• Journal of Information Processing Systems
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• 제18권6호
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• pp.813-821
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• 2022

Intelligent monitoring, life entertainment, medical rehabilitation, and other fields are only a few examples where visual image technology is becoming increasingly sophisticated and playing a significant role. Recognizing Wushu, or martial arts, movements through the use of visual image technology helps promote and develop Wushu. In order to segment and extract the signals of Wushu movements, this study analyzes the denoising of the original data using the wavelet transform and provides a sliding window data segmentation technique. Wushu movement The Wushu movement recognition model is built based on the hidden Markov model (HMM). The HMM model is trained and taught with the help of the Baum-Welch algorithm, which is then enhanced using the frequency weighted training approach and the mean training method. To identify the dynamic Wushu movement, the Viterbi algorithm is used to determine the probability of the optimal state sequence for each Wushu movement model. In light of the foregoing, an HMM-based martial arts movements recognition model is developed. The recognition accuracy of the HMM model increases to 99.60% when the number of samples is 4,000, which is greater than the accuracy of the SVM (by 0.94%), the CNN (by 1.12%), and the BP (by 1.14%). From what has been discussed, it appears that the suggested system for detecting martial arts acts is trustworthy and effective, and that it may contribute to the growth of martial arts.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권10호
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• pp.4952-4975
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• 2018

An induced hesitant linguistic aggregation operator is investigated in the paper, in which, hesitant fuzzy linguistic evaluation values are associated with probabilistic information. To deal with these hesitant fuzzy linguistic information, an induced hesitant fuzzy linguistic probabilistic ordered weighted averaging (IHFLPOWA) operator is proposed, monotonicity, boundary and idempotency of IHFLPOWA are proved. Then andness, orness and the entropy of dispersion of IHFLPOWA are analyzed, which are used to characterize the weighting vector of the operator, these properties show that IHFLPOWA is extensions of the induced linguistic ordered weighted averaging operator and linguistic probabilistic aggregation operator. In this paper, IHFLPOWA is utilized to gather linguistic information and create fuzzy ontologies, and a movie fuzzy ontology as an illustrative case study is used to show the elaboration of the proposed method and comparison with the existing linguistic aggregation operators, it seems that the IHFLPOWA operator is an useful and alternative operator for dealing with hesitant fuzzy linguistic information with probabilistic information.

• 한국도로학회논문집
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• 제19권5호
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• pp.89-96
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• 2017

PURPOSES: This study aims to develop a rational procedure for estimating the pavement roughness index considering vehicle wandering. METHODS : The location analysis of the passing vehicle in the lane was performed by approximately 1.2 million vehicles for verification of the wandering distribution. According to verification result, the distribution follows the normal distribution pattern. The probability density function was estimated using each lane's wandering distribution model. Then the procedure for applying a weighted value into the lane profile was conducted using this function. RESULTS : The modified index, MRIw, with consideration towards applying the wandering weighted value application was computed then compared with MRI. It was found that the Coefficient of Variation for distribution of lateral roughness index in the lane was high in the case of a large difference between each index (i.e., MRIw and MRI) observed. CONCLUSIONS : This result confirms that the new procedure with consideration of the weight factor can successfully improve the lane representative characteristics of the roughness index.

• Imaging Science in Dentistry
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• 제52권4호
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• pp.393-398
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• 2022

Purpose: This study proposed a generative adversarial network (GAN) model for T2-weighted image (WI) synthesis from proton density (PD)-WI in a temporomandibular joint(TMJ) magnetic resonance imaging (MRI) protocol. Materials and Methods: From January to November 2019, MRI scans for TMJ were reviewed and 308 imaging sets were collected. For training, 277 pairs of PD- and T2-WI sagittal TMJ images were used. Transfer learning of the pix2pix GAN model was utilized to generate T2-WI from PD-WI. Model performance was evaluated with the structural similarity index map (SSIM) and peak signal-to-noise ratio (PSNR) indices for 31 predicted T2-WI (pT2). The disc position was clinically diagnosed as anterior disc displacement with or without reduction, and joint effusion as present or absent. The true T2-WI-based diagnosis was regarded as the gold standard, to which pT2-based diagnoses were compared using Cohen's ĸ coefficient. Results: The mean SSIM and PSNR values were 0.4781(±0.0522) and 21.30(±1.51) dB, respectively. The pT2 protocol showed almost perfect agreement(ĸ=0.81) with the gold standard for disc position. The number of discordant cases was higher for normal disc position (17%) than for anterior displacement with reduction (2%) or without reduction (10%). The effusion diagnosis also showed almost perfect agreement(ĸ=0.88), with higher concordance for the presence (85%) than for the absence (77%) of effusion. Conclusion: The application of pT2 images for a TMJ MRI protocol useful for diagnosis, although the image quality of pT2 was not fully satisfactory. Further research is expected to enhance pT2 quality.

