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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2018-09-21 09:50 |
Fukuoka | Anomaly Detection for Various Operations of Machine Kazuki Kobayashi, Masatoshi Sekine, Satoshi Ikada (OKI)  PRMU2018-56 IBISML2018-33 |
In this paper, we propose an anomaly level estimation method for various operation of machine. Our proposed method has t... [more] | PRMU2018-56 IBISML2018-33 pp.133-138 |
|
| NLC, IPSJ-DC | 2018-09-06 17:20 |
Tokyo | Seikei University | Latent co-occurrence words graph extraction using sparse structure estimation
-- Comparison of word vectors between topic model and distributed representation -- Norimitsu Kubono, Nozomi Hiyoshi, Daiju Akashi (PERSOL CAREER) NLC2018-19 |
We are considering application of "structural topic model" in order to extract customer insight from member questionnai... [more] | NLC2018-19 pp.51-56 |
| EMCJ, WPT (Joint) |
2018-06-14 13:35 |
Nagasaki | Experimental validation for waveform model of conducted disturbances below 150 kHz from power conversion equipment Farhan Mahmood, Yuichiro Okugawa, Jun kato (NTT) EMCJ2018-11 |
International Electrotechnical Commission (IEC) specifies conducted immunity test wave as continuous wave (CW) in IEC 61... [more] | EMCJ2018-11 pp.1-6 |
|
| PRMU, MI, IE, SIP | 2018-05-18 16:15 |
Gifu | Effects on Classification Performance of Time Windows in Feature Extraction Using Denoising Autoencoders for Activity Recognition from Acceleration Data Satoko Sugai, Toru Takeyama, Kiyoshi Kogure (KIT) SIP2018-18 IE2018-18 PRMU2018-18 MI2018-18 |
We have experimentally evaluated the effects on classification accuracy of time windows in feature extraction using deno... [more] | SIP2018-18 IE2018-18 PRMU2018-18 MI2018-18 pp.81-85 |
|
| PRMU, BioX | 2018-03-18 13:55 |
Tokyo | Feature extraction of object shape from motion parallax using convolutional neural network ChengJun Shao, Makoto Murakami (Toyo Univ.) BioX2017-41 PRMU2017-177 |
The convolution neural networks (CNN) have good feature extraction capability. In this paper, we propose a method which ... [more] | BioX2017-41 PRMU2017-177 pp.31-36 |
|
| PRMU, BioX | 2018-03-18 16:10 |
Tokyo | Pedestrian Detection with Multi-level Deep Features Misaki Kodaira, Yu Wang, Jien Kato (Nagoya Univ.) BioX2017-52 PRMU2017-188 |
In this research, we aim to clarify effective application of CNN features in pedestrian detection. In the experiment, fe... [more] | BioX2017-52 PRMU2017-188 pp.97-102 |
|
| MBE, NC (Joint) |
2018-03-14 14:25 |
Tokyo | Kikai-Shinko-Kaikan Bldg. | Effect of Alzheimer's Disease on factors in waveform features of Pupil Light Reflex Wioletta Nowak (Wroclaw Univ. of S&T), Minoru Nakayama (Tokyo Tech), Krecicki Tomasz (Wroclaw Medical Univ.), Andrzej Hachol (Wroclaw Univ. of S&T) MBE2017-107 |
Pupil light reflex (PLR) for 1 second chromatic stimuli was analysed to develop a procedure of detecting Alzheimer's D... [more] |
MBE2017-107 pp.131-136 |
| MSS, NLP (Joint) |
2018-03-14 10:05 |
Osaka | Improvement of Spaito-Temporal Situation Recognition Using Staff's Behavior Logs Shun Hayakashi, Kunihiko Hiraishi, Naoshi Uchihira (JAIST) MSS2017-90 |
Recently, assist technologies for work staff using ICT devices is expected to be introduced into various real fields. In... [more] | MSS2017-90 pp.67-72 |
|
| ITS, IE, ITE-MMS, ITE-HI, ITE-ME, ITE-AIT [detail] | 2018-02-16 11:30 |
Hokkaido | Hokkaido Univ. | A Study of Handwritten Chinese Character Recognition Method Based on Feature Points Extraction Using Small Scale Space Masato Suzuki, Daisuke Kitakoshi (Tokyo Kosen) ITS2017-83 IE2017-115 |
Recognition method using local feature extraction is one of the famous strategies to general object recognition. By cons... [more] | ITS2017-83 IE2017-115 pp.257-260 |
| ITS, IE, ITE-MMS, ITE-HI, ITE-ME, ITE-AIT [detail] | 2018-02-16 11:30 |
Hokkaido | Hokkaido Univ. | Accuracy Improvement of Preference Estimation for Video Using SFEM-GS Yoshiki Ito, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
In this paper, we present two kinds of canonical correlation analysis methods, supervised fractional-order embedding mul... [more] | |
