Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Sign up for free
Menu
Search
Features
All features
Private URLs
Password Protection
Custom URLS
Scheduled publishing
Remove Branding
Restrict embedding
Deck Collections
Notes
Features
All features
Private URLs
Password Protection
Custom URLS
Scheduled publishing
Remove Branding
Restrict embedding
Deck Collections
Notes
Explore
Featured decks
Featured speakers
Programming
Technology
Storyboards
Explore
Featured decks
Featured speakers
Programming
Technology
Storyboards
Pricing
Search
Sign in
Sign up for free
SIGGRAPH Asia 2020 勉強会 "Computational Holography"
Search
yamdeck
February 28, 2021
Research
81
0
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
SIGGRAPH Asia 2020 勉強会 "Computational Holography"
yamdeck
February 28, 2021
More Decks by yamdeck
See All by yamdeck
SIGGRAPH2020勉強会 "VR Hardware"
yamdeck
1
210
SIGGRAPH2020勉強会 "Creative Fabrication"
yamdeck
1
96
“HCI Research as Problem-Solving”(CHI’16) で学ぶ What is HCI Research ?
yamdeck
0
420
Other Decks in Research
See All in Research
敵対生成プロンプト同時探索による内省型プロンプト最適化
kinoue_smarthr
0
410
SAKURAONE:An Open Ethernet-based AI HPC System And Its Observed Workload Dynamicsin a Single-Tenant LLM Development Environment
yuukit
1
590
AIエージェント時代のLLM-jpモデルのあるべき姿
k141303
0
620
横浜市長(山中氏)の言動にかかる第三者による調査報告書
y150saya
0
