Dongjun Im
Sungkyunkwan University · Medicine
About the Lab
Professor Dongjun Im's research lab specializes in systems engineering and technology innovation, with a strong focus on R&D planning, decision-making models, and advanced diagnostic technologies. The lab develops novel frameworks such as the System Alternatives Tree (SAT) and integrates them with analytical methods like AHP for systematic technology planning and optimization. Key research directions include AI-driven cybersecurity systems (e.g., unsupervised NIDS using autoencoders), precision oncology using molecular diagnostics (e.g., ddPCR for TERT promoter mutations), and multimodal medical imaging (e.g., photoacoustic and ultrasound fusion for thyroid nodule diagnosis). The lab bridges engineering, data science, and biomedical applications to solve complex system-level challenges in defense, healthcare, and technology development.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
6In this study, we propose a「System Alternatives Tree : SAT」 model, as a novel model of「Technology Tree Structure : TTS」, to set ‘Technology Planning Alternatives (TPA)’ which can achieve the final goal of R&D project. While existing studies have been mostly based on 「Decision Theory Approach」, this research attempts a 「Systems Engineering Approach」and therefore, it provides the distinguishing features through which staff in charge can analyze the total system and grasp an ideal combination of al
This paper is an investigation of the structure of success & failure model in the product development. Findings of this research are: Firstly, the R&D management and support system for the product development made a significant contribution to the success of products, especially in the development of high-tech products in defence industry. Secondly, it is important to keep the balance of management between technological factors and marketable factors to accelerate the product development. High-t
AI-based Network Intrusion Detection Systems (AI-NIDS) detect network attacks using machine learning and deep learning models. Recently, unsupervised AI-NIDS methods are getting more attention since there is no need for labeling, which is crucial for building practical NIDS systems. This paper aims to test the impact of designing autoencoder models that can be applied to unsupervised an AI-NIDS in real network systems. We collected security events of legacy network security system and carried ou
This study evaluated the reliability of droplet digital polymerase chain reaction (ddPCR) for detecting TERT promoter (pTERT) mutations in formalin-fixed, paraffin-embedded (FFPE) thyroid cancer samples and examined their association with clinicopathological features. A retrospective cohort of 296 postoperative patients with papillary thyroid carcinoma (PTC) was analyzed. DNA extracted from archived FFPE thyroidectomy specimens was examined for TERT promoter mutations using ddPCR. pTERT mutation
Thyroid nodule diagnosis relies heavily on ultrasound (US) screening, but conventional imaging exhibits poor specificity, resulting in excessive fine-needle aspiration biopsies. This study presents a dual-modality approach integrating photoacoustic (PA) and US technologies to enhance biopsy decisions. We examined 106 thyroid nodules across three pathological categories: benign nodules (n=29), papillary thyroid carcinomas (n=45), and follicular neoplasms (n=32). Three PA biomarkers—spectral gradi
In this study, we propose an independent utility measurement model, as a combined model of「System Alternatives Tree : SAT」and「Analytic Hierarchy Process : AHP」, in order to set ‘Technology Planning Alternatives (TPA)’ which can achieve the final goal of R&D project. While existing studies have been mostly based on「Decision Theory Approach」or 「Qualitative Measurement」, this research attempts a 「Systems Engineering Approach」and「Quantitative Measurement」. Therefore, it provides the distinguishing f
Research Areas
Dive deeper into Dongjun Im's research on Nubint
Open this lab's papers in the app to read with AI, summarize, and cite in your writing.