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| ○ | Erratum | Corrigendum to “Taxonomic review of the subtribe Masoreina (Coleoptera: Carabidae: Lebiinae) from Cambodia” [J Asia Pac Biodivers 17(3) (2024) 437–450, (S2287884X2400030X), (10.1016/j.japb.2024.01.011)] | The authors regret that a small portion of the text in page 438 must be deleted. The updated text below the sentence “Preliminary key to species of the subgenus Aephnidius from Oriental region (Schmidt-Göbel,1846:88e91; Chaudoir,1876:15e23;Andrewes, 1922: 169; Jedlicka, 1963:284e286; Habu,1967: 56, Pl. X)” must be 1. Elytral interval 3 without setigerous discal puncture.2- Elytral interval 3 with two or more setigerous discal punctures .42. Small, body length approximately 3.5 mm. Elytra with distinct yellowish spot.A. submaculatus (Bates, 1892): 405.- Larger, body length 4.0 to 4.75 mm. Elytra without distinct spot .33. Dorsum dark brown to black. Elytral striae relatively shallow .A. opaculus (Zimmermann, 1832): 120.- Dorsum reddish brown. Elytral striae relatively distinct. .A. rubidus (Andrewes, 1922): 169.4. Elytral surface with mottled reflection.5- Elytral surface without mottled reflection.65. Pronotum with lateral margins widest in the middle and weakly rugose around the median longitudinal impression. Body relatively flat. Dorsum more yellowish brown and pronotal disc faintly lighter along the lateral margins. .A. fuscipennis (Schmidt-Göbel, 1846): 89.- Pronotum with lateral margins widest behind the middle and distinctly rugose around the median longitudinal impression. Body convex. Dorsum generally uniformly blackish.A. adelioides MacLeay, 1825: 23.6. Anterior margin of pronotum moderately concave. Elytral humeri rounded and striae relatively distinct. .A. ruficornis (Chaudoir, 1850): 452.- Anterior margin of pronotum much more concave. Elytral humeri more or less rectangular and striae shallow. .A. pleuronectes (Zimmermann, 1832): 120.> on page 438.The authors would like to apologise for any inconvenience caused. © 2024 National Science Museum of Korea (NSMK) and Korea National Arboretum (KNA) | Choi, Jong Bong; Kwon, Taeyeong; Choi, Eun Young; Kim, Myeonghwan; Lee, Hee Soo; Gnim, Sodavy; Shin, Seungmin; Park, Jong Kyun | College of Ecology and Environmental Science, Kyungpook National University, Sangju, South Korea; College of Ecology and Environmental Science, Kyungpook National University, Sangju, South Korea; College of Ecology and Environmental Science, Kyungpook National University, Sangju, South Korea; College of Ecology and Environmental Science, Kyungpook National University, Sangju, South Korea; College of Ecology and Environmental Science, Kyungpook National University, Sangju, South Korea; College of Ecology and Environmental Science, Kyungpook National University, Sangju, South Korea; College of Ecology and Environmental Science, Kyungpook National University, Sangju, South Korea; College of Ecology and Environmental Science, Kyungpook National University, Sangju, South Korea, Research Institute of Invertebrate Vector, Kyungpook National University, Gyeongbuk, Sangju, South Korea | 57193335227; 57224825878; 57026862700; 57224780236; 58315082100; 58997128900; 58996209800; 37661967000 | entopark@knu.ac.kr; | Journal of Asia-Pacific Biodiversity | 2287-884X | 0 | 2025-05-07 | 0 | English | Article in press | 2024 | 10.1016/j.japb.2024.10.001 | 바로가기 | 바로가기 | |||||||||||||||||||
| ○ | Conference paper | Couple Effect of Loading Frequency and Uniformity Coefficient on the Liquefaction Resistance of Sand | The uniformity coefficient (Cu) of sand and the cyclic loading frequency (f) is unclear in affecting soil's cyclic undrained behavior. This note presents experimental research on the combined effect of f and Cu on the liquefaction resistance of the sand. A series of constant-volume, stress-controlled, cyclic direct simple shear tests (CDSS) were performed on silica sands mixed with different particle proportions to make both poorly (SP) and well-graded (SW) samples. SP and SW samples are deposited in a medium-density dry state, consolidated to vertical stress of 100 kPa, and cyclically loaded under a cyclic stress ratio of 0.1 with various loading frequency (f = 0.03, 0.05, 0.1, 0.2, and 0.5 Hz). In SP sand, the number of cycles to cause liquefaction (Ncyc) remains unchanged when the load frequency rises from 0.03 Hz to 0.1 Hz, and increases when the load frequency rises from 0.1 Hz to 0.5 Hz. It can be stated that SP sand's liquefaction resistance is affected by the high loading frequency. On the contrary, the effect of loading frequency on the Ncyc of SW sand is negligible. Furthermore, SP sand is more resistant to liquefaction than SW sand. According to this study, Cu and f should be included in the sand's liquefaction resistance analysis. