• The Journal of Korean Medicine
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• v.44 no.1
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• pp.38-55
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• 2023

Objectives: The purpose of this study was to provide direction for future research in the field of Korean medicine by analyzing microbiome based technologies emerging as a new diagnostic and treatment paradigm. Methods: To achieve the purpose of the study intellectual property data was used. After establishing citation network from registered microbiome-related US patents, citation network was analyzed by knowledge persistence-based main path approach to understanding technological trajectories. Furthermore, community detection algorithms were used to quantitatively identifying specific technological domain in a particular time period. Results: Results shows that early technologies in livestock industry contribute most to the recent patents. Knowledge in the patents flow through the path of food and beverage technological domain, and finally are inherited to the recent development of diagnosis, treatment and prevention technic. Conclusions: This study indicate that developing diagnostic tools which can link the composition of microbiome to specific diseases should be given high priority. Researches should lead to novel therapeutic strategies. Specifically, improving reliability of pattern identification and finding effective therapeutic compositions based on principles of Korean medicine is necessary.

• Current Optics and Photonics
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• v.7 no.4
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• pp.362-377
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• 2023

Optical information processing technology is characterized by high speed and parallelism, and the light features short wavelength and large information capacity; At the same time, it has various attributes including amplitude, phase, wavelength and polarization, and is a carrier of multi-dimensional information. Therefore, optical encryption is of great significance in the field of information security transmission, and is widely used in the field of image encryption. For multi-image encryption, this paper proposes a multi-image encryption algorithm based on a modified Gerchberg-Saxton algorithm (MGSA) in the Fresnel-transform domain and computational ghost imaging. First, MGSA is used to realize "one code, one key"; Second, phase function superposition and normalization are used to reduce the amount of ciphertext transmission; Finally, computational ghost imaging is used to improve the security of the whole encryption system. This method can encrypt multiple images simultaneously with high efficiency, simple calculation, safety and reliability, and less data transmission. The encryption effect of the method is evaluated by using correlation coefficient and structural similarity, and the effectiveness and security of the method are verified by simulation experiments.

• Mycobiology
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• v.51 no.5
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• pp.360-371
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• 2023

Hispidin is an important styrylpyrone produced by Sanghuangporus sanghuang. To analyze hispidin biosynthesis in S. sanghuang, the transcriptomes of hispidin-producing and non-producing S. sanghuang were determined by Illumina sequencing. Five PKSs were identified using genome annotation. Comparative analysis with the reference transcriptome showed that two PKSs (ShPKS3 and ShPKS4) had low expression levels in four types of media. The gene expression pattern of only ShPKS1 was consistent with the yield variation of hispidin. The combined analyses of gene expression with qPCR and hispidin detection by liquid chromatography-mass spectrometry coupled with ion-trap and time-of-flight technologies (LCMS-IT-TOF) showed that ShPKS1 was involved in hispidin biosynthesis in S. sanghuang. ShPKS1 is a partially reducing PKS gene with extra AMP and ACP domains before the KS domain. The domain architecture of ShPKS1 was AMP-ACP-KS-AT-DH-KR-ACP-ACP. Phylogenetic analysis shows that ShPKS1 and other PKS genes from Hymenochaetaceae form a unique monophyletic clade closely related to the clade containing Agaricales hispidin synthase. Taken together, our data indicate that ShPKS1 is a novel PKS of S. sanghuang involved in hispidin biosynthesis.

• Human Ecology Research
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• v.62 no.2
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• pp.233-248
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• 2024

The purpose of this study was to describe the family life and relationships of national family center users in 2023. A sample of 1,086 adults was recruited from 48 family centers across South Korea. Participants, both online and offline, reported on various aspects of family life, including satisfaction, gender equality, work-home-life balance, family leisure, and internet usage for family and household purposes. Additionally, family relationships were explored in terms of dynamics with a spouse, children, and adult children. Data were analyzed using descriptive statistics and t-tests. In the family life domain, center users reported a high level of satisfaction, with women exhibiting greater awareness of gender equality and higher engagement in household chores than men. Furthermore, although a moderate balance among work, home, and life was reported, time emerged as a significant challenge for family leisure. Regarding technology, center users frequently utilized the internet for family communication, leisure, shopping, and household management. In the family relationships domain, men were more satisfied with spousal relationships than women. Center users reported high satisfaction with parent-child relationships. Parents of school-aged children perceived economic burdens, while those with adult children frequently provided psychological and instrumental support. These findings suggest the need for program modification and the development of new initiatives within national family centers to better support the diverse needs of families in terms of life satisfaction and relationship dynamics.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.39-70
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• 2017

