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Effect of Limestone on NO Emission during the Co-Combustion of Semi-Coke and Bituminous Coal

Journal of Thermal Science 2024年第6期33卷 2413-2425页

作者： LIU Yanquan TAN Wenyi LIANG Shaohua BI Xiaolong School of Energy&Engineering Nanjing Institute of TechnologyNanjing 211167China School of Environment Engineering Nanjing Institute of TechnologyNanjing 211167China

When semi-coke is co-combusted with bituminous coal with higher sulfur content in a circulating fluidized bed(CFB)boiler,the necessity of desulfurization in furnace ***,limestone,which is a widely used desulfurizing agent,also has an effect on NO *** order to explore its effect during co-combustion,the combustion experiments were conducted in both a CFB test rig and a fixed bed *** results show that blending semi-coke with bituminous coal will change the occurrence forms of nitrogen in the fuel and more fuel NO is released during the devolatilization *** the desulfurization process,CaO will be generated through the calcination *** has catalytic effects on both the oxidation and reduction reactions of NO,and the catalytic strength in these two types of reactions decides the final effect on NO *** the blended fuel with 50%semi-coke and 50%bituminous coal(SC50BC50),the NO emission initially increases and then decreases as the Ca/S molar ratio increases from 0 to 4 at 900℃.Compared to the situation of burning semi-coke alone,semi-coke in the blended fuel has more opportunities to contact with CaO under the same Ca/S molar ratio,leading to the heterogeneous reduction reaction of NO *** the combustion temperature increases from 800℃ to 1000℃,the effect of limestone on NO emission will change from promotion to *** is because the higher combustion temperature can intensify not only the catalytic reduction of NO precursors in the dense-phase region,but also the reaction between NO and unburnt char in the dilute-phase region in the ***,the lower O_(2) concentration in the atmosphere is also favorable for enhancing the catalytic effect of CaO on the NO-char reduction reaction for semi-coke and SC50BC50,so the conversion of fuel-N/NO will be inhibited compared with the cases without *** achievements of this study are beneficial for the coordinated control of NOx and SO_(2) during the

关键词： semi-coke co-combustion limestone NO emission

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Lightweight Rice Planthopper Identification Method Based on YOLOv5

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2024年第8期51卷 1079-1085页

作者： Li, Siquan Wang, Yi Shi, Teng Chen, Xi Tang, Zhen Zeng, Ziyu Wen, Xin Shang, Yanling School of Automation Nanjing Institute of Technology Nanjing211167 China School of Mechanical Engineering Nanjing Institute of Technology Nanjing211167 China School of Automation Nanjing Institute of Technology Nanjing211167 China School of Computer engineering Nanjing Institute of Technology Nanjing211167 China School of Automation Nanjing Institute of Technology Nanjing211167 China

The identification and classification of pests in rice field are the prerequisites of early warning systems for pest disasters. Among these pests, the rice planthoppers cause the most serious damage. However, the existing deep learning models for rice planthopper recognition are characterized by large size and numerous parameters, which makes them unsuitable for the deployment on embedded devices with limited computational resources. To address this issue, a lightweight rice planthopper recognition model based on YOLOv5 is proposed in this paper. In the model, a lightweight convolutional network named GhostNet is employed as the backbone to reduce the operational parameters. Additionally, a convolutional attention module (CBAM) is integrated into the backbone network to effectively enhance the transmission of deep information, so as to improve the model’s ability of recognizing rice planthopper images. The original CIoU loss function is replaced by the SIoU loss function to expedite model convergence. Experimental results demonstrate that the modified model achieves the mAP@0.5 as 82.8%, with the parameter count of 3.12×106 and the model size of 7.2MB. Compared to the original model, it is a reduction of 46.7% in size and 43.3% in parameters, with a minor accuracy loss of 0.1%. Clearly, the improved model can achieve lightweight characteristics and robust performance, and thus it provides a theoretical and practical foundation for early warning systems against rice planthopper infestations in rice fields. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Convolution

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TGAIN:Geospatial Data Recovery Algorithm Based on GAIN-LSTM

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computers, Materials & Continua 2024年第10期81卷 1471-1489页

