化实现多轴同步运动控制。虽然系统基于工业4.0的自动化和信息技术,但其模块化设计、易于扩展的配置监控界面和支持人工干预的手动模式,为向工业5.0所倡导的“人机协作”范式过渡提供了良好的技术基础。
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引用次数: 0
A Multimodal Affective Computing Framework for Real-Time Student Engagement Assessment in IoT-Enabled English Classrooms: An Edge-Cloud Collaborative Approach 物联网英语课堂中实时学生参与评估的多模态情感计算框架:边缘云协作方法
IF 0.5 Q4 TELECOMMUNICATIONS Pub Date : 2026-01-15 DOI: 10.1002/itl2.70223
Peirong He

In the English teaching environment driven by the Internet of Things, real-time assessment of students' emotional states based on video data is particularly crucial for understanding student engagement and improving teaching quality. Existing server-based deep networks are limited by long-distance video data transmission, which seriously restricts the real-time performance of sentiment analysis. Moreover, simple video data cannot guarantee robustness in complex scenes. To address these issues, this paper proposes a multimodal fusion emotion recognition framework based on the edge-cloud collaboration mechanism. Firstly, on the edge node, we exploit two complementary modalities of data: video sequences and facial landmark sequences, and design a lightweight dual-stream neural network based on the 3D MobileNetV3 and graph convolutional network to efficiently extract multimodal features. On the server, we adopt the Transformer-based cross fusion mechanism to implement multimodal fusion and emotion evaluation. The edge side is responsible for real-time preprocessing and primary feature extraction. In our proposed framework, the server is responsible for aggregating feature data from multiple edge nodes. The experimental results indicate that the proposed framework can achieve high-precision student engagement assessment with low latency.

在物联网驱动的英语教学环境中,基于视频数据实时评估学生情绪状态,对于了解学生参与度,提高教学质量尤为重要。现有基于服务器的深度网络受远程视频数据传输的限制,严重制约了情感分析的实时性。此外,简单的视频数据并不能保证在复杂场景下的鲁棒性。针对这些问题,本文提出了一种基于边缘云协同机制的多模态融合情感识别框架。首先,在边缘节点上,利用数据的视频序列和面部地标序列两种互补模式,设计基于3D MobileNetV3和图卷积网络的轻量级双流神经网络,高效提取多模态特征;在服务器端,我们采用基于transformer的交叉融合机制实现多模态融合和情感评估。边缘侧负责实时预处理和主要特征提取。在我们提出的框架中,服务器负责聚合来自多个边缘节点的特征数据。实验结果表明,该框架可以实现高精度、低延迟的学生参与度评估。
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引用次数: 0
Bidirectional Converter-Based Power Conditioning Unit for Self-Sustained Wireless Sensor Networks 基于双向转换器的自维持无线传感器网络功率调节装置
IF 0.5 Q4 TELECOMMUNICATIONS Pub Date : 2026-01-15 DOI: 10.1002/itl2.70186
W. Margaret Amutha, Premalatha Santhanamari, Karthikeyan Murugesan

The growing use of Wireless Sensor Networks (WSNs) in remote and energy-limited areas calls for compact and efficient power converters. This paper proposes a bidirectional step-up/down DC–DC converter with a Modified Active Switched Inductor (MASL) cell, designed for energy harvesting powered WSNs. By integrating a switched capacitor network and an auxiliary control switch, the converter improves voltage gain, reduces ripple, and lowers voltage stress. It enables efficient energy transfer between renewable sources and storage elements like batteries or supercapacitors. Controlled synchronous rectification enhances efficiency, reaching a peak of 93.5%. MATLAB/Simulink simulations and a 200 W hardware prototype confirm its suitability for reliable, self-powered WSN applications.

无线传感器网络(wsn)在偏远地区和能源有限地区的应用越来越广泛,这就需要一种紧凑、高效的电源转换器。本文提出了一种带有改进有源开关电感(MASL)单元的双向升压/降压DC-DC变换器,用于能量收集供电的WSNs。通过集成开关电容网络和辅助控制开关,变换器提高了电压增益,减少了纹波,降低了电压应力。它可以在可再生能源和电池或超级电容器等存储元件之间实现有效的能量传输。可控同步整流提高了效率,最高可达93.5%。MATLAB/Simulink仿真和200w硬件原型验证了其适用于可靠的自供电WSN应用。
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引用次数: 0
Research on Prediction and Management of IoT Resource Supply Based on Machine Learning 基于机器学习的物联网资源供给预测与管理研究
IF 0.5 Q4 TELECOMMUNICATIONS Pub Date : 2026-01-09 DOI: 10.1002/itl2.652
Yuyao Li

