29 CFR 779.355 - Classification of lumber and building materials sales.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 29 Labor 3 2010-07-01 2010-07-01 false Classification of lumber and building materials sales. 779... building materials sales. (a) General. In determining, for purposes of the section 13(a)(2) and (4) exemptions, whether 75 percent of the annual dollar volume of the establishment's sales which are not for...

Building and Solving Odd-One-Out Classification Problems: A Systematic Approach

ERIC Educational Resources Information Center

Ruiz, Philippe E.

2011-01-01

Classification problems ("find the odd-one-out") are frequently used as tests of inductive reasoning to evaluate human or animal intelligence. This paper introduces a systematic method for building the set of all possible classification problems, followed by a simple algorithm for solving the problems of the R-ASCM, a psychometric test derived…

Satellite Image Classification of Building Damages Using Airborne and Satellite Image Samples in a Deep Learning Approach

NASA Astrophysics Data System (ADS)

Duarte, D.; Nex, F.; Kerle, N.; Vosselman, G.

2018-05-01

The localization and detailed assessment of damaged buildings after a disastrous event is of utmost importance to guide response operations, recovery tasks or for insurance purposes. Several remote sensing platforms and sensors are currently used for the manual detection of building damages. However, there is an overall interest in the use of automated methods to perform this task, regardless of the used platform. Owing to its synoptic coverage and predictable availability, satellite imagery is currently used as input for the identification of building damages by the International Charter, as well as the Copernicus Emergency Management Service for the production of damage grading and reference maps. Recently proposed methods to perform image classification of building damages rely on convolutional neural networks (CNN). These are usually trained with only satellite image samples in a binary classification problem, however the number of samples derived from these images is often limited, affecting the quality of the classification results. The use of up/down-sampling image samples during the training of a CNN, has demonstrated to improve several image recognition tasks in remote sensing. However, it is currently unclear if this multi resolution information can also be captured from images with different spatial resolutions like satellite and airborne imagery (from both manned and unmanned platforms). In this paper, a CNN framework using residual connections and dilated convolutions is used considering both manned and unmanned aerial image samples to perform the satellite image classification of building damages. Three network configurations, trained with multi-resolution image samples are compared against two benchmark networks where only satellite image samples are used. Combining feature maps generated from airborne and satellite image samples, and refining these using only the satellite image samples, improved nearly 4 % the overall satellite image classification of building damages.

Combining Unsupervised and Supervised Classification to Build User Models for Exploratory Learning Environments

ERIC Educational Resources Information Center

Amershi, Saleema; Conati, Cristina

2009-01-01

In this paper, we present a data-based user modeling framework that uses both unsupervised and supervised classification to build student models for exploratory learning environments. We apply the framework to build student models for two different learning environments and using two different data sources (logged interface and eye-tracking data).…

Assessment of Pansharpening Methods Applied to WorldView-2 Imagery Fusion.

PubMed

Li, Hui; Jing, Linhai; Tang, Yunwei

2017-01-05

Since WorldView-2 (WV-2) images are widely used in various fields, there is a high demand for the use of high-quality pansharpened WV-2 images for different application purposes. With respect to the novelty of the WV-2 multispectral (MS) and panchromatic (PAN) bands, the performances of eight state-of-art pan-sharpening methods for WV-2 imagery including six datasets from three WV-2 scenes were assessed in this study using both quality indices and information indices, along with visual inspection. The normalized difference vegetation index, normalized difference water index, and morphological building index, which are widely used in applications related to land cover classification, the extraction of vegetation areas, buildings, and water bodies, were employed in this work to evaluate the performance of different pansharpening methods in terms of information presentation ability. The experimental results show that the Haze- and Ratio-based, adaptive Gram-Schmidt, Generalized Laplacian pyramids (GLP) methods using enhanced spectral distortion minimal model and enhanced context-based decision model methods are good choices for producing fused WV-2 images used for image interpretation and the extraction of urban buildings. The two GLP-based methods are better choices than the other methods, if the fused images will be used for applications related to vegetation and water-bodies.

Assessment of Pansharpening Methods Applied to WorldView-2 Imagery Fusion

PubMed Central

Li, Hui; Jing, Linhai; Tang, Yunwei

2017-01-01

Since WorldView-2 (WV-2) images are widely used in various fields, there is a high demand for the use of high-quality pansharpened WV-2 images for different application purposes. With respect to the novelty of the WV-2 multispectral (MS) and panchromatic (PAN) bands, the performances of eight state-of-art pan-sharpening methods for WV-2 imagery including six datasets from three WV-2 scenes were assessed in this study using both quality indices and information indices, along with visual inspection. The normalized difference vegetation index, normalized difference water index, and morphological building index, which are widely used in applications related to land cover classification, the extraction of vegetation areas, buildings, and water bodies, were employed in this work to evaluate the performance of different pansharpening methods in terms of information presentation ability. The experimental results show that the Haze- and Ratio-based, adaptive Gram-Schmidt, Generalized Laplacian pyramids (GLP) methods using enhanced spectral distortion minimal model and enhanced context-based decision model methods are good choices for producing fused WV-2 images used for image interpretation and the extraction of urban buildings. The two GLP-based methods are better choices than the other methods, if the fused images will be used for applications related to vegetation and water-bodies. PMID:28067770

Creating a three level building classification using topographic and address-based data for Manchester

NASA Astrophysics Data System (ADS)

Hussain, M.; Chen, D.

2014-11-01

Buildings, the basic unit of an urban landscape, host most of its socio-economic activities and play an important role in the creation of urban land-use patterns. The spatial arrangement of different building types creates varied urban land-use clusters which can provide an insight to understand the relationships between social, economic, and living spaces. The classification of such urban clusters can help in policy-making and resource management. In many countries including the UK no national-level cadastral database containing information on individual building types exists in public domain. In this paper, we present a framework for inferring functional types of buildings based on the analysis of their form (e.g. geometrical properties, such as area and perimeter, layout) and spatial relationship from large topographic and address-based GIS database. Machine learning algorithms along with exploratory spatial analysis techniques are used to create the classification rules. The classification is extended to two further levels based on the functions (use) of buildings derived from address-based data. The developed methodology was applied to the Manchester metropolitan area using the Ordnance Survey's MasterMap®, a large-scale topographic and address-based data available for the UK.

A new method of building footprints detection using airborne laser scanning data and multispectral image

NASA Astrophysics Data System (ADS)

Luo, Yiping; Jiang, Ting; Gao, Shengli; Wang, Xin

2010-10-01

It presents a new approach for detecting building footprints in a combination of registered aerial image with multispectral bands and airborne laser scanning data synchronously obtained by Leica-Geosystems ALS40 and Applanix DACS-301 on the same platform. A two-step method for building detection was presented consisting of selecting 'building' candidate points and then classifying candidate points. A digital surface model(DSM) derived from last pulse laser scanning data was first filtered and the laser points were classified into classes 'ground' and 'building or tree' based on mathematic morphological filter. Then, 'ground' points were resample into digital elevation model(DEM), and a Normalized DSM(nDSM) was generated from DEM and DSM. The candidate points were selected from 'building or tree' points by height value and area threshold in nDSM. The candidate points were further classified into building points and tree points by using the support vector machines(SVM) classification method. Two classification tests were carried out using features only from laser scanning data and associated features from two input data sources. The features included height, height finite difference, RGB bands value, and so on. The RGB value of points was acquired by matching laser scanning data and image using collinear equation. The features of training points were presented as input data for SVM classification method, and cross validation was used to select best classification parameters. The determinant function could be constructed by the classification parameters and the class of candidate points was determined by determinant function. The result showed that associated features from two input data sources were superior to features only from laser scanning data. The accuracy of more than 90% was achieved for buildings in first kind of features.

Automatic Building Detection based on Supervised Classification using High Resolution Google Earth Images

NASA Astrophysics Data System (ADS)

Ghaffarian, S.; Ghaffarian, S.

2014-08-01

This paper presents a novel approach to detect the buildings by automization of the training area collecting stage for supervised classification. The method based on the fact that a 3d building structure should cast a shadow under suitable imaging conditions. Therefore, the methodology begins with the detection and masking out the shadow areas using luminance component of the LAB color space, which indicates the lightness of the image, and a novel double thresholding technique. Further, the training areas for supervised classification are selected by automatically determining a buffer zone on each building whose shadow is detected by using the shadow shape and the sun illumination direction. Thereafter, by calculating the statistic values of each buffer zone which is collected from the building areas the Improved Parallelepiped Supervised Classification is executed to detect the buildings. Standard deviation thresholding applied to the Parallelepiped classification method to improve its accuracy. Finally, simple morphological operations conducted for releasing the noises and increasing the accuracy of the results. The experiments were performed on set of high resolution Google Earth images. The performance of the proposed approach was assessed by comparing the results of the proposed approach with the reference data by using well-known quality measurements (Precision, Recall and F1-score) to evaluate the pixel-based and object-based performances of the proposed approach. Evaluation of the results illustrates that buildings detected from dense and suburban districts with divers characteristics and color combinations using our proposed method have 88.4 % and 853 % overall pixel-based and object-based precision performances, respectively.

Building Change Detection from Bi-Temporal Dense-Matching Point Clouds and Aerial Images.

PubMed

Pang, Shiyan; Hu, Xiangyun; Cai, Zhongliang; Gong, Jinqi; Zhang, Mi

2018-03-24

In this work, a novel building change detection method from bi-temporal dense-matching point clouds and aerial images is proposed to address two major problems, namely, the robust acquisition of the changed objects above ground and the automatic classification of changed objects into buildings or non-buildings. For the acquisition of changed objects above ground, the change detection problem is converted into a binary classification, in which the changed area above ground is regarded as the foreground and the other area as the background. For the gridded points of each period, the graph cuts algorithm is adopted to classify the points into foreground and background, followed by the region-growing algorithm to form candidate changed building objects. A novel structural feature that was extracted from aerial images is constructed to classify the candidate changed building objects into buildings and non-buildings. The changed building objects are further classified as "newly built", "taller", "demolished", and "lower" by combining the classification and the digital surface models of two periods. Finally, three typical areas from a large dataset are used to validate the proposed method. Numerous experiments demonstrate the effectiveness of the proposed algorithm.

Automatic 3D Extraction of Buildings, Vegetation and Roads from LIDAR Data

NASA Astrophysics Data System (ADS)

Bellakaout, A.; Cherkaoui, M.; Ettarid, M.; Touzani, A.

2016-06-01

Aerial topographic surveys using Light Detection and Ranging (LiDAR) technology collect dense and accurate information from the surface or terrain; it is becoming one of the important tools in the geosciences for studying objects and earth surface. Classification of Lidar data for extracting ground, vegetation, and buildings is a very important step needed in numerous applications such as 3D city modelling, extraction of different derived data for geographical information systems (GIS), mapping, navigation, etc... Regardless of what the scan data will be used for, an automatic process is greatly required to handle the large amount of data collected because the manual process is time consuming and very expensive. This paper is presenting an approach for automatic classification of aerial Lidar data into five groups of items: buildings, trees, roads, linear object and soil using single return Lidar and processing the point cloud without generating DEM. Topological relationship and height variation analysis is adopted to segment, preliminary, the entire point cloud preliminarily into upper and lower contours, uniform and non-uniform surface, non-uniform surfaces, linear objects, and others. This primary classification is used on the one hand to know the upper and lower part of each building in an urban scene, needed to model buildings façades; and on the other hand to extract point cloud of uniform surfaces which contain roofs, roads and ground used in the second phase of classification. A second algorithm is developed to segment the uniform surface into buildings roofs, roads and ground, the second phase of classification based on the topological relationship and height variation analysis, The proposed approach has been tested using two areas : the first is a housing complex and the second is a primary school. The proposed approach led to successful classification results of buildings, vegetation and road classes.

Model-Based Building Detection from Low-Cost Optical Sensors Onboard Unmanned Aerial Vehicles

NASA Astrophysics Data System (ADS)

Karantzalos, K.; Koutsourakis, P.; Kalisperakis, I.; Grammatikopoulos, L.

2015-08-01

The automated and cost-effective building detection in ultra high spatial resolution is of major importance for various engineering and smart city applications. To this end, in this paper, a model-based building detection technique has been developed able to extract and reconstruct buildings from UAV aerial imagery and low-cost imaging sensors. In particular, the developed approach through advanced structure from motion, bundle adjustment and dense image matching computes a DSM and a true orthomosaic from the numerous GoPro images which are characterised by important geometric distortions and fish-eye effect. An unsupervised multi-region, graphcut segmentation and a rule-based classification is responsible for delivering the initial multi-class classification map. The DTM is then calculated based on inpaininting and mathematical morphology process. A data fusion process between the detected building from the DSM/DTM and the classification map feeds a grammar-based building reconstruction and scene building are extracted and reconstructed. Preliminary experimental results appear quite promising with the quantitative evaluation indicating detection rates at object level of 88% regarding the correctness and above 75% regarding the detection completeness.

Property Specification Patterns for intelligence building software

NASA Astrophysics Data System (ADS)

Chun, Seungsu

2018-03-01

In this paper, through the property specification pattern research for Modal MU(μ) logical aspects present a single framework based on the pattern of intelligence building software. In this study, broken down by state property specification pattern classification of Dwyer (S) and action (A) and was subdivided into it again strong (A) and weaknesses (E). Through these means based on a hierarchical pattern classification of the property specification pattern analysis of logical aspects Mu(μ) was applied to the pattern classification of the examples used in the actual model checker. As a result, not only can a more accurate classification than the existing classification systems were easy to create and understand the attributes specified.

Body Build Satisfaction and the Congruency of Body Build Perceptions.

ERIC Educational Resources Information Center

Hankins, Norman E.; Bailey, Roger C.

1979-01-01

Females were administered the somatotype rating scale. Satisfied subjects showed greater congruency between their own and wished-for body build, and greater congruency between their own and friend/date body builds, but less congruency between their own body build and the female stereotype. (Author/BEF)

Basis of Criminalistic Classification of a Person in Republic Kazakhstan and Republic Mongolia

ERIC Educational Resources Information Center

Abdilov, Kanat S.; Zusbaev, Baurzan T.; Naurysbaev, Erlan A.; Nukiev, Berik A.; Nurkina, Zanar B.; Myrzahanov, Erlan N.; Urazalin, Galym T.

2016-01-01

In this article reviewed problems of the criminalistic classification building of a person. In the work were used legal formal, logical, comparative legal methods. The author describes classification kinds. Reveal the meaning of classification in criminalistic systematics. Shows types of grounds of criminalistic classification of a person.…

Crowd-sourced data collection to support automatic classification of building footprint data

NASA Astrophysics Data System (ADS)

Hecht, Robert; Kalla, Matthias; Krüger, Tobias

2018-05-01

Human settlements are mainly formed by buildings with their different characteristics and usage. Despite the importance of buildings for the economy and society, complete regional or even national figures of the entire building stock and its spatial distribution are still hardly available. Available digital topographic data sets created by National Mapping Agencies or mapped voluntarily through a crowd via Volunteered Geographic Information (VGI) platforms (e.g. OpenStreetMap) contain building footprint information but often lack additional information on building type, usage, age or number of floors. For this reason, predictive modeling is becoming increasingly important in this context. The capabilities of machine learning allow for the prediction of building types and other building characteristics and thus, the efficient classification and description of the entire building stock of cities and regions. However, such data-driven approaches always require a sufficient amount of ground truth (reference) information for training and validation. The collection of reference data is usually cost-intensive and time-consuming. Experiences from other disciplines have shown that crowdsourcing offers the possibility to support the process of obtaining ground truth data. Therefore, this paper presents the results of an experimental study aiming at assessing the accuracy of non-expert annotations on street view images collected from an internet crowd. The findings provide the basis for a future integration of a crowdsourcing component into the process of land use mapping, particularly the automatic building classification.

Review of Occupational Therapy Research in the Practice Area of Children and Youth

PubMed Central

Bendixen, Roxanna M.; Kreider, Consuelo M.

2011-01-01

A systematic review was conducted focusing on articles in the Occupational Therapy (OT) practice category of Childhood and Youth (C&Y) published in the American Journal of Occupational Therapy (AJOT) over the two-year period of 2009–2010. The frameworks of the International Classification of Functioning, Disability and Health (ICF) and Positive Youth Development (PYD) were used to explore OT research progress toward the goals of the Centennial Vision (CV). Forty-six research articles were organized by research type and were classified within these two frameworks. The majority of reviewed published research investigated variables representing constructs falling within the ICF domains of Body Functioning and Activity. The effect of OT interventions on PYD resided primarily in building competence. In order to meet the tenets of the CV, OTs must document changes in children’s engagement in everyday life situations and build the evidence of OT’s efficacy in facilitating participation. PMID:21675342

Classification of Obesity Varies between Body Mass Index and Direct Measures of Body Fat in Boys and Girls of Asian and European Ancestry

ERIC Educational Resources Information Center

McConnell-Nzunga, J.; Naylor, P. J.; Macdonald, H.; Rhodes, R. E.; Hofer, S. M.; McKay, H.

2018-01-01

Body mass index is a common proxy for proportion of body fat. However, body mass index may not classify youth similarly across ages and ethnicities. We used sex- and ethnic-specific receiver operating characteristic curves to determine how obesity classifications compared between body mass index and dual energy x-ray absorptiometry-based body fat…

Automatic 3d Building Model Generations with Airborne LiDAR Data

NASA Astrophysics Data System (ADS)

Yastikli, N.; Cetin, Z.

2017-11-01

LiDAR systems become more and more popular because of the potential use for obtaining the point clouds of vegetation and man-made objects on the earth surface in an accurate and quick way. Nowadays, these airborne systems have been frequently used in wide range of applications such as DEM/DSM generation, topographic mapping, object extraction, vegetation mapping, 3 dimensional (3D) modelling and simulation, change detection, engineering works, revision of maps, coastal management and bathymetry. The 3D building model generation is the one of the most prominent applications of LiDAR system, which has the major importance for urban planning, illegal construction monitoring, 3D city modelling, environmental simulation, tourism, security, telecommunication and mobile navigation etc. The manual or semi-automatic 3D building model generation is costly and very time-consuming process for these applications. Thus, an approach for automatic 3D building model generation is needed in a simple and quick way for many studies which includes building modelling. In this study, automatic 3D building models generation is aimed with airborne LiDAR data. An approach is proposed for automatic 3D building models generation including the automatic point based classification of raw LiDAR point cloud. The proposed point based classification includes the hierarchical rules, for the automatic production of 3D building models. The detailed analyses for the parameters which used in hierarchical rules have been performed to improve classification results using different test areas identified in the study area. The proposed approach have been tested in the study area which has partly open areas, forest areas and many types of the buildings, in Zekeriyakoy, Istanbul using the TerraScan module of TerraSolid. The 3D building model was generated automatically using the results of the automatic point based classification. The obtained results of this research on study area verified that automatic 3D building models can be generated successfully using raw LiDAR point cloud data.

Buildings classification from airborne LiDAR point clouds through OBIA and ontology driven approach

NASA Astrophysics Data System (ADS)

Tomljenovic, Ivan; Belgiu, Mariana; Lampoltshammer, Thomas J.

2013-04-01

In the last years, airborne Light Detection and Ranging (LiDAR) data proved to be a valuable information resource for a vast number of applications ranging from land cover mapping to individual surface feature extraction from complex urban environments. To extract information from LiDAR data, users apply prior knowledge. Unfortunately, there is no consistent initiative for structuring this knowledge into data models that can be shared and reused across different applications and domains. The absence of such models poses great challenges to data interpretation, data fusion and integration as well as information transferability. The intention of this work is to describe the design, development and deployment of an ontology-based system to classify buildings from airborne LiDAR data. The novelty of this approach consists of the development of a domain ontology that specifies explicitly the knowledge used to extract features from airborne LiDAR data. The overall goal of this approach is to investigate the possibility for classification of features of interest from LiDAR data by means of domain ontology. The proposed workflow is applied to the building extraction process for the region of "Biberach an der Riss" in South Germany. Strip-adjusted and georeferenced airborne LiDAR data is processed based on geometrical and radiometric signatures stored within the point cloud. Region-growing segmentation algorithms are applied and segmented regions are exported to the GeoJSON format. Subsequently, the data is imported into the ontology-based reasoning process used to automatically classify exported features of interest. Based on the ontology it becomes possible to define domain concepts, associated properties and relations. As a consequence, the resulting specific body of knowledge restricts possible interpretation variants. Moreover, ontologies are machinable and thus it is possible to run reasoning on top of them. Available reasoners (FACT++, JESS, Pellet) are used to check the consistency of the developed ontologies, and logical reasoning is performed to infer implicit relations between defined concepts. The ontology for the definition of building is specified using the Ontology Web Language (OWL). It is the most widely used ontology language that is based on Description Logics (DL). DL allows the description of internal properties of modelled concepts (roof typology, shape, area, height etc.) and relationships between objects (IS_A, MEMBER_OF/INSTANCE_OF). It captures terminological knowledge (TBox) as well as assertional knowledge (ABox) - that represents facts about concept instances, i.e. the buildings in airborne LiDAR data. To assess the classification accuracy, ground truth data generated by visual interpretation and calculated classification results in terms of precision and recall are used. The advantages of this approach are: (i) flexibility, (ii) transferability, and (iii) extendibility - i.e. ontology can be extended with further concepts, data properties and object properties.

Classification of Informal Settlements Through the Integration of 2d and 3d Features Extracted from Uav Data

NASA Astrophysics Data System (ADS)

Gevaert, C. M.; Persello, C.; Sliuzas, R.; Vosselman, G.

2016-06-01

Unmanned Aerial Vehicles (UAVs) are capable of providing very high resolution and up-to-date information to support informal settlement upgrading projects. In order to provide accurate basemaps, urban scene understanding through the identification and classification of buildings and terrain is imperative. However, common characteristics of informal settlements such as small, irregular buildings with heterogeneous roof material and large presence of clutter challenge state-of-the-art algorithms. Especially the dense buildings and steeply sloped terrain cause difficulties in identifying elevated objects. This work investigates how 2D radiometric and textural features, 2.5D topographic features, and 3D geometric features obtained from UAV imagery can be integrated to obtain a high classification accuracy in challenging classification problems for the analysis of informal settlements. It compares the utility of pixel-based and segment-based features obtained from an orthomosaic and DSM with point-based and segment-based features extracted from the point cloud to classify an unplanned settlement in Kigali, Rwanda. Findings show that the integration of 2D and 3D features leads to higher classification accuracies.

14 CFR Section 3 - Chart of Balance Sheet Accounts

Code of Federal Regulations, 2014 CFR

2014-01-01

... buildings and equipment 1639 1739 General classification Buildings 1640 1740 Maintenance buildings and... 1654 1754 Furniture, fixtures, and office equipment 1656 1756 Buildings 1660 1760 Maintenance buildings... 1510.3 Other investments and receivables 1530 Special funds 1550 Property and equipment 1600-1700...

14 CFR Section 3 - Chart of Balance Sheet Accounts

Code of Federal Regulations, 2013 CFR

2013-01-01

... buildings and equipment 1639 1739 General classification Buildings 1640 1740 Maintenance buildings and... 1654 1754 Furniture, fixtures, and office equipment 1656 1756 Buildings 1660 1760 Maintenance buildings... 1510.3 Other investments and receivables 1530 Special funds 1550 Property and equipment 1600-1700...

14 CFR 3 - Chart of Balance Sheet Accounts

Code of Federal Regulations, 2012 CFR

2012-01-01

... buildings and equipment 1639 1739 General classification Buildings 1640 1740 Maintenance buildings and... 1654 1754 Furniture, fixtures, and office equipment 1656 1756 Buildings 1660 1760 Maintenance buildings... 1510.3 Other investments and receivables 1530 Special funds 1550 Property and equipment 1600-1700...

Comparison of Object-Based Image Analysis Approaches to Mapping New Buildings in Accra, Ghana Using Multi-Temporal QuickBird Satellite Imagery

PubMed Central

Tsai, Yu Hsin; Stow, Douglas; Weeks, John

2013-01-01

The goal of this study was to map and quantify the number of newly constructed buildings in Accra, Ghana between 2002 and 2010 based on high spatial resolution satellite image data. Two semi-automated feature detection approaches for detecting and mapping newly constructed buildings based on QuickBird very high spatial resolution satellite imagery were analyzed: (1) post-classification comparison; and (2) bi-temporal layerstack classification. Feature Analyst software based on a spatial contextual classifier and ENVI Feature Extraction that uses a true object-based image analysis approach of image segmentation and segment classification were evaluated. Final map products representing new building objects were compared and assessed for accuracy using two object-based accuracy measures, completeness and correctness. The bi-temporal layerstack method generated more accurate results compared to the post-classification comparison method due to less confusion with background objects. The spectral/spatial contextual approach (Feature Analyst) outperformed the true object-based feature delineation approach (ENVI Feature Extraction) due to its ability to more reliably delineate individual buildings of various sizes. Semi-automated, object-based detection followed by manual editing appears to be a reliable and efficient approach for detecting and enumerating new building objects. A bivariate regression analysis was performed using neighborhood-level estimates of new building density regressed on a census-derived measure of socio-economic status, yielding an inverse relationship with R2 = 0.31 (n = 27; p = 0.00). The primary utility of the new building delineation results is to support spatial analyses of land cover and land use and demographic change. PMID:24415810

Obesity classification in military personnel: A comparison of body fat, waist circumference, and body mass index measurements

USDA-ARS?s Scientific Manuscript database

The purpose of this study was to evaluate obesity classifications from body fat percentage (BF%), body mass index (BMI), and waist circumference (WC). A total of 451 overweight/obese active duty military personnel completed all three assessments. Most were obese (men, 81%; women, 98%) using National...

A fuzzy hill-climbing algorithm for the development of a compact associative classifier

NASA Astrophysics Data System (ADS)

Mitra, Soumyaroop; Lam, Sarah S.

2012-02-01

Classification, a data mining technique, has widespread applications including medical diagnosis, targeted marketing, and others. Knowledge discovery from databases in the form of association rules is one of the important data mining tasks. An integrated approach, classification based on association rules, has drawn the attention of the data mining community over the last decade. While attention has been mainly focused on increasing classifier accuracies, not much efforts have been devoted towards building interpretable and less complex models. This paper discusses the development of a compact associative classification model using a hill-climbing approach and fuzzy sets. The proposed methodology builds the rule-base by selecting rules which contribute towards increasing training accuracy, thus balancing classification accuracy with the number of classification association rules. The results indicated that the proposed associative classification model can achieve competitive accuracies on benchmark datasets with continuous attributes and lend better interpretability, when compared with other rule-based systems.

Urban Heat Island ın Ankara

NASA Astrophysics Data System (ADS)

Yılmaz, Erkan

2016-04-01

In this study, the seasonal variation of the surface temperature of Ankara urban area and its enviroment have been analyzed by using Landsat 7 image. The Landsat 7 images of each month from 2007 to 2011 have been used to analyze the annually changes of the surface temperature. The land cover of the research area was defined with supervised classification method on the basis of the satellite image belonging to 2008 July. After determining the surface temperatures from 6-1 bands of satellite images, the monthly mean surface temperatures were calculated for land cover classification for the period between 2007 and 2011. According to the results obtained, the surface temperatures are high in summer and low in winter from the airtemperatures. all satellite images were taken at 10:00 am, it is found that urban areas are cooler than rural areas at 10:00 am. Regarding the land cover classification, the water surfaces are the coolest surfaces during the whole year.The warmest areas are the grasslands and dry farming areas. While the parks are warmer than the urban areas during the winter, during the summer they are cooler than artificial land covers. The urban areas with higher building density are the cooler surfaces after water bodies.

Preliminary Results of Earthquake-Induced Building Damage Detection with Object-Based Image Classification

NASA Astrophysics Data System (ADS)

Sabuncu, A.; Uca Avci, Z. D.; Sunar, F.

2016-06-01

Earthquakes are the most destructive natural disasters, which result in massive loss of life, infrastructure damages and financial losses. Earthquake-induced building damage detection is a very important step after earthquakes since earthquake-induced building damage is one of the most critical threats to cities and countries in terms of the area of damage, rate of collapsed buildings, the damage grade near the epicenters and also building damage types for all constructions. Van-Ercis (Turkey) earthquake (Mw= 7.1) was occurred on October 23th, 2011; at 10:41 UTC (13:41 local time) centered at 38.75 N 43.36 E that places the epicenter about 30 kilometers northern part of the city of Van. It is recorded that, 604 people died and approximately 4000 buildings collapsed or seriously damaged by the earthquake. In this study, high-resolution satellite images of Van-Ercis, acquired by Quickbird-2 (Digital Globe Inc.) after the earthquake, were used to detect the debris areas using an object-based image classification. Two different land surfaces, having homogeneous and heterogeneous land covers, were selected as case study areas. As a first step of the object-based image processing, segmentation was applied with a convenient scale parameter and homogeneity criterion parameters. As a next step, condition based classification was used. In the final step of this preliminary study, outputs were compared with streetview/ortophotos for the verification and evaluation of the classification accuracy.

Objected-oriented remote sensing image classification method based on geographic ontology model

NASA Astrophysics Data System (ADS)

Chu, Z.; Liu, Z. J.; Gu, H. Y.

2016-11-01

Nowadays, with the development of high resolution remote sensing image and the wide application of laser point cloud data, proceeding objected-oriented remote sensing classification based on the characteristic knowledge of multi-source spatial data has been an important trend on the field of remote sensing image classification, which gradually replaced the traditional method through improving algorithm to optimize image classification results. For this purpose, the paper puts forward a remote sensing image classification method that uses the he characteristic knowledge of multi-source spatial data to build the geographic ontology semantic network model, and carries out the objected-oriented classification experiment to implement urban features classification, the experiment uses protégé software which is developed by Stanford University in the United States, and intelligent image analysis software—eCognition software as the experiment platform, uses hyperspectral image and Lidar data that is obtained through flight in DaFeng City of JiangSu as the main data source, first of all, the experiment uses hyperspectral image to obtain feature knowledge of remote sensing image and related special index, the second, the experiment uses Lidar data to generate nDSM(Normalized DSM, Normalized Digital Surface Model),obtaining elevation information, the last, the experiment bases image feature knowledge, special index and elevation information to build the geographic ontology semantic network model that implement urban features classification, the experiment results show that, this method is significantly higher than the traditional classification algorithm on classification accuracy, especially it performs more evidently on the respect of building classification. The method not only considers the advantage of multi-source spatial data, for example, remote sensing image, Lidar data and so on, but also realizes multi-source spatial data knowledge integration and application of the knowledge to the field of remote sensing image classification, which provides an effective way for objected-oriented remote sensing image classification in the future.

Classification of building infrastructure and automatic building footprint delineation using airborne laser swath mapping data

NASA Astrophysics Data System (ADS)

Caceres, Jhon

Three-dimensional (3D) models of urban infrastructure comprise critical data for planners working on problems in wireless communications, environmental monitoring, civil engineering, and urban planning, among other tasks. Photogrammetric methods have been the most common approach to date to extract building models. However, Airborne Laser Swath Mapping (ALSM) observations offer a competitive alternative because they overcome some of the ambiguities that arise when trying to extract 3D information from 2D images. Regardless of the source data, the building extraction process requires segmentation and classification of the data and building identification. In this work, approaches for classifying ALSM data, separating building and tree points, and delineating ALSM footprints from the classified data are described. Digital aerial photographs are used in some cases to verify results, but the objective of this work is to develop methods that can work on ALSM data alone. A robust approach for separating tree and building points in ALSM data is presented. The method is based on supervised learning of the classes (tree vs. building) in a high dimensional feature space that yields good class separability. Features used for classification are based on the generation of local mappings, from three-dimensional space to two-dimensional space, known as "spin images" for each ALSM point to be classified. The method discriminates ALSM returns in compact spaces and even where the classes are very close together or overlapping spatially. A modified algorithm of the Hough Transform is used to orient the spin images, and the spin image parameters are specified such that the mutual information between the spin image pixel values and class labels is maximized. This new approach to ALSM classification allows us to fully exploit the 3D point information in the ALSM data while still achieving good class separability, which has been a difficult trade-off in the past. Supported by the spin image analysis for obtaining an initial classification, an automatic approach for delineating accurate building footprints is presented. The physical fact that laser pulses that happen to strike building edges can produce very different 1st and last return elevations has been long recognized. However, in older generation ALSM systems (<50 kHz pulse rates) such points were too few and far between to delineate building footprints precisely. Furthermore, without the robust separation of nearby trees and vegetation from the buildings, simply extracting ALSM shots where the elevation of the first return was much higher than the elevation of the last return, was not a reliable means of identifying building footprints. However, with the advent of ALSM systems with pulse rates in excess of 100 kHz, and by using spin-imaged based segmentation, it is now possible to extract building edges from the point cloud. A refined classification resulting from incorporating "on-edge" information is developed for obtaining quadrangular footprints. The footprint fitting process involves line generalization, least squares-based clustering and dominant points finding for segmenting individual building edges. In addition, an algorithm for fitting complex footprints using the segmented edges and data inside footprints is also proposed.

Automated Classification of Heritage Buildings for As-Built Bim Using Machine Learning Techniques

NASA Astrophysics Data System (ADS)

Bassier, M.; Vergauwen, M.; Van Genechten, B.

2017-08-01

Semantically rich three dimensional models such as Building Information Models (BIMs) are increasingly used in digital heritage. They provide the required information to varying stakeholders during the different stages of the historic buildings life cyle which is crucial in the conservation process. The creation of as-built BIM models is based on point cloud data. However, manually interpreting this data is labour intensive and often leads to misinterpretations. By automatically classifying the point cloud, the information can be proccesed more effeciently. A key aspect in this automated scan-to-BIM process is the classification of building objects. In this research we look to automatically recognise elements in existing buildings to create compact semantic information models. Our algorithm efficiently extracts the main structural components such as floors, ceilings, roofs, walls and beams despite the presence of significant clutter and occlusions. More specifically, Support Vector Machines (SVM) are proposed for the classification. The algorithm is evaluated using real data of a variety of existing buildings. The results prove that the used classifier recognizes the objects with both high precision and recall. As a result, entire data sets are reliably labelled at once. The approach enables experts to better document and process heritage assets.

Building rooftop classification using random forests for large-scale PV deployment

NASA Astrophysics Data System (ADS)

Assouline, Dan; Mohajeri, Nahid; Scartezzini, Jean-Louis

2017-10-01

Large scale solar Photovoltaic (PV) deployment on existing building rooftops has proven to be one of the most efficient and viable sources of renewable energy in urban areas. As it usually requires a potential analysis over the area of interest, a crucial step is to estimate the geometric characteristics of the building rooftops. In this paper, we introduce a multi-layer machine learning methodology to classify 6 roof types, 9 aspect (azimuth) classes and 5 slope (tilt) classes for all building rooftops in Switzerland, using GIS processing. We train Random Forests (RF), an ensemble learning algorithm, to build the classifiers. We use (2 × 2) [m2 ] LiDAR data (considering buildings and vegetation) to extract several rooftop features, and a generalised footprint polygon data to localize buildings. The roof classifier is trained and tested with 1252 labeled roofs from three different urban areas, namely Baden, Luzern, and Winterthur. The results for roof type classification show an average accuracy of 67%. The aspect and slope classifiers are trained and tested with 11449 labeled roofs in the Zurich periphery area. The results for aspect and slope classification show different accuracies depending on the classes: while some classes are well identified, other under-represented classes remain challenging to detect.

A compressed sensing method with analytical results for lidar feature classification

NASA Astrophysics Data System (ADS)

Allen, Josef D.; Yuan, Jiangbo; Liu, Xiuwen; Rahmes, Mark

2011-04-01

We present an innovative way to autonomously classify LiDAR points into bare earth, building, vegetation, and other categories. One desirable product of LiDAR data is the automatic classification of the points in the scene. Our algorithm automatically classifies scene points using Compressed Sensing Methods via Orthogonal Matching Pursuit algorithms utilizing a generalized K-Means clustering algorithm to extract buildings and foliage from a Digital Surface Models (DSM). This technology reduces manual editing while being cost effective for large scale automated global scene modeling. Quantitative analyses are provided using Receiver Operating Characteristics (ROC) curves to show Probability of Detection and False Alarm of buildings vs. vegetation classification. Histograms are shown with sample size metrics. Our inpainting algorithms then fill the voids where buildings and vegetation were removed, utilizing Computational Fluid Dynamics (CFD) techniques and Partial Differential Equations (PDE) to create an accurate Digital Terrain Model (DTM) [6]. Inpainting preserves building height contour consistency and edge sharpness of identified inpainted regions. Qualitative results illustrate other benefits such as Terrain Inpainting's unique ability to minimize or eliminate undesirable terrain data artifacts.

The Classification of Romanian High-Schools

ERIC Educational Resources Information Center

Ivan, Ion; Milodin, Daniel; Naie, Lucian

2006-01-01

The article tries to tackle the issue of high-schools classification from one city, district or from Romania. The classification criteria are presented. The National Database of Education is also presented and the application of criteria is illustrated. An algorithm for high-school multi-rang classification is proposed in order to build classes of…

Raman spectroscopy coupled with advanced statistics for differentiating menstrual and peripheral blood.

PubMed

Sikirzhytskaya, Aliaksandra; Sikirzhytski, Vitali; Lednev, Igor K

2014-01-01

Body fluids are a common and important type of forensic evidence. In particular, the identification of menstrual blood stains is often a key step during the investigation of rape cases. Here, we report on the application of near-infrared Raman microspectroscopy for differentiating menstrual blood from peripheral blood. We observed that the menstrual and peripheral blood samples have similar but distinct Raman spectra. Advanced statistical analysis of the multiple Raman spectra that were automatically (Raman mapping) acquired from the 40 dried blood stains (20 donors for each group) allowed us to build classification model with maximum (100%) sensitivity and specificity. We also demonstrated that despite certain common constituents, menstrual blood can be readily distinguished from vaginal fluid. All of the classification models were verified using cross-validation methods. The proposed method overcomes the problems associated with currently used biochemical methods, which are destructive, time consuming and expensive. Copyright © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Novel Strength Test Battery to Permit Evidence-Based Paralympic Classification

PubMed Central

Beckman, Emma M.; Newcombe, Peter; Vanlandewijck, Yves; Connick, Mark J.; Tweedy, Sean M.

2014-01-01

Abstract Ordinal-scale strength assessment methods currently used in Paralympic athletics classification prevent the development of evidence-based classification systems. This study evaluated a battery of 7, ratio-scale, isometric tests with the aim of facilitating the development of evidence-based methods of classification. This study aimed to report sex-specific normal performance ranges, evaluate test–retest reliability, and evaluate the relationship between the measures and body mass. Body mass and strength measures were obtained from 118 participants—63 males and 55 females—ages 23.2 years ± 3.7 (mean ± SD). Seventeen participants completed the battery twice to evaluate test–retest reliability. The body mass–strength relationship was evaluated using Pearson correlations and allometric exponents. Conventional patterns of force production were observed. Reliability was acceptable (mean intraclass correlation = 0.85). Eight measures had moderate significant correlations with body size (r = 0.30–61). Allometric exponents were higher in males than in females (mean 0.99 vs 0.30). Results indicate that this comprehensive and parsimonious battery is an important methodological advance because it has psychometric properties critical for the development of evidence-based classification. Measures were interrelated with body size, indicating further research is required to determine whether raw measures require normalization in order to be validly applied in classification. PMID:25068950

The Effect of Body Build and BMI on Aerobic Test Performance in School Children (10-15 Years)

PubMed Central

Slinger, Jantine; Verstappen, Frans; Breda, Eric Van; Kuipers, Harm

2006-01-01

Body Mass Index (BMI) has often questionably been used to define body build. In the present study body build was defined more specifically using fat free mass index (FFMI = fat free mass normalised to the stature) and fat mass index (FMI = fat mass normalised to stature). The body build of an individual is ‘solid’ in individuals with a high FFMI for their FMI and is ‘slender’ in individuals with a low FFMI relative to their FMI. The aim of the present study was to investigate the association between aerobic test performance and body build defined as solid, average or slender in 10 to 15 year old children. Five-hundred-and-two children (53% boys) aged 10 to 15 years of age were included in the study. Aerobic test performance was estimated with an incremental cycle ergometer protocol and a shuttle run test. BMI and percentage fat (by skin folds) were determined to calculate FMI and FFMI. After adjustment for differences in age, gender and body mass the solid group achieved a significantly higher maximal power output (W) and power output relative to body mass (W/kg) during the cycle test (p < 0.05) and a higher shuttle-run score (p < 0.05) compared to the slender group. The power output relative to FFM (W/kg FFM) was comparable (p > 0.05) between different body build groups. This study showed that body build is an important determinant of the aerobic test performance. In contrast, there were no differences in aerobic test performance per kilogramme FFM over the body build groups. This suggests that the body build may be determined by genetic predisposition. Key Points Children with a solid body build perform better in aerobic exercise tests than slender children. The power output relative to fat free mass was comparable in the solid, slender and average group. Besides body composition, body build should be considered related to other performance measurements. PMID:24357967

Determining urban land uses through building-associated element attributes derived from lidar and aerial photographs

NASA Astrophysics Data System (ADS)

Meng, Xuelian

Urban land-use research is a key component in analyzing the interactions between human activities and environmental change. Researchers have conducted many experiments to classify urban or built-up land, forest, water, agriculture, and other land-use and land-cover types. Separating residential land uses from other land uses within urban areas, however, has proven to be surprisingly troublesome. Although high-resolution images have recently become more available for land-use classification, an increase in spatial resolution does not guarantee improved classification accuracy by traditional classifiers due to the increase of class complexity. This research presents an approach to detect and separate residential land uses on a building scale directly from remotely sensed imagery to enhance urban land-use analysis. Specifically, the proposed methodology applies a multi-directional ground filter to generate a bare ground surface from lidar data, then utilizes a morphology-based building detection algorithm to identify buildings from lidar and aerial photographs, and finally separates residential buildings using a supervised C4.5 decision tree analysis based on the seven selected building land-use indicators. Successful execution of this study produces three independent methods, each corresponding to the steps of the methodology: lidar ground filtering, building detection, and building-based object-oriented land-use classification. Furthermore, this research provides a prototype as one of the few early explorations of building-based land-use analysis and successful separation of more than 85% of residential buildings based on an experiment on an 8.25-km2 study site located in Austin, Texas.

Ensemble Sparse Classification of Alzheimer’s Disease

PubMed Central

Liu, Manhua; Zhang, Daoqiang; Shen, Dinggang

2012-01-01

The high-dimensional pattern classification methods, e.g., support vector machines (SVM), have been widely investigated for analysis of structural and functional brain images (such as magnetic resonance imaging (MRI)) to assist the diagnosis of Alzheimer’s disease (AD) including its prodromal stage, i.e., mild cognitive impairment (MCI). Most existing classification methods extract features from neuroimaging data and then construct a single classifier to perform classification. However, due to noise and small sample size of neuroimaging data, it is challenging to train only a global classifier that can be robust enough to achieve good classification performance. In this paper, instead of building a single global classifier, we propose a local patch-based subspace ensemble method which builds multiple individual classifiers based on different subsets of local patches and then combines them for more accurate and robust classification. Specifically, to capture the local spatial consistency, each brain image is partitioned into a number of local patches and a subset of patches is randomly selected from the patch pool to build a weak classifier. Here, the sparse representation-based classification (SRC) method, which has shown effective for classification of image data (e.g., face), is used to construct each weak classifier. Then, multiple weak classifiers are combined to make the final decision. We evaluate our method on 652 subjects (including 198 AD patients, 225 MCI and 229 normal controls) from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database using MR images. The experimental results show that our method achieves an accuracy of 90.8% and an area under the ROC curve (AUC) of 94.86% for AD classification and an accuracy of 87.85% and an AUC of 92.90% for MCI classification, respectively, demonstrating a very promising performance of our method compared with the state-of-the-art methods for AD/MCI classification using MR images. PMID:22270352

Typological diversity of tall buildings and complexes in relation to their functional structure

NASA Astrophysics Data System (ADS)

Generalov, Viktor P.; Generalova, Elena M.; Kalinkina, Nadezhda A.; Zhdanova, Irina V.

2018-03-01

The paper focuses on peculiarities of tall buildings and complexes, their typology and its formation in relation to their functional structure. The research is based on the analysis of tall buildings and complexes and identifies the following main functional elements of their formation: residential, administrative (office), hotel elements. The paper also considers the following services as «disseminated» in the space-planning structure: shops, medicine, entertainment, kids and sports facilities, etc., their location in the structure of the total bulk of the building and their impact on typological diversity. Research results include suggestions to add such concepts as «single-function tall buildings» and «mixed-use tall buildings and complexes» into the classification of tall buildings. In addition, if a single-function building or complex performs serving functions, it is proposed to add such concepts as «a residential tall building (complex) with provision of services», «an administrative (public) tall building (complex) with provision of services» into the classification of tall buildings. For mixed-use buildings and complexes the following terms are suggested: «a mixed-use tall building with provision of services», «a mixed-use tall complex with provision of services».

