• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.205-218
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• 2022

Recently, digital transformation in manufacturing has been accelerating. It results in that the data collection technologies from the shop-floor is becoming important. These approaches focus primarily on obtaining specific manufacturing data using various sensors and communication technologies. In order to expand the channel of field data collection, this study proposes a method to automatically collect manufacturing data based on vision-based artificial intelligence. This is to analyze real-time image information with the object detection and tracking technologies and to obtain manufacturing data. The research team collects object motion information for each frame by applying YOLO (You Only Look Once) and DeepSORT as object detection and tracking algorithms. Thereafter, the motion information is converted into two pieces of manufacturing data (production performance and time) through post-processing. A dynamically moving factory model is created to obtain training data for deep learning. In addition, operating scenarios are proposed to reproduce the shop-floor situation in the real world. The operating scenario assumes a flow-shop consisting of six facilities. As a result of collecting manufacturing data according to the operating scenarios, the accuracy was 96.3%.

• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.113-123
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• 2023

This paper introduces the Cherry system, a user-independent blockchain donation system. This is a procedure that is delivered to the beneficiary's bank account through a virtual account when a donor makes a donation, so there is no difference from the existing donation delivery method from the user's point of view However, within the blockchain, Cherry Points, a virtual currency based on the user ID, are issued and delivered to the beneficiary, while all transactions and the beneficiary's usage history are managed on the blockchain. By adopting this method, there was an improvement in blockchain performance, with transaction processing exceeding 1,000 TPS in typical transaction condition and service completion within 21.3 seconds. By applying the automatic influence control algorithm to this system, the influence according to stake, which is an individual donation, is greatly reduced to 0.3 after 2 months, thereby concentrating influence could be controlled automatically. In addition, it was designed to enable micro tracking by adding a tracking function by timestamp to the donation ledger for each individual ID, which greatly improved the transparency in the use of donations. From a service perspective, existing blockchain donation systems were handled as limited donation delivery methods. Since it is a direct service in a user-independent method, convenience has been greatly improved by delivering donations in various forms.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.10
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• pp.461-470
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• 2023

While speech animation generation employing deep learning has been actively researched for English, there has been no prior work for Korean. Given the fact, this paper for the very first time employs supervised deep learning to generate Korean speech animation. By doing so, we find out the significant effect of deep learning being able to make speech animation research come down to speech recognition research which is the predominating technique. Also, we study the way to make best use of the effect for Korean speech animation generation. The effect can contribute to efficiently and efficaciously revitalizing the recently inactive Korean speech animation research, by clarifying the top priority research target. This paper performs this process: (i) it chooses blendshape animation technique, (ii) implements the deep-learning model in the master-servant pipeline of the automatic speech recognition (ASR) module and the facial action coding (FAC) module, (iii) makes Korean speech facial motion capture dataset, (iv) prepares two comparison deep learning models (one model adopts the English ASR module, the other model adopts the Korean ASR module, however both models adopt the same basic structure for their FAC modules), and (v) train the FAC modules of both models dependently on their ASR modules. The user study demonstrates that the model which adopts the Korean ASR module and dependently trains its FAC module (getting 4.2/5.0 points) generates decisively much more natural Korean speech animations than the model which adopts the English ASR module and dependently trains its FAC module (getting 2.7/5.0 points). The result confirms the aforementioned effect showing that the quality of the Korean speech animation comes down to the accuracy of Korean ASR.

• Journal of the Korean Geotechnical Society
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• v.39 no.12
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• pp.75-91
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• 2023

In this study, we explore the use of ground penetrating radar (GPR) for the nondestructive survey of subsurface conduits, focusing on the challenges posed by multichannel environments. A key concern is the shadow regions created by conduits, which significantly impact survey results. The shadow regions, which are influenced by conduit position and diameter, hinder signal propagation, thereby making detection within these regions challenging. Using finite-difference time-domain numerical analysis, we examined the characteristics of conduit signals, which typically manifest in hyperbolic patterns. Particularly, we investigated three conduit arrangements: horizontal, vertical, and diagonal. Automatic gain control was applied to amplify the signals, enabling the analysis of variations in shadow regions and signal characteristics for each arrangement. In the horizontal arrangement, the proximity of the two conduits resulted in the emergence of a new hyperbolic pattern between the existing conduits. In the vertical arrangement, the lower conduit could be detected using hyperbolic signals on either side, but the detection was challenging when the upper conduit diameter exceeded that of the lower conduit. In the diagonal arrangement, signal characteristics varied based on the position of shadow regions relative to the detection range of the equipment. Asymmetrical signal patterns were observed when the shadow regions fell within the detection range, whereas the signals of the two conduits were minimally impacted when the shadow regions were outside the detection range. This study provides vital insights into accurately detecting and characterizing subsurface multichannel conduits using GPR-a significant contribution to the field of subsurface exploration and management.

