The automotive industry is under increasingscrutiny to improve sustainability,and one of thekey approaches to addressing this is sustainablematerial choice.As an industry,the automotivesector uses over l4 million tonn...The automotive industry is under increasingscrutiny to improve sustainability,and one of thekey approaches to addressing this is sustainablematerial choice.As an industry,the automotivesector uses over l4 million tonnes of plastics inpassenger automotive vehicles each year.展开更多
To solve the challenges of connecting and coordinating multiple platforms in the automotive industry and to enhance collaboration among different participants,this research focuses on addressing the complex supply rel...To solve the challenges of connecting and coordinating multiple platforms in the automotive industry and to enhance collaboration among different participants,this research focuses on addressing the complex supply relationships in the automotive market,improving data sharing and interactions across various platforms,and achieving more detailed integration of data and operations.We propose a trust evaluation permission delegation method based on the automotive industry chain.The proposed method combines smart contracts with trust evaluation mechanisms,dynamically calculating the trust value of users based on the historical behavior of the delegated entity,network environment,and other factors to avoid malicious node attacks during the permission delegation process.We also introduce strict control over the cross-domain permission granting and revocation mechanisms to manage the delegation path,prevent information leakage caused by malicious node interception,and effectively protect data integrity and privacy.Experimental analysis shows that this method meets the realtime requirements of collaborative interaction in the automotive industry chain and provides a feasible solution to permission delegation issues in the automotive industry chain,offering dynamic flexibility in authorization and scalability compared to most existing solutions.展开更多
The automotive sector is crucial in modern society,facilitating essential transportation needs across personal,commercial,and logistical domains while significantly contributing to national economic development and em...The automotive sector is crucial in modern society,facilitating essential transportation needs across personal,commercial,and logistical domains while significantly contributing to national economic development and employment generation.The transformative impact of Artificial Intelligence(AI)has revolutionised multiple facets of the automotive industry,encompassing intelligent manufacturing processes,diagnostic systems,control mechanisms,supply chain operations,customer service platforms,and traffic management solutions.While extensive research exists on the above aspects of AI applications in automotive contexts,there is a compelling need to synthesise this knowledge comprehensively to guide and inspire future research.This review introduces a novel taxonomic framework that provides a holistic perspective on AI integration into the automotive sector,focusing on next-generation AI methods and their critical implementation aspects.Additionally,the proposed conceptual framework for real-time condition monitoring of electric vehicle subsystems delivers actionable maintenance recommendations to stakeholders,addressing a critical gap in the field.The review highlights that AI has significantly expedited the development of autonomous vehicles regarding navigation,decision-making,and safety features through the use of advanced algorithms and deep learning structures.Furthermore,it identifies advanced driver assistance systems,vehicle health monitoring,and predictive maintenance as the most impactful AI applications,transforming operational safety and maintenance efficiency in modern automotive technologies.The work is beneficial to understanding the various use cases of AI in the different automotive domains,where AI maintains a state-of-the-art for sector-specific applications,providing a strong foundation for meeting Industry 4.0 needs and encouraging AI use among more nascent industry segments.The current work is intended to consolidate previous works while shedding some light on future research directions in promoting further growth of AI-based innovations in the scope of automotive applications.展开更多
China Automotive Standardization Research Institute of CATARC is specialized in the standardization research and application in the automotive industry.The secretariat of SAC/TC 114 on automobiles is held by CATARC.Up...China Automotive Standardization Research Institute of CATARC is specialized in the standardization research and application in the automotive industry.The secretariat of SAC/TC 114 on automobiles is held by CATARC.Up to now,a standards system has been established,which consists of 1,586 standards(874 sectoral standards,593 voluntary national standards,and 119 mandatory national standards).展开更多
Shiyan,located in the northwestern part of Hubei Province,China,is a city with a population of approximately 3.2 million.As a prefecture-level city,Shiyan is known for its mountainous terrain and rich natural resource...Shiyan,located in the northwestern part of Hubei Province,China,is a city with a population of approximately 3.2 million.As a prefecture-level city,Shiyan is known for its mountainous terrain and rich natural resources.Historically,Shiyan has been a strategic transportation hub connecting Hubei,Shaanxi,and Chongqing.展开更多