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. | Tran, Dong-Kiem-Lam; Woo, Seung-Wook; Lee, Seong-Dong; Nguyen, Nhut-Nhut; Park, Sung-Sik | Department of Civil Engineering, Kyungpook National University, Daegu, South Korea; Department of Civil Engineering, Kyungpook National University, Daegu, South Korea; Department of Civil Engineering, Kyungpook National University, Daegu, South Korea; Department of Civil Engineering, Kyungpook National University, Daegu, South Korea; Department of Civil Engineering, Kyungpook National University, Daegu, South Korea | 57217738208; 57212917862; 58771516200; 57211211964; 36241850300 | sungpark@knu.ac.kr; | Lecture Notes in Civil Engineering | 2366-2557 | 442 | 5.19 | 2025-04-16 | 1 | Liquefaction Resistance; Loading Frequency; Poorly graded sand; Uniformity Coefficient; Well-graded sand | Silica sand; Soil liquefaction; Stress analysis; Couple effects; Cyclic loading frequency; Graded sand; Liquefaction resistance; Load-frequency; Loading frequencies; Poorly graded sand; Sand liquefaction; Uniformity coefficient; Well graded sand; Silica | English | Final | 2024 | 10.1007/978-981-99-7434-4_114 | 바로가기 | 바로가기 | ||||||||||||||||
| ○ | Review | Current Concepts and Medical Management for Patients with Radiographic Axial Spondyloarthritis | Radiographic axial spondyloarthritis (r-axSpA), a chronic inflammatory disease, can cause significant radiographic damage to the axial skeleton. Regarding the pathogenic mechanism, association of r-axSpA with tumor necrosis factor (TNF) and the interleukin-23/17 (IL23/IL17) pathway has been reported. Development of extraarticular manifestations, including uveitis, inflammatory bowel disease, and psoriasis, has been reported in some patients. The pivotal role of human leukocyte antigen-B27 in the pathogenesis of r-axSpA remains to be clarified. Symptoms usually start in late adolescence or early adulthood, and disease progression can vary in each patient, with clinical manifestations ranging from mild joint stiffness without radiographic changes to advanced manifestations including complete fusion of the spine, and severe arthritis of the hip, and could include peripheral arthritis and extraarticular manifestations. The modified New York criteria was used previously in diagnosis of r-axSpA. However, early diagnosis of the disease prior to development of bone deformity was required due to development of biological agents. As a result of Assessment of SpondyloArthritis international Society (ASAS), the classification was improved in part for diagnosis of spondyloarthritis prior to development of bone deformity. The diagnosis is based on comprehensive laboratory findings, physical examinations, and radiologic findings. Medical treatment for r-axSpA involves the use of a stepwise strategy, starting with administration of nonsteroidal anti-inflammatory drugs and physiotherapy, and progressing to sulfasalazine or methotrexate and biologics including TNF-α inhibitors or IL-17 inhibitors as needed. Use of Janus kinase inhibitors has been recently reported. © Korean Hip Society. | Baek, Seung-Hoon; Oh, Seungbae; Shim, Bum-Jin; Yoo, Jeong Joon; Hwang, Jung-Mo; Kim, Tae-Young; Shim, Seung-Cheol | Department of Orthopedic Surgery, Kyungpook National University Hospital, Kyungpook National University College of Medicine, Daegu, South Korea; Department of Orthopedic Surgery, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea; Department of Orthopedic Surgery, Kyungpook National University Chilgok Hospital, Kyungpook National University College of Medicine, Daegu, South Korea; Department of Orthopedic Surgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, South Korea; Department of Orthopedic Surgery, Chungnam National University School of Medicine, Daejeon, South Korea; Department of Orthopaedic Surgery, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, South Korea; Division of Rheumatology, Daejeon Rheumatoid & Degenerative Arthritis Center, Chungnam National University Hospital, Daejeon, South Korea | 56232924900; 57192693353; 57201499293; 55675762800; 56655791200; 57049785800; 7202796196 | syty-chan@hanmail.net;shimsc@cnuh.co.kr; | Hip and Pelvis | 2287-3260 | 36 | 4 | 0.54 | 2025-05-07 | 1 | Ankylosing spondylitis; Axial spondyloarthritis | English | Final | 2024 | 10.5371/hp.2024.36.4.234 | 바로가기 | 바로가기 | ||||||||||||||||
