Vision-based technologies have been extensively applied for on-street parking space sensing,aiming at providing timely and accurate information for drivers and improving daily travel convenience.However,it faces great...Vision-based technologies have been extensively applied for on-street parking space sensing,aiming at providing timely and accurate information for drivers and improving daily travel convenience.However,it faces great challenges as a partial visualization regularly occurs owing to occlusion from static or dynamic objects or a limited perspective of camera.This paper presents an imagery-based framework to infer parking space status by generating 3D bounding box of the vehicle.A specially designed convolutional neural network based on ResNet and feature pyramid network is proposed to overcome challenges from partial visualization and occlusion.It predicts 3D box candidates on multi-scale feature maps with five different 3D anchors,which generated by clustering diverse scales of ground truth box according to different vehicle templates in the source data set.Subsequently,vehicle distribution map is constructed jointly from the coordinates of vehicle box and artificially segmented parking spaces,where the normative degree of parked vehicle is calculated by computing the intersection over union between vehicle’s box and parking space edge.In space status inference,to further eliminate mutual vehicle interference,three adjacent spaces are combined into one unit and then a multinomial logistic regression model is trained to refine the status of the unit.Experiments on KITTI benchmark and Shanghai road show that the proposed method outperforms most monocular approaches in 3D box regression and achieves satisfactory accuracy in space status inference.展开更多
Short-term prediction of on-street parking occupancy is essential to the ITS system,which can guide drivers in finding vacant parking spaces.And the spatial dependencies and exogenous dependencies need to be considere...Short-term prediction of on-street parking occupancy is essential to the ITS system,which can guide drivers in finding vacant parking spaces.And the spatial dependencies and exogenous dependencies need to be considered simultaneously,which makes short-term prediction of on-street parking occupancy challenging.Therefore,this paper proposes a deep learning model for predicting block-level parking occupancy.First,the importance of multiple points of interest(POI)in different buffers is sorted by Boruta,used for feature selection.The results show that different types of POI data should consider different buffer radii.Then based on the real on-street parking data,long short-term memory(LSTM)that can address the time dependencies is applied to predict the parking occupancy.The results demonstrate that LSTM considering POI data after Boruta selection(LSTM(+BORUTA))outperforms other baseline methods,including LSTM,with an average testing MAPE of 11.78%.The selection process of POI data helps LSTM reduce training time and slightly improve the prediction performance,which indicates that complex correlations among the same type of POI data in different buffer zones will also affect the prediction accuracy of LSTM.When there are more restaurants on both sides of the street,the prediction performance of LSTM(+BORUTA)is significantly better than that of LSTM.展开更多
Parking in a small parking lot within limited space poses a difficult task. It often leads to deviations between the final parking posture and the target posture. These deviations can lead to partial occupancy of adja...Parking in a small parking lot within limited space poses a difficult task. It often leads to deviations between the final parking posture and the target posture. These deviations can lead to partial occupancy of adjacent parking lots, which poses a safety threat to vehicles parked in these parking lots. However, previous studies have not addressed this issue. In this paper, we aim to evaluate the impact of parking deviation of existing vehicles next to the target parking lot(PDEVNTPL) on the automatic ego vehicle(AEV) parking, in terms of safety, comfort, accuracy, and efficiency of parking. A segmented parking training framework(SPTF) based on soft actor-critic(SAC) is proposed to improve parking performance. In the proposed method, the SAC algorithm incorporates strategy entropy into the objective function, to enable the AEV to learn parking strategies based on a more comprehensive understanding of the environment. Additionally, the SPTF simplifies complex parking tasks to maintain the high performance of deep reinforcement learning(DRL). The experimental results reveal that the PDEVNTPL has a detrimental influence on the AEV parking in terms of safety, accuracy, and comfort, leading to reductions of more than 27%, 54%, and 26%respectively. However, the SAC-based SPTF effectively mitigates this impact, resulting in a considerable increase in the parking success rate from 71% to 93%. Furthermore, the heading angle deviation is significantly reduced from 2.25 degrees to 0.43degrees.展开更多
