Korea Aerospace University

In the autonomous driving environment, an obstructed view of the road can cause a delayed response time, making it challenging to avoid potential accidents. Thus, the method for detecting the blind spot is required, and the demand for sharing the detection results between vehicles through communication systems is increasing. In this letter, we propose a novel method of sharing the detection results between vehicles in automotive radar systems. We propose to use frequency-division multiplexing to reallocate the frequency band designated for automotive radar systems into two separate bands, one for the target detection and the other for the vehicle-to-vehicle (V2V) communications. In addition, the detection results are encoded by using amplitude modulation in the signals for V2V communications. The proposed method of sharing the detection results between vehicles is verified through several simulation scenarios. Through the simulations, the detection result of the preceding vehicle can be successfully shared to the following vehicle. Moreover, the average error between the actual and shared detection results is lower than 1%.

This paper presents a comprehensive review of the partitioned pipe mixer (PPM) and its design variants: the barrier-embedded partitioned pipe mixer (BPPM) and the groove-embedded partitioned pipe mixer (GPPM). These mixers utilize chaotic advection as their mixing mechanism in the laminar flow regime. The review first focuses on the flow and mixing characteristics of these mixers, considering the influence of the operating conditions and design variables. The advantages and flexibility of the BPPM and GPPM over the original PPM are highlighted. The investigation covers mixing performance in both the creeping and non-creeping flow regimes. In addition, this review examines the impact of thixotropy and fluid inertia on mixing performance of the mixers, revealing irregular trends. It emphasizes the importance of carefully considering thixotropy and inertia when selecting appropriate mixing protocols and operating conditions. Furthermore, the potential use of chaotic mixing by the BPPM in filtration processes is briefly reviewed. In conclusion, the review summarizes the limitations of the previous studies and suggesting future research directions. Further studies are expected to explore the potential of these types of mixers in improving mixing performance in various industries, particularly those dealing with rheologically complex fluids.

The hydroxyapatite/silver nanoparticles (HA/AgNPs) composites were synthesized using a two-step physiochemical method. Firstly, a mixture of Ca(NO3)2·4H2O and K2HPO4 was combined at pH 9 and subjected to various microwave treatments (5, 10, and 15 min). Secondly, Ag was incorporated into the HA to produce HA/AgNPs. Through FT-IR study, the peaks at 945.12, 1014.52, 1346, and 1633.7 cm⁻¹ correspond to the deformation of PO4³⁻ ions, stretching vibration of PO4³⁻ ions, stretching mode of CO3²⁻, and the O–H functional group’s bending vibration, respectively. The scanning electron microscopic analysis revealed the presence of finely dispersed AgNPs within a size range of 30–90 nm, with an average particle size of approximately 40 ± 5 nm. The HA particles exhibited a size distribution ranging from 60 to 120 nm, with an average particle size of about 55 ± 5 nm. Furthermore, the HA/AgNPs nanocomposites antibacterial properties were evaluated against Gram negative bacterium, Escherichia coli, and Gram-positive bacterium, Staphylococcus aureus. HA/AgNPs exhibited remarkable antibacterial performance at a concentration of 100 g mL⁻¹ and MIC values of about 6.25 and 12.5 g mL⁻¹ against Escherichia coli and Staphylococcus aureus, respectively. A dental amalgam prepared by mixing mercury and HA/AgNPs. This biomaterial can potentially serve as a dental filling material.

In this study, the effects of several geometry factors (fin front angle, clearance, number of fins, and honeycomb cell diameter and depth) on the mass flow function of solid and honeycomb land were studied experimentally. The fin front angle considered in the experiment ranged from 60 to 90 degrees, the number of fins was varied between two and three, and the diameter and depth of the honeycomb cell ranged from 1.33 to 4.00 times and 8.08 to 13.08 times the thickness of the fin tip, respectively. The experimental results showed that the mass flow function decreased as the number of fins increased for the solid land labyrinth seal, and the mass flow function increased as the clearance increased. A fin front angle of 60 degrees was found to have the minimum mass flow function. For the honeycomb land labyrinth seal, the mass flow function decreased as the number of fins increased, and the effect of the cell depth was shown to be insignificant compared to the effect of the cell diameter. The effects of cell diameter and cell depth on the mass flow function depended on the conditions of other variables. In addition, the correlation equations of the mass flow functions of the solid land and honeycomb land labyrinth seals are presented based on the experimental results, which represent the effects of the fin front angle, clearance, pressure ratio, and diameter and depth of the honeycomb cell. The correlation equation for the solid land labyrinth seal had an r2 value of 0.9822, while the correlation equation for honeycomb land had an r2 value of 0.9621.

