• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.263-271
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• 2016

Software requirement analysis is necessary for successful software development project. Specially, incomplete requirement is the most influential causes of software project failure. Incomplete requirement can bring late delay and over budget because of the misunderstanding and ambiguous criteria for project validation. Software requirement patterns can help writing more complete requirement. These can be a reference model and standards when author writing or validating software requirement. Furthermore, when a novice writes the software scenario, the requirement patterns can be one of the guideline. In this paper proposes an automatic approach to identifying software scenario patterns from various software scenarios. In this paper, we gathered 83 scenarios from eight industrial systems, and show how to extract 54 scenario patterns and how to find omitted action of the scenario using extracted patterns for the feasibility of the approach.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.4
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• pp.42-53
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• 2012

In pattern recognition and machine learning society, classification has been a classical problem and the most widely researched area. Adaptive boosting also known as Adaboost has been successfully applied to binary classification problems. It is a kind of boosting algorithm capable of constructing a strong classifier through a weighted combination of weak classifiers. On the other hand, the PCA and LDA algorithms are the most popular linear feature extraction methods used mainly for dimensionality reduction. In this paper, the combination of Adaboost and feature extraction methods is proposed for efficient classification of two class data. Conventionally, in classification problems, the roles of feature extraction and classification have been distinct, i.e., a feature extraction method and a classifier are applied sequentially to classify input variable into several categories. In this paper, these two steps are combined into one resulting in a good classification performance. More specifically, each projection vector is treated as a weak classifier in Adaboost algorithm to constitute a strong classifier for binary classification problems. The proposed algorithm is applied to UCI dataset and FRGC dataset and showed better recognition rates than sequential application of feature extraction and classification methods.

• Journal of KIISE:Software and Applications
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• v.37 no.12
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• pp.928-935
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• 2010

Identifying the referents of omitted elements in a text is an important task to many natural language processing applications such as machine translation, information extraction and so on. These omitted elements are often called zero anaphors or zero pronouns, and are regarded as one of the most common forms of reference. However, since all zero elements do not refer to explicit objects which occur in the same text, recent work on zero anaphora resolution have attempted to identify the anaphoricity of zero pronouns. This paper focuses on intra-sentential anaphoricity determination of subject zero pronouns that frequently occur in Korean. Unlike previous studies on pair-wise comparisons, this study attempts to determine the intra-sentential anaphoricity of zero pronouns by learning directly the structure of clauses in which either non-anaphoric or inter-sentential subject zero pronouns occur. The proposed method outperforms baseline methods, and anaphoricity determination of zero pronouns will play an important role in resolving zero anaphora.

• Korean Journal of Adult Nursing
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• v.27 no.5
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• pp.493-504
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• 2015

Purpose: This study was a qualitative study to explore and understand the adaptation experiences of hemodialysis among women with End-Stage Renal Disease (ESRD) and to develop a substantive theory using the grounded theory method. Methods: Participants were 15 female patients who underwent hemodialysis for ESRD treatment from three general hospitals. The data were collected through in-depth individual interviews. Results: The adaptation experience of participants was emerged as a process of taking care and enduring. There were four adaptation stages as a negative, despair, receptive, and maintenance period in reference to hemodialysis. The causal conditions were a vague expectations of recovery and refusal to undergo hemodialysis. The core phenomenon was that of confinement to dialysis machine. The contextual conditions for this phenomenon were the loss of femininity. They used action/interaction strategies such as transition their life with a focus on hemodialysis, seeking information, and learning how to take care of their body. Through this process, they had a strong will to live or had sustained their life. Conclusion: These results indicate that there is a need for nurses to understand the different steps of adaptation experiences of the given patient population. It is necessary for nurses to support them to lead their life as much normal as possible and improve the adaptation experience of ESRD.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.9
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• pp.615-620
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• 2013

With the development of smartphones, the number of applications for smartphone increases sharply. As a result, users need to try several times to find their favorite apps. In order to solve this problem, we propose a recommendation system to provide an appropriate app list based on the user's log information including time stamp, location, application list, and so on. The proposed approach learns three recommendation models including Naive-Bayesian model, SVM model, and Most-Frequent Usage model using temporal and spatial attributes. In order to figure out the best model, we compared the performance of these models with variant features, and suggest an hybrid method to improve the performance of single models.

• Korean Journal of Remote Sensing
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• v.36 no.1
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• pp.15-27
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• 2020

