Value of personnel classification information.

1975 ◽  
Author(s):  
James W. Abellera ◽  
Cecil J. Mullins ◽  
James A. Earles
Keyword(s):  
Classification Information

THE CLASSIFICATION OF LAND ACCORDING TO THE DEGREE OF RABBIT INFESTATION IN CENTRAL OTAGO

1987 ◽  
pp. 65-70
Author(s):  
I.G.C. Kerr ◽  
J.M. Williams ◽  
W.D. Ross ◽  
J.M. Pollard
Keyword(s):  
Carrying Capacity ◽  
Mediterranean Climate ◽  
Natural Habitat ◽  
Public Funding ◽  
Soil Survey ◽  
European Rabbit ◽  
Cultural Modification ◽  
Classification Information ◽  
Semi Arid

The European rabbit (Oryctolagus cuniculus) introduced into New Zealand in the 183Os, has consistently flourished in Central Otago, the upper Waitaki, and inland Marlborough, all areas of mediterranean climate. It has proved difficult to manage in these habitats. The 'rabbit problem' is largely confined to 105,000 ha of low producing land mostly in semi arid areas of Central Otago. No field scale modifications of the natural habitat have been successful in limiting rabbit numbers. The costs of control exceed the revenue from the land and continued public funding for control operations appears necessary. A system for classifying land according to the degree of rabbit proneness is described. Soil survey and land classification information for Central Otago is related to the distribution and density of rabbits. This intormation can be used as a basis for defining rabbit carrying capacity and consequent land use constraints and management needs. It is concluded that the natural rabbit carrying capacity of land can be defined by reference to soil survey information and cultural modification to the natural vegetation. Classification of land according to rabbit proneness is proposed as a means of identifying the need for, and allocation of, public funding tor rabbit management. Keywords: Rabbit habitat, rabbit proneness, use of rabbit prone land.

scholarly journals A comprehensive annotation dataset of intact LTR retrotransposons of 300 plant genomes

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Shan-Shan Zhou ◽  
Xue-Mei Yan ◽  
Kai-Fu Zhang ◽  
Hui Liu ◽  
Jie Xu ◽  
...  
Keyword(s):  
Evolutionary Dynamics ◽  
Age Determination ◽  
Genome Plasticity ◽  
Ltr Retrotransposons ◽  
Functional Variation ◽  
Plant Genomes ◽  
Sequencing Technologies ◽  
Plant Groups ◽  
Functional Implications ◽  
Classification Information

AbstractLTR retrotransposons (LTR-RTs) are ubiquitous and represent the dominant repeat element in plant genomes, playing important roles in functional variation, genome plasticity and evolution. With the advent of new sequencing technologies, a growing number of whole-genome sequences have been made publicly available, making it possible to carry out systematic analyses of LTR-RTs. However, a comprehensive and unified annotation of LTR-RTs in plant groups is still lacking. Here, we constructed a plant intact LTR-RTs dataset, which is designed to classify and annotate intact LTR-RTs with a standardized procedure. The dataset currently comprises a total of 2,593,685 intact LTR-RTs from genomes of 300 plant species representing 93 families of 46 orders. The dataset is accompanied by sequence, diverse structural and functional annotation, age determination and classification information associated with the LTR-RTs. This dataset will contribute valuable resources for investigating the evolutionary dynamics and functional implications of LTR-RTs in plant genomes.

scholarly journals Multiview Deep Feature Learning Network for SAR Automatic Target Recognition

2021 ◽  
Vol 13 (8) ◽  
pp. 1455
Author(s):  
Jifang Pei ◽  
Weibo Huo ◽  
Chenwei Wang ◽  
Yulin Huang ◽  
Yin Zhang ◽  
...  
Keyword(s):  
Target Recognition ◽  
Feature Learning ◽  
Automatic Target Recognition ◽  
Operating Conditions ◽  
Sar Images ◽  
Single View ◽  
Learning Network ◽  
Deep Feature ◽  
Deep Feature Learning ◽  
Classification Information

