The Study of Wireless Collision Avoidance and Early Warning System in Metro Vehicles

The basic composition and working principle of wireless collision avoidance and early warning system based on spread spectrum ranging which is used in urban mass transit is introduced in this paper. Some performance indicators such as maximum measured distance and range errors are theoretically analyzed and numerically calculated. According to the characteristics of the urban mass transit, the applicability of the system is evaluated.

Early warning method for thermal runaway of lithium-ion batteries under thermal abuse condition based on online electrochemical impedance monitoring

Early warning of thermal runaway(TR)of lithium-ion batteries(LIBs)is a significant challenge in current application scenarios.Timely and effective TR early warning technology is urgently required considering the current fire safety situation of LIBs.In this work,we report an early warning method of TR with online electrochemical impedance spectroscopy(EIS)monitoring,which overcomes the shortcomings of warning methods based on traditional signals such as temperature,gas,and pressure with obvious delay and high cost.With in-situ data acquisition through accelerating rate calorimeter(ARC)-EIS test,the crucial features of TR were extracted using the RReliefF algorithm.TR mechanisms corresponding to the features at specific frequencies were analyzed.Finally,a three-level warning strategy for single battery,series module,and parallel module was formulated,which can successfully send out an early warning signal ahead of the self-heating temperature of battery under thermal abuse condition.The technology can provide a reliable basis for the timely intervention of battery thermal management and fire protection systems and is expected to be applied to electric vehicles and energy storage devices to realize early warning and improve battery safety.

Path-Following Control With Obstacle Avoidance of Autonomous Surface Vehicles Subject to Actuator Faults

This paper investigates the path-following control problem with obstacle avoidance of autonomous surface vehicles in the presence of actuator faults,uncertainty and external disturbances.Autonomous surface vehicles inevitably suffer from actuator faults in complex sea environments,which may cause existing obstacle avoidance strategies to fail.To reduce the influence of actuator faults,an improved artificial potential function is constructed by introducing the lower bound of actuator efficiency factors.The nonlinear state observer,which only depends on measurable position information of the autonomous surface vehicle,is used to address uncertainties and external disturbances.By using a backstepping technique and adaptive mechanism,a path-following control strategy with obstacle avoidance and fault tolerance is designed which can ensure that the tracking errors converge to a small neighborhood of zero.Compared with existing results,the proposed control strategy has the capability of obstacle avoidance and fault tolerance simultaneously.Finally,the comparison results through simulations are given to verify the effectiveness of the proposed method.

Real-time debris flow monitoring and automated warning system

At present,debris flow warning uses precipitation threshold and issues regional warning throughout the world.Precipitation threshold warning is less accurate and in most of the time large portion of unaffected population are evacuated.More precise warning should use direct monitoring.There are many debris flow monitoring stations but no real time warning system in use.The main reason is that the identification and confirmation of debris flow occurrence requires human interaction and it is too slow.A debris flow monitoring and warning system has been installed in the midstream section of Yusui Stream,Taiwan China.The monitoring station operates fully automatically,providing early warnings without the need for manual intervention.The system comprises two webcam cameras,two Micro-Electro-Mechanical Systems(MEMS),and a rain gauge.The arrival of debris flows is detected and confirmed through both webcam images and MEMS signals.Once debris flow is detected,the system automatically issues a warning to the affected areas via voice messages,line messages,broadcasts,and web-based alerts.The webcam cameras are also used to estimate debris flow velocity and flow height,while the MEMS sensors are utilized to determine the phase speed and flow rate.On July 24th,2014,Typhoon Gaemi triggered several debris flows,and the system successfully issued several warnings automatically.The entire video record,along with depth variation data,was recorded automatically.