A study of earthquake-induced building detection by object oriented classification approach

NASA Astrophysics Data System (ADS)

Sabuncu, Asli; Damla Uca Avci, Zehra; Sunar, Filiz

2017-04-01

Among the natural hazards, earthquakes are the most destructive disasters and cause huge loss of lives, heavily infrastructure damages and great financial losses every year all around the world. According to the statistics about the earthquakes, more than a million earthquakes occur which is equal to two earthquakes per minute in the world. Natural disasters have brought more than 780.000 deaths approximately % 60 of all mortality is due to the earthquakes after 2001. A great earthquake took place at 38.75 N 43.36 E in the eastern part of Turkey in Van Province on On October 23th, 2011. 604 people died and about 4000 buildings seriously damaged and collapsed after this earthquake. In recent years, the use of object oriented classification approach based on different object features, such as spectral, textural, shape and spatial information, has gained importance and became widespread for the classification of high-resolution satellite images and orthophotos. The motivation of this study is to detect the collapsed buildings and debris areas after the earthquake by using very high-resolution satellite images and orthophotos with the object oriented classification and also see how well remote sensing technology was carried out in determining the collapsed buildings. In this study, two different land surfaces were selected as homogenous and heterogeneous case study areas. In the first step of application, multi-resolution segmentation was applied and optimum parameters were selected to obtain the objects in each area after testing different color/shape and compactness/smoothness values. In the next step, two different classification approaches, namely "supervised" and "unsupervised" approaches were applied and their classification performances were compared. Object-based Image Analysis (OBIA) was performed using e-Cognition software.

Ontology for Life-Cycle Modeling of Electrical Distribution Systems: Model View Definition

DTIC Science & Technology

2013-06-01

building information models ( BIM ) at the coordinated design stage of building construction. 1.3 Approach To...standard for exchanging Building Information Modeling ( BIM ) data, which defines hundreds of classes for common use in software, currently supported by...specifications, Construction Operations Building in- formation exchange (COBie), Building Information Modeling ( BIM ) 16. SECURITY CLASSIFICATION OF:

Hybrid Optimization of Object-Based Classification in High-Resolution Images Using Continous ANT Colony Algorithm with Emphasis on Building Detection

NASA Astrophysics Data System (ADS)

Tamimi, E.; Ebadi, H.; Kiani, A.

2017-09-01

Automatic building detection from High Spatial Resolution (HSR) images is one of the most important issues in Remote Sensing (RS). Due to the limited number of spectral bands in HSR images, using other features will lead to improve accuracy. By adding these features, the presence probability of dependent features will be increased, which leads to accuracy reduction. In addition, some parameters should be determined in Support Vector Machine (SVM) classification. Therefore, it is necessary to simultaneously determine classification parameters and select independent features according to image type. Optimization algorithm is an efficient method to solve this problem. On the other hand, pixel-based classification faces several challenges such as producing salt-paper results and high computational time in high dimensional data. Hence, in this paper, a novel method is proposed to optimize object-based SVM classification by applying continuous Ant Colony Optimization (ACO) algorithm. The advantages of the proposed method are relatively high automation level, independency of image scene and type, post processing reduction for building edge reconstruction and accuracy improvement. The proposed method was evaluated by pixel-based SVM and Random Forest (RF) classification in terms of accuracy. In comparison with optimized pixel-based SVM classification, the results showed that the proposed method improved quality factor and overall accuracy by 17% and 10%, respectively. Also, in the proposed method, Kappa coefficient was improved by 6% rather than RF classification. Time processing of the proposed method was relatively low because of unit of image analysis (image object). These showed the superiority of the proposed method in terms of time and accuracy.

Integrated Change Detection and Classification in Urban Areas Based on Airborne Laser Scanning Point Clouds.

PubMed

Tran, Thi Huong Giang; Ressl, Camillo; Pfeifer, Norbert

2018-02-03

This paper suggests a new approach for change detection (CD) in 3D point clouds. It combines classification and CD in one step using machine learning. The point cloud data of both epochs are merged for computing features of four types: features describing the point distribution, a feature relating to relative terrain elevation, features specific for the multi-target capability of laser scanning, and features combining the point clouds of both epochs to identify the change. All these features are merged in the points and then training samples are acquired to create the model for supervised classification, which is then applied to the whole study area. The final results reach an overall accuracy of over 90% for both epochs of eight classes: lost tree, new tree, lost building, new building, changed ground, unchanged building, unchanged tree, and unchanged ground.

The Design of Archives Buildings.

ERIC Educational Resources Information Center

Faye, Bernard

1982-01-01

Studies specific problems arising from design of archives buildings and examines three main purposes of this type of building, namely conservation, classification and restoration of archives, and the provision of access to them by administrators and research workers. Three references are listed. (Author/EJS)

Designing and Implementation of River Classification Assistant Management System

NASA Astrophysics Data System (ADS)

Zhao, Yinjun; Jiang, Wenyuan; Yang, Rujun; Yang, Nan; Liu, Haiyan

2018-03-01

In an earlier publication, we proposed a new Decision Classifier (DCF) for Chinese river classification based on their structures. To expand, enhance and promote the application of the DCF, we build a computer system to support river classification named River Classification Assistant Management System. Based on ArcEngine and ArcServer platform, this system implements many functions such as data management, extraction of river network, river classification, and results publication under combining Client / Server with Browser / Server framework.

A Hierarchical Object-oriented Urban Land Cover Classification Using WorldView-2 Imagery and Airborne LiDAR data

NASA Astrophysics Data System (ADS)

Wu, M. F.; Sun, Z. C.; Yang, B.; Yu, S. S.

2016-11-01

In order to reduce the “salt and pepper” in pixel-based urban land cover classification and expand the application of fusion of multi-source data in the field of urban remote sensing, WorldView-2 imagery and airborne Light Detection and Ranging (LiDAR) data were used to improve the classification of urban land cover. An approach of object- oriented hierarchical classification was proposed in our study. The processing of proposed method consisted of two hierarchies. (1) In the first hierarchy, LiDAR Normalized Digital Surface Model (nDSM) image was segmented to objects. The NDVI, Costal Blue and nDSM thresholds were set for extracting building objects. (2) In the second hierarchy, after removing building objects, WorldView-2 fused imagery was obtained by Haze-ratio-based (HR) fusion, and was segmented. A SVM classifier was applied to generate road/parking lot, vegetation and bare soil objects. (3) Trees and grasslands were split based on an nDSM threshold (2.4 meter). The results showed that compared with pixel-based and non-hierarchical object-oriented approach, proposed method provided a better performance of urban land cover classification, the overall accuracy (OA) and overall kappa (OK) improved up to 92.75% and 0.90. Furthermore, proposed method reduced “salt and pepper” in pixel-based classification, improved the extraction accuracy of buildings based on LiDAR nDSM image segmentation, and reduced the confusion between trees and grasslands through setting nDSM threshold.

Compact and Hybrid Feature Description for Building Extraction

NASA Astrophysics Data System (ADS)

Li, Z.; Liu, Y.; Hu, Y.; Li, P.; Ding, Y.

2017-05-01

Building extraction in aerial orthophotos is crucial for various applications. Currently, deep learning has been shown to be successful in addressing building extraction with high accuracy and high robustness. However, quite a large number of samples is required in training a classifier when using deep learning model. In order to realize accurate and semi-interactive labelling, the performance of feature description is crucial, as it has significant effect on the accuracy of classification. In this paper, we bring forward a compact and hybrid feature description method, in order to guarantees desirable classification accuracy of the corners on the building roof contours. The proposed descriptor is a hybrid description of an image patch constructed from 4 sets of binary intensity tests. Experiments show that benefiting from binary description and making full use of color channels, this descriptor is not only computationally frugal, but also accurate than SURF for building extraction.

The new intra-articular calcaneal fracture classification system in term of sustentacular fragment configurations and incorporation of posterior calcaneal facet fractures with fracture components of the calcaneal body.

PubMed

Harnroongroj, Thossart; Harnroongroj, Thos; Suntharapa, Thongchai; Arunakul, Marut

2016-10-01

The aim of this study was to develop a new calcaneal fracture classification system which will consider sustentacular fragment configuration and relation of posterior calcaneal facet to calcaneal body. The new classification system used sustentacular fragment configuration and relation of posterior calcaneal facet fracture with fracture components of calcaneal body as key aspects of main types and subtypes. Between 2000 and 2014, 126 intraarticular calcaneal fractures were classified according to the new classification system by using computed tomography images. The new classification system was studied in term of reliability, correlation to choices of treatment, implant fixation and quality of fracture reduction. Types of sustentacular fragment comprised type A, B and C. Type A sustentacular fragment included sustentacular tali containing middle calcaneal facet. In Type B and C fractures sustentacular fragment included medial aspect and entire posterior calcaneal facet as a single unit, respectively. The fractures with type A, B and C sustentacular fragments were classified as main type A, B and C intra-articular calcaneal fractures. The main type A and B comprised 4 subtypes. Subtypes A1, A3, B1, and B3 associated with avulsion and bending fragments of calcaneal body. Subtype A2, B2, and B4 associated with burst calcaneal body. Subtype B4 was not found in the study. Main type C had no subtype and associated with burst calcaneal body. The data showed good reliability. The study showed that our new intra-articular calcaneal fracture classification system correlates to choices of treatment, implant fixation and quality of fracture reduction. Level IV, Study of Diagnostic Test. Copyright © 2016 Turkish Association of Orthopaedics and Traumatology. Production and hosting by Elsevier B.V. All rights reserved.

Semantic classification of urban buildings combining VHR image and GIS data: An improved random forest approach

NASA Astrophysics Data System (ADS)

Du, Shihong; Zhang, Fangli; Zhang, Xiuyuan

2015-07-01

While most existing studies have focused on extracting geometric information on buildings, only a few have concentrated on semantic information. The lack of semantic information cannot satisfy many demands on resolving environmental and social issues. This study presents an approach to semantically classify buildings into much finer categories than those of existing studies by learning random forest (RF) classifier from a large number of imbalanced samples with high-dimensional features. First, a two-level segmentation mechanism combining GIS and VHR image produces single image objects at a large scale and intra-object components at a small scale. Second, a semi-supervised method chooses a large number of unbiased samples by considering the spatial proximity and intra-cluster similarity of buildings. Third, two important improvements in RF classifier are made: a voting-distribution ranked rule for reducing the influences of imbalanced samples on classification accuracy and a feature importance measurement for evaluating each feature's contribution to the recognition of each category. Fourth, the semantic classification of urban buildings is practically conducted in Beijing city, and the results demonstrate that the proposed approach is effective and accurate. The seven categories used in the study are finer than those in existing work and more helpful to studying many environmental and social problems.

EXTENDING AQUATIC CLASSIFICATION TO THE LANDSCAPE SCALE HYDROLOGY-BASED STRATEGIES

EPA Science Inventory

Aquatic classification of single water bodies (lakes, wetlands, estuaries) is often based on geologic origin, while stream classification has relied on multiple factors related to landform, geomorphology, and soils. We have developed an approach to aquatic classification based o...

Object-based analysis of multispectral airborne laser scanner data for land cover classification and map updating

NASA Astrophysics Data System (ADS)

Matikainen, Leena; Karila, Kirsi; Hyyppä, Juha; Litkey, Paula; Puttonen, Eetu; Ahokas, Eero

2017-06-01

During the last 20 years, airborne laser scanning (ALS), often combined with passive multispectral information from aerial images, has shown its high feasibility for automated mapping processes. The main benefits have been achieved in the mapping of elevated objects such as buildings and trees. Recently, the first multispectral airborne laser scanners have been launched, and active multispectral information is for the first time available for 3D ALS point clouds from a single sensor. This article discusses the potential of this new technology in map updating, especially in automated object-based land cover classification and change detection in a suburban area. For our study, Optech Titan multispectral ALS data over a suburban area in Finland were acquired. Results from an object-based random forests analysis suggest that the multispectral ALS data are very useful for land cover classification, considering both elevated classes and ground-level classes. The overall accuracy of the land cover classification results with six classes was 96% compared with validation points. The classes under study included building, tree, asphalt, gravel, rocky area and low vegetation. Compared to classification of single-channel data, the main improvements were achieved for ground-level classes. According to feature importance analyses, multispectral intensity features based on several channels were more useful than those based on one channel. Automatic change detection for buildings and roads was also demonstrated by utilising the new multispectral ALS data in combination with old map vectors. In change detection of buildings, an old digital surface model (DSM) based on single-channel ALS data was also used. Overall, our analyses suggest that the new data have high potential for further increasing the automation level in mapping. Unlike passive aerial imaging commonly used in mapping, the multispectral ALS technology is independent of external illumination conditions, and there are no shadows on intensity images produced from the data. These are significant advantages in developing automated classification and change detection procedures.

Adipose Tissue Quantification by Imaging Methods: A Proposed Classification

PubMed Central

Shen, Wei; Wang, ZiMian; Punyanita, Mark; Lei, Jianbo; Sinav, Ahmet; Kral, John G.; Imielinska, Celina; Ross, Robert; Heymsfield, Steven B.

2007-01-01

Recent advances in imaging techniques and understanding of differences in the molecular biology of adipose tissue has rendered classical anatomy obsolete, requiring a new classification of the topography of adipose tissue. Adipose tissue is one of the largest body compartments, yet a classification that defines specific adipose tissue depots based on their anatomic location and related functions is lacking. The absence of an accepted taxonomy poses problems for investigators studying adipose tissue topography and its functional correlates. The aim of this review was to critically examine the literature on imaging of whole body and regional adipose tissue and to create the first systematic classification of adipose tissue topography. Adipose tissue terminology was examined in over 100 original publications. Our analysis revealed inconsistencies in the use of specific definitions, especially for the compartment termed “visceral” adipose tissue. This analysis leads us to propose an updated classification of total body and regional adipose tissue, providing a well-defined basis for correlating imaging studies of specific adipose tissue depots with molecular processes. PMID:12529479

Some Implications of Body Build Stereotypes for the Development of Body Concept and Interpersonal Relations.

ERIC Educational Resources Information Center

Lerner, Richard M.

In this paper the author tries to indicate, through a review of his research, that the scope of the study of body build stereotypes has been broadened to address the larger issues involved in assessing some of the implications of body build stereotypes for the development of body concept and interpersonal relations. Among the topics discussed are:…

A Review of Equation of State Models, Chemical Equilibrium Calculations and CERV Code Requirements for SHS Detonation Modelling

DTIC Science & Technology

2009-10-01

parameters for a large number of species. These authors provide many sample calculations with the JCZS database incorporated in CHEETAH 2.0, including...FORM (highest classification of Title, Abstract, Keywords) DOCUMENT CONTROL DATA (Security classification of title, body of abstract and...CLASSIFICATION OF FORM 13. ABSTRACT (a brief and factual summary of the document. It may also appear elsewhere in the body of the document itself

Differences in Body Build in Children of Different Ethnic Groups and their Impact on the Prevalence of Stunting, Thinness, Overweight, and Obesity.

PubMed

Poh, Bee Koon; Wong, Jyh Eiin; Norimah, A Karim; Deurenberg, Paul

2016-03-01

The prevalence of stunting, thinness, overweight, and obesity among children differs by ethnicity. It is not known whether differences in body build across the ethnic groups influence the interpretation of nutritional parameters. To explore the differences in body build across the 5 main ethnic groups in Malaysia and to determine whether differences in body build have an impact on the interpretation of nutrition indicators. A total of 3227 children aged 2.0 to 12.9 years who participated in the South East Asian Nutrition Surveys (SEANUTS) in Malaysia were included in this analysis. Body weight, height, sitting height, wrist and knee breadths, and biceps and subscapular skinfolds were measured, and relative leg length, slenderness index, and sum of skinfolds were calculated. Z scores for height-for-age (HAZ) and body mass index-for-age (BAZ) were calculated using the World Health Organization (WHO) 2007 growth standards. Differences in relative leg length and slenderness across the ethnic groups were correlated with HAZ and BAZ. Correction for differences in body build did, in some ethnic groups, have significant impact on the prevalence of stunting, thinness, overweight, and obesity, and the pattern of prevalence across ethnic groups changed. At the population level, corrections for body build had only minor and mostly nonsignificant effects on prevalence, but at an individual level, corrections for body build placed a substantial number of children in different height or weight categories. Whether these misclassifications warrant additional assessment of body build in clinical practice will need further investigation. © The Author(s) 2016.

Creating a behavioural classification module for acceleration data: using a captive surrogate for difficult to observe species.

PubMed

Campbell, Hamish A; Gao, Lianli; Bidder, Owen R; Hunter, Jane; Franklin, Craig E

2013-12-15

Distinguishing specific behavioural modes from data collected by animal-borne tri-axial accelerometers can be a time-consuming and subjective process. Data synthesis can be further inhibited when the tri-axial acceleration data cannot be paired with the corresponding behavioural mode through direct observation. Here, we explored the use of a tame surrogate (domestic dog) to build a behavioural classification module, and then used that module to accurately identify and quantify behavioural modes within acceleration collected from other individuals/species. Tri-axial acceleration data were recorded from a domestic dog whilst it was commanded to walk, run, sit, stand and lie-down. Through video synchronisation, each tri-axial acceleration sample was annotated with its associated behavioural mode; the feature vectors were extracted and used to build the classification module through the application of support vector machines (SVMs). This behavioural classification module was then used to identify and quantify the same behavioural modes in acceleration collected from a range of other species (alligator, badger, cheetah, dingo, echidna, kangaroo and wombat). Evaluation of the module performance, using a binary classification system, showed there was a high capacity (>90%) for behaviour recognition between individuals of the same species. Furthermore, a positive correlation existed between SVM capacity and the similarity of the individual's spinal length-to-height above the ground ratio (SL:SH) to that of the surrogate. The study describes how to build a behavioural classification module and highlights the value of using a surrogate for studying cryptic, rare or endangered species.

Classification method, spectral diversity, band combination and accuracy assessment evaluation for urban feature detection

NASA Astrophysics Data System (ADS)

Erener, A.

2013-04-01

Automatic extraction of urban features from high resolution satellite images is one of the main applications in remote sensing. It is useful for wide scale applications, namely: urban planning, urban mapping, disaster management, GIS (geographic information systems) updating, and military target detection. One common approach to detecting urban features from high resolution images is to use automatic classification methods. This paper has four main objectives with respect to detecting buildings. The first objective is to compare the performance of the most notable supervised classification algorithms, including the maximum likelihood classifier (MLC) and the support vector machine (SVM). In this experiment the primary consideration is the impact of kernel configuration on the performance of the SVM. The second objective of the study is to explore the suitability of integrating additional bands, namely first principal component (1st PC) and the intensity image, for original data for multi classification approaches. The performance evaluation of classification results is done using two different accuracy assessment methods: pixel based and object based approaches, which reflect the third aim of the study. The objective here is to demonstrate the differences in the evaluation of accuracies of classification methods. Considering consistency, the same set of ground truth data which is produced by labeling the building boundaries in the GIS environment is used for accuracy assessment. Lastly, the fourth aim is to experimentally evaluate variation in the accuracy of classifiers for six different real situations in order to identify the impact of spatial and spectral diversity on results. The method is applied to Quickbird images for various urban complexity levels, extending from simple to complex urban patterns. The simple surface type includes a regular urban area with low density and systematic buildings with brick rooftops. The complex surface type involves almost all kinds of challenges, such as high dense build up areas, regions with bare soil, and small and large buildings with different rooftops, such as concrete, brick, and metal. Using the pixel based accuracy assessment it was shown that the percent building detection (PBD) and quality percent (QP) of the MLC and SVM depend on the complexity and texture variation of the region. Generally, PBD values range between 70% and 90% for the MLC and SVM, respectively. No substantial improvements were observed when the SVM and MLC classifications were developed by the addition of more variables, instead of the use of only four bands. In the evaluation of object based accuracy assessment, it was demonstrated that while MLC and SVM provide higher rates of correct detection, they also provide higher rates of false alarms.

Ontology for Life-Cycle Modeling of Electrical Distribution Systems: Application of Model View Definition Attributes

DTIC Science & Technology

2013-06-01

Building in- formation exchange (COBie), Building Information Modeling ( BIM ) 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF...to develop a life-cycle building model have resulted in the definition of a “core” building information model that contains general information de...develop an information -exchange Model View Definition (MVD) for building electrical systems. The objective of the current work was to document the

Building a Multi-Discipline Digital Library Through Extending the Dienst Protocol

NASA Technical Reports Server (NTRS)

Nelson, Michael L.; Maly, Kurt; Shen, Stewart N. T.

1997-01-01

The purpose of this project is to establish multi-discipline capability for a unified, canonical digital library service for scientific and technical information (STI). This is accomplished by extending the Dienst Protocol to be aware of subject classification of a servers holdings. We propose a hierarchical, general, and extendible subject classification that can encapsulate existing classification systems.

An approach for combining airborne LiDAR and high-resolution aerial color imagery using Gaussian processes

NASA Astrophysics Data System (ADS)

Liu, Yansong; Monteiro, Sildomar T.; Saber, Eli

2015-10-01

Changes in vegetation cover, building construction, road network and traffic conditions caused by urban expansion affect the human habitat as well as the natural environment in rapidly developing cities. It is crucial to assess these changes and respond accordingly by identifying man-made and natural structures with accurate classification algorithms. With the increase in use of multi-sensor remote sensing systems, researchers are able to obtain a more complete description of the scene of interest. By utilizing multi-sensor data, the accuracy of classification algorithms can be improved. In this paper, we propose a method for combining 3D LiDAR point clouds and high-resolution color images to classify urban areas using Gaussian processes (GP). GP classification is a powerful non-parametric classification method that yields probabilistic classification results. It makes predictions in a way that addresses the uncertainty of real world. In this paper, we attempt to identify man-made and natural objects in urban areas including buildings, roads, trees, grass, water and vehicles. LiDAR features are derived from the 3D point clouds and the spatial and color features are extracted from RGB images. For classification, we use the Laplacian approximation for GP binary classification on the new combined feature space. The multiclass classification has been implemented by using one-vs-all binary classification strategy. The result of applying support vector machines (SVMs) and logistic regression (LR) classifier is also provided for comparison. Our experiments show a clear improvement of classification results by using the two sensors combined instead of each sensor separately. Also we found the advantage of applying GP approach to handle the uncertainty in classification result without compromising accuracy compared to SVM, which is considered as the state-of-the-art classification method.

Analysis of Turbulent Boundary-Layer over Rough Surfaces with Application to Projectile Aerodynamics

DTIC Science & Technology

1988-12-01

12 V. APPLICATION IN COMPONENT BUILD-UP METHODOLOGIES ....................... 12 1. COMPONENT BUILD-UP IN DRAG...dimensional roughness. II. CLASSIFICATION OF PREDICTION METHODS Prediction methods can be classified into two main approache-: 1) Correlation methodologies ...data are availaNe. V. APPLICATION IN COMPONENT BUILD-UP METHODOLOGIES 1. COMPONENT BUILD-UP IN DRAG The new correlation can be used for an engine.ring

Voice classification and vocal tract of singers: a study of x-ray images and morphology.

PubMed

Roers, Friederike; Mürbe, Dirk; Sundberg, Johan

2009-01-01

This investigation compares vocal tract dimensions and the classification of singer voices by examining an x-ray material assembled between 1959 and 1991 of students admitted to the solo singing education at the University of Music, Dresden, Germany. A total of 132 images were available to analysis. Different classifications' values of the lengths of the total vocal tract, the pharynx, and mouth cavities as well as of the relative position of the larynx, the height of the palatal arch, and the estimated vocal fold length were analyzed statistically, and some significant differences were found. The length of the pharynx cavity seemed particularly influential on the total vocal tract length, which varied systematically with classification. Also studied were the relationships between voice classification and the body height and weight and the body mass index. The data support the hypothesis that there are consistent morphological vocal tract differences between singers of different voice classifications.

European validation of The Comprehensive International Classification of Functioning, Disability and Health Core Set for Osteoarthritis from the perspective of patients with osteoarthritis of the knee or hip.

PubMed

Weigl, Martin; Wild, Heike

2017-09-15

To validate the International Classification of Functioning, Disability and Health Comprehensive Core Set for Osteoarthritis from the patient perspective in Europe. This multicenter cross-sectional study involved 375 patients with knee or hip osteoarthritis. Trained health professionals completed the Comprehensive Core Set, and patients completed the Short-Form 36 questionnaire. Content validity was evaluated by calculating prevalences of impairments in body function and structures, limitations in activities and participation and environmental factors, which were either barriers or facilitators. Convergent construct validity was evaluated by correlating the International Classification of Functioning, Disability and Health categories with the Short-Form 36 Physical Component Score and the SF-36 Mental Component Score in a subgroup of 259 patients. The prevalences of all body function, body structure and activities and participation categories were >40%, >32% and >20%, respectively, and all environmental factors were relevant for >16% of patients. Few categories showed relevant differences between knee and hip osteoarthritis. All body function categories and all but two activities and participation categories showed significant correlations with the Physical Component Score. Body functions from the ICF chapter Mental Functions showed higher correlations with the Mental Component Score than with the Physical Component Score. This study supports the validity of the International Classification of Functioning, Disability and Health Comprehensive Core Set for Osteoarthritis. Implications for Rehabilitation Comprehensive International Classification of Functioning, Disability and Health Core Sets were developed as practical tools for application in multidisciplinary assessments. The validity of the Comprehensive International Classification of Functioning, Disability and Health Core Set for Osteoarthritis in this study supports its application in European patients with osteoarthritis. The differences in results between this Europe validation study and a previous Singaporean validation study underscore the need to validate the International Classification of Functioning, Disability and Health Core Sets in different regions of the world.

Building "Bob": A Project Exploring the Human Body at Western Illinois University Preschool Center

ERIC Educational Resources Information Center

Brouette, Scott

2008-01-01

When the children at Western Illinois University Preschool Center embarked on a study of human bodies, they decided to build a life-size model of a body, organ by organ from the inside out, to represent some of the things they were learning. This article describes the building of "Bob," the human body model, highlighting the children's…

Classification of Foreign Body Reactions due to Industrial Silicone Injection.

PubMed

Harlim, Ago; Kanoko, Mpu; Aisah, Siti

2018-05-02

A foreign body reaction (FBR) is a typical tissue response to a biomaterial that has been injected or implanted in human body tissue. There has been a lack of data on the classification of foreign body reaction to silicone injection, which can describe the pattern of body tissue responses to silicone. Determine the foreign body reaction to silicone injection. We modified the classification proposed by Duranti and colleagues, which has categorized a FBR to hyaluronic acid injection into a new classification of an FBR to silicone injection. A cohort study of 31 women suffering from silicone-induced granulomas on their chin was conducted. Granulomatous tissue and submental skin were stained with hematoxylin-eosin and evaluated. Our data revealed that there were at least 7 categories of FBRs to silicone injection that could be developed. Categories 1 to 4 showed inflammatory activity, and categories 5 to 8 showed tissue repair by fibrosis. Using histopathological staining, we are able to sequence the steps of body reactions to silicone injection. Initial inflammatory reaction is then replaced by fibrosis process repairing the damaged tissues. The process depends on the host immune tolerance.This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

Concordance of obesity classification between body mass index and percent body fat among school children in Saudi Arabia.

PubMed

Al-Mohaimeed, Abdulrahman; Ahmed, Saifuddin; Dandash, Khadiga; Ismail, Mohammed Saleh; Saquib, Nazmus

2015-03-05

In Saudi Arabia, where childhood obesity is a major public health issue, it is important to identify the best tool for obesity classification. Hence, we compared two field methods for their usefulness in epidemiological studies. The sample consisted of 874 primary school (grade I-IV) children, aged 6-10 years, and was obtained through a multi-stage random sampling procedure. Weight and height were measured, and BMI (kg/m(2)) was calculated. Percent body fat was determined with a Futrex analyzer that uses near infrared reactance (NIR) technology. Method specific cut-off values were used for obesity classification. Sensitivity, specificity, positive and negative predictive values were determined for BMI, and the agreement between BMI and percent body fat was calculated. Compared to boys, the mean BMI was higher in girls whereas the mean percent body fat was lower (p-values < 0.0001). According to BMI, the prevalence of overweight or obesity was significantly higher in girls (34.3% vs. 17.3%); as oppose to percent body fat, which was similar between the sexes (6.6% vs. 7.0%). The sensitivity of BMI to classify overweight or obesity was high (boys = 93%, girls = 100%); and its false-positive detection rate was also high (boys = 63%, girls = 81%). The agreement rate was low between these two methods (boys = 0.48, girls =0.24). There is poor agreement in obesity classification between BMI and percent body fat, using NIR method, among Saudi school children.

Reuse of Material Containing Natural Radionuclides - 12444

DOE Office of Scientific and Technical Information (OSTI.GOV)

Metlyaev, E.G.; Novikova, N.J.

2012-07-01

Disposal of and use of wastes containing natural radioactive material (NORM) or technologically enhanced natural radioactive material (TENORM) with excessive natural background as a building material is very important in the supervision body activity. At the present time, the residents of Octyabrsky village are under resettlement. This village is located just near the Priargunsky mining and chemical combine (Ltd. 'PPGHO'), one of the oldest uranium mines in our country. The vacated wooden houses in the village are demolished and partly used as a building material. To address the issue of potential radiation hazard of the wooden beams originating from demolitionmore » of houses in Octyabrsky village, the contents of the natural radionuclides (K-40, Th-232, Ra-226, U- 238) are being determined in samples of the wooden beams of houses. The NORM contents in the wooden house samples are higher, on average, than their content in the reference sample of the fresh wood shavings, but the range of values is rather large. According to the classification of waste containing the natural radionuclides, its evaluation is based on the effective specific activity. At the effective specific activity lower 1.5 kBq/kg and gamma dose rate lower 70 μR/h, the material is not considered as waste and can be used in building by 1 - 3 classes depending upon A{sub eff} value. At 1.5 kBq/kg < A{sub eff} ≤ 4 kBq/kg (4 class), the wooden beams might be used for the purpose of the industrial building, if sum of ratios between the radionuclide specific activity and its specific activity of minimum significance is lower than unit. The material classified as the waste containing the natural radionuclides has A{sub eff} higher 1.5 kBq /kg, and its usage for the purpose of house-building and road construction is forbidden. As for the ash classification and its future usage, such usage is unreasonable, because, according to the provided material, more than 50% of ash samples are considered as radioactive waste containing natural radionuclides. All materials originated from demolition of houses in Octyabrsky village are subjected to the obligatory radiation control. The decision to use the wooden beams shall enter into force after agreement with the State Sanitary and Epidemiological Supervision bodies. Conclusions: 1 - The wooden beam originated from the house demolition in Octyabrsky village might be used as the construction material only in case of compliance with the requirements of the regulatory documents, as well as under approval of the authorities responsible for the state sanitary and epidemiological supervision in this area. 2 - The industrial control is introduced to verify the compliance with the current regulations. 3 - The material originated from the house demolition might be used only if such usage does not cause increasing radiation exposure to the public. (authors)« less

Multisensor multiresolution data fusion for improvement in classification

NASA Astrophysics Data System (ADS)

Rubeena, V.; Tiwari, K. C.

2016-04-01

The rapid advancements in technology have facilitated easy availability of multisensor and multiresolution remote sensing data. Multisensor, multiresolution data contain complementary information and fusion of such data may result in application dependent significant information which may otherwise remain trapped within. The present work aims at improving classification by fusing features of coarse resolution hyperspectral (1 m) LWIR and fine resolution (20 cm) RGB data. The classification map comprises of eight classes. The class names are Road, Trees, Red Roof, Grey Roof, Concrete Roof, Vegetation, bare Soil and Unclassified. The processing methodology for hyperspectral LWIR data comprises of dimensionality reduction, resampling of data by interpolation technique for registering the two images at same spatial resolution, extraction of the spatial features to improve classification accuracy. In the case of fine resolution RGB data, the vegetation index is computed for classifying the vegetation class and the morphological building index is calculated for buildings. In order to extract the textural features, occurrence and co-occurence statistics is considered and the features will be extracted from all the three bands of RGB data. After extracting the features, Support Vector Machine (SVMs) has been used for training and classification. To increase the classification accuracy, post processing steps like removal of any spurious noise such as salt and pepper noise is done which is followed by filtering process by majority voting within the objects for better object classification.

Validity of total and segmental impedance measurements for prediction of body composition across ethnic population groups.

PubMed

Deurenberg, P; Deurenberg-Yap, M; Schouten, F J M

2002-03-01

To test the impact of body build factors on the validity of impedance-based body composition predictions across (ethnic) population groups and to study the suitability of segmental impedance measurements. Cross-sectional observational study. Ministry of Health and School of Physical Education, Nanyang Technological University, Singapore. A total of 291 female and male Chinese, Malays and Indian Singaporeans, aged 18-69, body mass index (BMI) 16.0-40.2 kg/ m2. Anthropometric parameters were measured in addition to impedance (100 kHz) of the total body, arms and legs. Impedance indexes were calculated as height2/impedance. Arm length (span) and leg length (sitting height), wrist and knee width were measured from which body build indices were calculated. Total body water (TBW) was measured using deuterium oxide dilution. Extra cellular water (ECW) was measured using bromide dilution. Body fat percentage was determined using a chemical four-compartment model. The bias of TBW predicted from total body impedance index (bias: measured minus predicted TBW) was different among the three ethnic groups, TBW being significantly underestimated in Indians compared to Chinese and Malays. This bias was found to be dependent on body water distribution (ECW/TBW) and parameters of body build, mainly relative (to height) arm length. After correcting for differences in body water distribution and body build parameters the differences in bias across the ethnic groups disappeared. The impedance index using total body impedance was better correlated with TBW than the impedance index of arm or leg impedance, even after corrections for body build parameters. The study shows that ethnic-specific bias of impedance-based prediction formulas for body composition is due mainly to differences in body build among the ethnic groups. This means that the use of 'general' prediction equations across different (ethnic) population groups without prior testing of their validity should be avoided. Total body impedance has higher predictive value than segmental impedance.

Segmentation of human upper body movement using multiple IMU sensors.

PubMed

Aoki, Takashi; Lin, Jonathan Feng-Shun; Kulic, Dana; Venture, Gentiane

2016-08-01

This paper proposes an approach for the segmentation of human body movements measured by inertial measurement unit sensors. Using the angular velocity and linear acceleration measurements directly, without converting to joint angles, we perform segmentation by formulating the problem as a classification problem, and training a classifier to differentiate between motion end-point and within-motion points. The proposed approach is validated with experiments measuring the upper body movement during reaching tasks, demonstrating classification accuracy of over 85.8%.

A Temporal Mining Framework for Classifying Un-Evenly Spaced Clinical Data: An Approach for Building Effective Clinical Decision-Making System.

PubMed

Jane, Nancy Yesudhas; Nehemiah, Khanna Harichandran; Arputharaj, Kannan

2016-01-01

Clinical time-series data acquired from electronic health records (EHR) are liable to temporal complexities such as irregular observations, missing values and time constrained attributes that make the knowledge discovery process challenging. This paper presents a temporal rough set induced neuro-fuzzy (TRiNF) mining framework that handles these complexities and builds an effective clinical decision-making system. TRiNF provides two functionalities namely temporal data acquisition (TDA) and temporal classification. In TDA, a time-series forecasting model is constructed by adopting an improved double exponential smoothing method. The forecasting model is used in missing value imputation and temporal pattern extraction. The relevant attributes are selected using a temporal pattern based rough set approach. In temporal classification, a classification model is built with the selected attributes using a temporal pattern induced neuro-fuzzy classifier. For experimentation, this work uses two clinical time series dataset of hepatitis and thrombosis patients. The experimental result shows that with the proposed TRiNF framework, there is a significant reduction in the error rate, thereby obtaining the classification accuracy on an average of 92.59% for hepatitis and 91.69% for thrombosis dataset. The obtained classification results prove the efficiency of the proposed framework in terms of its improved classification accuracy.

A graduated food addiction classification approach significantly differentiates obesity among people with type 2 diabetes.

PubMed

Raymond, Karren-Lee; Kannis-Dymand, Lee; Lovell, Geoff P

2016-10-01

This study examined a graduated severity level approach to food addiction classification against associations with World Health Organization obesity classifications (body mass index, kg/m 2 ) among 408 people with type 2 diabetes. A survey including the Yale Food Addiction Scale and several demographic questions demonstrated four distinct Yale Food Addiction Scale symptom severity groups (in line with Diagnostic and Statistical Manual of Mental Disorders (5th ed.) severity indicators): non-food addiction, mild food addiction, moderate food addiction and severe food addiction. Analysis of variance with post hoc tests demonstrated each severity classification group was significantly different in body mass index, with each grouping being associated with increased World Health Organization obesity classifications. These findings have implications for diagnosing food addiction and implementing treatment and prevention methodologies of obesity among people with type 2 diabetes.

1961-1968 New Construction Report.

ERIC Educational Resources Information Center

National Association of Physical Plant Administrators of Universities and Colleges, Richmond, IN.

137 NAPPA colleges and universities provided data for this summary. Projects are summarized by thirteen building classifications. Under each classification the following information headings are used--(1) name of institution, (2) project completion date, (3) gross square feet, (4) net assignable area, (5) construction costs, (6) number of stories,…

Towards Automatic Classification of Exoplanet-Transit-Like Signals: A Case Study on Kepler Mission Data

NASA Astrophysics Data System (ADS)

Valizadegan, Hamed; Martin, Rodney; McCauliff, Sean D.; Jenkins, Jon Michael; Catanzarite, Joseph; Oza, Nikunj C.

2015-08-01

Building new catalogues of planetary candidates, astrophysical false alarms, and non-transiting phenomena is a challenging task that currently requires a reviewing team of astrophysicists and astronomers. These scientists need to examine more than 100 diagnostic metrics and associated graphics for each candidate exoplanet-transit-like signal to classify it into one of the three classes. Considering that the NASA Explorer Program's TESS mission and ESA's PLATO mission survey even a larger area of space, the classification of their transit-like signals is more time-consuming for human agents and a bottleneck to successfully construct the new catalogues in a timely manner. This encourages building automatic classification tools that can quickly and reliably classify the new signal data from these missions. The standard tool for building automatic classification systems is the supervised machine learning that requires a large set of highly accurate labeled examples in order to build an effective classifier. This requirement cannot be easily met for classifying transit-like signals because not only are existing labeled signals very limited, but also the current labels may not be reliable (because the labeling process is a subjective task). Our experiments with using different supervised classifiers to categorize transit-like signals verifies that the labeled signals are not rich enough to provide the classifier with enough power to generalize well beyond the observed cases (e.g. to unseen or test signals). That motivated us to utilize a new category of learning techniques, so-called semi-supervised learning, that combines the label information from the costly labeled signals, and distribution information from the cheaply available unlabeled signals in order to construct more effective classifiers. Our study on the Kepler Mission data shows that semi-supervised learning can significantly improve the result of multiple base classifiers (e.g. Support Vector Machines, AdaBoost, and Decision Tree) and is a good technique for automatic classification of exoplanet-transit-like signal.

Granular support vector machines with association rules mining for protein homology prediction.

PubMed

Tang, Yuchun; Jin, Bo; Zhang, Yan-Qing

2005-01-01

Protein homology prediction between protein sequences is one of critical problems in computational biology. Such a complex classification problem is common in medical or biological information processing applications. How to build a model with superior generalization capability from training samples is an essential issue for mining knowledge to accurately predict/classify unseen new samples and to effectively support human experts to make correct decisions. A new learning model called granular support vector machines (GSVM) is proposed based on our previous work. GSVM systematically and formally combines the principles from statistical learning theory and granular computing theory and thus provides an interesting new mechanism to address complex classification problems. It works by building a sequence of information granules and then building support vector machines (SVM) in some of these information granules on demand. A good granulation method to find suitable granules is crucial for modeling a GSVM with good performance. In this paper, we also propose an association rules-based granulation method. For the granules induced by association rules with high enough confidence and significant support, we leave them as they are because of their high "purity" and significant effect on simplifying the classification task. For every other granule, a SVM is modeled to discriminate the corresponding data. In this way, a complex classification problem is divided into multiple smaller problems so that the learning task is simplified. The proposed algorithm, here named GSVM-AR, is compared with SVM by KDDCUP04 protein homology prediction data. The experimental results show that finding the splitting hyperplane is not a trivial task (we should be careful to select the association rules to avoid overfitting) and GSVM-AR does show significant improvement compared to building one single SVM in the whole feature space. Another advantage is that the utility of GSVM-AR is very good because it is easy to be implemented. More importantly and more interestingly, GSVM provides a new mechanism to address complex classification problems.

High-rise architecture in Ufa, Russia, based on crystallography canons

NASA Astrophysics Data System (ADS)

Narimanovich Sabitov, Ildar; Radikovna Kudasheva, Dilara; Yaroslavovich Vdovin, Denis

2018-03-01

The article considers fundamental steps of high-rise architecture forming stylistic tendencies, based on C. Willis and M. A. Korotich's studies. Crystallographic shaping as a direction is assigned on basis of classification by M. A. Korotich's. This direction is particularly examined and the main high-rise architecture forming aspects on basis of natural polycrystals forming principles are assigned. The article describes crystal forms transformation into an architectural composition, analyzes constructive systems within the framework of CTBUH (Council on Tall Buildings and Urban Habitat) classification, and picks out one of its types as the most optimal for using in buildings-crystals. The last stage of our research is the theoretical principles approbation into an experimental project of high-rise building in Ufa with the description of its contextual dislocation aspects.

Revision of seismic design codes corresponding to building damages in the ``5.12'' Wenchuan earthquake

NASA Astrophysics Data System (ADS)

Wang, Yayong

2010-06-01

A large number of buildings were seriously damaged or collapsed in the “5.12” Wenchuan earthquake. Based on field surveys and studies of damage to different types of buildings, seismic design codes have been updated. This paper briefly summarizes some of the major revisions that have been incorporated into the “Standard for classification of seismic protection of building constructions GB50223-2008” and “Code for Seismic Design of Buildings GB50011-2001.” The definition of seismic fortification class for buildings has been revisited, and as a result, the seismic classifications for schools, hospitals and other buildings that hold large populations such as evacuation shelters and information centers have been upgraded in the GB50223-2008 Code. The main aspects of the revised GB50011-2001 code include: (a) modification of the seismic intensity specified for the Provinces of Sichuan, Shanxi and Gansu; (b) basic conceptual design for retaining walls and building foundations in mountainous areas; (c) regularity of building configuration; (d) integration of masonry structures and pre-cast RC floors; (e) requirements for calculating and detailing stair shafts; and (f) limiting the use of single-bay RC frame structures. Some significant examples of damage in the epicenter areas are provided as a reference in the discussion on the consequences of collapse, the importance of duplicate structural systems, and the integration of RC and masonry structures.

Comparing supervised and unsupervised multiresolution segmentation approaches for extracting buildings from very high resolution imagery.

PubMed

Belgiu, Mariana; Dr Guţ, Lucian

2014-10-01

Although multiresolution segmentation (MRS) is a powerful technique for dealing with very high resolution imagery, some of the image objects that it generates do not match the geometries of the target objects, which reduces the classification accuracy. MRS can, however, be guided to produce results that approach the desired object geometry using either supervised or unsupervised approaches. Although some studies have suggested that a supervised approach is preferable, there has been no comparative evaluation of these two approaches. Therefore, in this study, we have compared supervised and unsupervised approaches to MRS. One supervised and two unsupervised segmentation methods were tested on three areas using QuickBird and WorldView-2 satellite imagery. The results were assessed using both segmentation evaluation methods and an accuracy assessment of the resulting building classifications. Thus, differences in the geometries of the image objects and in the potential to achieve satisfactory thematic accuracies were evaluated. The two approaches yielded remarkably similar classification results, with overall accuracies ranging from 82% to 86%. The performance of one of the unsupervised methods was unexpectedly similar to that of the supervised method; they identified almost identical scale parameters as being optimal for segmenting buildings, resulting in very similar geometries for the resulting image objects. The second unsupervised method produced very different image objects from the supervised method, but their classification accuracies were still very similar. The latter result was unexpected because, contrary to previously published findings, it suggests a high degree of independence between the segmentation results and classification accuracy. The results of this study have two important implications. The first is that object-based image analysis can be automated without sacrificing classification accuracy, and the second is that the previously accepted idea that classification is dependent on segmentation is challenged by our unexpected results, casting doubt on the value of pursuing 'optimal segmentation'. Our results rather suggest that as long as under-segmentation remains at acceptable levels, imperfections in segmentation can be ruled out, so that a high level of classification accuracy can still be achieved.

Mapping land cover in urban residential landscapes using fine resolution imagery and object-oriented classification

USDA-ARS?s Scientific Manuscript database

A knowledge of different types of land cover in urban residential landscapes is important for building social and economic city-wide policies including landscape ordinances and water conservation programs. Urban landscapes are typically heterogeneous, so classification of land cover in these areas ...

On the International Agency for Research on Cancer classification of glyphosate as a probable human carcinogen.

PubMed

Tarone, Robert E

2018-01-01

The recent classification by International Agency for Research on Cancer (IARC) of the herbicide glyphosate as a probable human carcinogen has generated considerable discussion. The classification is at variance with evaluations of the carcinogenic potential of glyphosate by several national and international regulatory bodies. The basis for the IARC classification is examined under the assumptions that the IARC criteria are reasonable and that the body of scientific studies determined by IARC staff to be relevant to the evaluation of glyphosate by the Monograph Working Group is sufficiently complete. It is shown that the classification of glyphosate as a probable human carcinogen was the result of a flawed and incomplete summary of the experimental evidence evaluated by the Working Group. Rational and effective cancer prevention activities depend on scientifically sound and unbiased assessments of the carcinogenic potential of suspected agents. Implications of the erroneous classification of glyphosate with respect to the IARC Monograph Working Group deliberative process are discussed.

IET. Tank building (TAN627). Plans, elevation, details. shows position of ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

IET. Tank building (TAN-627). Plans, elevation, details. shows position of tanks within building and concrete supports. Ralph M. Parsons 902-4-ANP-627-A&S 420. Date: Fabruary 1954. Approved by INEEL Classification Office for public release. INEEL index code no. 035-0627-00-693-106975 - Idaho National Engineering Laboratory, Test Area North, Scoville, Butte County, ID

Non-linear molecular pattern classification using molecular beacons with multiple targets.

