In order to ease congestion and ground delays in major hub airports, an aircraft taxiing scheduling optimization model is proposed with schedule time as the object function. In the new model, the idea of a classical j...In order to ease congestion and ground delays in major hub airports, an aircraft taxiing scheduling optimization model is proposed with schedule time as the object function. In the new model, the idea of a classical job shop-schedule problem is adopted and three types of special aircraft-taxi conflicts are considered in the constraints. To solve such nondeterministic polynomial time-complex problems, the immune clonal selection algorithm（ICSA） is introduced. The simulation results in a congested hour of Beijing Capital International Airport show that, compared with the first-come-first-served（FCFS） strategy, the optimization-planning strategy reduces the total scheduling time by 13.6 min and the taxiing time per aircraft by 45.3 s, which improves the capacity of the runway and the efficiency of airport operations.展开更多
With the rapid progress of deep convolutional neural networks,several applications of crowd counting have been proposed and explored in the literature.In congested scene monitoring,a variety of crowd density estimatin...With the rapid progress of deep convolutional neural networks,several applications of crowd counting have been proposed and explored in the literature.In congested scene monitoring,a variety of crowd density estimating approaches has been developed.The understanding of highly congested scenes for crowd counting during Muslim gatherings of Hajj and Umrah is a challenging task,as a large number of individuals stand nearby and,it is hard for detection techniques to recognize them,as the crowd can vary from low density to high density.To deal with such highly congested scenes,we have proposed the Congested Scene Crowd Counting Network(CSCC-Net)using VGG-16 as a core network with its first ten layers due to its strong and robust transfer learning rate.A hole dilated convolutional neural network is used at the back end to widen the relevant field to extract a large range of information from the image without losing its original resolution.The dilated convolution neural network is mainly chosen to expand the kernel size without changing other parameters.Moreover,several loss functions have been applied to strengthen the evaluation accuracy of the model.Finally,the entire experiments have been evaluated using prominent data sets namely,ShanghaiTech parts A,B,UCF_CC_50,and UCF_QNRF.Our model has achieved remarkable results i.e.,68.0 and 9.0 MAE on ShanghaiTech parts A,B,199.1 MAE on UCF_CC_50,and 99.8 on UCF_QNRF data sets respectively.展开更多
Statistics shed light on a tremendous imbalance inherent inChina’s urbanization process.Secondary and tertiary industriesmake up the lion’s share of GDP with figures of48.9%and39.4%respectively,while the primary ind...Statistics shed light on a tremendous imbalance inherent inChina’s urbanization process.Secondary and tertiary industriesmake up the lion’s share of GDP with figures of48.9%and39.4%respectively,while the primary industry only accounts for11.7%.By this measure,China can be said to be an industrializedcountry,or at least to have entered the intermediate phase ofindustrialization.In terms of demographic composition,展开更多
Cascading failures often occur in congested networks such as the Internet. A cascading failure can be described as a three-phase process： generation, diffusion, and dissipation of the congestion. In this account, we ...Cascading failures often occur in congested networks such as the Internet. A cascading failure can be described as a three-phase process： generation, diffusion, and dissipation of the congestion. In this account, we present a function that represents the extent of congestion on a given node. This approach is different from existing fimctions based on betweenness centrality. By introducing the concept of ＇delay time＇, we designate an intergradation between permanent removal and nonremoval. We also construct an evaluation fimction of network efficiency, based on congestion, which measures the damage caused by cascading failures. Finally, we investigate the effects of network structure and size, delay time, processing ability and packet generation speed on congestion propagation. Also, we uncover the relationship between the cascade dynamics and some properties of the network such as structure and size.展开更多
Congestion management in an electricity market is introduced in this paper and a new method of allocating congestion cost to transactions is proposed. The proposed method is a two-step process, in which the total cong...Congestion management in an electricity market is introduced in this paper and a new method of allocating congestion cost to transactions is proposed. The proposed method is a two-step process, in which the total congestion cost is firstly allocated to congested facilities and then to each transaction involved. The cost of relieving a congested facility allocated to each transaction is proportional to the power flow change on the congested facility caused by the transaction. The more the power flow change is on the congested facility caused by the transaction, the deeper the degree of involvement by the transaction. Therefore, cutting down the magnitudes of such transactions contributes to relieving congestion. Test results on a 5-bus system indicate that the proposed method can reflect reasonably the degree of involvement by each transaction in the congestion and provide correct price signals contributing to relieving congestion.展开更多
This study involved investigating the sensitivity of Measures of Effectiveness （MOEs） to different simulation initialization time （7, 10, and 13 minutes）; observing the trend of variation of MOEs with increasing s...This study involved investigating the sensitivity of Measures of Effectiveness （MOEs） to different simulation initialization time （7, 10, and 13 minutes）; observing the trend of variation of MOEs with increasing simulation runs; and identifying the major contributors of variation in MOEs using CORSIM and SimTraffic. The results showed that （1） the MOEs of a simulated intersection approaches were indeed sensitive to initialization times; （2） the variation within MOEs reached a steady state with increased number of simulation runs, while CORSIM required at least 50 simulation runs, SimTraffic required even higher number of runs for congested approaches; （3） lane changing and gap acceptance parameters play a major role as a source of variation of MOEs （delay/vehicle） in CORSIM and SimTraffic respectively.展开更多
Unmanned Aerial Vehicles(UAVs)are enabled to be fast and flexible in managing traffic compared to the conventional methods.However,in emergencies,this system takes more time to identify and clear the traffic because o...Unmanned Aerial Vehicles(UAVs)are enabled to be fast and flexible in managing traffic compared to the conventional methods.However,in emergencies,this system takes more time to identify and clear the traffic because of fixed time control.To overcome this problem,an automated intelligent traffic monitoring and controlling system is designed using YOLO V3 neural architecture and implemented to detect the emergency vehicles from video stream data from UAVs using deep Convolution Neural Network(CNN)along with rerouting algorithm to provide the safest alternate route from current position to destination,in a heavy traffic environment.The real-time visual data collected through UAV video cameras are trained using machine learning algorithms to produce statistical profiles that are used continuously as updated inputs to the existing traffic simulation models for improving predictions.The proposed automated system performs exemplary in recognizing emergency vehicles and diverting them to an alternate route for quick transportation in various scenarios.展开更多
