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Prediction Model of the End-point Carbon Content in BOF by Constructing the Graph of Heats

ISIJ International 2025年第5期65卷 613-618页

作者： Ning, Wen Hongbing, Wang Li, Li Anjun, Xu School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China School of Metallurgical and Ecological Engineering University of Science and Technology Beijing Beijing100083 China

For the differences in composition and proportions of input materials, end-point quality requirements and slagging specifications, it is difficult to construct a generalized model to guide steelmaking production. There is a complex connection among different heats in BOF, and this combination of correlations can be thought of as a hidden working pattern. A method named "attribute level division" is developed to exploit the correlations among heats to construct the graph of heats. The model based on the combination of label propagation algorithm (LPA) and back propagation neural network (BPNN), LPA-BP, is proposed for end-point carbon content prediction in BOF. LPA is used to discover the different community in the graph of heats and BPNN is trained to construct different models for end-point carbon content prediction for the heats from different community. The results of comparative experiment show that the LPA-BP model is higher than the baseline 2.5% when prediction error is within ±0.012%. The LPA-BP model also outperforms in some metrics, such as RMSE, MAE. This model provides a novel idea to improve the endpoint hit rate by distinguishing different communities to uncover the hidden working patterns among heats and constructing different models. © 2025 The Iron and Steel Institute of Japan.

关键词： Steelmaking

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Advanced Multiplier Architecture Optimization for Accelerated Arithmetic Operations and its Integration in Wireless Sensor Network Applications

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Tehnicki Vjesnik 2025年第3期32卷 1054-1065页

作者： Nirmal Kumar, R. Valarmathi, R.S. Kalamani, M. Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Tamil Nadu Erode638 401 India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai600062 India Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Tamil Nadu Coimbatore641407 India

Wireless sensor networks (WSNs) are becoming more valuable in environmental monitoring and industrial automation. Many WSN applications need high-performance adders and multipliers for efficient computation. Due to power efficiency, latency reduction, and resource use, optimum designs are required. The Optimized Multiplier Architecture for Wireless Sensor Networks (OMA-WSN) proposed in this paper addresses these issues. WSN mathematical procedures and significance are the subject of this study. WSNs with efficient multipliers and adders are crucial. WSNs cannot use current multiplier designs because of their sluggish operation and high power consumption. OMA-WSN combines RTSBA, Booth Multiplier, and Binary Common Sub-Expression Elimination (BCS2E) algorithms for a new viewpoint. This system's architecture implements efficient computation methods that minimize logic levels and propagation delays. The cutting-edge technique improves power consumption, latency, and resource usage for WSN applications. Numerous simulations demonstrate that the OMA-WSN performs better. The performance of the proposed OMA-WSN was found to be enhanced in various aspects. Specifically, the delay was reduced to 7.56 ns in simulation and 6.32 ns in hardware. The area utilization improved to 98.56 sq. micrometres in simulation and 90.23 sq. in hardware. The power consumption was lowered to 9.87 mW in simulation and 7.56 mW in hardware. The speed increased to 160.43 MHz in simulation and 145.67 MHz in hardware. The energy efficiency was enhanced to 0.98 pJ/bit in simulation and 0.87 pJ/bit in hardware. Lastly, the adder cell utilization improved to 47.43% in simulation and 40.67% in hardware. © 2025, Strojarski Facultet. All rights reserved.

关键词： Digital arithmetic

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A Comparative Study of Anomaly Detection: Integrating Joint Detection and Tracking Techniques 3

A Comparative Study of Anomaly Detection: Integrating Joint ...

