Satoshi Uemura

Bio: Satoshi Uemura is an academic researcher from Tokyo Institute of Technology. The author has contributed to research in topics: Power (physics) & Microphone array. The author has an hindex of 6, co-authored 17 publications receiving 84 citations. Previous affiliations of Satoshi Uemura include Honda.

A Method for Compensating Customer Voltage Drops due to Nighttime Simultaneous Charging of EVs Utilizing Reactive Power Injection from Battery Chargers

Abstract: When we consider global warming, the reduction of CO2 emissions is one of the most important issues which require urgent solutions. One option is to integrate low-CO2-emission generators to the grid as much as possible. Another option is to replace inefficient vehicles based on internal-combustion engines with electric ones (EVs). Due to the latter, we can easily predict that most consumers will charge EVs' batteries during nighttime. Thus, excessive voltage drops due to nighttime simultaneous charging are expected to be a possible future problem. This paper proposes a method for compensating the voltage drops by injecting reactive power from EV battery chargers. © 2013 Wiley Periodicals, Inc. Electr Eng Jpn, 184(1): 19–29, 2013; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/eej.22390

A method for compensating customer voltage drops due to nighttime simultaneous charging of evs utilizing reactive power injection from battery chargers

Abstract: When we consider global warming, the reduction of CO2 emissions is one of the most important issues which require urgent solutions. One option is to integrate low-CO2-emission generators to the grid as much as possible. Another option is to replace inefficient vehicles based on internal-combustion engines with electric ones (EVs). Due to the latter, we can easily predict that most consumers will charge EVs' batteries during nighttime. Thus, excessive voltage drops due to nighttime simultaneous charging are expected to be a possible future problem. This paper proposes a method for compensating the voltage drops by injecting reactive power from EV battery chargers. © 2013 Wiley Periodicals, Inc. Electr Eng Jpn, 184(1): 19–29, 2013; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/eej.22390

Fast Response Power Stabilizer using the AC-Excited Flywheel Generator

Abstract: The flywheel generator in use as a power source for fusion experimental device is enough large to generate the electric power to make it suitable for application in wide range of industries.The authors propose an AC-excited variable speed flywheel generator for short-period load leveling device and utility line power stabilizing device such as power factor, unbalanced load, flicker etc. In this paper, the validity of the flywheel for this purpose are described. And the new control method which applies the voltage control of Vector Control scheme are proposed.Compared to a conventional generator withflywheel, the following advantages are offered: (1) The necessary converter capacity is small; (2) It is possible to accumulate and yield electricity rapidly with large power; (3) Reactive power can be supplied through phase-modified operation of generater, making it possible to reduce in capacity of static power factor compensator.The experiments using the M-G set simulator with 4kW wound type induction motor with flywheel are carried out to demonstrate the control ability. It is reported that the system proposed here controls the active and reactive power with so fast step response of less than 5ms that the power-input attains the perfect leveling of even in unbalanced loads.

Outdoor Acoustic Event Identification with DNN Using a Quadrotor-Embedded Microphone Array

Abstract: 54 Kawaharacho, Syogoin, Sakyo-ku, Kyoto City 606-8507, Japan E-mail: sugiyama@kuhp.kyoto-u.ac.jp ∗2Graduate School of Information Science and Engineering, Tokyo Institute of Technology 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8552, Japan ∗3Department of Electrical and Electronic Engineering, School of Engineering, Tokyo Institute of Technology 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8552, Japan ∗4Honda Research Institute Japan Co., Ltd. 8-1 Honcho, Wako, Saitama 351-0188, Japan

Design of UAV-Embedded Microphone Array System for Sound Source Localization in Outdoor Environments

Abstract: In search and rescue activities, unmanned aerial vehicles (UAV) should exploit sound information to compensate for poor visual information. This paper describes the design and implementation of a UAV-embedded microphone array system for sound source localization in outdoor environments. Four critical development problems included water-resistance of the microphone array, efficiency in assembling, reliability of wireless communication, and sufficiency of visualization tools for operators. To solve these problems, we developed a spherical microphone array system (SMAS) consisting of a microphone array, a stable wireless network communication system, and intuitive visualization tools. The performance of SMAS was evaluated with simulated data and a demonstration in the field. Results confirmed that the SMAS provides highly accurate localization, water resistance, prompt assembly, stable wireless communication, and intuitive information for observers and operators.