Automotive radar has emerged as a critical component in Advanced Driver Assistance Systems(ADAS)and autonomous driving,enabling robust environmental perception through precise range-Doppler and angular measurements.It...Automotive radar has emerged as a critical component in Advanced Driver Assistance Systems(ADAS)and autonomous driving,enabling robust environmental perception through precise range-Doppler and angular measurements.It plays a pivotal role in enhancing road safety by supporting accurate detection and localization of surrounding objects.However,real-world deployment of automotive radar faces significant challenges,including mutual interference among radar units and dense clutter due to multiple dynamic targets,which demand advanced signal processing solutions beyond conventional methodologies.This paper presents a comprehensive review of traditional signal processing techniques and recent advancements specifically designed to address contemporary operational challenges in automotive radar.Emphasis is placed on direction-of-arrival(DoA)estimation algorithms such as Bartlett beamforming,Minimum Variance Distortionless Response(MVDR),Multiple Signal Classification(MUSIC),and Estimation of Signal Parameters via Rotational Invariance Techniques(ESPRIT).Among these,ESPRIT offers superior resolution for multi-target scenarios with reduced computational complexity compared to MUSIC,making it particularly advantageous for real-time applications.Furthermore,the study evaluates state-of-the-art tracking algorithms,including the Kalman Filter(KF),Extended KF(EKF),Unscented KF,and Bayesian filter.EKF is especially suitable for radar systems due to its capability to linearize nonlinear measurement models.The integration of machine learning approaches for target detection and classification is also discussed,highlighting the trade-off between the simplicity of implementation in K-Nearest Neighbors(KNN)and the enhanced accuracy provided by Support Vector Machines(SVM).A brief overview of benchmark radar datasets,performance metrics,and relevant standards is included to support future research.The paper concludes by outlining ongoing challenges and identifying promising research directions in automotive radar signal processing,particularly in the context of increasingly complex traffic scenarios and autonomous navigation systems.展开更多
As the demand for intelligent and flexible production in the automotive manufacturing industry continues to intensify,industrial automation enterprises are gaining ever-greater market opportunities and competitive adv...As the demand for intelligent and flexible production in the automotive manufacturing industry continues to intensify,industrial automation enterprises are gaining ever-greater market opportunities and competitive advantages in this field.Based on a literature review and representative case studies,this paper constructs a theoretical framework for growth strategies and systematically analyzes the current application status and growth paths of automation enterprises in both complete vehicle and component production.The research finds that different growth strategies(such as vertical integration,horizontal diversification,and digital service transformation)exhibit varying applicability across upstream and downstream segments of automotive manufacturing,while simultaneously facing challenges related to technology integration,business models,and organizational change.In response to these issues,this paper proposes countermeasures such as optimizing R&D and customer relationship management,improving branding and after-sales service systems,and strengthening policy and industry environment support,thereby offering guidance for sustainable growth of industrial automation enterprises in the automotive manufacturing sector.展开更多
This paper presented a novel and environmentally friendly approach for recovering platinum group metals(PGMs)from spent automotive exhaust catalysts.The study employed lead slag and waste graphite electrodes as raw ma...This paper presented a novel and environmentally friendly approach for recovering platinum group metals(PGMs)from spent automotive exhaust catalysts.The study employed lead slag and waste graphite electrodes as raw materials,incorporating CaO as an additive to fine-tune the slag's viscosity and density.By reducing FeO in the lead slag using waste graphite electrodes,pure Fe was obtained,effectively trapping the PGMs from the exhausted catalysts.The study explored the effects of reductant addition,trapping duration,slag basicity,and trapping temperature on the recovery rate of PGMs.The results indicated that a maximum recovery rate of 97.86%was achieved when the reductant was added at 1.5 times the theoretical amount,with a trapping duration of 60 minutes,a slag basicity of 0.7,and a trapping temperature of 1600℃.This research offered a greener pathway for the recovery of PGMs from spent automotive exhaust catalysts.展开更多
As the global textile market continues to expand, demand for sewing threads-a critical auxiliary material-is rising. In recent years, automotive interiors, smart outdoor equipment, and medical and ecofriendly products...As the global textile market continues to expand, demand for sewing threads-a critical auxiliary material-is rising. In recent years, automotive interiors, smart outdoor equipment, and medical and ecofriendly products have emerged as new growth engines for the textile industry. This has propelled specialized niche markets for sewing threads designed for these applications, revealing significant and undeniable potential.展开更多
Car manufacturers aim to enhance the use of two-factor authentication (2FA) to protect keyless entry systems in contemporary cars. Despite providing significant ease for users, keyless entry systems have become more s...Car manufacturers aim to enhance the use of two-factor authentication (2FA) to protect keyless entry systems in contemporary cars. Despite providing significant ease for users, keyless entry systems have become more susceptible to appealing attacks like relay attacks and critical fob hacking. These weaknesses present considerable security threats, resulting in unauthorized entry and car theft. The suggested approach combines a conventional keyless entry feature with an extra security measure. Implementing multi-factor authentication significantly improves the security of systems that allow keyless entry by reducing the likelihood of unauthorized access. Research shows that the benefits of using two-factor authentication, such as a substantial increase in security, far outweigh any minor drawbacks.展开更多
