• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.5
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• pp.1-14
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• 1997

We consider transmission beamforming techniques for frequency-division-duplex (FDD) wireless communication systems using adaptive arrays to improve the signal quality of the array transmission link. We develop a simple effective transmission beamforming technique based on an approximated frequency tranlsation (AFT) to derive the tranmsiion beamforming weights from the uplink channel vector. This technique exploits the invariance of the short-time averaged fast fading statistics to small frequency translations. A simple approximate relationship that relates the transmission channel vector to the reception channel vector is derived. We have developed its practical alternative in which the frequency translation of the channel vector is performed at the principal angle of arrival (AOA) of the u;link synthestic angular spectrum instead of the mean AOA. To analyze the performance of the proposed methods, we consider the power loss incurred by applying the estimated channel vector instead of the true downlink channel vector. The performance is analyzed as a function of the mean AOA, the angular spread, the number of elements, frequncy difference between the uplink and the downlink, and the angle distribution. Their performance is also compared with that of the direct weight reuse method and the AOA based methods.

• Journal of the Korea Society for Simulation
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• v.14 no.2
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• pp.93-103
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• 2005

This paper presents characteristics of a virtual human's navigation using a limited perception-based mapping. Previous approaches to virtual human navigation have used an omniscient perception requiring full layout of a virtual environment in advance. However, these approaches have a limitation on being a fundamental solution for a human-likeness of a virtual human, because behaviors of humans are basically based on their limited perception instead of omniscient perception. In this paper, we integrated Hill's mapping algorithm with a virtual human to experiment virtual human's navigation with the limited perception. This approach does not require full layout of the virtual environment, 360-degree's field of view, and vision through walls. In addition to static objects such as buildings, we consider enemy emergence that can affect an virtual human's navigation. The enemy emergence is used as the variable on the experiment of this present research. As the number of enemies varies, the changes in arrival rate and time taken to reach the goal position were observed. The virtual human navigates by two conditions. One is to take the shortest path to the goal position, and the other is to avoid enemies when the virtual human encounters them. The acquired result indicates that the virtual human's navigation corresponds to a human cognitive process, and thus this research can be a framework for human-likeness of virtual humans.

• Nuclear Engineering and Technology
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• v.53 no.3
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• pp.752-762
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• 2021

The Smoothed Particle Hydrodynamics is one of the most widely used mesh-free numerical method for thermo-fluid dynamics. Due to its Lagrangian nature and simplicity, it is recently gaining popularity in simulating complex physics with large deformations. In this study, the 3D single/two-phase numerical simulations are performed on the Liquid Metal Reactor (LMR) centralized sloshing benchmark experiment using the SPH parallelized using a GPU. In order to capture multi-phase flows with a large density ratio more effectively, the original SPH density and continuity equations are re-formulated in terms of the normalized-density. Based upon this approach, maximum sloshing height and arrival time in various experimental cases are calculated by using both single-phase and multi-phase SPH framework and the results are compared with the benchmark results. Overall, the results of SPH simulations show excellent agreement with all the benchmark experiments both in qualitative and quantitative manners. According to the sensitivity study of the particle-size, the prediction accuracy is gradually increasing with decreasing the particle-size leading to a higher resolution. In addition, it is found that the multi-phase SPH model considering both liquid and air provides a better prediction on the experimental results and the reality.

• Journal of Korean Society of Transportation
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• v.32 no.4
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• pp.401-409
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• 2014

This study deals with capacity expansion planning of airport infrastructure in view of economic validation that reflect construction costs and social benefits according to the reduction of passengers' delay time. We first forecast the airport peak-demand which has a seasonal and cyclical feature with ARIMA model that has been one of the most widely used linear models in time series forecasting. A discrete event simulation model is built for estimating actual delay time of passengers that consider the passenger's dynamic flow within airport infrastructure after arriving at the airport. With the trade-off relationship between cost and benefit, we determine an economic quantity of conveyor that will be expanded. Through the experiment performed with the case study of Incheon international airport, we demonstrate that our approach can be an effective method to solve the airport expansion problem with seasonal passenger arrival and dynamic operational aspects in airport infrastructure.

• Journal of the Korea Society for Simulation
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• v.22 no.1
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• pp.63-75
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• 2013

The School Bus Routing Problem(SBRP) seeks to plan an efficient schedule of a fleet of school buses that must pick up student from various bus stops and deliver them by satisfying various constraints; maximum capacity of the bus, maximum riding time of students, arrival time between a school's time window. By extending the existing SBRP, we consider a case study of SBRP with allowance of mixed-loading and dynamic arrivals reflecting the school bus operation of university in Korea. Our solution procedure is based on constructing the initial solution using sweep algorithm and then improving solution within the framework of the evolutionary approach known as efficient meta-heuristics. By comparing the various scenarios through the constraints relaxation for reflecting the real operational strategies, we assess the merit of our proposed procedure.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.222-234
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• 2021

Airborne-pests can be introduced into Korea from overseas areas by wind, which can cause considerable damage to major crops. Meteorological models have been used to estimate the wind trajectories of airborne insects. The objective of this study is to analyze the effect of input settings on the prediction of areas where airborne pests arrive by wind. The wind trajectories were predicted using the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. The HYSPLIT model was used to track the wind dispersal path of particles under the assumption that brown plant hopper (Nilaparvata lugens) was introduced into Korea from sites where the pest was reported in China. Meteorological input data including instantaneous and average wind speed were generated using meso-scale numerical weather model outputs for the domain where China, Korea, and Japan were included. In addition, the calculation time intervals were set to 1, 30, and 60 minutes for the wind trajectory calculation during early June in 2019 and 2020. It was found that the use of instantaneous and average wind speed data resulted in a considerably large difference between the arrival areas of airborne pests. In contrast, the spatial distribution of arrival areas had a relatively high degree of similarity when the time intervals were set to be 1 minute. Furthermore, these dispersal patterns predicted using the instantaneous wind speed were similar to the regions where the given pest was observed in Korea. These results suggest that the impact assessment of input settings on wind trajectory prediction would be needed to improve the reliability of an approach to predict regions where airborne-pest could be introduced.

