• The Korean Journal of Nuclear Medicine Technology
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• v.18 no.1
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• pp.127-133
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• 2014

Purpose: Low dose of PET/CT is important because of Patient's X-ray exposure. The aim of this study was to evaluate the effectiveness of low-dose PET/ CT image through the CTAC and QAC of patient study and phantom study. Materials and Methods: We used the discovery 710 PET/CT (GE). We used the NEMA IEC body phantom for evaluating the PET data corrected by ultra-low dose CT attenuation correction method and NU2-94 phantom for uniformity. After injection of 70.78 MBq and 22.2 MBq of 18 F-FDG were done to each of phantom, PET/CT scans were obtained. PET data were reconstructed by using of CTAC of which dose was for the diagnosis CT and Q. AC of which was only for attenuation correction. Quantitative analysis was performed by use of horizontal profile and vertical profile. Reference data which were corrected by CTAC were compared to PET data which was corrected by the ultra-low dose. The relative error was assessed. Patients with over weighted and normal weight also underwent a PET/CT scans according to low dose protocol and standard dose protocol. Relative error and signal to noise ratio of SUV were analyzed. Results: In the results of phantom test, phantom PET data were corrected by CTAC and Q.AC and they were compared each other. The relative error of Q.AC profile was been calculated, and it was shown in graph. In patient studies, PET data for overweight patient and normal weight patient were reconstructed by CTAC and Q.AC under routine dose and ultra-low dose. When routine dose was used, the relative error was small. When high dose was used, the result of overweight patient was effectively corrected by Q.AC. Conclusion: In phantom study, CTAC method with 80 kVp and 10 mA was resulted in bead hardening artifact. PET data corrected by ultra- low dose CTAC was not quantified, but those by the same dose were quantified properly. In patients' cases, PET data of over weighted patient could be quantified by Q.AC method. Its relative difference was not significant. Q.AC method was proper attenuation correction method when ultra-low dose was used. As a result, it is expected that Q.AC is a good method in order to reduce patient's exposure dose.

• Asian-Australasian Journal of Animal Sciences
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• v.17 no.4
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• pp.523-532
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• 2004

An experiment was conducted to investigate the effect of microbial phytase ($Natuphos^{R}$) supplementation in combination with enzyme complex (composed of enzymes targeted to SBM dietary components such as $\alpha$-galactosides and galactomannans; $Endo-Power^{R}$) to diet with low nutrient levels on growth performance and ileal nutrient digestibility of weaned pigs. A total of 210 crossbred weaned pigs (Landrace$\times$Yorkshire$\times$Duroc), 6.68$\pm$0.98 kg of initial body weight, were randomly allotted to five dietary treatments, based on weight and age, according to a randomized complete block design. There were three pens per treatment and 14 pigs per pen. The dietary treatments were 1) CON (Control diet with no phytase and enzyme complex (EC)), 2) LP+EC 100 (Control diet with 0.15% unit lower available phosphorus (aP) level+0.1% phytase (500 FTU/kg diet) and 0.1% enzyme complex), 3) LP+EC 80 (Control diet with 0.15% unit lower aP level+0.08% phytase (400 FTU/kg diet) and 0.08% enzyme complex, 4) LPEA+EC 100 (Control diet with 0.15% unit lower aP and 3% lower ME and amino acid levels (lysine, methionine, threonine and typtophan)+0.1% phytase (500 FTU/kg diet) and 0.1% enzyme complex), 5) LPEA+EC 80 (Control diet with 0.15% unit lower aP and 3% lower ME and amino acid levels+0.08% phytase (400 FTU/ kg diet) and 0.08% enzyme complex). For the determination of ileal nutrients digestibility, a total of 15 T-cannulated pigs (initial body weight; 7.52$\pm$1.24 kg; 3 replicates per treatment) were used in the present study. Piglets were weighted and allotted into same dietary treatments as one in growth trial and phase I experimental diets were provided for ileal digestibility study. There was no significant difference (p>0.05) in average daily gain (ADG) and average daily feed intake (ADFI) among dietary treatments during the whole experimental period (0 to 5 weeks). However, piglets in LP+EC 100 group had a significantly higher gain/feed ratio (G:F) than piglets had in control (p<0.05). Crude protein, energy and phosphorus digestibilities were significantly improved when both of phytase and enzyme complex were supplemented at the revel of 0.1%, respectively to diets with low nutrient level (aP or (and) ME and amino acids) (p<0.05). Piglets in LP+EC 100 and LPEA+EC 100 groups showed significantly higher phosphorus content (%) in bone than that of piglets in control group (p<0.05). Supplementation of both of phytase and enzyme complex at 0.1%, respectively, to diet with low nutrient levels (aP or (and) ME and amino acids) significantly improved total ileal essential amino acid and nonessential amino acid digestibilities compared to control group (p<0.05). In conclusion, the results from the present study suggest that the simultaneous inclusion of phytase and enzyme complex to diets at recommended level is advantageous with respect to improving growth performance and nutrient digestibility of weaned pigs and may contribute to increased economic return when added to corn-soy based weaned pig diets.

