• Journal of the Korean Association of Geographic Information Studies
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• v.10 no.2
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• pp.152-163
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• 2007

The on-going pre-environmental investigation at present is performed by separate numerical analysis of each provision which makes integrated pre-environmental investigation is difficult. The application of numerical data is insufficient, which results to the deterioration of environmental investigation result's objectivity. A lot of time and money is required for the investigation. In this study, the spacial analysis function of GIS was applied on the 8 pre-environmental investigation factors. Pre-environmental investigation GIS DMS(Decision Making System) using AHP was constructed to make integrated investigation possible through the use of investigation results for each factor. Through the use of the developed pre-environmental investigation GIS DMS and the pre-constructed GIS data, the objectivity of environmental investigation is sufficient and time and cost are reduced. Therefore, this system can be used for pre-environmental investigation during route selection in the initial stages of road construction. Through the numerical and visual data obtained from the system developed in this paper, it is easier to gain the approval of the public. Furthermore, environmental problems due to road construction can be investigated with less time and money during the initial stages of road construction.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.5C
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• pp.393-402
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• 2012

In this study, we propose a signal processing algorithm to measure the arousal level of a human subject using a PPG(Photoplethysmography) sensor. From the measured PPG signals, the arousal level is determined by PPI(Pulse to Pulse Interval) and discrete-time signal processing. We ran psychophysical experiments displaying visual stimuli on TV display while measuring PPG signal from a finger, where the nature landscape scenes were used for restorative effect, and the urban environments were used to stimulate the stress. However, the measured PPG signals may include noise due to subject movement and measurement error, which results in incorrect detections. In this paper, to mitigate the noise impact on stimulus detection, we propose a detecting algorithm using digital signal processing methods and statistics of measured signals. A filter is adopted to remove a high frequency noise and adaptively designed taking into account the statistics of the measured PPG signals. Moreover we employ a hysteresis method to reduce the distortion of PPI in decision of emotional. Via experiment, we show that the proposed scheme reduces signal noise and improves stimulus detection.

• Archives of design research
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• v.18 no.3 s.61
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• pp.209-218
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• 2005

Consciously or not, producers have to make many aesthetic choices in creative process of video production. If there are general acceptable aesthetic principles to make right choice it would be guideline of aesthetic decision to somewhat reduce mistakes and errors in the process. This paper proposes a theoretical approach on establishing the media aesthetic principle of 3D animation camera working, which is the most suitable for animation production context. We describe the Herbert Zettl's applied media aesthetics related directly to the camera, which is about the two-Dimensional field focusing on aspect radio and forces within the screen, three-dimensional field focusing on depth, volume, and four-dimensional field focusing on time and motion. In order to have theoretical approach we made an analysis on comparing a camera working of movie with 3D computer animation's one, and reconstructed these basic principles to be suited for the 3D animation production. When applied media aesthetics of the traditional camera working are applied to the 3D animation production, it could be an efficient guideline for it. Futhermore, if we develop the research for the relationship with various visual languages with the basis of these principles, the theory of creative picture composition method for the 3D animation production will be logically and systematically established.

• Science of Emotion and Sensibility
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• v.24 no.3
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• pp.41-60
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• 2021

The purpose of this research was to determine whether multimodal cues in an online shopping environment could enhance tactile consumer mental imagery, purchase intentions, and attitudes towards an apparel product. One limitation of online retail is that consumers are unable to physically touch the items. However, as tactile information plays an important role in consumer decisions especially for apparel products, this study investigated the effects of multimodal cues on overcoming the lack of tactile stimuli. In experiment 1, to explore the product, the participants were randomly assigned to four conditions; picture only, video without sound, video with corresponding sound, and video with discordant sound; after which tactile mental imagery vividness, ease of imagination, attitude, and purchase intentions were measured. It was found that the video with discordant sound had the lowest average scores of all dependent variables. A within-participants design was used in experiment 2, in which all participants explored the same product in the four conditions in a random order. They were told that they were visiting four different brands on a price comparison web site. After the same variables as in experiment 1, including the need for touch, were measured, the repeated measures ANCOVA results revealed that compared to the other conditions, the video with the corresponding sound significantly enhanced tactile mental imagery vividness, attitude, and purchase intentions. However, the discordant condition had significantly lower attitudes and purchase intentions. The dual mediation analysis also revealed that the multimodal cue conditions significantly predicted attitudes and purchase intentions by sequentially mediating the imagery vividness and ease of imagination. In sum, vivid tactile mental imagery triggered using audio-visual stimuli could have a positive effect on consumer decision making by making it easier to imagine a situation where consumers could touch and use the product.

