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2024 IEEE International Conference on Unmanned Systems, ICUS 2024

作者： Han, Sheng Ji, Yuning Lin, Youfang Lv, Kai School of Computer and Information Technology Beijing Jiaotong University Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing China

ISBN: (纸本)9798350384185

Existing motion coordination simulation environments can only support a limited number of preset scenarios and lack the capability to simulate complex dynamic environments, which hinders the fulfillment of practical needs and restricts the generalization ability and training effectiveness of algorithms. This paper proposes an innovative simulation environment that supports the simulation of complex dynamic environments and offers flexible configuration. It can dynamically adjust to meet different testing requirements, thereby enhancing the generalization ability and training speed of algorithms while maintaining sufficient environmental complexity to ensure training effectiveness. Experimental results show that the new simulation environment performs excellently in complex dynamic scenarios, significantly improving the generalization ability and training efficiency of algorithms. This provides a more reliable and efficient solution for the training and validation of algorithms for motion coordination tasks. © 2024 IEEE.

关键词： dynamic environment simulation motion coordination for multiple moving objects motion coordination simulation environment

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Estimation of Carbon Stock in Chinese Fir Stands Based on Stacking Ensemble Learning and LSTM Models

Estimation of Carbon Stock in Chinese Fir Stands Based on St...

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2024 International Conference on Computational Intelligence and Communication System, CCICS 2024

作者： Wang, Zhiyuan Shen, Wanling Chen, Lingyan Xiao, Jiawen Zhang, Chaofei Zhang, Shanwei Xu, Zhanghua Liu, Jinfu Forestry College Fujian Agriculture and Forestry University Fuzhou China College of Environment and Safety Engineering Academy of Geography and Ecological Environment Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou China

Forest carbon sinks, a critical component of the global carbon cycle, constitute nearly half of the total terrestrial carbon pool. This study employed correlation analysis and factor effect analysis to quantify the influences of various factors on the volume of Chinese fir stands. A novel modeling framework was developed using the stacking ensemble learning and LSTM (LongShort-TermMemory) models. This framework incorporated diverse base learners, including XGBoost, Adaboost, KNN, and DT, which are intelligently ensemble via GBDT as a meta learner. The model was further optimized by comparing activation functions and optimizers, with ReLU selected as the activation function and Adam as the optimizer. Model accuracy was evaluated using RMSE, MAE, and R2 metrics, significantly enhancing its learning ability and generalization performance. The findings are as follows: (1) Stand stocking showed strong positive correlations with depression, age group, average diameter at breast height (ADBH), average tree height (ATH), slope, and elevation. Conversely, it exhibited significant negative correlations with origin, stand density, and slope position. In investigating Chinese fir growth on slopes, no significant growth differences were observed between downslopes and midslopes;however, both differed significantly from upper slopes. (2) The stacking ensemble learning method constructed here surpassed all existing single models in terms of estimation and assessment indices, demonstrating superior comprehensive performance. (3) Among the LSTM models, Adam-LSTM performed the best (R2=0.844), followed by Sigmoid-LSTM (R2=0.656), while the RMSprop-LSTM model performed the worst (R2=0.618). Combined with artificial intelligence methods, our optimized carbon stock estimation model can help to improve the ability of forest land management and provide a theoretical basis for the scientific management of forest areas. © Published under licence by IOP Publishing Ltd.

关键词： Carbon cycle

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ReFrame: A Resource-Friendly Cloud-Assisted On-Device Deep Learning Framework for Vision Services

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IEEE Transactions on Services Computing 2025年第03期18卷 1711-1723页

作者： Xie, Jianhang Ding, Chuntao Guan, Qingji Zhou, Ao Li, Yidong Beijing Jiaotong University Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education School of Computer and Information Technology Beijing China Beijing Normal University School of Artificial Intelligence Beijing China Beijing Jiaotong University Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China

