• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1397-1404
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• 2018

Due to the technical difficulties and high cost for obtaining 3-dimensional structure data, sequence-based approaches in proteins have not been widely acknowledged. A motif can be defined as any segments in protein or gene sequences. With this simplicity, motifs have been actively and widely used in various areas. However, the motif itself has not been studied comprehensively. The value of this study can be categorized in three fields in order to analyze the human proteins using artificial intelligence method: (1) Based on our best knowledge, this research is the first comprehensive motif analysis by analyzing motifs with all human proteins in Protein Data Bank (PDB) associated with the database of Enzyme Commission (EC) number and Structural Classification of Proteins (SCOP). (2) We deeply analyze the motif in three different categories: pattern, statistical, and functional analysis of clusters. (3) At the last and most importantly, we proposed random motif frequency(RMF) matric that can efficiently distinct the characteristics of proteins by identifying interface residues from non-interface residues and clustering protein functions based on big data while varying the size of random motif.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.42 no.5 s.305
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• pp.27-34
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• 2005

Ventricular fibrillation(VF) is generally caused by chaotic behavior of electrical propagation in heart and may result in sudden cardiac death. In this study, we proposed a ventricular fibrillation detection algorithm based on support vector machine classifier, which could offer benefits to reduce the teaming costs as well as good classification performance. Before the extraction of input features, raw ECG signal was applied to preprocessing procedures, as like wavelet transform based bandpass filtering, R peak detection and segment assignment for feature extraction. We selected input features which of some are related to the rhythm information and of others are related to wavelet coefficients that could describe the morphology of ventricular fibrillation well. Parameters for SVM classifier, C and ${\alpha}$, were chosen as 10 and 1 respectively by trial and error experiments. Each average performance for normal sinus rhythm ventricular tachycardia and VF, was 98.39%, 96.92% and 99.88%. And, when the VF detection performance of SVM classifier was compared to that of multi-layer perceptron and fuzzy inference methods, it showed similar or higher values. Consequently, we could find that the proposed input features and SVM classifier would one of the most useful algorithm for VF detection.

• Journal of Cardiovascular Imaging
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• v.30 no.4
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• pp.263-275
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• 2022

BACKGROUND: Cardiac allograft vasculopathy (CAV) is a complication beyond the first-year post-heart transplantation (HTx). We aimed to test the utility of cardiac magnetic resonance (CMR) to detect functional/structural changes in HTx recipients with CAV. METHODS: Seventy-seven prospectively recruited HTx recipients beyond the first-year post-HTx and 18 healthy controls underwent CMR, including cine imaging of ventricular function and T1- and T2-mapping to assess myocardial tissue changes. Data analysis included quantification of global cardiac function and regional T2, T1 and extracellular volume based on the 16-segment model. International Society for Heart and Lung Transplantation criteria was used to adjudicate CAV grade (0-3) based on coronary angiography. RESULTS: The majority of HTx recipients (73%) presented with CAV (1: n = 42, 2/3: n = 14, 0: n = 21). Global and segmental T2 (49.5 ± 3.4 ms vs 50.6 ± 3.4 ms, p < 0.001;16/16 segments) were significantly elevated in CAV-0 compared to controls. When comparing CAV-2/3 to CAV-1, global and segmental T2 were significantly increased (53.6 ± 3.2 ms vs. 50.6 ± 2.9 ms, p < 0.001; 16/16 segments) and left ventricular ejection fraction was significantly decreased (54 ± 9% vs. 59 ± 9%, p < 0.05). No global, structural, or functional differences were seen between CAV-0 and CAV-1. CONCLUSIONS: Transplanted hearts display functional and structural alteration compared to native hearts, even in those without evidence of macrovasculopathy (CAV-0). In addition, CMR tissue parameters were sensitive to changes in CAV-1 vs. 2/3 (mild vs. moderate/severe). Further studies are warranted to evaluate the diagnostic value of CMR for the detection and classification of CAV.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.157-176
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• 2018

