An improved DBSCAN Algorithm for hazard recognition of obstacles in unmanned scenes

The environmental perception system is the foundation of unmanned driving systems and also the fundamental guarantee of the safety and intelligence of unmanned vehicles. The obstacle hazard identification technology is the core of the environment perception system, and it is also the basic condition...

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Vydáno v:	Soft computing (Berlin, Germany) Ročník 27; číslo 24; s. 18585 - 18604
Hlavní autor:	Zhang, Wenying
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2023 Springer Nature B.V
Témata:	Algorithms Artificial Intelligence Autonomous cars Clustering Computational Intelligence Control Cost analysis Data processing Decision making Density distribution Design Efficiency Embedded systems Engineering Hazard identification Heat detection Identification systems Internet of Things Lasers Mathematical Logic and Foundations Mechatronics Obstacle avoidance Optimization Perception Power Recognition Robotics Sensors Smoothing Traffic accidents & safety Trains Unmanned vehicles Vehicles AI image processing Unmanned Obstacles Bounding box Hazard identification DBSCAN Algorithm
ISSN:	1432-7643, 1433-7479
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Abstract	The environmental perception system is the foundation of unmanned driving systems and also the fundamental guarantee of the safety and intelligence of unmanned vehicles. The obstacle hazard identification technology is the core of the environment perception system, and it is also the basic condition for the autonomous driving of unmanned vehicles. In view of the complexity of obstacle danger identification, this research paper designs an improved Density-Based Spatial Clustering of Applications with Noise (DBSCAN) Algorithm for hazard recognition of obstacles in unmanned scenes through a systematic approach. First, it highlights the significance of morphological component analysis in identifying non-smooth regions within images where obstacles are likely to be present. Second, it introduces a novel approach for core point definition by identifying an optimal MinDensity value based on the curvature of the density distribution curve. Third, it addresses variations in density sequences through smoothing and normalization. Finally, it constructs an improved DBSCAN Algorithm for hazard recognition of obstacles in unmanned scenes. It addresses limitations in the traditional DBSCAN by refining the core point definition using an adaptive density threshold. It identifies the “elbow point” in density distribution, enhancing its ability to distinguish density states. Additionally, it incorporates density curve smoothing, normalization, and a merger step for handling stationary objects. The results show that it has high accuracy (95.6%), precision (96.3%), recall (94.5%), and F-Score (95.4%), as well as increased consistency (92.5%) and dependability (93.2%). It also has fast real-time data processing, lasting only 0.12 s, making it an excellent choice for obstacle detection and unmanned hazard avoidance.
