• Korean Journal of Agricultural Science
• /
• v.32 no.1
• /
• pp.81-94
• /
• 2005

This study is aimed at giving the basic information for individual farm households to make decisions for optimizing their farm sizes and for the government to implement farm size optimization policies through the identification of combinations among rice production factors in plain areas like Cheolwon district and the suggestion of the optimal farm sizes of individual farmers based on the scale of economy calculated. The data of agricultural production costs of 50 rice farmers in the plain area which is located in Dongsong-eup Cholwon district, Kangwon province were used in the analysis. The 'translog' cost function among various methods which is a flexible function type was adopted to calculate the scale of economy in rice production. Seemingly unrelated regression(SUR) method was used in forecasting functions and processing other statistics by SHAZAM which is one of the computer aid program for quantitative econometric analysis. In conclusion, the long-run average cost(LAC) curve showed 'U-shape' which was different from 'L-type' one which was shown in the previous studies by others. The lowest point of the LAC was 9.764ha and the concerned production cost amounted to 633 Won/kg. Based on these results, it have to be suggested that around 10 ha of paddy is the target size for policy assistances to save costs under the present level of farming practices and technology. The above results show that the rice production costs could be saved up to 10ha in Cheolwon plain area which is a typical paddy field. However, land use, land condition, land ownership and manager's ability which may affect scale of economy should be considered. Furthermore, reasonable management will have to be realized by means of labor saving technology and cost saving management skill like enlargement of farm size of rice.

• Proceedings of the Korean Society of Propulsion Engineers Conference
• /
• 2004.03a
• /
• pp.513-520
• /
• 2004

A steady-state/transient performance simulation model was newly developed for the propulsion system of the CRW (Canard Rotor Wing) type UAV (Unmanned Aerial Vehicle) during flight mode transition. The CRW type UAV has a new concept RPV (Remotely Piloted Vehicle) which can fly at two flight modes such as the take-off/landing and low speed forward flight mode using the rotary wing driven by engine bypass exhaust gas and the high speed forward flight mode using the stopped wing and main engine thrust. The propulsion system of the CRW type UAV consists of the main engine system and the duct system. The flight vehicle may generally select a proper type and specific engine with acceptable thrust level to meet the flight mission in the propulsion system design phase. In this study, a turbojet engine with one spool was selected by decision of the vehicle system designer, and the duct system is composed of main duct, rotor duct, master valve, rotor tip-jet nozzles, and variable area main nozzle. In order to establish the safe flight mode transition region of the propulsion system, steady-state and transient performance simulation should be needed. Using this simulation model, the optimal fuel flow schedules were obtained to keep the proper surge margin and the turbine inlet temperature limitation through steady-state and transient performance estimation. Furthermore, these analysis results will be used to the control optimization of the propulsion system, later. In the transient performance model, ICV (Inter-Component Volume) model was used. The performance analysis using the developed models was performed at various flight conditions and fuel flow schedules, and these results could set the safe flight mode transition region to satisfy the turbine inlet temperature overshoot limitation as well as the compressor surge margin. Because the engine performance simulation results without the duct system were well agreed with the engine manufacturer's data and the analysis results using a commercial program, it was confirmed that the validity of the proposed performance model was verified. However, the propulsion system performance model including the duct system will be compared with experimental measuring data, later.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• v.9 no.2
• /
• pp.508-511
• /
• 2005

In the near future various new services will be created by using ubiquitous computing and ubiquitous network. Especially u-LBS(Ubiquitous Location Based Services) is recognized as one of the most important services. U-LBS is based on the data created by recognizing objects including both human and matters at any time and anywhere. Many researches related with object locating method by using RF are in the process of studying However there are few researches on the location of objects. In this paper we propose the recognition method of the location of objects by using RF and USN technology. In detail, the strength of RF signal is used to recognize the location of objects. Also we discuss about the future work to enhance the recognition rate of location by using a number of conditions including the weather, temperature etc. And Genetic Algorithm is used to get the optimal parameters with which we can get the more exact recognition rate of location.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• v.1
• /
• pp.171-174
• /
• 2006

A large number of service providers in countries all over the world have established GNSS reference station networks in the last years and are using network software today to provide a correction stream to the user as a routine service. In current GNSS network processing, all the geometric related information such as ionospheric free carrier phase ambiguities from all stations and satellites, tropospheric effects, orbit errors, receiver and satellite clock errors are estimated in one centralized Kalman filter. Although this approach provides an optimal solution to the estimation problem, however, the processing time increases cubically with the number of reference stations in the network. Until now one single Personal Computer with Pentium 3.06 GHz CPU can only process data from a network consisting of no more than 50 stations in real time. In order to process data for larger networks in real time and to lower the computational load, a federated filter approach can be considered. The main benefit of this approach is that each local filter runs with reduced number of states and the computation time for the whole system increases only linearly with the number of local sensors, thus significantly reduces the computational load compared to the centralized filter approach. This paper presents the technical aspect and performance analysis of the federated filter approach. Test results show that for a network of 100 reference stations, with the centralized approach, the network processing including ionospheric modeling and network ambiguity fixing needs approximately 60 hours to process 24 hours network data in a 3.06 GHz computer, which means it is impossible to run this network in real time. With the federated filter approach, only less than 1 hour is needed, 66 times faster than the centralized filter approach. The availability and reliability of network processing remain at the same high level.

