On-line real-time path planning of mobile robots in dynamic uncertain environment

A new path planning method for mobile robots in globally unknown environment with moving obstacles is pre- sented. With an autoregressive (AR) model to predict the future positions of moving obstacles, and the predicted position taken as the next position of moving obstacles, a motion path in dynamic uncertain environment is planned by means of an on-line real-time path planning technique based on polar coordinates in which the desirable direction angle is taken into consideration as an optimization index. The effectiveness, feasibility, high stability, perfect performance of obstacle avoidance, real-time and optimization capability are demonstrated by simulation examples.

Application of A* Algorithm for Real-time Path Re-planning of an Unmanned Surface Vehicle Avoiding Underwater Obstacles

This paper describes path re-planning techniques and underwater obstacle avoidance for unmanned surface vehicle(USV) based on multi-beam forward looking sonar(FLS). Near-optimal paths in static and dynamic environments with underwater obstacles are computed using a numerical solution procedure based on an A* algorithm. The USV is modeled with a circular shape in 2 degrees of freedom(surge and yaw). In this paper, two-dimensional(2-D) underwater obstacle avoidance and the robust real-time path re-planning technique for actual USV using multi-beam FLS are developed. Our real-time path re-planning algorithm has been tested to regenerate the optimal path for several updated frames in the field of view of the sonar with a proper update frequency of the FLS. The performance of the proposed method was verified through simulations, and sea experiments. For simulations, the USV model can avoid both a single stationary obstacle, multiple stationary obstacles and moving obstacles with the near-optimal trajectory that are performed both in the vehicle and the world reference frame. For sea experiments, the proposed method for an underwater obstacle avoidance system is implemented with a USV test platform. The actual USV is automatically controlled and succeeded in its real-time avoidance against the stationary undersea obstacle in the field of view of the FLS together with the Global Positioning System(GPS) of the USV.

Real-time UAV path planning based on LSTM network

To address the shortcomings of single-step decision making in the existing deep reinforcement learning based unmanned aerial vehicle(UAV)real-time path planning problem,a real-time UAV path planning algorithm based on long shortterm memory(RPP-LSTM)network is proposed,which combines the memory characteristics of recurrent neural network(RNN)and the deep reinforcement learning algorithm.LSTM networks are used in this algorithm as Q-value networks for the deep Q network(DQN)algorithm,which makes the decision of the Q-value network has some memory.Thanks to LSTM network,the Q-value network can use the previous environmental information and action information which effectively avoids the problem of single-step decision considering only the current environment.Besides,the algorithm proposes a hierarchical reward and punishment function for the specific problem of UAV real-time path planning,so that the UAV can more reasonably perform path planning.Simulation verification shows that compared with the traditional feed-forward neural network(FNN)based UAV autonomous path planning algorithm,the RPP-LSTM proposed in this paper can adapt to more complex environments and has significantly improved robustness and accuracy when performing UAV real-time path planning.

Prevalence and Antibiotic Resistance of Urinary Tract Pathogens, with Molecular Identification of Klebsiella pneumoniae, Klebsiella oxytoca, and Acinetobacter spp., Using Multiplex Real-Time PCR

Urinary tract infections (UTIs) caused by uropathogens are a significant public health problem, and their treatment primarily relies on antibiotic therapy. However, the increasing global development of antibiotic resistance necessitates updating diagnostic techniques to ensure higher sensitivity and specificity, especially with advancements in science and medicine. This study aimed to evaluate the prevalence of UTIs and antibiotic resistance profiles through urine culture, as well as to identify Klebsiella pneumoniae, Klebsiella oxytoca, and Acinetobacter spp. in urine samples using a molecular approach with multiplex real-time PCR. From May 3 to July 25, 2023, at the Pietro Annigoni Biomolecular Research Center (CERBA) and Saint Camille Hospital of Ouagadougou (HOSCO), 209 urine samples collected from patients with suspected UTIs were analyzed using both urine culture and multiplex real-time PCR. Among the 209 patients, 52.15% were male and 47.85% female, with an average age of 46.87 ± 21.33 years. Urine cultures revealed an overall UTI prevalence of 23.44%, with a prevalence of 8.13% in men versus 15.31% in women (P = 0.023). The bacterial prevalence rates were as follows: Escherichia coli (12.92%), Klebsiella spp. (7.18%), Enterobacter cloacae (1.44%), Staphylococcus aureus (0.96%), and other bacteria. Klebsiella spp. demonstrated 100% resistance to Amoxicillin and Amoxicillin/Clavulanic Acid, while Escherichia coli showed 96.2% and 65.4% resistance to Amoxicillin and Amoxicillin/Clavulanic Acid, respectively. PCR analysis of the target bacteria revealed mono-infection prevalence rates of Klebsiella pneumoniae (10.39%), Klebsiella oxytoca (7.79%), and Acinetobacter spp. (7.79%), along with a co-infection prevalence rate of Klebsiella pneumoniae/Acinetobacter spp. (1.30%). This study demonstrated that PCR, with its high sensitivity and specificity, could effectively distinguish Klebsiella pneumoniae from Klebsiella oxytoca and detect Acinetobacter spp. in less than 24 hours—something urine culture alone could not achieve. The relative ease of automating urine PCR testing, combined with its diagnostic accuracy and rapid turnaround time, makes it a valuable addition to modern medical practice for the laboratory diagnosis of UTIs.

