• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.273-276
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• 2019

There is a lot of information in our world, quick access to the most accurate information or finding the information we need is more difficult and complicated. The recommendation system has become important for users to quickly find the product according to user's preference. A social recommendation system using community detection based on label propagation is proposed. In this paper, we applied community detection based on label propagation and collaborative filtering in the movie recommendation system. We implement with MovieLens dataset, the users will be clustering to the community by using label propagation algorithm, Our proposed algorithm will be recommended movie with finding the most similar community to the new user according to the personal propensity of users. Mean Absolute Error (MAE) is used to shown efficient of our proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.5
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• pp.1252-1271
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• 2013

Automatic image annotation has become an increasingly important research topic owing to its key role in image retrieval. Simultaneously, it is highly challenging when facing to large-scale dataset with large variance. Practical approaches generally rely on similarity measures defined over images and multi-label prediction methods. More specifically, those approaches usually 1) leverage similarity measures predefined or learned by optimizing for ranking or annotation, which might be not adaptive enough to datasets; and 2) predict labels separately without taking the correlation of labels into account. In this paper, we propose a method for image annotation through collaborative similarity metric learning from dataset and modeling the label correlation of the dataset. The similarity metric is learned by simultaneously optimizing the 1) image ranking using structural SVM (SSVM), and 2) image annotation using correlated label propagation, with respect to the similarity metric. The learned similarity metric, fully exploiting the available information of datasets, would improve the two collaborative components, ranking and annotation, and sequentially the retrieval system itself. We evaluated the proposed method on Corel5k, Corel30k and EspGame databases. The results for annotation and retrieval show the competitive performance of the proposed method.

• Journal of KIISE
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• v.44 no.7
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• pp.686-691
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• 2017

Telemarketing has become the center of marketing action of the industry in the information society. Recently, machine learning has emerged in many areas, especially, financial prediction. Financial data consists of lots of unlabeled data in most parts, and therefore, it is difficult for humans to perform their labeling. In this paper, we propose a fusion method of semi-supervised learning for automatic labeling of unlabeled data to predict telemarketing. Specifically, we integrate labeling results of label propagation and co-training with a decision tree. The data with lower reliabilities are removed, and the data are extracted that have consistent label from two labeling methods. After adding them to the training set, a decision tree is learned with all of them. To confirm the usefulness of the proposed method, we conduct the experiments with a real telemarketing dataset in a Portugal bank. Accuracy of the proposed method is 83.39%, which is 1.82% higher than that of the conventional method, and precision of the proposed method is 19.37%, which is 2.67% higher than that of the conventional method. As a result, we have shown that the proposed method has a better performance as assessed by the t-test.

• Journal of Satellite, Information and Communications
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• v.9 no.2
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• pp.101-106
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• 2014

As ESL(Electronic Shelf Label) system is increased at the market in the world, the interference between ESL system and Wi-Fi(Wireless Fidelity) Cellular Phone at 2.4GHz becomes issue. The interference scenario and propagation of the Extended HATA Model were established to analyze the interference from Wi-Fi Cellular Phone into ESL system. Through simulation results based on SEAMCAT(Spectrum Engineering Advanced Monte Carlo Analysis Tool), separation distance was obtained to protect ESL system from Wi-Fi Cellular Phone interference.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.2
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• pp.53-60
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• 2020

Sentiment analysis is the automated process of understanding attitudes and opinions about a given topic from written or spoken text. One of the sentiment analysis approaches is a dictionary-based approach, in which a sentiment dictionary plays an much important role. In this paper, we propose a method to automatically generate Korean sentiment lexicon from the well-known English sentiment lexicon called VADER (Valence Aware Dictionary and sEntiment Reasoner). The proposed method consists of three steps. The first step is to build a Korean-English bilingual lexicon using a Korean-English parallel corpus. The bilingual lexicon is a set of pairs between VADER sentiment words and Korean morphemes as candidates of Korean sentiment words. The second step is to construct a bilingual words graph using the bilingual lexicon. The third step is to run the label propagation algorithm throughout the bilingual graph. Finally a new Korean sentiment lexicon is generated by repeatedly applying the propagation algorithm until the values of all vertices converge. Empirically, the dictionary-based sentiment classifier using the Korean sentiment lexicon outperforms machine learning-based approaches on the KMU sentiment corpus and the Naver sentiment corpus. In the future, we will apply the proposed approach to generate multilingual sentiment lexica.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.06a
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• pp.377-381
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• 2001