作者： Lechan Yang Li Li Shouming Ma School of Soft Engineering Jinling Institute of TechnologyNanjing211169China School of Computer Jinling Institute of TechnologyNanjing211169China School of Network Security Jinling Institute of TechnologyNanjing211169China

Accurate geospatial data are essential for geographic information systems(GIS),environmental monitoring,and urban *** deep integration of the open Internet and geographic information technology has led to increasing challenges in the integrity and security of spatial *** this paper,we consider abnormal spatial data as missing data and focus on abnormal spatial data *** geospatial data recovery methods require complete datasets for training,resulting in time-consuming data recovery and lack of *** address these issues,we propose a GAIN-LSTM-based geospatial data recovery method(TGAIN),which consists of two main works:(1)it uses a long-short-term recurrent neural network(LSTM)as a generator to analyze geospatial temporal data and capture its temporal correlation;(2)it constructs a complete TGAIN network using a cue-masked fusion matrix mechanism to obtain data that matches the original distribution of the input *** experimental results on two publicly accessible datasets demonstrate that our proposed TGAIN approach surpasses four contemporary and traditional models in terms of mean absolute error(MAE),root mean square error(RMSE),mean square error(MSE),mean absolute percentage error(MAPE),coefficient of determination(R2)and average computational time across various data missing ***,TGAIN exhibits superior accuracy and robustness in data recovery compared to existing models,especially when dealing with a high rate of missing *** model is of great significance in improving the integrity of geospatial data and provides data support for practical applications such as urban traffic optimization prediction and personal mobility analysis.

关键词： Geospatial data data recovery generative adversarial networks temporal correlation

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A Lightweight ST-YOLO Based Model for Detection of Tea Bud in Unstructured Natural Environments

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IAENG International Journal of Applied Mathematics 2024年第3期54卷 342-349页

作者： Wen, Xin Yao, Yi Cai, Ying Zhao, Zixing Chen, Tianjiao Zeng, Ziyu Tang, Zhen Gao, Fangzheng the School of Automation of Nanjing Institute of Technology Nanjing211167 China the School of Automation Nanjing Institute of Technology Nanjing211167 China the School of Computer engineering Nanjing Institute of Technology Nanjing211167 China the School of Automation Nanjing Institute of Technology Nanjing211167 China

To address the difficulty of tea shoot recognition in natural environments, an enhanced YOLOX-Nano model(denoted as ST-YOLO) is introduced in this paper. In the proposed algorithm, the Depthwise Separable Convolution is incorporated into the backbone network to alleviate the feature extraction workload. And the Swin Transformer structure is also integrated into the neck network to augment feature extraction capabilities and to enhance the recognition of tea leaf shoots across diverse environmental conditions. Furthermore, to expedite model convergence, the SIoU loss function is employed in localization loss computation. Experimental results shows the efficacy of the proposed ST-YOLO algorithm, with achieved Average Precision (AP) and F1 scores of 86.2% and 88%, respectively. Comparing ST-YOLO with the baseline YOLOX-Nano in the same setting, it reveals notable improvements of 3.3% in AP and 5% in F1. Additionally, compared with the widely adopted target detection algorithms like YOLO v5-s, YOLO v4, and Faster R-CNN, ST-YOLO exhibits the superiority of a remarkable reduction in model size by half while simultaneously enhancing AP values by 6.0%, 6.1%, and 6.88%, respectively. As a result, the proposed algorithm not only excels in terms of model lightweights but also significantly improves detection accuracy, which offers a promising solution for the deployment of intelligent tea-picking systems on embedded hardware platforms. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Feature extraction

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Multiscale Feature Fusion for Gesture Recognition Using Commodity Millimeter-Wave Radar

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computers, Materials & Continua 2024年第10期81卷 1613-1640页

作者： Lingsheng Li Weiqing Bai Chong Han College of Computer Engineering Jinling Institute of TechnologyNanjing211169China College of Computer Nanjing University of Posts and TelecommunicationsNanjing210003China