With a surge in the university’s student and staff population, parking problems and congestion have rapidly intensified. The recent inclusion of women drivers, particularly during official working hours, has exacerba...With a surge in the university’s student and staff population, parking problems and congestion have rapidly intensified. The recent inclusion of women drivers, particularly during official working hours, has exacerbated these challenges. This pressing issue underscores the critical necessity for a structured approach to managing university entries and overseeing parking at the gates. The proposed smart parking management system aims to address these concerns by introducing a design concept that restricts unauthorized access and provides exclusive parking privileges to authorized users. Through image processing, the system identifies available parking spaces, relaying real-time information to users via a mobile application. This comprehensive solution also generates detailed reports (daily, weekly, and monthly), aiding university safety authorities in future gate management decisions.展开更多
Ensuring adequate access to truck parking is critical to the safe and efficient movement of freight traffic. There are strict federal guidelines for commercial truck driver rest periods. Rest areas and private truck s...Ensuring adequate access to truck parking is critical to the safe and efficient movement of freight traffic. There are strict federal guidelines for commercial truck driver rest periods. Rest areas and private truck stops are the only places for the trucks to stop legally and safely. In locations without sufficient parking areas, trucks often park on interstate ramps, which create safety risks for other interstate motorists. Historically, agencies have employed costly and time intensive manual counting methods, camera surveillance, and driver surveys to assess truck parking. Connected truck data, available in near real-time, offers an efficient alternative to practitioners to assess truck parking patterns and identify areas where there may be insufficient safe parking spaces. This paper presents a case study of interstate I-70 in east central Indiana and documents the observed spatiotemporal impacts of a rest area closure on truck parking on nearby interstate ramps. Results showed that there was a 28% increase in parking on ramps during the rest area closure. Analysis also found that ramps closest to the rest area were most impacted by the closure, seeing a rise in truck parking sessions as high as 2.7 times. Parking duration on the ramps during rest area closure also increased drastically. Although it was expected that this would result in increased parking by trucks on adjacent ramps, this before, during, after scenario provided an ideal scenario to evaluate the robustness of these techniques to assess changing parking characteristics of long-haul commercial trucks. The data analytics and visualization tools presented in this study are scalable nationwide and will aid stakeholders in informed data-driven decision making when allocating resources towards improving the nations commercial vehicle parking infrastructure.展开更多
In recent years,disorderly parking and difficult charging of electric bicycles have been challenges in urban management.The rapid growth of electric bicycles is in contradiction with the lack of dedicated parking spac...In recent years,disorderly parking and difficult charging of electric bicycles have been challenges in urban management.The rapid growth of electric bicycles is in contradiction with the lack of dedicated parking spaces and charging service facilities in towns and villages.To solve the issue of parking and charging electric bicycles in limited urban and rural spaces,prefabricated building technology is applied to the design of a multi-story electric bicycle parking lot.The multi-story prefabricated electric bicycle parking lot is utilized in urban and rural planning and design to upgrade parking facilities in old urban areas,land-constrained commercial areas,as well as counties,towns,and rural areas with inadequate municipal facilities.Multi-story prefabricated electric bicycle parking lots are the application exploration of industrial buildings,and promote the high-quality development planning and construction of towns and counties and villages.Compared with the single-story metal charging station,the multi-story assembled electric bicycle parking lot has the characteristics of integrating parking and charging,being more durable and safer in structure,accommodating a large number of vehicles,and improving the space utilization rate.展开更多
In light of growing urban traffic,car parking becomes increasingly critical for cities to manage.As a result,the prediction of parking occupancy has sparked significant research interest in recent years.While many ext...In light of growing urban traffic,car parking becomes increasingly critical for cities to manage.As a result,the prediction of parking occupancy has sparked significant research interest in recent years.While many external data sources have been considered in the prediction models,the underlying geographic context has mostly been ignored.Thus,in order to study the contribution of geospatial information to parking occupancy prediction models,road network centrality,land use,and Point of Interest(POI)data were incorporated in Random Forest(RF)and Artificial Neural Network(ANN,specifically Feedforward Neural Network FFNN)prediction models in this work.Model performances were compared to a baseline,which only considers historical and temporal input data.Moreover,the