• International Journal of Computer Science & Network Security
• /
• v.22 no.10
• /
• pp.171-176
• /
• 2022

A dramatic rise in the number of people dying from heart disease has prompted efforts to find a way to identify it sooner using efficient approaches. A variety of variables contribute to the condition and even hereditary factors. The current estimate approaches use an automated diagnostic system that fails to attain a high level of accuracy because it includes irrelevant dataset information. This paper presents an effective neural network with convolutional layers for classifying clinical data that is highly class-imbalanced. Traditional approaches rely on massive amounts of data rather than precise predictions. Data must be picked carefully in order to achieve an earlier prediction process. It's a setback for analysis if the data obtained is just partially complete. However, feature extraction is a major challenge in classification and prediction since increased data increases the training time of traditional machine learning classifiers. The work integrates the CNN-MDRP classifier (convolutional neural network (CNN)-based efficient multimodal disease risk prediction with TANFIS (tuned adaptive neuro-fuzzy inference system) for earlier accurate prediction. Perform data cleaning by transforming partial data to informative data from the dataset in this project. The recommended TANFIS tuning parameters are then improved using a Laplace Gaussian mutation-based grasshopper and moth flame optimization approach (LGM²G). The proposed approach yields a prediction accuracy of 98.40 percent when compared to current algorithms.

• The Transactions of the Korean Institute of Electrical Engineers P
• /
• v.59 no.2
• /
• pp.179-184
• /
• 2010

The digital substation automation system has contributed hugely to increasing the stability of power systems by providing not only protection and control of power systems but diagnostic features alongside them. Digital substation automation systems in the scale of substations consist of integrated operation systems and intelligent electronic devices. The main intelligent electronic devices currently in use are digital protection relays and the bay controllers in Gas insulated switchgears. Proficiently accomplishing the coordination of protection within the power system as a means of ensuring reliability and contriving for the stability of power supply through connection of function, the application of bay controllers is crucial, which collectively manage the protection relay at the bay level in order to achieve both. In this research, the bay controllers to be used in high-voltage Gas insulated switchgear has been localized, and in particular, the logic function and editor required in order to minimize the complicated hardware-like cable connections in the local panel have been developed. In addition, to ensure the strength and reliability of the bay controller hardware developed herein, the type tests from KERI have been successfully completed.

• Proceedings of the Korean Society of Marine Engineers Conference
• /
• 2005.06a
• /
• pp.777-782
• /
• 2005

The importance of emergency diesel generator(EDG) has confirmed in the safety evaluation of PSA and the study on aging of EDG has been progressed actively as a part of the project of nuclear plant aging research in the U.S.A. As the result, the concept of performance evaluation is being transferred from statistical analysis of test results to performance monitoring and trending analysis for monitoring of aging and reliability. Recently, the study related aging characteristic and reliability for EDGS has begun in Korea. Consequently, the efficient performance monitoring based systematic and integrated monitoring and failure diagnostic technology is necessary. In the research, the knowledge basis of monitoring parameters for EDGS is constructed, and the prototype monitoring and diagnosis system applicable to Pielstick EDG is developed.

• Tribology and Lubricants
• /
• v.36 no.6
• /
• pp.307-314
• /
• 2020

In this paper, studies on application cases of machine condition monitoring using oil sensors are reviewed. Owing to rapid industrial advancements, maintenance strategies play a crucial role in reducing the cost of downtime and improving system reliability. Consequently, machine condition monitoring plays an important role in maintaining operation stability and extending the period of usage for various machines. Machine condition monitoring through oil analysis is an effective method for assessing a machine's condition and providing early warnings regarding a machine's breakdown or failure. Among the three prevalent methods, the online analysis method is predominantly employed because this method incorporates oil sensors in real-time and has several advantages (such as prevention of human errors). Wear debris sensors are widely employed for implementing machine condition monitoring through oil sensors. Furthermore, various types of oil sensors are used in different machines and systems. Integrated oil sensors that can measure various oil attributes by incorporating a single sensor are becoming popular. By monitoring wear debris, machine condition monitoring using oil sensors is implemented for engines, automotive transmission, tanks, armored vehicles, and construction equipment. Additionally, such monitoring systems are incorporated in aircrafts such as passenger airplanes, fighter airplanes, and helicopters. Such monitoring systems are also employed in chemical plants and power plants for managing overall safety. Furthermore, widespread application of oil condition diagnosis requires the development of diagnostic programs.

