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| WoS | SCOPUS | Document Type | Document Title | Abstract | Authors | Affiliation | ResearcherID (WoS) | AuthorsID (SCOPUS) | Author Email(s) | Journal Name | JCR Abbreviation | ISSN | eISSN | Volume | Issue | WoS Edition | WoS Category | JCR Year | IF | JCR (%) | FWCI | FWCI Update Date | WoS Citation | SCOPUS Citation | Keywords (WoS) | KeywordsPlus (WoS) | Keywords (SCOPUS) | KeywordsPlus (SCOPUS) | Language | Publication Stage | Publication Year | Publication Date | DOI | JCR Link | DOI Link | WOS Link | SCOPUS Link |
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| ○ | Conference paper | A Multi-Criteria Approach toward Accelerating for Artificial Intelligence Business Ecosystems: A Perspective of AI Startups | AI startups play a crucial role in introducing new ideas and technologies to the market, thereby driving the proliferation of AI. Considering the influence of AI startups within the AI business ecosystem, it is essential to support AI startups as a means of fostering economic growth. While previous studies have predominantly focused on AI adoption by startups (Booyse & Scheepers, 2023; Filieri et al., 2021), there is a gap in understanding the multi-criteria factors that specifically drive the activation of AI startup ecosystems. This necessitates recognizing policies related to AI startups as a critical agenda and formulating appropriate strategies to invigorate the AI business ecosystem. In other words, practical and sophisticated solutions are required to realize the potential of AI startups. This study aims to bridge the gap between rapidly advancing AI technology and the social sciences that need to support technological development. By inviting CEO and managers of major 27 AI startups in Korea, this study proposes a model for evaluating the activation of AI startups business ecosystem. Our findings indicate market demand, training AI professionals, and high-quality data is the most important factors for activation AI startup ecosystem. The implications of our findings underline the importance of strategic policymaking. © 2025 IEEE Computer Society. All rights reserved. | Han, Kyunghyun; Park, Jonghwa | Kyungpook National University, South Korea; Kyungpook National University, South Korea | 59899136900; 59388235000 | Proceedings of the Annual Hawaii International Conference on System Sciences | 1530-1605 | 0 | 2025-06-11 | 0 | AI startups; Analytic Hierarchical Process; Business Ecosystems; CEOs; Multi-Criteria Decision Model | Economic analysis; Industrial economics; AI startup; Analytic hierarchical process; Business ecosystem; CEO; Decision modeling; Economic growths; Multi-criteria; Multi-criteria approach; Multi-criteria decision model; Multicriteria decision; Economic and social effects | English | Final | 2025 | 바로가기 | ||||||||||||||||||||
| ○ | Article | A Physical LDMOST Model and Predictive Simulations for Advanced Technology CAD | This article describes a compact Lateral DMOS Transistor (LDMOST) model incorporated directly into SPICE source code and presents its application to power IC technology CAD. The complete model combines a previously developed semi-numerical static model and a built-in parasitic component model with a charge-based dynamic model. This composite model is based on device physics; thus, it accounts well for important power MOSFET characteristics such as non-uniformly doped channels, reverse-recovery transients and the non-planar drift region. The measurements from the power MOSFET samples support the predictive model, verified in extensive SPICE simulations of several high-voltage circuits. This LDMOST model might be useful in computer-aided optimal design of smart power ICs. © 2025 Seventh Sense Research Group. | Chung, Yeonbae | School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, South Korea | 7404387325 | ybchung@ee.knu.ac.kr; | SSRG International Journal of Electrical and Electronics Engineering | 2348-8379 | 12 | 2 | 0 | 2025-05-07 | 0 | Charge-based dynamic model; High-voltage MOSFET; Lateral DMOS transistor; Parasitic BJT model; Power IC technology CAD | English | Final | 2025 | 10.14445/23488379/ijeee-v12i2p105 | 바로가기 | 바로가기 | ||||||||||||||||
