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![螻牛 覲伎 蟯 覯
る瑚概螻 谿碁 覲伎 蟆 蠍一
語蟲螻牛螻旧 螻螳 螻螳 豕
1. 襦
磯Μ螳 煙朱 蟲伎 リ碓襴 蟲螳
企 伎 螻褐 螻牛企. 蠍襦覯
觜讀 螻 螳 蠍磯伎
螻牛 伎 觜襯願 讀螳螻 朱, 企れ
蠍 螻牛 豢企 螳豢 譴
蠍磯 ′ 襦 讌螻 .[1]. 蠏碁
螻牛 豢企 螳豢 豢 豈朱
蟆曙朱 覲企 覦 . 磯殊,
襦 覦 覈 蠍讀 螻牛 螳
襴 觜るゼ 螻牛 覦 覈
. 螻牛 覦 螻殊 危ｴ覲企 (蠏碁1)螻 螳
螻牛 1.0朱覿 螻牛 3.0 蟇一 螻牛 4.0朱 觜
れ 覲螻 .[2]
螻牛 1.0 誤 蟲豢 牛 螻
觜るゼ, 螻牛 2.0 れ螻
觜るゼ 牛 螳 螳 觜るゼ , 螻牛 3,0 螻
螳譴 蠏殊 牛 郁 螳 襷豢ろ
觜るゼ 螻牛螻, 螻牛 4.0 襦 ICT 蠍
覈 螻螳 讌レ ｸ旧 螻牛 觜
襦 覦螻 . 讀, 螻牛 4.0 4谿 一覈
旧 蠍一れ る 螻牛(Smart
Airport) 朱 覦螻 る 蟆企, 覈 觜
讌レ 螻牛 誤殊 螻旧 譴 螳譴
朱 觜 れ 覲螻
. 覓語 螳蟲 螻牛襷 朱 螻牛
る誤襯 碁レ 蠍一語願 .
る瑚概 觜れ 覯 企, 磯
企, 伎企 螻牛 覿朱ゼ 讌襷,
螳 覿覿 讌 蟯 磯碁 企螳 螳
譴 螻る . 磯碁 企
螳 螻牛 谿 觜蠍一 麹 螳螻 觜
蠍一 企れ 螻牛 蟾讌襯 . 螻
螻牛 豢 覦 螻牛 蠍, 豌危, 覓
, 豢蟲 , 覲伎蟆, , 豢蟲, 觜蠍
, 谿, 覓 豬 煙 襦語るゼ 朱
螻 . [2],[3].
磯碁 螻牛 襦語 譴 螳 螳 覿ｸ
危螻, 覿 螳 螳 螻殊 覲伎蟆螻殊
.[4] (蠏碁 2) 螻牛 豢蟲 谿れ 螳
朱 蠍 螳螻 覿 螳 譟一
襭企.[2]
磯殊, 覲伎 蟆 螻殊 る誤螳 譴
蟆 覿螳螻 螻 れ 豈, 蠍一 れ
螻 朱 讌 螻 . 豕蠏
企ゼ 蠍一朱 蠏豪概蠍 谿碁 螻牛 覲
蟆 蠍一 殊螳 覦 讌螻
,螳 ｸ 螻, 螳 殊企 覲危, 覲伎蟆
螳 豢, 覲伎 蟆 語 る 豕
煙 覈朱 螻 .
谿碁 螻牛 覲伎 蟆 ろ 譴 襦 螳
一る所 覲危覃伎 磯 糾骸覃 螳 讌
螻 企 覓殊 朱 蟆 譯朱 Walk
Through(WT) 覲伎 蟆 蠍一 襦蟆 螻
.[5],[6]
覲 螻 螻牛 覦 覦レ 危ｴ覲願, 螻
4.0朱 覦螻 る 螻牛 螳 覦
讌, 蠏碁Μ螻 る 螻牛 ICT 蠍一
覲願 蠏 る瑚概 譴煙 覿螳螻
谿碁 覲伎 蟆 炎骸 譴煙 る
螻, 襷讌襷朱 WT る 覲伎蟆ろ
る.
2. る瑚概 螳 覦 轟
1. る瑚概 螳
2016 2 ENISA 覦 襭 覃
襷瑚概 覓殊誤磯(IoT) 蠍一 蠍磯 螳豌襴
螻殊 螳螻 襦 蠍磯レ 螻牛蠍 企
郁骸 郁屋 ICT 蟆曙 蟲豢 覦 豕 覃
襦語るゼ 螻牛企手 螻
.[7].
