• Journal of Biosystems Engineering
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• v.24 no.5
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• pp.445-452
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• 1999

This study was conducted to develop a searching algorithm for optimal daily temperature setpoint greenhouse. An algorithm using crop growth and energy models was developed to determine optimum crop growth environment. The results of this study were as follows: 1. Mathematical models for crop growth and energy consumption were derived to define optimal daily temperature setpoint. 2. Optimum temperature setpoint, which could maximize performance criterion, was determined by using Pontryagin maximum principle. 3. Dynamic control of daily temperature using the developed algorithm showed higher performance criterion than static control with fixed temperature setpoint. Performance criteria for dynamic control models were with simulated periodic weather data and with real weather data, increased by 48% and 60%, respectively.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.22 no.7
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• pp.905-914
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• 1998

View factor determination is very important in thermal analysis problems with surface radiation but it is very difficult to determine view factors for complex geometries. Exact calculation of view factors for crystal growth furnace is essential due to not only its high surface temperature but the radiation shield, complicated heating system. In this study, view factor calculation algorithm is introduced and applied to cylindrical crystal growth furnace. This algorithm is based on the Hemi-Cube Algorithm and the results obtained with this algorithm show good agreements with those of analytical solution. As an application of this algorithm, temperature profiles and heating value distributions for various furnaces are calculated and the shape criteria for better furnace are suggested.

• ETRI Journal
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• v.31 no.2
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• pp.240-242
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• 2009

A computationally efficient implementation of the progressive edge-growth algorithm is presented. This implementation uses an array of red-black (RB) trees to manage the layered structure of check nodes and adopts a new strategy to expand the Tanner graph. The complexity analysis and the simulation results show that the proposed approach reduces the computational effort effectively. In constructing a low-density parity check code with a length of $10^4$, the RB-tree-array-based implementation takes no more 10% of the time required by the original method.

• Journal of Digital Convergence
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• v.15 no.6
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• pp.229-236
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• 2017

An efficient frequent pattern algorithm is essential for mining association rules as well as many other mining tasks for convergence with its application spread over a very broad spectrum. Models for mining pattern have been proposed using a FP-tree for storing compressed information about frequent patterns. In this paper, we propose a centroid frequent pattern growth algorithm which we called "CAWFP-Growth" that enhances he FP-Growth algorithm by making the center of weights and frequencies for the itemsets. Because the conventional constraint of maximum weighted support is not necessary to maintain the downward closure property, it is more likely to reduce the search time and the information loss of the frequent patterns. The experimental results show that the proposed algorithm achieves better performance than other algorithms without scarifying the accuracy and increasing the processing time via the centroid of the items. The MapReduce framework model is provided to handle large amounts of data via a pseudo-distributed computing environment. In addition, the modeling of the proposed algorithm is required in the fully distributed mode.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.5
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• pp.621-626
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• 2003

In this paper, we modeled a virtual life (VL) that reacts to the user s action according to its own behavioral characteristics and grows itself. We established some conditions with which such a VL is designed. Genetic Algorithm is used for the growth process that changes the VL s properties. In this process, the parameter values of the VL s properties are encoded as one chromosome, and the GA operations change this chromosome. The VL s reaction to the user s action is determined by these properties as well as the general expectation of each reaction. These properties are evaluated through 5 fitness measures so as to deal with multi-objective criteria. Here, we present the simulation of the growth Process, and show some experimental results.

• Journal of KIISE:Computer Systems and Theory
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• v.29 no.11
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• pp.634-639
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• 2002

A Shortest Path Algorithm is the method to find the most efficient route among many routes from a start node to an end node. It is based on Labeling methods. In Labeling methods, there are Label-Setting method and Label-Correcting method. Label-Setting method is known as the fastest one among One-to-One shortest path algorithms. But Benjamin[1,2] shows Label-Correcting method is faster than Label-Setting method by the experiments using large road data. Since Graph Growth algorithm which is based on Label-Correcting method is made to find One-to-All shortest path, it is not suitable to find One-to-One shortest path. In this paper, we propose a new One-to-One shortest path algorithm. We show that our algorithm is faster than Graph Growth algorithm by extensive experiments.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.329-332
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• 2007

