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5th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2023

作者： Wang, Songtao Liu, Qianlong Li, Jun Liang, Kun Cai, Hui Tian, Hangwei Shandong Hi-Speed Construction Management Group Co. Ltd. Jinan China Shandong Scicom Information and Economy Research Institute Co. Ltd. Jinan China Shandong Hi-Speed Group Linteng Highway Co. Ltd. Jinan China School of Qilu Transportation Shandong University Jinan China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China

ISBN: (纸本)9798350357950

In the field of mechanical manufacturing, rolling bearings are important core components. To achieve stable rotational operation, improving diagnostic accuracy has become an urgent issue. In this paper, a hybrid fault diagnosis method is proposed, which combines attention and convolutional neural network and introduces Savitzky-Golay filter, called Savitzky-Golay Convolutional Attention network (SGCA-Net). The method adopts a dual-branch structure for rolling bearing fault diagnosis, which can effectively eliminate noise, and combines attention mechanism and convolutional neural network to perform feature learning on data. The experimental results show that the proposed method can accurately identify various faults, and has high accuracy and robustness. © 2023 IEEE.

关键词： Artificial Intelligence Attention component Convolutional Neural network Fault Diagnosis

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Effcient poisoning attacks and defenses for unlabeled data in DDoS prediction of intelligent transportation systems

Security and Safety

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security and Safety 2022年第1期1卷 145-165页

作者： Zhong Li Xianke Wu Changjun Jiang College of Information Science and Technology Donghua UniversityShanghai 201620China Key Laboratory of the Ministry of Education for Embedded System and Service Computing Department of Computer ScienceTongji UniversityShanghai 201804China Shanghai Network Financial Security Collaborative Innovation Center Tongji UniversityShanghai 201804China

Nowadays,large numbers of smart sensors(e.g.,road-side cameras)which com-municate with nearby base stations could launch distributed denial of services(DDoS)attack storms in intelligent transportation *** attacks disable the services provided by base *** in this paper,considering the uneven communication traffic ows and privacy preserving,we give a hidden Markov model-based prediction model by utilizing the multi-step characteristic of DDoS with a federated learning framework to predict whether DDoS attacks will happen on base stations in the ***,in the federated learning,we need to consider the problem of poisoning attacks due to malicious *** poisoning attacks will lead to the intelligent transportation systems paralysis without security *** poisoning attacks mainly apply to the classi cation model with labeled *** this paper,we propose a reinforcement learning-based poisoningmethod speci cally for poisoning the prediction model with unlabeled ***,previous related defense strategies rely on validation datasets with labeled data in the ***,it is unrealistic since the local training datasets are not uploaded to the server due to privacy preserving,and our datasets are also ***,we give a validation dataset-free defense strategy based on Dempster-Shafer(D-S)evidence theory avoiding anomaly aggregation to obtain a robust global model for precise DDoS *** our experiments,we simulate 3000 points in combination with DARPA2000 dataset to carry out *** results indicate that our poisoning method can successfully poison the global prediction model with unlabeled data in a short ***,we compare our proposed defense algorithm with three popularly used defense *** results show that our defense method has a high accuracy rate of excluding poisoners and can obtain a high attack prediction probability.

关键词： Poisoning attacks Defenses Multi-step DDoS prediction Unlabeled data Intel-ligent transportation systems

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Identification and addressing of internet of things based on distributed ID

Identification and addressing of internet of things based on...

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作者： Ma, Rui Liu, Yue Ma, Ke Wang, Xu An Beijing Key Laboratory of Software Security Engineering Technology School of Software Beijing Institute of Technology Beijing China Internet Center Institute of Communication Standard Research China Academy of Information and Communication Technology Beijing China Key Laboratory of Information and Network Security Engineering University of CAPF Xi’an Shanxi China

Identification and addressing the entity which accesses the internet through the wireless network with various short distance transmission protocols is a key issue. To solve the problem, by combining with the existing identification and addressing technology of the internet and the IOT, this paper proposes a method based on distributed ID. This method can be divided into two stages. It first designs the structure of distributed ID. Using the distributed address allocation algorithm, it implements the automatic allocation of the distributed ID as well as the distributed ID resolution among the global IOT. After that, an addressing scheme is designed to meet demands of the IOT addressing. It first defines the structure of addressing and then implements the routing addressing algorithm which combines the cluster-tree algorithm with the ad hoc on-demand distance vector routing algorithm. This scheme could improve the routing efficiency as well as achieve lower cost, lower energy consumption and higher reliability of the addressing. By the simulation on NS-2 platform, the experimental results highlight the feasibility and effectiveness of the proposed method from three aspects: the correctness of identification allocation, the effectiveness of addressing scheme, and the stability of data transmission. Copyright © 2020 Inderscience Enterprises Ltd.

