• Asian-Australasian Journal of Animal Sciences
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• v.17 no.6
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• pp.777-783
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• 2004

Samples of 13 species of forest tree leaves fed to livestock in the semi-hilly arid zone of Punjab State in India were collected at 30 d interval for 12 months, in order to assess their nutritional worth for livestock. The ground samples were pooled for 4 different seasons viz. dry hot, hot humid, fall and winter. The chemical composition irrespective of the season revealed that CP content varied between 8.9 (Carrisa) to 22.0% (Leucaena). Globulin was the major protein fraction in most of the leaves. The lowest concentration of cell wall constituents was observed in Morus alba and Grewea. The leaves in general became fiberous and lignified during winter and fall as compared to summer season. The leaves of Grewea, Morus alba, Leucaena, Carrisa and Acacia were rich in Ca, P and most of the trace elements. The total phenolics ranged between 1.88% (Azardirachta) to 15.82% (Acacia). The leaves of Acacia had the highest concentration of hydrolysable tannins (14.6%) whereas that of Carrisa had that of condensed tannins (5.9%). The condensed tannins (more than 3%) were negatively correlated to the digestibility of dry matter (DM), neutral detergent fiber (NDF) and crude protein (CP). The digestion kinetic parameters for DM, NDF and CP revealed that leaves of Morus alba, Zizyphus and Ehretia had highest insoluble but potentially degradable fraction. The minimum rumen fill values also revealed that leaves of Grewea, Azardirachta, Morus, Ehretia and Leucaena had great potential for voluntary DM intake. The leaves of Ougeinia, Malha, Dodenia and Carrisa had significantly higher rumen fill value indicating poor potential for voluntary DM intake. Season did not have any significant impact on digestion kinetic parameters except that most of the leaves had low potentially degradable fraction, which was degraded at slow rate during winter. It was concluded that the leaves of Morus, Ehretia, Grewea and Leucaena had great potential as livestock feed, while feeding of Ougeinia, Malha and Dodonea leaves should be avoided.

• Journal of Korea Multimedia Society
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• v.9 no.12
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• pp.1529-1541
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• 2006

Moving objects have been widely employed in traffic and logistic applications. Spatio-temporal aggregations mainly describe the moving object's behavior in the spatial data warehouse. The previous works usually express the object moving in some certain region, but ignore the object often moving along as the trajectory. Other researches focus on aggregation and comparison of trajectories. They divide the spatial region into units which records how many times the trajectories passed in the unit time. It not only makes the storage space quite ineffective, but also can not maintain spatial data property. In this paper, a spatio-temporal aggregation index structure for moving object trajectory in constrained network is proposed. An extended B-tree node contains the information of timestamp and the aggregation values of trajectories with two directions. The network is divided into segments and then the spatial index structure is constructed. There are the leaf node and the non leaf node. The leaf node contains the aggregation values of moving object's trajectory and the pointer to the extended B-tree. And the non leaf node contains the MBR(Minimum Bounding Rectangle), MSAV(Max Segment Aggregation Value) and its segment ID. The proposed technique overcomes previous problems efficiently and makes it practicable finding moving object trajectory in the time interval. It improves the shortcoming of R-tree, and makes some improvement to the spatio-temporal data in query processing and storage.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.6
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• pp.11-30
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• 2019

Smartcards are used as the basic data for utilizing the various transportation policies and evaluations, etc. and provided the transportation basic statistics index. However, the main problem of the smartcard data is that the most of users do not take the alighting tag at the stop, so there is a limit to the scope of use for the total O-D trip data because incomplete O-D traffic data of transportation card users. In this study, a decision tree of learning model is estimated for the alighting stop of smartcard users. The model estimation accuracy in range less than 2 stops interval was 89.7% on average. By eliminating the incompleteness alighting stop of smartcard data through this model, it is expected to be used as the basic data for various transportation analyses and evaluations.

