3,721 research outputs found

    Towards Efficient Incident Detection in Real-time Traffic Management

    Get PDF
    Incident detection is a key component in real-time traffic management systems that allows efficient response plan generation and decision making by means of risk alerts at critical affected sections in the network. State-of-the-art incident detection techniques traditionally require: i) good quality data from closely located sensor pairs, ii) a minimum of two reliable measurements from the flow- occupancy-speed triad, and iii) supervised adjustment of thresholds that will trigger anomalous traffic states. Despite such requirements may be reasonably achieved in simulated scenarios, real-time downstream applications rarely work under such ideal conditions and must deal with low reliability data, missing measurements, and scarcity of curated incident labelled datasets, among other challenges. This paper proposes an unsupervised technique based on univariate timeseries anomaly detection for computationally efficient incident detection in real-world scenarios. Such technique is proved to successfully work when only flow measurements are available, and to dynamically adjust thresholds that adapt to changes in the supply. Moreover, results show good performance with low-reliability and missing data

    Characterizing Distances of Networks on the Tensor Manifold

    Full text link
    At the core of understanding dynamical systems is the ability to maintain and control the systems behavior that includes notions of robustness, heterogeneity, or regime-shift detection. Recently, to explore such functional properties, a convenient representation has been to model such dynamical systems as a weighted graph consisting of a finite, but very large number of interacting agents. This said, there exists very limited relevant statistical theory that is able cope with real-life data, i.e., how does perform analysis and/or statistics over a family of networks as opposed to a specific network or network-to-network variation. Here, we are interested in the analysis of network families whereby each network represents a point on an underlying statistical manifold. To do so, we explore the Riemannian structure of the tensor manifold developed by Pennec previously applied to Diffusion Tensor Imaging (DTI) towards the problem of network analysis. In particular, while this note focuses on Pennec definition of geodesics amongst a family of networks, we show how it lays the foundation for future work for developing measures of network robustness for regime-shift detection. We conclude with experiments highlighting the proposed distance on synthetic networks and an application towards biological (stem-cell) systems.Comment: This paper is accepted at 8th International Conference on Complex Networks 201

