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A Data Transmission Approach Based on Ant Colony Optimization and Threshold Proxy Re-encryption in WSNs

Journal of Artificial Intelligence and Technology 2022年第1期2卷 23-31页

作者： Jing Liu Zenghui Liu Chenyu Sun Junxi Zhuang College of Computer Science Faculty of Information TechnologyBeijing University of TechnologyBeijing 100124China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of EducationBeijing 100124China College of Automation Engineering Beijing PolytechnicBeijing 100176China

Wireless sensor networks(WSNs)have become increasingly popular due to the rapid growth of the Internet of *** open wireless transmission media are easy to attack,security is one of the primary design concerns for *** solutions consider routing and data encryption as two isolated issues,providing incomplete ***,in this paper,we divide the WSN communication process into a data path selection phase and a data encryption *** propose an improved transmission method based on ant colony optimization(ACO)and threshold proxy re-encryption for WSNs,and we named it as *** method resists internal and external attacks and ensures safe and efficient data *** the data path selection stage,the ACO algorithm is used for network *** improvement of the pheromone concentration is *** order to resist attacks from external attackers,proxy re-encryption is extended to WSN in the data encryption *** threshold secret sharing algorithm is introduced to generate a set of re-encryption key fragments composed of random numbers at the source *** confirm the performance of our model via simulation studies.

关键词： wireless sensors network ant colony optimization pheromone proxy re-encryption threshold

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TZEAMM: An Efficient and Secure Active Measurement Method Based on TrustZone

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Security and Communication Networks 2023年第1期2023卷

作者： Liu, Xiaoqing Lai, Yingxu Liu, Jing Luo, Shiyao Faculty of Information Technology Beijing University of Technology Beijing100124 China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing100124 China

With the rapid development of computer and communication technology, embedded systems are widely used in smart devices. The increasing connectivity of these systems and the difficulties in providing comprehensive security have made such devices vulnerable to malicious attacks. Passive defense technologies and traffic-based intrusion detection technologies are not fully effective against such attacks. Trusted execution environment (TEE) technology can ensure system security against unknown attacks to some extent. Most researchers use TrustZone to implement TEE. However, the problem is that the API interface of the TEE module which provides the service is not secure. Therefore, to actively defend against attacks, we developed a trusted computing active measurement architecture based on TrustZone. To overcome the serious problem that modules in the trusted execution environment need to be passively invoked to provide services, we have proposed an active measurement closed-loop immune mechanism. To reduce the trusted computing base and reduce the performance overhead, we removed certain functional modules from the trusted execution environment. In addition, based on this architecture, we developed a trust chain and dynamic measurement method to ensure the security of the target applications. We changed the traditional attack response method, which requires the entire system to be restarted after an attack, by developing a fallback mechanism that is more suitable for the system. Finally, we verified the effectiveness of the architecture by developing an attack model. Performance testing and analysis showed that the architecture reduced the impact of the security mechanisms on the system. In the future, we will extend our research to more fine-grained measurements. © 2023 Xiaoqing Liu et al.

关键词： Application programming interfaces (API)

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OPS-YOLO: A Lightweight Foreign Objectors Detection Model for Overhead Power System

OPS-YOLO: A Lightweight Foreign Objectors Detection Model fo...

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第43届中国控制会议

作者： Boning Liu Rui Shi Heng Deng Liguo Zhang Faculty of Information Technology Beijing University of Technology Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

The performance of modern railway transit systems heavily relies on the overhead power system(OPS).However,OPSs built on the ground are vulnerable to foreign objects that can pose a threat to railway *** this paper,an improved lightweight detection method called OPS-YOLO,which is based on YOLO-v5,is proposed to detect foreign objects in ***,the Ghostnet-v2 is improved to compress the lightweight backbone model to slim down the original YOLO-v5 and reduce the complexity and ***,a parameter-free attention mechanism SimAM is introduced to enhance the detection precision of ***,the Focal-EIoU is chosen as the loss function of the OPS-YOLO to improve object border localization precision while accelerating bounding box regression *** evaluate the performance of OPS-YOLO,we generate a customized dataset of OPS scenarios by using augmentation methods to expand the existing samples and improve training ***,we extensive experimental tests on the dataset are conducted to verify the proposed *** show that the proposed OPS-YOLO improves the mAP by 0.5% compared to the classical YOLO-v5s ***,OPS-YOLO compresses the model parameters by 37.9% and reduces the model size by 37.5%.It is demonstrated that OPSYOLO can achieve a balance between computational efficiency and detection accuracy,making it suitable for automating OPS supervision and reducing reliance on human inspection.

