• ETRI Journal
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• v.46 no.2
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• pp.277-289
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• 2024

In this paper, we describe a neural network-based application that recommends multiple items using dialog context input and simultaneously outputs a response sentence. Further, we describe a multi-item recommendation by specifying it as a set of clothing recommendations. For this, a multimodal fusion approach that can process both cloth-related text and images is required. We also examine achieving the requirements of downstream models using a pretrained language model. Moreover, we propose a gate-based multimodal fusion and multiprompt learning based on a pretrained language model. Specifically, we propose an automatic evaluation technique to solve the one-to-many mapping problem of multi-item recommendations. A fashion-domain multimodal dataset based on Koreans is constructed and tested. Various experimental environment settings are verified using an automatic evaluation method. The results show that our proposed method can be used to obtain confidence scores for multi-item recommendation results, which is different from traditional accuracy evaluation.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.2
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• pp.99-105
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• 2013

In rapidly developing urban areas that include high-rise, large, and complex buildings, indoor and outdoor maps in GIS become a basis for utilizing and sharing information pertaining to various aspects of the real world. Although an indoor mapping has gained much attentions, research efforts are mostly in 2D and 3D modeling of terrain and buildings. Therefore, to facilitate fast and accurate construction of indoor GIS, this paper proposes a semi-automatic method consisting of preprocessing, 2D mapping, and 3D mapping stages. The preprocessing is designed to estimate heights of building interiors and to identify noise data from point clouds. In the 2D mapping, a floor map is extracted with a tracing grid and a refinement method. In the 3D mapping, a 3D wireframe model is created with heights from the preprocessing stage. 3D mesh data converted from noise data is combined with the 3D wireframe model for detail modeling. The proposed method was applied to point clouds depicting a hallway in a building. Experiment results indicate that the proposed method can be utilized to construct 2D and 3D maps for indoor GIS.

• Journal of Korea Multimedia Society
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• v.23 no.7
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• pp.850-861
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• 2020

Hangul is a language that is composed of initial, medial, and final syllables. It has 11,172 characters. For this reason, the current method of designing all the characters by hand is very expensive and time-consuming. In order to solve the problem, this paper proposes an automatic Hangul font generation system and evaluates the standards for mapping Hangul characters to produce an effective automated Hangul font generation system. The system was implemented using character generation engine based on deep learning CycleGAN. In order to evaluate the criteria when mapping characters in pairs, each criterion was designed based on Hangul structure and character shape, and the quality of the generated characters was evaluated. As a result of the evaluation, the standards designed based on the Hangul structure did not affect the quality of the automated Hangul font generation system. On the other hand, when tried with similar characters, the standards made based on the shape of Hangul characters produced better quality characters than when tried with less similar characters. As a result, it is better to generate automated Hangul font by designing a learning method based on mapping characters in pairs that have similar character shapes.

• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.67-80
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• 2022

In this paper, we propose a system which automatically generates the ShEx schemas to describe and validate RDF knowledge graphs constructed by RML mapping. ShEx schemas consist of constraints. The proposed system generates most of the constraints by converting the RML mapping rules. The schemas consisting only of constraints obtained from mapping rules can help users to figure out the structure of the graphs generated by RML mapping, but they are not sufficient for sophisticated validation purposes. For users who need a schema for validation, the proposed system is also able to provide the schema with added constraints generated from metadata extracted from the input data sources for RML mapping. The proposed system has the ability to handle CSV, XML, JSON or RDBMS as input data sources. Testing results from 297 cases show that the proposed system can be applied for RDF graph validation in various practical cases.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1177-1179
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• 2003

Recently, the ground-based laser profiler is used for acquisition of 3D spatial information of a rchaeological objects. However, it is very difficult to measure complicated objects, because of a relatively low-resolution. On the other hand, texture mapping can be a solution to complement the low resolution, and to generate 3D model with higher fidelity. But, a huge cost is required for the construction of textured 3D model, because huge labor is demanded, and the work depends on editor's experiences and skills . Moreover, the accuracy of data would be lost during the editing works. In this research, using the laser profiler and a non-calibrated digital camera, a method is proposed for the automatic generation of 3D model by integrating these data. At first, region segmentation is applied to laser range data to extract geometric features of an object in the laser range data. Various information such as normal vectors of planes, distances from a sensor and a sun-direction are used in this processing. Next, an image segmentation is also applied to the digital camera images, which include the same object. Then, geometrical relations are determined by corresponding the features extracted in the laser range data and digital camera’ images. By projecting digital camera image onto the surface data reconstructed from laser range image, the 3D texture model was generated automatically.

