28 research outputs found

    Development of immunodiagnostic test for paragonimiasis using a recombinant protein

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    ์˜ํ•™๊ณผ/๋ฐ•์‚ฌ[ํ•œ๊ธ€] ํํก์ถฉ ๊ฐ์—ผ์— ๋Œ€ํ•œ ์ง„๋‹จ์€ ๊ธฐ์ƒ์ถฉํ•™์  ๊ฒ€์‚ฌ๋ฒ•์™ธ์— ๋ฉด์—ญ์ง„๋‹จ๋ฒ•์ด ์œ ์šฉํ•˜๋‹ค. ํํก์ถฉ ํ•ญ์› ์ค‘ ๊ต์ฐจ๋ฐ˜์‘์ด ๋งค์šฐ ์ ์œผ๋ฉฐ ๋ฏผ๊ฐ๋„๊ฐ€ ๋†’์€ ์ƒˆ๋กœ์šด ํ•ญ์›์„ ์ฐพ๊ณ , ์ด์— ๋Œ€ํ•œ ์žฌ์กฐํ•ฉ ๋‹จ๋ฐฑ์งˆ์„ ์ œ์กฐํ•˜์—ฌ ํšจ์†Œ๋ฉด์—ญ์ง„๋‹จ๋ฒ•์— ์ ์šฉํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ํํก์ถฉ cDNA library๋ฅผ immunoscreeningํ•˜์—ฌ ํํก์ถฉ ์–‘์„ฑ ํ˜ˆ์ฒญ๊ณผ ๊ฐ•ํ•˜๊ฒŒ ๋ฐ˜์‘ํ•˜๋Š” ํ•ญ์›์„ฑ์ด ๋†’์€ ํด๋ก  6๊ฐœ ์ค‘ 2๊ฐœ ํด๋ก ์ด ์ค‘์š”ํ•œ ์‚ฌ๋žŒ ๊ธฐ์ƒ ํก์ถฉ์ธ ๋งŒ์†์ฃผํ˜ˆํก์ถฉ (Schistosoma mansoni, GenBank accession number A54521)๊ณผ ์ผ๋ณธ์ฃผํ˜ˆํก์ถฉ (S. japonicum, AAW25328)์˜ 40k major egg antigen๊ณผ 59% ์™€ 63%์˜ ์œ ์‚ฌ์„ฑ์„ ๊ฐ€์ง€๋Š” ์œ ์ „์ž์ด์—ˆ๊ณ , ์ด๋ฅผ ํํก์ถฉ major egg antigen๋กœ ๋ช…๋ช…ํ•˜์˜€๋‹ค. ํํก์ถฉ major egg antigen (GenBank accession number AY526873) ์œ ์ „์ž๋Š” 322์˜ ์•„๋ฏธ๋…ธ์‚ฐ์„ ์•”ํ˜ธํ•˜๋Š” 966 bp์˜ open reading frame์„ ๊ฐ€์ง€๊ณ  ์žˆ์—ˆ์œผ๋ฉฐ, ๋ถ„์ž๋Ÿ‰์€ 37,276.82 Da์ด์˜€๋‹ค. ๊ฐ„์ ‘ํ˜•๊ด‘ํ•ญ์ฒด๋ฒ•์„ ์ˆ˜ํ–‰ํ•˜์—ฌ ํํก์ถฉ ์„ฑ์ถฉ ๋‚ด major egg antigen์˜ ๋ถ„ํฌ๋ฅผ ์•Œ์•„๋ณธ ๊ฒฐ๊ณผ ์ถฉ๋ž€์—๋งŒ ๋ถ„ํฌํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. Major egg antigen ์œ ์ „์ž๋ฅผ ์žฌ์กฐํ•ฉ ๋‹จ๋ฐฑ์งˆ๋กœ ๊ณผ๋ฐœํ˜„์„ ์œ ๋„ํ•œ ํ›„ Ni-์นœํ™”์„ฑ ํฌ๋กœ๋งˆํ† ๊ทธ๋ผํ”ผ๋ฅผ ์ˆ˜ํ–‰ํ•˜์—ฌ 2.03 ใŽŽ (1.015 ใŽŽ/ใŽ–)์˜ ์žฌ์กฐํ•ฉ ๋‹จ๋ฐฑ์งˆ์„ ๋ถ„๋ฆฌํ•˜์˜€๋‹ค.ํํก์ถฉ ์žฌ์กฐํ•ฉ major egg antigen์˜ ํ•ญ์›์„ฑ๊ณผ ๊ต์ฐจ๋ฐ˜์‘์„ฑ์„ ์•Œ์•„๋ณด๊ธฐ ์œ„ํ•ด immunoblotting์„ ์ˆ˜ํ–‰ํ•œ ๊ฒฐ๊ณผ, 20๋ช…์˜ ํํก์ถฉ ์–‘์„ฑ ํ˜ˆ์ฒญ ์ค‘ 17๋ช…์ธ 85%๊ฐ€ ์žฌ์กฐํ•ฉ major egg antigen์— ๋Œ€ํ•œ ์–‘์„ฑ band๋ฅผ ๋ณด์ธ ๋ฐ˜๋ฉด, 20๋ช…์˜ ๊ฐ„ํก์ถฉ ์–‘์„ฑ ํ˜ˆ์ฒญ ๋ฐ 3๋ช…์˜ ์ •์ƒ ํ˜ˆ์ฒญ ๋ชจ๋‘ ์Œ์„ฑ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋˜ํ•œ ํํก์ถฉ์ฆ ํ˜ˆ์ฒญํ•™์  ์ง„๋‹จ์—์˜ ํŠน์ด๋„์™€ ๋ฏผ๊ฐ๋„๋ฅผ ์•Œ์•„๋ณด๊ธฐ ์œ„ํ•ด, ํํก์ถฉ ์–‘์„ฑ ํ˜ˆ์ฒญ(n=41), ๊ฐ„ํก์ถฉ ์–‘์„ฑ ํ˜ˆ์ฒญ(32), ์š”์ฝ”๊ฐ€์™€ํก์ถฉ ์–‘์„ฑ ํ˜ˆ์ฒญ(17), ์œ ๊ตฌ๋‚ญ๋ฏธ์ถฉ์ฆ ์–‘์„ฑ ํ˜ˆ์ฒญ (38), ๊ณ ์ถฉ์ฆ ์–‘์„ฑ ํ˜ˆ์ฒญ (23)๊ณผ ์ •์ƒ ํ˜ˆ์ฒญ (30)์œผ๋กœ ํšจ์†Œ๋ฉด์—ญ์ง„๋‹จ๋ฒ•์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. 30๋ช…์˜ ์ •์ƒ ํ˜ˆ์ฒญ์˜ ํก๊ด‘๋„๋ฅผ ํ† ๋Œ€๋กœ ์–ป์€ ์–‘์„ฑ ํŒ์ •์„ ์œ„ํ•œ cut-off ์ˆ˜์น˜๋Š” 0.24 (ํ‰๊ท  + 10 SD)์˜€์œผ๋ฉฐ, 41 ๋ช…์˜ ํํก์ถฉ ์–‘์„ฑ ํ˜ˆ์ฒญ ์ค‘, 37 ํ˜ˆ์ฒญ์ด ์–‘์„ฑ์ด์—ˆ๋‹ค(90.2%์˜ ๋ฏผ๊ฐ๋„). ๊ทธ ์™ธ ๊ฐ„ํก์ถฉ ์–‘์„ฑ ํ˜ˆ์ฒญ(32), ์š”์ฝ”๊ฐ€์™€ํก์ถฉ ์–‘์„ฑ ํ˜ˆ์ฒญ(17), ์œ ๊ตฌ๋‚ญ๋ฏธ์ถฉ์ฆ ์–‘์„ฑ ํ˜ˆ์ฒญ (38), ๊ณ ์ถฉ์ฆ ์–‘์„ฑ ํ˜ˆ์ฒญ (23)๋“ค์€ ๋ชจ๋‘ ์Œ์„ฑ์ด์—ˆ๋‹ค(100%์˜ ํŠน์ด๋„). ์ด ๊ฒฐ๊ณผ๋Š” ์žฌ์กฐํ•ฉ major egg antigen์„ ์ด์šฉํ•œ ํšจ์†Œ๋ฉด์—ญ์ง„๋‹จ๋ฒ•์ด ๋†’์€ ํŠน์ด๋„ ๋ฐ ๋ฏผ๊ฐ๋„๋กœ ํํก์ถฉ์ฆ ๋ฉด์—ญ ์ง„๋‹จ์— ๋งค์šฐ ์œ ์šฉํ•  ๊ฒƒ์œผ๋กœ ์‚ฌ๋ฃŒ๋œ๋‹ค. [์˜๋ฌธ]Diagnosis of paragonimiasis can be done by immunological technique as well as parasitological exam. A recombinant protein of a Paragonimus westermani major egg antigen was produced and tested as an antigen for the serologic diagnosis of P. westermani infection. The P. westermani major egg antigen gene contains a single open reading frame of 966 base pairs encoding 322 amino acids from 5'' methionine to the 3'' stop codon. The predicted amino acid sequence this major egg antigen was 59 and was 63% identical to 40k major egg antigen from Schistosoma japonicum and Schistosoma mansoni, respectively. Western blot analysis showed that sera from patients infected with P. westermani strongly reacted with the recombinant protein. The distribution of this antigen was investigated in adult worms by indirect immunofluorescence assay, and found to be distributed in eggs.The specificity and sensitivity of the recombinant antigen were assessed by enzyme-linked immunosorbent assay (ELISA) using sera from patients infected with different parasites, which included 41 patients with paragonimiasis, and negative controls. The sensitivity of ELISA using the recombinant antigen was 90.2%, and its specificity 100%. Our results suggest that the developed recombinant major egg antigen based ELISA offers a highly sensitive and specific assay for the diagnosis of paragonimiasis.ope