220
Spatial Active Noise Control Based onSound Field Interpolation Incorporating Physical Constraints
skoyamalab
0
190
全国町字単位空き家率推定データver1.0データ仕様
microbaseinc
0
230
MIRU2026 チュートリアル講演2:三次元データ処理の動向
nnchiba
6
4.8k
Pretrain Where? Investigating How Pretraining Data Diversity Impacts Geospatial Foundation Model Performance
satai
3
130
[Fishers] DIVER OSINT CTF 2026 特化AIエージェントハーネスで挑戦するOSINT CTF
analokmaus
0
560
多様なデータを許容し学習し続ける模倣学習 / Advanced Imitation Learning for VLA
prinlab
0
300
Language and AI
ayaniwa
0
230
高性能計算機クラスタを用いた大規模点群処理による森林の単木抽出と構造解析
kentaitakura
1
130
Featured
See All Featured
How to audit for AI Accessibility on your Front & Back End
davetheseo
0
530
A Guide to Academic Writing Using Generative AI - A Workshop
ks91
PRO
1
440
XXLCSS - How to scale CSS and keep your sanity
sugarenia
249
1.3M
Highjacked: Video Game Concept Design
rkendrick25
PRO
1
450
Building Flexible Design Systems
yeseniaperezcruz
330
41k
Automating Front-end Workflow
addyosmani
1369
210k
Building Experiences: Design Systems, User Experience, and Full Site Editing
marktimemedia
0
600
Large-scale JavaScript Application Architecture
addyosmani
515
110k
End of SEO as We Know It (SMX Advanced Version)
ipullrank
3
4.4k
WCS-LA-2024
lcolladotor
0
820
Tips & Tricks on How to Get Your First Job In Tech
honzajavorek
1
730