influence of the amount of training data,the prediction horizon,and the spatial variation of the prediction were explored.The results show that the inclusion of geospatial information led to a performance improvement of up to 25%compared to the baseline.Besides,as the prediction horizon expanded,predictions became less reliable,while the relevance of geospatial data increased.In general,land use and POI data proved to be more beneficial than road network centrality.The amount of training data did not have a significant influence on the performance of the RF model.The ANN model,conversely,achieved optimal results on a training input of 5 days.Likely attributable to varying occupancy patterns,prediction performance disparities could be identified for different parking districts and street segments.Generally,the RF model outperformed the ANN model on all predictions.展开更多
In today’s smart city transportation,traffic congestion is a vexing issue,and vehicles seeking parking spaces have been identified as one of the causes leading to approximately 40%of traffic congestion.Identifying pa...In today’s smart city transportation,traffic congestion is a vexing issue,and vehicles seeking parking spaces have been identified as one of the causes leading to approximately 40%of traffic congestion.Identifying parking spaces alone is insufficient because an identified available parking space may have been taken by another vehicle when it arrives,resulting in the driver’s frustration and aggravating traffic jams while searching for another parking space.This explains the need to predict the availability of parking spaces.Recently,deep learning(DL)has been shown to facilitate drivers to find parking spaces efficiently,leading to a promising performance enhancement in parking identification and prediction systems.However,no work reviews DL approaches applied to solve parking identification and prediction problems.Inspired by this gap,the purpose of this work is to investigate,highlight,and report on recent advances inDLapproaches applied to predict and identify the availability of parking spaces.Ataxonomy of DL-based parking identification and prediction systems is established as a methodology by classifying and categorizing existing literature,and by doing so,the salient and supportive features of different DL techniques for providing parking solutions are presented.Moreover,several open research challenges are outlined.This work identifies that there are various DL architectures,datasets,and performance measures used to address parking identification and prediction problems.Moreover,there are some open-source implementations available that can be used directly either to extend existing works or explore a new domain.This is the first short survey article that focuses on the use of DL-based techniques in parking identification and prediction systems for smart cities.This study concludes that although the deployment of DL in parking identification and prediction systems provides various benefits,the convergence of these two types of systems and DL brings about new issues that must be resolved in the near future.展开更多
在规模化养殖模式下,畜禽在生长过程中会不可避免地面对各种不利因素,导致体内产生氧化应激,严重影响其生长发育。畜禽体内存在多种抗氧化因子来应对氧化应激。其中,帕金森相关蛋白7(Parkinson’s Disease-Associated Protein 7,PARK7)...在规模化养殖模式下,畜禽在生长过程中会不可避免地面对各种不利因素,导致体内产生氧化应激,严重影响其生长发育。畜禽体内存在多种抗氧化因子来应对氧化应激。其中,帕金森相关蛋白7(Parkinson’s Disease-Associated Protein 7,PARK7)基因是细胞氧化应激中重要的调控因子之一。PARK7通过自身半胱氨酸的氧化水平感受氧化应激信号,并通过Nrf2、ERK1/2和PI3K/Akt等途径促进抗氧化酶的生成,提高细胞的抗氧化能力,从而减轻氧化应激引起的损伤。本文对PARK7基因在氧化应激中的调控作用和通路进行综述,将有助于畜禽氧化应激的调节机制研究。展开更多
In crowded cities,searching for the availability of parking lots is a herculean task as it results in the wastage of drivers’time,increases air pollution,and traffic congestion.Smart parking systems facilitate the dr...In crowded cities,searching for the availability of parking lots is a herculean task as it results in the wastage of drivers’time,increases air pollution,and traffic congestion.Smart parking systems facilitate the drivers to determine the information about the parking lot in real time and book them depending on the requirement.But the existing smart parking systems necessitate the drivers to reveal their sensitive information that includes their mobile number,personal identity,and desired destination.This disclosure of sensitive information makes the existing centralized smart parking systems more vulnerable to service providers’security breaches,single points of failure,and bottlenecks.In this paper,an Improved Asymmetric Consortium Blockchain and Homomorphically Computing Univariate Polynomial-based private information retrieval(IACB-HCUPPIR)scheme is proposed to ensure parking lots’availability with transparency security in a privacy-preserving smart parking system.In specific,an improved Asymmetric Consortium Blockchain is used for achieving secure transactions between different parties interacting in the smart parking environment.It further adopted the method of Homomorphically Computing Univariate Polynomial-based private information retrieval(HCUPPIR)scheme for preserving the location privacy of drivers.The results of IACB-HCUPPIR confirmed better results in terms of minimized computation and communication overload with throughput,latency,and response time with maximized drivers’privacy preservation.Moreover,the proposed fully homomorphic algorithm(FHE)was compared against partial-homomorphic encryption(PHE)and technique without encryption and found that the proposed model has quick communication in allocating the parking slots starting with 24.3 s,whereas PHE starts allocating from 24.7 s and the technique without encryption starts at 27.4 s.Thus,we ensure the proposed model performs well in allocating parking slots with less time and high security with privacy preservation.展开更多