| ○ | Conference paper | A Power-Efficient Reconfigurable Hybrid CNN-SNN Accelerator for High Performance AI Applications | Deep learning-based object detection requires high computation, making real-time processing difficult due to excessive power consumption and irregular workloads in conventional accelerators. Event-driven hybrid model training has been explored as a method to reduce power consumption. However, its implementation on traditional hardware remains challenging due to the lack of efficient sparse computation optimization. To address this issue, this paper proposes a power-efficient CNNSNN hybrid accelerator that leverages event-driven spiking computation and adaptive reconfiguration. Unlike conventional CNN accelerators that rely on continuous activation functions and fixed processing pipelines, the proposed architecture selectively converts energy-intensive layers into SNNs. This hybrid approach minimizes power-hungry multiply-accumulate operations by leveraging sparse, event-driven spike processing. The accelerator uses a reconfigurable dual-lane processor that switches between CNN and SNN operations for efficient workload distribution. To efficiently manage the dynamic switching between CNN and SNN operations, the accelerator employs adaptive dynamic memory optimization to minimize data movement overhead, while multistage pipeline optimizes temporal accumulation to maximize the benefits of event-driven SNN processing. The proposed hybrid CNN-SNN accelerator reduces power consumption by 32 % while maintaining 97.5% accuracy, improving FPS per watt by 47-67% over conventional CNN architectures. Its dynamic workload adaptation increases inference speed by up to 16 %, making it highly efficient for real-time edge AI. © 2025 IEEE. | Yun, Heuijee; Park, Daejin | School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, South Korea; School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, South Korea | 57222516795; 55463943600 | boltanut@knu.ac.kr; | IEEE Symposium on Low-Power and High-Speed Chips and Systems, COOL CHIPS 2025 - Proceedings | 0 | Event-driven processing; Hybrid CNN/SNN accelerator; Low-power deep learning; Reconfigurable computing | Acceleration; Computational efficiency; Deep learning; Electric power utilization; Energy efficiency; Green computing; Memory architecture; Object recognition; Pipeline processing systems; Reconfigurable architectures; Event-driven; Event-driven processing; Hybrid CNN/SNN accelerator; Low Power; Low-power deep learning; Power; Power efficient; Reconfigurable; Reconfigurable computing; Reconfigurable- computing; Pipelines | English | Final | 2025 | 10.1109/coolchips65488.2025.11018586 | 바로가기 | 바로가기 | ||||||||||||||||||||
| ○ | Article | A Preliminary Study on Softness Optimization Using Machine Learning for the Digital Twin of Soft Robots | This study presents a machine learning-based approach for optimizing Young’s modulus, a critical physical parameter of soft robots. Instead of directly utilizing conventional material property data, the method predicts Young's modulus based on positional coordinate data measured from key points on the deformed soft robot. The research consists of simulation and experimental phases. In the simulation phase, the convergence of the Young’s modulus estimation framework is first validated through gradient descent optimization. Subsequently, random forest and neural network models are trained using coordinate data collected over a Young’s modulus range of 10²–10¹⁰ Pa. The random forest model exhibits the lowest RMSE for predicting specific Young’s modulus values (10⁶ and 10⁸ Pa), demonstrating optimal performance. In the experimental phase, deformation data from a TPU-based 3D-printed soft robot are applied to the optimized random forest model to predict Young’s modulus in real-world conditions. The proposed method provides realistic predictions compared to publicly available modulus values. These findings confirm that simulation-trained machine learning models can be effectively applied to optimize soft robot design and control, enhancing the reliability of digital twins and soft robot engineering. © ICROS 2025. | Kwon, Taejun; Nam, Saekwang | Graduate School of Data Science, Kyungpook National University, South Korea; Graduate School of Data Science, Kyungpook National University, South Korea | 59899170600; 56091917700 | s.nam@knu.ac.kr; | Journal of Institute of Control, Robotics and Systems | 1976-5622 | 31 | 5 | 0 | 2025-06-11 | 0 | machine learning; nvidia isaacsim; soft robot; young’s modulus | Conventional materials; Learning-based approach; Machine-learning; Modulus values; Nvidia isaacsim; Optimisations; Physical parameters; Random forest modeling; Soft robot; Young’s modulus; Machine design | Korean | Final | 2025 | 10.5302/j.icros.2025.25.0044 | 바로가기 | 바로가기 | |||||||||||||||
