Fuzzy Multiple Discrimminant Analysis (FMDA) 5) (SOM) 6) SOM 3 6) SOM SOM SOM SOM SOM SOM 7) 8) SOM SOM SOM GPU 2. n k f(x) m g(x) (1) 12) { min(max)
|
|
|
- きみかず のあき
- 7 years ago
- Views:
Transcription
1 SOM (SOM: Self-Organizing Maps) 3 SOM SOM SOM SOM GPU A Study on Visualization of Pareto Solutions by Spherical Self-Organizing Maps MASATO YOSHIMI, 1 KANAME NISHIMOTO, 2 LUYI WANG, 2 TOMOYUKI HIROYASU 3 and MITSUNORI MIKI 1 In this paper, Self-Organizing Maps (SOM) which is one of neural network systems was applied to illustrate the Pareto solution set which is derived by multi-objective optimization. Especially, this paper described that Spherical SOM is effective for illustrating the Pareto solution set. These discussions have been performed through the real world problem. At the same time, the implementation and parallel method of Spherical SOM by GPUs were also discussed. 1 Faculty of Science and Engineering, Doshisha University 2 Faculty of Engineering, Doshisha University 3 Faculty of department of life and medical science, Doshisha University 1. Computer Aided Design(CAD) Finite Element Method(FEM) 1) 2) 4) c2010 Information
2 Fuzzy Multiple Discrimminant Analysis (FMDA) 5) (SOM) 6) SOM 3 6) SOM SOM SOM SOM SOM SOM 7) 8) SOM SOM SOM GPU 2. n k f(x) m g(x) (1) 12) { min(max) f i(x 1, x 2,..., x n) (i = 1, 2,..., k) subject to g j (x 1, x 2,..., x n ) 0 (j = 1, 2,..., m) f i (x) 1 (1) 13) x 1, x 2 I(x= (x 1, x 2,..., x n)) (a) f i (x 1 ) f i (x 2 )( i = 1, 2,..., k) x 1 x 2 (b) f i (x 1 ) < f i (x 2 )( i = 1, 2,..., k) x 1 x 2 x 1 x 2 x 1 x 2 x 0 I(x= (x 1, x 2,..., x n )) (a) x 0 x I x 0 Weak Pareto-optimal solution (b) x 0 x I x 0 Pareto-optimal solution (1) 2 k = 2 Feasible Region 3. SOM 3.1 SOM SOM SOM SOM SOM SOM i n t m i (t) x(t) 2 c2010 Information
3 f (x) 2 Feasible region Weak pareto-optimal solutions Pareto-optimal solutions 1 f (x) 1 α(t) h ci(t) σ(t) SOM ( 1 ) t 0 m i (0) ( 2 ) x(t) m i (t) m i (t) x(t) ( 3 ) c x(t) = min m i (t) x(t) c ( 4 ) c d ci (2) (2) h ci(t) α(t) (3) α(t) σ(t) (4) (5) m i (t + 1) = m i (t) + h ci (t)(x(t) m i (t)) (2) { 0 (d ci > σ(t)) h ci(t) = (3) α(t) (d ci σ(t)) α(t) = α(0)(t t)/t (4) σ(t) = σ(0)(t t)/t (5) ( 5 ) t < T t t + 1 (2) t = T 4. SOM 4.1 SOM SOM SOM 3 SOM 3 SOM SOM SOM c2010 Information
4 SOM SOM a spherical surface U development V U' 2 3D V' U' rectangular coordinates put neurons on the apices 3D SOM SOM V' 2 2 SOM 3 4 c2010 Information
5 h d i map e j f k m n o p q r s a b c g l d 3 e tree j f k o n i a b c g l p s r not-learned neurons learn and add tree pre-learned neurons adjacent neurons e HSV (Saturation) (Value) (Hue) [0, 360] [0, 280] 5. SOM GPU 5.1 SOM SOM 1 3 SOM 1987 Carpenter SOM 1992 Speckmann SOM SIMD COKOS(COprocessor for KOhonen s Self-organizing map) COKOS 8 SOM SOM FPGA Porrmann j k p s r n f k o n i 11) Porrmann 6 SOM Intel Xeon 2.80GHz CPU GPU SOM 10) NVIDIA GeForce GTX280 CPU 150 SOM GPU GPU SOM GPU 5.2 SOM SOM (2) SOM GPU ) SOM SOM GPU 5.3 SOM SOM GPU 18) C++ NVIDIA CUDA Opteron 1210 HE(2.50GHz, Linux amd64, 5 c2010 Information
6 G , -O3) 1 3 GPU ( ) [0, 9] CPU SP ( ) (GHz) (GHz) GeForce8400GS GeForce GTX Tesla C GPU SOM SOM 10) SOM GPU 6. SOM 4 4 (SP) GPU 10 SP 5 SOM SP 8400GS 240 SP GPU 10 SP SP 240 GPU SOM. SOM SOM SOM 3) 3) SOM SOM 6 c2010 Information
7 6.1 SOM, 4. SOM )9) SOM 6.2 CO2 14) 16) (SFC) (NOX) NOX (Soot) SFC NOX Soot NOX SFC 6 2 Start Angle Exhaust gas recirculation Rate EGR RateSwirl Ratio Boost Pressure ) NOX (Soot) SFC NOX SFC f 1 NOX f 2 Soot f 3 x 1 x 2 x 3 x 4 x 5 x 6 x 7 ( ) x SOM SOM SOM SOM SOM PAK SOM PAK SOM SOM SOM c2010 Information
