Human hand gestures are a widely accepted form of real-time input for devices providing a human-machine interface. However, hand gestures have limitations in terms of effectively conveying the complexity and diversity of human intentions. This study attempted to address these limitations by proposing a multi-modal input device, based on the observation that each application program requires different user intentions (and demanding functions) and the machine already acknowledges the running application. When the running application changes, the same gesture now offers a new function required in the new application, and thus, we can greatly reduce the number and complexity of required hand gestures. As a simple wearable sensor, we employ one miniature wireless three-axis gyroscope, the data of which are processed by correlation analysis with normalized covariance for continuous gesture recognition. Recognition accuracy is improved by considering both gesture patterns and signal strength and by incorporating a learning mode. In our system, six unit hand gestures successfully provide most functions offered by multiple input devices. The characteristics of our approach are automatically adjusted by acknowledging the application programs or learning user preferences. In three application programs, the approach shows good accuracy (90–96%), which is very promising in terms of designing a unified solution. Furthermore, the accuracy reaches 100% as the users become more familiar with the system.

This study addresses the role of metal spacer design in reducing the thermomechanical stress of solder joint in automotive double-sided cooling power electronics modules. The performance and reliability of power modules are becoming increasingly important in electrified vehicles. A double-sided cooling structure has dual heat sinks placed on the top and bottom of the module and is the most promising solution for improving the thermal reliability of power modules. One of unique components of this structure is its metal spacers, which are responsible for both electrical and thermal conductions via the connecting embedded power semiconductor dies and their top-side substrates. Reported spacers are typically cuboid in shape, providing a relatively large solder-contact area with the power dies. This large contact area is beneficial in reducing electrical and thermal resistance, but it increases thermomechanical stress and may accelerate the deterioration of die-bonded solders. This study explores the role of three new spacer shapes in reducing thermomechanical stress of the solder. Their performances are validated using both finite element method (FEM) simulations and thermal cycling experiments. In this study, an octagon-surface spacer demonstrates the best solder reliability compared to the two other designs and the conventional cuboid spacer.

This article proposes a new architecture for the hybrid distribution transformer (HDT) with a partially rated power converter connected in series with the existing distribution transformer. The proposed single-phase system is constructed by adding a three-legged power converter in series to the existing transformer's primary side. Here, the way the converter is connected to the transformer's two taps provides the following advantages: 1) It is possible to reduce the power converter's power rating, and 2) An additional current loop, which gives a degree of freedom, is added. For better power quality, the proposed system synthesizes a voltage vector to compensate for the voltage sags/swells and the grid voltage harmonics. Furthermore, it improves the grid's power factor to increase the power transmission efficiency. The proposed system meets the needs of smart grids in terms of resilience. The configuration, control strategies, analysis, and activation/deactivation sequence of the proposed system are discussed in this article. Real-scale simulations and lab-scale experiments were performed to validate the proposed system, and the system's effectiveness was verified through the obtained results.

Automotive Lidar sensors are highly susceptible to their environment. One of its major limitations results from the effects of rain environments, which should be seriously considered while designing a Lidar system. This study addresses the impact of rain on the Lidar system by considering the raindrop distributions of different regions. The regional distributions are derived from the rainfall data of three locations, which were reported by previous works, and converted using the constrained-gamma model. The regional distribution reveals different characteristics of raindrops, such as sizes, shapes, and numbers. The derived raindrop distributions are imported to a custom-built Lidar model, providing three models representing the three regions. The simulation results demonstrate that the signal power received by a Lidar attenuates, which is modeled using Mie scattering theory, and the amount of attenuation clearly differs in the regional models. Therefore, the attenuation characteristics change according to the regions; consequently, their effect on the Lidar sensor performances are quantitatively evaluated. In addition, the custom-built Lidar model is mounted on a virtual vehicle, which is simulated using a commercial automobile testing software, PreScan. The driving simulation also demonstrates similar conclusion that the regional raindrop distribution is critical in determining the Lidar performances.

Conditional autonomous vehicles have attracted significant attention, and their wide-spread use is expected to further increase in the near future. However, such vehicles can send take-over requests (TORs) to a driver who may struggle to accommodate the request while immersed in non-driving-related tasks. Previous studies have focused on TOR times and cues; however, the effects of environmental conditions have not been examined rigorously. This study, with the aim of addressing the aforementioned issue, is divided into two parts: in Study 1, we examine driver responses to TORs under different environmental conditions (i.e., sunny, rainy, snowy, foggy, and night-time), and in Study 2, we examine the effects of the proposed TOR cues (augmented reality $+$ smartphone alert) under different environmental conditions. For this investigation, a driving simulator was used for the participants’ safety. Each study involves 33 participants. The results of Study 1 indicate significant differences in the take-over time, lane-change time, time-to-collision, maximum acceleration, and subjective mental workload corresponding to different environmental conditions. Furthermore, the results of Study 2 suggest that the proposed TOR cues significantly reduce the effects of environmental conditions on various take-over performances. We discuss the implication of these results in terms of the improvements in responses to TORs and investigation of the effects of environmental conditions on the responses to TORs.

This paper proposes a new estimation process of the actual maximum junction temperature in multi-chip SiC MOSFET power modules. Temperature-sensitive electrical parameters (TSEPs) are common methods to estimate the junction temperature of power devices, but they have limitations in specifying the actual maximum junction temperature in multi-chip power modules with anti-parallel diodes. Common TSEPs includes body-diode forward voltage or on-resistance of MOSFETs, but it is difficult to distinguish the impact of the anti-parallel diodes and the on-resistance-based method has low sensitivity under low-current calibration. Especially, the TSEPs has difficulty to calibrate the actual maximum junction temperature of a power module, which should solely detect the hottest device in the module. Therefore, a new dynamic calibration method is proposed using fiber optics while simultaneously monitoring factors related to the maximum junction temperature. Four critical parameters were measured from two different combinations: 4-paralleled and 10-paralleled SiC MOSFET power modules with anti-parallel diodes. Because of the abundant amount of the measured data, data-driven modeling was conducted with a deep neural network (DNN). The trained models were evaluated, demonstrating distinctly better accuracy in the actual maximum temperature estimation, for both 4-paralleled (MAE of 0.61 °C) and 10-paralleled (MAE of 0.72 °C) MOSFET modules.