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3rd International Conference on electronics and Renewable Systems, ICEARS 2025

作者： Lekha, T. R. Jaya Chandra Aravind, T. Ramesh, R. Lourdes Mount College of Engineering and Technology Department of Electronics and Communication Engineering Tamilnadu India Saveetha Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai India Tagore Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai India

ISBN: (纸本)9798331509675

As video data becomes increasingly pervasive in various domains, the need for robust and efficient anomaly detection methods has grown exponentially. This comparative analysis explores and evaluates diverse methods for video anomaly detection, ranging from traditional techniques to advanced deep learning methodologies. The study aims to unravel the complexities inherent in identifying anomalies within dynamic environments and offers insights into each method's strengths, limitations, and adaptability. The research investigates the impact of these anomaly detection methods across different datasets, assessing their generalizability and performance consistency. The study further addresses the computational aspects, comparing the efficiency and resource requirements of the various models under consideration. This comparative analysis contributes to the ongoing discourse on enhancing surveillance and security measures in diverse real-world applications by delving into the nuances of video anomaly detection. © 2025 IEEE.

关键词： Generative adversarial networks

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Disease Diagnosis and Sustainable Prevention in Arecanut Farming 3

Disease Diagnosis and Sustainable Prevention in Arecanut Far...

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3rd International Conference on electronics and Renewable Systems, ICEARS 2025

作者： Siddalingadevaru, Sneha Chikkanayakanahalli Ravindra, Poojitha Devarahalli Gangadhar, Vanitha Srinivas, Srija Basavapatna Singh, Vivek Dwivedi, Ajay Kumar Nagarjuna College of Engineering and Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798331509675

One of India's main crops is arecanut. It is grown more in India and other subtropical climates due to its higher market value. Numerous diseases that affect this plant might result in significant losses for farmers. These illnesses are brought on by abrupt changes in temperature and other meteorological factors;early disease identification is crucial. In order to minimize losses for farmers, this work focuses on early and precise disease diagnosis. A Convolutional Neural Network is a Deep Learning technique that uses an image as input, gives different features in the image learnable weights and biases, and then uses the results to determine which features are different. This method improves process efficiency and drastically lowers the amount of manual labor needed for illness diagnosis. To train and assess the CNN with Extension model, a dataset including several images of both healthy and diseased areca nut leaves is created. It was found that the recommended approach to areca nut illness diagnosis was accurate and effective. This study supports further initiatives to use technology in agriculture to increase crop productivity and health. According to this study, the suggested approach is the most reliable for identifying illnesses in areca nut leaves. In addition to providing suitable treatments for any diseases it identified, the system was able to differentiate between healthy and sick leaves. These results highlight the potential use of deep learning techniques and convolutional neural networks in agriculture, specifically in the control of plant diseases. One of the diseases of areca nuts that the suggested system can identify and cure is yellow leaf spot. The experimental results demonstrate different levels of disease identification accuracy depending on the stage of the disease and the quality of the input image. The system's overall accuracy is estimated to be 98.9%. © 2025 IEEE.

关键词： Convolutional neural networks

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See-Through Walls With Wi-Fi: Joint Utilization of RSSI and CSI for Enhancing Building Layout Reconstruction

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IEEE Sensors Journal 2025年第10期25卷 17717-17726页

作者： Chen, Jiahui Guo, Shisheng Yu, Fangrui Xu, Zihan Huang, Xue Li, Nian Cui, Guolong University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China

In building layout reconstruction (BLR), electromagnetic (EM) wave propagation in indoor environments involves not only direct paths (DPs) but also multiple non-DPs due to reflections, diffractions, and scattering. Traditionally, received signal strength indicator (RSSI)-based methods have been employed for BLR. However, RSSI is highly susceptible to multipath interference, leading to degraded reconstruction accuracy. To address this limitation, this article leverages the fine-grained characterization capabilities of channel state information (CSI) to mitigate the weaknesses of RSSI in multipath environments. Specifically, we propose a novel method that integrates CSI phase fluctuations with RSSI measurements to achieve high-quality BLR for various building structures. This approach enables the independent reconstruction of different building features, which are subsequently fused to enhance the overall BLR image. Experimental results demonstrate that, compared to conventional RSSI-based BLR methods, the proposed approach significantly reduces multipath interference, resulting in improved reconstruction accuracy. © 2001-2012 IEEE.

关键词： Image fusion

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SAP-ISTA-Net: Synthetic Aperture Processing Assisted ISTA Network for Multichannel Radar Forward-Looking Superresolution Imaging

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IEEE Sensors Journal 2025年第9期25卷 15668-15678页

作者： Li, Wenchao Chen, Rui Zhou, Mingming Zhang, Kun Wang, Ziwen Pu, Wei Wu, Junjie Yang, Jianyu University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China

With azimuth multichannel receiving echoes, multichannel radar can realize forward-looking imaging, but its azimuth resolution is greatly restricted by the platform size. Although many superresolution algorithms have been developed in recent years, they always face problems such as difficulty in parameter tuning, high computational complexity, and noise sensitivity. In this article, a synthetic aperture processing assisted iterative shrinkage thresholding algorithm (ISTA) network, that is, the synthetic aperture processing assisted ISTA network (SAP-ISTA-Net), is proposed to achieve multichannel radar forward-looking superresolution imaging. In the network, the reconstruction process with ISTA is mapped into a deep unfolding network, and the real aperture superresolution imaging with ISTA-Net is achieved by processing the instantaneous data of multiple channels first. Then, the superresolution result is enhanced by the synthetic aperture processing of one-channel data with different time instants. At last, simulated and measured data experiments are presented to verify the effectiveness of the proposed method. © 2001-2012 IEEE.

关键词： Radar imaging

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An integrated PHM framework for radar systems through system structural decomposition

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Journal of Systems engineering and electronics 2025年第1期36卷 95-107页

作者： WANG Hong KULEVOME Delanyo Kwame Bensah ZHAO Zi’an School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China

Implementing an efficient real-time prognostics and health management (PHM) framework improves safety and reduces maintenance costs in complex engineering ***, research on PHM framework development for radar systems is limited. Furthermore, typical PHM approaches are centralized, do not scale well, and are challenging to *** paper proposes an integrated PHM framework for radar systems based on system structural decomposition to enhance reliability and support maintenance actions. The complexity challenge associated with implementing PHM at the system level is addressed by dividing the radar system into subsystems. Subsequently, optimal measurement point selection and sensor placement algorithms are formulated for effective data acquisition. Local modules are developed for each subsystem health assessment, fault diagnosis, and fault prediction without a centralized controller. Maintenance decisions are based on each local module’s fault diagnosis and prediction results. To further improve the effectiveness of the prognostics stage, the feasibility of integrating deep learning (DL) models is also *** experiments with different degradation patterns are performed to evaluate the effectiveness of the framework’s DLbased prognostics model. The proposed framework facilitates transitioning from traditional reactive maintenance practices to a predictive maintenance approach, thereby reducing downtime and improving the overall availability of radar systems.

关键词： deep learning prognostics and health management(PHM) radar systems remaining useful life(RUL)

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A Hybrid Machine Learning Approach for Top-N Bike Recommendations

A Hybrid Machine Learning Approach for Top-N Bike Recommenda...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Kumar, V. Vinoth Raj, T. Selvin Retna Raj, J. Relin Francis Sornavalli, M. Krishnan, R. Santhana Soundiraraj, N. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Avadi Chennai India Dmi College of Engineering Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Velammal College of Engineering and Technology Department of Mathematics Madurai India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Psna College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul India