Money Talks: Using Revenue to Get Sh*t Done
nikkihalliwell
0
490
Transcript
$PNQVUBUJPOBM)PMPHSBQIZ 4*((3"1)"TJB5FDIOJDBM1BQFST
3 %JTUSJCVUJPOPG5PEBZT1SFTFOUBUJPO /FVSBM)PMPHSBQIZ -FBSOFE)BSEXBSFJOUIFMPPQ )0& 3FOEFSJOH4QFDLMF
/FVSBM)PMPHSBQIZXJUI$BNFSBJOUIFMPPQ 5SBJOJOH%JTQMBZT :*'"/1&/( 46:&0/$)0* /*5*4)1"%."/"#"/ (03%0/8&5;45&*/ 4UBOGPSE6OJWFSTJUZ
લఏࣝͷڞ༗
6 • ޫͷճંɾׯবʹΑͬͯɼ̏࣍ݩʹݟ͑Δޫͷ࠶ੜɾอଘٕज़ʢྫɿࠨਤʣ • ҰൠతʹӈਤͷΑ͏ʹϨʔβʔޫΛࡱӨΦϒδΣΫτͱه༻ͷϓϨʔτʹͯͯࡱ૾͢Δ લఏࣝ ϗϩάϥϜͬͯͳΜͰ͔͢ʁ https://www.litiholo.com/hologram-kits.html ʢൃද࣌লུʣ
7 • ۭؒޫมௐثʢ4-.ʣͱݺΕΔӷথ੍ޚػࡐͷൃలʹΑΓɼػցతʹϗϩάϥϜΛ࡞ɾ੍ޚ͢Δ͜ͱ͕Մೳͱͳͬͯ ͖͍ͯΔ • ݴͬͯ͠·͑ɼ4-.ͱ͍͏֎෦σΟεϓϨΠʹͳΜΒ͔ͷύλʔϯΛදࣔ͢Δͱ ̏࣍ݩը૾ΛදࣔͰ͖Δͱ͍͏͜ͱ • Αͬͯɼ͜ͷ4-.ʹͲΜͳύλʔϯΛදࣔ͢Δ͔Λܭࢉ͢Δ͜ͱ͕ͱͯॏཁʂʂ ࠷ۙͷϗϩάϥϜࣄ
લఏࣝ ʢൃද࣌লུʣ ʢൃද࣌লུʣ
8 • ࠷؆୯ͳ࠷దԽͷྫɿ̎࣍ؔͷ࠷খ୳ࡧ ࠷దԽͬͯͳΜͰ͔͢ʁ https://www.youtube.com/watch?v=_Q4QJO8SEsY લఏࣝ ʢൃද࣌লུʣ
9 • ࠷؆୯ͳ࠷దԽͷྫɿ̎࣍ؔͷ࠷খ୳ࡧ • ࠷దԽͱͯ͠هड़͢Δͱ ࠷దԽͬͯͳΜͰ͔͢ʁ https://www.youtube.com/watch?v=_Q4QJO8SEsY લఏࣝ ʢൃද࣌লུʣ
10 • Ͳ͏ͬͯ࠷খΛͱΔYΛ୳ࡧ͢Δ͔ʁ • ࠷جຊతͳख๏ɼ͖Λͬͯ୳ࡧ͢Δख๏ • ͱ͋Δʹ͓͚Δޯͷٯํ ʹਐΉͱ࠷খʹ͔͏ •
ӈਤͰ͍͏ͱɼͷઓͷ͖ϚΠφεʢԾʹͱ͢Δʣ • XΛ ͷํͣΒ͢ʢX X ʣ • ͢Δͱ࠷খΛͱΔXʹۙͮ͘ • ඍΛ͖ͯ͠ʢޯʣΛऔಘ͢Δ͜ͱ͕࠷దԽʹඞਢ ࠷దԽʹඞཁͳޯ લఏࣝ
11 • ̎࣍ؔͷΑ͏ͳ؆୯ͳؔͳΒී௨ʹඍͯ͠ྑ͍͕ɼ࣮ࡍͷͰѻ͏ํఔࣜͬͱෳࡶ • Ұൠతʹɼ͜Ε·Ͱ̏ͭͷख๏͕ଟ͔ͬͨ • खܭࢉ • ඍ •
γϯϘϦοΫඍ • ۙɼػցֶशʢಛʹ/FVSBM/FUXPSLʣʹ͓͚Δ όοΫϓϩύήʔγϣϯͷ࣮ʹද͞ΕΔࣗಈඍ͕ ྲྀߦ Ͳ͏ͬͯඍʢޯʣΛܭࢉ͢Δ͔ʁ લఏࣝ "Automatic Differentiation in Machine Learning: a Survey" (2018) https://jmlr.org/papers/v18/17-468.html