Accurate knowledge of land use/land cover (LULC) features and their relative changes over upon the time are essential for sustainable urban management. Urban sprawl growth has been always also a worldwide concern that needs to carefully monitor particularly in a developing country where unplanned building constriction has been expanding at a high rate. Recently, remotely sensed imageries with a very high spatial/spectral resolution and state of the art machine learning approaches sent the urban classification and growth monitoring to a higher level. In this research, we classified the Quickbird satellite imagery by object-based image analysis of Dempster-Shafer (OBIA-DS) for the years of 2002 and 2015 at Karbala-Iraq. The real LULC changes including, residential sprawl expansion, amongst these years, were identified via change detection procedure. In accordance with extracted features of LULC and detected trend of urban pattern, the future LULC dynamic was simulated by using land transformation model (LTM) in geospatial information system (GIS) platform. Both classification and prediction stages were successfully validated using ground control points (GCPs) through accuracy assessment metric of Kappa coefficient that indicated 0.87 and 0.91 for 2002 and 2015 classification as well as 0.79 for prediction part. Detail results revealed a substantial growth in building over fifteen years that mostly replaced by agriculture and orchard field. The prediction scenario of LULC sprawl development for 2030 revealed a substantial decline in green and agriculture land as well as an extensive increment in build-up area especially at the countryside of the city without following the residential pattern standard. The proposed method helps urban decision-makers to identify the detail temporal-spatial growth pattern of highly populated cities like Karbala. Additionally, the results of this study can be considered as a probable future map in order to design enough future social services and amenities for the local inhabitants.

• Proceedings of the Safety Management and Science Conference
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• 2009.11a
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• pp.15-24
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• 2009

Cycling that transform human energy into mechanical energy is one of the man-machine systems out of sports fields. Benchmarking means " improving ourselves by learning from others ", therefore benchmarking toward dominant cyclist is necessary on field. the goals of this study were to provide important factors on multi-disciplines (kinematics, physiology, power, psychology) for a tailored-training program that is suitable to individual characteristics. Two cyclist participated in this study and gave consent to the experimental procedure. one was dominant cyclist (years:21 yrs, height:177 cm, mass:70 kg), and the other was non-dominant cyclist(years:21, height:176, mass:70). Kinematic data were recorded using six infrared cameras (240Hz) and QTM (software). Physiological data (VO2max, AT) were acquired according to graded exercising test with cycle ergometer and power with Wingate test used by Bar-Or et. al ( 1977) and to evaluate muscle function with Cybex. Psychological data were collected with competitive state anxiety inventory (CSAI-2) that were devised by Martens et. al (1990) and with athletes' self-management questionnaire (ASMQ) of Huh (2003). It appears that the dominant's CV(coefficient of variability) was higher than non-dominant's CV in Sports Biomechanics domain, that the dominant's values for all factors ware higher than non-dominant's values in physical, and physiological domain, and their values between cognitive anxiety and somatic anxiety were contrary to each other in psychology. Further research on multi-disciplines may lead to the development of tailored-optimal training programs applicable with key factors to enhance athletic performance by means of research including athlete, coach and parents.

• The Journal of Society for e-Business Studies
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• v.17 no.2
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• pp.149-163
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• 2012

Logistics systems provide their service to customers by coordinating the resources with limited capacity throughout the underlying processes involved to each other. To maintain the high level of service under such complicated condition, it is essential to carry out the real-time monitoring and continuous management of logistics processes. In this study, we propose a method of estimating the service completion time of key processes based on process-state information collected in real time. We first identify the factors that influence the process completion time by modeling and analyzing an influence diagram, and then suggest algorithms for quantifying the factors. We suppose the container terminal logistics and the process of discharging and loading containers to a vessel. The remaining service time of a vessel is estimated using a decision tree which is the result of machine-learning using historical data. We validated the estimation model using container terminal simulation. The proposed model is expected to improve competitiveness of logistics systems by forecasting service completion in real time, as well as to prevent the waste of resources.

• KIISE Transactions on Computing Practices
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• v.21 no.1
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• pp.94-99
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• 2015

With the recent machine learning paradigm of using nonparametric Bayesian statistics and statistical inference based on random sampling, the Dirichlet distribution finds many uses in a variety of graphical models. It is a multivariate generalization of the gamma distribution and is defined on a continuous (K-1)-simplex. This paper presents a sampling method for a Dirichlet distribution for the problem of dividing an integer X into a sequence of K integers which sum to X. The target samples in our problem are all positive integer vectors when multiplied by a given X. They must be sampled from the correspondingly gridded simplex. In this paper we develop a Markov Chain Monte Carlo (MCMC) proposal distribution for the neighborhood grid points on the simplex and then present the complete algorithm based on the Metropolis-Hastings algorithm. The proposed algorithm can be used for the Markov model, HMM, and Semi-Markov model for accurate state-duration modeling. It can also be used for the Gamma-Dirichlet HMM to model q the global-local duration distributions.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.12
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• pp.123-129
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• 2014

In accordance with the development of various convergence devices, cameras are being used in many types of the systems such as security system, driver assistance device and so on, and a lot of people are exposed to these system. Therefore the system should be able to recognize the human behavior and support some useful functions with the information that is obtained from detected human behavior. In this paper we use a machine learning approach based on 2D image and propose the human behavior pattern recognition methods. The proposed methods can provide valuable information to support some useful function to user based on the recognized human behavior. First proposed one is "phone call behavior" recognition. If a camera of the black box, which is focused on driver in a car, recognize phone call pose, it can give a warning to driver for safe driving. The second one is "looking ahead" recognition for driving safety where we propose the decision rule and method to decide whether the driver is looking ahead or not. This paper also shows usefulness of proposed recognition methods with some experiment results in real time.