8 情報処理学会研究報告 となる ニューロン数 (個) 平面 SOM 球面 SOM 900 (30 30) 1000 (20 50) 表 3 SOM の設定 学習回数 (回) 学習率係数の初期値 p2: f1: f3: 近傍半径の初期値 p1: f1: f3: p3: f1: f3: 提案する球面 SOM によって得られた結果を図 7 に示す ここでは 90 度ずつ回転させ た様子を示している 球面上には 目的関数 f1 (燃料消費量) の値を表示している すなわ ち f1 の値が高ければ赤く 低ければ紫となる 従って 球面 SOM で解の分布を学習さ せると f1 の値が高いものが島状に配置される p5: f1: f3: p1 p4: f1: f3: 図8 平面 SOM における SFC の空間の可視化 p2 p3 わかる 従って大林が平面 SOM での設計空間の分類 構造の把握を行えると示したことと p4 0 同様に 球面 SOM でも可能であると言え パレート解表示は有効であることが再確認され た9) 平面 SOM と球面 SOM におけるデータ配置の違い 平面 SOM が パレート解表示する上で有効であることは再確認されたが 境界において は 各競合層に配置されたデータにゆがみが生じる可能性がある そのため 図 8 において 示した平面 SOM に配置された各データが 球面 SOM ではどのように配置されているかを 検討する p5 図 8 における点 p1 から p5 までの各目的関数の値および設計変数の値を表 4 にまとめて 示す まず 図 8 における点 p2 および p4 に着目する これらの目的関数値および設計変数の 値は比較的類似しており SOM 上では 近傍に配置されることが期待される しかしなが ら 実際には平面 SOM 上では 右端に対峙して配置されている これは 類似したデータ 図 7 球面 SOM における SFC の空間の可視化 がすべて右端に偏って配置されたため 実際には近いデータにもかかわらず 距離をおいて 平面 SOM での学習結果を図 8 に示す f1 の値が高いものが左上に集まって表示され 右 配置されてしまったためであると考えられる 一方で 図 7 からもわかるように球面 SOM 上 右下 左下という順番で値が小さくなりながら配置されていることがわかる においては p2 および p4 は近傍に配置されている よって 球面 SOM によるパレート解 球面 SOM の結果も平面 SOM と同様に値を徐々に低下させながら配置されていることが 表示では境界のゆがみの影響を受けていないことが分かる Information
9 4 data f 1 f 3 f 3 x 1 x 2 x 3 x 4 x 5 x 6 x 7 x 8 p p p p p p1 p3 8 f SOM p1 p3 7 SOM 4 x 4 x 8 SOM p1 p3 SOM SOM SOM SOM SOM SOM 7. (Self-Organizing Maps, SOM) SOM SOM SOM SOM 2 3 SOM SOM SOM 1 SOM SOM SOM SOM SOM SOM SOM SOM SOM GPU SOM 1) (TOM) Vol.2, No.3 pp (2009) 2) N ) (TOM) Vol.1 No.1 pp.27-42, (2008) 4), Vol. 46, No. SIG 17(TOM13) pp (2005) 5) 9 c2010 Information
10 Vol.20, No.6, pp , (2008) 6), : Vol.58, pp (2005/5) 7) Ying Xin WU SOM Vol.19, No.6, pp , (2007) 8),,, SOM Vol.8, No.1, pp ), 14, pp (2001) 10) HOKKE-2009 IPSJ SIG Notes pp (2009) 11) H. Tamukoh, T. Aso, K. Horio, T. Yamakawa : Self-organizing map hardware accelerator system and its application to realtime image enlargement, Neural Networks, Proceedings IEEE International Joint Conference on, Vol. 4, (2004) 12), 2 (1995) 13),,, :,, 17, (1998) 14), Vol.55, No.9, pp.17-22, (2001) 15) :, : Vol.55, No.9, pp.10-16, (2001) 16), :, : Vol.55, No.9, pp.41-45, (2001) 17), :, : Vol.55, No.9, pp.46-52, (2001) 18) GPU SOM HOKKE-17 IPSJ SIG Note (2009) 10 c2010 Information
11 ( 1 ) ( 2 ) 1 CPU GPU MASATO YOSHIMI KANAME NISHIMOTO LUYI WANG TOMOYUKI HIROYASU MITSUNORI MIKI KANAME NISHIMOTO MASATO YOSHIMI LUYI WANG TOMOYUKI HIROYASU MITSUNORI MIKI
GPGPU
GPGPU 2013 1008 2015 1 23 Abstract In recent years, with the advance of microscope technology, the alive cells have been able to observe. On the other hand, from the standpoint of image processing, the
23 Fig. 2: hwmodulev2 3. Reconfigurable HPC 3.1 hw/sw hw/sw hw/sw FPGA PC FPGA PC FPGA HPC FPGA FPGA hw/sw hw/sw hw- Module FPGA hwmodule hw/sw FPGA h
23 FPGA CUDA Performance Comparison of FPGA Array with CUDA on Poisson Equation ([email protected]), ([email protected]), ([email protected]), ([email protected]),
A Study on Throw Simulation for Baseball Pitching Machine with Rollers and Its Optimization Shinobu SAKAI*5, Yuichiro KITAGAWA, Ryo KANAI and Juhachi
A Study on Throw Simulation for Baseball Pitching Machine with Rollers and Its Optimization Shinobu SAKAI*5, Yuichiro KITAGAWA, Ryo KANAI and Juhachi ODA Department of Human and Mechanical Systems Engineering,
258 5) GPS 1 GPS 6) GPS DP 7) 8) 10) GPS GPS 2 3 4 5 2. 2.1 3 1) GPS Global Positioning System
Vol. 52 No. 1 257 268 (Jan. 2011) 1 2, 1 1 measurement. In this paper, a dynamic road map making system is proposed. The proposition system uses probe-cars which has an in-vehicle camera and a GPS receiver.