讀 る 螻牛 AI, 觜一危, IoT, 襦覺 豌伎
訖襷 螻牛 伎 ｸ煙 企 覦
譴 螻 る 蟆朱 危危 .
豕蠏 蟲伎 螻牛れ 伎螳 蠍讀覃伎 る
螻牛 豢讌 覦 讌螻 . 譴讌
螻牛朱 覦 蟲螻牛 磯碁 , 豢
蟲 螳 豢蠍 40Gbps smartgate襯
蟲磯碁 T1, T2 . Smart Gate
豌伎 覲 覦 Smart Gate ID 豺企 煙 伎
, 豢蟲 襦語るゼ 螻 企ゼ 牛 企 襦
語れ 螳 10豐 伎碁 豢る 蟆郁骸襯
覦. [8], 譴蟲 覓伎 覦 煙レ朱 蟲
伎 蠍讀 螻 螳 磯 る 螻牛
蠍一 螳覦 覦 蠏轟朱 豢讌螻 朱,
伎 殊螻牛 る瑚概 2.0 伎語磯 蟯
襭 螳覦螻, 朱 豈螻 朱, CEBOT](https://image.slidesharecdn.com/airportsecurityver1-190729011355/85/Airport-security-ver1-1-320.jpg)
![2018 觜一危, 瑚概讌, IoT 蠍一 蠍磯朱
弰 覦.[9]
蟲伎 蟆曙, 蟲蟲給 2017 9 る
螻牛 譬螻 覦朱, 2022 蟾讌 る
螻牛 蟲 觜, 覈 る 蠏, 蟲旧
6螳 覦 伎 磯ジ 豢讌 螻殊襯 <1>螻
螳 螻 豢讌譴企.[10]
語蟲螻旧 2023 蟾讌 語蟲螻牛 る
瑚概 ろ SIMPLE, EASY, ON-TIME,
ENJOY襯 れ襦 100螳 譯殊 れ 覦レ
螻 豢讌螻 . 麹 螳 蟯 螻牛
襦語れ 覦 螳誤襯 豌危,
企 蠍磯 豈覺(Chat-bot), 螻牛 襦覺,
襷豢ろ 覃伎語狩, 覓 豢覲 螻 煙 螻牛
螻企.[11]
2. る 螻牛 轟
螻 伎 螳れ (蠏碁 3)螻 螳 蟆′覿
譯殊姶, 螻牛 谿伎 覓殊 螻, 豌危
, 覲伎 蟆襯 糾骸 豢蟲 襯 螻, 覃伎
蟲 煙 蠍壱蟆 . 觜蠍 覈讌
谿 覓殊 豬螻, 蟠, 觜 煙 誤
覃 蟲 襯 蟇一 螻牛 郁 蟲牛ｸ 煙
豕譬 覈讌蟾讌 螳 螻 螳 襦語れるゼ 蟇
豺蟆 . 蠏蟲 企 襦語れ 朱
.[12]
る瑚概 覈 襦語るゼ 螳蟆 螻牛
螳螻 螻糾 伎 ｸ 蟆渚 螻
蟆 朱 螻 蠍 覓語 れ
觜り 轟炎骸 覲危ｸ煙 螳讌 轟 .
蟲旧郁規(ETRI) 螳 螻牛 伎
襦語る 讀 覦 伎 蟲 觜るゼ
2018 朱 れ 朱 豸 覃伎 牛 <
2> 螳 譟一朱, 蟆郁骸 覃 覲伎蟆,
豌危, 覓 豺覲 觜 煙 讀
觜れ 螳 蟆 蟲螻, 蠏 襷
譴煙 る 蟆 . [13]](https://image.slidesharecdn.com/airportsecurityver1-190729011355/85/Airport-security-ver1-2-320.jpg)
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Final
FinalHyun Wong Choi
油
1) The document discusses the past, current, and future of smartphone technology.
2) In the past, "Pen on Projection" technology allowed writing on any surface using a Bluetooth pen and projected screen.
3) Currently, Qualcomm uses fingerprint sensor technology for authentication and security.
