• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.6 no.3
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• pp.31-39
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• 2016

As the computer technology is developed, score-writers that can edit music have more improved functions. A user not only can insert notes, chords, and musical symbols conveniently, but also can listen to music displayed in the score. Existing commercial score-writers make scores look good and play notes through VSTi(Virtual Studio Technology instrument). However chord accompaniment function is so vulnerable that it is hard to know whether or not displayed chords are suitable for music. If a score-writer has a proper function that plays chord sound good to hear, it will be a good reference to band music sheet writers. In this paper, to improve chord playback function in existing score-writers, we propose an algorithm that selects suitable chord tones for chord playback and implemented a program. If we apply this algorithm, the chord play function of a score-writer will be strengthened and resultantly a music sheet writer who makes a band music can make scores more conveniently.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.37-42
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• 2022

Recently, with the development of mobile communication technologies such as 5G, interest in oscilloscope technology based on high bandwidth and user-friendly UI is increasing. In this paper, we proposed a Python-based UI(user interface) SW for a high-bandwidth digital oscilloscope in connection with the study of a 13GHz band digital oscilloscope system. The proposed UI SW is designed not only to be executed integrally with the oscilloscope, but also to be run on a separate PC or laptop cooperating with the instrument through WiFi communication. Functions of the UI SW consists of displaying and analyzing signal data, storing signal data in an external storage device, generating test signal data, and reconfiguring the toolbar. Finally, we have shown that the proposed digital oscilloscope system operates normally by interworking test with the signal generator.

• Journal of the Korean Society for Library and Information Science
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• v.57 no.2
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• pp.51-77
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• 2023

This study analyzed the respective teaching and learning-related services offered by the Centers for Teaching and Learning and the academic libraries to find the proper roles of libraries regarding this type of service. We interviewed librarians to collect the data. The content analysis of the qualitative interview data enabled us to identify the librarians' perceptions of teaching and learning support, service provision method, strengthening relationships with other academic units, recognition of libraries' roles within the universities, and generating more investment for the libraries. Finally, the analysis led to six suggestions for libraries' teaching and learning support functions, such as advertising the availability of specific academic discipline or unit-oriented library services, strengthening librarian's capabilities as educators, bolstering digital information literacy of the faculty members and students, injecting libraries' views into the development and maintaining fundamental knowledge-related programs, emphasizing the notion of human-centered libraries, and finding new ways to utilize library space.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.348-369
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• 2024

With the advancement of Industry 4.0 and Industrial Internet of Things (IIoT), manufacturing increasingly seeks automation and intelligence. Temperature and vibration monitoring are essential for machinery health. Traditional abnormal state detection methodologies often overlook the intricate frequency characteristics inherent in vibration time series and are susceptible to erroneously reconstructing temperature abnormalities due to the highly similar waveforms. To address these limitations, we introduce synergistic, end-to-end, unsupervised Frequency-Time Domain Memory-Enhanced Autoencoders (FTD-MAE) capable of identifying abnormalities in both temperature and vibration datasets. This model is adept at accommodating time series with variable frequency complexities and mitigates the risk of overgeneralization. Initially, the frequency domain encoder processes the spectrogram generated through Short-Time Fourier Transform (STFT), while the time domain encoder interprets the raw time series. This results in two disparate sets of latent representations. Subsequently, these are subjected to a memory mechanism and a limiting function, which numerically constrain each memory term. These processed terms are then amalgamated to create two unified, novel representations that the decoder leverages to produce reconstructed samples. Furthermore, the model employs Spectral Entropy to dynamically assess the frequency complexity of the time series, which, in turn, calibrates the weightage attributed to the loss functions of the individual branches, thereby generating definitive abnormal scores. Through extensive experiments, FTD-MAE achieved an average ACC and F1 of 0.9826 and 0.9808 on the CMHS and CWRU datasets, respectively. Compared to the best representative model, the ACC increased by 0.2114 and the F1 by 0.1876.

• IMMUNE NETWORK
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• v.24 no.2
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• pp.14.1-14.26
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• 2024

The inflammatory response during cutaneous leishmaniasis (CL) involves immune and non-immune cell cooperation to contain and eliminate Leishmania parasites. The orchestration of these responses is coordinated primarily by CD4⁺ T cells; however, the disease outcome depends on the Th cell predominant phenotype. Although Th1 and Th2 phenotypes are the most addressed as steers for the resolution or perpetuation of the disease, Th17 cell activities, especially IL-17 release, are recognized to be vital during CL development. Th17 cells perform vital functions during both acute and chronic phases of CL. Overall, Th17 cells induce the migration of phagocytes (neutrophils, macrophages) to the infection site and CD8⁺ T cells and NK cell activation. They also provoke granzyme and perforin secretion from CD8⁺ T cells, macrophage differentiation towards an M2 phenotype, and expansion of B and Treg cells. Likewise, immune cells from the inflammatory infiltrate have modulatory activities over Th17 cells involving their differentiation from naive CD4⁺ T cells and further expansion by generating a microenvironment rich in optimal cytokines such as IL-1β, TGF-β, IL-6, and IL-21. Th17 cell activities and synergies are crucial for the resistance of the infection during the early and acute stages; however, if unchecked, Th17 cells might lead to a chronic stage. This review discusses the synergies between Th17 cells and the inflammatory infiltrate and how these interactions might destine the course of CL.

