• 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.

• Journal of IKEEE
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• v.9 no.2 s.17
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• pp.87-100
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• 2005

In this paper, a design of an integral augmented optimal variable structure system(IOVSS) is presented for the prescribed output control of uncertain SISO systems under persistent disturbances. This algorithm aims at removing the problems of the reaching phase by incorporating advanced optimal control theory. By means of an integral sliding surface, the reaching phase is completely removed, and the integral sliding surface can be defined from a given initial state to origin without any reaching phase. The ideal sliding dynamics of the integral sliding surface is obtained in the form of the state equation and is designed in an optimal sense by targeting the design of the integral sliding surface and equivalent control input. The corresponding control input is selected in order to generate the sliding mode on the predetermined integral sliding surface. As a result, the whole sliding output from a given initial state to origin is completely guaranteed against persistent disturbances. Moreover the prediction/predetermination of output is enabled, which helps in improving the performance over previously implemented VSS's. Through an illustrative example, the usefulness of the algorithm is shown.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.6
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• pp.1243-1251
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• 2017

When a Ship sails in the ocean, it is significant to find one's position for safe navigation. Most of ships have been using GPS navigation since its development after 1990's. The celestial navigation was used as the navigation method when sailing in the ocean, but time-consuming process such as complicated calculation and plotting the result on chart diminished its utilization. The thesis explains convenience and utilization of existing celestial navigation by resolving challenges it has. As a way of enhancing the celestial navigation, the author developed a software which incudes a numerical formula based on the previous calculation process. When a navigator inputs the altitude of sun, GHA and dec into computer while sailing, the position of the ship will be displayed as the coordinates. The improved method thus reaffirmed the usefulness of the celestial navigation and will greatly serve as means of navigation in the occurrence of distress. Abstract should be placed here.

• Journal of Navigation and Port Research
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• v.29 no.10 s.106
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• pp.865-870
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• 2005

Because there are only a limited number of means of action that are available for the master to pursue in the event of flooding, onboard decision support system has been required. The majority of systems activated during a flooding emergency (such as watertight and semi-watertight doors, bulkhead valves, dewatering pumps etc.) almost exclusively aim to restore a sufficiently high level of subdivision to prevent flooding from spreading through the ship. Even though assuming the flooding scenario is not catastrophic, the use of ballast tanks can be an additional and very effective tool to ensure both prevention of flooding spreading and also improve ship stability. This paper describes an optimization algorithm devised to choose the set of ballast tanks that should be filled in order to achieve an optimal response to a flooding accident.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1297-1307
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• 2013

Several multi-hop applications developed for vehicular ad hoc networks use broadcast as a means to either discover nearby neighbors or disseminate useful traffic information to othet vehicles located within a certain geographical area. However, the conventional broadcast mechanism may lead to the so-called broadcast storm problem, a scenario in which there is a high level of contention and collision at the link layer due to an excessive number of broadcast packets. To solve broadcast storm problem, we propose an RPB-MACn-based LV-CAST that is a vehicular broadcast algorithm for disseminating safety-related emergency message. The proposed LV-CAST identifies the last node within transmission range by computing the distance extending on 1 hop from the sending node of an emergency message to the next node of receiving node of the emergency message, and the last node only re-broadcasts the emergency message. The performance of LV-CAST is evaluated through simulation and compared with other message dissemination algorithms.