1 Fig. 1 Extraction of motion,.,,, 4,,, 3., 1, 2. 2.,. CHLAC,. 2.1,. (256 ).,., CHLAC. CHLAC, HLAC. 2.3 (HLAC ) r,.,. HLAC. N. 2 HLAC Fig. 2
CHLAC 1 2 3 3,. (CHLAC), 1).,.,, CHLAC,.,. Suspicious Behavior Detection based on CHLAC Method Hideaki Imanishi, 1 Toyohiro Hayashi, 2 Shuichi Enokida 3 and Toshiaki Ejima 3 We have proposed a method for
SOM SOM(Self-Organizing Maps) SOM SOM SOM SOM SOM SOM i
20 SOM Development of Syllabus Vsualization System using Spherical Self-Organizing Maps 1090366 2009 3 5 SOM SOM(Self-Organizing Maps) SOM SOM SOM SOM SOM SOM i Abstract Development of Syllabus Vsualization
平成○○年度知能システム科学専攻修士論文
A Realization of Robust Agents in an Agent-based Virtual Market Makio Yamashige 3 7 A Realization of Robust Agents in an Agent-based Virtual Market Makio Yamashige Abstract There are many people who try
Journal of Geography 116 (6) Configuration of Rapid Digital Mapping System Using Tablet PC and its Application to Obtaining Ground Truth
Journal of Geography 116 (6) 749-758 2007 Configuration of Rapid Digital Mapping System Using Tablet PC and its Application to Obtaining Ground Truth Data: A Case Study of a Snow Survey in Chuetsu District,
28 Horizontal angle correction using straight line detection in an equirectangular image
28 Horizontal angle correction using straight line detection in an equirectangular image 1170283 2017 3 1 2 i Abstract Horizontal angle correction using straight line detection in an equirectangular image
untitled
Studies in Human Geography 32 Geoenvironmental Sciences, Graduate School of Life and Environmental Sciences, University of Tsukuba, Japan ArcGIS Geography Network ArcGIS ArcGIS ArcGIS Geography Network
main.dvi
PC 1 1 [1][2] [3][4] ( ) GPU(Graphics Processing Unit) GPU PC GPU PC ( 2 GPU ) GPU Harris Corner Detector[5] CPU ( ) ( ) CPU GPU 2 3 GPU 4 5 6 7 1 [email protected] 45 2 ( ) CPU ( ) ( ) () 2.1
1 Web [2] Web [3] [4] [5], [6] [7] [8] S.W. [9] 3. MeetingShelf Web MeetingShelf MeetingShelf (1) (2) (3) (4) (5) Web MeetingShelf
![1 Web [2] Web [3] [4] [5], [6] [7] [8] S.W. [9] 3. MeetingShelf Web MeetingShelf MeetingShelf (1) (2) (3) (4) (5) Web MeetingShelf](/thumbs/79/80104027.jpg "1 Web [2] Web [3] [4] [5], [6] [7] [8] S.W. [9] 3. MeetingShelf Web MeetingShelf MeetingShelf (1) (2) (3) (4) (5) Web MeetingShelf")
1,a) 2,b) 4,c) 3,d) 4,e) Web A Review Supporting System for Whiteboard Logging Movies Based on Notes Timeline Taniguchi Yoshihide 1,a) Horiguchi Satoshi 2,b) Inoue Akifumi 4,c) Igaki Hiroshi 3,d) Hoshi
第６２巻 第１号 平成２４年４月／石こうを用いた木材ペレット
Bulletin of Japan Association for Fire Science and Engineering Vol. 62. No. 1 (2012) Development of Two-Dimensional Simple Simulation Model and Evaluation of Discharge Ability for Water Discharge of Firefighting
1 [1, 2, 3, 4, 5, 8, 9, 10, 12, 15] The Boston Public Schools system, BPS (Deferred Acceptance system, DA) (Top Trading Cycles system, TTC) cf. [13] [
![1 [1, 2, 3, 4, 5, 8, 9, 10, 12, 15] The Boston Public Schools system, BPS (Deferred Acceptance system, DA) (Top Trading Cycles system, TTC) cf. [13] [](/thumbs/91/106628466.jpg "1 [1, 2, 3, 4, 5, 8, 9, 10, 12, 15] The Boston Public Schools system, BPS (Deferred Acceptance system, DA) (Top Trading Cycles system, TTC) cf. [13] [")
Vol.2, No.x, April 2015, pp.xx-xx ISSN xxxx-xxxx 2015 4 30 2015 5 25 253-8550 1100 Tel 0467-53-2111( ) Fax 0467-54-3734 http://www.bunkyo.ac.jp/faculty/business/ 1 [1, 2, 3, 4, 5, 8, 9, 10, 12, 15] The
149 (Newell [5]) Newell [5], [1], [1], [11] Li,Ryu, and Song [2], [11] Li,Ryu, and Song [2], [1] 1) 2) ( ) ( ) 3) T : 2 a : 3 a 1 :