The purpose of this research is to develop a detection model for companies designated as administrative issue in KOSDAQ market using financial data. Administration issue designates the companies with high potential for delisting, which gives them time to overcome the reasons for the delisting under certain restrictions of the Korean stock market. It acts as an alarm to inform investors and market participants of which companies are likely to be delisted and warns them to make safe investments. Despite this importance, there are relatively few studies on administration issues prediction model in comparison with the lots of studies on bankruptcy prediction model. Therefore, this study develops and verifies the detection model of the companies designated as administrative issue using financial data of KOSDAQ companies. In this study, logistic regression and decision tree are proposed as the data mining models for detecting administrative issues. According to the results of the analysis, the logistic regression model predicted the companies designated as administrative issue using three variables - ROE(Earnings before tax), Cash flows/Shareholder's equity, and Asset turnover ratio, and its overall accuracy was 86% for the validation dataset. The decision tree (Classification and Regression Trees, CART) model applied the classification rules using Cash flows/Total assets and ROA(Net income), and the overall accuracy reached 87%. Implications of the financial indictors selected in our logistic regression and decision tree models are as follows. First, ROE(Earnings before tax) in the logistic detection model shows the profit and loss of the business segment that will continue without including the revenue and expenses of the discontinued business. Therefore, the weakening of the variable means that the competitiveness of the core business is weakened. If a large part of the profits is generated from one-off profit, it is very likely that the deterioration of business management is further intensified. As the ROE of a KOSDAQ company decreases significantly, it is highly likely that the company can be delisted. Second, cash flows to shareholder's equity represents that the firm's ability to generate cash flow under the condition that the financial condition of the subsidiary company is excluded. In other words, the weakening of the management capacity of the parent company, excluding the subsidiary's competence, can be a main reason for the increase of the possibility of administrative issue designation. Third, low asset turnover ratio means that current assets and non-current assets are ineffectively used by corporation, or that asset investment by corporation is excessive. If the asset turnover ratio of a KOSDAQ-listed company decreases, it is necessary to examine in detail corporate activities from various perspectives such as weakening sales or increasing or decreasing inventories of company. Cash flow / total assets, a variable selected by the decision tree detection model, is a key indicator of the company's cash condition and its ability to generate cash from operating activities. Cash flow indicates whether a firm can perform its main activities(maintaining its operating ability, repaying debts, paying dividends and making new investments) without relying on external financial resources. Therefore, if the index of the variable is negative(-), it indicates the possibility that a company has serious problems in business activities. If the cash flow from operating activities of a specific company is smaller than the net profit, it means that the net profit has not been cashed, indicating that there is a serious problem in managing the trade receivables and inventory assets of the company. Therefore, it can be understood that as the cash flows / total assets decrease, the probability of administrative issue designation and the probability of delisting are increased. In summary, the logistic regression-based detection model in this study was found to be affected by the company's financial activities including ROE(Earnings before tax). However, decision tree-based detection model predicts the designation based on the cash flows of the company.

• Journal of Yeungnam Medical Science
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• v.9 no.1
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• pp.110-120
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• 1992

The purpose of this study was to prevent the dilutional effect of excessive absorption of irrigating solution by using furosemide intraoperatively during transurethral resection of the prostate, 30 patients, who belonged to physical status II or III of ASA classification, were selected randomly and divided with two groups as follows : G1(N=15) : Not-administrated furosemide(control group) G2(N=15) : Administrated furosemide(Experimental group). All patients were premedicated with Hydroxyzine(1mg/kg, IM) and were performed continous epidural anesthesia with 2% lidocaine(1-1.5mg/segment). For irrigating solution, 5% D-sorbitol was used and Hartman's solution were given for maintenance fluid and fixed the height of irrigating container to 60 cm from symphysis pubis. With the starting of operation, 20mg furosemide was administrated to experimental group. The blood samples for the values of $Na^+$, $K^+$, Glucose and BUN were performed at the periods of preoperation, 10 min, 20 min, 30 min after the starting of operation and immediate postoperation. Based these data, serum osmolality and effective osmolality were calculated. The results were as follows : 1. The sodium concentration of control group was decreased statistically significantly at 10 min, 20 min, 30 min after the starting of operation and immediate postoperative period as comparing with the preoperation value(p<0.05). But that of experimental Group was not changed significantly. 2. The serum osmolality and effective osmolality were decreased statistically significantly at 30 min after the starting of operation and immediate postoperative period as comparing with the preoperation value(p<0.05). But those of experimental group were not changed significantly. These results show that the dilutional effect of excessive absorption of irrigating solution might be prevented by using furosemide intraopertively. And so we recommend the use of furosemide during TURP, especially in patients with congestive heart failure or renal failure.