    GHG ๋ฐฐ์ถœ์— ๋”ฐ๋ฅธ ๊ธ์ •์  ํŒŒ๊ธ‰ํšจ๊ณผ๊ฐ€ ์žˆ๋Š” ๊ต์ฐจ๋กœ ๊ตํ†ต์ƒํ™ฉ์— ๋Œ€ํ•œ ํ•ต์‹ฌ ์ •์ฑ…์š”์†Œ๋กœ์„œ์˜ ์Šค๋งˆํŠธ ์‹ ํ˜ธ๋“ฑ ๊ธฐ์ˆ ์— ๋Œ€ํ•œ AHP ํ‰๊ฐ€.

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ๊ธฐ์ˆ ๊ฒฝ์˜ยท๊ฒฝ์ œยท์ •์ฑ…์ „๊ณต, 2023. 2. ํ™ฉ์ค€์„.๊ธฐํ›„๋ณ€ํ™”๋Š” ์ „์„ธ๊ณ„์ ์œผ๋กœ ์ค‘์š”ํ•œ ๋ฌธ์ œ๊ฐ€ ๋˜์—ˆ๋‹ค. ์˜ค์—ผ, ํŠนํžˆ ์œ ํ•ด๊ฐ€์Šค ๋ฐฐ์ถœ์— ์˜ํ•œ ์„ธ๊ณ„์ ์ธ ๊ธฐ์˜จ ์ƒ์Šน์€ ์ƒ๋ฌผ, ํŠนํžˆ 2022๋…„ ๊ธฐ์ค€ 7์‹ญ์–ต 9์ฒœ๋งŒ๋ช…์ด ๋„˜๋Š” ์ธ๊ฐ„์˜ ์ƒ์กด์„ ์œ„ํ˜‘ํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ์˜ค์—ผ ๊ฒฝํ–ฅ์€ 1์ฐจ ์‚ฐ์—… ํ˜๋ช…์œผ๋กœ ๊ฑฐ์Šฌ๋Ÿฌ ์˜ฌ๋ผ๊ฐ€๋ฉฐ ์ž๋™์ฐจ ์‚ฐ์—…์—์„œ ํœ˜๋ฐœ์œ  ์ฒจ๊ฐ€์ œ๋ฅผ ๋„์ž…ํ•˜๋ฉด์„œ ์ „ํ™˜์ ์— ๋„๋‹ฌํ–ˆ๋‹ค. ์˜ค๋Š˜๋‚  ์ฐจ๋Ÿ‰ ๋ถ€๋ฌธ์€ ์„ธ๊ณ„ ์ฒซ๋ฒˆ์งธ ์˜ค์—ผ์›์ด์ž ์ง€๊ตฌ ๊ธฐ์˜จ ์ƒ์Šน๊ณผ ๊ทธ์— ๋”ฐ๋ฅธ ๊ธฐํ›„ ๋ณ€ํ™”์˜ ์ฃผ์š” ์›์ธ์ด๋‹ค. ๊ณผํ•™ ์ „๋ฌธ์ง€๋Š” ๊ตํ†ต ์—ญํ•™์„ ๋ถ„์„ํ•˜๊ณ  ๋ฐฐ์ถœ๋Ÿ‰ ์ฆ๊ฐ€์˜ ์ค‘์š”ํ•œ ์ˆœ๊ฐ„์€ ์ฐจ๋Ÿ‰์ด ๊ฐ€์žฅ ํšจ์œจ์ ์ธ ์—ฐ๋ฃŒ ์†Œ๋น„ ์†๋„๋กœ ์ด๋™ํ•ด์•ผ ํ•˜๋Š” ๊ตํ†ต ํ˜ผ์žก ์‹œ๊ฐ„ ๋™์•ˆ์ž„์„ ๋ฐœ๊ฒฌํ–ˆ๋‹ค. ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๊ต์ฐจ๋กœ๊ฐ€ ์ฐจ๋Ÿ‰์˜ ๊ตํ†ต์ˆ˜์š”๋ฅผ ์ฒ˜๋ฆฌํ•  ์ˆ˜ ์žˆ๋Š” ์‹ค์‹œ๊ฐ„ ๋Œ€์‘๊ธฐ์ˆ ์ด๋‚˜ ์žฅ์น˜ ๋ถ€์กฑ์œผ๋กœ ์ธํ•œ ๊ตํ†ต์ฒด์ฆ์˜ ๊ฐ€์žฅ ํ”ํ•œ ์›์ธ์œผ๋กœ ํŒ๋‹จ๋œ๋‹ค. ์ด์™€ ๊ด€๋ จํ•˜์—ฌ ์ค‘์‚ฐ์ธต ๋ฐ ๊ณ ์†Œ๋“ ๊ตญ๊ฐ€๋Š” ๊ตํ†ต ๊ด€๋ฆฌ ์‹œ์Šคํ…œ์˜ ๋””์ง€ํ„ธ ์ „ํ™˜์— ๋Œ€ํ•œ ๋Œ€๊ทœ๋ชจ ํˆฌ์ž๋ฅผ ํ†ตํ•ด ์ฐจ๋Ÿ‰ ๊ตํ†ต ํ˜ผ์žก์„ ์ค„์ด๊ธฐ ์œ„ํ•ด ๊ตํ†ต ๋ฐ ๋„์‹œ ์ •์ฑ…์œผ๋กœ ์ธํ”„๋ผ๋ฅผ ๊ฐœ์„ ํ•˜๊ณ  ๋„์‹œ๋ฅผ ์Šค๋งˆํŠธํ™”ํ•˜๊ธฐ ์œ„ํ•ด ์—ฌ๋Ÿฌ ํ˜„๋Œ€ ๊ธฐ์ˆ ์„ ๋„์ž…ํ•˜๊ณ  ์žˆ๋‹ค. ์ด ๋ฌธ์ œ๋Š” ์ €์†Œ๋“ ๊ตญ๊ฐ€๊ฐ€ ์ธ๊ตฌ ์š”๊ตฌ๋ฅผ ์šฐ์„ ํ•˜๊ณ  ์˜ˆ์‚ฐ์„ ๊ธฐํ›„๋ณ€ํ™”๋ณด๋‹ค ์‹๋Ÿ‰, ์ฃผ๊ฑฐ, ๊ฑด๊ฐ•, ๊ต์œก, ์•ˆ๋ณด, ๊ตํ†ต์— ํ• ๋‹นํ•  ๋•Œ ๋ฐœ์ƒํ•œ๋‹ค. ๊ทธ๋ž˜์„œ, ์˜จ์‹ค๊ฐ€์Šค ์˜ค์—ผ์œผ๋กœ ์ธํ•œ ๊ตํ†ต ๋ถ„์•ผ์— ์—ฐ๊ด€๋œ ๊ตฌ์กฐ์  ๋ฌธ์ œ๋Š” ๊ณ„์†๋œ๋‹ค. ์ด ์‹œ๋‚˜๋ฆฌ์˜ค์—์„œ๋Š” ์˜ค์—ผ์ด ์ œ๊ฑฐ๋˜๊ฑฐ๋‚˜ ๊ฐ์†Œ๋˜๊ฑฐ๋‚˜ ์ฆ๊ฐ€ํ•˜๋“ , ์ตœ์ข… ์˜ํ–ฅ์€ ์„ธ๊ณ„์ ์ธ ๊ธฐ์˜จ ๋ณ€ํ™”์— ๋‹ฌ๋ ค ์žˆ๋‹ค. ์ด ์ด์Šˆ๋ฅผ ๋” ์ฒ ์ €ํ•˜๊ฒŒ ์กฐ์‚ฌํ•˜๊ธฐ ์œ„ํ•ด์„œ, ํ˜„์žฌ์˜ ์—ฐ๊ตฌ๋Š” ๋‘ ๊ฐ€์ง€ ๋…ผ์ ์„ ์ œ๊ธฐํ•œ๋‹ค. ์ฒซ๋ฒˆ์งธ๋Š” ์ด์‚ฐํ™”ํƒ„์†Œ ๋ฐฐ์ถœ ์ฆ๊ฐ€์™€ ๊ต์ฐจ๋กœ์—์„œ์˜ ๊ตํ†ต ์ •์ฒด๊ฐ€ ์—ฐ๊ด€๋˜์–ด ์žˆ๋Š”๊ฐ€?์ด๊ณ . ๋ฐฉ๋ฒ•๋ก ์œผ๋กœ์„œ์˜ ์ฒด๊ณ„์ ์ธ ๋ฌธํ—Œ ๊ฒ€ํ† ๋ฅผ ์‚ฌ์šฉํ–ˆ๋‹ค. 135๊ฑด ์ด์ƒ์˜ ๋ฌธ์„œ ์Šค๋งˆํŠธ ๊ตํ†ต์‹ ํ˜ธ์™€ ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ์ด. SLR ๋…ผ๋ฌธ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค์—์„œ ํ‚ค์›Œ๋“œ ์ถ”์ถœ๊ธฐ๊ฐ€ ๊ตฌํ˜„๋˜์–ด ์•„ํ‚คํ…์ฒ˜, ํ”Œ๋žซํผ, ํ”„๋ ˆ์ž„์›Œํฌ, ์‹œ๋ฎฌ๋ ˆ์ดํ„ฐ, ์„ผ์„œ, ๋ฐฉ๋ฒ• ๋ฐ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์‹๋ณ„ํ•˜๊ณ  ๊ฐ ํ•ญ๋ชฉ์—์„œ ์ถ”์ถœํ–ˆ๋‹ค. ๊ทœํ™” ๋‹จ์–ด ํด๋ผ์šฐ๋“œ ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•˜์—ฌ, ์ด 241๊ฐœ์˜ ์„œ๋กœ ๊ด€๋ จ๋œ STL ๊ธฐ์ˆ ์„ ํ™•์ธํ•˜์˜€๊ณ , 2๋‹จ๊ณ„์—์„œ ์ด 135๊ฐœ์˜ ์šฉ์–ด๋กœ ๊ฐ์†Œํ•˜์˜€๋‹ค. ๊ด€๋ จ ๋˜๋Š” ๋ฐ€์ ‘ํ•˜๊ฒŒ ๊ด€๋ จ๋œ ๊ธฐ์ˆ ์„ ์กฐ์‚ฌํ•œ ํ›„์—๋Š” ๋ถ„๋ฅ˜ ํŠธ๋ฆฌ ๋งต์„ ์‚ฌ์šฉํ•˜์—ฌ ๊ฒฐ๊ณผ๋ฅผ 27 STL ์ฃผ์š” ์šฉ์–ด๋กœ ์ œํ•œํ–ˆ๋‹ค. ์—ฐ๊ตฌ ์งˆ๋ฌธ์€ Lu Jie, Watson, Bates ๋ฐ Kennedy, Towjua ๋ฐ Felix Isholab, Addy Majewski์˜ ์—ฐ๊ตฌ์— ๋Œ€ํ•œ SLR ์‹๋ณ„์œผ๋กœ ํ•ด๊ฒฐ๋˜์—ˆ์Šต๋‹ˆ๋‹ค; ๊ทธ๋“ค ๋ชจ๋‘๋Š” ๊ตํ†ต ์ฒด์ฆ๊ณผ ์ •์ฒด ๊ทธ๋ฆฌ๊ณ  ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ ์ฆ๊ฐ€์œจ ์‚ฌ์ด์˜ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ํ™•์ธํ•˜๊ธฐ ์œ„ํ•ด ์„œ๋กœ ๋‹ค๋ฅธ ๋ฐฉ๋ฒ•์— ๋™์˜ํ•˜๊ณ  ์ œ๊ณตํ–ˆ๋‹ค. SLR์˜ ์ง‘์ค‘์ ์ธ ๊ธฐ์ˆ  ์„ค๋ช…, ์ถ”์ถœ ๋ฐ ์ •๊ทœํ™”๋ฅผ ํ†ตํ•ด ์Šค๋งˆํŠธ ์‹ ํ˜ธ๋“ฑ ๊ด€๋ จ ๊ธฐ์ˆ , ์•„ํ‚คํ…์ฒ˜ ๋ฐ ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ๋ช…ํ™•ํ•˜๊ฒŒ ์‹๋ณ„ํ•  ์ˆ˜ ์žˆ๋‹ค.๋Œ€์ฒด ๊ณ„์ธต ๋˜๋Š” ์ฐจ์›์„ ์ œ๊ณตํ•จ์œผ๋กœ์จ AHP ํ”„๋กœ์„ธ์Šค์—์„œ ์ค‘์š”ํ•œ ๋‹จ๊ณ„ ์ค‘ ํ•˜๋‚˜๊ฐ€ ๋˜๋„๋ก ์˜๋„๋œ STL ๊ธฐ์ˆ  ๋งต์„ ์ƒ์„ฑํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ๋‘ ๋ฒˆ์งธ ์—ฐ๊ตฌ ์งˆ๋ฌธ: "STL ์‹œ์Šคํ…œ ๊ธฐ์ˆ ์˜ SLR ์‹๋ณ„์„ ๋ฐ”ํƒ•์œผ๋กœ ๊ตํ†ต ํ๋ฆ„์„ ๊ฐœ์„ ํ•˜๊ณ  GHG-Co2 ๋ฐฐ์ถœ๋Ÿ‰์„ ์ค„์ด๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ์˜ˆ์‚ฐ ์ œ์•ฝ ํ•˜์—์„œ ๊ต์ฐจ๋กœ(์‹ ํ˜ธ๋“ฑ)์˜ ๊ตํ†ต ์ธํ”„๋ผ ์š”์†Œ๋กœ ๊ตฌํ˜„ํ•˜๊ธฐ์— ๊ฐ€์žฅ ์ ํ•ฉํ•œ ๊ธฐ์ˆ ์€ ๋ฌด์—‡์ž…๋‹ˆ๊นŒ?" ์˜์‚ฌ๊ฒฐ์ •์ž์™€ ์ •์ฑ… ์ž…์•ˆ์ž๊ฐ€ ๊ฐ€์žฅ ์ ํ•ฉํ•œ ๊ฒƒ์„ ๊ฒฐ์ •ํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•˜๊ธฐ ์œ„ํ•ด ๋ถ„์„ ๊ณ„์ธต ํ”„๋กœ์„ธ์Šค(AHP)์— ๊ธฐ๋ฐ˜ํ•œ ๋‹ค์ค‘ ๊ธฐ์ค€ ์˜์‚ฌ๊ฒฐ์ • ๋ถ„์„(MCDA)์— ๋”ฐ๋ผ ์—ฐ๊ตฌ๋˜์—ˆ๋‹ค.๊ต์ฐจ๋กœ์˜ ์ฐจ๋Ÿ‰ ์ •์ฒด ๊ด€๋ฆฌ์™€ ๊ด€๋ จ๋œ IR ๊ธฐ์ˆ . 1970๋…„๋Œ€ ํ† ๋งˆ์Šค ์ƒˆํ‹ฐ ๊ต์ˆ˜๊ฐ€ ๊ฐœ๋ฐœํ•œ AHP ๋ฐฉ๋ฒ•๋ก ์€ ์ „ํ˜•์ ์œผ๋กœ ๊ณ„์ธต์ ์ด๊ณ  ์„œ๋กœ ์ž์ฃผ ๋Œ€๋ฆฝํ•˜๋Š” ๋‹ค์ˆ˜์˜ ์„ ํƒ ๊ธฐ์ค€ ๋˜๋Š” ๋ณ€์ˆ˜๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ๋งŽ์€ ๋Œ€์•ˆ ์ค‘์—์„œ ์„ ํƒํ•˜๋Š” ๋ฐ ๋„์›€์ด ๋˜๋Š” ๋‹ค์ค‘ ๊ธฐ์ค€ ๊ฒฐ์ • ๊ณผ์ •์ด๋‹ค. ์„ ํƒ ๊ธฐ์ค€๊ณผ ํ•˜์œ„ ๊ธฐ์ค€์„ ์‹ ์ค‘ํ•˜๊ฒŒ ์„ ํƒํ•˜๊ณ , ์ด๋ฅผ ์˜ฌ๋ฐ”๋ฅด๊ฒŒ ์ •์˜ํ•˜๋ฉฐ, SLR ๊ธฐ์ˆ , ์‹๋ณ„ ๋ฐ ๋ถ„๋ฅ˜๋ฅผ ํ†ตํ•ด ์ƒํ˜ธ ๋ฐฐํƒ€์ ์ธ ๋ฌธ์ œ์ž„์„ ํ™•์ธํ•˜๋Š” ๊ฒƒ์ด ํ”„๋กœ์„ธ์Šค์˜ ํ•„์ˆ˜ ๊ตฌ์„ฑ ์š”์†Œ ์ค‘ ํ•˜๋‚˜์ž…๋‹ˆ๋‹ค. ์ƒˆํ‹ฐ ๊ธฐ๋ณธ ์ฒ™๋„๋Š” ์กฐ์‚ฌ ๊ณผ์ •์—์„œ ์Œ์ฒด ๋น„๊ต๋ฅผ ์ˆ˜ํ–‰ํ•˜๋Š” ๋ฐ ์‚ฌ์šฉ๋ฉ๋‹ˆ๋‹ค. ๊ณ„์ธต ๊ตฌ์กฐ๋Š” ํ•˜ํ–ฅ์‹์ž…๋‹ˆ๋‹ค. ์ด ๋ฐฉ๋ฒ•์˜ ์ฃผ์ œ๋Š” ์งˆ์  ์ธก๋ฉด์„ ์–‘์  ์ธก๋ฉด์œผ๋กœ ์ „ํ™˜ํ•  ์ˆ˜ ์žˆ๋Š” ๋ชฉํ‘œ > ์น˜์ˆ˜(STL ๊ธฐ๋Šฅ, STL ๋น„์šฉ ๋ฐ ๊ตํ†ต ๋ฐฐ์ถœ) > ๊ธฐ์ค€ > ๋Œ€์•ˆ, ๋‹ค์–‘ํ•œ ๋Œ€์•ˆ ๊ฐ„์˜ ๋น„๊ต๋ฅผ ์ƒ๋‹นํžˆ ์šฉ์ดํ•˜๊ฒŒ ํ•˜๊ณ  ๋ณด๋‹ค ๊ฐ๊ด€์ ์ด๊ณ  ์‹ ๋ขฐํ•  ์ˆ˜ ์žˆ๋Š” ๊ฒฐ๊ณผ๋ฅผ ๋„์ถœํ•œ๋‹ค. ์ „๋ฌธ๊ฐ€ ์„ค๋ฌธ์กฐ์‚ฌ ๋ฌธํ•ญ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ AHP ๋ถ„์„์— ๋”ฐ๋ฅด๋ฉด,๊ธฐ์กด ์‹ ํ˜ธ๋“ฑ ์ธํ”„๋ผ ์—…๊ทธ๋ ˆ์ด๋“œ๋ฅผ ์œ„ํ•œ STL ๊ธฐ์ˆ ์„ ๊ตฌํ˜„ํ•˜๊ธฐ ์œ„ํ•ด ๋น„์šฉ ์ฐจ์›์ด ํ˜„์žฌ 45.79%๋กœ ๊ฐ€์žฅ ์ค‘์š”ํ•œ ์š”์†Œ์ด๋ฉฐ, ๊ทธ ๋‹ค์Œ์ด ํšจ์œจ ์ฐจ์›(41.61%)์ด๋‹ค. ๋Œ€์•ˆ ์ˆ˜์ค€์—์„œ๋Š” ์œ ๋„ ๋ฃจํ”„ ์„ผ์„œ๊ฐ€ 23.67% ๋™์˜๋กœ GHG ์ €๊ฐ๊ณผ ํ•จ๊ป˜ ๊ต์ฐจ๋กœ ๊ณ ๋„ํ™” ๋ฐ ๊ตํ†ตํ๋ฆ„ ๊ฐœ์„ ์— ๊ฐ€์žฅ ์ ํ•ฉํ•œ ๊ธฐ์ˆ ๋กœ ํŒŒ์•…๋์œผ๋ฉฐ ์˜์ƒ์ฐจ๋Ÿ‰ ๊ฐ์ง€ 15.02%, GPS ๊ธฐ๋ฐ˜ ๊ธฐ์ˆ  13.37% ์ˆœ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ์ €์†Œ๋“์ธต ์ •๋ถ€๊ฐ€ ๋””์ง€ํ„ธ ์ „ํ™˜์ด๋‚˜ ์Šค๋งˆํŠธํ™”์— ํˆฌ์žํ•˜์ง€ ๋ชปํ•˜๊ฒŒ ํ•˜๋Š” ์žฌ์ •์  ์ œ์•ฝ์„ ํ•ด๊ฒฐํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ์ œ์•ˆ์€ SLR์„ ๊ตฌํ˜„ํ•˜์—ฌ STL๊ณผ ๊ด€๋ จ๋œ ์Šค๋งˆํŠธ ๊ธฐ์ˆ , IoT, AI์˜ ์ตœ์ฒจ๋‹จ ๊ธฐ์ˆ ์„ ํŒŒ์•…ํ•˜๊ณ  ๋„๋กœ ๊ต์ฐจ๋กœ์˜ ํŠธ๋ž˜ํ”ฝ๊ณผ GHG ๋ฐฐ์ถœ๋Ÿ‰ ์ฆ๊ฐ€ ๊ฐ„์˜ ์ƒ๊ด€๊ด€๊ณ„ ๋ฐ ๊ณผํ•™์  ์ฆ๊ฑฐ๋ฅผ ์ฐพ๋Š” ๊ฒƒ์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด ์—ฐ๊ตฌ๋Š” ๊ณผํ•™์  ๊ทผ๊ฑฐ๋ฅผ ์‹๋ณ„ํ•˜๊ณ  ์ œ๊ณตํ•˜๋ ค๋Š” ์‹œ๋„ ์™ธ์—๋„ ๊ตํ†ต ๊ด€๋ฆฌ ์ „๋ฌธ๊ฐ€์™€ ์‹ค๋ฌด์ž์˜ ๊ด€์ ์—์„œ ์ด๋Ÿฌํ•œ ๊ธฐ์ˆ ์„ ํ‰๊ฐ€ํ•จ์œผ๋กœ์จ ๊ฒฐ๊ณผ์— ๋Œ€ํ•œ ๋†’์€ ์ˆ˜์ค€์˜ ์‹ ๋ขฐ์„ฑ์„ ์ œ๊ณตํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ์˜์‚ฌ ๊ฒฐ์ •์ž์™€ ์ •์ฑ… ์ž…์•ˆ์ž ๋ชจ๋‘ ํ˜„์žฌ์˜ ์—ฐ๊ตฌ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์œ ๋„ ๋ฃจํ”„ ์„ผ์„œ๊ฐ€ ๊ต์ฐจ๋กœ์˜ ๊ตํ†ต ํ๋ฆ„์„ ๊ฐœ์„ ํ•˜๊ณ  ์‹ ํ˜ธ๋“ฑ์— ์‹ค์‹œ๊ฐ„ ์ •๋ณด๋ฅผ ๊ณต๊ธ‰ํ•˜๋Š” ์ตœ๊ณ ์˜ ์Šค๋งˆํŠธ ๊ธฐ์ˆ ์ž„์„ ๊ฒฐ์ •ํ•  ์ˆ˜ ์žˆ๋‹ค, ๋‹จ๊ธฐ์ ์œผ๋กœ๋Š” ๋†’์€ ๋น„์šฉ์œผ๋กœ ์ดํ•ดํ•  ์ˆ˜ ์žˆ์ง€๋งŒ ์žฅ๊ธฐ์ ์œผ๋กœ๋Š” ํšจ์œจ์„ฑ ์ธก๋ฉด์—์„œ ์ด์ ์ด ์žˆ๋Š” ์ดˆ๊ธฐ ํˆฌ์ž์˜ ๋†’์€ ๋น„์šฉ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ .Climate change has become a critical issue around the world. Rising global temperatures caused by pollution, specifically noxious gas emissions, is threatening the survival of all living species, particularly humans, who will number more than 7.9 billion by 2022. This contamination proclivity dates back to the first industrial revolution and reached a tipping point with the implementation of gasoline additives by the automotive industry. Nowadays, the vehicular sector is the world's first source of pollution and the primary cause of rising global temperatures and the subsequent consequences of climate change. Scientific literature analyzes transportation dynamics and finds that critical moments in emission boost are during the traffic congestion hours when the vehicles are obligated to transit at the most efficient fuel consumption speed. Based on this, it is determined that road intersections are the most common source of traffic congestion due to lack of real-time responsive technologies or devices to handle vehicular traffic demand. Middle-upper and high-income nations have been working on implementing several modern technologies along with city infrastructure upgrades on the back of transportation and urban policies to reduce vehicular traffic congestion through large investments in the digital transformation of traffic management systems and moving the cities towards smartification. The problem arises when low- or low-middle-income governments are required to prioritize the needs of their populations and allocate budgets to projects, positioning climate change far behind food, housing, health, education, security, and transportation. Thus, structural problems related to the transportation field continue, resulting in Green House Gas (GHG) contamination. In this scenario, no matter whether the contamination