성균관대학교 · Computer Science
Khan Muhammad 교수의 연구실은 인공지능의 투명성과 신뢰성 향상을 위한 설명 가능성 기반 AI(XAI) 기술, 특히 저해상도 영상 복원 및 고해상도 전환 기술, 블록체인 기반 분산 신뢰 관리 시스템, 그리고 운전자 상태 모니터링을 위한 효율적인 딥러닝 기반 솔루션을 핵심 연구 분야로 다룹니다. 특히, 실시간 도로 및 인프라 손상 진단, 차량 번호판 인식 향상, 양자 정보 기반 보안 합의 알고리즘 등 실생활 응용에 초점을 맞춘 기술 개발을 진행하고 있습니다. 연구는 실용성과 효율성을 기반으로 하여 자원 제약이 있는 환경에서도 작동 가능한 경량 모델 및 혁신적 알고리즘 개발에 기여하고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated applications, but the outcomes of many AI models are challenging to comprehend and trust due to their black-box nature. Usually, it is essential to understand the reasoning behind an AI model’s decision-making. Thus, the need for eXplainable AI (XAI) methods for improving trust in AI models has arisen. XAI has become a popular research subject within the AI field in recent years. Existing survey papers have
No system entity within a contemporary distributed cyber system can be entirely trusted. Hence, the classic centralized trust management method cannot be directly applied to it. Blockchain technology is essential to achieving decentralized trust management, its consensus mechanism is useful in addressing large-scale data sharing and data consensus challenges. Herein, an n-party quantum detectable Byzantine agreement (DBA) based on the GHZ state to realize the data consensus in a quantum blockcha
Vehicle license plate images are often low resolution and blurry because of the large distance and relative motion between the vision sensor and vehicle, making license plate identification arduous. The extensive use of expensive, high-quality vision sensors is uneconomical in most cases; thus, images are initially captured and then translated from low resolution to high resolution. For this purpose, several traditional techniques such as bilinear, bicubic, super-resolution convolutional neural
Nowadays, the use of public transportation is reducing and people prefer to use private transport because of its low cost, comfortable ride, and personal preferences. However, personal transport causes numerous real-world road accidents due to the conditions of the drivers’ state such as drowsiness, stress, tiredness, and age during driving. In such cases, driver fatigue detection is mandatory to avoid road accidents and ensure a comfortable journey. To date, several complex systems have been pr
Currently, real-time assessment of surface damage to bridges is crucial for ensuring infrastructure safety. Unfortunately, existing methods often present a challenge: overly complex computational models are incompatible with systems that have limited resources, while lightweight models struggle to achieve sufficient detection accuracy. This task is further complicated by the diverse nature of bridge damages, such as cracks, exposed reinforcement, and efflorescence, as well as the challenges of d