The high melting point element W and the rare earth element Ce were added to 18Cr-Mo(444-type)ferritic stainless steel to improve its high-temperature oxidation resistance in exhaust gas.A simulated exhaust gas was fi...The high melting point element W and the rare earth element Ce were added to 18Cr-Mo(444-type)ferritic stainless steel to improve its high-temperature oxidation resistance in exhaust gas.A simulated exhaust gas was filled in the simultaneous thermal analyzer to simulate the service environment,and the oxidation behavior in high-temperature exhaust gas environment of 444-type ferritic stainless steel alloyed with W and Ce was investigated.The oxide structure and composition formed in this process were analyzed and characterized by scanning electron microscopy/energy-dispersive spectroscopy and electron probe analysis,and the mechanism of W and Ce in the oxidation process was revealed.The results show that 18Cr-Mo ferritic stainless steel containing W and Ce has better oxidation resistance in high-temperature exhaust gas.The element W can promote the precipitation of Laves phase at the matrix/interface,inhibit the interface diffusion of oxidizing elements and prevent the inward growth of the oxide film.The element Ce can suppress the volume of SiO_(2)at the oxide film/interface,reducing the breakaway oxidation caused by cracking of the oxide film.The CeO_(2)provides nucleation sites for oxide particles,promoting the healing of cracks and voids within the oxide film.展开更多
This paper proposes an integrated multi-stage framework to enhance frequency modulated continuous wave(FMCW)automotive radar performance under high noise and interference.The four-stage pipeline is applied consecutive...This paper proposes an integrated multi-stage framework to enhance frequency modulated continuous wave(FMCW)automotive radar performance under high noise and interference.The four-stage pipeline is applied consecutively:(i)an improved independent component analysis(ICA)blindly separates the two-channel echoes,isolating target and interference components;(ii)a recursive least-squares(RLS)filter compensates amplitude-and phase-mismatches,restoring signal fidelity;(iii)variational mode decomposition(VMD)followed by the Hilbert-Huang Transform(HHT)extracts noise-free intrinsic mode functions(IMFs)and sharpens their time-frequency signatures;and(iv)HHT-based beat-frequency estimation reconstructs a clean echo and delivers accurate range information.Finally,key IMFs are reconstructed into a clean signal,and a beat-frequency estimation via HHT confirms accurate distance results,closely aligning with theoretical predictions.On synthetic data with an input signal-to-noise ratio(SNR)of 12.7 dB,the pipeline delivers a 7.6 dB SNR gain,yields a mean-squared error of 0.25 m2,and achieves a range root-mean-square error(Range-RMSE)of 0.50 m.Empirical evaluations demonstrate that this enhanced ICA and VMD/HHT scheme effectively restores the fundamental echo signature,providing a robust approach for advanced driver assistance systems(ADAS).展开更多
Manufacturers must identify and classify various defects in automotive sealing rings to ensure product quality.Deep learning algorithms show promise in this field,but challenges remain,especially in detecting small-sc...Manufacturers must identify and classify various defects in automotive sealing rings to ensure product quality.Deep learning algorithms show promise in this field,but challenges remain,especially in detecting small-scale defects under harsh industrial conditions with multimodal data.This paper proposes an enhanced version of You Only Look Once(YOLO)v8 for improved defect detection in automotive sealing rings.We introduce the Multi-scale Adaptive Feature Extraction(MAFE)module,which integrates Deformable ConvolutionalNetwork(DCN)and Spaceto-Depth(SPD)operations.This module effectively captures long-range dependencies,enhances spatial aggregation,and minimizes information loss of small objects during feature extraction.Furthermore,we introduce the Blur-Aware Wasserstein Distance(BAWD)loss function,which improves regression accuracy and detection capabilities for small object anchor boxes,particularly in scenarios involving defocus blur.Additionally,we have constructed a high-quality dataset of automotive sealing ring defects,providing a valuable resource for evaluating defect detection methods.Experimental results demonstrate our method’s high performance,achieving 98.30% precision,96.62% recall,and an inference speed of 20.3 ms.展开更多
To enhance direction of arrival(DOA)estimation accuracy,this paper proposes a low-cost method for calibrating farfield steering vectors of large aperture millimeter wave radar(mmWR).To this end,we first derive the ste...To enhance direction of arrival(DOA)estimation accuracy,this paper proposes a low-cost method for calibrating farfield steering vectors of large aperture millimeter wave radar(mmWR).To this end,we first derive the steering vectors with amplitude and phase errors,assuming that mmWR works in the time-sharing mode.Then,approximate relationship between the near-field calibration steering vector and the far-field calibration steering vector is analyzed,which is used to accomplish the mapping between the two of them.Finally,simulation results verify that the proposed method can effectively improve the angle measurement accuracy of mmWR with existing amplitude and phase errors.展开更多
This paper introduces the key design aspects of automotive center console instrument systems,including hardware architecture,ergonomics,antenna layout,etc.It elaborates on the application and advantages of various adv...This paper introduces the key design aspects of automotive center console instrument systems,including hardware architecture,ergonomics,antenna layout,etc.It elaborates on the application and advantages of various advanced technologies,such as 3D printing and dual-color injection molding.Additionally,it discusses advancements in structural design,as well as future challenges and the trend of multidisciplinary collaborative innovation.展开更多
