• Journal of Chest Surgery
• /
• v.33 no.8
• /
• pp.688-692
• /
• 2000

Descending Necrotizing Mediastinitis(DNM) is a complication of oropharyngeal infections that can spread to the mediastinum. It is difficult to diagnose early because clinical and radiologic findings appear in the late stage of the infection. late diagnosis is the principal reason for the high mortality in DNM. An 18-year-old female admitted with Ludwig's angina from dental caries. Despite of combined antibiotics, dental extraction and drainge of submental abscess, infection spread to the cervical area. Chest computed tomogram revealed extension of the abscess to the pretracheal and periaortic space and development of bilateral pleural empyema. We performed bilateral cervical mediastinotomy and thoracotomy for drainage and debridement. Tracheostomy to secure the airway and postoperative pleural irrigation were performed. Postoperative course was uneventful and patient was discharged on the 40th postoperative day. It is important to perform chest CT scanning for early diagnosis of DNM when oropharyngeal infection spreads to the cervical area. Improved survival of patients with DNM implies early and radical surgical drainage and debridement via a cervical mediastinomy and thoracotomy.

• Proceedings of the KIEE Conference
• /
• 2001.07d
• /
• pp.2723-2725
• /
• 2001

Nuclear power generations have been developed gradually since 1950. Nowadays, 440 nuclear power generations are taking charge of 16% of electric power production in the world. The most important factor to operate the nuclear power generations is safety. It is not easy way to control nuclear power generations with safety because nuclear power generations are very complicated systems. In the main control room of the nuclear power generations, about 4000 numbers of alarms and monitoring devices are equipped to handle the signals corresponding to operating equipments. Thus, operators have to deal with massive information and to grasp the situation immediately. If they could not achieve these task, then they should make big problem in the power generations Owing to too many variables, operators could be also in the uncontrolled situation. So in this paper, automatic systems to diagnose the fault are constructed using 2 steps neural networks. This diagnosis method is based on the pattern of the principal variables which could represent the type and severity of faults.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.26 no.6_spc
• /
• pp.714-720
• /
• 2016

Rubbing and misalignment are the most usual faults that occurs in rotating machinery and with them severe effect on power plant availability. Especially blade rubbing is hard to detect on FFT spectrum using the vibration signal. In this paper, the possibility of feature analysis of vibration signal is confirmed under blade rubbing and misalignment condition. And the lab-scale rotor test device provides the blade rubbing and shaft misalignment modes. Feature selection based on GA (genetic algorithm) is processed by the extracted feature of the time domain. Then, classification of the features is analyzed by using SVM (support vector machine) which is one of the machine learning algorithm. The results of features selection based on GA compared with those based on PCA (principal component analysis). According to the results, the possibility of feature analysis is confirmed. Therefore, blade rubbing and shaft misalignment can be diagnosed by feature of vibration signal.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.20 no.9
• /
• pp.28-34
• /
• 2021

The Fourth Industrial Revolution has led to the development of drones for commercial and private applications. Therefore, the malfunction of drones has become a prominent problem. Failure mode and effect analysis was used in this study to analyze the primary cause of drone failure, and blade breakage was observed to have the highest frequency of failure. This was tested using a vibration sensor placed on drones along the breakage length of the blades. The data exhibited a significant increase in vibration within the drone body for blade fracture length. Principal component analysis was used to reduce the data dimension and classify the state with machine learning algorithms such as support vector machine, k-nearest neighbor, Gaussian naive Bayes, and random forest. The performance of machine learning was higher than 0.95 for the four algorithms in terms of accuracy, precision, recall, and f1-score. A follow-up study on failure prediction will be conducted based on the results of fault diagnosis.

• Journal of Korea Multimedia Society
• /
• v.24 no.5
• /
• pp.667-675
• /
• 2021

The treatments for symptoms of Alzheimer's disease are being provided and for the early diagnosis several researches are undergoing. In this regard, by using T1-weighted images several classification techniques had been proposed to distinguish among AD, MCI, and Healthy Control (HC) patients. In this paper, we also used some traditional Machine Learning (ML) approaches in order to diagnose the AD. This paper consists of an improvised feature selection method which is used to reduce the model complexity which accounted an issue while utilizing the ML approaches. In our presented work, combination of subcortical and cortical features of 308 subjects of ADNI dataset has been used to diagnose AD using structural magnetic resonance (sMRI) images. Three classification experiments were performed: binary classification. i.e., AD vs eMCI, AD vs lMCI, and AD vs HC. Proposed Feature Selection method consist of a combination of Principal Component Analysis and Recursive Feature Elimination method that has been used to reduce the dimension size and selection of best features simultaneously. Experiment on the dataset demonstrated that SVM is best suited for the AD vs lMCI, AD vs HC, and AD vs eMCI classification with the accuracy of 95.83%, 97.83%, and 97.87% respectively.

