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Autonomous driving recognition technology that can quickly and accurately recognize even small objects must be developed in high-speed situations. This study proposes an object point extraction method using rule-based LiDAR ring data and edge triggers to increase both speed and performance. The LiDAR’s ring information is interpreted as a digital pulse to remove the ground, and object points are extracted by detecting discontinuous edges of the z value aligned with the ring ID and azimuth. A bounding box was simply created using DBSCAN and PCA to check recognition performance from the extracted object points. Verification of the results of removing the ground and extracting points through Ring Edge was conducted using SemanticKITTI and Waymo Open Dataset, and it was confirmed that both F1 scores were superior to RANSAC. In addition, extracting bounding boxes of objects also showed higher PDR index performance when verified in open datasets, virtual driving environments, and actual driving environments.

As the automotive industry transitions from internal combustion engine vehicles to the era of electric cars, extensive research is being conducted in the field of electric vehicles. While a significant portion of this research focuses on the electrification of passenger cars, commercial vehicles have experienced relatively modest changes towards electric propulsion. Particularly, challenges related to power and efficiency have prompted a concentrated effort in addressing these issues. However, improvements in the efficiency of motors and inverters are reaching their limits, necessitating the development of multi-speed transmissions for electric commercial vehicles to enhance overall system efficiency. In this paper, the development of a 4-speed transmission with a synchronizer designed for electric commercial vehicles is presented as part of a project. A transmission shift map was developed, and verification of increased power and efficiency was conducted through a comparison with the existing product (a pneumatic 4-speed internal combustion engine transmission) installed in the target commercial vehicle. The study utilized vehicle dynamics, component modeling, and simulation environments to assess the improvements in performance.

Polylactic acid (PLA) and polybutylene succinate (PBS) are gaining prominence as environmentally friendly alternatives to petroleum-based polymers due to their inherent biodegradability. For their textile applications, this research is focused on exploring the effects of PBS content on the rheological properties of PLA/PBS blends and the characteristics of PLA/PBS blend fibers. PLA/PBS blends and fibers with varying PBS contents (0 to 10 wt.%) were prepared using melt-blending and spinning methods. Uniform morphologies of the PLA/PBS blends indicated that PBS was compatible with PLA, except at 10% PBS content, where phase separation occurred. The introduction of PBS reduced the complex viscosity of the blends, influencing fiber properties. Notably, PLA/PBS fibers with 7% PBS exhibited improved crystallinity, orientation factor, and elasticity (~16.58%), with a similar tensile strength to PLA fiber (~3.58 MPa). The results suggest that an optimal amount of PBS enhances alignment along the drawing direction and improves the molecular motion in PLA/PBS blend fiber. This study highlights the potential of strategically blending PBS to improve PLA fiber characteristics, promising advancement in textile applications.

This paper describes a new algorithm that independently manages braking and driving forces to improve the lateral stability of a vehicle equipped with independent drive motors on all wheels. In a similar way to previous research, the proposed algorithm controls yaw rate to improve lateral stability. However, unlike in previous research that only used differential braking, our algorithm controls both driving and braking forces on all four wheels independently to achieve the target yaw rate. The core contribution of this paper is the distribution logic that determines the braking and driving forces to apply at each wheel. To develop this distribution logic, we introduce the concept of yaw moment contour line. Using this concept, the optimal distribution strategy can be derived by considering yaw moment control performance, lateral movement performance, and deceleration minimization performance in eight different driving situations. Based on this strategy, we design a lateral stability control algorithm that is made up of a target yaw rate, a yaw moment controller, and a distributor. Simulations were performed to investigate the performance of the proposed algorithm using MATLAB/Simulink and the CarSim vehicle dynamics software. The simulation results show that the proposed control algorithm improves vehicle motion in terms of yaw rate tracking, lateral movement, and minimization of deceleration.