Cloud-assisted Internet of Things (IoT) device deployment of deep neural networks (DNNs) promotes On-device deep learning to provide users with ubiquitous high-quality services by solving the contradiction between insufficient IoT device resources and intensive demand for high-performance DNN resources. However, most existing methods optimize DNNs by considering one or two terms of transmission, computation, and storage resources, but do not consider all three terms at the same time in cloud-assisted IoT device deployment and updating DNNs. To this end, we propose a non-learnable module-based ResNet and a cloud-assisted on-device deep learning framework, ReFrame, based on the consideration of three indicators: model transmission parameters, computation resources, and storage resources. In the proposed method, we first specify that some parameters in DNNs are non-learnable and randomly initialized, so that, these parameters can be saved and reproduced with a few random seeds. By doing so, the cloud only transmits random seeds and learnable parameters to reduce the number of parameter transmissions. Secondly, we reduce the computation resource consumption of the model by introducing computation-friendly operators, such as pooling, to replace vanilla convolutions. Finally, since random seeds are used to save non-learnable model parameters, on IoT devices we only need to store random seeds and learnable parameters to reproduce the well-trained model. Compared with saving the complete model, our method greatly reduces IoT device storage resource consumption. Experimental results on image classification, object detection, and semantic segmentation tasks demonstrate the effectiveness of the proposed method. Specifically, on the CIFAR-10, our proposed method reduces approximately 89% of FLOPs and 90% of transmitted data in the prototype system compared to ResNet-18. © 2008-2012 IEEE.

关键词： Cloud platforms

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Tracking Topics and Hot Issues in Marine Remote Sensing Research Between 2018 and 2022 24

Tracking Topics and Hot Issues in Marine Remote Sensing Rese...

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2024 International Conference on Intelligent Education and Computer Technology, IECT 2024

作者： Yang, Yang Zhang, Hongyue College of Geography and Oceanography Minjiang University Fujian Fuzhou China Key Lab of Spatial Data Mining and Information Sharing of Ministry of Education Fuzhou University Fujian Fuzhou China

ISBN: (纸本)9798400709920

Large number of publications have been published in marine remote sensing study, and it is of great importance to explore and discover the key research topics and hotspots for domain researchers. In this study, a combination of topic modeling and keywords co-occurrence analysis was applied to 7,509 articles published between 2018 and 2022 obtained from the Web of Science database. The findings of this research indicate that marine remote sensing can be categorized into five major themes: Land Surface Temperature (LST), Sea Surface Temperature (SST), SAR/InSAR for target identification and detection, SAR, and altimetry technology for monitoring oceanographic and atmospheric phenomena, and water quality and organic substance monitoring. Changes in topic strengths reflect a growing emphasis on SAR-based target identification and detection research. keyword co-occurrence evolution results highlight the increasing importance of SAR and Sentinel theme clusters. In terms of research trends, applications in marine remote sensing are likely to prioritize feature extraction, sea measurement, and marine vehicles. In addition to traditional data sources such as MODIS and Landsat, Sentinel, Cryosat-2, AMSR-E and TerraSAR-X data offer significant potential for marine remote sensing applications. © 2024 ACM.

关键词： Surface temperature

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Horizontal Federated Traffic Speed Prediction Base on Secure Node Attribute Aggregation 17th

Horizontal Federated Traffic Speed Prediction Base on Secur...

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17th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2022

作者： Ye, Enjie Guo, Kun Guo, Wenzhong Chen, Dangrun Zhang, Zihan Li, Fuan Zheng, JiaChen Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China College of Computer and Data Science/College of Software Fuzhou University Fuzhou350108 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789819923847

