• Land and Housing Review
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• v.9 no.4
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• pp.10-18
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• 2018

As the volatility increasement of the number of tourist, there was been controversy over supply-demand imbalance in hotel market. The purpose of this study is to analysis on determinants of hotel occupancy rate in Jeju Island. The quantitative method is based on cointegrating regression, using an empirical dataset with hotel from 2000 to 2017. The primary results of research is briefly summarized as follows; First, there are high relationship between total hotel occupancy rate and hotel occupancy of foreign tourist. The volatility of hotel occupancy is caused by foreigner user than local tourists though local tourist high propotion of hotel occupancy in Jeju Island. Second, hotel occupancy of local tourist has not relationship with demand and supply variables. Because some hotel users are not local tourists but local resident, and effects to other variables of hotel consumer trend, accommodation such as Guest house, Airbnb. Third, there are high relationship between foreign hotel occupancy rate and demand-supply variables. These research imply that total management of supply-demand is very important to seek stability of hotel occupancy rate in Jeju Island. Also it can provide a useful solution regarding mismatch problem between supply-demand as well as development the systematic forecasting model for hotel market participants.

• Journal of Animal Science and Technology
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• v.65 no.6
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• pp.1254-1269
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• 2023

This study aims to predict the change in corn share according to the grazing of 20 gestational sows in a mature corn field by taking images with a camera-equipped unmanned air vehicle (UAV). Deep learning based on convolutional neural networks (CNNs) has been verified for its performance in various areas. It has also demonstrated high recognition accuracy and detection time in agricultural applications such as pest and disease diagnosis and prediction. A large amount of data is required to train CNNs effectively. Still, since UAVs capture only a limited number of images, we propose a data augmentation method that can effectively increase data. And most occupancy prediction predicts occupancy by designing a CNN-based object detector for an image and counting the number of recognized objects or calculating the number of pixels occupied by an object. These methods require complex occupancy rate calculations; the accuracy depends on whether the object features of interest are visible in the image. However, in this study, CNN is not approached as a corn object detection and classification problem but as a function approximation and regression problem so that the occupancy rate of corn objects in an image can be represented as the CNN output. The proposed method effectively estimates occupancy for a limited number of cornfield photos, shows excellent prediction accuracy, and confirms the potential and scalability of deep learning.

• Journal of the Korean Institute of Landscape Architecture International Edition
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• no.1
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• pp.183-191
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• 2001

The purpose of this study is to provide guidelines for planning, designing and managing neighborhood parks. Results form POE(Post-Occupancy-Evaluation) and RPOE(Re-Post-Occupancy-Evaluation) are analyzed for this study. Bundang Central Park in Bundang Newtown was selected for this study. This study compared the previous POE completed in 1996 with a RPOE conducted in 2001 to find out how the user, proximate environmental context, and the park administration changed in time and apply feedback for purposes of immediate problem solving. The results of this study showed that RPOE has to be initiated and utilized periodically as a device and the guideline for neighborhood park design, conservation, administration and operation.

• Journal of the Korean Solar Energy Society
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• v.37 no.2
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• pp.23-33
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• 2017

Accurate prediction of stochastic behavior of occupants is a well known problem for improving prediction performance of building energy use. Many researchers have been tried various sensors that have information on the status of occupant such as $CO_2$ sensor, infrared motion detector, RFID etc. to predict occupants, while others have been developed some algorithm to find occupancy probability with those sensors or some indirect monitoring data such as energy consumption in spaces. In this research, various sensor data and energy consumption data are utilized for decision tree algorithms (C4.5 & CART) for estimation of sub-hourly occupancy status. Although the experiment is limited by space (private room) and period (cooling season), the prediction result shows good agreement of above 95% accuracy when energy consumption data are used instead of measured $CO_2$ value. This result indicates potential of IoT data for awareness of indoor environmental status.

