• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 한국해양정보통신학회 2011년도 춘계학술대회
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• pp.263-266
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• 2011

Drivers check always vehicle state for own safety and is necessary to understand about vehicle state. If driver know vehicle state, because driver request vehicle state at vehicle specialty company, driver pay a lot of money and waste a lot of time. Now, drivers have checked vehicle state by using variety features of smart phone due to progress of IT(Information Technology). but existing smartphone vehicle diagnostic system learned professional knowledge of vehicle and know vehicle state. drives not need vehicle diagnostic. to overcome these disadvantages, there use easily drivers by using smartphone and request system to know own all vehicle state. In this paper, there implement vehicle diagnostic system with smartphone based on android OS to use easily this system and know check information of vehicle supplies replacement cycles, vehicle internal problems, Eco driving by using OBD-II data to receive by OBD-II Protocol convert Bluetooth connector.

• Biomedical Science Letters
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• 제18권4호
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• pp.399-405
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• 2012

The brain is the body's most organized and controlled organ, and it governs various psychological and mental functions. A brain abnormality could greatly affect one's physical and mental abilities, and consequently one's social life. Brain disorders can be broadly categorized into three main afflictions: stroke, brain tumor, and dementia. Among these, stroke is a common disease that occurs owing to a disorder in blood flow, and it is accompanied by a sudden loss of consciousness and motor paralysis. The main types of strokes are infarction and hemorrhage. The exact diagnosis and early treatment of an infarction are very important for the patient's prognosis and for the determination of the treatment direction. In this study, texture features were analyzed in order to develop a prototype auto-diagnostic system for infarction using computer auto-diagnostic software. The analysis results indicate that of the six parameters measured, the average brightness, average contrast, flatness, and uniformity show a high cognition rate whereas the degree of skewness and entropy show a low cognition rate. On the basis of these results, it was suggested that a digital CT image obtained using the computer auto-diagnostic software can be used to provide valuable information for general CT image auto-detection and diagnosis for pre-reading. This system is highly advantageous because it can achieve early diagnosis of the disease and it can be used as supplementary data in image reading. Further, it is expected to enable accurate medical image detection and reduced diagnostic time in final-reading.

• The Journal of the Korean dental association
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• 제55권11호
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• pp.778-788
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• 2017

Ultrasound sonography(US) is used to evaluate various diseases of oral and maxillofacial region including salivary glands, soft tissue and jaw lesions because of easy accessibility and no hazard of ionizing radiation. Also, US can offer dynamic study showing real-time images during diagnostic or surgical procedure. US images provide accurate information about the internal features of lesions on the jaw prior to surgical treatment. Doppler images are used to visualize the vascular distribution of the lesions and to provide additional information to enhance diagnostic value. It is necessary to evaluate the diagnostic value of US and evaluate its usefulness by looking at clinical cases using US images. Therefore, US imaging may be recommended as an assistant image in evaluating jaw lesions. US images provided accurate information about the internal structure of lesions on the jaw prior to surgical treatment, and diagnostic value was enhanced by visualizing the vascular distribution of the lesion using doppler imaging. We report the protocol and suggest the effectiveness of US for various lesions and US-guided sialography.

• The Journal of Churna Manual Medicine for Spine and Nerves
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• 제9권1호
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• pp.1-14
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• 2014

Objective : The purpose of this study is to offer a new approach to diagnostic X-ray in perspective of Chuna manual medicine for clinical application. Methods : Characteristics of each malposition in X-ray were analyzed comprehensively, based on the listing system. By verifying these results, find out the methods of X-ray diagnosis according to the each malposition. Results : 1. Vertebral malposition can be explained by alignment and relative position of vertebral body in the X-ray. To obtain more accurate estimation of subluxation, features of other structures should be considered, such as spinous process, intervertebral foramen and disc space. 2. Pelvic malposition can be determined by relative location of anterior superior iliac spine (ASIS) and posterior superior iliac spine (PSIS) in the X-ray. Also other pelvic parameters should be utilized to make a diagnosis of sacral malposition. Conclusions : Diagnostic X-ray should be applied to many clinicians for reasonable Chuna manual medicine application. And further studies are needed to use the diagnostic X-ray in the perspective of Chuna manual medicine.

• Journal of Korean Neurosurgical Society
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• 제60권5호
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• pp.540-549
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• 2017

Objective : To evaluate the diagnostic value of computed tomography-myelography (CTM) compared to that of magnetic resonance imaging (MRI) in patients with lumbar radiculopathy. Methods : The study included 91 patients presenting with radicular leg pain caused by herniated nucleus pulposus or lateral recess stenosis in the lumbar spine. The degree of nerve root compression on MRI and CTM was classified into four grades. The results of each imaging modality as assessed by two different observers were compared. Visual analog scale score for pain and electromyography result were the clinical parameters used to evaluate the relationships between clinical features and nerve root compression grades on both MRI and CTM. These relationships were quantified by calculating the receiver-operating characteristic curves, and the degree of relationship was compared between MRI and CTM. Results : McNemar's test revealed that the two diagnostic modalities did not show diagnostic concurrence (p<0.0001). Electromyography results did not correlate with grades on either MRI or CTM. The visual analog pain scale score results were correlated better with changes of the grades on CTM than those on MRI (p=0.0007). Conclusion : The present study demonstrates that CTM could better define the pathology of degenerative lumbar spine diseases with radiculopathy than MRI. CTM can be considered as a useful confirmative diagnostic tool when the exact cause of radicular pain in a patient with lumbar radiculopathy cannot be identified by using MRI. However, the invasiveness and potential complications of CTM are still considered to be pending questions to settle.