An HPC Based Real-time Path Rating Calculation Tool for Congestion Management with High Penetration of Renewable Energy

Every year, transmission congestion costs billions ofdollars for electricity customers. This clearly identifies the criticalneed for more transmission capacity and also poses big challengesfor power grid reliability in stressed conditions due to heavyloading and in uncertain situations due to variable renewableresources and responsive smart loads. However, it becomesincreasingly difficult to build new transmission lines, whichtypically involve both economic and environmental constraints.In this paper, advanced computing techniques are developedto enable a non-wire solution that realizes unused transfercapabilities of existing transmission facilities. An integratedsoftware prototype powered by high-performance computing(HPC) is developed to calculate ratings of key transmission pathsin real time for relieving transmission congestion and facilitatingrenewable integration, while complying with the North AmericanElectric Reliability Corporation (NERC) standards on assessingtotal transfer capabilities. The innovative algorithms include: (1)massive contingency analysis enabled by dynamic load balancing,(2) parallel transient simulation to speed up single dynamicsimulation, (3) a non-iterative method for calculating voltagesecurity boundary and (4) an integrated package consideringall NERC required limits. This tool has been tested on realisticpower system models in the Western Interconnection of NorthAmerica and demonstrates satisfactory computational speedusing parallel computers. Various benefits of real-time path ratingare investigated at Bonneville Power Administration using realtime EMS snapshots, demonstrating a significant increase in pathlimits. These technologies would change the traditional goals ofpath rating studies, fundamentally transforming how the grid isoperated, and maximizing the utilization of national transmissionassets, as well as facilitating integration of renewable energy andsmart loads.

基于Real-time PCR法检测乳粉中牛源性成分定量研究

基于Real-timePCR建立了乳粉中牛源性成分相对定量检测方法,并对牛的特异性引物与探针进行了特异性、灵敏度和稳定性测试。通过模拟不同浓度牛乳粉与马乳粉混合样本,根据其△Ct值的函数关系进行线性拟合进而绘制标准曲线,建立乳粉中牛源性成分的相对定量检测。结果显示,该方法的最低检测限为0.00001 mg/mL,回收率为91.11%~119.2%,组间变异系数≤0.58%、组内变异系数≤1.44%。说明该方法在特异性与稳定性上适用于乳粉中牛源性成分及含量的掺假检测。

A multifunctional shear apparatus for rocks subjected to true triaxial stress and high temperature in real-time

Deep engineering disasters,such as rockbursts and collapses,are more related to the shear slip of rock joints.A novel multifunctional device was developed to study the shear failure mechanism in rocks.Using this device,the complete shearedeformation process and long-term shear creep tests could be performed on rocks under constant normal stiffness(CNS)or constant normal loading(CNL)conditions in real-time at high temperature and true-triaxial stress.During the research and development process,five key technologies were successfully broken through:(1)the ability to perform true-triaxial compressioneshear loading tests on rock samples with high stiffness;(2)a shear box with ultra-low friction throughout the entire stress space of the rock sample during loading;(3)a control system capable of maintaining high stress for a long time and responding rapidly to the brittle fracture of a rock sample as well;(4)a refined ability to measure the volumetric deformation of rock samples subjected to true triaxial shearing;and(5)a heating system capable of maintaining uniform heating of the rock sample over a long time.By developing these technologies,loading under high true triaxial stress conditions was realized.The apparatus has a maximum normal stiffness of 1000 GPa/m and a maximum operating temperature of 300C.The differences in the surface temperature of the sample are constant to within5C.Five types of true triaxial shear tests were conducted on homogeneous sandstone to verify that the apparatus has good performance and reliability.The results show that temperature,lateral stress,normal stress and time influence the shear deformation,failure mode and strength of the sandstone.The novel apparatus can be reliably used to conduct true-triaxial shear tests on rocks subjected to high temperatures and stress.