AbstractList	The environmental perception system is the foundation of unmanned driving systems and also the fundamental guarantee of the safety and intelligence of unmanned vehicles. The obstacle hazard identification technology is the core of the environment perception system, and it is also the basic condition for the autonomous driving of unmanned vehicles. In view of the complexity of obstacle danger identification, this research paper designs an improved Density-Based Spatial Clustering of Applications with Noise (DBSCAN) Algorithm for hazard recognition of obstacles in unmanned scenes through a systematic approach. First, it highlights the significance of morphological component analysis in identifying non-smooth regions within images where obstacles are likely to be present. Second, it introduces a novel approach for core point definition by identifying an optimal MinDensity value based on the curvature of the density distribution curve. Third, it addresses variations in density sequences through smoothing and normalization. Finally, it constructs an improved DBSCAN Algorithm for hazard recognition of obstacles in unmanned scenes. It addresses limitations in the traditional DBSCAN by refining the core point definition using an adaptive density threshold. It identifies the “elbow point” in density distribution, enhancing its ability to distinguish density states. Additionally, it incorporates density curve smoothing, normalization, and a merger step for handling stationary objects. The results show that it has high accuracy (95.6%), precision (96.3%), recall (94.5%), and F-Score (95.4%), as well as increased consistency (92.5%) and dependability (93.2%). It also has fast real-time data processing, lasting only 0.12 s, making it an excellent choice for obstacle detection and unmanned hazard avoidance. The environmental perception system is the foundation of unmanned driving systems and also the fundamental guarantee of the safety and intelligence of unmanned vehicles. The obstacle hazard identification technology is the core of the environment perception system, and it is also the basic condition for the autonomous driving of unmanned vehicles. In view of the complexity of obstacle danger identification, this research paper designs an improved Density-Based Spatial Clustering of Applications with Noise (DBSCAN) Algorithm for hazard recognition of obstacles in unmanned scenes through a systematic approach. First, it highlights the significance of morphological component analysis in identifying non-smooth regions within images where obstacles are likely to be present. Second, it introduces a novel approach for core point definition by identifying an optimal MinDensity value based on the curvature of the density distribution curve. Third, it addresses variations in density sequences through smoothing and normalization. Finally, it constructs an improved DBSCAN Algorithm for hazard recognition of obstacles in unmanned scenes. It addresses limitations in the traditional DBSCAN by refining the core point definition using an adaptive density threshold. It identifies the “elbow point” in density distribution, enhancing its ability to distinguish density states. Additionally, it incorporates density curve smoothing, normalization, and a merger step for handling stationary objects. The results show that it has high accuracy (95.6%), precision (96.3%), recall (94.5%), and F-Score (95.4%), as well as increased consistency (92.5%) and dependability (93.2%). It also has fast real-time data processing, lasting only 0.12 s, making it an excellent choice for obstacle detection and unmanned hazard avoidance.
Author	Zhang, Wenying
