๐Ÿน STUDY 5

์›น์–ด์…ˆ๋ธ”๋ฆฌ(WebAssembly)

https://developer.mozilla.org/en-US/docs/WebAssembly WebAssembly | MDN WebAssembly is a new type of code that can be run in modern web browsers โ€” it is a low-level assembly-like language with a compact binary format that runs with near-native performance and provides languages such as C/C++, C# and Rust with a compilation t developer.mozilla.org WebAssembly๋Š” ์ตœ์‹  ์›น ๋ธŒ๋ผ์šฐ์ €..

[์šด์˜์ฒด์ œ] 2. System Structure & Program Execution

์ปดํ“จํ„ฐ ์‹œ์Šคํ…œ ๊ตฌ์กฐ memory : CPU์˜ ์ž‘์—… ๊ณต๊ฐ„ local buffer : device ์˜ ์ž‘์—… ๊ณต๊ฐ„ CPU register : memory๋ณด๋‹ค ๋น ๋ฅด๊ฒŒ ์ •๋ณด๋ฅผ ์ €์žฅํ•  ์ˆ˜ ์žˆ๋Š” ์ž‘์€ ๊ณต๊ฐ„ CPU mode bit : CPU์—์„œ ์‹คํ–‰๋˜๋Š” ๊ฒƒ์ด ์šด์˜์ฒด์ œ ์ธ์ง€ ์‚ฌ์šฉ์ž ํ”„๋กœ๊ทธ๋žจ์ธ์ง€ ๊ตฌ๋ถ„ํ•ด์คŒ CPU Interrupt line : CPU๋Š” ํ•ญ์ƒ ๋ฉ”๋ชจ๋ฆฌ์— ์žˆ๋Š” instruction๋งŒ ์‹คํ–‰. ๋‹ค๋ฅธ I/O์™€ ํ”„๋กœ๊ทธ๋žจ์„ ์ฒ˜๋ฆฌํ•  ๋•Œ ์ ‘๊ทผํ•˜๊ธฐ ์œ„ํ•ด์„œ ์‚ฌ์šฉํ•˜๋Š”.. ์ง์ ‘ ์ ‘๊ทผ ํ•˜์ง€ ์•Š์Œ. memory์™€๋งŒ ์†Œํ†ต. I/O ์—์„œ ๋ฌด์–ธ๊ฐ€ ๋ถˆ๋Ÿฌ์™€๋ผ ํ•˜๋Š” ์š”์ฒญ โ†’ device controller์— ๋ถ€ํƒ. controller์—์„œ ์ผ ์ฒ˜๋ฆฌ, buffer์— ์ €์žฅ. CPU๋Š” ๋ญ ์‹œํ‚ค๊ณ  ๋˜ memeory๋ž‘ ์†Œํ†ต timer : ํ•˜๋‚˜์˜ ํ”„๋กœ๊ทธ๋žจ์ด ..

