• Journal of Korean Medicine Rehabilitation
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• v.34 no.2
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• pp.173-180
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• 2024

Objectives The purpose of this study is to report the effects of Korean medicine treatment on tibia, fibula, and patellar fractures with sequelae of poliomyelitis. Methods A 64-year-old male patient was treated with acupuncture, herbal medicine, cupping, moxibustion, and exercise treatment for 59 days after open reduction and internal fixation and tension band wiring surgery. The effects were evaluated using a visual analog scale (VAS), manual muscle testing (MMT), range of motion (ROM), Western Ontario and McMaster Universities (WOMAC), and EuroQol-5 dimension (EQ-5D) index. Results After treatment, VAS decreased from 7 to 2. MMT, ROM, WOMAC, EQ-5D and walking ability were improved. Conclusions This case study suggests that Korean medicine treatment could be effective for tibia, fibula, patellar fractures in patients with sequelae of poliomyelitis.

• Wind and Structures
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• v.38 no.1
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• pp.1-13
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• 2024

In order to effectively simulate nonstationary stochastic turbulent wind fields, four separation hybrid (SEP-H) models are proposed in the present study. Based on the assumption that the lateral turbulence component at one single-point is uncorrelated with the longitudinal and vertical turbulence components, the fluctuating wind is separated into 2nV-1D and nV1D nonstationary stochastic vector processes. The first process can be expressed as double proper orthogonal decomposition (DPOD) or proper orthogonal decomposition and spectral representation method (POD-SRM), and the second process can be expressed as POD or SRM. On this basis, four SEP-H models of nonstationary stochastic turbulent wind fields are developed. In addition, the orthogonal random variables in the SEP-H models are presented as random orthogonal functions of elementary random variables. Meanwhile, the number theoretical method (NTM) is conveniently adopted to select representative points set of the elementary random variables. The POD-FFT (Fast Fourier transform) technique is introduced in frequency to give full play to the computational efficiency of the SEP-H models. Finally, taking a long-span bridge as the engineering background, the SEP-H models are compared with the dimension-reduction DPOD (DR-DPOD) model to verify the effectiveness and superiority of the proposed models.

• Journal of Environmental Health Sciences
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• v.21 no.4
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• pp.1-9
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• 1995

This study was carried to investigate the biodegradability of phenol in the wastewater with the two sludge blanket-packed bed reactor in series. Each reactor had a dimension of 0.09 m i.d. and 1.5 m height and consisted of two regions. The lower region was a sludge blanket of 0.5 m height and the upper region was a packed-bed of 1 m height. The packed bed region was charged with ceramic raschig rings of 10 mm i.d., 15 mm o.d. and 20 mm length. The reactors were operated at 35$\circ$C and the hydraulic retention time(HRT) was maintained 24 hours. The synthetic wastewater composed of glucose and phenol as major components was fed into the reactor in a continuous mode with incereasing phenol concentration. In addition, the nutrient trace metals($Na^+, Mg^{2+}, Ca^{2+}, PO_4^{3-}, NH_4^+, Co^{2+}, Fe^{2+}$ etc.) were added for growing anaerobes. The phenol concentration of the effluent, the overall gas production, the composition of product gas, the efficiency of COD reduction and the duration of acclimation period were measured to determine the performance of the anaerobic wastewater treatment system as the phenol concentration of the influent was increased from 600 to 2400 mg//l. Successfully stable biodegradation of phenol could be achieved with the anaerobic treatment system from 600 to 1, 800 mg/l of the influent phenol concentration. The upper level of influent phenol loading was high enough to meet most of the practical requirement. The duration of acclimation increased with the phenol loading. At steady state of the influent phenol concentration of 1800 mg/l, the treatment performance indicated the phenol reduction efficiency of 99%, the COD reduction efficiency of 99% and the gas production rate of 37 l/day. At the influent phenol concentration of 2400 mg/l, however, the operation of the treatment system was noted unstable. While the concentration of methane in biogas decreased with increasing the influent phenol loading, the carbon dioxide was increased. However, the concentration of hydrogen was varied negligibly. The concentration of methane was high enough to be used as a fuel. As a result, it is suggested that anaerobic phenol wastewater treament was economical in the sense of energy recovery and wastewater treatment.

