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One of the key technological elements of the wind power generation system is a generator, and the use of synchronous generators using heavy rare earth permanent magnets is increasing according to the demand for high efficiency. However, heavy rare earth permanent magnets are expensive and supply and demand are unbalanced. Therefore, a spoke-type was developed and used in a variety of ways to compensate for insufficient performance by using a ferrite magnet that is inexpensive and stable in supply and demand. However, research and application cases of permanent magnet synchronous generators of 1kW or higher to which the spoke-type is applied are still insufficient. Also, since the target specifications of the motor and the wind turbine are different, a design taking this into account must be carried out.In this paper, we design and analyze the characteristics of the hybrid spoke-type PMSG, modeled on a generator for 3kW class wind turbines. Unlike conventional spoke-type features, we propose a hybrid spoke-model with minimal bridge and cross-located bridge, and compare performance with conventional spoke-type models through FEM. Furthermore, we proceed with 3D interpretation to verify the performance considering 3D effect, and perform a design to improve the performance degradation caused by 3D effect. Finally, we confirm the tendency according to the number of slots in the stator, overhang length, to select the final model and produce the start-up to validate the validity of the design model. Finally, we confirm the tendency according to the number of slots in the stator, overhang length, to select the final model and produce the prototype to validate the validity of the design model..

Some of the researches on indoor positioning have been conducted, but there are still many constraints on indoor positioning approaches. Among these approaches, ultra-wideband (UWB) provides a fast and precise positioning performance but requires a sufficient infrastructure and a clear line-of-sight (LOS) channel. However, inertial sensor-based pedestrian dead reckoning (PDR) operates without infrastructure, but it requires position initialization and has error drift problems. In this study, we propose a hybrid positioning system that fully combines UWB and PDR to overcome such constraints and improve the positioning performance. This hybrid positioning system uses a Kalman filter (KF) based fusion method that identifies non-line-of-sight (NLOS) environments and mitigates UWB errors through PDR. We also evaluated the proposed system implemented using practical testbed devices at indoor environments classified as LOS, weak NLOS, and hard NLOS. The evaluation results showed that the proposed system significantly improves the positioning performance and alleviates the positioning constraints, as compared to the single positioning system. Our system has been designed to be lightweight compared to the existing extended KF-based convergence system, but is more robust to both weak and hard NLOS environments. Eventually, it improved positioning performance by 35.5% than existing hybrid systems in the hard NLOS environments.

In this paper, we compare conventional saddle type FinFETs to partial isolation type saddle FinFETs (Pi-FinFETs) using 3D TCAD simulations to examine the effect of single charge traps for proper prediction of leakage current. We simulated single charge traps at various locations in the drain region, and analyzed how the traps affect leakage current. Our results show that Pi-FinFETs enhanced the leakage current characteristics given the presence of a single charge trap. Also, it was found that Pi-FinFETs exhibit half the $\text{F}_{\mathrm {TAT}}$ of S-FinFETs. Based on the results from our analysis method, where we use $\text{I}_{\mathrm {off}}$ fluctuation, the $\text{F}_{\mathrm {TAT}}$ , the $\sigma _{\mathrm {F}}$ and the $\text{P}_{\mathrm {F}}$ parameters to accurately compare performance, and present device design guidelines aimed at improving DRAM refresh characteristics.

This paper examines the protocol structure of ATSC 3.0, the next-generation terrestrial broadcasting standard, and explains in-depth the technology of Real-Time Object Delivery over Unidirectional Transport (ROUTE) and MPEG Dynamic Adaptive Streaming over HTTP (DASH). Specifically, it considers both low and service signaling methodologies of ROUTE/DASH, that enable immersive media, stereoscopic 3D, and 360 virtual reality (VR), delivery in ATSC 3.0. Furthermore, this paper also provides the signaling examples to give readers new perspectives on immersive broadcasting through ROUTE/DASH in ATSC 3.0. In the era of COVID-19 pandemics, this kind of type, the ATSC 3.0 immersive media service method is expected to become an alternative to performance and media consumption in the future.

Recent technical advancements in both fields of unmanned aerial vehicles (UAV) control and artificial intelligence (AI) have made a certain realm of applications possible. However, one of the main problems in integration of these two areas is the bottle-neck of computing AI applications on UAV's resource limited platform. One of the main solution for this problem is that AI and control software from one side and computing hardware mounted on UAV from the other side be adopted together based on the main constraints of the resource limited computing platform on UAV. Basically, the target constraints of such adaptation are performance, energy efficiency, and accuracy. In this paper, we propose a strategy to integrate and adopt the commonly used object detection and tracking algorithm and UAV control software to be executed on a heterogeneous resource limited computing units on a UAV. For object detection, a convolutional neural network (CNN) algorithm is used. For object tracking, a novel algorithm is proposed that can execute along with object tracking via sequential stream data. For UAV control, a Gain-Scheduled PID controller is designed that steers the UAV by continuously manipulation of the actuators based on the stream data from the tracking unit and dynamics of the UAV. All the algorithms are adopted to be executed on a heterogeneous platform including NVIDIA Jetson TX2 embedded computer and an ARM Cortex M4. The observation from real-time operation of the platform shows that using the proposed platform reduces the power consumption by 53.69% in contrast with other existing methods while having marginal penalty for object detection and tracking parts.