A novel deceleration traffic flow model is established based on the oscillatory congested states and the slow-tostart rule.The novel model considers human overreaction and mechanical restrictions as limited decelerati...A novel deceleration traffic flow model is established based on the oscillatory congested states and the slow-tostart rule.The novel model considers human overreaction and mechanical restrictions as limited deceleration capacity,effectively avoiding the unrealistic deceleration behavior found in most existing traffic flow models.In order to consider that the acceleration of a stationary vehicle is slower than that of a moving vehicle due to reasons such as driver inattention,the slow-to-start rule is introduced.In actual traffic,the driver will take different deceleration measures according to local traffic conditions,divided into ordinary and emergency deceleration.The deceleration setting in the deceleration model with only ordinary deceleration is modified.Computer simulations show that the novel model can achieve smooth,comfortable acceleration and deceleration behavior.Introducing the slow-to-start rule can realize the first-order transition from free flow to synchronized flow.The oscillatory congested states enable a first-order transition from synchronized flow to wide moving jam.Under periodic boundary conditions,the novel model can reproduce three traffic flow phases(free flow,synchronized flow,and wide moving jam)and two first-order transitions between three phases.In addition,the novel model can reproduce empirical results such as linear synchronized flow and headway distribution of free flow below 1 s.Under open boundary conditions,different congested patterns caused by on-ramps are analyzed.Compared with the classic deceleration model,this model can better reproduce the phenomenon and characteristics of actual traffic flow and provide more accurate decision support for daily traffic management of expressways.展开更多
Lane-changing behaviour is one of the complex|driving behaviours.The lane-changing behaviour of drivers may exacerbate congestion,however driver behavioural characteristics are difficult to accurately acquire and quan...Lane-changing behaviour is one of the complex|driving behaviours.The lane-changing behaviour of drivers may exacerbate congestion,however driver behavioural characteristics are difficult to accurately acquire and quantify,and thus tend to be simplified or ignored in existing lane-changing models.In this paper,the Bik-means clustering algorithm is used to analyse the urban road congestion state discrimination method.Then,simulated driving tests were conducted for different traffic congestion conditions.Through the force feedback system and infrared camera,the data of driver lane-changing behaviours at different traffic congestion levels are obtained separately,and the definitions of the start and end points of a vehicle changing lanes are determined.Furthermore,statistical analysis and discussion of key feature parameters including driver lane-changing behaviour data and visual data under different levels of traffic congestion were conducted.It is found that the average lane-change intention times in each congestion state are 2 s,4 s,6 s and 7 s,while the turn-signal duration and the number of rear-view mirror observations have similar patterns of change to the data on lane-changing intention duration.Moreover,drivers’pupil diameters become smaller during the lane-changing intention phase,and then relatively enlarge during lane-changing;the range of pupil variation is roughly 3.5 mm to 4 mm.The frequency of observing the vehicle in front of the target lane increased as the level of congestion increased,and the frequency of observation in the driver’s mirrors while changing lanes approximately doubled compared to driving straight ahead,and this ratio increased as the level of congestion increased.展开更多
When paths share a common congested link, they will all suffer from a performance degradation. Boolean tomography exploits these performance-level correlations between different paths to identify the congested links. ...When paths share a common congested link, they will all suffer from a performance degradation. Boolean tomography exploits these performance-level correlations between different paths to identify the congested links. It is clear that the congestion of a path will be distinctly intensive when it traverses multiple congested links. We adopt an enlarged state space model to mirror different congestion levels and employ a system of integer equations, instead of Boolean equations, to describe relationships between the path states and the link states. We recast the problem of identifying congested links into a constraint optimization problem, including Boolean tomography as a special case. For a logical tree, we propose an up-to-bottom algorithm and prove that it always achieves a solution to the problem. Compared with existing algorithms, the simulation results show that our proposed algorithm achieves a higher detection rate while keeping a low false positive rate.展开更多
According to the Japanese Ministry of Health,Labour,and Welfare,14.2%of people were aged>75 years in Japan in 2018,and this number continues to rise.With population aging,the incidence of congestive heart failure(C...According to the Japanese Ministry of Health,Labour,and Welfare,14.2%of people were aged>75 years in Japan in 2018,and this number continues to rise.With population aging,the incidence of congestive heart failure(CHF)is also increasing.[1–3]Reports have shown that the presence of cognitive impairment(CI)in patients with CHF is associated with poor prognosis,[4–6]and the degree of CI is related to CHF severity.展开更多
Restructuring of power market not only introduces competition but also brings complexity which increases overloading of Transmission Lines(TL).To obviate this complexity,this paper aims to mitigate the overloading and...Restructuring of power market not only introduces competition but also brings complexity which increases overloading of Transmission Lines(TL).To obviate this complexity,this paper aims to mitigate the overloading and estimate the optimal location of Static Synchronous Compensator(STATCOM) by reducing congestion for a deregulated power system.The proposed method is based on the use of Locational Marginal Price(LMP) difference technique and congestion cost.LMPs are obtained as a by-product of Optimal Power Flow(OPF),whereas Congestion Cost(CC) is a function of difference in LMP and power flows.The effiectiveness of this approach is demonstrated by reducing the CC and solution space which can identify the TLs more suitable for placement of STATCOM.Importantly,total real power loss,reactive power loss and total CC are the three main objective functions in this optimization process.The process is implemented by developing an IEEE-69 bus test system which verifies and validates the effectiveness of proposed optimization technique.Additionally,a comparative analysis is enumerated by implementing two optimization techniques:Flower Pollination Algorithm(FPA) and Particle Swarm Optimization(PSO).The comparative analysis is sufficient to demonstrate the superiority of FPA technique over PSO technique in estimating an optimal placement of a STATCOM.The results from the load-flow analysis illustrate the reduction in CC,total real and reactive power loss using FPA technique compared to PSO technique.Overall,satisfactory results are obtained without using complex calculations which verify the effectiveness of optimization techniques.展开更多