• Journal of Veterinary Clinics
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• v.19 no.2
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• pp.186-190
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• 2002

This crossover study was performed in order to compare the effects of cetirizine, loratadine, and terfenadine in canine skin. Five healthy dogs were used. Cetirizine 0.5 mg/kg, loratadine 5 mg/kg and terfenadine 5 mg/kg were administered orally 4 hours before the experiment. Erythema indices and wheal size were assessed by Hexameter ($MX^{\circledR}$ 18, CK, Germany) and skin reaction guide, respectively. Cetirizine-induced erythema inhibition was generally higher than other drugs and was significantly different from placebo. Cetirizine was superior to placebo at 3, 4, 5, 6, 7, and 8 minutes (p< 0.01). Cetirizine also was superior to placebo at 9 minutes (p< 0.05). Loratadine and terfenadine erythema inhibition were better than after placebo treatment from 4 to 9 minutes, but erythema index of terfenadine at 7 minutes was not observed probability of 95% and 99%. At 10 minutes, intradermal injection of the histamine caused a mean wheal dimension for placebo, cetirizine, loratadine and terfenadine, which were 13.25$\pm$0.75 mm,7.5$\pm$ 1.02 mm (53% reduction, p<0.007),6.2$\pm$0.58 mm(43% reduction, p <0.01), and 8.4 $\pm$0.67 mm(37% reduction, p< 0.05), respectively, comparing with placebo. Loratadine and cetirizine were good antihistamines for clinical therapy for atopic dermatitis in dog.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.131-154
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• 2022

Recently, researches on unstructured data analysis have been actively conducted with the development of information and communication technology. In particular, topic modeling is a representative technique for discovering core topics from massive text data. In the early stages of topic modeling, most studies focused only on topic discovery. As the topic modeling field matured, studies on the change of the topic according to the change of time began to be carried out. Accordingly, interest in dynamic topic modeling that handle changes in keywords constituting the topic is also increasing. Dynamic topic modeling identifies major topics from the data of the initial period and manages the change and flow of topics in a way that utilizes topic information of the previous period to derive further topics in subsequent periods. However, it is very difficult to understand and interpret the results of dynamic topic modeling. The results of traditional dynamic topic modeling simply reveal changes in keywords and their rankings. However, this information is insufficient to represent how the meaning of the topic has changed. Therefore, in this study, we propose a method to visualize topics by period by reflecting the meaning of keywords in each topic. In addition, we propose a method that can intuitively interpret changes in topics and relationships between or among topics. The detailed method of visualizing topics by period is as follows. In the first step, dynamic topic modeling is implemented to derive the top keywords of each period and their weight from text data. In the second step, we derive vectors of top keywords of each topic from the pre-trained word embedding model. Then, we perform dimension reduction for the extracted vectors. Then, we formulate a semantic vector of each topic by calculating weight sum of keywords in each vector using topic weight of each keyword. In the third step, we visualize the semantic vector of each topic using matplotlib, and analyze the relationship between or among the topics based on the visualized result. The change of topic can be interpreted in the following manners. From the result of dynamic topic modeling, we identify rising top 5 keywords and descending top 5 keywords for each period to show the change of the topic. Existing many topic visualization studies usually visualize keywords of each topic, but our approach proposed in this study differs from previous studies in that it attempts to visualize each topic itself. To evaluate the practical applicability of the proposed methodology, we performed an experiment on 1,847 abstracts of artificial intelligence-related papers. The experiment was performed by dividing abstracts of artificial intelligence-related papers into three periods (2016-2017, 2018-2019, 2020-2021). We selected seven topics based on the consistency score, and utilized the pre-trained word embedding model of Word2vec trained with 'Wikipedia', an Internet encyclopedia. Based on the proposed methodology, we generated a semantic vector for each topic. Through this, by reflecting the meaning of keywords, we visualized and interpreted the themes by period. Through these experiments, we confirmed that the rising and descending of the topic weight of a keyword can be usefully used to interpret the semantic change of the corresponding topic and to grasp the relationship among topics. In this study, to overcome the limitations of dynamic topic modeling results, we used word embedding and dimension reduction techniques to visualize topics by era. The results of this study are meaningful in that they broadened the scope of topic understanding through the visualization of dynamic topic modeling results. In addition, the academic contribution can be acknowledged in that it laid the foundation for follow-up studies using various word embeddings and dimensionality reduction techniques to improve the performance of the proposed methodology.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.1
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• pp.195-201
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• 2014

In this study, face recognition system was designed based on polynomial Radial Basis Function Neural Networks(pRBFNNs) pattern classifier using 2-directional 2-dimensional principal component analysis algorithm. Existing one dimensional PCA leads to the reduction of dimension of image expressed by the multiplication of rows and columns. However $(2D)^2PCA$(2-Directional 2-Dimensional Principal Components Analysis) is conducted to reduce dimension to each row and column of image. and then the proposed intelligent pattern classifier evaluates performance using reduced images. The proposed pRBFNNs consist of three functional modules such as the condition part, the conclusion part, and the inference part. In the condition part of fuzzy rules, input space is partitioned with the aid of fuzzy c-means clustering. In the conclusion part of rules. the connection weight of RBFNNs is represented as the linear type of polynomial. The essential design parameters (including the number of inputs and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. Using Yale and AT&T dataset widely used in face recognition, the recognition rate is obtained and evaluated. Additionally IC&CI Lab dataset is experimented with for performance evaluation.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.31 no.2
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• pp.202-209
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• 1998