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Cites_doi	10.1109/TNSM.2016.2541171 10.1016/j.nanoen.2022.108013 10.1007/s00500-023-07923-5 10.1109/TCSVT.2022.3182426 10.23919/CCC50068.2020.9188843 10.1109/TITS.2021.3115823 10.1117/12.2540362 10.1007/s00500-023-09037-4 10.1109/TCSVT.2022.3194169 10.23919/ChiCC.2019.8866334 10.1049/cth2.12136 10.1016/j.ymssp.2022.109930 10.1049/iet-cta.2018.5469 10.1109/TKDE.2020.2970044 10.7717/peerj-cs.1400 10.1002/rnc.4839 10.1109/JSEN.2022.3201015 10.1109/TCSVT.2021.3069838 10.1109/TNET.2017.2705239 10.1109/TMM.2023.3282465 10.3390/jmse10081153 10.1007/s11071-018-4732-x 10.1038/s41377-022-00815-7 10.1007/s00500-023-08026-x 10.1109/JOE.2021.3126090 10.1093/comjnl/bxac085 10.1109/TITS.2023.3269794 10.3390/jmse10101399 10.1007/s44196-023-00233-6 10.23919/ChiCC.2017.8028015 10.1155/2022/3815306 10.1007/s10489-020-01894-y 10.1016/j.tre.2016.01.011 10.1002/asjc.2762 10.1145/3230644 10.1145/3511603 10.1109/TCSVT.2021.3107035 10.3389/fnbot.2022.928863 10.1109/TIP.2021.3096060 10.1109/TITS.2022.3161977
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References	AslamXDHouJLiQUllahRNiZLiuYReliable control design for composite-driven scheme based on delay networked T-S fuzzy systemInt J Robust Nonlinear Control202030416221642408539310.1002/rnc.48391465.93103 ChenPLiuHXinRCarvalTZhaoJXiaYEffectively detecting operational anomalies in large-scale IoT Data infrastructures by using a GAN-based predictive modelComput J202265112909292510.1093/comjnl/bxac085 LuSBanYZhangXYangBLiuSYinLZhengWAdaptive control of time delay teleoperation system with uncertain dynamicsFront Neurorobot20221610.3389/fnbot.2022.928863 ZhangHLuoGLiJWangF-YC2FDA: coarse-to-fine domain adaptation for traffic object detectionIEEE Trans Intell Transport Syst2022238126331264710.1109/TITS.2021.3115823 LiuQYuanHHamzaouiRSuHHouJReduced reference perceptual quality model with application to rate control for video-based point cloud compressionIEEE Trans Image Process2021306623663610.1109/TIP.2021.3096060 XuHSunZCaoYA data-driven approach for intrusion and anomaly detection using automated machine learning for the Internet of ThingsSoft Comput202310.1007/s00500-023-09037-4 UllahRDaiXShengAEvent-triggered scheme for fault detection and isolation of non-linear system with time-varying delayIET Control Theory Appl2020141624292438441797310.1049/iet-cta.2018.5469 LiuAZhaiYXuNNieWLiWRegion-aware image captioning via interaction learningIEEE Trans Circ Syst Video Technol20223263685369610.1109/TCSVT.2021.3107035 ZhengYLvXQianLLiuXAn optimal BP neural network track prediction method based on a GA–ACO hybrid algorithmJ Mar Sci Eng20221010139910.3390/jmse10101399 LiJHanLZhangCLiQLiuZSpherical convolution empowered viewport prediction in 360 video multicast with limited FoV feedbackACM Trans Multimed Comput Commun Appl202310.1145/3511603 YangSLiQLiWLiXLiuADual-level representation enhancement on characteristic and context for image-text retrievalIEEE Trans Circuits Syst Video Technol202232118037805010.1109/TCSVT.2022.3182426 CongRShengHYangDCuiZChenRExploiting spatial and angular correlations with deep efficient transformers for light field image super-resolutionIEEE Trans Multimed202310.1109/TMM.2023.3282465 ChengBZhuDZhaoSChenJSituation-aware IoT service coordination using the event-driven SOA paradigmIEEE Trans Netw Serv Manage201613234936110.1109/TNSM.2016.2541171 ZhengYLiuPQianLQinSLiuXMaYRecognition and depth estimation of ships based on binocular stereo visionJ Mar Sci Eng202210115310.3390/jmse10081153 ChengBWangMZhaoSZhaiZZhuDSituation-aware dynamic service coordination in an IoT environmentIEEE/ACM Trans Netw20172542082209510.1109/TNET.2017.2705239 LuSLiuMYinLYinZLiuXZhengWThe multi-modal fusion in visual question answering: a review of attention mechanismsPeerJ Comput Sci2023910.7717/peerj-cs.1400 WangFWangHZhouXFuRA driving fatigue feature detection method based on multifractal theoryIEEE Sens J20222219190461905910.1109/JSEN.2022.3201015 KumarAShaikhAMLiYPruning filters with L1-norm and capped L1-norm for CNN compressionAppl Intell2021511152116010.1007/s10489-020-01894-y LuSDingYLiuMYinZYinLMultiscale