Herein, sol–gel-processed Y2O3–Al2O3 mixed oxide-based resistive random-access-memory (RRAM) devices with different proportions of the involved Y2O3 and Al2O3 precursors were fabricated on indium tin oxide/glass substrates. The corresponding structural, chemical, and electrical properties were investigated. The fabricated devices exhibited conventional bipolar RRAM characteristics without requiring a high-voltage forming process. With an increase in the percentage of Al2O3 precursor above 50 mol%, the crystallinity reduced, with the amorphous phase increasing owing to internal stress. Moreover, with increasing Al2O3 percentage, the lattice oxygen percentage increased and the oxygen vacancy percentage decreased. A 50% Y2O3–50% Al2O3 mixed oxide-based RRAM device exhibited the maximum high-resistance-state/low-resistance-state (HRS/LRS) ratio, as required for a large readout margin and array size. Additionally, this device demonstrated good endurance characteristics, maintaining stability for approximately 100 cycles with a high HRS/LRS ratio (>104). The HRS and LRS resistances were also retained up to 104 s without considerable degradation.

In this study, a novel multifunctional film known as susceptor, containing silver nanoparticles, was proposed to improve the induction welding performance of unidirectional carbon fiber-reinforced polyetherketoneketone (CF/PEKK) thermoplastic composites. A group of Ag/PEKK films was synthesized with different amounts of silver nanoparticles. Good thermal performance was obtained, including the maximum temperature and thermal growth time constant, leading to the selection of films with 60 wt.% and 70 wt.% silver nanoparticles. In addition, the mechanical performance was evaluated for single-lap joints fabricated with the selected films and stainless-steel mesh. Specifically, the highest interlaminar shear strength and single-lap shear strength of 25.2 MPa and 20.9 MPa, respectively, were achieved with a susceptor containing 60 wt.% silver nanoparticles. The mechanism of these increases in thermal and mechanical performance is discussed using numerical and experimental methods (non-destructive test and optical microscope observation). In conclusion, the multifunctional heating film incorporated into the adhesive can improve the thermal and mechanical efficiency of single-lap joints of thermoplastic composites fabricated using the induction welding method.

Yttrium oxide (Y2O3) resistive random-access memory (RRAM) devices were fabricated using the sol–gel process on indium tin oxide/glass substrates. These devices exhibited conventional bipolar RRAM characteristics without requiring a high-voltage forming process. The effect of current compliance on the Y2O3 RRAM devices was investigated, and the results revealed that the resistance values gradually decreased with increasing set current compliance values. By regulating these values, the formation of pure Ag conductive filament could be restricted. The dominant oxygen ion diffusion and migration within Y2O3 leads to the formation of oxygen vacancies and Ag metal-mixed conductive filaments between the two electrodes. The filament composition changes from pure Ag metal to Ag metal mixed with oxygen vacancies, which is crucial for realizing multilevel cell (MLC) switching. Consequently, intermediate resistance values were obtained, which were suitable for MLC switching. The fabricated Y2O3 RRAM devices could function as a MLC with a capacity of two bits in one cell, utilizing three low-resistance states and one common high-resistance state. The potential of the Y2O3 RRAM devices for neural networks was further explored through numerical simulations. Hardware neural networks based on the Y2O3 RRAM devices demonstrated effective digit image classification with a high accuracy rate of approximately 88%, comparable to the ideal software-based classification (~92%). This indicates that the proposed RRAM can be utilized as a memory component in practical neuromorphic systems.

Internal damage to carbon fibre-reinforced plastic (CFRP) laminated plates was detected and visualized. This internal damage was generated via low-velocity impact testing. Prior to the experiment, the impact velocity was established through finite element analysis based on LS-DYNA. The damage size, determined through this analysis and subsequent experiment, presented a maximum error of 17%. Piezoelectric sensors were used to detect and visualize the internal damage. This damage was identified using a diagnostic approach, along with a novel signal separation method. The signal separation method was employed because the distance between the plate edge and the sensor was smaller than the distance between the two sensors on the CFRP-laminated plates. The visualized damage was verified though a comparison with the low-velocity impact experimental results. Both the location and size of damage could be detected with an error of approximately 9.7% and 10%, respectively.