| CW (2nd) |
2017-12-16 - 2017-12-17 |
Okinawa | Musical feature based on correlation between bands of undecimated wavelet coefficients Takuya Kobayashi, Akira Kubota (Chuo Univ,), Yuusuke Suzuki |
Content based filtering is a basic element of a music information retrieval system and attracts more attention as digita... [more] | ||
| ET | 2017-12-02 11:20 |
Ishikawa | Kanazawa Institute of Technology | A study of feature extraction and visualization for hand calculation algorithm for false prediction Keita Yamzaki, Yuzuki Sato, Arimitsu shikoda (TGU) ET2017-70 |
A feature extraction and visualization technique on column calculation algorithm for false prediction utilizing a machin... [more] | ET2017-70 pp.13-16 |
| MBE, NC (Joint) |
2017-11-25 15:10 |
Miyagi | Tohoku University | Ensemble Learning with Feature Extraction for EEG Signal Discrimination using Source Separation Shuichi Nishino, Tomohiro Yoshikawa, Takeshi Furuhashi (Nagoya Univ.) NC2017-36 |
BCI allows a user to control external devices and to communicate with other people by measuring and discriminating EEG. ... [more] | NC2017-36 pp.49-52 |
| SANE | 2017-11-23 11:00 |
Overseas | Malaysia (Borneo Island) | [Special Talk]
SAR Image Segmentation Based Framework to Ship Detection Yang-Lang Chang, Amare Anagaw Ayele (NTUT), Lena Chang, Wei-Lin Chen (NTOU), Meng-Che Wu (NSPO), Chihyuan Chu (NTUT) SANE2017-63 |
Synthetic aperture radar (SAR) imagery has proven to be a promising data source for the surveillance of maritime activit... [more] | SANE2017-63 pp.1-6 |
| IBISML | 2017-11-10 13:00 |
Tokyo | Univ. of Tokyo | Feature Extraction Using Empirical Mode Decomposition for Tire Sensing Keita Ishii, Takato Goto (BS), Matsui Tomoko (ISM), Gareth Peters (HW), Nourddine Azzaoui (UCA) IBISML2017-78 |
This paper describes a novel feature-extraction method for classifying road conditions by using acceleration signals tha... [more] | IBISML2017-78 pp.315-320 |
| MVE | 2017-09-21 10:00 |
Chiba | Chiba Univ. | Feature Extraction and Impression Prediction of Presentation Slides Shinji Oyama, Toshihiko Yamasaki, Kiyoharu Aizawa (Univ. of Tokyo) MVE2017-14 |
Presentation using slides is an effective way to deliver information in various fields. Although it has become easier to... [more] | MVE2017-14 pp.1-6 |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2017-09-16 09:45 |
Tokyo | Feature Extraction Using Denoising Autoencoders for Activity Recognition from Acceleration Data Toru Takeyama, Kiyoshi Kogure (KIT) PRMU2017-58 IBISML2017-30 |
We have experimentally evaluated how the performance of activity recognition from acceleration data depends on the way o... [more] | PRMU2017-58 IBISML2017-30 pp.161-166 |
|
| MBE, BioX | 2017-07-29 16:00 |
Tokushima | Tokushima University | Influence of Alzheimer's Disease and Aging factors on Features of Pupil Light Reflex Wioletta Nowak (Wroclaw Univ. of Tech.), Minoru Nakayama (Tokyo Inst. of Tech.), Tomasz Krecicki (Wroclaw Medical Univ.), Andrzej Hachol (Wroclaw Univ. of Tech.) BioX2017-20 MBE2017-29 |
Pupil light reflex (PLR) with melanopsin-containing retinal ganglion cells (ipRGCs), which consisted of 1 second chrom... [more] |
BioX2017-20 MBE2017-29 pp.57-62 |
| HCS, HIP, HI-SIGCOASTER [detail] | 2017-05-16 15:45 |
Okinawa | Okinawa Industry Support Center | Extraction of acoustic features of emotional speech and their characteristics Takashi Yamazaki, Minoru Nakayama (Tokyo Tech.) HCS2017-17 HIP2017-17 |
In this paper, we extracted the acoustic features of emotional speech and examined the effect of the feature on emotiona... [more] | HCS2017-17 HIP2017-17 pp.127-130 |
| CPSY, DC, IPSJ-SLDM, IPSJ-EMB, IPSJ-ARC [detail] | 2017-03-10 09:10 |
Okinawa | Kumejima Island | Data Mining and Private Information Detection Method using Power Demand Yoshida Masahiro, Imanishi Tomoya, Nishi Hiroaki (Keio Univ.) CPSY2016-143 DC2016-89 |
Recently, detailed power demand information of the building is being aggregated, accompanied by the spread of smart mete... [more] | CPSY2016-143 DC2016-89 pp.285-290 |
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