Gestures are one of the most natural and intuitive approach for human-computer *** with traditional camera-based or wearable sensors-based solutions,gesture recognition using the millimeter wave radar has attracted growing attention for its characteristics of contact-free,privacy-preserving and less *** there have been many recent studies on hand gesture recognition,the existing hand gesture recognition methods still have recognition accuracy and generalization ability shortcomings in shortrange *** this paper,we present a hand gesture recognition method named multiscale feature fusion(MSFF)to accurately identify micro hand *** MSFF,not only the overall action recognition of the palm but also the subtle movements of the fingers are taken into ***,we adopt hand gesture multiangle Doppler-time and gesture trajectory range-angle map multi-feature fusion to comprehensively extract hand gesture features and fuse high-level deep neural networks to make it pay more attention to subtle finger *** evaluate the proposed method using data collected from 10 users and our proposed solution achieves an average recognition accuracy of 99.7%.Extensive experiments on a public mmWave gesture dataset demonstrate the superior effectiveness of the proposed system.

关键词： Gesture recognition millimeter-wave(mmWave)radar radio frequency(RF)sensing human-computer interaction multiscale feature fusion

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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 2024年第6期35卷 5407-5418页

作者： Su, Yumeng Zhang, Jiachao Yan, Rui Li, Pengpeng Xie, Guosen Shu, Xiangbo Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China Nanjing Institute of Technology Artificial Intelligence Industrial Technology Research Institute 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: (i) The presence of numerous individuals and varying spatial positions makes it difficult for traditional token pruning methods to maintain accuracy. (ii) Simply discarding entire frames may result in the loss of crucial clues. (iii) 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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A novel matrix topology-based analysis method of thermal circuits for reliability optimization of HSER

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Chinese Journal of Aeronautics 2024年第4期37卷 479-492页

作者： Huimin LIANG Bo LI Jiaxin YOU Shan JIANG Mengtong ZHU Aobo WANG Department of Electrical Engineering Harbin Institute of TechnologyHarbin 150001China Nanjing Research Institute of Electronics Technology Nanjing 210008China

Hermetically Sealed Electromagnetic Relay(HSER), used in aviation and aerospace,demands high reliability due to its critical applications. Given its complex operating conditions, efficient thermal analysis is essential for optimizing reliability. The commonly used Finite Element Method(FEM) is often time-consuming and may not be efficient or adaptable for complex multi-dimensional system calculations and design processes. This paper introduces an analysis method for thermal networks based on matrix perspective technology, encompassing matrix transformation, backpropagation of the heat path model, temperature rise calculation, solution comparison, and product implementation. Using the similarity theory of heat circuits, a basic thermal unit is established. Based on the fundamental connection between key components, a thermal network for a typical HSER is designed. An experimental system is set up, and the thermal network model's accuracy is confirmed using test data. Employing the topology analysis method, the topology of the thermal network is analyzed under both coil-energized and de-energized states. Potential thermal paths are identified, leading to optimized solutions for the HSER. Utilizing these solutions, the thermal path matrix topology model is backpropagated to the thermal path for temperature rise calculations. When compared to prototype HSER test data, the efficiency and accuracy of this matrix topology-based analysis method are confirmed.

关键词： Thermal networks HSER(Hermetically Sealed Electromagnetic Relay) Heat-circuit topology model Heat-circuit matrix Optimization design

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PriChain: Efficient Privacy-Preserving Fine-Grained Redactable Blockchains in Decentralized Settings

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Chinese Journal of Electronics 2025年第1期34卷 82-97页

作者： Hongchen Guo Weilin Gan Mingyang Zhao Chuan Zhang Tong Wu Liehuang Zhu Jingfeng Xue School of Computer Science and Technology Beijing Institute of Technology School of Cyberspace Science and Technology Beijing Institute of Technology School of Computer and Communication Engineering University of Science and Technology Beijing

