In Hyun Kim
Hanyang University · 情報科学
研究室紹介
Professor In Hyun Kim's research lab specializes in intelligent systems and automated decision-making, with a focus on personalized recommendation systems, motion planning for autonomous agents, and natural language processing for mental health assessment. The lab develops advanced algorithms in data mining, artificial intelligence, and rule-based reasoning to address real-world challenges in e-commerce, robotics, and healthcare. Key research directions include kinodynamic motion planning for complex systems, automated web service composition with task dependency modeling, and NLP-driven depression symptom detection from social media. The lab integrates theoretical innovation with practical applications in biomedical informatics and human-centered computing.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15Abstract Customization and personalization services are a critical success factor for Internet stores and Web service providers. This paper studies personalized recommendation techniques that suggest products or services to the customers of Internet storefronts based on their demographics or past purchasing behavior. The underlining theories of recommendation techniques are statistics, data mining, artificial intelligence, and rule-based matching. In the rule-based approach to personalized recom
We describe some recent work on randomized motion planning algorithms and consider the problem of motion planning for systems with both kinematic and dynamic constraints. Such a problem is often referred to as kinodynamic motion planning. A rapidly-exploring random tree (RRT) is used for the motion planning of a blimp system, and some techniques for improving the performance of the planner are proposed. Based on a dynamic model of a blimp, dynamic constraints are introduced into the algorithm de
We address the problem of testing complex reactive control systems and validating the effectiveness of multi-agent controllers. Testing and validation involve searching for conditions that lead to system failure by exploring all adversarial inputs and disturbances for errant trajectories. This problem of testing is related to motion planning, with one main difference. Unlike motion planning problems, systems are typically not controllable with respect to disturbances or adversarial inputs and th
The National Library of Medicine (NLM) is developing an automated system to produce bibliographic records for its MEDLINE<SUP>R</SUP> database. This system, named Medical Article Record System (MARS), employs document image analysis and understanding techniques and optical character recognition (OCR). This paper describes a key module in MARS called the Automated Labeling (AL) module, which labels all zones of interest (title, author, affiliation, and abstract) automatically. The AL algorithm is
Objective: Although depression in modern people is emerging as a major social problem, it shows a low rate of use of mental health services. The purpose of this study was to classify sentences written by social media users based on the nine symptoms of depression in the Patient Health Questionnaire-9, using natural language processing to assess naturally users' depression based on their results. Methods: First, train two sentence classifiers: the Y/N sentence classifier, which categorizes whethe
Web services composition is becoming increasingly important as organizations are now getting ready to provide more complex service-based applications. Contemporary literature on the web services composition primarily addresses various aspects of composition for example, automated composition, quality-driven composition, and semantic composition. These approaches, however, do not adequately address the various dependencies among the tasks performed by the web services. Such dependencies could pot