Data-drive analytics techniques have been recently applied to public surveys. Instead of simply gathering survey results or expert opinions to research the preference for a recently launched product, enterprises need a way to collect and analyze various types of online data and then accurately figure out customer preferences. In the main concept of existing data-based survey methods, the sentiment lexicon for a particular domain is first constructed by domain experts who usually judge the positive, neutral, or negative meanings of the frequently used words from the collected text documents. In order to research the preference for a particular product, the existing approach collects (1) review posts, which are related to the product, from several product review web sites; (2) extracts sentences (or phrases) in the collection after the pre-processing step such as stemming and removal of stop words is performed; (3) classifies the polarity (either positive or negative sense) of each sentence (or phrase) based on the sentiment lexicon; and (4) estimates the positive and negative ratios of the product by dividing the total numbers of the positive and negative sentences (or phrases) by the total number of the sentences (or phrases) in the collection. Furthermore, the existing approach automatically finds important sentences (or phrases) including the positive and negative meaning to/against the product. As a motivated example, given a product like Sonata made by Hyundai Motors, customers often want to see the summary note including what positive points are in the 'car design' aspect as well as what negative points are in thesame aspect. They also want to gain more useful information regarding other aspects such as 'car quality', 'car performance', and 'car service.' Such an information will enable customers to make good choice when they attempt to purchase brand-new vehicles. In addition, automobile makers will be able to figure out the preference and positive/negative points for new models on market. In the near future, the weak points of the models will be improved by the sentiment analysis. For this, the existing approach computes the sentiment score of each sentence (or phrase) and then selects top-k sentences (or phrases) with the highest positive and negative scores. However, the existing approach has several shortcomings and is limited to apply to real applications. The main disadvantages of the existing approach is as follows: (1) The main aspects (e.g., car design, quality, performance, and service) to a product (e.g., Hyundai Sonata) are not considered. Through the sentiment analysis without considering aspects, as a result, the summary note including the positive and negative ratios of the product and top-k sentences (or phrases) with the highest sentiment scores in the entire corpus is just reported to customers and car makers. This approach is not enough and main aspects of the target product need to be considered in the sentiment analysis. (2) In general, since the same word has different meanings across different domains, the sentiment lexicon which is proper to each domain needs to be constructed. The efficient way to construct the sentiment lexicon per domain is required because the sentiment lexicon construction is labor intensive and time consuming. To address the above problems, in this article, we propose a novel product reputation mining algorithm that (1) extracts topics hidden in review documents written by customers; (2) mines main aspects based on the extracted topics; (3) measures the positive and negative ratios of the product using the aspects; and (4) presents the digest in which a few important sentences with the positive and negative meanings are listed in each aspect. Unlike the existing approach, using hidden topics makes experts construct the sentimental lexicon easily and quickly. Furthermore, reinforcing topic semantics, we can improve the accuracy of the product reputation mining algorithms more largely than that of the existing approach. In the experiments, we collected large review documents to the domestic vehicles such as K5, SM5, and Avante; measured the positive and negative ratios of the three cars; showed top-k positive and negative summaries per aspect; and conducted statistical analysis. Our experimental results clearly show the effectiveness of the proposed method, compared with the existing method.