• Journal of Korean Society of Transportation
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• v.35 no.4
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• pp.348-359
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• 2017

Recently, various public transportation activation policies are being implemented in order to mitigate traffic congestion in metropolitan areas. Especially in the metropolitan area, the bus information system has been introduced to provide information on the current location of the bus and the estimated arrival time. However, it is difficult to predict the travel time due to repetitive traffic congestion in buses passing through complex urban areas due to repetitive traffic congestion and bus bunching. The previous bus travel time study has difficulties in providing information on route travel time of bus users and information on long-term travel time due to short-term travel time prediction based on the data-driven method. In this study, the path based long-term bus travel time prediction methodology is studied. For this purpose, the training data is composed of 2015 bus travel information and the 2016 data are composed of verification data. We analyze bus travel information and factors affecting bus travel time were classified into departure time, day of week, and weather factors. These factors were used into clusters with similar patterns using self organizing map. Based on the derived clusters, the reference table for bus travel time by day and departure time for sunny and rainy days were constructed. The accuracy of bus travel time derived from this study was verified using the verification data. It is expected that the prediction algorithm of this paper could overcome the limitation of the existing intuitive and empirical approach, and it is possible to improve bus user satisfaction and to establish flexible public transportation policy by improving prediction accuracy.

• Journal of the Korea Society of Computer and Information
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• v.9 no.4 s.32
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• pp.117-125
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• 2004

The berth and crane scheduling problem in a container terminal encompasses the whole process of assigning berth to each ship. determining the duration of berthing, assigning container cranes to each ship, and determining the specific start and end time of each crane service, for all the ships scheduled to be arriving at the terminal during a certain scheduling horizon. This problem is basically a constraint satisfaction problem in which all the constraints should be satisfied. However, it is also an optimization problem because the requested arrival and departure time should be met for as many of the scheduled ships as possible. while the operation cost of the terminal should be minimized. In this paper. I present an effective approach to solving this problem, which combines both constraint satisfaction search and iterative improvement search. I test this method on a real world container terminal problem and the results show that the method can produce better results than any other existing method.

• The Journal of Information Systems
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• v.29 no.2
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• pp.131-147
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• 2020

Purpose The purpose of this study is to develop intelligent vehicle parking distribution system based on LoRa network at the circumstance of traffic congestion during cultural festival in a local city. This paper proposes a parking dispatch and distribution system using a Q-learning algorithm to rapidly disperse traffics that increases suddenly because of in-bound traffics from the outside of a city in the real-time base as well as to increase parking probability in a parking lot which is widely located in a city. Design/methodology/approach The system get information on realtime-base from the sensor network of IoT (LoRa network). It will contribute to solve the sudden increase in traffic and parking bottlenecks during local cultural festival. We applied the simulation system with Queuing model to the Yudeung Festival in Jinju, Korea. We proposed a Q-learning algorithm that could change the learning policy by setting the acceptability value of each parking lot as a threshold from the Jinju highway IC (Interchange) to the 7 parking lots. LoRa Network platform supports to browse parking resource information to each vehicle in realtime. The system updates Q-table periodically using Q-learning algorithm as soon as get information from parking lots. The Queuing Theory with Poisson arrival distribution is used to get probability distribution function. The Dijkstra algorithm is used to find the shortest distance. Findings This paper suggest a simulation test to verify the efficiency of Q-learning algorithm at the circumstance of high traffic jam in a city during local festival. As a result of the simulation, the proposed algorithm performed well even when each parking lot was somewhat saturated. When an intelligent learning system such as an O-learning algorithm is applied, it is possible to more effectively distribute the vehicle to a lot with a high parking probability when the vehicle inflow from the outside rapidly increases at a specific time, such as a local city cultural festival.

• Quality Improvement in Health Care
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• v.21 no.2
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• pp.12-24
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• 2015

Purpose: The need for the rapid evaluation and treatment of emergency department patients with major trauma is essential. A computerized physician order entry (CPOE) system can improve communication and provide immediate access to information with the goal of reducing ED time delays. The aim of this study was to report on the operation of a trauma CPOE program and demonstrate its usefulness by comparing time intervals from ED arrival to various evaluation steps before and after implementation of the program. Methods: This was a before-and-after observational study from a single emergency department at an academic center. The CPOE program was implemented for 6 months and compared with the data collected from the pre-CPOE implementation period. The efficacy of the program was assessed by comparing the time difference before and after CPOE implementation based on the following factors: total boarding time in ED, door-to-disposition decision time, door-to-blood-test report time, door-to-X-ray time, door-to-CT time, and door-to-transfusion time. Results: Over a period of 6 months, the CPOE was activated for a total of 17 patients. Total boarding time was reduced significantly after implementation [median, 641.5 minutes (IQR, 367.3-859.3) versus289.0 minutes (IQR, 140.0-508.0) for pre-CPOE vs. post-CPOE, respectively, p< 0.05). Time intervals for all evaluation steps were reduced after implementation of the program. The improvements in the door-to-blood-test and door-to-CT times were both statistically significant. Conclusion: This study demonstrated that a standard CPOE system can be successfully implemented and can reduce ED time delays in managing trauma patients.