is reduced, diminished, increased, or augmented, the final effect is accounted for as a global temperature change. To delve deeper into these issues, the current study poses two research questions: If a relationship between increasing GHG-Co2 emissions and vehicular traffic congestion levels at intersections exists? Using a systematic literature review (SLR) as the methodology, over 135 documents related to Smart Traffic Light (STL) and GHG emissions were categorized and filtered, yielding a total of 13 key papers. From the SLR papers database, a keyword extractor was implemented to identify and extract the architecture, platforms, frameworks, simulators, sensors, methods, and algorithms from each entry. A total of two hundred forty-one STL related technologies were identified, by using a normalization word cloud method it was reduced the total to one hundred thirty-five terms. In a second stage the results were limited to twenty-seven STL terms using a categorization tree map the related or closely related technologies were examined. The research question was addressed by the SLR identification of studies by Lu Jie, Watson, Bates, and Kennedy, Towojua and Felix Isholab, (Table 1). All these studies provide different methods for identifying the correlation between traffic jams and congestion and increasing GHG emissions. SLR's intensive technology description, extraction, and normalization resulted in a clear identification of smart traffic light-related technologies, architectures, and frameworks, allowing the creation of a STL technology map, which is intended to be one of the critical steps in the Analytical Hierarchy Process (AHP) by providing an alternative layer or dimension. The second research question is: Based on the SLRs identification of STL system technologies, which of these technologies are the most suitable to be implemented as an element of the traffic infrastructure at intersections (traffic lights) under budget constraints, targeted at improving traffic flows and reducing GHG-Co2 emissions? This was studied under a multicriteria decision analysis (MCDA), based on an (AHP), aimed to allow decision-makers and policymakers to determine which were the most suitable Fourth Industrial Revolution (4IR) technologies related to vehicular traffic congestion management at intersections. Developed by Professor Thomas Saaty in the 1970s, the AHP methodology is a multicriteria decision process that helps in choosing from among many alternatives based on a number of selection criteria or variables that are typically hierarchical and frequently at odds with one another. Choosing the selection criteria and sub-criteria carefully, defining them correctly, and ensuring that they are mutually exclusive are issues that were addressed by the SLR technologies. Identification and categorization are essential components of the process. The