• Journal of Navigation and Port Research
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• v.45 no.3
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• pp.148-154
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• 2021

With advancements of quay side handling equipment and technologies, congestion in terminal operation has moved to the storage yard side from the quay side. The importance of storage yard management has increased in overall terminal operation. Thus, many studies have been conducted to optimize the storage yard management of container terminals. However, there is no academic work to estimate the change of storage yard occupancy ratio by itself in the future. This paper examines the probability of storage yard occupancy ratio in the container terminal of Busan New port using the Markov chain analysis which explains probability change with passing time. The result shows that it is most likely to have the probability of maintaining a high level of storage yard occupancy ratio in the container terminal of Busan New Port.

• 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 applied mathematics & informatics
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• v.32 no.1_2
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• pp.129-136
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• 2014

One of the fundamental issues in the design of dynamic spectrum access policy is the modeling of the dynamic behavior of channel occupancy by primary users. Under a Markovian modeling of channel occupancy, a periodic sensing and greedy access policy is known as one of the simple and practical dynamic spectrum access policies in cognitive radio networks. In this paper, the primary occupancy of each channel is modeled as a discrete-time alternating renewal process with generally distributed on- and off-periods. A periodic sensing and greedy access policy is constructed based on the general channel occupancy model. Simulation results show that the proposed policy has better throughput than the policies using channel models with exponentially distributed on- or off-periods.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.900-903
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• 1996

The objective of this paper is to make the weighted graph map for path planning using the ultrasonic sensor measurements that are acquired when an A.M.R (autonomous mobile robot) explores the unknown circumstance. First, The A.M.R navigates on unknown space with wall-following and gathers the sensor data from the environments. After this, we constructs the occupancy grid map by interpreting the gathered sensor data to occupancy probability. For the path planning of roadmap method, the weighted graph map is extracted from the occupancy grid map using morphological image processing and thinning algorithm. This methods is implemented on an A.M.R having a ultrasonic sensor.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.138.4-138
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• 2001

The purpose of this research is to build a quality-improved occupancy grid map for path-planning of an autonomous mobile robot(AMR) based on the measurements from a single ultrasonic sensor, which are acquired when the autonomous mobile robot explores unknown indoor environment. The AMR navigates in the unknown space by following the wall and gathers the range data using the ultrasonic sensor, from which the occupancy grid map is constructed by associating the range data with occupancy certainties. In order to increase the quality of the map we modify the Bayesian probability updating rule, reject non-systematic measurement errors and correct the predictable error of the AMR itself. These procedures are implemented and tested using an AMR, and primary results are presented in this paper.

• Journal of Family Resource Management and Policy Review
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• v.9 no.4
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• pp.21-40
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• 2005

The purpose of this study was to exam the activities by the distribution of time and space occupancy on their weekday and weekend. to study the space and labor segregation by sex. The sample population included 23 wives and their husbands(30-40 years old). The major findings of the research are as follows: First, it shows that wives' time use by activity was consistent with the space occupancy on weekday. Second, on weekend, wives was used to stay in living room most of time. Thirdly, husbands show the stereotype of time use on weekday, and substitute leisure time for labor time. Finally, on weekend, the wives and husbands have the joint time in the household labor or leisure activities.

• Journal of the Korea Safety Management & Science
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• v.13 no.2
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• pp.147-155
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• 2011

For an effective energy management in intelligent buildings it is necessary to gather information about position/absence of people and the level of population. In this paper the smart occupancy detection system based on the active RFID is developed to satisfy such a demand. The performance of the developed system is tested and verified through various experiments. Furthermore the feasibility test of the active RFID tag is performed to verify whether it can be used as a location-based occupancy sensor. The developed core technology can be also applied to other fields such as security, healthcare, smart home, etc.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.3156-3158
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• 1999

Occupancy sensor is a efficient light control system which light on/off automatically and reduces the unnecessary power waste. From now the research on occupancy sensor is restricted to the selection of the appropriate place or sensor. Exist occupancy sensor changes the delay time by manually to the place or situation, so it is unreliable. The delay time can been changed by the average time of the occupied and the preset time, but it is not enough to reliable. In this paper, so to acquire the reliance the average and the standard deviation of the occupied time change the delay time automatically and protect the malfunction from the detector. And to embody it, AT89C52 microcontroller is adopted to the control circuit. It is verified by simulation and experiment results of output characteristic for randomized input.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2021.06a
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• pp.151-153
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• 2021

포인트 클라우드는 특정 개체 혹은 장면을 다수의 3 차원 포인터를 사용하여 표현하는 데이터의 표현 방식 중 하나로 3D 데이터를 정밀하게 수집하고 표현할 수 있는 방법이다. 하지만 방대한 양의 데이터를 필요로 하기 때문에 효율적인 압축이 필수적이다. 이에 따라 국제 표준화 단체인 Moving Picture Experts Group 에서는 포인트 클라우드 데이터의 효율적인 압축 방법 중 하나로 Video based Point Cloud Compression(V-PCC)에 대한 표준을 제정하였다. V-PCC 는 포인트 클라우드 정보를 Occupancy, Geometry, Texture 와 같은 다수의 2D 영상으로 변환하고 각 2D 영상을 전통적인 2D 비디오 코덱을 활용하여 압축하는 방법이다. 본 논문에서는 V-PCC 에서 변환하는 Occupancy 의 정보를 활용하여 효율적으로 Texture 영상을 압축할 수 있은 방법을 소개한다. 또한 제안방법이 V-PCC 에서 약 1%의 부호화 효율을 얻을 수 있음을 보인다.

• Journal of Korean Port Research
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• v.14 no.1
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• pp.66-75
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• 2000

From the traffic analysis, and model constructions and verifications for speed prediction on the freeway ramp junctions with 70mph speed limit, the following results were obtained : ⅰ) The traffic flow distribution showed a big difference depending on the time periods. Especially, more traffic flows were concentrated on the freeway junctions in the morning peak period when compared with the afternoon peak period. ⅱ) The occupancy distribution was also shown to be varied by a big difference depending on the time periods. Especially, the occupancy in the morning peak period showed over 100% increase when compared with the 24hours average occupancy, and the occupancy in the afternoon peak period over 25% increase when compared with the same occupancy. ⅲ) The speed distribution was not shown to have a big difference depending on the time periods. Especially, the speed in the morning peak period showed 10mph decrease when compared with the 24hours'average speed, but the speed did not show a big difference in the afternoon peak period. ⅳ) The analyses of variance showed a high explanatory power between the speed predictive models(SPM) constructed and the variables used, especially the upstream speed. ⅴ) The analysis of correlation for verifying the speed predictive models(SPM) constructed on the ramp junctions were shown to have a high correlation between observed data and predicted data. Especially, the correlation coefficients showed over 0.95 excluding the unstable condition on the diverge section. ⅵ) Speed predictive models constructed were shown to have the better results than the HCM models, even if the speed limits on the freeway were different between the HCM models and speed predictive models constructed.