Wearable electronic device for X-ray warning and health monitoring

The well-developed multifunctional wearable electronic device has fed the demand for human medicine and health monitoring in complex situations.However,the advancement of nuclear technology,especially irradiation medicine and safety inspections,has increased the exposure risk of irradiation safety workers.Traditional irradiation detectors are stiff and incompatible with the skin,and lack human health monitoring function,thus it’s vital to apply these flexible sensors for irradiation warning.Here,we report a novel composite gel device synthesized through solution processes by combining the Cs_(3)Cu_(2)I_(5):Zn nanoscintillator with the pre-patterned biocompatible gel,exhibiting a bi-functional response to motion/vibration sensing and sensitive irradiation warning.These wearable devices achieve a pressure sensitivity of up to 34 kPa^(-1)in a low-pressure range (0–3 kPa),a low limit of detection (LoD) down to 1.4 Pa,enabling health monitoring functions of pulse monitoring,finger bending,and elbow bending.Simultaneously,the device scintillates under X-ray irradiation among a wide dose rate range of 54–1167μGy_(air)s^(-1).The robust device shows no obvious signal loss after 4000 compression cycles and also excellent irradiation resistance over 50 days,broadening the path for designing and realizing new functional wearable devices.

Ultrafast Response and Threshold Adjustable Intelligent Thermoelectric Systems for Next‑Generation Self‑Powered Remote IoT Fire Warning

Fire warning is vital to human life,economy and ecology.However,the development of effective warning systems faces great challenges of fast response,adjustable threshold and remote detecting.Here,we propose an intelligent self-powered remote IoT fire warning system,by employing single-walled carbon nanotube/titanium carbide thermoelectric composite films.The flexible films,prepared by a convenient solution mixing,display p-type characteristic with excellent high-temperature stability,flame retardancy and TE(power factor of 239.7±15.8μW m^(-1) K^(-2))performances.The comprehensive morphology and structural analyses shed light on the underlying mechanisms.And the assembled TE devices(TEDs)exhibit fast fire warning with adjustable warning threshold voltages(1–10 mV).Excitingly,an ultrafast fire warning response time of~0.1 s at 1 mV threshold voltage is achieved,rivaling many state-of-the-art systems.Furthermore,TE fire warning systems reveal outstanding stability after 50 repeated cycles and desired durability even undergoing 180 days of air exposure.Finally,a TED-based wireless intelligent fire warning system has been developed by coupling an amplifier,analogto-digital converter and Bluetooth module.By combining TE characteristics,high-temperature stability and flame retardancy with wireless IoT signal transmission,TE-based hybrid system developed here is promising for next-generation self-powered remote IoT fire warning applications.

Method for rapid warning and activity concentration estimates in online waterγ-spectrometry systems

Onlineγ-spectrometry systems for inland waters,most of which extract samples in situ and in real time,are able to produce reliable activity concentration measurements for waterborne radionuclides only when they are distributed relatively uniformly and enter into a steady-state diffusion regime in the measurement chamber.To protect residents’health and ensure the safety of the living environment,better timeliness is required for this measurement method.To address this issue,this study established a mathematical model of the online waterγ-spectrometry system so that rapid warning and activity estimates can be obtained for water under non-steady-state(NSS)conditions.In addition,the detection efficiency of the detector for radionuclides during the NSS diffusion process was determined by applying the computational fluid dynamics technique in conjunction with Monte Carlo simulations.On this basis,a method was developed that allowed the online waterγ-spectrometry system to provide rapid warning and activity concentration estimates for radionuclides in water.Subsequent analysis of the NSS-mode measurements of^(40)K radioactive solutions with different activity concentrations determined the optimum warning threshold and measurement time for producing accurate activity concentration estimates for radionuclides.The experimental results show that the proposed NSS measurement method is able to give warning and yield accurate activity concentration estimates for radionuclides 55.42 and 69.42 min after the entry of a 10 Bq/L^(40)K radioactive solution into the measurement chamber,respectively.These times are much shorter than the 90 min required by the conventional measurement method.Furthermore,the NSS measurement method allows the measurement system to give rapid(within approximately 15 min)warning when the activity concentrations of some radionuclides reach their respective limits stipulated in the Guidelines for Drinking-water Quality of the WHO,suggesting that this method considerably enhances the warning capacity of in situ online waterγ-spectrometry systems.