feature extraction and fusion of image and text in VQAInt J Comput Intell Syst20231615410.1007/s44196-023-00233-6 Yin B, Khan J, Wang L, Zhang J and Kumar A (2019) Real-time lane detection and tracking for advanced driver assistance systems. In: 2019 Chinese Control Conference (CCC) (pp 6772–6777). IEEE. https://doi.org/10.23919/ChiCC.2019.8866334 JiangSZhaoCZhuYWangCDuYLeiWA practical and economical ultra-wideband base station placement approach for indoor autonomous driving systemsJ Adv Transp2022202211210.1155/2022/3815306 LiuHYuanHLiuQHouJZengHA hybrid compression framework for color attributes of static 3D point cloudsIEEE Trans Circ Syst Video Technol20223231564157710.1109/TCSVT.2021.3069838 LiangXHuangZYangSQiuLDevice-free motion and trajectory detection via RFIDACM Trans Embed Comput Syst20181747810.1145/3230644 Ali M, Yin B, Kumar A, Sheikh AM et al (2020) Reduction of multiplications in convolutional neural networks. In: 2020 39th Chinese Control Conference (CCC) (pp 7406–7411). IEEE. https://doi.org/10.23919/CCC50068.2020.9188843 ChenZObserver-based dissipative output feedback control for network T-S fuzzy systems under time delays with mismatch premiseNonlinear Dyn2019952923294110.1007/s11071-018-4732-x1437.93063 Wang L, Zhai Q, Yin B et al (2019) Second-order convolutional network for crowd counting. In: Proceeding of SPIE 11198, Fourth International Workshop on Pattern Recognition, 111980T. https://doi.org/10.1117/12.2540362 XiaoYKonakAThe heterogeneous green vehicle routing and scheduling problem with time-varying traffic congestionTransport Res Part E20168814616610.1016/j.tre.2016.01.011 ChenJXuMXuWLiDPengWA flow feedback traffic prediction based on visual quantified featuresIEEE Trans Intell Transp Syst202310.1109/TITS.2023.3269794 HouXZhangLSuYGaoGLiuYNaZA space crawling robotic bio-paw (SCRBP) enabled by triboelectric sensors for surface identificationNano Energy202310510.1016/j.nanoen.2022.108013 YangMWangHHuKYinGWeiZIA-net $: $ an inception–attention-module-based network for classifying underwater images from othersIEEE J Oceanic Eng202247370471710.1109/JOE.2021.3126090 ShamroozMLiQHouJFault detection for asynchronous T-S fuzzy networked Markov jump systems with new event-triggered schemeIET Control Theory Appl2021151114611473458335110.1049/cth2.12136 LiJZhouNSunJZhouSBaiZLuLTransport of intensity diffraction tomography with non-interferometric synthetic aperture for three-dimensional label-free microscopyLight Sci Appl202211115410.1038/s41377-022-00815-7 ShenYDingNZhengH-TLiYYangMModeling relation paths for knowledge graph completionIEEE Trans Knowl Data Eng202133113607361710.1109/TKDE.2020.2970044 YinBAslamMSA practical study of active disturbance rejection control for rotary flexible joint robot manipulatorSoft Comput2023274987500110.1007/s00500-023-08026-x ChenJWangQPengWXuHLiXDisparity-based multiscale fusion network for transportation detectionIEEE Trans Intell Transp Syst20222310188551886310.1109/TITS.2022.3161977 MuhammadIQMajidAShamroozSAdaptive event-triggered robust H∞ control for Takagi-Sugeno fuzzy networked Markov jump systems with time-varying delayAsian J Control2023251213228456232910.1002/asjc.2762 Yao W, Guo Y, Wu Y and Guo J (2017) Experimental validation of fuzzy PID control of flexible joint system in presence of uncertainties. In: 2017 36th Chinese Control Conference (CCC) (pp 4192–4197). IEEE. https://doi.org/10.23919/ChiCC.2017.8028015 MaXDongZQuanWDongYTanYReal-time assessment of asphalt pavement moduli and traffic loads using monitoring data from Built-in Sensors: optimal sensor placement and identification algorithmMech Syst Signal