• Journal of Biosystems Engineering
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• v.24 no.3
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• pp.267-274
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• 1999

An internet and network based software and related interface have been developed, which can remotely control and manage an on-site operating system. Developed software modules were composed of two parts: monitoring/management modules and control/diagnosis modules were developed for the network status, warehouse, production and selling status. Modules of control with diagnosis were developed for the on-site operating system and interface. Each module was integrated and the whole modules have been tested with an automatic mushroom grading/sorting system which was built in a laboratory. Developed software modules worked successfully without any uncommon situations such as system down caused by the software or data transfer error. Each software module was developed independently in order to apply easily to other existing on-site systems such as rice processing centers, fruit and vegetable sorting, packaging and distribution centers scattered over the country.

• Journal of Veterinary Clinics
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• v.15 no.2
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• pp.242-246
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• 1998

These studies were Performed to provide some basic informations for developing an automatic system in dairy farming in order that the farmers may easily and automatically detect the mastitis. Electrical conductivity of each milk sample was measured by micro-ohm meter and also the number of somatic cell was detected by somecounter. The major microorganisms causing mastitis were also investigated. The rate of infected cattle with mastitis was 33.0% among 2,540 dairy cattle and the rate of infected quarters with mastitis was 13.9 % among 9.660 quarters. When the number of somatic cell was under lost electrical conductivity of the milk was 0.073, whereas number of somatic cell was over $3{\times}10^{6}$, electrical conductivity was increased by 0.167. When electrical conductivity of milk was over 0.073, the cattle was diagnosised as mastitis. The major micmorganisms of mastitis were Staphylococcus spp. (55-60%) and Streptococcus spp. (15-20%).

• Proceedings of the KSME Conference
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• 2005.11a
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• pp.205.2-205.2
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• 2005

• Transactions of the Korean Society of Automotive Engineers
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• v.25 no.2
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• pp.149-156
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• 2017

Various kinds of accessories(e.g., clock, radio, automatic door locks, alarm devices, etc.) or unit components (e.g., black box, navigation system, alarm, private audio, etc.) require dark current even when the vehicle power is turned off. However, accessories or unit components can be the causes of excessive dark current generation. It results in battery discharge and the vehicle's failure to start. Therefore, immediate detection of abnormal dark current and response are very important for a successful repair job. In this paper, we can increase the maintenance efficiency by presenting a standardized diagnostic process for the measurement of the dark current and the existing problem. As a result of the absence of a system to block the dark current in a vehicle, diagnosis and repair were performed immediately by using a standardized dark current diagnostic process.

• Journal of the Korean Society of Systems Engineering
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• v.17 no.2
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• pp.91-97
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• 2021

User interface for cancer classification system is a software application with clinician's friendly tools and functions to diagnose cancer from pathology images. Pathology evolved from manual diagnosis to computer-aided diagnosis with the help of Artificial Intelligence tools and algorithms. In this paper, we explained each block of the project life cycle for the implementation of automated breast cancer classification software using AI and machine learning algorithms to classify normal and invasive breast histology images. The system was designed to help the pathologists in an automatic and efficient diagnosis of breast cancer. To design the classification model, Hematoxylin and Eosin (H&E) stained breast histology images were obtained from the ICIAR Breast Cancer challenge. These images are stain normalized to minimize the error that can occur during model training due to pathological stains. The normalized dataset was fed into the ResNet-34 for the classification of normal and invasive breast cancer images. ResNet-34 gave 94% accuracy, 93% F Score, 95% of model Recall, and 91% precision.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.6
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• pp.267-275
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• 2023