4) In the future, Qualcomm will introduce ultrasonic fingerprint sensors that can scan fingerprints through OLED displays of various thicknesses.003
003Hyun Wong Choi
油
The document analyzes electricity consumption at home through K-means clustering and evaluates different cluster validity indices, including the Silhouette score, to determine the optimal number of clusters in the dataset. It performs K-means clustering on a household electricity consumption dataset and compares the results of the Silhouette score and other indices at different values of K to identify the best number of clusters. The analysis aims to help optimize home electricity usage through machine learning clustering techniques.002
002Hyun Wong Choi
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This document summarizes a master's dissertation that analyzes electricity consumption at home through K-means clustering and silhouette scoring. It contains two papers. Paper 1 analyzes a household electricity usage dataset using K-means clustering to identify the optimal number of clusters, as determined by the Calinski-Harabasz Index, Davis-Boulden index, and silhouette score. Paper 2 performs a similar analysis but with a reduced 1/8 size dataset to compare results. The dissertation concludes that both analyses produce similar silhouette scores even with a smaller dataset.001
001Hyun Wong Choi
油
The document analyzes electricity consumption data from homes using K-means clustering to determine optimal clusters in the data. It evaluates different cluster validity indices like the Calinski-Harabasz Index, Davis-Boulden index, and Silhouette score to find the optimal number of clusters. The analysis is also performed on a reduced 1/8th dataset to see if the results are similar when using less data.Hyun wong thesis 2019 06_22_rev40_final_grammerly
Hyun wong thesis 2019 06_22_rev40_final_grammerlyHyun Wong Choi
油
The document analyzes electricity consumption at home through K-means clustering and evaluates different cluster validity indices, including the Silhouette score, to determine the optimal number of clusters in the dataset. It performs K-means clustering on a household electricity consumption dataset and compares the results of the Silhouette score and other indices at different values of K to identify the best clustering. The analysis aims to optimize home electricity usage through unsupervised machine learning clustering techniques.Hyun wong thesis 2019 06_22_rev40_final_Submitted_online
Hyun wong thesis 2019 06_22_rev40_final_Submitted_onlineHyun Wong Choi
油
The document summarizes a master's dissertation that analyzes electricity consumption at home through K-means clustering and silhouette scoring. It introduces machine learning and clustering techniques. It then describes the experimental environment, dataset used, previous work on related topics, and the proposed approach of applying K-means clustering to analyze the electricity consumption dataset. The key aspects analyzed are the optimal number of clusters determined by indices like Calinski-Harabasz, Davis-Boulden, and silhouette score. Results are compared between the full and 1/8 reduced datasets.Hyun wong thesis 2019 06_22_rev40_final_printed
Hyun wong thesis 2019 06_22_rev40_final_printedHyun Wong Choi
油
This document summarizes a master's dissertation that analyzes electricity consumption at home through k-means clustering. The dissertation contains two papers:
1. The first paper analyzes electricity usage data from homes using k-means clustering to identify optimal clusters of usage patterns. It evaluates different metrics like silhouette score and clustering indices to determine the optimal number of clusters in the data.
2. The second paper performs a comparative analysis using a reduced 1/8th dataset to validate that the silhouette score and optimal number of clusters is similar even with smaller data.