In this study, side-by-side bicomponent fibers were prepared by melt spinning using bio-based thermoplastic polyurethane (TPU) and TPU/polylactic acid (PLA) blends. The morphology, thermal and mechanical properties of the fibers were investigated. To this end, the synthesis of TPU using biomass-based polyols and the preparation of TPU/PLA blends were preceded. Their morphological and structural characteristics were investigated. The synthesis of TPU was confirmed through Fourier transform infrared analysis, and as a result of gel permeation chromatograph analysis, a compound having a weight average molecular weight of 196,107 was synthesized. The TPU/PLA blends were blended in the ratio of 80/20, 60/40, 40/60, and 20/80 through a melt extruder. They formed a sea–island structure as a result of scanning electron microscope analysis, and an increase in the PLA content in the TPU matrix caused a decrease in the melt flow index. Finally, TPU/(TPU/PLA) side-by-side bicomponent fibers were prepared by utilizing the above two materials. These fibers exhibited tensile strengths of up to 3624 MPa, with improved biocarbon content of up to 71.5%. These results demonstrate the potential of TPU/(TPU/PLA) side-by-side bicomponent fibers for various applications.

OBJECTIVES This study compared the results of meta-analysis with and without adjustment for the healthy worker effect on the association between working in the semiconductor industry and cancer mortality. METHODS Six studies that reported standardized mortality ratios (SMRs) for cancers were selected for meta-analysis. Using a random-effects model, the SMR results from each study were combined for all cancers and leukemias to estimate the summary SMRs (95% confidence interval, CI). To adjust for the healthy worker effect, the relative standardized mortality ratio (rSMR=SMRx/SMRnot x) were calculated using observed and expected counts for the specific cause of interest (i.e., all cancers and leukemias) and the observed and expected counts for all other causes of mortality. Then, the rSMR results were combined to estimate the summary rSMRs (95% CIs). RESULTS The SMRs for all causes of mortality among semiconductor industry workers ranged from 0.25 to 0.80, which reflects a significant healthy worker effect. A remarkable difference was found between the summary SMRs and the summary rSMRs. The summary SMR for all cancers was 0.70 (95% CI, 0.63 to 0.79) whereas the summary rSMR was 1.38 (95% CI, 1.20 to 1.59). The summary SMR for leukemia was 0.88 (95% CI, 0.72 to 1.07), and the summary rSMR was 1.88 (95% CI, 1.20 to 2.95). CONCLUSIONS Our results suggest that adjustment for the healthy worker effect (i.e., rSMR) may be useful in meta-analyses of cohort studies reporting SMRs.

Among the starting materials of thermoplastic polyurethanes (TPUs), it was confirmed that succinic acid-based polyester biopolyols having different molecular weights (Mn = 1000, 2000, and 4000) affect the physicochemical properties of the final polymer significantly. Bio-TPUs synthesized through a solvent-free one-shot polymerization process were synthesized with a polyester polyol, 1,4 butanediol (BDO), and 4,4′-methylene diphenyl diisocyanate (MDI) in a molar ratio of 1:1:2. As a control group, one typical petroleum-based TPU was synthesized and characterized along with other bio-based TPUs. Representative petroleum-based and bio-based TPUs synthesized were manufactured as monofilaments with a diameter of about 0.2 mm through an extrusion process with different draw ratios (4, 5, and 6 times). The molecular weight and structural properties of the TPUs were characterized by GPC and FT-IR analysis and thermal characterization by DSC and TGA analysis. Petroleum-based TPU and bio-based TPU having the same molecular weight soft segment (SS) tended to have similar molecular weight and hard segment (HS) content. TPUs with high HS content had excellent thermal stability, enabling stable extrusion of TPUs. In addition, it was confirmed that the bio-based TPU fibers produced in this way had a tensile strength corresponding to the physical properties of petroleum-based TPU fibers and an excellent elastic recovery rate of almost 100 %. These results indicate the application potential of bio-TPU.

