NAVIGATION STRATEGY FOR MOBILE ROBOT BASED ON COMPUTER VISION AND YOLOV5 NETWORK IN THE UNKNOWN ENVIRONMENT

Intelligent mobile robots must possess the ability to navigate in complex environments. The field of mobile robot navigation is continuously evolving, with various technologies being developed. Deep learning has gained attention from researchers, and numerous navigation models utilizing deep learning have been proposed. In this study, the YOLOv5 model is utilized to identify objects to aid the mobile robot in determining movement conditions. However, the limitation of deep learning models being trained on insufficient data, leading to inaccurate recognition in unforeseen scenarios, is addressed by introducing an innovative computer vision technology that detects lanes in real-time. Combining the deep learning model with computer vision technology, the robot can identify different types of objects, allowing it to estimate distance and adjust speed accordingly. Additionally, the paper investigates the recognition reliability in varying light intensities. The findings of this study offer promising directions for future breakthroughs in mobile robot navigatio

Effect of shear deformations due to bending and warping on the buckling resistances of thin-walled steel beams

The present paper successfully develops a closed form solution based on a shear deformation theory for elastic lateral-torsional buckling analyses of simply supported thin-walled steel beams. The theory captures the shear effects caused by transverse bending, lateral bending and warping deformations. The closed form solution is successfully validated against 3 dimensional finite element analyses conducted in commercial software. Through various comparisons between the buckling resistances based on a non-shear deformation theory and the buckling resistances based on the present shear deformation theory, the present study finds that (i) the effect of shear deformations on the buckling resistances decreases when the beam span increases, (ii) the effect of shear deformations on the buckling resistance is sensitive with the change of the flange width, and (iii) the effect of shear deformations in general is also influenced by the change of the section depth, and the flange and web thicknesses

SURVEYING THE VIETNAMESE YOUTH ON THE NEGATIVE IMPACT OF SOCIAL MEDIA

In the context of globalization and the rapid development of the Internet, social networks have become an indispensable part of the lives of citizens in the 21st century. In addition to helping people communicate and connect, wireless platforms bring benefits to work, study, and entertainment. However, faced with the staggering increase in the use of social networks, many argue that they can have negative impacts on users, particularly those who are studying or working. This study aims to provide readers with an overview of the negative impacts of social networks on Vietnamese youth. The research data was collected by gathering reputable sources and surveying young people born between 1995 and 2010, belonging to Generation Z, who are living, studying, and working in major cities in Vietnam and using social networks. Through statistical analysis and data processing, the results show that the use of communication platforms has a negative impact on the productivity and health of Vietnamese youth. To minimize the negative impacts on daily life, young people should consider the amount of time they spend using social networks and the content they publish. Additionally, protecting personal information and building positive communities is necessary to avoid unnecessary risks

Mimicking To Dominate: Imitation Learning Strategies for Success in Multiagent Competitive Games

Training agents in multi-agent competitive games presents significant challenges due to their intricate nature. These challenges are exacerbated by dynamics influenced not only by the environment but also by opponents' strategies. Existing methods often struggle with slow convergence and instability. To address this, we harness the potential of imitation learning to comprehend and anticipate opponents' behavior, aiming to mitigate uncertainties with respect to the game dynamics. Our key contributions include: (i) a new multi-agent imitation learning model for predicting next moves of the opponents -- our model works with hidden opponents' actions and local observations; (ii) a new multi-agent reinforcement learning algorithm that combines our imitation learning model and policy training into one single training process; and (iii) extensive experiments in three challenging game environments, including an advanced version of the Star-Craft multi-agent challenge (i.e., SMACv2). Experimental results show that our approach achieves superior performance compared to existing state-of-the-art multi-agent RL algorithms

Inverse Factorized Q-Learning for Cooperative Multi-agent Imitation Learning

This paper concerns imitation learning (IL) (i.e, the problem of learning to mimic expert behaviors from demonstrations) in cooperative multi-agent systems. The learning problem under consideration poses several challenges, characterized by high-dimensional state and action spaces and intricate inter-agent dependencies. In a single-agent setting, IL has proven to be done efficiently through an inverse soft-Q learning process given expert demonstrations. However, extending this framework to a multi-agent context introduces the need to simultaneously learn both local value functions to capture local observations and individual actions, and a joint value function for exploiting centralized learning. In this work, we introduce a novel multi-agent IL algorithm designed to address these challenges. Our approach enables the centralized learning by leveraging mixing networks to aggregate decentralized Q functions. A main advantage of this approach is that the weights of the mixing networks can be trained using information derived from global states. We further establish conditions for the mixing networks under which the multi-agent objective function exhibits convexity within the Q function space. We present extensive experiments conducted on some challenging competitive and cooperative multi-agent game environments, including an advanced version of the Star-Craft multi-agent challenge (i.e., SMACv2), which demonstrates the effectiveness of our proposed algorithm compared to existing state-of-the-art multi-agent IL algorithms

