• Parasites, Hosts and Diseases
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• v.59 no.6
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• pp.615-623
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• 2021

Human sparganosis is a food-borne parasitic disease caused by the plerocercoids of Spirometra species. Clinical diagnosis of sparganosis is crucial for effective treatment, thus it is important to identify sensitive and specific antigens of plerocercoids. The aim of the current study was to identify and characterize the immunogenic proteins of Spirometra erinaceieuropaei plerocercoids that were recognized by patient sera. Crude soluble extract of the plerocercoids were separated using 2-dimensional gel electrophoresis coupled with immunoblot and mass spectrometry analysis. Based on immunoblotting patterns and mass spectrometry results, 8 antigenic proteins were identified from the plerocercoid. Among the proteins, cysteine protease protein might be developed as an antigen for diagnosis of sparganosis.

• Parasites, Hosts and Diseases
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• v.58 no.3
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• pp.301-308
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• 2020

Owing to global climate change, the global resurgence of vector-borne infectious diseases and their potential to inflict widespread casualties among human populations has emerged as a pivotal burden on public health systems. Tsutsugamushi disease (scrub typhus) in the Republic of Korea is steadily increasing and was designated as a legal communicable disease in 1994. The disease is a mite-borne acute febrile disease most commonly contracted from October to December. In this study, we tried to determine the prevalence of tsutsugamushi disease transmitted by chigger mites living on rodents and investigated their target vector diversity, abundance, and distribution to enable the mapping of hotspots for this disease in 2015. A total of 5 species belonging to 4 genera (109 mites): Leptotrombidium scutellare 60.6%, L. pallidum 28.4% Neotrombicula tamiyai 9.2%, Euschoengastia koreaensis/0.9%), and Neoschoengastia asakawa 0.9% were collected using chigger mite collecting traps mimicking human skin odor and sticky chigger traps from April to November 2015. Chigger mites causing tsutsugamushi disease in wild rodents were also collected in Hwaseong for the zoonotic surveillance of the vector. A total of 77 rodents belonging to 3 genera: Apodemus agrarius (93.5%), Crocidura lasiura (5.2%), and Micromys minutus (1.3%) were collected in April, October, and November 2015. The most common mite was L. pallidum (46.9%), followed by L. scutellare (18.6%), and L. orientale (18.0%). However, any of the chigger mite pools collected from rodent hosts was tested positive for Orientia tsutsugamushi, the pathogen of tsutsugamushi disease, in this survey.

• Journal of Electrical Engineering and Technology
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• v.10 no.6
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• pp.2326-2333
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• 2015

Stator turn faults in permanent magnet synchronous motors (PMSMs) are more dangerous than those in induction motors (IMs) because of the presence of spinning rotor magnets that can be turned off at will. Condition monitoring and fault detection and diagnosis of the PMSM have been receiving a growing amount of attention among scientists and engineers in the past few years. The aim of this study is to propose a new detection technique of stator winding faults in a three-phase PMSM. This technique is based on the image analysis and recognition of the stator current Concordia patterns, and will allow the identification of turn faults in the stator winding as well as its correspondent fault index severity. A test bench of a vector controlled PMSM motor behaviors under short circuited turn in two phases stator windings has been built. Some experimental results of the phase to phase short circuits have been performed for diagnosis purpose.

• Parasites, Hosts and Diseases
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• v.55 no.1
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• pp.55-60
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• 2017

Fascioliasis is a foodborne zoonotic parasitic disease. We report 4 cases occurring in the same family, in whom diagnosis of acute fascioliasis was established after series of tests. One case was hospitalized with fever, eosinophilia, and hepatic lesions. MRI showed hypodense changes in both liver lobes. The remaining 3 cases presented with the symptom of stomachache only. Stool analysis was positive for Fasciola eggs in 2 adult patients. The immunological test and molecular identification of eggs were confirmed at the National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, China. The results of serological detection were positive in all the 4 patients. DNA sequencing of PCR products of the eggs demonstrated 100% homology with ITS and cox1 of Fasciola hepatica. The conditions of the patients were not improved by broad-spectrum anti-parasitic drugs until administration of triclabendazole.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.01a
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• pp.19-20
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• 2016

본 논문에서는 서포트 벡터 머신 (SVM)의 분류 성능에 영향을 주는 인수인 C와 ${\sigma}$ 값을 적응적으로 최적화할 수 있는 적응적 자가튜닝 SVM을 이용한 베어링의 상태 진단 방법을 제안한다. SVM의 각 인수의 변화에 따른 베어링 상태 진단의 성능 변화 패턴을 분석하여 적합한 인수를 적응적으로 찾을 수 있는 방법을 제안하고, 제안한 방법의 우수성을 검증하기 위해 실제 베어링 신호를 이용하여 기존방법인 격자탐색과의 성능을 비교하였다.