The dissertation applies machine learning clustering techniques to analyze electricity consumption data from homes with the goal of optimizing costs and identifying factors for overcharging.Hyun wong thesis 2019 06_22_rev40_final
Hyun wong thesis 2019 06_22_rev40_finalHyun Wong Choi
油
This master's dissertation analyzes electricity consumption at home through a K-means clustering algorithm and silhouette score. The document contains two papers that analyze a household electricity consumption dataset from the University of California, Irvine using K-means clustering. Paper 1 uses the Calinski-Harabasz Index, Davis-Boulden index, and silhouette score to determine the optimal number of clusters. Paper 2 performs a comparative analysis using a 1/8 subset of the full dataset and finds that the silhouette scores are similar even when using a smaller dataset. The dissertation aims to optimize household electricity usage and costs through machine learning clustering techniques.Hyun wong thesis 2019 06_22_rev39_final
Hyun wong thesis 2019 06_22_rev39_finalHyun Wong Choi
油
This document summarizes a master's dissertation that analyzes electricity consumption at home through K-means clustering and silhouette scoring. The dissertation contains two papers. Paper 1 analyzes household electricity consumption data from UC Irvine using K-means clustering to determine the optimal number of clusters based on silhouette scoring and other indices. The analysis finds seven clusters to be optimal. Paper 2 performs a comparative analysis using a 1/8 subset of the full dataset, finding that silhouette scores are approximately half of the full dataset but the optimal number of clusters is similar. The dissertation concludes that machine learning clustering can effectively analyze electricity consumption patterns and predict optimal clustering even with smaller datasets.Hyun wong thesis 2019 06_22_rev41_final
Hyun wong thesis 2019 06_22_rev41_finalHyun Wong Choi
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This document appears to be a master's dissertation that analyzes electricity consumption in homes using k-means clustering. It contains chapters that introduce the topic, provide an overview and motivation, describe two papers analyzing electricity consumption data through k-means clustering with silhouette scores to determine optimal cluster numbers, present results, and conclude. The dissertation applies machine learning techniques to optimize home electricity usage by reducing costs and overcharging through clustering and prediction.Hyun wong thesis 2019 06_22_rev40_final
Hyun wong thesis 2019 06_22_rev40_finalHyun Wong Choi
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This document appears to be a master's dissertation that analyzes electricity consumption in homes using k-means clustering. It contains chapters that introduce the topic, provide an overview and motivation, describe two papers analyzing electricity consumption data through k-means clustering with silhouette scores to determine optimal cluster numbers, present results of experiments on datasets, and conclude with findings. The dissertation aims to optimize home electricity usage through machine learning clustering techniques by reducing costs and overcharging factors while enabling prediction of consumption. It applies k-means clustering to electricity usage data from homes to predict consumption patterns and determine the optimal number of clusters using silhouette scores.Hyun wong thesis 2019 06_22_rev39_final
Hyun wong thesis 2019 06_22_rev39_finalHyun Wong Choi
油
This document appears to be a master's dissertation that analyzes electricity consumption in homes using k-means clustering. It contains chapters that introduce the topic, provide an overview and motivation, describe two papers analyzing electricity consumption data through k-means clustering with silhouette scores to determine optimal cluster numbers, present results of clustering a full and 1/8 sized dataset, and conclude. The dissertation aims to optimize home electricity usage through k-means clustering and determine factors influencing overcharges or costs by analyzing household consumption data.Hyun wong thesis 2019 06_19_rev38_final
Hyun wong thesis 2019 06_19_rev38_finalHyun Wong Choi
油
This document summarizes a master's dissertation that analyzes electricity consumption at home through K-means clustering. The dissertation contains two papers:
1. The first paper analyzes household electricity usage data through K-means clustering to obtain optimal data points. It uses the Calinski-Harabasz Index and Silhouette_score to determine the optimal number of clusters.
2. The second paper performs a comparative analysis on a dataset that is 1/8 the size of the original. It finds that the Silhouette score is half of the original dataset, even with the smaller data.
The dissertation applies unsupervised machine learning clustering techniques to analyze household electricity consumption data, in order to optimize costs and identify factorsMore Related Content
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001Hyun Wong Choi
油
The document analyzes electricity consumption data from homes using K-means clustering to determine optimal clusters in the data. It evaluates different cluster validity indices like the Calinski-Harabasz Index, Davis-Boulden index, and Silhouette score to find the optimal number of clusters. The analysis is also performed on a reduced 1/8th dataset to see if the results are similar when using less data.Hyun wong thesis 2019 06_22_rev40_final_grammerly
Hyun wong thesis 2019 06_22_rev40_final_grammerlyHyun Wong Choi
油
The document analyzes electricity consumption at home through K-means clustering and evaluates different cluster validity indices, including the Silhouette score, to determine the optimal number of clusters in the dataset. It performs K-means clustering on a household electricity consumption dataset and compares the results of the Silhouette score and other indices at different values of K to identify the best clustering. The analysis aims to optimize home electricity usage through unsupervised machine learning clustering techniques.Hyun wong thesis 2019 06_22_rev40_final_Submitted_online
Hyun wong thesis 2019 06_22_rev40_final_Submitted_onlineHyun Wong Choi
油
The document summarizes a master's dissertation that analyzes electricity consumption at home through K-means clustering and silhouette scoring. It introduces machine learning and clustering techniques. It then describes the experimental environment, dataset used, previous work on related topics, and the proposed approach of applying K-means clustering to analyze the electricity consumption dataset. The key aspects analyzed are the optimal number of clusters determined by indices like Calinski-Harabasz, Davis-Boulden, and silhouette score. Results are compared between the full and 1/8 reduced datasets.Hyun wong thesis 2019 06_22_rev40_final_printed
Hyun wong thesis 2019 06_22_rev40_final_printedHyun Wong Choi
油
This document summarizes a master's dissertation that analyzes electricity consumption at home through k-means clustering. The dissertation contains two papers:
1. The first paper analyzes electricity usage data from homes using k-means clustering to identify optimal clusters of usage patterns. It evaluates different metrics like silhouette score and clustering indices to determine the optimal number of clusters in the data.