• The Journal of the Acoustical Society of Korea
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• v.28 no.2
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• pp.112-118
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• 2009

This paper proposes a rapid detection algorithm of a salient region for music video browsing system, which can be applied to mobile device and digital video recorder (DVR). The input music video is decomposed into the music and video tracks. For the music track, the music highlight including musical chorus is detected based on structure analysis using energy-based peak position detection. Using the emotional models generated by SVM-AdaBoost learning algorithm, the music signal of the music videos is classified into one of the predefined emotional classes of the music automatically. For the video track, the face scene including the singer or actor/actress is detected based on a boosted cascade of simple features. Finally, the salient region is generated based on the alignment of boundaries of the music highlight and the visual face scene. First, the users select their favorite music videos from various music videos in the mobile devices or DVR with the information of a music video's emotion and thereafter they can browse the salient region with a length of 30-seconds using the proposed algorithm quickly. A mean opinion score (MOS) test with a database of 200 music videos is conducted to compare the detected salient region with the predefined manual part. The MOS test results show that the detected salient region using the proposed method performed much better than the predefined manual part without audiovisual processing.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.109-122
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• 2014

People are nowadays creating a tremendous amount of data on Social Network Service (SNS). In particular, the incorporation of SNS into mobile devices has resulted in massive amounts of data generation, thereby greatly influencing society. This is an unmatched phenomenon in history, and now we live in the Age of Big Data. SNS Data is defined as a condition of Big Data where the amount of data (volume), data input and output speeds (velocity), and the variety of data types (variety) are satisfied. If someone intends to discover the trend of an issue in SNS Big Data, this information can be used as a new important source for the creation of new values because this information covers the whole of society. In this study, a Twitter Issue Tracking System (TITS) is designed and established to meet the needs of analyzing SNS Big Data. TITS extracts issues from Twitter texts and visualizes them on the web. The proposed system provides the following four functions: (1) Provide the topic keyword set that corresponds to daily ranking; (2) Visualize the daily time series graph of a topic for the duration of a month; (3) Provide the importance of a topic through a treemap based on the score system and frequency; (4) Visualize the daily time-series graph of keywords by searching the keyword; The present study analyzes the Big Data generated by SNS in real time. SNS Big Data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. In addition, such analysis requires the latest big data technology to process rapidly a large amount of real-time data, such as the Hadoop distributed system or NoSQL, which is an alternative to relational database. We built TITS based on Hadoop to optimize the processing of big data because Hadoop is designed to scale up from single node computing to thousands of machines. Furthermore, we use MongoDB, which is classified as a NoSQL database. In addition, MongoDB is an open source platform, document-oriented database that provides high performance, high availability, and automatic scaling. Unlike existing relational database, there are no schema or tables with MongoDB, and its most important goal is that of data accessibility and data processing performance. In the Age of Big Data, the visualization of Big Data is more attractive to the Big Data community because it helps analysts to examine such data easily and clearly. Therefore, TITS uses the d3.js library as a visualization tool. This library is designed for the purpose of creating Data Driven Documents that bind document object model (DOM) and any data; the interaction between data is easy and useful for managing real-time data stream with smooth animation. In addition, TITS uses a bootstrap made of pre-configured plug-in style sheets and JavaScript libraries to build a web system. The TITS Graphical User Interface (GUI) is designed using these libraries, and it is capable of detecting issues on Twitter in an easy and intuitive manner. The proposed work demonstrates the superiority of our issue detection techniques by matching detected issues with corresponding online news articles. The contributions of the present study are threefold. First, we suggest an alternative approach to real-time big data analysis, which has become an extremely important issue. Second, we apply a topic modeling technique that is used in various research areas, including Library and Information Science (LIS). Based on this, we can confirm the utility of storytelling and time series analysis. Third, we develop a web-based system, and make the system available for the real-time discovery of topics. The present study conducted experiments with nearly 150 million tweets in Korea during March 2013.

• Journal of Korea Spatial Information System Society
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• v.3 no.2 s.6
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• pp.21-43
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• 2001

The environment to development information system including a GIS has been drastically changed in recent years in the perspectives of the complexity and diversity of the software, and the distributed processing and network computing, etc. This leads the paradigm of the software development to the CBD(Component Based Development) based object-oriented technology. As an effort to support these movements, OGC has released the abstract and implementation standards to enable approaching to the service for heterogeneous geographic information processing. It is also common trend in domestic field to develop the GIS application based on the component technology for municipal governments. Therefore, it is imperative to adopt the component technology considering current movements, yet related research works have not been made. This research is to propose a component-based GIS development methodology-ATOM(Advanced Technology Of Methodology)-and to verify its adoptability through the case study. ATOM can be used as a methodology to develop component itself and enterprise GIS supporting the whole procedure for the software development life cycle based on conventional reusable component. ATOM defines stepwise development process comprising activities and work units of each process. Also, it provides input and output, standardized items and specs for the documentation, detailed instructions for the easy understanding of the development methodology. The major characteristics of ATOM would be the component-based development methodology considering numerous features of the GIS domain to generate a component with a simple function, the smallest size, and the maximum reusability. The case study to validate the adoptability of the ATOM showed that it proves to be a efficient tool for generating a component providing relatively systematic and detailed guidelines for the component development. Therefore, ATOM would lead to the promotion of the quality and the productivity for developing application GIS software and eventually contribute to the automatic production of the GIS software, the our final goal.