关键词： Internet of things

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A Drug Box Recognition System Based on Deep Learning and Cloud-Edge Collaboration for Pharmacy Automation

A Drug Box Recognition System Based on Deep Learning and Clo...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Honglei Zhu Anming Dong Jiguo Yu Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Key Laboratory of Computing Power Network and Information Security Qilu University of Technology (Shandong Academy of Sciences) Jinan China School of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Big Data Institute Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

Drug box recognition is an integral part of the pharmacy automation system (PAS), which checks for discrepancies between the drugs provided by the system and the doctor’s prescription with the help of modern image recognition techniques. Although deep learning has facilitated the development of pharmacy automation systems, practical applications still face challenges. First, pharmacies update drugs frequently, and many pharmacies across the country are trained independently, which requires consuming massive computational resources. Secondly, the equipment performance of pharmacies is poor, and it’s hard to guarantee the real-time and accuracy of recognition. Therefore, we propose a cloud-side collaborative drug box recognition system. When new drugs are added, the new model is trained in the cloud and uniformly distributed to each edge through this architecture. In terms of real-time recognition, an improved lightweight YOLOv5 model is added to the edge part, and a text recognition module is deployed in the cloud to validate the recognition results at the edge. This cloud-edge collaboration architecture not only ensures the accuracy of recognition, but also reduces the burden on the compute edge, ensures the real-time *** results show that the proposed cloud-edge architecture reduces the recognition latency by 37.18% and achieves a precision of 97.47%. The improved YOLOv5 model reduces GFLOPs by 45% while achieving a 1.6% precision improvement.

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A Trajectory Tracking System for Zebrafish Based on Embedded Edge Artificial Intelligence

A Trajectory Tracking System for Zebrafish Based on Embedded...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Chuanhao Zang Anming Dong Jiguo Yu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China School of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Big Data Institute Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

Trajectory tracking of zebrafish is an important requirement in studying neurological disorders and developing new psychoactive drug. However, many challenges emerge for stable tracking, since zebrafish are similar in appearance, occlusion, agile, non-linear in moving and easy to swarm, all of which will lead to mistrack for multiple fish. And there is no precedent for tracking zebrafish through embedded edge artificial intelligence device. To overcome these difficulties, we present a tracking system for zebrafish based on RK3588-S. First, We construct an embedded edge AI hardware system consisting of two cameras driven by the RK3588-S, one for the front view and the other for top view of the fish. Then, we develop a 2D tracking algorithm based on YOLOv5 and the Observation-Centric Simple online and real-time tracking (OC-SORT) algorithm, which are transplanted to the RK3588-S for tracking the top and front views of the fish at the edge device. Compared with the previous methods, our method has fewer ID exchanges and highly real-time. Moreover, we apply the MQTT mechanism to establish communication links between the edge and the cloud to reliably transmit data to the cloud. The correspondence between cloud server and embedded AI is 1: N. Finally, we design a multi-view data fusion association algorithm to fuse the data of the two views in the cloud, which are further utilized to build the 3D tracklets of the zebrafish.

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A residual attention-based privacy-preserving biometrics model of transcriptome prediction from genome

A residual attention-based privacy-preserving biometrics mod...

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IEEE International Conference on Trust, security and Privacy in computing and Communications (TrustCom)

作者： Cheng Tian Song Liu Jinbao Li Guangchen Wang Luyue Kong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Department of Medicine Shandong Medical College Jinan China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Transcriptome prediction from genetic variation data is an important task in the privacy-preserving and biometrics field, which can better protect genomic data and achieve biometric recognition through transcriptome. Many transcriptome prediction methods have achieved good accuracy from genetic variation data. However, these traditional transcriptome prediction methods have the problems of linear assumption, overfitting, expose personal privacy, and extensive manual optimization. To solve these shortcomings, we propose an attention-based transcriptome prediction model from genetic variation named RATPM that improves the accuracy of transcriptome prediction and protects participant genomic data. In RATPM, we introduce and improve the deep learning model with multi-head self-attention into the transcriptome prediction stage of Predixcan, which uncovers the non-linear relationship between genetic variation and transcriptome. Moreover, we introduce a residual attention module to generate attention-aware features and extract more accurate features at different levels from genetic variation. Furthermore, we introduce the BERT pre-training module to encode genetic variation fully utilizing their contextual information. Our research enables scientific institutions to publish only predicted transcriptomic data for biometric purposes, thus protecting the genomic information of the subjects. Finally, we evaluated our model on the 1000 Genomes and Geuvadis projects datasets to compare with other baseline models.