| ○ | Conference paper | Curve Monitoring and Alert System for Smart Transportation | The proposed IoT-based Smart Curve Monitoring and Alert System is designed to improve road safety through the fusion of embedded systems, machine learning and edge computing technologies. The proposed system is primarily powered by ESP32 microcontroller, continuously gathers data from an array of sensors, including accelerometers, gyroscopes and a potentiometer to set the speed limit. This collected data is then subjected to analysis by a Convolutional Neural Network (CNN) model, which has been trained to recognize driving patterns, especially when navigating curves. Here, CNN classifies the vehicle's behavior as safe and risky, and if a risky behavior is identified, it triggers an alert and dispatches relevant data to a NodeMCU device. The NodeMCU plays a crucial role in the system by monitoring the communication tasks. It receives the alert from the ESP32, validates the vehicle's speed against the preset limit, and, if necessary, activates a GSM module to send an immediate notification to designated guardian. To ensure real-time monitoring and awareness, all pertinent information will be displayed on the LCD screen, including the vehicle's behavior classification, current speed, speed limit and alert status. By addressing reckless driving behaviors, particularly on curves, the system contributes significantly to safer roads and reinforces the importance of responsible driving practices. © 2024 IEEE. | Natarajan, Yuvaraj; Sri Preethaa K, R.; Viswanathan, Shrinithi; Jayarathinam, Sandhiya; Balasubramaniam, Vikas | Kpr Institute of Engineering and Technology, Department of Computer Science and Engineering, Coimbatore, India; Kyungpook National University, Department of Robot and Smart System Engineering, Daegu, South Korea; Kpr Institute of Engineering and Technology, Department of Computer Science and Engineering, Coimbatore, India; Kpr Institute of Engineering and Technology, Department of Computer Science and Engineering, Coimbatore, India; Kpr Institute of Engineering and Technology, Department of Computer Science and Engineering, Coimbatore, India | 57204528689; 59155390800; 57197534144; 59155290900; 59155485600 | yuvaraj.n@kpriet.ac.in; | 2024 International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024 | 0 | 2025-04-16 | 1 | Convolutional Neural Network (CNN); curve monitoring; edge computing; embedded systems; ESP32; GSM communication; guardian notification; IoT; LCD display; machine learning; NodeMCU; real-time alert; reckless driving; road safety; sensor data; speed limit | Accident prevention; Classification (of information); Edge computing; Embedded systems; Internet of things; Liquid crystal displays; Machine learning; Motor transportation; Roads and streets; Vehicles; Convolutional neural network; Curve monitoring; Edge computing; Embedded-system; ESP32; GSM Communications; Guardian notification; IoT; LCD displays; Machine-learning; NodeMCU; Real- time; Real-time alert; Reckless driving; Road safety; Sensors data; Speed limit; Convolution | English | Final | 2024 | 10.1109/icc-robins60238.2024.10533946 | 바로가기 | 바로가기 | ||||||||||||||||||
| ○ | Article | Cutaneous Manifestations of COVID-19 Vaccination and COVID-19 Infection: a Questionnaire-based, Multi-center Study in Korea | Background: Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can cause various cutaneous complications, including dermatologic adverse reactions to SARS-CoV-2 vaccines reported by several studies. Objective: To describe the clinical characteristics of cutaneous complications of SARS-CoV-2 infection and adverse reactions to SARS-CoV-2 vaccines, and to determine the risk factors for cutaneous manifestations. Methods: A questionnaire-based survey in 12 hospitals in Korea. Results: After receiving SARS-CoV-2 vaccinations, 20.23% and 5.94% of the respondents reported new-onset cutaneous lesions or aggravation of preexisting cutaneous conditions, respectively. Respondents who developed new cutaneous lesions after COVID-19 were significantly older than those who did not (p = 0.001). Systemic symptoms of SARS-CoV-2 vaccination (fever, chill, cough, sore throat, and myalgia) were associated with higher risk for new-onset cutaneous lesions (p < 0.05). Myalgia was the only systemic symptom of SARS-CoV-2 vaccination that was associated with higher risk for the aggravation of preexisting cutaneous conditions (p = 0.011). Following coronavirus 2019 (COVID-19) diagnosis, 13.3% and 9.7% of the respondents reported new skin lesions and aggravation of preexisting cutaneous conditions, respectively. Respondents with new cutaneous lesions were significantly older than those without new cutaneous lesions (p = 0.046). Systemic COVID-19 symptoms were significantly more common in respondents who developed new cutaneous lesions than in those who did not (p < 0.001). The proportion of respondents with underlying autoimmune diseases was significantly higher in those with cutaneous COVID-19 complications than in those without such complications (p = 0.038). Conclusion: This study offers insights into the characteristics of cutaneous manifestations of SARS-CoV-2 vaccination and infection in Korea. Copyright@2024 by The Korean Society for Medical Mycology. | Moon, Ik Jun; Lee, Woo Jin; Ko, Hyun Chang; Kim, Hyojin; Na, Chan Ho; Park, Joonsoo; Park, Jin; Seo, Hyun-Min; Shin, Min Kyung; Lee, Young Bok; Jang, Yong Hyun; Jung, Hye Jung; Lee, Yangwon | Department of