Commercial exoskeletons currently utilize multiple sensors, including inertial measurement units, electromyography sensors, and torque/force sensors, to detect human motion. While these sensors improve motion recognition by leveraging their unique strengths, they can also lead to discomfort due to direct skin contact, added weight, and complex wiring. In this paper, we propose a simplified motion recognition method that relies solely on encoders embedded in the motors. Our approach aims to accurately classify various movements by learning their distinctive features through a deep learning model. Specifically, we employ a convolutional neural network algorithm optimized for motion classification. Experimental results show that our model can effectively differentiate between movements such as standing, lifting, level walking, and inclined walking, achieving a test accuracy of 98.76%. Additionally, by implementing a sliding window maximum algorithm that tracks three consecutive classifications, we achieved a real-time motion recognition accuracy of 97.48% with a response time of 0.25 seconds. This approach provides a cost-effective and simplified solution for lower limb motion recognition, with potential applications in rehabilitation-focused exoskeletons.

In the case of TV products, space constraints and design requirements make it advantageous to use a worm gear that has a small volume and a self-locking function. Single enveloping worm gear teeth are classified as ZA, ZN, ZK, ZI, and ZC according to international standards. However, combining worm shafts and worm wheels with different tooth profiles can significantly worsen meshing transmission errors and reduce the lifespan of the worm gear. Despite these challenges, due to processing limitations, ease of manufacturing, and cost reduction, combinations of worm shafts and worm wheels with different tooth profiles are still considered. In this study, we confirmed the meshing transmission error for a worm gear that combined a ZA tooth shape worm shaft with a ZI tooth shape worm wheel. Additionally, we examined the contact stress and fatigue life characteristics of the material combinations using finite element analysis (FEM).

The manufacturing industry faces two significant challenges: declining added value due to industrial restructuring and an aging workforce stemming from demographic shifts. In the machining sector, leveraging big data from machine tools has become increasingly critical for enhancing productivity and implementing intelligent manufacturing systems. However, varying data formats and communication methods across different equipment hinder efficient integration, posing a major barrier to the digital transformation of manufacturing. This study develops an integrated server system to facilitate the digital transformation of the machining industry by enabling effective collection, storage, processing, and analysis of data from machine tools. The system features a standardized protocol-based interface for consistent data collection and control across heterogeneous CNC machine tools. By leveraging IEC 62541 OPC UA (Open Platform Communication Unified Architecture) and OPC 40501-1 UMATI (Universal Machine Technology Interface), it ensures interoperability with upper-level applications through standardized information models. The proposed approach addresses inefficiencies in vendor-dependent CNC data systems, providing consistent data management for diverse equipment. By enhancing real-time data handling and eliminating integration challenges, the system contributes to the digital transformation of the manufacturing sector and the creation of an efficient production environment.

External stores on low-speed rotorcraft are subjected to various external forces depending on the aircraft's operating conditions. While there are different types of external forces, this paper focuses on flight loads as defined by US defense specifications. Flight loads consist of static and dynamic loads. Static loads on aircraft external stores include inertial loads resulting from aircraft maneuvers and aerodynamic loads caused by the downward flow of the main wing. To define the inertial load, the inertial load factor on external stores was calculated, while the minimum analysis case for aerodynamic load was derived from trim analysis of rotorcraft blades. The critical design load diagram was developed by combining these factors, and ANSYS was utilized to analyze the structural robustness under static loads. Based on the characteristics of the main wing, a finite element analysis was conducted using a vibration profile tailored to the actual operating environment and an impact profile suitable for the impact conditions. Structural robustness was further assessed through actual tests. This analysis provides essential data for airworthiness certification, allowing for the safe installation of external stores on low-speed rotorcraft.

The use of environmentally friendly, lubricant-free plastic seals in the rotating parts of robots and machines is on the rise. However, variations in seal geometry and operating conditions can influence the contact pressure between the seal and shaft, potentially leading to poor sealing performance, premature wear, or debris ingress. Therefore, advanced design optimization is essential. In this study, we conduct a parametric study and sensitivity analysis to enhance the performance of plastic seals. Finite element analysis (FEA) is carried out using a 2D axisymmetric model with interference fit contact conditions to accurately simulate the behavior of the seal and shaft. We verify the reliability of the analysis by comparing the deformation of the seal diameter before and after shaft insertion with experimental measurements obtained using a 3D tactile measurement device. We analyze four design variables: pressure, temperature, seal diameter, and coefficient of friction, considering seal contact pressure as the objective function. Sensitivity analysis is performed to determine the impact of these design variables on contact pressure and to identify trends.

