• Journal of KIBIM
• /
• v.8 no.3
• /
• pp.31-42
• /
• 2018

This study focuses on the BIM request for information(RFI) processing performance and quantitatively analyzes the performance of the BIM coordinator and the loss due to the waiting of the project participants. For these purposes, a method to quantitatively evaluate the performance of the BIM coordinator was proposed using a queueing model. For the verification, two projects in which BIM was applied in the construction phase were selected, and the BIM RFI data were collected through the analysis of the BIM monthly report and BIM coordinator work log of each project. In addition, the BIM input personnel, labor cost, and productivity data were collected through interviews with the experts of the case projects. The analysis of the BIM RFI processing performance of the BIM coordinator using the queueing model exhibited on a probabilistic basis that the waiting status of the project participants could vary depending on the preliminary BIM application to the design verification as well as the input number and level of the BIM coordinator personnel. In addition, the loss cost due to the waiting of the project participants was analyzed using the number of BIM RFIs waiting to be processed in the queueing system. Finally, the economic feasibility analysis for the optimal BIM coordinator input was performed considering the loss cost. The results of this study can be used to make decisions about the optimal BIM coordinator input and can provide grounds for the BIM return on investment (ROI) analysis considering the waiting cost of the project participants.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• v.9 no.1
• /
• pp.1145-1148
• /
• 2005

In this paper, we introduce a probabilistic model based on Bayesian networks (BNs) for inferring human fatigue by integrating information from various visual cues and certain relevant contextual information. Visual parameters, typically characterizing the cognitive states of a person including parameters related to eyelid movement, gaze, head movement, and facial expression, serve as the sensory observations. But, an individual visual cue or contextual Information does not provide enough information to determine human fatigue. Therefore in this paper, a Bayesian network model was developed to fuse as many as possible contextual and visual cue information for monitoring human fatigue. At the experiment results, display the utility of the proposed BNs for predicting and modeling fatigue.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.33 no.9A
• /
• pp.891-899
• /
• 2008

The cdma2000 1xEV-DO system controls the data rates of mobile terminals based on a binary overload indicator from the base station and a simple probabilistic model. However, this traffic control scheme has difficulty in controlling the reverse-link traffic load effectively and in guaranteeing a stable operation of the reverse link because each mobile terminal determines the next data rate autonomously. This paper proposes a new trafRc control scheme to improve the system stability, and analyzes the proposed scheme by modeling it as a discrete-time Markov process. The numerical results show that the maximum data rate of the proposed scheme is much higher than that of the conventional one. Moreover, the proposed scheme does not modify the standard physical channel structure, so it is compatible to the existing 1xEV-DO system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2010.05a
• /
• pp.411-413
• /
• 2010

The wireless AP positioning system is under active progress regarding research and commercialization due to its merit of being able to overcome the representing demerits of existing GPS positioning, which are signal distortion and poor signal reception. This system's feature is to collect AP information distributed throughout the real world, store it on database, and execute positioning by comparing with searched AP information. The positioning process uses collected data, whereas comparison of database data uses the fingerprinting method. The fingerprinting method is a probabilistic modeling method that acquires as much of the data collected from one location upon database composition, to store the value's average value and use it in positioning. Yet, using the average value may contain the probability of errors. Such errors are fatal weaknesses for services based on the background of accurate positioning. This paper deals with the characteristics and problems of the previously used wireless AP positioning system, and proposes measures of using AP information for outdoor positioning in order to solve the aforementioned problems.

• Journal of Information Processing Systems
• /
• v.16 no.3
• /
• pp.612-628
• /
• 2020

This paper presents a new approach for detecting abnormal behaviors in complex surveillance scenes where anomalies are subtle and difficult to distinguish due to the intricate correlations among multiple objects' behaviors. Specifically, a cascaded probabilistic topic model was put forward for learning the spatial context of local behavior and the temporal context of global behavior in two different stages. In the first stage of topic modeling, unlike the existing approaches using either optical flows or complete trajectories, spatio-temporal correlations between the trajectory fragments in video clips were modeled by the latent Dirichlet allocation (LDA) topic model based on Markov random fields to obtain the spatial context of local behavior in each video clip. The local behavior topic categories were then obtained by exploiting the spectral clustering algorithm. Based on the construction of a dictionary through the process of local behavior topic clustering, the second phase of the LDA topic model learns the correlations of global behaviors and temporal context. In particular, an abnormal behavior recognition method was developed based on the learned spatio-temporal context of behaviors. The specific identification method adopts a top-down strategy and consists of two stages: anomaly recognition of video clip and anomalous behavior recognition within each video clip. Evaluation was performed using the validity of spatio-temporal context learning for local behavior topics and abnormal behavior recognition. Furthermore, the performance of the proposed approach in abnormal behavior recognition improved effectively and significantly in complex surveillance scenes.