As the usability of and demand for unmanned aerial vehicles (UAVs) have increased, it has become necessary to establish a UAS traffic management (UTM) system for efficient UAV operations at low altitudes. To avoid collisions with ground obstacles, other UAVs, and manned aircraft, in building a safe path, the UTM needs to determine the time and space allocated to each flight. Ideas for discretizing and structuring airspace in various forms have been proposed to enhance the efficiency of system operation and improve traffic congestion through effectual airspace allocation. Additionally, various methods of allocating UAVs to structured unit spaces have been studied in the literature. In this paper, the methods and structural designs for allocating airspace that have appeared in related studies are classified into several types, and their strengths and weaknesses are analyzed. The structured airspace designs are categorized into three models: Air-Matrix, Air-Network, and Air-Tube, and analyzed according to their sub-structures and temporal allocation methods. In addition, a quantitative analysis is conducted by re-categorizing the structured airspace and operation methods and building their combinations.

A novel passive vibration-damping device is proposed and investigated for a large deployable solar array. One strategy for achieving high damping in a solar panel is using a yoke structure comprising a hyperelastic shape memory alloy and multiple viscous adhesive layers of acrylic tape. The effectiveness of the proposed system in achieving a high damping performance was demonstrated by conducting free vibration and low-level sine sweep tests using a solar array, and a 0.75-m-long flexible dummy structure was simulated. We also investigated the dependence of the damping performance of the proposed structure on the number of viscous lamina layers. Finally, the damping characteristics of the proposed system were assessed under predictable on-orbit temperature conditions.

Chitosan is a bio-polymer made up of repeating units of N-acetyl glucosamine and glucosamine joined together by (1-4)-glycosidic linkages. Various bioresources have been used to develop bioactive materials that have a wide range of applications in different fields, including industry and medicine. Borassus flabellifer is a well-known source of chitin in the sub-Indian continent and is used in digestion, pharmaceuticals, and other applications. Chitin can be extracted from B. flabellifer fruit shells through demineralization and deproteinization and then converted into chitosan through deacetylation. This study aimed to investigate the biological activity of chitosan extracted from B. flabellifer fruit shells and to analyze its molecular structure using FT-IR analysis. Results showed the presence of NH, OH, and CO stretching, indicating the presence of various functional groups in chitosan. Scanning electron microscopic study revealed the topography of the chitosan. Well-diffusion and MIC tests showed that chitosan exhibited activity against E. coli and S. aureus. The chitosan extract also exhibited potential antioxidant polymer by scavenging free radicals.

The reliability and safety of advanced driver assistance systems and autonomous vehicles are highly dependent on the accuracy of automotive sensors such as radar, lidar, and camera. However, these sensors can be misaligned compared to the initial installation state due to external shocks, and it can cause deterioration of their performance. In the case of the radar sensor, when the mounting angle is distorted and the sensor tilt toward the ground or sky, the sensing performance deteriorates significantly. Therefore, to guarantee stable detection performance of the sensors and driver safety, a method for determining the misalignment of these sensors is required. In this paper, we propose a method for estimating the vertical tilt angle of the radar sensor using a deep neural network (DNN) classifier. Using the proposed method, the mounting state of the radar can be easily estimated without physically removing the bumper. First, to identify the characteristics of the received signal according to the radar misalignment states, radar data are obtained at various tilt angles and distances. Then, we extract range profiles from the received signals and design a DNN-based estimator using the profiles as input. The proposed angle estimator determines the tilt angle of the radar sensor regardless of the measured distance. The average estimation accuracy of the proposed DNN-based classifier is over 99.08%. Therefore, through the proposed method of indirectly determining the radar misalignment, maintenance of the vehicle radar sensor can be easily performed.

In this study, our focus is on the drop test simulation of an MR (Magnetorheological) damper-based main landing gear (MRMLG), aiming to explore multi-degree-of-freedom (DOF) dynamic models during aircraft landing. Three different 6-DOF dynamic models are proposed in this work, and their drop performances are compared with results achieved by commercial software. The proposed models include a nonlinear aircraft model (NLAM); a linearized approximated aircraft model (LAAM) linearizing from the nonlinear equations of motion in NLAM; and a fully approximated aircraft model (FAAM) which linearizes the MRMLG’s strut force model. In order to evaluate the drop performance of the aircraft landing gear system with MR dampers, a 7-DOF aircraft model incorporating the nonlinear MRMLG was formulated using RecurDyn. The principal comparative parameters are the coefficient of determination (R2) for the system response of each model with the RecurDyn model and root mean square error (RMSE), which is the ensemble of CG displacement data for each model. In addition, the ensemble of time series data is created for diverse drop scenarios, providing valuable insights into the performance of the proposed drop test models of an aircraft landing gear system featuring MR dampers.