For the people of Masaka,Kabuga and Muyumbu in Rwanda,the daily commute often takes longer than it should.A stretch of just 10 km along the Prince House-Giporoso-Masaka road can take half an hour during peak hours.The...For the people of Masaka,Kabuga and Muyumbu in Rwanda,the daily commute often takes longer than it should.A stretch of just 10 km along the Prince House-Giporoso-Masaka road can take half an hour during peak hours.The narrow two-lane artery,clogged with long-haul trucks from the Rwanda-Tanzania border and commuter traffic,has long tested the patience of drivers and pedestrians alike.In May,a long-awaited announcement finally arrived.Rwanda’s Ministry of Infrastructure confirmed plans to expand the road from two lanes to four,adding a 1.2-km flyover at Giporoso-Remera and an underpass to keep tra"c flowing smoothly.The$60.5 million(Rwf86 billion)project will be fully funded by China,a testament to the deepening friendship and cooperation between the two nations.For many residents,it signals the end of years of lost time and daily frustration.展开更多
Advancements in healthcare technology have improved mortality rates and extended lifespans,resulting in a population with multiple comorbidities that complicate patient care.Traditional assessments often fall short,un...Advancements in healthcare technology have improved mortality rates and extended lifespans,resulting in a population with multiple comorbidities that complicate patient care.Traditional assessments often fall short,underscoring the need for integrated care strategies.Among these,fluid management is particularly challenging due to the difficulty in directly assessing volume status especially in critically ill patients who frequently have peripheral oedema.Effective fluid ma-nagement is essential for optimal tissue oxygen delivery,which is crucial for cellular metabolism.Oxygen transport is dependent on arterial oxygen levels,haemoglobin concentration,and cardiac output,with the latter influenced by preload,afterload,and cardiac contractility.A delicate balance of these factors ensures that the cardiovascular system can respond adequately to varying ph-ysiological demands,thereby safeguarding tissue oxygenation and overall organ function during states of stress or illness.The Venous Excess Ultrasound(VExUS)Grading System is instrumental in evaluating fluid intolerance,providing detailed insights into venous congestion and fluid status.It was originally developed to assess the risk of acute kidney injury in postoperative cardiac patients,but its versatility has enabled broader applications in nephrology and critical care settings.This mini review explores VE×US’s application and its impact on fluid management and patient outcomes in critically ill patients.展开更多
During the sizing process,yarn congestion fault occurs at the reed teeth of a sizing machine.At present,the yarn congestion fault is generally handled by manual detection.The sizing production line operates on a large...During the sizing process,yarn congestion fault occurs at the reed teeth of a sizing machine.At present,the yarn congestion fault is generally handled by manual detection.The sizing production line operates on a large scale and runs continuously.Untimely handling of the yarn congestion fault causes a large amount of yarn waste.In this research,a machine vision-based algorithm for yarn congestion fault detection is developed.Through the analysis of the congestion fault and interference contour characteristics,the basic idea of image phase subtraction to identify the congestion fault is determined.To address the interference information appearing after image phase subtraction,the image pre-processing methods of Canny edge extraction and mean filtering are employed.According to the fault size and location characteristics,the fault contour detection algorithm based on inter-frame difference is designed.To mitigate the camera vibration interference,the anti-vibration interference algorithm based on affine transformation is studied,and the fault detection algorithm for the total yarn congestion fault is determined.The detection of 20 sets of field data is carried out,and the detection rate reaches 90%.This fault detection algorithm realizes the automatic detection of yarn congestion fault of sizing machine with certain real-time performance and accuracy.展开更多
High-quality services in today’s mobile networks require stable delivery of bandwidth-intensive network content.Multipath QUIC(MPQUIC),as a multipath protocol that extends QUIC,can utilize multiple paths to support s...High-quality services in today’s mobile networks require stable delivery of bandwidth-intensive network content.Multipath QUIC(MPQUIC),as a multipath protocol that extends QUIC,can utilize multiple paths to support stable and efficient transmission.The standard coupled congestion control algorithm in MPQUIC synchronizes these paths to manage congestion,meeting fairness requirements and improving transmission efficiency.However,current algorithms’Congestion Window(CWND)reduction approach significantly decreases CWND upon packet loss,which lowers effective throughput,regardless of the congestion origin.Furthermore,the uncoupled Slow-Start(SS)in MPQUIC leads to independent exponential CWND growth on each path,potentially causing buffer overflow.To address these issues,we propose the CC-OLIA,which incorporates Packet Loss Classifcation(PLC)and Coupled Slow-Start(CSS).The PLC distinguishes between congestion-induced and random packet losses,adjusting CWND reduction accordingly to maintain throughput.Concurrently,the CSS module coordinates CWND growth during the SS,preventing abrupt increases.Implementation on MININET shows that CC-OLIA not only maintains fair performance but also enhances transmission efficiency across diverse network conditions.展开更多