    The direction of development of car forensics and How to analyze extracted data

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์œตํ•ฉ๊ณผํ•™๊ธฐ์ˆ ๋Œ€ํ•™์› ์ˆ˜๋ฆฌ์ •๋ณด๊ณผํ•™๊ณผ, 2022.2. ์ด๋ณ‘์˜.As automobiles are electrical powered and developed in the direction of self-driving, the role of computers is becoming important. These smart cars have become common technologies with the development of innovative automobile technology and computer technology. This means that new approaches and research on automobiles are needed from a forensic point of view. In the past, car forensics consisted of physical forensics that measure the length of the skid mark in case of a vehicle accident and calculate the speed, analyze the destination or route of the navigation, and analyze videos recorded in the black box. This was not a forensics of the vehicle itself, but rather an investigation of external mounting devices or surrounding environments. In addition, there was a problem in securing reliability because the acquired data was inaccurate or cross-validation was not possible. Current automobile forensics have already developed to the point of analyzing EDR data installed inside cars, connecting acquisition devices to OBD terminals, and analyzing smartphone-linked infotainment systems such as Android Auto and Apple CarPlay. However, it is also not legally mandatory to install devices and systems for acquisition and analysis, making it difficult to obtain data. In addition, there is very little data stored inside the vehicle, and there is a limitation in that data is stored in external devices such as smartphones, so evidence must be obtained through separate mobile forensics. However, in the case of electric vehicles with partial autonomous driving functions, which have been released and are increasing sales, the structure of the vehicle is expected to be very developed, enabling the application of a new forensic method. The characteristic of these cars is that first, as numerous additional sensors are installed for self-driving and data generated here are accumulating, the number of information that can be collected is increasing. Second, the automobile ECU is integrated, equipped with a centralized computer, and an integrated operating system is installed and operated. This has the advantage that automobiles can be computerized and standardized for forensics. Finally, by having OTA and V2X functions, data can be transmitted and received wirelessly by being connected to the outside, which has the advantage of enabling cross-validation of the generated data. In addition, in the case of self-driving cars, it is legally forced to have a device to store vehicle driving data, and the type and storage method of collected data are stipulated. Commercially, these data are expected to generate profits through automobile design and insurance premium calculation, which is an incentive to store and acquire data. The accumulated data may be stored in a memory inside the vehicle or stored in a smartphone connected thereto or in a server of a manufacturer. The problem is that the capacity of the data is too vast. In mobile forensics, as the storage capacity of smartphones increases, it takes a considerable amount of time to obtain, select, and analyze data. In the case of data collected in automobiles, the capacity is expected to be much larger than that of mobile, so it is necessary to change the data analysis method. Therefore, in this paper, propose the introduction of FOQA (Flight Operations Quality Assurance), an accident investigation and maintenance program for aircraft that has already been used for a long time and has secured