Comparing 11 early warning scores and three shock indices in early sepsis prediction in the emergency department

BACKGROUND:This study aimed to evaluate the discriminatory performance of 11 vital sign-based early warning scores(EWSs)and three shock indices in early sepsis prediction in the emergency department(ED).METHODS:We performed a retrospective study on consecutive adult patients with an infection over 3 months in a public ED in Hong Kong.The primary outcome was sepsis(Sepsis-3 definition)within 48 h of ED presentation.Using c-statistics and the DeLong test,we compared 11 EWSs,including the National Early Warning Score 2(NEWS2),Modified Early Warning Score,and Worthing Physiological Scoring System(WPS),etc.,and three shock indices(the shock index[SI],modified shock index[MSI],and diastolic shock index[DSI]),with Systemic Inflammatory Response Syndrome(SIRS)and quick Sequential Organ Failure Assessment(qSOFA)in predicting the primary outcome,intensive care unit admission,and mortality at different time points.RESULTS:We analyzed 601 patients,of whom 166(27.6%)developed sepsis.NEWS2 had the highest point estimate(area under the receiver operating characteristic curve[AUROC]0.75,95%CI 0.70-0.79)and was significantly better than SIRS,qSOFA,other EWSs and shock indices,except WPS,at predicting the primary outcome.However,the pooled sensitivity and specificity of NEWS2≥5 for the prediction of sepsis were 0.45(95%CI 0.37-0.52)and 0.88(95%CI 0.85-0.91),respectively.The discriminatory performance of all EWSs and shock indices declined when used to predict mortality at a more remote time point.CONCLUSION:NEWS2 compared favorably with other EWSs and shock indices in early sepsis prediction but its low sensitivity at the usual cut-off point requires further modification for sepsis screening.

A Lightweight UAV Visual Obstacle Avoidance Algorithm Based on Improved YOLOv8

The importance of unmanned aerial vehicle(UAV)obstacle avoidance algorithms lies in their ability to ensure flight safety and collision avoidance,thereby protecting people and property.We propose UAD-YOLOv8,a lightweight YOLOv8-based obstacle detection algorithm optimized for UAV obstacle avoidance.The algorithm enhances the detection capability for small and irregular obstacles by removing the P5 feature layer and introducing deformable convolution v2(DCNv2)to optimize the cross stage partial bottleneck with 2 convolutions and fusion(C2f)module.Additionally,it reduces the model’s parameter count and computational load by constructing the unite ghost and depth-wise separable convolution(UGDConv)series of lightweight convolutions and a lightweight detection head.Based on this,we designed a visual obstacle avoidance algorithm that can improve the obstacle avoidance performance of UAVs in different environments.In particular,we propose an adaptive distance detection algorithm based on obstacle attributes to solve the ranging problem for multiple types and irregular obstacles to further enhance the UAV’s obstacle avoidance capability.To verify the effectiveness of the algorithm,the UAV obstacle detection(UAD)dataset was created.The experimental results show that UAD-YOLOv8 improves mAP50 by 3.4%and reduces GFLOPs by 34.5%compared to YOLOv8n while reducing the number of parameters by 77.4%and the model size by 73%.These improvements significantly enhance the UAV’s obstacle avoidance performance in complex environments,demonstrating its wide range of applications.

Effect of preload forces on multidimensional signal dynamic behaviours for battery early safety warning

Providing early safety warning for batteries in real-world applications is challenging.In this study,comprehensive thermal abuse experiments are conducted to clarify the multidimensional signal evolution of battery failure under various preload forces.The time-sequence relationship among expansion force,voltage,and temperature during thermal abuse under five categorised stages is revealed.Three characteristic peaks are identified for the expansion force,which correspond to venting,internal short-circuiting,and thermal runaway.In particular,an abnormal expansion force signal can be detected at temperatures as low as 42.4°C,followed by battery thermal runaway in approximately 6.5 min.Moreover,reducing the preload force can improve the effectiveness of the early-warning method via the expansion force.Specifically,reducing the preload force from 6000 to 1000 N prolongs the warning time(i.e.,227 to 398 s)before thermal runaway is triggered.Based on the results,a notable expansion force early-warning method is proposed that can successfully enable early safety warning approximately 375 s ahead of battery thermal runaway and effectively prevent failure propagation with module validation.This study provides a practical reference for the development of timely and accurate early-warning strategies as well as guidance for the design of safer battery systems.

