• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.93-98
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• 2023

Instagram is one of the fastest-growing online photo social web services where users share their life images and videos with other users. Image tagging is an essential step for developing Automatic Image Annotation (AIA) methods that are based on the learning by example paradigm. Hashtags can be used on just about any social media platform, but they're most popular on Twitter and Instagram. Using hashtags is essentially a way to group together conversations or content around a certain topic, making it easy for people to find content that interests them. Practically on average, 20% of the Instagram hashtags are related to the actual visual content of the image they accompany, i.e., they are descriptive hashtags, while there are many irrelevant hashtags, i.e., stophashtags, that are used across totally different images just for gathering clicks and for search ability enhancement. Hence in this work, Sorting instagram hashtags all the way through mass tagging using HITS (Hyperlink-Induced Topic Search) algorithm is presented. The hashtags can sorted to several groups according to Jensen-Shannon divergence between any two hashtags. This approach provides an effective and consistent way for finding pairs of Instagram images and hashtags, which lead to representative and noise-free training sets for content-based image retrieval. The HITS algorithm is first used to rank the annotators in terms of their effectiveness in the crowd tagging task and then to identify the right hashtags per image.

• Proceedings of the KAIS Fall Conference
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• 2010.11a
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• pp.272-275
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• 2010

인터넷의 발달로 인하여 다양한 온라인 카드 지불 방식이 생겨나게 되었다. 그 중, 기존의 국민카드와, BC카드의 카드 결제 방식인 ISP(Internet Secure Payment) 시스템에서 ISP를 생성하는 과정이 현재의 인증 방법으로는 잠재적인 취약점이 있음을 발견하고, 이에 본인 인증을 강화하는 프로토콜을 제안한다.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.3
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• pp.90-101
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• 2000

In this paper, we propose a controller for differentially steered wheeled mobile robots. The controller uses input-output linearization algorithm and artificial neural network to stabilize the dynamic model and compensate uncertainties. The proposed neural network part has 6 inputs, 1 hidden layer, 2 torque outputs and features fast online learning and good performance on structure error learning basis. Simulation results show that the proposed controller perform precisely tracking of reference path and is robust to uncertainties.

• 한국정보통신설비학회:학술대회논문집
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• 2009.08a
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• pp.143-146
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• 2009

There are some image hash methods, in the paper four image hash methods have been compared: FJLT (Fast Johnson- Lindenstrauss Transform), SVD (Singular Value Decomposition), NMF (Non-Negative Matrix Factorization), FP (Feature Point). From the compared result, FJLT method can't be used in the online. the search time is very slow because of the KNN algorithm. So FJLT method has been improved in the paper.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2149-2151
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• 2002

In this paper, a novel speed estimation method of an induction motor using neural networks(NNs) is presented. The NN speed estimator is trained online by using the error backpropagation algorithm, and the training starts simultaneously with the induction motor working. The neural network based vector controller has the advantage of robustness against machine parameter variation. The simulation results using Matlab/Simulink verify the useful of the proposed method.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1736-1737
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• 2007

Significant increase of container flows in marine terminals requires more efficient automatic port systems. This paper presents a novel routing and collision avoidance algorithm of linear motor based shuttle cars using random access sequence dynamic programming (RAS DP). The proposed RAS DP is accomplished online for determining optimal paths for each shuttle car.

• Proceedings of the KIPE Conference
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• 2015.11a
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• pp.13-14
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• 2015

본 논문은 추정 정확도를 높이기 위해 비례적분 관측기를 이용한 실시간 잔존용량 추정 알고리즘을 제안한다. 시뮬레이션을 통해 제안하는 알고리즘의 타당성을 검증하였고, 초기 잔존 용량이 불명확하거나 배터리 모델 파라미터 값이 실제와 일치하지 않더라도 평균 추정오차는 0.3% 미만으로 확인되었다.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11a
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• pp.973-975
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• 2005

With the continuous growth of e-commerce there is a huge amount of products information available online. Shop managers expect to apply information techniques to increase profit and perfect service. Hence many e-commerce systems use association rule mining to further refine their management. However previous association rule algorithms have two limitations. Firstly, they only use the number to weight item's essentiality and ignore essentiality of item profit. Secondly, they did not consider the relationship between number and profit of item when they do mining. We address a novel algorithm, profit-based association rule algorithm that uses profit-based technique to generate 1-itemsets and the multiple minimum supports mining technique to generate N-items large itemsets.

• Proceedings of the KIEE Conference
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• 2006.04b
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• pp.344-346
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• 2006

In this paper, a novel algorithm for increasing the steady state efficiency during light load operation of the induction generator that integrated with a wind power generation system is presented. The proposed algorithm based on the flux level reduction, where the flux level is estimated using Support-Vector -Machines for regression (SVR) for the optimum d-axis current of the generator. SVR is trained off-line to estimate the unknown mapping between the system's inputs and outputs, and then is used online to calculate the optimum d-axis current for minimizing generator loss. The experimental results show that SVR can define the flux-power loss accurately and determine the optimum d-axis current value precisely. The loss minimization process is more effective at low wind speed and the percent of power saving can approach to 40%.