| ○ | Conference paper | A Security Analysis of "A Privacy-Preserving Three-Factor Authentication System for IoT-Enabled Wireless Sensor Networks" | Wireless sensor network (WSN) is a main component of the internet of things (IoT) technology, it can be predicted to apply in various areas including smart city, smart home, healthcare, vehicular network, and so on. However, in WSN environments, sensors and data users communicate wirelessly and it can be prone to malicious attacks such as forgery, impersonation, denial-of-service. Therefore, many researchers have proposed to establish a session key securely in WSN environments. In 2024, Thakur et al. designed a three-factor based authentication protocol for IoT-enabled WSNs. They indicated that Sahoo et al.'s protocol has weaknesses, and therefore, they suggested an enhanced scheme that resolved the previous security weaknesses. Nevertheless, we reviewed Thakur et al.'s scheme and we analyze that their scheme fails to support mutual authentication and does not provide perfert forward secrecy. Furthermore, their scheme is also prone to DoS attack because of lack of mutual authentication. We provide a detailed analysis of Thakur et al.'s scheme and propose countermeasures to address them. © 2025 IEEE. | Son, Seunghwan; Kwon, DeokKyu; Park, Youngho | Kyungpook National University, School of Electronic and Electrical Engineering, Daegu, South Korea; Kyungpook National University, School of Electronic and Electrical Engineering, Daegu, South Korea; Kyungpook National University, School of Electronic and Electrical Engineering, Daegu, South Korea | 57221744477; 57221739597; 56962990300 | sonshawn@knu.ac.kr; | International Conference on Information Networking | 1976-7684 | 0 | 2025-06-11 | 0 | Internet of Things (IoT); mutual authentication; security; sensor; wireless sensors networks (WSNs) | Authentication; Authentication Protocol; Sensitive data; Authentication systems; Internet of thing; Mutual authentication; Privacy preserving; Security; Security analysis; Sensor network environment; Sensors network; Wireless sensor; Wireless sensor network; Wireless sensor networks | English | Final | 2025 | 10.1109/icoin63865.2025.10993149 | 바로가기 | 바로가기 | |||||||||||||||||
| ○ | Article | A self-study of a physics teacher at a science-gifted school to align teaching philosophy with practice; [수업 철학과 실천을 일치시키기 위한 한 과학영재학교 물리교사의 셀프스터디] | This study is a self-study of a physics teacher who transitioned from a public middle school to a gifted school, facing challenges in lesson design and practice while redefining his teaching identity. In middle school, I pursued learner-centered teaching, but in the gifted school, I relied on structured, lecture-based lessons due to the students' advanced levels and complex content, causing confusion and frustration. (In this study, 'I' refers to the first author.) For a year, I engaged in critical self-reflection and discussions with colleague to answer questions like, “What classes do I want to practice at the science school for gifted students?” and “How can I align my teaching beliefs with my practice at the gifted school?” Through the self-study process, reflecting on my educational beliefs and conducting critical discussions with colleague helped resolve my confusion and identify learner-centered teaching approaches suitable for the gifted school. This experience clarified my goal of designing and practicing effective instruction aligned with my teaching philosophy and growing as a physics teacher for gifted students. © 2025 Korean Physical Society. All rights reserved. | Jung, Jaehwan; Ha, Sangwoo | Daegu Science High School for the Gifted, Daegu, 42110, South Korea; Daegu Science High School for the Gifted, Daegu, 42110, South Korea, Department of Physics Education, Kyungpook National University, Daegu, 41566, South Korea, Science Education Research Institute of Kyungpook National University, Daegu, 41566, South Korea | 59717605300; 55215468100 | hswgcb@knu.ac.kr; | New Physics: Sae Mulli | 0374-4914 | 75 | 3 | 0 | 2025-05-07 | 0 | Good lesson; Learner centered lesson; Physics teacher; Science gifited school; Selfstudy | Korean | Final | 2025 | 10.3938/npsm.75.257 | 바로가기 | 바로가기 | ||||||||||||||||
