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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data science and systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Wang, Yuhao Li, Peng Huang, Weiyi School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China

ISBN: (纸本)9798350319934

The classical influence maximization (IM) prob-lem aims to select a set of nodes as seed nodes for information propagation. Most related studies focus on one-dimensional information dissemination. However, the diffusion of information should be a multi-dimensional (or multi-feature) diffusion process. The user's judgment of information often depends on the combined judgment of multiple-dimensional information. Based on this, we consider a multi-feature diffusion model. In-formation will be decomposed into multi-dimensional features for diffusion. The user will receive these features and integrate them into a complete message. In addition, we consider seed node selection as a network node classification task. Consid-ering that social networks satisfy a graph structure, we use the node classification technique of graph neural networks. This technique classifies the nodes by the network topology to find the set of influential nodes. We propose a multi-feature influence (MFI) algorithm to improve the performance of the prediction. The set of candidate nodes obtained by classification is used as a benchmark to find the set of nodes with maximum influence. Our experiment results on real datasets show that the proposed algorithm has better algorithmic efficiency than the traditional greedy algorithm and better effectiveness than similar heuristic algorithms. © 2022 IEEE.

关键词： information dissemination

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Hotel information system Management Audit Using COBIT 2019 - APO12 2

Hotel Information System Management Audit Using COBIT 2019 -...

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2nd International Conference for Innovation in Technology, INOCON 2023

作者： Utamajaya, Joy Nashar Supangat, Suhono Harso Gaol, Ford Lumban Ranti, Benny BINUS University BINUS Graduate Program School of Information System Jakarta Indonesia Bandung Institute of Technology School of Electrical Engineering and Informatic Bandung Indonesia Bina Nusantara University Computer Science Department BINUS Graduate Program Computer Science Jakarta Indonesia University of Indonesia Computer Science Department Faculty of Computer Science Depok Indonesia

ISBN: (纸本)9798350320923

In general, public or private organizations or companies have used information-based technology as a support to improve business performance to be more effective and efficient in order to achieve a company's business goals. Given the large contribution of information technology in the application of hotel applications as a supporting information system, it is a vital system that must avoid risks that can hinder and cause harm to the hotel management business processes. Therefore, it is necessary to carry out risk management using the COBIT 5 framework to manage possible risks that may occur based on the APO12 (Manage Risk) domain. From the evaluation results of the data obtained through observation and interviews as well as the calculation of the results of the questionnaire based on 6 APO12 subdomains, the results of the risk management level capability assessment in hotel applications are still at level 3 or have reached the level of established process with the target to be achieved at level 4 resulting in a gap of 1 level. Based on the results obtained, it is necessary to propose recommendations that can be used by hotels in improving the application of information technology risk management so that in the future it can achieve the expected target level so that the APO12 level of capability can increase and become more optimal. © 2023 IEEE.

关键词： Risk management

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Tracking Control in Presence of Obstacles and Uncertainties for Bioinspired Spherical Underwater Robots

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Journal of Bionic Engineering 2023年第1期20卷 323-337页

作者： Chunying Li Shuxiang Guo Jian Guo Graduate School of Engineering Kagawa UniversityTakamatsuKagawa761-0396Japan Key Laboratory of Convergence Medical Engineering System and Healthcare Technology the Ministry of Industry and Information TechnologySchool of Life Science and TechnologyBeijing Institute of TechnologyBeijing100081China Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems and Intelligent Robot Laboratory Tianjin University of TechnologyBinshui Xidao Extension 391Tianjin300384China

During marine missions,AUVs are susceptible to external disturbances,such as obstacles,ocean currents,etc.,which can easily cause mission failure or *** this paper,considering the strong nonlinearities,external disturbances and obstacles,the kinematic and dynamic model of bioinspired Spherical Underwater Robot(SUR)was ***,the waypoints-based trajectory tracking with obstacles and uncertainties was proposed for SUR to guarantee its safety and ***,the Lyapunov theory was adopted to verify the stability and the Slide Mode Control(SMC)method is used to verify the robustness of the control *** addition,a series of simulations were conducted to evaluate the effectiveness of proposed control *** tests,including path-following,static and moving obstacle avoidance were performed which verified the feasibility,robustness and effectiveness of the designed control ***,a series of experiments in real environment were performed to verify the performance of the control *** simulation and experimental results of the study supplied clues to the improvement of the path following capability and multi-obstacle avoidance of AUVs.

关键词： Path following Obstacle avoidance Bioinspired.Spherical underwater robot(SUR) Lyapunov theory

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Duration-Aware Task Assignment for Heterogeneous Mobility Users in Crowdsensing 25

Duration-Aware Task Assignment for Heterogeneous Mobility Us...

