Evaluating allometric shrub biomass equations fit to generated data

1985 ◽  
Vol 63 (1) ◽  
pp. 64-67 ◽  
Author(s):  
Gary J. Brand ◽  
W. Brad Smith
Keyword(s):  
Original Data ◽  
Single Equation ◽  
Biomass Equations ◽  
Species Groups ◽  
Hardwood Tree ◽  
Weighted Data ◽  
Tall Shrub ◽  
Shrub Biomass ◽  
Better Than ◽  
Salix Spp

Several published allometric biomass equations may be available for a particular species. For some applications a method is needed to produce a single equation for the species. Several investigators have produced such an equation by refitting the combined original data used to develop the separate equations. We evaluated a method, previously examined for several hardwood tree species, for producing an equation fit to data generated from the published equations when the original data are not available. For the three tall shrub species groups (Acer spicatum Lam., Amelanchier spp., and Salix spp.) examined, the generated weighted data equation fit the combined weighted data as well as or better than the published equations.

Inscribing in to an Control Attempt against Jammer Attack Using FB Decoding Mechanism

2014 ◽  
Vol 573 ◽  
pp. 412-417
Author(s):  
G. Sona ◽  
P. Annapandi ◽  
Boopathy Yamni
Keyword(s):  
Spread Spectrum ◽  
Cost Effective ◽  
Original Data ◽  
Overhead Cost ◽  
Storage Overhead ◽  
Node Spreading ◽  
Time Required ◽  
Key Scheduling ◽  
Secured Communication ◽  
Better Than

Previously Spread Spectrum (SS) communication involve by setting up preconfigured keys among the communicating nodes that are constrained to possess synchronous behaviour. This extends to several issues creating circular dependency problem, offering less energy efficiency and thereby leading to insecure short-lived communication. In this paper, an opponent resilient secret sharing concept is introduced without any establishment of pre-shared keys by FB (Forward Backward) decoding. It illustrates using time reversed message extraction and key scheduling at receiver side that enables secured transmission over wireless communication even when the receiver node remains inactive and attaining jammer not to obtain the original data sent by the sender node. Spreading the data involves use of DSSS as it would be more compatible in adjusting to multiple bandwidths. Main goal is to transmit the message in such a way that the time required to deliver the secret must be less than the time for the opponent to find key during transmission. Further, it come up with minimal storage overhead, cost effective and sustains long-lived secured communication among the interacting nodes. Evaluation of various parameters is performed using NS-2 toolkit to prove that this newer approach is better than earlier work.

Regional Greenweight Biomass Equations for the Northeast

1990 ◽  
Vol 7 (4) ◽  
pp. 187-189
Author(s):  
James Barrett ◽  
John Jastrembski
Keyword(s):  
New York ◽  
New Hampshire ◽  
Regional Differences ◽  
Species Group ◽  
Site Quality ◽  
Data Sets ◽  
Stem Form ◽  
Hardwood Species ◽  
Biomass Equations ◽  
Species Groups

Abstract Total stem green-weight biomass equations are given for four northeastern softwood species and ten northeastern hardwood species groups. Data, compiled from studies conducted in New York, New Hampshire, Michigan, Ohio, and two independent studies in West Virginia, suggest that for some data sets there were regional differences for species group. The regional differences may reflect differences in stem form, site quality, and moisture content of the trees due to soil moisture and seasonal variation in data collection analysis. North. J. Appl. For. 7:187-189, December 1990.

An Empirical Study on Feature Subsampling-Based Ensembles

2012 ◽  
Vol 239-240 ◽  
pp. 848-852
Author(s):  
Yan Yan Wei ◽  
Tao Sheng Li
Keyword(s):  
Empirical Study ◽  
Ensemble Methods ◽  
Original Data ◽  
Data Sets ◽  
Random Subspace ◽  
Subspace Method ◽  
Rotation Forest ◽  
Tree Classifier ◽  
Noise Data ◽  
Better Than

Feature subsampling techniques help to create diverse for classifiers ensemble. In this article we investigate two feature subsampling-base ensemble methods - Random Subspace Method (RSM) and Rotation Forest Method (RFM) to explore their usability with different learning algorithms and the robust on noise data. The experiments show that RSM with IBK work better than RFM and AdaBoost, and RFM with tree classifier and rule classifier achieve prominent improvement than others. We also find that Logistic algorithm is not suitable for any of the three ensembles. When adding classification noise into original data sets, ensembles outperform singles at lower noisy level but fail to maintain such superior at higher noisy level.

scholarly journals Interactive Entertainment Image Restoration Method Based on Hopfield Neural Network

2018 ◽  
Vol 232 ◽  
pp. 01008
Author(s):  
Shuangqing lv
Keyword(s):  
Neural Network ◽  
Image Restoration ◽  
Hopfield Neural Network ◽  
Original Data ◽  
Small Scale ◽  
Interactive Entertainment ◽  
Degraded Image ◽  
Restoration Method ◽  
Traditional Image ◽  
Better Than

The traditional image restoration methods of interactive entertainment are based on the original data. This paper proposes an interactive entertainment image restoration method based on Hopfield neural network. Firstly, the nonlinear mapping relationship between the degraded image and the real image is preliminarily established through the network, and then optimized by the algorithm. Finally, the image restoration can be achieved through the network. The experiments show that it has higher feasibility and the recovery effect on small-scale blur is better than the existing method.