| ○ | Conference paper | A Similarity-Based Training Strategy with Network-Level Perturbation for Semi-supervised Semantic Segmentation | Semantic segmentation, a pixel-level classification task, is crucial for the fine-grained classification of objects within images. However, its reliance on precise pixel-level labeling poses a significant challenge, increasing costs and limiting its applicability in real-world scenarios. Despite the semi-supervised learning methods that have alleviated the need for extensive labeled data, many still involve complex processes or substantial additional resources. We propose a similarity-based training strategy and a simple model configured with the online and the target network to perform semi-supervised semantic segmentation while reducing the required resources and maintaining a simpler configuration than conventional methods. To assess the effectiveness of our method, we conducted evaluations using various splits of the PASCAL VOC 2012 dataset, comparing it with other semi-supervised semantic segmentation approaches. Experimental results demonstrate that our proposed method outperforms conventional methods that rely on intricate processes or additional computational resources. This suggests the potential for a more practical and resource-efficient approach to semi-supervised semantic segmentation tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. | Chae, Jongbin; Lee, Dong-Gyu | Kyungpook National University, Daegu, 41566, South Korea; Kyungpook National University, Daegu, 41566, South Korea | 59660809300; 57169003900 | dglee@knu.ac.kr; | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | 0302-9743 | 14893 LNCS | 0 | 2025-05-07 | 0 | Semantic Segmentation; Semi-supervised Learning; Semi-supervised Semantic Segmentation | Adversarial machine learning; Contrastive Learning; Federated learning; Labeled data; Latent semantic analysis; Self-supervised learning; Semi-supervised learning; Classification tasks; Conventional methods; Network level; Pixel level; Semantic segmentation; Semi-supervised; Semi-supervised learning; Semi-supervised semantic segmentation; Training strategy; Semantic Segmentation | English | Final | 2025 | 10.1007/978-981-97-8705-0_18 | 바로가기 | 바로가기 | ||||||||||||||||
| ○ | Article | A Study of the Effects of Perceived Restorativeness of Urban Street Gardens on User Satisfaction and Reuse Intention | Background and objective: The objective of this study was to analyze the effect of the perceived restorativeness of urban street gardens on user satisfaction and reuse intention, and further to analyze the mediating effect of user satisfaction on the relationship between perceived restorativeness and reuse intention. Methods: A survey was conducted of users of street gardens in downtown Daegu, and 341 valid responses were used in the final analysis. Results: The analysis found that the perceived restorativeness of urban street gardens has a positive effect on user satisfaction, which in turn enhances their intention to reuse these spaces. In particular, the "scope" of urban gardens had the largest effect among the factors of perceived restorativeness of the urban street gardens. Conclusion: It was confirmed that urban street gardens have a positive effect on urban residents as restorative environmental spaces. This study has academic significance, in that it identifies the factors that affect user satisfaction with and reuse of restorative environments. © 2025 by the Society for People, Plants, and Environment. | Son, Dong Gu | Department of Landscape Architecture, Kyungpook National University, Daegu, 41566, South Korea | 59914782100 | sonson3915@naver.com; | Journal of People, Plants, and Environment | 2508-7673 | 28 | 2 | 0 | 2025-06-11 | 0 | perceived restorativeness; reuse intention; street garden; user satisfaction | English | Final | 2025 | 10.11628/ksppe.2025.28.2.193 | 바로가기 | 바로가기 | ||||||||||||||||
| ○ | Article | A Study on Spatial Improvement of Nursing Home through Evidence-based Design: Focusing on the Case of Seoul Nursing Home | In this study, I conducted a space improvement study targeting Seoul Nursing Home through evidence-based design. I sought to apply empirical research evidence to the design, and thus explored architectural methods that can maximize the healing aspect of the elderly medical environment and improve the health of elderly patients. As a research method, I derived results for improving elderly health through interviews. In addition, I conducted a prior research survey on specific evidence-based space design strategies that can achieve this, and mapped each correlation and importance. Based on this, I conducted a space analysis of Seoul Nursing Home and derived a space improvement design. I presented the results of space improvement based on evidence design that can visually compare and confirm three improvement plans and one excellent status for four detailed spaces of Seoul Nursing Home. A good building can be created only when research and practice, that is, experimental knowledge and experiential common sense, are in harmony. © 2025 Architectural Institute of Korea. | Youn, Hyun-Chul | School of Architecture, Kyungpook National University, South Korea | 57607640200 | enagoris@knu.ac.kr; | Journal of the Architectural Institute of Korea | 2733-6239 | 41 | 4 | N/A | 0 | Evidence-based design; Nursing home; Quality of life; Safety; Spatial improvement | Korean | Final | 2025 | 10.5659/jaik.2025.41.4.31 | 바로가기 | 바로가기 | |||||||||||||||||
