• Journal of Forest and Environmental Science
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• 제35권3호
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• pp.159-172
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• 2019

Madhupur National Park (MNP) is one of the last remaining patches of old-growth natural Sal forest left in Bangladesh where the forest is tropical moist deciduous type. A study was revealed to assess the tree species diversity and composition in this area. For determining tree species the study was conducted through extensive random quadrat survey methods with $20m{\times}20m$ sized plots. Results of the study indicated that there were 139 tree species belonging to 100 genera and 40 families. The quadrat survey assessed the basal area, stem density, diversity indices and importance value index of the tree species having ${\geq}5cm$ D.B.H (Diameter at Brest Height). The basal area and stem density of the tree species were $20.689{\pm}1.08m^2/ha$ and $1412.93{\pm}64.27stem\;ha^{-1}$ while, diversity indices, i.e. Shannon-Wiener's diversity, Simpson's evenness, Margalef's species richness and Pielou's dominance indices indicated poor diversity in comparison to that of other PAs (Protected Areas) in South-Eastern region of Bangladesh. The structural composition based on height and D.B.H through reverse-J shaped curve indicated higher regeneration and recruitment but removal of trees of large growth classes. Sal (Shorea robusta) was the most dominant tree species that accounts 75% of the total tree individuals in the natural forest patches. However, some associates of Sal, i.e. Bhutum (Hymenodictyon orixensis), Gadila (Careya arborea), and Kusum (Schleichera oleosa) etc. were seemed to be rare in MNP.

• Journal of Electrical Engineering and Technology
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• 제10권5호
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• pp.2018-2030
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• 2015

A two stage ac drive configuration consisting of a single-phase line commutated rectifier and a three-phase voltage source inverter (VSI) is very common in low and medium power applications. The deterministic pulse width modulation (PWM) methods like sinusoidal PWM (SPWM) could not be considered as an ideal choice for modern drives since they result mechanical vibration and acoustic noise, and limit the application scope. This is due to the incapability of the deterministic PWM strategies in sprawling the harmonic power. The random PWM (RPWM) approaches could solve this issue by creating continuous harmonic profile instead of discrete clusters of dominant harmonics. Insufficient filtering at dc link results in the amplitude distortion of the input dc voltage to the VSI and has the most significant impact on the spectral errors (difference between theoretical and practical spectra). It is obvious that the sprawling effect of RPWM undoubtedly influenced by input fluctuation and the discrete harmonic clusters may reappear. The influence of dc link fluctuation on harmonics and their spreading effect in the VSI remains invalidated. A case study is done with four different filter capacitor values in this paper and results are compared with the constant dc input operation. This paper also proposes an ingenious RPWM, a ripple dosed sinusoidal reference-random carrier PWM (RDSRRCPWM), which has the innate capacity of suppressing the effect of input fluctuation in the output than the other modern PWM methods. MATLAB based simulation study reveals the fundamental component, total harmonic distortion (THD) and harmonic spread factor (HSF) for various modulation indices. The non-ideal dc link is managed well with the developed RDSRRCPWM applied to the VSI and tested in a proto type VSI using the field programmable gate array (FPGA).

• 전력전자학회논문지
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• 제9권6호
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• pp.525-535
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• 2004

본 연구에서는 높은 변조지수 M 영역에서는 소음 및 고조파 스펙트럼의 광대역화 효과가 저하되는 2상 RCD-PWM(Random Centered Distribution PWM)기법의 문제점을 개선하고자, 듀얼 영 벡터 모드를 갖는 2상 RCD(Dual Zero Vector Modes RCD : DZRCD)기법을 제안하였다. 제안된 2상 DZRCD기법은 변조지수 M=0.8을 기준으로 하여 M이 0.8보다 큰 영역에서는 영 벡터로 $V_0$(111)를 선택하고, 0.8보다 작은 영역에서는 $V_0$(000)를 선택한다. 제안된 방법을 16비트 마이크로 콘트롤러인 C-167기반의 유도 모터 구동 시스템에 적용해 본 결과, M이 0.8이상인 영역에서 모터의 전압 / 전류 및 스위칭 소음 스펙트럼의 광대역화 특성이 종전의 방법과 비하여 우수함을 확인할 수 있었다.