Epilepsy Diagnosis Using Multi-view & Multi-medoid Entropy-based Clustering with Privacy Protection

2021 ◽  
Vol 21 (2) ◽  
pp. 1-21
Author(s):  
Yuanpeng Zhang ◽  
Yizhang Jiang ◽  
Lianyong Qi ◽  
Md Zakirul Alam Bhuiyan ◽  
Pengjiang Qian
Keyword(s):  
Collaborative Learning ◽  
Original Data ◽  
Extraction Techniques ◽  
Eeg Signals ◽  
Learning Mechanisms ◽  
Eeg Data ◽  
Epilepsy Diagnosis ◽  
Pattern Information ◽  
Weight Learning ◽  
Better Than

Using unsupervised learning methods for clinical diagnosis is very meaningful. In this study, we propose an unsupervised multi-view & multi-medoid variant-entropy-based fuzzy clustering (M 2 VEFC) method for epilepsy EEG signals detecting. Comparing with existing related studies, M 2 VEFC has four main merits and contributions: (1) Features in original EEG data are represented from different perspectives that can provide more pattern information for epilepsy signals detecting. (2) During multi-view modeling, multi-medoids are used to capture the structure of clusters in each view. Furthermore, we assume that the medoids in a cluster observed from different views should keep invariant, which is taken as one of the collaborative learning mechanisms in this study. (3) A variant entropy is designed as another collaborative learning mechanism in which view weight learning is controlled by a user-free parameter. The parameter is derived from the distribution of samples in each view such that the learned weights have more discrimination. (4) M 2 VEFC does not need original data as its input—it only needs a similarity matrix and feature statistical information. Therefore, the original data are not exposed to users and hence the privacy is protected. We use several different kinds of feature extraction techniques to extract several groups of features as multi-view data from original EEG data to test the proposed method M 2 VEFC. Experimental results indicate M 2 VEFC achieves a promising performance that is better than benchmarking models.

scholarly journals Multiscale Bidirectional Input Convolutional and Deep Neural Network for Human Activity Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yishu Qiu ◽  
Lanliang Lin ◽  
Lvqing Yang ◽  
Dingzhao Li ◽  
Runhan Song ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Original Data ◽  
Learning Ability ◽  
Activity Data ◽  
Input Module ◽  
Single Input ◽  
Input Model ◽  
Forward Sequence ◽  
Better Than

In this paper, we proposed a multiscale and bidirectional input model based on convolutional neural network and deep neural network, named MBCDNN. In order to solve the problem of inconsistent activity segments, a multiscale input module is constructed to make up for the noise caused by filling. In order to solve the problem that single input is not enough to extract features from original data, we propose to manually design aggregation features combined with forward sequence and reverse sequence and use five cross-validation and stratified sampling to enhance the generalization ability of the model. According to the particularity of the task, we design an evaluation index combined with scene and action weight, which enriches the learning ability of the model to a great extent. In the 19 kinds of activity data based on scene+action, the accuracy and robustness are significantly improved, which is better than other mainstream traditional methods.

scholarly journals Novelty Ranking Approach with Z-Score and Fuzzy Multi- Attribute Decision Making Combination

2018 ◽  
Vol 7 (4.7) ◽  
pp. 476
Author(s):  
Basri . ◽  
Syarli .
Keyword(s):  
Decision Making ◽  
Statistical Technique ◽  
Z Score ◽  
New Approach ◽  
Score Method ◽  
Multi Attribute Decision Making ◽  
Weighted Data ◽  
Hierarchy Process ◽  
Better Than

This study aims to recommend a new approach in the ranking system by analyzing the combination of the Z-Score method and the Fuzzy Multi-Attribute Decision Making (FMADM) method. This fusion is based on the merging of the advantages of Z-Score and FMADM as a superiority method in statistical rank data processing with weighting data distribution. The lack of Z-Score in processing multi-attributes weighted data can be improved by the FMADM method. In this study, the integration of the Analytical Hierarchy Process (AHP) and Weighted Product (WP) methods was used as the FMADM method with the Z-Score statistical technique. The results of the analysis in the case study show that the integration of the Z-Score and AHP-Weighted Product (Z-WeP) methods can provide maximum results with similarities to the Z-Score results by 86%. Analysis of criterion values on alternatives also shows that Z-WeP can work better than some other of FMADM approaches.   