• Journal for History of Mathematics
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• v.24 no.3
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• pp.37-58
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• 2011

The development of recent Mathematical Logic is mostly originated in Hilbert's Proof Theory. The purpose of the plan so called Hilbert's Program lies in the formalization of mathematics by formal axiomatic method, rescuing classical mathematics by means of verifying completeness and consistency of the formal system and solidifying the foundations of mathematics. In 1931, the completeness encounters crisis by the existence of undecidable proposition through the 1st Theorem of G?del, and the establishment of consistency faces a risk of invalidation by the 2nd Theorem. However, relative of partial realization of Hilbert's Program still exists as a fruitful research program. We have tried to bring into relief through Curry-Howard Correspondence the fact that Hilbert's program serves as source of power for the growth of mathematical constructivism today. That proof in natural deduction is in truth equivalent to computer program has allowed the formalization of mathematics to be seen in new light. In other words, Hilbert's program conforms best to the concept of algorithm, the central idea in computer science.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.2
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• pp.116-127
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• 2002

A new approach to improve the speaker adaptation algorithm by means of the variable number of observation density functions for CDHMM speech recognizer has been proposed. The proposed method uses the observation density function with more than one mixture in each state to represent speech characteristics in detail. The number of mixtures in each state is determined by the number of frames and the determinant of the variance, respectively. The each MAP Parameter is extracted in every mixture determined by these two methods. In addition, the state segmentation method requiring speaker adaptation can segment the adapting speech more Precisely by using speaker-independent model trained from sufficient database as a priori knowledge. And the state duration distribution is used lot adapting the speech duration information owing to speaker's utterance habit and speed. The recognition rate of the proposed methods are significantly higher than that of the conventional method using one mixture in each state.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2005.10a
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• pp.75-80
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• 2005

Because there are only a limited number of means of action that are available for the master to pursue in the event of flooding, onboard decision support system has been required The majority of systems activated during a flooding emergency (such as watertight and semi-watertight doors, bulkhead valves, dewatering pumps etc.) almost exclusively aim to restore a sufficiently high level of subdivision to prevent flooding from spreading through the ship. Even though assuming the flooding scenario is not catastrophic, the use of ballast tanks can be an additional and very effective tool to ensure both prevention of flooding spreading and also improve ship stability. This paper describes an optimization algorithm devised to choose the set of ballast tanks that should be filled in order to achieve an optimal response to a flooding accident.

• Journal of IKEEE
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• v.23 no.3
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• pp.910-916
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• 2019

Non-local similarity of natural images is one of highly exploited features in various applications dealing with images. Unique edges, texture, and pattern of the images are frequently repeated over the entire image. Once the similar image blocks are classified into a cluster, representative features of the image blocks can be extracted from the cluster. The bigger the size of the cluster is the better the additive white noise can be separated. Denoising is one of major research topics in the image processing field suppressing the additive noise. In this paper, a denoising algorithm is proposed which first clusters the noisy image blocks based on similarity, extracts the feature of the cluster, and finally recovers the original image. Performance experiments with several images under various noise strengths show that the proposed algorithm recovers the details of the image such as edges, texture, and patterns while outperforming the previous methods in terms of PSNR in removing the additive Gaussian noise.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.195-207
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• 2022

COVID-19 has remained one of the most serious health crises in recent history, resulting in the tragic loss of lives and significant economic impacts on the entire world. The difficulty of controlling COVID-19 poses a threat to the global health sector. Considering that Artificial Intelligence (AI) has contributed to improving research methods and solving problems facing diverse fields of study, AI algorithms have also proven effective in disease detection and early diagnosis. Specifically, acoustic features offer a promising prospect for the early detection of respiratory diseases. Motivated by these observations, this study conceptualized a speech-based diagnostic model to aid in COVID-19 diagnosis. The proposed methodology uses speech signals from confirmed positive and negative cases of COVID-19 to extract features through the pre-trained Visual Geometry Group (VGG-16) model based on Mel spectrogram images. This is used in addition to the K-means algorithm that determines effective features, followed by a Genetic Algorithm-Support Vector Machine (GA-SVM) classifier to classify cases. The experimental findings indicate the proposed methodology's capability to classify COVID-19 and NOT COVID-19 of varying ages and speaking different languages, as demonstrated in the simulations. The proposed methodology depends on deep features, followed by the dimension reduction technique for features to detect COVID-19. As a result, it produces better and more consistent performance than handcrafted features used in previous studies.