• Korean Journal of Construction Engineering and Management
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• v.23 no.4
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• pp.3-14
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• 2022

Autonomous equipment requires a large amount of data from various environments. However, it takes a lot of time and cost for an experiment in a real construction sites, which are difficulties in data collection and processing. Therefore, this study aims to develop a virtual environment for autonomous tower cranes technology development and validation. The authors defined automation functions and operation conditions of tower cranes with three performance criteria: operational design domain, object and event detection and response, and minimum functional conditions. Afterward, this study developed a virtual environment for learning and validation for autonomous functions such as recognition, decision making, and control using the Unity game engine. Validation was conducted by construction industry experts with a fidelity which is the representative matrix for virtual environment assessment. Through the virtual environment platform developed in this study, it will be possible to reduce the cost and time for data collection and technology development. Also, it is also expected to contribute to autonomous driving for not only tower cranes but also other construction equipment.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.30-31
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• 2009

Early detection of schedule delay in field construction activities is vital to project management. It provides the opportunity to initiate remedial actions and increases the chance of controlling such overruns or minimizing their impacts. This entails project managers to design, implement, and maintain a systematic approach for progress monitoring to promptly identify, process and communicate discrepancies between actual and as-planned performances as early as possible. Despite importance, systematic implementation of progress monitoring is challenging: (1) Current progress monitoring is time-consuming as it needs extensive as-planned and as-built data collection; (2) The excessive amount of work required to be performed may cause human-errors and reduce the quality of manually collected data and since only an approximate visual inspection is usually performed, makes the collected data subjective; (3) Existing methods of progress monitoring are also non-systematic and may also create a time-lag between the time progress is reported and the time progress is actually accomplished; (4) Progress reports are visually complex, and do not reflect spatial aspects of construction; and (5) Current reporting methods increase the time required to describe and explain progress in coordination meetings and in turn could delay the decision making process. In summary, with current methods, it may be not be easy to understand the progress situation clearly and quickly. To overcome such inefficiencies, this research focuses on exploring application of unsorted daily progress photograph logs - available on any construction site - as well as IFC-based 4D models for progress monitoring. Our approach is based on computing, from the images themselves, the photographer's locations and orientations, along with a sparse 3D geometric representation of the as-built scene using daily progress photographs and superimposition of the reconstructed scene over the as-planned 4D model. Within such an environment, progress photographs are registered in the virtual as-planned environment, allowing a large unstructured collection of daily construction images to be interactively explored. In addition, sparse reconstructed scenes superimposed over 4D models allow site images to be geo-registered with the as-planned components and consequently, a location-based image processing technique to be implemented and progress data to be extracted automatically. The result of progress comparison study between as-planned and as-built performances can subsequently be visualized in the D4AR - 4D Augmented Reality - environment using a traffic light metaphor. In such an environment, project participants would be able to: 1) use the 4D as-planned model as a baseline for progress monitoring, compare it to daily construction photographs and study workspace logistics; 2) interactively and remotely explore registered construction photographs in a 3D environment; 3) analyze registered images and quantify as-built progress; 4) measure discrepancies between as-planned and as-built performances; and 5) visually represent progress discrepancies through superimposition of 4D as-planned models over progress photographs, make control decisions and effectively communicate those with project participants. We present our preliminary results on two ongoing construction projects and discuss implementation, perceived benefits and future potential enhancement of this new technology in construction, in all fronts of automatic data collection, processing and communication.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.2
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• pp.181-186
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• 2022