Liver transplantation(LT)is the most effective treatment for patients with end-stage liver disease,and maintaining vascular patency of the transplanted liver is one of the crucial prerequisites for surgical success.De...Liver transplantation(LT)is the most effective treatment for patients with end-stage liver disease,and maintaining vascular patency of the transplanted liver is one of the crucial prerequisites for surgical success.Despite hepatic vein complic-ations following LT occurring at a relatively low frequency,ranging between 2%to 11%,delayed diagnosis and treatment may lead to graft dysfunction and even patient mortality.Clinical manifestations of hepatic vein complications are often subtle and nonspecific,posing challenges for early diagnosis.Signs may initially present as mild abnormalities in liver function,delayed recovery of liver function,unexplained ascites,lower limb edema,and perineal edema.Prolonged duration of these complications can lead to hepatic sinusoidal dilatation and eventual liver failure due to prolonged hepatic congestion.Ultrasonography has become the preferred imaging modality for post-liver transplant evaluation due to its convenience and non-invasiveness.Although hepatic vein complications may manifest as disappearance or flattening of the hepatic vein spectrum on routine ultrasound imaging,these findings lack specificity.Contrast-enhanced ultrasound that visualizes the filling of contrast agent in the hepatic veins and dynamically displays blood flow perfusion information in the drainage area can,however,significantly improve diagnostic confidence and provide additional information beyond routine ultrasound examination.展开更多
Congestion control is an inherent challenge of V2X(Vehicle to Everything)technologies.Due to the use of a broadcasting mechanism,channel congestion becomes severe with the increase in vehicle density.The researchers s...Congestion control is an inherent challenge of V2X(Vehicle to Everything)technologies.Due to the use of a broadcasting mechanism,channel congestion becomes severe with the increase in vehicle density.The researchers suggested reducing the frequency of packet dissemination to relieve congestion,which caused a rise in road driving risk.Obviously,high-risk vehicles should be able to send messages timely to alarm surrounding vehicles.Therefore,packet dissemination frequency should be set according to the corresponding vehicle’s risk level,which is hard to evaluate.In this paper,a two-stage fuzzy inference model is constructed to evaluate a vehicle’s risk level,while a congestion control algorithm DRG-DCC(Driving Risk Game-Distributed Congestion Control)is proposed.Moreover,HPSO is employed to find optimal solutions.The simulation results show that the proposed method adjusts the transmission frequency based on driving risk,effectively striking a balance between transmission delay and channel busy rate.展开更多
As vehicular networks grow increasingly complex due to high node mobility and dynamic traffic conditions,efficient clustering mechanisms are vital to ensure stable and scalable communication.Recent studies have emphas...As vehicular networks grow increasingly complex due to high node mobility and dynamic traffic conditions,efficient clustering mechanisms are vital to ensure stable and scalable communication.Recent studies have emphasized the need for adaptive clustering strategies to improve performance in Intelligent Transportation Systems(ITS).This paper presents the Grasshopper Optimization Algorithm for Vehicular Network Clustering(GOAVNET)algorithm,an innovative approach to optimal vehicular clustering in Vehicular Ad-Hoc Networks(VANETs),leveraging the Grasshopper Optimization Algorithm(GOA)to address the critical challenges of traffic congestion and communication inefficiencies in Intelligent Transportation Systems(ITS).The proposed GOA-VNET employs an iterative and interactive optimization mechanism to dynamically adjust node positions and cluster configurations,ensuring robust adaptability to varying vehicular densities and transmission ranges.Key features of GOA-VNET include the utilization of attraction zone,repulsion zone,and comfort zone parameters,which collectively enhance clustering efficiency and minimize congestion within Regions of Interest(ROI).By managing cluster configurations and node densities effectively,GOA-VNET ensures balanced load distribution and seamless data transmission,even in scenarios with high vehicular densities and varying transmission ranges.Comparative evaluations against the Whale Optimization Algorithm(WOA)and Grey Wolf Optimization(GWO)demonstrate that GOA-VNET consistently outperforms these methods by achieving superior clustering efficiency,reducing the number of clusters by up to 10%in high-density scenarios,and improving data transmission reliability.Simulation results reveal that under a 100-600 m transmission range,GOA-VNET achieves an average reduction of 8%-15%in the number of clusters and maintains a 5%-10%improvement in packet delivery ratio(PDR)compared to baseline algorithms.Additionally,the algorithm incorporates a heat transfer-inspired load-balancing mechanism,ensuring equitable distribution of nodes among cluster leaders(CLs)and maintaining a stable network environment.These results validate GOA-VNET as a reliable and scalable solution for VANETs,with significant potential to support next-generation ITS.Future research could further enhance the algorithm by integrating multi-objective optimization techniques and exploring broader applications in complex traffic scenarios.展开更多
The aim of this study is to determine the level to which the public is aware about ITS(intelligent transportation systems)technologies and how they perceive the potential advantages and inhibitors of ITS in Michigan.A...The aim of this study is to determine the level to which the public is aware about ITS(intelligent transportation systems)technologies and how they perceive the potential advantages and inhibitors of ITS in Michigan.A survey was performed with 200 participants living in Michigan,in urban,suburban and rural areas.Questions covered in the survey included how often and how bad traffic congestion occurred,how familiar travelers were with ITS technologies(adaptive traffic signals,real time monitoring of the traffic)and how much support travelers would provide for ITS initiatives.Results reveal that there is a high degree of traffic congestion awareness,there is low public awareness of ITS technologies.While respondents who were aware of ITS solutions had positive views about deploying them,especially in urban areas,they were less supportive of ITS solutions than they were among those who did not know much about these.Factors including area of residence,commute time and age were perceived to influence ITS along with more positive attitudes to ITS amongst urban dwellers and younger respondents.Analysis of key barriers to ITS implementation reflected high initial costs,challenges with technical integration and users’concerns surrounding privacy.展开更多
基金Supported by the Basic Scientific Research Projects of the Central University of China(ZXH2010D010)the National Natural Science Foundation of China(60979021/F01)~~
文摘In order to ease congestion and ground delays in major hub airports, an aircraft taxiing scheduling optimization model is proposed with schedule time as the object function. In the new model, the idea of a classical job shop-schedule problem is adopted and three types of special aircraft-taxi conflicts are considered in the constraints. To solve such nondeterministic polynomial time-complex problems, the immune clonal selection algorithm（ICSA） is introduced. The simulation results in a congested hour of Beijing Capital International Airport show that, compared with the first-come-first-served（FCFS） strategy, the optimization-planning strategy reduces the total scheduling time by 13.6 min and the taxiing time per aircraft by 45.3 s, which improves the capacity of the runway and the efficiency of airport operations.
