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.

CNN is one of the deep learning technologies useful for image-based pattern recognition and classification. For machining processes, this technique can be used to predict machining parameters and surface roughness. In electrical discharge machining (EDM), the machined surface is covered with many craters, the shape of which depends on the workpiece material and pulse parameters. In this study, CNN was applied to predict EDM parameters including capacitor, workpiece material, and surface roughness. After machining three metals (brass, stainless steel, and cemented carbide) with different discharge energies, images of machined surfaces were collected using a scanning electron microscope (SEM) and a digital microscope. Surface roughness of each surface was then measured. The CNN model was used to predict machining parameters and surface roughness.

In the semiconductor manufacturing industry, efficient operation of wafer transfer robots has a direct impact on productivity and product quality. Ball screw misalignment anomalies are a critical factor affecting precision transport of robots. Early diagnosis of these anomalies is essential to maintaining system efficiency. This study proposed a method to effectively diagnose ball screw misalignment anomalies using 1D-CNN and 2D-CNN models. This method mainly uses binary classification to distinguish between normal and abnormal states. Additionally, explainable artificial intelligence (XAI) technology was applied to interpret diagnostic decisions of the two deep learning models, allowing users to convince prediction results of the AI model. This study was based on data collected through acceleration sensors and torque sensors. It compared accuracies of 1D-CNN and 2D-CNN models. It presents a method to explain the model"s predictions through XAI. Experimental results showed that the proposed method could diagnose ball screw misalignment anomalies with high accuracy. This is expected to contribute to the establishment of reliable abnormality diagnosis and preventive maintenance strategies in industrial sites.

Elderly monitoring systems are gaining significant attention in our increasingly aging society. Existing monitoring systems, which utilize RGB and infrared cameras, often encounter errors when recognizing human-like objects, photos, and videos as actual humans. Additionally, privacy concerns arise due to this issue. However, these challenges can potentially be overcome by employing thermal images. Thus, our study aimed to investigate the feasibility of identifying and categorizing human postures depicted in thermal images using deep learning models and algorithms. To conduct our experiment, we developed a system that utilizes a thermal pose algorithm and a convolutional neural network. As a result, we achieved an average accuracy of 88.3%, with the highest accuracy reaching 91.2%.

In order to monitor the machining status of a machine tool, it is necessary to measure the signal of the machine tool and establish the relationship between the machining status and the signal. One effective approach is to utilize an AIbased analysis model. To improve the accuracy and reliability of AI models, it is crucial to identify the features of the model through signal analysis. However, when dealing with time series data, it has been challenging to identify these features. Therefore, instead of directly applying time series data, a method was used to extract the best features by processing the data using techniques such as RMS and FFT. Recently, there have been numerous reported cases of designing AI models with high accuracy and reliability by directly applying time series data to find the best features, particularly in the case of AI models combining CNN and LSTM. In this paper, time series data obtained through a gap sensor are directly applied to an AI model that combines CNN, LSTM, and MLP (Multi-Layer Perceptron) to determine tool wear. The machine tool and tool status were monitored and evaluated through an AI model trained using time series data from the machining process.

Brain-computer interface (BCI) is a technology used in various fields to analyze electroencephalography (EEG) signals to recognize an individual"s intention or state and control a computer or machine. However, most of the research on BCI is on motor imagery, and research on active movement is concentrated on upper limb movement. In the case of lower limb movement, most of the research is on the static state or single movements. Therefore, in this research, we developed a deep-learning model for classifying walking behavior(1: walking, 2: upstairs, 3: downstairs) based on EEG signals in a dynamic environment to verify the possibility of classifying EEG signals in a dynamic state. We developed a model that combined a convolutional neural network (CNN) and a bidirectional long short-term memory (BiLSTM). The model obtained an average recognition performance of 82.01%, with an average accuracy of 93.77% for walking, 76.52% for upstairs, and 75.75% for downstairs. It is anticipated that various robotic devices aimed at assisting people with disabilities and the elderly could be designed in the future with multiple features, such as human-robot interaction, object manipulation, and path-planning utilizing BCI for control.

The Technical Specification for Interoperability (TSI) legally mandates the prediction and verification process of the Reliability, Availability, Maintainability and Safety (RAMS) in signaling and communication systems. Recently, domestic regulations, including the Railroad Safety Act, have been strengthened in order to better meet the requirements for participating in international projects. To comply with these regulatory requirements, manufacturers and development organizations must prepare verification data pertaining to the reliability and safety of railway components and related systems. This paper aims to analyze the requirements of Failure Mode, Effects and Criticality Analysis (FMECA) through international laws and standards, and subsequently propose a compliant FMECA system for the domestic railway industry. The proposed FMECA system is then compared with the analysis results of actual failure data to determine its suitability for establishing a Reliability, Availability, Maintainability (RAM) verification standard for railway products in relation to conformity assessment.

In this study, we proposed an AI-algorithm for face mask recognition based on the MobileNetV2 network to implement automatic door control in intensive care units. The proposed network was constructed using four bottleneck blocks, incorporating depth-wise separable convolution with channel expansion/projection to minimize computational costs. The performance of the proposed network was compared with other networks trained with an identical dataset. Our network demonstrated higher accuracy than other networks. It also had less trainable total parameters. Additionally, we employed the CVzone-based machine learning model to automatically detect face location. The neural network for mask recognition and the face detection model were integrated into a system for real-time door control using Arduino. Consequently, the proposed algorithm could automatically verify the wearing of masks upon entry to intensive care units, thereby preventing respiratory disease infections among patients and medical staff. The low computational cost and high accuracy of the proposed algorithm also provide excellent performance for real-time mask recognition in actual environments.

