• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.2
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• pp.370-375
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• 2011

In this paper, we propose an antenna module with high isolation between Tx and Rx in RFID reader module. The proposed module consists of a quadrafilar antenna and a directional coupler with a switchable dummy load to improve the directivity. The proposed module achieves the isolation higher than 20 dB between Tx and Rx. To show the validity of the proposed scheme, we have performed the measurement of tagging range and multi-tagging ability. The experiment results show that the detecting range and multi-tagging ability are enhanced by 81% and 200%, respectively.

• Journal of Digital Convergence
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• v.17 no.12
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• pp.243-248
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• 2019

Multi-Task Learning(MTL) is a training method that trains a single neural network with multiple tasks influences each other. In this paper, we compare performance of MTL Named entity recognition(NER) model trained with Korean traditional culture corpus and other NER model. In training process, each Bi-LSTM layer of Part of speech tagging(POS-tagging) and NER are propagated from a Bi-LSTM layer to obtain the joint loss. As a result, the MTL based Bi-LSTM model shows 1.1%~4.6% performance improvement compared to single Bi-LSTM models.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.10
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• pp.1010-1014
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• 2010

Article-related product recommender system is an emerging e-commerce service which recommends items based on association in contexts between items and articles. Current services recommend based on the similarity between tags of articles and items, which is deficient not only due to the high cost in manual tagging but also low accuracies in recommendation. As a component of novel article-related item recommender system, we propose a new method for tagging item images based on pre-defined categories. We suggest a hypernetwork-based algorithm for learning association between images, which is represented by visual words, and categories of products. Learned hypernetwork are used to assign multiple tags to unlabeled item images. We show the ability of our method with a product set of real-world online shopping-mall including 1,251 product images with 10 categories. Experimental results not only show that the proposed method has competitive tagging performance compared with other classifiers but also present that the proposed multi-tagging method based on hypernetworks improves the accuracy of tagging.

• KIISE Transactions on Computing Practices
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• v.23 no.6
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• pp.386-391
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• 2017

In this paper, we propose a system that automatically assigns user's experience-based emotion tags from wearable sensor data collected in real life. Four types of emotional tags are defined considering the user's own emotions and the information which the user sees and listens to. Based on the collected wearable sensor data from multiple sensors, we have trained a machine learning-based tagging system that combines the known auxiliary tools from the existing affective computing research and assigns emotional tags. In order to show the usefulness of this multi-modality-based emotion tagging system, quantitative and qualitative comparison with the existing single-modality-based emotion recognition approach are performed.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2017.11a
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• pp.39-41
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• 2017

본 연구에서는 해사영어어휘의 특징인 ballast water, fore peak bulkhead, container, freight station charges와 같은 n-gram의 복수 단어로 구성된 합성어 (multi-word compounds) 태깅(tagging)처리가 포함된 해사영어코퍼스를 구축하였다. 해사영어코퍼스는 백만 단어씩 수집한 학술, 법, 신문, 교과서 4개 하위 코퍼스로 구성된 총 400만 단어의 해사영어코퍼스로 구성되어 있다.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.18
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• pp.8351-8358
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• 2016

Electronic cleansing is an image post processing technique in which the tagged colonic content is subtracted from colon using CTC images. There are post processing artefacts, like: 1) soft tissue degradation; 2) incomplete cleansing; 3) misclassification of polyp due to pseudo enhanced voxels; and 4) pseudo soft tissue structures. The objective of the study was to subtract the tagged colonic content without losing the soft tissue structures. This paper proposes a novel adaptive method to solve the first three problems using a multi-step algorithm. It uses a new edge model-based method which involves colon segmentation, priori information of Hounsfield units (HU) of different colonic contents at specific tube voltages, subtracting the tagging materials, restoring the soft tissue structures based on selective HU, removing boundary between air-contrast, and applying a filter to clean minute particles due to improperly tagged endoluminal fluids which appear as noise. The main finding of the study was submerged soft tissue structures were absolutely preserved and the pseudo enhanced intensities were corrected without any artifact. The method was implemented with multithreading for parallel processing in a high performance computer. The technique was applied on a fecal tagged dataset (30 patients) where the tagging agent was not completely removed from colon. The results were then qualitatively validated by radiologists for any image processing artifacts.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.11B
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• pp.722-729
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• 2005

Ethernet passive optical networks (EPONs) are an emerging access network technology which has a point-to-multipoint topology. EPONs operate point-to-multipoint in the OLT-ONU direction, and point-to-point in the ONU-OLT direction. To support point-to-point emulation and shared LAM emulation, EPONs use multi-point control protocol (MPCP). The MPCP uses logical link identification (LLID) field for frame tagging and filtering between the OLT and ONUs. In this paper, I propose logical-group identification (LGID) for logical link-based multicasting or VLAN services in EPONs. Using LGID with new frame tagging and filtering rules, EPONs support differentiated multimedia broadcasting or multicasting services. Additionally, EPONs can support logical link-based VLAN services that divides ONUs into several subsets.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.308-310
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• 2022

With the rise of the Al wave, task-oriented dialogue systems have become one of the popular research directions in academia and industry. Currently, task-oriented dialogue systems mainly adopt pipelined form, which mainly includes natural language understanding, dialogue state decision making, dialogue state tracking and natural language generation. However, pipelining is prone to error propagation, so many task-oriented dialogue systems in the market are only for single-round dialogues. Usually single- domain dialogues have relatively accurate semantic understanding, while they tend to perform poorly on multi-domain, multi-round dialogue datasets. To solve these issues, we developed a paddle-based multi-domain task-oriented Chinese dialogue system. It is based on NEZHA-base pre-training model and CrossWOZ dataset, and uses intention recognition module, dichotomous slot recognition module and NER recognition module to do DST and generate replies based on rules. Experiments show that the dialogue system not only makes good use of the context, but also effectively addresses long-term dependencies. In our approach, the DST of dialogue tracking state is improved, and our DST can identify multiple slotted key-value pairs involved in the discourse, which eliminates the need for manual tagging and thus greatly saves manpower.

• Journal of Information Processing Systems
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• v.12 no.3
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• pp.358-380
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• 2016

In order to considerably reduce the ambiguity rate, we propose in this article a disambiguation approach that is based on the selection of the right diacritics at different analysis levels. This hybrid approach combines a linguistic approach with a multi-criteria decision one and could be considered as an alternative choice to solve the morpho-lexical ambiguity problem regardless of the diacritics rate of the processed text. As to its evaluation, we tried the disambiguation on the online Alkhalil morphological analyzer (the proposed approach can be used on any morphological analyzer of the Arabic language) and obtained encouraging results with an F-measure of more than 80%.

• Journal of KIISE:Software and Applications
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• v.36 no.9
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• pp.749-757
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• 2009

A Internet user can easily access to the target information by web searching using some key-words or categories in the present Internet environment. When some meta-data which represent attributes of several data structures well are used, then more accurate result which is matched with the intention of users can be provided. This study proposes a multiple dimensional vector tagging method for the small web user group who interest in maintaining and sharing the bookmark for common interesting topics. The proposed method uses vector tag method for increasing the effect of categorization, management, and retrieval of target information. The vector tag composes with two or more components of the user defined priority. The basic vector space is created time of information and reference value. The calculated vector value shows the usability of information and became the metric of ranking. The ranking accuracy of the proposed method compares with that of a simply link structure, The proposed method shows better results for corresponding the intention of users.