• Parasites, Hosts and Diseases
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• v.62 no.2
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• pp.205-216
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• 2024

Sigma-class glutathione transferase (GST) proteins with dual GST and prostaglandin synthase (PGS) activities play a crucial role in the establishment of Clonorchis sinensis infection. Herein, we analyzed the structural and enzymatic properties of sigma-class GST (CsGST-σ) proteins to obtain insight into their antioxidant and immunomodulatory functions in comparison with mu-class GST (CsGST-µ) proteins. CsGST-σ proteins conserved characteristic structures, which had been described in mammalian hematopoietic prostaglandin D₂ synthases. Recombinant forms of these CsGST-σ and CsGST-µ proteins expressed in Escherichia coli exhibited considerable degrees of GST and PGS activities with substantially different specific activities. All recombinant proteins displayed higher affinities toward prostaglandin H₂ (PGS substrate; average K_m of 30.7 and 3.0 ㎛ for prostaglandin D₂ [PGDS] and E₂ synthase [PGES], respectively) than those toward CDNB (GST substrate; average K_m of 1,205.1 ㎛). Furthermore, the catalytic efficiency (K_cat/K_m) of the PGDS/PGES activity was higher than that of GST activity (average K_cat/K_m of 3.1, 0.7, and 7.0×10^-3 s^-1㎛^-1 for PGDS, PGES, and GST, respectively). Our data strongly suggest that the C. sinensis sigma- and mu-class GST proteins are deeply involved in regulating host immune responses by generating PGD₂ and PGE₂ in addition to their roles in general detoxification.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.25-38
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• 2019

Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.2
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• pp.687-697
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• 2014

This study ranges from planning phase to the detailed design phase of steel box girder bridge and proposes ways to generate semantic information of components through Industry Foundation Classes (IFC), a data model for Building Information Modeling (BIM). The classification of components of steel box girder bridge was performed to define information items required for identifying semantic information based on IFC, and spatial information items based on topology and physical information items based on functions of components were classified to create additional properties that does not support IFC by applying user-defined property set within the IFC framework. Steel box girder bridge information model based on IFC was implemented through BIM software and semantic information input interface, which was developed in this study to examine the effectiveness of the additionally created user-defined property. Furthermore, the quantity take-off of components was performed through information model of steel box girder bridge, and the applicability of the proposed method was tested by comparing the quantity take-off based on design document with the result.

• The Journal of the Korea Contents Association
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• v.20 no.6
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• pp.99-113
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• 2020

In the future, robots and drones for the convenience of our lives in everyday life will increase. As a method for controlling this, a remote control or a human voice method is most commonly used. However, the remote control needs to be operated by a person and can not ignore ambient noise in the case of voice. In this paper, we propose an economical attitude information acquisition method to accurately acquire the posture information of the arm in real time under the assumption that the surround drones or robots can be controlled wirelessly with the posture information of the arm. For this purpose, the extended Kalman filter was used to eliminate the noise of the arm position information. in order to detect the arm movement, a low cost MEMS type sensor was applied to secure the economical efficiency of the apparatus. To increase the wear ability of the arm, We developed a compact and lightweight attitude information acquisition system by integrating all functions into one chip as much as possible. As a result, the real-time performance of 1 ms was secured and the extended Kalman filter was applied to acquire the accurate attitude information of the arm with noise removed and display the attitude information of the arm in real time. This provides a basis for generating commands using real-time attitude information of the arm.

• The KIPS Transactions:PartC
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• v.12C no.5 s.101
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• pp.623-632
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• 2005

In ad-hoc network, there is no fixed infrastructure such as base stations or mobile switching centers. The security of ad-hoc network is more vulnerable than traditional networks because of the basic characteristics of ad-hoc network, and current muting protocols for ad-hoc networks allow many different types of attacks by malicious nodes. Malicious nodes can disrupt the correct functioning of a routing protocol by modifying routing information, by fabricating false routing information and by impersonating other nodes. We propose a routing suity mechanism based on one-time digital signature. In our proposal, we use one-time digital signatures based on one-way hash functions in order to limit or prevent attacks of malicious nodes. For the purpose of generating and keeping a large number of public key sets, we derive multiple sets of the keys from hash chains by repeated hashing of the public key elements in the first set. After that, each node publishes its own public keys, broadcasts routing message including one-time digital signature during route discovery and route setup. This mechanism provides authentication and message integrity and prevents attacks from malicious nodes. Simulation results indicate that our mechanism increases the routing overhead in a highly mobile environment, but provides great security in the route discovery process and increases the network efficiency.