Deep Learning in Robotics: Survey on Model Structures and Training Strategies

Abstract: The ever-increasing complexity of robot applications induces the need for methods to approach problems with no (viable) analytical solution. Deep learning (DL) provides a set of tools to address this kind of problems. This survey presents a categorization of the major challenges in robotics that leverage DL technologies and introduces representative examples of successful solutions for the described problems. We also consider the question when and whether to use modular, monolithic models or end-to-end DL, in order to provide a guideline for the selection of the correct model structure and training strategy. By doing so, the current role and adaptability of different techniques at different hierarchical levels of a robot-application can be highlighted, thus providing a well-structured basis to assist future approaches.

Sound source estimation using neural networks

Abstract: A system for estimating the location of a stationary or moving sound source includes multiple microphones, which need not be physically aligned in a linear array or a regular geometric pattern in a given environment, an auralizer that generates auralized multi-channel signals based at least on array-related transfer functions and room impulse responses of the microphones as well as signal labels corresponding to the auralized multi-channel signals, a feature extractor that extracts features from the auralized multi-channel signals for efficient processing, and a neural network that can be trained to estimate the location of the sound source based at least on the features extracted from the auralized multi-channel signals and the corresponding signal labels.

An On-Demand Compensation Function for an EV as a Reactive Power Service Provider

Abstract: Rapid growth in the deployment of electric vehicles (EVs) is creating opportunities for system operators to improve the reliability and sustainability of electricity delivery system, while reducing the operating cost. EVs can respond quickly, improve system stability, and provide reactive power support without battery wear. Therefore, EVs could be a promising source of reactive power when they are connected to smart charging equipment. However, under certain scenarios, reactive power support may raise the vehicle charging cost by shifting part of charging power to times of the day when the price of electricity is higher. Participation of EVs in an ancillary service market for reactive power will thus require some level of compensation for EV owners. In this paper, we present a framework for the calculation of reactive power supply function for EVs. We define an objective function representing charging/discharging cost of EVs and then minimize the cost under realistic constraints. An algorithm is then introduced that extracts the EV's supply function for providing on-demand reactive power service at minimum cost. Thus, the system operator can include EVs, as reactive power service providers (RPSPs) along with other service providers, to meet the on-demand need of the reactive power. Simulation results indicate the EV's ability to provide reactive power service during on-peak periods, when the system operator's need for reactive power is most likely high, at almost no cost to the EV owner.

Microphone-Array Ego-Noise Reduction Algorithms for Auditory Micro Aerial Vehicles

Abstract: When a micro aerial vehicle (MAV) captures sounds emitted by a ground or aerial source, its motors and propellers are much closer to the microphone(s) than the sound source, thus leading to extremely low signal-to-noise ratios (SNR), e.g., −15 dB. While microphone-array techniques have been investigated intensively, their application to MAV-based ego-noise reduction has been rarely reported in the literature. To fill this gap, we implement and compare three types of microphone-array algorithms to enhance the target sound captured by an MAV. These algorithms include a recently emerged technique, time-frequency spatial filtering, and two well-known techniques, beamforming and blind source separation. In particular, based on the observation that the target sound and the ego-noise usually have concentrated energy at sparsely isolated time-frequency bins, we propose to use the time-frequency processing approach, which formulates a spatial filter that can enhance a target direction based on local direction of arrival estimates at individual time-frequency bins. By exploiting the time-frequency sparsity of the acoustic signal, this spatial filter works robustly for sound enhancement in the presence of strong ego-noise. We analyze in details the three techniques and conduct a comparative evaluation with real-recorded MAV sounds. Experimental results show the superiority of blind source separation and time-frequency filtering in low-SNR scenarios.