12 • ΊͬͪΌΊͪΌࡶʹݴ͏ͱʮඍΛϓϩάϥϜͰ؆୯ʹͬͯ͘ΕΔͭʯ • ܭࢉաఔΛϓϩάϥϜʹ͢Δͱ͍͏͜ͱɼԼਤͷΑ͏ʹجຊతͳܭࢉͷΈ߹ΘͤͰ࣮͢Δ͜ͱ • ̍ͭ̍ͭͷܭࢉ୯७ͳͷͰɼ̍ͭ̍ͭͷඍܭࢉ͍͢͠ • ͜ͷ̍ͭ̍ͭͷඍΛͬͯɼతؔͷඍʢޯʣΛܭࢉ͢Δ •
େࣄͳ͜ͱɼ࣮Ͱ͖Εඍ͕ՄೳʹͳΔʹޯ͕ٻΊΒΕΔͱ͍͏͜ͱ • ʔʼ࠷దԽʹ͑Δʂʂʂ ࣗಈඍͬͯͳΜͰ͔͢ʁ લఏࣝ
13 • ϗϩάϥϜ͕Ͳ͏͍͏ͷ͔ͷհ • ࠷దԽʹ͍ͭͯͷجຊతͳհ • ࣗಈඍͱ͍͏ٕज़ʹؔ͢Δجຊతͳհ લఏࣝͷཧ ѻͬͨ༰ ʢൃද࣌লུʣ
ຊจͷհ
15
16 • ࣗಈඍΛ༻͍ͨ࠷దԽ͕͜Ε·Ͱͷશͯͷ࠷దԽख๏Λ্ճΔਫ਼Λୡͨ͠ͱ͍͏'JOEJOHT • $BNFSBJOUIFMPPQΛߏஙͯ͠ϗϩάϥϜΛ͞Βʹ࠷దԽ • ϦΞϧλΠϜॲཧͷͨΊͷ/FVSBM/FUXPSLߏங ಋೖ จͷίϯτϦϏϡʔγϣϯ ,FZXPSETࣗಈඍɾ࠷దԽɾ*OGFBTJCMF.PEFM%JGGFSFOUJBUJPO
0QUJNJ[BUJPO ɾ999JOUIFMPPQ
ίϯτϦϏϡʔγϣϯ̍ɿ ࣗಈඍΛ༻͍ͨϗϩάϥϜ࠷దԽ
18 • ͳΜΒ͔ͷύλʔϯПΛ4-.ʹදࣔ͢Δͱɼ݁Ռ ͕ಘΒΕΔ • ͜ΕΛඪͱͷ͕ࠩ࠷খ͘͞ͳΔΑ͏ʹʢ ʣ͢Δͷ͕ຊจͰͷ࠷దԽ • ࠷దԽͷߋ৽ʹ͋ͨͬͯɼࣗಈඍʹΑͬͯٻΊΒΕΔޯΛ׆༻ ̂
f(ϕ) ̂ f(ϕ) − Atarget = 0 ຊจʹ͓͚ΔͷఆࣜԽ ຊจʹ͓͚Δ࠷దԽ ೖྗɿП ඍՄೳ γϛϡϨʔγϣϯ ̂ f ग़ྗɿ ̂ f(ϕ)
19 • ·ͣӈଆͷάϥϑͷΈʹ • 4(%͕ఏҊख๏Ͱɼ8)ɾ(4͕طଘख๏ • ಛʹTUBUFPGUIFBSUͷख๏Ͱ͋Δ8)Λ্ճΔͷڻ͖ ίϯτϦϏϡʔγϣϯ̍ ࣗಈඍΛ༻͍ͨ࠷దԽ
20 • ͜ͷࣸਅͩͱຊʹେ͖͘վળ͍ͯ͠Δ͔֬ೝͮ͠Β͍͕ɼ14/3ɾ44*.࠷ߴ͍݁ՌΛ͍ࣔͯ͠Δ • ָ࣮͕ͱ͍͏ͷඇৗʹخ͍͠ϙΠϯτ • ຊจ1ZUPSDIͰ࣮͞ΕɼࣗಈඍΛ༻͍ͯޯܭࢉ͕ͳ͞Ε͍ͯΔ ίϯτϦϏϡʔγϣϯ̍ ࣗಈඍΛ༻͍ͨ࠷దԽ
21 • ࠓݟͨख๏ͱ͍͏ͷࡢࠓͷඍՄೳͳγϛϡϨʔγϣϯͱಉ͡ϫʔΫϑϩʔͰ͋Δ͜ͱ͕Ӑ͑Δ • ඍՄೳϨϯμϦϯάʢFY.JUTVCBʣɾඍՄೳϓϩάϥϛϯάʢFY%JGG5BJDIJʣͳͲ • ೖྗมʢPS/FVSBM/FUXPSLʣΛ࠷దԽ͢Δʹద༻ՄೳͰɼ ࠷దԽʹ͓͚ΔޯܭࢉͷͨΊʹࣗಈඍʹରԠͨ͠ඍՄೳͳγϛϡϨʔλΛ׆༻͍ͯ͠Δ ࣗಈඍʢඍՄೳγϛϡϨʔγϣϯʣͷࡢࠓ ඍՄೳγϛϡϨʔγϣϯͷྲྀߦ
ೖྗɿП ඍՄೳ γϛϡϨʔγϣϯ ̂ f ग़ྗɿ ̂ f(ϕ) ʢൃද࣌লུʣ
22 • ࣗಈඍʹରԠͨ͠ి࣓ܭࢉʢ'%'%๏ʣΛ࣮͠ɼܗঢ়࠷దԽʹద༻ͨ͠ • ԼਤޫͷʹԠͯ͡ܦ࿏ΛΓସ͑Δܗঢ়࠷దԽ • ࠷దԽରʢೖྗมʣɿփ৭ྖҬͷܗঢ় • ඍՄೳγϛϡϨʔγϣϯɿ'%'% •
