• Journal of Apiculture
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• v.32 no.3
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• pp.191-198
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• 2017

Vespa hornets are notorious predators of honeybees in Korean beekeeping. Detection of vespa hornet attacking on honeybee colony was tried through analysis of wing beat frequency profiling from Vespa mandarinia. Wing beat profiles of V. mandarinia during active flight and resting were distinctively different. From the wing beat profiling, algorithm of automated detection of vespa attack was encoded, and alert system was developed using Teensy 3.2 and Raspberry pi 3. From the laboratory testing, the prototype system successfully detected vespa wing beats and delivered the vespa attack information to the user wirelessly. Further development of the system could help precision alert system of the vespa attack to apiary.

• Korean Journal of Remote Sensing
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• v.40 no.3
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• pp.257-268
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• 2024

Drone-mounted hyperspectral sensors (DHSs) have revolutionized remote sensing in agriculture by offering a cost-effective and flexible platform for high-resolution spectral data acquisition. Their ability to capture data at low altitudes minimizes atmospheric interference, enhancing their utility in agricultural monitoring and management. This study focused on addressing the challenges of radiometric and geometric distortions in preprocessing drone-acquired hyperspectral data. Radiometric correction, using the empirical line method (ELM) and spectral reference panels, effectively removed sensor noise and variations in solar irradiance, resulting in accurate surface reflectance values. Notably, the ELM correction improved reflectance for measured reference panels by 5-55%, resulting in a more uniform spectral profile across wavelengths, further validated by high correlations (0.97-0.99), despite minor deviations observed at specific wavelengths for some reflectors. Geometric correction, utilizing a rubber sheet transformation with ground control points, successfully rectified distortions caused by sensor orientation and flight path variations, ensuring accurate spatial representation within the image. The effectiveness of geometric correction was assessed using root mean square error(RMSE) analysis, revealing minimal errors in both east-west(0.00 to 0.081 m) and north-south directions(0.00 to 0.076 m).The overall position RMSE of 0.031 meters across 100 points demonstrates high geometric accuracy, exceeding industry standards. Additionally, image mosaicking was performed to create a comprehensive representation of the study area. These results demonstrate the effectiveness of the applied preprocessing techniques and highlight the potential of DHSs for precise crop health monitoring and management in smart agriculture. However, further research is needed to address challenges related to data dimensionality, sensor calibration, and reference data availability, as well as exploring alternative correction methods and evaluating their performance in diverse environmental conditions to enhance the robustness and applicability of hyperspectral data processing in agriculture.

• Journal of KIISE:Software and Applications
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• v.35 no.12
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• pp.716-730
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• 2008

In Service-Oriented Architecture (SOA), service providers develop and deploy reusable services on the repositories, and service consumers utilize blackbox form of services through their interfaces. Services are also highly evolvable and often heterogeneous. Due to these characteristics of the service, it is hard to manage the faults if faults occur on the services. Autonomic Computing (AC) is a way of designing systems which can manage themselves without direct human intervention. Applying the key disciplines of AC to service management is appealing since key technical issues for service management can be effectively resolved by AC. In this paper, we present a theoretical model, Symptom-Cause-Actuator (SCA), to enable autonomous service fault management in SOA. We derive SCA model from our rigorous observation on how physicians treat patients. In this paper, we first define a five-phase computing model and meta-model of SCA. And, we define a schema of SCA profile, which contains instances of symptoms, causes, actuators and their dependency values in a machine readable form. Then, we present detailed algorithms for the five phases that are used to manage faults the services. To show the applicability of our approach, we demonstrate the result of our case study for the domain of 'Flight Ticket Management Services'.