• Journal of the Korean Society of Hazard Mitigation
• /
• v.8 no.1
• /
• pp.117-123
• /
• 2008

In this study, PHABSIM which is a sample for 1D physical habitat and River2D, which is a sample for 2D physical habitat were applied to the main streams of Han River in order to calculate an optimum flow considering the habitats of fishes in determining the instream flow. Moreover, the Weighted Usable Area (WUA) of the two samples in each growth step (adults and spawning) of the target fish type was compared and reviewed. The optimal flow value was calculated by considering the conditions for inhabiting fishes. As a result of the correlation analysis for WUA from 1D and 2D samples was 0.87 to 0.99. The optimum flow considering the conditions of inhabiting fishes showed insignificant difference of $3m^3/s\;to\;5m^3/s$ with the exception of adults in Moon-Mak and spawning in Dal-Chun.

• Journal of Satellite, Information and Communications
• /
• v.7 no.2
• /
• pp.97-103
• /
• 2012

A very low complexity QRD-M algorithm based on limited search area is proposed for MIMO systems. The conventional QRD-M algorithm calculates Euclidean distance between all constellation symbols and the temporary detection symbol at each layer. We found that performance will not be degraded even if we adaptively restrict the search area of the candidate symbols only to the neighboring points of temporary detection symbol according to the channel condition at each layer. As a channel condition indicator, we employ the channel gain ratio among the layers without necessity of SNR estimation. The simulation results show that the proposed scheme effectively achieves near optimal performance while maintaining the overall average computation complexity much smaller than the conventional QRD-M algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.10 no.11
• /
• pp.2002-2007
• /
• 2006

The path search method in the telematics system should consider traffic flow of the roads as well as the shortest time because the optimal path with minimized travel time could be continuously changed by the traffic flow. The existing path search methods are not able to cope efficiently with the change of the traffic flow. The search method to use traffic information also needs more computation time than the existing shortest path search. In this paper, a method for efficiency improvement of path search is implemented and its performance is evaluated. The method employs the fixed grid for adjustable heuristic to traffic flow. Moreover, in order to simplify the computation of estimation values, it only adds graded decimal values instead of multiplication operation of floating point numbers with due regard to the gradient between a departure and a destination. The results obtained from the experiments show that it achieves the high accuracy and short execution time as well.

• The Korean Journal of Applied Statistics
• /
• v.29 no.4
• /
• pp.643-656
• /
• 2016

High dimensional data are frequently encountered in various fields where the number of variables is greater than the number of samples. It is usually necessary to select variables to estimate regression coefficients and avoid overfitting in high dimensional data. A penalized regression model simultaneously obtains variable selection and estimation of coefficients which makes them frequently used for high dimensional data. However, the penalized regression model also needs to select the optimal model by choosing a tuning parameter based on the model selection criterion. This study deals with the bias effect of LASSO regression for model selection criteria. We numerically describes the bias effect to the model selection criteria and apply the proposed correction to the identification of biomarkers for lung cancer based on gene expression data.

• Journal of Korean Society of Transportation
• /
• v.31 no.1
• /
• pp.15-24
• /
• 2013

To accommodate urban concentration of population, multi-purpose super-tall buildings have been introduced, but they induce many travel demands causing regional traffic problems. While several travel demand management policies such as transit promotion or parking limits are presented to alleviate such problems, transportation infrastructure are still insufficient to meet high demands. In this study, super-tall buildings are categorized by scale and purpose, then mode-specific derived demand is estimated using modal share of each category. Optimal transportation infrastructure level is determined by condition-based average changing amount yielded by street network delay (in case of road) or the number of transit routes (in case of transit).

• Journal of KIISE:Software and Applications
• /
• v.30 no.11
• /
• pp.1054-1061
• /
• 2003

Q-Learning, a representative algorithm of reinforcement learning, experiences repeatedly until estimation values about all state-action pairs of state space converge and achieve optimal policies. When the state space is high dimensional or continuous, complex reinforcement learning tasks involve very large state space and suffer from storing all individual state values in a single table. We introduce Q-Map that is new function approximation method to get classified policies. As an agent learns on-line, Q-Map groups states of similar situations and adapts to new experiences repeatedly. State-action pairs necessary for fine control are treated in the form of rule. As a result of experiment in maze environment and mountain car problem, we can achieve classified knowledge and extract easily rules from Q-Map