Dermatology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea; Department of Dermatology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea; Department of Dermatology, School of Medicine, Pusan National University, Busan, South Korea; Department of Dermatology, Inje University Busan Paik Hospital, Busan, South Korea; Department of Dermatology, Chosun University Hospital, Gwangju, South Korea; Department of Dermatology, Daegu Catholic University Hospital, Daegu, South Korea; Department of Dermatology, Jeonbuk National Univeristy Hospital, Jeonju, South Korea; Department of Dermatology, College of Medicine, Hanyang University, Seoul, South Korea; Department of Dermatology, College of Medicine, Kyung Hee University, Seoul, South Korea; Department of Dermatology, College of Medicine, The Catholic University of Korea, Seoul, South Korea; Department of Dermatology, School of Medicine, Kyungpook National University, Daegu, South Korea; Department of Dermatology, National Medical Center, Seoul, South Korea; Department of Dermatology, Konkuk University Medical Center, Seoul, South Korea | 57114383100; 55619313075; 57202265435; 57202104039; 24475283700; 55717191000; 35076360600; 55642437700; 57201780499; 36066866100; 57016046400; 56382664000; 59043287100 | 20050078@kuh.ac.kr; | Journal of Mycology and Infection | 3058-423X | 29 | 3 | 0 | 2025-05-07 | 0 | Coronavirus disease 2019; Severe acute respiratory syndrome coronavirus 2; Skin disease; Skin eruption; Survey; Vaccination | antibiotic agent; antihistaminic agent; antimalarial agent; antipyretic agent; antivirus agent; corticosteroid; elasomeran; ibacovavec; nvx-cov2373 vaccine; tozinameran; vaxzevria; acne; adult; Article; chill; clinical feature; controlled study; coronavirus disease 2019; coughing; disease exacerbation; eczema; female; fever; folliculitis; hair loss; herpes zoster; human; hyperpigmentation; hypopigmentation; injection site reaction; major clinical study; male; measles like rash; medical history; multicenter study; myalgia; outpatient department; psoriasis; questionnaire; risk factor; sensory dysfunction; skin burning sensation; skin manifestation; skin necrosis; skin pain; skin pruritus; skin tumor; skin ulcer; sore throat; systemic therapy; urticaria; vaccination; vesicular rash | English | Final | 2024 | 10.17966/jmi.2024.29.3.117 | 바로가기 | 바로가기 | |||||||||||||||
| ○ | Article | Cutaneous Nontuberculous Mycobacterial Infection Misdiagnosed as Rheumatoid Nodule in a Patient with Rheumatoid Arthritis | Owing to advances in diagnostics and the increase in invasive procedures, and immunocompromised patients, cutaneous nontuberculous mycobacteria (NTM) infection is rising. NTM should be suspected in patients with persistent skin lesions refractory to treatment with a history of immunosuppression or skin injury. A 59-year-old woman presented with a 4-week history of multiple erythematous tender nodules on left arm. A year prior, multiple nodules appeared on left hand dorsum, followed by recurrent suppurative nodules in left arm. She has been taking methotrexate and leflunomide for 7 years due to rheumatoid arthritis (RA). Skin biopsy revealed granulomatous inflammation, and NTM polymerase chain reaction test was positive. Furthermore, she had cut her left finger with a knife 14 months ago. Based on these findings, cutaneous NTM infection was diagnosed. Herein, we report a case of cutaneous NTM infection in an immunosuppressed patient with RA, emphasizing differentiating subcutaneous nodules from rheumatoid nodules in RA. © 2024 Korean Dermatological Association. All rights reserved. | Ha, Nam Gyoung; Ha, Dae-Lyong; Kim, Jun Young; Jang, Yong Hyung; Lee, Weon Ju; Lee, Seok Jong; Park, Kyung Duck | Department of Dermatology, Kyungpook National University School of Medicine, Daegu, South Korea; Department of Dermatology, Kyungpook National University School of Medicine, Daegu, South Korea; Department of Dermatology, Kyungpook National University School of Medicine, Daegu, South Korea; Department of Dermatology, Kyungpook National University School of Medicine, Daegu, South Korea; Department of Dermatology, Kyungpook National University School of Medicine, Daegu, South Korea; Department of Dermatology, Kyungpook National University School of Medicine, Daegu, South Korea; Department of Dermatology, Kyungpook National University School of Medicine, Daegu, South Korea | 57772600900; 57201367090; 35310922800; 59821699900; 24474659000; 56013454400; 55767995700 | gdpk1217@naver.com; | Korean Journal of Dermatology | 0494-4739 | 62 | 4 | 0 | 2025-05-07 | 0 | Immunosuppression therapy; Nontuberculous mycobacteria; Rheumatoid arthritis | English | Final | 2024 | 바로가기 | ||||||||||||||||||
