• Smart Media Journal
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• v.8 no.2
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• pp.58-65
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• 2019

Automatic text summarization is a process of shortening a text document by either extraction or abstraction. The abstraction approach inspired by deep learning methods scaling to a large amount of document is applied in recent work. Abstractive text summarization involves utilizing pre-generated word embedding information. Low-frequent but salient words such as terminologies are seldom included to dictionaries, that are so called, out-of-vocabulary(OOV) problems. OOV deteriorates the performance of Encoder-Decoder model in neural network. In order to address OOV words in abstractive text summarization, we propose a copy mechanism to facilitate copying new words in the target document and generating summary sentences. Different from the previous studies, the proposed approach combines accurate pointing information and selective copy mechanism based on bidirectional RNN and bidirectional LSTM. In addition, neural network gate model to estimate the generation probability and the loss function to optimize the entire abstraction model has been applied. The dataset has been constructed from the collection of abstractions and titles of journal articles. Experimental results demonstrate that both ROUGE-1 (based on word recall) and ROUGE-L (employed longest common subsequence) of the proposed Encoding-Decoding model have been improved to 47.01 and 29.55, respectively.

• Proceedings of the ESK Conference
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• 1998.04a
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• pp.65-69
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• 1998

기존에 사용자 인터페이스 디자인 개발시 다양한 사용자 니즈들이 수집되고 이러한 정보들의 효과적 활용을 위하여 여러 가지 분석방법들이 활용, 개발되고 있다. 그중 Video Ethnography는 특정 시스템 상에서 나타나는 사용자의 행동을 중심으로 한 환경내의 변화를 비디오를 이용해 저장하고 저장된 상황 의 변화요인을 해석 체계에 의거해 분석하는 기법이다. 이는 기존의 다른 방법에 비해 사용자의 자연스 러운 작업의 수행을 분석하는 데 초점을 맞추고 있기 때문에 실험실에서 행해지는 계획된 실험에서 얻 을 수 없는 시스템에 관한 발견점을 찾아낼 수 있다는 장점이 있다. 하지만 현재 Video Ethnography를 수행하는 과정에서 수집된 사용성 정보들이 총체적인 관점에서 체계적으로 관리, 분석되지 못하고 있고, 관리 시스템의 부재로 인하여 비디오 데이터를 분석, 관리하는데 필요이상의 시간과 노력이 필요한 실정 이다. 본 연구에서는 이러한 어려움을 해결하고자 Video Ethnography를 통해 얻어진 사용자 니즈를 체 계적으로 수집,해석, 관리하는 효과적인 도구의 개발에 중점을 두었다. 특정한 사용상황에 맞추어 수집 된 다양한 형태의 사용자 니즈들은 먼저 컴푸터를 통해 입력되고 입력된 데이터는 과업의 목적, 주변상황, 시스템과 사용자와의 상호작용 등 다양한 변수에 의해 분석된다. 이러한 분석의 과정을 통해특정 시스템 에 대한 사용자의 니즈가 도출되고 새로운 디자인 해결안이 제시될 수 있는 것이다. 이러한 일련의 과정 은 사용자 니즈 데이터베이스로 구축되며 추후 제품 개발의 근거로서 활용될 수 있다. 앞으로 다양한 사 용환경에 대한 사용자 니즈 데이터베이스가 확충되면 각 사용상황하의 사용성 문제 해결안뿐만 아니라 서로 관련이 있는 사용상황간의 연계 연구를 통해 좀 더 광범위한 개념의 제품 개발도 가능해 질 수 있을 것이다.와 만족도와의 관계 및 이상형에 대해 구체적으로 파악할 필요가 있다. 또한, 신체에 대한 이상형은 시대의 여러 여건에 따라서 변화할 수 있으므로 의복 착용자가 의복을 통해서 표현하고자 하는 이상형의 시대적 변화를 살펴볼 필요가 있다. 따라서 본 연구에서는 신체에 대한 인식도 및 만족도, 이상형에 대한 설문지 조사와 신체측정을 통하여 신체 크기에대한 만족도를 객관적인 척도로 고찰하고, 이상형과 실제 체형에 관하여 고찰하고자 한다. 도한, 1992년도 자료와의 비교를 통하여 시대에 따른 신체만족도와 이상형의 변화를 파악하고자 한다. 이를 기초로 한 의복원형 제작 및 의복 디자인에 대한 연구를 통해 의복의 맞음새가 좋을뿐만 아니라 의복착용자들 에게 심리적 만족을 줄 수 있는 의복 제작에 도움이 될 수 있을 것이다.적입지로 분석되었다.등 다양한 모형들을 고려해 본 뒤, 적절한 모형을 적용할 것이다. 가로망 설계 모형에서 신호제어를 고려하기 위해서는 주어진 가로망에 대한 통행 배정과정에서 고려되는 통행시간을 링크통행시간과 교차로 지체시간을 동시에 고려해야 하는데, 이러한 문제의 해결을 위해서 최근 활발히 논의되고 있는 교차로에서의 신호제어에 대응하는 통행배정 모형을 도입하여 고려하고자 한다. 이를 위해서 지금까지 연구되어온 Global Solution Approach와 Iterative Approach를 비교, 검토한 뒤 모형에 보다 알맞은 방법을 선택한다. 차량의 교차로 통행을 고려하는 performance function의 경우 비신호 교차로와 신호교차로에 대한 적절한 비교가 현재로서는 고려되고 있지 못하기 때문에, 구성되는 가로망의 경우 신호교차로들로만 구성되며, 부득이한 경우 입체교차의 형태로 구성되는 것으로 가정한다. 실제 가로망의 경우, 교통향이 많은 도시부의 경우 주가로망은 대부분 신호교차로와 입체교차로 구성되기 때문에