A three-dimensional numerical method based on the Green's integral equation is developed to predict the motion response and drift force in waves for the ocean monitoring facilities. In this method, we use source and doublet distribution, and triangular and rectangular eliments. To eliminate the irregular frequency phenomenon, the method of improved integral equation is applied and the time-mean drift force is calculated by the method of direct pressure integration over the body surface. To conform the validity of the present numerical method, some calculations for the floating sphere are performed and it is shown that the present method provides sufficiently reliable results. As a calculation example for the real facilities, the motion response and the drift force of the vertical cylinder type ocean monitoring buoy with 2.6 m diameter and 3,77 m draft are calculated and discussed. The obtained results of motion response can be used to determine the shape and dimension of the buoy to reduce the motion response, and other data such as the effect of motion reduction due to a damper can be predictable through these motion calculations. Also, the calculation results of drift force can be used in the design procedure of mooring system to predict the maximum wave load acting on the mooring system. The present method has, in principle, no restriction in the application to the arbitrary shape facilities. So, this method can be a robust tool for the design, installation, and operation of various kinds of the floating-type ocean monitoring facilities.

• Journal of Broadcast Engineering
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• v.14 no.6
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• pp.733-741
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• 2009

The design rule of digital communication systems is the reliable data transmission with high spectral efficiency and minimum allowable power. This paper suggests the method that saves the average power by implementing a multi-dimensional constellation in case of multi-carrier communication system. By using multi-dimensional constellations we can relocate constellation points in the form of a sphere. If we simply convert the two-dimensional QAM modulation into multi-dimensional QAM, constellation points of 2 N dimensional cube form are made up. Relocating outermost constellation points of 2 N dimensional cube form into low energy constellation points, the constellation of the 2 N-dimensional sphere form is made up which decreases power consumption. In this paper, the multi-dimensional constellations of 2 N-dimensional sphere form are designed from 16-QAM to 2,048-QAM, and power reductions are obtained by comparing constellations of 2-dimensional QAMs and multi-dimensional constellations of 2 N-dimensional sphere form. The result shows that the average power consumption of higher dimensional constellations increases, because the more a dimension elevates, the more the relocatable constellation points increase. But, the increment of the average power savings decreases as the a dimension elevates. The transmission of the data by using multi-dimensional constellations of the sphere form is effective to save the average power consumption with little hardware complexity.

• The Korean Journal of Applied Statistics
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• v.29 no.6
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• pp.1117-1128
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• 2016

In genetic association studies with high-dimensional genomic data, regularization procedures based on penalized likelihood are often applied to identify genes or genetic regions associated with diseases or traits. A network-based regularization procedure can utilize biological network information (such as genetic pathways and signaling pathways in genetic association studies) with an outstanding selection performance over other regularization procedures such as lasso and elastic-net. However, network-based regularization has a limitation because cannot be applied to high-dimension genomic data with a group structure. In this article, we propose to combine data dimension reduction techniques such as principal component analysis and a partial least square into network-based regularization for the analysis of high-dimensional genomic data with a group structure. The selection performance of the proposed method was evaluated by extensive simulation studies. The proposed method was also applied to real DNA methylation data generated from Illumina Innium HumanMethylation27K BeadChip, where methylation beta values of around 20,000 CpG sites over 12,770 genes were compared between 123 ovarian cancer patients and 152 healthy controls. This analysis was also able to indicate a few cancer-related genes.

• Science of Emotion and Sensibility
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• v.8 no.4
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• pp.333-344
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• 2005

The sensitivity adjectives on wall paper are collected. With the collected sensitivity adjective, we are going to develop the model which can recommend the wallpaper to customer. A large number of adjectives describing affective responses were collected from such diverse sources as questionnaire survey results, field survey results and internet survey result. To search the representative adjective of collected adjective, we used the diverse statistical analysis method. We attempted to decide the axis name of dimension through the MDS(Multi-Dimensional Scale) analysis method using the similarity matrix an4 to find a three or four reduced factors through the factor analysis method using the varimax rotation method. The result of the analysis showed that the reduced factors could account about $82\%$ when the number of factor is three(popular, elegance, and passable) ant about $93\%$ when the number of factor is four (elegance, passable, beautiful, and affectionate) On the basis of this result, we expect it can be used to develop the model recommending the wallpaper.