This paper deeply discusses the causes of gear howling noise,the identification and analysis of multi-source excitation,the transmission path of dynamic noise,simulation and experimental research,case analysis,optimiz...This paper deeply discusses the causes of gear howling noise,the identification and analysis of multi-source excitation,the transmission path of dynamic noise,simulation and experimental research,case analysis,optimization effect,etc.,aiming to better provide a certain guideline and reference for relevant researchers.展开更多
The 36th China International Automotive Service&Parts Exhibition(CIAACE),one of the most significant events in the automotive industry,is currently in full swing,attracting global attention and fostering internati...The 36th China International Automotive Service&Parts Exhibition(CIAACE),one of the most significant events in the automotive industry,is currently in full swing,attracting global attention and fostering international collaboration.With the theme“Global Resources,Chinese Market”,the exhibition spans an impressive 250,000 square meters and features nearly 6,000 domestic and international exhibitors showcasing over 80,000 products,including more than 5,000 new product launches.This year’s event is not only a comprehensive showcase of automotive innovation but also a strategic platform for Chinese enterprises to expand their global footprint,with a particular focus on the rapidly growing new energy vehicle(NEV)sector.展开更多
This study evaluates the development of a testing process for the automotive software domain, highlighting challenges stemming from the absence of adequate processes. The research demonstrates the application of Desig...This study evaluates the development of a testing process for the automotive software domain, highlighting challenges stemming from the absence of adequate processes. The research demonstrates the application of Design Science Research methodology in developing, an automotive software testing process—ProTSA, using six functional testing modules. Additionally, the study evaluates the benefits of implementing ProTSA in a specific Original Equipment Manufacturer (OEM) using an experimental single-case approach with industry professionals’ participation through a survey. The study concludes that combining testing techniques with effective communication and alignment is crucial for enhancing software quality. Furthermore, survey data indicates that implementing ProTSA leads to productivity gains by initiating tests early, resulting in time savings in the testing program and increased productivity for the testing team. Future work will explore implementing ProTSA in cybersecurity, over-the-air software updates, and autonomous vehicle testing processes. .展开更多
Identifying objects in real-time is a technology that is developing rapidly and has a huge potential for expansion in many technical fields.Currently,systems that use image processing to detect objects are based on th...Identifying objects in real-time is a technology that is developing rapidly and has a huge potential for expansion in many technical fields.Currently,systems that use image processing to detect objects are based on the information from a single frame.A video camera positioned in the analyzed area captures the image,monitoring in detail the changes that occur between frames.The You Only Look Once(YOLO)algorithm is a model for detecting objects in images,that is currently known for the accuracy of the data obtained and the fast-working speed.This study proposes a comprehensive literature review of YOLO research,as well as a bibliometric analysis to map the trends in the automotive field from 2020 to 2024.Object detection applications using YOLO were categorized into three primary domains:road traffic,autonomous vehicle development,and industrial settings.A detailed analysis was conducted for each domain,providing quantitative insights into existing implementations.Among the various YOLO architectures evaluated(v2–v8,H,X,R,C),YOLO v8 demonstrated superior performance with a mean Average Precision(mAP)of 0.99.展开更多
In response to the challenges posed by insufficient real-time performance and suboptimal matching accuracy of traditional feature matching algorithms within automotive panoramic surround view systems,this paper has pr...In response to the challenges posed by insufficient real-time performance and suboptimal matching accuracy of traditional feature matching algorithms within automotive panoramic surround view systems,this paper has proposed a high-performance dimension reduction parallel matching algorithm that integrates Principal Component Analysis(PCA)and Dual-Heap Filtering(DHF).The algorithm employs PCA to map the feature points into the lower-dimensional space and employs the square of Euclidean distance for feature matching,which significantly reduces computational complexity.To ensure the accuracy of feature matching,the algorithm utilizes Dual-Heap Filtering to filter and refine matched point pairs.To further enhance matching speed and make optimal use of computational resources,the algorithm introduces a multi-core parallel matching strategy,greatly elevating the efficiency of feature matching.Compared to Scale-Invariant Feature Transform(SIFT)and Speeded Up Robust Features(SURF),the proposed algorithm reduces matching time by 77%to 80%and concurrently enhances matching accuracy by 5%to 15%.Experimental results demonstrate that the proposed algorithmexhibits outstanding real-time matching performance and accuracy,effectivelymeeting the feature-matching requirements of automotive panoramic surround view systems.展开更多
文摘The automotive industry is under increasingscrutiny to improve sustainability,and one of thekey approaches to addressing this is sustainablematerial choice.As an industry,the automotivesector uses over l4 million tonnes of plastics inpassenger automotive vehicles each year.