reliability and accuracy. By visualizing the acquired data in 3D simulation, in the case of an accident investigation, the handle position, transmission position, accelerator pedal position, etc. can be reproduced as the situation at the time of the accident to increase the accuracy of judgment. In the case of general evidence collection, the driver's biometric information, passenger information, destination information, and payment information are collected and reproduced through the vehicle's internal camera and infotainment information, enabling mobile forensics and cross-validation as well as independent evidence. By adding an automatic analysis function, forensic investigators can grasp the overall matters necessary for the investigation, such as vehicle and driver information, at a glance. In addition, data on rapid acceleration, rapid deceleration, drowsy driving, etc. will be standardized and registered as event code to indicate event code, occurrence point, and end point in the program, reducing data analysis time and improving accuracy.์ž๋™์ฐจ๊ฐ€ ์ „๋™ํ™” ๋˜๊ณ , ์Šค์Šค๋กœ ์šด์ „ํ•˜๋Š” ๋ฐฉํ–ฅ์œผ๋กœ ๋ฐœ์ „ํ•˜๋ฉฐ ์ปดํ“จํ„ฐ์˜ ์—ญํ• ์ด ์ค‘์š”ํ•ด์ง€๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ์Šค๋งˆํŠธ์นด๋“ค์€ ํ˜์‹ ์  ์ž๋™์ฐจ ๊ธฐ์ˆ  ๋ฐœ์ „ ๋ฐ ์ปดํ“จํ„ฐ ๊ธฐ์ˆ  ๋ฐœ์ „๊ณผ ๋”๋ถˆ์–ด ๋ณดํŽธํ™”๋œ ๊ธฐ์ˆ ์ด ๋˜์—ˆ๋‹ค. ์ด๋Š” ๊ณง ํฌ๋ Œ์‹ ๊ด€์ ์—์„œ ์ž๋™์ฐจ์— ๋Œ€ํ•œ ์ƒˆ๋กœ์šด ์ ‘๊ทผ ๋ฐ ์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•˜๋‹ค๋Š” ๊ฒƒ์„ ์˜๋ฏธํ•œ๋‹ค. ๊ณผ๊ฑฐ์˜ ์ž๋™์ฐจ ํฌ๋ Œ์‹์€ ์ฐจ๋Ÿ‰ ์‚ฌ๊ณ  ์‹œ ์Šคํ‚ค๋“œ ๋งˆํฌ์˜ ๊ธธ์ด๋ฅผ ์ธก์ •ํ•˜์—ฌ ์†๋„๋ฅผ ๊ณ„์‚ฐํ•˜๋Š” ๋ฌผ๋ฆฌ์  ํฌ๋ Œ์‹, ๊ทธ๋ฆฌ๊ณ  ๋„ค๋น„๊ฒŒ์ด์…˜์˜ ๋ชฉ์ ์ง€๋‚˜ ๊ฒฝ๋กœ๋ฅผ ๋ถ„์„ํ•˜๊ณ , ๋ธ”๋ž™๋ฐ•์Šค์— ๊ธฐ๋ก๋œ ๋™์˜์ƒ์„ ๋ถ„์„ํ•˜๋Š” ๋“ฑ์˜ ํฌ๋ Œ์‹์ด ์ด๋ฃจ์–ด์กŒ๋‹ค. ์ด๋Š” ์ฐจ๋Ÿ‰ ์ž์ฒด์— ๋Œ€ํ•œ ํฌ๋ Œ์‹์ด๋ผ๊ธฐ ๋ณด๋‹ค๋Š” ์™ธ๋ถ€ ์žฅ์ฐฉ๊ธฐ๊ธฐ ๋˜๋Š” ์ฃผ๋ณ€ ํ™˜๊ฒฝ์— ๋Œ€ํ•œ ์กฐ์‚ฌ์˜€๋‹ค. ๋˜ํ•œ ํš๋“ ๋ฐ์ดํ„ฐ๊ฐ€ ๋ถ€์ •ํ™• ํ•˜๊ฑฐ๋‚˜ ๊ต์ฐจ ๊ฒ€์ฆ์ด ๋ถˆ๊ฐ€๋Šฅํ•˜์—ฌ ์‹ ๋ขฐ์„ฑ ํ™•๋ณด ์ฐจ์›์—๋„ ๋ฌธ์ œ๊ฐ€ ์žˆ์—ˆ๋‹ค. ํ˜„์žฌ์˜ ์ž๋™์ฐจ ํฌ๋ Œ์‹์€ ์ž๋™์ฐจ ๋‚ด๋ถ€์— ์žฅ์ฐฉ๋œ EDR ์ž๋ฃŒ ๋ถ„์„ ๋˜๋Š” OBD ๋‹จ์ž์— ํš๋“ ๊ธฐ๊ธฐ๋ฅผ ์—ฐ๊ฒฐํ•œ ๋ถ„์„, ์•ˆ๋“œ๋กœ์ด๋“œ ์˜คํ† ์™€ ์• ํ”Œ ์นดํ”Œ๋ ˆ์ด ๊ฐ™์€ ์Šค๋งˆํŠธํฐ ์—ฐ๋™ ์ธํฌํ…Œ์ธ๋จผํŠธ ์‹œ์Šคํ…œ ๋ถ„์„์— ์ด๋ฅผ ์ •๋„๋กœ ๋ฐœ์ „๋˜์—ˆ๋‹ค. ํ•˜์ง€๋งŒ ์ด ์—ญ์‹œ๋„ ํš๋“ ๋ฐ ๋ถ„์„์„ ์œ„ํ•œ ์žฅ์น˜์™€ ์‹œ์Šคํ…œ ์„ค์น˜๊ฐ€ ๋ฒ•์ ์œผ๋กœ ์˜๋ฌด ์‚ฌํ•ญ์ด ์•„๋‹ˆ๋ผ, ๋ฐ์ดํ„ฐ ํš๋“์ด ์ˆ˜์›”ํ•˜์ง€ ์•Š๋‹ค. ๋˜ํ•œ ์ฐจ๋Ÿ‰ ๋‚ด๋ถ€์— ์ €์žฅ๋˜๋Š” ๋ฐ์ดํ„ฐ๊ฐ€ ๋งค์šฐ ์ ์„ ๋ฟ๋”๋Ÿฌ ์Šค๋งˆํŠธํฐ ๊ฐ™์€ ์™ธ๋ถ€ ๊ธฐ๊ธฐ์— ๋ฐ์ดํ„ฐ๊ฐ€ ์ €์žฅ๋˜์–ด ๋ณ„๋„์˜ ๋ชจ๋ฐ”์ผ ํฌ๋ Œ์‹ ๋“ฑ์„ ํ†ตํ•ด ์ฆ๊ฑฐ๋ฅผ ํš๋“ํ•ด์•ผ ํ•˜๋Š” ํ•œ๊ณ„์ ์ด ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ์ง€๊ธˆ ์ถœ์‹œ๋˜์–ด ํŒ๋งค๊ฐ€ ๋Š˜๊ณ  ์žˆ๋Š” ๋ถ€๋ถ„์ž์œจ์ฃผํ–‰๊ธฐ๋Šฅ์„ ๊ฐ–์ถ˜ ์ „๊ธฐ์ฐจ๋“ค์˜ ๊ฒฝ์šฐ ์ฐจ๋Ÿ‰์˜ ๊ตฌ์กฐ๊ฐ€ ๋งค์šฐ ๋ฐœ์ „๋˜์–ด ์ƒˆ๋กœ์šด ํฌ๋ Œ์‹ ๋ฐฉ๋ฒ•์˜ ์ ์šฉ์ด ๊ฐ€๋Šฅํ•ด์งˆ ๊ฒƒ์œผ๋กœ ๋ณด๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ž๋™์ฐจ๋“ค์˜ ํŠน์ง•์€ ์ฒซ์งธ ์ž์œจ์ฃผํ–‰์„ ์œ„ํ•ด ์ˆ˜๋งŽ์€ ์„ผ์„œ๊ฐ€ ์ถ”๊ฐ€ ํƒ‘์žฌ๋˜๊ณ  ์—ฌ๊ธฐ์„œ ์ƒ์„ฑ๋˜๋Š” ๋ฐ์ดํ„ฐ๋“ค์ด ์ถ•์ ๋˜๊ณ  ์žˆ์–ด ์ˆ˜์ง‘ํ•  ์ˆ˜ ์žˆ๋Š” ์ •๋ณด๋“ค์ด ๋Š˜์–ด๋‚˜๊ณ  ์žˆ๋‹ค. ๋‘˜์งธ ์ž๋™์ฐจ ECU๊ฐ€ ํ†ตํ•ฉ๋˜์–ด ์ค‘์•™ ์ง‘์ค‘์‹ ์ปดํ“จํ„ฐ๊ฐ€ ํƒ‘์žฌ๋˜๊ณ  ํ†ตํ•ฉ ์šด์˜์ฒด์ œ๊ฐ€ ์„ค์น˜๋˜์–ด ์šด์˜๋œ๋‹ค. ์ด๋Š” ์ž๋™์ฐจ๊ฐ€ ์ปดํ“จํ„ฐํ™” ๋˜์–ด ํฌ๋ Œ์‹์„ ์œ„ํ•œ ํ‘œ์ค€ํ™”๊ฐ€ ๊ฐ€๋Šฅํ•˜๋‹ค๋Š” ์žฅ์ ์„ ๊ฐ€์ง„๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ OTA, V2X ๊ธฐ๋Šฅ๋“ค์„ ๊ฐ–์ถค์œผ๋กœ์จ ์™ธ๋ถ€์™€ ์—ฐ๊ฒฐ๋˜์–ด ๋ฌด์„ ์œผ๋กœ ๋ฐ์ดํ„ฐ์˜ ์†ก์ˆ˜์‹ ์ด ๊ฐ€๋Šฅํ•ด์ง€๊ณ  ์ด๋Š” ์ƒ์„ฑ๋œ ๋ฐ์ดํ„ฐ๋“ค์— ๋Œ€ํ•œ ๊ต์ฐจ ๊ฒ€์ฆ์ด ๊ฐ€๋Šฅํ•ด์ง„๋‹ค๋Š” ์žฅ์ ์„ ๊ฐ€์ง„๋‹ค. ๋˜ํ•œ ์ž์œจ์ฃผํ–‰ ์ž๋™์ฐจ์˜ ๊ฒฝ์šฐ ๋ฒ•์ ์œผ๋กœ ์ž๋™์ฐจ ์šดํ–‰ ๋ฐ์ดํ„ฐ๋ฅผ ์ €์žฅํ•˜๋Š” ์žฅ์น˜๋ฅผ ๊ฐ–์ถ”๋„๋ก ๊ฐ•์ œํ•˜๊ณ  ์ˆ˜์ง‘ ๋ฐ์ดํ„ฐ์˜ ์ข…๋ฅ˜์™€ ์ €์žฅ ๋ฐฉ์‹ ๋“ฑ์„ ๊ทœ์ •ํ•˜๊ณ  ์žˆ๋‹ค. ์ƒ์—…์ ์œผ๋กœ๋„ ์ด๋Ÿฐ ๋ฐ์ดํ„ฐ๋“ค์„ ํ†ตํ•ด ์ž๋™์ฐจ ์„ค๊ณ„, ๋ณดํ—˜๋ฃŒ ๊ณ„์‚ฐ ๋“ฑ์˜ ๋ฐฉ์‹์œผ๋กœ ์ˆ˜์ต์ด ์ฐฝ์ถœ๋  ๊ฒƒ์œผ๋กœ ๋ณด์—ฌ ๋ฐ์ดํ„ฐ์˜ ์ €์žฅ ๋ฐ ํš๋“์— ์œ ์ธ์ด ๋˜๊ณ  ์žˆ๋‹ค. ์ถ•์ ๋œ ๋ฐ์ดํ„ฐ๋Š” ์ž๋™์ฐจ ๋‚ด๋ถ€์˜ ๋ฉ”๋ชจ๋ฆฌ์— ์ €์žฅ๋˜๊ฑฐ๋‚˜ ์—ฐ๊ฒฐ๋œ ์Šค๋งˆํŠธํฐ ๋˜๋Š” ์ œ์ž‘์‚ฌ์˜ ์„œ๋ฒ„์— ์ €์žฅ๋  ๊ฒƒ์ด๋‹ค. ๋ฌธ์ œ์ ์€ ๋ฐ์ดํ„ฐ์˜ ์šฉ๋Ÿ‰์ด ๋„ˆ๋ฌด ๋ฐฉ๋Œ€ํ•˜๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. ๋ชจ๋ฐ”์ผ ํฌ๋ Œ์‹์—์„œ๋„ ์Šค๋งˆํŠธํฐ์˜ ์ €์žฅ ์šฉ๋Ÿ‰์ด ์ปค์ง€๋ฉด์„œ ๋ฐ์ดํ„ฐ์˜ ํš๋“ ๋ฐ ์„ ๋ณ„, ๋ถ„์„์— ์ƒ๋‹นํ•œ ์‹œ๊ฐ„์ด ์†Œ์š”๋˜๋Š” ์–ด๋ ค์›€์ด ๋ฐœ์ƒํ•˜๊ณ  ์žˆ๋‹ค. ์ž๋™์ฐจ์— ์ˆ˜์ง‘๋˜๋Š” ๋ฐ์ดํ„ฐ์˜ ๊ฒฝ์šฐ ๋ชจ๋ฐ”์ผ๋ณด๋‹ค ์šฉ๋Ÿ‰์ด ํ›จ์”ฌ ํด ๊ฒƒ์œผ๋กœ ์˜ˆ์ƒ๋˜์–ด ๋ฐ์ดํ„ฐ ๋ถ„์„ ๋ฐฉ๋ฒ•์˜ ๋ณ€ํ™”๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ๋”ฐ๋ผ์„œ ์ด ๋…ผ๋ฌธ์—์„œ๋Š” ์ด๋ฏธ ์˜ค๋žœ ๊ธฐ๊ฐ„ ์‚ฌ์šฉ๋˜์–ด ์‹ ๋ขฐ์„ฑ๊ณผ ์ •ํ™•๋„๊ฐ€ ํ™•๋ณด๋œ ํ•ญ๊ณต๊ธฐ์˜ ์‚ฌ๊ณ ์กฐ์‚ฌ ๋ฐ ์œ ์ง€๋ณด์ˆ˜ ํ”„๋กœ๊ทธ๋žจ์ธ FOQA(Flight Operations Quality Assurance) ๋„์ž…์„ ์ œ์•ˆํ•œ๋‹ค. ํš๋“ํ•œ ๋ฐ์ดํ„ฐ๋ฅผ 3D ์‹œ๋ฎฌ๋ ˆ์ด์…˜์œผ๋กœ ์‹œ๊ฐํ™”ํ•จ์œผ๋กœ์จ ์‚ฌ๊ณ  ์กฐ์‚ฌ์˜ ๊ฒฝ์šฐ ํ•ธ๋“ค์œ„์น˜, ๋ณ€์†๊ธฐ ์œ„์น˜, ๊ฐ€์†ํŽ˜๋‹ฌ ์œ„์น˜ ๋“ฑ์„ ์‚ฌ๊ณ  ๋‹น์‹œ ์ƒํ™ฉ์œผ๋กœ ์žฌํ˜„ํ•˜์—ฌ ํŒ๋‹จ์˜ ์ •ํ™•๋„๋ฅผ ๋†’์ผ ์ˆ˜ ์žˆ๋‹ค. ์ผ๋ฐ˜ ์ฆ๊ฑฐ ์ˆ˜์ง‘์˜ ๊ฒฝ์šฐ์—๋„ ์ฐจ๋Ÿ‰ ๋‚ด๋ถ€ ์นด๋ฉ”๋ผ ๋ฐ ์ธํฌํ…Œ์ธ๋จผํŠธ ์ •๋ณด๋ฅผ ํ†ตํ•˜์—ฌ ์šด์ „์ž์˜ ์ƒ์ฒด์ •๋ณด, ๋™์Šน์ž์ •๋ณด, ๋ชฉ์ ์ง€์ •๋ณด, ๊ฒฐ์ œ์ •๋ณด ๋“ฑ์„ ์ˆ˜์ง‘, ์žฌํ˜„ํ•˜์—ฌ ๋…์ž์ ์ธ ์ฆ๊ฑฐ๋ฟ ์•„๋‹ˆ๋ผ ๋ชจ๋ฐ”์ผ ํฌ๋ Œ์‹๊ณผ ๊ต์ฐจ๊ฒ€์ฆ์ด ๊ฐ€๋Šฅํ•ด์ง„๋‹ค. ์ž๋™ ๋ถ„์„ ๊ธฐ๋Šฅ์„ ์ถ”๊ฐ€ํ•˜์—ฌ ํฌ๋ Œ์‹ ์ˆ˜์‚ฌ๊ด€์ด ํ•œ๋ˆˆ์— ์ฐจ๋Ÿ‰ ๋ฐ ์šด์ „์ž ์ •๋ณด ๋“ฑ ์ˆ˜์‚ฌ์— ํ•„์š”ํ•œ ์ „๋ฐ˜์ ์ธ ์‚ฌํ•ญ์˜ ํŒŒ์•…์ด ๊ฐ€๋Šฅํ•˜๋‹ค. ๋˜ํ•œ ๊ธ‰๊ฐ€์†, ๊ธ‰๊ฐ์†, ์กธ์Œ ์šด์ „, ๋˜๋Š” ์šด์ „์ž ์‹ ์ฒด ์ด์ƒ, ์ฐจ๋Ÿ‰ ๋‚ด ๊ฒฐ์ œ ๋“ฑ์ด ์ด๋ฃจ์–ด์กŒ์„ ๊ฒฝ์šฐ์— ๋Œ€ํ•œ ๋ฐ์ดํ„ฐ๋ฅผ ํ‘œ์ค€ํ™” ํ•˜๊ณ  ์ด๋ฅผ ์ด๋ฒคํŠธ ์ฝ”๋“œ๋กœ ๋“ฑ๋กํ•˜์—ฌ ํŠน์ •์ƒํ™ฉ์ด ๋ฐœ์ƒํ•  ๊ฒฝ์šฐ ํ”„๋กœ๊ทธ๋žจ์— ์ด๋ฒคํŠธ ์ฝ”๋“œ์™€ ๋ฐœ์ƒ ์‹œ์  ๋ฐ ์ข…์  ํ‘œ์‹œํ•จ์œผ๋กœ์จ ๊ทธ ๋ถ€๋ถ„์˜ ๋ฐ์ดํ„ฐ๋งŒ ์ƒ์„ธ ๋ถ„์„ํ•˜์—ฌ ๋ฐ์ดํ„ฐ ๋ถ„์„ ์‹œ๊ฐ„์„ ์ค„์ด๊ณ  ์ •ํ™•์„ฑ์„ ๋†’์ผ ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค.์ œ1์žฅ ์„œ๋ก  1 ์ œ2์žฅ ์‹œ๊ธฐ๋ณ„ ์ฐจ๋Ÿ‰ํฌ๋ Œ์‹ ๋ฐฉ๋ฒ• 3 ์ œ1์ ˆ ์•„๋‚ ๋กœ๊ทธ, ๋ฌผ๋ฆฌ์  ํฌ๋ Œ์‹ 3 ์ œ2์ ˆ ์™ธ๋ถ€์žฅ์ฐฉ๊ธฐ๊ธฐ ํฌ๋ Œ์‹ 4 1. ๋„ค๋น„๊ฒŒ์ด์…˜ 4 2. ๋ธ”๋ž™๋ฐ•์Šค 6 ์ œ3์ ˆ ๋‚ด๋ถ€์žฅ์ฐฉ๊ธฐ๊ธฐ ํฌ๋ Œ์‹ 8 1. EDR 8 2. OBD ์Šค์บ๋„ˆ ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜ 14 ์ œ4์ ˆ ์ฐจ๋Ÿ‰ ์ธํฌํ…Œ์ธ๋จผํŠธ ํฌ๋ Œ์‹ 20 1. ์ฐจ๋Ÿ‰ ์ธํฌํ…Œ์ธ๋จผํŠธ ์‹œ์Šคํ…œ 20 2. ์ฐจ๋Ÿ‰ ์ธํฌํ…Œ์ธ๋จผํŠธ ํฌ๋ Œ์‹ ์—ฐ๊ตฌ 23 3. ํŠน์ง•๊ณผ ํ•œ๊ณ„์  26 ์ œ3์žฅ ๋ถ„์•ผ๋ณ„ ๋ฐœ์ „๋ฐฉํ–ฅ 27 ์ œ1์ ˆ ํ•˜๋“œ์›จ์–ด 27 1. ์„ผ์„œ ๋“ฑ ์ •๋ณด์ˆ˜์ง‘ ์žฅ์น˜์˜ ์ฆ๊ฐ€ 27 2. ์ค‘์•™์ง‘์ค‘ํ™” ๋œ ์ œ์–ด 32 3. OTA 34 4. V2X 34 ์ œ2์ ˆ ์†Œํ”„ํŠธ์›จ์–ด 35 1. ์ „์žฅ๋ถ€ํ’ˆ ํ†ตํ•ฉ์ œ์–ด์šฉ OS 36 2. ์ธํฌํ…Œ์ธ๋จผํŠธ (Infortainment) ์†Œํ”„ํŠธ์›จ์–ด 38 ์ œ3์ ˆ ๋ฒ•์ ์ธ ์ธก๋ฉด 40 1. ํ˜„์žฌ ์ž๋™์ฐจ ๊ธฐ๋ก์žฅ์น˜ ๊ด€๋ จ ๋ฒ•๋ น 40 2. ์ž๋™์ฐจ ๊ธฐ๋ก์žฅ์น˜ ๊ด€๋ จ ๋ฒ•๋ น์˜ ๋ณ€ํ™” 44 3. ๋ฒ•์ ์ธ ์ œ๋„์˜ ๋ณด์™„ 44 ์ œ4์ ˆ ์ƒ์—…์  ์ธก๋ฉด 45 1. ์„ค๊ณ„ ๋ฐ ์œ ์ง€๋ณด์ˆ˜ 45 2. ๋ณดํ—˜์‚ฌ ํ™œ์šฉ 46 ์ œ4์žฅ ํฌ๋ Œ์‹ ๋ฐ์ดํ„ฐ์˜ ๋ถ„์„ 47 ์ œ1์ ˆ ์ˆ˜์ง‘ ๋ฐ์ดํ„ฐ ์ข…๋ฅ˜ 47 1. ECU ์ฐจ๋Ÿ‰ ์„ผ์„œ 47 2. ADAS ์ฐจ๋Ÿ‰ ์„ผ์„œ 15 3. ์ธํฌํ…Œ์ธ๋จผํŠธ์— ์ €์žฅ๋˜๋Š” ๋””์ง€ํ„ธ ์„ผ์„œ ๋ฐ์ดํ„ฐ 49 4. ์—ฐ๋ฝ์ฒ˜ 51 5. ๋„ค๋น„๊ฒŒ์ด์…˜ ์ •๋ณด ๋ฐ์ดํ„ฐ 52 6. ์ฐจ๋Ÿ‰ ์šดํ–‰ ๊ธฐ๋ก์‹œ์Šคํ…œ ๋กœ๊ทธ ๋ฐ์ดํ„ฐ 52 ์ œ2์ ˆ ๋ฐ์ดํ„ฐ ์ €์žฅ๋ฐฉ์‹ ๋ฐ ์ถ”์ถœ์ ˆ์ฐจ 54 1. ์ฐจ๋Ÿ‰ ์ฃผํ–‰์ •๋ณด 54 2. ์ž์œจ์ฃผํ–‰ ๊ด€๋ จ ์ •๋ณด 54 3. ์ธํฌํ…Œ์ธ๋จผํŠธ ์ •๋ณด 55 ์ œ3์ ˆ ๋ฐ์ดํ„ฐ๋ถ„์„ ๋ฐฉ๋ฒ• 55 1. FOQA 56 2. ์ž๋™๋ถ„์„ 59 3. ์ด๋ฒคํŠธ ์ฝ”๋“œ 60 ์ œ5์žฅ ๊ฒฐ๋ก  61์„

    General population's view on euthanasia

