• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.17 no.12
• /
• pp.3001-3008
• /
• 2013

To diagnose the cardiac valve abnormalities using analysis of phonocardiogram, first of all, accurate detection of S1, S2 components is needed for heart sound segmentation. In this paper, a new method that uses the third moment characteristics of an envelope of the PCG is proposed for accurate detection of S1 and S2 components of the heart sound with cardiac murmurs. The envelope of the PCG is obtained from the short-time energy profile, and its third moment profile with slope information is used for accurate time gating of the S1, S2 components. Experimental results have shown that the proposed method is superior to the conventional second moment method for detection of S1 and S2 regions from the heart sound signals with cardiac murmurs.

• The Journal of Society for e-Business Studies
• /
• v.23 no.4
• /
• pp.153-169
• /
• 2018

Previous e-financial anomalies analysis and detection technology collects large amounts of electronic financial transaction logs generated from electronic financial business systems into big-data-based storage space. And it detects abnormal transactions in real time using detection rules that analyze transaction pattern profiling of existing customers and various accident transactions. However, deep analysis such as attempts to access e-finance by insiders of financial institutions with large scale of damages and social ripple effects and stealing important information from e-financial users through bypass of internal control environments is not conducted. This paper analyzes the management status of e-financial security programs of financial companies and draws the possibility that they are allies in security control of insiders who exploit vulnerability in management. In order to efficiently respond to this problem, it will present a comprehensive e-financial security management environment linked to insider threat monitoring as well as the existing e-financial transaction detection system.

• The Korean Journal of Applied Statistics
• /
• v.34 no.1
• /
• pp.99-114
• /
• 2021

With the Fourth Industrial Revolution based on new technology, the semiconductor manufacturing industry researches various analysis methods such as detecting process abnormalities and predicting yield based on equipment sensor data generated in the manufacturing process. The semiconductor manufacturing process consists of hundreds of processes and thousands of measurement processes associated with them, each of which has properties that cannot be defined by chemical or physical equations. In the individual measurement process, the actual measurement ratio does not exceed 0.1% to 5% of the target product, and it cannot be kept constant for each measurement point. For this reason, efforts are being made to determine whether to manage by using equipment sensor data that can indirectly determine the normal state of each step of the process. In this study, the Functional Data Analysis (FDA) was proposed to define a process abnormality detection process based on equipment sensor data and compensate for the disadvantages of the currently applied statistics-based diagnosis method. Anomaly detection accuracy was compared using machine learning on actual field case data, and its effectiveness was verified.

• Journal of Genetic Medicine
• /
• v.17 no.2
• /
• pp.68-72
• /
• 2020

Purpose: Trisomy 21, the cause of Down syndrome (DS) with various medical problems, is the most common aneuploidy during the fetal period. For diagnosis, a non-invasive screening test using maternal blood, which cannot be confirmed and invasive confirmation test with a risk of miscarriage, may be performed. The trophoblast retrieval and isolation of the cervix (TRIC) have been proposed by some researchers as an alternative to overcome the limitations of current tests. We experimented using TRIC to identify the possibility of trisomy 21 for the first time in Asia. Materials and Methods: Three cases of DS were analyzed confirmed by invasive tests (chorionic villus sampling, amniocentesis). All samples of trophoblasts immediately were immersed in phosphate-buffered saline and processed with formalin for fixation. The trophoblasts were isolated using an anti-human leukocyte antigen-G antibody coupled to magnetic nanoparticles. β-human chorionic gonadotropin (hCG)-expressing cells were considered as trophoblast cells, and the detection rate calculated. DS was confirmed by fluorescence in situ hybridization (FISH). Results: The mean trophoblast detection rate using β-hCG was 78.1%, and the detection rate using FISH was 22.2%. In all cases, the trisomy of chromosome 21 was identified. Conclusion: Trophoblast can be obtained from the five weeks of gestation and has a high detection rate, so it is noted that it can replace the current prenatal genetic test. To realize the clinical application as a prenatal genetic test, we will need additional efforts to identify trisomy 21 as well as other chromosomal abnormalities in future large-scale studies.

• Journal of Internet Computing and Services
• /
• v.24 no.2
• /
• pp.37-45
• /
• 2023

In line with the 4th Industrial Revolution, facility maintenance technologies for building smart factories are receiving attention and are being advanced. In addition, technology is being applied to smart farms and smart fisheries following smart factories. Among them, in the case of a recirculating aquaculture system, there is a motor pump that circulates water for a stable quality environment in the tank. Motor pump maintenance activities for recirculating aquaculture system are carried out based on preventive maintenance and data obtained from vibration sensor. Preventive maintenance cannot cope with abnormalities that occur before prior planning, and vibration sensors are affected by the external environment. This paper proposes an anomaly detection algorithm that utilizes ADTK, a Python open source, for motor pump anomaly detection based on data collected through current sensors that are less affected by the external environment than noise, temperature and vibration sensors.