Network transmission plays an important role in information system services of IoT, and network transmission quality is also an important indicator of the quality of information export in China. To measure the development of Internet quality in China's foreign trade, this paper calculates and analyzes the time series data of Internet export quality based on open source. This paper first analyzes the necessity and significance of the research. After comparing several types of time series methods, this paper chooses a statistics-based method to describe the Internet quality data, compares the statistical data before and after the epidemic, and draws a conclusion that the epidemic is irrelevant to the Internet quality. Further, this paper combined the cyclic neural network LSTM and principal component analysis methods to carry out calculations. LSTM method completed the analysis and prediction of the quality data of the Internet export, selected packet loss rate, RTT, and delay jitter as the indicators of Internet quality, and completed the preprocessing of the indicator data. To obtain comprehensive network quality indicators, this paper constructs a single index network quality evaluation model based on principal component analysis and completes the analysis and calculation of data analysis. The computation results demonstrate the great prediction accuracy of the LSTM referenced in this research for index data, and the only index result for measuring the quality of Internet exports can be obtained by using principal component analysis. In practical applications, the research in this paper can provide support for the subsequent network performance optimization and bandwidth utilization improvement of the IOT industry. Github link and data source of the paper is https://github.com/superpeace90/iotproject.

网络传输在物联网信息系统服务中发挥着重要作用,网络传输质量也是衡量中国信息输出质量的重要指标。为了衡量中国对外贸易中互联网质量的发展,本文基于开源计算并分析了互联网出口质量的时间序列数据。本文首先分析了研究的必要性和意义。在比较了几种时间序列方法之后,本文选择了一种基于统计的方法来描述互联网质量数据,对比了疫情前后的统计数据,得出疫情与互联网质量无关的结论。并结合循环神经网络LSTM和主成分分析方法进行计算。LSTM方法完成了对Internet导出质量数据的分析和预测,选择丢包率、RTT和时延抖动作为Internet质量的指标,并完成了指标数据的预处理。为获得综合的网络质量指标,本文构建了基于主成分分析的单指标网络质量评价模型,并完成了数据分析的分析计算。计算结果表明,本文引用的LSTM对指标数据的预测精度较高,主成分分析只能得到衡量互联网出口产品质量的指标结果。在实际应用中,本文的研究可以为物联网行业后续的网络性能优化和带宽利用率提升提供支持。本文的Github链接和数据来源为https://github.com/superpeace90/iotproject。
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引用次数: 0
Enhanced Medical Security Based Whale Optimization Algorithm (EMSWOA) in Wireless Body Area Networks 无线体域网络中基于增强医疗安全的鲸鱼优化算法(EMSWOA
IF 0.5 Q4 TELECOMMUNICATIONS Pub Date : 2025-12-28 DOI: 10.1002/itl2.70099
Blanie Scrimshaw William, Y. Bevish Jinila

In today's world, most individuals are suffering from health complications due to sedentary lifestyles, food habits, and aging. This might have distracted them from their daily activities, and many individuals are not seeking medical help with their busy work schedules. To overcome these challenges, Wireless Body Area Network (WBAN) provides challenging solutions with continuous and remote monitoring systems. Tiny sensors are either embedded in the body or worn by the patients for tracking physiological factors, including heart pulse rate (bilirubin), pressures (alkphos), and levels of hemoglobin (albumin). The inserted or implanted sensors collect the data and transmit it to the central sink node for the aggregation of the readings, and reports are comprehensively generated and transmitted to the medical professionals for analysis. While transmitting the sensitive data, a security breach might occur with the wireless channels. To enhance security, Enhanced Juels and Sudan (EJS) encryption algorithm is deployed to safeguard both the data and sensor systems. This prevents unauthorized access to data. Whale Optimization Algorithm is deployed for improving optimized network performance and security. Simulations are conducted in the Cooja simulator and demonstrate enhanced convergence efficacy, optimal weight computation, and mitigated error rates, including False Rejection Rate (FRR) and False Acceptance Rate (FAR) for a robust health monitoring system.