PubMed

Lee, In-Hee; Lee, Seung Hwan; Park, Tai Hyun; Zhang, Byoung-Tak

2013-12-01

In vitro pattern classification has been highlighted as an important future application of DNA computing. Previous work has demonstrated the feasibility of linear classifiers using DNA-based molecular computing. However, complex tasks require non-linear classification capability. Here we design a molecular beacon that can interact with multiple targets and experimentally shows that its fluorescent signals form a complex radial-basis function, enabling it to be used as a building block for non-linear molecular classification in vitro. The proposed method was successfully applied to solving artificial and real-world classification problems: XOR and microRNA expression patterns. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Hybrid Automatic Building Interpretation System

NASA Astrophysics Data System (ADS)

Pakzad, K.; Klink, A.; Müterthies, A.; Gröger, G.; Stroh, V.; Plümer, L.

2011-09-01

HABIS (Hybrid Automatic Building Interpretation System) is a system for an automatic reconstruction of building roofs used in virtual 3D building models. Unlike most of the commercially available systems, HABIS is able to work to a high degree automatically. The hybrid method uses different sources intending to exploit the advantages of the particular sources. 3D point clouds usually provide good height and surface data, whereas spatial high resolution aerial images provide important information for edges and detail information for roof objects like dormers or chimneys. The cadastral data provide important basis information about the building ground plans. The approach used in HABIS works with a multi-stage-process, which starts with a coarse roof classification based on 3D point clouds. After that it continues with an image based verification of these predicted roofs. In a further step a final classification and adjustment of the roofs is done. In addition some roof objects like dormers and chimneys are also extracted based on aerial images and added to the models. In this paper the used methods are described and some results are presented.

Scapula fractures: interobserver reliability of classification and treatment.

PubMed

Neuhaus, Valentin; Bot, Arjan G J; Guitton, Thierry G; Ring, David C; Abdel-Ghany, Mahmoud I; Abrams, Jeffrey; Abzug, Joshua M; Adolfsson, Lars E; Balfour, George W; Bamberger, H Brent; Barquet, Antonio; Baskies, Michael; Batson, W Arnold; Baxamusa, Taizoon; Bayne, Grant J; Begue, Thierry; Behrman, Michael; Beingessner, Daphne; Biert, Jan; Bishop, Julius; Alves, Mateus Borges Oliveira; Boyer, Martin; Brilej, Drago; Brink, Peter R G; Brunton, Lance M; Buckley, Richard; Cagnone, Juan Carlos; Calfee, Ryan P; Campinhos, Luiz Augusto B; Cassidy, Charles; Catalano, Louis; Chivers, Karel; Choudhari, Pradeep; Cimerman, Matej; Conflitti, Joseph M; Costanzo, Ralph M; Crist, Brett D; Cross, Brian J; Dantuluri, Phani; Darowish, Michael; de Bedout, Ramon; DeCoster, Thomas; Dennison, David G; DeNoble, Peter H; DeSilva, Gregory; Dienstknecht, Thomas; Duncan, Scott F; Duralde, Xavier A; Durchholz, Holger; Egol, Kenneth; Ekholm, Carl; Elias, Nelson; Erickson, John M; Esparza, J Daniel Espinosa; Fernandes, C H; Fischer, Thomas J; Fischmeister, Martin; Forigua Jaime, E; Getz, Charles L; Gilbert, Richard S; Giordano, Vincenzo; Glaser, David L; Gosens, Taco; Grafe, Michael W; Filho, Jose Eduardo Grandi Ribeiro; Gray, Robert R L; Gulotta, Lawrence V; Gummerson, Nigel William; Hammerberg, Eric Mark; Harvey, Edward; Haverlag, R; Henry, Patrick D G; Hobby, Jonathan L; Hofmeister, Eric P; Hughes, Thomas; Itamura, John; Jebson, Peter; Jenkinson, Richard; Jeray, Kyle; Jones, Christopher M; Jones, Jedediah; Jubel, Axel; Kaar, Scott G; Kabir, K; Kaplan, F Thomas D; Kennedy, Stephen A; Kessler, Michael W; Kimball, Hervey L; Kloen, Peter; Klostermann, Cyrus; Kohut, Georges; Kraan, G A; Kristan, Anze; Loebenberg, Mark I; Malone, Kevin J; Marsh, L; Martineau, Paul A; McAuliffe, John; McGraw, Iain; Mehta, Samir; Merchant, Milind; Metzger, Charles; Meylaerts, S A; Miller, Anna N; Wolf, Jennifer Moriatis; Murachovsky, Joel; Murthi, Anand; Nancollas, Michael; Nolan, Betsy M; Omara, Timothy; Omid, Reza; Ortiz, Jose A; Overbeck, Joachim P; Castillo, Alberto Pérez; Pesantez, Rodrigo; Polatsch, Daniel; Porcellini, G; Prayson, Michael; Quell, M; Ragsdell, Matthew M; Reid, James G; Reuver, J M; Richard, Marc J; Richardson, Martin; Rizzo, Marco; Rowinski, Sergio; Rubio, Jorge; Guerrero, Carlos G Sánchez; Satora, Wojciech; Schandelmaier, Peter; Scheer, Johan H; Schmidt, Andrew; Schubkegel, Todd A; Schulte, Leah M; Schumer, Evan D; Sears, Benjamin W; Shafritz, Adam B; Shortt, Nicholas L; Siff, Todd; Silva, Dario Mejia; Smith, Raymond Malcolm; Spruijt, Sander; Stein, Jason A; Pemovska, Emilija Stojkovska; Streubel, Philipp N; Swigart, Carrie; Swiontkowski, Marc; Thomas, George; Tolo, Eric T; Turina, Matthias; Tyllianakis, Minos; van den Bekerom, Michel P J; van der Heide, Huub; van de Sande, M A J; van Eerten, P V; Verbeek, Diederik O F; Hoffmann, David Victoria; Vochteloo, A J H; Wagenmakers, Robert; Wall, Christopher J; Wallensten, Richard; Wascher, Daniel C; Weiss, Lawrence; Wiater, J Michael; Wills, Brian P D; Wint, Jeffrey; Wright, Thomas; Young, Jason P; Zalavras, Charalampos; Zura, Robert D; Zyto, Karol

2014-03-01

There is substantial variation in the classification and management of scapula fractures. The first purpose of this study was to analyze the interobserver reliability of the OTA/AO classification and the New International Classification for Scapula Fractures. The second purpose was to assess the proportion of agreement among orthopaedic surgeons on operative or nonoperative treatment. Web-based reliability study. Independent orthopaedic surgeons from several countries were invited to classify scapular fractures in an online survey. One hundred three orthopaedic surgeons evaluated 35 movies of three-dimensional computerized tomography reconstruction of selected scapular fractures, representing a full spectrum of fracture patterns. Fleiss kappa (κ) was used to assess the reliability of agreement between the surgeons. The overall agreement on the OTA/AO classification was moderate for the types (A, B, and C, κ = 0.54) with a 71% proportion of rater agreement (PA) and for the 9 groups (A1 to C3, κ = 0.47) with a 57% PA. For the New International Classification, the agreement about the intraarticular extension of the fracture (Fossa (F), κ = 0.79) was substantial and the agreement about a fractured body (Body (B), κ = 0.57) or process was moderate (Process (P), κ = 0.53); however, PAs were more than 81%. The agreement on the treatment recommendation was moderate (κ = 0.57) with a 73% PA. The New International Classification was more reliable. Body and process fractures generated more disagreement than intraarticular fractures and need further clear definitions.

85. Neg. No. F51, Apr 13, 1930, INTERIORASSEMBLY BUILDING, BODY ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

85. Neg. No. F-51, Apr 13, 1930, INTERIOR-ASSEMBLY BUILDING, BODY AND CUSHION LINE - Ford Motor Company Long Beach Assembly Plant, Assembly Building, 700 Henry Ford Avenue, Long Beach, Los Angeles County, CA

79. Neg. No. F61A, Apr 13, 1930, INTERIORASSEMBLY BUILDING, BODY ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

79. Neg. No. F-61A, Apr 13, 1930, INTERIOR-ASSEMBLY BUILDING, BODY CONSTRUCTION - Ford Motor Company Long Beach Assembly Plant, Assembly Building, 700 Henry Ford Avenue, Long Beach, Los Angeles County, CA

86. Neg. No. F64, Apr 13, 1930, INTERIORASSEMBLY BUILDING, BODY ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

86. Neg. No. F-64, Apr 13, 1930, INTERIOR-ASSEMBLY BUILDING, BODY STORAGE CONVEYOR - Ford Motor Company Long Beach Assembly Plant, Assembly Building, 700 Henry Ford Avenue, Long Beach, Los Angeles County, CA

"SAFEGUARDING THE INTERESTS OF THE STATE" FROM DEFECTIVE DELINQUENT GIRLS.

PubMed

Sohasky, Kate E

2016-01-01

The 1911 mental classification, "defective delinquent," was created as a temporary legal-medical category in order to identify a peculiar class of delinquent girls in a specific institutional setting. The defective delinquent's alleged slight mental defect, combined with her appearance of normalcy, rendered her a "dangerous" and "incurable" citizen. At the intersection of institutional history and the history of ideas, this article explores the largely overlooked role of borderline mental classifications of near-normalcy in the medicalization of intelligence and criminality during the first third of the twentieth-century United States. Borderline classifications served as mechanisms of control over women's bodies through the criminalization of their minds, and the advent of psychometric tests legitimated and facilitated the spread of this classification beyond its original and intended context. The borderline case of the defective delinquent girl demonstrates the significance of marginal mental classifications to the policing of bodies through the medicalization of intellect. © 2015 Wiley Periodicals, Inc.

Classification of buildings mold threat using electronic nose

NASA Astrophysics Data System (ADS)

Łagód, Grzegorz; Suchorab, Zbigniew; Guz, Łukasz; Sobczuk, Henryk

2017-07-01

Mold is considered to be one of the most important features of Sick Building Syndrome and is an important problem in current building industry. In many cases it is caused by the rising moisture of building envelopes surface and exaggerated humidity of indoor air. Concerning historical buildings it is mostly caused by outdated raising techniques among that is absence of horizontal isolation against moisture and hygroscopic materials applied for construction. Recent buildings also suffer problem of mold risk which is caused in many cases by hermetization leading to improper performance of gravitational ventilation systems that make suitable conditions for mold development. Basing on our research there is proposed a method of buildings mold threat classification using electronic nose, based on a gas sensors array which consists of MOS sensors (metal oxide semiconductor). Used device is frequently applied for air quality assessment in environmental engineering branches. Presented results show the interpretation of e-nose readouts of indoor air sampled in rooms threatened with mold development in comparison with clean reference rooms and synthetic air. Obtained multivariate data were processed, visualized and classified using a PCA (Principal Component Analysis) and ANN (Artificial Neural Network) methods. Described investigation confirmed that electronic nose - gas sensors array supported with data processing enables to classify air samples taken from different rooms affected with mold.

A deep learning pipeline for Indian dance style classification

NASA Astrophysics Data System (ADS)

Dewan, Swati; Agarwal, Shubham; Singh, Navjyoti

2018-04-01

In this paper, we address the problem of dance style classification to classify Indian dance or any dance in general. We propose a 3-step deep learning pipeline. First, we extract 14 essential joint locations of the dancer from each video frame, this helps us to derive any body region location within the frame, we use this in the second step which forms the main part of our pipeline. Here, we divide the dancer into regions of important motion in each video frame. We then extract patches centered at these regions. Main discriminative motion is captured in these patches. We stack the features from all such patches of a frame into a single vector and form our hierarchical dance pose descriptor. Finally, in the third step, we build a high level representation of the dance video using the hierarchical descriptors and train it using a Recurrent Neural Network (RNN) for classification. Our novelty also lies in the way we use multiple representations for a single video. This helps us to: (1) Overcome the RNN limitation of learning small sequences over big sequences such as dance; (2) Extract more data from the available dataset for effective deep learning by training multiple representations. Our contributions in this paper are three-folds: (1) We provide a deep learning pipeline for classification of any form of dance; (2) We prove that a segmented representation of a dance video works well with sequence learning techniques for recognition purposes; (3) We extend and refine the ICD dataset and provide a new dataset for evaluation of dance. Our model performs comparable or better in some cases than the state-of-the-art on action recognition benchmarks.

87. Neg. No. F74A, Jun 14, 1930, INTERIORASSEMBLY BUILDING, BODY ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

87. Neg. No. F-74A, Jun 14, 1930, INTERIOR-ASSEMBLY BUILDING, BODY STORAGE CONVEYORS - Ford Motor Company Long Beach Assembly Plant, Assembly Building, 700 Henry Ford Avenue, Long Beach, Los Angeles County, CA

Fully Convolutional Network Based Shadow Extraction from GF-2 Imagery

NASA Astrophysics Data System (ADS)

Li, Z.; Cai, G.; Ren, H.

2018-04-01

There are many shadows on the high spatial resolution satellite images, especially in the urban areas. Although shadows on imagery severely affect the information extraction of land cover or land use, they provide auxiliary information for building extraction which is hard to achieve a satisfactory accuracy through image classification itself. This paper focused on the method of building shadow extraction by designing a fully convolutional network and training samples collected from GF-2 satellite imagery in the urban region of Changchun city. By means of spatial filtering and calculation of adjacent relationship along the sunlight direction, the small patches from vegetation or bridges have been eliminated from the preliminary extracted shadows. Finally, the building shadows were separated. The extracted building shadow information from the proposed method in this paper was compared with the results from the traditional object-oriented supervised classification algorihtms. It showed that the deep learning network approach can improve the accuracy to a large extent.

Assessment of Life Cycle Information Exchanges (LCie): Understanding the Value-Added Benefit of a COBie Process

DTIC Science & Technology

2013-10-01

exchange (COBie), Building Information Modeling ( BIM ), value-added analysis, business processes, project management 16. SECURITY CLASSIFICATION OF: 17...equipment. The innovative aspect of Building In- formation Modeling ( BIM ) is that it creates a computable building descrip- tion. The ability to use a...interoperability. In order for the building information to be interoperable, it must also con- form to a common data model , or schema, that defines the class

Isolation and characterization of urinary extracellular vesicles: implications for biomarker discovery.

PubMed

Merchant, Michael L; Rood, Ilse M; Deegens, Jeroen K J; Klein, Jon B

2017-12-01

Urine is a valuable diagnostic medium and, with the discovery of urinary extracellular vesicles, is viewed as a dynamic bioactive fluid. Extracellular vesicles are lipid-enclosed structures that can be classified into three categories: exosomes, microvesicles (or ectosomes) and apoptotic bodies. This classification is based on the mechanisms by which membrane vesicles are formed: fusion of multivesicular bodies with the plasma membranes (exosomes), budding of vesicles directly from the plasma membrane (microvesicles) or those shed from dying cells (apoptotic bodies). During their formation, urinary extracellular vesicles incorporate various cell-specific components (proteins, lipids and nucleic acids) that can be transferred to target cells. The rigour needed for comparative studies has fueled the search for optimal approaches for their isolation, purification, and characterization. RNA, the newest extracellular vesicle component to be discovered, has received substantial attention as an extracellular vesicle therapeutic, and compelling evidence suggests that ex vivo manipulation of microRNA composition may have uses in the treatment of kidney disorders. The results of these studies are building the case that urinary extracellular vesicles act as mediators of renal pathophysiology. As the field of extracellular vesicle studies is burgeoning, this Review focuses on primary data obtained from studies of human urine rather than on data from studies of laboratory animals or cultured immortalized cells.

A Genealogy of Convex Solids Via Local and Global Bifurcations of Gradient Vector Fields

NASA Astrophysics Data System (ADS)

Domokos, Gábor; Holmes, Philip; Lángi, Zsolt

2016-12-01

Three-dimensional convex bodies can be classified in terms of the number and stability types of critical points on which they can balance at rest on a horizontal plane. For typical bodies, these are non-degenerate maxima, minima, and saddle points, the numbers of which provide a primary classification. Secondary and tertiary classifications use graphs to describe orbits connecting these critical points in the gradient vector field associated with each body. In previous work, it was shown that these classifications are complete in that no class is empty. Here, we construct 1- and 2-parameter families of convex bodies connecting members of adjacent primary and secondary classes and show that transitions between them can be realized by codimension 1 saddle-node and saddle-saddle (heteroclinic) bifurcations in the gradient vector fields. Our results indicate that all combinatorially possible transitions can be realized in physical shape evolution processes, e.g., by abrasion of sedimentary particles.

Hyaluronic Acid and Hyaluronidase in Prostate Cancer: Evaluation of Their Therapeutic and Prognostic Potential

DTIC Science & Technology

2005-01-01

PAGES No subject terms provided. 75 16. PRICE CODE 17. SECURITY CLASSIFICATION 18 . SECURITY CLASSIFICATION 19. SECURITY CLASSIFICATION 20. LIMITATION OF...Prescribed by ANSI Std. Z39- 18 298-102 Lokeshwar, Vinata B Table of Contents Cover...1 Body ................................................................................................. 2- 18 Key Research

Maritime Pre-Positioning Force-Future: Bill Payer or Sea Basing Enabler?

DTIC Science & Technology

2008-03-25

Ship Building Plan , UAV CLASSIFICATION: Unclassified Actions at sea no longer suffice to influence world events; actions from the sea must...in amphibious ships or fall victim to an untenable Navy ship building plan . Premature consideration of cost issues hindered MPF-F program...fiscal environment and an illusory Navy ship building plan . Given the demonstrated capability and success of the current Maritime Pre-positioning

Spring Ankle with Regenerative Kinetics to Build a New Generation of Transtibial Prostheses

DTIC Science & Technology

2008-07-31

form factor that is portable to the wearer. The objective is to build a transtibial prosthesis that will support a Military amputee’s return to...active duty. 15. SUBJECT TERMS Transtibial Prosthesis , regenerative, spring, wearable robot 16. SECURITY CLASSIFICATION OF: 17. LIMITATION...Regenerative Kinetics” to build a new generation of transtibial prostheses Keywords: Transtibial Prosthesis , regenerative, spring, wearable robot

19. VIEW LOOKING SOUTHWEST TOWARDS THE ANCILLARY BUILDINGS. FROM LEFT ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

19. VIEW LOOKING SOUTHWEST TOWARDS THE ANCILLARY BUILDINGS. FROM LEFT TO RIGHT BACKGROUND, RETORT BUILDING, STORAGE, SMELTER BUILDING, GARAGE. THE NORTHWEST CORNER OF THE VANNER ROOM IS IN THE FOREGROUND AND LEFT. A 2000 DODGE GRAND CARAVAN IS IN THE FOREGROUND CENTER. THEIR USE OF THE SHED TO THE REAR OF THE AUTOMOBILE IS UNCERTAIN, ALTHOUGH IT IS CONNECTED TO THE MILL AT THE BASE OF THE WEST SIDE OF THE AMALGAMATING PANS ROOM. - Standard Gold Mill, East of Bodie Creek, Northeast of Bodie, Bodie, Mono County, CA

Quantifying physical characteristics of wildland fuels using the fuel characteristic classification system.

Treesearch

Cynthia L. Riccardi; Susan J. Prichard; David V. Sandberg; Roger D. Ottmar

2007-01-01

Wildland fuel characteristics are used in many applications of operational fire predictions and to understand fire effects and behaviour. Even so, there is a shortage of information on basic fuel properties and the physical characteristics of wildland fuels. The Fuel Characteristic Classification System (FCCS) builds and catalogues fuelbed descriptions based on...

Interactive Classification of Construction Materials: Feedback Driven Framework for Annotation and Analysis of 3d Point Clouds

NASA Astrophysics Data System (ADS)

Hess, M. R.; Petrovic, V.; Kuester, F.

2017-08-01

Digital documentation of cultural heritage structures is increasingly more common through the application of different imaging techniques. Many works have focused on the application of laser scanning and photogrammetry techniques for the acquisition of threedimensional (3D) geometry detailing cultural heritage sites and structures. With an abundance of these 3D data assets, there must be a digital environment where these data can be visualized and analyzed. Presented here is a feedback driven visualization framework that seamlessly enables interactive exploration and manipulation of massive point cloud data. The focus of this work is on the classification of different building materials with the goal of building more accurate as-built information models of historical structures. User defined functions have been tested within the interactive point cloud visualization framework to evaluate automated and semi-automated classification of 3D point data. These functions include decisions based on observed color, laser intensity, normal vector or local surface geometry. Multiple case studies are presented here to demonstrate the flexibility and utility of the presented point cloud visualization framework to achieve classification objectives.

Review of Development Survey of Phase Change Material Models in Building Applications

PubMed Central

Akeiber, Hussein J.; Wahid, Mazlan A.; Hussen, Hasanen M.; Mohammad, Abdulrahman Th.

2014-01-01

The application of phase change materials (PCMs) in green buildings has been increasing rapidly. PCM applications in green buildings include several development models. This paper briefly surveys the recent research and development activities of PCM technology in building applications. Firstly, a basic description of phase change and their principles is provided; the classification and applications of PCMs are also included. Secondly, PCM models in buildings are reviewed and discussed according to the wall, roof, floor, and cooling systems. Finally, conclusions are presented based on the collected data. PMID:25313367

Improved wavelet packet classification algorithm for vibrational intrusions in distributed fiber-optic monitoring systems

NASA Astrophysics Data System (ADS)

Wang, Bingjie; Pi, Shaohua; Sun, Qi; Jia, Bo

2015-05-01

An improved classification algorithm that considers multiscale wavelet packet Shannon entropy is proposed. Decomposition coefficients at all levels are obtained to build the initial Shannon entropy feature vector. After subtracting the Shannon entropy map of the background signal, components of the strongest discriminating power in the initial feature vector are picked out to rebuild the Shannon entropy feature vector, which is transferred to radial basis function (RBF) neural network for classification. Four types of man-made vibrational intrusion signals are recorded based on a modified Sagnac interferometer. The performance of the improved classification algorithm has been evaluated by the classification experiments via RBF neural network under different diffusion coefficients. An 85% classification accuracy rate is achieved, which is higher than the other common algorithms. The classification results show that this improved classification algorithm can be used to classify vibrational intrusion signals in an automatic real-time monitoring system.

Low-power wireless ECG acquisition and classification system for body sensor networks.

PubMed

Lee, Shuenn-Yuh; Hong, Jia-Hua; Hsieh, Cheng-Han; Liang, Ming-Chun; Chang Chien, Shih-Yu; Lin, Kuang-Hao

2015-01-01

A low-power biosignal acquisition and classification system for body sensor networks is proposed. The proposed system consists of three main parts: 1) a high-pass sigma delta modulator-based biosignal processor (BSP) for signal acquisition and digitization, 2) a low-power, super-regenerative on-off keying transceiver for short-range wireless transmission, and 3) a digital signal processor (DSP) for electrocardiogram (ECG) classification. The BSP and transmitter circuits, which are the body-end circuits, can be operated for over 80 days using two 605 mAH zinc-air batteries as the power supply; the power consumption is 586.5 μW. As for the radio frequency receiver and DSP, which are the receiving-end circuits that can be integrated in smartphones or personal computers, power consumption is less than 1 mW. With a wavelet transform-based digital signal processing circuit and a diagnosis control by cardiologists, the accuracy of beat detection and ECG classification are close to 99.44% and 97.25%, respectively. All chips are fabricated in TSMC 0.18-μm standard CMOS process.

Jobs in Construction. Job Family Series.

ERIC Educational Resources Information Center

Science Research Associates, Inc., Chicago, IL.

The booklet describes jobs in the construction industry under the classifications of public and private building. Separate chapters discuss the process of building a city hospital, a model home, and a State highway. Chapters outline miscellaneous jobs in the industry such as elevator constructors, lathers, plasterers, roofers, and sheet metal…

Effects of Vaporized Decontamination Systems on Selected Building Interior Materials: Vaporized Hydrogen Peroxide

DTIC Science & Technology

2009-01-01

surfaces in buildings following a terrorist attack using CB agents. Vaporized hydrogen peroxide ( VHP ) and Cl02 are decontamination technologies that...decontaminant. The focus of this technical report is the evaluation of the building interior materials and the Steris VHP technology. 15. SUBJECT...TERMS Material Compatibility VHP vaporized hydrogen peroxide 16. SECURITY CLASSIFICATION OF: a. REPORT U b. ABSTRACT U c. THIS PAGE U 17

Semi-supervised classification tool for DubaiSat-2 multispectral imagery

NASA Astrophysics Data System (ADS)

Al-Mansoori, Saeed

2015-10-01

This paper addresses a semi-supervised classification tool based on a pixel-based approach of the multi-spectral satellite imagery. There are not many studies demonstrating such algorithm for the multispectral images, especially when the image consists of 4 bands (Red, Green, Blue and Near Infrared) as in DubaiSat-2 satellite images. The proposed approach utilizes both unsupervised and supervised classification schemes sequentially to identify four classes in the image, namely, water bodies, vegetation, land (developed and undeveloped areas) and paved areas (i.e. roads). The unsupervised classification concept is applied to identify two classes; water bodies and vegetation, based on a well-known index that uses the distinct wavelengths of visible and near-infrared sunlight that is absorbed and reflected by the plants to identify the classes; this index parameter is called "Normalized Difference Vegetation Index (NDVI)". Afterward, the supervised classification is performed by selecting training homogenous samples for roads and land areas. Here, a precise selection of training samples plays a vital role in the classification accuracy. Post classification is finally performed to enhance the classification accuracy, where the classified image is sieved, clumped and filtered before producing final output. Overall, the supervised classification approach produced higher accuracy than the unsupervised method. This paper shows some current preliminary research results which point out the effectiveness of the proposed technique in a virtual perspective.

Fire potential rating for wildland fuelbeds using the Fuel Characteristic Classification System.

Treesearch

David V. Sandberg; Cynthia L. Riccardi; Mark D. Schaff

2007-01-01

The Fuel Characteristic Classification System (FCCS) is a systematic catalog of inherent physical properties of wildland fuelbeds that allows land managers, policymakers, and scientists to build and calculate fuel characteristics with complete or incomplete information. The FCCS is equipped with a set of equations to calculate the potential of any real-world or...

[Remote sensing monitoring and screening for urban black and odorous water body: A review.

PubMed

Shen, Qian; Zhu, Li; Cao, Hong Ye

2017-10-01

Continuous improvement of urban water environment and overall control of black and odorous water body are not merely national strategic needs with the action plan for prevention and treatment of water pollution, but also the hot issues attracting the attention of people. Most previous researches concentrated on the study of cause, evaluation and treatment measures of this phenomenon, and there are few researches on the monitoring using remote sensing, which is often a strain to meet the national needs of operational monitoring. This paper mainly summarized the urgent research problems, mainly including the identification and classification standard, research on the key technologies, and the frame of remote sensing screening systems for the urban black and odorous water body. The main key technologies were concluded too, including the high spatial resolution image preprocessing and extraction technique for black and odorous water body, the extraction of water information in city zones, the classification of the black and odorous water, and the identification and classification technique based on satellite-sky-ground remote sensing. This paper summarized the research progress and put forward research ideas of monitoring and screening urban black and odorous water body via high spatial resolution remote sensing technology, which would be beneficial to having an overall grasp of spatial distribution and improvement progress of black and odorous water body, and provide strong technical support for controlling urban black and odorous water body.

Factors affecting breeding soundness classification of beef bulls examined at the Western College of Veterinary Medicine.

PubMed

Barth, Albert D; Waldner, Cheryl L

2002-04-01

Breeding soundness evaluation records from 2110 beef bulls, for the period of 1986 to 1999, were analyzed to determine the prevalence and importance of factors affecting breeding soundness classification. The percentage of all bulls classified as satisfactory ranged from 49.0% in January to 73.3% in May. The percentage of physically normal bulls with satisfactory semen quality ranged from 65.7% in January to 87.5% in June. Poor body condition or excessive body condition, below average or below the recommended minimum scrotal circumference, lameness, and severe scrotal frostbite significantly reduced the probability of a satisfactory breeding soundness classification. The percentage of sperm with midpiece defects declined significantly and the percentage of sperm with head defects increased significantly with the approach of summer. Photoperiod, cold stress, poor or excessive body condition, and reduced feed quality may interact to reduce semen quality in the winter months.

VIEW OF A BODY COUNTING ROOM IN BUILDING 122. BODY ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

VIEW OF A BODY COUNTING ROOM IN BUILDING 122. BODY COUNTING MEASURES RADIOACTIVE MATERIAL IN THE BODY. DESIGNED TO MINIMIZE EXTERNAL SOURCES OF RADIATION, BODY COUNTING ROOMS ARE CONSTRUCTED OF PRE-WORLD WAR II (WWII) STEEL. PRE-WWII STEEL, WHICH HAS NOT BEEN AFFECTED BY NUCLEAR FALLOUT, IS LOWER IS RADIOACTIVITY THAN STEEL CREATED AFTER WWII. (10/25/85) - Rocky Flats Plant, Emergency Medical Services Facility, Southwest corner of Central & Third Avenues, Golden, Jefferson County, CO

Gender classification of running subjects using full-body kinematics

NASA Astrophysics Data System (ADS)

Williams, Christina M.; Flora, Jeffrey B.; Iftekharuddin, Khan M.

2016-05-01

This paper proposes novel automated gender classification of subjects while engaged in running activity. The machine learning techniques include preprocessing steps using principal component analysis followed by classification with linear discriminant analysis, and nonlinear support vector machines, and decision-stump with AdaBoost. The dataset consists of 49 subjects (25 males, 24 females, 2 trials each) all equipped with approximately 80 retroreflective markers. The trials are reflective of the subject's entire body moving unrestrained through a capture volume at a self-selected running speed, thus producing highly realistic data. The classification accuracy using leave-one-out cross validation for the 49 subjects is improved from 66.33% using linear discriminant analysis to 86.74% using the nonlinear support vector machine. Results are further improved to 87.76% by means of implementing a nonlinear decision stump with AdaBoost classifier. The experimental findings suggest that the linear classification approaches are inadequate in classifying gender for a large dataset with subjects running in a moderately uninhibited environment.

Adolescent Attractiveness Standards for Self and Significant Other.

ERIC Educational Resources Information Center

Papini, Dennis R.

Previous research investigating the relationship between body build and personality stereotypes found that the mesomorphic build was attributed positive qualities while the endomorph and ectomorph were assigned negative qualities by both males and females. More recent research has revealed a potential shift in body build preferences as standards…

[The classification of the injuries inflicted to the human body by gunshots from the pneumatic weapons].

PubMed

Kozachenko, I N

2016-01-01

The classification of the injuries inflicted to the human body by gunshots from the pneumatic weapons remains to be developed. The objective of the present work was to elaborate the classification of the injuries caused by gunshots from the pneumatic weapons based on the analysis of 98 expert and acts of forensic medical expertises (surveys) of living subjects (n=76) and corpses (n=22) affected by gunshots from the pneumatic weapons. These materials were collected from the bureaus of forensic medical expertise in different regions of the Ukraine during the period from 2006 till 2015. In addition, scientific publications concerned with the problem of interest were used along with the relevant explanatory and terminological dictionaries. The terminology and the conceptual framework proposed by the author in the earlier papers provided a basis for the development of the first standard classification of the injuries inflicted to the human body by gunshots from the pneumatic weapons categorized into 15 groups. It is believed that this classification will lay the foundation for the common approach of forensic medical experts to the examination and analysis of the data on the gunshots from the pneumatic weapons used to be found on the bodies of living subjects and the corpses. Moreover, it may be useful for the clinicians in their diagnostic and therapeutic practices and for the legal practitioners engaged in the quality assessment of the results of forensic medical expertises. It is recommended to present information about the gunshots from the pneumatic weapons in the accounting documents in a separate line.

Intended Use of a Building in Terms of Updating the Cadastral Database and Harmonizing the Data with other Public Records

NASA Astrophysics Data System (ADS)

Buśko, Małgorzata

2017-06-01

According to the original wording of the Regulation on the register of land and buildings of 2001, in the real estate cadastre there was one attribute associated with the use of a building structure - its intended use, which was applicable until the amendment to the Regulation was introduced in 2013. Then, additional attributes were added, i.e. the type of the building according to the Classification of Fixed Assets (KST), the class of the building according to the Polish Classification of Types of Constructions (PKOB) and, at the same time, the main functional use and other functions of the building remained in the Regulation as well. The record data on buildings are captured for the real estate cadastre from other data sets, for example those maintained by architectural and construction authorities. At the same time, the data contained in the cadastre, after they have been entered or changed in the database, are transferred to other registers, such as tax records, or land and mortgage court registers. This study is the result of the analysis of the laws applicable to the specific units and registers. A list of discrepancies in the attributes occurring in the different registers was prepared. The practical part of the study paid particular attention to the legal bases and procedures for entering the function of a building in the real estate cadastre, which is extremely significant, as it is the attribute determining the property tax basis.

Hollow-Wall Heat Shield for Fuel Injector Component

NASA Technical Reports Server (NTRS)

Hanson, Russell B. (Inventor)

2018-01-01

A fuel injector component includes a body, an elongate void and a plurality of bores. The body has a first surface and a second surface. The elongate void is enclosed by the body and is integrally formed between portions of the body defining the first surface and the second surface. The plurality of bores extends into the second surface to intersect the elongate void. A process for making a fuel injector component includes building an injector component body having a void and a plurality of ports connected to the void using an additive manufacturing process that utilizes a powdered building material, and removing residual powdered building material from void through the plurality of ports.

Roof Type Selection Based on Patch-Based Classification Using Deep Learning for High Resolution Satellite Imagery

NASA Astrophysics Data System (ADS)

Partovi, T.; Fraundorfer, F.; Azimi, S.; Marmanis, D.; Reinartz, P.

2017-05-01

3D building reconstruction from remote sensing image data from satellites is still an active research topic and very valuable for 3D city modelling. The roof model is the most important component to reconstruct the Level of Details 2 (LoD2) for a building in 3D modelling. While the general solution for roof modelling relies on the detailed cues (such as lines, corners and planes) extracted from a Digital Surface Model (DSM), the correct detection of the roof type and its modelling can fail due to low quality of the DSM generated by dense stereo matching. To reduce dependencies of roof modelling on DSMs, the pansharpened satellite images as a rich resource of information are used in addition. In this paper, two strategies are employed for roof type classification. In the first one, building roof types are classified in a state-of-the-art supervised pre-trained convolutional neural network (CNN) framework. In the second strategy, deep features from deep layers of different pre-trained CNN model are extracted and then an RBF kernel using SVM is employed to classify the building roof type. Based on roof complexity of the scene, a roof library including seven types of roofs is defined. A new semi-automatic method is proposed to generate training and test patches of each roof type in the library. Using the pre-trained CNN model does not only decrease the computation time for training significantly but also increases the classification accuracy.

Building a common pipeline for rule-based document classification.

PubMed

Patterson, Olga V; Ginter, Thomas; DuVall, Scott L

2013-01-01

Instance-based classification of clinical text is a widely used natural language processing task employed as a step for patient classification, document retrieval, or information extraction. Rule-based approaches rely on concept identification and context analysis in order to determine the appropriate class. We propose a five-step process that enables even small research teams to develop simple but powerful rule-based NLP systems by taking advantage of a common UIMA AS based pipeline for classification. Our proposed methodology coupled with the general-purpose solution provides researchers with access to the data locked in clinical text in cases of limited human resources and compact timelines.

The research on medical image classification algorithm based on PLSA-BOW model.

PubMed

Cao, C H; Cao, H L

2016-04-29

With the rapid development of modern medical imaging technology, medical image classification has become more important for medical diagnosis and treatment. To solve the existence of polysemous words and synonyms problem, this study combines the word bag model with PLSA (Probabilistic Latent Semantic Analysis) and proposes the PLSA-BOW (Probabilistic Latent Semantic Analysis-Bag of Words) model. In this paper we introduce the bag of words model in text field to image field, and build the model of visual bag of words model. The method enables the word bag model-based classification method to be further improved in accuracy. The experimental results show that the PLSA-BOW model for medical image classification can lead to a more accurate classification.

Effects of the body mass index on menopausal symptoms among Asian American midlife women using two different classification systems.

PubMed

Chang, Sun Ju; Chee, Wonshik; Im, Eun-Ok

2014-01-01

To explore the effects of the body mass index (BMI) on menopausal symptoms among Asian American midlife women using two different classification systems: the international classification and the BMI classification for public health action among Asian populations. Secondary analysis using data from two large Internet survey studies. Communities and groups of midlife women on the Internet. A total of 223 Asian American midlife women who were recruited over the Internet. The Midlife Women's Symptom Index and self-reports of height and weight were used to collect data. The data were analyzed using multiple analyses of covariance. No significant differences in the prevalence and severity scores among three subscales and total menopausal symptoms according to the international classification were found. When the BMI classification for public health action among Asian populations was used as an independent variable, significant differences were found in the severity scores of three subscales and total menopausal symptoms. Results of the post-hoc analyses showed that Asian American midlife women who were in the BMI classification for high risk had significantly more severe menopausal symptoms than those who were in the BMI classification for increased risk. For Asian American women, BMI categorized using the BMI classification for Asian populations is more closely related to the severity of menopausal symptoms than BMI categorized using the international classification. Nurses need to consider the BMI classification for Asian populations when they develop interventions to prevent and alleviate menopausal symptoms among Asian American midlife women. © 2013 AWHONN, the Association of Women's Health, Obstetric and Neonatal Nurses.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zarate, M.A.; Slotnick, J.; Ramos, M.

The development and implementation of a solid waste management program served to build local capacity in San Mateo Ixtatan between 2002 and 2003 as part of a public health action plan. The program was developed and implemented in two phases: (1) the identification and education of a working team from the community; and (2) the completion of a solid waste classification and quantification study. Social capital and the water cycle were two public health approaches utilized to build a sustainable program. The activities accomplished gained support from the community and municipal authorities. A description of the tasks completed and findingsmore » of the solid waste classification and quantification performed by a local working group are presented in this paper.« less

Building a robust vehicle detection and classification module

NASA Astrophysics Data System (ADS)

Grigoryev, Anton; Khanipov, Timur; Koptelov, Ivan; Bocharov, Dmitry; Postnikov, Vassily; Nikolaev, Dmitry

2015-12-01

The growing adoption of intelligent transportation systems (ITS) and autonomous driving requires robust real-time solutions for various event and object detection problems. Most of real-world systems still cannot rely on computer vision algorithms and employ a wide range of costly additional hardware like LIDARs. In this paper we explore engineering challenges encountered in building a highly robust visual vehicle detection and classification module that works under broad range of environmental and road conditions. The resulting technology is competitive to traditional non-visual means of traffic monitoring. The main focus of the paper is on software and hardware architecture, algorithm selection and domain-specific heuristics that help the computer vision system avoid implausible answers.

ADM. Service Building (TAN603). Floor plan. Names of functional areas. ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

ADM. Service Building (TAN-603). Floor plan. Names of functional areas. Ralph M. Parsons 902-2-ANY-603-A 43. Date: December 1952. Approved by INEEL Classification Office for public release. INEEL index code no. 033-0603-00-693-106718 - Idaho National Engineering Laboratory, Test Area North, Scoville, Butte County, ID

FET. Control and equipment building (TAN630). Sections. Ralph M. Parsons ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

FET. Control and equipment building (TAN-630). Sections. Ralph M. Parsons 1229-2 ANP/GE-5-630-A-4. Date: March 1957. Approved by INEEL Classification Office for public release. INEEL index code no. 036-0630-00-693-107083 - Idaho National Engineering Laboratory, Test Area North, Scoville, Butte County, ID

FET. Chlorination building, TAN637. Elevations, section. Ralph M. Parsons 12292 ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

FET. Chlorination building, TAN-637. Elevations, section. Ralph M. Parsons 1229-2 ANP/GE-5-637-A-S-H&V-1. Date: March 1957. Approved by INEEL Classification Office for public release. INEEL index code no. 036-0637-00-693-107148 - Idaho National Engineering Laboratory, Test Area North, Scoville, Butte County, ID

IET. Control and equipment building (TAN620). Blast roof details. Ralph ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

IET. Control and equipment building (TAN-620). Blast roof details. Ralph M. Parsons 902-4-ANP-620-A-323. Date: February 1954. Approved by INEEL Classification Office for public release. INEEL index code no. 035-620-00-693-106908 - Idaho National Engineering Laboratory, Test Area North, Scoville, Butte County, ID

IET. Control and equipment building (TAN620). Details and room finish ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

IET. Control and equipment building (TAN-620). Details and room finish schedule. Ralph M. Parsons 902-4-ANP-620-A 322. Approved by INEEL Classification Office for public release. INEEL index code no. 035-0629-00-693-106907 - Idaho National Engineering Laboratory, Test Area North, Scoville, Butte County, ID

ADM. Administration Building (TAN602). Early room layout, door and room ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

ADM. Administration Building (TAN-602). Early room layout, door and room schedules. Ralph M. Parsons 902-2-ANP-602-A 31. Date: December 1952. Approved by INEEL Classification Office for public release. INEEL index code no. 033-0602-00-693-106710 - Idaho National Engineering Laboratory, Test Area North, Scoville, Butte County, ID

Building Performance Optimization while Empowering Occupants Toward Environmentally Sustainable Behavior through Continuous Monitoring and Diagnostics

DTIC Science & Technology

2016-12-01

conservation, building occupant comfort and satisfaction 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF PAGES 19a...21 3.2.7 Occupant Comfort and Satisfaction ............................................................................. 22 3.2.8 Facility...50 6.7 PO-VII: INCREASE IN OCCUPANT SATISFACTION ......................................... 51 6.8 PO-VIII

A Framework for Text Mining in Scientometric Study: A Case Study in Biomedicine Publications

NASA Astrophysics Data System (ADS)

Silalahi, V. M. M.; Hardiyati, R.; Nadhiroh, I. M.; Handayani, T.; Rahmaida, R.; Amelia, M.

2018-04-01

The data of Indonesians research publications in the domain of biomedicine has been collected to be text mined for the purpose of a scientometric study. The goal is to build a predictive model that provides a classification of research publications on the potency for downstreaming. The model is based on the drug development processes adapted from the literatures. An effort is described to build the conceptual model and the development of a corpus on the research publications in the domain of Indonesian biomedicine. Then an investigation is conducted relating to the problems associated with building a corpus and validating the model. Based on our experience, a framework is proposed to manage the scientometric study based on text mining. Our method shows the effectiveness of conducting a scientometric study based on text mining in order to get a valid classification model. This valid model is mainly supported by the iterative and close interactions with the domain experts starting from identifying the issues, building a conceptual model, to the labelling, validation and results interpretation.

Village Building Identification Based on Ensemble Convolutional Neural Networks

PubMed Central

Guo, Zhiling; Chen, Qi; Xu, Yongwei; Shibasaki, Ryosuke; Shao, Xiaowei

2017-01-01

In this study, we present the Ensemble Convolutional Neural Network (ECNN), an elaborate CNN frame formulated based on ensembling state-of-the-art CNN models, to identify village buildings from open high-resolution remote sensing (HRRS) images. First, to optimize and mine the capability of CNN for village mapping and to ensure compatibility with our classification targets, a few state-of-the-art models were carefully optimized and enhanced based on a series of rigorous analyses and evaluations. Second, rather than directly implementing building identification by using these models, we exploited most of their advantages by ensembling their feature extractor parts into a stronger model called ECNN based on the multiscale feature learning method. Finally, the generated ECNN was applied to a pixel-level classification frame to implement object identification. The proposed method can serve as a viable tool for village building identification with high accuracy and efficiency. The experimental results obtained from the test area in Savannakhet province, Laos, prove that the proposed ECNN model significantly outperforms existing methods, improving overall accuracy from 96.64% to 99.26%, and kappa from 0.57 to 0.86. PMID:29084154

Neural Networks for the Classification of Building Use from Street-View Imagery

NASA Astrophysics Data System (ADS)

Laupheimer, D.; Tutzauer, P.; Haala, N.; Spicker, M.

2018-05-01

Within this paper we propose an end-to-end approach for classifying terrestrial images of building facades into five different utility classes (commercial, hybrid, residential, specialUse, underConstruction) by using Convolutional Neural Networks (CNNs). For our examples we use images provided by Google Street View. These images are automatically linked to a coarse city model, including the outlines of the buildings as well as their respective use classes. By these means an extensive dataset is available for training and evaluation of our Deep Learning pipeline. The paper describes the implemented end-to-end approach for classifying street-level images of building facades and discusses our experiments with various CNNs. In addition to the classification results, so-called Class Activation Maps (CAMs) are evaluated. These maps give further insights into decisive facade parts that are learned as features during the training process. Furthermore, they can be used for the generation of abstract presentations which facilitate the comprehension of semantic image content. The abstract representations are a result of the stippling method, an importance-based image rendering.

High-throughput screening of chemicals as functional ...