Saaty Fundamental Scale is used in the survey to perform a paired comparison. The hierarchical structure is top-down: the subject of this method is Objectives> Dimensions (STL Functions, STL Costs, and Traffic Emissions)> Criteria> Alternatives, which allows the transformation of qualitative aspects into quantitative ones, significantly facilitating a comparison between the various alternatives and producing more objective and reliable results. According to an AHP analysis which was based on an expert survey questionnaire, the cost dimension is the most important factor in implementing STL technologies for upgrading existing traffic light infrastructure at 45.79 percent, followed by the efficiency dimension (41.61 percent). At the alternatives level, experts identified that Inductive Loop Sensors were the best technology for upgrading the intersections and obtaining traffic flow improvements along with a GHG reduction with 23.67 percent agreement, followed by Video Vehicle Detection at 15.02 percent, and GPS-based technologies at 13.37 percent. The current study aims to address low-income governments' financial constraints which prevent them from investing in digital transformation or smartification. The study uses a SLR to identify the smart technologies, Internet of Things (IoT), and Artificial Intelligence (AI) related to STL state of art to find a correlation and scientific evidence between the traffic at road intersections and the increase in GHG emissions. However, in addition to identifying and providing scientific evidence, the research goes further by evaluating those technologies from the perspective of traffic management experts and practitioners, providing a high degree of reliability of the outcomes. Thus, both decision-makers and policymakers can base their policies on the present study to determine that the Inductive Loop Sensor is the best smart technology for improving traffic flows at intersections and feeding traffic lights with real-time information, despite the high initial investments, which can be understood as a high cost in the short-run but with benefits in terms of efficiency in the long run.Chapter 1. Introduction 1 1.1 Research Background 1 1.1.1 Environmental background 1 1.1.2 Vehicle industry background 3 1.1.3 Developing countries backgrounds 7 1.2 Definitions 10 1.3 Motivation 16 1.4 Problem statement 16 1.5 Research objective 18 1.6 Research questions 19 1.7 Research methodology 19 1.8 Research contribution 21 1.9 Research novelty 22 1.10 Outline 23 Chapter 2. Literature Review 23 Chapter 3.Data and Methodology 26 3.1 Systematic Literature Review (SLR) 26 3.1.1 Journal search and indexing databases 27 3.1.2 SLR Methodology 30 3.2 The Analytic Hierarchy Process (AHP) 34 3.2.1 AHP Survey questionnaire 38 3.2.2 Criteria description 39 3.2.3 Data normalizing 41 3.2.4 The AHP Methodology 46 Chapter 4. Data 50 4.1 AHPs Objective 50 4.2 First Layer: Dimensions 51 4.3 Second layer: Criteria 52 4.3.1 Efficiency dimension data analysis 52 4.3.2 Cost dimension data analysis 53 4.3.3 Emission dimensions data analysis 53 4.4 Third layer: Alternatives 54 Chapter 5. Results 55 Chapter 6. Conclusions 58 Bibliography. 