๐Ÿฌ ML & Data/๐Ÿ“˜ ๋…ผ๋ฌธ & ๋ชจ๋ธ ๋ฆฌ๋ทฐ

    [Paper Review] Transforming Cooling Optimization for Green Data Center via Deep Reinforcement Learning

    * ๊ฐœ์ธ์ ์œผ๋กœ ์ฝ๊ณ  ๊ฐ€๋ณ๊ฒŒ ์ •๋ฆฌํ•ด๋ณด๋Š” ์šฉ๋„๋กœ ์ž‘์„ฑํ•œ ๊ธ€์ด๋ผ ๋ฏธ์ˆ™ํ•˜๊ณ  ์ •ํ™•ํ•˜์ง€ ์•Š์Šต๋‹ˆ๋‹ค. ์–‘ํ•ด ๋ถ€ํƒ๋“œ๋ฆฝ๋‹ˆ๋‹ค :D Transforming Cooling Optimization for Green Data Center via Deep Reinforcement Learning Cooling system plays a critical role in a modern data center (DC). Developing an optimal control policy for DC cooling system is a challenging task. The prevailing approaches often rely on approximating system models that are built upon the knowled..

    [Model Review] TadGAN(Time series Anomaly Detection GAN)

    ์ด๋ฒˆ์— ๊ณ ์žฅ์ง„๋‹จ์— ๊ด€ํ•œ ๊ณผ์ œ๋ฅผ ํ•˜๊ฒŒ ๋˜๋ฉด์„œ LSTM AE๋‚˜ CNN ๋ณด๋‹ค ์ตœ๊ทผ ๋ชจ๋ธ์„ ์ ์šฉํ•ด๋ณด๊ณ  ์‹ถ์–ด์„œ TadGAN์„ ๊ณจ๋ž๋‹ค. ์•„์ง ์™„์ „ํžˆ ์ดํ•ดํ–ˆ๋Š”์ง€๋Š” ๋ชจ๋ฅด๊ฒ ์œผ๋‚˜ ์•Œ๊ฒŒ๋œ๋Œ€๋กœ ์กฐ๊ธˆ ์ ์–ด๋ณด๋ ค๊ณ  ํ•œ๋‹ค. TadGAN(Time series Anomaly Detection GAN) TadGAN์€ 2020๋…„ ๋ฐœํ‘œ๋œ ๋ชจ๋ธ๋กœ, ์ด๋ฆ„ ๊ทธ๋Œ€๋กœ ์‹œ๊ณ„์—ด ๋ฐ์ดํ„ฐ์˜ ์ด์ƒ ํƒ์ง€์šฉ GAN ๋ชจ๋ธ์ด๋‹ค. GAN ๋ชจ๋ธ์€ ๋ณต์›, ์ด๋ฏธ์ง€ ์ƒ์„ฑ ๋“ฑ์— ํŠนํ™”๋˜์–ด ์žˆ๋Š”๋ฐ, ์ด ์„ฑ์งˆ์„ ์ด์šฉํ•˜์—ฌ LSTM Auto Encoder์ฒ˜๋Ÿผ ํŒจํ„ด์„ ๋ณต์›ํ•˜๋ฉฐ ํ•™์Šตํ•˜๊ณ , ์ดํ›„์— ๋“ค์–ด์˜ค๋Š” ์ƒˆ๋กœ์šด ๋ฐ์ดํ„ฐ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ์˜ˆ์ธกํ–ˆ์„ ๋•Œ ์—๋Ÿฌ๊ฐ€ ํฐ ๋ถ€๋ถ„์„ ์ด์ƒ์น˜๋กœ ํƒ์ง€ํ•˜๋Š” ๊ฒƒ์ด๋‹ค. TadGAN์˜ ๊ตฌ์กฐ TadGAN์€ 2๊ฐœ์˜ Generator์™€ 2๊ฐœ์˜ Critic ์œผ๋กœ ๊ตฌ์„ฑ๋œ๋‹ค. Gene..

    [Model Review] YOLOv5 + Roboflow Annotation

    ! ์ฃผ์˜ ! ์ด ๊ธ€์—๋Š” ์ ์€ yolo v5์— ๋Œ€ํ•œ ์š”์•ฝ๊ณผ ์งง์€ ์‚ฌ์šฉ๋ฒ•, ๊ทธ๋ฆฌ๊ณ  roboflow annotation์— ๋Œ€ํ•œ ๊ฐœ์ธ์ ์ธ ๊ฒฌํ•ด๊ฐ€ ์“ฐ์—ฌ์žˆ์Šต๋‹ˆ๋‹ค. 1. YOLOv5 Summary You Only Look Once - one stage detection ๋ชจ๋ธ R-CNN์ด๋‚˜ Faster R-CNN๊ณผ ๋‹ฌ๋ฆฌ ์ด๋ฏธ์ง€ ๋ถ„ํ•  ์—†์ด ์ด๋ฏธ์ง€๋ฅผ ํ•œ ๋ฒˆ๋งŒ ๋ณด๋Š” ํŠน์ง• ์ „์ฒ˜๋ฆฌ๋ชจ๋ธ๊ณผ ์ธ๊ณต์‹ ๊ฒฝ๋ง ํ†ตํ•ฉ ์‹ค์‹œ๊ฐ„ ๊ฐ์ฒดํƒ์ง€ Backbone : input image → feature map CSP-Darknet https://keyog.tistory.com/30 Head : predict classes / bounding boxes Dense Prediction : One stage detector(predict classes + b..

    [Model Review] MobileNet SSD ๋…ผ๋ฌธ ํ€ต ๋ฆฌ๋ทฐ

    ํ€„๋ฆฌํ‹ฐ๊ฐ€ ๋†’์ง€ ์•Š์Šต๋‹ˆ๋‹ค. ์ฃผ์˜! Mobile Object Detection model - based on VGG- 16 https://arxiv.org/abs/1704.04861 1. Summary VGG-16 ๊ธฐ๋ฐ˜ ๊ธฐ๋ณธ ๋ชจ๋ธ์ด๋‹ค. ๊ธฐ์กด VGG-16 ๋ชจ๋ธ์ด 3x3x3 convolution์„ 3-dimention์œผ๋กœ ์‚ฌ์šฉํ–ˆ๊ธฐ ๋•Œ๋ฌธ์— ์ด parameter ๊ฐœ์ˆ˜๊ฐ€ 81๊ฐœ์˜€๋Š”๋ฐ, mobile ๊ธฐ๊ธฐ ์œ„์— ์˜ฌ๋ฆฌ๊ธฐ ์œ„ํ•ด depthwise convolution๊ณผ pointwise convolution์„ ํ•จ๊ป˜ ์‚ฌ์šฉํ•˜์—ฌ 331 x 3 + 311 x 3 = 27 + 9 = 36๊ฐœ์˜ parameter๋กœ ์ค„์ธ ๋ฐฉ์‹์˜ ๋ชจ๋ธ์ด๋‹ค. → ์ด๋ฅผ Depth separable convolution ์ด๋ผ๊ณ  ๋ถ€๋ฅธ๋‹ค. 2. Architectur..