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    ์˜๊ณผํ•™์‚ฌ์—…๋‹จ/์„์‚ฌ[ํ•œ๊ธ€]์ตœ๊ทผ ์˜๋ฃŒ ๊ธฐ์ˆ ์ด ๋ฐœ๋‹ฌํ•˜๋ฉด์„œ ๋Œ€๋‘๋œ ์—ฌ๋Ÿฌ ์ƒ๋ช… ์œค๋ฆฌ์˜ ๋ฌธ์ œ์ ๋“ค ์ค‘ ํŠนํžˆ ์•ˆ๋ฝ์‚ฌ์— ๋Œ€ํ•œ ๊ด€์‹ฌ์ด ๋†’์•„์ง€๊ณ  ์žˆ๊ณ  ์ด์— ๋Œ€ํ•ด ์ ์ฐจ ๊ฐœ๋ฐฉ์ ์œผ๋กœ ์ƒ๊ฐํ•˜๋Š” ๊ฒƒ์ด ์ „์ฒด์ ์ธ ํ๋ฆ„์ด๋‹ค. ๋ฏธ๊ตญ, ์œ ๋Ÿฝ, ํ˜ธ์ฃผ ๋“ฑ์—์„œ๋Š” ์ด์— ๋Œ€ํ•œ ์—ฐ๊ตฌ ์ž๋ฃŒ๊ฐ€ ๊ตฌ์ฒด์ ์œผ๋กœ ๋‚˜์™€ ์žˆ๊ณ  ์‹ค์ œ๋กœ ์‹œํ–‰๋˜๊ณ  ์žˆ์œผ๋‚˜ ๊ตญ๋‚ด์—์„œ๋Š” ์•„์ง ์—ฐ๊ตฌ๊ฐ€ ๋ถ€์กฑํ•œ ์‹ค์ •์ด๋ฉฐ ํ˜„์žฌ๊นŒ์ง€ ๋ฐœํ‘œ๋œ ์—ฐ๊ตฌ๋“ค๋„ ์ฃผ๋กœ ์˜๋ฃŒ์ธ์„ ๋Œ€์ƒ์œผ๋กœ ํ•œ ๊ฒƒ๋“ค์ด ๋งŽ์•„ ์ผ๋ฐ˜์ธ๋“ค์˜ ์ธ์‹๋„๊ฐ€ ์–ด๋Š ์ •๋„์ธ์ง€์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ์—ฐ๊ตฌ๋ฐฉ๋ฒ•์€ 2000๋…„ 5์›”์—์„œ 7์›”๊นŒ์ง€ ์„œ์šธ์˜ ์ผ๊ฐœ ๊ตฌ์™€ ๊ฒฝ๊ธฐ๋„๋‚ด ํ•œ ์ง€์—ญ์˜ ๊ณ ๋“ฑํ•™์ƒ ์ด์ƒ ๋‚จ๋…€ 600๋ช…์„ ๋Œ€์ƒ์œผ๋กœ ์„ค๋ฌธ ์กฐ์‚ฌ๋ฅผ ์‹œํ–‰ํ•˜์˜€๋‹ค. ํšŒ์ˆ˜๋œ ์„ค๋ฌธ์ง€ ์ค‘ ๊ธฐ์žฌ๊ฐ€ ๋ฏธ๋น„๋œ ๊ฒƒ์„ ์ œ์™ธํ•œ 413๋ช…์ด ์ตœ์ข…๋ถ„์„์— ์‚ฌ์šฉ๋˜์—ˆ๋‹ค. ์ธ๊ตฌ์‚ฌํšŒํ•™์  ๋ณ€์ˆ˜์™€ ์•ˆ๋ฝ์‚ฌ์— ๋Œ€ํ•œ ์ธ์‹๋„์™€์˜ ๊ด€๊ณ„๋ฅผ SAS ํ†ต๊ณ„ ํ”„๋กœ๊ทธ๋žจ 6.12๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ๋ถ„์„ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ๊ฒฐ๊ณผ ์‘๋‹ต์ž ์ค‘ ์•ˆ๋ฝ์‚ฌ๋ฅผ ๋ฒ•์ œํ™”ํ•ด์•ผ ํ•œ๋‹ค๋Š” ์‚ฌ๋žŒ์€ 304๋ช…(73.6%)์ด์—ˆ๊ณ  ์•ˆ๋ฝ์‚ฌ์˜ ๋ฒ”์œ„๋ฅผ ๋Šฅ๋™์  ์•ˆ๋ฝ์‚ฌ๊นŒ์ง€ ํ—ˆ์šฉํ•œ๋‹ค๋Š” ์‚ฌ๋žŒ์ด 156๋ช…(37.8%), ์ˆ˜๋™์  ์•ˆ๋ฝ์‚ฌ๋ฅผ ํ—ˆ์šฉํ•œ๋‹ค๋Š” ์‚ฌ๋žŒ์ด 234๋ช…(56.6%)์ด์—ˆ๋‹ค. ์•ˆ๋ฝ์‚ฌ์˜ ๋Œ€์ƒ์ด ๋ณธ์ธ์ธ ๊ฒฝ์šฐ ๋Šฅ๋™์  ์•ˆ๋ฝ์‚ฌ๋Š” 35์„ธ ์ด์ƒ(P=0.001)์—์„œ ๋” ๋งŽ์ด ์‹œํ–‰ํ•œ๋‹ค๊ณ  ํ•˜์˜€๊ณ  ์ˆ˜๋™์  ์•ˆ๋ฝ์‚ฌ์˜ ๊ฒฝ์šฐ์—๋Š” ํ•™๋ ฅ์ด ๋‚ฎ์„์ˆ˜๋ก(P=0.046), ๊ฒฝ์ œ๋ ฅ์ด ๋‚ฎ์„์ˆ˜๋ก(P=0.040) ๋” ๋งŽ์ด ์‹œํ–‰ํ•˜๊ฒ ๋‹ค๊ณ  ํ•˜์˜€๊ณ , ์•ˆ๋ฝ์‚ฌ์˜ ๋Œ€์ƒ์ด ํƒ€์ธ์ผ ๋•Œ ๋Šฅ๋™์  ์•ˆ๋ฝ์‚ฌ์˜ ๊ฒฝ์šฐ์—๋Š” ๋‚จ์ž(P=0.001), 35์„ธ ์ด์ƒ (P=0.001), ๊ธฐํ˜ผ(P=0.002)์—์„œ ๋” ๋งŽ์ด ํ—ˆ์šฉํ•˜์˜€๊ณ  ์ˆ˜๋™์  ์•ˆ๋ฝ์‚ฌ์˜ ๊ฒฝ์šฐ์—๋Š” ์ง์—…(P=0.016)์— ๋”ฐ๋ผ ์œ ์˜ํ•œ ์ฐจ์ด๊ฐ€ ์žˆ์—ˆ๋‹ค. ์•ˆ๋ฝ์‚ฌ๊ฐ€ ํ•ฉ๋ฒ•ํ™”๋˜์—ˆ์„ ๊ฒฝ์šฐ ๋ณธ์ธ์—๊ฒŒ ๋Šฅ๋™์  ์•ˆ๋ฝ์‚ฌ๋ฅผ ์‹œํ–‰ํ•˜๊ฒ ๋‹ค๋Š” ๊ฒฝ์šฐ๋Š” 35์„ธ์ด์ƒ(P=0.001), ๊ธฐํ˜ผ(P=0.022)์—์„œ ๋งŽ์•˜๊ณ  ์ˆ˜๋™์  ์•ˆ๋ฝ์‚ฌ๋ฅผ ์‹œํ–‰ํ•˜๊ฒ ๋‹ค๋Š” ๊ฒฝ์šฐ๋„ ๋‚˜์ด(P=0.001), ๊ฒฐํ˜ผ์—ฌ๋ถ€(P=0.001)์™€ ๊ด€๋ จ์ด ์žˆ์—ˆ๋‹ค. ํƒ€์ธ์—๊ฒŒ ๋Šฅ๋™์  ์•ˆ๋ฝ์‚ฌ๋ฅผ ์‹œํ–‰ํ•˜๊ฒ ๋‹ค๊ณ  ํ•œ ๊ฒฝ์šฐ๋Š” ์„ฑ๋ณ„(P=0.004), ๊ฒฐํ˜ผ์—ฌ๋ถ€(P=0.001)์—์„œ, ์ˆ˜๋™์  ์•ˆ๋ฝ์‚ฌ๋ฅผ ์‹œํ–‰ํ•˜๊ฒ ๋‹ค๊ณ  ํ•œ ๊ฒฝ์šฐ๋Š” ๋‚˜์ด(P=0.002), ๊ฒฐํ˜ผ์—ฌ๋ถ€(P=0.017), ๊ต์œก์ •๋„(P=0.025), ๊ฒฝ์ œ๋ ฅ(P=0.001)์— ๋”ฐ๋ผ ์œ ์˜ํ•œ ์ฐจ์ด๋ฅผ ๋ณด์˜€๋‹ค. ๊ฒฐ๋ก ์ ์œผ๋กœ ๋Œ€๋‹ค์ˆ˜์˜ ์ผ๋ฐ˜์ธ๋“ค์€ ์•ˆ๋ฝ์‚ฌ์— ๋Œ€ํ•œ ๋ฒ•์ด ํ•„์š”ํ•˜๋‹ค๊ณ  ํ•˜์˜€๊ณ  ์—ฐ๋ น๊ณผ ๊ต์œก, ๊ฒฝ์ œ๋ ฅ์ด ์•ˆ๋ฝ์‚ฌ ์‹œํ–‰ ์—ฌ๋ถ€์— ์˜ํ–ฅ์„ ์ฃผ๋Š” ๊ฒƒ์œผ๋กœ ๋ณด์—ฌ์ง„๋‹ค. [์˜๋ฌธ]Background : The concerns on euthanasia, among the various life ethic problems raised according as the medical technology has been developed recently, is getting high. Thus, the general public tends to have more liberal opinion. They have detail research data and real practices in US, Europe and Australia, but we lack of studies in the country. This study is undertaken owing to the need of studies on the recognition of euthanasia among the public because the existing studies have been focused on the medical staff. Methods : Survey 413 people the age of 17 or more, from May to July 2000. Testify the data on the variation of demography and the recognition of euthanasia by using SAS 6.12, the statistic program. Results: 1. 304 people (73.6%) think that euthanasia should be legislated, 156 people (37.8%) permit euthanasia to the rage of voluntary one, and 234 people (56.6%) permit passive euthanasia. 2. When the subject of voluntary euthanasia is the respondent of himself, more people whose age is 35 or more(P=0.001) responded that they will undertake euthanasia. And, related to the passive euthanasia, one's educational background(P=0.046) and economic power(P=0.040) arrangement show meaningful differences. 3. When the subject of voluntary euthanasia is the respondent of other people, more people whose age is 35 or more than 35(P=0.001), whose sex is male(P=0.001), and married people(P=0.002) were allowing the matter. For the subject of passive euthanasia, survey participant's occupation(P=0.016) created meaningful difference. 4. More people whose age is 35 or more than 35 responded that they want voluntary euthanasia for themselves(P=0.001), and in the case euthanasia is legislated, marital status(P=0.002) also shows meaningful difference. Passive euthanasia is permitted by the more people whose age is less than 35 for respondents other people(P=0.001), marital status show meaningful difference in case for respondent himself. 