• KIPS Transactions on Software and Data Engineering
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• v.11 no.4
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• pp.179-188
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• 2022

There have been many successful researches on a bearing fault diagnosis based on Deep Learning, but there is still a critical issue of the data distribution difference between training data and test data from their different working conditions causing performance degradation in applying those methods to the machines in the field. As a solution, a data adaptation method has been proposed and showed a good result, but each and every approach is strictly limited to a specific applying scenario or presupposition, which makes it still difficult to be used as a real-world application. Therefore, in this study, we have proposed a method that, using a data transformation with MFCCs and a simple CNN architecture, can perform a robust diagnosis on a target domain data without an additional learning or tuning on the model generated from a source domain data and conducted an experiment and analysis on the proposed method with the CWRU bearing dataset, which is one of the representative datasests for bearing fault diagnosis. The experimental results showed that our method achieved an equal performance to those of transfer learning based methods and a better performance by at least 15% compared to that of an input transformation based baseline method.

• Journal of Multimedia Information System
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• v.4 no.4
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• pp.233-238
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• 2017

Automatic identification of disease in plants from their leaves is one of the most challenging task to researchers. Diseases among plants degrade their performance and results into a huge reduction of agricultural products. Therefore, early and accurate diagnosis of such disease is of the utmost importance. The advancement in deep Convolutional Neural Network (CNN) has change the way of processing images as compared to traditional image processing techniques. Deep learning architectures are composed of multiple processing layers that learn the representations of data with multiple levels of abstraction. Therefore, proved highly effective in comparison to many state-of-the-art works. In this paper, we present a plant disease identification methodology from their leaves using deep CNNs. For this, we have adopted GoogLeNet that is considered a powerful architecture of deep learning to identify the disease types. Transfer learning has been used to fine tune the pre-trained model. An accuracy of 85.04% has been recorded in the identification of four disease class in Apple plant leaves. Finally, a comparison with other models has been performed to show the effectiveness of the approach.

• Journal of Biomedical Engineering Research
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• v.30 no.1
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• pp.66-78
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• 2009

A mobile ultrasound scanner developed for use in point-of-care applications is introduced, which can not only provide ultrasound images but can also measure various bio-signals. The mobile ultrasound scanner is also designed to meet the demanding requirements for point-of-care diagnosis, such as battery-powered operation, portability in terms of size and weigh, and real-time wireless communications capability for remote diagnosis. To meet these requirements, an efficient beamforming method for high resolution imaging with a small number of active elements, a hardware efficient beamformer architecture, and echo processing algorithms with greatly reduced computational complexity have been developed. Experimental results show that the prototype mobile ultrasound scanner is fully functional and satisfies most of the design requirements.

• Journal of the Korea institute for structural maintenance and inspection
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• v.23 no.7
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• pp.129-136
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• 2019

In this study, the magnetic flux leakage technique was applied to diagnose steel plate damage, imaging technique was applied through those signals. Steel plate specimens with different thicknesses were prepared for the imaging the magnetic flux leakage signal, and 6 different depths of damage were artificially processed at the same locations on each specimen. The sensor head consist hall sensor and magnetization yoke was fabricated to magnetize the steel plate specimen and measure the magnetic flux leakage signal. In order to remove the noise and increase the resolution of the image in the signal collected from the hall sensor, various of signal processing was performed. P-P value was analyzed for each channel to analyze the magnetic flux leakage signals measured from each damaged part. Based on the above processed signals and analysis, it was converted into heatmap image. Through this, it was possible to identify the damage on the steel plate at glance by imaging magnetic flux leakage signal.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.3
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• pp.83-90
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• 2022

Diabetes is a common disease today, and there are also many cases of developing into serious complications called Diabetic Foot Ulcers(DFU). Diagnosis and prevention of DFU in advance is an important task, and this paper proposes the method. Based on existing studies introduced in the paper, it can be seen that foot pressure and temperature information are deeply correlated with DFU. Introduce the process and architecture of SmarTinsole, an IoT device that measures these indicators. Also, the paper describes the preprocessing process for AI-based diagnosis of DFU. Through the comparison of the measured pressure graph and the actual human step distribution, it presents the results that multiple information collected in real-time from SmarTinsole are more efficient and reliable than the previous study.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.343-351
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• 2001