In laparoscopic surgeries, robotic systems commonly use trocar fixation to achieve remote center motion (RCM). However, this fixation occupies the surgeon's operational space and limits surgical flexibility. It is essential to ensure adequate workspace while maintaining RCM to enhance procedural efficiency and safety. This paper introduces a novel approach to preserve RCM without relying on trocar fixation. The proposed method integrates a six-degree-of-freedom robotic arm with a dual end-effector system, employing tool coordinate storage and remote center point definition to achieve precise four- degree-of-freedom RCM motion control. To validate this method, an experimental setup with an optical tracking system was utilized to measure and calibrate the remote center position. The results indicate that the robot maintained RCM with mean positional errors of 0.672, 0.318, and 0.704 mm along the x, y, and z axes, respectively, yielding a three-dimensional mean error of 1.136 mm. These findings demonstrate the effectiveness of the method in maintaining RCM while maximizing surgical workspace and operational flexibility.

Hyperspectral imaging is a promising technology utilized in various fields, including physics, chemistry, and astrophysics. It can be categorized into point, line, and spectral scanning techniques based on the principle of obtaining hyperspectral cubes. Recently, snapshot hyperspectral imaging techniques have been developed to reduce acquisition time. This review introduces various types of hyperspectral imaging techniques, along with their basic principles and applications. Additionally, it discusses the advantages and disadvantages highlighted in recent research on hyperspectral imaging. This review aims to provide insight into the development of hyperspectral imaging techniques and their appropriate applications.

Human activity recognition (HAR) has been actively researched in fields such as healthcare to understand and analyze human behavior in human-robot interaction. However, most studies have struggled to recognize activities like turning and motion transitions, which are often associated with dynamic balance. Therefore, we propose a novel HAR approach using a single sensor to collect and early fuse motion and position data. The aim is to enhance the accuracy of motion classification for daily activities and those that cause imbalance, which have traditionally been difficult to recognize. We constructed a quarantine room environment for data collection and to evaluate the impact of the suggested features on behavior. Five deep learning models were trained and evaluated to identify the optimal model. The collected data was classified and analyzed by the selected model, which demonstrated an average accuracy of 98.96%.

Recently, flexible pressure sensors featuring enhanced sensitivity and durability through nano/micro additive manufacturing have been employed in various fields, including medical monitoring, E-skin technology, and soft robotics. This study focuses on the fabrication and verification of an interdigitated electrode (IDE) based flexible pressure sensor that incorporates microstructures, utilizing a direct patterning-based additive process. The IDE-patterned sample was designed with a total size of 7.95 × 10 mm2, a line width of 150 µm, a spacing of 200 µm, and a probe pad measuring 1.25 × 2 mm2. It was fabricated using AgNP ink on a primed 100 µm thick polyethylene naphthalate (PEN) substrate. The electrode layer was subsequently covered with a sensing layer made of a MWCNT/Ecoflex composite material, resulting in the final pressure sensor sample. Measurements indicated that the sensor exhibited good sensitivity and response speed, and it was confirmed that further improvements in sensitivity could be achieved by optimizing the size, spacing, and height of the microstructures. Building on the flexible pressure sensor structure developed in this study, we plan to pursue future research aimed at fabricating array sensors with integrated circuits and exploring their applicability in wearable devices for pressure sensing and control functions.

This study numerically investigates the spreading and retracting dynamics of Janus drops on the inner surfaces of cylinders using the Volume of Fluid method. The results indicate that increasing surface curvature enhances spreading in the axial direction and promotes the detachment of the low-viscosity water component, particularly under conditions of high viscosity ratio and Weber number. A regime map is constructed to identify the critical conditions for separation, revealing that surfaces with intermediate curvature exhibit higher separation efficiency compared to those with high curvature. The temporal evolution of axial momenta in the x and z directions highlights the role of viscosity contrast in inducing asymmetric deformation. A scaling law for residence time is proposed as a function of Weber number, which aligns well with simulation results in the high Weber number regime. These findings provide fundamental insights for optimizing surface curvature and fluid composition to enhance drop separation and may benefit applications such as selective liquid extraction, surface cleaning, and microfluidic manipulation.