| ○ | Conference paper | A Survey on Weighted Bergman Spaces of Holomorphic Ball Bundles | The present article aims to review some results related to the Levi problem for holomorphic ball bundles over compact complex manifolds. In particular, we introduce a relation between symmetric differentials on compact complex hyperbolic space forms and the weighted L2 holomorphic functions on certain ball bundles. This connection provides a method to understand the weighted Bergman spaces of these bundles without using any ergodicity arguments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. | Lee, Seungjae | Department of Mathematics, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu, 41566, South Korea | 57219791511 | seungjae@knu.ac.kr; | Springer Proceedings in Mathematics and Statistics | 2194-1009 | 481 | 0 | 2025-05-07 | 0 | 32A05; 32L05; 32W05; Holomorphic ball bundle; Primary 32A36; Secondary 32E40; Symmetric differential; Weakly 1-complete domain; Weighted Bergman space; ∂¯-equation | Choquet integral; 32a05; 32l05; 32w05; Holomorphic ball bundle; Primary 32a36; Secondary 32e40; Symmetric differential; Symmetrics; Weakly 1-complete domain; Weighted bergman space; ∂¯-equation; Hyperbolic functions | English | Final | 2025 | 10.1007/978-981-96-0447-0_11 | 바로가기 | 바로가기 | ||||||||||||||||
| ○ | Article | ABUNDANCE, DIVERSITY AND FORAGING BEHAVIOR OF THE FLOWER-VISITING INSECTS OF MANGO IN GAZIPUR, BANGLADESH | To investigate the abundance, diversity, and diurnal dynamics of insect visitors associated with mango flowers, the present study was conducted in a mango orchard of Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh from June 2020 to August 2021. The observed insect visitors represented 13 families of four taxonomic orders: Hymenoptera, Diptera, Lepidoptera, and Coleoptera. Hymenoptera showed the highest abundance (47.2%), with ants being the most abundant species (21.2±3.5/30 sweeps), followed by honeybees (11.2±0.9/30 sweeps). Furthermore, Hymenopteran insects showed the highest diversity (H’= 2.85), richness (Dmg= 0.56), and dominance (DBP=0.27) among the four orders. Epilachna beetles had the highest foraging speed (27.8±2.4 s/flower), followed by blister beetles (27.1±1.9 s/flower). Blow flies showed the lowest foraging speed (16.5±1.3 s/flower) but the highest visitation frequency (13.7±0.9 flowers/min). The abundance of ants, honeybees, and blowflies showed significant positive correlations with visitation frequency (0.905, 0.972, and 0.926, respectively) but a significant negative correlation with foraging speed (−0.968, −0.933, and −0.931, respectively). The diurnal pattern of insect visitors showed that the highest foraging activities occurred in the first part of the day (7.00 to 11.00 h) and then declined, with the lowest activity at 15.00 h. © 2025, Penerbit Universiti Kebangsaan Malaysia. All rights reserved. | Hasan, Md Zahid; Miah, Md Ramiz Uddin; Islam, Md Moshiul; Afroz, Mansura; Suh, Sang Jae; Amin, Md Ruhul | Department of Entomology, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh; Department of Entomology, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh; Department of Agronomy, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh; Department of Entomology, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh; School of Applied Biology, Kyungpook National University, Daegu, South Korea; Department of Entomology, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh | 59768180900; 57225799974; 59349261100; 57226501699; 59768181000; 59584307500 | mramin@bsmrau.edu.bd; | Serangga | 1394-5130 | 30 | 1 | N/A | 0 | Abundance; diversity; Mangifera indica; richness; visitation | English | Final | 2025 | 10.17576/serangga-2025-3001-04 | 바로가기 | 바로가기 | |||||||||||||||||