With the development of automatic driving and fuzzy theory, people pay more and more attention to the application of fuzzy logic in engineering technology. The automatic parking module in the automatic driving system ...With the development of automatic driving and fuzzy theory, people pay more and more attention to the application of fuzzy logic in engineering technology. The automatic parking module in the automatic driving system has always been the focus of research. Automatic parking modules can greatly assist drivers in parking operations, greatly reduce parking difficulties and make people more convenient and fast parking. In this paper, an automatic parking system based on the fuzzy controller is proposed. The fuzzy controller of automatic parking system is constructed by using fuzzy theory, and the robustness of the whole system is examined by fuzzy logic. Firstly, the vehicle motion rules and trajectory changes are analyzed in detail, and the real parking lot model is simulated. Then, the input and output variables of the whole system are analyzed by fuzzy theory and the membership function is constructed. Based on the experience of human experts, the parking rules are tested and summarized, and a reasonable and practical rule base is established. Finally, MATLAB is used to code, build the visual interface of parking lot and vehicles, and draw the cyclic iterative function to detect the vehicle position and direction angle, so as to act as a sensor. The results show that using a fuzzy controller to construct an automatic parking system can effectively improve the parking level.展开更多
Parkinson's disease is characterized by the selective degeneration of dopamine neurons in the nigrostriatal pathway and dopamine deficiency in the striatum.The precise reasons behind the specific degeneration of t...Parkinson's disease is characterized by the selective degeneration of dopamine neurons in the nigrostriatal pathway and dopamine deficiency in the striatum.The precise reasons behind the specific degeneration of these dopamine neurons remain largely elusive.Genetic investigations have identified over 20 causative PARK genes and 90 genomic risk loci associated with both familial and sporadic Parkinson's disease.Notably,several of these genes are linked to the synaptic vesicle recycling process,particularly the clathrinmediated endocytosis pathway.This suggests that impaired synaptic vesicle recycling might represent an early feature of Parkinson's disease,followed by axonal degeneration and the eventual loss of dopamine cell bodies in the midbrain via a"dying back"mechanism.Recently,several new animal and cellular models with Parkinson's disease-linked mutations affecting the endocytic pathway have been created and extensively characterized.These models faithfully recapitulate certain Parkinson's disease-like features at the animal,circuit,and cellular levels,and exhibit defects in synaptic membrane trafficking,further supporting the findings from human genetics and clinical studies.In this review,we will first summarize the cellular and molecular findings from the models of two Parkinson's disease-linked clathrin uncoating proteins:auxilin(DNAJC6/PARK19)and synaptojanin 1(SYNJ1/PARK20).The mouse models carrying these two PARK gene mutations phenocopy each other with specific dopamine terminal pathology and display a potent synergistic effect.Subsequently,we will delve into the involvement of several clathrin-mediated endocytosis-related proteins(GAK,endophilin A1,SAC2/INPP5 F,synaptotagmin-11),identified as Parkinson's disease risk factors through genome-wide association studies,in Parkinson's disease pathogenesis.We will also explore the direct or indirect roles of some common Parkinson's disease-linked proteins(alpha-synuclein(PARK1/4),Parkin(PARK2),and LRRK2(PARK8))in synaptic endocytic trafficking.Additionally,we will discuss the emerging novel functions of these endocytic proteins in downstream membrane traffic pathways,particularly autophagy.Given that synaptic dysfunction is considered as an early event in Parkinson's disease,a deeper understanding of the cellular mechanisms underlying synaptic vesicle endocytic trafficking may unveil novel to rgets for early diagnosis and the development of interventional therapies for Parkinson's disease.Future research should aim to elucidate why generalized synaptic endocytic dysfunction leads to the selective degeneration of nigrostriatal dopamine neurons in Parkinson's disease.展开更多
基金This work was supported in part by National Natural Science Foundation of China(No.51805312)in part by Shanghai Sailing Program(No.18YF1409400)+2 种基金in part by Training and Funding Program of Shanghai College young teachers(No.ZZGCD15102)in part by Scientific Research Project of Shanghai University of Engineering Science(No.2016-19)in part by the Shanghai University of Engineering Science Innovation Fund for Graduate Students(No.18KY0613).