• Journal of KIISE:Databases
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• v.33 no.2
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• pp.188-200
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• 2006

Once a continuous query, which is commonly used in sensor networks, is issued, the query is executed many times with a certain interval and the results of those query executions are collected to the base station. Since this comes many communication messages continuously, it is important to reduce communication cost for collecting data to the base station. In sensor networks, in-network processing reduces the number of message transmissions by partially aggregating results of an aggregate query in intermediate nodes, or merging the results in one message, resulting in reduction of communication cost. In this paper, we propose a routing tree for sensor nodes that qualify the given query predicate, called the query specific routing tree(QSRT). The idea of the QSRT is to maximize in-network processing opportunity. A QSRT is created seperately for each query during dissemination of the query. It is constructed in such a way that during the collection of query results partial aggregation and packet merging of intermediate results can be fully utilized. Our experimental results show that our proposed method can reduce message transmissions more than 18% compared to the existing one.

• Journal of IKEEE
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• v.23 no.2
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• pp.529-533
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• 2019

Combined cycle power plants are often used to produce power. These days prediction of power plant output based on operating parameters is a major concern. This paper presents an approach to using computational intelligence technique to predict the output power of combined cycle power plant. Computational intelligence techniques have been developed and applied to many real world problems. In this paper, tree architectures of fuzzy neural networks are considered to predict the output power. Tree architectures of fuzzy neural networks have an advantage of reducing the number of rules by selecting fuzzy neurons as nodes and relevant inputs as leaves optimally. For the optimization of the networks, two-step optimization method is used. Genetic algorithms optimize the binary structure of the networks by selecting the nodes and leaves as binary, and followed by random signal-based learning further refines the optimized binary connections in the unit interval. To verify the effectiveness of the proposed method, combined cycle power plant dataset obtained from the UCI Machine Learning Repository Database is considered.

• Journal of Korean Society of Forest Science
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• v.61 no.1
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• pp.37-44
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• 1983

This study was to estimate total annual volume growth by the measurement of mean tree growth during the last 10 years. Surveyed Forest stand was the second block (20.80 ha.)of Kyung Hee University Forests located at San 58 and 64, Gaegok-Ri, Gapyung-Yeup, Gapyung-Goon, Kyunggi province in Korea. The stand was mainly composed of uneven-aged Pinus densiflora and the estimation of tree volume was conducted by taking the cores at the D.B.H. of the sample tree which was selected by sub-sampling. The results obtained were as follows; 1) The regression between the diameter (D) and diameter growth ($\hat{I}$) was $\hat{I}=0.5499+0.0101D$. 2) The estimated equation of confidence interval for the diameter growth was $S^2{\hat{I}}=0.00817(0.09538-0.00952D+0.00027D^2$) 3) The equation for estimating tree height (H) from diameter was $H=1.32376D^{0.77958}$ 4) The equation for estimating tree volume from diameter and height $V=0.0000622D^{1.6918}H^{1.1397}$ 5) Total annual tree volume growth was $5.4041m^3/ha$, and ranged from 5.6131 to $5.1984m^3/ha$. 6) Annual growth rate of total tree volume and its error were 8.8% and 3.9%, respectively. The annual volume growth per tree for any districts can be estimated by this method, and the annual volume growth will be successfully predicted. Because of poor forest growing stock in Korea, annual amount of allowable cut should not exceed annual tree volume growth for better forest management. Accordingly, annual amount of allowable cut should be either equal to or less than annual tree volume growth for the balanced establishment between timber supply and demand in Korea. Demand shortage will be substituted with imported timber. Such plans enable Korean Government to develop a better policy of forest resources management.

• Journal of KIISE:Information Networking
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• v.35 no.6
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• pp.497-509
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• 2008

Various algorithms and architectures for efficient packet classification have been widely studied. Packet classification algorithms based on a decision tree structure such as HiCuts and HyperCuts are known to be the best by exploiting the geometrical representation of rules in a classifier. However, the algorithms are not practical since they involve complicated heuristics in selecting a dimension of cuts and determining the number of cuts at each node of the decision tree. Moreover, the cutting is not efficient enough since the cutting is based on regular interval which is not related to the actual range that each rule covers. In this paper, we proposed a new efficient packet classification algorithm using a range cutting. The proposed algorithm primarily finds out the ranges that each rule covers in 2-dimensional prefix plane and performs cutting according to the ranges. Hence, the proposed algorithm constructs a very efficient decision tree. The cutting applied to each node of the decision tree is optimal and deterministic not involving the complicated heuristics. Simulation results for rule sets generated using class-bench databases show that the proposed algorithm has better performance in average search speed and consumes up to 3-300 times less memory space compared with previous cutting algorithms.