在当今世界,由于久坐不动的生活方式、饮食习惯和衰老,大多数人都患有健康并发症。这可能会分散他们的日常活动,许多人在繁忙的工作安排中没有寻求医疗帮助。为了克服这些挑战,无线体域网络(WBAN)为连续和远程监控系统提供了具有挑战性的解决方案。微小的传感器要么嵌入体内,要么由患者佩戴,用于跟踪生理因素,包括心率(胆红素)、血压(alkphos)和血红蛋白(白蛋白)水平。插入或植入的传感器采集数据并传输到中央汇聚节点汇总读数,综合生成报告并传输给医疗专业人员进行分析。在传输敏感数据时,无线通道可能会出现安全漏洞。为了提高安全性,部署了增强型Juels和苏丹(EJS)加密算法来保护数据和传感器系统。这可以防止对数据进行未经授权的访问。采用Whale Optimization Algorithm优化网络性能和安全性。在Cooja模拟器中进行了仿真,并证明了增强的收敛效率,最优的权重计算和降低的错误率,包括假拒绝率(FRR)和假接受率(FAR),用于稳健的健康监测系统。
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引用次数: 0
Enhance Reliability and Security in VANET Using Clustering Based on ANT Colony Optimization and Fuzzy Logic 利用基于蚁群优化和模糊逻辑的聚类技术提高VANET的可靠性和安全性
IF 0.5 Q4 TELECOMMUNICATIONS Pub Date : 2025-12-28 DOI: 10.1002/itl2.70202
Gulista Khan, Sumit Kumar, Wajid Ali, Kamal Kumar Gola, Rohit Kanauzia

Vehicular Ad Hoc Networks (VANETs) play a crucial role in intelligent transportation by enabling communication between vehicles and infrastructure. However, ensuring secure, reliable, and consistent data transfer remains challenging due to their dynamic nature. This paper proposes a Clustering-Based Ant Colony Optimization (CB-ACO) and Fuzzy Logic algorithm to address these issues. Clustering reduces network load and enhances stability, while ACO selects optimal cluster heads and routes, supported by Fuzzy Logic-based trust assessments for secure access control. Compared to existing algorithms, the proposed method improves packet delivery, reduces latency, and enhances security, offering a robust solution for next-generation VANETs.

车辆自组织网络(vanet)通过实现车辆与基础设施之间的通信,在智能交通中发挥着至关重要的作用。然而,由于数据传输的动态性,确保安全、可靠和一致的数据传输仍然具有挑战性。本文提出了一种基于聚类的蚁群优化(CB-ACO)和模糊逻辑算法来解决这些问题。聚类可以减少网络负载,提高网络稳定性,蚁群算法选择最优簇头和路由,并基于模糊逻辑的信任评估实现安全访问控制。与现有算法相比,该方法改进了分组传输,降低了延迟,提高了安全性,为下一代vanet提供了强大的解决方案。
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引用次数: 0
Research on Online Monitoring System of Generator Slip Ring Temperature Based on the Internet of Things and Edge Computing 基于物联网和边缘计算的发电机滑环温度在线监测系统研究
IF 0.5 Q4 TELECOMMUNICATIONS Pub Date : 2025-12-28 DOI: 10.1002/itl2.70115
Tang Li

To address overheating and maintenance challenges in wind turbine slip ring systems operating in harsh environments, this study develops an online temperature monitoring system integrating Industrial Internet of Things (IIoT) and edge computing. A deep learning-driven distributed resource allocation algorithm (DDLRA) is deployed on edge devices to achieve real-time slip ring temperature monitoring, intelligent fault prediction, and reduced latency. A multi-layer edge intelligence architecture is constructed, optimizing offloading strategies via distributed neural networks to balance energy efficiency and resource utilization. Experimental simulations using over 1000 temperature datasets from key components demonstrate that the model maintains prediction errors within 0.3°C, effectively identifying anomalies and adapting to variable conditions. The system also enables real-time line load scheduling and carbon brush temperature prediction. This study pioneers the application of online updatable AI models in slip ring monitoring, combining edge responsiveness and cloud collaboration, offering significant engineering value for improving wind turbine reliability.

为了解决在恶劣环境下运行的风力涡轮机滑环系统的过热和维护挑战,本研究开发了一种集成工业物联网(IIoT)和边缘计算的在线温度监测系统。在边缘设备上部署深度学习驱动的分布式资源分配算法(DDLRA),实现滑环温度实时监测、故障智能预测、降低时延。构建了多层边缘智能架构,通过分布式神经网络优化卸载策略,平衡能源效率和资源利用率。使用来自关键部件的1000多个温度数据集进行的实验模拟表明,该模型将预测误差保持在0.3°C以内,有效地识别异常并适应可变条件。该系统还可以实现实时线负荷调度和碳刷温度预测。该研究率先将可在线更新的人工智能模型应用于滑环监测,结合边缘响应和云协作,为提高风力涡轮机的可靠性提供了重要的工程价值。
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引用次数: 0
Lightweight Neural Networks on Edge Devices for Real-Time Analysis of Student Movement in Cloud-Assisted Physical Education 基于边缘设备的轻量级神经网络用于云辅助体育教学中学生运动的实时分析
IF 0.5 Q4 TELECOMMUNICATIONS Pub Date : 2025-12-28 DOI: 10.1002/itl2.70215
Jianjun Yin