Pore-pressure and stress-coupled creep behavior in deep coal:Insights from real-time NMR analysis

Understanding the variations in microscopic pore-fracture structures(MPFS) during coal creep under pore pressure and stress coupling is crucial for coal mining and effective gas treatment. In this manuscript, a triaxial creep test on deep coal at various pore pressures using a test system that combines in-situ mechanical loading with real-time nuclear magnetic resonance(NMR) detection was conducted.Full-scale quantitative characterization, online real-time detection, and visualization of MPFS during coal creep influenced by pore pressure and stress coupling were performed using NMR and NMR imaging(NMRI) techniques. The results revealed that seepage pores and microfractures(SPM) undergo the most significant changes during coal creep, with creep failure gradually expanding from dense primary pore fractures. Pore pressure presence promotes MPFS development primarily by inhibiting SPM compression and encouraging adsorption pores(AP) to evolve into SPM. Coal enters the accelerated creep stage earlier at lower stress levels, resulting in more pronounced creep deformation. The connection between the micro and macro values was established, demonstrating that increased porosity at different pore pressures leads to a negative exponential decay of the viscosity coefficient. The Newton dashpot in the ideal viscoplastic body and the Burgers model was improved using NMR experimental results, and a creep model that considers pore pressure and stress coupling using variable-order fractional operators was developed. The model’s reasonableness was confirmed using creep experimental data. The damagestate adjustment factors ω and β were identified through a parameter sensitivity analysis to characterize the effect of pore pressure and stress coupling on the creep damage characteristics(size and degree of difficulty) of coal.

一种基于real-time PCR技术的TTV检测方法的建立及应用

目的:本研究旨在开发一种具有更高灵敏度和特异性的TTV检测技术,为揭示TTV在多种疾病过程中的作用提供重要的技术支持。方法:为了更精确、灵敏的检测TTV,本研究分析了目前公布的所有亚型的TTV基因序列,在此基础上建立了一种基于UTR区域的real-time PCR检测方法,并与文献报道应用较为广泛的PCR检测方法进行了对比。结果:本研究建立的方法在1×10^(7)~1×10^(1) copies/μL标准品浓度范围内具有良好的线性关系,相关系数为1.000,斜率为-3.446,检测下限为1×10^(1) copies/μL。重复性试验结果显示,组内变异系数为7.22%,表明本方法重复性、稳定性较强。针对30份临床样本,使用本研究建立的real-time PCR检测方法及目前被多个研究所使用的4套引物进行对比。结果表明,本研究所建立的方法灵敏度显著高于文献中报道的4种方法(P<0.01);Sanger测序结果表明,本方法检测出的30份阳性样本均为TTV,检测特异性为100%。结论:本研究采用基于TaqMan探针的real-time PCR检测方法,检测灵敏性高、覆盖基因型范围广,尤其对于TTV病毒载量较低的情况下能够进行定量检测,对于TTV病毒的致病性及作为免疫标志物的应用提供重要的技术支持。

Efficient Route Planning for Real-Time Demand-Responsive Transit

Demand Responsive Transit (DRT) responds to the dynamic users’ requests without any fixed routes and timetablesand determines the stop and the start according to the demands. This study explores the optimization of dynamicvehicle scheduling and real-time route planning in urban public transportation systems, with a focus on busservices. It addresses the limitations of current shared mobility routing algorithms, which are primarily designedfor simpler, single origin/destination scenarios, and do not meet the complex demands of bus transit systems. Theresearch introduces an route planning algorithm designed to dynamically accommodate passenger travel needsand enable real-time route modifications. Unlike traditional methods, this algorithm leverages a queue-based,multi-objective heuristic A∗ approach, offering a solution to the inflexibility and limited coverage of suburbanbus routes. Also, this study conducts a comparative analysis of the proposed algorithm with solutions based onGenetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO), focusing on calculation time, routelength, passenger waiting time, boarding time, and detour rate. The findings demonstrate that the proposedalgorithmsignificantly enhances route planning speed, achieving an 80–100-fold increase in efficiency over existingmodels, thereby supporting the real-time demands of Demand-Responsive Transportation (DRT) systems. Thestudy concludes that this algorithm not only optimizes route planning in bus transit but also presents a scalablesolution for improving urban mobility.

Real-time data processing method for CO_(2) dispersion interferometer on EAST

A real-time data processing system is designed for the carbon dioxide dispersion interferometer(CO_(2)-DI)on EAST.The system utilizes the parallel and pipelining capabilities of an fieldprogrammable gate array(FPGA)to digitize and process the intensity of signals from the detector.Finally,the real-time electron density signals are exported through a digital-to-analog converter(DAC)module in the form of analog signals.The system has been successfully applied in the CO_(2)-DI system to provide low-latency electron density input to the plasma control system on EAST.Experimental results of the latest campaign with long-pulse discharges on EAST(2022–2023)demonstrate that the system can respond effectively in the case of rapid density changes,proving its reliability and accuracy for future electron density calculation.