• Journal of KIISE:Software and Applications
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• v.29 no.3
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• pp.167-179
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• 2002

In this paper, we propose a new acoustic model for characterizing segmental features and an algorithm based upon a general framework of hidden Markov models (HMMs) in order to compensate the weakness of HMM assumptions. The segmental features are represented as a trajectory of observed vector sequences by a polynomial regression function because the single frame feature cannot represent the temporal dynamics of speech signals effectively. To apply the segmental features to pattern classification, we adopted segmental HMM(SHMM) which is known as the effective method to represent the trend of speech signals. SHMM separates observation probability of the given state into extra- and intra-segmental variations that show the long-term and short-term variabilities, respectively. To consider the segmental characteristics in acoustic model, we present segmental-feature HMM(SFHMM) by modifying the SHMM. The SFHMM therefore represents the external- and internal-variation as the observation probability of the trajectory in a given state and trajectory estimation error for the given segment, respectively. We conducted several experiments on the TIMIT database to establish the effectiveness of the proposed method and the characteristics of the segmental features. From the experimental results, we conclude that the proposed method is valuable, if its number of parameters is greater than that of conventional HMM, in the flexible and informative feature representation and the performance improvement.

• Journal of Environmental Science International
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• v.24 no.11
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• pp.1343-1362
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• 2015

As a result of analysis based on the observed data for BOD, COD and TOC in order to manage non-biodegradable organics in the Geumho River, COD/BOD ratio was analyzed as the occupying predominance proportion. In this study, the classification(changes in water quality measurement : increase, equal, decrease) and measurement of BOD and COD were analyzed for trends over the past 10 years from 2005 to 2014 in the Geumho River. The Geumho River is expected to need non-biodegradable organics management because BOD was found to be reduced 61.1% and COD was found to be increased 50%. As a result of the analysis of land use, the Geumho-A is a unit watershed area of $921.13km^2$, which is the most common area that is occupied by forests. The Geumho-B is a unit watershed area of $436.8km^2$, which is the area that is highest occupied by agriculture and grass of 24.84%. The Geumho-C is a unit watershed area of $704.56km^2$ accounted for 40.29% of the entire watershed, which is the area that is occupied by urban of 15.12%. Load of non-biodegradable organics, which is not easy biodegradable according to the discharge, appeared to be increased because flow coefficient of COD and TOC at the Geumho-B were estimated larger than 1 value. The management of non-point sources of agricultural land is required because the Geumho-B watershed area occupied by the high proportion of agriculture and field. In this segment it showed to increase the organics that biodegradation is difficult because the ratio of BOD and TOC was decreased rapidly from GR7 to GR8. Thus, countermeasures will be required for this.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.5
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• pp.619-628
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• 2009

LiDAR has capability of rapid data acquisition and provides useful information for reconstructing surface of the Earth. However, Extracting information from LiDAR data is not easy task because LiDAR data consist of irregularly distributed point clouds of 3D coordinates and lack of semantic and visual information. This thesis proposed methods for automatic extraction of buildings and 3D detail modeling using airborne LiDAR data. As for preprocessing, noise and unnecessary data were removed by iterative surface fitting and then classification of ground and non-ground data was performed by analyzing histogram. Footprints of the buildings were extracted by tracing points on the building boundaries. The refined footprints were obtained by regularization based on the building hypothesis. The accuracy of building footprints were evaluated by comparing with 1:1,000 digital vector maps. The horizontal RMSE was 0.56m for test areas. Finally, a method of 3D modeling of roof superstructure was developed. Statistical and geometric information of the LiDAR data on building roof were analyzed to segment data and to determine roof shape. The superstructures on the roof were modeled by 3D analytical functions that were derived by least square method. The accuracy of the 3D modeling was estimated using simulation data. The RMSEs were 0.91m, 1.43m, 1.85m and 1.97m for flat, sloped, arch and dome shapes, respectively. The methods developed in study show that the automation of 3D building modeling process was effectively performed.