5. In the case euthanasia is legislated, more people whose age is 35 or more than 35(P=0.001), sex is male(P=0.004) and more married people(P=0.001) response that they want voluntary euthanasia for other people. And, age(P=0.002), sex(P=0.017), education(P=0.025) and economic power(P=0.001) show meaningful difference for case the subject of passive euthanasia. Conclusions : Most of general public responded that the legislation on euthanasia is required; and, age, education and economic power seem to have an influence on their decisions on euthanasia. Not only such a study of demographic and sociological correlation; but, various basic data on the legislation of euthanasia are needed.ope

    The direction of development of car forensics and How to analyze extracted data

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    As automobiles are electrical powered and developed in the direction of self-driving, the role of computers is becoming important. These smart cars have become common technologies with the development of innovative automobile technology and computer technology. This means that new approaches and research on automobiles are needed from a forensic point of view. In the past, car forensics consisted of physical forensics that measure the length of the skid mark in case of a vehicle accident and calculate the speed, analyze the destination or route of the navigation, and analyze videos recorded in the black box. This was not a forensics of the vehicle itself, but rather an investigation of external mounting devices or surrounding environments. In addition, there was a problem in securing reliability because the acquired data was inaccurate or cross-validation was not possible. Current automobile forensics have already developed to the point of analyzing EDR data installed inside cars, connecting acquisition devices to OBD terminals, and analyzing smartphone-linked infotainment systems such as Android Auto and Apple CarPlay. However, it is also not legally mandatory to install devices and systems for acquisition and analysis, making it difficult to obtain data. In addition, there is very little data stored inside the vehicle, and there is a limitation in that data is stored in external devices such as smartphones, so evidence must be obtained through separate mobile forensics. However, in the case of electric vehicles with partial autonomous driving functions, which have been released and are increasing sales, the structure of the vehicle is expected to be very developed, enabling the application of a new forensic method. The characteristic of these cars is that first, as numerous additional sensors are installed for self-driving and data generated here are accumulating, the number of information that can be collected is increasing. Second, the automobile ECU is integrated, equipped with a centralized computer, and an integrated operating system is installed and operated. This has the advantage that automobiles can be computerized and standardized for forensics. Finally, by having OTA and V2X functions, data can be transmitted and received wirelessly by being connected to the outside, which has the advantage of enabling cross-validation of the generated data. In addition, in the case of self-driving cars, it is legally forced to have a device to store vehicle driving data, and the type and storage method of collected data are stipulated. Commercially, these data are expected to generate profits through automobile design and insurance premium calculation, which is an incentive to store and acquire data. The accumulated data may be stored in a memory inside the vehicle or stored in a smartphone connected thereto or in a server of a manufacturer. The problem is that the capacity of the data is too vast. In mobile