![149 (Newell [5]) Newell [5], [1], [1], [11] Li,Ryu, and Song [2], [11] Li,Ryu, and Song [2], [1] 1) 2) ( ) ( ) 3) T : 2 a : 3 a 1 :](/thumbs/92/109497872.jpg "149 (Newell [5]) Newell [5], [1], [1], [11] Li,Ryu, and Song [2], [11] Li,Ryu, and Song [2], [1] 1) 2) ( ) ( ) 3) T : 2 a : 3 a 1 :")
Transactions of the Operations Research Society of Japan Vol. 58, 215, pp. 148 165 c ( 215 1 2 ; 215 9 3 ) 1) 2) :,,,,, 1. [9] 3 12 Darroch,Newell, and Morris [1] Mcneil [3] Miller [4] Newell [5, 6], [1]
(MIRU2008) HOG Histograms of Oriented Gradients (HOG)
(MIRU2008) 2008 7 HOG - - E-mail: [email protected], {takigu,ariki}@kobe-u.ac.jp Histograms of Oriented Gradients (HOG) HOG Shape Contexts HOG 5.5 Histograms of Oriented Gradients D Human
Input image Initialize variables Loop for period of oscillation Update height map Make shade image Change property of image Output image Change time L
1,a) 1,b) 1/f β Generation Method of Animation from Pictures with Natural Flicker Abstract: Some methods to create animation automatically from one picture have been proposed. There is a method that gives
untitled
2007 55 2 235 254 c 2007 1 2 3 3 2007 6 12 2007 11 1 20 8 2 1. 2004 Sakata et al. 2004 1 610 0394 1 3 2 176 8525 2 42 1 3 525 8577 1 1 1 236 55 2 2007 2003 2004 Camurri et al. 1999 2002 2005 CG 1987 1
理学療法検査技術習得に向けた客観的臨床能力試験(OSCE)の試行
An Application of an Objective Structured Clinical Examination for Learning Skills in Assessment of Physical Therapy A Task Involving Measurements on the Range of Motion Test Chikako Fujita1) Hiroyasu
IPSJ SIG Technical Report Vol.2009-DPS-141 No.20 Vol.2009-GN-73 No.20 Vol.2009-EIP-46 No /11/27 1. MIERUKEN 1 2 MIERUKEN MIERUKEN MIERUKEN: Spe
1. MIERUKEN 1 2 MIERUKEN MIERUKEN MIERUKEN: Speech Visualization System Based on Augmented Reality Yuichiro Nagano 1 and Takashi Yoshino 2 As the spread of the Augmented Reality(AR) technology and service,
07-二村幸孝・出口大輔.indd
GPU Graphics Processing Units HPC High Performance Computing GPU GPGPU General-Purpose computation on GPU CPU GPU GPU *1 Intel Quad-Core Xeon E5472 3.0 GHz 2 6 MB L2 cache 1600 MHz FSB 80 GFlops 1 nvidia
HASC2012corpus HASC Challenge 2010,2011 HASC2011corpus( 116, 4898), HASC2012corpus( 136, 7668) HASC2012corpus HASC2012corpus
HASC2012corpus 1 1 1 1 1 1 2 2 3 4 5 6 7 HASC Challenge 2010,2011 HASC2011corpus( 116, 4898), HASC2012corpus( 136, 7668) HASC2012corpus HASC2012corpus: Human Activity Corpus and Its Application Nobuo KAWAGUCHI,
Study on Throw Accuracy for Baseball Pitching Machine with Roller (Study of Seam of Ball and Roller) Shinobu SAKAI*5, Juhachi ODA, Kengo KAWATA and Yu
Study on Throw Accuracy for Baseball Pitching Machine with Roller (Study of Seam of Ball and Roller) Shinobu SAKAI*5, Juhachi ODA, Kengo KAWATA and Yuichiro KITAGAWA Department of Human and Mechanical
1 GPU GPGPU GPU CPU 2 GPU 2007 NVIDIA GPGPU CUDA[3] GPGPU CUDA GPGPU CUDA GPGPU GPU GPU GPU Graphics Processing Unit LSI LSI CPU ( ) DRAM GPU LSI GPU
![1 GPU GPGPU GPU CPU 2 GPU 2007 NVIDIA GPGPU CUDA[3] GPGPU CUDA GPGPU CUDA GPGPU GPU GPU GPU Graphics Processing Unit LSI LSI CPU ( ) DRAM GPU LSI GPU](/thumbs/89/99132402.jpg "1 GPU GPGPU GPU CPU 2 GPU 2007 NVIDIA GPGPU CUDA[3] GPGPU CUDA GPGPU CUDA GPGPU GPU GPU GPU Graphics Processing Unit LSI LSI CPU ( ) DRAM GPU LSI GPU")
GPGPU (I) GPU GPGPU 1 GPU(Graphics Processing Unit) GPU GPGPU(General-Purpose computing on GPUs) GPU GPGPU GPU ( PC ) PC PC GPU PC PC GPU GPU 2008 TSUBAME NVIDIA GPU(Tesla S1070) TOP500 29 [1] 2009 AMD
Visual Evaluation of Polka-dot Patterns Yoojin LEE and Nobuko NARUSE * Granduate School of Bunka Women's University, and * Faculty of Fashion Science,