• Smart Media Journal
• /
• v.12 no.3
• /
• pp.30-37
• /
• 2023

Alzheimer's disease (AD) symptoms are being treated by early diagnosis, where we can only slow the symptoms and research is still undergoing. In consideration, using T1-weighted images several classification models are proposed in Machine learning to identify AD. In this paper, we consider the improvised feature selection, to reduce the complexity by using wrapping techniques and Restricted Boltzmann Machine (RBM). This present work used the subcortical and cortical features of 278 subjects from the ADNI dataset to identify AD and sMRI. Multi-class classification is used for the experiment i.e., AD, EMCI, LMCI, HC. The proposed feature selection consists of Forward feature selection, Backward feature selection, and Combined PCA & RBM. Forward and backward feature selection methods use an iterative method starting being no features in the forward feature selection and backward feature selection with all features included in the technique. PCA is used to reduce the dimensions and RBM is used to select the best feature without interpreting the features. We have compared the three models with PCA to analysis. The following experiment shows that combined PCA &RBM, and backward feature selection give the best accuracy with respective classification model RF i.e., 88.65, 88.56% respectively.

• Journal of the Korea Society of Computer and Information
• /
• v.18 no.4
• /
• pp.45-55
• /
• 2013

In this study, we propose a tongue diagnosis system which detects the tongue from face image and divides the tongue area into six areas, and finally generates tongue fur ratio of each area. To detect the tongue area from face image, we use ASM as one of the active shape models. Detected tongue area is divided into six areas and the distribution of tongue coating of six areas is examined by SVM. For SVM, we use a 3-dimensional vector calculated by PCA from a 12-dimensional vector consisting of RGB, HSV, Lab, and Luv. As a result, we stably detected the tongue area using ASM. Furthermore, we recognized that PCA and SVM helped to raise the ratio of tongue coating detection.

• The Korean Journal of Cytopathology
• /
• v.7 no.2
• /
• pp.192-196
• /
• 1996

The principal significance of the urothelial changes caused by polyomavirus activation is in an erroneous diagnosis of urothelial cancer; however, the clue to their benign nature is the smooth structureless nuclear configuration and the relative paucity of affected cells. Though virologic studies and electron microscopy are usually needed to firmly establish the diagnosis, cytology is the most readily available and rapid means of establishing a presumptive diagnosis of human polyomavirus infection. A urine specimen of a 24-year-old man with hemorrhagic cystitis beginning two months after bone marrow transplantation for acute myeloblastic leukemia(M2) was submitted for cytologic evaluation. Cytologic findings revealed a few inclusion-bearing epithelial cells intermingled with erythrocytes, neutrophils, lymphocytes, and macrophages. Most of the inclusion-bearing fells had large, round to ovoid nuclei almost completely filled with homogeneous dark, basophilic inclusion. The chromatin was clumped along the periphery and the cytoplasm was mostly degenerated. The other cells exhibited irregular inclusions attached to the nuclear membrane surrounded by an indistinct halo. These findings were consistent with polyomavirus infection.

• Journal of the Korea Convergence Society
• /
• v.8 no.5
• /
• pp.87-96
• /
• 2017

This study was carried out to provide basic data for prevention of death from injuries and traumas by analyzing the characteristics of length of hospital stay of patients with injuries and traumas, utilizing in-depth investigation data of discharged injuries. The study subjects were 233 patients discharged from January 1 to December 31 in 2014 whom the final treatment result was 'death' and the main diagnosis were injuries and accidental external causes(S00-T98). According to the research findings, the length of hospital stay of females was longer than that of males. Based on the main diagnosis, the longest length of hospital stay had complication of other internal prosthetic devices, implants and grafts(T85). In conclusion, it is necessary to develop a policy to identify the factors affecting the length of hospital stays of patients and to manage them intensively.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.35 no.11
• /
• pp.1529-1535
• /
• 2011

Improvement of the thermal efficiency in operation and maintenance of low- and medium-speed engines is a kind of never-ending requirement in the maritime power plant business. For the purpose of improving engine management efficiency, a principal factor that represents the fitness of the engine should be identified. Gas pressure, gas temperature, and vibration have all been used as this factor. However, they have limitations in terms of response speed and diagnosis accuracy. The EFR (engine fitness ratio) is suggested as a new diagnostic factor in this paper. The EFR is defined as the ratio of particular frequencies in the frequency domain and represents the fitness of an engine. It is calculated from the fluctuation pattern of the crankshaft angular speed. The EFR was verified using an experimental method for a low-speed engine and an analytic method for a medium-speed engine.