• The Journal of Korean Academy of Sensory Integration
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• v.6 no.1
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• pp.1-12
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• 2008

Objective : The purpose of this study is to provide reference to functional level of sensory integration of in the low-grads school age, based on the Clinical Observation of Motor and Postural Skills (COMPS) and to examine correlation between the function of sensory integration and academic achievement. Method : Two schools ("J" and "S") have been selected indiscriminately among 56 elementary schools located in Gimhae-si, GyeongNam and then one class from each school was voluntarily chosen among all second-grade classes of the schools. The total number of students in those two classes was 69 (34 boys and 35 girls). Subjects had no developmental problem and no history of referral regarding neurological conditions. Three skilled researchers administrated the COMPS together, and each researcher executed two sub-items of the COMPS. As result of the academic achievement, score data of midterm- and final-exam in the spring semester were collected. The scores of 'Korean language' and 'Math', common examination subjects in both schools, were utilized for data analysis in this study. Results : Statically, there was no significant correlation between the COMPS Weighted Scores and any academic achievements. In a dispersion graphic analysis, however, the total achievement showed significant negative-correlation with the area of 'Rapid Forearm Rotation' and significant positive-correlation with the area of 'Supine Flexion'. In terms of the Math achievement, there are significant negative-correlation with rapid forearm rotation and asymmetrical tonic neck reflex, and significant positive-correlation with the area of 'Supine Flexion'. Students with higher score of the Korean language showed a tendency to get higher Weighted Score and Minus Adjustment Score, and those with lower score of the Math showed a tendency to get higher COMPS scores in all area except the area of 'Supine Flexion'. There was a statically significant difference in the COMPS scores depend on the age among general characteristics. As student older, all COMPS scores, except those in the area of 'Slow Motion' and 'Supine Flexion, were higher. Conclusions : There is somehow reliable correlation between sensory integration function and academic achievement although no statistical significance found in this study. The information from this study may contribute to initiate developing a normative-reference to screen earlier and more alertly sensory integration dysfunctions for school-age children. Further study is recommended trying to find out more reliable matter regarding low grade- schooler's academic achievement.

• The Korean Journal of Nuclear Medicine Technology
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• v.19 no.1
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• pp.12-16
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• 2015

Purpose Principal component analysis (PCA) is a method often used in the neuroimagre analysis as a multivariate analysis technique for describing the structure of high dimensional correlation as the structure of lower dimensional space. PCA is a statistical procedure that uses an orthogonal transformation to convert a set of observations of correlated variables into a set of values of linearly independent variables called principal components. In this study, in order to investigate the usefulness of PCA in the brain PET image analysis, we tried to analyze C[11]-PIB PET image as a representative case. Materials and Methods Nineteen subjects were included in this study (normal = 9, AD/MCI = 10). For C[11]-PIB, PET scan were acquired for 20 min starting 40 min after intravenous injection of 9.6 MBq/kg C[11]-PIB. All emission recordings were acquired with the Biograph 6 Hi-Rez (Siemens-CTI, Knoxville, TN) in three-dimensional acquisition mode. Transmission map for attenuation-correction was acquired using the CT emission scans (130 kVp, 240 mA). Standardized uptake values (SUVs) of C[11]-PIB calculated from PET/CT. In normal subjects, 3T MRI T1-weighted images were obtained to create a C[11]-PIB template. Spatial normalization and smoothing were conducted as a pre-processing for PCA using SPM8 and PCA was conducted using Matlab2012b. Results Through the PCA, we obtained linearly uncorrelated independent principal component images. Principal component images obtained through the PCA can simplify the variation of whole C[11]-PIB images into several principal components including the variation of neocortex and white matter and the variation of deep brain structure such as pons. Conclusion PCA is useful to analyze and extract the main pattern of C[11]-PIB image. PCA, as a method of multivariate analysis, might be useful for pattern recognition of neuroimages such as FDG-PET or fMRI as well as C[11]-PIB image.

