• Smart Structures and Systems
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• v.6 no.5_6
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• pp.423-438
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• 2010

Wireless smart sensors enable new approaches to improve structural health monitoring (SHM) practices through the use of distributed data processing. Such an approach is scalable to the large number of sensor nodes required for high-fidelity modal analysis and damage detection. While much of the technology associated with smart sensors has been available for nearly a decade, there have been limited numbers of fulls-cale implementations due to the lack of critical hardware and software elements. This research develops a flexible wireless smart sensor framework for full-scale, autonomous SHM that integrates the necessary software and hardware while addressing key implementation requirements. The Imote2 smart sensor platform is employed, providing the computation and communication resources that support demanding sensor network applications such as SHM of civil infrastructure. A multi-metric Imote2 sensor board with onboard signal processing specifically designed for SHM applications has been designed and validated. The framework software is based on a service-oriented architecture that is modular, reusable and extensible, thus allowing engineers to more readily realize the potential of smart sensor technology. Flexible network management software combines a sleep/wake cycle for enhanced power efficiency with threshold detection for triggering network wide operations such as synchronized sensing or decentralized modal analysis. The framework developed in this research has been validated on a full-scale a cable-stayed bridge in South Korea.

• Journal of Veterinary Science
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• v.22 no.4
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• pp.50.1-50.10
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• 2021

Background: Bovine papillomatosis is a type of proliferative tumor disease of skin and mucosae caused by bovine papillomavirus (BPV). As a transboundary and emerging disease in cattle, it poses a potential threat to the dairy industry. Objectives: The aim of this study is to detect and clarify the genetic diversity of BPV circulating in dairy cows in Xinjiang, China. Methods: 122 papilloma skin lesions from 8 intensive dairy farms located in different regions of Xinjiang, China were detected by polymerase chain reaction. The genetic evolution relationships of various types of BPVs were analyzed by examining this phylogenetic tree. Results: Ten genotypes of BPV (BPV1, BPV2, BPV3, BPV6, BPV7, BPV8, BPV10, BPV11, BPV13, and BPV14) were detected and identified in dairy cows. These were the first reported detections of BPV13 and BPV14 in Xinjiang, Mixed infections were detected, and there were geographical differences in the distribution of the BPV genotypes. Notably, the BPV infection rate among young cattle (< 1-year-old) developed from the same supply of frozen sperm was higher than that of the other young cows naturally raised under the same environmental conditions. Conclusions: Genotyping based on the L1 gene of BPV showed that BPVs circulating in Xinjiang China displayed substantial genetic diversity. This study provided valuable data at the molecular epidemiology level, which is conducive to developing deep insights into the genetic diversity and pathogenic characteristics of BPVs in dairy cows.

• Korean Journal of Agricultural Science
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• v.46 no.2
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• pp.381-393
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• 2019

A smart farm is a system that combines information and communication technology (ICT), internet of things (IoT), and agricultural technology that enable a farm to operate with minimal labor and to automatically control of a greenhouse environment. Machine learning based on recently data-driven techniques has emerged with big data technologies and high-performance computing to create opportunities to quantify data intensive processes in agricultural operational environments. This paper presents research on the application of machine learning technology to diagnose the growth status of crops and predicting the harvest time of strawberries in a greenhouse according to image processing techniques. To classify the growth stages of the strawberries, we used object inference and detection with machine learning model based on deep learning neural networks and TensorFlow. The classification accuracy was compared based on the training data volume and training epoch. As a result, it was able to classify with an accuracy of over 90% with 200 training images and 8,000 training steps. The detection and classification of the strawberry maturities could be identified with an accuracy of over 90% at the mature and over mature stages of the strawberries. Concurrently, the experimental results are promising, and they show that this approach can be applied to develop a machine learning model for predicting the strawberry harvesting time and can be used to provide key decision support information to both farmers and policy makers about optimal harvest times and harvest planning.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4290-4309
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• 2020

In recent years, advancements in machine learning capabilities have allowed it to see widespread adoption for tasks such as object detection, image classification, and anomaly detection. However, despite their promise, a limitation lies in the fact that a network's performance quality is based on the data which it receives. A well-trained network will still have poor performance if the subsequent data supplied to it contains artifacts, out of focus regions, or other visual distortions. Under normal circumstances, images of the same scene captured from differing points of focus, angles, or modalities must be separately analysed by the network, despite possibly containing overlapping information such as in the case of images of the same scene captured from different angles, or irrelevant information such as images captured from infrared sensors which can capture thermal information well but not topographical details. This factor can potentially add significantly to the computational time and resources required to utilize the network without providing any additional benefit. In this study, we plan to explore using image fusion techniques to assemble multiple images of the same scene into a single image that retains the most salient key features of the individual source images while discarding overlapping or irrelevant data that does not provide any benefit to the network. Utilizing this image fusion step before inputting a dataset into the network, the number of images would be significantly reduced with the potential to improve the classification performance accuracy by enhancing images while discarding irrelevant and overlapping regions.