基金funded by the Sichuan Science and Technology Program,Grant Nos.2024NSFSC0515,2024ZHCG0182 and MZGC20230013.
文摘To solve the challenges of connecting and coordinating multiple platforms in the automotive industry and to enhance collaboration among different participants,this research focuses on addressing the complex supply relationships in the automotive market,improving data sharing and interactions across various platforms,and achieving more detailed integration of data and operations.We propose a trust evaluation permission delegation method based on the automotive industry chain.The proposed method combines smart contracts with trust evaluation mechanisms,dynamically calculating the trust value of users based on the historical behavior of the delegated entity,network environment,and other factors to avoid malicious node attacks during the permission delegation process.We also introduce strict control over the cross-domain permission granting and revocation mechanisms to manage the delegation path,prevent information leakage caused by malicious node interception,and effectively protect data integrity and privacy.Experimental analysis shows that this method meets the realtime requirements of collaborative interaction in the automotive industry chain and provides a feasible solution to permission delegation issues in the automotive industry chain,offering dynamic flexibility in authorization and scalability compared to most existing solutions.
基金The authors are grateful to the Universiti Malaysia Pahang Al-Sultan Abdullah and the Malaysian Ministry of Higher Education for their generous support and funding provided through University Distinguished Research Grants(Project No.RDU223016)as well as financial assistance provided through the Fundamental Research Grant Scheme(No.FRGS/1/2022/TK10/UMP/02/35).
文摘The automotive sector is crucial in modern society,facilitating essential transportation needs across personal,commercial,and logistical domains while significantly contributing to national economic development and employment generation.The transformative impact of Artificial Intelligence(AI)has revolutionised multiple facets of the automotive industry,encompassing intelligent manufacturing processes,diagnostic systems,control mechanisms,supply chain operations,customer service platforms,and traffic management solutions.While extensive research exists on the above aspects of AI applications in automotive contexts,there is a compelling need to synthesise this knowledge comprehensively to guide and inspire future research.This review introduces a novel taxonomic framework that provides a holistic perspective on AI integration into the automotive sector,focusing on next-generation AI methods and their critical implementation aspects.Additionally,the proposed conceptual framework for real-time condition monitoring of electric vehicle subsystems delivers actionable maintenance recommendations to stakeholders,addressing a critical gap in the field.The review highlights that AI has significantly expedited the development of autonomous vehicles regarding navigation,decision-making,and safety features through the use of advanced algorithms and deep learning structures.Furthermore,it identifies advanced driver assistance systems,vehicle health monitoring,and predictive maintenance as the most impactful AI applications,transforming operational safety and maintenance efficiency in modern automotive technologies.The work is beneficial to understanding the various use cases of AI in the different automotive domains,where AI maintains a state-of-the-art for sector-specific applications,providing a strong foundation for meeting Industry 4.0 needs and encouraging AI use among more nascent industry segments.The current work is intended to consolidate previous works while shedding some light on future research directions in promoting further growth of AI-based innovations in the scope of automotive applications.
文摘China Automotive Standardization Research Institute of CATARC is specialized in the standardization research and application in the automotive industry.The secretariat of SAC/TC 114 on automobiles is held by CATARC.Up to now,a standards system has been established,which consists of 1,586 standards(874 sectoral standards,593 voluntary national standards,and 119 mandatory national standards).
文摘Shiyan,located in the northwestern part of Hubei Province,China,is a city with a population of approximately 3.2 million.As a prefecture-level city,Shiyan is known for its mountainous terrain and rich natural resources.Historically,Shiyan has been a strategic transportation hub connecting Hubei,Shaanxi,and Chongqing.