| ○ | Article | Activated Carbon Production from Jatropha Pyrolysis Biochar | The depletion of fossil fuels and growing environmental concerns highlight the need for renewable energy sources and efficient waste management. This study explores the production of activated carbon from Jatropha de-oiled cake through atmospheric pyrolysis and chemical activation, where the atmospheric pyrolysis of the de-oiled cake generates biochar, which serves as the precursor for activated carbon. The activation process using potassium hydroxide (KOH) was optimized with response surface methodology (RSM) to evaluate the effects of impregnation ratio, activation temperature, and activation time. Under optimal conditions, the process achieved a maximum activated carbon yield of 69.8% and a surface area of 285 m²/g. The activated carbon exhibited high adsorption efficiency, removing 90.3% of acetaminophen from aqueous solutions. These findings demonstrate the potential of Jatropha de-oiled cake for high-value material synthesis, contributing to renewable energy development and sustainable waste management. © 2025, Korea Society of Waste Management. All rights reserved. | Owkusumsirisakul, Jinjuta; Park, Kyungdu; Jang, Eunho; Capareda, Sergio; Nam, Hyungseok | Department of Physics, Faculty of Science, Burapha University, Thailand, Biological and Agricultural Engineering Department, Texas A&M University, United States; School of Mechanical Engineering & IEDT, Kyungpook National University, South Korea; School of Mechanical Engineering & IEDT, Kyungpook National University, South Korea; Biological and Agricultural Engineering Department, Texas A&M University, United States; Biological and Agricultural Engineering Department, Texas A&M University, United States, School of Mechanical Engineering & IEDT, Kyungpook National University, South Korea | 57202707188; 59696671200; 59697099800; 8656086100; 57190418228 | namhs219@knu.ac.kr; | Journal of Korea Society of Waste Management | 2093-2332 | 42 | 1 | 0 | 2025-05-07 | 0 | Activated carbon; Jatropha; Pyrolysis; Response surface methodology (RSM) | English | Final | 2025 | 10.9786/kswm.2025.42.1.34 | 바로가기 | 바로가기 | ||||||||||||||||
| ○ | Conference paper | Adaptive Bias Discovery for Learning Debiased Classifier | Training deep neural networks with empirical risk minimization (ERM) often captures dataset biases, hindering generalization to new or unseen data. Previous solutions either require prior knowledge of biases or utilize training intentionally biased models as auxiliaries; however, they still suffer from multiple biases. To address this, we introduce Adaptive Bias Discovery (ABD), a novel learning framework designed to mitigate the impact of multiple unknown biases. ABD trains an auxiliary model to be adapted to biases based on the debiased parameters from the debiasing phase, allowing it to navigate through multiple biases. Then, samples are reweighted based on the discovered biases to update debiased parameters. Extensive evaluations of synthetic experiments and real-world datasets demonstrate that ABD consistently outperforms existing methods, particularly in real-world applications where multiple unknown biases are prevalent. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. | Bae, Jun-Hyun; Lee, Minho; Jung, Heechul | Kyungpook National University, Daegu, South Korea; Kyungpook National University, Daegu, South Korea, ALI Co., Ltd., Daegu, South Korea; Kyungpook National University, Daegu, South Korea | 57222760538; 57191730119; 55652175200 | heechul@knu.ac.kr; | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | 0302-9743 | 15479 LNCS | 0 | 2025-05-07 | 0 | Classification; Debiasing; Deep Learning; Spurious Correlations | Adversarial machine learning; Federated learning; Adaptive bias; Auxiliary models; De-biasing; Deep learning; Empirical risk minimization; Generalisation; Learning frameworks; Neural-networks; Prior-knowledge; Spurious correlation; Contrastive Learning | English | Final | 2025 | 10.1007/978-981-96-0966-6_3 | 바로가기 | 바로가기 | ||||||||||||||||
| ○ | Article | Adaptive Frequency Hopping Technique for Anti-Jamming of UAV Systems Using Lightweight Messaging Protoco | This study proposes an adaptive frequency hopping technique to enhance the anti-jamming capability of small Unmanned Aerial Vehicle (UAV) systems using lightweight messaging protocols. The proposed method consists of three phases: Channel state monitoring, channel state information integration and distribution, and frequency hopping pattern adjustment, designed to operate effectively in dynamic jamming environments. The performance of the proposed technique was evaluated through simulations, comparing it with existing methods in both stationary and dynamic jamming scenarios. Results show that the proposed adaptive frequency hopping technique demonstrates the most stable and high performance in dynamic jamming environments, effectively addressing the problem of false detection accumulation and ensuring long-term communication stability. The technique is designed considering hardware constraints, making it immediately applicable to existing lightweight messaging protocol-based drone systems. This research is expected to significantly enhance the security