文摘Vision-based technologies have been extensively applied for on-street parking space sensing,aiming at providing timely and accurate information for drivers and improving daily travel convenience.However,it faces great challenges as a partial visualization regularly occurs owing to occlusion from static or dynamic objects or a limited perspective of camera.This paper presents an imagery-based framework to infer parking space status by generating 3D bounding box of the vehicle.A specially designed convolutional neural network based on ResNet and feature pyramid network is proposed to overcome challenges from partial visualization and occlusion.It predicts 3D box candidates on multi-scale feature maps with five different 3D anchors,which generated by clustering diverse scales of ground truth box according to different vehicle templates in the source data set.Subsequently,vehicle distribution map is constructed jointly from the coordinates of vehicle box and artificially segmented parking spaces,where the normative degree of parked vehicle is calculated by computing the intersection over union between vehicle’s box and parking space edge.In space status inference,to further eliminate mutual vehicle interference,three adjacent spaces are combined into one unit and then a multinomial logistic regression model is trained to refine the status of the unit.Experiments on KITTI benchmark and Shanghai road show that the proposed method outperforms most monocular approaches in 3D box regression and achieves satisfactory accuracy in space status inference.
基金supported in part by the National Key Research and Development Program of China(Project No.2018YFB1600900)the Jiangsu Province Transportation Key Project of Science(Project No.2019Z01)Zhejiang Provincial Natural Science Foundation of China(No.LTGG23E080005).
文摘Short-term prediction of on-street parking occupancy is essential to the ITS system,which can guide drivers in finding vacant parking spaces.And the spatial dependencies and exogenous dependencies need to be considered simultaneously,which makes short-term prediction of on-street parking occupancy challenging.Therefore,this paper proposes a deep learning model for predicting block-level parking occupancy.First,the importance of multiple points of interest(POI)in different buffers is sorted by Boruta,used for feature selection.The results show that different types of POI data should consider different buffer radii.Then based on the real on-street parking data,long short-term memory(LSTM)that can address the time dependencies is applied to predict the parking occupancy.The results demonstrate that LSTM considering POI data after Boruta selection(LSTM(+BORUTA))outperforms other baseline methods,including LSTM,with an average testing MAPE of 11.78%.The selection process of POI data helps LSTM reduce training time and slightly improve the prediction performance,which indicates that complex correlations among the same type of POI data in different buffer zones will also affect the prediction accuracy of LSTM.When there are more restaurants on both sides of the street,the prediction performance of LSTM(+BORUTA)is significantly better than that of LSTM.