2. The second paper performs a comparative analysis using a reduced 1/8th dataset to validate that the silhouette score and optimal number of clusters is similar even with smaller data.
The dissertation applies machine learning clustering techniques to analyze electricity consumption data from homes with the goal of optimizing costs and identifying factors for overcharging.Hyun wong thesis 2019 06_22_rev40_final
Hyun wong thesis 2019 06_22_rev40_finalHyun Wong Choi
油
This master's dissertation analyzes electricity consumption at home through a K-means clustering algorithm and silhouette score. The document contains two papers that analyze a household electricity consumption dataset from the University of California, Irvine using K-means clustering. Paper 1 uses the Calinski-Harabasz Index, Davis-Boulden index, and silhouette score to determine the optimal number of clusters. Paper 2 performs a comparative analysis using a 1/8 subset of the full dataset and finds that the silhouette scores are similar even when using a smaller dataset. The dissertation aims to optimize household electricity usage and costs through machine learning clustering techniques.Hyun wong thesis 2019 06_22_rev39_final
Hyun wong thesis 2019 06_22_rev39_finalHyun Wong Choi
油
This document summarizes a master's dissertation that analyzes electricity consumption at home through K-means clustering and silhouette scoring. The dissertation contains two papers. Paper 1 analyzes household electricity consumption data from UC Irvine using K-means clustering to determine the optimal number of clusters based on silhouette scoring and other indices. The analysis finds seven clusters to be optimal. Paper 2 performs a comparative analysis using a 1/8 subset of the full dataset, finding that silhouette scores are approximately half of the full dataset but the optimal number of clusters is similar. The dissertation concludes that machine learning clustering can effectively analyze electricity consumption patterns and predict optimal clustering even with smaller datasets.Hyun wong thesis 2019 06_22_rev41_final
Hyun wong thesis 2019 06_22_rev41_finalHyun Wong Choi
油
This document appears to be a master's dissertation that analyzes electricity consumption in homes using k-means clustering. It contains chapters that introduce the topic, provide an overview and motivation, describe two papers analyzing electricity consumption data through k-means clustering with silhouette scores to determine optimal cluster numbers, present results, and conclude. The dissertation applies machine learning techniques to optimize home electricity usage by reducing costs and overcharging through clustering and prediction.Hyun wong thesis 2019 06_22_rev40_final
Hyun wong thesis 2019 06_22_rev40_finalHyun Wong Choi
油
This document appears to be a master's dissertation that analyzes electricity consumption in homes using k-means clustering. It contains chapters that introduce the topic, provide an overview and motivation, describe two papers analyzing electricity consumption data through k-means clustering with silhouette scores to determine optimal cluster numbers, present results of experiments on datasets, and conclude with findings. The dissertation aims to optimize home electricity usage through machine learning clustering techniques by reducing costs and overcharging factors while enabling prediction of consumption. It applies k-means clustering to electricity usage data from homes to predict consumption patterns and determine the optimal number of clusters using silhouette scores.Hyun wong thesis 2019 06_22_rev39_final
Hyun wong thesis 2019 06_22_rev39_finalHyun Wong Choi
油
This document appears to be a master's dissertation that analyzes electricity consumption in homes using k-means clustering. It contains chapters that introduce the topic, provide an overview and motivation, describe two papers analyzing electricity consumption data through k-means clustering with silhouette scores to determine optimal cluster numbers, present results of clustering a full and 1/8 sized dataset, and conclude. The dissertation aims to optimize home electricity usage through k-means clustering and determine factors influencing overcharges or costs by analyzing household consumption data.Hyun wong thesis 2019 06_19_rev38_final
Hyun wong thesis 2019 06_19_rev38_finalHyun Wong Choi
油
This document summarizes a master's dissertation that analyzes electricity consumption at home through K-means clustering. The dissertation contains two papers:
1. The first paper analyzes household electricity usage data through K-means clustering to obtain optimal data points. It uses the Calinski-Harabasz Index and Silhouette_score to determine the optimal number of clusters.