| ○ | Book chapter | Cybersecurity measures for logistics industry | The logistics sector plays a crucial role in the worldwide economy by facilitating the efficient and punctual transportation of commodities and services. Nevertheless, the growing reliance on technological and digital infrastructure has highlighted the significance of implementing resilient cybersecurity protocols within the logistics industry. The occurrence of cybersecurity threats within the logistics industry may result in notable ramifications such as financial losses, impairment of reputation, and disturbances to the supply chain. The chapter investigates the various categories of cybersecurity threats confronting the logistics sector while furnishing instances of recent cybersecurity assaults perpetrated against companies within this industry. Furthermore, this chapter delves into several cybersecurity frameworks and strategies that can be adopted to forestall or alleviate these threats. By implementing these measures, logistics companies can secure their operations, clients, and affiliates from cyber threats. © 2024, IGI Global. All rights reserved. | Sindiramutty, Siva Raja; Jhanjhi, Noor Zaman; Tan, Chong Eng; Khan, Navid Ali; Shah, Bhavin; Manchuri, Amaranadha Reddy | Taylor's University, Malaysia; Taylor's University, Malaysia; Universiti Malaysia Sarawak, Malaysia; Taylor's University, Malaysia; Lok Jagruti University, India; Kyungpook National University, South Korea | 57216348438; 36088700700; 25825561000; 57216537861; 58976933600; 58343647900 | Navigating Cyber Threats and Cybersecurity in the Logistics Industry | 9.98 | 2025-05-07 | 14 | English | Final | 2024 | 10.4018/979-8-3693-3816-2.ch001 | 바로가기 | 바로가기 | |||||||||||||||||||||
| ○ | Book chapter | Cytokinin Signaling in Plant Response to Abiotic Stresses | Cytokinins, a type of plant hormone, play a vital and diverse role in regulating various aspects of plant growth and development. They are crucial for fundamental plant processes such as cell division, shoot initiation, leaf aging, and nutrient distribution (Mok and Mok 2001, Kieber and Schaller 2014). Typically cytokinins are synthesized in the roots, and then transported throughout the plant, exerting their effects through intricate signal transduction pathways (Hwang et al. 2012). One significant effect of cytokinins is their ability to stimulate cell division, resulting in an increase in cell numbers and subsequent growth of plant organs. Moreover, cytokinins play a crucial role in shoot initiation, ensuring the development of new shoots from dormant buds. Additionally, these hormones regulate leaf aging by extending the lifespan of leaves and sustaining photosynthetic activity for an extended period. Furthermore, cytokinins facilitate the mobilization of nutrients, aiding in the efficient uptake and transportation of essential elements required for plant growth and metabolism. Extensive research has provided substantial evidence for the remarkable impact of cytokinins on plant growth. Their application has consistently shown improvements in shoot and root development, promotion of enhanced flowering, and overall increased yield (Werner et al. 2001, Sakakibara 2006). By influencing cellular processes, cytokinins actively contribute to the formation of new tissues and the activation of growth-promoting genes. © 2024 CRC Press. | Ullah, Izhar; Danish, Muhammad; Basit, Toor Abdul; Mohamed, Heba I. | Department of Horticulture, Faculty of Agriculture, Ondokuz Mayis University, Samsun, Atakum, 55139, Turkey; Department of Soil Science and Plant Nutrition, Ondokuz Mayis University, Samsun, Atakum, 55139, Turkey; Department of Horticultural Science, Kyungpook National University, Daegu, 41566, South Korea; Department of Biological and Geological Sciences, Faculty of Education, Ain Shams, University, Cairo, 11341, Egypt | 57211559793; 7004009956; 59392677600; 37102371200 | hebaibrahim79@gmail.com; | Plant Growth Regulators to Manage Biotic and Abiotic Stress in Agroecosystems | 3.89 | 2025-05-07 | 1 | English | Final | 2024 | 10.1201/9781003389507-15 | 바로가기 | 바로가기 | ||||||||||||||||||||
| ○ | Conference paper | DAFT-GAN: Dual Affine Transformation Generative Adversarial Network for Text-Guided Image Inpainting | In recent years, there has been a significant focus on research related to text-guided image inpainting. However, the task remains challenging due to several constraints, such as ensuring alignment between the image and the text, and maintaining consistency in distribution between corrupted and uncorrupted regions. In this paper, thus, we propose a dual affine transformation generative adversarial network (DAFT-GAN) to maintain the semantic consistency for text-guided inpainting. DAFT-GAN integrates two affine transformation networks to combine text and image features gradually for each decoding block. Moreover, we minimize information leakage of uncorrupted features for fine-grained image generation by encoding corrupted and uncorrupted regions of the masked image separately. Our proposed model outperforms the existing GAN-based models in both qualitative