Visual Evaluation of Polka-dot Patterns Yoojin LEE and Nobuko NARUSE * Granduate School of Bunka Women's University, and * Faculty of Fashion Science, Bunka Women's University, Shibuya-ku, Tokyo 151-8523
IPSJ SIG Technical Report Vol.2013-ARC-203 No /2/1 SMYLE OpenCL (NEDO) IT FPGA SMYLEref SMYLE OpenCL SMYLE OpenCL FPGA 1
SMYLE OpenCL 128 1 1 1 1 1 2 2 3 3 3 (NEDO) IT FPGA SMYLEref SMYLE OpenCL SMYLE OpenCL FPGA 128 SMYLEref SMYLE OpenCL SMYLE OpenCL Implementation and Evaluations on 128 Cores Takuji Hieda 1 Noriko Etani
IPSJ SIG Technical Report Vol.2010-GN-74 No /1/ , 3 Disaster Training Supporting System Based on Electronic Triage HIROAKI KOJIMA, 1 KU
1 2 2 1, 3 Disaster Training Supporting System Based on Electronic Triage HIROAKI KOJIMA, 1 KUNIAKI SUSEKI, 2 KENTARO NAGAHASHI 2 and KEN-ICHI OKADA 1, 3 When there are a lot of injured people at a large-scale
xx/xx Vol. Jxx A No. xx 1 Fig. 1 PAL(Panoramic Annular Lens) PAL(Panoramic Annular Lens) PAL (2) PAL PAL 2 PAL 3 2 PAL 1 PAL 3 PAL PAL 2. 1 PAL
PAL On the Precision of 3D Measurement by Stereo PAL Images Hiroyuki HASE,HirofumiKAWAI,FrankEKPAR, Masaaki YONEDA,andJien KATO PAL 3 PAL Panoramic Annular Lens 1985 Greguss PAL 1 PAL PAL 2 3 2 PAL DP
rank ”«‘‚“™z‡Ì GPU ‡É‡æ‡éŁÀŠñ›»
rank GPU ERATO 2011 11 1 1 / 26 GPU rank/select wavelet tree balanced parenthesis GPU rank 2 / 26 GPU rank/select wavelet tree balanced parenthesis GPU rank 2 / 26 GPU rank/select wavelet tree balanced
2017 (413812)
2017 (413812) Deep Learning ( NN) 2012 Google ASIC(Application Specific Integrated Circuit: IC) 10 ASIC Deep Learning TPU(Tensor Processing Unit) NN 12 20 30 Abstract Multi-layered neural network(nn) has
1 3DCG [2] 3DCG CG 3DCG [3] 3DCG 3 3 API 2 3DCG 3 (1) Saito [4] (a) 1920x1080 (b) 1280x720 (c) 640x360 (d) 320x G-Buffer Decaudin[5] G-Buffer D
![1 3DCG [2] 3DCG CG 3DCG [3] 3DCG 3 3 API 2 3DCG 3 (1) Saito [4] (a) 1920x1080 (b) 1280x720 (c) 640x360 (d) 320x G-Buffer Decaudin[5] G-Buffer D](/thumbs/91/105248386.jpg "1 3DCG [2] 3DCG CG 3DCG [3] 3DCG 3 3 API 2 3DCG 3 (1) Saito [4] (a) 1920x1080 (b) 1280x720 (c) 640x360 (d) 320x G-Buffer Decaudin[5] G-Buffer D")
3DCG 1) ( ) 2) 2) 1) 2) Real-Time Line Drawing Using Image Processing and Deforming Process Together in 3DCG Takeshi Okuya 1) Katsuaki Tanaka 2) Shigekazu Sakai 2) 1) Department of Intermedia Art and Science,
日立金属技報 Vol.34
Influence of Misorientation Angle between Adjacent Grains on Magnetization Reversal in Nd-Fe-B Sintered Magnet Tomohito Maki Rintaro Ishii Mitsutoshi Natsumeda Takeshi Nishiuchi Ryo Uchikoshi Masaaki Takezawa
JFE.dvi
,, Department of Civil Engineering, Chuo University Kasuga 1-13-27, Bunkyo-ku, Tokyo 112 8551, JAPAN E-mail : [email protected] E-mail : [email protected] SATO KOGYO CO., LTD. 12-20, Nihonbashi-Honcho
soturon.dvi
12 Exploration Method of Various Routes with Genetic Algorithm 1010369 2001 2 5 ( Genetic Algorithm: GA ) GA 2 3 Dijkstra Dijkstra i Abstract Exploration Method of Various Routes with Genetic Algorithm
W W - W. m - km. km..c - mm. km. ,,. m/, -,, -,/m - -. - - W- kw - -. - / / - - - k rated output powerwww WWW kw kw kw kw k m/w W. m kw W m/ W - m m mw. m m kw kw W. m m W W kw kg kw kg kw kg kw, kg k
A Feasibility Study of Direct-Mapping-Type Parallel Processing Method to Solve Linear Equations in Load Flow Calculations Hiroaki Inayoshi, Non-member
A Feasibility Study of Direct-Mapping-Type Parallel Processing Method to Solve Linear Equations in Load Flow Calculations Hiroaki Inayoshi, Non-member (University of Tsukuba), Yasuharu Ohsawa, Member (Kobe