࠷దԽɿܗঢ়Λೖྗͱͯ͠'%'%γϛϡϨʔγϣϯΛ࣮ߦɽ࣮ߦ݁Ռ͔ΒࣗಈඍͰޯΛಋग़͠ɼܗঢ়Λߋ৽ɽ ࣗಈඍʢඍՄೳγϛϡϨʔγϣϯʣͷࡢࠓ ۩ମྫ̍ɿ'PSXBSE.PEF%JGGFSFOUJBUJPOPG.BYXFMM`T&RVBUJPOT ॳظܗঢ় ࠷దԽܗঢ় ೖྗɿ ܗঢ় ඍՄೳ γϛϡϨʔγϣϯɿ '%5%๏ ग़ྗɿ ޫͷൖܦ࿏ ʢൃද࣌লུʣ
23 • ෳͷϏϡʔϙΠϯτը૾͔Βɼ͋ΒΏΔํͷϏϡʔϙΠϯτը૾ΛੜͰ͖ΔΑ͏ʹ͢Δݚڀ • //ೖྗɿY Z [ В П •
//ग़ྗɿ3(#М • ඍՄೳγϛϡϨʔγϣϯɿ7PMVNF3FOEFSJOH • ࠷దԽɿ3FOEFSJOH݁ՌʹΑΔ-PTT͔ΒඍͰ୧͍ͬͯͬͯ//Λߋ৽ ࣗಈඍʢඍՄೳγϛϡϨʔγϣϯʣͷࡢࠓ ۩ମྫ̎ɿ/F3'3FQSFTFOUJOH4DFOFTBT/FVSBM3BEJBODF'JFMETGPS7JFX4ZOUIFTJT ೖྗɿ ࠲ඪɾํ ඍՄೳԋࢉɿ /FVSBM/FUXPSL 7PMVNF3FOEFSJOH ग़ྗɿ ϏϡʔϙΠϯτը૾ ʢൃද࣌লུʣ
ίϯτϦϏϡʔγϣϯ̎ɿ %JSFDUMZ*OGFBTJCMFϞσϧͷ࠷దԽ $BNFSBJOUIFMPPQ
25 • γϛϡϨʔγϣϯͰ΄΅ϊΠζͷͳ͍ը૾͕ੜ͞Ε͍ͯΔʢࣼઢࠨʣ ͕ɼ࣮ࡍͷޫֶܥΛ௨͢ͱϊΠζͷ͋Δը૾͕؍ଌ͞ΕΔʢࣼઢӈʣ • ͜Ε࣮ࡍͷޫֶܥʹ֤ޫֶܥݻ༗ͷΈ͕͋ΔͨΊ • ͜ͷΈΛղফ͢ΔͨΊʹɼ$BNFSBJOUIFMPPQPQUJNJ[BUJPOΛ࣮ ίϯτϦϏϡʔγϣϯ̎ ࣮ࡍͷޫֶܥʹΈ͕͋Δ
Simulation Result Physical Result ݻ༗ͷΈ͋Γ
26 • ίϯτϦϏϡʔγϣϯ̍ͰɼγϛϡϨʔγϣϯ্ͷؔ ʹରͯ͠࠷దԽΛ ߦ͍ͬͯͨʢӈ্ࣜʣ • ͜Εͱಉ͡Α͏ʹ࣮ࡍͷޫֶܥʹରͯ͠࠷దԽΛߦ͍͍͕ͨɼ ࣮ࡍͷޫֶܥͷൖॲཧΛඍ͢Δ͜ͱͰ͖ͳ͍ ̂ f
Ͳ͏࣮ͬͯޫֶܥʹ࠷దԽॲཧΛΈࠐΉ͔ʁ ίϯτϦϏϡʔγϣϯ̎ ͜ΕඍͰ͖ͳ͍ γϛϡϨʔγϣϯ্ͷൖؔɿ ࣮ࡍͷޫֶܥͰͷൖؔɿ ̂ f f
27 • ͔͠͠ɼγϛϡϨʔγϣϯϞσϧͱ࣮ޫֶܥ΄΅Ұக͍ͯ͠Δͱݟͳ͢͜ͱͰ͖Δ ˠඍύʔτ͚ͩγϛϡϨʔγϣϯϕʔεʹஔ͖͑ͯ͠·͓͏ʂ Ͳ͏࣮ͬͯޫֶܥʹ࠷దԽॲཧΛΈࠐΉ͔ʁ ίϯτϦϏϡʔγϣϯ̎ ࣮ޫֶܥͷ ൖɿG ग़ྗɿG П
ඍՄೳγϛϡϨʔγϣϯ Ͱ ஔ͖͑ͯඍ ̂ f ೖྗɿП ೖྗПΛ࠷దԽ ஔ͖͑ ஔ͖͑
• ΧϝϥͰ࣮ࡍʹࡱӨͨ͠ϗϩάϥϜͷ݁ՌΛͬͯ࠷దԽ͠Α͏ • ΧϝϥͰࡱӨͨ͠ը૾ΛMPTTؔʹΈࠐΉ ͭ·ΓɼΧϝϥը૾ͱඪը૾ͷࠩΛMPTTͱఆٛ͢Δ • ޯγϛϡϨʔγϣϯϞσϧΛ׆༻ͯ͠ɼҐ૬Λߋ৽͠Α͏ ίϯτϦϏϡʔγϣϯ̎ Ͳ͏࣮ͬͯޫֶܥʹ࠷దԽॲཧΛΈࠐΉ͔ʁ Captured
Image: f(ϕk−1) SLM Phase: ϕk−1 Propagation Function: f ࣮ޫֶܥͷࡱӨ݁ՌΛΈࠐΜͩߋ৽ࣜ Χϝϥͱඪը૾ͷࠩ ஔ͖͑ඍܭࢉ 28
29 • ϊΠζ͕ܰݮ͞ΕɼΒ͔ͳ݁Ռ͕ಘΒΕΔΑ͏ʹͳͬͨ • ࠨɿγϛϡϨʔγϣϯ্ͷ࠷దԽͷΈɼӈɿΧϝϥࡱӨΛؚΊͨ࠷దԽ ࣮ޫֶܥͷ݁ՌΛͱʹ࠷దԽͨ݁͠Ռ ίϯτϦϏϡʔγϣϯ̎