and quantitative assessments with three benchmark datasets (MS-COCO, CUB, and Oxford) for text-guided image inpainting. © 2024 Owner/Author. | Lee, Jihoon; Min, Yunhong; Kim, Hwidong; Ahn, Sangtae | School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, South Korea; School of Electronics Engineering, Kyungpook National University, Daegu, South Korea; School of Electronics Engineering, Kyungpook National University, Daegu, South Korea; School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, South Korea | 59448690600; 59884155000; 59317419800; 55468016100 | stahn@knu.ac.kr; | MM 2024 - Proceedings of the 32nd ACM International Conference on Multimedia | 1.43 | 2025-05-07 | 1 | dual affine transformation; semantic consistency; separated mask convolution; text-guided image inpainting | Adversarial machine learning; Affine transforms; Adversarial networks; Affine transformations; Dual affine transformation; Guided images; Image Inpainting; Inpainting; Semantic consistency; Separated mask convolution; Text feature; Text-guided image inpainting; Generative adversarial networks | English | Final | 2024 | 10.1145/3664647.3681662 | 바로가기 | 바로가기 | ||||||||||||||||||
| ○ | Book chapter | Data security and privacy concerns in drone operations | The widespread use of drones across various industries is leading to significant transformations. However, the resulting concerns about data security and privacy are quite significant. This section offers a thorough exploration of these important issues, providing insights into the challenges they pose and potential ways to address them. Starting with an overview of the increasing utility of drones, this chapter highlights the importance of strong protocols for data security and privacy. By examining the complexities of data collection and storage, it reveals the different types of data that drones gather, delves into storage techniques, and reveals vulnerabilities, setting the stage for effective countermeasures. At the core of this discussion are cybersecurity risks, which range from cyberattacks on drone systems to unauthorized access and tampering of data. To sum up, this chapter serves as a comprehensive guide to understanding, addressing, and mitigating concerns related to data security and privacy in drone operations. © 2024, IGI Global. All rights reserved. | Sindiramutty, Siva Raja; Jhanjhi, Noor Zaman; Tan, Chong Eng; Yun, Khor Jia; Manchuri, Amaranadha Reddy; Ashraf, Humaira; Murugesan, Raja Kumar; Tee, Wee Jing; Hussain, Manzoor | Taylor's University, Malaysia; School of Computing Science, Taylor's University, Malaysia; Universiti Malaysia Sarawak, Malaysia; Tunku Abdul Rahman University of Management and Technology, Malaysia; Kyungpook National University, South Korea; Taylor's University, Malaysia; Taylor's University, Malaysia; Taylor's University, Malaysia; Indus University, Pakistan | 57216348438; 36088700700; 25825561000; 59000456800; 58343647900; 57210684343; 57198406478; 57202002663; 59000687600 | Cybersecurity Issues and Challenges in the Drone Industry | 18.51 | 2025-05-07 | 17 | English | Final | 2024 | 10.4018/979-8-3693-0774-8.ch010 | 바로가기 | 바로가기 | |||||||||||||||||||||
| ○ | Article | DC Motor Current Control Based on Inverse Model Using Recurrent Neural Network | This paper presents a method for microcontrollers control using a recurrent neural network-based inverse model. Limited computational power in microcontrollers makes applying complex neural network structures challenging. We use the Elman network with a simple structure as an inverse model to address this issue. Elman network was used to model nonlinear control systems. The proposed method constructs a recurrent neural network-based inverse model in parallel to enhance the performance of the PID controller. The recurrent neural network uses the output generated by the PID controller as the past control input and compensates for the control inputs generated by the PID controller. We applied the proposed controller to a DC motor current control system and compared its performance with the PID controller that uses a deep neural network as an inverse model. We evaluated the control performance by applying a sine wave. The results show that the proposed controller has better tracking performance at 1, 3, and 5 Hz than the other controllers. © ICROS 2024. | Baek, Dong-Min; Joe, Hyun-Min | Department of Robot and Smart System Engineering, Kyungpook National University, South Korea; Department of Robot and Smart System Engineering, Kyungpook National University, South Korea | 58040177100; 57188687051 | hmjoe@knu.ac.kr; | Journal of Institute of Control, Robotics and Systems | 1976-5622 | 30 | 1 | 0.46 | 2025-04-16 | 1 | current control; DC motor; Inverse model; microcontroller; Recurrent Neural Network | Controllers; DC motors; Deep neural networks; Electric control equipment; Electric current control; Electric machine control; Inverse problems; Microcontrollers; Proportional control systems; Three term control systems; Complex neural networks; Computational power; Control inputs; D.C. motors; Elman network; Inverse modelling; Motor currents; Network-based; Performance; PID controllers; Recurrent neural networks | Korean | Final | 2024 | 10.5302/j.icros.2024.23.0186 | 바로가기 | 바로가기 | |||||||||||||||