• Journal of the Korean Wood Science and Technology
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• 제22권3호
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• pp.39-44
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• 1994

This study was carried out to investigate features pertaining to spiral thickenings, which was considered one of the most important diagnostic values, for wood identification. Species, kind of cells with spiral thickening, and ridge numbers of spiral thickening per axial mm were recorded in 71 families, 144 genera, 316 species of Korean hardwoods. Spiral thickening was observed in 128 of 316 species, about 40.5 % of all the investigated, and classified into 6 types on the basis of distributional patterns and morphological features as follows: 1. Type 1, present throughout all vessel element, which was found in 14 families, 19 genera, 43 species. 2. Type 2, present only in small vessel element, which was found in 18 families, 29 genera, 41 species. 3. Type 3, present both in small vessel element and wood fibers, which was found in 8 families, 17 genera, 29 species. 4. Type 4, present in wood fibers, which was found in 1 family, 1 genus, 1 species. 5. Type 5, present only in tail of vessel element, which was found in 4 families, 5 genera, 9 species. 6. Type 6, being present in vessel element faintly or partially, which was found in 2 families, 3 genera, 5 species.

• Journal of Genetic Medicine
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• 제16권1호
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• pp.1-9
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• 2019

Noonan syndrome (NS) and NS-related disorders (cardio-facio-cutaneous syndrome, Costello syndrome, NS with multiple lentigines, or LEOPARD [lentigines, ECG conduction abnormalities, ocular hypertelorism, pulmonic stenosis, abnormal genitalia, retardation of growth and sensory neural deafness] syndrome) are collectively named as RASopathies. Clinical presentations are similar, featured with typical facial features, short stature, intellectual disability, ectodermal abnormalities, congenital heart diseases, chest & skeletal deformity and delayed puberty. During past decades, molecular etiologies of RASopathies have been growingly discovered. The functional perturbations of the RAS-mitogen-activated protein kinase pathway are resulted from the mutation of more than 20 genes (PTPN11, SOS1, RAF1, SHOC2, BRAF, KRAS, NRAS, HRAS, MEK1, MEK2, CBL, SOS2, RIT, RRAS, RASA2, SPRY1, LZTR1, MAP3K8, MYST4, A2ML1, RRAS2). The PTPN11 (40-50%), SOS1 (10-20%), RAF1 (3-17%), and RIT1 (5-9%) mutations are common in NS patients. In this review, the constellation of overlapping clinical features of RASopathies will be described based on genotype as well as their differential diagnostic points and management.

• Steel and Composite Structures
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• 제29권6호
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• pp.703-716
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• 2018

Rapid detection of damages in civil engineering structures, in order to assess their possible disorders and as a result produce competent decision making, are crucial to ensure their health and ultimately enhance the level of public safety. In traditional intelligent health monitoring methods, the features are manually extracted depending on prior knowledge and diagnostic expertise. Inspired by the idea of unsupervised feature learning that uses artificial intelligence techniques to learn features from raw data, a two-stage learning method is proposed here for intelligent health monitoring of civil engineering structures. In the first stage, $Nystr{\ddot{o}}m$ method is used for automatic feature extraction from structural vibration signals. In the second stage, Moving Kernel Principal Component Analysis (MKPCA) is employed to classify the health conditions based on the extracted features. In this paper, KPCA has been implemented in a new form as Moving KPCA for effectively segmenting large data and for determining the changes, as data are continuously collected. Numerical results revealed that the proposed health monitoring system has a satisfactory performance for detecting the damage scenarios of a three-story frame aluminum structure. Furthermore, the enhanced version of KPCA methods exhibited a significant improvement in sensitivity, accuracy, and effectiveness over conventional methods.

• Journal of Biomedical Engineering Research
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• 제42권5호
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• pp.201-210
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• 2021

In Korean medicine, tongue diagnosis is one of the important diagnostic methods for diagnosing abnormalities in the body. Representative features that are used in the tongue diagnosis include color, shape, texture, cracks, and tooth marks. When diagnosing a patient through these features, the diagnosis criteria may be different for each oriental medical doctor, and even the same person may have different diagnosis results depending on time and work environment. In order to overcome this problem, recent studies to automate and standardize tongue diagnosis using machine learning are continuing and the basic process of such a machine learning-based tongue diagnosis system is tongue segmentation. In this paper, image data is augmented based on the main tongue features, and backbones of various famous deep learning architecture models are used for automatic tongue segmentation. The experimental results show that the proposed augmentation technique improves the accuracy of tongue segmentation, and that automatic tongue segmentation can be performed with a high accuracy of 99.12%.

• Journal of Korean Society of Industrial and Systems Engineering
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• 제46권3호
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• pp.161-169
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• 2023

Degenerative arthritis is a common joint disease that affects many elderly people and is typically diagnosed through radiography. However, the need for remote diagnosis is increasing because knee pain and walking disorders caused by degenerative arthritis make face-to-face treatment difficult. This study collects three-dimensional joint coordinates in real time using Azure Kinect DK and calculates 6 gait features through visualization and one-way ANOVA verification. The random forest classifier, trained with these characteristics, classified degenerative arthritis with an accuracy of 97.52%, and the model's basis for classification was identified through classification algorithm by features. Overall, this study not only compensated for the shortcomings of existing diagnostic methods, but also constructed a high-accuracy prediction model using statistically verified gait features and provided detailed prediction results.