• Korean Journal of Plant Resources
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• v.30 no.5
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• pp.565-570
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• 2017

In a plumcot 'Harmony' cultivar, which is an interspecific hybrid between plum and apricot, canopy occupation and productivity according to tree training system, Y shape with no trellis (YNT) and Y-palmette with trellis (YPT), were compared. According to the survey results for 5 years of planting, tree growth was similar in two training systems. However, canopy occupation and fruit yield of YPT were significantly higher than those of YNT. The fruit weight and sugar content were not significantly different between two systems. The fruit drop rate tended to be lower in YPT than in YNT. From the above results, it is expected that the YPT type will contribute to the increase of canopy occupation and fruit yield and reducing the fruit drop rate compared to the YNT.

• Journal of the military operations research society of Korea
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• v.33 no.2
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• pp.101-113
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• 2007

The period of military personnel service will be phased down by 2014 according to 'The law of National Defense Reformation' issued by the Ministry of National Defense. For this reason, the ROK army provides discrimination education to 'newly recruited privates' for more effective individual performance in the on-the-job training. For the training to be more effective, it would be essential to predict the degree of achievements by new privates in the training. Thus, we used data mining techniques to develop a classification model which classifies the new privates into one of two achievements groups, so that different skills of education are applied to each group. The target variable for this model is a binary variable, whose value can be either 'a group of general control' or 'a group of special control'. We developed four pure classification models using Neural Network, Decision Tree, Support Vector Machine and Naive Bayesian. We also built four hybrid models, each of which combines k-means clustering algorithm with one of these four mining technique. Experimental results demonstrated that the highest performance model was the hybrid model of k-means and Neural Network. We expect that various military education programs could be supported by these classification models for better educational performance.

• Journal of Internet Computing and Services
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• v.14 no.5
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• pp.1-10
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• 2013

RFID(Radio Frequency Identification) system is non-contact identification technology. A basic RFID system consists of a reader, and a set of tags. RFID tags can be divided into active and passive tags. Active tags with power source allows their own operation execution and passive tags are small and low-cost. So passive tags are more suitable for distribution industry than active tags. A reader processes the information receiving from tags. RFID system achieves a fast identification of multiple tags using radio frequency. RFID systems has been applied into a variety of fields such as distribution, logistics, transportation, inventory management, access control, finance and etc. To encourage the introduction of RFID systems, several problems (price, size, power consumption, security) should be resolved. In this paper, we proposed an algorithm to significantly alleviate the collision problem caused by simultaneous responses of multiple tags. In the RFID systems, in anti-collision schemes, there are three methods: probabilistic, deterministic, and hybrid. In this paper, we introduce ALOHA-based protocol as a probabilistic method, and Tree-based protocol as a deterministic one. In Aloha-based protocols, time is divided into multiple slots. Tags randomly select their own IDs and transmit it. But Aloha-based protocol cannot guarantee that all tags are identified because they are probabilistic methods. In contrast, Tree-based protocols guarantee that a reader identifies all tags within the transmission range of the reader. In Tree-based protocols, a reader sends a query, and tags respond it with their own IDs. When a reader sends a query and two or more tags respond, a collision occurs. Then the reader makes and sends a new query. Frequent collisions make the identification performance degrade. Therefore, to identify tags quickly, it is necessary to reduce collisions efficiently. Each RFID tag has an ID of 96bit EPC(Electronic Product Code). The tags in a company or manufacturer have similar tag IDs with the same prefix. Unnecessary collisions occur while identifying multiple tags using Query Tree protocol. It results in growth of query-responses and idle time, which the identification time significantly increases. To solve this problem, Collision Tree protocol and M-ary Query Tree protocol have been proposed. However, in Collision Tree protocol and Query Tree protocol, only one bit is identified during one query-response. And, when similar tag IDs exist, M-ary Query Tree Protocol generates unnecessary query-responses. In this paper, we propose Adaptive M-ary Query Tree protocol that improves the identification performance using m-bit recognition, collision information of tag IDs, and prediction technique. We compare our proposed scheme with other Tree-based protocols under the same conditions. We show that our proposed scheme outperforms others in terms of identification time and identification efficiency.

• Journal of Intelligence and Information Systems
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• v.14 no.2
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• pp.193-206
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• 2008

Medical diagnosis can be considered a classification task which classifies disease types from patient's condition data represented by a set of pre-defined attributes. This study proposes a hybrid genetic algorithm based classification method to develop classifiers for multidimensional pattern classification problems related with medical decision making. The classification problem can be solved by identifying separation boundaries which distinguish the various classes in the data pattern. The proposed method fits a finite number of regional agents to the data pattern by combining genetic algorithms and local adaptive operations. The local adaptive operations of an agent include expansion, avoidance and relocation, one of which is performed according to the agent's fitness value. The classifier system has been tested with well-known medical data sets from the UCI machine learning database, showing superior performance to other methods such as the nearest neighbor, decision tree, and neural networks.

• Journal of Forest and Environmental Science
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• v.28 no.4
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• pp.212-219
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• 2012

An experiment was set to understand the seedlings growth and nodulation capabilities of five potentially important leguminous agroforestry tree species of Bangladesh in response to application of organic fertilizer. Study was carried out in nursery bed on the seedlings of Acacia mangium Willd., Acacia hybrid, Acacia auriculiformis A. Cunn. ex Benth, Albizia lebbeck (L.) Benth. and Leucaena leucocephala (Lam.) de Wit. after sowing their seeds in polybags filled with different medium. Farmyard manure or decomposed cow dung was applied as the source of organic fertilizer with other potting media. The growth and nodulation performance of seedlings of the selected species were measured three moths after the first seed was emerged. The different soil: organic manure ratios' used during the experiment were, 1:1, 2:1 and 3:1. The effects of organic fertilizer on growth and nodulation were compared to that of seedlings grown in control. The results revealed a positive correlation between seedling growth and the different doses of organic fertilization (i.e. seedling growth was enhanced significantly with different treatments containing higher proportion of organic fertilizer). In few cases a negative effect was observed after applying higher levels of organic manure. It was also apparent that the nodulation in terms of nodule number and size was inhibited significantly by the application of organic fertilizer.