关键词： Privacy Biometrics (access control) Biological system modeling Genomics Predictive models Feature extraction Gene expression

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Motion compensation and object detection for neuromorphic camera

Motion compensation and object detection for neuromorphic ca...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Wan, Yuxin Fei, Rong Tang, Yu Bai, Xueru Xie, Guo Li, Aimin Xi'An University of Technology School of Computer Science and Engineering Xi'an China Xi'An University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xi'an China Xidian University Guangzhou Institute Guangzhou China Xi'An University of Technology School of Computer Science and Engineering Xi'an China Xi'An University of Technology School of Automation and Information Engineering Xi'an China

ISBN: (纸本)9781665401265

Compared to conventional cameras, the new type of vision camera-neuromorphic cameras, which can avoid motion blur and have the advantages of high spatiotemporal resolution, high dynamic range, low latency, etc. In this paper, two motion compensation methods are introduced for operating event flow based on differential neuromorphic camera, one is motion compensation based on event counting image and time image, another is motion compensation based on image of warped events. Comprehensive experimental study is carried on two datasets, the motion compensation method based on event counting image and time image is better at generating clear motion compensation images, highlighting moving objects inconsistent with the background motion, while the motion compensation method based on image of warped events tends to generate motion compensated images of moving objects with more prominent or clearer edges. Simultaneously, two motion compensation methods are further applied to realize the object detection method based on threshold or maximum contrast. The former method can be adapted in many cases, while the latter method can well detect the translational moving target. © 2021 IEEE.

关键词： Object detection

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A Shortest Path Query Approach for Encrypted Graphs Based on Padding Dictionary Structure

A Shortest Path Query Approach for Encrypted Graphs Based on...

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Chinese Automation Congress (CAC)

作者： Ming Yang Kaiyang Zhang Chao Mu Xin Wang Yuanlong Liu Heng Zhang Key Laboratory of Computing Power Network and Information Security Shandong Computer Science Center Ministry of Education Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China State Grid Shandong Electric Power Company Jinan China School of Computer Engineering Jiangsu Ocean University Lianyungang China

Hybrid intelligence has emerged as an innovative approach that synergistically combines the strengths of human intelligence and artificial intelligence (AI) to address complex problems. In this paradigm, knowledge graphs play a pivotal role by supplying vital background information and contextual understanding to hybrid intelligence systems. However, due to limitations in local storage capacity, managing large-scale graph data often requires outsourcing to cloud servers. Nevertheless, the use of untrusted cloud servers poses a risk of potential user data leaks. To address these concerns, this paper presents an encryption scheme specifically designed to protect graph data, enabling queries involving intermediate nodes in the shortest path between two nodes within the graph. The proposed scheme ensures the privacy and security of outsourced data by employing a novel method that utilizes padding dictionary structure encryption. This method ensures strict limitations on the cloud server's capabilities, restricting it solely to query operations, it effectively prevents any compromise of user data or unauthorized access to sensitive information, users can securely store and process their graph data on servers, significantly reducing the potential risk of data leakage and unauthorized breaches. To validate the feasibility and effectiveness of the proposed solution, practical experiments were conducted using real-world data.

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EMO: An Ontology for Supporting the Management of Epidemic

EMO: An Ontology for Supporting the Management of Epidemic

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Applied Physics and computing (ICAPC), International Conference on

作者： Yongchao Gao Qiuyue Wang Mark S. Fox Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Qilu University of Technology (Shandong Academy of Sciences) Jinan China Standard Institute of Emerging Technologies and Innovations Jinan China Urban Data Centre University of Toronto Toronto Canada

In order to improve the coverage of concepts in epidemic management, this paper constructs an epidemic management ontology EMO to help manage and analyse epidemic data. First, based on the ISO/lEC 5087 city data model series of standards and TOVE method, EMO is constructed using Protégé. Secondly, the data types and their relationships required in the epidemic management use cases are defined. Finally, the instance data help to verify the ability of EMO to represent the city epidemic management data. Results show that EMO provides a clear, accurate and unified definition of epidemic management data, improves the efficiency of data sharing and integration, and reduces the difficulty of developing epidemic management tools.

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A Hybrid Routing Algorithm based on Destruction Resistance Cost and Reliability in Flying Ad-Hoc networks 7

A Hybrid Routing Algorithm based on Destruction Resistance C...

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7th International Conference on Computer and Communications, ICCC 2021

作者： Guo, Kun Wang, Dongbin Zhi, Hui Gao, Yibo Zhang, Tao Lv, Tiejun Lu, Yueming Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing China Beijing University of Posts and Telecommunications National Engineering Laboratory For Mobile Network Beijing China TravelSky Technology Limited Beijing China Beijing University of Posts and Telecommunications Cyberspace Security Research Center Peng Cheng Laboratory Beijing China China Academy of Launch Vehicle Technology Beijing China

ISBN: (纸本)9781665409506

Existing routing algorithms in Flying Ad-Hoc Net- works (FANETs) lack the optimization consideration of destruction resistance cost caused by routing recovery when some nodes fail suddenly. In this paper, we propose a hybrid routing algorithm based on destruction resistance cost and reliability. Based on the real-time network topology and network state, the algorithm can provide a multi-parameters-based routing path with low destruction resistance cost for communication transmission. It can ensure that the main routing path can be quickly recovered after interruption and reduce the process of discovering new routing paths. We have constructed a variety of simulation scenarios to analyze the proposed algorithm and the existing algorithms. The simulation results show that the pro- posed algorithm has good performance in reducing destruction resistance cost. © 2021 IEEE.

关键词： Routing algorithms

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