62 Appendix 71 Appendix 1: Spearman Coefficient Correlation GSโ€“ WoS 73 Appendix 2: Spearman Coefficient Correlation GS - Scopus 74 Appendix 3: PRISMA 2020 Checklist 75 Appendix 4: AHP Expert Questionary 78 Appendix 5: AHP Electronic Survey Form 85 Appendix 6: AHP Top-Down Hierarchy Model 86 Acknowledgments 88 Abstract (Korean) 88์„

    VELOS: A VR Platform for Ship-Evacuation Analysis

    Get PDF
    โ€œVirtual Environment for Life On Shipsโ€ (VELOS) is a multi-user Virtual Reality (VR) system that aims to support designers to assess (early in the design Process) passenger and crew activities on a ship for both normal and hectic Conditions of operations and to improve ship design accordingly. This paper focuses On presenting the novel features of VELOS related to both its VR and Evacuation-specific functionalities. These features include: i) capability of multiple Usersโ€™ immersion and active participation in the evacuation process, ii) Real-time interactivity and capability for making on-the-fly alterations of environment Events and crowd-behavior parameters, iii) capability of agents and Avatars to move continuously on decks, iv) integrated framework for both the Simplified and the advanced method of analysis according to the IMO/MSC 1033 Circular, v) enrichment of the ship geometrical model with a topological model Suitable for evacuation analysis, vi) efficient interfaces for the dynamic specification and handling of the required heterogeneous input data, and vii) post Processing of the calculated agent trajectories for extracting useful information For the evacuation process. VELOS evacuation functionality is illustrated using Three evacuation test cases for a ro-ro passenger ship

    AI/ML Algorithms and Applications in VLSI Design and Technology

    Full text link
    An evident challenge ahead for the integrated circuit (IC) industry in the nanometer regime is the investigation and development of methods that can reduce the design complexity ensuing from growing process variations and curtail the turnaround time of chip manufacturing. Conventional methodologies employed for such tasks are largely manual; thus, time-consuming and resource-intensive. In contrast, the unique learning strategies of artificial intelligence (AI) provide numerous exciting automated approaches for handling complex and data-intensive tasks in very-large-scale integration (VLSI) design and testing. Employing AI and machine learning (ML) algorithms in VLSI design and manufacturing reduces the time and effort for understanding and processing