It is essential for autonomous vehicles at level 3 or higher to have the ability to predict the trajectories of surrounding vehicles to safely and effectively plan and drive along trajectories in complex traffic situations. However, predicting the future behavior of vehicles is a challenging issue because traffic vehicles each have different drivers with different driving tendencies and intentions and they interact with each other. This paper presents a Long Short-Term Memory (LSTM) encoder–decoder model that utilizes an attention mechanism that focuses on certain information to predict vehicles’ trajectories. The proposed model was trained using the Highway Drone (HighD) dataset, which is a high-precision, large-scale traffic dataset. We also compared this model to previous studies. Our model effectively predicted future trajectories by using an attention mechanism to manage the importance of the driving flow of the target and adjacent vehicles and the target vehicle’s dynamics in each driving situation. Furthermore, this study presents a method of linearizing the road geometry such that the trajectory prediction model can be used in a variety of road environments. We verified that the road geometry linearization mechanism can improve the trajectory prediction model’s performance on various road environments in a virtual test-driving simulator constructed based on actual road data.

In this study, highly porous carbon fiber was prepared for hydrogen storage. Porous carbon fiber (PCF) and activated porous carbon fiber (APCF) were derived by carbonization and chemical activation after selectively removing polyvinyl alcohol from a bi-component fiber composed of polyvinyl alcohol and polyacrylonitrile (PAN). The chemical activation created more pores on the surface of the PCF, and consequently, highly porous APCF was obtained with an improved BET surface area (3058 m2 g−1) and micropore volume (1.18 cm3 g−1) compare to those of the carbon fiber, which was prepared by calcination of monocomponent PAN. APCF was revealed to be very efficient for hydrogen storage, its hydrogen capacity of 5.14 wt% at 77 K and 10 MPa. Such hydrogen storage capacity is much higher than that of activated carbon fibers reported previously. To further enhance hydrogen storage capacity, catalytic Pd nanoparticles were deposited on the surface of the APCF. The Pd-deposited APCF exhibits a high hydrogen storage capacity of 5.45 wt% at 77 K and 10 MPa. The results demonstrate the potential of Pd-deposited APCF for efficient hydrogen storage.

According to SAE J3016, autonomous driving can be divided into six levels, and partially automated driving is possible from level three up. A partially or highly automated vehicle can encounter situations involving total system failure. Here, we studied a strategy for safe takeover in such situations. A human-in-the-loop simulator, driver–vehicle interface, and driver monitoring system were developed, and takeover experiments were performed using various driving scenarios and realistic autonomous driving situations. The experiments allowed us to draw the following conclusions. The visual–auditory–haptic complex alarm effectively delivered warnings and had a clear correlation with the user’s subjective preferences. There were scenario types in which the system had to immediately enter minimum risk maneuvers or emergency maneuvers without requesting takeover. Lastly, the risk of accidents can be reduced by the driver monitoring system that prevents the driver from being completely immersed in non-driving-related tasks. We proposed a safe takeover strategy from these results, which provides meaningful guidance for the development of autonomous vehicles. Considering the subjective questionnaire evaluations of users, it is expected to improve the acceptance of autonomous vehicles and increase the adoption of autonomous vehicles.

In a battery-electric vehicle, a representative electric vehicle, there is a growing demand for performance and one-charge mileage improvement. As an alternative to such improvements, the capacity of the battery has been increased; however, due to the corresponding increase in the weight of the battery and the limited space in the vehicle, increasing the capacity of the battery also has limitations. Therefore, researches are being actively conducted to improve system operation efficiency to overcome such limitations. This paper proposes a distributing method of the driving forces to a battery-powered electric shuttle bus for last-mile mobility equipped with the decentralized driving system while taking into account voltage changes of the input terminals due to changes in the battery charge. The system operation efficiency changes were compared and evaluated by performing energy consumption analysis using ‘Manhattan Bus Driving Cycle’ at low voltage condition (SOC 20%). Various analyzes were performed and compared, such as the uniform distribution method of driving forces of the front and rear wheels (Uniform), the optimization method without considering the input terminal voltage change (Vnorm = 90 V), and the optimization method considering the input terminal voltage change (Vdclink). As a result, it shows that the proposed algorithm can improve 6.0% compared to the conventional uniform driving force distribution method (Uniform). Moreover, it shows that the real-time optimization method without considering the input voltage change (Vnorm = 90 V) can improve 5.3% compared to the uniform distribution method. The proposed method can obtain an additional 0.7% increase in total cost compared to the existing optimization method, which shows that the vehicle system has cost-effectiveness by reducing the battery capacity required to achieve the same mileage.