1 2 4 5 9 10 12 3 6 11 13 14 0 8 7 15 Iteration 0 Iteration 1 1 Iteration 2 Iteration 3 N N N! N 1 MOPT(Merge Optimization) 3) MOPT 8192 2 16384 5 MOP
10000 SFMOPT / / MOPT(Merge OPTimization) MOPT FMOPT(Fast MOPT) FMOPT SFMOPT(Subgrouping FMOPT) SFMOPT 2 8192 31 The Proposal and Evaluation of SFMOPT, a Task Mapping Method for 10000 Tasks Haruka Asano
Ł×
March OECD eds. OECD eds. ab March 100.0 80.0 60.0 40.0 20.0 95.7 94.9 91.9 93.8 88.0 94.4 78.1 77.0 35.2 38.7 0.0 6069 7079 8089 9099 0009 March W W B B W B W W B B W B March 2.5 2.0 1.5 2.03 1.79 1.95
06’ÓŠ¹/ŒØŒì
FD. FD FD FD FD FD FD / Plan-Do-See FD FD FD FD FD FD FD FD FD FD FD FD FD FD JABEE FD A. C. A B .. AV .. B Communication Space A FD FD ES FD FD The approach of the lesson improvement in Osaka City University
Title 社 会 化 教 育 における 公 民 的 資 質 : 法 教 育 における 憲 法 的 価 値 原 理 ( fulltext ) Author(s) 中 平, 一 義 Citation 学 校 教 育 学 研 究 論 集 (21): 113-126 Issue Date 2010-03 URL http://hdl.handle.net/2309/107543 Publisher 東 京
2010 : M0107189 3DCG 3 (3DCG) 3DCG 3DCG 3DCG S
2010 M0107189 2010 : M0107189 3DCG 3 (3DCG) 3DCG 3DCG 3DCG S 1 1 1.1............................ 1 1.2.............................. 4 2 5 2.1............................ 5 2.2.............................
17 Proposal of an Algorithm of Image Extraction and Research on Improvement of a Man-machine Interface of Food Intake Measuring System
1. (1) ( MMI ) 2. 3. MMI Personal Computer(PC) MMI PC 1 1 2 (%) (%) 100.0 95.2 100.0 80.1 2 % 31.3% 2 PC (3 ) (2) MMI 2 ( ),,,, 49,,p531-532,2005 ( ),,,,,2005,p66-p67,2005 17 Proposal of an Algorithm of
1: ( 1) 3 : 1 2 4
RippleDesk Using Ripples to Represent Conversational Noise on Internet Shigaku Iwabuchi Takaomi Hisamatsu Shin Takahashi Buntarou Shizuki Kazuo Misue Jiro Tanaka Department of Comupter Science, University
1: A/B/C/D Fig. 1 Modeling Based on Difference in Agitation Method artisoc[7] A D 2017 Information Processing
![1: A/B/C/D Fig. 1 Modeling Based on Difference in Agitation Method artisoc[7] A D 2017 Information Processing](/thumbs/88/115893223.jpg "1: A/B/C/D Fig. 1 Modeling Based on Difference in Agitation Method artisoc[7] A D 2017 Information Processing")
1,a) 2,b) 3 Modeling of Agitation Method in Automatic Mahjong Table using Multi-Agent Simulation Hiroyasu Ide 1,a) Takashi Okuda 2,b) Abstract: Automatic mahjong table refers to mahjong table which automatically
( ) [1] [4] ( ) 2. [5] [6] Piano Tutor[7] [1], [2], [8], [9] Radiobaton[10] Two Finger Piano[11] Coloring-in Piano[12] ism[13] MIDI MIDI 1 Fig. 1 Syst
![( ) [1] [4] ( ) 2. [5] [6] Piano Tutor[7] [1], [2], [8], [9] Radiobaton[10] Two Finger Piano[11] Coloring-in Piano[12] ism[13] MIDI MIDI 1 Fig. 1 Syst](/thumbs/91/107597950.jpg "( ) [1] [4] ( ) 2. [5] [6] Piano Tutor[7] [1], [2], [8], [9] Radiobaton[10] Two Finger Piano[11] Coloring-in Piano[12] ism[13] MIDI MIDI 1 Fig. 1 Syst")
情報処理学会インタラクション 2015 IPSJ Interaction 2015 15INT014 2015/3/7 1,a) 1,b) 1,c) Design and Implementation of a Piano Learning Support System Considering Motivation Fukuya Yuto 1,a) Takegawa Yoshinari 1,b) Yanagi
2. CABAC CABAC CABAC 1 1 CABAC Figure 1 Overview of CABAC 2 DCT 2 0/ /1 CABAC [3] 3. 2 値化部 コンテキスト計算部 2 値算術符号化部 CABAC CABAC
![2. CABAC CABAC CABAC 1 1 CABAC Figure 1 Overview of CABAC 2 DCT 2 0/ /1 CABAC [3] 3. 2 値化部 コンテキスト計算部 2 値算術符号化部 CABAC CABAC](/thumbs/92/108208872.jpg "2. CABAC CABAC CABAC 1 1 CABAC Figure 1 Overview of CABAC 2 DCT 2 0/ /1 CABAC [3] 3. 2 値化部 コンテキスト計算部 2 値算術符号化部 CABAC CABAC")
