People with hemiplegia require ongoing rehabilitation exercises to regain function in their upper limbs. However, due to the increasing number of elderly and disabled people, the number of rehabilitation professionals is insufficient. As a solution to this problem, researchers have been exploring various upper limb rehabilitation exercise robots. Unfortunately, these robots are often large and heavy, making them cumbersome to wear and use. The proposed exoskeleton rehabilitation robot consists of two robotic modules: an elbow module (1 DOF) and a wrist module (1 DOF). In order to analyze the robot"s workspace, the kinematics were calculated using the D-H parameters. To generate the trajectories, five able-bodied individuals wore the robot and performed the hand-wash motion, resulting in a total of 10 trajectory data sets. The reference trajectories were then generated by polynomial regression based on the collected data. Lastly, a passive mode control was experimented with in the rehabilitation process, and the results demonstrated the promising effectiveness of the proposed robot.

Belt-pulley looseness is a crucial factor in ensuring the safe operation of machinery used in industrial applications, such as compressors and fans. Traditionally, belt looseness has been inspected using contact-based current and vibration sensors. However, these methods are time-consuming and require manual attachment of the sensors. In order to overcome the limitations of these traditional methods, we propose a remote diagnosis method for detecting belt looseness using a smartphone. By utilizing a four-mirror system, the smartphone can construct a stereo system that enables 3D reconstruction of the object. This allows us to reconstruct the 3D trajectory of the belt and diagnose the level of looseness. To further enhance the accuracy of our proposed system, we have developed a calibration algorithm specifically designed for the four-mirror system. In our actual experiment, we successfully diagnosed four levels of belt looseness. As the level of looseness increased, we observed a curved shape in the 3D trajectory of the belt, along with noticeable quantitative differences. To quantitatively analyze these differences, we introduced a measure called the residual, which reflects the curvilinearity of the 3D trajectory. Our findings confirmed a significant correlation between the residual and the level of belt looseness.

In Hopkinson bar theory, stress, strain, and strain rate can be determined by analyzing the dimensions of the specimen. When conducting Split-Hopkinson Pressure Bar (SHPB) experiments, the stress-strain curve is obtained by considering the entire length and width of the specimen. However, in Split-Hopkinson Tensile Bar (SHTB) experiments, it is important to only consider the regions where deformation occurs in order to accurately determine the dynamic material properties. This study introduces a method for selecting the dimensions of the deformed region (LD and WD) in plate specimens for SHTB experiments using Finite Element Analysis (FEA). The analysis involved varying the length and width of a 1 mm thick SUS430 specimen, and the deformed region was determined using the proposed method. The stress-strain curves obtained from this region were then compared with the input Cowper-Symonds model. The validity of the proposed approach was confirmed, as the percentage error between them ranged from 2.54 to 6.62%.

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.

In recent years, the demand for lightweight parts has been gradually increasing, particularly in the E-mobility industry. Among lightweight materials, aluminum alloys are highly beneficial for improving the fuel efficiency of automobile engines due to their lighter weight compared to iron-based materials. As electric vehicles become more prevalent, aluminum alloys are also extensively used in components such as battery housings and EV platform frames. To enhance productivity, aluminum parts processing companies require Polycrystalline Diamond (PCD) cutting tools for high-speed and ultraprecision processing. PCD cutting tools are known for their excellent cutting performance and wear resistance in highspeed aluminum machining, and they are anticipated to have significant growth potential in the global cutting tool market. In this study, we manufactured three types of PCD cutting tools (Drill, Endmill, and Reamer) using a self-developed brazing device and analyzed the machining surface quality through experiments. The results showed that the brazing joint quality of the PCD cutting tools was high, and the differences in surface roughness values under various machining conditions were minimal, confirming no issues with machining performance. Future research will focus on evaluating machining precision and tool life through comparative experiments with advanced PCD cutting tools from overseas.

In this study, a module combining various types of sensors was developed to increase search efficiency inside collapsed buildings. It was designed to be less than 70 mm in diameter so that it can be put into narrow spaces, and is equipped with a small & high-performance processor to process multiple sensor data. To increase sensor data processing efficiency, multi thread based software was configured, and the images were combined and transmitted to ensure time synchronization of multi-channel video data. A human detection function based on sound source detection using two microphones was implemented. The developed multi-sensor module was tested for operation by mounting it on a snake-type robot in a test bed simulating a disaster site. It was confirmed that the visible range of the robot to which the multi-sensor module was applied was expanded, and the ability to detect human and low-light human detect was secured.

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%.

The fast steering mirror is now being used in industries beyond precision processing, such as space and defense. The piezoelectric fast steering mirror (PFSM), which utilizes a piezoelectric actuator, is particularly suitable for these industries as they often require devices like electro-optic devices to withstand external vibrations and impacts. While the PFSM has inherent high stiffness, its complex structure makes it difficult to control. To address this, an accurate dynamic model is necessary. In this paper, we derived a dynamic model for the PFSM using a two-inertial system model that takes into account its structural characteristics. This dynamic model consists of both a mechanical system model and an electrical system model. We measured the frequency response function from electrical input to mechanical output and compared it with the derived frequency response model to verify its accuracy. The derived model can be used not only for control design, but also for instrument design and interpretation.

In this study, we developed a new vertical thermal gradient rig that uses methane-oxygen fuel. We conducted thermal gradient testing on a thermal barrier coating system, with a flame temperature of 1,900℃. Our results showed that the maximum surface temperature reached 1,065℃, while the temperature difference between the surface temperature and the temperature of the middle substrate (ΔT) was 70oC. Using the same torch as in this study, our finding suggest that the total flow rate of the flame should be above 12.4 LPM, and the gun distance should be less than 8 cm, to simulate a surface temperature of 1,300℃, while keeping the substrate temperature below 1,000℃. This will ensure that the flame is wide enough to cover the entire surface area of the thermal barrier coating.

This paper outlines the fabrication process of the partition component, a crucial element in digital PCR. The partition component consists of thousands of micro-wells capable of holding small volumes of reagents. In this study, the partition component was created in a honeycomb structure, with hexagonally shaped micro-wells measuring 100 μm in size and spaced 20 μm apart. The fabrication process involved using photolithography, lift-off, and electroplating techniques. Photolithography and lift-off processes were employed to create a pattern of Cu metal layers in a hexagonal honeycomb arrangement on a glass substrate. Subsequently, the Cu metal-patterned substrate was used to produce pillar patterns of SU-8 with a high aspect ratio using photolithography. Finally, the gaps between the SU-8 pillar patterns were filled with nickel through electroplating, completing the partition component. The micro-wells in the partition component were designed to have an aspect ratio of 4-5; however, in this study, micro-wells with an aspect ratio of 2 and a depth of 200 μm were fabricated.