Real-Time Intelligent Diagnosis of Co-frequency Vibration Faults in Rotating Machinery Based on Lightweight-Convolutional Neural Networks

The co-frequency vibration fault is one of the common faults in the operation of rotating equipment,and realizing the real-time diagnosis of the co-frequency vibration fault is of great significance for monitoring the health state and carrying out vibration suppression of the equipment.In engineering scenarios,co-frequency vibration faults are highlighted by rotational frequency and are difficult to identify,and existing intelligent methods require more hardware conditions and are exclusively time-consuming.Therefore,Lightweight-convolutional neural networks(LW-CNN)algorithm is proposed in this paper to achieve real-time fault diagnosis.The critical parameters are discussed and verified by simulated and experimental signals for the sliding window data augmentation method.Based on LW-CNN and data augmentation,the real-time intelligent diagnosis of co-frequency is realized.Moreover,a real-time detection method of fault diagnosis algorithm is proposed for data acquisition to fault diagnosis.It is verified by experiments that the LW-CNN and sliding window methods are used with high accuracy and real-time performance.

Integrated strategy for real-time wind power fluctuation mitigation and energy storage system control

To address the impact of wind-power fluctuations on the stability of power systems,we propose a comprehensive approach that integrates multiple strategies and methods to enhance the efficiency and reliability of a system.First,we employ a strategy that restricts long-and short-term power output deviations to smoothen wind power fluctuations in real time.Second,we adopt the sliding window instantaneous complete ensemble empirical mode decomposition with adaptive noise(SW-ICEEMDAN)strategy to achieve real-time decomposition of the energy storage power,facilitating internal power distribution within the hybrid energy storage system.Finally,we introduce a rule-based multi-fuzzy control strategy for the secondary adjustment of the initial power allocation commands for different energy storage components.Through simulation validation,we demonstrate that the proposed comprehensive control strategy can smoothen wind power fluctuations in real time and decompose energy storage power.Compared with traditional empirical mode decomposition(EMD),ensemble empirical mode decomposition(EEMD),and complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)decomposition strategies,the configuration of the energy storage system under the SW-ICEEMDAN control strategy is more optimal.Additionally,the state-of-charge of energy storage components fluctuates within a reasonable range,enhancing the stability of the power system and ensuring the secure operation of the energy storage system.

Nanomaterial-assisted wearable glucose biosensors for noninvasive real-time monitoring:Pioneering point-of-care and beyond

This review explores glucose monitoring and management strategies,emphasizing the need for reliable and userfriendly wearable sensors that are the next generation of sensors for continuous glucose detection.In addition,examines key strategies for designing glucose sensors that are multi-functional,reliable,and cost-effective in a variety of contexts.The unique features of effective diabetes management technology are highlighted,with a focus on using nano/biosensor devices that can quickly and accurately detect glucose levels in the blood,improving patient treatment and control of potential diabetes-related infections.The potential of next-generation wearable and touch-sensitive nano biomedical sensor engineering designs for providing full control in assessing implantable,continuous glucose monitoring is also explored.The challenges of standardizing drug or insulin delivery doses,low-cost,real-time detection of increased blood sugar levels in diabetics,and early digital health awareness controls for the adverse effects of injectable medication are identified as unmet needs.Also,the market for biosensors is expected to expand significantly due to the rising need for portable diagnostic equipment and an ever-increasing diabetic population.The paper concludes by emphasizing the need for further research and development of glucose biosensors to meet the stringent requirements for sensitivity and specificity imposed by clinical diagnostics while being cost-effective,stable,and durable.

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.

Real-Time Detection and Instance Segmentation of Strawberry in Unstructured Environment

The real-time detection and instance segmentation of strawberries constitute fundamental components in the development of strawberry harvesting robots.Real-time identification of strawberries in an unstructured envi-ronment is a challenging task.Current instance segmentation algorithms for strawberries suffer from issues such as poor real-time performance and low accuracy.To this end,the present study proposes an Efficient YOLACT(E-YOLACT)algorithm for strawberry detection and segmentation based on the YOLACT framework.The key enhancements of the E-YOLACT encompass the development of a lightweight attention mechanism,pyramid squeeze shuffle attention(PSSA),for efficient feature extraction.Additionally,an attention-guided context-feature pyramid network(AC-FPN)is employed instead of FPN to optimize the architecture’s performance.Furthermore,a feature-enhanced model(FEM)is introduced to enhance the prediction head’s capabilities,while efficient fast non-maximum suppression(EF-NMS)is devised to improve non-maximum suppression.The experimental results demonstrate that the E-YOLACT achieves a Box-mAP and Mask-mAP of 77.9 and 76.6,respectively,on the custom dataset.Moreover,it exhibits an impressive category accuracy of 93.5%.Notably,the E-YOLACT also demonstrates a remarkable real-time detection capability with a speed of 34.8 FPS.The method proposed in this article presents an efficient approach for the vision system of a strawberry-picking robot.