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Liu, Zhiyuan Li, Peng Huang, Weiyi Wang, Jiahu Nie, Lei Wuhan University of Science and Technology School of Computer Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing Real-Time Industrial System China

ISBN: (纸本)9781665405270

This paper investigates the duration-aware task assignment problem for heterogeneous mobility users in MCS (DTAH problem). There are two types of users in MCS, vehicle users and pedestrian users. The vehicle user gets to the task fast, but the task duration is short. Pedestrian user meets the task duration limit, but it takes a long time to reach the task. Given a set of tasks and two types of users, each user, and each task has a duration limit. The DTAH problem is how to assign tasks to maximize the utility of the system. We prove that the DTAH problem is NP-hard by reducing the weighted maximum set coverage problem to the DTAH problem. Then, we solve the problem from a pedestrian perspective and a vehicle perspective. From the pedestrian user perspective, we propose a pedestrian user task assignment (PTA) algorithm based on the Kuhn-Munkres algorithm. From the vehicle user perspective, we propose a greedy vehicle user task assignment (VTA) algorithm. We prove that the VTA algorithm can obtain the approximate ratio of 1-1/e to the optimal value. Finally, we design the heterogeneous user task assignment (HTA) algorithm based on these PTA and VTA algorithms. Extensive experiments have proved that our proposed HTA algorithm achieves more efficient performance than other comparison algorithms. © 2022 IEEE.

关键词： Combinatorial optimization

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Safe Tracker: A Robust Aerial system for Safe Tracking in Cluttered Environments 42

Safe Tracker: A Robust Aerial System for Safe Tracking in Cl...

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42nd Chinese Control Conference, CCC 2023

作者： Hui, Yulin Tian, Bailing Zhang, Xuewei Lu, Hanchen Shen, Hongming School of Electrical and Information Engineering Tianjin University Tianjin300072 China Beijing Electro-Mechanical Engineering Institute Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (纸本)9789887581543

This paper proposes Safe Tracker, an autonomous aerial tracking framework based on vision sensor that can deal with challenging tracking missions and guarantee safety and visibility. Firstly, a RGB-D sensor based segmentation method is employed to locate the target in real-time. Then target motion is predicted for a short time horizon, based on which a heuristic path searching method is applied to generate an occlusion-free path. Finally, particular formulations to balance visibility and safety are designed, and an effective non-linear trajectory optimization method enables to generate an optimal tracking trajectory. Autonomous aerial tracking experiments using a vision sensor are conducted to demonstrate the effectiveness of the proposed Safe Tracker in a challenging cluttered forest and a room full of sharp turns using Gazebo. Benchmark comparisons validate that our method tracks more safely and robustly than the state-of-art method. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Trajectories

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Time Series Data Association Mining With High-Frequency Data Interference 24

Time Series Data Association Mining With High-Frequency Data...

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作者： Zhu, Rongjing Li, Peng Liu, Yu Deng, Miao School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China

ISBN: (纸本)9798350319934

Time series data mining is an important problem in data mining. It discovers the intrinsic correlation rules by analyzing a large amount of time series data. However, high-frequency data may exist in the dataset. High-frequency data is the data that occurs frequently in time series data, which will affect the mining of association rules. Traditional time series association mining tends to classify high-frequency data as correlated data because they co-occur too many times. In this paper, we propose the list mining framework to mine the association data by list co-occurrence to achieve the mining of massive data. We divide it into three phases, list generation phase, matrix generation phase, and rule generation phase. First, we create data lists based on time windows. Second, we create the initial count matrix, update the count matrix based on the list, and propose a GCPM algorithm to generate a conditional probability matrix based on the count matrix. Third, we propose a List Mining (LM) algorithm to generate the initial rules based on the conditional probability matrix and optimize the initial rules to get the final association rules. In addition, we weigh the rules according to the list mining method and propose a Weighted List Mining (WLM) algorithm to filter rules by reducing the weight of frequent item interference. The experimental results show that LM and WLM can effectively improve the accuracy of mining results. © 2022 IEEE.

关键词： Data mining

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Identity Re recognition Technology Based on Improved Lightweight ConvNetx Model 42

Identity Re recognition Technology Based on Improved Lightwe...

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42nd Chinese Control Conference, CCC 2023

作者： Siting, Peng Lei, Li Zhu, Hongbo Jun, Ren Zhichao, Song Yang, Jiabing The Beijing Electro-Mechanical Engineering Institute Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Beihang University School of Electronics and Information Engineering Beijing China

ISBN: (纸本)9789887581543

In the engineering field, there is a requirement for re recognition, including the identity recognition of cooperative and non-cooperative targets, that is, different sensors shoot targets in the same scene, and the identity recognition of targets from two perspectives is required, also known as target accurate retrieval. This method is widely used in fields such as automatic driving and military strikes. In the actual application scenario, deploying the re-recognition algorithm on the embedded processor needs to ensure the real-time computing speed and accuracy, so the complexity of the deep learning algorithm must be reduced, and pruning and lightweight processing are required. In the engineering field, when the image acquired by the sensor is subject to natural interference such as cloud and fog weather or fire and smoke, the difficulty of re-recognition increases. This paper adopts the lightweight improvement of ConvNeXt network model, and fine-tuned it on the basis of its original feature extraction function, so that it can successfully migrate to infrared image classification and also complete the extraction of infrared image target detail distinguishing features, and at the same time introduce the alignment of similar targets in infrared cross-view recognition. In the original similarity calculation, the average value is directly obtained from the spatial dimension of the output feature layer, which is replaced by the weight of the position in each space for estimation. The feature information of the multi-view marine ship target is extracted from the multi-sensor marine image, and its similarity is calculated, so as to determine the identity, thus reducing the support conditions and reconnaissance costs, and improving the accurate detection and tracking ability under the diverse dynamic reconnaissance conditions. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Feature extraction

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Tightly-Coupled Lidar-Visual-Inertial Odometry and Mapping in Real Time 42

Tightly-Coupled Lidar-Visual-Inertial Odometry and Mapping i...