• Korean Journal of Radiology
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• v.23 no.12
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• pp.1281-1289
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• 2022

Objective: Radiomic modeling using multiple regions of interest in MRI of the brain to diagnose juvenile myoclonic epilepsy (JME) has not yet been investigated. This study aimed to develop and validate radiomics prediction models to distinguish patients with JME from healthy controls (HCs), and to evaluate the feasibility of a radiomics approach using MRI for diagnosing JME. Materials and Methods: A total of 97 JME patients (25.6 ± 8.5 years; female, 45.5%) and 32 HCs (28.9 ± 11.4 years; female, 50.0%) were randomly split (7:3 ratio) into a training (n = 90) and a test set (n = 39) group. Radiomic features were extracted from 22 regions of interest in the brain using the T1-weighted MRI based on clinical evidence. Predictive models were trained using seven modeling methods, including a light gradient boosting machine, support vector classifier, random forest, logistic regression, extreme gradient boosting, gradient boosting machine, and decision tree, with radiomics features in the training set. The performance of the models was validated and compared to the test set. The model with the highest area under the receiver operating curve (AUROC) was chosen, and important features in the model were identified. Results: The seven tested radiomics models, including light gradient boosting machine, support vector classifier, random forest, logistic regression, extreme gradient boosting, gradient boosting machine, and decision tree, showed AUROC values of 0.817, 0.807, 0.783, 0.779, 0.767, 0.762, and 0.672, respectively. The light gradient boosting machine with the highest AUROC, albeit without statistically significant differences from the other models in pairwise comparisons, had accuracy, precision, recall, and F1 scores of 0.795, 0.818, 0.931, and 0.871, respectively. Radiomic features, including the putamen and ventral diencephalon, were ranked as the most important for suggesting JME. Conclusion: Radiomic models using MRI were able to differentiate JME from HCs.

• The Journal of the Acoustical Society of Korea
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• v.25 no.6
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• pp.277-281
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• 2006

This paper presents a method for the automatic recognition of pitch accents over syllables. The method that we propose is based on the time-delay recursive neural network (TDRNN). which is a neural network classifier with two different representation of dynamic context: the delayed input nodes allow the representation of an explicit trajectory F0(t) along time. while the recursive nodes provide long-term context information that reflects the characteristics of pitch accentuation in spoken English. We apply the TDRNN to pitch accent recognition in two forms: in the normal TDRNN. all of the prosodic features (pitch. energy, duration) are used as an entire set in a single TDRNN. while in the distributed TDRNN. the network consists of several TDRNNs each taking a single prosodic feature as the input. The final output of the distributed TDRNN is weighted sum of the output of individual TDRNN. We used the Boston Radio News Corpus (BRNC) for the experiments on the speaker-independent pitch accent recognition. π 1e experimental results show that the distributed TDRNN exhibits an average recognition accuracy of 83.64% over both pitch events and non-events.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.11
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• pp.471-480
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• 2023

The Korean construction order volume in South Korea grew significantly from 91.3 trillion won in public orders in 2013 to a total of 212 trillion won in 2021, particularly in the private sector. As the size of the domestic and overseas markets grew, the scale and complexity of EPC (Engineering, Procurement, Construction) projects increased, and risk management of project management and ITB (Invitation to Bid) documents became a critical issue. The time granted to actual construction companies in the bidding process following the EPC project award is not only limited, but also extremely challenging to review all the risk terms in the ITB document due to manpower and cost issues. Previous research attempted to categorize the risk terms in EPC contract documents and detect them based on AI, but there were limitations to practical use due to problems related to data, such as the limit of labeled data utilization and class imbalance. Therefore, this study aims to develop an AI model that can categorize the contract terms based on the FIDIC Yellow 2017(Federation Internationale Des Ingenieurs-Conseils Contract terms) standard in detail, rather than defining and classifying risk terms like previous research. A multi-text classification function is necessary because the contract terms that need to be reviewed in detail may vary depending on the scale and type of the project. To enhance the performance of the multi-text classification model, we developed the ELECTRA PLM (Pre-trained Language Model) capable of efficiently learning the context of text data from the pre-training stage, and conducted a four-step experiment to validate the performance of the model. As a result, the ensemble version of the self-developed ITB-ELECTRA model and Legal-BERT achieved the best performance with a weighted average F1-Score of 76% in the classification of 57 contract terms.

