• Journal of Intelligence and Information Systems
• /
• v.29 no.2
• /
• pp.219-240
• /
• 2023

Conversational AI which allows users to interact with satisfaction is a long-standing research topic. To develop conversational AI, it is necessary to build training data that reflects real conversations between people, but current Korean datasets are not in question-answer format or use honorifics, making it difficult for users to feel closeness. In this paper, we propose a conversation dataset (KOMUChat) consisting of 30,767 question-answer sentence pairs collected from online communities. The question-answer pairs were collected from post titles and first comments of love and relationship counsel boards used by men and women. In addition, we removed abuse records through automatic and manual cleansing to build high quality dataset. To verify the validity of KOMUChat, we compared and analyzed the result of generative language model learning KOMUChat and benchmark dataset. The results showed that our dataset outperformed the benchmark dataset in terms of answer appropriateness, user satisfaction, and fulfillment of conversational AI goals. The dataset is the largest open-source single turn text data presented so far and it has the significance of building a more friendly Korean dataset by reflecting the text styles of the online community.

• Korean Journal of Remote Sensing
• /
• v.39 no.2
• /
• pp.183-192
• /
• 2023

The remotely sensed data, such as satellite imagery and aerial photos, can be used to extract and detect some objects in the image through image interpretation and processing techniques. Significantly, the possibility for utilizing digital map updating and land monitoring has been increased through automatic object detection since spatial resolution of remotely sensed data has improved and technologies about deep learning have been developed. In this paper, we tried to extract plastic greenhouses into aerial orthophotos by using fully convolutional densely connected convolutional network (FC-DenseNet), one of the representative deep learning models for semantic segmentation. Then, a quantitative analysis of extraction results had performed. Using the farm map of the Ministry of Agriculture, Food and Rural Affairsin Korea, training data was generated by labeling plastic greenhouses into Damyang and Miryang areas. And then, FC-DenseNet was trained through a training dataset. To apply the deep learning model in the remotely sensed imagery, instance norm, which can maintain the spectral characteristics of bands, was used as normalization. In addition, optimal weights for each band were determined by adding attention modules in the deep learning model. In the experiments, it was found that a deep learning model can extract plastic greenhouses. These results can be applied to digital map updating of Farm-map and landcover maps.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.4
• /
• pp.21-29
• /
• 2023

In this paper, we propose an AI-based system for automatically classifying industry and occupation codes in the population census. The accurate classification of industry and occupation codes is crucial for informing policy decisions, allocating resources, and conducting research. However, this task has traditionally been performed by human coders, which is time-consuming, resource-intensive, and prone to errors. Our system represents a significant improvement over the existing rule-based system used by the statistics agency, which relies on user-entered data for code classification. In this paper, we trained and evaluated several models, and developed an ensemble model that achieved an 86.76% match accuracy in industry and 81.84% in occupation, outperforming the best individual model. Additionally, we propose process improvement work based on the classification probability results of the model. Our proposed method utilizes an ensemble model that combines transfer learning techniques with pre-trained models. In this paper, we demonstrate the potential for AI-based systems to improve the accuracy and efficiency of population census data classification. By automating this process with AI, we can achieve more accurate and consistent results while reducing the workload on agency staff.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.26 no.4D
• /
• pp.671-678
• /
• 2006

In this study, to describe vehicle-structure dynamic interaction phenomena with 1/4 vehicle model, nonlinear Hertzian contact spring and nonlinear contact damper are adopted. The external loads acting on 1/4 vehicle model are selfweight of vehicle and geometry information of running surface. The constraint equation on contact surface is implemented by the Penalty method with stabilization and the reaction from constraint violation. To describe pitching motion of various vehicles two types of the displacement constraint equations are exerted to connect between car bodies and between bogie frames, i.e., the rigid body connection and the rigid body connection with pin, respectively. For the time integration of dynamic equations of vehicles and structure Newmark time integration scheme is adopted. To reduce the error caused by inadequate time step size, adaptive time-stepping technique is also adopted. Thus, it is expected that more versatile dynamic interaction phenomena can be described by this approach and it can be applied to various railway dynamic problems with low computational cost.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.28 no.6B
• /
• pp.709-721
• /
• 2008

This paper is to test the applicability of ModKIMSTORM (Modified KIneMatic Wave STOrm Runoff Model) by applying it to Namgangdam watershed of $2,293km^2$. Model inputs (DEM, land use, soil related information) were prepared in 500 m spatial resolution. Using five typhoon events (Saomi in 2000, Rusa in 2002, Maemi in 2003, Megi in 2004 and Ewiniar in 2006) and two storm events (May of 2003 and July of 2004), the model was calibrated and verified by comparing the simulated streamflow with the observed one at the outlet of the watershed. The Pearson's coefficient of determination $R^2$, Nash and Sutcliffe model efficiency E, the deviation of runoff volumes $D_v$, relative error of the peak runoff rate $EQ_p$, and absolute error of the time to peak runoff $ET_p$ showed the average value of 0.984, 0.981, 3.63%, 0.003, and 0.48 hr for 4 storms calibration and 0.937, 0.895, 8.08%, 0.138, and 0.73 hr for 3 storms verification respectively. Among the model parameters, the stream Manning's roughness coefficient was the most sensitive for peak runoff and the initial soil moisture content was highly sensitive for runoff volume fitting. We could look into the behavior of hyrologic components from the spatial results during the storm periods and get some clue for the watershed management by storms.