关键词： Object Detection Overhead Power System(OPS) Convolutional neural network(CNN) Intrusion Detection

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A Novel Cross-Ratio Based Gaze Estimation Method

A Novel Cross-Ratio Based Gaze Estimation Method

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2022 International Conference on Machine Learning and Knowledge engineering, MLKE 2022

作者： Fan, Shuo Chi, Jiannan Liu, Jiahui University of Science and Technology Beijing School of Automation and Electrical Engineering Beijing China Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Beijing China

ISBN: (纸本)9781665495677

Eye movement is the most natural expression of human thoughts, therefore it plays an important role in human-computer interaction. Over the past few decades, researchers have been pursuing for high-accuracy, easy-to-use eye-tracking techniques. The gaze tracking technology based on cross-ratio is a hot topic in the field due to its advantages of simple hardware device and allowing free head movement. Given the complexity of the user calibration procedure and the error caused by the coplanar assumption in the cross-ratio based method, we propose a new method to approximate the pupil plane step by step through constructing the front and rear planes, and then estimate the point-of-regard based on cross-ratio invariance. Simulation results show that the proposed method not only simplifies the system configuration and user calibration procedure, but also avoids the error caused by the non-coplanarity between the corneal surface reflection points and the pupil center. © 2022 IEEE.

关键词： Eye tracking

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Federated Multi-View Multi-Label Classification

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IEEE Transactions on Big Data 2024年

作者： Meng, Hongdao Deng, Yongjian Zhong, Qiyu Wang, Yipeng Yang, Zhen Lyu, Gengyu Beijing University of Technology College of Computer Science China Ministry of Education Engineering Research Center of Intelligence Perception and Autonomous Control Beijing100124 China

Multi-view multi-label classification is a crucial machine learning paradigm aimed at building robust multi-label predictors by integrating heterogeneous features from various sources while addressing multiple correlated labels. However, in real-world applications, concerns over data confidentiality and security often prevent data exchange or fusion across different sources, leading to the challenging issue of data islands. To tackle this problem, we propose a general federated multi-view multi-label classification method, FMVML, which integrates a novel multi-view multi-label classification technique into a federated learning framework. This approach enables cross-view feature fusion and multi-label semantic classification while preserving the data privacy of each independent source. Within this federated framework, we first extract view-specific information from each individual client to capture unique characteristics and then consolidate consensus information from different views on the global server to represent shared features. Unlike previous methods, our approach enhances cross-view fusion and semantic expression by jointly capturing both feature and semantic aspects of specificity and commonality. The final label predictions are generated by combining the view-specific predictions from individual clients and the consensus predictions from the global server. Extensive experiments across various applications demonstrate that FMVML fully leverages multi-view data in a privacy-preserving manner and consistently outperforms state-of-the-art methods. © 2015 IEEE.

关键词： Federated learning

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A novel self-attention weight transformer for air pollution smoke detection 5

A novel self-attention weight transformer for air pollution ...

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5th International Conference on Green Energy, Environment, and Sustainable Development, GEESD 2024

作者： Zhou, Chengxu Fan, Xiaojie Jiang, Yanlin Liu, Yuchen Gu, Ke Faculty of Information Technology Beijing University of Technology Beijing100083 China Engineering Research Center of Intelligent Perception and Autonomous Control of Ministry of Education Beijing102400 China Beijing Laboratory of Smart Environmental Protection Beijing100124 China Beijing Artificial Intelligence Institute Beijing100000 China School of Electronic & Information Engineering Liaoning University of Technology Liaoning Anshan114051 China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Liaoning Dalian116024 China

ISBN: (纸本)9781510683006

There is a necessary combustion facility called the flare stack to ensure production safety in every petrochemical plant, smelter, and refinery all over the world. Because of the incomplete combustion of flare gas, the flare stack discharges a large amount of smoke into the atmosphere and make it dirty. The air pollution is becoming more and more serious caused worldwide concern. Hence, there is in desperate need of an efficient and available smoke detection method to protect the atmosphere. To this end, we present a novel self-attention weight transformer (SAWT) that can detect accurately smoke, then guarantee full combustion of the flare stack. First, we are concerned about the weight relationship between channels and draw on the self-attention mechanism in the MobileViT block structure to adaptively adjust channel-wise feature weight. Second, we leverage short connections to thoroughly learn and fuse local and global features. Results of experiments on a real smoke dataset reveal that the proposed SAWT achieves superior performance to the popular deep CNNs and state-of-the-art smoke detection algorithms. © 2024 SPIE.

关键词： Flare stacks

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A Hybrid Music Recommendation Algorithm Based on Attention Mechanism 27th

A Hybrid Music Recommendation Algorithm Based on Attention M...

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27th International Conference on MultiMedia Modeling, MMM 2021

作者： Feng, Weite Li, Tong Yu, Haiyang Yang, Zhen Faculty of Information Technology Beijing University of Technology Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing China

ISBN: (纸本)9783030678319

Music recommendation systems based on deep learning have been actively explored using hybrid approaches. However, most of the models proposed by previous studies adopt coarse-grained embedding approaches (e.g., CNNs) to characterize audio features. Users’ fine-grained preferences for music content have not been effectively explored yet. In this work, we propose a hybrid music recommendation model based on attention mechanism, which integrates user’s historical behaviour records and audio content and can capture the user’s fine-grained preferences for music content due to the introduction of attention mechanism. We experimented with a subset of the ***-1b dataset (30,753 users, 10,000 songs, 1533,245 interactions). The experimental results show that our method outperforms baselines approaches. © 2021, Springer Nature Switzerland AG.