• Korean Journal of Remote Sensing
• /
• v.10 no.2
• /
• pp.37-48
• /
• 1994

In spatial information processing, particularly in non-renewable resource exploration, the spatial data sets, including remote sensing, geophysical and geochemical data, have to be geocoded onto a reference map and integrated for the final analysis and interpretation. Application of a computer based GIS(Geographical Information System of Geological Information System) at some point of the spatial data integration/fusion processing is now a logical and essential step. It should, however, be pointed out that the basic concepts of the GIS based spatial data fusion were developed with insufficient mathematical understanding of spatial characteristics or quantitative modeling framwork of the data. Furthermore many remote sensing and geological data sets, available for many exploration projects, are spatially incomplete in coverage and interduce spatially uneven information distribution. In addition, spectral information of many spatial data sets is often imprecise due to digital rescaling. Direct applications of GIS systems to spatial data fusion can therefore result in seriously erroneous final results. To resolve this problem, some of the important mathematical information representation techniques are briefly reviewed and discussed in this paper with condideration of spatial and spectral characteristics of the common remote sensing and exploration data. They include the basic probabilistic approach, the evidential belief function approach (Dempster-Shafer method) and the fuzzy logic approach. Even though the basic concepts of these three approaches are different, proper application of the techniques and careful interpretation of the final results are expected to yield acceptable conclusions in cach case. Actual tests with real data (Moon, 1990a; An etal., 1991, 1992, 1993) have shown that implementation and application of the methods discussed in this paper consistently provide more accurate final results than most direct applications of GIS techniques.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.32 no.3
• /
• pp.173-181
• /
• 2019

To estimate probabilistic distribution function from experimental data, kernel density estimation(KDE) is mostly used in cases when data is insufficient. The estimated distribution using KDE depends on bandwidth selectors that smoothen or overfit a kernel estimator to experimental data. In this study, various bandwidth selectors such as the Silverman's rule of thumb, rule using adaptive estimates, and oversmoothing rule, were compared for accuracy and conservativeness. For this, statistical simulations were carried out using assumed true models including unimodal and multimodal distributions, and, accuracies and conservativeness of estimating distribution functions were compared according to various data. In addition, it was verified how the estimated distributions using KDE with different bandwidth selectors affect reliability analysis results through simple reliability examples.

• Composites Research
• /
• v.34 no.1
• /
• pp.70-75
• /
• 2021

This paper presents a novel algorithm to reconstruct meso-scale representative volume elements (RVE), referring to experimentally observed features of Sheet Molding Compound (SMC) composites. Predicting anisotropic mechanical properties of SMC composites is challenging in the multiscale virtual test using finite element (FE) models. To this end, an SMC RVE modeler consisting of a series of image processing techniques, the novel reconstruction algorithm, and a FE mesh generator for the SMC composites are developed. First, micro-CT image processing is conducted to estimate probabilistic distributions of two critical features, such as fiber chip orientation and distribution that are highly related to mechanical performance. Second, a reconstruction algorithm for 3D fiber chip packing is developed in consideration of the overlapping effect between fiber chips. Third, the macro-scale behavior of the SMC is predicted by the multiscale analysis.

• Journal of KIISE:Software and Applications
• /
• v.35 no.12
• /
• pp.780-788
• /
• 2008

Modeling hand poses and tracking its movement are one of the challenging problems in computer vision. There are two typical approaches for the reconstruction of hand poses in 3D, depending on the number of cameras from which images are captured. One is to capture images from multiple cameras or a stereo camera. The other is to capture images from a single camera. The former approach is relatively limited, because of the environmental constraints for setting up multiple cameras. In this paper we propose a method of reconstructing 3D hand poses from a 2D input image sequence captured from a single camera by means of Belief Propagation in a graphical model and recognizing a finger clicking motion using a hidden Markov model. We define a graphical model with hidden nodes representing joints of a hand, and observable nodes with the features extracted from a 2D input image sequence. To track hand poses in 3D, we use a Belief Propagation algorithm, which provides a robust and unified framework for inference in a graphical model. From the estimated 3D hand pose we extract the information for each finger's motion, which is then fed into a hidden Markov model. To recognize natural finger actions, we consider the movements of all the fingers to recognize a single finger's action. We applied the proposed method to a virtual keypad system and the result showed a high recognition rate of 94.66% with 300 test data.

• Korean Journal of Remote Sensing
• /
• v.37 no.6_2
• /
• pp.1869-1880
• /
• 2021

Korean Journal of Remote Sensing (KJRS), leading the field of remote sensing and GIS in South Korea for over 37 years, has published interdisciplinary research papers. In this study, we performed the topic modeling based on Latent Dirichlet Allocation (LDA), a probabilistic generative model, to identify the research topics and trends using 1) the whole articles, and 2) specific articles related to natural and environmental disasters published in KJRS by analyzing titles, keywords, and abstracts. The results of LDA showed that 4 topics('Polar', 'Hydrosphere', 'Geosphere', and 'Atmosphere') were identified in the whole articles and the topic of 'Polar' was dominant among them (linear slope=3.51 × 10^-3, p<0.05) over time. For the specific articles related to natural and environmental disasters, the optimal number of topics were 7 ('Marine pollution', 'Air pollution', 'Volcano', 'Wildfire', 'Flood', 'Drought', and 'Heavy rain') and the topic of 'Air pollution' was dominant (linear slope=2.61 × 10^-3, p<0.05) over time. The results from this study provide the history and insight into natural and environmental disasters in KRJS with multidisciplinary researchers.