• Journal of Ethics
• /
• no.82
• /
• pp.73-125
• /
• 2011

This article aims to search for moral educational implication of J. D. Greene's recent neuro-scientific approaches to deontological ethics. Recently new technique in neuroscience such as fMRI is applied to moral and social psychological concepts or terms, and 'affective primacy' and 'automaticity' principles are highlighted as basic concepts of the new paradigm. When these principles are introduced to ethical theories, it makes rooms of new and different interpretations of them. J. D. Greene et al. claim that deontological moral judgments or theories are just a kind of post hoc rationalization for intuitions or emotions by ways of neuroscientific findings and evolutionary interpretation. For example, Kant's categorical imperative in which a maxim should be universalizable to be as a principle, might be a product of moral intuition. Firstly this article tries to search for intellectual backgrounds of the social intuitionalism where Greens' thought originates. Secondly, this article tries to collect and summarize his arguments about moral dilemma responses, personal-impersonal dilemma catergorizing hypothesis, fMRI data interpretations by ways of evolutionary theory, cultural and social psychological theories, application to deontological and consequential theories, and his suggestion that deontological ethics shoud be rejected as a normative ethical thought and consequentialism be a promising theory etc. Thirdly, this tries to analyse and critically exam those aspects and argumentation, especially from viewpoints of the ethicists whose various strategies seek to defeat Greene's claims. Fourthly, this article criticizes that his arguments make a few critical mistakes in methodology and data interpretation. Last, this article seeks to find its implications for moral education in korea, in which in spite of incomplete argumentation of his neuroscientific approach to morality, neuroethics needs to be introduced as a new approach and educational content, and critical materials as well.

• Journal of Advanced Navigation Technology
• /
• v.26 no.6
• /
• pp.496-503
• /
• 2022

In the reality that the fishing population is decreasing and the single-man fishing vessels is increasing, mandatory equipment for navigation and radio equipments for the safety of fishing boats has continued to be added. Therefore, many equipment such as navigation, communication and fishing are installed in the narrow steering room, so it is very confusing and a number of monitors are placed in the front, which is a factor that degrades the function of maritime observation. To solve this problem, we studied an interface that integrates and operates to major radio facilities such as very high frequency-digital selective calling equipment (VHF-DSC), automatic identification system (AIS) and fishing boat location transmission device (V-pass) into one multi function display (MFD) based on LAN. In addition, IEC61162-450 UDP packets and IEC61162 sentence were applied to exchange data through link between MFD and radio equipments, and additional messages needed for each equipment and function were defined. The integrated MFD monitor is easily operated by the menu method, and the performance of the interface was evaluated by checking the distress and emergency communication functions related to maritime safety and the message transmission status by equipment.

• Journal of the Korea Computer Graphics Society
• /
• v.30 no.3
• /
• pp.99-107
• /
• 2024

While the demand for extrapolating video content horizontally or vertically is increasing, even the most advanced techniques cannot successfully extrapolate all videos. Therefore, it is important to determine if a given video can be well extrapolated before attempting the actual extrapolation. This can help avoid wasting computing resources. This paper proposes a video classifier that can identify if a video is suitable for horizontal extrapolation. The classifier utilizes optical flow and an adaptive Gaussian blur network, which can be applied to flow-based video extrapolation methods. The labeling for training was rigorously conducted through user tests and quantitative evaluations. As a result of learning from this labeled dataset, a network was developed to determine the extrapolation capability of a given video. The proposed classifier achieved much more accurate classification performance than methods that simply use the original video or fixed blur alone by effectively capturing the characteristics of the video through optical flow and adaptive Gaussian blur network. This classifier can be utilized in various fields in conjunction with automatic video extrapolation techniques for immersive viewing experiences.

• Journal of Internet Computing and Services
• /
• v.14 no.6
• /
• pp.71-84
• /
• 2013