Research on Anthropomorphic Obstacle Avoidance Trajectory Planning for Adaptive Driving Scenarios Based on Inverse Reinforcement Learning Theory

The forward design of trajectory planning strategies requires preset trajectory optimization functions,resulting in poor adaptability of the strategy and an inability to accurately generate obstacle avoidance trajectories that conform to real driver behavior habits.In addition,owing to the strong time-varying dynamic characteristics of obstacle avoidance scenarios,it is necessary to design numerous trajectory optimization functions and adjust the corresponding parameters.Therefore,an anthropomorphic obstacle-avoidance trajectory planning strategy for adaptive driving scenarios is proposed.First,numerous expert-demonstrated trajectories are extracted from the HighD natural driving dataset.Subsequently,a trajectory expectation feature-matching algorithm is proposed that uses maximum entropy inverse reinforcement learning theory to learn the extracted expert-demonstrated trajectories and achieve automatic acquisition of the optimization function of the expert-demonstrated trajectory.Furthermore,a mapping model is constructed by combining the key driving scenario information that affects vehicle obstacle avoidance with the weight of the optimization function,and an anthropomorphic obstacle avoidance trajectory planning strategy for adaptive driving scenarios is proposed.Finally,the proposed strategy is verified based on real driving scenarios.The results show that the strategy can adjust the weight distribution of the trajectory optimization function in real time according to the“emergency degree”of obstacle avoidance and the state of the vehicle.Moreover,this strategy can generate anthropomorphic trajectories that are similar to expert-demonstrated trajectories,effectively improving the adaptability and acceptability of trajectories in driving scenarios.

Linear-quadratic and norm-bounded combined differential game guidance scheme with obstacle avoidance for attacking defended aircraft in three-player engagement

A differential game guidance scheme with obstacle avoidance,based on the formulation of a combined linear quadratic and norm-bounded differential game,is designed for a three-player engagement scenario,which includes a pursuer,an interceptor,and an evader.The confrontation between the players is divided into four phases(P1-P4)by introducing the switching time,and proposing different guidance strategies according to the phase where the static obstacle is located:the linear quadratic game method is employed to devise the guidance scheme for the energy optimization when the obstacle is located in the P1 and P3 stages;the norm-bounded differential game guidance strategy is presented to satisfy the acceleration constraint under the circumstance that the obstacle is located in the P2 and P4 phases.Furthermore,the radii of the static obstacle and the interceptor are taken as the design parameters to derive the combined guidance strategy through the dead-zone function,which guarantees that the pursuer avoids the static obstacle,and the interceptor,and attacks the evader.Finally,the nonlinear numerical simulations verify the performance of the game guidance strategy.

The Relationship between Overparenting and Adolescent Anxiety:The Mediating Role of Cognitive Avoidance

Background:Adolescent anxiety has a significant impact on physical and mental health,and overparenting is recognized as one of the major factors affecting adolescent anxiety.The objective of this study was to investigate the relationship between overparenting and adolescent anxiety,while also examining the mediating role of cognitive avoidance.Methods:Data were collected through a cross-sectional survey with 1931 valid responses using the Overparenting Scale,the Cognitive Avoidance Scale,and the Anxiety Self-Rating Scale.A structural equation modelling approach was used to test the mediating role of cognitive avoidance between overparenting and adolescent anxiety and to reveal the underlying mechanisms.The significance of the mediating effect was assessed based on maximum likelihood estimation.Differences in the mediating role of cognitive avoidance in the male and female samples were comparatively analyzed in the mediation effect analysis.Results:The study’s findings reveal a significant positive correlation between overparenting and adolescent anxiety(p<0.01),between overparenting and cognitive avoidance(p<0.01),and between cognitive avoidance and adolescent anxiety(p<0.01).Cognitive avoidance mediated the relationship between overparenting and adolescent anxiety.Overparenting can not only directly predict adolescent anxiety but also indirectly predict it through the mediating role of cognitive avoidance.Conclusion:This study validates the direct effect of overparenting on adolescent anxiety and reveals the mechanism of cognitive avoidance as a mediator.