| ○ | Conference paper | De-Identification of Sensitive Personal Data in Datasets Derived from IIT-CDIP | The IIT-CDIP document collection is the source of several widely used and publicly accessible document understanding datasets. In this paper, manual inspection of 5 datasets derived from IIT-CDIP uncovers the presence of thousands of instances of sensitive personal data, including US Social Security Numbers (SSNs), birth places and dates, and home addresses of individuals. The presence of such sensitive personal data in commonly-used and publicly available datasets is startling and has ethical and potentially legal implications; we believe such sensitive data ought to be removed from the internet. Thus, in this paper, we develop a modular data de-identification pipeline that replaces sensitive data with synthetic, but realistic, data. Via experiments, we demonstrate that this de-identification method preserves the utility of the de-identified documents so that they can continue be used in various document understanding applications. We will release redacted versions of these datasets publicly. © 2024 Association for Computational Linguistics. | Larson, Stefan; Joshi, Amogh Manoj; Mathur, Yash; Shen, Junjie; Lima, Nicole Cornehl; Betala, Siddharth; Prajapati, Kaushal Kumar; Okotore, Temi; Díaz, Santiago Pedroza; Suleiman, Jamiu Tunde; Alakraa, Ramla; Leach, Kevin | Vanderbilt University, United States, ML Collective; ML Collective, Arizona State University, United States; ML Collective, Carnegie Mellon University, United States; University of Michigan, United States; University of Michigan, United States; ML Collective, IIT Madras, India; ML Collective; University of Michigan, United States; ML Collective, Universidad Panamericana, Mexico; ML Collective, Kyungpook National University, South Korea; University of Michigan, United States; Vanderbilt University, United States | 57216693314; 57223939719; 58508764400; 59556896600; 59556694600; 58679655800; 58654279900; 59556896700; 59557909700; 58571666300; 59557094800; 57908411800 | stefan.larson@vanderbilt.edu; | EMNLP 2024 - 2024 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference | 0 | 2025-05-07 | 0 | Data assimilation; Sensitive data; De-identification; Document collection; Document understanding; Home address; Identification method; Legal implications; Manual inspection; Modular data; Publicly accessible; Social security numbers; Data collection | English | Final | 2024 | 10.18653/v1/2024.emnlp-main.1198 | 바로가기 | 바로가기 | |||||||||||||||||||
| ○ | Conference paper | Decoding White Matter Fiber ODFs: A Mixture Learning Framework in x-q Space | Diffusion magnetic resonance imaging (dMRI), as a powerful non-invasive white matter imaging technology, plays an important role in studying brain white matter. The fiber orientation distribution functions (fODFs) derived from dMRI data provide the key directional information of fiber tracts for revealing the 3D geometric structure of brain white matter. The estimation of fODFs faces two challenges, including (i) the demand for dMRI data densely sampled in q-space and (ii) the joint consideration of x-q space. To address these challenges, we propose a mixture learning framework with q-space sparely sampled dMRI data as input. Specifically, we propose an x-space learning module based on 3D U-Net to learn x-space features and a q-space learning module based on spherical convolutional neural networks to learn q-space features. Two kinds of features are then fused with a mixture learning fusion module for fODFs estimation. The whole framework is supervised with an x-q space loss function. Our framework makes full use of joint x-q space information for fODFs estimation with clinically available q-space sparsely sampled dMRI data. Extensive experiments on three public datasets show that our framework is effective in fODFs estimation and outperforms cutting-edge models. © 2024 IEEE. | Ma, Jiquan; Deng, Chengdong; Chen, Geng; Jiang, Haotian; Huang, Shijie; Kim, Jaeil; Wen, Xuyun; Shen, Dinggang | Heilongjiang University, Department of Computer Science and Technology, Harbin, China; Heilongjiang University, Department of Computer Science and Technology, Harbin, China; Northwestern Polytechnical University, School of Computer Science and Engineering, Xi'an, China; Northwestern Polytechnical University, School of Computer Science and Engineering, Xi'an, China; ShanghaiTech University, School of Biomedical Engineering, State Key Laboratory of Advanced