基金This research is supported by the Ministry of Education Saudi Arabia under Project Number QURDO001.
文摘With the rapid progress of deep convolutional neural networks,several applications of crowd counting have been proposed and explored in the literature.In congested scene monitoring,a variety of crowd density estimating approaches has been developed.The understanding of highly congested scenes for crowd counting during Muslim gatherings of Hajj and Umrah is a challenging task,as a large number of individuals stand nearby and,it is hard for detection techniques to recognize them,as the crowd can vary from low density to high density.To deal with such highly congested scenes,we have proposed the Congested Scene Crowd Counting Network(CSCC-Net)using VGG-16 as a core network with its first ten layers due to its strong and robust transfer learning rate.A hole dilated convolutional neural network is used at the back end to widen the relevant field to extract a large range of information from the image without losing its original resolution.The dilated convolution neural network is mainly chosen to expand the kernel size without changing other parameters.Moreover,several loss functions have been applied to strengthen the evaluation accuracy of the model.Finally,the entire experiments have been evaluated using prominent data sets namely,ShanghaiTech parts A,B,UCF_CC_50,and UCF_QNRF.Our model has achieved remarkable results i.e.,68.0 and 9.0 MAE on ShanghaiTech parts A,B,199.1 MAE on UCF_CC_50,and 99.8 on UCF_QNRF data sets respectively.
文摘Statistics shed light on a tremendous imbalance inherent inChina’s urbanization process.Secondary and tertiary industriesmake up the lion’s share of GDP with figures of48.9%and39.4%respectively,while the primary industry only accounts for11.7%.By this measure,China can be said to be an industrializedcountry,or at least to have entered the intermediate phase ofindustrialization.In terms of demographic composition,
基金the National Natural Science Foundation of China (No. 60573128)the Ph.D. Programs Foundation of Ministry of Education of China (No. 20060183043)+1 种基金the China–British Columbia Innovation and Commercialization Strategic Develop-ment Grant (No. 2008DFA12140)the Jilin University 985 Graduate Student Innovation Foundation (No. 20080235)
文摘Cascading failures often occur in congested networks such as the Internet. A cascading failure can be described as a three-phase process： generation, diffusion, and dissipation of the congestion. In this account, we present a function that represents the extent of congestion on a given node. This approach is different from existing fimctions based on betweenness centrality. By introducing the concept of ＇delay time＇, we designate an intergradation between permanent removal and nonremoval. We also construct an evaluation fimction of network efficiency, based on congestion, which measures the damage caused by cascading failures. Finally, we investigate the effects of network structure and size, delay time, processing ability and packet generation speed on congestion propagation. Also, we uncover the relationship between the cascade dynamics and some properties of the network such as structure and size.
文摘Congestion management in an electricity market is introduced in this paper and a new method of allocating congestion cost to transactions is proposed. The proposed method is a two-step process, in which the total congestion cost is firstly allocated to congested facilities and then to each transaction involved. The cost of relieving a congested facility allocated to each transaction is proportional to the power flow change on the congested facility caused by the transaction. The more the power flow change is on the congested facility caused by the transaction, the deeper the degree of involvement by the transaction. Therefore, cutting down the magnitudes of such transactions contributes to relieving congestion. Test results on a 5-bus system indicate that the proposed method can reflect reasonably the degree of involvement by each transaction in the congestion and provide correct price signals contributing to relieving congestion.
文摘This study involved investigating the sensitivity of Measures of Effectiveness （MOEs） to different simulation initialization time （7, 10, and 13 minutes）; observing the trend of variation of MOEs with increasing simulation runs; and identifying the major contributors of variation in MOEs using CORSIM and SimTraffic. The results showed that （1） the MOEs of a simulated intersection approaches were indeed sensitive to initialization times; （2） the variation within MOEs reached a steady state with increased number of simulation runs, while CORSIM required at least 50 simulation runs, SimTraffic required even higher number of runs for congested approaches; （3） lane changing and gap acceptance parameters play a major role as a source of variation of MOEs （delay/vehicle） in CORSIM and SimTraffic respectively.
