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IEEE Vehicular Networking Conference (VNC)

作者： Obst, Marcus Hobert, Laurens Reisdorf, Pierre BASELABS GmbH Ebertstr 10 D-09126 Chemnitz Germany Hitachi Europe SAS F-06560 Sophia Antipolis France Tech Univ Chemnitz D-09126 Chemnitz Germany

ISBN: (纸本)9781479976607

Automated vehicles require an adequate and reliable perception of the surrounding world in order to make good decisions. Using vehicle-to-vehicle (V2V) communication to exchange location data (i.e. time, position, heading and speed) can improve the perception beyond the capabilities of traditional on-board sensors (e.g. radar, lidar). However, it is vital to trust the data before it is being used. Cryptographic mechanisms can protect the exchange and authenticity of data but do not guarantee the correctness of the content. In this paper we present a vision-based multi-object tracking system for checking the plausibility of V2V communication. The system is addressing the challenge of fusing relative sensor observations as provided by a MobilEye vision-system with time-delayed absolute GNSS-based measurements from Cooperative Awareness Messages (CAMs) as provided by V2V. The plausibility check is implemented in a prototype and based on a state-of-the-art multiple-object tracking algorithm. The proposed system is evaluated and validated under real-world conditions by conducting several test drives under urban conditions.

关键词： computer vision driver information systems image fusion image sensors mobile communication object tracking satellite navigation CAM MobilEye vision-system V2V communications automated vehicles cooperative awareness messages driver support location data exchange multisensor data fusion plausibility checking relative sensor observation fusion time-delayed absolute GNSS-based measurements vehicle-to-vehicle communication vision-based multiple-object tracking system Cams Computer aided manufacturing data integration Radar tracking sensor fusion Vehicles Computer aided manufacturing sensor data fusion Automated vehicles Vehicle to vehicle communications driver information systems Satellite navigation sensors (image) data integration V2V sensor fusion image fusion mobile communication multi-sensor data fusion computer vision tracking objects

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multi-sensor data fusion BASED ON THE EIGEN VECTOR OF RELATION MATRIX 12

MULTI-SENSOR DATA FUSION BASED ON THE EIGEN VECTOR OF RELATI...

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12th IEEE International Conference on Signal Processing (ICSP)

作者： Sun Yue Zhao Jinzhong Li Bing Harbin Engn Univ Dept Automat Harbin Peoples R China

ISBN: (纸本)9781479921867

A data fusion approach based on eigenvalues and eigenvectors of relation matrix of multi-sensor is proposed. Features of relation matrix are extracted and the information of relation matrix is fully utilized by eigenvalues and eigenvectors of relation matrix. This approach overcomes the problem of influenced by expert experience in threshold settings by the optimal sensor group selected through fusion weight Finally, the result of digital simulation demonstrates that this approach can be simply calculated with high accuracy and excellent robustness.

关键词： multi-sensor data fusion relation matrix eigenvalues and eigenvectors optimal sensor group

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multi-sensor data fusion method to discern point targets

Multi-sensor data fusion method to discern point targets

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Conference on Signal and data Processing of Small Targets 1997

作者： Li, H An, W Xu, H Sun, ZK National Univ. of Defense Technology (China)

ISBN: (纸本)0819425850

Two different vehicles which have the same shapes and sizes fly together in upper space at the same velocity. The vehicles are so far away from the sensors that the acquired images are point targets. No shape information can be gotten. These two vehicles fly together and no motion characteristics may be used. Therefore the infrared (IR) radiation characteristics of them are important for discerning these two vehicles. In this paper, three ground-based IR sensors are used to get the TR radiation spectrum of the point targets, and twelve IR characteristics are selected for recognizing them. First, a BP network is used to recognize the point targets at each base. Then a Subjective Bayesian Method is adopted to fuse the recognized results given by BP networks on three bases at the same time. And the result given by Bayesian is fused by D-S Evidence Theory with the result at next time till the belief function is more than threshold. The emulation shows that the last outputs is satisfactory.

关键词： multi-sensor data fusion neural networks subjective Bayesian method evidence theory point targets recognition

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multi-sensor data fusion and Feature Extraction for Cutting Tool Condition Monitoring: A Review 10

Multi-sensor Data Fusion and Feature Extraction for Cutting ...

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IEEE 10th International Conference on Information, Communication and Networks (ICICN)

作者： Liu, Riliang Zhu, Zhiying Zeng, Yunfei Shandong Univ Sch Mech Engn Jinan Peoples R China

ISBN: (纸本)9781665490825

Cutting tools are the key component for removing material in machining process. Tool wear will definitely impair machining quality and efficiency and even result in more serious consequences. With the development of intelligent algorithms, it is necessary to reveal the relation between tool condition and the data collected in actual machining, and to develop a reasonable monitoring system to ensure that the tool be replaced appropriately under certain circumstances. Aiming at tool wear monitoring in milling process, this paper discusses the commonly used sensor data types. Then, data fusion technology is addressed, and the feature recognition and tool wear monitoring methods using machine learning are covered. Finally, an implementation of residual convolution neural network is presented.

