In this paper, we propose a deep Q-network-based resource allocation method for efficient communication between a base station and multiple Unmanned Aerial Vehicles (UAVs) in environments with limited wireless resources. This method focused on maximizing the throughput of UAV to Infrastructure (U2I) links while ensuring that UAV to UAV (U2U) links could meet their data transmission time constraints, even when U2U links share the wireless resource used by U2I links. The deep Q-network agent uses the Channel State Information (CSI) of both U2U and U2I links, along with the remaining time for data transmission, as state, and determines optimal Resource Block (RB) and transmission power for each UAV. Simulation results demonstrated that the proposed method significantly outperformed both random allocation and CSI-based greedy algorithms in terms of U2I link throughput and the probability of meeting U2U link time constraints.

Holonic Manufacturing Systems (HMSs) are regarded as a foundation of cyber-physical production systems as they enable computers to conduct intelligent process planning, scheduling, and control by endowing manufacturing components with autonomy and collaboration. In an HMS, autonomy is realized by specifying holons that represent virtual agents of manufacturing components, while collaboration is facilitated through a communication mechanism that enables data exchange and decision making throughout a holarchy of holons without human intervention. This study presents the development of a virtualized holon model and a predictive process planning procedure using the Asset Administration Shell (AAS), i.e., a standardized model that can identify digital representation of manufacturing components to ensure interoperability. Specifically, an AAS-based information model was proposed to define operator, machine, product, and order holons. In addition, a predictive process planning procedure based on the Contract Net Protocol was developed to automatically allocate tasks while predicting task execution times. This study can contribute to the designing of an AAS- domain specific information model for HMS to increase interoperability in the holon holarchy, exhibiting the feasibility of AAS applications in predictive process planning on HMS.

In this paper, a deburring tool with 3-axis compliance is presented for deburring using a robot manipulator. Compliance is provided with beam structures instead of pneumatic pressure, which enables integrated 3-axis force sensing and variable stiffness. Two radial compliances were achieved using 4-PSS (Prismatic-Spherical-Spherical) legs, with P joints composed of cantilever beams. The one axial compliance was configured with two ball bushings and a linear spring. Strain gauges were attached to cantilever beams and a load cell was mounted between the linear spring and the universal joint to perform force sensing. The stability of vibrations and force sensing were verified through deburring experiments using the proposed deburring tool. Additionally, experiments on automatic offset for applying a constant force during deburring were conducted and results were validated by comparing the workpiece before and after the deburring process.

Drones are increasingly used in various fields such as agriculture, logistics, and disaster response due to their agility and versatility. In indoor plant factories, small drones are used to monitor crop conditions and collect environmental data. However, small drones require frequent recharging due to their limited battery capacity, making autonomous charging systems essential for uninterrupted operation of drones. This study proposes an autonomous charging station designed for small drones in indoor plant factories. The system employs a wired charging mechanism to enhance charging efficiency, and a 3-degree-of-freedom (DOF) pose alignment system, utilizing an XY plotter and turntable, to correct drone landing errors. The alignment system ensures that drones, landing with random positions and orientations, are automatically adjusted to the correct position for charging. Experiments demonstrated that the charging station successfully aligned and charged drones with a 93% success rate on the first attempt. Even in cases of failure, the system automatically retried until a 100% success rate was achieved. This autonomous drone charging system has the potential to significantly enhance operational efficiency in indoor plant factories and can be adapted for various drone models in future applications.

This study presents results of Computational fluid dynamics (CFD) analysis conducted to evaluate performances of various functional products developed for smart bathroom systems. The primary objective was to analyze the efficiency of space heating, direct drying, and dehumidification functions in a winter bathroom environment. Representative bathroom models in South Korea were selected and detailed CFD simulations were performed on these models. Results showed that bathtub models exhibited higher efficiency overall in space heating and dehumidification than shower booth models. This was attributed to differences in bathroom structure and internal air flow. Additionally, the direct drying function showed higher efficiency in bathtub models, determined by the placement of air outlets and inlets. This study provides essential foundational data that can contribute to the design and enhancement of smart bathroom systems' functionality, offering valuable insights for the development of optimized smart bathroom products.

In soft robotics, gripper technology based on granular jamming offers the capability to adapt flexibly to objects of diverse shapes and material properties. Specifically, small-scale jamming grippers can address tasks challenging for conventional grippers either by enhancing gripping performance or by extending functionality when combined with rigid grippers. This study investigated effects of membrane morphology, thickness, and material on performances of small-scale jamming grippers to identify optimal design parameters. Experiments were conducted with three membrane morphologies, two thickness levels, and two material types. Results indicated that a concentric-pocket membrane morphology, a membrane thickness of 1.5 mm, and a soft material such as Dragon Skin 10 achieved a superior holding force of 430.7 gf. These findings indicate that softer materials can improve the membrane's ability to conform to objects, while increasing thickness can minimize deformation due to tensile forces, thereby enhancing gripping stability. Furthermore, experiments demonstrated that this configuration could enable the gripper to safely grasp objects of various shapes and perform additional tasks, such as rotating valves and handles, with effectiveness.

This study aimed to develop a regression-based model for predicting tool life in manufacturing environments, with goals of enhancing productivity and reducing costs. In machining operations, particularly roughing processes, high cutting forces can accelerate tool wear, often leading to process interruptions and increased defect rates. Previous research on tool life prediction has frequently relied on empirical models and statistical methods, which face limitations in reliability across diverse machining conditions. To address this issue, we proposed a data-driven approach that could collects tool wear data under varying machining conditions (such as cutting speed, feed rate, and depth of cut) and applied regression models to predict tool life effectively. The model’s performance was validated under multiple conditions to assess its predictive accuracy. This study offers a practical tool life management solution for manufacturing settings, optimizing tool usage and enhancing operational efficiency.

Facility Layout Problem (FLP) aims to optimize arrangement of facilities to enhance productivity and minimize costs. Traditional methods face challenges in dealing with the complexity and non-linearity of modern manufacturing environments. This study introduced an approach combining Reinforcement Learning (RL) and simulation to optimize manufacturing line layouts. Deep Q-Network (DQN) learns to reduce unused space, improve path efficiency, and maximize space utilization by optimizing facility placement and material flow. Simulations were used to validate layouts and evaluate performance based on production output, path length, and bending frequency. This RL-based method offers a more adaptable and efficient solution for FLP than traditional techniques, addressing both physical and operational optimization.