Cloud-assisted physical education teaching is an important direction for the development of smart education. However, the data processing and transmission of videos severely restrict the real-time performance of action analysis. To this end, this paper proposes an efficient edge cloud-assisted student movement recognition framework based on the graph convolutional network and human skeleton data. On the edge server, we use the YOLO-pose algorithm to generate robust human skeleton sequences and design an improved spatial–temporal dual stream graph convolutional neural network with an early fusion structure, which introduces the node weight module and the dynamic graph module to exploit long-distance dependency relationships of nodes. In the cloud server, we use a federated learning framework based on the density clustering mechanism to collaboratively train and aggregate parameters of models scattered across edge nodes. The experimental results show that our proposed model achieves excellent recognition accuracy on the self-built sports action dataset, providing an effective solution for intelligent and real-time feedback in outdoor sports teaching.

云辅助体育教学是智慧教育发展的重要方向。然而,视频的数据处理和传输严重制约了动作分析的实时性。为此,本文提出了一种基于图卷积网络和人体骨骼数据的高效边缘云辅助学生运动识别框架。在边缘服务器上,采用YOLO-pose算法生成鲁棒人体骨架序列,设计了一种改进的具有早期融合结构的时空双流图卷积神经网络,引入节点权值模块和动态图模块,利用节点之间的远程依赖关系。在云服务器端,我们使用基于密度聚类机制的联邦学习框架来协同训练和聚合分散在边缘节点上的模型参数。实验结果表明,该模型在自建运动动作数据集上取得了优异的识别精度,为户外运动教学中的智能实时反馈提供了有效的解决方案。
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引用次数: 0
IIoT-Driven Digital Cockpits Carbon Neutral Assimilation System: Exploratory and Exploitative Green Innovation 工业物联网驱动的数字驾驶舱碳中和同化系统:探索性和开发性绿色创新
IF 0.5 Q4 TELECOMMUNICATIONS Pub Date : 2025-12-28 DOI: 10.1002/itl2.70214
Zemin Zhang, Tao Li, Yingjun Du

IIoT and digital twin-empowered micro level carbon governance is transforming EU-China carbon neutrality; this study defines the mechanism of DCCNAS (digital cockpits carbon neutral assimilation system) that facilitates real-time vehicle emission behavior interactions; however, it lacks theoretical support and remains unexplained. By employing a mixed-methods, first define the DCCNAS, subsequently describe the first-stage and second-stage structure of DCCNAS mechanism, demonstrate the exploitative and explorative green innovation with dual tacit knowledge transfer in through four cases. Second, develop a tripartite evolutionary model to quantitatively examine the micro-level asymptotic stability of exploratory versus exploitative green innovation strategies under dual tacit knowledge transfer. Automaker achieves dual tacit knowledge transfer through the diffusion of low-carbon technologies in driving behavior and community culture, empowers behavioral optimization and energy efficiency management within the DCCNAS framework to facilitate green transformation from individuals to organization. This study contributes in identifying DCCNAS and offers theoretical support and practical guidance for the green innovation practices of automakers, reveal the mechanism in achieving carbon neutrality goals in product design, technological upgrades, and system optimization.

工业物联网和数字孪生驱动的微观碳治理正在改变中欧碳中和;本研究定义了DCCNAS(数字驾驶舱碳中和同化系统)促进实时车辆排放行为交互的机制;然而,它缺乏理论支持,仍然无法解释。采用混合方法,首先对DCCNAS进行了定义,然后描述了DCCNAS机制的第一阶段和第二阶段结构,通过四个案例展示了具有双重隐性知识转移的探索性和探索性绿色创新。其次,建立三方演化模型,定量考察双重隐性知识转移下探索性与开发性绿色创新策略的微观渐近稳定性。汽车制造商通过低碳技术在驾驶行为和社区文化中的传播实现双重隐性知识转移,在DCCNAS框架内实现行为优化和能效管理,促进从个人到组织的绿色转型。本研究有助于识别碳中和目标,为汽车制造商的绿色创新实践提供理论支持和实践指导,揭示汽车制造商在产品设计、技术升级和系统优化等方面实现碳中和目标的机制。
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引用次数: 0
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