• Journal of information and communication convergence engineering
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• v.3 no.2
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• pp.105-109
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• 2005

This paper proposed a fault diagnosis technique of induction motors winding fault based on an artificial neural network (ANN). This method used Park's vector pattern as input data of ANN. The ANN are firstly learned using this pattern, and then classify between 'healthy' and 'winding fault' (with 2, 10, and 20 shorted turn) induction motor under 0, 50, and $100\%$ load condition. Also the possibility of classification of untrained turn-fault and load condition are tested. The proposed method has been experimentally tested on a 3-phase, 1 HP squirrel-cage induction motor. The obtained results provided a high level of accuracy especially in small turn fault, and showed that it is a reliable method for industrial application

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.18 no.4
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• pp.104-111
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• 2004

In this paper, we propose a diagnosis algorithm to detect faults of induction motor using LDA First, after reducing the input dimension of a current value measured by experiment at each period using PCA method, we extract characteristic vectors for each fault using LDA Next, we analyze the driving condition of an induction motor using the Euclidean distance between a precalculated characteristic vector and an input vector. Finally, from the experiments under various noise conditions showing the properties of the LDA method, we obtained better results than the case of using the PCA method.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.1
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• pp.97-102
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• 2013

Objective: The aim of this study was to develop an automated computer-aided diagnostic system for diagnosis of thyroid cancer pattern in fine needle aspiration cytology (FNAC) microscopic images with high degree of sensitivity and specificity using statistical texture features and a Support Vector Machine classifier (SVM). Materials and Methods: A training set of 40 benign and 40 malignant FNAC images and a testing set of 10 benign and 20 malignant FNAC images were used to perform the diagnosis of thyroid cancer. Initially, segmentation of region of interest (ROI) was performed by region-based morphology segmentation. The developed diagnostic system utilized statistical texture features derived from the segmented images using a Gabor filter bank at various wavelengths and angles. Finally, the SVM was used as a machine learning algorithm to identify benign and malignant states of thyroid nodules. Results: The SVMachieved a diagnostic accuracy of 96.7% with sensitivity and specificity of 95% and 100%, respectively, at a wavelength of 4 and an angle of 45. Conclusion: The results show that the diagnosis of thyroid cancer in FNAC images can be effectively performed using statistical texture information derived with Gabor filters in association with an SVM.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.25 no.9
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• pp.637-643
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• 2015

In recent years, pattern recognition methods have been widely used by many researchers for fault diagnoses of mechanical systems. The soundness of a mechanical system can be checked by analyzing the variation of the system vibration characteristic along with a pattern recognition method. Recently, the hidden Markov model has been widely used as a pattern recognition method in various fields. In this paper, the hidden Markov model is employed for the fault diagnosis of the mass unbalance of a rotating system. Mass unbalance is one of the critical faults in the rotating system. A procedure to identity the location and size of the mass unbalance is proposed and the accuracy of the procedure is validated through experiment.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.15 no.4
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• pp.23-29
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• 2016

Rotary machine is major equipment in industry. The rotary machine is applied for a machine tool, ship, vehicle, power plant, and so on. But a spindle fault increase product's expense and decrease quality of a workpiece in machine tool. A turbine in power plant is directly connected to human safety. National crisis could be happened by stopping of rotary machine in nuclear plant. Therefore, it is very important to know rotary machine condition in industry field. This study mentioned fault diagnosis algorithm with statistical parameter and empirical mode decomposition. Vibration locations can be found by analyze kurtosis of data from triaxial axis. Support vector of data determine threshold using hyperplane with fault location. Empirical mode decomposition is used to find fault caused by intrinsic mode. This paper suggested algorithm to find direction and causes from generated fault.