H.264 CABAC 1 1 1 1 1 2, CABAC(Context-based Adaptive Binary Arithmetic Coding) H.264, CABAC, A Parallelization Technology of H.264 CABAC For Real Time Encoder of Moving Picture YUSUKE YATABE 1 HIRONORI
パナソニック技報
Panasonic Technical Journal Vol. 64 No. 2 Nov. 2018 Optical Disc Archiving System with 100 Years Lifespan of Digital Data Takuto Yamazaki Yasushi Kobayashi Blu-ray Disc 1 Archival Disc 2 3300 GB 10012
untitled
2010 58 1 39 59 c 2010 20 2009 11 30 2010 6 24 6 25 1 1953 12 2008 III 1. 5, 1961, 1970, 1975, 1982, 1992 12 2008 2008 226 0015 32 40 58 1 2010 III 2., 2009 3 #3.xx #3.1 #3.2 1 1953 2 1958 12 2008 1 2
2 22006 2 e-learning e e 2003 1 4 e e e-learning 2 Web e-leaning 2004 2005 2006 e 4 GP 4 e-learning e-learning e-learning e LMS LMS Internet Navigware
2 2 Journal of Multimedia Aided Education Research 2006, Vol. 2, No. 2, 19 e 1 1 2 2 1 1 GP e 2004 e-learning 2004 e-learning 2005 e-learning e-learning e-learning e-learning 2004 e-learning HuWeb 2005
2). 3) 4) 1.2 NICTNICT DCRA Dihedral Corner Reflector micro-arraysdcra DCRA DCRA DCRA 3D DCRA PC USB PC PC ON / OFF Velleman K8055 K8055 K8055
1 1 1 2 DCRA 1. 1.1 1) 1 Tactile Interface with Air Jets for Floating Images Aya Higuchi, 1 Nomin, 1 Sandor Markon 1 and Satoshi Maekawa 2 The new optical device DCRA can display floating images in free
21 e-learning Development of Real-time Learner Detection System for e-learning
21 e-learning Development of Real-time Learner Detection System for e-learning 1100349 2010 3 1 e-learning WBT (Web Based training) e-learning LMS (Learning Management System) LMS WBT e-learning e-learning
EQUIVALENT TRANSFORMATION TECHNIQUE FOR ISLANDING DETECTION METHODS OF SYNCHRONOUS GENERATOR -REACTIVE POWER PERTURBATION METHODS USING AVR OR SVC- Ju
EQUIVALENT TRANSFORMATION TECHNIQUE FOR ISLANDING DETECTION METHODS OF SYNCHRONOUS GENERATOR -REACTIVE POWER PERTURBATION METHODS USING AVR OR SVC- Jun Motohashi, Member, Takashi Ichinose, Member (Tokyo
Table 1. Assumed performance of a water electrol ysis plant. Fig. 1. Structure of a proposed power generation system utilizing waste heat from factori
Proposal and Characteristics Evaluation of a Power Generation System Utilizing Waste Heat from Factories for Load Leveling Pyong Sik Pak, Member, Takashi Arima, Non-member (Osaka University) In this paper,
Vol.57 No
Title 合併と企業統治 : 大正期東洋紡と大日本紡の比較 Author(s) 川井, 充 Citation 大阪大学経済学. 57(3) P.38-P.72 Issue 2007-12 Date Text Version publisher URL http://hdl.handle.net/11094/17848 DOI Rights Osaka University Vol.57 No.3
Journal of the Combustion Society of Japan Vol.56 No.178 (2014) FEATURE /Issues and Solutions for Engine Combustion φ-t マッ
56 178 2014 291-297 Journal of the Combustion Society of Japan Vol.56 No.178 (2014) 291-297 FEATURE /Issues and Solutions for Engine Combustion φ-t マップとエンジン燃焼コンセプトの接点 A Point of Contact between a φ-t Map
On the Wireless Beam of Short Electric Waves. (VII) (A New Electric Wave Projector.) By S. UDA, Member (Tohoku Imperial University.) Abstract. A new e
On the Wireless Beam of Short Electric Waves. (VII) (A New Electric Wave Projector.) By S. UDA, Member (Tohoku Imperial University.) Abstract. A new electric wave projector is proposed in this paper. The
(a) 1 (b) 3. Gilbert Pernicka[2] Treibitz Schechner[3] Narasimhan [4] Kim [5] Nayar [6] [7][8][9] 2. X X X [10] [11] L L t L s L = L t + L s
![(a) 1 (b) 3. Gilbert Pernicka[2] Treibitz Schechner[3] Narasimhan [4] Kim [5] Nayar [6] [7][8][9] 2. X X X [10] [11] L L t L s L = L t + L s](/thumbs/75/72066128.jpg "(a) 1 (b) 3. Gilbert Pernicka[2] Treibitz Schechner[3] Narasimhan [4] Kim [5] Nayar [6] [7][8][9] 2. X X X [10] [11] L L t L s L = L t + L s")