• Investigative Magnetic Resonance Imaging
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• v.6 no.2
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• pp.129-136
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• 2002

Purpose : To evaluate the usefulness of MRCP in the diagnosis of the variable causes of common bile duct(CBD) dilatation, except stone or tumor Materials and methods : Twenty-six patients(M:F=15:11, mean age; 62 years) with both MRCP and ERCP were included in this study. Dynamic MRCP(n=12) and contrast-enhanced MRI(n=10) of abdomen were also added. Dilatation of CBD, intrahepatic ducts and pancreatic duct was evaluated, including coexistence of intrahepatic ductal stone, pancreatic pseudocyst, and papillary or papillary edema. The criteria of CBD dilatation was over than 7mm(n= 21, without cholecystectomy) or 10 mm(n=5, with cholecystecto-my) in diameter on T2-weighted coronal image. Results : The mean diameter of CBD was 12.7mm without cholecystectomy(9-19 mm) and 13.0 mm with cholecystectomy(10-15mm), respectively(p 〉0.05). Cholangitis(n=11, 42.3%), chronic pancreatitis(n=8, 30.8%), stenosis of distal CBD(n= 6, 23.1%), periampullary diverticulum(n=3, 11.5%), stenosis of ampulla of Vater(n=2, 7.7%), dysfunction of sphincter of Oddi(n=2, 7.7%), acute focal pancreatitis in the pancreatic head(n=2, 7.7%), papillitis(n=1, 3.8%), pseudocyst in the pancre atic head(n = 1, 3.8%), and ascaris in CBD(n=1, 3.8%) were noted. Pancreatic duct dilatation(n=10, 38.5%) and duodenal diverticulum(n=3, 11.5%) were also seen on MRC P. On dynamic MRCP(12 patients), distal CBD was visualized in 2 patients(16.7%), which was not shown on routine MRCP. Only 1 patient(10.0%) showed papillitis with slightly enhancing papilla on contrast-enhanced MRI (10 patients). Conclusion : MRCP was thought to be helpful in the evaluation of the causes of CBD dilatation, not caused by stone or tumor, especially in the cases of stenosis of distal CBD and chronic pancreatitis, dysfunction of sphincter of Oddi on dynamic MRCP and cholangitis and pericholangitic abnormality on contrast-enhanced MRI.

• Investigative Magnetic Resonance Imaging
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• v.4 no.2
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• pp.85-93
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• 2000

Purpose: To evaluate the availability of magnetic resonance (MRI voiding cystography for the diagnosis of vesicoureteral reflux (VUR) and to compare the sensitivity of MR voiding cystography (MRVC) with that of radiographic voiding cystourethrography (VCUG) in the detection of VUR. Material and Methods : MRVC was performed upon 20 children referred for investigation of VUR. Either coronal T1-weighted spin-echo or spoiled gradient-echo images were obtained before and after transurethral administration of a mixture of normal saline and gadopentetate dimeglumine, and immediately after voiding. The findings of MRVC were compared with those of VCUG performed within 6 months of MRVC. Results 1 VUR was detected in 23 ureterorenal units f16 VUR's by both methods, five VUR's by VCUG, and two VUR's by MRVC). The sensitivity of VCUG and MRVC in detecting VUR was 91.3% (21/23) and 78.3% (18/23), respectively. MRVC detected renal scarring in 15 out of 17 kidneys with scintigraphically detected renal scarring. Conclusion : Although MRVC is slightly less sensitive than VCUG in the detection of VUR, it can be used for the diagnosis of VUR and renal scarring simultaneouslyl and thus will reduce the radiation hazard.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.137-148
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• 2014

Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used