EPA Pesticide Factsheets

Identifying chemicals that provide a specific function within a product, yet have minimal impact on the human body or environment, is the goal of most formulation chemists and engineers practicing green chemistry. We present a methodology to identify potential chemical functional substitutes from large libraries of chemicals using machine learning based models. We collect and analyze publicly available information on the function of chemicals in consumer products or industrial processes to identify a suite of harmonized function categories suitable for modeling. We use structural and physicochemical descriptors for these chemicals to build 41 quantitative structure–use relationship (QSUR) models for harmonized function categories using random forest classification. We apply these models to screen a library of nearly 6400 chemicals with available structure information for potential functional substitutes. Using our Functional Use database (FUse), we could identify uses for 3121 chemicals; 4412 predicted functional uses had a probability of 80% or greater. We demonstrate the potential application of the models to high-throughput (HT) screening for “candidate alternatives” by merging the valid functional substitute classifications with hazard metrics developed from HT screening assays for bioactivity. A descriptor set could be obtained for 6356 Tox21 chemicals that have undergone a battery of HT in vitro bioactivity screening assays. By applying QSURs, we wer

Optimized spatio-temporal descriptors for real-time fall detection: comparison of support vector machine and Adaboost-based classification

NASA Astrophysics Data System (ADS)

Charfi, Imen; Miteran, Johel; Dubois, Julien; Atri, Mohamed; Tourki, Rached

2013-10-01

We propose a supervised approach to detect falls in a home environment using an optimized descriptor adapted to real-time tasks. We introduce a realistic dataset of 222 videos, a new metric allowing evaluation of fall detection performance in a video stream, and an automatically optimized set of spatio-temporal descriptors which fed a supervised classifier. We build the initial spatio-temporal descriptor named STHF using several combinations of transformations of geometrical features (height and width of human body bounding box, the user's trajectory with her/his orientation, projection histograms, and moments of orders 0, 1, and 2). We study the combinations of usual transformations of the features (Fourier transform, wavelet transform, first and second derivatives), and we show experimentally that it is possible to achieve high performance using support vector machine and Adaboost classifiers. Automatic feature selection allows to show that the best tradeoff between classification performance and processing time is obtained by combining the original low-level features with their first derivative. Hence, we evaluate the robustness of the fall detection regarding location changes. We propose a realistic and pragmatic protocol that enables performance to be improved by updating the training in the current location with normal activities records.

Fetal programming of body dimensions and percentage body fat measured in prepubertal children with a 4-component model of body composition, dual-energy X-ray absorptiometry, deuterium dilution, densitometry, and skinfold thicknesses.

PubMed

Elia, Marinos; Betts, Peter; Jackson, Diane M; Mulligan, Jean

2007-09-01

Intrauterine programming of body composition [percentage body fat (%BF)] has been sparsely examined with multiple independent reference techniques in children. The effects on and consequences of body build (dimensions, mass, and length of body segments) are unclear. The study examined whether percentage fat and relation of percentage fat to body mass index (BMI; in kg/m2) in prepubertal children are programmed during intrauterine development and are dependent on body build. It also aimed to examine the extent to which height can be predicted by parental height and birth weight. Eighty-five white children (44 boys, 41 girls; aged 6.5-9.1 y) had body composition measured with a 4-component model (n = 58), dual-energy X-ray absorptiometry (n = 84), deuterium dilution (n = 81), densitometry (n = 62), and skinfold thicknesses (n = 85). An increase in birth weight of 1 SD was associated with a decrease of 1.95% fat as measured by the 4-component model (P = 0.012) and 0.82-2.75% by the other techniques. These associations were independent of age, sex, socioeconomic status, physical activity, BMI, and body build. Body build did not decrease the strength of the associations. Birth weight was a significantly better predictor of height than was self-reported midparental height, accounting for 19.4% of the variability at 5 y of age and 10.3% at 7.8 y of age (17.8% and 8.8% of which were independent of parental height at these ages, respectively). Consistent trends across body-composition measurement techniques add strength to the suggestion that percentage fat in prepubertal children is programmed in utero (independently of body build and BMI). It also suggests birth weight is a better predictor of prepubertal height than is self-reported midparental height.

Relative significance of heat transfer processes to quantify tradeoffs between complexity and accuracy of energy simulations with a building energy use patterns classification

NASA Astrophysics Data System (ADS)

Heidarinejad, Mohammad

This dissertation develops rapid and accurate building energy simulations based on a building classification that identifies and focuses modeling efforts on most significant heat transfer processes. The building classification identifies energy use patterns and their contributing parameters for a portfolio of buildings. The dissertation hypothesis is "Building classification can provide minimal required inputs for rapid and accurate energy simulations for a large number of buildings". The critical literature review indicated there is lack of studies to (1) Consider synoptic point of view rather than the case study approach, (2) Analyze influence of different granularities of energy use, (3) Identify key variables based on the heat transfer processes, and (4) Automate the procedure to quantify model complexity with accuracy. Therefore, three dissertation objectives are designed to test out the dissertation hypothesis: (1) Develop different classes of buildings based on their energy use patterns, (2) Develop different building energy simulation approaches for the identified classes of buildings to quantify tradeoffs between model accuracy and complexity, (3) Demonstrate building simulation approaches for case studies. Penn State's and Harvard's campus buildings as well as high performance LEED NC office buildings are test beds for this study to develop different classes of buildings. The campus buildings include detailed chilled water, electricity, and steam data, enabling to classify buildings into externally-load, internally-load, or mixed-load dominated. The energy use of the internally-load buildings is primarily a function of the internal loads and their schedules. Externally-load dominated buildings tend to have an energy use pattern that is a function of building construction materials and outdoor weather conditions. However, most of the commercial medium-sized office buildings have a mixed-load pattern, meaning the HVAC system and operation schedule dictate the indoor condition regardless of the contribution of internal and external loads. To deploy the methodology to another portfolio of buildings, simulated LEED NC office buildings are selected. The advantage of this approach is to isolate energy performance due to inherent building characteristics and location, rather than operational and maintenance factors that can contribute to significant variation in building energy use. A framework for detailed building energy databases with annual energy end-uses is developed to select variables and omit outliers. The results show that the high performance office buildings are internally-load dominated with existence of three different clusters of low-intensity, medium-intensity, and high-intensity energy use pattern for the reviewed office buildings. Low-intensity cluster buildings benefit from small building area, while the medium- and high-intensity clusters have a similar range of floor areas and different energy use intensities. Half of the energy use in the low-intensity buildings is associated with the internal loads, such as lighting and plug loads, indicating that there are opportunities to save energy by using lighting or plug load management systems. A comparison between the frameworks developed for the campus buildings and LEED NC office buildings indicates these two frameworks are complementary to each other. Availability of the information has yielded to two different procedures, suggesting future studies for a portfolio of buildings such as city benchmarking and disclosure ordinance should collect and disclose minimal required inputs suggested by this study with the minimum level of monthly energy consumption granularity. This dissertation developed automated methods using the OpenStudio API (Application Programing Interface) to create energy models based on the building class. ASHRAE Guideline 14 defines well-accepted criteria to measure accuracy of energy simulations; however, there is no well-accepted methodology to quantify the model complexity without the influence of the energy modeler judgment about the model complexity. This study developed a novel method using two weighting factors, including weighting factors based on (1) computational time and (2) easiness of on-site data collection, to measure complexity of the energy models. Therefore, this dissertation enables measurement of both model complexity and accuracy as well as assessment of the inherent tradeoffs between energy simulation model complexity and accuracy. The results of this methodology suggest for most of the internal load contributors such as operation schedules the on-site data collection adds more complexity to the model compared to the computational time. Overall, this study provided specific data on tradeoffs between accuracy and model complexity that points to critical inputs for different building classes, rather than an increase in the volume and detail of model inputs as the current research and consulting practice indicates. (Abstract shortened by UMI.).

Informal settlement classification using point-cloud and image-based features from UAV data

NASA Astrophysics Data System (ADS)

Gevaert, C. M.; Persello, C.; Sliuzas, R.; Vosselman, G.

2017-03-01

Unmanned Aerial Vehicles (UAVs) are capable of providing very high resolution and up-to-date information to support informal settlement upgrading projects. In order to provide accurate basemaps, urban scene understanding through the identification and classification of buildings and terrain is imperative. However, common characteristics of informal settlements such as small, irregular buildings with heterogeneous roof material and large presence of clutter challenge state-of-the-art algorithms. Furthermore, it is of interest to analyse which fundamental attributes are suitable for describing these objects in different geographic locations. This work investigates how 2D radiometric and textural features, 2.5D topographic features, and 3D geometric features obtained from UAV imagery can be integrated to obtain a high classification accuracy in challenging classification problems for the analysis of informal settlements. UAV datasets from informal settlements in two different countries are compared in order to identify salient features for specific objects in heterogeneous urban environments. Findings show that the integration of 2D and 3D features leads to an overall accuracy of 91.6% and 95.2% respectively for informal settlements in Kigali, Rwanda and Maldonado, Uruguay.

Quantification of urban structure on building block level utilizing multisensoral remote sensing data

NASA Astrophysics Data System (ADS)

Wurm, Michael; Taubenböck, Hannes; Dech, Stefan

2010-10-01

Dynamics of urban environments are a challenge to a sustainable development. Urban areas promise wealth, realization of individual dreams and power. Hence, many cities are characterized by a population growth as well as physical development. Traditional, visual mapping and updating of urban structure information of cities is a very laborious and cost-intensive task, especially for large urban areas. For this purpose, we developed a workflow for the extraction of the relevant information by means of object-based image classification. In this manner, multisensoral remote sensing data has been analyzed in terms of very high resolution optical satellite imagery together with height information by a digital surface model to retrieve a detailed 3D city model with the relevant land-use / land-cover information. This information has been aggregated on the level of the building block to describe the urban structure by physical indicators. A comparison between the indicators derived by the classification and a reference classification has been accomplished to show the correlation between the individual indicators and a reference classification of urban structure types. The indicators have been used to apply a cluster analysis to group the individual blocks into similar clusters.

32 CFR 2001.92 - Definitions.

Code of Federal Regulations, 2013 CFR

2013-07-01

.... (c) Classification management means the life-cycle management of classified national security information from original classification to declassification. (d) Cleared commercial carrier means a carrier that is authorized by law, regulatory body, or regulation, to transport Secret and Confidential...

Prefrontal gray matter volume mediates genetic risks for obesity.

PubMed

Opel, N; Redlich, R; Kaehler, C; Grotegerd, D; Dohm, K; Heindel, W; Kugel, H; Thalamuthu, A; Koutsouleris, N; Arolt, V; Teuber, A; Wersching, H; Baune, B T; Berger, K; Dannlowski, U

2017-05-01

Genetic and neuroimaging research has identified neurobiological correlates of obesity. However, evidence for an integrated model of genetic risk and brain structural alterations in the pathophysiology of obesity is still absent. Here we investigated the relationship between polygenic risk for obesity, gray matter structure and body mass index (BMI) by the use of univariate and multivariate analyses in two large, independent cohorts (n=330 and n=347). Higher BMI and higher polygenic risk for obesity were significantly associated with medial prefrontal gray matter decrease, and prefrontal gray matter was further shown to significantly mediate the effect of polygenic risk for obesity on BMI in both samples. Building on this, the successful individualized prediction of BMI by means of multivariate pattern classification algorithms trained on whole-brain imaging data and external validations in the second cohort points to potential clinical applications of this imaging trait marker.

The estimation of nonlinearity in problems of the building of initial confidence regions for small bodies motion. (Russian Title: Оценивание нелинейности в задачах построения начальных доверительных областей движения малых тел)

NASA Astrophysics Data System (ADS)

Syusina, O. M.; Chernitsov, A. M.; Tamarov, V. A.

2011-07-01

Simple and mathematically rigorous methods for calculating of nonlinearity coefficients are proposed. These coefficients allow us to make classification for the least squares problem as strongly or weakly nonlinear one. The advices are given on how to reduce a concrete estimation problem to weakly nonlinear one where a more efficient linear approach can be used.

Remote measurement of surface roughness, surface reflectance, and body reflectance with LiDAR.

PubMed

Li, Xiaolu; Liang, Yu

2015-10-20

Light detection and ranging (LiDAR) intensity data are attracting increasing attention because of the great potential for use of such data in a variety of remote sensing applications. To fully investigate the data potential for target classification and identification, we carried out a series of experiments with typical urban building materials and employed our reconstructed built-in-lab LiDAR system. Received intensity data were analyzed on the basis of the derived bidirectional reflectance distribution function (BRDF) model and the established integration method. With an improved fitting algorithm, parameters involved in the BRDF model can be obtained to depict the surface characteristics. One of these parameters related to surface roughness was converted to a most used roughness parameter, the arithmetical mean deviation of the roughness profile (R_a), which can be used to validate the feasibility of the BRDF model in surface characterizations and performance evaluations.

Hyperspectral imaging of polymer banknotes for building and analysis of spectral library

NASA Astrophysics Data System (ADS)

Lim, Hoong-Ta; Murukeshan, Vadakke Matham

2017-11-01

The use of counterfeit banknotes increases crime rates and cripples the economy. New countermeasures are required to stop counterfeiters who use advancing technologies with criminal intent. Many countries started adopting polymer banknotes to replace paper notes, as polymer notes are more durable and have better quality. The research on authenticating such banknotes is of much interest to the forensic investigators. Hyperspectral imaging can be employed to build a spectral library of polymer notes, which can then be used for classification to authenticate these notes. This is however not widely reported and has become a research interest in forensic identification. This paper focuses on the use of hyperspectral imaging on polymer notes to build spectral libraries, using a pushbroom hyperspectral imager which has been previously reported. As an initial study, a spectral library will be built from three arbitrarily chosen regions of interest of five circulated genuine polymer notes. Principal component analysis is used for dimension reduction and to convert the information in the spectral library to principal components. A 99% confidence ellipse is formed around the cluster of principal component scores of each class and then used as classification criteria. The potential of the adopted methodology is demonstrated by the classification of the imaged regions as training samples.

Indoor transformer stations and ELF magnetic field exposure: use of transformer structural characteristics to improve exposure assessment.

PubMed

Okokon, Enembe Oku; Roivainen, Päivi; Kheifets, Leeka; Mezei, Gabor; Juutilainen, Jukka

2014-01-01

Previous studies have shown that populations of multiapartment buildings with indoor transformer stations may serve as a basis for improved epidemiological studies on the relationship between childhood leukaemia and extremely-low-frequency (ELF) magnetic fields (MFs). This study investigated whether classification based on structural characteristics of the transformer stations would improve ELF MF exposure assessment. The data included MF measurements in apartments directly above transformer stations ("exposed" apartments) in 30 buildings in Finland, and reference apartments in the same buildings. Transformer structural characteristics (type and location of low-voltage conductors) were used to classify exposed apartments into high-exposure (HE) and intermediate-exposure (IE) categories. An exposure gradient was observed: both the time-average MF and time above a threshold (0.4 μT) were highest in the HE apartments and lowest in the reference apartments, showing a statistically significant trend. The differences between HE and IE apartments, however, were not statistically significant. A simulation exercise showed that the three-category classification did not perform better than a two-category classification (exposed and reference apartments) in detecting the existence of an increased risk. However, data on the structural characteristics of transformers is potentially useful for evaluating exposure-response relationship.

[Local foreign body reactions to biodegradable implants. A classification].

PubMed

Hoffmann, R; Weller, A; Helling, H J; Krettek, C; Rehm, K E

1997-08-01

Biodegradable implants are increasingly used in orthopedic and trauma surgery. Many different implants consisting of different biodegradable polymers are currently available. Different factors contribute to the biocompatibility of these implants, and local foreign-body reactions remain a matter of concern. Therefore, it is mandatory to document and compare the tissue reactions caused by various biodegradable implants in experimental or clinical studies. We have developed a standardized system of classification based on our previous experimental and clinical observations. Foreign-body reactions are differentiated into osteolysis (0-0 to 0-4), extra-articular (EA-0 to EA-4) and intraarticular (IA-0 to A-4) soft-tissue reactions.

About decomposition approach for solving the classification problem

NASA Astrophysics Data System (ADS)

Andrianova, A. A.

2016-11-01

This article describes the features of the application of an algorithm with using of decomposition methods for solving the binary classification problem of constructing a linear classifier based on Support Vector Machine method. Application of decomposition reduces the volume of calculations, in particular, due to the emerging possibilities to build parallel versions of the algorithm, which is a very important advantage for the solution of problems with big data. The analysis of the results of computational experiments conducted using the decomposition approach. The experiment use known data set for binary classification problem.

Application of the International Classification of Functioning, Disability and Health system to symptoms of the Duchenne and Becker muscular dystrophies.

PubMed

Conway, Kristin M; Ciafaloni, Emma; Matthews, Dennis; Westfield, Chris; James, Kathy; Paramsothy, Pangaja; Romitti, Paul A

2018-07-01

Duchenne and Becker muscular dystrophies, collectively referred to as dystrophinopathies, are X-linked recessive diseases that affect dystrophin production resulting in compromised muscle function across multiple systems. The International Classification of Functioning, Disability and Health provides a systematic classification scheme from which body functions affected by a dystrophinopathy can be identified and used to examine functional health. The infrastructure of the Muscular Dystrophy Surveillance, Tracking, and Research Network was used to identify commonly affected body functions and link selected functions to clinical surveillance data collected through medical record abstraction. Seventy-one (24 second-, 41 third- and 7 fourth-level) body function categories were selected via clinician review and consensus. Of these, 15 of 24 retained second-level categories were linked to data elements from the Muscular Dystrophy Surveillance, Tracking, and Research Network surveillance database. Our findings support continued development of a core set of body functions from the International Classification of Functioning, Disability and Health system that are representative of disease progression in dystrophinopathies and the incorporation of these functions in standardized evaluations of functional health and implementation of individualized rehabilitation care plans. Implications for Rehabilitation Duchenne and Becker muscular dystrophies, collectively referred to as dystrophinopathies, are X-linked recessive disorders that affect the production of dystrophin resulting in compromised muscle function across multiple systems. The severity and progressive nature of dystrophinopathies can have considerable impact on a patient's participation in activities across multiple life domains. Our findings support continued development of an International Classification of Functioning, Disability and Health core set for childhood-onset dystrophinopathies. A standardized dystrophinopathy International Classification of Functioning, Disability and Health documentation form can be used as a screening tool by rehabilitation professionals and for patient goal setting when developing rehabilitation plans. Patient reports of perceived functional health should be incorporated into the rehabilitation plan and therapeutic progress monitored by a standardized form.

New insight into defining the lakes of the southern Baltic coastal zone.

PubMed

Cieśliński, Roman; Olszewska, Alicja

2018-01-29

There exist many classification systems of hydrographic entities such as lakes found along the coastlines of seas and oceans. Each system has its advantages and can be used with some success in the area of protection and management. This paper aims to evaluate whether the studied lakes are only coastal lakes or rather bodies of water of a completely different hydrological and hydrochemical nature. The attempt to create a new classification system of Polish coastal lakes is related to the incompleteness of lake information in existing classifications. Thus far, the most frequently used are classifications based solely on lake basin morphogenesis or hydrochemical properties. The classifications in this paper are based not only on the magnitude of lake water salinity or hydrochemical analysis but also on isolation from the Baltic Sea and other sources of water. The key element of the new classification system for coastal bodies of water is a departure from the existing system used to classify lakes in Poland and the introduction of ion-"tracking" methods designed to identify anion and cation distributions in each body of water of interest. As a result of the work, a new classification of lakes of the southern Baltic Sea coastal zone was created. Featured objects such as permanently brackish lakes, brackish lakes that may turn into freshwater lakes from time to time, freshwater lakes that may turn into brackish lakes from time to time, freshwater lakes that experience low levels of salinity due to specific incidents, and permanently freshwater lakes. The authors have adopted 200 mg Cl -  dm -3 as a maximum value of lake water salinity. There are many conditions that determine the membership of a lake to a particular group, but the most important is the isolation lakes from the Baltic Sea. Changing a condition may change the classification of a lake.

Caracterisation des occupations du sol en milieu urbain par imagerie radar

NASA Astrophysics Data System (ADS)

Codjia, Claude

This study aims to test the relevance of medium and high-resolution SAR images on the characterization of the types of land use in urban areas. To this end, we have relied on textural approaches based on second-order statistics. Specifically, we look for texture parameters most relevant for discriminating urban objects. We have used in this regard Radarsat-1 in fine polarization mode and Radarsat-2 HH fine mode in dual and quad polarization and ultrafine mode HH polarization. The land uses sought were dense building, medium density building, low density building, industrial and institutional buildings, low density vegetation, dense vegetation and water. We have identified nine texture parameters for analysis, grouped into families according to their mathematical definitions in a first step. The parameters of similarity / dissimilarity include Homogeneity, Contrast, the Differential Inverse Moment and Dissimilarity. The parameters of disorder are Entropy and the Second Angular Momentum. The Standard Deviation and Correlation are the dispersion parameters and the Average is a separate family. It is clear from experience that certain combinations of texture parameters from different family used in classifications yield good results while others produce kappa of very little interest. Furthermore, we realize that if the use of several texture parameters improves classifications, its performance ceils from three parameters. The calculation of correlations between the textures and their principal axes confirm the results. Despite the good performance of this approach based on the complementarity of texture parameters, systematic errors due to the cardinal effects remain on classifications. To overcome this problem, a radiometric compensation model was developed based on the radar cross section (SER). A radar simulation from the digital surface model of the environment allowed us to extract the building backscatter zones and to analyze the related backscatter. Thus, we were able to devise a strategy of compensation of cardinal effects solely based on the responses of the objects according to their orientation from the plane of illumination through the radar's beam. It appeared that a compensation algorithm based on the radar cross section was appropriate. Some examples of the application of this algorithm on HH polarized RADARSAT-2 images are presented as well. Application of this algorithm will allow considerable gains with regard to certain forms of automation (classification and segmentation) at the level of radar imagery thus generating a higher level of quality in regard to visual interpretation. Application of this algorithm on RADARSAT-1 and RADARSAT-2 images with HH, HV, VH, and VV polarisations helped make considerable gains and eliminate most of the classification errors due to the cardinal effects.

Profile of patients with chronic obstructive pulmonary disease classified as physically active and inactive according to different thresholds of physical activity in daily life

PubMed Central

Furlanetto, Karina C.; Pinto, Isabela F. S.; Sant’Anna, Thais; Hernandes, Nidia A.; Pitta, Fabio

2016-01-01

ABSTRACT Objective To compare the profiles of patients with chronic obstructive pulmonary disease (COPD) considered physically active or inactive according to different classifications of the level of physical activity in daily life (PADL). Method Pulmonary function, dyspnea, functional status, body composition, exercise capacity, respiratory and peripheral muscle strength, and presence of comorbidities were assessed in 104 patients with COPD. The level of PADL was quantified with a SenseWear Armband activity monitor. Three classifications were used to classify the patients as physically active or inactive: 30 minutes of activity/day with intensity >3.2 METs, if age ≥65 years, and >4 METs, if age <65 years; 30 minutes of activity/day with intensity >3.0 METs, regardless of patient age; and 80 minutes of activity/day with intensity >3.0 METs, regardless of patient age. Results In all classifications, when compared with the inactive group, the physically active group had better values of anthropometric variables (higher fat-free mass, lower body weight, body mass index and fat percentage), exercise capacity (6-minute walking distance), lung function (forced vital capacity) and functional status (personal care domain of the London Chest Activity of Daily Living). Furthermore, patients classified as physically active in two classifications also had better peripheral and expiratory muscle strength, airflow obstruction, functional status, and quality of life, as well as lower prevalence of heart disease and mortality risk. Conclusion In all classification methods, physically active patients with COPD have better exercise capacity, lung function, body composition, and functional status compared to physically inactive patients. PMID:27683835

Selective classification and quantification model of C&D waste from material resources consumed in residential building construction.

PubMed

Mercader-Moyano, Pilar; Ramírez-de-Arellano-Agudo, Antonio

2013-05-01

The unfortunate economic situation involving Spain and the European Union is, among other factors, the result of intensive construction activity over recent years. The excessive consumption of natural resources, together with the impact caused by the uncontrolled dumping of untreated C&D waste in illegal landfills have caused environmental pollution and a deterioration of the landscape. The objective of this research was to generate a selective classification and quantification model of C&D waste based on the material resources consumed in the construction of residential buildings, either new or renovated, namely the Conventional Constructive Model (CCM). A practical example carried out on ten residential buildings in Seville, Spain, enabled the identification and quantification of the C&D waste generated in their construction and the origin of the waste, in terms of the building material from which it originated and its impact for every m(2) constructed. This model enables other researchers to establish comparisons between the various improvements proposed for the minimization of the environmental impact produced by building a CCM, new corrective measures to be proposed in future policies that regulate the production and management of C&D waste generated in construction from the design stage to the completion of the construction process, and the establishment of sustainable management for C&D waste and for the selection of materials for the construction on projected or renovated buildings.

Emergent equilibrium in many-body optical bistability

NASA Astrophysics Data System (ADS)

Foss-Feig, Michael; Niroula, Pradeep; Young, Jeremy; Hafezi, Mohammad; Gorshkov, Alexey; Wilson, Ryan; Maghrebi, Mohammad

2017-04-01

Many-body systems constructed of quantum-optical building blocks can now be realized in experimental platforms ranging from exciton-polariton fluids to Rydberg gases, establishing a fascinating interface between traditional many-body physics and the non-equilibrium setting of cavity-QED. At this interface the standard intuitions of both fields are called into question, obscuring issues as fundamental as the role of fluctuations, dimensionality, and symmetry on the nature of collective behavior and phase transitions. We study the driven-dissipative Bose-Hubbard model, a minimal description of atomic, optical, and solid-state systems in which particle loss is countered by coherent driving. Despite being a lattice version of optical bistability-a foundational and patently non-equilibrium model of cavity-QED-the steady state possesses an emergent equilibrium description in terms of an Ising model. We establish this picture by identifying a limit in which the quantum dynamics is asymptotically equivalent to non-equilibrium Langevin equations, which support a phase transition described by model A of the Hohenberg-Halperin classification. Simulations of the Langevin equations corroborate this picture, producing results consistent with the behavior of a finite-temperature Ising model. M.F.M., J.T.Y., and A.V.G. acknowledge support by ARL CDQI, ARO MURI, NSF QIS, ARO, NSF PFC at JQI, and AFOSR. R.M.W. acknowledges partial support from the NSF under Grant No. PHYS-1516421. M.H. acknowledges support by AFOSR-MURI, ONR and Sloan Foundation.

Overweight and obesity prevalence among Cree youth of Eeyou Istchee according to three body mass index classification systems.

PubMed

St-Jean, Audray; Meziou, Salma; Ayotte, Pierre; Lucas, Michel

2017-11-22

Little is known about the suitability of three commonly used body mass index (BMI) classification systems for Indigenous youth. We estimated overweight and obesity prevalence among Cree youth of Eeyou Istchee according to three BMI classification systems, assessed the level of agreement between them, and evaluated their accuracy through body fat and cardiometabolic risk factors. Data on 288 youth (aged 8-17 years) were collected. Overweight and obesity prevalence were estimated with Centers for Disease Control and Prevention (CDC), International Obesity Task Force (IOTF) and World Health Organization (WHO) criteria. Agreement was measured with weighted kappa (κw). Associations with body fat and cardiometabolic risk factors were evaluated by analysis of variance. Obesity prevalence was 42.7% with IOTF, 47.2% with CDC, and 49.3% with WHO criteria. Agreement was almost perfect between IOTF and CDC (κw = 0.93), IOTF and WHO (κw = 0.91), and WHO and CDC (κw = 0.94). Means of body fat and cardiometabolic risk factors were significantly higher (P trend  < 0.001) from normal weight to obesity, regardless of the system used. Youth considered overweight by IOTF but obese by CDC or WHO exhibited less severe clinical obesity. IOTF seems to be more accurate in identifying obesity in Cree youth.

FET. Tank Building, TAN631. Elevations, sections, details. Tank pads and ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

FET. Tank Building, TAN-631. Elevations, sections, details. Tank pads and saddles. RAlph M. Parsons 1229-2 ANP/GE-5-631-A-1. Date: March 1957. Approved by INEEL Classification Office for public release. INEEL index code no. 036-0631-00-693-107142 - Idaho National Engineering Laboratory, Test Area North, Scoville, Butte County, ID

IET. Control and equipment building (TAN620) floor plan. Schedule of ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

IET. Control and equipment building (TAN-620) floor plan. Schedule of furniture and equipment. Ralph M. Parsons 902-4-ANP-A 320. Date: February 1954. Approved by INEEL Classification Office for public release. INEEL index code no. 035-0620-00-693-106905 - Idaho National Engineering Laboratory, Test Area North, Scoville, Butte County, ID

FINAL REPORT: Building Performance Optimization while Empowering Occupants Toward Environmentally Sustainable Behavior through Continuous Monitoring and Diagnostics

DTIC Science & Technology

2016-12-05

conservation, building occupant comfort and satisfaction 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF PAGES 19a...21 3.2.7 Occupant Comfort and Satisfaction ............................................................................. 22 3.2.8 Facility...50 6.7 PO-VII: INCREASE IN OCCUPANT SATISFACTION ......................................... 51 6.8 PO-VIII

Overweight and Obesity Prevalence Among School-Aged Nunavik Inuit Children According to Three Body Mass Index Classification Systems.

PubMed

Medehouenou, Thierry Comlan Marc; Ayotte, Pierre; St-Jean, Audray; Meziou, Salma; Roy, Cynthia; Muckle, Gina; Lucas, Michel

2015-07-01

Little is known about the suitability of three commonly used body mass index (BMI) classification system for Indigenous children. This study aims to estimate overweight and obesity prevalence among school-aged Nunavik Inuit children according to International Obesity Task Force (IOTF), Centers for Disease Control and Prevention (CDC), and World Health Organization (WHO) BMI classification systems, to measure agreement between those classification systems, and to investigate whether BMI status as defined by these classification systems is associated with levels of metabolic and inflammatory biomarkers. Data were collected on 290 school-aged children (aged 8-14 years; 50.7% girls) from the Nunavik Child Development Study with data collected in 2005-2010. Anthropometric parameters were measured and blood sampled. Participants were classified as normal weight, overweight, and obese according to BMI classification systems. Weighted kappa (κw) statistics assessed agreement between different BMI classification systems, and multivariate analysis of variance ascertained their relationship with metabolic and inflammatory biomarkers. The combined prevalence rate of overweight/obesity was 26.9% (with 6.6% obesity) with IOTF, 24.1% (11.0%) with CDC, and 40.4% (12.8%) with WHO classification systems. Agreement was the highest between IOTF and CDC (κw = .87) classifications, and substantial for IOTF and WHO (κw = .69) and for CDC and WHO (κw = .73). Insulin and high-sensitivity C-reactive protein plasma levels were significantly higher from normal weight to obesity, regardless of classification system. Among obese subjects, higher insulin level was observed with IOTF. Compared with other systems, IOTF classification appears to be more specific to identify overweight and obesity in Inuit children. Copyright © 2015 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Building Extraction Based on Openstreetmap Tags and Very High Spatial Resolution Image in Urban Area

NASA Astrophysics Data System (ADS)

Kang, L.; Wang, Q.; Yan, H. W.

2018-04-01

How to derive contour of buildings from VHR images is the essential problem for automatic building extraction in urban area. To solve this problem, OSM data is introduced to offer vector contour information of buildings which is hard to get from VHR images. First, we import OSM data into database. The line string data of OSM with tags of building, amenity, office etc. are selected and combined into completed contours; Second, the accuracy of contours of buildings is confirmed by comparing with the real buildings in Google Earth; Third, maximum likelihood classification is conducted with the confirmed building contours, and the result demonstrates that the proposed approach is effective and accurate. The approach offers a new way for automatic interpretation of VHR images.

Estimating probabilities of infestation and extent of damage by the roundheaded pine beetle in ponderosa pine in the Sacramento Mountains, New Mexico

Treesearch

Jose Negron

1997-01-01

Classification trees and linear regression analysis were used to build models to predict probabilities of infestation and amount of tree mortality in terms of basal area resulting from roundheaded pine beetle, Dendroctonus adjunctus Blandford, activity in ponderosa pine, Pinus ponderosa Laws., in the Sacramento Mountains, New Mexico. Classification trees were built for...

Formalizing Resources for Planning

NASA Technical Reports Server (NTRS)

Bedrax-Weiss, Tania; McGann, Conor; Ramakrishnan, Sailesh

2003-01-01

In this paper we present a classification scheme which circumscribes a large class of resources found in the real world. Building on the work of others we also define key properties of resources that allow formal expression of the proposed classification. Furthermore, operations that change the state of a resource are formalized. Together, properties and operations go a long way in formalizing the representation and reasoning aspects of resources for planning.

[Management of chemical products and European standards: new classification criteria according to the 1272/2008 (CLP) regulation].

PubMed

Fanghella, Paola Di Prospero; Aliberti, Ludovica Malaguti

2013-01-01

The European Union adopted regulations (EC) 1907/2006 REACH e (EC)1272/2008 CLP, to manage chemicals. REACH requires for evaluation and management of risks connected to the use of chemical substances, while o CLP provides for the classification, labelling and packagings of dangerous substances and mixtures by implementing in the EU the UN Globally Harmonised System of Classification and Labelling applying the building block approach, that is taking on board the hazard classes and categories which are close to the existing EU system in order to maintain the level of protection of human health and environment. This regulation provides also for the notification of the classification and labelling of substances to the Classification & Labelling Inventory established by the European Chemicals Agency (ECHA). Some european downstream regulations making reference to the classification criteria, as the health and safety laws at workplace, need to be adapted to these regulations.

Site Classification using Multichannel Channel Analysis of Surface Wave (MASW) method on Soft and Hard Ground

NASA Astrophysics Data System (ADS)

Ashraf, M. A. M.; Kumar, N. S.; Yusoh, R.; Hazreek, Z. A. M.; Aziman, M.

2018-04-01

Site classification utilizing average shear wave velocity (Vs(30) up to 30 meters depth is a typical parameter. Numerous geophysical methods have been proposed for estimation of shear wave velocity by utilizing assortment of testing configuration, processing method, and inversion algorithm. Multichannel Analysis of Surface Wave (MASW) method is been rehearsed by numerous specialist and professional to geotechnical engineering for local site characterization and classification. This study aims to determine the site classification on soft and hard ground using MASW method. The subsurface classification was made utilizing National Earthquake Hazards Reduction Program (NERHP) and international Building Code (IBC) classification. Two sites are chosen to acquire the shear wave velocity which is in the state of Pulau Pinang for soft soil and Perlis for hard rock. Results recommend that MASW technique can be utilized to spatially calculate the distribution of shear wave velocity (Vs(30)) in soil and rock to characterize areas.

Simultaneous Co-Clustering and Classification in Customers Insight

NASA Astrophysics Data System (ADS)

Anggistia, M.; Saefuddin, A.; Sartono, B.

2017-04-01

Building predictive model based on the heterogeneous dataset may yield many problems, such as less precise in parameter and prediction accuracy. Such problem can be solved by segmenting the data into relatively homogeneous groups and then build a predictive model for each cluster. The advantage of using this strategy usually gives result in simpler models, more interpretable, and more actionable without any loss in accuracy and reliability. This work concerns on marketing data set which recorded a customer behaviour across products. There are some variables describing customer and product as attributes. The basic idea of this approach is to combine co-clustering and classification simultaneously. The objective of this research is to analyse the customer across product characteristics, so the marketing strategy implemented precisely.

Exploring point-cloud features from partial body views for gender classification

NASA Astrophysics Data System (ADS)

Fouts, Aaron; McCoppin, Ryan; Rizki, Mateen; Tamburino, Louis; Mendoza-Schrock, Olga

2012-06-01

In this paper we extend a previous exploration of histogram features extracted from 3D point cloud images of human subjects for gender discrimination. Feature extraction used a collection of concentric cylinders to define volumes for counting 3D points. The histogram features are characterized by a rotational axis and a selected set of volumes derived from the concentric cylinders. The point cloud images are drawn from the CAESAR anthropometric database provided by the Air Force Research Laboratory (AFRL) Human Effectiveness Directorate and SAE International. This database contains approximately 4400 high resolution LIDAR whole body scans of carefully posed human subjects. Success from our previous investigation was based on extracting features from full body coverage which required integration of multiple camera images. With the full body coverage, the central vertical body axis and orientation are readily obtainable; however, this is not the case with a one camera view providing less than one half body coverage. Assuming that the subjects are upright, we need to determine or estimate the position of the vertical axis and the orientation of the body about this axis relative to the camera. In past experiments the vertical axis was located through the center of mass of torso points projected on the ground plane and the body orientation derived using principle component analysis. In a natural extension of our previous work to partial body views, the absence of rotational invariance about the cylindrical axis greatly increases the difficulty for gender classification. Even the problem of estimating the axis is no longer simple. We describe some simple feasibility experiments that use partial image histograms. Here, the cylindrical axis is assumed to be known. We also discuss experiments with full body images that explore the sensitivity of classification accuracy relative to displacements of the cylindrical axis. Our initial results provide the basis for further investigation of more complex partial body viewing problems and new methods for estimating the two position coordinates for the axis location and the unknown body orientation angle.

Accelerometer-based on-body sensor localization for health and medical monitoring applications

PubMed Central

Vahdatpour, Alireza; Amini, Navid; Xu, Wenyao; Sarrafzadeh, Majid

2011-01-01

In this paper, we present a technique to recognize the position of sensors on the human body. Automatic on-body device localization ensures correctness and accuracy of measurements in health and medical monitoring systems. In addition, it provides opportunities to improve the performance and usability of ubiquitous devices. Our technique uses accelerometers to capture motion data to estimate the location of the device on the user’s body, using mixed supervised and unsupervised time series analysis methods. We have evaluated our technique with extensive experiments on 25 subjects. On average, our technique achieves 89% accuracy in estimating the location of devices on the body. In order to study the feasibility of classification of left limbs from right limbs (e.g., left arm vs. right arm), we performed analysis, based of which no meaningful classification was observed. Personalized ultraviolet monitoring and wireless transmission power control comprise two immediate applications of our on-body device localization approach. Such applications, along with their corresponding feasibility studies, are discussed. PMID:22347840

Feature selection gait-based gender classification under different circumstances

NASA Astrophysics Data System (ADS)

Sabir, Azhin; Al-Jawad, Naseer; Jassim, Sabah

2014-05-01

This paper proposes a gender classification based on human gait features and investigates the problem of two variations: clothing (wearing coats) and carrying bag condition as addition to the normal gait sequence. The feature vectors in the proposed system are constructed after applying wavelet transform. Three different sets of feature are proposed in this method. First, Spatio-temporal distance that is dealing with the distance of different parts of the human body (like feet, knees, hand, Human Height and shoulder) during one gait cycle. The second and third feature sets are constructed from approximation and non-approximation coefficient of human body respectively. To extract these two sets of feature we divided the human body into two parts, upper and lower body part, based on the golden ratio proportion. In this paper, we have adopted a statistical method for constructing the feature vector from the above sets. The dimension of the constructed feature vector is reduced based on the Fisher score as a feature selection method to optimize their discriminating significance. Finally k-Nearest Neighbor is applied as a classification method. Experimental results demonstrate that our approach is providing more realistic scenario and relatively better performance compared with the existing approaches.

A Review of Major Nursing Vocabularies and the Extent to Which They Have the Characteristics Required for Implementation in Computer-based Systems

PubMed Central

Henry, Suzanne Bakken; Warren, Judith J.; Lange, Linda; Button, Patricia

1998-01-01

Building on the work of previous authors, the Computer-based Patient Record Institute (CPRI) Work Group on Codes and Structures has described features of a classification scheme for implementation within a computer-based patient record. The authors of the current study reviewed the evaluation literature related to six major nursing vocabularies (the North American Nursing Diagnosis Association Taxonomy 1, the Nursing Interventions Classification, the Nursing Outcomes Classification, the Home Health Care Classification, the Omaha System, and the International Classification for Nursing Practice) to determine the extent to which the vocabularies include the CPRI features. None of the vocabularies met all criteria. The Omaha System, Home Health Care Classification, and International Classification for Nursing Practice each included five features. Criteria not fully met by any systems were clear and non-redundant representation of concepts, administrative cross-references, syntax and grammar, synonyms, uncertainty, context-free identifiers, and language independence. PMID:9670127

FET. Control and equipment building (TAN630). Basement floor plan. Tunnel ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

FET. Control and equipment building (TAN-630). Basement floor plan. Tunnel to hangar (TAN-629). Electrical and chemical services. Ralph M. Parsons 1229-2 ANP/GE-630-A-1. Date: March 1957. Approved by INEEL Classification Office for public release. INEEL index code no. 036-0630-00-693-107080 - Idaho National Engineering Laboratory, Test Area North, Scoville, Butte County, ID

IET. Fuel transfer pumping building (TAN625). Elevations, foundation. Detail of ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

IET. Fuel transfer pumping building (TAN-625). Elevations, foundation. Detail of access stairway to coupling station. Ralph M. Parsons 902-a-ANY-620-625-A&S 414. Date: February 1954. Approved by INEEL Classification Office for public release. INEEL index code no. 035-0625-00-693-106971 - Idaho National Engineering Laboratory, Test Area North, Scoville, Butte County, ID

FET. Control and equipment building (TAN630). East elevation and section. ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

FET. Control and equipment building (TAN-630). East elevation and section. Shielded roadway and personnel entrances. Ralph M. Parsons 1229-2 ANP/GE-5-630-A-5. Date: March 1957. Approved by INEEL Classification Office for public release. INEEL index code no. 036-0630-00-693-107084 - Idaho National Engineering Laboratory, Test Area North, Scoville, Butte County, ID

FET. Control and equipment building, TAN630. Main floor plan. Control ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

FET. Control and equipment building, TAN-630. Main floor plan. Control room. Room numbers and functions. Ralph M. Parsons. 1229-2-ANP/GE-5-630-A-2. Date: March 1957. Approved by INEEL Classification Office for public release. INEEL index code no. 036-0630-00-693-107081 - Idaho National Engineering Laboratory, Test Area North, Scoville, Butte County, ID

FET. Control and equipment building (TAN630). Sections. Earth cover. Shielded ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

FET. Control and equipment building (TAN-630). Sections. Earth cover. Shielded access entries for personnel and vehicles. Ralph M. Parsons 1229-2 ANP/GE-5-630-A-3. Date: March 1957. Approved by INEEL Classification Office for public release. INEEL index code no. 036-0630-00-693-107082 - Idaho National Engineering Laboratory, Test Area North, Scoville, Butte County, ID

ADM. Administration Building (TAN602). Elevations, sections, details. Shows areas that ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

ADM. Administration Building (TAN-602). Elevations, sections, details. Shows areas that were soon remodeled or added onto. Ralph M. Parsons 902-2-ANP-602-A 32 Date: August 1955. Approved by INEEL Classification Office for public release. INEEL index code no. 033-0602-00-693-106711 - Idaho National Engineering Laboratory, Test Area North, Scoville, Butte County, ID

[Age-related changes of somatotype and body mass components in girls].

PubMed

Tambovtseva, R V; Zhukova, S G

2005-01-01

The longitudinal and transverse studies of girls aged 7 to 17 years living in Moscow and the town of Yelabuga were performed to monitor the dynamics of their growth processes, parameters of ectomorphism, mesomorphism and endomorphism depending on the type of body build. Anthropometric, anthroposcopic metods and cluster analysis were used to evaluate the type of body build according to V.G. Shtefko and A.G. Ostrovskiy (1928). Quantitative assessment of parameters of endo-, meso- and ectomorphism was performed using Heath-Carter method (1980). It was shown that the age-related variability of the types of body build appeared in association with the developmental heterochronism, which resulted from the uneven growth rate of different body components. The least variable parameters were found in the girls of digestive and asthenoid types of body build, while in girls of muscular and thoracic types these parameters changed more frequently. The critical periods during which the significant changes of somatotype were increased in number, were defined as 9 to 10 years and puberty period--11 to 14 years. Most sensitive time points in the time-course of somatotype establishment in girls are the ages of 12 and 14 years.

Predicting nutrient excretion of aquatic animals with metabolic ecology and ecological stoichiometry: a global synthesis.

PubMed

Vanni, Michael J; McIntyre, Peter B

2016-12-01

The metabolic theory of ecology (MTE) and ecological stoichiometry (ES) are both prominent frameworks for understanding energy and nutrient budgets of organisms. We tested their separate and joint power to predict nitrogen (N) and phosphorus (P) excretion rates of ectothermic aquatic invertebrate and vertebrate animals (10,534 observations worldwide). MTE variables (body size, temperature) performed better than ES variables (trophic guild, vertebrate classification, body N:P) in predicting excretion rates, but the best models included variables from both frameworks. Size scaling coefficients were significantly lower than predicted by MTE (<0.75), were lower for P than N, and varied greatly among species. Contrary to expectations under ES, vertebrates excreted both N and P at higher rates than invertebrates despite having more nutrient-rich bodies, and primary consumers excreted as much nutrients as carnivores despite having nutrient-poor diets. Accounting for body N:P hardly improved upon predictions from treating vertebrate classification categorically. We conclude that basic data on body size, water temperature, trophic guild, and vertebrate classification are sufficient to make general estimates of nutrient excretion rates for any animal taxon or aquatic ecosystem. Nonetheless, dramatic interspecific variation in size-scaling coefficients and counter-intuitive patterns with respect to diet and body composition underscore the need for field data on consumption and egestion rates. Together, MTE and ES provide a powerful conceptual basis for interpreting and predicting nutrient recycling rates of aquatic animals worldwide. © 2016 by the Ecological Society of America.

Ethnic differences in the relationship between body mass index and percentage body fat among Asian children from different backgrounds.

