2023년11월고1모의고사변형문제(37번)와 정답자료가 필요하시다고요?
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오늘은 2023년11월고1모의고사변형문제(37번)와 정답자료를 제공해 드립니다.
좋은 내신 성적은 진심에서 나오는 거 아시죠?
37번 지문에 대한 완벽한 이해는 기본이죠?
37번 내용 확실히 익히셨으리라 믿고,
변형문제 도전 시작합니다!
Here we go!
2023년11월고1모의고사변형문제(37번)
1. 다음 밑줄 친 부분 중 어법상 틀린 것은?
Despite the remarkable progress in deep‑learning based facial recognition approaches in recent years, in terms of identification performance, they still have limitations. These limitations relate to the database ①used in the learning stage. If the selected database does not contain enough instances, the result may be systematically affected. For example, the performance of a facial biometric system may decrease if the person ②to identify was enrolled over 10 years ago. The factor to consider is ③that this person may experience changes in the texture of the face, particularly with the appearance of wrinkles and sagging skin. These changes may ④be highlighted by weight gain or loss. To counteract this problem, researchers have developed models for face aging or digital de‑aging. It is used to compensate for the differences in facial characteristics, ⑤which appear over a given time period.
정답은?
②to be identified
-> identify 는 '식별하다' 혹은 '구분하다' 라는 의미죠?
하지만, 문맥상, '식별되어야 할 사람'이라는 '수동'의 의미죠?
따라서, be p.p. 의 형태가 포함되어야 합니다. 그리고, '~할' 이라는 의미가 들어가면, to 부정사로 써야 하죠?
그래서, to be p.p. 의 형태인 to be identified가 옳습니다.
2023년11월고1모의고사변형문제(37번)
2 다음 밑줄 친 부분 중 문맥상 어색한 것은? (원문 일부 변형 문제입니다.)
Despite the remarkable ①advancement in deep‑learning based facial recognition approaches in recent years, in terms of identification performance, they still have limitations. These limitations relate to the database used in the learning stage. If the selected database does not contain enough instances, the result may be ②methodically affected. For example, the performance of a facial biometric system may decrease if the person to be ③recognized was enrolled over 10 years ago. The factor to consider is that this person may experience changes in the texture of the face, particularly with the appearance of wrinkles and sagging skin. These changes may be highlighted by weight gain or loss. To ④neutralize this problem, researchers have developed models for face aging or digital de‑aging. It is used to compensate for the ⑤defects in facial characteristics, which appear over a given time period.
정답은?
⑤defects (결점들) -> differences
참고 :
①progress (원문 어휘)
②systematically (원문 어휘)
③identified (원문 어휘)
④counteract (원문 어휘)
2023년11월고1모의고사변형문제(37번)
3. 글의 흐름으로 보아, 다음 주어진 문장이 들어갈 가장 알맞은 곳은?
To counteract this problem, researchers have developed models for face aging or digital de‑aging.
Despite the remarkable progress in deep‑learning based facial recognition approaches in recent years, in terms of identification performance, they still have limitations. These limitations relate to the database used in the learning stage. ① If the selected database does not contain enough instances, the result may be systematically affected. ② For example, the performance of a facial biometric system may decrease if the person to be identified was enrolled over 10 years ago. ③ The factor to consider is that this person may experience changes in the texture of the face, particularly with the appearance of wrinkles and sagging skin. ④ These changes may be highlighted by weight gain or loss. ⑤ It is used to compensate for the differences in facial characteristics, which appear over a given time period.
정답은?
⑤
2023년11월고1모의고사변형문제(37번)
4. 다음 빈칸에 들어갈 가장 알맞은 것은? (원문 전체 변형 문제입니다.)
Although significant advancements have been made in deep learning-based facial recognition techniques in recent years, they still face certain limitations regarding identification performance. These challenges are primarily tied to the database used during the learning phase. If the database lacks sufficient examples, the results can be systematically influenced. For instance, the accuracy of a facial biometric system may decline if the individual being identified was enrolled more than a decade ago. This is because facial features may change over time due to factors such as wrinkles, sagging skin, and weight fluctuations. To address these issues, researchers have developed face-aging or digital de-aging models .
① designed to spread the ability to distinguish facial expressions to peripheral devices
② designed to adjust for the variations in facial features that occur over time
③ designed to control and remove all surrounding distractions if necessary
④ designed to restore a changed appearance over time to a previous state
⑤ designed to identify other physical elements besides facial expressions
정답은?
② designed to adjust for the variations in facial features that occur over time
② 시간이 지남에 따라 발생하는 얼굴 특징의 변화를 조정하도록 설계된
참고 :
① 얼굴 표정을 구분할 수 있는 능력을 주변 기기에게도 전파할 수 있도록 설계된
③ 주변의 모든 방해 요소들을 통제하고 필요할 경우 제거하도록 설계된
④ 시간이 흘러 변화된 모습을 이전 상태로 복구할 수 있도록 설계된
⑤ 얼굴 표정 이외의 다른 신체적 요소들도 파악할 수 있도록 설계된
2023년11월고1모의고사변형문제(37번)
5. 다음 글의 내용을 한 문장으로 요약하고자 한다. 빈칸 (A), (B)에 들어갈 말로 가장 적절한 것은?
Despite the remarkable progress in deep‑learning based facial recognition approaches in recent years, in terms of identification performance, they still have limitations. These limitations relate to the database used in the learning stage. If the selected database does not contain enough instances, the result may be systematically affected. For example, the performance of a facial biometric system may decrease if the person to be identified was enrolled over 10 years ago. The factor to consider is that this person may experience changes in the texture of the face, particularly with the appearance of wrinkles and sagging skin. These changes may be highlighted by weight gain or loss. To counteract this problem, researchers have developed models for face aging or digital de‑aging. It is used to compensate for the differences in facial characteristics, which appear over a given time period.
↓
Facial recognition systems face [A] issues due to aging, but digital de-aging models help [B] .
[A] [B]
① balance develop
② fairness promote
③ justification change
④ accuracy compensate
⑤ privacy operate
정답은?
④ accuracy compensate
Facial recognition systems face accuracy issues due to aging, but digital de-aging models help compensate.
얼굴 인식 시스템은 노화로 인해 정확도 문제에 직면해 있지만 디지털 노화 방지 모델은 이를 보완하는 데 도움이 된다.
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