查看更多>>摘要：In response to the uncertainty of information of the injured in post disaster situations,considering constraints such as random chance and the quantity of rescue resource,the split deliv-ery vehicle routing problem with stochastic demands(SDVRPSD)model and the multi-depot split delivery heterogeneous vehicle routing problem with stochastic demands(MDSDHVRPSD)model are established.A two-stage hybrid variable neighborhood tabu search algorithm is designed for unmanned vehicle task planning to minimize the path cost of rescue plans.Simulation experiments show that the solution obtained by the algorithm can effectively reduce the rescue vehicle path cost and the rescue task completion time,with high optimization quality and certain portability.
查看更多>>摘要：Road lanes and markings are the bases for autonomous driving environment perception.In this paper,we propose an end-to-end multi-task network,Road All Information Extractor named RAIENet,which aims to extract the full information of the road surface including road lanes,road markings and their correspondences.Based on the prior knowledge of pavement information,we explore and use the deep progressive relationship between lane segmentation and pavement mark-ing detection.Then,different attention mechanisms are adapted for different tasks.A lane detec-tion accuracy of 0.807 F1-score and a ground marking accuracy of 0.971 mean average precision at intersection over union(IOU)threshold 0.5 were achieved on the newly labeled see more on road plus(CeyMo+)dataset.Of course,we also validated it on two well-known datasets Berkeley Deep-Drive 100K(BDD100K)and CULane.In addition,a post-processing method for generating bird's eye view lane(BEVLane)using lidar point cloud information is proposed,which is used for the construction of high-definition maps and subsequent decision-making planning.The code and data are available at https://github.com/mayberpf/RAIEnet.
查看更多>>摘要：The complexity of unknown scenarios and the dynamics involved in target entrapment make designing control strategies for swarm robots a formidable task,which in turn impacts their efficiency in complex and dynamic settings.To address these challenges,this paper introduces an adaptive swarm robot entrapment control model grounded in the transformation of gene regulatory networks(AT-GRN).This innovative model enables swarm robots to dynamically adjust entrap-ment strategies by assessing current environmental conditions via real-time sensory data.Further-more,an improved motion control model for swarm robots is designed to dynamically shape the for-mation generated by the AT-GRN.Through two sets of rigorous experimental environments,the proposed model significantly enhances the trapping performance of swarm robots in complex envi-ronments,demonstrating remarkable adaptability and stability.
查看更多>>摘要：Strabismus significantly impacts human health as a prevalent ophthalmic condition.Early detection of strabismus is crucial for effective treatment and prognosis.Traditional deep learning models for strabismus detection often fail to estimate prediction certainty precisely.This paper employed a Bayesian deep learning algorithm with knowledge distillation,improving the model's performance and uncertainty estimation ability.Trained on 6 807 images from two tertiary hospitals,the model showed significantly higher diagnostic accuracy than traditional deep-learning models.Experimental results revealed that knowledge distillation enhanced the Bayesian model's performance and uncertainty estimation ability.These findings underscore the combined benefits of using Bayesian deep learning algorithms and knowledge distillation,which improve the reliability and accuracy of strabismus diagnostic predictions.
查看更多>>摘要：Gesture recognition plays an increasingly important role as the requirements of intelli-gent systems for human-computer interaction methods increase.To improve the accuracy of the millimeter-wave radar gesture detection algorithm with limited computational resources,this study improves the detection performance in terms of optimized features and interference filtering.The accuracy of the algorithm is improved by refining the combination of gesture features using a self-constructed dataset,and biometric filtering is introduced to reduce the interference of inanimate object motion.Finally,experiments demonstrate the effectiveness of the proposed algorithm in both mitigating interference from inanimate objects and accurately recognizing gestures.Results show a notable 93.29%average reduction in false detections achieved through the integration of biometric filtering into the algorithm's interpretation of target movements.Additionally,the algorithm adeptly identifies the six gestures with an average accuracy of 96.84%on embedded systems.