• Journal of Digital Convergence
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• v.14 no.2
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• pp.183-190
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• 2016

This paper proposes algorithm to reduce the computing time in a neural network that reduces transmission of data for tracking mobile objects in surveillance networks in terms of detection and communication load. Object Detection can be defined as follows : Given image sequence, which can forom a digitalized image, the goal of object detection is to determine whether or not there is any object in the image, and if present, returns its location, direction, size, and so on. But object in an given image is considerably difficult because location, size, light conditions, obstacle and so on change the overall appearance of objects, thereby making it difficult to detect them rapidly and exactly. Therefore, this paper proposes fast and exact object detection which overcomes some restrictions by using neural network. Proposed system can be object detection irrelevant to obstacle, background and pose rapidly. And neural network calculation time is decreased by reducing input vector size of neural network. Principle Component Analysis can reduce the dimension of data. In the video input in real time from a CCTV was experimented and in case of color segment, the result shows different success rate depending on camera settings. Experimental results show proposed method attains 30% higher recognition performance than the conventional method.

• Asian Spine Journal
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• v.12 no.6
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• pp.1060-1068
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• 2018

Study Design: Retrospective study. Purpose: Lumbar intervertebral disc degeneration is an important cause of low back pain. Overview of Literature: Spinal fusion is often reported to have a good course for adolescent idiopathic scoliosis (AIS). However, many studies have reported that adjacent segment degeneration is accelerated after lumbar spinal fusion. Radiography is a simple method used to evaluate the orientation of the vertebral column. magnetic resonance imaging (MRI) is the method most often used to specifically evaluate intervertebral disc degeneration. The Pfirrmann classification is a well-known method used to evaluate degenerative lumbar disease. After spinal fusion, an increase in stress, excess mobility, increased intra-disc pressure, and posterior displacement of the axis of motion have been observed in the adjacent segments. Methods: we retrospectively secured and analyzed the data of 15 patients (four boys and 11 girls) with AIS who underwent a spinal fusion surgery. We studied the full-length view of the spine (anterior-posterior and lateral) from the X-ray and MRI obtained from all patients before surgery. Postoperatively, another full-length spine X-ray and lumbosacral MRI were obtained from all participants. Then, pelvic tilt, sacral slope, curve correction, and fused and free segments before and after surgery were calculated based on X-ray studies. MRI images were used to estimate the degree to which intervertebral discs were degenerated using Pfirrmann grading system. Pfirrmann grade before and after surgery were compared with Wilcoxon signed rank test. While analyzing the contribution of potential risk factors for the post-spinal fusion Pfirrmann grade of disc degeneration, we used generalized linear models with robust standard error estimates to account for intraclass correlation that may have been present between discs of the same patient. Results: The mean age of the participant was 14 years, and the mean curvature before and after surgery were 67.8 and 23.8, respectively (p<0.05). During the median follow-up of 5 years, the mean degree of the disc degeneration significantly increased in all patients after surgery (p<0.05) with a Pfirrmann grade of 1 and 2.8 in the L2-L3 before and after surgery, respectively. The corresponding figures at L3-L4, L4-L5, and L5-S1 levels were 1.28 and 2.43, 1.07 and 2.35, and 1 and 2.33, respectively. The lower was the number of free discs below the fusion level, the higher was the Pfirrmann grade of degeneration (p<0.001). Conversely, the higher was the number of the discs fused together, the higher was the Pfirrmann grade. Conclusions: we observed that the disc degeneration aggravated after spinal fusion for scoliosis. While the degree of degeneration as measured by Pfirrmann grade was directly correlated by the number of fused segments, it was negatively correlated with the number of discs that remained free below the lowermost level of the fusion.