[์šด์˜์ฒด์ œ] 1. Introduction to Operating Systems

์šด์˜์ฒด์ œ(Operating System, OS)๋ž€? ์ปดํ“จํ„ฐ ํ•˜๋“œ์›จ์–ด ๋ฐ”๋กœ ์œ„์— ์„ค์น˜๋˜์–ด ์‚ฌ์šฉ์ž ๋ฐ ๋‹ค๋ฅธ ๋ชจ๋“  ์†Œํ”„ํŠธ์›จ์–ด์™€ ํ•˜๋“œ์›จ์–ด๋ฅผ ์—ฐ๊ฒฐํ•˜๋Š” ์†Œํ”„ํŠธ์›จ์–ด ๊ณ„์ธต ํ•˜๋“œ์›จ์–ด์™€ ๊ฐ์ข… ์†Œํ”„ํŠธ์›จ์–ด, ์‚ฌ์šฉ์ž๋ฅผ ์—ฐ๊ฒฐ์‹œ์ผœ์ฃผ๋Š” ์†Œํ”„ํŠธ์›จ์–ด ํ˜‘์˜์˜ ์šด์˜์ฒด์ œ (์ปค๋„) ์šด์˜์ฒด์ œ์˜ ํ•ต์‹ฌ ๋ถ€๋ถ„์œผ๋กœ ๋ฉ”๋ชจ๋ฆฌ์— ์ƒ์ฃผํ•˜๋Š” ๋ถ€๋ถ„ ๊ด‘์˜์˜ ์šด์˜์ฒด์ œ ์ปค๋„ ๋ฟ ์•„๋‹ˆ๋ผ ๊ฐ์ข… ์ฃผ๋ณ€ ์‹œ์Šคํ…œ ์œ ํ‹ธ๋ฆฌํ‹ฐ๋ฅผ ํฌํ•จํ•œ ๊ฐœ๋… ์šด์˜์ฒด์ œ์˜ ๋ชฉํ‘œ ์ปดํ“จํ„ฐ ์‹œ์Šคํ…œ์„ ํŽธ๋ฆฌํ•˜๊ฒŒ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ํ™˜๊ฒฝ์„ ์ œ๊ณต ์šด์˜์ฒด์ œ๋Š” ๋™์‹œ ์‚ฌ์šฉ์ž/ํ”„๋กœ๊ทธ๋žจ๋“ค์ด ๊ฐ๊ฐ ๋…์ž์  ์ปดํ“จํ„ฐ์—์„œ ์ˆ˜ํ–‰๋˜๋Š” ๊ฒƒ ๊ฐ™์€ ํ™˜์ƒ์„ ์ œ๊ณต ํ•˜๋“œ์›จ์–ด๋ฅผ ์ง์ ‘ ๋‹ค๋ฃจ๋Š” ๋ณต์žกํ•œ ๋ถ€๋ถ„์„ ์šด์˜์ฒด์ œ๊ฐ€ ๋Œ€ํ–‰ ์ปดํ“จํ„ฐ ์‹œ์Šคํ…œ์˜ ์ž์›์„ ํšจ์œจ์ ์œผ๋กœ ๊ด€๋ฆฌ ํ”„๋กœ์„ธ์„œ, ๊ธฐ์–ต์žฅ์น˜, ์ž…์ถœ๋ ฅ ์žฅ์น˜ ๋“ฑ์˜ ํšจ์œจ์  ๊ด€๋ฆฌ ์‚ฌ์šฉ์ž๊ฐ„์˜ ํ˜•ํ‰์„ฑ ์žˆ๋Š” ์ž์› ๋ถ„๋ฐฐ ์ฃผ์–ด์ง„ ์ž..

Wireless Channel Characteristics - Fading Channel

์ด๋™ํ†ต์‹  ์ฑ„๋„์—๋Š” Large-Scale propagation๊ณผ Small-Scale propagation์˜ Fading์„ ๊ฐ–๋Š”๋‹ค. ์ด๋™ํ†ต์‹ ์‹œ์Šคํ…œ์—์„œ ์‹ ํ˜ธ ์ „ํŒŒ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” 3๊ฐ€์ง€์˜ ๊ธฐ๋ณธ ๋ฉ”์ปค๋‹ˆ์ฆ˜ Relflection : ์ „ํŒŒํ•˜๋Š” ์ „์ž๊ธฐํŒŒ๊ฐ€ RF ์‹ ํ˜ธ ํŒŒ์žฅ์— ๋น„ํ•ด ๋งค์šฐ ํฐ ํฌ๊ธฐ์˜ ๋งค๋„๋Ÿฌ์šด ํ‘œ๋ฉด์— ์ถฉ๋Œํ•  ๋•Œ ๋ฐœ์ƒ Diffaction : ์†ก์‹ ๊ธฐ์™€ ์ˆ˜์‹ ๊ธฐ ์‚ฌ์ด์˜ ๋ฌด์„ ๊ฒฝ๋กœ๊ฐ€ ฮป์— ๋น„ํ•ด ๋งค์šฐ ๋ฐ€์ง‘๋œ ๋ฌผ์ฒด์— ์˜ํ•ด ์ฐจ๋‹จ๋˜์–ด ๋ฐฉํ•ด๋˜๋Š” ๋ฌผ์ฒด์˜ ๋’ค์— 2์ฐจํŒŒ๊ฐ€ ํ˜•์„ฑ๋  ๋•Œ ๋ฐœ์ƒ Scattering : ์ „ํŒŒ๊ฐ€ ํฌ๊ณ  ๊ฑฐ์นœ ํ‘œ๋ฉด์ด๋‚˜ ฮป์™€ ๋น„์Šทํ•˜๊ฑฐ๋‚˜ ์ž‘์€ ํ‘œ๋ฉด์— ์ถฉ๋™ํ•˜์—ฌ ๋ฐ˜์‚ฌ๋œ ์—๋„ˆ์ง€๊ฐ€ ์‚ฌ๋ฐฉ์œผ๋กœ ํผ์ ธ ๋‚˜๊ฐˆ ๋•Œ ๋ฐœ์ƒ Large-Scale Fading Mobile Cannel Environment ๊ฑฐ๋ฆฌ์— ๋”ฐ๋ฅธ Path Loss, ์žฅ์• ..