查看更多>>摘要：Automatic pavement crack detection plays an important role in ensuring road safety.In images of cracks,information about the cracks can be conveyed through high-frequency and low-fre-quency signals that focus on fine details and global structures,respectively.The output features obtained from different convolutional layers can be combined to represent information about both high-frequency and low-frequency signals.In this paper,we propose an encoder-decoder framework called octave hierarchical network(Octave-H),which is based on the U-Network(U-Net)architec-ture and utilizes an octave convolutional neural network and a hierarchical feature learning module for performing crack detection.The proposed octave convolution is capable of extracting multi-fre-quency feature maps,capturing both fine details and global cracks.We propose a hierarchical fea-ture learning module that merges multi-frequency-scale feature maps with different levels(high and low)of octave convolutional layers.To verify the superiority of the proposed Octave-H,we employed the CrackForest dataset(CFD)and AigleRN databases to evaluate this method.The experimental results demonstrate that Octave-H outperforms other algorithms with satisfactory per-formance.
查看更多>>摘要：With the prevalence of machine learning in malware defense,hackers have tried to attack machine learning models to evade detection.It is generally difficult to explore the details of malware detection models,hackers can adopt fuzzing attack to manipulate the features of the mal-ware closer to benign programs on the premise of retaining their functions.In this paper,attack and defense methods on malware detection models based on machine learning algorithms were studied.Firstly,we designed a fuzzing attack method by randomly modifying features to evade detection.The fuzzing attack can effectively descend the accuracy of machine learning model with single fea-ture.Then an adversarial malware detection model MaliFuzz is proposed to defend fuzzing attack.Different from the ordinary single feature detection model,the combined features by static and dynamic analysis to improve the defense ability are used.The experiment results show that the adversarial malware detection model with combined features can deal with the attack.The meth-ods designed in this paper have great significance in improving the security of malware detection models and have good application prospects.
查看更多>>摘要：The closed-loop flight control system of fly by wire is generally adopted in modern air-craft.Based on the frequency-domain stability analysis,the aeroservoelastic model of closed-loop aircraft is established,and aeroservoelastic stability parameterized calculation of design improve-ment is conducted after the preliminary analysis.The design variables are mounted location of inte-grated sensors and damping coefficients ζ1,ζ2 of notch filter,with stability margin of the system as design objective.Results indicate that aeroservoelastic margin of the aircraft in certain states is insufficient.While the mounted location of integrated sensors is adjusted,the system stability can be improved to certain extent.It's more appropriate to mount the integrated sensors in the overlap-ping field between the nodal lines of vertical and lateral bending for the fuselage.The system stabil-ity is also significantly improved by adding notch filter,both gain margin and phase margin increase when the real number pair ζ1-ζ2 is located in the zone above the 45° diagonal of ζ1,ζ2 con-struction plane,and the farther the ζ1-ζ2 is from the 45° diagonal,the stronger the system stability.Also the decrease in the gain peak of frequency response and the enhancement of relative stability of the system are achieved by the appropriate ζ1-ζ2 of notch filter.
查看更多>>摘要：The economic operation of integrated energy system(IES)faces new challenges such as multi-timescale characteristics of heterogeneous energy sources,and cooperative operation of hybrid energy storage system(HESS).To this end,this paper investigates the multi-timescale rolling opti-mization problem for IES integrated with HESS.Firstly,the architecture of IES with HESS is established,a comparative analysis is conducted to evaluate the advantages of the HESS over a sin-gle energy storage system(SESS)in stabilizing power fluctuations.Secondly,the day-ahead and real-time scheduling cost functions of IES are established,the day-ahead scheduling mainly depends on operation costs of the components in IES,the real-time optimal scheduling adopts the Lya-punov optimization method to schedule the battery and hydrogen energy storage in each time slot,so as to minimize the real-time average scheduling operation cost,and the problem of day-ahead and real-time scheduling error,which caused by the uncertainty of the energy storage is solved by online optimization.Finally,the proposed model is verified to reduce the scheduling operation cost and the dispatching error by performing an arithmetic example analysis of the IES in Shanghai,which provides a reference for the safe and stable operation of the IES.
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