2. The second paper performs a comparative analysis on a dataset that is 1/8 the size of the original. It finds that the Silhouette score is half of the original dataset, even with the smaller data.
The dissertation applies unsupervised machine learning clustering techniques to analyze household electricity consumption data, in order to optimize costs and identify factorsAirport security ver1
- 1. 螻牛 覲伎 蟯 覯 る瑚概螻 谿碁 覲伎 蟆 蠍一 語蟲螻牛螻旧 螻螳 螻螳 豕 1. 襦 磯Μ螳 煙朱 蟲伎 リ碓襴 蟲螳 企 伎 螻褐 螻牛企. 蠍襦覯 觜讀 螻 螳 蠍磯伎 螻牛 伎 觜襯願 讀螳螻 朱, 企れ 蠍 螻牛 豢企 螳豢 譴 蠍磯 ′ 襦 讌螻 .[1]. 蠏碁 螻牛 豢企 螳豢 豢 豈朱 蟆曙朱 覲企 覦 . 磯殊, 襦 覦 覈 蠍讀 螻牛 螳 襴 觜るゼ 螻牛 覦 覈 . 螻牛 覦 螻殊 危ｴ覲企 (蠏碁1)螻 螳 螻牛 1.0朱覿 螻牛 3.0 蟇一 螻牛 4.0朱 觜 れ 覲螻 .[2] 螻牛 1.0 誤 蟲豢 牛 螻 觜るゼ, 螻牛 2.0 れ螻 觜るゼ 牛 螳 螳 觜るゼ , 螻牛 3,0 螻 螳譴 蠏殊 牛 郁 螳 襷豢ろ 觜るゼ 螻牛螻, 螻牛 4.0 襦 ICT 蠍 覈 螻螳 讌レ ｸ旧 螻牛 觜 襦 覦螻 . 讀, 螻牛 4.0 4谿 一覈 旧 蠍一れ る 螻牛(Smart Airport) 朱 覦螻 る 蟆企, 覈 觜 讌レ 螻牛 誤殊 螻旧 譴 螳譴 朱 觜 れ 覲螻 . 覓語 螳蟲 螻牛襷 朱 螻牛 る誤襯 碁レ 蠍一語願 . る瑚概 觜れ 覯 企, 磯 企, 伎企 螻牛 覿朱ゼ 讌襷, 螳 覿覿 讌 蟯 磯碁 企螳 螳 譴 螻る . 磯碁 企 螳 螻牛 谿 觜蠍一 麹 螳螻 觜 蠍一 企れ 螻牛 蟾讌襯 . 螻 螻牛 豢 覦 螻牛 蠍, 豌危, 覓 , 豢蟲 , 覲伎蟆, , 豢蟲, 觜蠍 , 谿, 覓 豬 煙 襦語るゼ 朱 螻 . [2],[3]. 磯碁 螻牛 襦語 譴 螳 螳 覿ｸ 危螻, 覿 螳 螳 螻殊 覲伎蟆螻殊 .[4] (蠏碁 2) 螻牛 豢蟲 谿れ 螳 朱 蠍 螳螻 覿 螳 譟一 襭企.[2] 磯殊, 覲伎 蟆 螻殊 る誤螳 譴 蟆 覿螳螻 螻 れ 豈, 蠍一 れ 螻 朱 讌 螻 . 豕蠏 企ゼ 蠍一朱 蠏豪概蠍 谿碁 螻牛 覲 蟆 蠍一 殊螳 覦 讌螻 ,螳 ｸ 螻, 螳 殊企 覲危, 覲伎蟆 螳 豢, 覲伎 蟆 語 る 豕 煙 覈朱 螻 . 