30 • දࣔը૾̍ຕ̍ຕʹରͯ͠࠷దԽΛ͢Δඞཁ͕ൃੜ͍ͯ͠Δʢ͔͔࣌ؒΓ͗͢ʣ • ޫֶܥͷಛੑΛֶशͯ͠ɼͲΜͳදࣔը૾ʹରͯ͠ରԠͰ͖ΔϞσϧΛֶशͰ͖ͳ͍͔ʁ • ˠ$BNFSBJOUIFMPPQ.PEFM5SBJOJOH • ࢥ͍ͭ͘؆ܿͳख๏ $POWPMVUJPOBM
/FVSBM/FUXPSLΛ׆༻ͨ͠ख๏ • ͨͩ/FVSBM/FUXPSLʹ͢ΔͱͲ͏͍ͬͨཁૉ͕ىҼ͍ͯ͠Δ͔ͷੳ͕ࠔ • ˠຊจͰɼ1IZTJDBMMZ#BTFE.PEFMΛߏங ୯७ͳ$BNFSBJOUIFMPPQͷ ίϯτϦϏϡʔγϣϯ̎ γϛϡϨʔγϣϯ ࠷దԽҐ૬ɿϕ /FVSBM/FUXPSL *OQVU 0VUQVU ϕ ϕ′ ࣮ޫֶܥͰͷ ग़ྗɿf(ϕ′ ) NNΛֶश
31 • 1IZTJDBMMZ#BTFE.PEFM̐ཁૉ͔ΒΔʢӈԼࣜʹʣ • $POUFOU*OEFQFOEFOU4PVSDFBOE5BSHFU'JFME7BSJBUJPO • .PEFMJOH0QUJDBM1SPQBHBUJPOXJUI"CFSSBUJPOT • .PEFMJOH1IBTF/POMJOFBSJUJFT •
$POUFOUEFQFOEFOU6OEJSFDUFE-JHIU • શύϥϝʔλΛ͋ΘͤͯВͱఆٛ͠ɼͦΕΛֶश ίϯτϦϏϡʔγϣϯ̎ $BNFSBJOUIFMPPQ.PEFM5SBJOJOH มԽ ඍՄೳ γϛϡϨʔγϣϯ ̂ fθ ೖྗɿ ࠷దԽҐ૬ ϞσϧύϥϝʔλВ ϕ ࣮ޫֶܥͰͷ ग़ྗɿfθ (ϕ′ ) ௨ৗͷൖࣜ ϊΠζཁૉ͕ύϥϝλϥΠζ͞ΕͯΈࠐ·Εͨൖࣜ
32 • $*5-0QUJNJ[BUJPOϞσϧԽ͠ͳ͍Ͱը૾͝ͱʹ࠷దԽ ͢Δख๏ • $*5-DBMJCSBUFE.PEFM͕ը૾ʹґଘͤͣɼൖϞσϧΛֶश ͤͨ͞ख๏ • $*5-0QUJNJ[BUJPO͕ϕετύϑΥʔϚϯεΛൃش͢Δ͕ɼ $*5-DBMJCSBUFE.PEFMطଘख๏Λ্ճͬͨ
݁Ռͷൺֱ ίϯτϦϏϡʔγϣϯ̎
33 • ਓؒΛඍ͢Δ͜ͱͰ͖ͳ͍ͷͰɼਓؒΛ͋ΔϞσϧʹஔ͖͑ͯʢ#MBDLCPYγεςϜͱͯ͠औΓѻͬͯʣ ࠷దԽʹΈࠐΉΑ͏ͳ͕͋Δ • ྫɿ)VNBOJOUIFMPPQΛ׆༻ͨ͠ਓؒ("/ %JSFDUMZ*OGFBTJCMFϞσϧͷ࠷దԽ ඍͰ͖ͳ͍ྫɿਓؒ BΛೖྗͱͯ͠
ར༻ ਓ͕ؒஅ %JTDSJNJOBUPS ग़ྗC ඍՄೳͳϞσϧͰ ਓؒΛஔ͖͑ɼ ޯܭࢉʹ׆༻ //͕ੜ (FOFSBUPS ɿB NNΛֶश ʢൃද࣌লུʣ
ίϯτϦϏϡʔγϣϯ̏ɿ ܭࢉߴԽͷͨΊͷ/FVSBM/FUXPSLͷར༻
35 • ࠷దԽجຊతʹΠςϨʔγϣϯΛඞཁͱ͢ΔͷͰ͕͔͔࣌ؒΔ • ɼҐ૬Λܭࢉ͢ΔΑ͏ͳߴख๏͕ٻΊΒΕΔ • ຊจͰ)PMP/FUͱ͍͏ωοτϫʔΫΛΈɼߴԽΛ࣮ݱ ίϯτϦϏϡʔγϣϯ̏ ܭࢉߴԽ
36 • ͱ͍ͬͨඍՄೳͳཧԋࢉΛؚΊͯMPTTܭࢉʹ׆༻͢ΔωοτϫʔΫΞʔΩςΫνϟͷΈํ͕ಛతʁ ʢ࠷ۙ૿͍͑ͯΔؾ͢Δʣ ̂ f −1 ̂ fθ