A Secure Blockchain-Based Vehicular Collision Avoidance Protocol: Detecting and Preventing Blackhole Attacks

This work aims to examine the vulnerabilities and threats in the applications of intelligent transport systems,especially collision avoidance protocols.It focuses on achieving the availability of network communication among traveling vehicles.Finally,it aims to find a secure solution to prevent blackhole attacks on vehicular network communications.The proposed solution relies on authenticating vehicles by joining a blockchain network.This technology provides identification information and receives cryptography keys.Moreover,the ad hoc on-demand distance vector(AODV)protocol is used for route discovery and ensuring reliable node communication.The system activates an adaptive mode for monitoring communications and continually adjusts trust scores based on packet delivery performance.From the experimental study,we can infer that the proposed protocol has successfully detected and prevented blackhole attacks for different numbers of simulated vehicles and at different traveling speeds.This reduces accident rates by 60%and increases the packet delivery ratio and the throughput of the connecting network by 40%and 20%,respectively.However,extra overheads in delay and memory are required to create and initialize the blockchain network.

Identification of Early Warning Signals of Infectious Diseases in Hospitals by Integrating Clinical Treatment and Disease Prevention

The global incidence of infectious diseases has increased in recent years,posing a significant threat to human health.Hospitals typically serve as frontline institutions for detecting infectious diseases.However,accurately identifying warning signals of infectious diseases in a timely manner,especially emerging infectious diseases,can be challenging.Consequently,there is a pressing need to integrate treatment and disease prevention data to conduct comprehensive analyses aimed at preventing and controlling infectious diseases within hospitals.This paper examines the role of medical data in the early identification of infectious diseases,explores early warning technologies for infectious disease recognition,and assesses monitoring and early warning mechanisms for infectious diseases.We propose that hospitals adopt novel multidimensional early warning technologies to mine and analyze medical data from various systems,in compliance with national strategies to integrate clinical treatment and disease prevention.Furthermore,hospitals should establish institution-specific,clinical-based early warning models for infectious diseases to actively monitor early signals and enhance preparedness for infectious disease prevention and control.

ARHCS (Automatic Rainfall Half-Life Cluster System): A Landslides Early Warning System (LEWS) Using Cluster Analysis and Automatic Threshold Definition

A significant portion of Landslide Early Warning Systems (LEWS) relies on the definition of operational thresholds and the monitoring of cumulative rainfall for alert issuance. These thresholds can be obtained in various ways, but most often they are based on previous landslide data. This approach introduces several limitations. For instance, there is a requirement for the location to have been previously monitored in some way to have this type of information recorded. Another significant limitation is the need for information regarding the location and timing of incidents. Despite the current ease of obtaining location information (GPS, drone images, etc.), the timing of the event remains challenging to ascertain for a considerable portion of landslide data. Concerning rainfall monitoring, there are multiple ways to consider it, for instance, examining accumulations over various intervals (1 h, 6 h, 24 h, 72 h), as well as in the calculation of effective rainfall, which represents the precipitation that actually infiltrates the soil. However, in the vast majority of cases, both the thresholds and the rain monitoring approach are defined manually and subjectively, relying on the operators’ experience. This makes the process labor-intensive and time-consuming, hindering the establishment of a truly standardized and rapidly scalable methodology on a large scale. In this work, we propose a Landslides Early Warning System (LEWS) based on the concept of rainfall half-life and the determination of thresholds using Cluster Analysis and data inversion. The system is designed to be applied in extensive monitoring networks, such as the one utilized by Cemaden, Brazil’s National Center for Monitoring and Early Warning of Natural Disasters.

LSDA-APF:A Local Obstacle Avoidance Algorithm for Unmanned Surface Vehicles Based on 5G Communication Environment

In view of the complex marine environment of navigation,especially in the case of multiple static and dynamic obstacles,the traditional obstacle avoidance algorithms applied to unmanned surface vehicles(USV)are prone to fall into the trap of local optimization.Therefore,this paper proposes an improved artificial potential field(APF)algorithm,which uses 5G communication technology to communicate between the USV and the control center.The algorithm introduces the USV discrimination mechanism to avoid the USV falling into local optimization when the USV encounter different obstacles in different scenarios.Considering the various scenarios between the USV and other dynamic obstacles such as vessels in the process of performing tasks,the algorithm introduces the concept of dynamic artificial potential field.For the multiple obstacles encountered in the process of USV sailing,based on the International Regulations for Preventing Collisions at Sea(COLREGS),the USV determines whether the next step will fall into local optimization through the discriminationmechanism.The local potential field of the USV will dynamically adjust,and the reverse virtual gravitational potential field will be added to prevent it from falling into the local optimization and avoid collisions.The objective function and cost function are designed at the same time,so that the USV can smoothly switch between the global path and the local obstacle avoidance.The simulation results show that the improved APF algorithm proposed in this paper can successfully avoid various obstacles in the complex marine environment,and take navigation time and economic cost into account.