Trajectory tracking is a crucial aspect of controlling nonlinear systems and is an important area of research. Researchers have proposed several strategies to perform this task in the presence of perturbations, which are the sum of a system’s uncertainty, modeling errors, and external disturbances. Nonlinear systems, such as robot manipulators, have complex dynamics, and deriving their exact mathematical models is a tedious task. Therefore, the objective of this research is to design a model-free form of control for such systems. To achieve this goal, a sliding mode control (SMC) with a proportional-integral-derivative (PID) sliding surface was designed and integrated with a saturation-function-based extended-state observer (ESO). In an extended-state observer (ESO), the primary concept is to define the system’s perturbation. The ESO estimates the system’s states and perturbation, including the known and unknown dynamics, uncertainties, and external disturbances, which are considered as perturbations. The estimated perturbation is used in a closed loop to cancel the actual perturbation. This perturbation-rejection technique improved the controller’s performance, resulting in reduced position error, reduced sensitivity to low-frequency elements of perturbation, and a small magnitude of switching gain. The designed control algorithm requires minimal information about the system, specifically position feedback, and, therefore, there is no need to identify the system parameters. A mathematical analysis of the designed algorithm was performed in detail, and the algorithm was compared with the existing ESO-based SMC algorithm. Simulations were conducted using MATLAB/SimMechanics on two different systems, and the comparison results validated the performance of the designed algorithm in comparison to previous research.

In this article, we propose a geometric modeling method for an integrally stiffened panel using 3D woven composites. To observe the geometric shapes of the panel, we design weave patterns in which the skin and stiffener are integrated and manufacture the panel. Cross-sections of the manufactured panel are observed using a microscope, and paths and shapes of tows are modeled using weaving parameters and the obtained cross-sections. To verify the method, buckling analysis is performed on the panel and compared with test results. The proposed method can be effectively applied to the design of integrally stiffened panel using 3D woven composites.

In structural health monitoring, the damage detection method using Lamb wave has been actively performed for composite laminates. It is hard to apply the Lamb wave to composite laminate because of its dispersion and multi-mode characteristics. This paper derived the dispersion equation for a single layer assuming monoclinic to calculate more accurate group velocity. Furthermore, the general solution for wave propagation is assumed that the wavenumber vector considers the displacements in three directions. Using the transfer matrix method with the slowness curve, the Lamb wave behavior for a single layer is extended to the composite laminate. By solving the matrix equation, the group velocity is theoretically calculated. For comparison with the theoretical results, an experimental test has been performed on the unidirectional composite plates with 12ply and 16ply. As a result, it has been confirmed that the theoretical and the experimental group velocities are well-matched except for the fiber direction (0°) because of the monoclinic and wavenumber assumption. An error analysis of the experimental and theoretical values has been performed for the fiber direction. It is concluded that a compensation method for group velocity in the fiber direction is needed for damage detection using the Lamb wave in unidirectional composite plate.

With the development of deep learning technology and the abundance of sensors, machine vision applications that utilize vast amounts of image/video data are rapidly increasing in the autonomous vehicle, video surveillance and smart city fields. However, achieving a more compact image/video representation and lower latency solutions is challenging for such machine-based applications. Therefore, it is essential to develop a more efficient video coding standard for machine vision applications. Currently, the Moving Picture Experts Group (MPEG) is developing a new standard called video coding for machines (VCM) with two tracks, each mainly dealing with compression of the input image/video (Track 2) and compression of the features extracted from it (Track 1). In this paper, an enhanced multiscale feature compression (E-MSFC) method is proposed to efficiently compress multiscale features generated by a feature pyramid network (FPN), which is the backbone network of machine vision networks specified in the VCM evaluation framework. The proposed E-MSFC reduces the feature channels to be included in a single feature map and compresses the feature map using versatile video coding (VVC), the latest video standard, rather than the single stream feature compression (SSFC) module in the existing MSFC. In addition, the performance of the E-MSFC is further enhanced by adding a bottom-up structure to the multiscale feature fusion (MSFF) module, which performs the channel-wise reduction in the E-MSFC. Experimental results reveal that the proposed E-MSFC significantly outperforms the VCM image anchor with a BD-rate gain of up to 85.94%, which includes an additional gain of 0.96% achieved by the MSFF with the bottom-up structure.