Real-time accurate hand path tracking and joint trajectory planning for industrial robots(Ⅰ)

Previously, researchers raised the accuracy for a robot′s hand to track a specified path in Car-tesian space mainly through increasing the number of knots on the path and the number of the path′s segments, which results in the heavier online computational burden for the robot controller. Aiming at overcoming this drawback, the authors propose a new kind of real-time accurate hand path tracking and joint trajectory planning method. Through selecting some extra knots on the specified hand path by a certain rule and introducing a sinusoidal function to the joint displacement equation of each segment, this method can greatly raise the path tracking accuracy of robot′s hand and does not change the number of the path′s segments. It also does not increase markedly the computational burden of robot controller. The result of simulation indicates that this method is very effective, and has important value in increasing the application of industrial robots.

A real-time performance improvement method for composite time scale

The composite time scale(CTS)provides a stable,accurate,and reliable time scale for modern society.The improvement of CTS’s real-time performance will improve its stability,which strengths related applications’performance.Aiming at this goal,a method achieved by determining the optimal calculation interval and accelerating adjustment stage is proposed in this paper.The determinants of the CTS’s calculation interval(characteristics of the clock ensemble,the measurement noise,the time and frequency synchronization system’s noise and the auxiliary output generator noise floor)are studied and the optimal calculation interval is obtained.We also investigate the effect of ensemble algorithm’s initial parameters on the CTS’s adjustment stage.A strategy to get the reasonable initial parameters of ensemble algorithm is designed.The results show that the adjustment stage can be finished rapidly or even can be shorten to zero with reasonable initial parameters.On this basis,we experimentally generate a distributed CTS with a calculation interval of 500 s and its stability outperforms those of the member clocks when the averaging time is longer than1700 s.The experimental result proves that the CTS’s real-time performance is significantly improved.

FBCRI Based Real-time Path Planning for Unmanned Aerial Vehicles in Unknown Environments with Uncertainty

This paper presents an FBCRI(feedback based compositional rule of inference)based novel path planning method to satisfy the requirements of real-time navigation,smoothness optimization and probabilistic obstacle avoidance.With local path-searching behaviors in regional ranges and global goal-seeking behaviors in holistic ranges,the method infers behavior weights using fuzzy reasoning embedded with feedback,and then coordinates the behaviors to generate new reference waypoints.In view of the deterministic decisions and the uncertain states of a UAV(unmanned air vehicle),chance constraints are adopted to probabilistically guarantee the UAV’s safety at a required level.Simulation results in representative scenes prove that the method is able to rapidly generate convergent paths in obstacle-rich environments,as well as highly improve the path quality with respect to smoothness and probabilistic safety.

Models to Simulate Effective Coverage of Fire Station Based on Real-Time Travel Times

In recent years,frequent fire disasters have led to enormous damage in China.Effective firefighting rescues can minimize the losses caused by fires.During the rescue processes,the travel time of fire trucks can be severely affected by traffic conditions,changing the effective coverage of fire stations.However,it is still challenging to determine the effective coverage of fire stations considering dynamic traffic conditions.This paper addresses this issue by combining the traveling time calculationmodelwith the effective coverage simulationmodel.In addition,it proposes a new index of total effective coverage area(TECA)based on the time-weighted average of the effective coverage area(ECA)to evaluate the urban fire services.It also selects China as the case study to validate the feasibility of the models,a fire station(FS-JX)in Changsha.FS-JX station and its surrounding 9,117 fire risk points are selected as the fire service supply and demand points,respectively.A total of 196 simulation scenarios throughout a consecutiveweek are analyzed.Eventually,1,933,815 sets of valid sample data are obtained.The results showed that the TECA of FS-JX is 3.27 km^(2),which is far below the standard requirement of 7.00 km^(2) due to the traffic conditions.The visualization results showed that three rivers around FS-JX interrupt the continuity of its effective coverage.The proposed method can provide data support to optimize the locations of fire stations by accurately and dynamically determining the effective coverage of fire stations.