• Journal of Digital Contents Society
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• v.16 no.2
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• pp.335-343
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• 2015

This paper describes a quantitative approach to the BIM-enabled assessment of building circulation by using quantitative data and applying its weight. Assessment of building circulation plays an important role before construction stage because of related requirements regulated by design guide, building codes, etc. and design decision making as well. In this paper, Qualitative issue is mainly dealt with that are usually excluded from rule checking objects due to their implicit definition such as 'comfortable circulation'. A weighting framework is suggested using weighted data of circulation properties such as 1) metric distance, 2) number of passing spaces, 3) number of turning spaces, 4) window area of passing space, etc.

• Korean Journal of Construction Engineering and Management
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• v.16 no.5
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• pp.3-11
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• 2015

This paper aims to review and discuss a graph-based approach for the representation and analysis of building circulation using BIM models. To propose this approach, the authors survey diverse researches and developments which are related to building circulation issues such as circulation requirements in Korea Building Act, spatial network analysis, as well as BIM applications. As the basis of this paper, UCN (Universal Circulation Network) is the main reference of the research, and the major goal of this paper is to extend the coverage of UCN with additional features we examined in the survey. In this paper we restructured two major perspectives on top of UCN: 1) finding major factors of graph-based circulation analysis based on UCN and 2) restructuring the UCN approach and others for adjusting to Korean Building Act. As a result of the further studies in this paper, two major additions have demonstrated in the article: 1) the most remote point-based circulation representation, and 2) virtual space-based circulation analysis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.435-449
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• 2023

The state-of-the-art video-based point cloud compression(V-PCC) has a high efficiency of compressing 3D point cloud by projecting points onto 2D images. These images are then padded and compressed by High-Efficiency Video Coding(HEVC). Pixels in padded 2D images are classified into three groups including origin pixels, padded pixels and unoccupied pixels. Origin pixels are generated from projection of 3D point cloud. Padded pixels and unoccupied pixels are generated by copying values from origin pixels during image padding. For padded pixels, they are reconstructed to 3D space during geometry reconstruction as well as origin pixels. For unoccupied pixels, they are not reconstructed. The rate distortion optimization(RDO) used in HEVC is mainly aimed at keeping the balance between video distortion and video bitrates. However, traditional RDO is unreliable for padded pixels and unoccupied pixels, which leads to significant waste of bits in geometry reconstruction. In this paper, we propose a new RDO scheme which takes 3D-Distortion into account instead of traditional video distortion for padded pixels and unoccupied pixels. Firstly, these pixels are classified based on the occupancy map. Secondly, different strategies are applied to these pixels to calculate their 3D-Distortions. Finally, the obtained 3D-Distortions replace the sum square error(SSE) during the full RDO process in intra prediction and inter prediction. The proposed method is applied to geometry frames. Experimental results show that the proposed algorithm achieves an average of 31.41% and 6.14% bitrate saving for D1 metric in Random Access setting and All Intra setting on geometry videos compared with V-PCC anchor.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2577-2596
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• 2013

In this paper, we present an Energy-Aware Self-Stabilizing Distributed Clustering protocol based on message-passing model for Ad Hoc networks. The latter does not require any initialization. Starting from an arbitrary configuration, the network converges to a stable state in a finite time. Our contribution is twofold. We firstly give the formal proof that the stabilization is reached after at most n+2 transitions and requires at most $n{\times}log(2n+{\kappa}+3)$ memory space, where n is the number of network nodes and ${\kappa}$ represents the maximum hops number in the clusters. Furthermore, using the OMNeT++ simulator, we perform an evaluation of our approach. Secondly, we propose an adaptation of our solution in the context of Wireless Sensor Networks (WSNs) with energy constraint. We notably show that our protocol can be easily used for constructing clusters according to multiple criteria in the election of cluster-heads, such as nodes' identity, residual energy or degree. We give a comparison under the different election metrics by evaluating their communication cost and energy consumption. Simulation results show that in terms of number of exchanged messages and energy consumption, it is better to use the Highest-ID metric for electing CHs.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.2
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• pp.148-155
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• 2014

In this paper a commute time embedding is implemented by organizing patches according to the graph-based metric, and its properties are investigated via changing the number of nodes on the graph.. It is shown that manifold embedding methods generate the intrinsic geometric structures when waveforms such as speech or music instrumental sound signals are embedded on the low dimensional Euclidean space. Basically manifold embedding algorithms only project the training samples on the graph into an embedding subspace but can not generalize the learning results to test samples. They are very effective for data clustering but are not appropriate for classification or recognition. In this paper a commute time guided transform is adopted to enhance the generalization ability and its performance is analyzed by applying it to the classification of 6 kinds of music instrumental sounds.