• Geomechanics and Engineering
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• 제31권4호
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• pp.353-364
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• 2022

This study presents a hybrid algorithm for classifying the rock joints, where the improved artificial bee colony (IABC) and the fuzzy C-means (FCM) clustering algorithms are incorporated to take advantage of the artificial bee colony (ABC) algorithm by tuning the FCM clustering algorithm to obtain the more reasonable and stable result. A coefficient is proposed to reduce the amount of blind random searches and speed up convergence, thus achieving the goals of optimizing and improving the ABC algorithm. The results from the IABC algorithm are used as initial parameters in FCM to avoid falling to the local optimum in the local search, thus obtaining stable classifying results. Two validity indices are adopted to verify the rationality and practicability of the IABC-FCM algorithm in classifying the rock joints, and the optimal amount of joint sets is obtained based on the two validity indices. Two illustrative examples, i.e., the simulated rock joints data and the field-survey rock joints data, are used in the verification to check the feasibility and practicability in rock engineering for the proposed algorithm. The results show that the IABC-FCM algorithm could be applicable in classifying the rock joint sets.

• Asian Pacific Journal of Cancer Prevention
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• 제13권11호
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• pp.5517-5523
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• 2012

Purpose: To evaluate the diagnostic accuracy of ultrasonograph and fine-needle aspiration cytologic examination (USG-FNAC) in the staging of axillary lymph node metastasis in breast cancer patients.Methods: We conducted an electronic search of the literature addressing the performance of USG-FNAC in diagnosis of axillary lymph node metastasis in databases such as Pubmed, Medline, Embase, Ovid and Cochrane library. We introduced a series of diagnostic test indices to evaluate the performance of USG-FNAC by the random effect model (REM), including sensitivity, specificity, likelihood ratios, and diagnostic odds ratios and area under the curve (AUC). Results: A total of 20 studies including 1371 cases and 1289 controls were identified. The pooled sensitivity was determined to be 0.66 (95% CI 0.64-0.69), specificity 0.98 (95% CI 0.98-0.99), positive likelihood ratio 22.7 (95% CI 15.0-34.49), negative likelihood ratio 0.32 (95% CI 0.25-0.41), diagnostic OR 84.2 (95% CI 53.3-133.0). Due to the marginal threshold effect found in some indices of diagnostic validity, we used a summary SROC curve to aggregate data, and obtained a symmetrical curve with an AUC of 0.942. Conclusion: The results of this meta-analysis indicated that the USG-FNAC techniques have acceptable diagnostic validity indices and can be used for early staging of axillary lymph node in breast cancer patients.

• 응용통계연구
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• 제23권4호
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• pp.767-776
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• 2010

Broken-Stick 모형 (Barton과 David, 1956) 하에서 순서화된 분절구간의 기대길이를 기초로 유효차원의 개수를 결정하는 Frontier (1976)방법은 일관된 모의실험 결과를 제공하는 기준 중의 하나로 보고된 바 있다 (Jackson, 1993). 이 연구에서는 Broken-Stick 모형(BSM) 하에서 분절구간길이의 분포를 이용하여 주성분 상대공헌도의 크기를 확률적으로 평가하는 BSM 유의확률기준을 제안한다. 이에 부가하여 소득분포의 불균등성을 도식화한 로렌츠곡선과 이에 대응하는 지니계수를 통해 주성분 공헌도의 포괄적 균등성을 탐구한다.

• Journal of applied mathematics & informatics
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• 제13권1_2호
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• pp.441-446
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• 2003

Let { $X_{n}$ , n $\geq$ 1} be a sequence of independent and identically distributed random variables with a common continuous distribution function F(x) and probability density function f(x). Let $Y_{n}$ = max{ $X_1$, $X_2$, …, $X_{n}$ } for n $\geq$ 1. We say $X_{j}$ is an upper record value of { $X_{n}$ , n$\geq$1} if $Y_{j}$ > $Y_{j-1}$, j > 1. The indices at which the upper record values occur are given by the record times {u(n)}, n$\geq$1, where u(n) = min{j｜j>u(n-1), $X_{j}$ > $X_{u}$ (n-1), n$\geq$2} and u(1) = 1. We call the random variable X $\in$ Beta (1, c) if the corresponding probability cumulative function F(x) of x is of the form F(x) = 1-(1-x)$^{c}$ , c>0, 0$\leq$x$\leq$1. In this paper, we will give a characterization of the beta distribution of the first kind by considering conditional expectations of record values.s.