forensics, as the storage capacity of smartphones increases, it takes a considerable amount of time to obtain, select, and analyze data. In the case of data collected in automobiles, the capacity is expected to be much larger than that of mobile, so it is necessary to change the data analysis method. Therefore, in this paper, propose the introduction of FOQA (Flight Operations Quality Assurance), an accident investigation and maintenance program for aircraft that has already been used for a long time and has secured reliability and accuracy. By visualizing the acquired data in 3D simulation, in the case of an accident investigation, the handle position, transmission position, accelerator pedal position, etc. can be reproduced as the situation at the time of the accident to increase the accuracy of judgment. In the case of general evidence collection, the driver's biometric information, passenger information, destination information, and payment information are collected and reproduced through the vehicle's internal camera and infotainment information, enabling mobile forensics and cross-validation as well as independent evidence. By adding an automatic analysis function, forensic investigators can grasp the overall matters necessary for the investigation, such as vehicle and driver information, at a glance. In addition, data on rapid acceleration, rapid deceleration, drowsy driving, etc. will be standardized and registered as event code to indicate event code, occurrence point, and end point in the program, reducing data analysis time and improving accuracy.์ž๋™์ฐจ๊ฐ€ ์ „๋™ํ™” ๋˜๊ณ , ์Šค์Šค๋กœ ์šด์ „ํ•˜๋Š” ๋ฐฉํ–ฅ์œผ๋กœ ๋ฐœ์ „ํ•˜๋ฉฐ ์ปดํ“จํ„ฐ์˜ ์—ญํ• ์ด ์ค‘์š”ํ•ด์ง€๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ์Šค๋งˆํŠธ์นด๋“ค์€ ํ˜์‹ ์  ์ž๋™์ฐจ ๊ธฐ์ˆ  ๋ฐœ์ „ ๋ฐ ์ปดํ“จํ„ฐ ๊ธฐ์ˆ  ๋ฐœ์ „๊ณผ ๋”๋ถˆ์–ด ๋ณดํŽธํ™”๋œ ๊ธฐ์ˆ ์ด ๋˜์—ˆ๋‹ค. ์ด๋Š” ๊ณง ํฌ๋ Œ์‹ ๊ด€์ ์—์„œ ์ž๋™์ฐจ์— ๋Œ€ํ•œ ์ƒˆ๋กœ์šด ์ ‘๊ทผ ๋ฐ ์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•˜๋‹ค๋Š” ๊ฒƒ์„ ์˜๋ฏธํ•œ๋‹ค. ๊ณผ๊ฑฐ์˜ ์ž๋™์ฐจ ํฌ๋ Œ์‹์€ ์ฐจ๋Ÿ‰ ์‚ฌ๊ณ  ์‹œ ์Šคํ‚ค๋“œ ๋งˆํฌ์˜ ๊ธธ์ด๋ฅผ ์ธก์ •ํ•˜์—ฌ ์†๋„๋ฅผ ๊ณ„์‚ฐํ•˜๋Š” ๋ฌผ๋ฆฌ์  ํฌ๋ Œ์‹, ๊ทธ๋ฆฌ๊ณ  ๋„ค๋น„๊ฒŒ์ด์…˜์˜ ๋ชฉ์ ์ง€๋‚˜ ๊ฒฝ๋กœ๋ฅผ ๋ถ„์„ํ•˜๊ณ , ๋ธ”๋ž™๋ฐ•์Šค์— ๊ธฐ๋ก๋œ ๋™์˜์ƒ์„ ๋ถ„์„ํ•˜๋Š” ๋“ฑ์˜ ํฌ๋ Œ์‹์ด ์ด๋ฃจ์–ด์กŒ๋‹ค. ์ด๋Š” ์ฐจ๋Ÿ‰ ์ž์ฒด์— ๋Œ€ํ•œ ํฌ๋ Œ์‹์ด๋ผ๊ธฐ ๋ณด๋‹ค๋Š” ์™ธ๋ถ€ ์žฅ์ฐฉ๊ธฐ๊ธฐ ๋˜๋Š” ์ฃผ๋ณ€ ํ™˜๊ฒฝ์— ๋Œ€ํ•œ ์กฐ์‚ฌ์˜€๋‹ค. ๋˜ํ•œ ํš๋“ ๋ฐ์ดํ„ฐ๊ฐ€ ๋ถ€์ •ํ™• ํ•˜๊ฑฐ๋‚˜ ๊ต์ฐจ ๊ฒ€์ฆ์ด ๋ถˆ๊ฐ€๋Šฅํ•˜์—ฌ ์‹ ๋ขฐ์„ฑ ํ™•๋ณด ์ฐจ์›์—๋„ ๋ฌธ์ œ๊ฐ€ ์žˆ์—ˆ๋‹ค. ํ˜„์žฌ์˜ ์ž๋™์ฐจ ํฌ๋ Œ์‹์€ ์ž๋™์ฐจ ๋‚ด๋ถ€์— ์žฅ์ฐฉ๋œ EDR ์ž๋ฃŒ ๋ถ„์„ ๋˜๋Š” OBD ๋‹จ์ž์— ํš๋“ ๊ธฐ๊ธฐ๋ฅผ ์—ฐ๊ฒฐํ•œ ๋ถ„์„, ์•ˆ๋“œ๋กœ์ด๋“œ ์˜คํ† ์™€ ์• ํ”Œ ์นดํ”Œ๋ ˆ์ด ๊ฐ™์€ ์Šค๋งˆํŠธํฐ ์—ฐ๋™ ์ธํฌํ…Œ์ธ๋จผํŠธ ์‹œ์Šคํ…œ ๋ถ„์„์— ์ด๋ฅผ ์ •๋„๋กœ ๋ฐœ์ „๋˜์—ˆ๋‹ค. ํ•˜์ง€๋งŒ ์ด ์—ญ์‹œ๋„ ํš๋“ ๋ฐ ๋ถ„์„์„ ์œ„ํ•œ ์žฅ์น˜์™€ ์‹œ์Šคํ…œ ์„ค์น˜๊ฐ€ ๋ฒ•์ ์œผ๋กœ ์˜๋ฌด ์‚ฌํ•ญ์ด ์•„๋‹ˆ๋ผ, ๋ฐ์ดํ„ฐ ํš๋“์ด ์ˆ˜์›”ํ•˜์ง€ ์•Š๋‹ค. ๋˜ํ•œ ์ฐจ๋Ÿ‰ ๋‚ด๋ถ€์— ์ €์žฅ๋˜๋Š” ๋ฐ์ดํ„ฐ๊ฐ€ ๋งค์šฐ ์ ์„ ๋ฟ๋”๋Ÿฌ ์Šค๋งˆํŠธํฐ ๊ฐ™์€ ์™ธ๋ถ€ ๊ธฐ๊ธฐ์— ๋ฐ์ดํ„ฐ๊ฐ€ ์ €์žฅ๋˜์–ด ๋ณ„๋„์˜ ๋ชจ๋ฐ”์ผ ํฌ๋ Œ์‹ ๋“ฑ์„ ํ†ตํ•ด ์ฆ๊ฑฐ๋ฅผ ํš๋“ํ•ด์•ผ ํ•˜๋Š” ํ•œ๊ณ„์ ์ด ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ์ง€๊ธˆ ์ถœ์‹œ๋˜์–ด ํŒ๋งค๊ฐ€ ๋Š˜๊ณ  ์žˆ๋Š” ๋ถ€๋ถ„์ž์œจ์ฃผํ–‰๊ธฐ๋Šฅ์„ ๊ฐ–์ถ˜ ์ „๊ธฐ์ฐจ๋“ค์˜ ๊ฒฝ์šฐ ์ฐจ๋Ÿ‰์˜ ๊ตฌ์กฐ๊ฐ€ ๋งค์šฐ ๋ฐœ์ „๋˜์–ด ์ƒˆ๋กœ์šด ํฌ๋ Œ์‹ ๋ฐฉ๋ฒ•์˜ ์ ์šฉ์ด ๊ฐ€๋Šฅํ•ด์งˆ ๊ฒƒ์œผ๋กœ ๋ณด๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ž๋™์ฐจ๋“ค์˜ ํŠน์ง•์€ ์ฒซ์งธ ์ž์œจ์ฃผํ–‰์„ ์œ„ํ•ด ์ˆ˜๋งŽ์€ ์„ผ์„œ๊ฐ€ ์ถ”๊ฐ€ ํƒ‘์žฌ๋˜๊ณ  ์—ฌ๊ธฐ์„œ ์ƒ์„ฑ๋˜๋Š” ๋ฐ์ดํ„ฐ๋“ค์ด ์ถ•์ ๋˜๊ณ  ์žˆ์–ด ์ˆ˜์ง‘ํ•  ์ˆ˜ ์žˆ๋Š” ์ •๋ณด๋“ค์ด ๋Š˜์–ด๋‚˜๊ณ  ์žˆ๋‹ค. ๋‘˜์งธ ์ž๋™์ฐจ ECU๊ฐ€ ํ†ตํ•ฉ๋˜์–ด ์ค‘์•™ ์ง‘์ค‘์‹ ์ปดํ“จํ„ฐ๊ฐ€ ํƒ‘์žฌ๋˜๊ณ  ํ†ตํ•ฉ ์šด์˜์ฒด์ œ๊ฐ€ ์„ค์น˜๋˜์–ด ์šด์˜๋œ๋‹ค. ์ด๋Š” ์ž๋™์ฐจ๊ฐ€ ์ปดํ“จํ„ฐํ™” ๋˜์–ด ํฌ๋ Œ์‹์„ ์œ„ํ•œ ํ‘œ์ค€ํ™”๊ฐ€ ๊ฐ€๋Šฅํ•˜๋‹ค๋Š” ์žฅ์ ์„ ๊ฐ€์ง„๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ OTA, V2X ๊ธฐ๋Šฅ๋“ค์„ ๊ฐ–์ถค์œผ๋กœ์จ ์™ธ๋ถ€์™€ ์—ฐ๊ฒฐ๋˜์–ด ๋ฌด์„ ์œผ๋กœ ๋ฐ์ดํ„ฐ์˜ ์†ก์ˆ˜์‹ ์ด ๊ฐ€๋Šฅํ•ด์ง€๊ณ  ์ด๋Š” ์ƒ์„ฑ๋œ ๋ฐ์ดํ„ฐ๋“ค์— ๋Œ€ํ•œ ๊ต์ฐจ ๊ฒ€์ฆ์ด ๊ฐ€๋Šฅํ•ด์ง„๋‹ค๋Š” ์žฅ์ ์„ ๊ฐ€์ง„๋‹ค. ๋˜ํ•œ ์ž์œจ์ฃผํ–‰ ์ž๋™์ฐจ์˜ ๊ฒฝ์šฐ ๋ฒ•์ ์œผ๋กœ ์ž๋™์ฐจ ์šดํ–‰ ๋ฐ์ดํ„ฐ๋ฅผ ์ €์žฅํ•˜๋Š” ์žฅ์น˜๋ฅผ ๊ฐ–์ถ”๋„๋ก ๊ฐ•์ œํ•˜๊ณ  ์ˆ˜์ง‘ ๋ฐ์ดํ„ฐ์˜ ์ข…๋ฅ˜์™€ ์ €์žฅ ๋ฐฉ์‹ ๋“ฑ์„ ๊ทœ์ •ํ•˜๊ณ  ์žˆ๋‹ค. ์ƒ์—…์ ์œผ๋กœ๋„ ์ด๋Ÿฐ ๋ฐ์ดํ„ฐ๋“ค์„ ํ†ตํ•ด ์ž๋™์ฐจ ์„ค๊ณ„, ๋ณดํ—˜๋ฃŒ ๊ณ„์‚ฐ ๋“ฑ์˜ ๋ฐฉ์‹์œผ๋กœ ์ˆ˜์ต์ด ์ฐฝ์ถœ๋  ๊ฒƒ์œผ๋กœ ๋ณด์—ฌ ๋ฐ์ดํ„ฐ์˜ ์ €์žฅ ๋ฐ ํš๋“์— ์œ ์ธ์ด ๋˜๊ณ  ์žˆ๋‹ค. ์ถ•์ ๋œ ๋ฐ์ดํ„ฐ๋Š” ์ž๋™์ฐจ ๋‚ด๋ถ€์˜ ๋ฉ”๋ชจ๋ฆฌ์— ์ €์žฅ๋˜๊ฑฐ๋‚˜ ์—ฐ๊ฒฐ๋œ ์Šค๋งˆํŠธํฐ ๋˜๋Š” ์ œ์ž‘์‚ฌ์˜ ์„œ๋ฒ„์— ์ €์žฅ๋  ๊ฒƒ์ด๋‹ค. ๋ฌธ์ œ์ ์€ ๋ฐ์ดํ„ฐ์˜ ์šฉ๋Ÿ‰์ด ๋„ˆ๋ฌด ๋ฐฉ๋Œ€ํ•˜๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. ๋ชจ๋ฐ”์ผ ํฌ๋ Œ์‹์—์„œ๋„ ์Šค๋งˆํŠธํฐ์˜ ์ €์žฅ ์šฉ๋Ÿ‰์ด ์ปค์ง€๋ฉด์„œ ๋ฐ์ดํ„ฐ์˜ ํš๋“ ๋ฐ ์„ ๋ณ„, ๋ถ„์„์— ์ƒ๋‹นํ•œ ์‹œ๊ฐ„์ด ์†Œ์š”๋˜๋Š” ์–ด๋ ค์›€์ด ๋ฐœ์ƒํ•˜๊ณ  ์žˆ๋‹ค. ์ž๋™์ฐจ์— ์ˆ˜์ง‘๋˜๋Š” ๋ฐ์ดํ„ฐ์˜ ๊ฒฝ์šฐ ๋ชจ๋ฐ”์ผ๋ณด๋‹ค ์šฉ๋Ÿ‰์ด ํ›จ์”ฌ ํด ๊ฒƒ์œผ๋กœ ์˜ˆ์ƒ๋˜์–ด ๋ฐ์ดํ„ฐ ๋ถ„์„ ๋ฐฉ๋ฒ•์˜ ๋ณ€ํ™”๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ๋”ฐ๋ผ์„œ ์ด ๋…ผ๋ฌธ์—์„œ๋Š” ์ด๋ฏธ ์˜ค๋žœ ๊ธฐ๊ฐ„ ์‚ฌ์šฉ๋˜์–ด ์‹ ๋ขฐ์„ฑ๊ณผ ์ •ํ™•๋„๊ฐ€ ํ™•๋ณด๋œ ํ•ญ๊ณต๊ธฐ์˜ ์‚ฌ๊ณ ์กฐ์‚ฌ ๋ฐ ์œ ์ง€๋ณด์ˆ˜ ํ”„๋กœ๊ทธ๋žจ์ธ FOQA(Flight Operations Quality Assurance) ๋„์ž…์„ ์ œ์•ˆํ•œ๋‹ค. ํš๋“ํ•œ ๋ฐ์ดํ„ฐ๋ฅผ 3D ์‹œ๋ฎฌ๋ ˆ์ด์…˜์œผ๋กœ ์‹œ๊ฐํ™”ํ•จ์œผ๋กœ์จ ์‚ฌ๊ณ  ์กฐ์‚ฌ์˜ ๊ฒฝ์šฐ ํ•ธ๋“ค์œ„์น˜, ๋ณ€์†๊ธฐ ์œ„์น˜, ๊ฐ€์†ํŽ˜๋‹ฌ ์œ„์น˜ ๋“ฑ์„ ์‚ฌ๊ณ  ๋‹น์‹œ ์ƒํ™ฉ์œผ๋กœ ์žฌํ˜„ํ•˜์—ฌ ํŒ๋‹จ์˜ ์ •ํ™•๋„๋ฅผ ๋†’์ผ ์ˆ˜ ์žˆ๋‹ค. ์ผ๋ฐ˜ ์ฆ๊ฑฐ ์ˆ˜์ง‘์˜ ๊ฒฝ์šฐ์—๋„ ์ฐจ๋Ÿ‰ ๋‚ด๋ถ€ ์นด๋ฉ”๋ผ ๋ฐ ์ธํฌํ…Œ์ธ๋จผํŠธ ์ •๋ณด๋ฅผ ํ†ตํ•˜์—ฌ ์šด์ „์ž์˜ ์ƒ์ฒด์ •๋ณด, ๋™์Šน์ž์ •๋ณด, ๋ชฉ์ ์ง€์ •๋ณด, ๊ฒฐ์ œ์ •๋ณด ๋“ฑ์„ ์ˆ˜์ง‘, ์žฌํ˜„ํ•˜์—ฌ ๋…์ž์ ์ธ ์ฆ๊ฑฐ๋ฟ ์•„๋‹ˆ๋ผ ๋ชจ๋ฐ”์ผ ํฌ๋ Œ์‹๊ณผ ๊ต์ฐจ๊ฒ€์ฆ์ด ๊ฐ€๋Šฅํ•ด์ง„๋‹ค. ์ž๋™ ๋ถ„์„ ๊ธฐ๋Šฅ์„ ์ถ”๊ฐ€ํ•˜์—ฌ ํฌ๋ Œ์‹ ์ˆ˜์‚ฌ๊ด€์ด ํ•œ๋ˆˆ์— ์ฐจ๋Ÿ‰ ๋ฐ ์šด์ „์ž ์ •๋ณด ๋“ฑ ์ˆ˜์‚ฌ์— ํ•„์š”ํ•œ ์ „๋ฐ˜์ ์ธ ์‚ฌํ•ญ์˜ ํŒŒ์•…์ด ๊ฐ€๋Šฅํ•˜๋‹ค. ๋˜ํ•œ ๊ธ‰๊ฐ€์†, ๊ธ‰๊ฐ์†, ์กธ์Œ ์šด์ „, ๋˜๋Š” ์šด์ „์ž ์‹ ์ฒด ์ด์ƒ, ์ฐจ๋Ÿ‰ ๋‚ด ๊ฒฐ์ œ ๋“ฑ์ด ์ด๋ฃจ์–ด์กŒ์„ ๊ฒฝ์šฐ์— ๋Œ€ํ•œ ๋ฐ์ดํ„ฐ๋ฅผ ํ‘œ์ค€ํ™” ํ•˜๊ณ  ์ด๋ฅผ ์ด๋ฒคํŠธ ์ฝ”๋“œ๋กœ ๋“ฑ๋กํ•˜์—ฌ ํŠน์ •์ƒํ™ฉ์ด ๋ฐœ์ƒํ•  ๊ฒฝ์šฐ ํ”„๋กœ๊ทธ๋žจ์— ์ด๋ฒคํŠธ ์ฝ”๋“œ์™€ ๋ฐœ์ƒ ์‹œ์  ๋ฐ ์ข…์  ํ‘œ์‹œํ•จ์œผ๋กœ์จ ๊ทธ ๋ถ€๋ถ„์˜ ๋ฐ์ดํ„ฐ๋งŒ ์ƒ์„ธ ๋ถ„์„ํ•˜์—ฌ ๋ฐ์ดํ„ฐ ๋ถ„์„ ์‹œ๊ฐ„์„ ์ค„์ด๊ณ  ์ •ํ™•์„ฑ์„ ๋†’์ผ ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค.์ œ1์žฅ ์„œ๋ก  1 ์ œ2์žฅ ์‹œ๊ธฐ๋ณ„ ์ฐจ๋Ÿ‰ํฌ๋ Œ์‹ ๋ฐฉ๋ฒ• 3 ์ œ1์ ˆ ์•„๋‚ ๋กœ๊ทธ, ๋ฌผ๋ฆฌ์  ํฌ๋ Œ์‹ 3 ์ œ2์ ˆ ์™ธ๋ถ€์žฅ์ฐฉ๊ธฐ๊ธฐ ํฌ๋ Œ์‹ 4 1. ๋„ค๋น„๊ฒŒ์ด์…˜ 4 2. ๋ธ”๋ž™๋ฐ•์Šค 6 ์ œ3์ ˆ ๋‚ด๋ถ€์žฅ์ฐฉ๊ธฐ๊ธฐ ํฌ๋ Œ์‹ 8 1. EDR 8 2. OBD ์Šค์บ๋„ˆ ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜ 14 ์ œ4์ ˆ ์ฐจ๋Ÿ‰ ์ธํฌํ…Œ์ธ๋จผํŠธ ํฌ๋ Œ์‹ 20 1. ์ฐจ๋Ÿ‰ ์ธํฌํ…Œ์ธ๋จผํŠธ ์‹œ์Šคํ…œ 20 2. ์ฐจ๋Ÿ‰ ์ธํฌํ…Œ์ธ๋จผํŠธ ํฌ๋ Œ์‹ ์—ฐ๊ตฌ 23 3. ํŠน์ง•๊ณผ ํ•œ๊ณ„์  26 ์ œ3์žฅ ๋ถ„์•ผ๋ณ„ ๋ฐœ์ „๋ฐฉํ–ฅ 27 ์ œ1์ ˆ ํ•˜๋“œ์›จ์–ด 27 1. ์„ผ์„œ ๋“ฑ ์ •๋ณด์ˆ˜์ง‘ ์žฅ์น˜์˜ ์ฆ๊ฐ€ 27 2. ์ค‘์•™์ง‘์ค‘ํ™” ๋œ ์ œ์–ด 32 3. OTA 34 4. V2X 34 ์ œ2์ ˆ ์†Œํ”„ํŠธ์›จ์–ด 35 1. ์ „์žฅ๋ถ€ํ’ˆ ํ†ตํ•ฉ์ œ์–ด์šฉ OS 36 2. ์ธํฌํ…Œ์ธ๋จผํŠธ (Infortainment) ์†Œํ”„ํŠธ์›จ์–ด 38 ์ œ3์ ˆ ๋ฒ•์ ์ธ ์ธก๋ฉด 40 1. ํ˜„์žฌ ์ž๋™์ฐจ ๊ธฐ๋ก์žฅ์น˜ ๊ด€๋ จ ๋ฒ•๋ น 40 2. ์ž๋™์ฐจ ๊ธฐ๋ก์žฅ์น˜ ๊ด€๋ จ ๋ฒ•๋ น์˜ ๋ณ€ํ™” 44 3. ๋ฒ•์ ์ธ ์ œ๋„์˜ ๋ณด์™„ 44 ์ œ4์ ˆ ์ƒ์—…์  ์ธก๋ฉด 45 1. ์„ค๊ณ„ ๋ฐ ์œ ์ง€๋ณด์ˆ˜ 45 2. ๋ณดํ—˜์‚ฌ ํ™œ์šฉ 46 ์ œ4์žฅ ํฌ๋ Œ์‹ ๋ฐ์ดํ„ฐ์˜ ๋ถ„์„ 47 ์ œ1์ ˆ ์ˆ˜์ง‘ ๋ฐ์ดํ„ฐ ์ข…๋ฅ˜ 47 1. ECU ์ฐจ๋Ÿ‰ ์„ผ์„œ 47 2. ADAS ์ฐจ๋Ÿ‰ ์„ผ์„œ 15 3. ์ธํฌํ…Œ์ธ๋จผํŠธ์— ์ €์žฅ๋˜๋Š” ๋””์ง€ํ„ธ ์„ผ์„œ ๋ฐ์ดํ„ฐ 49 4. ์—ฐ๋ฝ์ฒ˜ 51 5. ๋„ค๋น„๊ฒŒ์ด์…˜ ์ •๋ณด ๋ฐ์ดํ„ฐ 52 6. ์ฐจ๋Ÿ‰ ์šดํ–‰ ๊ธฐ๋ก์‹œ์Šคํ…œ ๋กœ๊ทธ ๋ฐ์ดํ„ฐ 52 ์ œ2์ ˆ ๋ฐ์ดํ„ฐ ์ €์žฅ๋ฐฉ์‹ ๋ฐ ์ถ”์ถœ์ ˆ์ฐจ 54 1. ์ฐจ๋Ÿ‰ ์ฃผํ–‰์ •๋ณด 54 2. ์ž์œจ์ฃผํ–‰ ๊ด€๋ จ ์ •๋ณด 54 3. ์ธํฌํ…Œ์ธ๋จผํŠธ ์ •๋ณด 55 ์ œ3์ ˆ ๋ฐ์ดํ„ฐ๋ถ„์„ ๋ฐฉ๋ฒ• 55 1. FOQA 56 2. ์ž๋™๋ถ„์„ 59 3. ์ด๋ฒคํŠธ ์ฝ”๋“œ 60 ์ œ5์žฅ ๊ฒฐ๋ก  61์„
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