ISBN: (纸本)9798331509828

This research introduces a bike recommendation system aimed at providing personalized suggestions by integrating diverse data sources. The system leverages information from social media, user reviews, and product specifications to generate a comprehensive dataset. Social media interactions, such as likes, shares, and comments, offer valuable insights into the popularity and user engagement of various bike models. E-commerce platforms contribute detailed information regarding bike features, including price, brand, type, and user ratings, enhancing the dataset's depth. The data undergoes preprocessing to ensure consistency and relevance, which allows for effective feature extraction and the handling of missing values. This step is crucial in ensuring the quality of the dataset, which is then used to train the recommendation models. The system combines multiple recommendation techniques to tailor suggestions based on individual user preferences and behavior. By integrating content-based filtering and collaborative filtering, the model generates highly relevant recommendations suited to each user's specific needs. The models are evaluated using key performance metrics, such as accuracy, precision, recall, and F1 score, to ensure the most effective algorithms are selected for the task. This evaluation process guarantees that the system delivers high-quality recommendations. Once trained, the recommendation models are deployed via an efficient API, enabling real-time, personalized bike suggestions to users. The system's web interface allows users to input their preferences, such as budget, bike type, and other specifications, further enhancing the personalization of recommendations. Overall, this research demonstrates the value of integrating multiple data sources to create an advanced, personalized bike recommendation system capable of meeting diverse user needs. © 2025 IEEE.

关键词： Bike recommendation system data integration e-commerce data personalized suggestions social media data user reviews

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Adaptive Dwell Scheduling Based on Dual-Side Time Pointers for Simultaneous Multi-Beam Radar

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1190-1200页

作者： Siyu Heng Ting Cheng Jiaming Song Zishu He Luqing Liu Yuanqing Wang School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China

Adaptive dwell scheduling is essential to achieve full performance for a simultaneous multi-beam radar *** dwell scheduling for such a radar system considering desired execution time criterion is studied in this *** primary objective of this model is to achieve maximum scheduling gain and minimum scheduling cost while adhering to not only time,aperture,and frequency constraints,but also electromagnetic compatibility(EMC)*** dwell scheduling algorithm is proposed to solve the above optimization problem,where several separation points are set on the timeline,so that each separator divides the scheduling interval into two *** the two sides,the dual-side time pointers are introduced,which move from the separator to both ends of the scheduling *** dwell tasks are analyzed in sequence at each analysis point based on their two-level synthetical *** tasks are then executed simultaneously by sharing the whole aperture under various constraints to accomplish multiple tasks *** above process is respectively conducted at each separator,and the final scheduling result is the one with the minimal cost among *** results prove that the proposed algorithm can achieve real-time dwell scheduling and outperform the existing algorithms in terms of scheduling performance.

关键词： multifunctional radar resource management scheduling algorithms simultaneous multi-beam

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Signal Integrity Analysis of Tapered Through Packaging Multibit Glass Vias Using Exponential Matrix–Rational Approximation Technique

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IEEE Transactions on Components, Packaging and Manufacturing technology 2025年第5期15卷 1014-1024页

作者： Kumar, Ajay Dhiman, Rohit National Institute of Technology at Hamirpur Department of Electronics and Communication Engineering Himachal Pradesh Hamirpur177 005 India

In this article, we develop a wideband scalable analytical model of tapered through packaging differential multibit vias (TP-DMVs) with pads in glass interposer by developing an exponential matrix-rational approximation (EM-RA) technique. An electrical RLGC model, which also includes the skin effect, is proposed as a function of geometric parameters of tapered through glass vias. The scalability of RLGC equivalent circuit is verified in terms of S-parameters against the 3-D electromagnetic (EM) high-frequency structure simulator (HFSS) up to 100 GHz, for a wide variety of tapered TP-DMV dimensions, including the effects of slope angle and aspect ratio. The maximum reduction in capacitance and conductance values of tapered TP-DMVs with a slope angle is ~75%, while the maximum increase in resistance and inductance value is ~93%. The signal integrity parameters, such as crosstalk, time to reach peak crosstalk, and propagation delay under the influence of surface roughness (SR) and temperature variations, are computed using the EM-RA technique and industry-level simulator SPICE. The variations include temperature and glass SR ranges from (300–500 K) and (150– 1.5 μm). The comparison shows excellent conformity with less than 1% error. Through the proposed EM-RA technique and Nyquist stability criterion, it is shown that, by increasing the slope angle and pitch, the tapered TP-DMVs become relatively more stable. © 2011-2012 IEEE.

关键词： Aspect ratio

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