• Journal of Plant Biology
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• v.19 no.3
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• pp.85-91
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• 1976

A comparison betwen the productivity of Pinus rigida which is native in the Eastern United States, and P. rigitaeda which is F1 hybrid between P. rigida and P. taeda, has been established. For each tree the diameter at breast height (D) and the height of tree (H) were measured in three years. The standard sample trees were down and then weighed each organ. From obtained data the allometric relation between $D_2H$ and dry weight of the trunk (Ws), the branches (Wb) and the leaves (Wl) of P. rigida were approxmated by $$Ws=0.0592 (D^2H)^{0.837}$$ $$Wb=0.0065 (D^2H)^{0.989}$$ $$Wl=0.0447(D^2H)^{0.690}$$ and those of P. rigitaeda were approximated by $$Ws=0.0522 (D^2H)^{0.843}$$ $$Wb=0.0037 (D^2H)^{1.117}$$ $$Wl=0.0207 (D^2H)^{0.856}$$ From the above, the standing crops of above ground of P. rigida were estimated to be as much as 16.93-34.35 ton dry matter per ha, and those of P. rigitaeda were 20.45-45.55 ton per ha. Annual net production was appraised at 8.07-9.35 ton/ha.yr in P. rigida and at 11.59-13.41 ton/ha.yr in P. rigitaeda (1.0:1.4). It is assumed that high productivity of P. rigitaeda stand compared with P. rigida resulted from an increase of the leaf amount with age. Photosynthetic ability under the saturated light of the current and theold leaves of P. rigida were respectively 2.62 and 0.66mg CO2/g. d. wt..hr and those of P. rigitaeda were 1.17 and 0.96mg CO2/g.d. wt.hr. Respiration of the current and the old leaves at $25^{\circ}C$ were 1.00 and 0.90 mg CO2/g. d. wt..hr. in P. rigida and 0.90 and 0.45mg CO2/g.d.wt.hr in P. rigitaeda. It is assumed that photosynthetic longevity of P. rigitaeda leaves was vigorously maintained longer than that of P. rigida.

• Journal of The Korean Society of Agricultural Engineers
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• v.52 no.3
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• pp.39-45
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• 2010

Quantification of carbon absorption and understanding the human induced land use changes (LUC) forms one of the major study with respect to global climatic changes. An attempt study has been made to quantify the carbon absorption by LUC through remote sensing technology. The Landsat imagery four time periods was classified with the hybrid classification method in order to quantify carbon absorption by LUC. Thereafter, for estimating the amount of carbon absorption, the stand biomass of forest was estimated with the total weight, which was the sum of individual tree weight. Individual tree volumes could be estimated with the crown width extracted from digital forest cover type map. In particular, the carbon conversion index and the ratio of the $CO_2$ molecular weight to the C atomic weight, reported in the IPCC guideline, was used to convert the stand biomass into the amount of carbon absorption. Total carbon absorption has been modeled by taking areal estimates of LUC of four time periods and carbon factors for land use type and standing biomass. Results of this study, through LUC suggests that over a period of construction, 7.10 % of forest and 9.43 % of barren were converted into urban. In the conversion process, there has been a loss of 6.66 t/ha/y (7.94 %) of carbon absorption from the study area.

• Journal of Intelligence and Information Systems
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• v.12 no.2
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• pp.167-183
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• 2006

Since data mining attempts to find unknown facts or rules by dealing with also vaguely-known data sets, it always suffers from high error rate. In order to reduce the error rate, many researchers have employed multiple models in solving a problem. In this research, we present a new type of multiple models, called DyMoS, whose unique feature is that it classifies the input data and applies the different model developed appropriately for each class of data. In order to evaluate the performance of DyMoS, we applied it to a real customer churn problem of an automobile insurance company, The result shows that the DyMoS outperformed any model which employed only one data mining technique such as artificial neural network, decision tree and case-based reasoning.

• The KIPS Transactions:PartB
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• v.8B no.6
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• pp.657-668
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• 2001

It is difficult that web documents are classified with exact user intention because existing document classification systems are based on word frequency number using single keyword. To improve this defect, first, we use keyword, a query, domain knowledge. Like explanation based learning, first, query is analyzed with knowledge based information and then structured user intention information is extracted. We use this intention tree in the course of existing word frequency number based document classification as user information and constraints. Thus, we can classify web documents with more exact user intention. In classifying document, structured user intention information is helpful to keep more documents and information which can be lost in the system using single keyword information. Our hybrid approach integrating user intention information with existing statistics and probability method is more efficient to decide direction and range of document category than existing word frequency approach.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.4
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• pp.59-68
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• 2009

This paper addresses a hybrid multi-focus image fusion scheme using the recent curvelet transform constructions. Hybridization is obtained by combining the MS fusion rule with a novel "copy" method. The proposed scheme use MS rule to fuse the m most significant terms in spectrum of an image at each decomposition level. The scheme is dubbed in this work as m-term fusion in adherence to its use of the MSC (most significant coefficients) in the transform set at any given scale, orientation, and translation. We applied the edge-sensitive objective quality measure proposed by Xydeas and Petrovic to evaluate the method. Experimental results show that the proposed scheme is a potential alternative to the redundant, shift-invariant Dual-Tree Complex Wavelet transforms. In particular, it was confirmed that a 50% m-term fusion produces outputs with no visible quality degradation.