• The Journal of the Acoustical Society of Korea
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• v.32 no.4
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• pp.308-316
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• 2013

A set of experiments (Shallow Water 2006, SW06) was carried out in shallow water near the New Jersey shelf break in summer 2006. Significant fluctuations in direct and surface reflected arrivals were observed from the chirp data (1100~2900 Hz) measured on a vertical line array. This paper presents a geoacoustic inverssion technique for short-range acoustic data with fluctuating arrivals and inversion results of experimental data. In order to reduce effects of random sea surface on the inversion, the acoustic energy back-propagated from the array to the source through direct and bottom-reflected paths is defined as the objective function. A multi-step inversion scheme is applied to the data using VFSR (Very Fast Simulated Reannealing) optimization technique. The inversion results show a source depth oscillation period equal to the measured ocean surface wave period. The inverted bottom sound speed is 1645 m/s and is similar to that estimated by other work at the same site.

• Journal of Broadcast Engineering
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• v.25 no.4
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• pp.569-575
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• 2020

The serial arc is one of factors causing electrical fires. Over past decades, various researches have been carried out to detect arc occurrences. Even though frequency analysis, wavelet, and statistical features have been used, additional steps such as transformation and feature extraction are required. On the contrary, deep learning models directly use the raw data without any feature extraction processes. Therefore, the usage of time-domain data is preferred, but the performance is not satisfactory. To solve this problem, subsequent 1-D signals are transformed into 2-D data that can feed into a convolutional neural network (CNN). Experiments validated that CNN model outperforms deep neural network (DNN) by the classification accuracy of 8.6%. In addition, data augmentation is utilized, resulting in the accuracy improvement by 14%.

• Geophysics and Geophysical Exploration
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• v.19 no.2
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• pp.84-90
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• 2016

The validation of performance test for newly developed or old-used sensor is very important in the earthquake monitoring and seismology using earthquake data. Especially the frequency response of the sensor is mainly used to correct the earthquake data. The technique of the calculation of phase and amplitude with Hilbert transformation for earthquake data that is filtered with band limited frequency in time domain is applied to calculate the frequency response of the sensor. This technique was tested for the acceleration sensors, CMG-5T of 1g and 2g installed on the vibration table at the laboratory and we could obtain satisfactory result. Tohoku large earthquake in 2011 observed at the station SNU that has accelerometer, ES-T and seismometer, STS-2 operated by KIGAM was also used to test the field data applicability. We could successfully get the low frequency response of broad band sensor, STS-2. The technique by using band limited frequency filter and Hilbert transformation showed the superior frequency response to the frequency spectrum ratio method for noisy part in data.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.736-741
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• 2006

The design of a classification system generally involves data acquisition module, learning module and decision module, considering their functions and it is often an important component of intelligent systems. The learning module provides a priori information and it has been playing a key role for the classification. The conventional learning techniques for classification are based on a winner take all fashion which does not reflect the description of real data where boundarues might be fuzzy Moreover they need all data for the learning of its problem domain. Generally, in many practical applications, it is not possible to prepare them at a time. In this paper, we design an adaptive classification model using incremental training fuzzy neural networks, FNN-I. To have a more useful information, it introduces the representation and membership degree by fuzzy theory. And it provides an incremental learning algorithm for continuously gathered data. We present tie experimental results on computer virus data. They show that the proposed system can learn incrementally and classify new viruses effectively.

• Smart Structures and Systems
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• v.6 no.5_6
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• pp.461-480
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• 2010

This paper analyses the data collected from the $2^{nd}$ Jindo Bridge, a cable-stayed bridge in Korea that is a structural health monitoring (SHM) international test bed for advanced wireless smart sensors network (WSSN) technology. The SHM system consists of a total of 70 wireless smart sensor nodes deployed underneath of the deck, on the pylons, and on the cables to capture the vibration of the bridge excited by traffic and environmental loadings. Analysis of the data is performed in both the time and frequency domains. Modal properties of the bridge are identified using the frequency domain decomposition and the stochastic subspace identification methods based on the output-only measurements, and the results are compared with those obtained from a detailed finite element model. Tension forces for the 10 instrumented stay cables are also estimated from the ambient acceleration data and compared both with those from the initial design and with those obtained during two previous regular inspections. The results of the data analyses demonstrate that the WSSN-based SHM system performs effectively for this cable-stayed bridge, giving direct access to the physical status of the bridge.