• 한국토양비료학회지
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• 제42권5호
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• pp.323-329
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• 2009

This study was conducted to analyze growth responses of fast growing tree species(8 clones of hybrid poplars, Salix alba, Metasequoia glyptostroboides, Liriodendron tulipifera, Acer okamotoanum, and Quercus palustris), the chemical characteristics of soil and $NO_3-N$ concentration of groundwater in a plantation applied with liquid pig manure. Concentrations of nitrogen and phosphorous in the soil treated with liquid pig manure were higher than that of the soil treated without liquid pig manure. With the exception of S. alba, DBH(Diameter at Breast Height) growth of all the fast growing tree species treated with liquid pig manure was higher than that of the species treated without liquid pig manure. In liquid pig manure treatment group, P. euramericana 'Eco28' clone showed the best performance in height and DBH growth. Concentration of nitrogen in the leaf with liquid pig manure was higher than that of the leaf treated without liquid pig manure. Based on the $NO_3-N$ concentration of groundwater analyzed during the experimental period, there was no evidence that groundwater was polluted by the liquid pig manure applied at the plantation.

• 한국산림과학회지
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• 제43권1호
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• pp.1-5
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• 1979

Poulusp alba P. glandulosa와 그의 양친종(兩親種)에 대(對)하여 엽병(葉柄)의 중간부위(中間部位)의 유관속(維管束)의 수(數)와 배열상태(配列狀態)를 조사하여 다음과 같은 결과(結果)를 얻었다. 1) 유관속(維管束)의 수(數)와 배열상태(配列狀態)의 변이(變異)는 엽병(葉炳)의 중간부위(中間部位)가 엽신측부위(葉身側部位)보다 변화(變化)가 적었다. 2) 유관속(維管束)의 배열(配列)과 수(數)의 변이(變異)는 한 개체(個體)에서, 또는 동일(同一) clone의 개체(個體)에 따라서, 그리고 동일(同一) 수종(樹種)의 개체(個體)에 따라서 다소(多少)의 변이(變異)가 있다. 3) P. alba${\times}$P. glandulosa의 $F_1$의 유관속형(維管束型)은 5 type이 있다. 그중(中) 26.7%는 P. alba와과 동일형(同一型)이고, 13.3%는 P. glandulosa와 동일형(同一型)이고, 그리고 53.3%은 유전(遺傳)에 의(依)하여 연유(緣由)된 $F_1$형(型)이다. 4) P. alba형(型)을 가진 clone number는 66-20-1, 66-6-8, 65-22-11, 64-6-44, P.이고, 그 중(中)에는 P. tomentiglandulosa와 유사(類似)한 clone도 있었다. P. glandulosa형(型)의 clone number는 65-95, 66-14-93,이다. $F_1$형(型)의 clone은 66-15-3, 67-6-3, 65-22-4, 66-26-55, 68-1-54, 66-14-99, 65-29-19, 66-25-5이다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권6호
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• pp.2492-2512
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• 2018

Multicast communication can effectively reduce network resources consumption in contrast with unicast. With the advent of SDN, current researches on multicast traffic are mainly conducted in the SDN scenario, thus to mitigate the problems of IP multicast such as the unavoidable difficulty in traffic engineering and high security risk. However, migration to SDN cannot be achieved in one step, hybrid SDN emerges as a transitional networking form for ISP network. In hybrid SDN, for acquiring similar TE and security performance as in SDN multicast, we redirect every multicast traffic to an appropriate SDN node before reaching the destinations of the multicast group, thus to build up a core-based multicast tree substantially which is first introduced in CBT. Based on the core SDN node, it is possible to realize dynamic control over the routing paths to benefit traffic engineering (TE), while multicast traffic manageability can also be obtained, e.g., access control and middlebox-supported network services. On top of that, multiple core-based multicast trees are constructed for each multicast group by fully taking advantage of the routing flexibility of SDN nodes, in order to further enhance the TE performance. The multicast routing and splitting (MRS) algorithm is proposed whereby we jointly and efficiently determine an appropriate core SDN node for each group, as well as optimizing the traffic splitting fractions for the corresponding multiple core-based trees to minimize the maximum link utilization. We conduct simulations with different SDN deployment rate in real network topologies. The results indicate that, when 40% of the SDN switches are deployed in HSDN as well as calculating 2 trees for each group, HSDN multicast adopting MRS algorithm can obtain a comparable TE performance to SDN multicast.