The development of a highly sensitive gas sensor for toxic gases is an important issue in that it can reduce the damage caused by unexpected gas leaks. In this regard, in order to make the sensor accurate and highly responsive, we have investigated which morphology is effective to improve the sensitivity and how the deposited nanoparticle affects the sensitivity by controlling the morphology of semiconductor oxides—either nanorod or nanoplate—and depositing metal nanoparticles on the semiconductor surface. In this study, we compared the CO gas sensitivity for sensors with different morphology (rod and plate) of ZnO nanostructure with metal nanoparticles (gold and copper) photodeposited and investigated the correlation between the gas sensitivity and some factors such as the morphology of ZnO and the properties of the deposited metal. Among the samples, Au/ZnO nanorod showed the best response (~86%) to the exposure of 100 ppm CO gas at 200 °C. The result showed that the electrical properties due to the deposition of metal species also have a strong influence on the sensor properties such as sensor response, working temperature, the response and recovery time, etc., together with the morphology of ZnO.

The in-wheel electric vehicle is expected to be a popular next-generation vehicle because an in-wheel system can simplify the powertrain and improve driving performance. In addition, it also has an advantage in that it maximizes driving efficiency through independent torque control considering the motor efficiency. However, there is an instability problem if only the driving torque is controlled in consideration of only the motor efficiency. In this paper, integrated torque distribution strategies are proposed to overcome these problems. The control algorithm consists of various strategies for optimizing driving efficiency, satisfying driver demands, and considering tire slip and vehicle cornering. Fuzzy logic is used to determine the appropriate timing of intervention for each distribution strategy. A performance simulator for in-wheel electric vehicles was developed by using MATLAB/Simulink and CarSim to validate the control strategies. From simulation results under complex driving conditions, the proposed algorithm was verified to improve both the driving stability and fuel economy of the in-wheel vehicle.

For the fast and easy detection of carbon monoxide (CO) gas, it was necessary to develop a CO gas sensor to operate in low temperatures. Herein, a novel Cu/CuO-decorated ZnO hollow nanofiber was prepared with the electrospinning, calcination, and photodeposition methods. In the presence of 100 ppm CO gas, the Cu/CuO-photodeposited ZnO hollow nanofiber (Cu/CuO@ZnO HNF) showed twice higher sensitivity than that of pure ZnO nanofiber at a relatively low working temperature of 300 °C. The hollow structure and p−n junction between Cu/CuO and ZnO would be considered to contribute to the enhancement of sensitivity to CO gas at 300 °C due to the improved specific surface area and efficient electron transfer.

In this paper, an integrated torque distribution strategy was developed to improve the stability and efficiency of the vehicle. To improve the stability of the low friction road surface, the vertical and lateral forces of the vehicle were estimated and the estimated forces were used to determine the driving torque limit. A turning stability index comprised of vehicle velocity and desired yaw rate was proposed to examine the driving stability of the vehicle while turning. The proposed index was used to subdivide turning situations and propose a torque distribution strategy, which can minimize deceleration of the vehicle while securing turning stability. The torque distribution strategy for increased driving stability and efficiency was used to create an integrated torque distribution (ITD) strategy. A vehicle stability index based on the slip rate and turning stability index was proposed to determine the overall driving stability of the vehicle, and the proposed index was used as a weight factor that determines the intervention of the control strategy for increased efficiency and driving stability. The simulation and actual vehicle test were carried out to verify the performance of the developed ITD. From these results, it can be verified that the proposed torque distribution strategy helps solve the poor handling performance problems of in-wheel electric vehicles.