Recently, redactable blockchain has been proposed and leveraged in a wide range of real systems for its unique properties of decentralization, traceability, and transparency while ensuring controllable on-chain data redaction. However, the development of redactable blockchain is now obstructed by three limitations, which are data privacy breaches, high communication overhead, and low searching efficiency, respectively. In this paper, we propose PriChain, the first efficient privacy-preserving fine-grained redactable blockchain in decentralized settings. PriChain provides data owners with rights to control who can read and redact on-chain data while maintaining downward compatibility, ensuring the one who can redact will be able to read. Specifically, inspired by the concept of multi-authority attribute-based encryption, we utilize the isomorphism of the access control tree, realizing fine-grained redaction mechanism, downward compatibility, and collusion resistance. With the newly designed structure, PriChain can realize O（n） communication and storage overhead compared to prior O（n2） schemes. Furthermore, we integrate multiple access trees into a tree-based dictionary, optimizing searching efficiency. Theoretical analysis proves that PriChain is secure against the chosen-plaintext attack and has competitive complexity. The experimental evaluations show that PriChain realizes 10× efficiency improvement of searching and 100× lower communication and storage overhead on average compared with existing schemes.

关键词： Resistance Access control Data privacy Dictionaries Blockchains Encryption Complexity theory Usability Protection

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A Generalized Autoregressive Model Fusing Both Linearity and Nonlinearity and Its Application

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IAENG International Journal of Applied Mathematics 2024年第1期54卷 117-127页

作者： Hao, Fei Gao, Chengchong Dong, Ying Chen, Ruwen Zhang, Tianqi School of Mechanical Engineering Nanjing Institute of Technology CO Nanjing211167 China School of Mechanical Engineering Nanjing Institute of Technology CO Nanjing211167 China School of Mechanical Engineering Nanjing Institute of Technology CO Nanjing211167 China School of Automotive and Rail Transit Nanjing Institute of Technology CO Nanjing211167 China School of Mechanical Engineering Nanjing Institute of Technology CO Nanjing211167 China

—A generalized autoregressive (GNAR) model fusing both linearity and nonlinearity is proposed to solve the complex nonlinear time series modeling problem. First, the mathematical model of the GNAR model is established while the mathematical mechanism and physical meaning of the GNAR model are both expounded from the two aspects of Weierstrass theory and Volterra theory. Then, an improved least squares parameter estimation method, namely robust Residuals Adjusted Least Squares (RALS) method, is introduced and its process is successionally proposed to improve the anti-outlier performance of the GNAR model. Next, the mathematical model of the computational complexity for the GNAR model is established and the complexity model is introduced into the AIC to propose an improved AIC (iAIC). Finally, a dataset containing 23 records is established and experiments are carried out. The results show that the GNAR model with RALS estimator has high fitting accuracy. The Mean Square Error (MSE) of the series predicted by the GNAR model with Least Squares (LS) estimator is 325.3% higher than that of the series predicted by the GNAR model with RALS estimator at most. The effectiveness of order determination method reaches 82.61%. Therefore, the GNAR model together with its parameter estimation method and order determination method proposed in this paper are effective. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Mean square error

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Abnormal Action Recognition with Lightweight Pose Estimation Network in Electric Power Training Scene

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computers, Materials & Continua 2024年第6期79卷 4979-4994页

作者： Yunfeng Cai Ran Qin Jin Tang Long Zhang Xiaotian Bi Qing Yang State Grid Jiangsu Electric Power Co. Ltd.Research InstituteNanjing211103China School of Computer Engineering Nanjing Institute of TechnologyNanjing211167China

Electric power training is essential for ensuring the safety and reliability of the *** this study,we introduce a novel Abnormal Action Recognition(AAR)system that utilizes a Lightweight Pose Estimation Network(LPEN)to efficiently and effectively detect abnormal fall-down and trespass incidents in electric power training *** LPEN network,comprising three stages—MobileNet,Initial Stage,and Refinement Stage—is employed to swiftly extract image features,detect human key points,and refine them for accurate ***,a Pose-aware Action Analysis Module(PAAM)captures the positional coordinates of human skeletal points in each ***,an Abnormal Action Inference Module(AAIM)evaluates whether abnormal fall-down or unauthorized trespass behavior is *** fall-down recognition,three criteria—falling speed,main angles of skeletal points,and the person’s bounding box—are *** identify unauthorized trespass,emphasis is placed on the position of the *** experiments validate the effectiveness and efficiency of the proposed system in ensuring the safety and reliability of electric power training.

关键词： Abnormal action recognition action recognition lightweight pose estimation electric power training

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