• 한국수자원학회논문집
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• 제45권2호
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• pp.229-242
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• 2012

본 연구에서는 괴산댐 하류 달천에서 성어기 피라미에 대한 물리 서식처 모의를 수행하였다. 이를 위하여 퍼지논리에 의한 서식처 적합도 지수를 산정하는 CASiMiR 모형을 이용하였다. 또한 모의결과를 서식처 선호도 곡선을 이용하여 서식처 적합도 지수를 산정할 수 있는 수리모형인 River2D 모형의 결과와 비교, 분석하였다. CASiMiR 모형의 수위 자료는River2D 모형을 통한 수위계산결과를 활용하였으며 현장측정자료와 비교한 결과 잘 반영하는 것을 확인하였다. 대상구간의 만곡부 상류 직선구간에 있는 여울에서 성어기 피라미의 서식처가 가장 적합한 것으로 나타났다. CASiMiR 모형의 경우$7.23m^3/s$의유량조건에서가중가용면적이최대값을보였고, River2D 모형은$9m^3/s$의 유량에서 최대 가중가용면적을 예측하였다. 또한 갈수량(Q355), 저수량(Q275), 평수량(Q185), 풍수량(Q95) 유량조건에서CASiMiR 모형은River2D 모형에 비해 가중가용면적을 0.3~25.3% 정도 과대 추정하는 결과를 보였다.

• 대한한의학회지
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• 제39권2호
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• pp.1-12
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• 2018

Objectives: The objective of this study is to develop a new animal model with Kidney-hypofunction for Sasang Constitutional Medicine, especially for partial Soyangin(one of four constitution which has good digestive function and poor renal function) by Unilateral Ureteral Obstruction, and to estimate the factor related to obesity, dyslipidemia, and metabolic syndrome. Methods: The C57BL/6J mice were divided into 3 groups : normal group, high fat diet(HFD) control group, and HFD group with Unilateral Ureteral Obstruction(UUO). Then, the HFD control group and the experimental group were fed with high fat diet for 6 weeks. Food intake and body weight were measured at regular time by week. After the final experiment, blood was gathered for bloodchemical examination and organs(liver, fatty tissue) were remoed, weighted, and mRNA was analyzed with real-time PCR. Results: The weight growth rate with High fat diet went down by 8.35% in experimental group and had similar FER with the normal group, while HFD control group had higher weight growth rate and FER than any other groups. Also The experimental group had lower triglyceride and LDL cholesterol rate and higher glucose rate in serum. and in mRNA expression, GLUT-9, the protein related to excretion of uric acid and metabolic syndrome, expressed lower rate than that of HFD control group. and IL-6, a kind of cytokine related to obesity and metabolic syndrome, expressed more than HFD control group. Conclusions: It was found that Kidney-hypofunction animal-experimental model is susceptible to metabolic syndrome.