关键词： tool wear monitoring multi-sensor data fusion deep learning feature extraction convolutional neural network

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multi-sensor data fusion Algorithm of Triangle Module Operator in WSN 10

Multi-sensor Data Fusion Algorithm of Triangle Module Operat...

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10th International Conference on Mobile Ad-hoc and sensor Networks MSN

作者： Yu, Yanan Feng, Xiufang Hu, Jingxian Taiyuan Univ Technol Dept Comp Sci & Technol Taiyuan Peoples R China Finance & Trade Training Ctr Shanxi Vocat & Tech Coll Taiyuan Peoples R China

ISBN: (纸本)9781479973941

data fusion is an important method for saving energy in Wireless sensor Networks (WSNs). For the applications of the WSNs most widely use numerical data, multi sensor data fusion TMMDF algorithm is designed in this paper. TMMDF algorithm uses Dixon method to eliminate negligent errors in the original data. Then we use OLSF support function to calculate data on each data set and the mean value of support. Finally, we need to select the appropriate triangle module operator fusion to obtain the final fusion results. TMMDF algorithm has low computational complexity and high integration accuracy. Simulation results show that TMMDF algorithm has higher accuracy and robustness.

关键词： Wireless sensor Networks multi-sensor data fusion TMMDF algorithm negligent errors OLSF support function triangle module operator

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A belief interval euclidean distance entropy of the mass function and its application in multi-sensor data fusion

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APPLIED INTELLIGENCE 2024年第17-18期54卷 7545-7569页

作者： Zhang, Fuxiao Chen, Zichong Cai, Rui Southwest Univ Business Coll Chongqing 402460 Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China

Dempster-Shafer (D-S) evidence theory has extensive applications in the field of data fusion. It uses the mass function to replace the probability distribution in Bayesian Probability theory, which has the advantages of weak constraints and representing uncertainty. However, many existing uncertainty measures can not be well applied to the mass function, leading to many defects in some scenarios. For example, many methods can not fully reflect the relationship between focal elements. Therefore, how to improve the uncertainty measurement, and make full use of the advantages of the mass function, so as to further accurately express the system state, is still a hot topic worth studying. To solve this problem, we propose a Belief Interval Euclidean Distance (BIED) entropy of the mass function. This method combines nonspecificity and discord measurement of the mass function and is suitable for various situations. In this paper, we introduce a large number of numerical examples to demonstrate the practicality of the BIED entropy. Compared with existing advanced methods, the BIED entropy can better identify the quantity relationship and similarity relationship between focal elements, and exhibit a linear trend with the changing cardinality of focal elements. Finally, we implement a BIED entropy-based multi-sensor data fusion on various kinds of datasets. The experimental results indicate that BIED entropy has relatively high robustness, with the target recognition accuracy close to 79.33% and the F1-Score close to 77.29%.

关键词： Uncertainty measure D-S evidence theory Entropy Belief interval multi-sensor data fusion

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An Innovative multi-sensor data fusion method based on thrice deeply-fusion architecture for multi-object tracking

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2023年第12期237卷 2679-2695页

作者： Wang, Bai-chao Liu, Cong-zhi Zhang, Li-tong Wang, Da-sen Changchun Univ Sci & Technol Sch Mech & Elect Engn Changchun Peoples R China Yanshan Univ Sch Mech Engn Qinhuangdao Peoples R China Inner Mongolia Met Mat Res Inst Baotou Peoples R China Changchun Univ Sci & Technol Sch Mech & Elect Engn 7089 Weixing Rd Changchun 130022 Peoples R China

For the purpose of track maintenance in a cluttered environment with some false alarms, false dismissals and measurement disturbances, this paper presents a new multi-object tracking procedure with uncertainty of the source of the measurement data returned from multi-sensor. The tracking method is a thrice deeply-fusion approach constructed by a primary fusion based on an improved probabilistic data association filter (IPDAF), a secondary fusion with historical motion trajectories, and a thrice fusion with road-markings. It incorporates the existence probabilities of the individual tracks and the variable number of objects based on the Bayesian estimation theory, which can improve the tracking performances effectively in an environment with high clutter density. A binary 2-D assignment is adopted for the optimal data association, which is established as a nonlinear optimization problem. In the motion modeling, it introduces multiple measurement models for different sensors into the method. Then, the estimation could be performed with greater reliability. The computational efficiency is satisfying and it can be used for real-time application, which is verified by two real test scenarios.