基金supported by National Natural Science Foundation of China(52222215, 52272420, 52072051)。
文摘Parking in a small parking lot within limited space poses a difficult task. It often leads to deviations between the final parking posture and the target posture. These deviations can lead to partial occupancy of adjacent parking lots, which poses a safety threat to vehicles parked in these parking lots. However, previous studies have not addressed this issue. In this paper, we aim to evaluate the impact of parking deviation of existing vehicles next to the target parking lot(PDEVNTPL) on the automatic ego vehicle(AEV) parking, in terms of safety, comfort, accuracy, and efficiency of parking. A segmented parking training framework(SPTF) based on soft actor-critic(SAC) is proposed to improve parking performance. In the proposed method, the SAC algorithm incorporates strategy entropy into the objective function, to enable the AEV to learn parking strategies based on a more comprehensive understanding of the environment. Additionally, the SPTF simplifies complex parking tasks to maintain the high performance of deep reinforcement learning(DRL). The experimental results reveal that the PDEVNTPL has a detrimental influence on the AEV parking in terms of safety, accuracy, and comfort, leading to reductions of more than 27%, 54%, and 26%respectively. However, the SAC-based SPTF effectively mitigates this impact, resulting in a considerable increase in the parking success rate from 71% to 93%. Furthermore, the heading angle deviation is significantly reduced from 2.25 degrees to 0.43degrees.
文摘With a surge in the university’s student and staff population, parking problems and congestion have rapidly intensified. The recent inclusion of women drivers, particularly during official working hours, has exacerbated these challenges. This pressing issue underscores the critical necessity for a structured approach to managing university entries and overseeing parking at the gates. The proposed smart parking management system aims to address these concerns by introducing a design concept that restricts unauthorized access and provides exclusive parking privileges to authorized users. Through image processing, the system identifies available parking spaces, relaying real-time information to users via a mobile application. This comprehensive solution also generates detailed reports (daily, weekly, and monthly), aiding university safety authorities in future gate management decisions.
文摘Ensuring adequate access to truck parking is critical to the safe and efficient movement of freight traffic. There are strict federal guidelines for commercial truck driver rest periods. Rest areas and private truck stops are the only places for the trucks to stop legally and safely. In locations without sufficient parking areas, trucks often park on interstate ramps, which create safety risks for other interstate motorists. Historically, agencies have employed costly and time intensive manual counting methods, camera surveillance, and driver surveys to assess truck parking. Connected truck data, available in near real-time, offers an efficient alternative to practitioners to assess truck parking patterns and identify areas where there may be insufficient safe parking spaces. This paper presents a case study of interstate I-70 in east central Indiana and documents the observed spatiotemporal impacts of a rest area closure on truck parking on nearby interstate ramps. Results showed that there was a 28% increase in parking on ramps during the rest area closure. Analysis also found that ramps closest to the rest area were most impacted by the closure, seeing a rise in truck parking sessions as high as 2.7 times. Parking duration on the ramps during rest area closure also increased drastically. Although it was expected that this would result in increased parking by trucks on adjacent ramps, this before, during, after scenario provided an ideal scenario to evaluate the robustness of these techniques to assess changing parking characteristics of long-haul commercial trucks. The data analytics and visualization tools presented in this study are scalable nationwide and will aid stakeholders in informed data-driven decision making when allocating resources towards improving the nations commercial vehicle parking infrastructure.
文摘In recent years,disorderly parking and difficult charging of electric bicycles have been challenges in urban management.The rapid growth of electric bicycles is in contradiction with the lack of dedicated parking spaces and charging service facilities in towns and villages.To solve the issue of parking and charging electric bicycles in limited urban and rural spaces,prefabricated building technology is applied to the design of a multi-story electric bicycle parking lot.The multi-story prefabricated electric bicycle parking lot is utilized in urban and rural planning and design to upgrade parking facilities in old urban areas,land-constrained commercial areas,as well as counties,towns,and rural areas with inadequate municipal facilities.Multi-story prefabricated electric bicycle parking lots are the application exploration of industrial buildings,and promote the high-quality development planning and construction of towns and counties and villages.Compared with the single-story metal charging station,the multi-story assembled electric bicycle parking lot has the characteristics of integrating parking and charging,being more durable and safer in structure,accommodating a large number of vehicles,and improving the space utilization rate.