Process202318710.1016/j.ymssp.2022.109930 ChengDChenLLvCGuoLKouQLight-guided and cross-fusion U-net for anti-illumination image super-resolutionIEEE Trans Circ Syst Video Technol202232128436844910.1109/TCSVT.2022.3194169 HazratBYinBKumarAAliMZhangJYaoJJerk-bounded trajectory planning for rotary flexible joint manipulator: an experimental approachSoft Comput20232774029403910.1007/s00500-023-07923-5 X Hou (9319_CR12) 2023; 105 IQ Muhammad (9319_CR25) 2023; 25 D Cheng (9319_CR9) 2022; 32 F Wang (9319_CR29) 2022; 22 R Cong (9319_CR10) 2023 9319_CR1 B Cheng (9319_CR7) 2016; 13 S Lu (9319_CR21) 2022; 16 B Cheng (9319_CR8) 2017; 25 S Lu (9319_CR23) 2023; 9 A Kumar (9319_CR14) 2021; 51 Z Chen (9319_CR3) 2019; 95 Y Zheng (9319_CR40) 2022; 10 XD Aslam (9319_CR2) 2020; 30 H Zhang (9319_CR38) 2022; 23 J Li (9319_CR15) 2022; 11 X Liang (9319_CR17) 2018; 17 J Li (9319_CR16) 2023 A Liu (9319_CR19) 2022; 32 Y Zheng (9319_CR39) 2022; 10 9319_CR30 X Ma (9319_CR24) 2023; 187 P Chen (9319_CR5) 2022; 65 9319_CR35 S Jiang (9319_CR13) 2022; 2022 R Ullah (9319_CR28) 2020; 14 B Hazrat (9319_CR11) 2023; 27 S Yang (9319_CR33) 2022; 32 Y Shen (9319_CR27) 2021; 33 M Yang (9319_CR34) 2022; 47 J Chen (9319_CR6) 2023 H Xu (9319_CR32) 2023 B Yin (9319_CR36) 2023; 27 Q Liu (9319_CR18) 2021; 30 S Lu (9319_CR22) 2023; 16 9319_CR37 Y Xiao (9319_CR31) 2016; 88 J Chen (9319_CR4) 2022; 23 M Shamrooz (9319_CR26) 2021; 15 H Liu (9319_CR20) 2022; 32
References_xml	– reference: ShamroozMLiQHouJFault detection for asynchronous T-S fuzzy networked Markov jump systems with new event-triggered schemeIET Control Theory Appl2021151114611473458335110.1049/cth2.12136 – reference: ChengDChenLLvCGuoLKouQLight-guided and cross-fusion U-net for anti-illumination image super-resolutionIEEE Trans Circ Syst Video Technol202232128436844910.1109/TCSVT.2022.3194169 – reference: JiangSZhaoCZhuYWangCDuYLeiWA practical and economical ultra-wideband base station placement approach for indoor autonomous driving systemsJ Adv Transp2022202211210.1155/2022/3815306 – reference: LuSDingYLiuMYinZYinLMultiscale feature extraction and fusion of image and text in VQAInt J Comput Intell Syst20231615410.1007/s44196-023-00233-6 – reference: YinBAslamMSA practical study of active disturbance rejection control for rotary flexible joint robot manipulatorSoft Comput2023274987500110.1007/s00500-023-08026-x – reference: XiaoYKonakAThe heterogeneous green vehicle routing and scheduling problem with time-varying traffic congestionTransport Res Part E20168814616610.1016/j.tre.2016.01.011 – reference: XuHSunZCaoYA data-driven approach for intrusion and anomaly detection using automated machine learning for the Internet of ThingsSoft Comput202310.1007/s00500-023-09037-4 – reference: Yao W, Guo Y, Wu Y and Guo J (2017) Experimental validation of fuzzy PID control of flexible joint system in presence of uncertainties. In: 2017 36th Chinese Control Conference (CCC) (pp 4192–4197). IEEE. https://doi.org/10.23919/ChiCC.2017.8028015 – reference: MaXDongZQuanWDongYTanYReal-time assessment of asphalt pavement moduli and traffic loads using monitoring data from Built-in Sensors: optimal sensor placement and identification algorithmMech Syst Signal Process202318710.1016/j.ymssp.2022.109930 – reference: Yin B, Khan J, Wang L, Zhang J and Kumar A (2019) Real-time lane detection and tracking for advanced driver assistance systems. In: 2019 Chinese Control Conference (CCC) (pp 6772–6777). IEEE. https://doi.org/10.23919/ChiCC.2019.8866334 – reference: CongRShengHYangDCuiZChenRExploiting spatial and angular correlations with deep efficient transformers for light field image super-resolutionIEEE Trans