An Intelligent Optimization Method of Reinforcing Bar Cutting for Construction Site

To meet the requirements of specifications,intelligent optimization of steel bar blanking can improve resource utilization and promote the intelligent development of sustainable construction.As one of the most important building materials in construction engineering,reinforcing bars(rebar)account for more than 30%of the cost in civil engineering.A significant amount of cutting waste is generated during the construction phase.Excessive cutting waste increases construction costs and generates a considerable amount of CO_(2)emission.This study aimed to develop an optimization algorithm for steel bar blanking that can be used in the intelligent optimization of steel bar engineering to realize sustainable construction.In the proposed algorithm,the integer linear programming algorithm was applied to solve the problem.It was combined with the statistical method,a greedy strategy was introduced,and a method for determining the dynamic critical threshold was developed to ensure the accuracy of large-scale data calculation.The proposed algorithm was verified through a case study;the results confirmed that the rebar loss rate of the proposed method was reduced by 9.124%compared with that of traditional distributed processing of steel bars,reducing CO_(2)emissions and saving construction costs.As the scale of a project increases,the calculation quality of the optimization algorithmfor steel bar blanking proposed also increases,while maintaining high calculation efficiency.When the results of this study are applied in practice,they can be used as a sustainable foundation for building informatization and intelligent development.

Never Lost Keys:A Novel Key Generation Scheme Based on Motor Imagery EEG in End-Edge-Cloud System

Biometric key is generated from the user’s unique biometric features,and can effectively solve the security problems in cryptography.However,the current prevailing biometric key generation techniques such as fingerprint recognition and facial recognition are poor in randomness and can be forged easily.According to the characteristics of Electroencephalographic(EEG)signals such as the randomness,nonlinear and non-stationary etc.,it can significantly avoid these flaws.This paper proposes a novel method to generate keys based on EEG signals with end-edgecloud collaboration computing.Using sensors to measure motor imagery EEG data,the key is generated via pre-processing,feature extraction and classification.Experiments show the total time consumption of the key generation process is about 2.45s.Our scheme is practical and feasible,which provides a research route to generate biometric keys using EEG data.

A car-following model based on the optimized velocity and its security analysis

An enhanced optimal velocity model(EOVM)that considers driving safety is established to alleviate traffic congestion and ensure driving safety.Time headway is introduced as a criterion for determining whether the car is safe.When the time headway is less discussed to ensure the model's safety and maintain the following state.A stability analysis of the model was carried out to determine than the minimum time headway(TH_(min))or more than the most comfortable time headway(TH_(com)),the acceleration constraints are the stability conditions of the model.The EOVM is compared with the optimal velocity model(OVM)and fuzzy car-following model using the real dataset.Experiments show that the EOVM model has the smallest error in average,maximum and median with the real dataset.To confirm the model's safety,design fleet simulation experiments were conducted for three actual scenarios of starting,stopping and uniform process.

Algorithm Contest of Calibration-free Motor Imagery BCI in the BCI Controlled Robot Contest in World Robot Contest 2021:A survey

Objective:From September 10 to 13,2021,the finals of the BCI Controlled Robot Contest in World Robot Contest 2021 were held in Beijing,China.Eleven teams participated in the Algorithm Contest of Calibration-free Motor Imagery BCI.The participants employed both traditional electroencephalograph(EEG)analysis methods and deep learning-based methods in the contest.In this paper,we reviewed the algorithms utilized by the participants,extracted the trends and highlighted interesting approaches from these methods to inform future contests and research recommendations.Method:First,we analyzed the algorithms in separate steps,including EEG channel and signal segment setup,prepossessing technology,and classification model.Then,we emphasized the highlights of each algorithm.Finally,we compared the competition algorithm with the SOTA algorithm.Results:The algorithm employed in the finals performed better than that of the SOTA algorithm.During the final stage of the contest,four of the top five teams used convolutional neural network models,suggesting that with the rapid development of deep learning,convolutional neural network-based models have been the most popular methods in the field of motor imagery BCI.

Deep Reinforcement Learning Object Tracking Based on Actor-Double Critic Network

Aiming at the problem of poor tracking robustness caused by severe occlusion,deformation,and object rotation of deep learning object tracking algorithm in complex scenes,an improved deep reinforcement learning object tracking algorithm based on actor-double critic network is proposed.In offline training phase,the actor network moves the rectangular box representing the object location according to the input sequence image to obtain the action value,that is,the horizontal,vertical,and scale transformation of the object.Then,the designed double critic network is used to evaluate the action value,and the output double Q value is averaged to guide the actor network to optimize the tracking strategy.The design of double critic network effectively improves the stability and convergence,especially in challenging scenes such as object occlusion,and the tracking performance is significantly improved.In online tracking phase,the well-trained actor network is used to infer the changing action of the bounding box,directly causing the tracker to move the box to the object position in the current frame.Several comparative tracking experiments were conducted on the OTB100 visual tracker benchmark and the experimental results show that more intensive reward settings significantly increase the actor network’s output probability of positive actions.This makes the tracking algorithm proposed in this paper outperforms the mainstream deep reinforcement learning tracking algorithms and deep learning tracking algorithms under the challenging attributes such as occlusion,deformation,and rotation.