the data within and across different abstraction levels via automated learning algorithms. It, in turn, improves the IC yield and reduces the manufacturing turnaround time. This paper thoroughly reviews the AI/ML automated approaches introduced in the past towards VLSI design and manufacturing. Moreover, we discuss the scope of AI/ML applications in the future at various abstraction levels to revolutionize the field of VLSI design, aiming for high-speed, highly intelligent, and efficient implementations

    Dynamic Message Sign and Diversion Traffic Optimization

    Get PDF
    This dissertation proposes a Dynamic Message Signs (DMS) diversion control system based on principles of existing Advanced Traveler Information Systems and Advanced Traffic Management Systems (ATMS). The objective of the proposed system is to alleviate total corridor traffic delay by choosing optimized diversion rate and alternative road signal-timing plan. The DMS displays adaptive messages at predefined time interval for guiding certain number of drivers to alternative roads. Messages to be displayed on the DMS are chosen by an on-line optimization model that minimizes corridor traffic delay. The expected diversion rate is assumed following a distribution. An optimization model that considers three traffic delay components: mainline travel delay, alternative road signal control delay, and the travel time difference between the mainline and alternative roads is constructed. Signal timing parameters of alternative road intersections and DMS message level are the decision variables; speeds, flow rates, and other corridor traffic data from detectors serve as inputs of the model. Traffic simulation software, CORSIM, served as a developmental environment and test bed for evaluating the proposed system. MATLAB optimization toolboxes have been applied to solve the proposed model. A CORSIM Run-Time-Extension (RTE) has been developed to exchange data between CORSIM and the adopted MATLAB optimization algorithms (Genetic Algorithm, Pattern Search in direct search toolbox, and Sequential Quadratic Programming). Among the three candidate algorithms, the Sequential Quadratic Programming showed the fastest execution speed and yielded the smallest total delays for numerical examples. TRANSYT-7F, the most credible traffic signal optimization software has been used as a benchmark to verify the proposed model. The total corridor delays obtained from CORSIM with the SQP solutions show average reductions of 8.97%, 14.09%, and 13.09% for heavy, moderate and light traffic congestion levels respectively when compared with TRANSYT-7F optimization results. The maximum model execution time at each MATLAB call is fewer than two minutes, which implies that the system is capable of real world implementation with a DMS message and signal update interval of two minutes
    • โ€ฆ
    corecore