Dam slope RIM is a highly important contact interface where the main body and the base surface are connected. In general, when the grouting for the slope of the dam structure is designed, it is planned using limited data (drilling, geological map, etc.). This makes it very difficult to accurately consider the original ground characteristics of the slope RIM grouting target, In addition, when the grouting volume planned during the design is drilled and injected into the original ground where the waterstop is secured, there is a possibility that the original ground with the waterstop is disturbed and the effect of the waterstop is rather diminished. In order to overcome such problems, it is more suitable to first consider geological conditions and determine whether to perform optimal grouting on the original ground through on-site repair tests before performing RIM grouting. In this paper, to determine the grouting of the RIM unit, a pilot hole water pressure test was performed on the rock of the slope in the target section. The analysis shows grouting volume of 1 Lugeon or less, and the cement injection amount also shows the injection result of 1 kg/m or less. In this case, performing grouting is rather counterproductive. This result can be evaluated through a rock of which some degree of order of mass is secured, as it is a dam design standard of 1 Lugeon or less when analyzed, using the results of visual observation and geological map creation during slope cutting. Therefore, in conclusion, it is preferable to make the decision for using RIM grouting on the slope of the dam body structure, based on 1 Lugeon in a rock state, and the cement injection amount also at 1 kg/m.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.2
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• pp.249-256
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• 2022

Various studies are being actively conducted to show that the real-time visualization technology that combines BIM (Building Information Modeling) and AR (Augmented Reality) helps to increase construction management decision-making and processing efficiency. However, when large-capacity BIM data is projected into AR, there are various limitations such as data transmission and connection problems and the image cut-off issue. To improve the high efficiency of visualizing, a mesh optimization algorithm based on the k-nearest neighbors (KNN) classification framework to reconstruct BIM data is proposed in place of existing mesh optimization methods that are complicated and cannot adequately handle meshes with numerous boundaries of the 3D models. In the proposed algorithm, our target BIM model is optimized with the Unity C# code based on triangle centroid concepts and classified using the KNN. As a result, the algorithm can check the number of mesh vertices and triangles before and after optimization of the entire model and each structure. In addition, it is able to optimize the mesh vertices of the original model by approximately 56 % and the triangles by about 42 %. Moreover, compared to the original model, the optimized model shows no visual differences in the model elements and information, meaning that high-performance visualization can be expected when using AR devices.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6A
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• pp.765-778
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• 2008

Performance Profiles are essential to predict the performance variation over time for the bridge management system (BMS) based on risk management. In general, condition profiles based on experts opinion and/or visual inspection records have been used widely because obtaining profiles based on real performance is not easy. However, those condition profiles usually don't give a good consistency to the safety of bridges, causing practical problems for the effective bridge management. The accuracy of performance evaluation is directly related to the accuracy of BMS. The reliability of the evaluation is important to produce the optimal solution for distributing maintenance budget reasonably. However, conventional methods of bridge assessment are not suitable for a more sophisticated decision making procedure. In this study, a method to compute quantitative performance profiles has been proposed to overcome the limitations of those conventional models. In Bridge Management Systems, the main role of performance profiles is to compute and predict the performance of bridges subject to lifetime activities with uncertainty. Therefore, the computation time for obtaining an optimal maintenance scenario is closely related to the efficiency of the performance profile. In this study, the Response Surface Method (RSM) based on independent and important design variables is developed for the rapid computation. Steel box bridges have been investigated because the number of independent design variables can be reduced significantly due to the high dependency between design variables.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.85-92
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• 2023

In this paper, we propose a method for automatically generating story-based abbreviated summaries from long-form dramas and movies. From the shooting stage, the basic premise was to compose a frame with illusion of depth considering the golden division as well as focus on the object of interest to focus the viewer's attention in terms of content delivery. To consider how to extract the appropriate frames for this purpose, we utilized elemental techniques that have been utilized in previous work on scene and shot detection, as well as work on identifying focus-related blur. After converting the videos shared on YouTube to frame-by-frame, we divided them into a entire frame and three partial regions for feature extraction, and calculated the results of applying Laplacian operator and FFT to each region to choose the FFT with relative consistency and robustness. By comparing the calculated values for the entire frame with the calculated values for the three regions, the target frames were selected based on the condition that relatively sharp regions could be identified. Based on the selected results, the final frames were extracted by combining the results of an offline change point detection method to ensure the continuity of the frames within the shot, and an edit decision list was constructed to produce an abbreviated summary of 62.77% of the footage with F1-Score of 75.9%