关键词： Deep learning

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YOLOv5-Pothole: An Improved Pothole perception Method Based on YOLOv5-Seg 2

YOLOv5-Pothole: An Improved Pothole Perception Method Based ...

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2nd International Conference on Artificial Intelligence, Human-Computer Interaction and Robotics, AIHCIR 2023

作者： Chen, Xiaokang Ma, Nan Wang, Mohan Beijing Union University Beijing Key Laboratory of Information Service Engineering Beijing China Beijing University of Technology Engineering Research Center of Intelligence Perception and Autonomous Control Beijing China Beijing University of Technology Faculty of Information and Technology Beijing China

ISBN: (纸本)9798350360363

Road pothole damage represents a significant challenge in transportation infrastructure. Numerous studies have aimed to automate pothole detection by employing diverse computer vision techniques. There is a critical need for an automated road pothole detection process that offers sufficient accuracy and speed, while also being cost-effective and straightforward to implement. We introduce an enhanced road pothole detection algorithm, YOLOv5-pothole, based on the YOLOv5-seg model. Inputs for this algorithm include RGB images, depth images, and camera position data, all captured by a D455 depth camera. Concurrently, LiDAR sensors are used to acquire 3D point cloud data. The RGB images are semantically segmented by the self-trained YOLOv5-seg network, and corresponding virtual point clouds are generated through coordinate transformation. Then we register the 3D point clouds with the virtual point clouds generated from 2D pothole images to supplement the semantic information of the pothole. Finally, the pothole point clouds are individually segmented using Euclidean clustering, providing the information of the pothole. The GRDDC2020 experimental dataset was used to train the YOLOv5-seg network. We tested the YOLOv5-pothole algorithm on five randomly selected road potholes, with experimental results indicating a mean detection error of 7.0%. The algorithm demonstrated effective detection across potholes of varying sizes. This study offers valuable insights for future real-time road pothole detection research. © 2023 IEEE.

关键词： Object detection

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Edge-Cloud Collaborative High-Quality Recommendation: A Meta-Learning Approach

Edge-Cloud Collaborative High-Quality Recommendation: A Meta...

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International Conference on intelligent System and Knowledge engineering, ISKE

作者： Hao Chen Ruiping Yin Zhen Yang Faculty of Information Technology Beijing University of Technology Beijing China The Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing China

Under traditional recommendation frameworks based on a central server, recommender systems provide rec-ommendations for different users by training a global model for all users on the cloud. However, this method cannot provide high-quality recommendations for the majority of low-active users as well as those who are not willing to provide their personal data to the central server. Additionally, training solely on a user's device with local data may lead to overfitting due to the insufficiency of local data. In this work, we propose a two-stage process of coarse and fine-grained data augmentation using clustering and filtering to alleviate data insufficiency at the edge. Furthermore, we use meta-learning to formulate a two-stage training process from cloud to edge. The meta-training process of the global model is conducted on the cloud, and fast training on the edge is suitable for resource-constrained edge devices, enabling high-quality recommendations for each user. The extensive experimental results demonstrate the effectiveness of our method.

关键词：

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Time Series Prediction Integrated RNN-DRL for Energy-Emission Management in HEVs 7

Time Series Prediction Integrated RNN-DRL for Energy-Emissio...

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7th CAA International Conference on Vehicular control and Intelligence, CVCI 2023

作者： Luan, Junzhi Guo, Lulu Ma, Baolin Chen, Hong Clean Energy Automotive Engineering Center Tongji University Shanghai China Department of Control Science and Engineering Tongji University Shanghai China Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China

ISBN: (纸本)9798350340488

Learning-based strategies have gained widespread adoption in the field of energy management strategy (EMS) for hybrid electric vehicles (HEVs). However, existing algorithms often struggle to maintain the effectiveness in different driving scenarios. Additionally, achieving a fully observable Markov decision process in real-world situations is impractical due to the limitation in sensor sensitivity and noise. Therefore, it is crucial to design a stable EMS for HEVs that can adapt to various driving scenarios. This paper proposes a novel approach called the twin-delayed deep deterministic policy gradient with a long-short-term combined algorithm. It aims to enhance the generalization performance and EMS capability of HEVs' learning-based EMS by integrating temporal predicted information using a recurrent neural network. Comparing this algorithm with other established deep reinforcement learning algorithms, the results demonstrate significant improvements in EMS performance for HEVs, including state of charge maintenance, energy conservation, and emission reduction. Notably, the algorithm achieves these improvements while ensuring robust generalization across diverse driving cycles and scenarios. © 2023 IEEE.

关键词： DRL EMS generalization HEVs predicted information

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