• International Journal of High-Rise Buildings
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• v.6 no.2
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• pp.165-175
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• 2017

The importance of designing urban building complexes so that they obtain 'urban' power, rather than become isolated from the surrounding urban context, has been well recognized by both researchers and practitioners. Nevertheless, most current discussions are made from architects' personal experiences and intuition, and lack a quantitative understanding, to which obstacles include an in-depth exploration of the 'urban' power between building complexes and the urban environment. This paper attempts to measure this feature of 'urban', i.e., 'urbanity,' through a new analytical approach derived from the opendata environment. Three measurements that can be easily collected though the Google Maps API and Open Street Map are applied herein to evaluate high or low values of urbanity. Specifically, these are 'metric depth', i.e., the scale of extended public space, 'development density', i.e., density and distribution of point of interests (POIs), and 'type diversity', i.e., diversity of different commercial types. Six cases located in Japan, China and Hong Kong respectively are ranked based on this analytical approach and compared with each other. It shows that Japanese cases, i.e., Osaka Station City and Namba Parks, Osaka, obtained clearly higher values than cases in Shanghai and Hong Kong. On one hand, the insight generated from measuring and explaining 'urban' power would help to assist better implementation of this feature in the design of urban building complexes. On the other hand, this analytical approach can be easily extended to achieve a large-scale measurement and comparison among different urban building complexes, which is also helpful for design practitioners.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.508-518
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• 2023

This paper presents a new approach for the automatic mapping of discontinuities in a tunnel face based on its 3D digital model reconstructed by LiDAR scan or photogrammetry techniques. The main idea revolves around the identification of discontinuity areas in the 3D digital model of a tunnel face by segmenting its 2D projected images using a deep-learning semantic segmentation model called U-Net. The proposed deep learning model integrates various features including the projected RGB image, depth map image, and local surface properties-based images i.e., normal vector and curvature images to effectively segment areas of discontinuity in the images. Subsequently, the segmentation results are projected back onto the 3D model using depth maps and projection matrices to obtain an accurate representation of the location and extent of discontinuities within the 3D space. The performance of the segmentation model is evaluated by comparing the segmented results with their corresponding ground truths, which demonstrates the high accuracy of segmentation results with the intersection-over-union metric of approximately 0.8. Despite still being limited in training data, this method exhibits promising potential to address the limitations of conventional approaches, which only rely on normal vectors and unsupervised machine learning algorithms for grouping points in the 3D model into distinct sets of discontinuities.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.9
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• pp.3856-3863
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• 2012

A 3D reconstruction technique from stereo images that requires minimal intervention from the user has been developed. The reconstruction problem consists of three steps of estimating specific geometry groups. The first step is estimating the epipolar geometry that exists between the stereo image pairs which includes feature matching in both images. The second is estimating the affine geometry, a process to find a special plane in the projective space by means of vanishing points. The third step, which includes camera self-calibration, is obtaining a metric geometry from which a 3D model of the scene could be obtained. The major advantage of this method is that the stereo images do not need to be calibrated for reconstruction. The results of camera calibration and reconstruction have shown the possibility of obtaining a 3D model directly from features in the images.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.2
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• pp.65-72
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• 2007

As embedded system has strict cost and space constraints, it is impossible to apply conventional fault-tolerant techniques directly for increasing the dependability of embedded system. In this paper, we propose software fault-tolerant mechanism which requires only minimum redundancy of system component. We define an utility metric that reflects the dependability of each embedded system component, and then measure the defined utility of each reconfiguration combinations to provide fault tolerance. The proposed utility evaluation process shows exponential complexity. However we reduce the complexity by hierachical subgrouping at the software level of each component. When some components of embedded system are tailed, reconfiguration operation changes the system state from current faulty state to pre-calculated one which has maximum utility combination.

• The Journal of Industrial Distribution & Business
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• v.11 no.7
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• pp.51-60
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• 2020

Purpose: Recently, Korean cosmetics distribution market has been reorganized with the H&B store. In the domestic cosmetics distribution market, existing brand road shops are decreasing, and multi-shops are leading the H & B stores, which have greatly improved their experience and content. In these environmental changes, the offline distribution channels are turning into the multi-editing shops that have introduced products of various brands and greatly enhanced experiences and contents. Nevertheless, most studies of factors and measurement items for measuring customer experience in the H&B store use Schmitt (1999)'s Strategic Experience Modules (SEMs). Therefore, the purpose of this study is to propose a measure that is practicable through consideration of the in-store customer experience components of the H&B store. Research design, data and methodology: Based on Schmitt's Strategic Experience Modules (SEMs), which are widely used in customer experience marketing, the metric pool was constructed through customer and literature research on H & B store managers. Since then, 101 preliminary surveys and 211 main surveys have been conducted in order to propose a dimension of customer experience and refine the metrics. Results: As a result of the research, H&B store's customer experience was derived from a measurement model consisting of 19 measurement items in total of five dimensions: environmental experience, intellectual experience, behavioral experience, tech experience, and relationship experience. This study analyzed that compared to the existing Schmitt's Strategic Experience Modules (SEMs), (1) emotional experience expanded to environmental experience, (2) Cognitive and relationship experiences are maintained (3) behavioral experience was subdivided into physical and technical experiences. In particular, the environmental experience has been proposed as a major component is an important point because the H&B store recently opened a large flagship store and is competitive in constructing a differentiated space. Conclusions: Related experience was seen as an important component of customer experience in the offline store, but in the process of refining the scale, interaction items with employees of the H&B store were removed, and rather, participation in the APP or SNS channel of the company, event Participation, interaction with other customers, etc. appear to be important, while suggesting the practical implications.