关键词： multi-object tracking multi-sensor data fusion Improved probabilistic data association filter Bayesian estimation theory 2-D assignment

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Structural damage identification based on self-fitting ARMAX model and multi-sensor data fusion

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2014年第4期13卷 445-460页

作者： Ay, Ali M. Wang, Ying Deakin Univ Fac Sci Engn & Built Environm Sch Engn Geelong Vic 3216 Australia

Statistical time series methods have proven to be a promising technique in structural health monitoring, since it provides a direct form of data analysis and eliminates the requirement for domain transformation. Latest research in structural health monitoring presents a number of statistical models that have been successfully used to construct quantified models of vibration response signals. Although a majority of these studies present viable results, the aspects of practical implementation, statistical model construction and decision-making procedures are often vaguely defined or omitted from presented work. In this article, a comprehensive methodology is developed, which essentially utilizes an auto-regressive moving average with exogenous input model to create quantified model estimates of experimentally acquired response signals. An iterative self-fitting algorithm is proposed to construct and fit the auto-regressive moving average with exogenous input model, which is capable of integrally finding an optimum set of auto-regressive moving average with exogenous input model parameters. After creating a dataset of quantified response signals, an unlabelled response signal can be identified according to a 'closest-fit' available in the dataset. A unique averaging method is proposed and implemented for multi-sensor data fusion to decrease the margin of error with sensors, thus increasing the reliability of global damage identification. To demonstrate the effectiveness of the developed methodology, a steel frame structure subjected to various bolt-connection damage scenarios is tested. Damage identification results from the experimental study suggest that the proposed methodology can be employed as an efficient and functional damage identification tool.

关键词： Damage identification auto-regressive moving average with exogenous input model self-fitting multi-sensor data fusion steel frame

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Crack propagation characteristics during progressive failure of circular tunnels and the early warning thereof based on multi-sensor data fusion

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GEOMECHANICS AND GEOPHYSICS FOR GEO-ENERGY AND GEO-RESOURCES 2022年第5期8卷 1-24页

作者： Zhang, Liming Chao, Wenwen Liu, Zhongyuan Cong, Yu Wang, Zaiquan Qingdao Univ Technol Sch Civil Engn Qingdao 266033 Peoples R China Qingdao Univ Technol Cooperat Innovat Ctr Engn Construct & Safety Shan Qingdao 266033 Peoples R China Qingdao Univ Technol Sch Sci Qingdao 266033 Peoples R China

Biaxial compression tests were conducted on circular tunnels that constructed in sandstone to reveal the evolution of macro-cracks, the strain field, and acoustic emission (AE) parameters during deformation of tunnels. An early-warning method for tunnel failure based on multi-sensor data fusion was studied. The results indicate that tensile cracks first initiate at the arch haunch of the tunnel;then shear cracks occur on both sides of the opening, forming two asymmetric V-shaped grooves;finally, tensile-shear cracks on both sides of the tunnel propagate to the vault, triggering brittle failure of the tunnel. The strain concentration zones appear earlier than cracks in the tunnel, and the location and range of strain concentration zones cover the crack-propagation paths. Before the failure of tunnels, a series of precursor events occur, including an AE quiet period, a significant, rapid drop in the AE-based b value, and abrupt increases in AE entropy and covariance of the strain field. The monitoring data of each physical field provide early warning asynchronously: the early-warning time of AE entropy is the earliest while that of the AE quiet period is the latest. Membership functions are constructed taking early-warning results of monitoring data in various physical cases as bodies of evidence, and their critical values are determined using normal distribution. multi-sensor data are fused using the Dempster-Shafer (D-S) evidence theory to realize time-varying prediction and provision of early warning with graded probabilities for tunnel failure based on collaborative multi-sensor data fusion.

关键词： Tunnel Crack propagation Acoustic emission Failure warning multi-sensor data fusion

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Improved Q-Learning-Based Motion Control for Basketball Intelligent Robots Under multi-sensor data fusion

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IEEE ACCESS 2024年 12卷 57059-57070页

作者： Lan, Jianping Dong, Xiujuan Macau Univ Sci & Technol Fac Innovat Engn Macau Peoples R China Hubei Univ Automot Technol Inst Automot Engineers Shiyan 442002 Peoples R China

With the continuous development of science and technology, the application of artificial intelligence in robot technology, especially in the progress of sports robot technology, has received great attention. This development provides new opportunities for the development of intelligent basketball robots. However, achieving high accuracy and stability in motion control remains a challenge in the research field. The motion control technology of basketball intelligent robots is crucial. The research proposes a study on enhancing Q-learning-based action control technology for basketball robots with multi-sensor data fusion to address the problem of inaccuracy and instability. The first step involves examining how multi-sensor data fusion technology can offer more accurate and comprehensive environmental information that can aid the robot in making more precise action decisions. The second area of study involves the development of enhanced algorithms for controlling basketball robots through improved Q-learning techniques. The goal is to increase the accuracy and stability of the robots' actions. The final focus is on creating a model for controlling the basketball robots' actions, which is based on the improved Q-learning algorithm. This model effectively increases the accuracy and stability of the robots' actions. The experiment was conducted on a computer equipped with Intel Core i9-11900K processor and NVIDIA GeForce RTX 3080 graphics card. MATLAB R2023a was used for system parameterization and simulation, while Webots R2023a software was used to simulate robot actions. The sensor adopted Hokuyo URG-04LX-UG01 laser rangefinder, and the robot model was KUKA youBot. At the same time, in order to improve the control performance of basketball intelligent robots, comparisons were made with existing methods such as PID control, trajectory planning, traditional control methods, multi-sensor fusion technology, and visual perception basketball shooting correction technology.

关键词： multi-sensor data fusion Q-learning algorithm motion control basketball robot DQN algorithm

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