๊ฐ•ํ™”ํ•™์Šต #1

๊ฐ•ํ™”ํ•™์Šต Reinforcement Learning ๊ฐ•ํ™”ํ•™์Šต์€ ๋จธ์‹ ๋Ÿฌ๋‹์˜ ํ•œ ์˜์—ญ์ด๋‹ค. Agent๋Š” ํ™˜๊ฒฝ์— ๋Œ€ํ•œ State ์ •๋ณด๋ฅผ ๋ฐ›๊ณ  ๊ทธ์— ๋”ฐ๋ฅธ Action์„ ์ทจํ•œ๋‹ค. Action์— ๋Œ€ํ•œ Reward๋ฅผ ๋ฐ›๊ณ , ๊ทธ ํ–‰๋™์— ๋Œ€ํ•ด ํ‰๊ฐ€๋ฅผ ํ•˜๋ฉฐ ์–ด๋Š ๋ฐฉํ–ฅ์œผ๋กœ ํ–‰๋™ํ•˜๋Š” ๊ฒƒ์ด ๋” ํฐ Rewrad๋ฅผ ๋ฐ›์„์ง€ ํ•™์Šตํ•œ๋‹ค. ๋จธ์‹ ๋Ÿฌ๋‹์˜ ์ข…๋ฅ˜๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™์ด ๋ณผ ์ˆ˜ ์žˆ๋‹ค. - supervised learning - unsupervised learning - reinforcement learning (๋น„)์ง€๋„ ํ•™์Šต์€ ์‚ฌ๋žŒ์ด ๋ฐ์ดํ„ฐ ์…‹์„ ๊ด€๋ฆฌํ•˜๊ณ  ์ž…๋ ฅํ•ด์•ผ ํ•˜๊ธฐ ๋•Œ๋ฌธ์— ๋งŽ์€ ์‹œ๊ฐ„๊ณผ ๋น„์šฉ์ด ํ•„์š”ํ•˜๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๋ชฉํ‘œ๋กœ ํ•˜๋Š” ๊ฐ’์€ ๊ฒฐ๊ตญ์—” ์‚ฌ๋žŒ์˜ ๊ฒฐ๊ณผ๊ฐ’์„ ํ‰๋‚ด ๋‚ด๋Š” ๊ฒƒ์ด๊ธฐ ๋•Œ๋ฌธ์— ์‚ฌ๋žŒ๋ณด๋‹ค ๋” ๋›ฐ์–ด๋‚œ ์„ฑ๋Šฅ์„ ๋‚ผ ๊ฒƒ์ด๋ผ๊ณ  ๊ธฐ๋Œ€ํ•˜๊ธฐ ์–ด๋ ต๋‹ค. Mar..

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