and reliability of small UAV systems, offering a practical solution for improving anti-jamming capabilities in lightweight messaging protocol environments. © 2025, Korean Institute of Communications and Information Sciences. All rights reserved. | Kwon, Jinsol; Seo, Kyunghee; Baek, Hoki | Kyungpook National University, School of Computer Science and Engineering, South Korea; Kyungpook National University, School of Computer Science and Engineering, South Korea; Kyungpook National University, School of Computer Science and Engineering, South Korea | 59517407800; 58934320900; 35112685500 | neloyou@knu.ac.kr; | Journal of Korean Institute of Communications and Information Sciences | 1226-4717 | 50 | 1 | 0 | 2025-05-07 | 0 | Anti-jamming; Frequency Hopping; Lightweight Messaging Protocol; Unmanned Aerial Vehicle | Korean | Final | 2025 | 10.7840/kics.2025.50.1.95 | 바로가기 | 바로가기 | ||||||||||||||||
| ○ | Review | Advances in vat photopolymerization: early-career researchers shine light on a path forward | Vat photopolymerization (VP) has emerged as a promising additive manufacturing technique to allow rapid light-based fabrication of 3D objects from a liquid resin. Research in the field of vat photopolymerization spans across multiple disciplines from engineering and materials science to applied chemistry and physics. This perspective brings together early-career researchers from various disciplines in academia and national laboratories around the world to summarize the most recent advancements with special emphasis on the research highlighted as part of the Gordon Research Conference (GRC) 2024 meeting on Additive Manufacturing of Soft Materials. We provide an outlook on next-generation polymer processing methods from synthesis of novel materials to multimodality manufacturing and performance engineering. Further, this article combines the ideas of many of these junior researchers to present a vision for the future of the field by highlighting the challenges and opportunities that lie ahead. © 2025 RSC. | Dhand, Abhishek P.; Bean, Ren H.; Chiaradia, Viviane; Commisso, Alex J.; Dranseike, Dalia; Fowler, Hayden E.; Fraser, Julia M.; Howard, Holden; Kaneko, Takashi; Kim, Ji-Won; Kronenfeld, Jason M.; Mason, Keldy S.; O'Dea, Connor J.; Pashley-Johnson, Fred; Porcincula, Dominique H.; Segal, Maddison I.; Yu, Siwei; Saccone, Max A. | Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States; Sandia National Laboratories, Albuquerque, NM, United States; Department of Chemistry, RCSI University of Medicine and Health Sciences, Dublin, Ireland; Sandia National Laboratories, Albuquerque, NM, United States; Macromolecular Engineering Laboratory, Department of Mechanical and Process Engineering, ETH Zurich, Zurich, Switzerland; Sandia National Laboratories, Albuquerque, NM, United States; Department of Chemistry, University of Wisconsin-Madison, Madison, WI, United States; Materials Engineering Division, Lawrence Livermore National Laboratory, United States; Department of Chemistry and Biochemistry, University of California, Santa Barbara, CA, United States; Department of Chemical Engineering, Kyungpook National University, Daegu, South Korea; Department of Chemistry, Stanford University, Stanford, CA, United States; Department of Chemistry, The University of Texas at Austin, Austin, TX, United States; Department of Chemistry, The University of Texas at Austin, Austin, TX, United States; School of Chemistry and Physics & Centre for Materials Science, Queensland University of Technology (QUT), Brisbane, QLD, Australia, Centre of Macromolecular Chemistry (CMaC), Department of Organic and Macromolecular Chemistry, Faculty of Sciences, Ghent University, Ghent, Belgium; Materials Engineering Division, Lawrence Livermore National Laboratory, United States; Thomas Lord Department of Mechanical Engineering & Material Science, Duke University, Durham, NC, United States; Department of Chemistry, University of Washington, Seattle, WA, United States; Department of Chemical Engineering, Stanford University, Stanford, CA, United States, Paul M. Rady Department of Mechanical Engineering, University of Colorado Boulder, Boulder, CO, United States | 57204002634; 57215853359; 36571391100; 57196055882; 58844119700; 57209847892; 59680447700; 59680447800; 57834234800; 59817405300; 57219331345; 57933338200; 58435850700; 57681806200; 57203589159; 58698927400; 58776849600; 57204398135 | adhand@seas.upenn.edu; max.saccone@colorado.edu; | RSC Applied Polymers | 2755-371X | 3 | 3 | 0 | 2025-05-07 | 0 | English | Final | 2025 | 10.1039/d5lp00010f | 바로가기 | 바로가기 |
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