Log data, which record the multitude of information created when operating computer systems, are utilized in many processes, from carrying out computer system inspection and process optimization to providing customized user optimization. In this paper, we propose a MongoDB-based unstructured log processing system in a cloud environment for processing the massive amount of log data of banks. Most of the log data generated during banking operations come from handling a client's business. Therefore, in order to gather, store, categorize, and analyze the log data generated while processing the client's business, a separate log data processing system needs to be established. However, the realization of flexible storage expansion functions for processing a massive amount of unstructured log data and executing a considerable number of functions to categorize and analyze the stored unstructured log data is difficult in existing computer environments. Thus, in this study, we use cloud computing technology to realize a cloud-based log data processing system for processing unstructured log data that are difficult to process using the existing computing infrastructure's analysis tools and management system. The proposed system uses the IaaS (Infrastructure as a Service) cloud environment to provide a flexible expansion of computing resources and includes the ability to flexibly expand resources such as storage space and memory under conditions such as extended storage or rapid increase in log data. Moreover, to overcome the processing limits of the existing analysis tool when a real-time analysis of the aggregated unstructured log data is required, the proposed system includes a Hadoop-based analysis module for quick and reliable parallel-distributed processing of the massive amount of log data. Furthermore, because the HDFS (Hadoop Distributed File System) stores data by generating copies of the block units of the aggregated log data, the proposed system offers automatic restore functions for the system to continually operate after it recovers from a malfunction. Finally, by establishing a distributed database using the NoSQL-based Mongo DB, the proposed system provides methods of effectively processing unstructured log data. Relational databases such as the MySQL databases have complex schemas that are inappropriate for processing unstructured log data. Further, strict schemas like those of relational databases cannot expand nodes in the case wherein the stored data are distributed to various nodes when the amount of data rapidly increases. NoSQL does not provide the complex computations that relational databases may provide but can easily expand the database through node dispersion when the amount of data increases rapidly; it is a non-relational database with an appropriate structure for processing unstructured data. The data models of the NoSQL are usually classified as Key-Value, column-oriented, and document-oriented types. Of these, the representative document-oriented data model, MongoDB, which has a free schema structure, is used in the proposed system. MongoDB is introduced to the proposed system because it makes it easy to process unstructured log data through a flexible schema structure, facilitates flexible node expansion when the amount of data is rapidly increasing, and provides an Auto-Sharding function that automatically expands storage. The proposed system is composed of a log collector module, a log graph generator module, a MongoDB module, a Hadoop-based analysis module, and a MySQL module. When the log data generated over the entire client business process of each bank are sent to the cloud server, the log collector module collects and classifies data according to the type of log data and distributes it to the MongoDB module and the MySQL module. The log graph generator module generates the results of the log analysis of the MongoDB module, Hadoop-based analysis module, and the MySQL module per analysis time and type of the aggregated log data, and provides them to the user through a web interface. Log data that require a real-time log data analysis are stored in the MySQL module and provided real-time by the log graph generator module. The aggregated log data per unit time are stored in the MongoDB module and plotted in a graph according to the user's various analysis conditions. The aggregated log data in the MongoDB module are parallel-distributed and processed by the Hadoop-based analysis module. A comparative evaluation is carried out against a log data processing system that uses only MySQL for inserting log data and estimating query performance; this evaluation proves the proposed system's superiority. Moreover, an optimal chunk size is confirmed through the log data insert performance evaluation of MongoDB for various chunk sizes.

• The Korean Journal of Nuclear Medicine
• /
• v.39 no.6
• /
• pp.430-437
• /
• 2005

Purpose: The objectives of this study were - First, to determine the normal range of left ventricular end diastolic volume (EDV), end systolic volume (ESV) and election fraction (EF) from gated myocardial perfusion SPECT for Quantitative Gated SPECT (QGS) and 4D-MSPECT (4DM), respectively. Second, to evaluate the relationships between values produced by both software packages. Materials & Methods: Tc-99m MIBI gated myocardial perfusion SPECT were performed for 77 patients (mean age: $49.6{\pm}13.7y$, n=37(M), 40(F)) with a low likelihood (<10%) of coronary artery disease (CAD) using dual head gamma camera (E.CAM, Siemens, USA). Left ventricular EDV, ESV and EF were automatically measured by means of QGS and 4DM, respectively. Results: in QGS, the mean EDV, ESV and EF for all patients were $78.2{\pm}25.2ml,\;27.4{\pm}12.9ml\;and\;66.6{\pm}8.0%$ at stress test respectively, not different from rest test (p>0.05). In 4DM, the mean EDV, ESV and EF for all patients were $89.1{\pm}26.4ml,\;29.1{\pm}12.8ml\;and\;68.5{\pm}6.7%$ at stress test. Most cases in 4DM, there was no significant difference statistically between stress and rest test (p>0.05). But statistically significant difference was found in EF ($68.5{\pm}6.7%$ at stress vs $70.9{\pm}8.0%$ at rest, p<0.05). Correlation coefficients between the methods for EDV, ESV and EF were comparatively high (0.95, 0.93, 0.71 at stress test and 0.95, 0.90, 0.69 at rest test, respectively). However, Bland-Altman plots showed a large range of the limit value of agreement for EDV, ESV and EF between both methods ($-30ml{\sim}10ml,\;-12ml{\sim}8ml,\;-14%{\sim}11%$ at stress test and $-32ml{\sim}5ml,\;-13ml{\sim}13ml,\;-18%{\sim}12%$ at rest test). Conclusion: We found the normal ranges of EDV, ESV and EF for patients with a low likelihood of CAD in both methods. We expect these values will be a good reference to interpret gated myocardial perfusion SPECT. Although good correlation was observed between both methods, they should not be used interchangeably. Therefore, when both programs are used at the same site, it will be important to apply normal limits specific to each method.