This study evaluates the structural design and safety of the CanSat in launch environments. The CanSat serves as an educational replica satellite, allowing users to experience the design and operation of small satellites. To ensure stable operation during launch, the structural analysis and design must consider external forces, including vibration and acceleration loads. We determined the material properties for the structure and conducted modal and random vibration analyses, comparing the results with launch environment data from NASA, ECSS, Falcon 9, and Soyuz-2. Additionally, we performed an acceleration load analysis using actual data from CanSat launches during competitions. The modal analysis indicated that the first natural frequency was 65.34 Hz, which exceeds the required threshold. The random vibration and acceleration load analyses further confirmed the structural safety of the design. While the data from NASA and ECSS were conservatively set, reflecting higher vibration intensities, the Falcon 9 and Soyuz-2 launch vehicles provided relatively lower vibration environments due to differences in their designs. Overall, the results demonstrate that the CanSat's structural integrity is maintained under the conditions analyzed for Falcon 9 and Soyuz-2.

Falls are common among older people. Age-related changes in toe strength and force steadiness may increase fall risk. This study aimed to evaluate the performance of a fall risk prediction model using toe strength and force steadiness data as input variables. Participants were four healthy adults (25.5±1.7 yrs). To indirectly reproduce physical conditions of older adults, an experiment was conducted by adding conditions for weight and fatigue increase. The maximal strength (MVIC) was measured for 5 s using a custom toe dynamometer. For force steadiness, toe flexion was measured for 10 s according to the target line, which was 40% of the MVIC. A one-leg-standing test was performed for 10 s with eyes-opened using a force plate. Deep learning experiments were performed with seven conditions using long short-term memory (LSTM) algorithms. Results of the deep learning model were randomly mixed and expressed through a confusion matrix. Results showed potential of the model"s fall risk prediction with force steadiness data as input variables. However, experiments were conducted on young adults. Additional experiments should be conducted on older adults to evaluate the predictive model.

Tire-related crashes account for a large proportion of all types of car accidents. The causes of tire-related accidents are inappropriate tire temperature, pressure, and wear. Although temperature and pressure can be monitored easily with TPMS, there exists no system to monitor tire wear regularly. This paper proposes a system that can estimate tire wear using a 3-axis accelerometer attached to the tread inside the tire. This system utilizes axial acceleration, extracts feature from data acquired with the accelerometer and estimates tire wear by feature classification using machine learning. In particular, the proposed tire wear estimation method is designed to estimate tread depth in four types (7, 5.6, 4.2, and 1.4 mm) at speeds of 40, 50, and 60 kmph. Based on the data obtained during several runs on a test track, it has been found that this system can estimate the tread depth with reasonable accuracy.

In the existing machine tool field, the focus was on the displacement of the feed system from the viewpoint of the motion of the machine tool. The displacement of the tool or spindle of a machine tool is useful for developing various functions. In this study, using the acceleration data of the spindle, we proposed an algorithm that tracked the displacement of the spindle with respect to the pseudo-step waveform motion. In order to solve the bandwidth problem of the pseudo-step waveform, the displacement data measured by the motor encoder of the feed system was used. In addition, in order to solve the drift problem due to double integration, a new drift removal filter was proposed and a displacement estimation algorithm was implemented. In order to examine the performance and possibility of the proposed spindle displacement estimation algorithm, it was applied to a gantry-type engraver and its excellent performance was confirmed compared to other algorithms.

Transfer robots for large-sized panels used in the display industry need to compensate for path error and reduce vibration. The iterative learning control (ILC) technique can simply compensate for the uncertainty of a control system in a repetitive motion. This study introduces an ILC compensation system applied with an accelerometer to a display panel transfer robot control system. The ILC technique was used to reduce the path error and vibration induced the flexibility of the large size robot. This method was applied to a robot system without the system model of the mechanical and measurement elements. To improve the iterative learning performance through the accelerometer, the ILC is configured by applying an acceleration element and time shift method to the PD-Offline ILC algorithm. In addition, based on the characteristics of repetitive motion, the ILC derives an acceleration data-based position estimation value. In this study, the ILC system and a large-sized panel transfer robot were implemented in MATLAB-Simulink with RECURDYN. The path errors and vibration level of the robot with a suggested ILC of 20 repeated learnings were reduced by more than 90%.

In this study we investigated Kalman filter-based attitude estimation algorithms, considering external acceleration and bias effects in several different scenarios. Towards these goals, gyro biases were first estimated, or calibrated, in all three applied algorithms. Whereas external acceleration effects were not considered in the first algorithm, external acceleration effects were compensated for in the second and third algorithms, using the Kalman filter’s residual and acceleration model. Low, intermediate, and high external acceleration scenarios were then implemented in our test-bed. Three different rotational frequencies (0.3, 3, and 6 ㎐) for roll and pitch rotations were applied. Performance of each estimation algorithm was analyzed using slopes, y-intercepts, and standard deviations obtained from the linear regression. Our results confirm that attitude estimation errors are linearly proportional to the magnitude of the applied external acceleration. Perhaps most importantly, our results show the second algorithm may be used to provide relatively uniform and accurate estimation performance for low- and high-frequency motions.

In this study, a numerical analysis on the impact response of HHA (High Hardness Armor Plate) sequences under a 7.62 mm projectile impact was performed to obtain the fundamental design data for a combat-vehicle platform. Recently, the ballistic-protection levels for combat vehicles have increased, and ballistic-protection designs should now be able to deflect multi-hit projectiles. To study the ballistic-impact characteristics, armor-plate sequences of one or two layers with a gap of 0 mm to 2 mm between the front and rear plate were defined under the same weight and thickness. For the certification of the reliability of the numerical model, ballistic tests and an analysis of the single plate under the 7.62 mm projectile impact were performed and analyzed. On the basis of a valid numerical model, a numerical analysis was performed and analyzed. Lastly, it was proved that the performances of the two-layer sequence with the 2 mm gap regarding the impact-response acceleration, deflection efficiency, and penetration depth are the highest.