PubMed

Liu, Ailing; Byrne, Nuala M; Kagawa, Masaharu; Ma, Guansheng; Poh, Bee Koon; Ismail, Mohammad Noor; Kijboonchoo, Kallaya; Nasreddine, Lara; Trinidad, Trinidad Palad; Hills, Andrew P

2011-11-01

Overweight and obesity in Asian children are increasing at an alarming rate; therefore a better understanding of the relationship between BMI and percentage body fat (%BF) in this population is important. A total of 1039 children aged 8-10 years, encompassing a wide BMI range, were recruited from China, Lebanon, Malaysia, The Philippines and Thailand. Body composition was determined using the 2H dilution technique to quantify total body water and subsequently fat mass, fat-free mass and %BF. Ethnic differences in the BMI-%BF relationship were found; for example, %BF in Filipino boys was approximately 2 % lower than in their Thai and Malay counterparts. In contrast, Thai girls had approximately 2.0 % higher %BF values than in their Chinese, Lebanese, Filipino and Malay counterparts at a given BMI. However, the ethnic difference in the BMI-%BF relationship varied by BMI. Compared with Caucasian children of the same age, Asian children had 3-6 units lower BMI at a given %BF. Approximately one-third of the obese Asian children (%BF above 25 % for boys and above 30 % for girls) in the study were not identified using the WHO classification and more than half using the International Obesity Task Force classification. Use of the Chinese classification increased the sensitivity. Results confirmed the necessity to consider ethnic differences in body composition when developing BMI cut-points and other obesity criteria in Asian children.

Accuracy assessments and areal estimates using two-phase stratified random sampling, cluster plots, and the multivariate composite estimator

Treesearch

Raymond L. Czaplewski

2000-01-01

Consider the following example of an accuracy assessment. Landsat data are used to build a thematic map of land cover for a multicounty region. The map classifier (e.g., a supervised classification algorithm) assigns each pixel into one category of land cover. The classification system includes 12 different types of forest and land cover: black spruce, balsam fir,...

Synergistic Use of WorldView-2 Imagery and Airborne LiDAR Data for Urban Land Cover Classification

NASA Astrophysics Data System (ADS)

Wu, M. F.; Sun, Z. C.; Yang, B.; Yu, S. S.

2017-02-01

There are lots of challenges for deriving urban land cover types for high resolution optical imagery because of spectral similarity of different objects, mixed pixels, shadows of buildings and large tree crowns. In order to reduce these uncertainties, recently, it’s a trend of the classification of urban land cover from multi-source sensors in the field of urban remote sensing. In this study, a hierarchical support vector machine (SVM) classification method was applied to the urban land cover mapping, using the WorldView-2 imagery and airborne Light Detection and Ranging (LiDAR) data. The results showed that: (1) The overall accuracy (OA) and overall kappa (OK) were 72.92% and 0.66 for WorldView-2 imagery alone; while the OA and OK were improved up to 89.44% and 0.87 for the synergistic use of the two types of data source. (2) Buildings and road/parking lots extracted from fused data were more precision and well-shaped. The two classes from fused data were optimally classified with higher producer’s accuracy and user’s accuracy than WorldView-2 imagery alone. The trees were also easily separated from the grasslands when the airborne LiDAR data was added. (3) The fused data could reduce the phenomenon of different spectral character of the complex and detailed objects. It was also helpful to address the problem of shadows from the high-rise buildings. The results from this study indicate that the synergistic use of high resolution optical imagery and airborne LiDAR data can be an efficient approach to improving the classification of urban land cover.

T-Shirt Tangle.

ERIC Educational Resources Information Center

Clark, Wilma

1986-01-01

Describes an exercise in which students cut out T-shirt drawings, sort the T-shirts into groups, and "write" a classification essay by pasting the T-shirts on sheet of paper. The T-shirts in each group become the examples used in one body paragraph of the classification essay. (HTH)

The Names of Heavenly Bodies and Luminaries as a Basis for Word-building and Metaphorical Application in the Poetry of Misak Metzarents

NASA Astrophysics Data System (ADS)

Arakelyan, Karine; Santryan, Liana

2016-12-01

The metaphorical applications of heavenly bodies and luminaries in the poetry of the famous Western Armenian poet Missak Metsarents are very diverse and of great variety. The poet admired the beauty of nature and the greater parts of his poems are devoted to representation of nature, where heavenly bodies and phenomena are of special interest. They often help to reveal one's inner life, emotions, expectations, and feelings. Many times these units are bases for word-building; new, also poetical words building and they create unique beauty.

Information support of monitoring of technical condition of buildings in construction risk area

NASA Astrophysics Data System (ADS)

Skachkova, M. E.; Lepihina, O. Y.; Ignatova, V. V.

2018-05-01

The paper presents the results of the research devoted to the development of a model of information support of monitoring buildings technical condition; these buildings are located in the construction risk area. As a result of the visual and instrumental survey, as well as the analysis of existing approaches and techniques, attributive and cartographic databases have been created. These databases allow monitoring defects and damages of buildings located in a 30-meter risk area from the object under construction. The classification of structures and defects of these buildings under survey is presented. The functional capabilities of the developed model and the field of it practical applications are determined.

Link prediction boosted psychiatry disorder classification for functional connectivity network

NASA Astrophysics Data System (ADS)

Li, Weiwei; Mei, Xue; Wang, Hao; Zhou, Yu; Huang, Jiashuang

2017-02-01

Functional connectivity network (FCN) is an effective tool in psychiatry disorders classification, and represents cross-correlation of the regional blood oxygenation level dependent signal. However, FCN is often incomplete for suffering from missing and spurious edges. To accurate classify psychiatry disorders and health control with the incomplete FCN, we first `repair' the FCN with link prediction, and then exact the clustering coefficients as features to build a weak classifier for every FCN. Finally, we apply a boosting algorithm to combine these weak classifiers for improving classification accuracy. Our method tested by three datasets of psychiatry disorder, including Alzheimer's Disease, Schizophrenia and Attention Deficit Hyperactivity Disorder. The experimental results show our method not only significantly improves the classification accuracy, but also efficiently reconstructs the incomplete FCN.

An estimation framework for building information modeling (BIM)-based demolition waste by type.

PubMed

Kim, Young-Chan; Hong, Won-Hwa; Park, Jae-Woo; Cha, Gi-Wook

2017-12-01

Most existing studies on demolition waste (DW) quantification do not have an official standard to estimate the amount and type of DW. Therefore, there are limitations in the existing literature for estimating DW with a consistent classification system. Building information modeling (BIM) is a technology that can generate and manage all the information required during the life cycle of a building, from design to demolition. Nevertheless, there has been a lack of research regarding its application to the demolition stage of a building. For an effective waste management plan, the estimation of the type and volume of DW should begin from the building design stage. However, the lack of tools hinders an early estimation. This study proposes a BIM-based framework that estimates DW in the early design stages, to achieve an effective and streamlined planning, processing, and management. Specifically, the input of construction materials in the Korean construction classification system and those in the BIM library were matched. Based on this matching integration, the estimates of DW by type were calculated by applying the weight/unit volume factors and the rates of DW volume change. To verify the framework, its operation was demonstrated by means of an actual BIM modeling and by comparing its results with those available in the literature. This study is expected to contribute not only to the estimation of DW at the building level, but also to the automated estimation of DW at the district level.

Feature selection and classification of multiparametric medical images using bagging and SVM

NASA Astrophysics Data System (ADS)

Fan, Yong; Resnick, Susan M.; Davatzikos, Christos

2008-03-01

This paper presents a framework for brain classification based on multi-parametric medical images. This method takes advantage of multi-parametric imaging to provide a set of discriminative features for classifier construction by using a regional feature extraction method which takes into account joint correlations among different image parameters; in the experiments herein, MRI and PET images of the brain are used. Support vector machine classifiers are then trained based on the most discriminative features selected from the feature set. To facilitate robust classification and optimal selection of parameters involved in classification, in view of the well-known "curse of dimensionality", base classifiers are constructed in a bagging (bootstrap aggregating) framework for building an ensemble classifier and the classification parameters of these base classifiers are optimized by means of maximizing the area under the ROC (receiver operating characteristic) curve estimated from their prediction performance on left-out samples of bootstrap sampling. This classification system is tested on a sex classification problem, where it yields over 90% classification rates for unseen subjects. The proposed classification method is also compared with other commonly used classification algorithms, with favorable results. These results illustrate that the methods built upon information jointly extracted from multi-parametric images have the potential to perform individual classification with high sensitivity and specificity.

Exercise-Associated Collapse in Endurance Events: A Classification System.

ERIC Educational Resources Information Center

Roberts, William O.

1989-01-01

Describes a classification system devised for exercise-associated collapse in endurance events based on casualties observed at six Twin Cities Marathons. Major diagnostic criteria are body temperature and mental status. Management protocol includes fluid and fuel replacement, temperature correction, and leg cramp treatment. (Author/SM)

[A cold/heat property classification strategy based on bio-effects of herbal medicines].

PubMed

Jiang, Miao; Lv, Ai-Ping

2014-06-01

The property theory of Chinese herbal medicine (CHM) is regarded as the core and basic of Chinese medical theory, however, the underlying mechanism of the properties in CHMs remains unclear, which impedes a barrier for the modernization of Chinese herbal medicine. The properties of CHM are often categorized into cold and heat according to the theory of Chinese medicine, which are essential to guide the clinical application of CHMs. There is an urgent demand to build a cold/heat property classification model to facilitate the property theory of Chinese herbal medicine, as well as to clarify the controversial properties of some herbs. Based on previous studies on the cold/heat properties of CHM, in this paper, we described a novel strategy on building a cold/heat property classification model based on herbal bio-effect. The interdisciplinary cooperation of systems biology, pharmacological network, and pattern recognition technique might lighten the study on cold/heat property theory, provide a scientific model for determination the cold/heat property of herbal medicines, and a new strategy for expanding the Chinese herbal medicine resources as well.

Lightweight Biometric Sensing for Walker Classification Using Narrowband RF Links

PubMed Central

Liang, Zhuo-qian

2017-01-01

This article proposes a lightweight biometric sensing system using ubiquitous narrowband radio frequency (RF) links for path-dependent walker classification. The fluctuated received signal strength (RSS) sequence generated by human motion is used for feature representation. To capture the most discriminative characteristics of individuals, a three-layer RF sensing network is organized for building multiple sampling links at the most common heights of upper limbs, thighs, and lower legs. The optimal parameters of sensing configuration, such as the height of link location and number of fused links, are investigated to improve sensory data distinctions among subjects, and the experimental results suggest that the synergistic sensing by using multiple links can contribute a better performance. This is the new consideration of using RF links in building a biometric sensing system. In addition, two types of classification methods involving vector quantization (VQ) and hidden Markov models (HMMs) are developed and compared for closed-set walker recognition and verification. Experimental studies in indoor line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios are conducted to validate the proposed method. PMID:29206188

Lightweight Biometric Sensing for Walker Classification Using Narrowband RF Links.

PubMed

Liu, Tong; Liang, Zhuo-Qian

2017-12-05

This article proposes a lightweight biometric sensing system using ubiquitous narrowband radio frequency (RF) links for path-dependent walker classification. The fluctuated received signal strength (RSS) sequence generated by human motion is used for feature representation. To capture the most discriminative characteristics of individuals, a three-layer RF sensing network is organized for building multiple sampling links at the most common heights of upper limbs, thighs, and lower legs. The optimal parameters of sensing configuration, such as the height of link location and number of fused links, are investigated to improve sensory data distinctions among subjects, and the experimental results suggest that the synergistic sensing by using multiple links can contribute a better performance. This is the new consideration of using RF links in building a biometric sensing system. In addition, two types of classification methods involving vector quantization (VQ) and hidden Markov models (HMMs) are developed and compared for closed-set walker recognition and verification. Experimental studies in indoor line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios are conducted to validate the proposed method.

Comprehensive decision tree models in bioinformatics.

PubMed

Stiglic, Gregor; Kocbek, Simon; Pernek, Igor; Kokol, Peter

2012-01-01

Classification is an important and widely used machine learning technique in bioinformatics. Researchers and other end-users of machine learning software often prefer to work with comprehensible models where knowledge extraction and explanation of reasoning behind the classification model are possible. This paper presents an extension to an existing machine learning environment and a study on visual tuning of decision tree classifiers. The motivation for this research comes from the need to build effective and easily interpretable decision tree models by so called one-button data mining approach where no parameter tuning is needed. To avoid bias in classification, no classification performance measure is used during the tuning of the model that is constrained exclusively by the dimensions of the produced decision tree. The proposed visual tuning of decision trees was evaluated on 40 datasets containing classical machine learning problems and 31 datasets from the field of bioinformatics. Although we did not expected significant differences in classification performance, the results demonstrate a significant increase of accuracy in less complex visually tuned decision trees. In contrast to classical machine learning benchmarking datasets, we observe higher accuracy gains in bioinformatics datasets. Additionally, a user study was carried out to confirm the assumption that the tree tuning times are significantly lower for the proposed method in comparison to manual tuning of the decision tree. The empirical results demonstrate that by building simple models constrained by predefined visual boundaries, one not only achieves good comprehensibility, but also very good classification performance that does not differ from usually more complex models built using default settings of the classical decision tree algorithm. In addition, our study demonstrates the suitability of visually tuned decision trees for datasets with binary class attributes and a high number of possibly redundant attributes that are very common in bioinformatics.

Comprehensive Decision Tree Models in Bioinformatics

PubMed Central

Stiglic, Gregor; Kocbek, Simon; Pernek, Igor; Kokol, Peter

2012-01-01

Purpose Classification is an important and widely used machine learning technique in bioinformatics. Researchers and other end-users of machine learning software often prefer to work with comprehensible models where knowledge extraction and explanation of reasoning behind the classification model are possible. Methods This paper presents an extension to an existing machine learning environment and a study on visual tuning of decision tree classifiers. The motivation for this research comes from the need to build effective and easily interpretable decision tree models by so called one-button data mining approach where no parameter tuning is needed. To avoid bias in classification, no classification performance measure is used during the tuning of the model that is constrained exclusively by the dimensions of the produced decision tree. Results The proposed visual tuning of decision trees was evaluated on 40 datasets containing classical machine learning problems and 31 datasets from the field of bioinformatics. Although we did not expected significant differences in classification performance, the results demonstrate a significant increase of accuracy in less complex visually tuned decision trees. In contrast to classical machine learning benchmarking datasets, we observe higher accuracy gains in bioinformatics datasets. Additionally, a user study was carried out to confirm the assumption that the tree tuning times are significantly lower for the proposed method in comparison to manual tuning of the decision tree. Conclusions The empirical results demonstrate that by building simple models constrained by predefined visual boundaries, one not only achieves good comprehensibility, but also very good classification performance that does not differ from usually more complex models built using default settings of the classical decision tree algorithm. In addition, our study demonstrates the suitability of visually tuned decision trees for datasets with binary class attributes and a high number of possibly redundant attributes that are very common in bioinformatics. PMID:22479449

Automated detection of radioisotopes from an aircraft platform by pattern recognition analysis of gamma-ray spectra.

PubMed

Dess, Brian W; Cardarelli, John; Thomas, Mark J; Stapleton, Jeff; Kroutil, Robert T; Miller, David; Curry, Timothy; Small, Gary W

2018-03-08

A generalized methodology was developed for automating the detection of radioisotopes from gamma-ray spectra collected from an aircraft platform using sodium-iodide detectors. Employing data provided by the U.S Environmental Protection Agency Airborne Spectral Photometric Environmental Collection Technology (ASPECT) program, multivariate classification models based on nonparametric linear discriminant analysis were developed for application to spectra that were preprocessed through a combination of altitude-based scaling and digital filtering. Training sets of spectra for use in building classification models were assembled from a combination of background spectra collected in the field and synthesized spectra obtained by superimposing laboratory-collected spectra of target radioisotopes onto field backgrounds. This approach eliminated the need for field experimentation with radioactive sources for use in building classification models. Through a bi-Gaussian modeling procedure, the discriminant scores that served as the outputs from the classification models were related to associated confidence levels. This provided an easily interpreted result regarding the presence or absence of the signature of a specific radioisotope in each collected spectrum. Through the use of this approach, classifiers were built for cesium-137 ( 137 Cs) and cobalt-60 ( 60 Co), two radioisotopes that are of interest in airborne radiological monitoring applications. The optimized classifiers were tested with field data collected from a set of six geographically diverse sites, three of which contained either 137 Cs, 60 Co, or both. When the optimized classification models were applied, the overall percentages of correct classifications for spectra collected at these sites were 99.9 and 97.9% for the 60 Co and 137 Cs classifiers, respectively. Copyright © 2018 Elsevier Ltd. All rights reserved.

LPT. Chlorination building (TAN643) and water well pumphouse (TAN644). Plans, ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

LPT. Chlorination building (TAN-643) and water well pumphouse (TAN-644). Plans, elevations, sections, and details. Ralph M. Parsons 1229-12 ANP/GE-7-643-A-S-H&V-1. November 1956. Approved by INEEL Classification Office for public release. INEEL index code no. 038-0643/0644-00-693-107307 - Idaho National Engineering Laboratory, Test Area North, Scoville, Butte County, ID

Alternative Missions for the Army

DTIC Science & Technology

1992-07-17

SECURITY CLASSIFICATION AUTHORITY 1.1I1 H I I BILITY OF REPORT 2b. DECLASSIFICATION /DOWNGRADING SCHEDULE Approved for public release; H ict-ri hiit ...construction, health care, transportation, and law enforcement. - Because they are already located in over 5,000 communities throughout the nation, the...various railway surveys. effectively building the nations first railroad, and also developed the cour.irys water resources through building or improving

Research and implementation on 3D modeling of geological body

NASA Astrophysics Data System (ADS)

Niu, Lijuan; Li, Ligong; Zhu, Renyi; Huang, Man

2017-10-01

This study based on GIS thinking explores the combination of the mixed spatial data model and GIS model to build three-dimensional(3d) model of geological bodies in the Arc Engine platform, describes the interface and method used in the construction of 3d geological body in Arc Engine component platform in detail, and puts forward an indirect method which constructs a set of geological grid layers through Rigging interpolation by the borehole data and then converts it into the geological layers of TIN, which improves the defect in building the geological layers of TIN directly and makes it better to complete the simulation of the real geological layer. This study makes a useful attempt to build 3d model of the geological body based on the GIS, and provides a certain reference value for simulating geological bodies in 3d and constructing the digital system of underground space.

Building a Joint-Service Classification Research Roadmap: Methodological Issues in Selection and Classification

DTIC Science & Technology

1994-02-01

Wijting , 1976). However, missing critical job elements may lead the J-coefficient to underestimate validity (Mossholder & Arvey, 1984), and variation...should be able to approximate the validity estimates derived empirically. Research on the J-Coefficient (Dickinson & Wijting , 1976) and the SYNVAL project...Measur.ment 8, 71-82. Dickinson, T. L, & Wijting , J. P. Q976). Poiiyvcapturingasaprocedute for synLthetic vraidation. Paper preented at the meeting of the

Effects of Age and Sex on the Development of Personal Space Schemata Towards Body Build

ERIC Educational Resources Information Center

Lerner, Richard M.; And Others

1975-01-01

This study assessed personal space schemata of children towards stimulus figures representing male and female body build stereotypes. Greater spatial distances were used towards the Endomorph than other physique types and significant sex differences were found. (GO)

Active Learning of Classification Models with Likert-Scale Feedback.

PubMed

Xue, Yanbing; Hauskrecht, Milos

2017-01-01

Annotation of classification data by humans can be a time-consuming and tedious process. Finding ways of reducing the annotation effort is critical for building the classification models in practice and for applying them to a variety of classification tasks. In this paper, we develop a new active learning framework that combines two strategies to reduce the annotation effort. First, it relies on label uncertainty information obtained from the human in terms of the Likert-scale feedback. Second, it uses active learning to annotate examples with the greatest expected change. We propose a Bayesian approach to calculate the expectation and an incremental SVM solver to reduce the time complexity of the solvers. We show the combination of our active learning strategy and the Likert-scale feedback can learn classification models more rapidly and with a smaller number of labeled instances than methods that rely on either Likert-scale labels or active learning alone.

Active Learning of Classification Models with Likert-Scale Feedback

PubMed Central

Xue, Yanbing; Hauskrecht, Milos

2017-01-01

Annotation of classification data by humans can be a time-consuming and tedious process. Finding ways of reducing the annotation effort is critical for building the classification models in practice and for applying them to a variety of classification tasks. In this paper, we develop a new active learning framework that combines two strategies to reduce the annotation effort. First, it relies on label uncertainty information obtained from the human in terms of the Likert-scale feedback. Second, it uses active learning to annotate examples with the greatest expected change. We propose a Bayesian approach to calculate the expectation and an incremental SVM solver to reduce the time complexity of the solvers. We show the combination of our active learning strategy and the Likert-scale feedback can learn classification models more rapidly and with a smaller number of labeled instances than methods that rely on either Likert-scale labels or active learning alone. PMID:28979827

Multi-element analysis of wines by ICP-MS and ICP-OES and their classification according to geographical origin in Slovenia.

PubMed

Selih, Vid S; Sala, Martin; Drgan, Viktor

2014-06-15

Inductively coupled plasma mass spectrometry and optical emission were used to determine the multi-element composition of 272 bottled Slovenian wines. To achieve geographical classification of the wines by their elemental composition, principal component analysis (PCA) and counter-propagation artificial neural networks (CPANN) have been used. From 49 elements measured, 19 were used to build the final classification models. CPANN was used for the final predictions because of its superior results. The best model gave 82% correct predictions for external set of the white wine samples. Taking into account the small size of whole Slovenian wine growing regions, we consider the classification results were very good. For the red wines, which were mostly represented from one region, even-sub region classification was possible with great precision. From the level maps of the CPANN model, some of the most important elements for classification were identified. Copyright © 2013 Elsevier Ltd. All rights reserved.

Land Covers Classification Based on Random Forest Method Using Features from Full-Waveform LIDAR Data

NASA Astrophysics Data System (ADS)

Ma, L.; Zhou, M.; Li, C.

2017-09-01

In this study, a Random Forest (RF) based land covers classification method is presented to predict the types of land covers in Miyun area. The returned full-waveforms which were acquired by a LiteMapper 5600 airborne LiDAR system were processed, including waveform filtering, waveform decomposition and features extraction. The commonly used features that were distance, intensity, Full Width at Half Maximum (FWHM), skewness and kurtosis were extracted. These waveform features were used as attributes of training data for generating the RF prediction model. The RF prediction model was applied to predict the types of land covers in Miyun area as trees, buildings, farmland and ground. The classification results of these four types of land covers were obtained according to the ground truth information acquired from CCD image data of the same region. The RF classification results were compared with that of SVM method and show better results. The RF classification accuracy reached 89.73% and the classification Kappa was 0.8631.

Stereotyped movement disorder in ICD-11.

PubMed

Stein, Dan J; Woods, Douglas W

2014-01-01

According to current proposals for ICD-11, stereotyped movement disorder will be classified in the grouping of neurodevelopmental disorders, with a qualifier to indicate whether self-injury is present, similar to the classification of stereotypic movement disorder in DSM-5. At the same time, the WHO ICD-11 Working Group on the Classification of Obsessive-Compulsive and Related Disorders has proposed a grouping of body-focused repetitive behavior disorders within the obsessive-compulsive and related disorders (OCRD) cluster to include trichotillomania and skin-picking disorder. DSM-5 has taken a slightly different approach: trichotillomania and excoriation (skin picking) disorder are included in the OCRD grouping, while body-focused repetitive behavior disorder is listed under other specified forms of OCRD. DSM-5 also includes a separate category of nonsuicidal self-injury in the section on "conditions for further study." There are a number of unresolved nosological questions regarding the relationships among stereotyped movement disorder, body-focused repetitive behavior disorders, and nonsuicidal self-injury. In this article, we attempt to provide preliminary answers to some of these questions as they relate to the ICD-11 classification of mental and behavioral disorders.

Ageing and exercise: building body capital in old age.

PubMed

Bergland, Astrid; Fougner, Marit; Lund, Anne; Debesay, Jonas

2018-01-01

Research that provides better understanding of the motivational processes in older age to maintain a healthy and active lifestyle is sought after. We apply theoretical approaches to cultural capital, active and healthy aging health to shed light on the women's experiences in maintaining physical capabilities through an active lifestyle, and thereby facilitating their own inclusion in society. Thus, the aim of this paper is to explore why older home dwelling women over the age of 70 years or more spend time in physical exercise and their experiences about the importance of participating in group exercise for their daily life.This paper reports on a qualitative study based on interviews with 16 older women aged 70 years or more and regularly attending group exercise classes in the community at an established workout center. The data were analyzed the data using an inductive content analysis approach. Three overreaching and interrelated themes emerged from the interviews: "Building body capital for independence", "Building body capital to maintain vitality and being in control" and "Building resources for social interaction". The findings suggest that group exercise is important for building body capital. The group exercise helped the women in building bodily ability to manage everyday life, maintain vitality, being in control, pursue social interaction and live independently. These body resources were important for these older women's experience of the manageability and meaningfulness of daily life. This study has provided insights into older women's understanding and experiences of the challenges of everyday life within a theoretical framework of cultural capital and health. The women acquired cultural health capital, and more specifically body capital, by participating in the group exercise classes. The women's investment in body capital through regular physical activity created resources which facilitated social participation. Therefore professionals need to be aware of this when performing group exercise.

Disability and Profiles of Functioning of Patients with Parkinson's Disease Described with ICF Classification

ERIC Educational Resources Information Center

Raggi, Alberto; Leonardi, Matilde; Ajovalasit, Daniela; Carella, Francesco; Soliveri, Paola; Albanese, Alberto; Romito, Luigi

2011-01-01

The objective of this study was to describe the functional profiles of patients with Parkinson's disease (PD), and the relationships between impairment in body functions, limitations in activities, and environmental factors, using the World Health Organization's International Classification of Functioning, Disability, and Health (ICF). Patients…

Body composition throughout the lifecycle: The role of dairy foods

USDA-ARS?s Scientific Manuscript database

There is an ongoing concern about the degree of obesity world-wide and the implications for health outcomes. Body mass index (BMI) is used as the measure for the classification of overweight and obesity. However, this index does not represent actual body fat levels or the amount of active lean bod...

Nutritional status in sick children and adolescents is not accurately reflected by BMI-SDS.

PubMed

Fusch, Gerhard; Raja, Preeya; Dung, Nguyen Quang; Karaolis-Danckert, Nadina; Barr, Ronald; Fusch, Christoph

2013-01-01

Nutritional status provides helpful information of disease severity and treatment effectiveness. Body mass index standard deviation scores (BMI-SDS) provide an approximation of body composition and thus are frequently used to classify nutritional status of sick children and adolescents. However, the accuracy of estimating body composition in this population using BMI-SDS has not been assessed. Thus, this study aims to evaluate the accuracy of nutritional status classification in sick infants and adolescents using BMI-SDS, upon comparison to classification using percentage body fat (%BF) reference charts. BMI-SDS was calculated from anthropometric measurements and %BF was measured using dual-energy x-ray absorptiometry (DXA) for 393 sick children and adolescents (5 months-18 years). Subjects were classified by nutritional status (underweight, normal weight, overweight, and obese), using 2 methods: (1) BMI-SDS, based on age- and gender-specific percentiles, and (2) %BF reference charts (standard). Linear regression and a correlation analysis were conducted to compare agreement between both methods of nutritional status classification. %BF reference value comparisons were also made between 3 independent sources based on German, Canadian, and American study populations. Correlation between nutritional status classification by BMI-SDS and %BF agreed moderately (r (2) = 0.75, 0.76 in boys and girls, respectively). The misclassification of nutritional status in sick children and adolescents using BMI-SDS was 27% when using German %BF references. Similar rates observed when using Canadian and American %BF references (24% and 23%, respectively). Using BMI-SDS to determine nutritional status in a sick population is not considered an appropriate clinical tool for identifying individual underweight or overweight children or adolescents. However, BMI-SDS may be appropriate for longitudinal measurements or for screening purposes in large field studies. When accurate nutritional status classification of a sick patient is needed for clinical purposes, nutritional status will be assessed more accurately using methods that accurately measure %BF, such as DXA.

Multi-Temporal Classification and Change Detection Using Uav Images

NASA Astrophysics Data System (ADS)

Makuti, S.; Nex, F.; Yang, M. Y.

2018-05-01

In this paper different methodologies for the classification and change detection of UAV image blocks are explored. UAV is not only the cheapest platform for image acquisition but it is also the easiest platform to operate in repeated data collections over a changing area like a building construction site. Two change detection techniques have been evaluated in this study: the pre-classification and the post-classification algorithms. These methods are based on three main steps: feature extraction, classification and change detection. A set of state of the art features have been used in the tests: colour features (HSV), textural features (GLCM) and 3D geometric features. For classification purposes Conditional Random Field (CRF) has been used: the unary potential was determined using the Random Forest algorithm while the pairwise potential was defined by the fully connected CRF. In the performed tests, different feature configurations and settings have been considered to assess the performance of these methods in such challenging task. Experimental results showed that the post-classification approach outperforms the pre-classification change detection method. This was analysed using the overall accuracy, where by post classification have an accuracy of up to 62.6 % and the pre classification change detection have an accuracy of 46.5 %. These results represent a first useful indication for future works and developments.

Gender classification under extended operating conditions

NASA Astrophysics Data System (ADS)

Rude, Howard N.; Rizki, Mateen

2014-06-01

Gender classification is a critical component of a robust image security system. Many techniques exist to perform gender classification using facial features. In contrast, this paper explores gender classification using body features extracted from clothed subjects. Several of the most effective types of features for gender classification identified in literature were implemented and applied to the newly developed Seasonal Weather And Gender (SWAG) dataset. SWAG contains video clips of approximately 2000 samples of human subjects captured over a period of several months. The subjects are wearing casual business attire and outer garments appropriate for the specific weather conditions observed in the Midwest. The results from a series of experiments are presented that compare the classification accuracy of systems that incorporate various types and combinations of features applied to multiple looks at subjects at different image resolutions to determine a baseline performance for gender classification.

The Mind-Body Building Equation.

ERIC Educational Resources Information Center

Dryfoos, Joy

2000-01-01

Full-service community schools combine three concepts--mind, body, and building--into an integrated approach placing quality education and comprehensive support services at one site. The DeWitt Wallace-Reader's Digest Fund is helping schools and communities replicate 4 such programs at 60 sites in 20 U.S. cities. (MLH)

Automated simultaneous multiple feature classification of MTI data

NASA Astrophysics Data System (ADS)

Harvey, Neal R.; Theiler, James P.; Balick, Lee K.; Pope, Paul A.; Szymanski, John J.; Perkins, Simon J.; Porter, Reid B.; Brumby, Steven P.; Bloch, Jeffrey J.; David, Nancy A.; Galassi, Mark C.

2002-08-01

Los Alamos National Laboratory has developed and demonstrated a highly capable system, GENIE, for the two-class problem of detecting a single feature against a background of non-feature. In addition to the two-class case, however, a commonly encountered remote sensing task is the segmentation of multispectral image data into a larger number of distinct feature classes or land cover types. To this end we have extended our existing system to allow the simultaneous classification of multiple features/classes from multispectral data. The technique builds on previous work and its core continues to utilize a hybrid evolutionary-algorithm-based system capable of searching for image processing pipelines optimized for specific image feature extraction tasks. We describe the improvements made to the GENIE software to allow multiple-feature classification and describe the application of this system to the automatic simultaneous classification of multiple features from MTI image data. We show the application of the multiple-feature classification technique to the problem of classifying lava flows on Mauna Loa volcano, Hawaii, using MTI image data and compare the classification results with standard supervised multiple-feature classification techniques.

GLCF: Gallery

Science.gov Websites

UMD Land Cover Classification Product External Galleries * ASTER at JPL * AVHRR at JHU * Earth Observatory at NASA * Landsat 7 at USGS * MODIS at NASA * Visible Earth at NASA e-link 4321 Hartwick Building

exprso: an R-package for the rapid implementation of machine learning algorithms.

PubMed

Quinn, Thomas; Tylee, Daniel; Glatt, Stephen

2016-01-01

Machine learning plays a major role in many scientific investigations. However, non-expert programmers may struggle to implement the elaborate pipelines necessary to build highly accurate and generalizable models. We introduce exprso , a new R package that is an intuitive machine learning suite designed specifically for non-expert programmers. Built initially for the classification of high-dimensional data, exprso uses an object-oriented framework to encapsulate a number of common analytical methods into a series of interchangeable modules. This includes modules for feature selection, classification, high-throughput parameter grid-searching, elaborate cross-validation schemes (e.g., Monte Carlo and nested cross-validation), ensemble classification, and prediction. In addition, exprso also supports multi-class classification (through the 1-vs-all generalization of binary classifiers) and the prediction of continuous outcomes.

An Optimal Set of Flesh Points on Tongue and Lips for Speech-Movement Classification

PubMed Central

Samal, Ashok; Rong, Panying; Green, Jordan R.

2016-01-01

Purpose The authors sought to determine an optimal set of flesh points on the tongue and lips for classifying speech movements. Method The authors used electromagnetic articulographs (Carstens AG500 and NDI Wave) to record tongue and lip movements from 13 healthy talkers who articulated 8 vowels, 11 consonants, a phonetically balanced set of words, and a set of short phrases during the recording. We used a machine-learning classifier (support-vector machine) to classify the speech stimuli on the basis of articulatory movements. We then compared classification accuracies of the flesh-point combinations to determine an optimal set of sensors. Results When data from the 4 sensors (T1: the vicinity between the tongue tip and tongue blade; T4: the tongue-body back; UL: the upper lip; and LL: the lower lip) were combined, phoneme and word classifications were most accurate and were comparable with the full set (including T2: the tongue-body front; and T3: the tongue-body front). Conclusion We identified a 4-sensor set—that is, T1, T4, UL, LL—that yielded a classification accuracy (91%–95%) equivalent to that using all 6 sensors. These findings provide an empirical basis for selecting sensors and their locations for scientific and emerging clinical applications that incorporate articulatory movements. PMID:26564030

A hazard and risk classification system for catastrophic rock slope failures in Norway

NASA Astrophysics Data System (ADS)

Hermanns, R.; Oppikofer, T.; Anda, E.; Blikra, L. H.; Böhme, M.; Bunkholt, H.; Dahle, H.; Devoli, G.; Eikenæs, O.; Fischer, L.; Harbitz, C. B.; Jaboyedoff, M.; Loew, S.; Yugsi Molina, F. X.

2012-04-01

The Geological Survey of Norway carries out systematic geologic mapping of potentially unstable rock slopes in Norway that can cause a catastrophic failure. As catastrophic failure we describe failures that involve substantial fragmentation of the rock mass during run-out and that impact an area larger than that of a rock fall (shadow angle of ca. 28-32° for rock falls). This includes therefore rock slope failures that lead to secondary effects, such as a displacement wave when impacting a water body or damming of a narrow valley. Our systematic mapping revealed more than 280 rock slopes with significant postglacial deformation, which might represent localities of large future rock slope failures. This large number necessitates prioritization of follow-up activities, such as more detailed investigations, periodic monitoring and permanent monitoring and early-warning. In the past hazard and risk were assessed qualitatively for some sites, however, in order to compare sites so that political and financial decisions can be taken, it was necessary to develop a quantitative hazard and risk classification system. A preliminary classification system was presented and discussed with an expert group of Norwegian and international experts and afterwards adapted following their recommendations. This contribution presents the concept of this final hazard and risk classification that should be used in Norway in the upcoming years. Historical experience and possible future rockslide scenarios in Norway indicate that hazard assessment of large rock slope failures must be scenario-based, because intensity of deformation and present displacement rates, as well as the geological structures activated by the sliding rock mass can vary significantly on a given slope. In addition, for each scenario the run-out of the rock mass has to be evaluated. This includes the secondary effects such as generation of displacement waves or landslide damming of valleys with the potential of later outburst floods. It became obvious that large rock slope failures cannot be evaluated on a slope scale with frequency analyses of historical and prehistorical events only, as multiple rockslides have occurred within one century on a single slope that prior to the recent failures had been inactive for several thousand years. In addition, a systematic analysis on temporal distribution indicates that rockslide activity following deglaciation after the Last Glacial Maximum has been much higher than throughout the Holocene. Therefore the classification system has to be based primarily on the geological conditions on the deforming slope and on the deformation rates and only to a lesser weight on a frequency analyses. Our hazard classification therefore is primarily based on several criteria: 1) Development of the back-scarp, 2) development of the lateral release surfaces, 3) development of the potential basal sliding surface, 4) morphologic expression of the basal sliding surface, 5) kinematic feasibility tests for different displacement mechanisms, 6) landslide displacement rates, 7) change of displacement rates (acceleration), 8) increase of rockfall activity on the unstable rock slope, 9) Presence post-glacial events of similar size along the affected slope and its vicinity. For each of these criteria several conditions are possible to choose from (e.g. different velocity classes for the displacement rate criterion). A score is assigned to each condition and the sum of all scores gives the total susceptibility score. Since many of these observations are somewhat uncertain, the classification system is organized in a decision tree where probabilities can be assigned to each condition. All possibilities in the decision tree are computed and the individual probabilities giving the same total score are summed. Basic statistics show the minimum and maximum total scores of a scenario, as well as the mean and modal value. The final output is a cumulative frequency distribution of the susceptibility scores that can be divided into several classes, which are interpreted as susceptibility classes (very high, high, medium, low, and very low). Today the Norwegian Planning and Building Act uses hazard classes with annual probabilities of impact on buildings producing damages (<1/100, <1/1000, <1/5000 and zero for critical buildings). However, up to now there is not enough scientific knowledge to predict large rock slope failures in these strict classes. Therefore, the susceptibility classes will be matched with the hazard classes from the Norwegian Building Act (e.g. very high susceptibility represents the hazard class with annual probability >1/100). The risk analysis focuses on the potential fatalities of a worst case rock slide scenario and its secondary effects only and is done in consequence classes with a decimal logarithmic scale. However we recommend for all high risk objects that municipalities carry out detailed risk analyses. Finally, the hazard and risk classification system will give recommendations where surveillance in form of continuous 24/7 monitoring systems coupled with early-warning systems (high risk class) or periodic monitoring (medium risk class) should be carried out. These measures are understood as to reduce the risk of life loss due to a rock slope failure close to 0 as population can be evacuated on time if a change of stability situation occurs. The final hazard and risk classification for all potentially unstable rock slopes in Norway, including all data used for its classification will be published within the national landslide database (available on www.skrednett.no).

Photovoltaic building sheathing element with anti-slide features

DOEpatents

Keenihan, James R.; Langmaid, Joseph A.; Lopez, Leonardo C.

2015-09-08

The present invention is premised` upon an assembly that includes at least a photovoltaic building sheathing element capable of being affixed on a building structure, the photovoltaic building sheathing element. The element including a photovoltaic cell assembly, a body portion attached to one or more portions of the photovoltaic cell assembly; and at feast a first and a second connector assembly capable of directly or indirectly electrically connecting the photovoltaic cell assembly to one or more adjoining devices; wherein the body portion includes one or more geometric features adapted to engage a vertically adjoining device before installation.

Modernism in Belgrade: Classification of Modernist Housing Buildings 1919-1980

NASA Astrophysics Data System (ADS)

Dragutinovic, Anica; Pottgiesser, Uta; De Vos, Els; Melenhorst, Michel

2017-10-01

Yugoslavian Modernist Architecture, although part of a larger cultural phenomenon, received hardly any international attention, since there are only a few internationally published studies about it. Nevertheless, Modernist Architecture of the Inter-war Yugoslavia (Kingdom of Yugoslavia), and specially Modernist Architecture of the Post-war Yugoslavia (Socialist Federal Republic of Yugoslavia under the “reign” of Tito), represents the most important architectural heritage of the 20th century in former Yugoslavian countries. Belgrade, as the capital city of both newly founded Yugoslavia(s), experienced an immediate economic, political and cultural expansion after the both wars, as well as a large population increase. The construction of sufficient and appropriate new housing was a major undertaking in both periods (1919-1940 and 1948-1980), however conceived and realized with deeply diverging views. The transition from villas and modest apartment buildings, as main housing typologies in the Inter-war period, to the mass housing of the Post-war period, was not only a result of the different socio-political context of the two Yugoslavia(s), but also the country’s industrialization, modernization and technological development. Through the classification of Modernist housing buildings in Belgrade, this paper will investigate on relations between the transformations of the main housing typologies executed under different socio-political contexts on the one side, and development of building technologies, construction systems and materials applied on those buildings on the other side. The paper wants to shed light on the Yugoslavian Modernist Architecture in order to increase the international awareness on its architectural and heritage values. The aim is an integrated re-evaluation of the buildings, presentation of their current condition and potentials for future (re)use with a specific focus on building envelopes and construction.

Processing of Crawled Urban Imagery for Building Use Classification

NASA Astrophysics Data System (ADS)

Tutzauer, P.; Haala, N.

2017-05-01

Recent years have shown a shift from pure geometric 3D city models to data with semantics. This is induced by new applications (e.g. Virtual/Augmented Reality) and also a requirement for concepts like Smart Cities. However, essential urban semantic data like building use categories is often not available. We present a first step in bridging this gap by proposing a pipeline to use crawled urban imagery and link it with ground truth cadastral data as an input for automatic building use classification. We aim to extract this city-relevant semantic information automatically from Street View (SV) imagery. Convolutional Neural Networks (CNNs) proved to be extremely successful for image interpretation, however, require a huge amount of training data. Main contribution of the paper is the automatic provision of such training datasets by linking semantic information as already available from databases provided from national mapping agencies or city administrations to the corresponding façade images extracted from SV. Finally, we present first investigations with a CNN and an alternative classifier as a proof of concept.

Body Build Perceptions in Male and Female College Students.

ERIC Educational Resources Information Center

Bailey, Roger C.; Hankins, Norman E.

1979-01-01

Results from scores on the Somatotype Rating Scale (SRS) indicated that, while there was close agreement between males and females on the measures, females exhibited more dissatisfaction with their body build and greater congruency between their self-concept and their same-sex stereotype than did males. (Author)

29 CFR 782.6 - Mechanics.

Code of Federal Regulations, 2010 CFR

2010-07-01

.... Hayes Freight Lines, 71 F. Supp. 755 (work of janitor and caretaker, carpentry work, body building.... 846 (body building, construction work, painting and lettering); Hutchinson v. Barry, 50 F. Supp. 292.... Tenn.), 1 Wage Hour Cases 920 (painter), reversed on other grounds 124 F. (2d) 549; Green v. Riss & Co...

Evaluation of gene expression classification studies: factors associated with classification performance.

PubMed

Novianti, Putri W; Roes, Kit C B; Eijkemans, Marinus J C

2014-01-01

Classification methods used in microarray studies for gene expression are diverse in the way they deal with the underlying complexity of the data, as well as in the technique used to build the classification model. The MAQC II study on cancer classification problems has found that performance was affected by factors such as the classification algorithm, cross validation method, number of genes, and gene selection method. In this paper, we study the hypothesis that the disease under study significantly determines which method is optimal, and that additionally sample size, class imbalance, type of medical question (diagnostic, prognostic or treatment response), and microarray platform are potentially influential. A systematic literature review was used to extract the information from 48 published articles on non-cancer microarray classification studies. The impact of the various factors on the reported classification accuracy was analyzed through random-intercept logistic regression. The type of medical question and method of cross validation dominated the explained variation in accuracy among studies, followed by disease category and microarray platform. In total, 42% of the between study variation was explained by all the study specific and problem specific factors that we studied together.

Urban Density Indices Using Mean Shift-Based Upsampled Elevetion Data

NASA Astrophysics Data System (ADS)

Charou, E.; Gyftakis, S.; Bratsolis, E.; Tsenoglou, T.; Papadopoulou, Th. D.; Vassilas, N.

2015-04-01

Urban density is an important factor for several fields, e.g. urban design, planning and land management. Modern remote sensors deliver ample information for the estimation of specific urban land classification classes (2D indicators), and the height of urban land classification objects (3D indicators) within an Area of Interest (AOI). In this research, two of these indicators, Building Coverage Ratio (BCR) and Floor Area Ratio (FAR) are numerically and automatically derived from high-resolution airborne RGB orthophotos and LiDAR data. In the pre-processing step the low resolution elevation data are fused with the high resolution optical data through a mean-shift based discontinuity preserving smoothing algorithm. The outcome is an improved normalized digital surface model (nDSM) is an upsampled elevation data with considerable improvement regarding region filling and "straightness" of elevation discontinuities. In a following step, a Multilayer Feedforward Neural Network (MFNN) is used to classify all pixels of the AOI to building or non-building categories. For the total surface of the block and the buildings we consider the number of their pixels and the surface of the unit pixel. Comparisons of the automatically derived BCR and FAR indicators with manually derived ones shows the applicability and effectiveness of the methodology proposed.

6. VIEW LOOKING NORTHWEST FROM THE IMAGE LEFT TO THE ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

6. VIEW LOOKING NORTHWEST FROM THE IMAGE LEFT TO THE IMAGE RIGHT IS THE CHARCOAL HOUSE, THE RETORT SHED IN THE BACKGROUND, THE MILL ANNEX, THE MACHINE SHOP, AND THE ELECTRIC MOTOR ROOM. THE MILL BUILDING IS IN THE BACKGROUND CENTER RIGHT AND ONE OF THE ORE DELIVERY TRESTLES EXTENDING FROM THE MILL BUILDING TO RIGHT IMAGE EDGE. - Standard Gold Mill, East of Bodie Creek, Northeast of Bodie, Bodie, Mono County, CA

Land use and land cover classification for rural residential areas in China using soft-probability cascading of multifeatures

NASA Astrophysics Data System (ADS)

Zhang, Bin; Liu, Yueyan; Zhang, Zuyu; Shen, Yonglin

2017-10-01

A multifeature soft-probability cascading scheme to solve the problem of land use and land cover (LULC) classification using high-spatial-resolution images to map rural residential areas in China is proposed. The proposed method is used to build midlevel LULC features. Local features are frequently considered as low-level feature descriptors in a midlevel feature learning method. However, spectral and textural features, which are very effective low-level features, are neglected. The acquisition of the dictionary of sparse coding is unsupervised, and this phenomenon reduces the discriminative power of the midlevel feature. Thus, we propose to learn supervised features based on sparse coding, a support vector machine (SVM) classifier, and a conditional random field (CRF) model to utilize the different effective low-level features and improve the discriminability of midlevel feature descriptors. First, three kinds of typical low-level features, namely, dense scale-invariant feature transform, gray-level co-occurrence matrix, and spectral features, are extracted separately. Second, combined with sparse coding and the SVM classifier, the probabilities of the different LULC classes are inferred to build supervised feature descriptors. Finally, the CRF model, which consists of two parts: unary potential and pairwise potential, is employed to construct an LULC classification map. Experimental results show that the proposed classification scheme can achieve impressive performance when the total accuracy reached about 87%.