文摘In light of growing urban traffic,car parking becomes increasingly critical for cities to manage.As a result,the prediction of parking occupancy has sparked significant research interest in recent years.While many external data sources have been considered in the prediction models,the underlying geographic context has mostly been ignored.Thus,in order to study the contribution of geospatial information to parking occupancy prediction models,road network centrality,land use,and Point of Interest(POI)data were incorporated in Random Forest(RF)and Artificial Neural Network(ANN,specifically Feedforward Neural Network FFNN)prediction models in this work.Model performances were compared to a baseline,which only considers historical and temporal input data.Moreover,the influence of the amount of training data,the prediction horizon,and the spatial variation of the prediction were explored.The results show that the inclusion of geospatial information led to a performance improvement of up to 25%compared to the baseline.Besides,as the prediction horizon expanded,predictions became less reliable,while the relevance of geospatial data increased.In general,land use and POI data proved to be more beneficial than road network centrality.The amount of training data did not have a significant influence on the performance of the RF model.The ANN model,conversely,achieved optimal results on a training input of 5 days.Likely attributable to varying occupancy patterns,prediction performance disparities could be identified for different parking districts and street segments.Generally,the RF model outperformed the ANN model on all predictions.
文摘In today’s smart city transportation,traffic congestion is a vexing issue,and vehicles seeking parking spaces have been identified as one of the causes leading to approximately 40%of traffic congestion.Identifying parking spaces alone is insufficient because an identified available parking space may have been taken by another vehicle when it arrives,resulting in the driver’s frustration and aggravating traffic jams while searching for another parking space.This explains the need to predict the availability of parking spaces.Recently,deep learning(DL)has been shown to facilitate drivers to find parking spaces efficiently,leading to a promising performance enhancement in parking identification and prediction systems.However,no work reviews DL approaches applied to solve parking identification and prediction problems.Inspired by this gap,the purpose of this work is to investigate,highlight,and report on recent advances inDLapproaches applied to predict and identify the availability of parking spaces.Ataxonomy of DL-based parking identification and prediction systems is established as a methodology by classifying and categorizing existing literature,and by doing so,the salient and supportive features of different DL techniques for providing parking solutions are presented.Moreover,several open research challenges are outlined.This work identifies that there are various DL architectures,datasets,and performance measures used to address parking identification and prediction problems.Moreover,there are some open-source implementations available that can be used directly either to extend existing works or explore a new domain.This is the first short survey article that focuses on the use of DL-based techniques in parking identification and prediction systems for smart cities.This study concludes that although the deployment of DL in parking identification and prediction systems provides various benefits,the convergence of these two types of systems and DL brings about new issues that must be resolved in the near future.
文摘在规模化养殖模式下,畜禽在生长过程中会不可避免地面对各种不利因素,导致体内产生氧化应激,严重影响其生长发育。畜禽体内存在多种抗氧化因子来应对氧化应激。其中,帕金森相关蛋白7(Parkinson’s Disease-Associated Protein 7,PARK7)基因是细胞氧化应激中重要的调控因子之一。PARK7通过自身半胱氨酸的氧化水平感受氧化应激信号,并通过Nrf2、ERK1/2和PI3K/Akt等途径促进抗氧化酶的生成,提高细胞的抗氧化能力,从而减轻氧化应激引起的损伤。本文对PARK7基因在氧化应激中的调控作用和通路进行综述,将有助于畜禽氧化应激的调节机制研究。
基金The research was funded by the School of Information Technology and Engineering,Vellore Institute of Technology,Vellore 632014,Tamil Nadu,India.
文摘In crowded cities,searching for the availability of parking lots is a herculean task as it results in the wastage of drivers’time,increases air pollution,and traffic congestion.Smart parking systems facilitate the drivers to determine the information about the parking lot in real time and book them depending on the requirement.But the existing smart parking systems necessitate the drivers to reveal their sensitive information that includes their mobile number,personal identity,and desired destination.This disclosure of sensitive information makes the existing centralized smart parking systems more vulnerable to service providers’security breaches,single points of failure,and bottlenecks.In this paper,an Improved Asymmetric Consortium Blockchain and Homomorphically Computing Univariate Polynomial-based private information retrieval(IACB-HCUPPIR)scheme is proposed to ensure parking lots’availability with transparency security in a privacy-preserving smart parking system.In specific,an improved Asymmetric Consortium Blockchain is used for achieving secure transactions between different parties interacting in the smart parking environment.It further adopted the method of Homomorphically Computing Univariate Polynomial-based private information retrieval(HCUPPIR)scheme for preserving the location privacy of drivers.The results of IACB-HCUPPIR confirmed better results in terms of minimized computation and communication overload with throughput,latency,and response time with maximized drivers’privacy preservation.Moreover,the proposed fully homomorphic algorithm(FHE)was compared against partial-homomorphic encryption(PHE)and technique without encryption and found that the proposed model has quick communication in allocating the parking slots starting with 24.3 s,whereas PHE starts allocating from 24.7 s and the technique without encryption starts at 27.4 s.Thus,we ensure the proposed model performs well in allocating parking slots with less time and high security with privacy preservation.