This paper presents the development of specialized software for annotating volume-of-interest on ¹⁸F-FDG PET/CT images with the goal of facilitating the studies and diagnosis of head and neck cancer (HNC). To achieve an efficient annotation process, we employed the SE-Norm-Residual Layer-based U-Net model. This model exhibited outstanding proficiency to segment cancerous regions within ¹⁸F-FDG PET/CT scans of HNC cases. Manual annotation function was also integrated, allowing researchers and clinicians to validate and refine annotations based on dataset characteristics. Workspace has a display with fusion of both PET and CT images, providing enhance user convenience through simultaneous visualization. The performance of deeplearning model was validated using a Hecktor 2021 dataset, and subsequently developed semi-automatic annotation functionalities. We began by performing image preprocessing including resampling, normalization, and co-registration, followed by an evaluation of the deep learning model performance. This model was integrated into the software, serving as an initial automatic segmentation step. Users can manually refine pre-segmented regions to correct false positives and false negatives. Annotation images are subsequently saved along with their corresponding ¹⁸F-FDG PET/CT fusion images, enabling their application across various domains. In this study, we developed a semi-automatic annotation software designed for efficiently generating annotated lesion images, with applications in HNC research and diagnosis. The findings indicated that this software surpasses conventional tools, particularly in the context of HNC-specific annotation with ¹⁸F-FDG PET/CT data. Consequently, developed software offers a robust solution for producing annotated datasets, driving advances in the studies and diagnosis of HNC.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.11
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• pp.2051-2057
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• 2007

In Oriental medicine, the status of a tongue is the important indicator to diagnose one's health like physiological and clinicopathological changes of inner parts of the body. The method of tongue diagnosis is not only convenient but also non-invasive and widely used in Oriental medicine. However, tongue diagnosis is affected by examination circumstances a lot like a light source, patient's posture and doctor's condition. To develop an automatic tongue diagnosis system for an objective and standardized diagnosis, segmenting a tongue is inevitable but difficult since the colors of a tongue, lips and skin in a mouth are similar. The proposed method includes preprocessing, graph-based over-segmentation, detecting positions with a local minimum over shading, detecting edge with color difference and estimating edge geometry from the probable structure of a tongue, where preprocessing performs down-sampling to reduce computation time, histogram equalization and edge enhancement. A tongue was segmented from a face image with a tongue from a digital tongue diagnosis system by the proposed method. According to three oriental medical doctors' evaluation, it produced the segmented region to include effective information and exclude a non-tongue region. It can be used to make an objective and standardized diagnosis.

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

• Journal of Korean Society of Transportation
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• v.23 no.5 s.83
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• pp.123-134
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• 2005

This research aims at developing a new method which can replace the existing method. known as the quality factor(Q factor) method by an L-R-C test for use in the performance test of inductive loop detectors(ILD) being installed and maintained. In this study, a sensor to detect a magnetic field in terms of frequency and intensity, a method to collect field data, the method of analysis, and the method of diagnosis were developed. An automatic diagnosis system which was developed to overcome those drawbacks has the following features : First, field data is collected automatically by a test vehicle equipped with magnetic field sensors that is running can be said to along the roadway and. thus, the new system completely overcome the roadway and, thus, the new system can be said to completely overcome the inefficiency of the existing method second, since the magnetic fold generated from the ILD is the final output of the whole system of ILD, the existing problem has been solved. third. since each of the detection area by height is collected by the magnetic sensors installed by height. a basic for the identification of the vehicle types to be detectable and the setting of adjustment factors has been made. For the automatic diagnosis system developed during in this study, a reliability test was carried out by comparing vehicle times of ILD installed ideally.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.8
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• pp.1424-1429
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• 2016

Segmentation is one of the first steps in most diagnosis systems for characterization of dental caries in an early stage. The purpose of automatic dental cavity detection system is helping dentist to make more precise diagnosis. We proposed the semi-automatic method for the segmentation of dental caries on digital x-ray images. Based on a manually and roughly selected ROI (Region of Interest), it calculated the contour for the dental cavity. A snake algorithm which is one of active contour models repetitively refined the initial contour and self-examination and correction on the segmentation result. Seven phantom tooth from incisor to molar were made for the evaluation of the developed algorithm. They contained a different form of cavities and each phantom tooth has two dental cavities. From 14 dental cavities, twelve cavities were accurately detected including small cavities. And two cavities were segmented partly. It demonstrates the practical feasibility of the dental lesion detection using Computer-aided Detection (CADe).