• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.1
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• pp.229-240
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• 2012

Advances in MEMS and CMOS technologies have motivated the development of low cost/power sensors and wireless multimedia sensor networks (WMSN). The WMSNs were created to ubiquitously harvest multimedia content. Such networks have allowed researchers and engineers to glimpse at new Machine-to-Machine (M2M) Systems, such as remote monitoring of biosignals for telemedicine networks. These systems require the acquisition of a large number of data streams that are simultaneously generated by multiple distributed devices. This paradigm of data generation and transmission is known as event-streaming. In order to be useful to the application, the collected data requires a preprocessing called data fusion, which entails the temporal alignment task of multimedia data. A practical way to perform this task is in a centralized manner, assuming that the network nodes only function as collector entities. However, by following this scheme, a considerable amount of redundant information is transmitted to the central entity. To decrease such redundancy, data fusion must be performed in a collaborative way. In this paper, we propose a collaborative data alignment approach for event-streaming. Our approach identifies temporal relationships by translating temporal dependencies based on a timeline to causal dependencies of the media involved.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.3
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• pp.203-215
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• 2022

Bayesian Networks are multivariate probabilistic factor graphs that are used to assess underlying factor relationships. From January 2005 to December 2018, the study examines how Dynamic Bayesian Networks can be utilized to estimate portfolio risk and return as well as determine inter-factor relationships among reversal profit-generating components in Pakistan's emerging market (PSX). The goal of this article is to uncover the factors that cause reversal profits in the Pakistani stock market. In visual form, Bayesian networks can generate causal and inferential probabilistic relationships. Investors might update their stock return values in the network simultaneously with fresh market information, resulting in a dynamic shift in portfolio risk distribution across the networks. The findings show that investments in low net profit margin, low investment, and high volatility-based designed portfolios yield the biggest dynamical reversal profits. The main triggering aspects related to generation reversal profits in the Pakistan market, in the long run, are net profit margin, market risk premium, investment, size, and volatility factor. Investors should invest in and build portfolios with small companies that have a low price-to-earnings ratio, small earnings per share, and minimal volatility, according to the most likely explanation.

• Korean Journal of Social Welfare
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• v.62 no.4
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• pp.223-248
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• 2010

This study is to explore the contents and interaction of residents voluntary network and propose the strategies to promote residents voluntary network. The grounded theory was utilized to attain our object. Total of seven social worker and 17 residents participated in the study. Data were collected through in-depth interviews and documents. The data were analyzed by using Strauss and Corbin's method. Results are the followings. In open coding 13 categories, 32 subcategories and 133 concepts were constructed. In axial coding causal conditions were qualitative ascent of needs, emergence of the right welfare consumer. Phenomenon was agitation of praxis ground and grope of exist. Contextual conditions were crisis resources, skepticism of welfare. Intervention conditions were maturation of welfare cognition and proliferation of the sense of community responsibility. Strategy were resocialization of voluntary organization and construction of field related service delivery system. Consequence were grass routing welfare strategic fitting service system. In selective coding we constructed the core category: The praxis revolution from bottom for break social welfare environment.

• Journal of Korean Academy of Nursing
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• v.28 no.2
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• pp.510-518
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• 1998

It was assumed that the maternal identity in primi-gravida is one of the most attribute of the motherhood, that is not biological but cognitive phenomena, appears active process as intelligent human being. The purposes of this study were that the identification the cognitive structure and the influencing factors of the maternal identity in primi-gravida. Theoretical framework in this study, maternal identity in primi-gravida was constructed as a cognitive output, has the cognitive structure of cognitive-perceptual factor, cognitive-behavioral factor, and cognitive-emotional factor. Influencing factors of maternal identity was constructed as a cognitive input, which were pregnancy related perceptions (pregnancy intention, minor discomfort, value of motherhood), interpersonal relationship(relationship with mother, relationship with husband, relationship with social network), preparation to motherhood(maternal knowledge, antenatal self care), and biological factor (gestation period). This study was the descriptive correlational research design, was done from the 3rd January to the 15th March 1996, and the research subjects were selected conviniently 226 the primi-gravida during the gestation period, data collection method was self reported questionnaire cross-sectionally. Descriptive data analysis was done by SAS PC$^{+}$, testing the hypothetical model was done by covariance structural analysis using LISREL 8.03 program. The result of the hypothesis testing, the value of motherhood(y=.650, T=4.26) the maternal knowledge (y=.137, T=2.030), the gestation period( y=.113, T=2.621), showed significant causal effect on the maternal identity in primi-gravida. In conclusion, the maternal identity in primi-gravida had interrelated cognitive structure consist of perceptual, behavioral, and emotional factors. Significant causal factors influencing the maternal identity were value identified. It seems to contribute toward the understanding the characteristics of the maternal identity as a cognitive domains that has been regarded highly abstract concept, so has not been validated empirically.y.