谿碁 螻牛 覲伎 蟆 ろ 譴 襦 螳 一る所 覲危覃伎 磯 糾骸覃 螳 讌 螻 企 覓殊 朱 蟆 譯朱 Walk Through(WT) 覲伎 蟆 蠍一 襦蟆 螻 .[5],[6] 覲 螻 螻牛 覦 覦レ 危ｴ覲願, 螻 4.0朱 覦螻 る 螻牛 螳 覦 讌, 蠏碁Μ螻 る 螻牛 ICT 蠍一 覲願 蠏 る瑚概 譴煙 覿螳螻 谿碁 覲伎 蟆 炎骸 譴煙 る 螻, 襷讌襷朱 WT る 覲伎蟆ろ る. 2. る瑚概 螳 覦 轟 1. る瑚概 螳 2016 2 ENISA 覦 襭 覃 襷瑚概 覓殊誤磯(IoT) 蠍一 蠍磯 螳豌襴 螻殊 螳螻 襦 蠍磯レ 螻牛蠍 企 郁骸 郁屋 ICT 蟆曙 蟲豢 覦 豕 覃 襦語るゼ 螻牛企手 螻 .[7]. 讀 る 螻牛 AI, 觜一危, IoT, 襦覺 豌伎 訖襷 螻牛 伎 ｸ煙 企 覦 譴 螻 る 蟆朱 危危 . 豕蠏 蟲伎 螻牛れ 伎螳 蠍讀覃伎 る 螻牛 豢讌 覦 讌螻 . 譴讌 螻牛朱 覦 蟲螻牛 磯碁 , 豢 蟲 螳 豢蠍 40Gbps smartgate襯 蟲磯碁 T1, T2 . Smart Gate 豌伎 覲 覦 Smart Gate ID 豺企 煙 伎 , 豢蟲 襦語るゼ 螻 企ゼ 牛 企 襦 語れ 螳 10豐 伎碁 豢る 蟆郁骸襯 覦. [8], 譴蟲 覓伎 覦 煙レ朱 蟲 伎 蠍讀 螻 螳 磯 る 螻牛 蠍一 螳覦 覦 蠏轟朱 豢讌螻 朱, 伎 殊螻牛 る瑚概 2.0 伎語磯 蟯 襭 螳覦螻, 朱 豈螻 朱, CEBOT
- 2. 2018 觜一危, 瑚概讌, IoT 蠍一 蠍磯朱 弰 覦.[9] 蟲伎 蟆曙, 蟲蟲給 2017 9 る 螻牛 譬螻 覦朱, 2022 蟾讌 る 螻牛 蟲 觜, 覈 る 蠏, 蟲旧 6螳 覦 伎 磯ジ 豢讌 螻殊襯 <1>螻 螳 螻 豢讌譴企.[10] 語蟲螻旧 2023 蟾讌 語蟲螻牛 る 瑚概 ろ SIMPLE, EASY, ON-TIME, ENJOY襯 れ襦 100螳 譯殊 れ 覦レ 螻 豢讌螻 . 麹 螳 蟯 螻牛 襦語れ 覦 螳誤襯 豌危, 企 蠍磯 豈覺(Chat-bot), 螻牛 襦覺, 襷豢ろ 覃伎語狩, 覓 豢覲 螻 煙 螻牛 螻企.[11] 2. る 螻牛 轟 螻 伎 螳れ (蠏碁 3)螻 螳 蟆′覿 譯殊姶, 螻牛 谿伎 覓殊 螻, 豌危 , 覲伎 蟆襯 糾骸 豢蟲 襯 螻, 覃伎 蟲 煙 蠍壱蟆 . 觜蠍 覈讌 谿 覓殊 豬螻, 蟠, 觜 煙 誤 覃 蟲 襯 蟇一 螻牛 郁 蟲牛ｸ 煙 豕譬 覈讌蟾讌 螳 螻 螳 襦語れるゼ 蟇 豺蟆 . 蠏蟲 企 襦語れ 朱 .[12] る瑚概 覈 襦語るゼ 螳蟆 螻牛 螳螻 螻糾 伎 ｸ 蟆渚 螻 蟆 朱 螻 蠍 覓語 れ 觜り 轟炎骸 覲危ｸ煙 螳讌 轟 . 蟲旧郁規(ETRI) 螳 螻牛 伎 襦語る 讀 覦 伎 蟲 觜るゼ 2018 朱 れ 朱 豸 覃伎 牛 < 2> 螳 譟一朱, 蟆郁骸 覃 覲伎蟆, 豌危, 覓 豺覲 觜 煙 讀 觜れ 螳 蟆 蟲螻, 蠏 襷 譴煙 る 蟆 . [13]