文摘Unmanned Aerial Vehicles(UAVs)are enabled to be fast and flexible in managing traffic compared to the conventional methods.However,in emergencies,this system takes more time to identify and clear the traffic because of fixed time control.To overcome this problem,an automated intelligent traffic monitoring and controlling system is designed using YOLO V3 neural architecture and implemented to detect the emergency vehicles from video stream data from UAVs using deep Convolution Neural Network(CNN)along with rerouting algorithm to provide the safest alternate route from current position to destination,in a heavy traffic environment.The real-time visual data collected through UAV video cameras are trained using machine learning algorithms to produce statistical profiles that are used continuously as updated inputs to the existing traffic simulation models for improving predictions.The proposed automated system performs exemplary in recognizing emergency vehicles and diverting them to an alternate route for quick transportation in various scenarios.
基金supported by the National Natural Science Foundation of China(Grant No.71671109)the National Key Research and Development Program of China(Grant No.2020YFB1600500)the Key Research and Development Program of Heilongjiang Province,China(Grant No.GZ20220089)。
文摘A novel deceleration traffic flow model is established based on the oscillatory congested states and the slow-tostart rule.The novel model considers human overreaction and mechanical restrictions as limited deceleration capacity,effectively avoiding the unrealistic deceleration behavior found in most existing traffic flow models.In order to consider that the acceleration of a stationary vehicle is slower than that of a moving vehicle due to reasons such as driver inattention,the slow-to-start rule is introduced.In actual traffic,the driver will take different deceleration measures according to local traffic conditions,divided into ordinary and emergency deceleration.The deceleration setting in the deceleration model with only ordinary deceleration is modified.Computer simulations show that the novel model can achieve smooth,comfortable acceleration and deceleration behavior.Introducing the slow-to-start rule can realize the first-order transition from free flow to synchronized flow.The oscillatory congested states enable a first-order transition from synchronized flow to wide moving jam.Under periodic boundary conditions,the novel model can reproduce three traffic flow phases(free flow,synchronized flow,and wide moving jam)and two first-order transitions between three phases.In addition,the novel model can reproduce empirical results such as linear synchronized flow and headway distribution of free flow below 1 s.Under open boundary conditions,different congested patterns caused by on-ramps are analyzed.Compared with the classic deceleration model,this model can better reproduce the phenomenon and characteristics of actual traffic flow and provide more accurate decision support for daily traffic management of expressways.
基金supported in part by funds from Heilongjiang Provincial Key R&D Programme(Grant No.JD22A014)the Fundamental Research Funds for the Central Universities(Grant No.2572021AW35).
文摘Lane-changing behaviour is one of the complex|driving behaviours.The lane-changing behaviour of drivers may exacerbate congestion,however driver behavioural characteristics are difficult to accurately acquire and quantify,and thus tend to be simplified or ignored in existing lane-changing models.In this paper,the Bik-means clustering algorithm is used to analyse the urban road congestion state discrimination method.Then,simulated driving tests were conducted for different traffic congestion conditions.Through the force feedback system and infrared camera,the data of driver lane-changing behaviours at different traffic congestion levels are obtained separately,and the definitions of the start and end points of a vehicle changing lanes are determined.Furthermore,statistical analysis and discussion of key feature parameters including driver lane-changing behaviour data and visual data under different levels of traffic congestion were conducted.It is found that the average lane-change intention times in each congestion state are 2 s,4 s,6 s and 7 s,while the turn-signal duration and the number of rear-view mirror observations have similar patterns of change to the data on lane-changing intention duration.Moreover,drivers’pupil diameters become smaller during the lane-changing intention phase,and then relatively enlarge during lane-changing;the range of pupil variation is roughly 3.5 mm to 4 mm.The frequency of observing the vehicle in front of the target lane increased as the level of congestion increased,and the frequency of observation in the driver’s mirrors while changing lanes approximately doubled compared to driving straight ahead,and this ratio increased as the level of congestion increased.
基金This work was partly supported by the National Natural Science Foundation of China under Grant Nos. 61171091 and 91438120, the Young Scientists Fund of the National Natural Science Foundation of China under Grant No. 61201127, the Fundamental Research Funds for Central Universities of China under Grant Nos. ZYGX2012J005 and 2014SCU11013, and the Science and Technology on Communication Security Laboratory under Grant No. 9140Cl10503140C11054.
文摘When paths share a common congested link, they will all suffer from a performance degradation. Boolean tomography exploits these performance-level correlations between different paths to identify the congested links. It is clear that the congestion of a path will be distinctly intensive when it traverses multiple congested links. We adopt an enlarged state space model to mirror different congestion levels and employ a system of integer equations, instead of Boolean equations, to describe relationships between the path states and the link states. We recast the problem of identifying congested links into a constraint optimization problem, including Boolean tomography as a special case. For a logical tree, we propose an up-to-bottom algorithm and prove that it always achieves a solution to the problem. Compared with existing algorithms, the simulation results show that our proposed algorithm achieves a higher detection rate while keeping a low false positive rate.
文摘According to the Japanese Ministry of Health,Labour,and Welfare,14.2%of people were aged>75 years in Japan in 2018,and this number continues to rise.With population aging,the incidence of congestive heart failure(CHF)is also increasing.[1–3]Reports have shown that the presence of cognitive impairment(CI)in patients with CHF is associated with poor prognosis,[4–6]and the degree of CI is related to CHF severity.