Medical Materials and Devices, Shanghai, China; Kyungpook National University, School of Computer Science and Engineering, Daegu, South Korea; Nanjing University of Aeronautics and Astronautics, School of Computer Science and Technology, Nanjing, China; ShanghaiTech University, School of Biomedical Engineering, State Key Laboratory of Advanced Medical Materials and Devices, Shanghai, China, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China, Shanghai Clinical Research and Trial Center, Shanghai, China | 36078806400; 58655791100; 56903235100; 57907593100; 58453621500; 59812532000; 57204364884; 57226254008 | geng.chen.cs@gmail.com; | Proceedings - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024 | 0 | 2025-05-07 | 0 | Diffusion MRI; Fiber ODFs; Spherical Convolution; x-q space | Brain mapping; Convolutional neural networks; Diffusion tensor imaging; Image coding; Image compression; Image segmentation; Image thinning; Intelligent systems; Spheres; Time difference of arrival; Diffusion magnetic resonance imaging; Diffusion MRI; Fiber ODF; Fiber orientation distribution; Orientation distribution function; Q-Space; Resonance imaging data; Spherical convolution; White matter; X-q space; Magnetic resonance imaging | English | Final | 2024 | 10.1109/bibm62325.2024.10822650 | 바로가기 | 바로가기 | ||||||||||||||||||
| ○ | Article | Deep belief networks based radar signal classification system | A threat library is used in most of the existing electronic warfare systems to identify or execute jamming against various radar signals. The conventional method uses frequency, pulse repetition interval, and pulse width sampled from the pulse description word column as characteristics of a signal. Such sampling technique cannot effectively model each radar signal when dealing with a complex signal array. In this paper, a new deep belief network model is proposed to generate a more efficient threat library for radar signal classification. The proposed model consists of independent restricted Boltzman machines (RBMs) of frequency, pulse repetition interval, pulse width respectively, and a RBM which fuses the result again. The performance of the existing system and the proposed system is evaluated by testing the signals with measurement errors and insufficient information. As a result, the proposed system shows more than 6% performance improvement over the existing system. © Springer-Verlag GmbH Germany, part of Springer Nature 2018. | Jeong, Chang Min; Jung, Young Giu; Lee, Sang Jo | Agency for Defense Development, Yuseong, Daejeon, South Korea; YM-Naeultech, Inharo, Namgu, Incheon, South Korea; Department of Computer Engineering, Kyungpook National University, Daegu, South Korea | 57194784873; 36960138200; 55716479700 | min@add.re.kr; | Journal of Ambient Intelligence and Humanized Computing | 1868-5137 | 15 | 2 | 0.48 | 2025-05-07 | 7 | BP; DBN; Radar signal classification; Threat library | Military applications; BP; DBN; Deep belief networks; Performance; Pulse repetition intervals; Pulsewidths; Pulswidths; Radar signal classifications; Radar signals; Threat library; Electronic warfare | English | Final | 2024 | 10.1007/s12652-018-0774-7 | 바로가기 | 바로가기 | |||||||||||||||
| ○ | ○ | Proceedings Paper | DEEP LEARNING APPROACH FOR CLASSIFICATION OF WATER BOTTOM AND SURFACE FROM BATHYMETRIC LIDAR POINT CLOUDS | This study investigates the application of PointNet, a deep learning architecture, to classify bathymetric LiDAR point clouds in shallow waters. Using a dataset from Marco Island's southern coast, Florida, the research categorized water levels into noise, surface, column, and bottom classes through Gaussian curve fitting and novel rule-based approaches. PointNet was trained considering critical parameters such as batch size, epochs, learning rate, and optimizer. Results indicated that a batch size of 8 yielded higher validation accuracy (0.7001) compared to 16 (0.6926). Evaluation showcased an approximate 70% accuracy, distinguishing noise, surface, bottom, and column points. While some ambiguity existed between surface and column points, differentiation between bottom, surface, and column was evident. This study demonstrates PointNet's feasibility for bathymetric LiDAR classification in shallow waters and emphasizes optimizing parameters for enhanced accuracy and performance. | Song, Ahram; Kim, Hyejin | Kyungpook Natl Univ, Dept Locat Based Informat Syst, Sangju, South Korea; Konkuk Univ, Social Ecotech Inst, Seoul, South Korea | 56496312900; 59342253700 | 2024 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2024) | 2153-6996 | 0 | 2025-05-07 | 0 | 0 | Bathymetric LiDAR; Point clouds; Water bottom; Water surface; PointNet | Bathymetric LiDAR; Point clouds; PointNet; Water bottom; Water surface | English | 2024 | 2024 | 10.1109/igarss53475.2024.10641485 | 바로가기 | 바로가기 | 바로가기 |
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