World practice is evidence of that computer systems of an intellectual support of medical activities bound up with examination of patients, their diagnosis, therapy and so on are the most effective means for attainment of a high level of physician\`s qualification. Such systems must contain large knowledge bases consistent with the modern level of science and practice. To from large knowledge bases for such systems it is necessary to have a medical ontology model reflecting contemporary notions of medicine. This paper presents a description of an observation ontology, knowledge base for the physician of general tipe, architecture, functions and implementation of problem independent shell of the system for intellectual supporting patient examination and mathematical model of the dialog. The system can be used by the following specialist: therapeutist, surgeon, gynecologist, urologist, otolaryngologist, ophthalmologist, endocrinologist, neuropathologist and immunologist. The system supports a high level of examination of patients, delivers doctors from routine work upon filling in case records and also automatically forms a computer archives of case records. The archives can be used for any statistical data processing, for producing accounts and also for debugging of knowledge bases of expert systems. Besides that, the system can be used for rise of medical education level of students, doctors in internship, staff physicians and postgraduate students.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.881-902
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• 2024

Fast decision support systems and accurate diagnosis have become significant in the rapidly growing healthcare sector. As the number of disparate medical IoT devices connected to the human body rises, fast and interrelated healthcare data retrieval gets harder and harder. One of the most important requirements for the Healthcare Internet of Things (HIoT) is semantic interoperability. The state-of-the-art HIoT systems have problems with bandwidth and latency. An extension of cloud computing called fog computing not only solves the latency problem but also provides other benefits including resource mobility and on-demand scalability. The recommended approach helps to lower latency and network bandwidth consumption in a system that provides semantic interoperability in healthcare organizations. To evaluate the system's language processing performance, we simulated it in three different contexts. 1. Polysemy resolution system 2. System for hyponymy-hypernymy resolution with polysemy 3. System for resolving polysemy, hypernymy, hyponymy, meronymy, and holonymy. In comparison to the other two systems, the third system has lower latency and network usage. The proposed framework can reduce the computation overhead of heterogeneous healthcare data. The simulation results show that fog computing can reduce delay, network usage, and energy consumption.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.7
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• pp.1088-1097
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• 2021

Vibration data of mechanical equipment inevitably have noise. This noise adversely af ects the maintenance of mechanical equipment. Accordingly, the performance of a learning model depends on how effectively the noise of the data is removed. In this study, the noise of the data was removed using the Denoising Auto Encoder (DAE) technique which does not include the characteristic extraction process in preprocessing time series data. In addition, the performance was compared with that of the Wavelet Transform, which is widely used for machine signal processing. The performance comparison was conducted by calculating the failure detection rate. For a more accurate comparison, a classification performance evaluation criterion, the F-1 Score, was calculated. Failure data were detected using the One-Class SVM technique. The performance comparison, revealed that the DAE technique performed better than the Wavelet Transform technique in terms of failure diagnosis and error rate.

• Journal of Korean Society of Disaster and Security
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• v.11 no.2
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• pp.53-60
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• 2018

To diagnosis the local damages of the steel plates, magnetic flux leakage (MFL) method that is known as a adaptable non-destructive evaluation (NDE) method for continuum ferromagnetic members was applied in this study. To analysis the sensitivity according to thickness of steel plate in MFL method based damage diagnosis, several steel plate specimens that have different thickness were prepared and three depths of artificial damage were formed to the each specimens. To measured the MFL signals, a MFL sensor head that have a constant magnetization intensity were fabricated using a hall sensor and a magnetization yoke using permanent magnets. The magnetic flux signals obtained by using MFL sensor head were improved through a series of signal processing methods. The capability of local damage detection was verified from the measured MFL signals from each damage points. And, the peak to peak values (P-P value) extracted from the detected MFL signals from each thickness specimen were compared each other to analysis the MFL based local damage detection sensitivity according to the thickness of steel plate.

• KIISE Transactions on Computing Practices
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• v.22 no.10
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• pp.521-531
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• 2016

In this paper, since most business process of D-agency is being performed through some information systems, including Onnara System is a government standard operating management system, computerized accumulated in the system documentation based on, even if there is no independent job analysis system, in a judgment that can be can be tissue diagnosis, it presented a job analysis plan that leverages the existing information system. Most material is passed online in business processing between departments and between colleagues, it is returned. In situations where most information systems for such business processing is built developed, grasp the work procedures and information systems D-agency data accumulated to derive the necessary elements for job analysis quantified, and verified the validity of the element in the regression statistics.In addition, classification system (BRM, Business Reference Model) of the existing functionality that is available only Onnara System, and to establish a job analysis architecture to be able to function diagnostic departments to leverage common also in other information systems, related implement illustrating additional features of the information system, to derive a department duties value calculation formula with it, and present various job analysis plan that can actually be utilized to diagnose and derived elements department appropriate personnel.