Damage detection in structural health monitoring using combination of deep neural networks

Structural Health Monitoring is a process of continuous evaluation of infrastructure status. In order to be able to detect the damage status, data collected from sensors have to be processed to identify the difference between the damaged and the undamaged states. In recent years, convolution neural network has been applied to detect the structural damage and with positive results. This paper proposes a new method of damage detection using combination of deep neural networks. The method uses a convolution neural network to extract deep features in time series and Long Short Term Memory network to find a statistically significant correlation of each lagged features in time series data. These two types of features are combined to increase discrimination ability compared to deep features only. Finally, the fully connected layer will be used to classify the time series into normal and damaged states. The accuracy of damaged detection was tested on a benchmark dataset from Los Alamos National Laboratory and the result shows that hybrid features provided a highly accurate damage identification

A hybrid heuristic optimization algorithm PSOGSA coupled with a hybrid objective function using ECOMAC and frequency in damage detection

Presence of damage leads to variation in modal properties of observed structures. The majority of studies use the changes in natural frequencies for damage detection. The reason is that the frequencies are often easily measurable with high accuracy by using reasonable sensors. However, frequencies are more sensitive to environmental effects, such as temperature, in comparison with mode shapes. Besides, defects in symmetric structures can cause the same changes in frequency. In contrast, mode shapes are more sensitive to local damage because they own local information and are independent of symmetric characteristics. These make mode shapes have dominant advantages in detecting nonlinear and multiple damage. ECOMAC is an index derived from mode shapes. It is a fact that these indices are not always possible to detect faults successfully in structures. Therefore, in this paper, a hybrid optimization algorithm, particle swarm optimization – gravitational search algorithm, namely PSOGSA, is used to improve the accuracy of infect detection using a hybrid objective function combined ECOMAC and frequency based on the inverse problem. Numerical studies of a two-span continuous beam, a simply supported truss, and a free-free beam, are utilized to verify the effectiveness and reliability of the proposal. From the obtained results, the proposed approach shows high potential in damage identification for different structures

Research on Using Dolomite Aggregate as Cement Treated Base for Highway Pavement Construction in Ninh Binh, Vietnam

Dolomite is commonly used in the construction of highway pavement in the world. However, there are still no concrete specifications or regulations on the use of dolomite for highway construction. Dolomite is available in huge quantities in NinhBinh Province. This is a high potential material for grain bases of highway pavement structure. The alternative material could be a considerable contribution to diversify the supply of aggregate resources for highway pavement construction in the province, and thus contribute to the conservation of natural landscape heritages and limestone resources for related building materials manufacturing industries. In order to evaluate the use of dolomite in highway pavement construction, a research program is conducted to test the working capacity of the cement treated dolomite aggregate, which is intended to use as upper base material in pavement structure. The experimental results showed that the mechanical indicators of the mixture satisfy the requirements for the base layers of highway pavement structure

A hybrid heuristic optimization algorithm PSOGSA coupled with a hybrid objective function using ECOMAC and frequency in damage detection

Presence of damage leads to variation in modal properties of observed structures. The majority of studies use the changes in natural frequencies for damage detection. The reason is that the frequencies are often easily measurable with high accuracy by using reasonable sensors. However, frequencies are more sensitive to environmental effects, such as temperature, in comparison with mode shapes. Besides, defects in symmetric structures can cause the same changes in frequency. In contrast, mode shapes are more sensitive to local damage because they own local information and are independent of symmetric characteristics. These make mode shapes have dominant advantages in detecting nonlinear and multiple damage. ECOMAC is an index derived from mode shapes. It is a fact that these indices are not always possible to detect faults successfully in structures. Therefore, in this paper, a hybrid optimization algorithm, particle swarm optimization – gravitational search algorithm, namely PSOGSA, is used to improve the accuracy of infect detection using a hybrid objective function combined ECOMAC and frequency based on the inverse problem. Numerical studies of a two-span continuous beam, a simply supported truss, and a free-free beam, are utilized to verify the effectiveness and reliability of the proposal. From the obtained results, the proposed approach shows high potential in damage identification for different structures