Multimed202310.1109/TMM.2023.3282465 – reference: ShenYDingNZhengH-TLiYYangMModeling relation paths for knowledge graph completionIEEE Trans Knowl Data Eng202133113607361710.1109/TKDE.2020.2970044 – reference: ChengBWangMZhaoSZhaiZZhuDSituation-aware dynamic service coordination in an IoT environmentIEEE/ACM Trans Netw20172542082209510.1109/TNET.2017.2705239 – reference: ZhengYLiuPQianLQinSLiuXMaYRecognition and depth estimation of ships based on binocular stereo visionJ Mar Sci Eng202210115310.3390/jmse10081153 – reference: LiuAZhaiYXuNNieWLiWRegion-aware image captioning via interaction learningIEEE Trans Circ Syst Video Technol20223263685369610.1109/TCSVT.2021.3107035 – reference: LuSBanYZhangXYangBLiuSYinLZhengWAdaptive control of time delay teleoperation system with uncertain dynamicsFront Neurorobot20221610.3389/fnbot.2022.928863 – reference: LiJHanLZhangCLiQLiuZSpherical convolution empowered viewport prediction in 360 video multicast with limited FoV feedbackACM Trans Multimed Comput Commun Appl202310.1145/3511603 – reference: AslamXDHouJLiQUllahRNiZLiuYReliable control design for composite-driven scheme based on delay networked T-S fuzzy systemInt J Robust Nonlinear Control202030416221642408539310.1002/rnc.48391465.93103 – reference: ChenJXuMXuWLiDPengWA flow feedback traffic prediction based on visual quantified featuresIEEE Trans Intell Transp Syst202310.1109/TITS.2023.3269794 – reference: WangFWangHZhouXFuRA driving fatigue feature detection method based on multifractal theoryIEEE Sens J20222219190461905910.1109/JSEN.2022.3201015 – reference: Ali M, Yin B, Kumar A, Sheikh AM et al (2020) Reduction of multiplications in convolutional neural networks. In: 2020 39th Chinese Control Conference (CCC) (pp 7406–7411). IEEE. https://doi.org/10.23919/CCC50068.2020.9188843 – reference: LiJZhouNSunJZhouSBaiZLuLTransport of intensity diffraction tomography with non-interferometric synthetic aperture for three-dimensional label-free microscopyLight Sci Appl202211115410.1038/s41377-022-00815-7 – reference: YangSLiQLiWLiXLiuADual-level representation enhancement on characteristic and context for image-text retrievalIEEE Trans Circuits Syst Video Technol202232118037805010.1109/TCSVT.2022.3182426 – reference: HazratBYinBKumarAAliMZhangJYaoJJerk-bounded trajectory planning for rotary flexible joint manipulator: an experimental approachSoft Comput20232774029403910.1007/s00500-023-07923-5 – reference: MuhammadIQMajidAShamroozSAdaptive event-triggered robust H∞ control for Takagi-Sugeno fuzzy networked Markov jump systems with time-varying delayAsian J Control2023251213228456232910.1002/asjc.2762 – reference: LiuQYuanHHamzaouiRSuHHouJReduced reference perceptual quality model with application to rate control for video-based point cloud compressionIEEE Trans Image Process2021306623663610.1109/TIP.2021.3096060 – reference: ZhangHLuoGLiJWangF-YC2FDA: coarse-to-fine domain adaptation for traffic object detectionIEEE Trans Intell Transport Syst2022238126331264710.1109/TITS.2021.3115823 – reference: LiuHYuanHLiuQHouJZengHA hybrid compression framework for color attributes of static 3D point cloudsIEEE Trans Circ Syst Video Technol20223231564157710.1109/TCSVT.2021.3069838 – reference: LuSLiuMYinLYinZLiuXZhengWThe multi-modal fusion in visual question answering: a review of attention mechanismsPeerJ Comput Sci2023910.7717/peerj-cs.1400 – reference: UllahRDaiXShengAEvent-triggered scheme for fault detection and isolation of non-linear system with time-varying delayIET Control Theory Appl2020141624292438441797310.1049/iet-cta.2018.5469 – reference: ChenJWangQPengWXuHLiXDisparity-based multiscale fusion network