ωοτϫʔΫΞʔΩςΫνϟ ίϯτϦϏϡʔγϣϯ̏ ֶशର ֶशର ඍՄೳԋࢉ ඍՄೳԋࢉ
37 • ࠷దԽϧʔϓΛඞཁͱ͠ͳ͍ख๏ಉ࢜ͰൺͯΈΔͱɼ طଘख๏Λ্ճ͍ͬͯΔ͜ͱ͕Θ͔Δ ݁Ռ ίϯτϦϏϡʔγϣϯ̏
૯ׅ
39 • ίϯτϦϏϡʔγϣϯ̍ɿࣗಈඍͱඍՄೳγϛϡϨʔλͱ࠷దԽ • ࣗಈඍʹରԠͨ͠ඍՄೳγϛϡϨʔγϣϯʹΑΔ࠷దԽ͕࠷ྑ͍݁ՌΛ࣮ݱ • ඍՄೳγϛϡϨʔγϣϯΛ׆༻ͨ͠࠷దԽࠓޙ৭ʑͳͰొ͢ΔͩΖ͏ • ίϯτϦϏϡʔγϣϯ̎ɿ%JSFDUMZ*OGFBTJCMFϞσϧͷ࠷దԽ •
࣮ޫֶܥͷΑ͏ʹܭࢉػͰѻ͑ͳ͍ͷͰ͋ͬͯɼஔ͖͑ͰඍՄೳʹ͢Δ͜ͱͰ࠷దԽॲཧͷதʹ ΈࠐΉ͜ͱ͕Ͱ͖Δ • ϋʔυΣΞͷΈͳΒͣɼਓؒͷΑ͏ͳੜରͱͳΓ͏Δߟ͑ํͰ͋Ζ͏ • ίϯτϦϏϡʔγϣϯ̏ɿ/FVSBM/FUXPSLʹΑΔߴԽ • ඍՄೳͳԋࢉͰ͋Εɼ//ͷܗΛऔΒͳͯ͘MPTTؔʹΈࠐΜͰɼֶशʹ׆༻Ͱ͖Δ • ͷࢪ͞Εͨ//"SDIJUFDUVSFࠓޙӹʑ૿͑ΔͩΖ͏ ૯ׅ
-FBSOFE)BSEXBSFJOUIFMPPQ1IBTF3FUSJFWBMGPS )PMPHSBQIJD/FBS&ZF%JTQMBZT 1SBOFFUI$IBLSBWBSUIVMB &UIBO5TFOH 5BSVO4SJWBTUBWB )FOSZ'VDIT 'FMJY)FJEF 6/$$IBQFM)JMM 1SJODFUPO6OJWFSTJUZ
41 • ޫֶܥͷಛੑΛֶशͯ͠ɼͲΜͳදࣔը૾ʹରͯ͠ରԠͰ͖ΔϞσϧΛֶशͰ͖ͳ͍͔ʁ • ࢥ͍ͭ͘؆ܿͳख๏ $POWPMVUJPOBM /FVSBM/FUXPSLΛ׆༻ͨ͠ख๏ //Λ༻͍ͨղܾํ๏ /FVSBM)PMPHSBQIZʹ͓͚ΔఏҊ γϛϡϨʔγϣϯ
࠷దԽҐ૬ɿϕ /FVSBM/FUXPSL *OQVU 0VUQVU ϕ ϕ′ ࣮ޫֶܥͰͷ ग़ྗɿf(ϕ′ ) NNΛֶश
42 • ຊจͷఏҊख๏ͷύΠϓϥΠϯԼਤ • ᶃҐ૬ 4-.໘ ͔Βൖܭࢉɹᶄൖܭࢉ͞Εͨཧతͳը૾Λݱ࣮ͱಉ͡ϊΠζ࠶ݱΛͰ͖Δ//ͰΞτϓοτ ᶅϊΠζ࠶ݱ͞Εͨը૾ͱλʔήοτը૾ͷࠩ MPTT ͔ΒɼೖྗҐ૬Їʹର͢ΔޯܭࢉɹᶆҐ૬ߋ৽
࠷దԽ ΞΠσΞࣗମ͔ͳΓ͍ۙʢҙࣝҰॹʹͲ͏ͬͯޫֶϊΠζΛແ͔͘͢ʣ -FBSOFE)BSEXBSFJOUIFMPPQͰͷ࣮
43 • %JTDSJNJOBUPSͱ(FOFSBUPS͔ΒΔ("/ͷωοτϫʔΫϞσϧͱͳ͍ͬͯΔ • ݱ࣮ͷΩϟϓνϟը૾ʹͳΔΑ͏ͳ(FOFSBUPSΛֶश্ͤͨ͞ͰɼͦͷϞσϧΛͬͯೖྗҐ૬Їͷ࠷దԽʹҠΔ ΞΠσΞࣗମ͔ͳΓ͍ۙʢҙࣝҰॹʹͲ͏ͬͯޫֶϊΠζΛແ͔͘͢ʣ -FBSOFE)BSEXBSFJOUIFMPPQͰͷ࣮
44 ηοτΞοϓී௨ 0QUJDBM4FUVQ
45 طଘख๏ͱͷൺֱ %JTQMBZ3FTVMUT