• Imaging Science in Dentistry
• /
• v.43 no.3
• /
• pp.153-161
• /
• 2013

Purpose: To prevent low bone mineral density (BMD), that is, osteoporosis, in postmenopausal women, it is essential to diagnose osteoporosis more precisely. This study presented an automatic approach utilizing a histogram-based automatic clustering (HAC) algorithm with a support vector machine (SVM) to analyse dental panoramic radiographs (DPRs) and thus improve diagnostic accuracy by identifying postmenopausal women with low BMD or osteoporosis. Materials and Methods: We integrated our newly-proposed histogram-based automatic clustering (HAC) algorithm with our previously-designed computer-aided diagnosis system. The extracted moment-based features (mean, variance, skewness, and kurtosis) of the mandibular cortical width for the radial basis function (RBF) SVM classifier were employed. We also compared the diagnostic efficacy of the SVM model with the back propagation (BP) neural network model. In this study, DPRs and BMD measurements of 100 postmenopausal women patients (aged >50 years), with no previous record of osteoporosis, were randomly selected for inclusion. Results: The accuracy, sensitivity, and specificity of the BMD measurements using our HAC-SVM model to identify women with low BMD were 93.0% (88.0%-98.0%), 95.8% (91.9%-99.7%) and 86.6% (79.9%-93.3%), respectively, at the lumbar spine; and 89.0% (82.9%-95.1%), 96.0% (92.2%-99.8%) and 84.0% (76.8%-91.2%), respectively, at the femoral neck. Conclusion: Our experimental results predict that the proposed HAC-SVM model combination applied on DPRs could be useful to assist dentists in early diagnosis and help to reduce the morbidity and mortality associated with low BMD and osteoporosis.

• Nuclear Engineering and Technology
• /
• v.53 no.1
• /
• pp.148-163
• /
• 2021

Timely fault identification is important for safe and reliable operation of the electric valve system. Many research works have utilized different data-driven approach for fault diagnosis in complex systems. However, they do not consider specific characteristics of critical control components such as electric valves. This work presents an integrated shallow-deep fault diagnostic model, developed based on signals extracted from DN50 electric valve. First, the local optimal issue of particle swarm optimization algorithm is solved by optimizing the weight search capability, the particle speed, and position update strategy. Then, to develop a shallow diagnostic model, the modified particle swarm algorithm is combined with support vector machine to form a hybrid improved particle swarm-support vector machine (IPs-SVM). To decouple the influence of the background noise, the wavelet packet transform method is used to reconstruct the vibration signal. Thereafter, the IPs-SVM is used to classify phase imbalance and damaged valve faults, and the performance was evaluated against other models developed using the conventional SVM and particle swarm optimized SVM. Secondly, three different deep belief network (DBN) models are developed, using different acoustic signal structures: raw signal, wavelet transformed signal and time-series (sequential) signal. The models are developed to estimate internal leakage sizes in the electric valve. The predictive performance of the DBN and the evaluation results of the proposed IPs-SVM are also presented in this paper.

• Smart Structures and Systems
• /
• v.16 no.3
• /
• pp.383-399
• /
• 2015

Today, as railways increase their capacity and speeds, it is more important than ever to be completely aware of the state of vehicles fleet's condition to ensure the highest quality and safety standards, as well as being able to maintain the costs as low as possible. Operation of a modern, dynamic and efficient railway demands a real time, accurate and reliable evaluation of the infrastructure assets, including signal networks and diagnostic systems able to acquire functional parameters. In the conventional system, measurement data are reliably collected using coaxial wires for communication between sensors and the repository. As sensors grow in size, the cost of the monitoring system can grow. Recently, auto-powered wireless sensor has been considered as an alternative tool for economical and accurate realization of structural health monitoring system, being provided by the following essential features: on-board micro-processor, sensing capability, wireless communication, auto-powered battery, and low cost. In this work, an original harvester device is designed to supply wireless sensor system battery using train bogie energy. Piezoelectric materials have in here considered due to their established ability to directly convert applied strain energy into usable electric energy and their relatively simple modelling into an integrated system. The mechanical and electrical properties of the system are studied according to the project specifications. The numerical formulation is implemented with in-house code using commercial software tool and then experimentally validated through a proof of concept setup using an excitation signal by a real application scenario.