for transportation detectionIEEE Trans Intell Transp Syst20222310188551886310.1109/TITS.2022.3161977 – reference: Wang L, Zhai Q, Yin B et al (2019) Second-order convolutional network for crowd counting. In: Proceeding of SPIE 11198, Fourth International Workshop on Pattern Recognition, 111980T. https://doi.org/10.1117/12.2540362 – reference: LiangXHuangZYangSQiuLDevice-free motion and trajectory detection via RFIDACM Trans Embed Comput Syst20181747810.1145/3230644 – reference: ChenPLiuHXinRCarvalTZhaoJXiaYEffectively detecting operational anomalies in large-scale IoT Data infrastructures by using a GAN-based predictive modelComput J202265112909292510.1093/comjnl/bxac085 – reference: ZhengYLvXQianLLiuXAn optimal BP neural network track prediction method based on a GA–ACO hybrid algorithmJ Mar Sci Eng20221010139910.3390/jmse10101399 – reference: ChengBZhuDZhaoSChenJSituation-aware IoT service coordination using the event-driven SOA paradigmIEEE Trans Netw Serv Manage201613234936110.1109/TNSM.2016.2541171 – reference: ChenZObserver-based dissipative output feedback control for network T-S fuzzy systems under time delays with mismatch premiseNonlinear Dyn2019952923294110.1007/s11071-018-4732-x1437.93063 – reference: YangMWangHHuKYinGWeiZIA-net $: $ an inception–attention-module-based network for classifying underwater images from othersIEEE J Oceanic Eng202247370471710.1109/JOE.2021.3126090 – reference: HouXZhangLSuYGaoGLiuYNaZA space crawling robotic bio-paw (SCRBP) enabled by triboelectric sensors for surface identificationNano Energy202310510.1016/j.nanoen.2022.108013 – reference: KumarAShaikhAMLiYPruning filters with L1-norm and capped L1-norm for CNN compressionAppl Intell2021511152116010.1007/s10489-020-01894-y – volume: 13 start-page: 349 issue: 2 year: 2016 ident: 9319_CR7 publication-title: IEEE Trans Netw Serv Manage doi: 10.1109/TNSM.2016.2541171 – volume: 105 year: 2023 ident: 9319_CR12 publication-title: Nano Energy doi: 10.1016/j.nanoen.2022.108013 – volume: 27 start-page: 4029 issue: 7 year: 2023 ident: 9319_CR11 publication-title: Soft Comput doi: 10.1007/s00500-023-07923-5 – volume: 32 start-page: 8037 issue: 11 year: 2022 ident: 9319_CR33 publication-title: IEEE Trans Circuits Syst Video Technol doi: 10.1109/TCSVT.2022.3182426 – ident: 9319_CR1 doi: 10.23919/CCC50068.2020.9188843 – volume: 23 start-page: 12633 issue: 8 year: 2022 ident: 9319_CR38 publication-title: IEEE Trans Intell Transport Syst doi: 10.1109/TITS.2021.3115823 – ident: 9319_CR30 doi: 10.1117/12.2540362 – year: 2023 ident: 9319_CR32 publication-title: Soft Comput doi: 10.1007/s00500-023-09037-4 – volume: 32 start-page: 8436 issue: 12 year: 2022 ident: 9319_CR9 publication-title: IEEE Trans Circ Syst Video Technol doi: 10.1109/TCSVT.2022.3194169 – ident: 9319_CR37 doi: 10.23919/ChiCC.2019.8866334 – volume: 15 start-page: 1461 issue: 11 year: 2021 ident: 9319_CR26 publication-title: IET Control Theory Appl doi: 10.1049/cth2.12136 – volume: 187 year: 2023 ident: 9319_CR24 publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2022.109930 – volume: 14 start-page: 2429 issue: 16 year: 2020 ident: 9319_CR28 publication-title: IET Control Theory Appl doi: 10.1049/iet-cta.2018.5469 – volume: 33 start-page: 3607 issue: 11 year: 2021 ident: 9319_CR27 publication-title: IEEE Trans Knowl Data Eng doi: 10.1109/TKDE.2020.2970044 – volume: 9 year: 2023 ident: 9319_CR23 publication-title: PeerJ Comput Sci doi: 10.7717/peerj-cs.1400 – volume: 30 start-page: 1622 issue: 4 year: 2020 ident: 9319_CR2 publication-title: Int J Robust Nonlinear Control doi: 10.1002/rnc.4839 – volume: 22 start-page: 19046 issue: 19 year: 2022 ident: 