基金supported in part by the National Science and Technology Council,Taiwan:NSTC 113-2410-H-030-077-MY2.
文摘Automotive radar has emerged as a critical component in Advanced Driver Assistance Systems(ADAS)and autonomous driving,enabling robust environmental perception through precise range-Doppler and angular measurements.It plays a pivotal role in enhancing road safety by supporting accurate detection and localization of surrounding objects.However,real-world deployment of automotive radar faces significant challenges,including mutual interference among radar units and dense clutter due to multiple dynamic targets,which demand advanced signal processing solutions beyond conventional methodologies.This paper presents a comprehensive review of traditional signal processing techniques and recent advancements specifically designed to address contemporary operational challenges in automotive radar.Emphasis is placed on direction-of-arrival(DoA)estimation algorithms such as Bartlett beamforming,Minimum Variance Distortionless Response(MVDR),Multiple Signal Classification(MUSIC),and Estimation of Signal Parameters via Rotational Invariance Techniques(ESPRIT).Among these,ESPRIT offers superior resolution for multi-target scenarios with reduced computational complexity compared to MUSIC,making it particularly advantageous for real-time applications.Furthermore,the study evaluates state-of-the-art tracking algorithms,including the Kalman Filter(KF),Extended KF(EKF),Unscented KF,and Bayesian filter.EKF is especially suitable for radar systems due to its capability to linearize nonlinear measurement models.The integration of machine learning approaches for target detection and classification is also discussed,highlighting the trade-off between the simplicity of implementation in K-Nearest Neighbors(KNN)and the enhanced accuracy provided by Support Vector Machines(SVM).A brief overview of benchmark radar datasets,performance metrics,and relevant standards is included to support future research.The paper concludes by outlining ongoing challenges and identifying promising research directions in automotive radar signal processing,particularly in the context of increasingly complex traffic scenarios and autonomous navigation systems.
文摘As the demand for intelligent and flexible production in the automotive manufacturing industry continues to intensify,industrial automation enterprises are gaining ever-greater market opportunities and competitive advantages in this field.Based on a literature review and representative case studies,this paper constructs a theoretical framework for growth strategies and systematically analyzes the current application status and growth paths of automation enterprises in both complete vehicle and component production.The research finds that different growth strategies(such as vertical integration,horizontal diversification,and digital service transformation)exhibit varying applicability across upstream and downstream segments of automotive manufacturing,while simultaneously facing challenges related to technology integration,business models,and organizational change.In response to these issues,this paper proposes countermeasures such as optimizing R&D and customer relationship management,improving branding and after-sales service systems,and strengthening policy and industry environment support,thereby offering guidance for sustainable growth of industrial automation enterprises in the automotive manufacturing sector.
基金Funded by the Natural Science Foundation of Henan(No.252300421563)the Key Research Projects of Henan Provincial Colleges and Universities(No.25B450001)+3 种基金the Basic and Frontier Research Project of Nanyang(No.24JCQY032)National Natural Science Foundation of China(No.52201044)the Key Specialized Research&Development and Promotion Project(Scientific and Technological Project)of Henan Province(No.232102221022)the Basic and Frontier Technology Research Project of Nanyang(No.23JCQY1001)。
文摘This paper presented a novel and environmentally friendly approach for recovering platinum group metals(PGMs)from spent automotive exhaust catalysts.The study employed lead slag and waste graphite electrodes as raw materials,incorporating CaO as an additive to fine-tune the slag's viscosity and density.By reducing FeO in the lead slag using waste graphite electrodes,pure Fe was obtained,effectively trapping the PGMs from the exhausted catalysts.The study explored the effects of reductant addition,trapping duration,slag basicity,and trapping temperature on the recovery rate of PGMs.The results indicated that a maximum recovery rate of 97.86%was achieved when the reductant was added at 1.5 times the theoretical amount,with a trapping duration of 60 minutes,a slag basicity of 0.7,and a trapping temperature of 1600℃.This research offered a greener pathway for the recovery of PGMs from spent automotive exhaust catalysts.
文摘As the global textile market continues to expand, demand for sewing threads-a critical auxiliary material-is rising. In recent years, automotive interiors, smart outdoor equipment, and medical and ecofriendly products have emerged as new growth engines for the textile industry. This has propelled specialized niche markets for sewing threads designed for these applications, revealing significant and undeniable potential.
