Stud Pose Detection Based on Photometric Stereo and Lightweight YOLOv4

There are hundreds of welded studs in a car. The posture of a welded stud determines the quality of the body assembly thus affecting the safety of cars. It is crucial to detect the posture of the welded studs. Considering the lack of accurate method in detecting the position of welded studs, this paper aims to detect the weld stud’s pose based on photometric stereo and neural network. Firstly, a machine vision-based stud dataset collection system is built to achieve the stud dataset labeling automatically. Secondly, photometric stereo algorithm is applied to estimate the stud normal map which as input is fed to neural network. Finally, we improve a lightweight YOLOv4 neural network which is applied to achieve the detection of stud position thus overcoming the shortcomings of traditional testing methods. The research and experimental results show that the stud pose detection system designed achieves rapid detection and high accuracy positioning of the stud. This research provides the foundation combining the photometric stereo and deep learning for object detection in industrial production.

UAV maneuvering decision -making algorithm based on Twin Delayed Deep Deterministic Policy Gradient Algorithm

Aiming at intelligent decision-making of UAV based on situation information in air combat, a novel maneuvering decision method based on deep reinforcement learning is proposed in this paper. The autonomous maneuvering model of UAV is established by Markov Decision Process. The Twin Delayed Deep Deterministic Policy Gradient(TD3) algorithm and the Deep Deterministic Policy Gradient (DDPG) algorithm in deep reinforcement learning are used to train the model, and the experimental results of the two algorithms are analyzed and compared. The simulation experiment results show that compared with the DDPG algorithm, the TD3 algorithm has stronger decision-making performance and faster convergence speed, and is more suitable forsolving combat problems. The algorithm proposed in this paper enables UAVs to autonomously make maneuvering decisions based on situation information such as position, speed, and relative azimuth, adjust their actions to approach and successfully strike the enemy, providing a new method for UAVs to make intelligent maneuvering decisions during air combat.

A data transmission approach based on ant colony optimization and threshold proxy re-encryption in WSNs

Wireless sensor networks have become increasingly popular due to the rapid growth of the Internet of Things. As open wireless transmission mediums are easy to attack, security is one of the primary design concerns for wireless sensor networks. Current solutions consider routing and data encryption as two isolated issues, providing incomplete security. Therefore, in this paper we divide the WSN communication process into a data path selection phase and a data encryption phase. By proposing an improved transmission method based on ant colony optimization and threshold proxy re-encryption for wireless sensor networks,named as ACOTPRE, it resists internal and external attacks and ensures safe and efficient data transmission. In the data path selection stage, the ant colony optimization algorithm is used for network routing. The improvement of the pheromone concentration is proposed. In order to resist attacks from external attackers, proxy re-encryption is extended to WSN in the data encryption stage. The threshold secret sharing algorithm is introduced to generate a set of re-encryption key fragments composed of random numbers at the source node. We confirm the performance of our model via simulation studies.

Data with Non-Euclidean Geometry and its Characterization

Recently, dealing the Non-Euclidean data and its characterization is considered as one of the major issues by researchers. The first problem arises while distinction of among Euclidean and non-Euclidean geometry. The second problem arises with dealing the Non-Euclidean geometry in true, false and uncertain regions. The third problem arises while investigating some pattern in Non-Euclidean data sets. This paper focused on tackling these issues with some real life examples.

Survey of NISQ Era Hybrid Quantum-Classical Machine Learning Research

Quantum computing is a rapidly growing field that has received a significant amount of support in the past decade in industry and academia. Several physical quantum computers are now freely available to use through cloud services, with some implementations supporting upwards of hundreds of qubits. These advances mark the beginning of the Noisy Intermediate-Scale Quantum (NISQ) era of quantum computing, paving the way for hybrid quantum-classical systems. This work provides an introductory overview of gate-model quantum computing through the Visual IoT/Robotics Programming Language Environment and a survey of recent applications of NISQ era quantum computers to hybrid quantum-classical machine learning.

Simulation of a Walking Robot-Exoskeleton Movement on a Movable Base

The paper studies the problem of movement of a two-legged walking machine on a movable base. This task is relevant for design rehabilitation and mechanotherapy complexes for people with impaired functions of the musculoskeletal system. Presents a mathematical model that allows obtaining the kinematic and dynamic parameters of the movement of the executive units of the device under study. The paper presents a method for planning the trajectory of exoskeleton links, its algorithmic and software implementation. The paper proposes the structure of the automatic link position control system, which ensures the movement of the executive links along a given trajectory. A mathematical apparatus is proposed for studying the dynamics of the controlled movement of the links of the human-machine system of the exoskeleton. The article presents the results of numerical experiments on the movement of the low-limb exoskeleton leg in the one step mode and analyzes them.

Modeling the Scheduling Problem in Cellular Manufacturing Systems Using Genetic Algorithm as an Efficient Meta-Heuristic Approach

This paper presents a new, bi-criteria mixed-integer programming model for scheduling cells and pieces within each cell in a manufacturing cellular system. The objective of this model is to minimize the makespan and inter-cell movements simultaneously, while considering sequence-dependent cell setup times. In the CMS design and planning, three main steps must be considered, namely cell formation (i.e., piece families and machine grouping), inter and intra-cell layouts, and scheduling issue. Due to the fact that the Cellular Manufacturing Systems (CMS) problem is NP-Hard, a Genetic Algorithm (GA) as an efficient meta-heuristic method is proposed to solve such a hard problem. Finally, a number of test problems are solved to show the efficiency of the proposed GA and the related computational results are compared with the results obtained by the use of an optimization tool.

On Determining the Optimal Lifting Law of the Walking Propulsion Device Foot of an Underwater Robot from the Bottom

The problem of lifting the foot of the walking propulsion device of an underwater mobile robot is considered, taking into account the additional “compression” force acting on it. A mathematical model has been developed for the detachment of a propulsion foot from the ground, based on Henry's laws establishing the concentration of dissolved air in a liquid, the law of gas expansion at a constant temperature, Darcy's law on fluid filtration and the theorem on the motion of the center of mass of a solid body. The linearized model allows to obtain and analytical solutions. Based on the solution of the variational problem, optimal modes of lifting the foot of the walking propulsion of an underwater mobile robot are established.

Analysis of Mechanical Adhesion Climbing Robot Design for Wind Tower Inspection

Mmaintenance of wind turbine farms is a huge task, with associated significant risks and potential hazard to the safety and wellbeing of people who are responsible for carrying the tower inspection tasks. Periodic inspections are required for wind turbine tower to ensure that the wind turbines are in full working order, with no signs of potential failure. Therefore, the development of an automated wind tower inspection system has been very crucial for the overall performance of the renewable wind power generation industry. In order to determine the life span of the tower, an investigation of robot design is discussed in this paper. It presents how a mechanical spring-loaded climbing robot can be designed and constructed to climb and rotate 360° around the tower. An adjustable circular shape robot is designed that allows the device to fit in different diameters of the wind generator tower. The rotational module is designed to allow the wheels to rotate and be able to go in a circular motion. The design further incorporates a suspension that allows the robot to go through any obstacle. This paper also presents afiniteelement spring stress analysis and Simulink control system model to find the optimal parameters that are required for the wind tower climbing robot.