• Tunnel and Underground Space
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• v.33 no.4
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• pp.281-298
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• 2023

Continuous research efforts are being devoted to unmanned mobile platforms for lunar exploration. There is an ongoing demand for real-time information processing to accurately determine the positioning and mapping of areas of interest on the lunar surface. To apply deep learning processing and analysis techniques to practical rovers, research on software integration and optimization is imperative. In this study, a foundational investigation has been conducted on real-time analysis of virtual lunar base construction site images, aimed at automatically quantifying spatial information of key objects. This study involved transitioning from an existing region-based object recognition algorithm to a boundary box-based algorithm, thus enhancing object recognition accuracy and inference speed. To facilitate extensive data-based object matching training, the Batch Hard Triplet Mining technique was introduced, and research was conducted to optimize both training and inference processes. Furthermore, an improved software system for object recognition and identical object matching was integrated, accompanied by the development of visualization software for the automatic matching of identical objects within input images. Leveraging satellite simulative captured video data for training objects and moving object-captured video data for inference, training and inference for identical object matching were successfully executed. The outcomes of this research suggest the feasibility of implementing 3D spatial information based on continuous-capture video data of mobile platforms and utilizing it for positioning objects within regions of interest. As a result, these findings are expected to contribute to the integration of an automated on-site system for video-based construction monitoring and control of significant target objects within future lunar base construction sites.

• Radiation Oncology Journal
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• v.25 no.1
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• pp.54-61
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• 2007

[ $\underline{Purpose}$ ]: The purpose of this study is to develop a practical method for determining accurate marker positions for prostate cancer radiotherapy using CT images and kV x-ray images obtained from the use of the on- board imager (OBI). $\underline{Materials\;and\;Methods}$: Three gold seed markers were implanted into the reference position inside a prostate gland by a urologist. Multiple digital image processing techniques were used to determine seed marker position and the center-of-mass (COM) technique was employed to determine a representative reference seed marker position. A setup discrepancy can be estimated by comparing a computed $COM_{OBI}$ with the reference $COM_{CT}$. A proposed algorithm was applied to a seed phantom and to four prostate cancer patients with seed implants treated in our clinic. $\underline{Results}$: In the phantom study, the calculated $COM_{CT}$ and $COM_{OBI}$ agreed with $COM_{actual}$ within a millimeter. The algorithm also could localize each seed marker correctly and calculated $COM_{CT}$ and $COM_{OBI}$ for all CT and kV x-ray image sets, respectively. Discrepancies of setup errors between 2D-2D matching results using the OBI application and results using the proposed algorithm were less than one millimeter for each axis. The setup error of each patient was in the range of $0.1{\pm}2.7{\sim}1.8{\pm}6.6\;mm$ in the AP direction, $0.8{\pm}1.6{\sim}2.0{\pm}2.7\;mm$ in the SI direction and $-0.9{\pm}1.5{\sim}2.8{\pm}3.0\;mm$ in the lateral direction, even though the setup error was quite patient dependent. $\underline{Conclusion}$: As it took less than 10 seconds to evaluate a setup discrepancy, it can be helpful to reduce the setup correction time while minimizing subjective factors that may be user dependent. However, the on-line correction process should be integrated into the treatment machine control system for a more reliable procedure.

• Korean Journal of Agricultural Science
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• v.9 no.2
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• pp.576-583
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• 1982

The variation of free amino acids during the tomato producing was studied using a tomato variety, Kagome 77. The concentration of free amino acids in fresh and heated pulp, and in puree and paste was analyzed by using automatic amino acid analyzer, Hitachi model KLA-5. 1. A significant difference in decomposition rate of glutamine and asparagine among amide group was recognized. For instance, the glutamine decomposed fast and no glutamine was found in the paste, while 56% of asparagine was found in the paste. 2. The diminishing quantity of glutamic acid among acid group was highest among all free amino acids. The quantity of aspartic acid was next to the glutamine. The percents of glutamic acid and aspartic acid left over were 38% and 24%, respectively. 3. Glycine, alanine, valine, isoleucine and leucine of neutral amino acids tended to be reduced a little during the heating, concentrating process. 4. No apparent variation was found for the lysine and histidine belonging to basic amino acids. while arginine increased a little. 5. Tyrosine, phenylalanine and tryptophane of aromatic group seemed to increase a little during the heating process. But the variations of them during the concentrating process were not recognized. 6. The methionine content, sulfur containing amino acid decreased a little throughout the process. But the decrease of ${\gamma}-amino$ butyric acid of non-protein was not apparently recognized. 7. The amino acid contents of fresh pulp were found as following order: glutamic acid>${\gamma}$-amino butyric acid>glutamine>aspartic acid>asparagine. The amino acid contents of paste were as glutamic acid>${\gamma}$-amino butyric acid>aspartic acid and aspargine. The percent distribution of aromatic and basic amino acids increased, even it was not great. 8. When amino acids were analyzed by Hitachi KLA-5, unknown peak which was never app eared in the fresh pulp before tryptophane was appeared when processed. The peak became greater when heated and concentrated. Later it was known that the peak was not due to lysinoalanine or ornithine.