文摘Car manufacturers aim to enhance the use of two-factor authentication (2FA) to protect keyless entry systems in contemporary cars. Despite providing significant ease for users, keyless entry systems have become more susceptible to appealing attacks like relay attacks and critical fob hacking. These weaknesses present considerable security threats, resulting in unauthorized entry and car theft. The suggested approach combines a conventional keyless entry feature with an extra security measure. Implementing multi-factor authentication significantly improves the security of systems that allow keyless entry by reducing the likelihood of unauthorized access. Research shows that the benefits of using two-factor authentication, such as a substantial increase in security, far outweigh any minor drawbacks.
基金the joint financial support from the National Natural Science Foundation of China and Baowu Group Co.,Ltd.(Grant No.U1660205)the Fundamental Research Funds for the Central Universities(Grant No.N2007001).
文摘The high melting point element W and the rare earth element Ce were added to 18Cr-Mo(444-type)ferritic stainless steel to improve its high-temperature oxidation resistance in exhaust gas.A simulated exhaust gas was filled in the simultaneous thermal analyzer to simulate the service environment,and the oxidation behavior in high-temperature exhaust gas environment of 444-type ferritic stainless steel alloyed with W and Ce was investigated.The oxide structure and composition formed in this process were analyzed and characterized by scanning electron microscopy/energy-dispersive spectroscopy and electron probe analysis,and the mechanism of W and Ce in the oxidation process was revealed.The results show that 18Cr-Mo ferritic stainless steel containing W and Ce has better oxidation resistance in high-temperature exhaust gas.The element W can promote the precipitation of Laves phase at the matrix/interface,inhibit the interface diffusion of oxidizing elements and prevent the inward growth of the oxide film.The element Ce can suppress the volume of SiO_(2)at the oxide film/interface,reducing the breakaway oxidation caused by cracking of the oxide film.The CeO_(2)provides nucleation sites for oxide particles,promoting the healing of cracks and voids within the oxide film.
基金the National Science and Technology Council,Taiwan,for financially supporting this research(grant No.NSTC 113-2221-E-018-011)the Ministry of Education's Teaching Practice Research Program,Taiwan(PSK1134099).
文摘This paper proposes an integrated multi-stage framework to enhance frequency modulated continuous wave(FMCW)automotive radar performance under high noise and interference.The four-stage pipeline is applied consecutively:(i)an improved independent component analysis(ICA)blindly separates the two-channel echoes,isolating target and interference components;(ii)a recursive least-squares(RLS)filter compensates amplitude-and phase-mismatches,restoring signal fidelity;(iii)variational mode decomposition(VMD)followed by the Hilbert-Huang Transform(HHT)extracts noise-free intrinsic mode functions(IMFs)and sharpens their time-frequency signatures;and(iv)HHT-based beat-frequency estimation reconstructs a clean echo and delivers accurate range information.Finally,key IMFs are reconstructed into a clean signal,and a beat-frequency estimation via HHT confirms accurate distance results,closely aligning with theoretical predictions.On synthetic data with an input signal-to-noise ratio(SNR)of 12.7 dB,the pipeline delivers a 7.6 dB SNR gain,yields a mean-squared error of 0.25 m2,and achieves a range root-mean-square error(Range-RMSE)of 0.50 m.Empirical evaluations demonstrate that this enhanced ICA and VMD/HHT scheme effectively restores the fundamental echo signature,providing a robust approach for advanced driver assistance systems(ADAS).
文摘Manufacturers must identify and classify various defects in automotive sealing rings to ensure product quality.Deep learning algorithms show promise in this field,but challenges remain,especially in detecting small-scale defects under harsh industrial conditions with multimodal data.This paper proposes an enhanced version of You Only Look Once(YOLO)v8 for improved defect detection in automotive sealing rings.We introduce the Multi-scale Adaptive Feature Extraction(MAFE)module,which integrates Deformable ConvolutionalNetwork(DCN)and Spaceto-Depth(SPD)operations.This module effectively captures long-range dependencies,enhances spatial aggregation,and minimizes information loss of small objects during feature extraction.Furthermore,we introduce the Blur-Aware Wasserstein Distance(BAWD)loss function,which improves regression accuracy and detection capabilities for small object anchor boxes,particularly in scenarios involving defocus blur.Additionally,we have constructed a high-quality dataset of automotive sealing ring defects,providing a valuable resource for evaluating defect detection methods.Experimental results demonstrate our method’s high performance,achieving 98.30% precision,96.62% recall,and an inference speed of 20.3 ms.