Detecting Water Bodies in LANDSAT8 Oli Image Using Deep Learning

NASA Astrophysics Data System (ADS)

Jiang, W.; He, G.; Long, T.; Ni, Y.

2018-04-01

Water body identifying is critical to climate change, water resources, ecosystem service and hydrological cycle. Multi-layer perceptron(MLP) is the popular and classic method under deep learning framework to detect target and classify image. Therefore, this study adopts this method to identify the water body of Landsat8. To compare the performance of classification, the maximum likelihood and water index are employed for each study area. The classification results are evaluated from accuracy indices and local comparison. Evaluation result shows that multi-layer perceptron(MLP) can achieve better performance than the other two methods. Moreover, the thin water also can be clearly identified by the multi-layer perceptron. The proposed method has the application potential in mapping global scale surface water with multi-source medium-high resolution satellite data.

Topological classification of the Goryachev integrable case in rigid body dynamics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nikolaenko, S S

2016-01-31

A topological analysis of the Goryachev integrable case in rigid body dynamics is made on the basis of the Fomenko-Zieschang theory. The invariants (marked molecules) which are obtained give a complete description, from the standpoint of Liouville classification, of the systems of Goryachev type on various level sets of the energy. It turns out that on appropriate energy levels the Goryachev case is Liouville equivalent to many classical integrable systems and, in particular, the Joukowski, Clebsch, Sokolov and Kovalevskaya-Yehia cases in rigid body dynamics, as well as to some integrable billiards in plane domains bounded by confocal quadrics -- in othermore » words, the foliations given by the closures of generic solutions of these systems have the same structure. Bibliography: 15 titles.« less

Application of normal mode theory to seismic source and structure problems: Seismic investigations of upper mantle lateral heterogeneity. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Okal, E. A.

1978-01-01

The theory of the normal modes of the earth is investigated and used to build synthetic seismograms in order to solve source and structural problems. A study is made of the physical properties of spheroidal modes leading to a rational classification. Two problems addressed are the observability of deep isotropic seismic sources and the investigation of the physical properties of the earth in the neighborhood of the Core-Mantle boundary, using SH waves diffracted at the core's surface. Data sets of seismic body and surface waves are used in a search for possible deep lateral heterogeneities in the mantle. In both cases, it is found that seismic data do not require structural differences between oceans and continents to extend deeper than 250 km. In general, differences between oceans and continents are found to be on the same order of magnitude as the intrinsic lateral heterogeneity in the oceanic plate brought about by the aging of the oceanic lithosphere.

Data Field Modeling and Spectral-Spatial Feature Fusion for Hyperspectral Data Classification.

PubMed

Liu, Da; Li, Jianxun

2016-12-16

Classification is a significant subject in hyperspectral remote sensing image processing. This study proposes a spectral-spatial feature fusion algorithm for the classification of hyperspectral images (HSI). Unlike existing spectral-spatial classification methods, the influences and interactions of the surroundings on each measured pixel were taken into consideration in this paper. Data field theory was employed as the mathematical realization of the field theory concept in physics, and both the spectral and spatial domains of HSI were considered as data fields. Therefore, the inherent dependency of interacting pixels was modeled. Using data field modeling, spatial and spectral features were transformed into a unified radiation form and further fused into a new feature by using a linear model. In contrast to the current spectral-spatial classification methods, which usually simply stack spectral and spatial features together, the proposed method builds the inner connection between the spectral and spatial features, and explores the hidden information that contributed to classification. Therefore, new information is included for classification. The final classification result was obtained using a random forest (RF) classifier. The proposed method was tested with the University of Pavia and Indian Pines, two well-known standard hyperspectral datasets. The experimental results demonstrate that the proposed method has higher classification accuracies than those obtained by the traditional approaches.

A comparison of the International Classification of Functioning, Disability, and Health to the disability tax credit.

PubMed

Conti-Becker, Angela; Doralp, Samantha; Fayed, Nora; Kean, Crystal; Lencucha, Raphael; Leyshon, Rhysa; Mersich, Jackie; Robbins, Shawn; Doyle, Phillip C

2007-01-01

The Disability Tax Credit (DTC) Certification is an assessment tool used to provide Canadians with disability tax relief The International Classification of Functioning, Disability and Health (ICF) provides a universal framework for defining disability. The purpose of this study was to evaluate the DTC and familiarize occupational therapists with the process of mapping measures to the ICF classification system. Concepts within the DTC were identified and mapped to appropriate ICF codes (Cieza et al., 2005). The DTC was linked to 45 unique ICF codes (16 Body Functions, 19 Activities and Participation, and 8 Environmental Factors). The DTC encompasses various domains of the ICF; however, there is no consideration of Personal Factors, Body Structures, and key aspects of Activities and Participation. Refining the DTC to address these aspects will provide an opportunity for fair and just determinations for those who experience disability.

A Fly-Inspired Mushroom Bodies Model for Sensory-Motor Control Through Sequence and Subsequence Learning.

PubMed

Arena, Paolo; Calí, Marco; Patané, Luca; Portera, Agnese; Strauss, Roland

2016-09-01

Classification and sequence learning are relevant capabilities used by living beings to extract complex information from the environment for behavioral control. The insect world is full of examples where the presentation time of specific stimuli shapes the behavioral response. On the basis of previously developed neural models, inspired by Drosophila melanogaster, a new architecture for classification and sequence learning is here presented under the perspective of the Neural Reuse theory. Classification of relevant input stimuli is performed through resonant neurons, activated by the complex dynamics generated in a lattice of recurrent spiking neurons modeling the insect Mushroom Bodies neuropile. The network devoted to context formation is able to reconstruct the learned sequence and also to trace the subsequences present in the provided input. A sensitivity analysis to parameter variation and noise is reported. Experiments on a roving robot are reported to show the capabilities of the architecture used as a neural controller.

Body Build Stereotypes and Self-Identification in Three Age Groups of Females.

ERIC Educational Resources Information Center

Brenner, David; Hinsdale, Gary

1978-01-01

Body build stereotypes of average-weight and heavy females, ages 6, 15, and 19, were studied through adjective checklists and drawings of endomorphs, ectomorphs, and mesomorphs. Mesomorph drawings were favored and the endomorphs least liked. But heavy subjects rejected for themselves behavioral stereotypes previously applied to the endomorph…

Mapping the Content of the Patient Reported Outcomes Measurement Information System (PROMIS®) Using the International Classification of Functioning, Health and Disability

PubMed Central

Tucker, Carole A; Escorpizo, Reuben; Cieza, Alarcos; Lai, Jin Shei; Stucki, Gerold; Ustun, T. Bedirhan; Kostanjsek, Nenad; Cella, David; Forrest, Christopher B.

2014-01-01

Background The Patient Reported Outcomes Measurement Information System (PROMIS®) is a U.S. National Institutes of Health initiative that has produced self-reported item banks for physical, mental, and social health. Objective To describe the content of PROMIS at the item level using the World Health Organization’s International Classification of Functioning, Disability and Health (ICF). Methods All PROMIS adult items (publicly available as of 2012) were assigned to relevant ICF concepts. The content of the PROMIS adult item banks were then described using the mapped ICF code descriptors. Results The 1006 items in the PROMIS instruments could all be mapped to ICF concepts at the second level of classification, with the exception of 3 items of global or general health that mapped across the first-level classification of ICF activity and participation component (d categories). Individual PROMIS item banks mapped from 1 to 5 separate ICF codes indicating one-to-one, one-to-many and many-to-one mappings between PROMIS item banks and ICF second level classification codes. PROMIS supports measurement of the majority of major concepts in the ICF Body Functions (b) and Activity & Participation (d) components using PROMIS item banks or subsets of PROMIS items that could, with care, be used to develop customized instruments. Given the focus of PROMIS is on measurement of person health outcomes, concepts in body structures (s) and some body functions (b), as well as many ICF environmental factor have minimal coverage in PROMIS. Discussion The PROMIS-ICF mapped items provide a basis for users to evaluate the ICF related content of specific PROMIS instruments, and to select PROMIS instruments in ICF based measurement applications. PMID:24760532

Lewy Body Disease

MedlinePlus

Lewy body disease is one of the most common causes of dementia in the elderly. Dementia is the loss of mental ... to affect normal activities and relationships. Lewy body disease happens when abnormal structures, called Lewy bodies, build ...

Earthquake Building Damage Mapping Based on Feature Analyzing Method from Synthetic Aperture Radar Data

NASA Astrophysics Data System (ADS)

An, L.; Zhang, J.; Gong, L.

2018-04-01

Playing an important role in gathering information of social infrastructure damage, Synthetic Aperture Radar (SAR) remote sensing is a useful tool for monitoring earthquake disasters. With the wide application of this technique, a standard method, comparing post-seismic to pre-seismic data, become common. However, multi-temporal SAR processes, are not always achievable. To develop a post-seismic data only method for building damage detection, is of great importance. In this paper, the authors are now initiating experimental investigation to establish an object-based feature analysing classification method for building damage recognition.

A new tool for post-AGB SED classification

NASA Astrophysics Data System (ADS)

Bendjoya, P.; Suarez, O.; Galluccio, L.; Michel, O.

We present the results of an unsupervised classification method applied on a set of 344 spectral energy distributions (SED) of post-AGB stars extracted from the Torun catalogue of Galactic post-AGB stars. This method aims to find a new unbiased method for post-AGB star classification based on the information contained in the IR region of the SED (fluxes, IR excess, colours). We used the data from IRAS and MSX satellites, and from the 2MASS survey. We applied a classification method based on the construction of the dataset of a minimal spanning tree (MST) with the Prim's algorithm. In order to build this tree, different metrics have been tested on both flux and color indices. Our method is able to classify the set of 344 post-AGB stars in 9 distinct groups according to their SEDs.

Towards Automatic Classification of Wikipedia Content

NASA Astrophysics Data System (ADS)

Szymański, Julian

Wikipedia - the Free Encyclopedia encounters the problem of proper classification of new articles everyday. The process of assignment of articles to categories is performed manually and it is a time consuming task. It requires knowledge about Wikipedia structure, which is beyond typical editor competence, which leads to human-caused mistakes - omitting or wrong assignments of articles to categories. The article presents application of SVM classifier for automatic classification of documents from The Free Encyclopedia. The classifier application has been tested while using two text representations: inter-documents connections (hyperlinks) and word content. The results of the performed experiments evaluated on hand crafted data show that the Wikipedia classification process can be partially automated. The proposed approach can be used for building a decision support system which suggests editors the best categories that fit new content entered to Wikipedia.

ADM. Service Building (TAN603). Elevations of all facades with door ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

ADM. Service Building (TAN-603). Elevations of all facades with door details and detail of kitchen. Section through garage area shows second level of steel decking. Equipment and laboratory furniture schedule. Ralph M. Parsons 902-2-ANP-603-A 44. Date: December 1952. Approved by INEEL Classification Office for public release. INEEL index code no. 033-0603-00-693-106719 - Idaho National Engineering Laboratory, Test Area North, Scoville, Butte County, ID

Research of information classification and strategy intelligence extract algorithm based on military strategy hall

NASA Astrophysics Data System (ADS)

Chen, Lei; Li, Dehua; Yang, Jie

2007-12-01

Constructing virtual international strategy environment needs many kinds of information, such as economy, politic, military, diploma, culture, science, etc. So it is very important to build an information auto-extract, classification, recombination and analysis management system with high efficiency as the foundation and component of military strategy hall. This paper firstly use improved Boost algorithm to classify obtained initial information, then use a strategy intelligence extract algorithm to extract strategy intelligence from initial information to help strategist to analysis information.

Building Combat Strength through Logistics: Translating the New Air Force Logistics Concept of Operations into Action

DTIC Science & Technology

1988-03-31

MARCI 1988 iAm U m WI 4EUnclT CLSIIAION OF THIS PAGE REPORT DOCUMENTATION PAGE is REPORT SECURITY CLASSIFICATION lb. RESTRICTIVE MARKINGS 2.. SECUR ...logistics system of the future more capable of supporting the full spectrumn of war 20 OISTRIaSUTION.’AVAILAeILiTY 0" ABSTRACT 21 ABSTRACT SECURITY ... SECURITY CLASSIFICATION OT: THIS PAGF Unclas ’SCUFUTY Cý= I!FICATION OF THIS PAGE 1,Qwcont.) scenarios. Today’s logistics processes assume wartime

Attributes of the Federal Energy Management Program's Federal Site Building Characteristics Database

DOE Office of Scientific and Technical Information (OSTI.GOV)

Loper, Susan A.; Sandusky, William F.

2010-12-31

Typically, the Federal building stock is referred to as a group of about one-half million buildings throughout the United States. Additional information beyond this level is generally limited to distribution of that total by agency and maybe distribution of the total by state. However, additional characterization of the Federal building stock is required as the Federal sector seeks ways to implement efficiency projects to reduce energy and water use intensity as mandated by legislation and Executive Order. Using a Federal facility database that was assembled for use in a geographic information system tool, additional characterization of the Federal building stockmore » is provided including information regarding the geographical distribution of sites, building counts and percentage of total by agency, distribution of sites and building totals by agency, distribution of building count and floor space by Federal building type classification by agency, and rank ordering of sites, buildings, and floor space by state. A case study is provided regarding how the building stock has changed for the Department of Energy from 2000 through 2008.« less

Transfer Learning for Class Imbalance Problems with Inadequate Data.

PubMed

Al-Stouhi, Samir; Reddy, Chandan K

2016-07-01

A fundamental problem in data mining is to effectively build robust classifiers in the presence of skewed data distributions. Class imbalance classifiers are trained specifically for skewed distribution datasets. Existing methods assume an ample supply of training examples as a fundamental prerequisite for constructing an effective classifier. However, when sufficient data is not readily available, the development of a representative classification algorithm becomes even more difficult due to the unequal distribution between classes. We provide a unified framework that will potentially take advantage of auxiliary data using a transfer learning mechanism and simultaneously build a robust classifier to tackle this imbalance issue in the presence of few training samples in a particular target domain of interest. Transfer learning methods use auxiliary data to augment learning when training examples are not sufficient and in this paper we will develop a method that is optimized to simultaneously augment the training data and induce balance into skewed datasets. We propose a novel boosting based instance-transfer classifier with a label-dependent update mechanism that simultaneously compensates for class imbalance and incorporates samples from an auxiliary domain to improve classification. We provide theoretical and empirical validation of our method and apply to healthcare and text classification applications.

Classification of basic facilities for high-rise residential: A survey from 100 housing scheme in Kajang area

NASA Astrophysics Data System (ADS)

Ani, Adi Irfan Che; Sairi, Ahmad; Tawil, Norngainy Mohd; Wahab, Siti Rashidah Hanum Abd; Razak, Muhd Zulhanif Abd

2016-08-01

High demand for housing and limited land in town area has increasing the provision of high-rise residential scheme. This type of housing has different owners but share the same land lot and common facilities. Thus, maintenance works of the buildings and common facilities must be well organized. The purpose of this paper is to identify and classify basic facilities for high-rise residential building hoping to improve the management of the scheme. The method adopted is a survey on 100 high-rise residential schemes that ranged from affordable housing to high cost housing by using a snowball sampling. The scope of this research is within Kajang area, which is rapidly developed with high-rise housing. The objective of the survey is to list out all facilities in every sample of the schemes. The result confirmed that pre-determined 11 classifications hold true and can provide the realistic classification for high-rise residential scheme. This paper proposed for redefinition of facilities provided to create a better management system and give a clear definition on the type of high-rise residential based on its facilities.

Drug related webpages classification using images and text information based on multi-kernel learning

NASA Astrophysics Data System (ADS)

Hu, Ruiguang; Xiao, Liping; Zheng, Wenjuan

2015-12-01

In this paper, multi-kernel learning(MKL) is used for drug-related webpages classification. First, body text and image-label text are extracted through HTML parsing, and valid images are chosen by the FOCARSS algorithm. Second, text based BOW model is used to generate text representation, and image-based BOW model is used to generate images representation. Last, text and images representation are fused with a few methods. Experimental results demonstrate that the classification accuracy of MKL is higher than those of all other fusion methods in decision level and feature level, and much higher than the accuracy of single-modal classification.

Sleep staging with movement-related signals.

PubMed

Jansen, B H; Shankar, K

1993-05-01

Body movement related signals (i.e., activity due to postural changes and the ballistocardiac effort) were recorded from six normal volunteers using the static-charge-sensitive bed (SCSB). Visual sleep staging was performed on the basis of simultaneously recorded EEG, EMG and EOG signals. A statistical classification technique was used to determine if reliable sleep staging could be performed using only the SCSB signal. A classification rate of between 52% and 75% was obtained for sleep staging in the five conventional sleep stages and the awake state. These rates improved from 78% to 89% for classification between awake, REM and non-REM sleep and from 86% to 98% for awake versus asleep classification.

Neuropathology in movement disorders.

PubMed Central

Gibb, W R

1989-01-01

This review concentrates on the definition and classification of degenerative movement disorders in which Parkinsonian symptoms are often prominent. The pathological spectrum and clinical manifestations of Lewy body disease are described, and associations with Alzheimer's disease and motor neuron disease are explored. A classification of pallidonigral degenerations is based on clinical features, distribution of pathology, and morphological abnormalities; some of these patients have mild nigral degeneration and no Parkinsonian features. Many other juvenile and familial Parkinsonian cases are not included among the pallidonigral degenerations. Most of these latter syndromes have been organised into preliminary groups, in particular, autosomal dominant dystonia-Parkinson syndrome, juvenile Parkinsonian disorder and autosomal dominant Lewy body disease. Images PMID:2547027

The Comprehensive AOCMF Classification System: Mandible Fractures- Level 2 Tutorial

PubMed Central

Cornelius, Carl-Peter; Audigé, Laurent; Kunz, Christoph; Rudderman, Randal; Buitrago-Téllez, Carlos H.; Frodel, John; Prein, Joachim

2014-01-01

This tutorial outlines the details of the AOCMF image-based classification system for fractures of the mandible at the precision level 2 allowing description of their topographical distribution. A short introduction about the anatomy is made. Mandibular fractures are classified by the anatomic regions involved. For this purpose, the mandible is delineated into an array of nine regions identified by letters: the symphysis/parasymphysis region anteriorly, two body regions on each lateral side, combined angle and ascending ramus regions, and finally the condylar and coronoid processes. A precise definition of the demarcation lines between these regions is given for the unambiguous allocation of fractures. Four transition zones allow an accurate topographic assignment if fractures end up in or run across the borders of anatomic regions. These zones are defined between angle/ramus and body, and between body and symphysis/parasymphysis. A fracture is classified as “confined” as long as it is located within a region, in contrast to a fracture being “nonconfined” when it extents to an adjoining region. Illustrations and case examples of mandible fractures are presented to become familiar with the classification procedure in daily routine. PMID:25489388

Relations of meeting national public health recommendations for muscular strengthening activities with strength, body composition, and obesity: the Women's Injury Study.

PubMed

Trudelle-Jackson, Elaine; Jackson, Allen W; Morrow, James R

2011-10-01

We examined the relations of meeting or not meeting the 2008 Physical Activity Guidelines for Americans recommendations for muscular strengthening activities with percentage of body fat, body mass index (BMI; defined as weight in kilograms divided by height in meters, squared), muscular strength, and obesity classification in women. We analyzed data on 918 women aged 20 to 83 years in the Women's Injury Study from 2007 to 2009. A baseline orthopedic examination included measurement of height, body weight, skinfolds, and muscle strength. Women who met muscle strengthening activity recommendations had significantly lower BMI and percentage of body fat and higher muscle strength. Women not meeting those recommendations were more likely to be obese (BMI ≥ 30) compared with women who met the recommendations after we adjusted for age, race, and aerobic physical activity (odds ratio = 2.28; 95% confidence interval = 1.61, 3.23). There was a small but significant positive association between meeting muscle strengthening activity recommendations and muscular strength, a moderate inverse association with body fat percentage, and a strong inverse association with obesity classification, providing preliminary support for the muscle strengthening activity recommendation for women.

Open Dataset for the Automatic Recognition of Sedentary Behaviors.

PubMed

Possos, William; Cruz, Robinson; Cerón, Jesús D; López, Diego M; Sierra-Torres, Carlos H

2017-01-01

Sedentarism is associated with the development of noncommunicable diseases (NCD) such as cardiovascular diseases (CVD), type 2 diabetes, and cancer. Therefore, the identification of specific sedentary behaviors (TV viewing, sitting at work, driving, relaxing, etc.) is especially relevant for planning personalized prevention programs. To build and evaluate a public a dataset for the automatic recognition (classification) of sedentary behaviors. The dataset included data from 30 subjects, who performed 23 sedentary behaviors while wearing a commercial wearable on the wrist, a smartphone on the hip and another in the thigh. Bluetooth Low Energy (BLE) beacons were used in order to improve the automatic classification of different sedentary behaviors. The study also compared six well know data mining classification techniques in order to identify the more precise method of solving the classification problem of the 23 defined behaviors. A better classification accuracy was obtained using the Random Forest algorithm and when data were collected from the phone on the hip. Furthermore, the use of beacons as a reference for obtaining the symbolic location of the individual improved the precision of the classification.

The United Nations Framework Classification for World Petroleum Resources

USGS Publications Warehouse

Ahlbrandt, T.S.; Blystad, P.; Young, E.D.; Slavov, S.; Heiberg, S.

2003-01-01

The United Nations has developed an international framework classification for solid fuels and minerals (UNFC). This is now being extended to petroleum by building on the joint classification of the Society of Petroleum Engineers (SPE), the World Petroleum Congresses (WPC) and the American Association of Petroleum Geologists (AAPG). The UNFC is a 3-dimansional classification. This: Is necessary in order to migrate accounts of resource quantities that are developed on one or two of the axes to the common basis; Provides for more precise reporting and analysis. This is particularly useful in analyses of contingent resources. The characteristics of the SPE/WPC/AAPG classification has been preserved and enhanced to facilitate improved international and national petroleum resource management, corporate business process management and financial reporting. A UN intergovernmental committee responsible for extending the UNFC to extractive energy resources (coal, petroleum and uranium) will meet in Geneva on October 30th and 31st to review experiences gained and comments received during 2003. A recommended classification will then be delivered for consideration to the United Nations through the Committee on Sustainable Energy of the Economic Commission for Europe (UN ECE).

Joint Feature Selection and Classification for Multilabel Learning.

PubMed

Huang, Jun; Li, Guorong; Huang, Qingming; Wu, Xindong

2018-03-01

Multilabel learning deals with examples having multiple class labels simultaneously. It has been applied to a variety of applications, such as text categorization and image annotation. A large number of algorithms have been proposed for multilabel learning, most of which concentrate on multilabel classification problems and only a few of them are feature selection algorithms. Current multilabel classification models are mainly built on a single data representation composed of all the features which are shared by all the class labels. Since each class label might be decided by some specific features of its own, and the problems of classification and feature selection are often addressed independently, in this paper, we propose a novel method which can perform joint feature selection and classification for multilabel learning, named JFSC. Different from many existing methods, JFSC learns both shared features and label-specific features by considering pairwise label correlations, and builds the multilabel classifier on the learned low-dimensional data representations simultaneously. A comparative study with state-of-the-art approaches manifests a competitive performance of our proposed method both in classification and feature selection for multilabel learning.

Feasibility of Multispectral Airborne Laser Scanning for Land Cover Classification, Road Mapping and Map Updating

NASA Astrophysics Data System (ADS)

Matikainen, L.; Karila, K.; Hyyppä, J.; Puttonen, E.; Litkey, P.; Ahokas, E.

2017-10-01

This article summarises our first results and experiences on the use of multispectral airborne laser scanner (ALS) data. Optech Titan multispectral ALS data over a large suburban area in Finland were acquired on three different dates in 2015-2016. We investigated the feasibility of the data from the first date for land cover classification and road mapping. Object-based analyses with segmentation and random forests classification were used. The potential of the data for change detection of buildings and roads was also demonstrated. The overall accuracy of land cover classification results with six classes was 96 % compared with validation points. The data also showed high potential for road detection, road surface classification and change detection. The multispectral intensity information appeared to be very important for automated classifications. Compared to passive aerial images, the intensity images have interesting advantages, such as the lack of shadows. Currently, we focus on analyses and applications with the multitemporal multispectral data. Important questions include, for example, the potential and challenges of the multitemporal data for change detection.

Classification of tumor based on magnetic resonance (MR) brain images using wavelet energy feature and neuro-fuzzy model

NASA Astrophysics Data System (ADS)

Damayanti, A.; Werdiningsih, I.

2018-03-01

The brain is the organ that coordinates all the activities that occur in our bodies. Small abnormalities in the brain will affect body activity. Tumor of the brain is a mass formed a result of cell growth not normal and unbridled in the brain. MRI is a non-invasive medical test that is useful for doctors in diagnosing and treating medical conditions. The process of classification of brain tumor can provide the right decision and correct treatment and right on the process of treatment of brain tumor. In this study, the classification process performed to determine the type of brain tumor disease, namely Alzheimer’s, Glioma, Carcinoma and normal, using energy coefficient and ANFIS. Process stages in the classification of images of MR brain are the extraction of a feature, reduction of a feature, and process of classification. The result of feature extraction is a vector approximation of each wavelet decomposition level. The feature reduction is a process of reducing the feature by using the energy coefficients of the vector approximation. The feature reduction result for energy coefficient of 100 per feature is 1 x 52 pixels. This vector will be the input on the classification using ANFIS with Fuzzy C-Means and FLVQ clustering process and LM back-propagation. Percentage of success rate of MR brain images recognition using ANFIS-FLVQ, ANFIS, and LM back-propagation was obtained at 100%.

Ultrasonic Apparatus and Method to Assess Compartment Syndrome

NASA Technical Reports Server (NTRS)

Yost, William T. (Inventor); Ueno, Toshiaki (Inventor); Hargens, Alan R. (Inventor)

2009-01-01

A process and apparatus for measuring pressure buildup in a body compartment that encases muscular tissue. The method includes assessing the body compartment configuration and identifying the effect of pulsatible components on compartment dimensions and muscle tissue characteristics. This process is used in preventing tissue necrosis, and in decisions of whether to perform surgery on the body compartment for prevention of Compartment Syndrome. An apparatus is used for measuring pressure build-up in the body compartment having components for imparting ultrasonic waves such as a transducer, placing the transducer to impart the ultrasonic waves, capturing the imparted ultrasonic waves, mathematically manipulating the captured ultrasonic waves and categorizing pressure build-up in the body compartment from the mathematical manipulations.

Comparative study of building footprint estimation methods from LiDAR point clouds

NASA Astrophysics Data System (ADS)

Rozas, E.; Rivera, F. F.; Cabaleiro, J. C.; Pena, T. F.; Vilariño, D. L.

2017-10-01

Building area calculation from LiDAR points is still a difficult task with no clear solution. Their different characteristics, such as shape or size, have made the process too complex to automate. However, several algorithms and techniques have been used in order to obtain an approximated hull. 3D-building reconstruction or urban planning are examples of important applications that benefit of accurate building footprint estimations. In this paper, we have carried out a study of accuracy in the estimation of the footprint of buildings from LiDAR points. The analysis focuses on the processing steps following the object recognition and classification, assuming that labeling of building points have been previously performed. Then, we perform an in-depth analysis of the influence of the point density over the accuracy of the building area estimation. In addition, a set of buildings with different size and shape were manually classified, in such a way that they can be used as benchmark.

21 CFR 862.2730 - Osmometer for clinical use.

Code of Federal Regulations, 2013 CFR

2013-04-01

...) MEDICAL DEVICES CLINICAL CHEMISTRY AND CLINICAL TOXICOLOGY DEVICES Clinical Laboratory Instruments § 862... to measure the osmotic pressure of body fluids. Osmotic pressure is the pressure required to prevent... device are used in the diagnosis and treatment of body fluid disorders. (b) Classification. Class I...

21 CFR 862.2730 - Osmometer for clinical use.

Code of Federal Regulations, 2011 CFR

2011-04-01

...) MEDICAL DEVICES CLINICAL CHEMISTRY AND CLINICAL TOXICOLOGY DEVICES Clinical Laboratory Instruments § 862... to measure the osmotic pressure of body fluids. Osmotic pressure is the pressure required to prevent... device are used in the diagnosis and treatment of body fluid disorders. (b) Classification. Class I...

21 CFR 862.2730 - Osmometer for clinical use.

Code of Federal Regulations, 2012 CFR

2012-04-01

...) MEDICAL DEVICES CLINICAL CHEMISTRY AND CLINICAL TOXICOLOGY DEVICES Clinical Laboratory Instruments § 862... to measure the osmotic pressure of body fluids. Osmotic pressure is the pressure required to prevent... device are used in the diagnosis and treatment of body fluid disorders. (b) Classification. Class I...

21 CFR 862.2730 - Osmometer for clinical use.

Code of Federal Regulations, 2014 CFR

2014-04-01

...) MEDICAL DEVICES CLINICAL CHEMISTRY AND CLINICAL TOXICOLOGY DEVICES Clinical Laboratory Instruments § 862... to measure the osmotic pressure of body fluids. Osmotic pressure is the pressure required to prevent... device are used in the diagnosis and treatment of body fluid disorders. (b) Classification. Class I...

Detection and classification of human body odor using an electronic nose.

PubMed

Wongchoosuk, Chatchawal; Lutz, Mario; Kerdcharoen, Teerakiat

2009-01-01

An electronic nose (E-nose) has been designed and equipped with software that can detect and classify human armpit body odor. An array of metal oxide sensors was used for detecting volatile organic compounds. The measurement circuit employs a voltage divider resistor to measure the sensitivity of each sensor. This E-nose was controlled by in-house developed software through a portable USB data acquisition card with a principle component analysis (PCA) algorithm implemented for pattern recognition and classification. Because gas sensor sensitivity in the detection of armpit odor samples is affected by humidity, we propose a new method and algorithms combining hardware/software for the correction of the humidity noise. After the humidity correction, the E-nose showed the capability of detecting human body odor and distinguishing the body odors from two persons in a relative manner. The E-nose is still able to recognize people, even after application of deodorant. In conclusion, this is the first report of the application of an E-nose for armpit odor recognition.

Detection and Classification of Human Body Odor Using an Electronic Nose

PubMed Central

Wongchoosuk, Chatchawal; Lutz, Mario; Kerdcharoen, Teerakiat

2009-01-01

An electronic nose (E-nose) has been designed and equipped with software that can detect and classify human armpit body odor. An array of metal oxide sensors was used for detecting volatile organic compounds. The measurement circuit employs a voltage divider resistor to measure the sensitivity of each sensor. This E-nose was controlled by in-house developed software through a portable USB data acquisition card with a principle component analysis (PCA) algorithm implemented for pattern recognition and classification. Because gas sensor sensitivity in the detection of armpit odor samples is affected by humidity, we propose a new method and algorithms combining hardware/software for the correction of the humidity noise. After the humidity correction, the E-nose showed the capability of detecting human body odor and distinguishing the body odors from two persons in a relative manner. The E-nose is still able to recognize people, even after application of deodorant. In conclusion, this is the first report of the application of an E-nose for armpit odor recognition. PMID:22399995

A further step toward an optimal ensemble of classifiers for peptide classification, a case study: HIV protease.

PubMed

Nanni, Loris; Lumini, Alessandra

2009-01-01

The focuses of this work are: to propose a novel method for building an ensemble of classifiers for peptide classification based on substitution matrices; to show the importance to select a proper set of the parameters of the classifiers that build the ensemble of learning systems. The HIV-1 protease cleavage site prediction problem is here studied. The results obtained by a blind testing protocol are reported, the comparison with other state-of-the-art approaches, based on ensemble of classifiers, allows to quantify the performance improvement obtained by the systems proposed in this paper. The simulation based on experimentally determined protease cleavage data has demonstrated the success of these new ensemble algorithms. Particularly interesting it is to note that also if the HIV-1 protease cleavage site prediction problem is considered linearly separable we obtain the best performance using an ensemble of non-linear classifiers.

Behavior identification based on geotagged photo data set.

PubMed

Liu, Guo-qi; Zhang, Yi-jia; Fu, Ying-mao; Liu, Ying

2014-01-01

The popularity of mobile devices has produced a set of image data with geographic information, time information, and text description information, which is called geotagged photo data set. The division of this kind of data by its behavior and the location not only can identify the user's important location and daily behavior, but also helps users to sort the huge image data. This paper proposes a method to build an index based on multiple classification result, which can divide the data set multiple times and distribute labels to the data to build index according to the estimated probability of classification results in order to accomplish the identification of users' important location and daily behaviors. This paper collects 1400 discrete sets of data as experimental data to verify the method proposed in this paper. The result of the experiment shows that the index and actual tagging results have a high inosculation.

A Parallel Adaboost-Backpropagation Neural Network for Massive Image Dataset Classification

NASA Astrophysics Data System (ADS)

Cao, Jianfang; Chen, Lichao; Wang, Min; Shi, Hao; Tian, Yun

2016-12-01

Image classification uses computers to simulate human understanding and cognition of images by automatically categorizing images. This study proposes a faster image classification approach that parallelizes the traditional Adaboost-Backpropagation (BP) neural network using the MapReduce parallel programming model. First, we construct a strong classifier by assembling the outputs of 15 BP neural networks (which are individually regarded as weak classifiers) based on the Adaboost algorithm. Second, we design Map and Reduce tasks for both the parallel Adaboost-BP neural network and the feature extraction algorithm. Finally, we establish an automated classification model by building a Hadoop cluster. We use the Pascal VOC2007 and Caltech256 datasets to train and test the classification model. The results are superior to those obtained using traditional Adaboost-BP neural network or parallel BP neural network approaches. Our approach increased the average classification accuracy rate by approximately 14.5% and 26.0% compared to the traditional Adaboost-BP neural network and parallel BP neural network, respectively. Furthermore, the proposed approach requires less computation time and scales very well as evaluated by speedup, sizeup and scaleup. The proposed approach may provide a foundation for automated large-scale image classification and demonstrates practical value.

A Parallel Adaboost-Backpropagation Neural Network for Massive Image Dataset Classification.

PubMed

Cao, Jianfang; Chen, Lichao; Wang, Min; Shi, Hao; Tian, Yun

2016-12-01

Image classification uses computers to simulate human understanding and cognition of images by automatically categorizing images. This study proposes a faster image classification approach that parallelizes the traditional Adaboost-Backpropagation (BP) neural network using the MapReduce parallel programming model. First, we construct a strong classifier by assembling the outputs of 15 BP neural networks (which are individually regarded as weak classifiers) based on the Adaboost algorithm. Second, we design Map and Reduce tasks for both the parallel Adaboost-BP neural network and the feature extraction algorithm. Finally, we establish an automated classification model by building a Hadoop cluster. We use the Pascal VOC2007 and Caltech256 datasets to train and test the classification model. The results are superior to those obtained using traditional Adaboost-BP neural network or parallel BP neural network approaches. Our approach increased the average classification accuracy rate by approximately 14.5% and 26.0% compared to the traditional Adaboost-BP neural network and parallel BP neural network, respectively. Furthermore, the proposed approach requires less computation time and scales very well as evaluated by speedup, sizeup and scaleup. The proposed approach may provide a foundation for automated large-scale image classification and demonstrates practical value.

A Parallel Adaboost-Backpropagation Neural Network for Massive Image Dataset Classification

PubMed Central

Cao, Jianfang; Chen, Lichao; Wang, Min; Shi, Hao; Tian, Yun

2016-01-01

Image classification uses computers to simulate human understanding and cognition of images by automatically categorizing images. This study proposes a faster image classification approach that parallelizes the traditional Adaboost-Backpropagation (BP) neural network using the MapReduce parallel programming model. First, we construct a strong classifier by assembling the outputs of 15 BP neural networks (which are individually regarded as weak classifiers) based on the Adaboost algorithm. Second, we design Map and Reduce tasks for both the parallel Adaboost-BP neural network and the feature extraction algorithm. Finally, we establish an automated classification model by building a Hadoop cluster. We use the Pascal VOC2007 and Caltech256 datasets to train and test the classification model. The results are superior to those obtained using traditional Adaboost-BP neural network or parallel BP neural network approaches. Our approach increased the average classification accuracy rate by approximately 14.5% and 26.0% compared to the traditional Adaboost-BP neural network and parallel BP neural network, respectively. Furthermore, the proposed approach requires less computation time and scales very well as evaluated by speedup, sizeup and scaleup. The proposed approach may provide a foundation for automated large-scale image classification and demonstrates practical value. PMID:27905520

Classification and Sequential Pattern Analysis for Improving Managerial Efficiency and Providing Better Medical Service in Public Healthcare Centers

PubMed Central

Chung, Sukhoon; Rhee, Hyunsill; Suh, Yongmoo

2010-01-01

Objectives This study sought to find answers to the following questions: 1) Can we predict whether a patient will revisit a healthcare center? 2) Can we anticipate diseases of patients who revisit the center? Methods For the first question, we applied 5 classification algorithms (decision tree, artificial neural network, logistic regression, Bayesian networks, and Naïve Bayes) and the stacking-bagging method for building classification models. To solve the second question, we performed sequential pattern analysis. Results We determined: 1) In general, the most influential variables which impact whether a patient of a public healthcare center will revisit it or not are personal burden, insurance bill, period of prescription, age, systolic pressure, name of disease, and postal code. 2) The best plain classification model is dependent on the dataset. 3) Based on average of classification accuracy, the proposed stacking-bagging method outperformed all traditional classification models and our sequential pattern analysis revealed 16 sequential patterns. Conclusions Classification models and sequential patterns can help public healthcare centers plan and implement healthcare service programs and businesses that are more appropriate to local residents, encouraging them to revisit public health centers. PMID:21818426

Spectral-spatial classification of hyperspectral imagery with cooperative game

NASA Astrophysics Data System (ADS)

Zhao, Ji; Zhong, Yanfei; Jia, Tianyi; Wang, Xinyu; Xu, Yao; Shu, Hong; Zhang, Liangpei

2018-01-01

Spectral-spatial classification is known to be an effective way to improve classification performance by integrating spectral information and spatial cues for hyperspectral imagery. In this paper, a game-theoretic spectral-spatial classification algorithm (GTA) using a conditional random field (CRF) model is presented, in which CRF is used to model the image considering the spatial contextual information, and a cooperative game is designed to obtain the labels. The algorithm establishes a one-to-one correspondence between image classification and game theory. The pixels of the image are considered as the players, and the labels are considered as the strategies in a game. Similar to the idea of soft classification, the uncertainty is considered to build the expected energy model in the first step. The local expected energy can be quickly calculated, based on a mixed strategy for the pixels, to establish the foundation for a cooperative game. Coalitions can then be formed by the designed merge rule based on the local expected energy, so that a majority game can be performed to make a coalition decision to obtain the label of each pixel. The experimental results on three hyperspectral data sets demonstrate the effectiveness of the proposed classification algorithm.

Cascaded deep decision networks for classification of endoscopic images

NASA Astrophysics Data System (ADS)

Murthy, Venkatesh N.; Singh, Vivek; Sun, Shanhui; Bhattacharya, Subhabrata; Chen, Terrence; Comaniciu, Dorin

2017-02-01

Both traditional and wireless capsule endoscopes can generate tens of thousands of images for each patient. It is desirable to have the majority of irrelevant images filtered out by automatic algorithms during an offline review process or to have automatic indication for highly suspicious areas during an online guidance. This also applies to the newly invented endomicroscopy, where online indication of tumor classification plays a significant role. Image classification is a standard pattern recognition problem and is well studied in the literature. However, performance on the challenging endoscopic images still has room for improvement. In this paper, we present a novel Cascaded Deep Decision Network (CDDN) to improve image classification performance over standard Deep neural network based methods. During the learning phase, CDDN automatically builds a network which discards samples that are classified with high confidence scores by a previously trained network and concentrates only on the challenging samples which would be handled by the subsequent expert shallow networks. We validate CDDN using two different types of endoscopic imaging, which includes a polyp classification dataset and a tumor classification dataset. From both datasets we show that CDDN can outperform other methods by about 10%. In addition, CDDN can also be applied to other image classification problems.

Application of classification tree and logistic regression for the management and health intervention plans in a community-based study.

PubMed

Teng, Ju-Hsi; Lin, Kuan-Chia; Ho, Bin-Shenq

2007-10-01

A community-based aboriginal study was conducted and analysed to explore the application of classification tree and logistic regression. A total of 1066 aboriginal residents in Yilan County were screened during 2003-2004. The independent variables include demographic characteristics, physical examinations, geographic location, health behaviours, dietary habits and family hereditary diseases history. Risk factors of cardiovascular diseases were selected as the dependent variables in further analysis. The completion rate for heath interview is 88.9%. The classification tree results find that if body mass index is higher than 25.72 kg m(-2) and the age is above 51 years, the predicted probability for number of cardiovascular risk factors > or =3 is 73.6% and the population is 322. If body mass index is higher than 26.35 kg m(-2) and geographical latitude of the village is lower than 24 degrees 22.8', the predicted probability for number of cardiovascular risk factors > or =4 is 60.8% and the population is 74. As the logistic regression results indicate that body mass index, drinking habit and menopause are the top three significant independent variables. The classification tree model specifically shows the discrimination paths and interactions between the risk groups. The logistic regression model presents and analyses the statistical independent factors of cardiovascular risks. Applying both models to specific situations will provide a different angle for the design and management of future health intervention plans after community-based study.

A web-based land cover classification system based on ontology model of different classification systems

NASA Astrophysics Data System (ADS)

Lin, Y.; Chen, X.

2016-12-01

Land cover classification systems used in remote sensing image data have been developed to meet the needs for depicting land covers in scientific investigations and policy decisions. However, accuracy assessments of a spate of data sets demonstrate that compared with the real physiognomy, each of the thematic map of specific land cover classification system contains some unavoidable flaws and unintended deviation. This work proposes a web-based land cover classification system, an integrated prototype, based on an ontology model of various classification systems, each of which is assigned the same weight in the final determination of land cover type. Ontology, a formal explication of specific concepts and relations, is employed in this prototype to build up the connections among different systems to resolve the naming conflicts. The process is initialized by measuring semantic similarity between terminologies in the systems and the search key to produce certain set of satisfied classifications, and carries on through searching the predefined relations in concepts of all classification systems to generate classification maps with user-specified land cover type highlighted, based on probability calculated by votes from data sets with different classification system adopted. The present system is verified and validated by comparing the classification results with those most common systems. Due to full consideration and meaningful expression of each classification system using ontology and the convenience that the web brings with itself, this system, as a preliminary model, proposes a flexible and extensible architecture for classification system integration and data fusion, thereby providing a strong foundation for the future work.

[Severity classification of chronic obstructive pulmonary disease based on deep learning].

PubMed

Ying, Jun; Yang, Ceyuan; Li, Quanzheng; Xue, Wanguo; Li, Tanshi; Cao, Wenzhe

2017-12-01

In this paper, a deep learning method has been raised to build an automatic classification algorithm of severity of chronic obstructive pulmonary disease. Large sample clinical data as input feature were analyzed for their weights in classification. Through feature selection, model training, parameter optimization and model testing, a classification prediction model based on deep belief network was built to predict severity classification criteria raised by the Global Initiative for Chronic Obstructive Lung Disease (GOLD). We get accuracy over 90% in prediction for two different standardized versions of severity criteria raised in 2007 and 2011 respectively. Moreover, we also got the contribution ranking of different input features through analyzing the model coefficient matrix and confirmed that there was a certain degree of agreement between the more contributive input features and the clinical diagnostic knowledge. The validity of the deep belief network model was proved by this result. This study provides an effective solution for the application of deep learning method in automatic diagnostic decision making.

One input-class and two input-class classifications for differentiating olive oil from other edible vegetable oils by use of the normal-phase liquid chromatography fingerprint of the methyl-transesterified fraction.

PubMed

Jiménez-Carvelo, Ana M; Pérez-Castaño, Estefanía; González-Casado, Antonio; Cuadros-Rodríguez, Luis

2017-04-15

A new method for differentiation of olive oil (independently of the quality category) from other vegetable oils (canola, safflower, corn, peanut, seeds, grapeseed, palm, linseed, sesame and soybean) has been developed. The analytical procedure for chromatographic fingerprinting of the methyl-transesterified fraction of each vegetable oil, using normal-phase liquid chromatography, is described and the chemometric strategies applied and discussed. Some chemometric methods, such as k-nearest neighbours (kNN), partial least squared-discriminant analysis (PLS-DA), support vector machine classification analysis (SVM-C), and soft independent modelling of class analogies (SIMCA), were applied to build classification models. Performance of the classification was evaluated and ranked using several classification quality metrics. The discriminant analysis, based on the use of one input-class, (plus a dummy class) was applied for the first time in this study. Copyright © 2016 Elsevier Ltd. All rights reserved.

Building the United States National Vegetation Classification

USGS Publications Warehouse

Franklin, S.B.; Faber-Langendoen, D.; Jennings, M.; Keeler-Wolf, T.; Loucks, O.; Peet, R.; Roberts, D.; McKerrow, A.