• Journal of KIISE:Software and Applications
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• v.31 no.3
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• pp.379-386
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• 2004

Video segmentation is a fundamental task in video indexing and it includes two kinds of shot change detections such as the abrupt transition and the gradual transition. The abrupt shot boundaries are detected by computing the image-based distance between adjacent frames and comparing this distance with a pre-determined threshold value. However, the gradual shot boundaries are difficult to detect with this approach. To overcome this difficulty, we propose the method that detects gradual transition in the MPEG compressed video using the HMM (Hidden Markov Model). We take two different HMMs such as a discrete HMM and a continuous HMM with a Gaussian mixture model. As image features for HMM's observations, we use two distinct features such as the difference of histogram of DC images between two adjacent frames and the difference of each individual macroblock's deviations at the corresponding macroblock's between two adjacent frames, where deviation means an arithmetic difference of each macroblock's DC value from the mean of DC values in the given frame. Furthermore, we obtain the DC sequences of P and B frame by the first order approximation for a fast and effective computation. Experiment results show that we obtain the best detection and classification performance of gradual transitions when a continuous HMM with one Gaussian model is taken and two image features are used together.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.121-131
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• 2014

Traffic speed is an important measure in transportation. It can be employed for various purposes, including traffic congestion detection, travel time estimation, and road design. Consequently, accurate speed prediction is essential in the development of intelligent transportation systems. In this paper, we present an analysis and speed prediction of a certain road section in Busan, South Korea. In previous works, only historical data of the target link are used for prediction. Here, we extract features from real traffic data by considering the neighboring links. After obtaining the candidate features, linear regression, model tree, and k-nearest neighbor (k-NN) are employed for both feature selection and speed prediction. The experiment results show that k-NN outperforms model tree and linear regression for the given dataset. Compared to the other predictors, k-NN significantly reduces the error measures that we use, including mean absolute percentage error (MAPE) and root mean square error (RMSE).

• Journal of KIISE:Databases
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• v.33 no.2
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• pp.201-213
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• 2006

A time-series database is a set of time-series data sequences, each of which is a list of changing values of the object in a given period of time. Subsequence matching is an operation that searches for such data subsequences whose changing patterns are similar to a query sequence from a time-series database. This paper addresses a performance issue of time-series subsequence matching. First, we quantitatively examine the performance degradation caused by the window size effect, and then show that the performance of subsequence matching with a single index is not satisfactory in real applications. We argue that index interpolation is fairly useful to resolve this problem. The index interpolation performs subsequence matching by selecting the most appropriate one from multiple indexes built on windows of their inherent sizes. For index interpolation, we first decide the sites of windows for multiple indexes to be built. In this paper, we solve the problem of selecting optimal window sizes in the perspective of physical database design. For this, given a set of query sequences to be peformed in a target time-series database and a set of window sizes for building multiple indexes, we devise a formula that estimates the cost of all the subsequence matchings. Based on this formula, we propose an algorithm that determines the optimal window sizes for maximizing the performance of entire subsequence matchings. We formally Prove the optimality as well as the effectiveness of the algorithm. Finally, we perform a series of extensive experiments with a real-life stock data set and a large volume of a synthetic data set. The results reveal that the proposed approach improves the previous one by 1.5 to 7.8 times.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1517-1524
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• 2022

In this paper, we consider a heterogeneous network (HetNet) consisting of one macro base station and multiple small base stations, and assume the coordinated multi-point transmission between the base stations. In addition, we assume that the channel between the base station and the user consists of path loss and Rayleigh fading. Under these assumptions, we present the energy efficiency (EE) achievable by the user for a given base station and we formulate an optimization problem of dynamic cell selection and transmit power allocation to maximize the total EE of the HetNet. In this paper, we propose an unsupervised deep learning method to solve the optimization problem. The proposed deep learning-based scheme can provide high EE while having low complexity compared to the conventional iterative convergence methods. Through the simulation, we show that the proposed dynamic cell selection scheme provides higher EE performance than the maximum signal-to-interference-plus-noise ratio scheme and the Lagrangian dual decomposition scheme, and the proposed transmit power allocation scheme provides the similar performance to the trust region interior point method which can achieve the maximum EE.