Federated graph learning has been widely used in distributed graph machine learning tasks. The data distribution of existing graph-based federated Spatio-temporal prediction methods is mainly segmented by graph topology. However, in the real-world Spatio-temporal traffic speed prediction task, a location will have data from different devices belonging to different companies. A node may have multi-party information in the real-world distributed traffic speed prediction scenario. The difference in multi-party information leads to the information not being fully utilized. Moreover, the direct transmission of node embedding in the federated learning process may also risk privacy leaks. Using homomorphic encryption and other encryption methods will bring a high computational overhead. Therefore we propose a new distributed privacy-preserving traffic speed prediction algorithm, which uses secure node attribute aggregation strategy(SNAAS) to apply to the multi-party collaborative traffic speed prediction scenario when the graph topology structure is public. At the same time, secret sharing technology is used in SNAAS to protect the attribute matrix and reduce the overhead of secret computing. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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Globally Consistent Vertical Federated Graph Autoencoder for Privacy-Preserving Community Detection 17th

Globally Consistent Vertical Federated Graph Autoencoder for...

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17th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2022

作者： Fang, Yutong Huang, Qingqing Ye, Enjie Guo, Wenzhong Guo, Kun Chen, Xiaoqi Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China College of Computer and Data Science/College of Software Fuzhou University Fuzhou350108 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789819923557

Community detection is a trendy area in research on complex network analysis and has a wide range of real-world applications, like advertising. As people become increasingly concerned about privacy, protecting participants’ privacy in distributed community detection has become a new challenge. When applied to not identically and independently distributed (Non-IID) data, most federated graph algorithms suffer from the weight divergence problem caused by diversified training of local and global models, resulting in accuracy degradation. Furthermore, the privacy protection approaches based on anonymization, such as differential privacy (DP), and cryptography, such as homomorphic encryption (HE), incur accuracy loss and high time consumption, respectively. In this paper, we propose a globally consistent vertical federated graph autoencoder (GCVFGAE) algorithm, which builds a globally consistent model among the coordinator and all participants to solve the Non-IID graph data problem. As well, an attribute blinding strategy based on security aggregation is developed to protect the network privacy of each participant without losing accuracy. Both real-world and artificial networks’ experiments show that our algorithm reaches higher accuracy than the existing vertical federated graph neural networks (GNNs) and the simple distributed graph autoencoder without federated learning and detects communities identical to those found by the standard graph autoencoder (GAE). © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Population dynamics

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Machine Learning for Signal Demodulation in Underwater Wireless Optical Communications

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China Communications 2024年第5期21卷 297-313页

作者： Ma Shuai Yang Lei Ding Wanying Li Hang Zhang Zhongdan Xu Jing Li Zongyan Xu Gang Li Shiyin Department of Strategic and Advanced Interdisciplinary Research Peng Cheng LaboratoryShenzhen 518055China School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China Data-driven Intelligent Information System Laboratory Shenzhen Research Institute of Big DataShenzhen 518172China Ocean College Zhejiang UniversityZhoushan 316021China State Key Laboratory of Millimeter Waves Southeast UniversityNanjing 210096China

The underwater wireless optical communication(UWOC)system has gradually become essential to underwater wireless communication *** other existing works on UWOC systems,this paper evaluates the proposed machine learningbased signal demodulation methods through the selfbuilt experimental *** on such a platform,we first construct a real signal dataset with ten modulation ***,we propose a deep belief network(DBN)-based demodulator for feature extraction and multi-class feature *** also design an adaptive boosting(Ada Boost)demodulator as an alternative scheme without feature filtering for multiple modulated ***,it is demonstrated by extensive experimental results that the Ada Boost demodulator significantly outperforms the other *** also reveals that the demodulator accuracy decreases as the modulation order increases for a fixed received optical power.A higher-order modulation may achieve a higher effective transmission rate when the signal-to-noise ratio(SNR)is higher.

关键词： AdaBoost DBN machine learning signal demodulation

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Review and prospect of remote sensing monitoring methods for inland black and malodorous water bodies 24

Review and prospect of remote sensing monitoring methods for...