9319_CR29 publication-title: IEEE Sens J doi: 10.1109/JSEN.2022.3201015 – volume: 32 start-page: 1564 issue: 3 year: 2022 ident: 9319_CR20 publication-title: IEEE Trans Circ Syst Video Technol doi: 10.1109/TCSVT.2021.3069838 – volume: 25 start-page: 2082 issue: 4 year: 2017 ident: 9319_CR8 publication-title: IEEE/ACM Trans Netw doi: 10.1109/TNET.2017.2705239 – year: 2023 ident: 9319_CR10 publication-title: IEEE Trans Multimed doi: 10.1109/TMM.2023.3282465 – volume: 10 start-page: 1153 year: 2022 ident: 9319_CR40 publication-title: J Mar Sci Eng doi: 10.3390/jmse10081153 – volume: 95 start-page: 2923 year: 2019 ident: 9319_CR3 publication-title: Nonlinear Dyn doi: 10.1007/s11071-018-4732-x – volume: 11 start-page: 154 issue: 1 year: 2022 ident: 9319_CR15 publication-title: Light Sci Appl doi: 10.1038/s41377-022-00815-7 – volume: 27 start-page: 4987 year: 2023 ident: 9319_CR36 publication-title: Soft Comput doi: 10.1007/s00500-023-08026-x – volume: 47 start-page: 704 issue: 3 year: 2022 ident: 9319_CR34 publication-title: IEEE J Oceanic Eng doi: 10.1109/JOE.2021.3126090 – volume: 65 start-page: 2909 issue: 11 year: 2022 ident: 9319_CR5 publication-title: Comput J doi: 10.1093/comjnl/bxac085 – year: 2023 ident: 9319_CR6 publication-title: IEEE Trans Intell Transp Syst doi: 10.1109/TITS.2023.3269794 – volume: 10 start-page: 1399 issue: 10 year: 2022 ident: 9319_CR39 publication-title: J Mar Sci Eng doi: 10.3390/jmse10101399 – volume: 16 start-page: 54 issue: 1 year: 2023 ident: 9319_CR22 publication-title: Int J Comput Intell Syst doi: 10.1007/s44196-023-00233-6 – ident: 9319_CR35 doi: 10.23919/ChiCC.2017.8028015 – volume: 2022 start-page: 1 year: 2022 ident: 9319_CR13 publication-title: J Adv Transp doi: 10.1155/2022/3815306 – volume: 51 start-page: 1152 year: 2021 ident: 9319_CR14 publication-title: Appl Intell doi: 10.1007/s10489-020-01894-y – volume: 88 start-page: 146 year: 2016 ident: 9319_CR31 publication-title: Transport Res Part E doi: 10.1016/j.tre.2016.01.011 – volume: 25 start-page: 213 issue: 1 year: 2023 ident: 9319_CR25 publication-title: Asian J Control doi: 10.1002/asjc.2762 – volume: 17 start-page: 78 issue: 4 year: 2018 ident: 9319_CR17 publication-title: ACM Trans Embed Comput Syst doi: 10.1145/3230644 – year: 2023 ident: 9319_CR16 publication-title: ACM Trans Multimed Comput Commun Appl doi: 10.1145/3511603 – volume: 32 start-page: 3685 issue: 6 year: 2022 ident: 9319_CR19 publication-title: IEEE Trans Circ Syst Video Technol doi: 10.1109/TCSVT.2021.3107035 – volume: 16 year: 2022 ident: 9319_CR21 publication-title: Front Neurorobot doi: 10.3389/fnbot.2022.928863 – volume: 30 start-page: 6623 year: 2021 ident: 9319_CR18 publication-title: IEEE Trans Image Process doi: 10.1109/TIP.2021.3096060 – volume: 23 start-page: 18855 issue: 10 year: 2022 ident: 9319_CR4 publication-title: IEEE Trans Intell Transp Syst doi: 10.1109/TITS.2022.3161977
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SubjectTerms	Algorithms Artificial Intelligence Autonomous cars Clustering Computational Intelligence Control Cost analysis Data processing Decision making Density distribution Design Efficiency Embedded systems Engineering Hazard identification Heat detection Identification systems Internet of Things Lasers Mathematical Logic and Foundations Mechatronics Obstacle avoidance Optimization Perception Power Recognition Robotics Sensors Smoothing Traffic accidents & safety Trains Unmanned vehicles Vehicles
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Title	An improved DBSCAN Algorithm for hazard recognition of obstacles in unmanned scenes
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