https://scholar.dgist.ac.kr/handle/20.500.11750/10139 Sun, 15 Nov 2026 15:19:47 GMT 2026-11-15T15:19:47Z https://scholar.dgist.ac.kr/handle/20.500.11750/61171 Title: Individual AI mechanism design based on the open innovation dynamics Author(s): Yun, JinHyo Joseph; Zhao, Xiaofei; Ahn, Heungju; Park, KyungBae Abstract: Purpose This study addresses key research gaps related to the singularity of artificial intelligence (AI). specifically, this study investigates whether it is possible to create an individual AI derived from open innovation dynamics, while ensuring that it does not exceed its capabilities beyond the singularity threshold. Design/methodology/approach It first develops a conceptual model of individual AI. Next, it introduces a mathematical framework that incorporates both the breadth dimensions of open innovation - such as reciprocity and location - and the depth dimensions, including customization and organization. Through simulations, it identifies the unique personalities of the individual AI and, using causal loop modeling, it covers its distinctive characteristics. Findings It highlights the advantages of individual AI approach based on open innovation dynamics over current general AI models. Originality/value Individual AI might propose a new future direction of the general AI, which has each individuality and does not have the possibility to grow up over the singularity. Sun, 31 May 2026 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/61171 2026-05-31T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/60966 Title: Interpretable In-Cabin Occupancy Detection Using FHRS-Based Residual Features from Fixed-ROI UWB Radar Signals Author(s): Cho, Hui-Sup; Park, Young-Jin Abstract: Vehicle in-cabin occupancy detection supports restraint-system control, child presence detection, and intelligent cabin sensing, with child presence detection linked to safety assessment frameworks such as the European New Car Assessment Program. High-dimensional radar representations show strong potential, but their physical basis can be difficult to interpret. Engineered-feature approaches improve interpretability, yet few explicitly separate the linear-delay component from the residual structure of the complex reflection response. This study proposes an interpretable representation derived from the complex reflection ratio within a fixed time-domain region of interest (ROI). The method extracts direct-delay cues, frequency-domain Hilbert residual signature (FHRS) scalar features, and continuous wavelet transform (CWT)-based residual-delay and log-magnitude-derivative shape features. In the original experiment, the residual-centered condition achieved 97.72% accuracy and a 97.73% macro F1-score, whereas the direct-delay-only condition achieved 100.00%. A separately acquired 20-trial challenge experiment used Empty Seat, Person, and Human-like Object-loaded Seat conditions, with a human-shaped test object used for the latter. In this challenge, the direct-delay-only, residual-centered, and all-feature conditions achieved accuracies of 79.44%, 96.92%, and 97.39%, respectively. These results demonstrate that residual features provide nonredundant discriminative information when direct-delay cues alone are insufficient. Participant-independent, cross-vehicle, and cross-installation generalization remain to be verified. Fri, 31 Jul 2026 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/60966 2026-07-31T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/60856 Title: End-to-End 자율주행 AI의 안전성 확보를 위한 기술 동향과 국제 안전 표준 관점의 분석 Author(s): Son, Joonwoo; Park, Myoungouk; Jeong, Seok Chan; Kim, Sung-Hee Abstract: End-to-end (E2E) learning-based autonomous driving has emerged as a promising paradigm that directly maps sensor inputs to vehicle control commands using data-driven models. Compared to conventional modular architectures, E2E approaches offer advantages in terms of architectural simplification and holistic learning of complex driving contexts from large-scale data. However, since E2E systems rely on probabilistic decision-making, exhibit limited explainability, and remain vulnerable to distribution shifts, edge cases, and long-tail scenarios, those benefits actually introduce fundamental challenges in safety assurance. This paper reviews recent technological developments in E2E autonomous driving and systematically analyzes key AI safety issues from a system-level perspective. Core challenges including rare operational scenarios, uncertainty under out-of-distribution conditions, and limitations in traceability and accountability are examined in relation to existing automotive safety frameworks. In particular, the paper investigates how ISO 21448 (Safety of the Intended Functionality, SOTIF) and UL 4600 can be reinterpreted and applied to learning-based autonomous driving systems to complement traditional failure-based functional safety standards. To address the structural mismatch between E2E architectures and existing safety standards, this paper discusses rule-based safety shields and scenario-based validation as practical and standard-compatible mechanisms for mitigating non-failure-based risks and constructing evidence-driven safety arguments. The analysis demonstrates that instead of E2E autonomous driving invalidating existing safety frameworks, it actually necessitates their complementary and systematic integration to achieve robust safety assurance in learning-based autonomous driving systems. Tue, 31 Mar 2026 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/60856 2026-03-31T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/60674 Title: TENG-Driven Electrotherapy: A Self-Powered Approach to Inducing Cancer Cell Apoptosis Author(s): Ramu, Dandugudumula; Hajra, Sugato; Panda, Swati; Kaja, Kushal Ruthvik; Mishra, Yogendra Kumar; Kim, Hoe Joon; Kim, Eunjoo Abstract: Most of the cancer-related deaths are caused by metastasis, which also remains a significant obstacle to successful clinical management. Even though several anti-metastatic treatments have been put forth, systemic toxicity, low cellular responsiveness, and drug resistance typically undermine their therapeutic efficacy. Although triboelectric nanogenerators (TENGs) have become highly effective self-powered electrical therapies for biomedical applications, their potential as an active treatment tool for metastasis suppression has not yet been fully investigated. Here, we present a self-powered TENG-driven electrotherapeutic approach that suppresses early lung cancer cell migration in vitro by carefully regulated electrical stimulation. Electrical stimulation at 60 V and 760 nA for 5 min disrupted redox homeostasis and induced caspase-3-mediated apoptotic death of A549 cells. TENG-based electrical therapy serves as a self-powered electrical stimulation source that triggers apoptosis in cancer cells by activating the caspase-3/PARP pathway. These results raise possibilities for TENGs not merely as energy-harvesting devices but as active, mechanistic electrotherapeutic platforms, converting mechanical energy into controlled electrical signals that initiate apoptotic cell death pathways in cancer cells. Tue, 30 Jun 2026 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/60674 2026-06-30T15:00:00Z