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42nd Chinese Control Conference, CCC 2023

作者： Ma, Wenyu Tian, Bailing Lu, Hanchen Shen, Hongming School of Electrical and Information Engineering Tianjin University Tianjin300072 China Beijing Electro-Mechanical Engineering Institute Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (纸本)9789887581543

We proposed a tightly-coupled Lidar-visual-inertial odometry and mapping method, which takes advantage of measurement of Lidar, visual and inertial sensors to achieve highly accurate, real-time 6DoF state estimation and map-building in GNSS-denied environments. The proposed odometry is a tightly-coupled optimization-based method, obtains robust and low drift odometry by fusing pre-integrated IMU measurements, visual features from the image, and geometric features from Lidar data. Further, we adapt an online method to mitigate degeneracy in optimization problems to improve robustness in environmentally degenerate cases. Simulation and real-world experiments show that the proposed method exhibits similar or better robustness and accuracy with the state-of-the-art SLAM methods. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Optical radar

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A Cross-Scale Multi-Branch Feature Fusion Based Method for Smoke and Fire Detection in Warehouses 24

A Cross-Scale Multi-Branch Feature Fusion Based Method for S...

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13th International Conference on Computing and Pattern Recognition, ICCPR 2024

作者： Bai, Yanbing Liu, Pengyu Liu, Weilong Deng, Yiyang Kong, Lili Chen, Shanji Chen, Jiali Yuan, Bowen School of Information Science and Technology Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University Of Technology Beijing China Information Technology Co. Ltd Dingzhou China Beijing-Dublin International College at BJUT Beijing University Of Technology Beijing China College of Physics and Electronic Information Engineering Qinghai Minzu University Qinghai China

ISBN: (纸本)9798400717482

To address the challenges of leakage, misdetection, and low accuracy caused by environmental complexity, object occlusion, and insufficient lighting in smoke and fire detection within warehouse scenarios, this paper proposes a cross-scale multi-branch feature fusion network for smoke and fire detection, named SF-YOLO. Firstly, a multi-branch feature enhancement module, Res2Net-C, is proposed to introduce an attention mechanism using a multi-branch feature fusion network, which not only extends the sensory field of the network, but also enhances the ability of extracting effective information, while effectively suppressing the interference of irrelevant information. It effectively improves detection performance under complex environmental conditions and in the presence of object occlusion. Secondly, a cross-scale feature fusion network structure, MSFPN, is proposed, which achieves effective complementation and fusion of high-level and low-level information by establishing hopping connections between features at different scales, and significantly improves the recognition accuracy of the type and location of the smoke and fire in low-light conditions. In addition, the added small target detection layer further improves the network's ability to detect small targets. Finally, in order to better adapt to pyrotechnic targets with variable morphology, EIoU is adopted as the bounding box loss function. Test results on a self-built dataset show that SF-YOLO achieves a mean accuracy rate (mAP) of 87.5% in the task of detecting smoke and fire under multiple environmental factors in a warehouse scenario, proving its effectiveness and usefulness. © 2024 Copyright held by the owner/author(s).

关键词： Deep learning

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Two-sided Online Stable Task Assignment with Incomplete Lists and Ties in Spatial Crowdsourcing 19

Two-sided Online Stable Task Assignment with Incomplete List...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Huang, Weiyi Li, Peng Li, Bo Liu, Jing Nie, Lei Bao, Haizhou Wuhan University of Science and Technology School of Computer Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China

ISBN: (纸本)9798350346558

For the application of task assignment in crowdsourcing, previous research has focused on offline version or one-sided online version. Some of them draw extra attention to stability in task assignment. Our work extends a one-sided task assignment and its stable generalization. In particular, we present a two-sided online stable task assignment (TS-OSTA) problem in which any vertex from two sides can maintain an incomplete neighbor list and ties. We first give the fundamental definitions of task assignment in spatial crowdsourcing and stable matching. Then, we analyze and present the matching condition and formulate the TS-OSTA problem. As a warm-up, we investigate and analyze two classical baseline algorithms Greedy and Random Threshold. Then we present a bipartite ranking algorithm that maintains the efficiency of Greedy and effectiveness of Random Threshold. Through extensive experiments, we examine the performance of our algorithm in size, memory, and time. The results show that our algorithm performs better than the baselines. © 2022 IEEE.

关键词： Crowdsourcing

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