• Korean Journal of Radiology
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• 제22권7호
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• pp.1021-1033
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• 2021

Objective: To assess the expression of vascular normalization genes in different molecular subtypes of breast cancer and to determine whether molecular subtypes with a higher vascular normalization gene expression can be identified using dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) and intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI). Materials and Methods: This prospective study evaluated 306 female (mean age ± standard deviation, 50 ± 10 years), recruited between January 2014 and August 2017, who had de novo breast cancer larger than 1 cm in diameter (308 tumors). DCE MRI followed by IVIM DWI studies using 11 different b-values (0 to 1200 s/mm²) were performed on a 1.5T MRI system. The Tofts model and segmented biexponential IVIM analysis were used. For each tumor, the molecular subtype (according to six [I-VI] subtypes and PAM50 subtypes), expression profile of genes for vascular normalization, pericytes, and normal vascular signatures were determined using freshly frozen tissue. Statistical associations between imaging parameters and molecular subtypes were examined using logistic regression or linear regression with a significance level of p = 0.05. Results: Breast cancer subtypes III and VI and PAM50 subtypes luminal A and normal-like exhibited a higher expression of genes for vascular normalization, pericyte markers, and normal vessel function signature (p < 0.001 for all) compared to other subtypes. Subtypes III and VI and PAM50 subtypes luminal A and normal-like, versus the remaining subtypes, showed significant associations with K^trans, k_ep, v_p, and IAUGC_BN90 on DEC MRI, with relatively smaller values in the former. The subtype grouping was significantly associated with D, with relatively less restricted diffusion in subtypes III and VI and PAM50 subtypes luminal A and normal-like. Conclusion: DCE MRI and IVIM parameters may identify molecular subtypes of breast cancers with a different vascular normalization gene expression.

• Journal of the Korean Statistical Society
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• 제26권1호
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• pp.1-22
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• 1997

In the linear regression model $y_{i}$ = .alpha. $x_{i}$ $^{T}$ .beta. + .epsilon.$_{i}$ , i = 1,2,...,n, the weighted pairwise absolute deviation (WPAD) estimator was defined by minimizing the dispersion function D (.beta.) = .sum..sum.$_{{i $w_{{ij}}$$\mid$ $r_{j}$ (.beta.) $r_{i}$ (.beta.)$\mid$, where $r_{i}$ (.beta.)'s are residuals and $w_{{ij}}$'s are weights. This estimator can achive bounded total influence with positive breakdown by choice of weights $w_{{ij}}$. In this paper, we consider a more general type of dispersion function than that of D(.beta.) and propose a pairwise GM-estimator based on the dispersion function. Under some regularity conditions, the proposed estimator has a bounded influence function, a high breakdown point, and asymptotically a normal distribution. Results of a small-sample Monte Carlo study are also presented. presented.

• Journal of Power Electronics
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• 제16권1호
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• pp.48-56
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• 2016

This paper proposes a method for detecting irreversible demagnetization using the harmonic analysis of back electromotive force (BEMF) in interior permanent magnet-type brushless DC motors. First, demagnetization patterns, such as equality, inequality, and weighted demagnetizations, are defined and classified by considering the possibility of demagnetization resulting from motor operating characteristics. Second, an available diagnostic model for the harmonic analysis of BEMFs is defined according to pole-slot coefficients because the characteristics of BEMFs under demagnetization conditions are affected by the combination of poles and slots. Third, BEMFs and their harmonic components under normal and demagnetization conditions are analyzed through simulation and experiment to verify the proposed demagnetization detection technique.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권8호
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• pp.3984-4005
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• 2017

Image recognition has become an increasingly important topic for its wide application. It is highly challenging when facing to large-scale database with large variance. The recognition systems rely on a key component, i.e. the low-level feature or the learned mid-level feature. The recognition performance can be potentially improved if the data distribution information is exploited using a more sophisticated way, which usually a function over hidden variable, model parameter and observed data. These methods are called generative score space. In this paper, we propose a discriminative extension for the existing generative score space methods, which exploits class label when deriving score functions for image recognition task. Specifically, we first extend the regular generative models to class conditional models over both observed variable and class label. Then, we derive the mid-level feature mapping from the extended models. At last, the derived feature mapping is embedded into a discriminative classifier for image recognition. The advantages of our proposed approach are two folds. First, the resulted methods take simple and intuitive forms which are weighted versions of existing methods, benefitting from the Bayesian inference of class label. Second, the probabilistic generative modeling allows us to exploit hidden information and is well adapt to data distribution. To validate the effectiveness of the proposed method, we cooperate our discriminative extension with three generative models for image recognition task. The experimental results validate the effectiveness of our proposed approach.