scholarly journals A biomass equation dataset for common shrub species in China

2021 ◽  
Author(s):  
Yang Wang ◽  
Wenting Xu ◽  
Zhiyao Tang ◽  
Zongqiang Xie
Keyword(s):  
Terrestrial Ecosystem ◽  
Structure And Function ◽  
Biomass Estimation ◽  
Ecosystem Structure ◽  
Biomass Equation ◽  
Shrub Species ◽  
Biomass Equations ◽  
Shrub Biomass ◽  
And Function ◽  
Ecosystem Structure And Function

Abstract. Shrub biomass equations provide an accurate, efficient and convenient method in estimating biomass of shrubland ecosystems and biomass of the shrub layer in forest ecosystems at various spatial and temporal scales. In recent decades, many shrub biomass equations have been reported mainly in journals, books and postgraduate's dissertations. However, these biomass equations are applicable for limited shrub species with respect to a large number of shrub species widely distributed in China, which severely restricted the study of terrestrial ecosystem structure and function, such as biomass, production, and carbon budge. Therefore, we firstly carried out a critical review of published literature (from 1982 to 2019) on shrub biomass equations in China, and then developed biomass equations for the dominant shrub species using a unified method based on field measurements of 738 sites in shrubland ecosystems across China. Finally, we constructed the first comprehensive biomass equation dataset for China’s common shrub species. This dataset consists of 822 biomass equations specific to 167 shrub species and has significant representativeness to the geographical, climatic and shrubland vegetation features across China. The dataset is freely available at https://doi.org/10.11922/sciencedb.00641 for noncommercial scientific applications, and this dataset fills a significant gap in woody biomass equations and provides key parameters for biomass estimation in studies on terrestrial ecosystem structure and function.

scholarly journals Learning from Scarce Information: Using Synthetic Data to Classify Roman Fine Ware Pottery

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1140
Author(s):  
Santos J. Núñez Jareño ◽  
Daniël P. van Helden ◽  
Evgeny M. Mirkes ◽  
Ivan Y. Tyukin ◽  
Penelope M. Allison
Keyword(s):  
Expert Knowledge ◽  
Hybrid Approach ◽  
Synthetic Data ◽  
Original Data ◽  
Initial Training ◽  
Training Set ◽  
Data Generator ◽  
The Impact ◽  
Learning Architectures ◽  
Better Than

In this article, we consider a version of the challenging problem of learning from datasets whose size is too limited to allow generalisation beyond the training set. To address the challenge, we propose to use a transfer learning approach whereby the model is first trained on a synthetic dataset replicating features of the original objects. In this study, the objects were smartphone photographs of near-complete Roman terra sigillata pottery vessels from the collection of the Museum of London. Taking the replicated features from published profile drawings of pottery forms allowed the integration of expert knowledge into the process through our synthetic data generator. After this first initial training the model was fine-tuned with data from photographs of real vessels. We show, through exhaustive experiments across several popular deep learning architectures, different test priors, and considering the impact of the photograph viewpoint and excessive damage to the vessels, that the proposed hybrid approach enables the creation of classifiers with appropriate generalisation performance. This performance is significantly better than that of classifiers trained exclusively on the original data, which shows the promise of the approach to alleviate the fundamental issue of learning from small datasets.

scholarly journals Computer Mathematical Statistics Analysis of downscaling data of different vegetation index inversion TRMM

2021 ◽  
Vol 2083 (4) ◽  
pp. 042052
Author(s):  
Shuangbao Qu ◽  
Miaoxing Zhao ◽  
Shuo Deng
Keyword(s):  
Geographically Weighted Regression ◽  
Vegetation Index ◽  
Original Data ◽  
Measured Data ◽  
Central China ◽  
Weighted Regression ◽  
Precipitation Data ◽  
Enhanced Vegetation Index ◽  
Geographically Weighted Regression Model ◽  
Better Than

Abstract This paper uses enhanced vegetation index (EVI) data, normalized vegetation index (NDVI) data, DEM, aspect data, and TRMM3B43 (V7) data, based on a geographically weighted regression model (GWR), and uses a statistical downscaling method to achieve Central China Downscaling of regional TRMM data from 2010 to 2019. The research results show: (1) TRMM data has good applicability in Central China, and the R2of TRMM data and weather station measured data is above 0.8. (2) Improve the ground resolution from 0.25°×0.25° (approximately 27.5km×27.5km) to 1km×1km while ensuring the same accuracy as the original data. (3) Overall, the accuracy of EVI downscaled precipitation data in Central China is better than that of NDVI downscaled precipitation data.

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