Multiview synthetic aperture radar (SAR) images contain much richer information for automatic target recognition (ATR) than a single-view one. It is desirable to establish a reasonable multiview ATR scheme and design effective ATR algorithm to thoroughly learn and extract that classification information, so that superior SAR ATR performance can be achieved. Hence, a general processing framework applicable for a multiview SAR ATR pattern is first given in this paper, which can provide an effective approach to ATR system design. Then, a new ATR method using a multiview deep feature learning network is designed based on the proposed multiview ATR framework. The proposed neural network is with a multiple input parallel topology and some distinct deep feature learning modules, with which significant classification features, the intra-view and inter-view features existing in the input multiview SAR images, will be learned simultaneously and thoroughly. Therefore, the proposed multiview deep feature learning network can achieve an excellent SAR ATR performance. Experimental results have shown the superiorities of the proposed multiview SAR ATR method under various operating conditions.

scholarly journals Methods of Population Spatialization Based on the Classification Information of Buildings from China’s First National Geoinformation Survey in Urban Area: A Case Study of Wuchang District, Wuhan City, China

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2558 ◽  
Author(s):  
Linze Li ◽  
Jiansong Li ◽  
Zilong Jiang ◽  
Lingli Zhao ◽  
Pengcheng Zhao
Keyword(s):  
Population Density ◽  
Accuracy Assessment ◽  
Spatial Relationship ◽  
Water Area ◽  
Level Population ◽  
Least Squares Regression ◽  
Street Level ◽  
Residential House ◽  
Multi Level ◽  
Classification Information

Most of the currently mature methods that are used globally for population spatialization are researched on a single level, and are dependent on the spatial relationship between population and land covers (city, road, water area, etc.), resulting in difficulties in data acquisition and an inability to identify precise features on the different levels. This paper proposes a multi-level population spatialization method on the different administrative levels with the support of China’s first national geoinformation survey, and then considers several approaches to verify the results of the multi-level method. This paper aims to establish a multi-level population spatialization method that is suitable for the administrative division of districts and streets. It is assumed that the same residential house has the same population density on the district level. Based on this assumption, the least squares regression model is used to obtain the optimized prediction model and accurate population space prediction results by dynamically segmenting and aggregating house categories.In addition, it is assumed that the distribution of the population is relatively regular in communities that are spatially close to each other, and that the population densities on the street level are similar, so the average population density is assessed by optimizing the community and surrounding residential houses on the street level. Finally, the scientificalness and rationality of the proposed method is proved by spatial autocorrelation analysis, overlay analysis, cross-validation analysis and accuracy assessment methods.

scholarly journals Solar flare forecasting using sunspot-groups classification and photospheric magnetic parameters

2010 ◽  
Vol 6 (S273) ◽  
pp. 446-450 ◽  
Author(s):  
Yuan Yuan ◽  
Frank Y. Shih ◽  
Ju Jing ◽  
Haimin Wang
Keyword(s):  
Solar Flare ◽  
Neutral Line ◽  
Forecasting Accuracy ◽  
Magnetic Parameters ◽  
Forecasting Method ◽  
Total Gradient ◽  
Strong Gradient ◽  
Classification Information ◽  
Year 2000 ◽  
Sunspot Groups

AbstractIn this paper, we investigate whether incorporating sunspot-groups classification information would further improve the performance of our previous logistic regression based solar flare forecasting method, which uses only line-of-sight photospheric magnetic parameters. A dataset containing 4913 samples from the year 2000 to 2005 is constructed, in which 2721 samples from the year 2000, 2002 and 2004 are used as a training set, and the remaining 2192 samples from the year 2001, 2003 and 2005 are used as a testing set. Experimental results show that sunspot-groups classification combined with total gradient on the strong gradient polarity neutral line achieve the highest forecasting accuracy and thus it testifies sunspot-groups classification does help in solar flare forecasting.

scholarly journals Compiling a new glacier inventory for southeastern Qinghai–Tibet Plateau from Landsat and PALSAR data

2016 ◽  
Vol 62 (233) ◽  
pp. 579-592 ◽  
Author(s):  
LINGHONG KE ◽  
XIAOLI DING ◽  
LEI ZHANG ◽  
JUN HU ◽  
C. K. SHUM ◽  
...  
Keyword(s):  
Shuttle Radar Topography Mission ◽  
Tibet Plateau ◽  
Landsat 8 ◽  
Glacier Change ◽  
Glacier Area ◽  
Glacier Inventory ◽  
Radar Images ◽  
Water Classification ◽  
Multi Temporal ◽  
Classification Information