文摘With the development of automatic driving and fuzzy theory, people pay more and more attention to the application of fuzzy logic in engineering technology. The automatic parking module in the automatic driving system has always been the focus of research. Automatic parking modules can greatly assist drivers in parking operations, greatly reduce parking difficulties and make people more convenient and fast parking. In this paper, an automatic parking system based on the fuzzy controller is proposed. The fuzzy controller of automatic parking system is constructed by using fuzzy theory, and the robustness of the whole system is examined by fuzzy logic. Firstly, the vehicle motion rules and trajectory changes are analyzed in detail, and the real parking lot model is simulated. Then, the input and output variables of the whole system are analyzed by fuzzy theory and the membership function is constructed. Based on the experience of human experts, the parking rules are tested and summarized, and a reasonable and practical rule base is established. Finally, MATLAB is used to code, build the visual interface of parking lot and vehicles, and draw the cyclic iterative function to detect the vehicle position and direction angle, so as to act as a sensor. The results show that using a fuzzy controller to construct an automatic parking system can effectively improve the parking level.
文摘Parkinson's disease is characterized by the selective degeneration of dopamine neurons in the nigrostriatal pathway and dopamine deficiency in the striatum.The precise reasons behind the specific degeneration of these dopamine neurons remain largely elusive.Genetic investigations have identified over 20 causative PARK genes and 90 genomic risk loci associated with both familial and sporadic Parkinson's disease.Notably,several of these genes are linked to the synaptic vesicle recycling process,particularly the clathrinmediated endocytosis pathway.This suggests that impaired synaptic vesicle recycling might represent an early feature of Parkinson's disease,followed by axonal degeneration and the eventual loss of dopamine cell bodies in the midbrain via a"dying back"mechanism.Recently,several new animal and cellular models with Parkinson's disease-linked mutations affecting the endocytic pathway have been created and extensively characterized.These models faithfully recapitulate certain Parkinson's disease-like features at the animal,circuit,and cellular levels,and exhibit defects in synaptic membrane trafficking,further supporting the findings from human genetics and clinical studies.In this review,we will first summarize the cellular and molecular findings from the models of two Parkinson's disease-linked clathrin uncoating proteins:auxilin(DNAJC6/PARK19)and synaptojanin 1(SYNJ1/PARK20).The mouse models carrying these two PARK gene mutations phenocopy each other with specific dopamine terminal pathology and display a potent synergistic effect.Subsequently,we will delve into the involvement of several clathrin-mediated endocytosis-related proteins(GAK,endophilin A1,SAC2/INPP5 F,synaptotagmin-11),identified as Parkinson's disease risk factors through genome-wide association studies,in Parkinson's disease pathogenesis.We will also explore the direct or indirect roles of some common Parkinson's disease-linked proteins(alpha-synuclein(PARK1/4),Parkin(PARK2),and LRRK2(PARK8))in synaptic endocytic trafficking.Additionally,we will discuss the emerging novel functions of these endocytic proteins in downstream membrane traffic pathways,particularly autophagy.Given that synaptic dysfunction is considered as an early event in Parkinson's disease,a deeper understanding of the cellular mechanisms underlying synaptic vesicle endocytic trafficking may unveil novel to rgets for early diagnosis and the development of interventional therapies for Parkinson's disease.Future research should aim to elucidate why generalized synaptic endocytic dysfunction leads to the selective degeneration of nigrostriatal dopamine neurons in Parkinson's disease.