文摘Restructuring of power market not only introduces competition but also brings complexity which increases overloading of Transmission Lines(TL).To obviate this complexity,this paper aims to mitigate the overloading and estimate the optimal location of Static Synchronous Compensator(STATCOM) by reducing congestion for a deregulated power system.The proposed method is based on the use of Locational Marginal Price(LMP) difference technique and congestion cost.LMPs are obtained as a by-product of Optimal Power Flow(OPF),whereas Congestion Cost(CC) is a function of difference in LMP and power flows.The effiectiveness of this approach is demonstrated by reducing the CC and solution space which can identify the TLs more suitable for placement of STATCOM.Importantly,total real power loss,reactive power loss and total CC are the three main objective functions in this optimization process.The process is implemented by developing an IEEE-69 bus test system which verifies and validates the effectiveness of proposed optimization technique.Additionally,a comparative analysis is enumerated by implementing two optimization techniques:Flower Pollination Algorithm(FPA) and Particle Swarm Optimization(PSO).The comparative analysis is sufficient to demonstrate the superiority of FPA technique over PSO technique in estimating an optimal placement of a STATCOM.The results from the load-flow analysis illustrate the reduction in CC,total real and reactive power loss using FPA technique compared to PSO technique.Overall,satisfactory results are obtained without using complex calculations which verify the effectiveness of optimization techniques.
文摘For the people of Masaka,Kabuga and Muyumbu in Rwanda,the daily commute often takes longer than it should.A stretch of just 10 km along the Prince House-Giporoso-Masaka road can take half an hour during peak hours.The narrow two-lane artery,clogged with long-haul trucks from the Rwanda-Tanzania border and commuter traffic,has long tested the patience of drivers and pedestrians alike.In May,a long-awaited announcement finally arrived.Rwanda’s Ministry of Infrastructure confirmed plans to expand the road from two lanes to four,adding a 1.2-km flyover at Giporoso-Remera and an underpass to keep tra"c flowing smoothly.The$60.5 million(Rwf86 billion)project will be fully funded by China,a testament to the deepening friendship and cooperation between the two nations.For many residents,it signals the end of years of lost time and daily frustration.
文摘Advancements in healthcare technology have improved mortality rates and extended lifespans,resulting in a population with multiple comorbidities that complicate patient care.Traditional assessments often fall short,underscoring the need for integrated care strategies.Among these,fluid management is particularly challenging due to the difficulty in directly assessing volume status especially in critically ill patients who frequently have peripheral oedema.Effective fluid ma-nagement is essential for optimal tissue oxygen delivery,which is crucial for cellular metabolism.Oxygen transport is dependent on arterial oxygen levels,haemoglobin concentration,and cardiac output,with the latter influenced by preload,afterload,and cardiac contractility.A delicate balance of these factors ensures that the cardiovascular system can respond adequately to varying ph-ysiological demands,thereby safeguarding tissue oxygenation and overall organ function during states of stress or illness.The Venous Excess Ultrasound(VExUS)Grading System is instrumental in evaluating fluid intolerance,providing detailed insights into venous congestion and fluid status.It was originally developed to assess the risk of acute kidney injury in postoperative cardiac patients,but its versatility has enabled broader applications in nephrology and critical care settings.This mini review explores VE×US’s application and its impact on fluid management and patient outcomes in critically ill patients.
基金National Key Research and Development Program of China(No.2017YFB1304001)。
文摘During the sizing process,yarn congestion fault occurs at the reed teeth of a sizing machine.At present,the yarn congestion fault is generally handled by manual detection.The sizing production line operates on a large scale and runs continuously.Untimely handling of the yarn congestion fault causes a large amount of yarn waste.In this research,a machine vision-based algorithm for yarn congestion fault detection is developed.Through the analysis of the congestion fault and interference contour characteristics,the basic idea of image phase subtraction to identify the congestion fault is determined.To address the interference information appearing after image phase subtraction,the image pre-processing methods of Canny edge extraction and mean filtering are employed.According to the fault size and location characteristics,the fault contour detection algorithm based on inter-frame difference is designed.To mitigate the camera vibration interference,the anti-vibration interference algorithm based on affine transformation is studied,and the fault detection algorithm for the total yarn congestion fault is determined.The detection of 20 sets of field data is carried out,and the detection rate reaches 90%.This fault detection algorithm realizes the automatic detection of yarn congestion fault of sizing machine with certain real-time performance and accuracy.
文摘High-quality services in today’s mobile networks require stable delivery of bandwidth-intensive network content.Multipath QUIC(MPQUIC),as a multipath protocol that extends QUIC,can utilize multiple paths to support stable and efficient transmission.The standard coupled congestion control algorithm in MPQUIC synchronizes these paths to manage congestion,meeting fairness requirements and improving transmission efficiency.However,current algorithms’Congestion Window(CWND)reduction approach significantly decreases CWND upon packet loss,which lowers effective throughput,regardless of the congestion origin.Furthermore,the uncoupled Slow-Start(SS)in MPQUIC leads to independent exponential CWND growth on each path,potentially causing buffer overflow.To address these issues,we propose the CC-OLIA,which incorporates Packet Loss Classifcation(PLC)and Coupled Slow-Start(CSS).The PLC distinguishes between congestion-induced and random packet losses,adjusting CWND reduction accordingly to maintain throughput.Concurrently,the CSS module coordinates CWND growth during the SS,preventing abrupt increases.Implementation on MININET shows that CC-OLIA not only maintains fair performance but also enhances transmission efficiency across diverse network conditions.
基金Supported by The Shenzhen Science and Technology Research and Development Fund,No.JCYJ20220530163011026 and No.JCYJ20210324131809027Shenzhen Medical Key Discipline Project,No.G2021008 and No.G2022008.