• The Journal of Korean Medicine Ophthalmology and Otolaryngology and Dermatology
• /
• v.29 no.1
• /
• pp.145-156
• /
• 2016

Objective : We performed a literature review for developing the clinical phenotype evaluation system of atopic dermatitis.Methods : We searched the papers that describe symptoms for atopic dermatitis through Oriental Medicine Advanced Searching Integrated System(OASIS) and Korean Studies Incategoryation Service System(KISS). We looked through all the papers and finally chose 47 papers that are suitable for inclusion. Then, we extracted symptoms from these papers and arranged them in order of frequency and validity through experts' conference.Results : We found 360 papers and chose 47 papers. We decided to include general information of patients, systemic and dermatologic symptoms in evaluation category of atopic dermatitis. Through experts' conference, it was decided that general information has age, sex and body type; Systemic symptoms have 9 items; Dermatologic symptoms have 15 items.Conclusion : To evaluate atopic dermatitis objectively, the standardization of diagnostic tool is needed. Therefore we developed a clinical phenotype evaluation system of atopic dermatitis.

• The Journal of Korean Medicine
• /
• v.41 no.2
• /
• pp.122-136
• /
• 2020

Objectives: A case report is a detailed report of the symptoms, signs, diagnosis, treatment, and follow-up of an individual patient. The purpose of this study is to evaluate the quality of case reports from the Journal of Korean Medicine by the CARE (CAse REport) Guideline. Methods: Case reports published in the Journal of Korean Medicine from January 2016 to March 2020 were searched from Oriental Medicine Advanced Searching Integrated System (OASIS). We assessed the quality of reporting based on CARE (CAse REport) guideline as 'Sufficient', 'Not-Sufficient' and 'Not-Report'. Results: A total of 22 case reports were finally included for the assessment. The reporting items were reported as of reporting quality. After checking the result, there was a deviation in the sub-item reporting rate by a maximum 89.29%, a minimum 66.67% and a median 82.14% in case reports. Also after checking the quality in case reports by 28 detailed items in CARE guidelines, there were not reported 77% or more in the 5 sub-items 'Intervention adherence and tolerability', 'Informed consent', 'Adverse and unanticipated events', 'Diagnostic challenges', 'Patient perspective'. Conclusion: There is a need to improve the quality of case reports in the journal of Korean Medicine based on various studies using CARE guideline.

• The Journal of Pediatrics of Korean Medicine
• /
• v.32 no.2
• /
• pp.26-42
• /
• 2018

Objectives The purpose of this study is to figure out the recent trend of the treatment for autism spectrum disorder (ASD) by comparing Korean and Chinese clinical studies. Methods National Digital Science Library (NDSL), Oriental medicine Advanced Searching Integrated System (OASIS), Research Information Sharing Service (RISS) and Korean Traditional Knowledge Portal (KTKP) were used to search Korean studies which were published from January, 2011 to May, 2017. Also Chinese National Knowledge Infrastructure (CNKI) and Wanfang data were used to search Chinese studies which were published from the same period. Key words of 'Autism' and 'Autism spectrum disorder' were used. Results 3 Korean studies and 21 Chinese studies were selected and analyzed to find out the most commonly used diagnostic criteria, treatments, including herbal medicine and acupuncture, and treatment assessment procedures. Conclusions As a result of comparing Korean and Chinese clinical studies for the treatment of ASD, both Korean and Chinese medicine treatments showed their effectiveness. However, there were some differences between two countries' clinical trends. In order for this study to be helpful, more highly evidenced clinical studies should be followed.