1 1 1, Extraction of Transmitted Light using Parallel High-frequency Illumination Kenichiro Tanaka 1 Yasuhiro Mukaigawa 1 Yasushi Yagi 1 Abstract: We propose a new sharpening method of transmitted scene
BOK body of knowledge, BOK BOK BOK 1 CC2001 computing curricula 2001 [1] BOK IT BOK 2008 ITBOK [2] social infomatics SI BOK BOK BOK WikiBOK BO
![BOK body of knowledge, BOK BOK BOK 1 CC2001 computing curricula 2001 [1] BOK IT BOK 2008 ITBOK [2] social infomatics SI BOK BOK BOK WikiBOK BO](/thumbs/91/105171019.jpg "BOK body of knowledge, BOK BOK BOK 1 CC2001 computing curricula 2001 [1] BOK IT BOK 2008 ITBOK [2] social infomatics SI BOK BOK BOK WikiBOK BO")
DEIM Forum 2012 C8-5 WikiBOK 252 5258 5 10 1 E-mail: [email protected], {kaz,masunaga}@si.aoyama.ac.jp, {yabuki,sakuta}@it.aoyama.ac.jp Body Of Knowledge, BOK BOK BOK BOK BOK, BOK Abstract Extention
i
21 Fault-Toleranted Authentication Data Distribution Protocol for Autonomous Distributed Networks 1125153 2010 3 2 i Abstract Fault-Toleranted Authentication Data Distribution Protocol for Autonomous Distributed
Vol. 48 No. 4 Apr LAN TCP/IP LAN TCP/IP 1 PC TCP/IP 1 PC User-mode Linux 12 Development of a System to Visualize Computer Network Behavior for L
Vol. 48 No. 4 Apr. 2007 LAN TCP/IP LAN TCP/IP 1 PC TCP/IP 1 PC User-mode Linux 12 Development of a System to Visualize Computer Network Behavior for Learning to Associate LAN Construction Skills with TCP/IP
& Vol.5 No (Oct. 2015) TV 1,2,a) , Augmented TV TV AR Augmented Reality 3DCG TV Estimation of TV Screen Position and Ro
TV 1,2,a) 1 2 2015 1 26, 2015 5 21 Augmented TV TV AR Augmented Reality 3DCG TV Estimation of TV Screen Position and Rotation Using Mobile Device Hiroyuki Kawakita 1,2,a) Toshio Nakagawa 1 Makoto Sato
2. ICA ICA () (Blind Source Separation BBS) 2) Fig. 1 Model of Optical Topography. ( ) ICA 2.2 ICA ICA 3) n 1 1 x 1 (t) 2 x 2 (t) n x(t) 1 x(t
ICA 1 2 2 (Independent Component Analysis) Denoising Method using ICA for Optical Topography Yamato Yokota, 1 Tomoyuki Hiroyasu 2 and Hisatake Yokouchi 2 Optical topography is one of the promising ways
2 The Bulletin of Meiji University of Integrative Medicine 3, Yamashita 10 11
1-122013 1 2 1 2 20 2,000 2009 12 1 2 1,362 68.1 2009 1 1 9.5 1 2.2 3.6 0.82.9 1.0 0.2 2 4 3 1 2 4 3 Key words acupuncture and moxibustion Treatment with acupuncture, moxibustion and Anma-Massage-Shiatsu
情報処理学会研究報告 IPSJ SIG Technical Report Vol.2015-GI-34 No /7/ % Selections of Discarding Mahjong Piece Using Neural Network Matsui
2 3 2000 3.3% Selections of Discarding Mahjong Piece Using Neural Network Matsui Kazuaki Matoba Ryuichi 2 Abstract: Mahjong is one of games with imperfect information, and its rule is very complicated
Fig. 3 Flow diagram of image processing. Black rectangle in the photo indicates the processing area (128 x 32 pixels).
Fig. 1 The scheme of glottal area as a function of time Fig. 3 Flow diagram of image processing. Black rectangle in the photo indicates the processing area (128 x 32 pixels). Fig, 4 Parametric representation
1., 1 COOKPAD 2, Web.,,,,,,.,, [1]., 5.,, [2].,,.,.,, 5, [3].,,,.,, [4], 33,.,,.,,.. 2.,, 3.., 4., 5., ,. 1.,,., 2.,. 1,,
![1., 1 COOKPAD 2, Web.,,,,,,.,, [1]., 5.,, [2].,,.,.,, 5, [3].,,,.,, [4], 33,.,,.,,.. 2.,, 3.., 4., 5., ,. 1.,,., 2.,. 1,,](/thumbs/50/25936361.jpg "1., 1 COOKPAD 2, Web.,,,,,,.,, [1]., 5.,, [2].,,.,.,, 5, [3].,,,.,, [4], 33,.,,.,,.. 2.,, 3.., 4., 5., ,. 1.,,., 2.,. 1,,")
THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE.,, 464 8601 470 0393 101 464 8601 E-mail: [email protected], {ide,murase,hirayama}@is.nagoya-u.ac.jp,