• Journal of Information Processing Systems
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• v.17 no.1
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• pp.51-62
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• 2021

On-chip caches of graphics processing units (GPUs) have contributed to improved GPU performance by reducing long memory access latency. However, cache efficiency remains low despite the facts that recent GPUs have considerably mitigated the bottleneck problem of L1 data cache. Although the cache miss rate is a reasonable metric for cache efficiency, it is not necessarily proportional to GPU performance. In this study, we introduce a second key determinant to overcome the problem of predicting the performance gains from L1 data cache based on the assumption that miss rate only is not accurate. The proposed technique estimates the benefits of the cache by measuring the balance between cache efficiency and throughput. The throughput of the cache is predicted based on the warp occupancy information in the warp pool. Then, the warp occupancy is used for a second bypass phase when workloads show an ambiguous miss rate. In our proposed architecture, the L1 data cache is turned off for a long period when the warp occupancy is not high. Our two-level bypassing technique can be applied to recent GPU models and improves the performance by 6% on average compared to the architecture without bypassing. Moreover, it outperforms the conventional bottleneck-based bypassing techniques.

• Proceeding of Spring/Autumn Annual Conference of KHA
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• 2009.04a
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• pp.73-77
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• 2009

The block housing type is coming to fore as a 'low-rise high-density housing type', which can resolve the all sorts of problem caused in Korea apartment housing complex and single-detached residental area in terms of livability, urbanity, and community. To analyze the characteristics of the block housing, the block housing in Eunpyeong New Town, in Korea was analyzed as a sample for post occupancy evaluation. The analysis show that the block housing is useful low-rise and high-density housing type, which ensures the livability as well as the urbanity and community. In details the analysis also shows the user satisfaction from the perspective of living and facility use within the block housing and individual unit.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.6
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• pp.492-497
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• 2002

Map building is the process of modeling the robot's environment. The map is usually built based on a grid-based or topological approach, which has its own merits and demerits. These two methods, therefore, can be integrated to provide a better way of map building, which compensates for each other's drawbacks. In this paper, a method of building the topological map based on the occupancy grid map through a Voronoi diagram is presented and verified by various simulations. This Voronoi diagram is made by using a labeled Voronoi diagram scheme which is suitable for the occupancy grid maps. It is shown that the Proposed method is efficient and simple fur building a topological map. The simple path-planning problem is simulated and experimented verify validity of the proposed approach.

• Journal of the Korea Institute of Building Construction
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• v.11 no.6
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• pp.576-586
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• 2011

In recent years, pollution in residential spaces has been a significant area of concern. In particular, the indoor air quality (IAQ) of an apartment building before occupancy, which is related to the interior material, is a serious problem. Unlike previous research, which has mainly focused on pollution control after construction, this study has derived influencing factors and priority of management with a controlling schedule for IAQ. The objectives of this research are 1) control of schedule or improvement of management for IAQ, 2) distribution of responsibility to the parties concerned (factory, material company, construction company, design and engineering, occupancy). The results show the relative priority of the four major items in wall?based apartment buildings and in column?based apartment buildings. An analysis of the parties responsible for improvement based on the IAQ results shows more efforts to improve IAQ are needed in material factories and engineering/design companies.

• Proceedings of the Korea Concrete Institute Conference
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• 2004.11a
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• pp.141-144
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• 2004

Nowadays, there has been substantial interest in whether there is an association between electromagnetic field exposure and living environment. It is increased the demand that electromagnetic wave environment and its countermeasure. In the present study, we has applied the electromagnetic absorbent inorganic paint of 'I' corporations, and measured electromagnetic waves generated in new apartment before occupancy using the standard field electromagnetic wave generating device we developed. The measurement before occupancy was $100\~131V/m$, but the measurement after occupancy was $6.9\~8.0V/m$ less than 10V/m, the comprehensive electromagnetic wave limit allowed by TCO in Sweden. The implication is that domestic apartment are exposed to extremely poor electromagnetic wave environment. Nevertheless, there have been neither serious efforts to overcome this problem, nor its alternatives and related standards. Therefore, it is necessary to continue research of related fields to establish standards and plans to improve the situation.

• Journal of Broadcast Engineering
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• v.23 no.2
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• pp.274-282
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• 2018

Most occupancy sensors installed in buildings, households and so forth are pyroelectric infra-red (PIR) sensors. One of disadvantages is that PIR sensor can not detect the stationary person due to its functionality of detecting the variation of thermal temperature. In order to overcome this problem, the utilization of camera vision sensors has gained interests, where object tracking is used for detecting the stationary persons. However, the object tracking has an inherent problem such as tracking drift. Therefore, the recognition of humans in static trackers is an important task. In this paper, we propose a CNN-based human recognition to determine whether a static tracker contains humans. Experimental results validated that human and non-humans are classified with accuracy of about 88% and that the proposed method can be incorporated into practical vision occupancy sensors.