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2024 International Conference on Image Processing, Intelligent Control and Computer Engineering, IPICE 2024

作者： Liao, Yansong Huang, Fenghua The Academy of Digital China Fuzhou University China Fujian University Engineering Research Center of Spatial Data Mining and Application Yango University China Fujian Key Laboratory of Spatial Information Perception and Intelligent Processing Yango University China College of Artificial Intelligence Yango University China

ISBN: (纸本)9798400710285

Black and malodorous water bodies represent a severe water pollution phenomenon that urgently needs addressing due to the discomfort and health risks they pose. Since the release of the"Action Plan for Prevention and Control of Water Pollution" in 2015 and the subsequent"Action Plan for the Treatment of Black and Malodorous Water Bodies in Urban Areas during the 14th Five-Year Plan Period" in 2022 by the Chinese government, monitoring efforts have become increasingly crucial. Remote sensing technology offers advantages such as high dynamics, low cost, and a macroscopic perspective in ecological environment monitoring, making it advantageous over traditional physical and chemical methods in monitoring black and malodorous water bodies. This paper summarizes remote sensing methods for identifying black and malodorous water bodies, including extraction of information through spectral, spatial, index, and temporal features, and analyzes the monitoring feasibility of these methods while pointing out potential issues. Finally, future research directions for identifying black and malodorous water bodies are discussed. © 2024 Copyright held by the owner/author(s).

关键词： Environmental monitoring

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A state‑domain robust autonomous integrity monitoring with an extrapolation method for single receiver positioning in the presence of slowly growing fault

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Satellite Navigation 2023年第3期4卷 198-209页

作者： Zhangjun Yu Qiuzhao Zhang Shubi Zhang Nanshan Zheng Keqiang Liu Jiangsu Key Laboratory of Resources and Environmental Information Engineering China University of Mining and TechnologyXuzhou 221116China School of Environment Science and Spatial Informatics China University of Mining and TechnologyXuzhou 221116China Zhejiang Deqing Zhilu Navigation Research Institute Co. Ltd.Huzhou 313299China

Single receiver positioning has been widely used as a standard and standalone positioning technique for about 25 *** detect the slowly growing faults caused by satellite and receiver clocks in single receiver positioning,the Autonomous Integrity Monitoring with an Extrapolation method(AIME)was proposed based on the Kalman filter measurement ***,AIME was designed with the assumption of there is the same number of visible satellites at each epoch,which limits its *** address this issue,this paper proposes a state-domain Robust Autonomous Integrity Monitoring with the Extrapolation Method(SRAIME).The slowly growing fault detection statistics is established based on the difference between the estimates of the state propagator and the posterior state estimation in Kalman ***,singular value decomposition is adopted to factor the covariance matrix of the difference to increase computational ***,the relevant formulas of the proposed method are theoretically derived,and it is proven that the proposed method is suitable for any positioning model based on the Kalman ***,the results of two experiments indicate that SRAIME can detect slowly growing faults in single receiver positioning earlier than AIME.

关键词： GNSS Single receiver positioning Slow growing fault State domain Fault detection

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Overview of indoor positioning technology 24

Overview of indoor positioning technology

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2024 International Conference on Image Processing, Intelligent Control and Computer Engineering, IPICE 2024

作者： Xu, Zeyu Huang, Fenghua The Academy of Digital China Fuzhou University China College of Artificial Intelligence Yango University China Fujian Key Laboratory of Spatial Information Perception and Intelligent Processing Yango University China Fujian University Engineering Research Center of Spatial Data Mining and Application Yango University China

ISBN: (纸本)9798400710285

This paper introduces the classification of indoor positioning technology, and elaborates the principles of three mainstream indoor positioning technologies based on external source, natural source and multi-source fusion and their development respectively. By analyzing the problems of existing indoor positioning technologies, it summarizes the difficulties and future trends of the current development of indoor positioning technologies at home and abroad. The comprehensive study shows that exploring combined positioning techniques and better positioning algorithms, and combining them with machine learning (including deep learning) to further improve indoor positioning accuracy, establishing a standard system and performance evaluation system for indoor positioning techniques, and focusing on user privacy are the main future development directions of indoor positioning techniques. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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