• Korean Journal of Forensic Psychology
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• v.11 no.3
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• pp.329-347
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• 2020

When a person suggests an estimate under uncertainty, (s)he tend to rely on the information and number provided in advance. As a result, their final estimate would be assimilated to the initial value. This phenomenon is called "anchoring effect". The present research examined anchoring effects observed in law courts. Sentencing decision of jurors can be influenced by the sentence demanded by the prosecutor. Specifically, this study demonstrated the condition in which anchoring effect would be stronger and practical solutions for lowering anchoring effect. Study 1 demonstrated whether gravity of criminal cases and levels of anchor influenced anchoring effects. As expected, anchoring effect was stronger in a heavier criminal case than in a lighter one. When a low anchor was provided in a lighter case, anchoring effect was stronger compared to when a high anchor was provided. Study 2 examined how emotion affects anchoring effects. The results showed that anchoring effect appeared to be significantly stronger with feelings of anger than of sadness. Study 3 examined the solution for reducing anchoring effects in a court. When activation of selective-accessibility model was prevented, anchoring effects significantly decreased. These results can help solve the problems about juror judgmental bias and contribute to the development of Korean jury trial.

• Journal of the Korean Data and Information Science Society
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• v.23 no.5
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• pp.949-959
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• 2012

This article is to find the right size of decision trees that performs better for boosting algorithm. First we defined the tree size D as the depth of a decision tree. Then we compared the performance of boosting algorithm with different tree sizes in the experiment. Although it is an usual practice to set the tree size in boosting algorithm to be small, we figured out that the choice of D has a significant influence on the performance of boosting algorithm. Furthermore, we found out that the tree size D need to be sufficiently large for some dataset. The experiment result shows that there exists an optimal D for each dataset and choosing the right size D is important in improving the performance of boosting. We also tried to find the model for estimating the right size D suitable for boosting algorithm, using variables that can explain the nature of a given dataset. The suggested model reveals that the optimal tree size D for a given dataset can be estimated by the error rate of stump tree, the number of classes, the depth of a single tree, and the gini impurity.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.281-286
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• 2003

A large number of features are collected for problem solving in real life, but to utilize ail the features collected would be difficult. It is not so easy to collect of correct data about all features. In case it takes advantage of all collected data to learn, complicated learning model is created and good performance result can't get. Also exist interrelationships or hierarchical relations among the features. We can reduce feature's number analyzing relation among the features using heuristic knowledge or statistical method. Heuristic technique refers to learning through repetitive trial and errors and experience. Experts can approach to relevant problem domain through opinion collection process by experience. These properties can be utilized to reduce the number of feature used in learning. Experts generate a new feature (highly abstract) using raw data. This paper describes machine learning model that reduce the number of features used in learning using heuristic function and use abstracted feature by neural network's input value. We have applied this model to the win/lose prediction in pro-baseball games. The result shows the model mixing two techniques not only reduces the complexity of the neural network model but also significantly improves the classification accuracy than when neural network and heuristic model are used separately.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.131-146
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• 2011

It is becoming more essential than ever for office professionals to become competent in information collection/gathering and problem solving in today's global business society. In particular, office professionals do not only assist simple chores but are also forced to make decisions as quickly and efficiently as possible in problematic situations that can end in either profit or loss to their company. Since office professionals rely heavily on their tacit knowledge to solve problems that arise in everyday business situations, it is truly helpful and efficient to refer to similar business cases from the past and share or reuse such previous business knowledge for better performance results. Case-based reasoning(CBR) is a problem-solving method which utilizes previous similar cases to solve problems. Through CBR, the closest case to the current business situation can be searched and retrieved from the case or knowledge base and can be referred to for a new solution. This reduces the time and resources needed and increase success probability. The main purpose of this study is to design a system called COKRS(Case-based reasoning Office Knowledge Recommender System) and develop a prototype for it. COKRS manages cases and their meta data, accepts key words from the user and searches the casebase for the most similar past case to the input keyword, and communicates with users to collect information about the quality of the case provided and continuously apply the information to update values on the similarity table. Core concepts like system architecture, definition of a case, meta database, similarity table have been introduced, and also an algorithm to retrieve all similar cases from past work history has also been proposed. In this research, a case is best defined as a work experience in office administration. However, defining a case in office administration was not an easy task in reality. We surveyed 10 office professionals in order to get an idea of how to define a case in office administration and found out that in most cases any type of office work is to be recorded digitally and/or non-digitally. Therefore, we have defined a record or document case as for COKRS. Similarity table was composed of items of the result of job analysis for office professionals conducted in a previous research. Values between items of the similarity table were initially set to those from researchers' experiences and literature review. The results of this study could also be utilized in other areas of business for knowledge sharing wherever it is necessary and beneficial to share and learn from past experiences. We expect this research to be a reference for researchers and developers who are in this area or interested in office knowledge recommendation system based on CBR. Focus group interview(FGI) was conducted with ten administrative assistants carefully selected from various areas of business. They were given a chance to try out COKRS in an actual work setting and make some suggestions for future improvement. FGI has identified the user-interface for saving and searching cases for keywords as the most positive aspect of COKRS, and has identified the most urgently needed improvement as transforming tacit knowledge and knowhow into recorded documents more efficiently. Also, the focus group has mentioned that it is essential to secure enough support, encouragement, and reward from the company and promote positive attitude and atmosphere for knowledge sharing for everybody's benefit in the company.