ABSTRACTGlacier change has been recognized as an important climate variable due to its sensitive response to climate change. Although there are a large number of glaciers distributed over the southeastern Qinghai–Tibetan Plateau, the region is poorly represented in glacier databases due to seasonal snow cover and frequent cloud cover. Here, we present an improved glacier inventory for this region by combining Landsat observations acquired over 2011–13 (Landsat 8/OLI and Landsat TM/ETM+), coherence images from Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar images and the Shuttle Radar Topography Mission (SRTM) DEM. We present a semi-automated scheme for integrating observations from multi-temporal Landsat scenes to mitigate cloud obscuration. Further, the clean-ice observations, together with coherence information, slope constraints, vegetation cover and water classification information extracted from the Landsat scenes, are integrated to determine the debris-covered glacier area. After manual editing, we derive a new glacier inventory containing 6892 glaciers >0.02 km2, covering a total area of 6566 ± 197 km2. This new glacier inventory indicates gross overestimation in glacier area (over 30%) in previously published glacier inventories, and reveals various spatial characteristics of glaciers in the region. Our inventory can be used as a baseline dataset for future studies including glacier change assessment.

Vehicle Detection and Classification Using Model-Based and Fuzzy Logic Approaches

2005 ◽  
Vol 1935 (1) ◽  
pp. 154-162
Author(s):  
Heng-Da Cheng ◽  
Haining Du ◽  
Liming Hu ◽  
Chris Glazier
Keyword(s):  
Fuzzy Logic ◽  
Traffic Control ◽  
Vehicle Detection ◽  
Classification Systems ◽  
Vehicle Classification ◽  
Highway Traffic ◽  
Control Center ◽  
Model Based ◽  
Fixed Orientation ◽  
Classification Information

Vehicle detection and classification information is invaluable in many transportation issues. Vehicle feature extraction and detection are the preprocesses required for vehicle classification. Current automatic vehicle classification systems have deficiencies: low accuracy, special requirements, fixed orientation of the camera, or additional hardware and devices. This paper discusses a vehicle detection and classification system using model-based and fuzzy logic approaches. The system was tested with the use of a variety of images captured by the highway traffic control center of the Utah Department of Transportation. In comparison with existing systems, major advantages of the proposed system are ( a) no special orientation of the camera is required, ( b) no additional devices are needed, and ( c) high classification accuracy is provided. Experimental results show that the performance of the proposed system exceeds that of the existing video-based vehicle classification systems.

scholarly journals An Antinoise Fault Diagnosis Method Based on Multiscale 1DCNN

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Jie Cao ◽  
Zhidong He ◽  
Jinhua Wang ◽  
Ping Yu
Keyword(s):  
Activation Function ◽  
Vibration Sensor ◽  
Training Set ◽  
Convolutional Network ◽  
Convolution Structure ◽  
Diagnosis Method ◽  
Intense Noise ◽  
Comparison Algorithms ◽  
Classification Information ◽  
Improved Methods

The bearing state signal collected by the vibration sensor contains a large amount of environmental noise in actual processes, which leads to a reduction in the accuracy of the convolutional network in identifying bearing faults. To solve this problem, a one-dimensional convolutional neural network with a multiscale kernel (MSK-1DCNN) is proposed for the classification information enhancement of the input. A two-layer multiscale convolution structure (MSK) is used at the front of the network. MSK has five convolutional kernels with different sizes, and those kernels are used to extract features with varying resolutions in the original signal. In the multiscale convolution structure, the ELU activation function is used instead of the ReLU function to improve the antinoise ability of MSK-1DCNN, also by adding pepper noise to the training set data to destroy the input data and forcing the network to learn more representative features to improve the robustness of the network. Experimental results illustrate that the improved methods proposed in this paper effectively enhance the diagnostic performance of MSK-1DCNN under intense noise, and the diagnostic accuracy is higher than that of other comparison algorithms.

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