2012-01-01

The Federal Geographic Data Committee (FGDC) Vegetation Subcommittee, the Ecological Society of America Panel on Vegetation Classification, and NatureServe have worked together to develop the United States National Vegetation Classification (USNVC). The current standard was accepted in 2008 and fosters consistency across Federal agencies and non-federal partners for the description of each vegetation concept and its hierarchical classification. The USNVC is structured as a dynamic standard, where changes to types at any level may be proposed at any time as new information comes in. But, because much information already exists from previous work, the NVC partners first established methods for screening existing types to determine their acceptability with respect to the 2008 standard. Current efforts include a screening process to assign confidence to Association and Group level descriptions, and a review of the upper three levels of the classification. For the upper levels especially, the expectation is that the review process includes international scientists. Immediate future efforts include the review of remaining levels and the development of a proposal review process.

The 7th lung cancer TNM classification and staging system: Review of the changes and implications.

PubMed

Mirsadraee, Saeed; Oswal, Dilip; Alizadeh, Yalda; Caulo, Andrea; van Beek, Edwin

2012-04-28

Lung cancer is the most common cause of death from cancer in males, accounting for more than 1.4 million deaths in 2008. It is a growing concern in China, Asia and Africa as well. Accurate staging of the disease is an important part of the management as it provides estimation of patient's prognosis and identifies treatment sterategies. It also helps to build a database for future staging projects. A major revision of lung cancer staging has been announced with effect from January 2010. The new classification is based on a larger surgical and non-surgical cohort of patients, and thus more accurate in terms of outcome prediction compared to the previous classification. There are several original papers regarding this new classification which give comprehensive description of the methodology, the changes in the staging and the statistical analysis. This overview is a simplified description of the changes in the new classification and their potential impact on patients' treatment and prognosis.

Application of partial least squares near-infrared spectral classification in diabetic identification

NASA Astrophysics Data System (ADS)

Yan, Wen-juan; Yang, Ming; He, Guo-quan; Qin, Lin; Li, Gang

2014-11-01

In order to identify the diabetic patients by using tongue near-infrared (NIR) spectrum - a spectral classification model of the NIR reflectivity of the tongue tip is proposed, based on the partial least square (PLS) method. 39sample data of tongue tip's NIR spectra are harvested from healthy people and diabetic patients , respectively. After pretreatment of the reflectivity, the spectral data are set as the independent variable matrix, and information of classification as the dependent variables matrix, Samples were divided into two groups - i.e. 53 samples as calibration set and 25 as prediction set - then the PLS is used to build the classification model The constructed modelfrom the 53 samples has the correlation of 0.9614 and the root mean square error of cross-validation (RMSECV) of 0.1387.The predictions for the 25 samples have the correlation of 0.9146 and the RMSECV of 0.2122.The experimental result shows that the PLS method can achieve good classification on features of healthy people and diabetic patients.

Comparison of World Health Organization and Asia-Pacific body mass index classifications in COPD patients.

PubMed

Lim, Jeong Uk; Lee, Jae Ha; Kim, Ju Sang; Hwang, Yong Il; Kim, Tae-Hyung; Lim, Seong Yong; Yoo, Kwang Ha; Jung, Ki-Suck; Kim, Young Kyoon; Rhee, Chin Kook

2017-01-01

A low body mass index (BMI) is associated with increased mortality and low health-related quality of life in patients with COPD. The Asia-Pacific classification of BMI has a lower cutoff for overweight and obese categories compared to the World Health Organization (WHO) classification. The present study assessed patients with COPD among different BMI categories according to two BMI classification systems: WHO and Asia-Pacific. Patients with COPD aged 40 years or older from the Korean COPD Subtype Study cohort were selected for evaluation. We enrolled 1,462 patients. Medical history including age, sex, St George's Respiratory Questionnaire (SGRQ-C), the modified Medical Research Council (mMRC) dyspnea scale, and post-bronchodilator forced expiratory volume in 1 second (FEV 1 ) were evaluated. Patients were categorized into different BMI groups according to the two BMI classification systems. FEV 1 and the diffusing capacity of the lung for carbon monoxide (DLCO) percentage revealed an inverse "U"-shaped pattern as the BMI groups changed from underweight to obese when WHO cutoffs were applied. When Asia-Pacific cutoffs were applied, FEV 1 and DLCO (%) exhibited a linearly ascending relationship as the BMI increased, and the percentage of patients in the overweight and obese groups linearly decreased with increasing severity of the Global Initiative for Chronic Obstructive Lung Disease criteria. From the underweight to the overweight groups, SGRQ-C and mMRC had a decreasing relationship in both the WHO and Asia-Pacific classifications. The prevalence of comorbidities in the different BMI groups showed similar trends in both BMI classifications systems. The present study demonstrated that patients with COPD who have a high BMI have better pulmonary function and health-related quality of life and reduced dyspnea symptoms. Furthermore, the Asia-Pacific BMI classification more appropriately reflects the correlation of obesity and disease manifestation in Asian COPD patients than the WHO classification.

Stratification of a cityscape using census and land use variables for inventory of building materials

USGS Publications Warehouse

Rosenfield, G.H.; Fitzpatrick-Lins, K.; Johnson, T.L.

1987-01-01

A cityscape (or any landscape) can be stratified into environmental units using multiple variables of information. For the purposes of sampling building materials, census and land use variables were used to identify similar strata. In the Metropolitan Statistical Area of a cityscape, the census tract is the smallest unit for which census data are summarized and digitized boundaries are available. For purposes of this analysis, census data on total population, total number of housing units, and number of singleunit dwellings were aggregated into variables of persons per square kilometer and proportion of housing units in single-unit dwellings. The level 2 categories of the U.S. Geological Survey's land use and land cover data base were aggregated into variables of proportion of residential land with buildings, proportion of nonresidential land with buildings, and proportion of open land. The cityscape was stratified, from these variables, into environmental strata of Urban Central Business District, Urban Livelihood Industrial Commercial, Urban Multi-Family Residential, Urban Single Family Residential, Non-Urban Suburbanizing, and Non-Urban Rural. The New England region was chosen as a region with commonality of building materials, and a procedure developed for trial classification of census tracts into one of the strata. Final stratification was performed by discriminant analysis using the trial classification and prior probabilities as weights. The procedure was applied to several cities, and the results analyzed by correlation analysis from a field sample of building materials. The methodology developed for stratification of a cityscape using multiple variables has application to many other types of environmental studies, including forest inventory, hydrologic unit management, waste disposal, transportation studies, and other urban studies. Multivariate analysis techniques have recently been used for urban stratification in England. ?? 1987 Annals of Regional Science.

Building Loads Analysis and System Thermodynamics (BLAST) Program Users Manual. Volume One. Supplement (Version 3.0).

DTIC Science & Technology

1981-03-01

AD-A B99 054 CONSTRUCTION EN INEERIN RESEARCH LAB (ARMY) CHAMPAIGN IL F/ 9/2 BUILDING LOADS ANALYSIS AND SYSTEM THERMOD NAMICS (BLAST) PROGR...continued. systems , (11) induction unit systems , (12) direct-drive chillers, and (13) purchased steam from utilities. BLAST Version 3.0 also offers the user...their BLAST input. II UNCLASSIFIED SECURITY CLASSIFICATION OF THIS PAGEftin Date Rnerod) FOREWORD This report was prepared for the Air Force Systems

A&M. TAN607. Elevation for secondphase expansion of A&M Building. Work ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

A&M. TAN-607. Elevation for second-phase expansion of A&M Building. Work areas south of the Carpentry Shop. High-bay shop, decontamination room at south-most end. Approved by INEEL Classification Office for public release. Ralph M. Parsons 1299-5-ANP/GE-3-607-A 106. Date: August 1956. INEEL index code no. 034-0607-00-693-107166 - Idaho National Engineering Laboratory, Test Area North, Scoville, Butte County, ID

Cascaded VLSI neural network architecture for on-line learning

NASA Technical Reports Server (NTRS)

Thakoor, Anilkumar P. (Inventor); Duong, Tuan A. (Inventor); Daud, Taher (Inventor)

1992-01-01

High-speed, analog, fully-parallel, and asynchronous building blocks are cascaded for larger sizes and enhanced resolution. A hardware compatible algorithm permits hardware-in-the-loop learning despite limited weight resolution. A computation intensive feature classification application was demonstrated with this flexible hardware and new algorithm at high speed. This result indicates that these building block chips can be embedded as an application specific coprocessor for solving real world problems at extremely high data rates.

Cascaded VLSI neural network architecture for on-line learning

NASA Technical Reports Server (NTRS)

Duong, Tuan A. (Inventor); Daud, Taher (Inventor); Thakoor, Anilkumar P. (Inventor)

1995-01-01

High-speed, analog, fully-parallel and asynchronous building blocks are cascaded for larger sizes and enhanced resolution. A hardware-compatible algorithm permits hardware-in-the-loop learning despite limited weight resolution. A comparison-intensive feature classification application has been demonstrated with this flexible hardware and new algorithm at high speed. This result indicates that these building block chips can be embedded as application-specific-coprocessors for solving real-world problems at extremely high data rates.

Development Index, A Proposed Pattern for Organizing and Facilitating the Flow of Information Needed By Man in Furthering His Own Development, With Particular Reference to the Development of Buildings and Communities and Other Forms of Environmental Control.

ERIC Educational Resources Information Center

Michigan Univ., Ann Arbor.

The organization of knowledge related to the development of the environment and the building industry is provided in this index which provides a framework or classification system for a broad range of information. Man's development in terms of environmental structuring and control is discussed as development goals, development cycle, and…

A Saliency Guided Semi-Supervised Building Change Detection Method for High Resolution Remote Sensing Images

PubMed Central

Hou, Bin; Wang, Yunhong; Liu, Qingjie

2016-01-01

Characterizations of up to date information of the Earth’s surface are an important application providing insights to urban planning, resources monitoring and environmental studies. A large number of change detection (CD) methods have been developed to solve them by utilizing remote sensing (RS) images. The advent of high resolution (HR) remote sensing images further provides challenges to traditional CD methods and opportunities to object-based CD methods. While several kinds of geospatial objects are recognized, this manuscript mainly focuses on buildings. Specifically, we propose a novel automatic approach combining pixel-based strategies with object-based ones for detecting building changes with HR remote sensing images. A multiresolution contextual morphological transformation called extended morphological attribute profiles (EMAPs) allows the extraction of geometrical features related to the structures within the scene at different scales. Pixel-based post-classification is executed on EMAPs using hierarchical fuzzy clustering. Subsequently, the hierarchical fuzzy frequency vector histograms are formed based on the image-objects acquired by simple linear iterative clustering (SLIC) segmentation. Then, saliency and morphological building index (MBI) extracted on difference images are used to generate a pseudo training set. Ultimately, object-based semi-supervised classification is implemented on this training set by applying random forest (RF). Most of the important changes are detected by the proposed method in our experiments. This study was checked for effectiveness using visual evaluation and numerical evaluation. PMID:27618903

A Saliency Guided Semi-Supervised Building Change Detection Method for High Resolution Remote Sensing Images.

PubMed

Hou, Bin; Wang, Yunhong; Liu, Qingjie

2016-08-27

Characterizations of up to date information of the Earth's surface are an important application providing insights to urban planning, resources monitoring and environmental studies. A large number of change detection (CD) methods have been developed to solve them by utilizing remote sensing (RS) images. The advent of high resolution (HR) remote sensing images further provides challenges to traditional CD methods and opportunities to object-based CD methods. While several kinds of geospatial objects are recognized, this manuscript mainly focuses on buildings. Specifically, we propose a novel automatic approach combining pixel-based strategies with object-based ones for detecting building changes with HR remote sensing images. A multiresolution contextual morphological transformation called extended morphological attribute profiles (EMAPs) allows the extraction of geometrical features related to the structures within the scene at different scales. Pixel-based post-classification is executed on EMAPs using hierarchical fuzzy clustering. Subsequently, the hierarchical fuzzy frequency vector histograms are formed based on the image-objects acquired by simple linear iterative clustering (SLIC) segmentation. Then, saliency and morphological building index (MBI) extracted on difference images are used to generate a pseudo training set. Ultimately, object-based semi-supervised classification is implemented on this training set by applying random forest (RF). Most of the important changes are detected by the proposed method in our experiments. This study was checked for effectiveness using visual evaluation and numerical evaluation.

Mechanization of Library Procedures in the Medium-Sized Medical Library: XIV. Correlations between National Library of Medicine Classification Numbers and MeSH Headings *

PubMed Central

Fenske, Ruth E.

1972-01-01

The purpose of this study was to determine the amount of correlation between National Library of Medicine classification numbers and MeSH headings in a body of cataloging which had already been done and then to find out which of two alternative methods of utilizing the correlation would be best. There was a correlation of 44.5% between classification numbers and subject headings in the data base studied, cataloging data covering 8,137 books. The results indicate that a subject heading index showing classification numbers would be the preferred method of utilization, because it would be more accurate than the alternative considered, an arrangement by classification numbers which would be consulted to obtain subject headings. PMID:16017607

The Psychosomatic Disorders Pertaining to Dental Practice with Revised Working Type Classification

PubMed Central

2014-01-01

Psychosomatic disorders are defined as disorders characterized by physiological changes that originate partially from emotional factors. This article aims to discuss the psychosomatic disorders of the oral cavity with a revised working type classification. The author has added one more subset to the existing classification, i.e., disorders caused by altered perception of dentofacial form and function, which include body dysmorphic disorder. The author has also inserted delusional halitosis under the miscellaneous disorders classification of psychosomatic disorders and revised the already existing classification proposed for the psychosomatic disorders pertaining to dental practice. After the inclusion of the subset (disorders caused by altered perception of dentofacial form and function), the terminology "psychosomatic disorders of the oral cavity" is modified to "psychosomatic disorders pertaining to dental practice". PMID:24478896

The psychosomatic disorders pertaining to dental practice with revised working type classification.

PubMed

Shamim, Thorakkal

2014-01-01

Psychosomatic disorders are defined as disorders characterized by physiological changes that originate partially from emotional factors. This article aims to discuss the psychosomatic disorders of the oral cavity with a revised working type classification. The author has added one more subset to the existing classification, i.e., disorders caused by altered perception of dentofacial form and function, which include body dysmorphic disorder. The author has also inserted delusional halitosis under the miscellaneous disorders classification of psychosomatic disorders and revised the already existing classification proposed for the psychosomatic disorders pertaining to dental practice. After the inclusion of the subset (disorders caused by altered perception of dentofacial form and function), the terminology "psychosomatic disorders of the oral cavity" is modified to "psychosomatic disorders pertaining to dental practice".

Approximate classification of mining tremors harmfulness based on free-field and building foundation vibrations

NASA Astrophysics Data System (ADS)

Kuzniar, Krystyna; Stec, Krystyna; Tatara, Tadeusz

2018-04-01

The paper compares the results of an approximate evaluation of mining tremors harmfulness performed on the basis of free-field and simultaneously measured building foundation vibrations. The focus is on the office building located in the Upper Silesian Basin (USB). The empirical Mining Intensity Scale GSI-GZWKW-2012 has been applied to classify the harmfulness of the rockbursts. This scale is based on the measurements of free-field vibrations but, for research purposes, it was also used in the cases of building foundation vibrations. The analysis was carried out using the set of 156 pairs ground - foundation of velocity vibration records as well as the set of 156 pairs of acceleration records induced by the same mining tremors.

Pastureland ESD concepts and current development

USDA-ARS?s Scientific Manuscript database

Bringing pastureland classification into an ecological site framework gives us the opportunity to build on the extensive experience of rangeland scientists and managers with this process. Unlike rangelands, pasture plant communities are dominated by naturalized species and are maintained by manageme...

Classifications for Cesarean Section: A Systematic Review

PubMed Central

Torloni, Maria Regina; Betran, Ana Pilar; Souza, Joao Paulo; Widmer, Mariana; Allen, Tomas; Gulmezoglu, Metin; Merialdi, Mario

2011-01-01

Background Rising cesarean section (CS) rates are a major public health concern and cause worldwide debates. To propose and implement effective measures to reduce or increase CS rates where necessary requires an appropriate classification. Despite several existing CS classifications, there has not yet been a systematic review of these. This study aimed to 1) identify the main CS classifications used worldwide, 2) analyze advantages and deficiencies of each system. Methods and Findings Three electronic databases were searched for classifications published 1968–2008. Two reviewers independently assessed classifications using a form created based on items rated as important by international experts. Seven domains (ease, clarity, mutually exclusive categories, totally inclusive classification, prospective identification of categories, reproducibility, implementability) were assessed and graded. Classifications were tested in 12 hypothetical clinical case-scenarios. From a total of 2948 citations, 60 were selected for full-text evaluation and 27 classifications identified. Indications classifications present important limitations and their overall score ranged from 2–9 (maximum grade = 14). Degree of urgency classifications also had several drawbacks (overall scores 6–9). Woman-based classifications performed best (scores 5–14). Other types of classifications require data not routinely collected and may not be relevant in all settings (scores 3–8). Conclusions This review and critical appraisal of CS classifications is a methodologically sound contribution to establish the basis for the appropriate monitoring and rational use of CS. Results suggest that women-based classifications in general, and Robson's classification, in particular, would be in the best position to fulfill current international and local needs and that efforts to develop an internationally applicable CS classification would be most appropriately placed in building upon this classification. The use of a single CS classification will facilitate auditing, analyzing and comparing CS rates across different settings and help to create and implement effective strategies specifically targeted to optimize CS rates where necessary. PMID:21283801

Weight Measurements and Standards for Soldiers, Phase 2

DTIC Science & Technology

2016-10-01

SUPPLEMENTARY NOTES 14. ABSTRACT The specific aims of the study are to: 1) examine body weight and fat changes associated with participation in a...maintenance of changes in body weight, body fat , and fitness after discontinuation of the promotion associated with the H.E.A.L.T.H. program. The study is a...physical fitness, health, weight, body fat 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF PAGES 26 19a. NAME OF

Engineering Change Management Method Framework in Mechanical Engineering

NASA Astrophysics Data System (ADS)

Stekolschik, Alexander

2016-11-01

Engineering changes make an impact on different process chains in and outside the company, and lead to most error costs and time shifts. In fact, 30 to 50 per cent of development costs result from technical changes. Controlling engineering change processes can help us to avoid errors and risks, and contribute to cost optimization and a shorter time to market. This paper presents a method framework for controlling engineering changes at mechanical engineering companies. The developed classification of engineering changes and accordingly process requirements build the basis for the method framework. The developed method framework comprises two main areas: special data objects managed in different engineering IT tools and process framework. Objects from both areas are building blocks that can be selected to the overall business process based on the engineering process type and change classification. The process framework contains steps for the creation of change objects (both for overall change and for parts), change implementation, and release. Companies can select singleprocess building blocks from the framework, depending on the product development process and change impact. The developed change framework has been implemented at a division (10,000 employees) of a big German mechanical engineering company.

Mammographic mass classification based on possibility theory

NASA Astrophysics Data System (ADS)

Hmida, Marwa; Hamrouni, Kamel; Solaiman, Basel; Boussetta, Sana

2017-03-01

Shape and margin features are very important for differentiating between benign and malignant masses in mammographic images. In fact, benign masses are usually round and oval and have smooth contours. However, malignant tumors have generally irregular shape and appear lobulated or speculated in margins. This knowledge suffers from imprecision and ambiguity. Therefore, this paper deals with the problem of mass classification by using shape and margin features while taking into account the uncertainty linked to the degree of truth of the available information and the imprecision related to its content. Thus, in this work, we proposed a novel mass classification approach which provides a possibility based representation of the extracted shape features and builds a possibility knowledge basis in order to evaluate the possibility degree of malignancy and benignity for each mass. For experimentation, the MIAS database was used and the classification results show the great performance of our approach in spite of using simple features.

Decoding memory features from hippocampal spiking activities using sparse classification models.

PubMed

Dong Song; Hampson, Robert E; Robinson, Brian S; Marmarelis, Vasilis Z; Deadwyler, Sam A; Berger, Theodore W

2016-08-01

To understand how memory information is encoded in the hippocampus, we build classification models to decode memory features from hippocampal CA3 and CA1 spatio-temporal patterns of spikes recorded from epilepsy patients performing a memory-dependent delayed match-to-sample task. The classification model consists of a set of B-spline basis functions for extracting memory features from the spike patterns, and a sparse logistic regression classifier for generating binary categorical output of memory features. Results show that classification models can extract significant amount of memory information with respects to types of memory tasks and categories of sample images used in the task, despite the high level of variability in prediction accuracy due to the small sample size. These results support the hypothesis that memories are encoded in the hippocampal activities and have important implication to the development of hippocampal memory prostheses.

Optical tomographic detection of rheumatoid arthritis with computer-aided classification schemes

NASA Astrophysics Data System (ADS)

Klose, Christian D.; Klose, Alexander D.; Netz, Uwe; Beuthan, Jürgen; Hielscher, Andreas H.

2009-02-01

A recent research study has shown that combining multiple parameters, drawn from optical tomographic images, leads to better classification results to identifying human finger joints that are affected or not affected by rheumatic arthritis RA. Building up on the research findings of the previous study, this article presents an advanced computer-aided classification approach for interpreting optical image data to detect RA in finger joints. Additional data are used including, for example, maximum and minimum values of the absorption coefficient as well as their ratios and image variances. Classification performances obtained by the proposed method were evaluated in terms of sensitivity, specificity, Youden index and area under the curve AUC. Results were compared to different benchmarks ("gold standard"): magnet resonance, ultrasound and clinical evaluation. Maximum accuracies (AUC=0.88) were reached when combining minimum/maximum-ratios and image variances and using ultrasound as gold standard.

Investigating Perceived vs. Medical Weight Status Classification among College Students: Room for Improvement Exists among the Overweight and Obese

ERIC Educational Resources Information Center

Duffrin, Christopher; Eakin, Angela; Bertrand, Brenda; Barber-Heidel, Kimberly; Carraway-Stage, Virginia

2011-01-01

The American College Health Association estimated that 31% of college students are overweight or obese. It is important that students have a correct perception of body weight status as extra weight has potential adverse health effects. This study assessed accuracy of perceived weight status versus medical classification among 102 college students.…

Body mass index: different nutritional status according to WHO, OPAS and Lipschitz classifications in gastrointestinal cancer patients.

PubMed

Barao, Katia; Forones, Nora Manoukian

2012-01-01

The body mass index (BMI) is the most common marker used on diagnoses of the nutritional status. The great advantage of this index is the easy way to measure, the low cost, the good correlation with the fat mass and the association to morbidity and mortality. To compare the BMI differences according to the WHO, OPAS and Lipschitz classification. A prospective study on 352 patients with esophageal, gastric or colorectal cancer was done. The BMI was calculated and analyzed by the classification of WHO, Lipschitz and OPAS. The mean age was 62.1 ± 12.4 years and 59% of them had more than 59 years. The BMI had not difference between the genders in patients <59 years (P = 0.75), but over 59 years the BMI was higher in women (P<0.01). The percentage of undernourished was 7%, 18% and 21% (P<0.01) by WHO, Lipschitz and OPAS, respectively. The overweight/obesity was also different among the various classifications (P<0.01). Most of the patients with gastrointestinal cancer had more than 65 years. A different cut off must be used for this patients, because undernourished patients may be wrongly considered well nourished.

[Multifaceted body. 2. The lived body].

PubMed

Wykretowicz, H; Saraga, M; Bourquin, C; Stiefel, F

2015-02-11

The human body is the object upon which medicine is acting, but also lived reality, image, symbol, representation and the object of elaboration and theory. All these elements which constitute the body influence the way medicine is treating it. In this series of three articles, we address the human body from various perspectives: medical (1), phenomenological (2), psychosomatic and socio-anthropological (3). This second article distinguishes between the body as an object of knowledge or representation and the way the body is lived. This distinction which originates in phenomenological psychiatry aims to understand how the patient experiences his body and to surpass the classical somatic and psychiatric classifications.

A mini review on the integration of resource recovery from wastewater into sustainability of the green building through phycoremediation

NASA Astrophysics Data System (ADS)

Yulistyorini, Anie

2017-09-01

Green building implementation is an important assessment for sustainable development to establish a good quality of the environment. To develop the future green building implementation, resource recovery from the building wastewater is significantly important to consider as a part of the green building development. Discharge of urban wastewater into water bodies trigger of eutrophication in the water catchment, accordingly need further treatment to recover the nutrient before it is reused or discharged into receiving water bodies. In this regard, integration of microalgae cultivation in closed photobioreactor as building façade is critically important to be considered in the implementation of the green building. Microalgae offer multi-function as bioremediation (phycoremediation) of the wastewater, production of the biofuels, and important algal bio-products. At the same time, algae façade boost the reduction of the operating cost in forms of light, thermal energy and add the benefit into the building for energy reduction and architecture function. It promises an environmental benefit to support green building spirit through nutrient recovery and wastewater reuse for algae cultivation and to enhance the aesthetic of the building façade.

21 CFR 866.5270 - C-reactive protein immuno-logical test system.

Code of Federal Regulations, 2013 CFR

2013-04-01

... the C-reactive protein in serum and other body fluids. Measurement of C-reactive protein aids in evaluation of the amount of injury to body tissues. (b) Classification. Class II (performance standards). ....5270 Section 866.5270 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN...

21 CFR 866.5270 - C-reactive protein immuno-logical test system.

Code of Federal Regulations, 2011 CFR

2011-04-01

... the C-reactive protein in serum and other body fluids. Measurement of C-reactive protein aids in evaluation of the amount of injury to body tissues. (b) Classification. Class II (performance standards). ....5270 Section 866.5270 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN...

21 CFR 866.5270 - C-reactive protein immuno-logical test system.

Code of Federal Regulations, 2012 CFR

2012-04-01

... the C-reactive protein in serum and other body fluids. Measurement of C-reactive protein aids in evaluation of the amount of injury to body tissues. (b) Classification. Class II (performance standards). ....5270 Section 866.5270 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN...

21 CFR 866.5270 - C-reactive protein immuno-logical test system.

Code of Federal Regulations, 2014 CFR

2014-04-01

... the C-reactive protein in serum and other body fluids. Measurement of C-reactive protein aids in evaluation of the amount of injury to body tissues. (b) Classification. Class II (performance standards). ....5270 Section 866.5270 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN...

21 CFR 888.4150 - Calipers for clinical use.

Code of Federal Regulations, 2012 CFR

2012-04-01

....4150 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED... or diameter of a part of the body or the distance between two body surfaces, such as for measuring an excised skeletal specimen to determine the proper replacement size of a prosthesis. (b) Classification...

21 CFR 888.4150 - Calipers for clinical use.

Code of Federal Regulations, 2013 CFR

2013-04-01

....4150 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED... or diameter of a part of the body or the distance between two body surfaces, such as for measuring an excised skeletal specimen to determine the proper replacement size of a prosthesis. (b) Classification...

21 CFR 888.4150 - Calipers for clinical use.

Code of Federal Regulations, 2014 CFR

2014-04-01

....4150 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED... or diameter of a part of the body or the distance between two body surfaces, such as for measuring an excised skeletal specimen to determine the proper replacement size of a prosthesis. (b) Classification...

21 CFR 888.4150 - Calipers for clinical use.

Code of Federal Regulations, 2011 CFR

2011-04-01

....4150 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED... or diameter of a part of the body or the distance between two body surfaces, such as for measuring an excised skeletal specimen to determine the proper replacement size of a prosthesis. (b) Classification...

Optimized hardware framework of MLP with random hidden layers for classification applications

NASA Astrophysics Data System (ADS)

Zyarah, Abdullah M.; Ramesh, Abhishek; Merkel, Cory; Kudithipudi, Dhireesha

2016-05-01

Multilayer Perceptron Networks with random hidden layers are very efficient at automatic feature extraction and offer significant performance improvements in the training process. They essentially employ large collection of fixed, random features, and are expedient for form-factor constrained embedded platforms. In this work, a reconfigurable and scalable architecture is proposed for the MLPs with random hidden layers with a customized building block based on CORDIC algorithm. The proposed architecture also exploits fixed point operations for area efficiency. The design is validated for classification on two different datasets. An accuracy of ~ 90% for MNIST dataset and 75% for gender classification on LFW dataset was observed. The hardware has 299 speed-up over the corresponding software realization.

Influence of crisp values on the object-based data extraction procedure from LiDAR data

NASA Astrophysics Data System (ADS)

Tomljenovic, Ivan; Rousell, Adam

2014-05-01

Nowadays a plethora of approaches attempt to automate the process of object extraction from LiDAR data. However, the majority of these methods require the fusion of the LiDAR dataset with other information such as photogrammetric imagery. The approach that has been used as the basis for this paper is a novel method which makes use of human knowledge and the CNL modelling language to automatically extract buildings solely from LiDAR point cloud data in a transferable method. A number of rules are implemented to generate an artificial intelligence algorithm which is used for the object extraction. Although the single dataset method has been found to successfully extract building footprints from the point cloud dataset, at this initial stage it has one restriction that may limit its effectiveness - a number of the rules that are used are based on crisp boundary values. If, for example, the slope of the ground surface is used as a rule for determining objects then the slope value of a pixel would be assessed to determine if it is suitable for a building structure. This check would be performed by identifying whether the slope value is less than or greater than a threshold value. However, in reality such a crisp classification process is likely not to be a true reflection of real world scenarios. For example, using the crisp methods a difference of 1° in slope could result in one region in a dataset being deemed suitable and its neighboring region being seen as not suitable. It is likely however that there is in reality little difference in the actual suitability of these two neighboring regions. A more suitable classification process may be the use of fuzzy set theory whereby each region is seen as having degree of membership to a number of sets (or classifications). In the above example, the two regions would likely be seen as having very similar membership values to the different sets, although this is obviously dependent on factors such as the extent of each region. The purpose of this study is to identify to what extent the use of explicit boundary values has on the overall building footprint dataset extracted. By performing the analysis multiple times using differing threshold values for rules, it is possible to compare the resultant datasets and thus identify the impact of using such classification procedures. If a significant difference is found between the resultant datasets, this would highlight that the use of such crisp methods in the extraction processes may not be optimal and that a future enhancement to the method would be to consider the use of fuzzy classification methods.

Lossless Compression of Classification-Map Data

NASA Technical Reports Server (NTRS)

Hua, Xie; Klimesh, Matthew

2009-01-01

A lossless image-data-compression algorithm intended specifically for application to classification-map data is based on prediction, context modeling, and entropy coding. The algorithm was formulated, in consideration of the differences between classification maps and ordinary images of natural scenes, so as to be capable of compressing classification- map data more effectively than do general-purpose image-data-compression algorithms. Classification maps are typically generated from remote-sensing images acquired by instruments aboard aircraft (see figure) and spacecraft. A classification map is a synthetic image that summarizes information derived from one or more original remote-sensing image(s) of a scene. The value assigned to each pixel in such a map is the index of a class that represents some type of content deduced from the original image data for example, a type of vegetation, a mineral, or a body of water at the corresponding location in the scene. When classification maps are generated onboard the aircraft or spacecraft, it is desirable to compress the classification-map data in order to reduce the volume of data that must be transmitted to a ground station.

Linking of the quality of life in neurological disorders (Neuro-QoL) to the international classification of functioning, disability and health.

PubMed

Wong, Alex W K; Lau, Stephen C L; Cella, David; Lai, Jin-Shei; Xie, Guanli; Chen, Lidian; Chan, Chetwyn C H; Heinemann, Allen W

2017-09-01

The quality of life in neurological disorders (Neuro-QoL) is a U.S. National Institutes of Health initiative that produced a set of self-report measures of physical, mental, and social health experienced by adults or children who have a neurological condition or disorder. To describe the content of the Neuro-QoL at the item level using the World Health Organization's international classification of functioning, disability and health (ICF). We assessed the Neuro-QoL for its content coverage of functioning and disability relative to each of the four ICF domains (i.e., body functions, body structures, activities and participation, and environment). We used second-level ICF three-digit codes to classify items into categories within each ICF domain and computed the percentage of categories within each ICF domain that were represented in the Neuro-QoL items. All items of Neuro-QoL could be mapped to the ICF categories at the second-level classification codes. The activities and participation domain and the mental functions category of the body functions domain were the areas most often represented by Neuro-QoL. Neuro-QoL provides limited coverage of the environmental factors and body structure domains. Neuro-QoL measures map well to the ICF. The Neuro-QoL-ICF-mapped items provide a blueprint for users to select appropriate measures in ICF-based measurement applications.

Texture operator for snow particle classification into snowflake and graupel

NASA Astrophysics Data System (ADS)

Nurzyńska, Karolina; Kubo, Mamoru; Muramoto, Ken-ichiro

2012-11-01

In order to improve the estimation of precipitation, the coefficients of Z-R relation should be determined for each snow type. Therefore, it is necessary to identify the type of falling snow. Consequently, this research addresses a problem of snow particle classification into snowflake and graupel in an automatic manner (as these types are the most common in the study region). Having correctly classified precipitation events, it is believed that it will be possible to estimate the related parameters accurately. The automatic classification system presented here describes the images with texture operators. Some of them are well-known from the literature: first order features, co-occurrence matrix, grey-tone difference matrix, run length matrix, and local binary pattern, but also a novel approach to design simple local statistic operators is introduced. In this work the following texture operators are defined: mean histogram, min-max histogram, and mean-variance histogram. Moreover, building a feature vector, which is based on the structure created in many from mentioned algorithms is also suggested. For classification, the k-nearest neighbourhood classifier was applied. The results showed that it is possible to achieve correct classification accuracy above 80% by most of the techniques. The best result of 86.06%, was achieved for operator built from a structure achieved in the middle stage of the co-occurrence matrix calculation. Next, it was noticed that describing an image with two texture operators does not improve the classification results considerably. In the best case the correct classification efficiency was 87.89% for a pair of texture operators created from local binary pattern and structure build in a middle stage of grey-tone difference matrix calculation. This also suggests that the information gathered by each texture operator is redundant. Therefore, the principal component analysis was applied in order to remove the unnecessary information and additionally reduce the length of the feature vectors. The improvement of the correct classification efficiency for up to 100% is possible for methods: min-max histogram, texture operator built from structure achieved in a middle stage of co-occurrence matrix calculation, texture operator built from a structure achieved in a middle stage of grey-tone difference matrix creation, and texture operator based on a histogram, when the feature vector stores 99% of initial information.

A tool for enhancing strategic health planning: a modeled use of the International Classification of Functioning, Disability and Health

PubMed Central

Sinclair, Lisa Bundara; Fox, Michael H.; Betts, Donald R.

2015-01-01

SUMMARY This article describes use of the International Classification of Functioning, Disability and Health (ICF) as a tool for strategic planning. The ICF is the international classification system for factors that influence health, including Body Structures, Body Functions, Activities and Participation and Environmental Factors. An overview of strategic planning and the ICF are provided. Selected ICF concepts and nomenclature are used to demonstrate its utility in helping develop a classic planning framework, objectives, measures and actions. Some issues and resolutions for applying the ICF are described. Applying the ICF for strategic health planning is an innovative approach that fosters the inclusion of social ecological health determinants and broad populations. If employed from the onset of planning, the ICF can help public health organizations systematically conceptualize, organize and communicate a strategic health plan. This article is a US Government work and is in the public domain in the USA. PMID:23147247

Image processing developments and applications for water quality monitoring and trophic state determination

NASA Technical Reports Server (NTRS)

Blackwell, R. J.

1982-01-01

Remote sensing data analysis of water quality monitoring is evaluated. Data anaysis and image processing techniques are applied to LANDSAT remote sensing data to produce an effective operational tool for lake water quality surveying and monitoring. Digital image processing and analysis techniques were designed, developed, tested, and applied to LANDSAT multispectral scanner (MSS) data and conventional surface acquired data. Utilization of these techniques facilitates the surveying and monitoring of large numbers of lakes in an operational manner. Supervised multispectral classification, when used in conjunction with surface acquired water quality indicators, is used to characterize water body trophic status. Unsupervised multispectral classification, when interpreted by lake scientists familiar with a specific water body, yields classifications of equal validity with supervised methods and in a more cost effective manner. Image data base technology is used to great advantage in characterizing other contributing effects to water quality. These effects include drainage basin configuration, terrain slope, soil, precipitation and land cover characteristics.

A tool for enhancing strategic health planning: a modeled use of the International Classification of Functioning, Disability and Health.

PubMed

Sinclair, Lisa Bundara; Fox, Michael H; Betts, Donald R

2013-01-01

This article describes use of the International Classification of Functioning, Disability and Health (ICF) as a tool for strategic planning. The ICF is the international classification system for factors that influence health, including Body Structures, Body Functions, Activities and Participation and Environmental Factors. An overview of strategic planning and the ICF are provided. Selected ICF concepts and nomenclature are used to demonstrate its utility in helping develop a classic planning framework, objectives, measures and actions. Some issues and resolutions for applying the ICF are described. Applying the ICF for strategic health planning is an innovative approach that fosters the inclusion of social ecological health determinants and broad populations. If employed from the onset of planning, the ICF can help public health organizations systematically conceptualize, organize and communicate a strategic health plan. Published 2012. This article is a US Government work and is in the public domain in the USA.

LPT. Plot plan and site layout. Includes shield test pool/EBOR ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

LPT. Plot plan and site layout. Includes shield test pool/EBOR facility. (TAN-645 and -646) low power test building (TAN-640 and -641), water storage tanks, guard house (TAN-642), pump house (TAN-644), driveways, well, chlorination building (TAN-643), septic system. Ralph M. Parsons 1229-12 ANP/GE-7-102. November 1956. Approved by INEEL Classification Office for public release. INEEL index code no. 038-0102-00-693-107261 - Idaho National Engineering Laboratory, Test Area North, Scoville, Butte County, ID

IET. Movable test cell building (TAN624). Plans, sections, and elevations ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

IET. Movable test cell building (TAN-624). Plans, sections, and elevations show trapezoidal shape of front/rear elevations, vertical sliding door panels, wheels, periscope and camera locations, fixed concrete wall, and relationship to coupling station (TAN-620) and rail track. Ralph M. Parson 902-4-ANP-624-A 329. Date: February 1954. Approved by INEEL Classification Office for public release. INEEL Index code no. 035-0624-00-693-106911 - Idaho National Engineering Laboratory, Test Area North, Scoville, Butte County, ID

Role of Rad23 and Dsk2 in Nucleotide Excision Repair and Spindle Pole Body Duplication

DTIC Science & Technology

2006-03-01

AD Award Number: W81XWH-05-1-0310 TITLE: Role of Rad23 and Dsk2 in Nucleotide Excision Repair and Spindle Pole Body Duplication PRINCIPAL...Feb 2006 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER Role of Rad23 and Dsk2 in Nucleotide Excision Repair and Spindle Pole Body Duplication Sb. GRANT...Degradation, Cell Cycle, Spindle Pole Body 16. SECURITY CLASSIFICATION OF: 17. LIMITATION 18. NUMBER 19a. NAME OF RESPONSIBLE PERSON OF ABSTRACT OF

Computer Aided Ballistic Orbit Classification Around Small Bodies

NASA Astrophysics Data System (ADS)

Villac, Benjamin F.; Anderson, Rodney L.; Pini, Alex J.

2016-09-01

Orbital dynamics around small bodies are as varied as the shapes and dynamical states of these bodies. While various classes of orbits have been analyzed in detail, the global overview of relevant ballistic orbits at particular bodies is not easily computed or organized. Yet, correctly categorizing these orbits will ease their future use in the overall trajectory design process. This paper overviews methods that have been used to organize orbits, focusing on periodic orbits in particular, and introduces new methods based on clustering approaches.

Measuring body structures and body functions from the International Classification of Functioning, Disability, and Health perspective: considerations for biomedical parameters in spinal cord injury research.

PubMed

Eriks-Hoogland, Inge E; Brinkhof, Martin W G; Al-Khodairy, Abdul; Baumberger, Michael; Brechbühl, Jörg; Curt, Armin; Mäder, Mark; Stucki, Gerold; Post, Marcel W M

2011-11-01

The aims of this study were to provide a selection of biomedical domains based on the comprehensive International Classification of Functioning, Disability, and Health (ICF) core sets for spinal cord injury (SCI) and to present an overview of the corresponding measurement instruments. Based on the Biomedical Domain Set, the SCI literature, the International Spinal Cord Society international data sets, and the Spinal Cord Injury Rehabilitation Evidence project publications were used to derive category specifications for use in SCI research. Expert opinion was used to derive a priority selection. The same sources were used to determine candidate measurement instruments for the specification of body functions and body structures using an example, and guiding principles were applied to select the most appropriate biomedical measurement instrument(s) for use in an SCI research project. Literature searches were performed for 41 second-level ICF body functions categories and for four second-level ICF body structures categories. For some of these categories, only a few candidate measurement instruments were found with limited variation in the type of measurement instruments. An ICF-based measurement set for biomedical aspects of functioning with SCI was established. For some categories of the ICF core sets for SCI, there is a need to develop measurement instruments.

Music-Elicited Emotion Identification Using Optical Flow Analysis of Human Face

NASA Astrophysics Data System (ADS)

Kniaz, V. V.; Smirnova, Z. N.

2015-05-01

Human emotion identification from image sequences is highly demanded nowadays. The range of possible applications can vary from an automatic smile shutter function of consumer grade digital cameras to Biofied Building technologies, which enables communication between building space and residents. The highly perceptual nature of human emotions leads to the complexity of their classification and identification. The main question arises from the subjective quality of emotional classification of events that elicit human emotions. A variety of methods for formal classification of emotions were developed in musical psychology. This work is focused on identification of human emotions evoked by musical pieces using human face tracking and optical flow analysis. Facial feature tracking algorithm used for facial feature speed and position estimation is presented. Facial features were extracted from each image sequence using human face tracking with local binary patterns (LBP) features. Accurate relative speeds of facial features were estimated using optical flow analysis. Obtained relative positions and speeds were used as the output facial emotion vector. The algorithm was tested using original software and recorded image sequences. The proposed technique proves to give a robust identification of human emotions elicited by musical pieces. The estimated models could be used for human emotion identification from image sequences in such fields as emotion based musical background or mood dependent radio.

Multispectral LiDAR Data for Land Cover Classification of Urban Areas

PubMed Central

Morsy, Salem; Shaker, Ahmed; El-Rabbany, Ahmed

2017-01-01

Airborne Light Detection And Ranging (LiDAR) systems usually operate at a monochromatic wavelength measuring the range and the strength of the reflected energy (intensity) from objects. Recently, multispectral LiDAR sensors, which acquire data at different wavelengths, have emerged. This allows for recording of a diversity of spectral reflectance from objects. In this context, we aim to investigate the use of multispectral LiDAR data in land cover classification using two different techniques. The first is image-based classification, where intensity and height images are created from LiDAR points and then a maximum likelihood classifier is applied. The second is point-based classification, where ground filtering and Normalized Difference Vegetation Indices (NDVIs) computation are conducted. A dataset of an urban area located in Oshawa, Ontario, Canada, is classified into four classes: buildings, trees, roads and grass. An overall accuracy of up to 89.9% and 92.7% is achieved from image classification and 3D point classification, respectively. A radiometric correction model is also applied to the intensity data in order to remove the attenuation due to the system distortion and terrain height variation. The classification process is then repeated, and the results demonstrate that there are no significant improvements achieved in the overall accuracy. PMID:28445432

Multispectral LiDAR Data for Land Cover Classification of Urban Areas.

PubMed

Morsy, Salem; Shaker, Ahmed; El-Rabbany, Ahmed

2017-04-26

Airborne Light Detection And Ranging (LiDAR) systems usually operate at a monochromatic wavelength measuring the range and the strength of the reflected energy (intensity) from objects. Recently, multispectral LiDAR sensors, which acquire data at different wavelengths, have emerged. This allows for recording of a diversity of spectral reflectance from objects. In this context, we aim to investigate the use of multispectral LiDAR data in land cover classification using two different techniques. The first is image-based classification, where intensity and height images are created from LiDAR points and then a maximum likelihood classifier is applied. The second is point-based classification, where ground filtering and Normalized Difference Vegetation Indices (NDVIs) computation are conducted. A dataset of an urban area located in Oshawa, Ontario, Canada, is classified into four classes: buildings, trees, roads and grass. An overall accuracy of up to 89.9% and 92.7% is achieved from image classification and 3D point classification, respectively. A radiometric correction model is also applied to the intensity data in order to remove the attenuation due to the system distortion and terrain height variation. The classification process is then repeated, and the results demonstrate that there are no significant improvements achieved in the overall accuracy.

Feasibility of Active Machine Learning for Multiclass Compound Classification.

PubMed

Lang, Tobias; Flachsenberg, Florian; von Luxburg, Ulrike; Rarey, Matthias

2016-01-25

A common task in the hit-to-lead process is classifying sets of compounds into multiple, usually structural classes, which build the groundwork for subsequent SAR studies. Machine learning techniques can be used to automate this process by learning classification models from training compounds of each class. Gathering class information for compounds can be cost-intensive as the required data needs to be provided by human experts or experiments. This paper studies whether active machine learning can be used to reduce the required number of training compounds. Active learning is a machine learning method which processes class label data in an iterative fashion. It has gained much attention in a broad range of application areas. In this paper, an active learning method for multiclass compound classification is proposed. This method selects informative training compounds so as to optimally support the learning progress. The combination with human feedback leads to a semiautomated interactive multiclass classification procedure. This method was investigated empirically on 15 compound classification tasks containing 86-2870 compounds in 3-38 classes. The empirical results show that active learning can solve these classification tasks using 10-80% of the data which would be necessary for standard learning techniques.

Third Generation Nigerian University Libraries.

ERIC Educational Resources Information Center

Agboola, A. T.