A pod mounted on an aircraft external must be installed on an aircraft after its structural safety is verified under flight conditions. This paper presents methods of flight load and test load generation. Evaluation of test result data and standards for failure mode are also presented. First of all, to verify the static structural stability, flight loads for the aircraft maneuvering conditions were calculated. Finite element analysis was then performed with flight loads. As a result of the analysis, structures were verified to have a margin of safety for a given design requirement. In addition, it was confirmed that the launcher tube had enough rigidity to support the missile. Thus, the role of stinger such as longeron and hardback was insignificant. Finally, based on results of tests and analysis, the static structural stability of pod was substantiated and the reliability and effectiveness of the analysis model were obtained. These results and dynamic stability verification results suggest that an optimal design is necessary.

Anomaly detection models using big data generated from facilities and equipment have been adopted for predictive maintenance in the manufacturing industry. When facility faults or defects occur, different patterns of abnormal data are shown owing to their component behaviors. By detecting these pattern changes, it is possible to determine whether a facility abnormality occurs. This study evaluated the anomaly detection results from a combined driving system consisting of three driving motors for about six months at a manufacturing site. The learning data with an autoencoder model for about a month at the beginning of vibration data collection and continuous monitoring of anomalies using reconstruction errors showed that a component defect occurred in one driving motor, and the reconstruction error increased progressively about three months earlier than a facility manager found the failure. In addition, the micro-electro-mechanical systems sensor showed high amplitude in the entire frequency domain when high reconstruction errors occurred. However, the integrated electronics piezoelectric sensor showed different patterns as high amplitude in a specific frequency domain. The results of this study will be helpful for detecting facility abnormalities in combined driving systems using vibration sensors.

Bone plates are a medical device used for fixing broken bones, which should not have a crack and hole defect. Defect detection is very important because bone plate defect is very dangerous. In this study, we proposed a defect detection model based on a parallel type convolution neural network for detecting bone plate crack and pore deformation. All size filters were different according to the defect shape. A convolution neural network detected pore defects. Another convolution neural network detected the crack. Two convolution neural networks simultaneously detected different defect types. The performance of the defect detection model was measured and used for the F1- score. We confirmed that performance of the defect detection model was 98.4%. We confirmed that the defect detection time was 0.21 seconds.

During digital microscopy of an oil painting surface it is inconvenient to analyze an entire image due to multiple defocused areas. The defocusing is usually caused by the small depth of the lens and the rough surface curve. Thus, these microscopic images in an oil painting have multiple focal points, which indicates multi-focus images. We present a multi-focus fusion synthesizing a focused image from scans based on focal direction and selection of focused places. Based on microscopic characteristics, a common scanned area of the images was defined to unify the lens multiplication. A focus index was applied to each pixel to identify well-focused pixels and generate a mapping image in the focal direction. Subsequently, a median filter was applied to the mapping image and a multifocal image was acquired based on actual pixel values obtained from the mapping image. The proposed method was utilized in analyzing oil painting samples carrying rough surface curves. The multifocal image facilitated the analysis of the oil painting surface and resulted in enhanced quality compared with other methods. The proposed method can be used to generate useful images in scientific and industrial microscopy.

As the size of semiconductor devices gradually decreases, it is important to measure and analyze semiconductor devices, to improve the image quality of semiconductors. We use VDSR, one of the Super-Resolution methods to improve the quality of semiconductor devices’ SEM images. VDSR is also a convolutional neural network that can be optimized with various parameters. In this study, a VDSR model for semiconductor devices’ SEM images was optimized using parameters such as depth of layers and amount of training data. Meanwhile, the quantitative evaluation and the qualitative evaluation did not match at the low scale factor. To solve this problem, we proposed an MTF measurement method using the slanted edge for better quantitative evaluation. This method was verified by comparing the results with the PSNR and SSIM index results, which are known as quality indicators. Based on the results, it was confirmed that using the MTF value could be a better approach for the evaluation of SEM images of the semiconductor device than using PSNR and SSIM.

Recently, interest in Prognostics and Health management (PHM) has been increasing as an advanced technology of maintenance. PHM technology is a technology that allows equipment to check its condition and predict failures in advance. To realize PHM technology, it is important to implement artificial intelligence technology that diagnoses failures based on data. Vibration data is often used to diagnose the state of the rotating machine. Additionally, there have been many efforts to convert vibration data into 2D images to apply a convolutional neural network (CNN), which is emerging as a powerful algorithm in the image processing field, to vibration data. In this study, a series of PHM processes for acquiring data from a rotary machine and using it to check the condition of the machine were applied to the rotary table. Additionally, a study was conducted to introduce and compare two methodologies for converting vibration data into 2D images. Finally, a GUI program to implement the PHM process was developed.

Nanoparticle laser sintering is one of the vital technologies in additive manufacturing. Numerous processes such as freeform surfaces or seamless parts have been proposed for the fabrication of complex components, however, the resolution and quality of the processes do not meet the standard necessary for practical applications. Therefore, selective laser sintering is used to fabricate electrode patterns in high-precision manufacturing field. Despite the various advantages, laser sintering process generates defects on the pattern with one of the major contributing factors being the Marangoni flow. In this study, the laser sintering process was used to determine the relationship between the nanoparticle blending conditions and the microstructure of the fabricated electrode pattern through the control of the nanoparticles density and laser characteristics such as power, pulse duration, and scan speed. As a result, the conditions for Marangoni flow were analyzed in relation to the concentration of the nanoparticle solution and laser irradiation parameters. More severe Marangoni flow was produced with the solution having a low weight percent of nanoparticles, while the width of the pattern was uniform when the pulsed laser was applied using a high peak power to achieve the same total amount of energy.