• 한국멀티미디어학회논문지
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• 제6권3호
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• pp.384-396
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• 2003

웹 GIS에서 인터넷 서비스 이용자의 집중 현상으로 발생하는 서버의 과부하 현상을 막기 위한 대표적인 방법으로 클라이언트와 서버가 모두 질의에 참여하는 하이브리드(Hybrid) 질의 처리 방식이 있다. 그러나 하이브리드 질의 처리 방식은 서버 확장에 제약이 존재하기 때문에 근본적인 해결책이 되지 못한다. 따라서 웹 GIS 서버의 안정적인 서비스 제공을 위해서는 웹 클러스터링 기술의 도입이 필요하다. 본 논문에서는 웹 GIS클러스터링 시스템을 위한 질의 영역의 인접성을 이용한 로드 밸런싱 기법을 제안한다. 제안하는 기법은 공간 데이터를 관리하는 타일을 기반으로 인접한 타일 그룹을 생성하여 각 서버에 할당하며, 질의 영역 및 공간 연산을 고려하여 서버에서 질의가 처리되는 동안 버퍼 재사용율이 최대가 되도록 클라이언트의 질의 요청을 서버에 전달한다. 제안하는 기법은 서버의 버퍼를 공간 인덱스 탐색에 최적화함으로써 서버의 버퍼 재사용율을 높이고, 클러스터링 시스템에서 디스크의 접근 횟수를 낮추어 전체적인 서버 시스템의 처리 능력을 향상시킨다.

• International Journal of Computer Science & Network Security
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• 제23권12호
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• pp.101-106
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• 2023

Chronic illnesses are among the most common serious problems affecting human health. Early diagnosis of chronic diseases can assist to avoid or mitigate their consequences, potentially decreasing mortality rates. Using machine learning algorithms to identify risk factors is an exciting strategy. The issue with existing feature selection approaches is that each method provides a distinct set of properties that affect model correctness, and present methods cannot perform well on huge multidimensional datasets. We would like to introduce a novel model that contains a feature selection approach that selects optimal characteristics from big multidimensional data sets to provide reliable predictions of chronic illnesses without sacrificing data uniqueness.[1] To ensure the success of our proposed model, we employed balanced classes by employing hybrid balanced class sampling methods on the original dataset, as well as methods for data pre-processing and data transformation, to provide credible data for the training model. We ran and assessed our model on datasets with binary and multivalued classifications. We have used multiple datasets (Parkinson, arrythmia, breast cancer, kidney, diabetes). Suitable features are selected by using the Hybrid feature model consists of Lassocv, decision tree, random forest, gradient boosting,Adaboost, stochastic gradient descent and done voting of attributes which are common output from these methods.Accuracy of original dataset before applying framework is recorded and evaluated against reduced data set of attributes accuracy. The results are shown separately to provide comparisons. Based on the result analysis, we can conclude that our proposed model produced the highest accuracy on multi valued class datasets than on binary class attributes.[1]

• Steel and Composite Structures
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• 제52권2호
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• pp.145-163
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• 2024

This paper presents six novel hybrid machine learning (ML) models that combine support vector machines (SVM), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), extreme gradient boosting (XGB), and categorical gradient boosting (CGB) with the Harris Hawks Optimization (HHO) algorithm. These models, namely HHO-SVM, HHO-DT, HHO-RF, HHO-GB, HHO-XGB, and HHO-CGB, are designed to predict the ultimate strength of both rectangular and circular reinforced concrete (RC) columns. The prediction models are established using a comprehensive database consisting of 325 experimental data for rectangular columns and 172 experimental data for circular columns. The ML model hyperparameters are optimized through a combination of cross-validation technique and the HHO. The performance of the hybrid ML models is evaluated and compared using various metrics, ultimately identifying the HHO-CGB model as the top-performing model for predicting the ultimate shear strength of both rectangular and circular RC columns. The mean R-value and mean a20-index are relatively high, reaching 0.991 and 0.959, respectively, while the mean absolute error and root mean square error are low (10.302 kN and 27.954 kN, respectively). Another comparison is conducted with four existing formulas to further validate the efficiency of the proposed HHO-CGB model. The Shapely Additive Explanations method is applied to analyze the contribution of each variable to the output within the HHO-CGB model, providing insights into the local and global influence of variables. The analysis reveals that the depth of the column, length of the column, and axial loading exert the most significant influence on the ultimate shear strength of RC columns. A user-friendly graphical interface tool is then developed based on the HHO-CGB to facilitate practical and cost-effective usage.