An ATMS(Assumption-based Truth Maintenance System) has been widely used for maintaining the truth of information by detecting and solving contradictions in nile-based systems. But the ATMS can not correctly maintain the truth of the information in case that the generated information is satisfied within a time interval or includes data about temporal relations of events in time varying situations, because it has no mechanism manipulating temporal data. In this paper, The extended ATMS method is proposed, which can maintain the truth of the information in the inference system using information changing over time or temporal relations of events. In order to maintain contexts generated by relations of events, the label representation method is modified, the disjunction, conjunction simplification method in the label-propagation procedure and nogood handling method of the conventional ATMS are modified, too.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.9
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• pp.4405-4418
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• 2016

Automatic image completion techniques have difficulty processing images in which the target region has multiple planes or is non-facade. Here, we propose a new image completion method that uses belief propagation based on planar priorities. We first calculate planar information, which includes planar projection parameters, plane segments, and repetitive regularity extractions within the plane. Next, we convert this planar information into planar guide knowledge using the prior probabilities of patch transforms and offsets. Using the energy of the discrete Markov Random Field (MRF), we then define an objective function for image completion that uses the planar guide knowledge. Finally, in order to effectively optimize the MRF, we propose a new optimization scheme, termed Planar Priority-belief propagation that includes message-scheduling-based planar priority and dynamic label cropping. The results of experiment show that our approach exhibits advanced performance compared with existing approaches.

• Broadcasting and Media Magazine
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• v.27 no.2
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• pp.10-18
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• 2022

본 고에서는 준지도 학습의 개념과 목표 그리고 대표 기법들의 동작 원리에 대해서 알아본다. 구체적으로, 영상 분류를 위한 준지도 학습 기법을 크게 label propagation 기반 기법과 representation learning 기반 기법으로 나누고, 이 두 가지 기법들의 특성을 분석하고, 대표 기법들의 동작 원리에 대해서 설명한다. 또한, 영상 분류 문제에서 위 두 가지 접근법들의 대표 기법들의 성능을 평가한다.

• Journal of Internet Computing and Services
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• v.21 no.2
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• pp.19-26
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• 2020

This paper analyses the feasibility of using implantable antennas to detect and monitor tumors. We analyze this setting according to the wireless propagation loss and signal fading produced by human bodies and their environment in an indoor scenario. The study is based on the ITU-R propagation recommendations and prediction models for the planning of indoor radio communication systems and radio local area networks in the frequency range of 300 MHz to 100 GHz. We conduct primary estimations on 915 MHz and 2.4 GHz operating frequencies. The path loss presented in most short-range wireless implant devices does not take into account the human body as a channel itself, which causes additional losses to wireless designs. In this paper, we examine the propagation through the human body, including losses taken from bones, muscles, fat, and clothes, which results in a more accurate characterization and estimation of the channel. The results obtained from our simulation indicates a variation of the return loss of the spiral antenna when a tumor is located near the implant. This knowledge can be applied in medical detection, and monitoring of early tumors, by analyzing the electromagnetic field behavior of the implant. The tumor was modeled under CST Microwave Studio, using Wisconsin Diagnosis Breast Cancer Dataset. Features like the radius, texture, perimeter, area, and smoothness of the tumor are included along with their label data to determine whether the external shape has malignant or benign physiognomies. An explanation of the feasibility of the system deployment and technical recommendations to avoid interference is also described.

• Current Optics and Photonics
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• v.7 no.5
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• pp.537-544
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• 2023

Differential phase contrast (DPC) microscopy, a central quantitative phase imaging (QPI) technique in cell biology, facilitates label-free, real-time monitoring of intrinsic optical phase variations in biological samples. The existing DPC imaging theory, while important for QPI, is grounded in paraxial diffraction theory. However, this theory lacks accuracy when applied to high numerical aperture (NA) systems that are vital for high-resolution cellular studies. To tackle this limitation, we have, for the first time, formulated a nonparaxial DPC imaging equation with a transmission cross-coefficient (TCC) for high NA DPC microscopy. Our theoretical framework incorporates the apodization of the high NA objective lens, nonparaxial light propagation, and the angular distribution of source intensity or detector sensitivity. Thus, our TCC model deviates significantly from traditional paraxial TCCs, influenced by both NA and the angular variation of illumination or detection. Our nonparaxial imaging theory could enhance phase retrieval accuracy in QPI based on high NA DPC imaging.