• Journal of Biomedical Engineering Research
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• v.16 no.1
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• pp.107-113
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• 1995

It is well known that multipoint and computerized intraoperative mapping systems improve the results of surgery for Wolff-Parkinson-White syndrome and show tremendous potential for opening an entirely new era of surgical intervention for the more common and lethal types of supraventricular tachyarrhythmias such as atrial flutter and atrial fibrillation. In addition, the ability to map and ablate the sometimes fleeting automatic atrial tachycardia is greatly enhanced by computerized mapping systems. In this study, we have developed 64 channel computerized data analysis system using microcomputer (Macintosh ${II}_{x}$) for basic research of electrophysiology and electrical propagation. The bipolar electrogram information is acquired from 64 cardiac sites simultaneously at a sampling rate of 1 ksampls/sec with continuous and total data storage of up to 30 seconds. When the reference electrogram is selected and reference point is picked up, delay time from the reference point is displayed on two dimensional diagram of the heart. System design permits easy expansion to almost 256 simultaneous sites. this system is expected to enable us to study pathophysiology of cardiac arrhythmia and to improve the result of diagnosis and surgical treatment for cardiac arrhythmia.

• Nuclear Engineering and Technology
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• v.54 no.12
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• pp.4684-4692
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• 2022

Radionuclide identification is an important part of the nuclear material identification system. The development of artificial intelligence and machine learning has made nuclide identification rapid and automatic. However, many methods directly use existing deep learning models to analyze the gamma-ray spectrum, which lacks interpretability for researchers. This study proposes an explainable radionuclide identification algorithm based on the convolutional neural network and class activation mapping. This method shows the area of interest of the neural network on the gamma-ray spectrum by generating a class activation map. We analyzed the class activation map of the gamma-ray spectrum of different types, different gross counts, and different signal-to-noise ratios. The results show that the convolutional neural network attempted to learn the relationship between the input gamma-ray spectrum and the nuclide type, and could identify the nuclide based on the photoelectric peak and Compton edge. Furthermore, the results explain why the neural network could identify gamma-ray spectra with low counts and low signal-to-noise ratios. Thus, the findings improve researchers' confidence in the ability of neural networks to identify nuclides and promote the application of artificial intelligence methods in the field of nuclide identification.

• MALSORI
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• no.52
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• pp.161-169
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• 2004

This paper describes the automated call routing for train ticket reservation aid system based on speech recognition. We focus on the task of automatically routing telephone calls based on user's fluently spoken response instead of touch tone menus in an interactive voice response system. Vector-based call routing algorithm is investigated and mapping table for key term is suggested. Korail database collected by KT is used for call routing experiment. We evaluate call-classification experiments for transcribed text from Korail database. In case of small training data, an average call routing error reduction rate of 14% is observed when mapping table is used.

• Journal of Biomedical Engineering Research
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• v.16 no.2
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• pp.157-166
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• 1995

The Electroencephalogram (EEG) and evoked potential (EP) t;ave widely been used for study of brain functions. The EEG and EP signals acquired from multi-channel electrodes placed on the head surface are often interfered by other relatively large physiological signals such as electromyogram (EMG) or electroculogram (EOG). Since these artifact-affected EEG signals degrade EEG mapping, the removal of the artifact-affected EEGs is one of the key elements in neuro-functional mapping. Conventionally this task has been carried out by human experts spending lots of examination time. In this paper a neural-network based classification is proposed to replace or to reduce human expert's efforts and time. From experiments, the neural-network based classification performs as good as human experts : variation of decisions between the neural network and human expert appears even smaller than that between human experts.

• Proceedings of the KOSOMBE Conference
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• v.1994 no.12
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• pp.81-84
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• 1994

A clinically oriented EEG and EP mapping system was developed with user-friendly interface and easy interactive operations. The system was based on the graphical user interface developed with C/C++ and Software Development Kit (SDK) operated under Microsoft Windows 3.1. Continuous acquisition for the EEG signal and burst mode acquisition for EEG signal syncronized to the external stimuli arc implemented with real time display. A neural network based automatic artifact discrimation is developed and implemented with which examination time can be reduced by a factor of 3 or more. Several bands of spectral maps and spectrums arc displayed for EEG diagnosis. Amplitude maps of EP signal at specified times by operator are displayed together with cine mode of EP maps for dynamic study. Source localization and other statistical signal processing are also included.