• Allergy, Asthma & Immunology Research
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• v.10 no.6
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• pp.628-647
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• 2018

Purpose: Obesity is associated with metabolic dysregulation, but the underlying metabolic signatures involving clinical and inflammatory profiles of obese asthma are largely unexplored. We aimed at identifying the metabolic signatures of obese asthma. Methods: Eligible subjects with obese (n = 11) and lean (n = 22) asthma underwent body composition and clinical assessment, sputum induction, and blood sampling. Sputum supernatant was assessed for interleukin $(IL)-1{\beta}$, -4, -5, -6, -13, and tumor necrosis factor $(TNF)-{\alpha}$, and serum was detected for leptin, adiponectin and C-reactive protein. Untargeted gas chromatography time-of-flight mass spectrometry (GC-TOF-MS)-based metabolic profiles in sputum, serum and peripheral blood monocular cells (PBMCs) were analyzed by orthogonal projections to latent structures-discriminate analysis (OPLS-DA) and pathway topology enrichment analysis. The differential metabolites were further validated by correlation analysis with body composition, and clinical and inflammatory profiles. Results: Body composition, asthma control, and the levels of $IL-1{\beta}$, -4, -13, leptin and adiponectin in obese asthmatics were significantly different from those in lean asthmatics. OPLS-DA analysis revealed 28 differential metabolites that distinguished obese from lean asthmatic subjects. The validation analysis identified 18 potential metabolic signatures (11 in sputum, 4 in serum and 2 in PBMCs) of obese asthmatics. Pathway topology enrichment analysis revealed that cyanoamino acid metabolism, caffeine metabolism, alanine, aspartate and glutamate metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, pentose phosphate pathway in sputum, and glyoxylate and dicarboxylate metabolism, glycerolipid metabolism and pentose phosphate pathway in serum are suggested to be significant pathways related to obese asthma. Conclusions: GC-TOF-MS-based metabolomics indicates obese asthma is characterized by a metabolic profile different from lean asthma. The potential metabolic signatures indicated novel immune-metabolic mechanisms in obese asthma with providing more phenotypic and therapeutic implications, which needs further replication and validation.

• Journal of Advanced Navigation Technology
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• v.22 no.6
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• pp.530-536
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• 2018

Recently, many studies for interoperability of UAV in the NAS has been performed since the application range and demand of UAV are continuously increased. For the interoperation of UAV in the NAS, technical standards and certification system for UAV which is equivalent to the commercial aircraft are required and test and evaluation methodology must be presented by standards. In this paper, qualification test and evaluation methodology aboutfor the UAV navigation system is proposed. For the research, the mission profile and operation environment of UAV were analyzed. Thereafter the test criteria were derived and the test methodology were established. Finally, the simulation and demonstration using test-bed UAV were performed. As a result of the test, it was confirmed that the navigation system of test UAV has a position accuracy about 1.4 meters at 95% confidence level in the entire flight stage.

• Food Science and Preservation
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• v.30 no.2
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• pp.235-246
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• 2023

Yellowball (Citrus hybrid cv. Yellowball ) is a new citrus hybrid between Haruka (C. tamurana × natsudaidai ) and Kiyomi (C. unshiu × sinensis) and is known to possess strong antioxidant activity. However, detailed information on the antioxidant components of its peel has not yet been reported. This study evaluated the antioxidant activity of the peel and identified the antioxidant components by fractionating a methanolic extract of Yellowball peels using liquid-liquid extraction with n-hexane, ethyl ether (ether), ethyl acetate (EA), butanol, and water. The phenolic contents and antioxidant activities of the n-hexane, ether, and EA fractions were higher than those of the other fractions, and these fractions were further separated by semi-preparative high-performance liquid chromatography (HPLC). Four antioxidant peaks, EA1, EA2, EA3, and He1, were isolated and analyzed using ultra-performance liquid chromatography-quadrupole-time- of-flight mass spectrometry (UPLC-Q-TOF MS). Sinapoyl glucoside and hesperidin were identified in EA2 and EA3, respectively, and a polymethoxylated flavone (PMF) complex (5-hydroxy-3,6,7,8,3',4'-hexamethoxyflavone, natsudaidain, tetrameth- oxyflavone, and tangeretin) was identified in He1. A compound in EA1 with m/z 223.0246 [M-H] could not be identified and was named unknown2. The antioxidant activity of unknown2 (IC₅₀=69.17 ㎍/mL) was similar to that of Trolox, which was noted as a major antioxidant in Yellowball peel. Further studies on the antioxidant capacity of Yellowball peel are required; however, these results provide a foundation for using Yellowball peel as an antioxidant.