Annual vegetation growth patterns are determined by the intrinsic phenological characteristics of each land cover types. So, if typical growth patterns of each land cover types are well-estimated, and a NDVI time-series data of a certain area is compared to those estimated patterns, we can implement more advanced analyses such as a land surface-type classification or a land surface type change detection. In this study, we utilized Terra MODIS NDVI 250m data and compressed full annual NDVI time series data into several indices using the Harmonic Analysis of Time Series(HANTS) algorithm which extracts the most significant frequencies expected to be presented in the original NDVI time-series data. Then, we found these frequencies patterns, described by amplitude and phase data, were significantly different from each other according to vegetation types and these could be used for land cover classification. However, in spite of the capabilities of the HANTS algorithm for detecting and interpolating cloud-contaminated NDVI values, some distorted NDVI pixels of June, July and August, as well as the long rainy season in Korea, are not properly corrected. In particular, in the case of two or three successive NDVI time-series data, which are severely affected by clouds, the HANTS algorithm outputted wrong results.

• Journal of Communications and Networks
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• v.17 no.4
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• pp.346-350
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• 2015

It is known that the progressive edge-growth (PEG) algorithm can be used to construct low-density parity-check (LDPC) codes at finite code lengths with large girths through the establishment of edges between variable and check nodes in an edge-by-edge manner. In [1], the authors derived a class of LDPC codes for relay communication systems by extending the full-diversity root-LDPC code. However, the submatrices of the parity-check matrix H corresponding to this code were constructed separately; thus, the girth of H was not optimized. To solve this problem, this paper proposes a modified PEG algorithm for use in the design of large girth and full-diversity LDPC codes. Simulation results indicated that the LDPC codes constructed using the modified PEG algorithm exhibited a more favorable frame error rate performance than did codes proposed in [1] over block-fading channels.

• Journal of Bio-Environment Control
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• v.2 no.1
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• pp.27-36
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• 1993

Carbon dioxide enrichment for greenhouse crops has generally been a standard commercial practice for many years. Vegetable crops such as tomato, cucumber, and lettuce respond positively to the $CO_2$ enrichment. But improper $CO_2$ enrichment leads to physiological damage and economical loss. This study was carried out to develop a $CO_2$ concentration control algorithm considering growth stage and efficiency. The measurand was $CO_2$ consumption rate and top fresh weight that represents growth stage. The weight of top fresh lettuce as a whole in the tray was measured through a non-destructive method. The demand in $CO_2$ concentration according to growth stage was investigated. The results are summarized as follows. 1. The $CO_2$ consumption rate could be measured within the error of $\pm$ 15.4mg$CO_2$/hr in the range of $CO_2$ concentration of 500-1500ppm. 2. The weight of top fresh lettuce could be measured within the error $\pm$ 4.3g in the range of 0-1400g. 3. The $CO_2$ control model developed could determine an economical $CO_2$ supply rate considering $CO_2$ consumption rate and leakage rate. 4. The $CO_2$ control algorithm based on the control model was composed of feedforward control for maintaining a stable $CO_2$ concentration level, and feedback control with $CO_2$ consumption rate and top fresh weight for adapting to the change in $CO_2$ demand by growth stage. 5. For the performance test with the developed control algorithm on lettuce the decrease in $CO_2$ supply rate was obtained without a significant decrease in top fresh weight.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.220-225
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• 2021

Human population growth rate is an important parameter for real-world planning. Common approaches rely upon fixed parameters like human population, mortality rate, fertility rate, which is collected historically to determine the region's population growth rate. Literature does not provide a solution for areas with no historical knowledge. In such areas, machine learning can solve the problem, but a multitude of machine learning algorithm makes it difficult to determine the best approach. Further, the missing feature is a common real-world problem. Thus, it is essential to compare and select the machine learning techniques which provide the best and most robust in the presence of missing features. This study compares 17 machine learning techniques (base learners and ensemble learners) performance in predicting the human population growth rate of the country. Among the 17 machine learning techniques, random forest outperformed all the other techniques both in predictive performance and robustness towards missing features. Thus, the study successfully demonstrates and compares machine learning techniques to predict the human population growth rate in settings where historical data and feature information is not available. Further, the study provides the best machine learning algorithm for performing population growth rate prediction.