• The Journal of the Korea Contents Association
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• v.10 no.4
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• pp.306-316
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• 2010

The purpose of study is to promote the development of contents industry by analysing the causal relationship between R&D collaboration and firms' performance. In study, we use the number of intellectual property and total sales as proxy variables of performance. we use the degrees of collaboration experience, firms' interaction and degree of collaboration as proxy variables of independent variables. The results of study are as follows. First, collaboration experience and firms' interaction are positively influence on technological performance. Second, collaboration experience is positively correlated to economic performance. Finally, firms' R&D collaboration revealed higher performance than R&D collaboration of firm and R&D institution This study emphases on the importances of R&D collaboration for developing new technology and improving economic performance.

• Journal of Information Technology Applications and Management
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• v.21 no.4
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• pp.247-261
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• 2014

Trust and perceived usefulness have been variables frequently used by IS researchers to explain consumers' online transaction intention. A group of prior studies have incorporated the two variables simultaneously to build better models to capture the phenomenon. However, the studies have largely ignored the role of trust by replacing trust with trustworthiness. The purpose of this study is to fill this gap. To do this, the study conceptualizes and tests relationships between trust, perceived usefulness and related variables. Based on the study results, the study discusses that trust and trustworthiness are distinct and have causal relationships, the dimensions (ability, integrity, and benevolence) of trustworthiness are related but theoretically distinct, and trust performs a mediating role within its nomological network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1796-1816
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• 2020

The health and safety of elderly and disabled patients who cannot live alone is an important issue. Timely detection of sudden events is necessary to protect these people, and anomaly detection in smart homes is an efficient approach to extracting such information. In the real world, there is a causal relationship between an occupant's behaviour and the order in which appliances are used in the home. Bayesian networks are appropriate tools for assessing the probability of an effect due to the occurrence of its causes, and vice versa. This paper defines different subsets of random variables on the basis of sensory data from a smart home, and it presents an anomaly detection system based on various models of Bayesian networks and drawing upon these variables. We examine different models to obtain the best network, one that has higher assessment scores and a smaller size. Experimental evaluations of real datasets show the effectiveness of the proposed method.

• Communications for Statistical Applications and Methods
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• v.8 no.1
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• pp.193-207
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• 2001

When designing a test set, we need to consider constraints on items that are deemed important by item developers or test specialists. The constraints are essentially on the components of the test domain or abilities relevant to a given test set. And so if the test domain could be represented in a more refined form, test construction would be made in a more efficient way. We assume that relationships among task abilities are representable by a causal model and that the item response theory (IRT) is not fully available for them. In such a case we can not apply traditional item selection methods that are based on the IRT. In this paper, we use entropy as an uncertainty measure for making inferences on task abilities and developed an optimal item selection algorithm which reduces most the entropy of task abilities when items are selected from an item pool.

• Proceedings of the Korea Information Processing Society Conference
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• 2001.10a
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• pp.267-270
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• 2001

본 논문에서는 이동통신망에서의 효율적인 인과순서 알고리즘을 채널전환 알고리즘과 함께 제안한다. 인과순서를 유지하기 위해서는 순서를 유지하기 위한 제어정보를 교환하여야 하며, 송수신 양측은 메시지를 보내거나 받은 전후에 각각의 제어정보를 수정하여야 한다. 특히 이동유닛이 다른 셀로 이동한 경우, 채널전환 프로토콜은 기존의 기지국과 새로운 기지국에서 동시에 제어정보 관리를 위한 작업을 수행하여야 하며, 기존의 기지국에 있는 이동 유닛의 제어정보는 새로운 기지국으로 전달되어야 한다. 따라서 제어정보의 크기가 채널전환시간과 메시지 지연시간에 직접적인 영향을 주기 때문에, 전송되는 제어정보의 양을 최소화하여야 한다. 본 논문에서는 제어정보의 양을 줄이기 위하여 유효한 통신패턴을 분석하여 중복으로 교환되는 제어정보가 최소화되는 인과순서 알고리즘을 제안하였으며, 모의실험을 통하여 제안된 알고리즘이 기존의 알고리즘에 비해 효율적임을 보였다.

• Journal of Electrical Engineering and Technology
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• v.9 no.3
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• pp.1051-1059
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• 2014

In this paper, we propose an intelligent situation recognition model by collecting and analyzing multiple sensor signals. Multiple sensor signals are collected for fixed time window. A training set of collected sensor data for each situation is provided to K2-learning algorithm to generate Bayesian networks representing causal relationship between sensors for the situation. Statistical characteristics of sensor values and topological characteristics of generated graphs are learned for each situation. A neural network is designed to classify the current situation based on the extracted features from collected multiple sensor values. The proposed method is implemented and tested with UCI machine learning repository data.