• 한국산림과학회지
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• 제66권1호
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• pp.109-117
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• 1984

잡종채종원상(雜種採種園上)의 리기다소나무 49가계(家系)로부터 종자(種字)를 채취하여 종자(種字)의 배유(胚乳) 및 배(胚)에 대한 Aspartate aminotransferase(AAT), Glutamate dehydrogenase(GDH) 및 Leucine aminopeptidase(LAP)등의 Allozyme 변이(變異)를 조사하여 다음과 같은 결과를 얻었다. 이들 세 가지 Allozyme system에서 AAT에 5개, GDH에 1개 및 LAP에 2개, 모두 8개의 유전자좌(遺傳子座)(Locus)가 발견되었으며 GDH를 제외한 모든 유전자좌에서 Allozyme Polymorpshism을 발견하였다. 각 유전자좌에 있어서 평균 대입유전자(對立遺傳子) 수(數)는 종자모수집단(種子母樹集團)에서 2.33개, 차대집단(次代集團)에서 2.67개였다. 평균이형접합성(平均異型接合性) 종자모수집단이 0.235, 차대집단(次代集團)이 0.238이었고 유전자의 유전적(遺傳的) 다양성(多樣性)은 종자모수집단이 5.409, 차대집단(次代集團)이 5.569로서 같은 수종의 다른 집단 또는 다른 침엽수 수종에 비하여 비교적 낮은 값을 나타냈다. Allozyme분석에 의하여 잡종채종원의 리기다소나무에 있어서 일대잡종(一代雜種) 종자(種子) 발생율(發生率)을 추정해 본 결과 일대잡종 종자의 발생빈도는 0.77%로서 묘포에서 조사한 일대잡종묘의 발생율 0.73%와 거의 일치하였다. 잡종채종원상의 종자모수 리기다소나무 및 이대 차대들 Allozyme변이에 있어서 Wahlund 효과(效果), 비교적 높은 수준의 자가수정(自家受精) 및 Non-random mating 등의 가능성이 발견되어 이들에 대한 Allozyme 변이 연구에 깊은 주의가 필요하다.

• 정보기술응용연구
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• 제1권
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• pp.173-211
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• 1999

The task of classification permeates all walks of life, from business and economics to science and public policy. In this context, nonlinear techniques from artificial intelligence have often proven to be more effective than the methods of classical statistics. The objective of knowledge discovery and data mining is to support decision making through the effective use of information. The automated approach to knowledge discovery is especially useful when dealing with large data sets or complex relationships. For many applications, automated software may find subtle patterns which escape the notice of manual analysis, or whose complexity exceeds the cognitive capabilities of humans. This paper explores the utility of a collaborative learning approach involving integrated models in the preprocessing and postprocessing stages. For instance, a genetic algorithm effects feature-weight optimization in a preprocessing module. Moreover, an inductive tree, artificial neural network (ANN), and k-nearest neighbor (kNN) techniques serve as postprocessing modules. More specifically, the postprocessors act as second0order classifiers which determine the best first-order classifier on a case-by-case basis. In addition to the second-order models, a voting scheme is investigated as a simple, but efficient, postprocessing model. The first-order models consist of statistical and machine learning models such as logistic regression (logit), multivariate discriminant analysis (MDA), ANN, and kNN. The genetic algorithm, inductive decision tree, and voting scheme act as kernel modules for collaborative learning. These ideas are explored against the background of a practical application relating to financial fraud management which exemplifies a binary classification problem.

• 한국통신학회논문지
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• 제19권8호
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• pp.1587-1594
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• 1994

피지 예측기는 강인한 트리 부호화기에서 가장 중요한 부분 중에 하나이다. 피치 예측기는 역방향으로 블록 적용 방법과 회귀적인 방법이 결합되어 구성되어진다. 부호화기의 전송에러에 대한 성능을 개선하고 입력 음성의 피치주기의 변화를 추적하기 위해 피치 예측기의 스무더를 부가하는 방법을 제시한다. 3개의 탭을 갖는 스무더는 고정된 계수를 가지거나 피치 합성기의 출력신호의 자기상관 함수에 따라 변화되는 가계변수를 가질 수 있다. 피치 예측기에 스무더의 부가는 한 블록 내에서의 피치주기의 변화를 추적할 수 있고 채널에러에 대한 영향도 줄일 수 있다.

• 디지털산업정보학회논문지
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• 제8권2호
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• pp.131-143
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• 2012

This paper introduces the double-density discrete wavelet transform using 3 direction separable processing method, which is a discrete wavelet transform that combines the double-density discrete wavelet transform and quincunx sampling method, each of which has its own characteristics and advantages. The double-density discrete wavelet transform is nearly shift-invariant. But there is room for improvement because not all of the wavelets are directional. That is, although the double-density DWT utilizes more wavelets, some lack a dominant spatial orientation, which prevents them from being able to isolate those directions. The dual-tree discrete wavelet transform has a more computationally efficient approach to shift invariance. Also, the dual-tree discrete wavelet transform gives much better directional selectivity when filtering multidimensional signals. But this transformation has more cost complexity Because it needs eight digital filters. Therefor, we need to hybrid transform which has the more directional selection and the lower cost complexity. A solution to this problem is a the double-density discrete wavelet transform using 3 direction separable processing method. The proposed wavelet transformation services good performance in image and video processing fields.