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390 results on '"Onoye, Takao"'

1. Data-driven HVAC Control Using Symbolic Regression: Design and Implementation

Author

Ozawa, Yuki, Zhao, Dafang, Watari, Daichi, Taniguchi, Ittetsu, Suzuki, Toshihiro, Shimoda, Yoshiyuki, and Onoye, Takao

Subjects
Electrical Engineering and Systems Science - Systems and Control, Computer Science - Artificial Intelligence
Abstract

The large amount of data collected in buildings makes energy management smarter and more energy efficient. This study proposes a design and implementation methodology of data-driven heating, ventilation, and air conditioning (HVAC) control. Building thermodynamics is modeled using a symbolic regression model (SRM) built from the collected data. Additionally, an HVAC system model is also developed with a data-driven approach. A model predictive control (MPC) based HVAC scheduling is formulated with the developed models to minimize energy consumption and peak power demand and maximize thermal comfort. The performance of the proposed framework is demonstrated in the workspace in the actual campus building. The HVAC system using the proposed framework reduces the peak power by 16.1\% compared to the widely used thermostat controller., Comment: 5 pages with 6 figures, accepted by IEEE PES GM 2023 (23PESGM0543) This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

Published
2023

3. A Picture May Be Worth a Hundred Words for Visual Question Answering

Author

Hirota, Yusuke, Garcia, Noa, Otani, Mayu, Chu, Chenhui, Nakashima, Yuta, Taniguchi, Ittetsu, and Onoye, Takao

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

How far can we go with textual representations for understanding pictures? In image understanding, it is essential to use concise but detailed image representations. Deep visual features extracted by vision models, such as Faster R-CNN, are prevailing used in multiple tasks, and especially in visual question answering (VQA). However, conventional deep visual features may struggle to convey all the details in an image as we humans do. Meanwhile, with recent language models' progress, descriptive text may be an alternative to this problem. This paper delves into the effectiveness of textual representations for image understanding in the specific context of VQA. We propose to take description-question pairs as input, instead of deep visual features, and fed them into a language-only Transformer model, simplifying the process and the computational cost. We also experiment with data augmentation techniques to increase the diversity in the training set and avoid learning statistical bias. Extensive evaluations have shown that textual representations require only about a hundred words to compete with deep visual features on both VQA 2.0 and VQA-CP v2.

Published
2021

8. Computer-Vision-Oriented Adaptive Sampling in Compressive Sensing †.

Author

Liu, Luyang, Nishikawa, Hiroki, Zhou, Jinjia, Taniguchi, Ittetsu, and Onoye, Takao

Subjects
ADAPTIVE sampling (Statistics), DATA acquisition systems, COMPUTER vision, INTERNET of things, ACQUISITION of data
Abstract

Compressive sensing (CS) is recognized for its adeptness at compressing signals, making it a pivotal technology in the context of sensor data acquisition. With the proliferation of image data in Internet of Things (IoT) systems, CS is expected to reduce the transmission cost of signals captured by various sensor devices. However, the quality of CS-reconstructed signals inevitably degrades as the sampling rate decreases, which poses a challenge in terms of the inference accuracy in downstream computer vision (CV) tasks. This limitation imposes an obstacle to the real-world application of existing CS techniques, especially for reducing transmission costs in sensor-rich environments. In response to this challenge, this paper contributes a CV-oriented adaptive CS framework based on saliency detection to the field of sensing technology that enables sensor systems to intelligently prioritize and transmit the most relevant data. Unlike existing CS techniques, the proposal prioritizes the accuracy of reconstructed images for CV purposes, not only for visual quality. The primary objective of this proposal is to enhance the preservation of information critical for CV tasks while optimizing the utilization of sensor data. This work conducts experiments on various realistic scenario datasets collected by real sensor devices. Experimental results demonstrate superior performance compared to existing CS sampling techniques across the STL10, Intel, and Imagenette datasets for classification and KITTI for object detection. Compared with the baseline uniform sampling technique, the average classification accuracy shows a maximum improvement of 26.23%, 11.69%, and 18.25%, respectively, at specific sampling rates. In addition, even at very low sampling rates, the proposal is demonstrated to be robust in terms of classification and detection as compared to state-of-the-art CS techniques. This ensures essential information for CV tasks is retained, improving the efficacy of sensor-based data acquisition systems. [ABSTRACT FROM AUTHOR]

Published
2024
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9. Online Energy Management Framework for Smart Buildings With Low-Complexity Estimators.