• Journal of the Korean Association of Geographic Information Studies
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• v.27 no.2
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• pp.1-18
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• 2024

This study aimed to identify the optimal street tree planting method to improve the summer thermal environment in Seoul, Republic of Korea. The effects of road direction and street tree planting patterns on urban thermal environments using ENVI-met simulations were analyzed. The 68 scenarios were analyzed based on four road directions and 17 planting patterns. The results showed that tree planting had a reducing air temperature, mean radiant temperature, human thermal sensation (PET and UTCI). The most effective planting pattern among all scenarios was low tree height (6m), wide crown width (9m), high leaf area index (3.0), and narrow planting interval (8m). The largest improvement in the thermal environment was the northern sidewalk of the east-west road. Since this study used computer simulations, the difference from real urban spaces should be considered, and further research is needed through field measurement and consideration of more variables.

• Journal of the Korean Institute of Landscape Architecture
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• v.42 no.5
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• pp.13-21
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• 2014

This study generated regression models to quantify storage and annual uptake of carbon from five native landscape tree species through a direct harvesting method, and established essential information to estimate carbon reduction effects from urban greenspaces. Tree species for the study included the Chionanthus retusus, Prunus armeniaca, Abies holophylla, Cornus officinalis, and Taxus cuspidata, which are usually planted in cities of middle Korea, but for which no information on carbon reduction is available. Ten tree individuals for each species were sampled reflecting various stem diameter sizes at a given interval. The study measured biomass for each part including the roots of sample trees to compute total carbon storage per tree. The annual carbon uptake per tree was quantified by analyzing the radial growth rates of stem samples at breast height or ground level. Regression models were developed using diameter at breast height (dbh) or ground level (dg) as an independent variable to easily estimate storage and annual uptake of carbon per tree for each species. All the regression models showed high fitness with $r^2$ values of 0.92~0.99. Storage and annual uptake of carbon from a tree with dbh of 10 cm were greatest with C. retusus (20.0 kg and 5.9 kg/yr, respectively), followed by P. armeniaca (17.5 kg and 4.5 kg/yr) and A. holophylla (13.2kg and 1.8 kg/yr) in order. A C. officinalis tree and T. cuspidata tree with dg of 10 cm stored 9.3 and 6.3 kg of carbon and annually sequestered 3.2 and 0.6 kg, respectively. The above-mentioned carbon storage equaled the amount of carbon emitted from gasoline consumption of about 23~35 L for C. retusus, P. armeniaca, and A. holophylla, and 11~16 L for C. officinalis and T. cuspidata. A tree with the diameter size of 10 cm annually offset carbon emissions from gasoline use of about 6~10 L for C. retusus, P. armeniaca, and C. officinalis, and 1~3 L for A. holophylla and T. cuspidata. The study breaks new ground to easily quantify biomass and carbon reduction for the tree species by overcoming difficulties in direct cutting and root digging of urban landscape trees.

• Journal of applied mathematics & informatics
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• v.17 no.1_2_3
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• pp.1-23
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• 2005

The shortest-paths problem is a fundamental problem in graph theory and finds diverse applications in various fields. This is why shortest path algorithms have been designed more thoroughly than any other algorithm in graph theory. A large number of optimization problems are mathematically equivalent to the problem of finding shortest paths in a graph. The shortest-path between a pair of vertices is defined as the path with shortest length between the pair of vertices. The shortest path from one vertex to another often gives the best way to route a message between the vertices. This paper presents an $O(n^2)$ time sequential algorithm and an $O(n^2/p+logn)$ time parallel algorithm on EREW PRAM model for solving all pairs shortest paths problem on circular-arc graphs, where p and n represent respectively the number of processors and the number of vertices of the circular-arc graph.