The sideslip angle is used to control a vehicle for safety, however, it is hard to estimate the sideslip angle. In many researches, the estimator needs many unattainable variables, such as yaw inertia or cornering stiffness, to estimate the sideslip angle. This paper proposes the observer uses geometrically derived equations. The estimator needs a compensation value to estimate the sideslip angle instead of unattainable variables. The compensation value compensates the error from the imperfections of the formulas and nonlinearity motion at high speed. Estimator tunes its compensation value automatically, and uses the least squares method for estimating the compensation value and to apply to a real vehicle. Estimator is developed by LabVIEW, and gets the simulation result through CarSim. The algorithm of finding a proper compensation value is simply using the sensor data that are already mounted on vehicles, such as yaw rate, longitudinal speed, and lateral acceleration. The real vehicle test was conducted to verify the proposed algorithm. From the results, the algorithm can estimate the sideslip angle without unattainable variables.

The water-soluble fullerenes [C60, C70] are prepared with fullerenes [C60, C70] and a mixture of oxidants (v/v) at the ratio of 3:1 under ultrasonic condition at room temperature. The MALDI-TOF MS confirmed that the water-soluble compounds were C60 and C70. The antioxidant effect of water-soluble fullerenes [C60, C70] in the PC 12 cells (Rat pheochromocytoma) line following exposure to hydrogen peroxide (H2O2) was investigated.

The mode transition from electric propulsion mode to hybrid propulsion mode is important with regard to the power management strategy of plug-in hybrid electric vehicles (PHEVs). This is because mode transitions can occur frequently depending on the power management strategies and driving cycles, and because inadequate mode transitions worsen the fuel efficiency and drivability. A pre-transmission parallel PHEV uses a clutch between the internal combustion engine (ICE) and the electric motor (EM) to connect or disconnect the power source of the ICE for a mode transition. The mode transition requires additional energy consumption for clutch speed synchronization, and is accompanied by a drivetrain shock due to clutch engagement. This paper proposes a control strategy for the mode transition with gear-shifting to resolve the problems of energy consumption and drivetrain shock. Through the development of a PHEV performance simulator, we analyze the mode transition characteristics and propose a control strategy considering the vehicle acceleration and gear state. The control strategy reduces the duration required for the mode transition by moving the start time of the mode transition. This helps to improve energy efficiency while maintaining adequate drivability.

Purpose: To assess the added value of radiomics models from preoperative chest CT in predicting the presence of spread through air spaces (STAS) in the early stage of surgically resected lung adenocarcinomas using multiple validation datasets. Materials and Methods: This retrospective study included 550 early-stage surgically resected lung adenocarcinomas in 521 patients, classified into training, test, internal validation, and temporal validation sets (n=211, 90, 91, and 158, respectively). Radiomics features were extracted from the segmented tumors on preoperative chest CT, and a radiomics score (Rad-score) was calculated to predict the presence of STAS. Diagnostic performance of the conventional model and the combined model, based on a combination of conventional and radiomics features, for the diagnosis of the presence of STAS were compared using the area under the curve (AUC) of the receiver operating characteristic curve. Results: Rad-score was significantly higher in the STAS-positive group compared to the STAS-negative group in the training, test, internal, and temporal validation sets. The performance of the combined model was significantly higher than that of the conventional model in the training set {AUC: 0.784 [95% confidence interval (CI): 0.722-0.846] vs. AUC: 0.815 (95% CI: 0.759-0.872), p=0.042}. In the temporal validation set, the combined model showed a significantly higher AUC than that of the conventional model (p=0.001). The combined model showed a higher AUC than the conventional model in the test and internal validation sets, albeit with no statistical significance. Conclusion: A quantitative CT radiomics model can assist in the non-invasive prediction of the presence of STAS in the early stage of lung adenocarcinomas. [ABSTRACT FROM AUTHOR]