Author

Watari, Daichi, Marantos, Charalampos, Taniguchi, Ittetsu, Catthoor, Francky, Siozios, Kostas, Soudris, Dimitrios, and Onoye, Takao

Abstract

This letter proposes an online energy management framework for smart buildings. Two low-complexity estimators for thermal comfort and electrical load are investigated and integrated into an optimization framework. The proposed framework optimizes operations of a battery system and multiple heating, ventilation, and air conditioning (HVAC) systems to minimize energy consumption and power peak while maximizing occupants’ thermal comfort. The effectiveness of the proposed framework is demonstrated using data measured in actual campus buildings in terms of system costs, thermal comfort, and computational complexity. The results show that the proposed framework can reduce electricity costs by 5.7% compared to the baseline. [ABSTRACT FROM AUTHOR]

Published
2024
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11. Radiation-Induced Soft Errors

Author

Ibe, Eishi H., Yoshimoto, Shusuke, Yoshimoto, Masahiko, Kawaguchi, Hiroshi, Kobayashi, Kazutoshi, Furuta, Jun, Mitsuyama, Yukio, Hashimoto, Masanori, Onoye, Takao, Kanbara, Hiroyuki, Ochi, Hiroyuki, Wakabayashi, Kazutoshi, Onodera, Hidetoshi, Sugihara, Makoto, and Asai, Shojiro, editor

Published
2019
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14. Fusion Networks for Air-Writing Recognition

Author

Yana, Buntueng, Onoye, Takao, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Schoeffmann, Klaus, editor, Chalidabhongse, Thanarat H., editor, Ngo, Chong Wah, editor, Aramvith, Supavadee, editor, O’Connor, Noel E., editor, Ho, Yo-Sung, editor, Gabbouj, Moncef, editor, and Elgammal, Ahmed, editor

Published
2018
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17. Duck Curve Aware Dynamic Pricing and Battery Scheduling Strategy Using Reinforcement Learning

Author

Watari, Daichi, Taniguchi, Ittetsu, and Onoye, Takao

Abstract

The duck curve is becoming a global problem in energy technology due to the rapid increase in solar power adoption and the rise of prosumers. To address this issue, a resource aggregator (RA) has emerged to provide flexible solutions through aggregating the prosumers and demand response such as dynamic pricing. This paper proposes an optimal strategy for the RA that dispatches dynamic pricing to the prosumers and leverages the battery system at both RA and prosumer levels. The proposed method is based on a model-free deep reinforcement learning (DRL) algorithm to optimize each prosumer’s retail prices and schedule usage of the RA’s battery power station. An objective reward function is used to maximize the RA’s profit, minimize the prosumer’s cost, and maximize the improvement of the duck curve. The performance of the proposed DRL-based strategy was demonstrated by simulation experiments using actual wholesale price, demand, and PV generation data. The results show that the proposed strategy can improve the standard deviation and peak-to-average ratio of net load by up to 57.1% and 23%, respectively.

Published
2024
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23. Radiation-Induced Soft Errors

Author

Ibe, Eishi H., primary, Yoshimoto, Shusuke, additional, Yoshimoto, Masahiko, additional, Kawaguchi, Hiroshi, additional, Kobayashi, Kazutoshi, additional, Furuta, Jun, additional, Mitsuyama, Yukio, additional, Hashimoto, Masanori, additional, Onoye, Takao, additional, Kanbara, Hiroyuki, additional, Ochi, Hiroyuki, additional, Wakabayashi, Kazutoshi, additional, Onodera, Hidetoshi, additional, and Sugihara, Makoto, additional

Published
2018
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30. NBTI Mitigation by Giving Random Scan-in Vectors during Standby Mode

Author

Kameda, Toshihiro, Konoura, Hiroaki, Mitsuyama, Yukio, Hashimoto, Masanori, Onoye, Takao, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Ayala, José L., editor, García-Cámara, Braulio, editor, Prieto, Manuel, editor, Ruggiero, Martino, editor, and Sicard, Gilles, editor

Published
2011
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31. A Design Procedure for Oscillator-Based Hardware Random Number Generator with Stochastic Behavior Modeling

Author

Amaki, Takehiko, Hashimoto, Masanori, Mitsuyama, Yukio, Onoye, Takao, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Chung, Yongwha, editor, and Yung, Moti, editor

Published
2011
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32. m-ActiveCube; Multimedia Extension of Spatial Tangible User Interface

Author

Ueda, Kyoko, Kosaka, Atsushi, Watanabe, Ryoichi, Takeuchi, Yoshinori, Onoye, Takao, Itoh, Yuichi, Kitamura, Yoshifumi, Kishino, Fumio, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Ijspeert, Auke Jan, editor, Masuzawa, Toshimitsu, editor, and Kusumoto, Shinji, editor

Published
2006
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37. Scalable Design Framework for JPEG2000 System Architecture

Author

Tsutsui, Hiroshi, Masuzaki, Takahiko, Hayashi, Yoshiteru, Taki, Yoshitaka, Izumi, Tomonori, Onoye, Takao, Nakamura, Yukihiro, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Yew, Pen-Chung, editor, and Xue, Jingling, editor

Published
2004
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