• Journal of Electrochemical Science and Technology
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• v.13 no.4
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• pp.431-437
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• 2022

As one of the most hazardous and deadliest pathogens, Listeria monocytogenes (LM) posed various serious diseases to the human being, thus designing effective strategy for its detection is of great significance. In this work, by preparing Ti₃C₂T_x MXenes nanoribbon (Ti₃C₂T_xR) as carrier and selecting thionine (Th) acted simultaneously as signal probe and functional monomer, a LM pathogen-imprinted polymers (PIP) integrated probe electrochemical sensor was design to monitor LM for the first time, that was carried out through the electropolymerization of Th on the Ti₃C₂T_xR/GCE surface in the existence of LM. Upon eluting the templates from the LM imprinted cavities, the fabricated PIP/Ti₃C₂T_xR/GCE sensor can rebound LM cells effectively. By recording the peak current of Th as the response signal, it can be weakened when LM cell was re-bound to the LM imprinted cavity on PIP/Ti₃C₂T_xR/GCE, and the absolute values of peak current change increase with the increasement of LM concentrations. After optimizing three key parameters, a considerable low analytical limit (2 CFU mL^-1) and wide linearity (10-10⁸ CFU mL^-1) for LM were achieved. In addition, the experiments demonstrated that the PIP/Ti₃C₂T_xR sensor offers satisfactory selectivity, reproducibility and stability.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.6
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• pp.1165-1170
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• 2022

In WSNs, a clustering algorithm groups sensor nodes on a unit called cluster and periodically selects a cluster head (CH) that acts as a communication relay on behalf of nodes in each cluster for the purpose of energy conservation and relay efficiency. Meanwhile, attack techniques also have emerged to intervene in the CH election process through compromised nodes (inside attackers) and have a fatal impact on network operation. However, existing countermeasures such as encryption key-based methods against outside attackers have a limitation to defend against such inside attackers. Therefore, we propose a statistical detection method that detects abnormal CH election behaviors occurs in a WSN cluster. We design two attack methods (Selfish and Greedy attacks) and our proposed defense method in WSNs with two clustering algorithms and conduct experiments to validate our proposed defense method works well against those attacks.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.2
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• pp.62-69
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• 2022

An electrocardiogram (ECG) is one of the most important biosignals, and in particular, continuous ECG monitoring is very important in patients with arrhythmia. There are many different types of arrhythmia (sinus node, sinus tachycardia, atrial premature beat (APB), and ventricular fibrillation) depending on the cause, and continuous ECG monitoring during daily life is very important for early diagnosis of arrhythmias and setting treatment directions. The ECG signal of arrhythmia patients is very unstable, and it is difficult to detect the R-peak point, which is a key feature for automatic arrhythmias detection. In this study, we develped a continuous measuring Holter ECG monitoring device and software for analysis and confirmed the utility of R-peak of the ECG signal with MIT-BIH arrhythmia database. In future studies, it needs the validation of algorithms and clinical data for morphological classification and prediction of arrhythmias due to various etiologies.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.569-582
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• 2023

Lead-acid Battery is the oldest rechargeable battery system and has maintained its position in the rechargeable battery field. The battery causes thermal runaway for various reasons, which can lead to major accidents. Therefore, preventing thermal runaway is a key part of the battery management system. Recently, research is underway to categorize thermal runaway battery cells into machine learning. In this paper, we present a thermal runaway hazard cell detection and verification algorithm using DBSCAN and statistical method. An experiment was conducted to classify thermal runaway hazard cells using only the resistance values as measured by the Battery Management System (BMS). The results demonstrated the efficacy of the proposed algorithms in accurately classifying thermal runaway cells. Furthermore, the proposed algorithm was able to classify thermal runaway cells between thermal runaway hazard cells and cells containing noise. Additionally, the thermal runaway hazard cells were early detected through the optimization of DBSCAN parameters using a grid search approach.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.1-6
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• 2023

In the realm of dental prosthesis fabrication, obtaining accurate impressions has historically been a challenging and inefficient process, often hindered by hygiene concerns and patient discomfort. Addressing these limitations, Company D recently introduced a cutting-edge solution by harnessing the potential of intraoral scan images to create 3D dental models. However, the complexity of these scan images, encompassing not only teeth and gums but also the palate, tongue, and other structures, posed a new set of challenges. In response, we propose a sophisticated real-time image segmentation algorithm that selectively extracts pertinent data, specifically focusing on teeth and gums, from oral scan images obtained through Company D's oral scanner for 3D model generation. A key challenge we tackled was the detection of the intricate molar regions, common in dental imaging, which we effectively addressed through intelligent data augmentation for enhanced training. By placing significant emphasis on both accuracy and speed, critical factors for real-time intraoral scanning, our proposed algorithm demonstrated exceptional performance, boasting an impressive accuracy rate of 0.91 and an unrivaled FPS of 92.4. Compared to existing algorithms, our solution exhibited superior outcomes when integrated into Company D's oral scanner. This algorithm is scheduled for deployment and commercialization within Company D's intraoral scanner.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.4
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• pp.67-79
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• 2023

Service environments through laptops, smart devices, and various IoT devices are developing very rapidly. Recent security measures in these Internet environments mainly consist of network application level solutions such as firewall(Intrusion Prevention Systems) and IDS (intrusion detection system). In addition, various security data have recently been used on-site, and issues regarding the management and destruction of such security data have been raised. Products such as DRM(Digital Rights Management) and DLP(Data Loss Prevention) are being used to manage these security data. However despite these security measures, data security measures taken out to be used in the field are operated to the extent that the data is encrypted, delivered, and stored in many environments, and measures for encryption key management or data destruction are insufficient. Based on these issues we aim to propose a SecureOS Module, an OS-based security module. With this module users can manage and operate security data through a consistent interface, addressing the problems mentioned above.