文摘Liver transplantation(LT)is the most effective treatment for patients with end-stage liver disease,and maintaining vascular patency of the transplanted liver is one of the crucial prerequisites for surgical success.Despite hepatic vein complic-ations following LT occurring at a relatively low frequency,ranging between 2%to 11%,delayed diagnosis and treatment may lead to graft dysfunction and even patient mortality.Clinical manifestations of hepatic vein complications are often subtle and nonspecific,posing challenges for early diagnosis.Signs may initially present as mild abnormalities in liver function,delayed recovery of liver function,unexplained ascites,lower limb edema,and perineal edema.Prolonged duration of these complications can lead to hepatic sinusoidal dilatation and eventual liver failure due to prolonged hepatic congestion.Ultrasonography has become the preferred imaging modality for post-liver transplant evaluation due to its convenience and non-invasiveness.Although hepatic vein complications may manifest as disappearance or flattening of the hepatic vein spectrum on routine ultrasound imaging,these findings lack specificity.Contrast-enhanced ultrasound that visualizes the filling of contrast agent in the hepatic veins and dynamically displays blood flow perfusion information in the drainage area can,however,significantly improve diagnostic confidence and provide additional information beyond routine ultrasound examination.
基金supported by the special key project of Chongqing Technology Innovation and Application Development under Grant No.cstc2021jscx-gksbX0057the Special Major Project of Chongqing Technology Innovation and Application Development under Grant No.CSTB2022TIADSTX0003.
文摘Congestion control is an inherent challenge of V2X(Vehicle to Everything)technologies.Due to the use of a broadcasting mechanism,channel congestion becomes severe with the increase in vehicle density.The researchers suggested reducing the frequency of packet dissemination to relieve congestion,which caused a rise in road driving risk.Obviously,high-risk vehicles should be able to send messages timely to alarm surrounding vehicles.Therefore,packet dissemination frequency should be set according to the corresponding vehicle’s risk level,which is hard to evaluate.In this paper,a two-stage fuzzy inference model is constructed to evaluate a vehicle’s risk level,while a congestion control algorithm DRG-DCC(Driving Risk Game-Distributed Congestion Control)is proposed.Moreover,HPSO is employed to find optimal solutions.The simulation results show that the proposed method adjusts the transmission frequency based on driving risk,effectively striking a balance between transmission delay and channel busy rate.
基金supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.RS-2024-00337489Development of Data Drift Management Technology to Overcome Performance Degradation of AI Analysis Models).
文摘As vehicular networks grow increasingly complex due to high node mobility and dynamic traffic conditions,efficient clustering mechanisms are vital to ensure stable and scalable communication.Recent studies have emphasized the need for adaptive clustering strategies to improve performance in Intelligent Transportation Systems(ITS).This paper presents the Grasshopper Optimization Algorithm for Vehicular Network Clustering(GOAVNET)algorithm,an innovative approach to optimal vehicular clustering in Vehicular Ad-Hoc Networks(VANETs),leveraging the Grasshopper Optimization Algorithm(GOA)to address the critical challenges of traffic congestion and communication inefficiencies in Intelligent Transportation Systems(ITS).The proposed GOA-VNET employs an iterative and interactive optimization mechanism to dynamically adjust node positions and cluster configurations,ensuring robust adaptability to varying vehicular densities and transmission ranges.Key features of GOA-VNET include the utilization of attraction zone,repulsion zone,and comfort zone parameters,which collectively enhance clustering efficiency and minimize congestion within Regions of Interest(ROI).By managing cluster configurations and node densities effectively,GOA-VNET ensures balanced load distribution and seamless data transmission,even in scenarios with high vehicular densities and varying transmission ranges.Comparative evaluations against the Whale Optimization Algorithm(WOA)and Grey Wolf Optimization(GWO)demonstrate that GOA-VNET consistently outperforms these methods by achieving superior clustering efficiency,reducing the number of clusters by up to 10%in high-density scenarios,and improving data transmission reliability.Simulation results reveal that under a 100-600 m transmission range,GOA-VNET achieves an average reduction of 8%-15%in the number of clusters and maintains a 5%-10%improvement in packet delivery ratio(PDR)compared to baseline algorithms.Additionally,the algorithm incorporates a heat transfer-inspired load-balancing mechanism,ensuring equitable distribution of nodes among cluster leaders(CLs)and maintaining a stable network environment.These results validate GOA-VNET as a reliable and scalable solution for VANETs,with significant potential to support next-generation ITS.Future research could further enhance the algorithm by integrating multi-objective optimization techniques and exploring broader applications in complex traffic scenarios.
文摘The aim of this study is to determine the level to which the public is aware about ITS(intelligent transportation systems)technologies and how they perceive the potential advantages and inhibitors of ITS in Michigan.A survey was performed with 200 participants living in Michigan,in urban,suburban and rural areas.Questions covered in the survey included how often and how bad traffic congestion occurred,how familiar travelers were with ITS technologies(adaptive traffic signals,real time monitoring of the traffic)and how much support travelers would provide for ITS initiatives.Results reveal that there is a high degree of traffic congestion awareness,there is low public awareness of ITS technologies.While respondents who were aware of ITS solutions had positive views about deploying them,especially in urban areas,they were less supportive of ITS solutions than they were among those who did not know much about these.Factors including area of residence,commute time and age were perceived to influence ITS along with more positive attitudes to ITS amongst urban dwellers and younger respondents.Analysis of key barriers to ITS implementation reflected high initial costs,challenges with technical integration and users’concerns surrounding privacy.