• 한국농공학회논문집
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• 제59권3호
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• pp.97-110
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• 2017

This study investigates the efficiencies of machine learning models, including artificial neural network (ANN), generalized regression neural network (GRNN), adaptive neuro-fuzzy inference system (ANFIS) and random forest (RF), for reservoir water level forecasting in the Chungju Dam, South Korea. The models' efficiencies are assessed based on model efficiency indices and graphical comparison. The forecasting results of the models are dependent on lead times and the combination of input variables. For lead time t = 1 day, ANFIS1 and ANN6 models yield superior forecasting results to RF6 and GRNN6 models. For lead time t = 5 days, ANN1 and RF6 models produce better forecasting results than ANFIS1 and GRNN3 models. For lead time t = 10 days, ANN3 and RF1 models perform better than ANFIS3 and GRNN3 models. It is found that ANN model yields the best performance for all lead times, in terms of model efficiency and graphical comparison. These results indicate that the optimal combination of input variables and forecasting models depending on lead times should be applied in reservoir water level forecasting, instead of the single combination of input variables and forecasting models for all lead times.

• Plant Resources
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• 제3권3호
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• pp.211-218
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• 2000

Intraspecific genetic relationship of 19 variation types of the Yam (Dioscorea alata) classified by their external morphological characteristics such as leaf and tuber shape were assessed by DNA using random and specific primer. Twenty two out of 113 primers (100 random[10-mer] primers, two 15 mer [M13 core sequence, and (GGAT)$_4$ sequence]) had been used in PCR-amplification. Only 12 primers, however, were success in DNA amplification in all of the analyzed plants, resulting in 93 randomly and specifically amplified DNA fragments. The analyzed taxa showed very high polymorphisms(69 bands, 71.0 %), allowing individual taxon to be identified based on DNA fingerprinting. Monomorphic bands among total amplified DNA bands of each primer was low under the 50%. Similarity indices between accessions were computed from PCR(polymerase chain reaction) data, and genetic relationships among intraspecific variations were closely related at the levels ranging from 0.66 to 0.90. These DNA data were not matched well with those of morphological characters since they were divided into two major groups at the similarity coefficient value of 0.70. Therefore, Grouping of species into variation types by mainly morphological charactistics was suggested unreasonable.

• Advances in Computational Design
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• 제7권3호
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• pp.253-279
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• 2022

This study investigated and predicted the Marshall stability of glass-fiber asphalt mix, carbon-fiber asphalt mix and glass-carbon-fiber asphalt (hybrid) mix by using machine learning techniques such as Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest(RF), The data was obtained from the experiments and the research articles. Assessment of results indicated that performance of the Artificial Neural Network (ANN) based model outperformed applied models in training and testing datasets with values of indices as; coefficient of correlation (CC) 0.8492 and 0.8234, mean absolute error (MAE) 2.0999 and 2.5408, root mean squared error (RMSE) 2.8541 and 3.3165, relative absolute error (RAE) 48.16% and 54.05%, relative squared error (RRSE) 53.14% and 57.39%, Willmott's index (WI) 0.7490 and 0.7011, Scattering index (SI) 0.4134 and 0.3702 and BIAS 0.3020 and 0.4300 for both training and testing stages respectively. The Taylor diagram also confirms that the ANN-based model outperforms the other models. Results of sensitivity analysis show that Carbon fiber has a major influence in predicting the Marshall stability. However, the carbon fiber (CF) followed by glass-carbon fiber (50GF:50CF) and the optimal combination CF + (50GF:50CF) are found to be most sensitive in predicting the Marshall stability of fibrous asphalt concrete.