%FTJHOBOE'BCSJDBUJPOPG'SFFGPSN)PMPHSBQIJD0QUJDBM &MFNFOUT $IBOHXPO+BOH 0MJWFS.FSDJFS ,JTFVOH#BOH (BOH-J :BOH;IBP %PVHMBT-BONBO 'BDFCPPL3FBMJUZ-BCT3FTFBSDI
47 • χΞΞΠσΟεϓϨΠʹ͓͍ͯ࠷ྑ͘ݟΔ)0&ͷ༻ํ๏ɼ ϝΨωܕσόΠεͷϨϯζ෦ʹूޫੑೳΛ࣋ͨͤͨ)0&Λஔ͢Δͱ͍͏ͷ • ΑΓෳࡶͳઃܭʹ͑Δͷ͕ཉ͍͠ɼͱ'#3FBMJUZ -BC͕ओு͢Δͷྑ͘Θ͔Δؾ͕͢Δ 73"3σόΠεͷখܕԽʹඞਢͷޫֶૉࢠ 8IBUJT)PMPHSBQIJD0QUJDBM&MFNFOU )0&
[Maimone et al. 2017]
48 • ಠࣗͷબੑΛߟྀͨ͠ϑϦʔϑΥʔϜ)0&ͷ࠷దԽख๏ • )0&༻ͷμΠϠϞϯυટʹΑΔϑϦʔϑΥʔϜද໘ɾͭͷ໘มௐΞʔϜΛඋ͑ͨϗϩάϥϑΟοΫϓϦϯλʔ ͷ༻ͱ͍ͬͨɼ̎छྨͷϑϦʔϑΥʔϜ)0&ख๏ • ྆ํͷΞϓϩʔνʹ߹Θͤͯௐ͞Εͨݎ࿚ͳ໘ղΞϧΰϦζϜ • "3ΠϝʔδίϯόΠφʔɾϔουΞοϓσΟεϓϨΠɾϨϯζΞϨΠͳͲͷ
σΟεϓϨΠ͓ΑͼΠϝʔδϯάΞϓϦέʔγϣϯͷྫ • ϑϧΧϥʔ$BVTUJDTӨ)0&ͷσϞ ຊจͷߩݙ $POUSJCVUJPOT
49 • ʢ͢Έ·ͤΜɼ͜͜ਂ͘ಡΊͯ·ͤΜʣ ϑϦʔϑΥʔϜ)0&ͷ࠷దԽʢσβΠϯʣ $POUSJCVUJPO
50 • )0&ޫͷׯবʹΑͬͯه͞ΕΔ • ఏҊख๏ͱͯ̎ͭ͠ͷΨϥεͷϑϦʔϑΥʔϜαʔϑΣεʹΑͬͯׯবͤͯ͞ɼ)0&ͷύλʔϯΛ࡞Δ ΨϥεʹΑΔϑϦʔϑΥʔϜαʔϑΣε $POUSJCVUJPO HOEͷম͖͚ ϑϦʔϑΥʔϜΨϥεද໘ͷ
51 • 4-.ʹΑͬͯมௐ͞Εͨޫ͕ೋํ͔Βൖ͖ͯͯ͠ɼͦͷׯবΛه͢Δख๏ ϗϩάϥϜϓϦϯλʔʹΑΔ)0&ͷ࡞ $POUSJCVUJPO ϗϩάϥϜϓϦϯλʔʹΑΔHOEͷম͖͚
52 3FTVMUT "QQMJDBUJPOT ඇٿ໘ϨϯζHOE (a,b,c) HUDϨϯζHOE (d,e,f) Printed HUDϨϯζHOE (g,h)
ϨϯζΞϨΠHOE (i) Caustic HOE (j,k,l)
3FOEFSJOH/FBS'JFME4QFDLMF4UBUJTUJDTJO4DBUUFSJOH .FEJB $IFO#BS *PBOOJT(LJPVMFLBT "OBU-FWJO %FQBSUNFOUPG&MFDUSJDBM&OHJOFFSJOH 5FDIOJPO *TSBFM 3PCPUJDT*OTUJUVUF $BSOFHJF.FMMPO6OJWFSTJUZ
64"
54 εϖοΫϧϊΠζͱ 8IBUJTTQFDLMFOPJTFʁ ϨʔβʔͷΑ͏ͳίώʔϨϯτޫΛࢄཚഔ࣭ʹͯΔͱɼεϖοΫϧϊΠζͱݺΕΔϥϯμϜͳϊΠζ͕ൃੜ͢Δ ʰࢄཚഔମதͷମΠϝʔδϯά͓Αͼମೝࣝʹؔ͢Δݚڀʱ(2016) ΑΓը૾Ҿ༻
55 • ͜͏ͨ͠ࢄཚഔ࣭Λ௨աͨ͠ޙͷεϖοΫϧϊΠζΛϨϯμϦϯά͢Δख๏ͷఏҊ • ʢ͢Έ·ͤΜʣ ຊݚڀͷߩݙ $POUSJCVUJPOPG5IJT3FTFBSDI