文摘To enhance direction of arrival(DOA)estimation accuracy,this paper proposes a low-cost method for calibrating farfield steering vectors of large aperture millimeter wave radar(mmWR).To this end,we first derive the steering vectors with amplitude and phase errors,assuming that mmWR works in the time-sharing mode.Then,approximate relationship between the near-field calibration steering vector and the far-field calibration steering vector is analyzed,which is used to accomplish the mapping between the two of them.Finally,simulation results verify that the proposed method can effectively improve the angle measurement accuracy of mmWR with existing amplitude and phase errors.
文摘This paper introduces the key design aspects of automotive center console instrument systems,including hardware architecture,ergonomics,antenna layout,etc.It elaborates on the application and advantages of various advanced technologies,such as 3D printing and dual-color injection molding.Additionally,it discusses advancements in structural design,as well as future challenges and the trend of multidisciplinary collaborative innovation.
文摘This paper deeply discusses the causes of gear howling noise,the identification and analysis of multi-source excitation,the transmission path of dynamic noise,simulation and experimental research,case analysis,optimization effect,etc.,aiming to better provide a certain guideline and reference for relevant researchers.
文摘The 36th China International Automotive Service&Parts Exhibition(CIAACE),one of the most significant events in the automotive industry,is currently in full swing,attracting global attention and fostering international collaboration.With the theme“Global Resources,Chinese Market”,the exhibition spans an impressive 250,000 square meters and features nearly 6,000 domestic and international exhibitors showcasing over 80,000 products,including more than 5,000 new product launches.This year’s event is not only a comprehensive showcase of automotive innovation but also a strategic platform for Chinese enterprises to expand their global footprint,with a particular focus on the rapidly growing new energy vehicle(NEV)sector.
文摘This study evaluates the development of a testing process for the automotive software domain, highlighting challenges stemming from the absence of adequate processes. The research demonstrates the application of Design Science Research methodology in developing, an automotive software testing process—ProTSA, using six functional testing modules. Additionally, the study evaluates the benefits of implementing ProTSA in a specific Original Equipment Manufacturer (OEM) using an experimental single-case approach with industry professionals’ participation through a survey. The study concludes that combining testing techniques with effective communication and alignment is crucial for enhancing software quality. Furthermore, survey data indicates that implementing ProTSA leads to productivity gains by initiating tests early, resulting in time savings in the testing program and increased productivity for the testing team. Future work will explore implementing ProTSA in cybersecurity, over-the-air software updates, and autonomous vehicle testing processes. .
文摘Identifying objects in real-time is a technology that is developing rapidly and has a huge potential for expansion in many technical fields.Currently,systems that use image processing to detect objects are based on the information from a single frame.A video camera positioned in the analyzed area captures the image,monitoring in detail the changes that occur between frames.The You Only Look Once(YOLO)algorithm is a model for detecting objects in images,that is currently known for the accuracy of the data obtained and the fast-working speed.This study proposes a comprehensive literature review of YOLO research,as well as a bibliometric analysis to map the trends in the automotive field from 2020 to 2024.Object detection applications using YOLO were categorized into three primary domains:road traffic,autonomous vehicle development,and industrial settings.A detailed analysis was conducted for each domain,providing quantitative insights into existing implementations.Among the various YOLO architectures evaluated(v2–v8,H,X,R,C),YOLO v8 demonstrated superior performance with a mean Average Precision(mAP)of 0.99.
基金the National Natural Science Foundation of China(61803206)the Key R&D Program of Jiangsu Province(BE2022053-2)the Nanjing Forestry University Youth Science and Technology Innovation Fund(CX2018004)for partly funding this project.
文摘In response to the challenges posed by insufficient real-time performance and suboptimal matching accuracy of traditional feature matching algorithms within automotive panoramic surround view systems,this paper has proposed a high-performance dimension reduction parallel matching algorithm that integrates Principal Component Analysis(PCA)and Dual-Heap Filtering(DHF).The algorithm employs PCA to map the feature points into the lower-dimensional space and employs the square of Euclidean distance for feature matching,which significantly reduces computational complexity.To ensure the accuracy of feature matching,the algorithm utilizes Dual-Heap Filtering to filter and refine matched point pairs.To further enhance matching speed and make optimal use of computational resources,the algorithm introduces a multi-core parallel matching strategy,greatly elevating the efficiency of feature matching.Compared to Scale-Invariant Feature Transform(SIFT)and Speeded Up Robust Features(SURF),the proposed algorithm reduces matching time by 77%to 80%and concurrently enhances matching accuracy by 5%to 15%.Experimental results demonstrate that the proposed algorithmexhibits outstanding real-time matching performance and accuracy,effectivelymeeting the feature-matching requirements of automotive panoramic surround view systems.