Robust Platoon Control of Mixed Autonomous and Human-Driven Vehicles for Obstacle Collision Avoidance:A Cooperative Sensing-Based Adaptive Model Predictive Control Approach

Obstacle detection and platoon control for mixed traffic flows,comprising human-driven vehicles(HDVs)and connected and autonomous vehicles(CAVs),face challenges from uncertain disturbances,such as sensor faults,inaccurate driver operations,and mismatched model errors.Furthermore,misleading sensing information or malicious attacks in vehicular wireless networks can jeopardize CAVs’perception and platoon safety.In this paper,we develop a two-dimensional robust control method for a mixed platoon,including a single leading CAV and multiple following HDVs that incorpo-rate robust information sensing and platoon control.To effectively detect and locate unknown obstacles ahead of the leading CAV,we propose a cooperative vehicle-infrastructure sensing scheme and integrate it with an adaptive model predictive control scheme for the leading CAV.This sensing scheme fuses information from multiple nodes while suppressing malicious data from attackers to enhance robustness and attack resilience in a distributed and adaptive manner.Additionally,we propose a distributed car-following control scheme with robustness to guarantee the following HDVs,considering uncertain disturbances.We also provide theoretical proof of the string stability under this control framework.Finally,extensive simulations are conducted to validate our approach.The simulation results demonstrate that our method can effectively filter out misleading sensing information from malicious attackers,significantly reduce the mean-square deviation in obstacle sensing,and approach the theoretical error lower bound.Moreover,the proposed control method successfully achieves obstacle avoidance for the mixed platoon while ensuring stability and robustness in the face of external attacks and uncertain disturbances.

Multi-Lever EarlyWarning forWind and Photovoltaic Power Ramp Events Based on Neural Network and Fuzzy Logic

With the increasing penetration of renewable energy in power system,renewable energy power ramp events(REPREs),dominated by wind power and photovoltaic power,pose significant threats to the secure and stable operation of power systems.This paper presents an early warning method for REPREs based on long short-term memory(LSTM)network and fuzzy logic.First,the warning levels of REPREs are defined by assessing the control costs of various power control measures.Then,the next 4-h power support capability of external grid is estimated by a tie line power predictionmodel,which is constructed based on the LSTMnetwork.Finally,considering the risk attitudes of dispatchers,fuzzy rules are employed to address the boundary value attribution of the early warning interval,improving the rationality of power ramp event early warning.Simulation results demonstrate that the proposed method can generate reasonable early warning levels for REPREs,guiding decision-making for control strategy.

Research on the driver fatigue early warning model of electric vehicles based on the fusion of EMG and ECG signals

Electric vehicles have been rapidly developing worldwide due to the use of new energy.However,at the same time,serious traffic accidents caused by driver fatigue in emergency situations have also drawn widespread attention.The lack of datasets in real vehicle test environments has always been a bottleneck in the research of driver fatigue in electric vehicles.Therefore,this study establishes a dataset from real vehicle test,applies the Bayesian optimization support vector machine(BOA-SVM)algorithm to take features of electromyography(EMG)and electrocardiography(ECG)signals as input and develop an early warning model for driving fatigue detection.Firstly,the driver’s EMG and ECG signals are collected through real vehicle testing experiments and then combined with the driver’s subjective fatigue evaluation scores to establish the dataset.Secondly,the study establishes a driver fatigue early warning model for emergency situations.Time-domain and frequency-domain features are extracted from the EMG signals.Principal component analysis(PCA)is applied for dimensionality reduction of these features.The experimental results show that based on the input of dimensionality reduced EMG features and ECG features,the BOA-SVM algorithm achieved an accuracy of 94.4%in classification.