• Annals of Clinical Neurophysiology
• /
• v.13 no.1
• /
• pp.13-20
• /
• 2011

Neuro-ophthalmological findings are common and occasionally prominent features in movement disorders. Accordingly, careful evaluation of the ocular motor functions may provide valuable information in early detection of the diseases and monitoring of the progression. Furthermore, accurate assessment of the abnormal ocular motor findings aids in understanding the pathophysiology and mechanisms of the movement disorders, and in their differential diagnosis. Ocular motility examination should include bedside evaluation and laboratory recording of the fixational abnormalities, saccades, smooth pursuit, the vestibulo-ocular reflex, optokinetic nystagmus, and vergence eye movements. In this review, we will discuss various ocular motor findings in ataxia and parkinsonian syndromes, and hyperkinetic movement disorders.

• Journal of Information Processing Systems
• /
• v.13 no.3
• /
• pp.491-499
• /
• 2017

In this study, we proposed a new approach to segment ground and nonground points gained from a 3D laser range sensor. The primary aim of this research was to provide a fast and effective method for ground segmentation. In each frame, we divide the point cloud into small groups. All threshold points and start-ground points in each group are then analyzed. To determine threshold points we depend on three features: gradient, lost threshold points, and abnormalities in the distance between the sensor and a particular threshold point. After a threshold point is determined, a start-ground point is then identified by considering the height difference between two consecutive points. All points from a start-ground point to the next threshold point are ground points. Other points are nonground. This process is then repeated until all points are labelled.

• The Korean Journal of Cytopathology
• /
• v.7 no.2
• /
• pp.138-143
• /
• 1996

The diagnostic accuracy of routine cytological preparations from effusions ranges from 60% to 70%. Immunohistochemical markers, especially tumor-associated antigens, have been successfully employed to increase diagnostic sensitivity in effusion cytology. However, more than two different antibodies in diagnosis of effusions are needed. In the view of prevalence of abnormalities of p53 gene in human malignancies we investigated the diagnostic usefulness of demonstration of p53 protein immunoreactivity in distinguishing benign changes versus malignant processes in effusions. p53 protein expression was studied immunohistochemically in 76 effusions(28 malignant and 48 benign) using anti-human p53 antibody p53 immunoreactivity was identified in 19 of 28(67.9%) malignant effusions. In contrast, no p53 immunoreactivity was observed in all benign effusions. A specificity of 100% and a sensitivity of 67.9% were observed. These results suggest that immunohistochemical detection of p53 protein seems to be helpful in distinguishing benign changes versus malignant processes in effusions, although its principal limitation is its relatively low sensitivity.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2003.09a
• /
• pp.372-375
• /
• 2003

The incidence of blindness resulting from diabetic retinopathy has significantly increased despite the intervention of insulin to control diabetes mellitus. Early signs are microaneurysms, exudates, intraretinal hemorrhages, cotton wool patches, microvascular abnormalities, and venous beading. Advanced stages include neovascularization, fibrous formations, preretinal and vitreous microhemorrhages, and retinal detachment. Microaneurysm count is important because it is an indicator of retinopathy progression. The purpose of this paper is to apply data mining to detect diabetic retinopathy patterns in routine fundus fluorescein angiography. Early symptoms are of principal interest and therefore the emphasis is on detecting microaneurysms rather than vessel tortuosity. The analysis does not involve image-recognition algorithms. Instead, mathematical filtering isolates microaneurysms, microhemorrhages, and exudates as objects of disconnected sets. A neural network is trained on their distribution to return fractal dimension. Hausdorff and box counting dimensions grade progression of the disease. The field is acquired on fluorescein angiography with resolution superior to color ophthalmoscopy, or on patterns produced by physical or mathematical simulations that model viscous fingering of water with additives percolated through porous media. A mathematical filter and neural network perform the screening process thereby eliminating the time consuming operation of determining fractal set dimension in every case.

• Sleep Medicine and Psychophysiology
• /
• v.9 no.1
• /
• pp.9-13
• /
• 2002

Alzheimer's disease (AD) is one of the most common and devastating dementing disorders of old age. Most AD patients showed significant alternation of sleep structure as well as cognitive deficit. Typical findings of sleep architecture in AD patients include lower sleep efficiency, higher stage 1 percentage, and greater frequency of arousals. The slowing of EEG activity is also noted. Abnormalities in REM sleep are of particular interest in AD because the cholinergic system is related to both REM sleep and AD. Several parameters representing REM sleep structure such as REM latency, the amount of REM sleep, and REM density are change in patients with AD. Especially, measurements of EEG slowing during tonic REM sleep can be used as an EEG marker for early detection of possible AD. In addition, a structural defect in the suprachiasmatic nucleus is suggested to cause various chronobiological alternations in AD. Most of alternations related to sleep make sleep disturbances common and disruptive symptoms of AD. In this article, the author reviewed the alternation of sleep structure and circadian rhythm in AD patients.