1993-01-01

Examines the development of Nigerian University libraries and the political factors that created them and continue to effect their development, with a focus on those established between 1980 and 1984. Users, governance, finance, buildings, staffing, collection development, services, cataloging and classification, and automation are described.…

77 FR 49991 - Small Business Size Standards; Adoption of 2012 North American Industry Classification System for...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-08-20

... Manufacturing. ......... 322215 Nonfolding Sanitary ......... 750 employees....... Food Container Manufacturing... Manufacturing. ......... 327113 Porcelain Electrical 500 employees. Supply Manufacturing. 327120 Clay Building N 2b 750 employees....... 327121 Brick and Structural 500 employees. Material and Clay Tile...

48 CFR 1845.7101-1 - Property classification.

Code of Federal Regulations, 2010 CFR

2010-10-01

... aeronautical and space programs, which are capable of stand-alone operation. Examples include research aircraft... characteristics. (ii) Examples of NASA heritage assets include buildings and structures designated as National...., it no longer provides service to NASA operations). Examples of obsolete property are items in...

48 CFR 1845.7101-1 - Property classification.

Code of Federal Regulations, 2011 CFR

2011-10-01

... aeronautical and space programs, which are capable of stand-alone operation. Examples include research aircraft... characteristics. (ii) Examples of NASA heritage assets include buildings and structures designated as National...., it no longer provides service to NASA operations). Examples of obsolete property are items in...

48 CFR 1845.7101-1 - Property classification.

Code of Federal Regulations, 2014 CFR

2014-10-01

... aeronautical and space programs, which are capable of stand-alone operation. Examples include research aircraft... characteristics. (ii) Examples of NASA heritage assets include buildings and structures designated as National...., it no longer provides service to NASA operations). Examples of obsolete property are items in...

48 CFR 1845.7101-1 - Property classification.

Code of Federal Regulations, 2013 CFR

2013-10-01

... aeronautical and space programs, which are capable of stand-alone operation. Examples include research aircraft... characteristics. (ii) Examples of NASA heritage assets include buildings and structures designated as National...., it no longer provides service to NASA operations). Examples of obsolete property are items in...

48 CFR 1845.7101-1 - Property classification.

Code of Federal Regulations, 2012 CFR

2012-10-01

... aeronautical and space programs, which are capable of stand-alone operation. Examples include research aircraft... characteristics. (ii) Examples of NASA heritage assets include buildings and structures designated as National...., it no longer provides service to NASA operations). Examples of obsolete property are items in...

23 CFR 750.703 - Definitions.

Code of Federal Regulations, 2012 CFR

2012-04-01

... classifications. (b) Erect means to construct, build, raise, assemble, place, affix, attach, create, paint, draw... frontage roads, turning roadways, or parking areas. (i) Sign, display or device, hereinafter referred to as “sign,” means an outdoor advertising sign, light, display, device, figure, painting, drawing, message...

23 CFR 750.703 - Definitions.

Code of Federal Regulations, 2010 CFR

2010-04-01

... classifications. (b) Erect means to construct, build, raise, assemble, place, affix, attach, create, paint, draw... frontage roads, turning roadways, or parking areas. (i) Sign, display or device, hereinafter referred to as “sign,” means an outdoor advertising sign, light, display, device, figure, painting, drawing, message...

23 CFR 750.703 - Definitions.

Code of Federal Regulations, 2011 CFR

2011-04-01

... classifications. (b) Erect means to construct, build, raise, assemble, place, affix, attach, create, paint, draw... frontage roads, turning roadways, or parking areas. (i) Sign, display or device, hereinafter referred to as “sign,” means an outdoor advertising sign, light, display, device, figure, painting, drawing, message...

23 CFR 750.703 - Definitions.

Code of Federal Regulations, 2014 CFR

2014-04-01

... classifications. (b) Erect means to construct, build, raise, assemble, place, affix, attach, create, paint, draw... frontage roads, turning roadways, or parking areas. (i) Sign, display or device, hereinafter referred to as “sign,” means an outdoor advertising sign, light, display, device, figure, painting, drawing, message...

23 CFR 750.703 - Definitions.

Code of Federal Regulations, 2013 CFR

2013-04-01

... classifications. (b) Erect means to construct, build, raise, assemble, place, affix, attach, create, paint, draw... frontage roads, turning roadways, or parking areas. (i) Sign, display or device, hereinafter referred to as “sign,” means an outdoor advertising sign, light, display, device, figure, painting, drawing, message...

Plant or Animal?

ERIC Educational Resources Information Center

Bowman, Frank; Matthews, Catherine E.

1996-01-01

Presents activities that use marine organisms with plant-like appearances to help students build classification skills and illustrate some of the less obvious differences between plants and animals. Compares mechanisms by which sessile plants and animals deal with common problems such as obtaining energy, defending themselves, successfully…

Identification, classification and differential expression of oleosin genes in tung tree (Vernicia fordii)

USDA-ARS?s Scientific Manuscript database

Triacylglycerols (TAG) are the major molecules of energy storage in eukaryotes. TAG are packed in subcellular structures called oil bodies or lipid droplets. Oleosins (OLE) are the major proteins in plant oil bodies. Multiple isoforms of OLE are present in plants such as tung tree (Vernicia fordii),...

Body mass index distribution affects discrepancies in weight classifications in children

USDA-ARS?s Scientific Manuscript database

The aim of the present study was to investigate the effect of body mass index (BMI) distribution, ethnicity, and age at menarche on the consistency in the prevalence of underweight and overweight as defined by the Centers for Disease Control and Prevention (CDC) and the International Obesity Task Fo...

Parent Reactions to a School-Based Body Mass Index Screening Program

ERIC Educational Resources Information Center

Johnson, Suzanne Bennett; Pilkington, Lorri L.; Lamp, Camilla; He, Jianghua; Deeb, Larry C.

2009-01-01

Background: This study assessed parent reactions to school-based body mass index (BMI) screening. Methods: After a K-8 BMI screening program, parents were sent a letter detailing their child's BMI results. Approximately 50 parents were randomly selected for interview from each of 4 child weight-classification groups (overweight, at risk of…

How Do Changes in Body Functions and Structures, Activity, and Participation Relate in Children with Cerebral Palsy?

ERIC Educational Resources Information Center

Wright, F. Virginia; Rosenbaum, Peter L.; Goldsmith, Charles H.; Law, Mary; Fehlings, Darcy L.

2008-01-01

Rehabilitation increasingly addresses the International Classification of Functioning, Disability and Health's (ICF) concepts of activity and participation, but little is known about associations between changes in body functions and structures, activity, and participation. We conducted a before-and-after study of 35 ambulatory children with…

Fall Risk Assessment Through Automatic Combination of Clinical Fall Risk Factors and Body-Worn Sensor Data.

PubMed

Greene, Barry R; Redmond, Stephen J; Caulfield, Brian

2017-05-01

Falls are the leading global cause of accidental death and disability in older adults and are the most common cause of injury and hospitalization. Accurate, early identification of patients at risk of falling, could lead to timely intervention and a reduction in the incidence of fall-related injury and associated costs. We report a statistical method for fall risk assessment using standard clinical fall risk factors (N = 748). We also report a means of improving this method by automatically combining it, with a fall risk assessment algorithm based on inertial sensor data and the timed-up-and-go test. Furthermore, we provide validation data on the sensor-based fall risk assessment method using a statistically independent dataset. Results obtained using cross-validation on a sample of 292 community dwelling older adults suggest that a combined clinical and sensor-based approach yields a classification accuracy of 76.0%, compared to either 73.6% for sensor-based assessment alone, or 68.8% for clinical risk factors alone. Increasing the cohort size by adding an additional 130 subjects from a separate recruitment wave (N = 422), and applying the same model building and validation method, resulted in a decrease in classification performance (68.5% for combined classifier, 66.8% for sensor data alone, and 58.5% for clinical data alone). This suggests that heterogeneity between cohorts may be a major challenge when attempting to develop fall risk assessment algorithms which generalize well. Independent validation of the sensor-based fall risk assessment algorithm on an independent cohort of 22 community dwelling older adults yielded a classification accuracy of 72.7%. Results suggest that the present method compares well to previously reported sensor-based fall risk assessment methods in assessing falls risk. Implementation of objective fall risk assessment methods on a large scale has the potential to improve quality of care and lead to a reduction in associated hospital costs, due to fewer admissions and reduced injuries due to falling.

Excessive bodybuilding as pathology? A first neurophysiological classification.

PubMed

Maier, Moritz Julian; Haeussinger, Florian Benedikt; Hautzinger, Martin; Fallgatter, Andreas Jochen; Ehlis, Ann-Christine

2017-11-15

Excessive bodybuilding as a pathological syndrome has been classified based on two different theories: bodybuilding as dependency or as muscle dysmorphic disorder (MDD). This study is a first attempt to find psychophysiological data supporting one of these classifications. Twenty-four participants (bodybuilders vs healthy controls) were presented with pictures of bodies, exercise equipment or general reward stimuli in a control or experimental condition, and were measured with functional near-infrared spectroscopy (fNIRS). Higher activation in the dorsolateral prefrontal cortex (DLPFC) and the orbitofrontal cortex (OFC) while watching bodies and training equipment in the experimental condition (muscular bodies and bodybuilding-typical equipment) would be an indicator for the addiction theory. Higher activation in motion-related areas would be an indicator for the MDD theory. We found no task-related differences between the groups in the DLPFC and OFC, but a significantly higher activation in bodybuilders in the primary somatosensory cortex (PSC) and left-hemispheric supplementary motor area (SMA) while watching body pictures (across conditions) as compared to the control group. These neurophysiological results could be interpreted as a first evidence for the MDD theory of excessive bodybuilding.

Comparative analysis of expert and machine-learning methods for classification of body cavity effusions in companion animals.

PubMed

Hotz, Christine S; Templeton, Steven J; Christopher, Mary M

2005-03-01

A rule-based expert system using CLIPS programming language was created to classify body cavity effusions as transudates, modified transudates, exudates, chylous, and hemorrhagic effusions. The diagnostic accuracy of the rule-based system was compared with that produced by 2 machine-learning methods: Rosetta, a rough sets algorithm and RIPPER, a rule-induction method. Results of 508 body cavity fluid analyses (canine, feline, equine) obtained from the University of California-Davis Veterinary Medical Teaching Hospital computerized patient database were used to test CLIPS and to test and train RIPPER and Rosetta. The CLIPS system, using 17 rules, achieved an accuracy of 93.5% compared with pathologist consensus diagnoses. Rosetta accurately classified 91% of effusions by using 5,479 rules. RIPPER achieved the greatest accuracy (95.5%) using only 10 rules. When the original rules of the CLIPS application were replaced with those of RIPPER, the accuracy rates were identical. These results suggest that both rule-based expert systems and machine-learning methods hold promise for the preliminary classification of body fluids in the clinical laboratory.

Advanced statistical analysis of Raman spectroscopic data for the identification of body fluid traces: semen and blood mixtures.

PubMed

Sikirzhytski, Vitali; Sikirzhytskaya, Aliaksandra; Lednev, Igor K

2012-10-10

Conventional confirmatory biochemical tests used in the forensic analysis of body fluid traces found at a crime scene are destructive and not universal. Recently, we reported on the application of near-infrared (NIR) Raman microspectroscopy for non-destructive confirmatory identification of pure blood, saliva, semen, vaginal fluid and sweat. Here we expand the method to include dry mixtures of semen and blood. A classification algorithm was developed for differentiating pure body fluids and their mixtures. The classification methodology is based on an effective combination of Support Vector Machine (SVM) regression (data selection) and SVM Discriminant Analysis of preprocessed experimental Raman spectra collected using an automatic mapping of the sample. This extensive cross-validation of the obtained results demonstrated that the detection limit of the minor contributor is as low as a few percent. The developed methodology can be further expanded to any binary mixture of complex solutions, including but not limited to mixtures of other body fluids. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Agricultural Body-Building: Incorporations of Gender, Body and Work

ERIC Educational Resources Information Center

Brandth, Berit

2006-01-01

This paper is concerned with gendered embodiment of agricultural work, particularly the connection between women's gender identity and the body at work. Focussing on how the body enters into relations with the tools of work, four processes are identified by which women's bodies, work and machinery are incorporated into each other and give each…

School Building Organisation in Greece.

ERIC Educational Resources Information Center

PEB Exchange, 2001

2001-01-01

Discusses the past and current organizational structure of Greece's School Building Organisation, a body established to work with government agencies in the design and construction of new buildings and the provisioning of educational equipment. Future planning to incorporate culture and creativity, sports, and laboratory learning in modern school…

Clustering-based classification of road traffic accidents using hierarchical clustering and artificial neural networks.

PubMed

Taamneh, Madhar; Taamneh, Salah; Alkheder, Sharaf

2017-09-01

Artificial neural networks (ANNs) have been widely used in predicting the severity of road traffic crashes. All available information about previously occurred accidents is typically used for building a single prediction model (i.e., classifier). Too little attention has been paid to the differences between these accidents, leading, in most cases, to build less accurate predictors. Hierarchical clustering is a well-known clustering method that seeks to group data by creating a hierarchy of clusters. Using hierarchical clustering and ANNs, a clustering-based classification approach for predicting the injury severity of road traffic accidents was proposed. About 6000 road accidents occurred over a six-year period from 2008 to 2013 in Abu Dhabi were used throughout this study. In order to reduce the amount of variation in data, hierarchical clustering was applied on the data set to organize it into six different forms, each with different number of clusters (i.e., clusters from 1 to 6). Two ANN models were subsequently built for each cluster of accidents in each generated form. The first model was built and validated using all accidents (training set), whereas only 66% of the accidents were used to build the second model, and the remaining 34% were used to test it (percentage split). Finally, the weighted average accuracy was computed for each type of models in each from of data. The results show that when testing the models using the training set, clustering prior to classification achieves (11%-16%) more accuracy than without using clustering, while the percentage split achieves (2%-5%) more accuracy. The results also suggest that partitioning the accidents into six clusters achieves the best accuracy if both types of models are taken into account.

Branch classification: A new mechanism for improving branch predictor performance

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chang, P.Y.; Hao, E.; Patt, Y.

There is wide agreement that one of the most significant impediments to the performance of current and future pipelined superscalar processors is the presence of conditional branches in the instruction stream. Speculative execution is one solution to the branch problem, but speculative work is discarded if a branch is mispredicted. For it to be effective, speculative work is discarded if a branch is mispredicted. For it to be effective, speculative execution requires a very accurate branch predictor; 95% accuracy is not good enough. This paper proposes branch classification, a methodology for building more accurate branch predictors. Branch classification allows anmore » individual branch instruction to be associated with the branch predictor best suited to predict its direction. Using this approach, a hybrid branch predictor can be constructed such that each component branch predictor predicts those branches for which it is best suited. To demonstrate the usefulness of branch classification, an example classification scheme is given and a new hybrid predictor is built based on this scheme which achieves a higher prediction accuracy than any branch predictor previously reported in the literature.« less

On feature augmentation for semantic argument classification of the Quran English translation using support vector machine

NASA Astrophysics Data System (ADS)

Khaira Batubara, Dina; Arif Bijaksana, Moch; Adiwijaya

2018-03-01

Research on the semantic argument classification requires semantically labeled data in large numbers, called corpus. Because building a corpus is costly and time-consuming, recently many studies have used existing corpus as the training data to conduct semantic argument classification research on new domain. But previous studies have proven that there is a significant decrease in performance when classifying semantic arguments on different domain between the training and the testing data. The main problem is when there is a new argument that found in the testing data but it is not found in the training data. This research carries on semantic argument classification on a new domain that is Quran English Translation by utilizing Propbank corpus as the training data. To recognize the new argument in the training data, this research proposes four new features for extending the argument features in the training data. By using SVM Linear, the experiment has proven that augmenting the proposed features to the baseline system with some combinations option improve the performance of semantic argument classification on Quran data using Propbank Corpus as training data.

Attribute-based Decision Graphs: A framework for multiclass data classification.

PubMed

Bertini, João Roberto; Nicoletti, Maria do Carmo; Zhao, Liang

2017-01-01

Graph-based algorithms have been successfully applied in machine learning and data mining tasks. A simple but, widely used, approach to build graphs from vector-based data is to consider each data instance as a vertex and connecting pairs of it using a similarity measure. Although this abstraction presents some advantages, such as arbitrary shape representation of the original data, it is still tied to some drawbacks, for example, it is dependent on the choice of a pre-defined distance metric and is biased by the local information among data instances. Aiming at exploring alternative ways to build graphs from data, this paper proposes an algorithm for constructing a new type of graph, called Attribute-based Decision Graph-AbDG. Given a vector-based data set, an AbDG is built by partitioning each data attribute range into disjoint intervals and representing each interval as a vertex. The edges are then established between vertices from different attributes according to a pre-defined pattern. Classification is performed through a matching process among the attribute values of the new instance and AbDG. Moreover, AbDG provides an inner mechanism to handle missing attribute values, which contributes for expanding its applicability. Results of classification tasks have shown that AbDG is a competitive approach when compared to well-known multiclass algorithms. The main contribution of the proposed framework is the combination of the advantages of attribute-based and graph-based techniques to perform robust pattern matching data classification, while permitting the analysis the input data considering only a subset of its attributes. Copyright © 2016 Elsevier Ltd. All rights reserved.

Using geometrical, textural, and contextual information of land parcels for classification of detailed urban land use

USGS Publications Warehouse

Wu, S.-S.; Qiu, X.; Usery, E.L.; Wang, L.

2009-01-01

Detailed urban land use data are important to government officials, researchers, and businesspeople for a variety of purposes. This article presents an approach to classifying detailed urban land use based on geometrical, textural, and contextual information of land parcels. An area of 6 by 14 km in Austin, Texas, with land parcel boundaries delineated by the Travis Central Appraisal District of Travis County, Texas, is tested for the approach. We derive fifty parcel attributes from relevant geographic information system (GIS) and remote sensing data and use them to discriminate among nine urban land uses: single family, multifamily, commercial, office, industrial, civic, open space, transportation, and undeveloped. Half of the 33,025 parcels in the study area are used as training data for land use classification and the other half are used as testing data for accuracy assessment. The best result with a decision tree classification algorithm has an overall accuracy of 96 percent and a kappa coefficient of 0.78, and two naive, baseline models based on the majority rule and the spatial autocorrelation rule have overall accuracy of 89 percent and 79 percent, respectively. The algorithm is relatively good at classifying single-family, multifamily, commercial, open space, and undeveloped land uses and relatively poor at classifying office, industrial, civic, and transportation land uses. The most important attributes for land use classification are the geometrical attributes, particularly those related to building areas. Next are the contextual attributes, particularly those relevant to the spatial relationship between buildings, then the textural attributes, particularly the semivariance texture statistic from 0.61-m resolution images.

An AdaBoost Based Approach to Automatic Classification and Detection of Buildings Footprints, Vegetation Areas and Roads from Satellite Images

NASA Astrophysics Data System (ADS)

Gonulalan, Cansu

In recent years, there has been an increasing demand for applications to monitor the targets related to land-use, using remote sensing images. Advances in remote sensing satellites give rise to the research in this area. Many applications ranging from urban growth planning to homeland security have already used the algorithms for automated object recognition from remote sensing imagery. However, they have still problems such as low accuracy on detection of targets, specific algorithms for a specific area etc. In this thesis, we focus on an automatic approach to classify and detect building foot-prints, road networks and vegetation areas. The automatic interpretation of visual data is a comprehensive task in computer vision field. The machine learning approaches improve the capability of classification in an intelligent way. We propose a method, which has high accuracy on detection and classification. The multi class classification is developed for detecting multiple objects. We present an AdaBoost-based approach along with the supervised learning algorithm. The combi- nation of AdaBoost with "Attentional Cascade" is adopted from Viola and Jones [1]. This combination decreases the computation time and gives opportunity to real time applications. For the feature extraction step, our contribution is to combine Haar-like features that include corner, rectangle and Gabor. Among all features, AdaBoost selects only critical features and generates in extremely efficient cascade structured classifier. Finally, we present and evaluate our experimental results. The overall system is tested and high performance of detection is achieved. The precision rate of the final multi-class classifier is over 98%.

Association Between Fish Oil Consumption and the Incidence of Mental Health Issues Among Active Duty Military Personnel

DTIC Science & Technology

2016-03-01

minerals, individual vitamins or minerals, antioxidants, legal body-building supplements, herbal supplements, weight loss products in the past twelve...minerals, individual vitamins or minerals, antioxidants, legal body-building supplements, herbal supplements, or weight loss products” (DOD 2011...These solutions they may be seeking could be medicinal or more alternative in health such as fish oil supplements. Finally, with regard to Navy

The Survey of Cultural Heritage after AN Earthquake: the Case of Emilia-Lombardia in 2012

NASA Astrophysics Data System (ADS)

Adami, A.; Chiarini, S.; Cremonesi, S.; Fregonese, L.; Taffurelli, L.; Valente, M. V.

2016-06-01

In recent years many earthquakes hit Italy and its Cultural Heritage. The topic of survey of buildings damaged by seismic events and their interpretation has become very relevant and involved many research groups and Italian Civil Protection. The damage survey has different roles: in the first stage, immediately after the emergency, the documentation is necessary for the shoring and protection of damaged structures (AEDES forms of Civil Protection). The aim of the second stage is the study and the documentation for the restoration, reconstruction and retrofitting of buildings. In this context, this study presents methods and instruments used in the survey of 24 churches in the province of Mantua, Lombardy, after the 2012 earthquake sequence. The paper examines the difficulties in surveying damaged buildings and presents the classification used to define, time by time, the most suitable survey approach in the field of Geomatics. In this classification, many aspects are taken into account, such as logistical and practical problems, safety conditions, time preserving methods, economic decisions, complexity of building and required results. The accurate documentation obtained as a three-dimensional architectural database allows for the observation and analysis of the damage, the definition of interpretative models and the development of intervention projects. Different results are obtained from the point cloud database: traditional 2D representations for architectural projects as well as 3D models for structural analysis or for the development of BIM.

New decision support tool for acute lymphoblastic leukemia classification

NASA Astrophysics Data System (ADS)

Madhukar, Monica; Agaian, Sos; Chronopoulos, Anthony T.

2012-03-01

In this paper, we build up a new decision support tool to improve treatment intensity choice in childhood ALL. The developed system includes different methods to accurately measure furthermore cell properties in microscope blood film images. The blood images are exposed to series of pre-processing steps which include color correlation, and contrast enhancement. By performing K-means clustering on the resultant images, the nuclei of the cells under consideration are obtained. Shape features and texture features are then extracted for classification. The system is further tested on the classification of spectra measured from the cell nuclei in blood samples in order to distinguish normal cells from those affected by Acute Lymphoblastic Leukemia. The results show that the proposed system robustly segments and classifies acute lymphoblastic leukemia based on complete microscopic blood images.

A framework for farmland parcels extraction based on image classification

NASA Astrophysics Data System (ADS)

Liu, Guoying; Ge, Wenying; Song, Xu; Zhao, Hongdan

2018-03-01

It is very important for the government to build an accurate national basic cultivated land database. In this work, farmland parcels extraction is one of the basic steps. However, during the past years, people had to spend much time on determining an area is a farmland parcel or not, since they were bounded to understand remote sensing images only from the mere visual interpretation. In order to overcome this problem, in this study, a method was proposed to extract farmland parcels by means of image classification. In the proposed method, farmland areas and ridge areas of the classification map are semantically processed independently and the results are fused together to form the final results of farmland parcels. Experiments on high spatial remote sensing images have shown the effectiveness of the proposed method.

Characterizing Geological Facies using Seismic Waveform Classification in Sarawak Basin

NASA Astrophysics Data System (ADS)

Zahraa, Afiqah; Zailani, Ahmad; Prasad Ghosh, Deva

2017-10-01

Numerous effort have been made to build relationship between geology and geophysics using different techniques throughout the years. The integration of these two most important data in oil and gas industry can be used to reduce uncertainty in exploration and production especially for reservoir productivity enhancement and stratigraphic identification. This paper is focusing on seismic waveform classification to different classes using neural network and to link them according to the geological facies which are established using the knowledge on lithology and log motif of well data. Seismic inversion is used as the input for the neural network to act as the direct lithology indicator reducing dependency on well calibration. The interpretation of seismic facies classification map provides a better understanding towards the lithology distribution, depositional environment and help to identify significant reservoir rock

Maintenance Budgeting.

ERIC Educational Resources Information Center

Smith, J. McCree

Three methods for the preparation of maintenance budgets are discussed--(1) a traditional method, inconclusive and obsolete, based on gross square footage, (2) the formula approach method based on building classification (wood-frame, masonry-wood, masonry-concrete) with maintenance cost factors for each type plus custodial service rates by type of…

The Modern View of Nature's Building Blocks.

ERIC Educational Resources Information Center

Akyeampong, D. A.

1985-01-01

Explains current knowledge about the makeup of matter at the microscopic (and even more infinitesimal) levels, accentuating the role of accelerators in the process and considering whether more fundamental particles may exist. Classification of subatomic particles, hadrons, quarks, and gluons are among the areas examined. (JN)

40 CFR 132.2 - Definitions.

Code of Federal Regulations, 2011 CFR

2011-07-01

... lipid) of a substance's lipid-normalized concentration in tissue of an aquatic organism to its organic... develop neoplasms, in animals or humans. The classification of carcinogens is discussed in section II.A of... Species Act. Existing Great Lakes discharger is any building, structure, facility, or installation from...

40 CFR 132.2 - Definitions.

Code of Federal Regulations, 2010 CFR

2010-07-01

... lipid) of a substance's lipid-normalized concentration in tissue of an aquatic organism to its organic... develop neoplasms, in animals or humans. The classification of carcinogens is discussed in section II.A of... Species Act. Existing Great Lakes discharger is any building, structure, facility, or installation from...

Building Diversified Multiple Trees for classification in high dimensional noisy biomedical data.

PubMed

Li, Jiuyong; Liu, Lin; Liu, Jixue; Green, Ryan

2017-12-01

It is common that a trained classification model is applied to the operating data that is deviated from the training data because of noise. This paper will test an ensemble method, Diversified Multiple Tree (DMT), on its capability for classifying instances in a new laboratory using the classifier built on the instances of another laboratory. DMT is tested on three real world biomedical data sets from different laboratories in comparison with four benchmark ensemble methods, AdaBoost, Bagging, Random Forests, and Random Trees. Experiments have also been conducted on studying the limitation of DMT and its possible variations. Experimental results show that DMT is significantly more accurate than other benchmark ensemble classifiers on classifying new instances of a different laboratory from the laboratory where instances are used to build the classifier. This paper demonstrates that an ensemble classifier, DMT, is more robust in classifying noisy data than other widely used ensemble methods. DMT works on the data set that supports multiple simple trees.

Metabolomics for organic food authentication: Results from a long-term field study in carrots.

PubMed

Cubero-Leon, Elena; De Rudder, Olivier; Maquet, Alain

2018-01-15

Increasing demand for organic products and their premium prices make them an attractive target for fraudulent malpractices. In this study, a large-scale comparative metabolomics approach was applied to investigate the effect of the agronomic production system on the metabolite composition of carrots and to build statistical models for prediction purposes. Orthogonal projections to latent structures-discriminant analysis (OPLS-DA) was applied successfully to predict the origin of the agricultural system of the harvested carrots on the basis of features determined by liquid chromatography-mass spectrometry. When the training set used to build the OPLS-DA models contained samples representative of each harvest year, the models were able to classify unknown samples correctly (100% correct classification). If a harvest year was left out of the training sets and used for predictions, the correct classification rates achieved ranged from 76% to 100%. The results therefore highlight the potential of metabolomic fingerprinting for organic food authentication purposes. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Searching for disability in electronic databases of published literature.

PubMed

Walsh, Emily S; Peterson, Jana J; Judkins, Dolores Z

2014-01-01

As researchers in disability and health conduct systematic reviews with greater frequency, the definition of disability used in these reviews gains importance. Translating a comprehensive conceptual definition of "disability" into an operational definition that utilizes electronic databases in the health sciences is a difficult step necessary for performing systematic literature reviews in the field. Consistency of definition across studies will help build a body of evidence that is comparable and amenable to synthesis. To illustrate a process for operationalizing the World Health Organization's International Classification of Disability, Functioning, and Health concept of disability for MEDLINE, PsycINFO, and CINAHL databases. We created an electronic search strategy in conjunction with a reference librarian and an expert panel. Quality control steps included comparison of search results to results of a search for a specific disabling condition and to articles nominated by the expert panel. The complete search strategy is presented. Results of the quality control steps indicated that our strategy was sufficiently sensitive and specific. Our search strategy will be valuable to researchers conducting literature reviews on broad populations with disabilities. Copyright © 2014 Elsevier Inc. All rights reserved.

Influence of Weight Classification on Walking and Jogging Energy Expenditure Prediction in Women

ERIC Educational Resources Information Center

Heden, Timothy D.; LeCheminant, James D.; Smith, John D.

2012-01-01

The purpose of this study was to determine the influence of weight classification on predicting energy expenditure (EE) in women. Twelve overweight (body mass index [BMI] = 25-29.99 kg/m[superscript 2]) and 12 normal-weight (BMI = 18.5-24.99 kg/m[superscript 2]) women walked and jogged 1,609 m at 1.34 m.s[superscript -1] and 2.23 m.s[superscript…

A Novel Approach to ECG Classification Based upon Two-Layered HMMs in Body Sensor Networks

PubMed Central

Liang, Wei; Zhang, Yinlong; Tan, Jindong; Li, Yang

2014-01-01

This paper presents a novel approach to ECG signal filtering and classification. Unlike the traditional techniques which aim at collecting and processing the ECG signals with the patient being still, lying in bed in hospitals, our proposed algorithm is intentionally designed for monitoring and classifying the patient's ECG signals in the free-living environment. The patients are equipped with wearable ambulatory devices the whole day, which facilitates the real-time heart attack detection. In ECG preprocessing, an integral-coefficient-band-stop (ICBS) filter is applied, which omits time-consuming floating-point computations. In addition, two-layered Hidden Markov Models (HMMs) are applied to achieve ECG feature extraction and classification. The periodic ECG waveforms are segmented into ISO intervals, P subwave, QRS complex and T subwave respectively in the first HMM layer where expert-annotation assisted Baum-Welch algorithm is utilized in HMM modeling. Then the corresponding interval features are selected and applied to categorize the ECG into normal type or abnormal type (PVC, APC) in the second HMM layer. For verifying the effectiveness of our algorithm on abnormal signal detection, we have developed an ECG body sensor network (BSN) platform, whereby real-time ECG signals are collected, transmitted, displayed and the corresponding classification outcomes are deduced and shown on the BSN screen. PMID:24681668

Plant species classification using flower images—A comparative study of local feature representations

PubMed Central

Seeland, Marco; Rzanny, Michael; Alaqraa, Nedal; Wäldchen, Jana; Mäder, Patrick

2017-01-01

Steady improvements of image description methods induced a growing interest in image-based plant species classification, a task vital to the study of biodiversity and ecological sensitivity. Various techniques have been proposed for general object classification over the past years and several of them have already been studied for plant species classification. However, results of these studies are selective in the evaluated steps of a classification pipeline, in the utilized datasets for evaluation, and in the compared baseline methods. No study is available that evaluates the main competing methods for building an image representation on the same datasets allowing for generalized findings regarding flower-based plant species classification. The aim of this paper is to comparatively evaluate methods, method combinations, and their parameters towards classification accuracy. The investigated methods span from detection, extraction, fusion, pooling, to encoding of local features for quantifying shape and color information of flower images. We selected the flower image datasets Oxford Flower 17 and Oxford Flower 102 as well as our own Jena Flower 30 dataset for our experiments. Findings show large differences among the various studied techniques and that their wisely chosen orchestration allows for high accuracies in species classification. We further found that true local feature detectors in combination with advanced encoding methods yield higher classification results at lower computational costs compared to commonly used dense sampling and spatial pooling methods. Color was found to be an indispensable feature for high classification results, especially while preserving spatial correspondence to gray-level features. In result, our study provides a comprehensive overview of competing techniques and the implications of their main parameters for flower-based plant species classification. PMID:28234999

Classification of Parkinson's disease utilizing multi-edit nearest-neighbor and ensemble learning algorithms with speech samples.

PubMed

Zhang, He-Hua; Yang, Liuyang; Liu, Yuchuan; Wang, Pin; Yin, Jun; Li, Yongming; Qiu, Mingguo; Zhu, Xueru; Yan, Fang

2016-11-16

The use of speech based data in the classification of Parkinson disease (PD) has been shown to provide an effect, non-invasive mode of classification in recent years. Thus, there has been an increased interest in speech pattern analysis methods applicable to Parkinsonism for building predictive tele-diagnosis and tele-monitoring models. One of the obstacles in optimizing classifications is to reduce noise within the collected speech samples, thus ensuring better classification accuracy and stability. While the currently used methods are effect, the ability to invoke instance selection has been seldomly examined. In this study, a PD classification algorithm was proposed and examined that combines a multi-edit-nearest-neighbor (MENN) algorithm and an ensemble learning algorithm. First, the MENN algorithm is applied for selecting optimal training speech samples iteratively, thereby obtaining samples with high separability. Next, an ensemble learning algorithm, random forest (RF) or decorrelated neural network ensembles (DNNE), is used to generate trained samples from the collected training samples. Lastly, the trained ensemble learning algorithms are applied to the test samples for PD classification. This proposed method was examined using a more recently deposited public datasets and compared against other currently used algorithms for validation. Experimental results showed that the proposed algorithm obtained the highest degree of improved classification accuracy (29.44%) compared with the other algorithm that was examined. Furthermore, the MENN algorithm alone was found to improve classification accuracy by as much as 45.72%. Moreover, the proposed algorithm was found to exhibit a higher stability, particularly when combining the MENN and RF algorithms. This study showed that the proposed method could improve PD classification when using speech data and can be applied to future studies seeking to improve PD classification methods.

Classification of jet fuels by fuzzy rule-building expert systems applied to three-way data by fast gas chromatography--fast scanning quadrupole ion trap mass spectrometry.

PubMed

Sun, Xiaobo; Zimmermann, Carolyn M; Jackson, Glen P; Bunker, Christopher E; Harrington, Peter B

2011-01-30

A fast method that can be used to classify unknown jet fuel types or detect possible property changes in jet fuel physical properties is of paramount interest to national defense and the airline industries. While fast gas chromatography (GC) has been used with conventional mass spectrometry (MS) to study jet fuels, fast GC was combined with fast scanning MS and used to classify jet fuels into lot numbers or origin for the first time by using fuzzy rule-building expert system (FuRES) classifiers. In the process of building classifiers, the data were pretreated with and without wavelet transformation and evaluated with respect to performance. Principal component transformation was used to compress the two-way data images prior to classification. Jet fuel samples were successfully classified with 99.8 ± 0.5% accuracy for both with and without wavelet compression. Ten bootstrapped Latin partitions were used to validate the generalized prediction accuracy. Optimized partial least squares (o-PLS) regression results were used as positively biased references for comparing the FuRES prediction results. The prediction results for the jet fuel samples obtained with these two methods were compared statistically. The projected difference resolution (PDR) method was also used to evaluate the fast GC and fast MS data. Two batches of aliquots of ten new samples were prepared and run independently 4 days apart to evaluate the robustness of the method. The only change in classification parameters was the use of polynomial retention time alignment to correct for drift that occurred during the 4-day span of the two collections. FuRES achieved perfect classifications for four models of uncompressed three-way data. This fast GC/fast MS method furnishes characteristics of high speed, accuracy, and robustness. This mode of measurement may be useful as a monitoring tool to track changes in the chemical composition of fuels that may also lead to property changes. Copyright © 2010 Elsevier B.V. All rights reserved.

Building a model for disease classification integration in oncology, an approach based on the national cancer institute thesaurus.

PubMed

Jouhet, Vianney; Mougin, Fleur; Bréchat, Bérénice; Thiessard, Frantz

2017-02-07

Identifying incident cancer cases within a population remains essential for scientific research in oncology. Data produced within electronic health records can be useful for this purpose. Due to the multiplicity of providers, heterogeneous terminologies such as ICD-10 and ICD-O-3 are used for oncology diagnosis recording purpose. To enable disease identification based on these diagnoses, there is a need for integrating disease classifications in oncology. Our aim was to build a model integrating concepts involved in two disease classifications, namely ICD-10 (diagnosis) and ICD-O-3 (topography and morphology), despite their structural heterogeneity. Based on the NCIt, a "derivative" model for linking diagnosis and topography-morphology combinations was defined and built. ICD-O-3 and ICD-10 codes were then used to instantiate classes of the "derivative" model. Links between terminologies obtained through the model were then compared to mappings provided by the Surveillance, Epidemiology, and End Results (SEER) program. The model integrated 42% of neoplasm ICD-10 codes (excluding metastasis), 98% of ICD-O-3 morphology codes (excluding metastasis) and 68% of ICD-O-3 topography codes. For every codes instantiating at least a class in the "derivative" model, comparison with SEER mappings reveals that all mappings were actually available in the model as a link between the corresponding codes. We have proposed a method to automatically build a model for integrating ICD-10 and ICD-O-3 based on the NCIt. The resulting "derivative" model is a machine understandable resource that enables an integrated view of these heterogeneous terminologies. The NCIt structure and the available relationships can help to bridge disease classifications taking into account their structural and granular heterogeneities. However, (i) inconsistencies exist within the NCIt leading to misclassifications in the "derivative" model, (ii) the "derivative" model only integrates a part of ICD-10 and ICD-O-3. The NCIt is not sufficient for integration purpose and further work based on other termino-ontological resources is needed in order to enrich the model and avoid identified inconsistencies.

Winning Bodies and Souls: State Building and the Necessity of Nationalism

DTIC Science & Technology

2008-12-01

the citizens that live within its territory. The RAND study, America’s Role in Nation Building: from Germany to Iraq, nicely encapsulates the...neglect of nationalism in the theory and practice of the state building when it blithely observes that:   What principally distinguishes Germany , Japan...Nation Building: From Germany to Iraq (Santa Monica: RAND, 2003), xix. 6 This argument views nation-building projects as primarily the result of what

Distinguishing body mass and activity level from the lower limb: can entheses diagnose obesity?

PubMed

Godde, Kanya; Taylor, Rebecca Wilson

2013-03-10

The ability to estimate body size from the skeleton has broad applications, but is especially important to the forensic community when identifying unknown skeletal remains. This research investigates the utility of using entheses/muscle skeletal markers of the lower limb to estimate body size and to classify individuals into average, obese, and active categories, while using a biomechanical approach to interpret the results. Eighteen muscle attachment sites of the lower limb, known to be involved in the sit-to-stand transition, were scored for robusticity and stress in 105 white males (aged 31-81 years) from the William M. Bass Donated Skeletal Collection. Both logistic regression and log linear models were applied to the data to (1) test the utility of entheses as an indicator of body weight and activity level, and (2) to generate classification percentages that speak to the accuracy of the method. Thirteen robusticity scores differed significantly between the groups, but classification percentages were only slightly greater than chance. However, clear differences could be seen between the average and obese and the average and active groups. Stress scores showed no value in discriminating between groups. These results were interpreted in relation to biomechanical forces at the microscopic and macroscopic levels. Even though robusticity alone is not able to classify individuals well, its significance may show greater value when incorporated into a model that has multiple skeletal indicators. Further research needs to evaluate a larger sample and incorporate several lines of evidence to improve classification rates. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Menstrual Changes in Body Composition of Female Athletes.

PubMed

Stachoń, Aleksandra Jadwiga

2016-06-01

The aim of the study was to determine whether the tendencies and scope of changes in body mass, body composition and body girths across the menstrual cycle were similar or different in women of different body build. Anthropometric examinations were carried out in a group of 40 naturally regularly menstruated females practicing team sports (aged 19-21, B-v 169.3+/-6.4 cm, body mass 59.6+/-7.0 kg), in the follicular, periovulatory and luteal phases of the menstrual cycle. The phases were determined on the basis of data from two consecutive menstrual cycles taking into account the cycle’s length. To establish the type of body build, Body Mass Index, hydration status and skinfold thickness were measured. For a statistical analysis, a multiple comparisons with multiple confidence intervals were applied. The increase in body mass between the follicular and the luteal phases was observed in all groups of women, the biggest gain was recorded in slim women, who in the luteal phase weighted 0.8 kg more. The amount of fat mass increased significantly across the menstrual cycle only in more hydrated (by about 0.66 kg) and slim women (by about 0.54 kg). Significant changes between consecutive phases of the menstrual cycle in waist and hip girths, and suprailiac skinfold thickness in some groups of women also indicate influence of fatness and hydration status and slenderness. In view of the presented results, the body build seems important for an analysis of the pattern of each component’s changes across the menstrual cycle, especially for female athletes. Certain changes can be seen only in some groups of women, therefore somatic features can be considered as a predictor of the intensity of changes.

Development of Accommodation Models for Soldiers in Vehicles: Squad

DTIC Science & Technology

2014-09-01

existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Send comments...Distribution Statement A. Approved for public release; distribution is unlimited 13. SUPPLEMENTARY NOTES 14. ABSTRACT Data from a previous study...body armor and body borne gear. 15. SUBJECT TERMS Anthropometry , Posture, Vehicle Occupants, Accommodation 16. SECURITY CLASSIFICATION OF

Suggestions for the New Social Entrepreneurship Initiative: Focus on Building a Body of Research-Proven Programs, Shown to Produce Major Gains in Education, Poverty Reduction, Crime Prevention, and Other Areas

ERIC Educational Resources Information Center

Coalition for Evidence-Based Policy, 2009

2009-01-01

This paper outlines a possible approach to implementing the Social Entrepreneurship initiative, focused on building a body of research-proven program models/strategies, and scaling them up, so as to produce major progress in education, poverty reduction, crime prevention, and other areas. The paper summarizes the rationale for this approach, then…

The impact of modeling the dependencies among patient findings on classification accuracy and calibration.

PubMed Central

Monti, S.; Cooper, G. F.

1998-01-01

We present a new Bayesian classifier for computer-aided diagnosis. The new classifier builds upon the naive-Bayes classifier, and models the dependencies among patient findings in an attempt to improve its performance, both in terms of classification accuracy and in terms of calibration of the estimated probabilities. This work finds motivation in the argument that highly calibrated probabilities are necessary for the clinician to be able to rely on the model's recommendations. Experimental results are presented, supporting the conclusion that modeling the dependencies among findings improves calibration. PMID:9929288

Criteria for classification of competitive housing projects in terms of their environmental friendliness

NASA Astrophysics Data System (ADS)

Nezhnikova, Ekaterina

2017-10-01

This article deals with social and economic essence of strategy of the housing industry development, both complex system of economic relations in field of production and consumption, which is regulated through the mechanism of prices and implemented through formation and realization of priority directions. Developed criteria for classification of housing construction projects as environmentally friendly and the quality criteria of variables for assessment of the environmental friendliness of residential buildings allowed to determine the ways of development of the industry on the basis of creation of competitive projects in interrelation with quality, environmental friendliness and price of consumption.

How automated image analysis techniques help scientists in species identification and classification?

PubMed

Yousef Kalafi, Elham; Town, Christopher; Kaur Dhillon, Sarinder

2017-09-04

Identification of taxonomy at a specific level is time consuming and reliant upon expert ecologists. Hence the demand for automated species identification increased over the last two decades. Automation of data classification is primarily focussed on images, incorporating and analysing image data has recently become easier due to developments in computational technology. Research efforts in identification of species include specimens' image processing, extraction of identical features, followed by classifying them into correct categories. In this paper, we discuss recent automated species identification systems, categorizing and evaluating their methods. We reviewed and compared different methods in step by step scheme of automated identification and classification systems of species images. The selection of methods is influenced by many variables such as level of classification, number of training data and complexity of images. The aim of writing this paper is to provide researchers and scientists an extensive background study on work related to automated species identification, focusing on pattern recognition techniques in building such systems for biodiversity studies.

Rapid identification and classification of bacteria by 16S rDNA restriction fragment melting curve analyses (RFMCA).

PubMed

Rudi, Knut; Kleiberg, Gro H; Heiberg, Ragnhild; Rosnes, Jan T

2007-08-01

The aim of this work was to evaluate restriction fragment melting curve analyses (RFMCA) as a novel approach for rapid classification of bacteria during food production. RFMCA was evaluated for bacteria isolated from sous vide food products, and raw materials used for sous vide production. We identified four major bacterial groups in the material analysed (cluster I-Streptococcus, cluster II-Carnobacterium/Bacillus, cluster III-Staphylococcus and cluster IV-Actinomycetales). The accuracy of RFMCA was evaluated by comparison with 16S rDNA sequencing. The strains satisfying the RFMCA quality filtering criteria (73%, n=57), with both 16S rDNA sequence information and RFMCA data (n=45) gave identical group assignments with the two methods. RFMCA enabled rapid and accurate classification of bacteria that is database compatible. Potential application of RFMCA in the food or pharmaceutical industry will include development of classification models for the bacteria expected in a given product, and then to build an RFMCA database as a part of the product quality control.

Wing Classification in the Virtual Research Center

NASA Technical Reports Server (NTRS)

Campbell, William H.

1999-01-01

The Virtual Research Center (VRC) is a Web site that hosts a database of documents organized to allow teams of scientists and engineers to store and maintain documents. A number of other workgroup-related capabilities are provided. My tasks as a NASA/ASEE Summer Faculty Fellow included developing a scheme for classifying the workgroups using the VRC using the various Divisions within NASA Enterprises. To this end I developed a plan to use several CGI Perl scripts to gather classification information from the leaders of the workgroups, and to display all the workgroups within a specified classification. I designed, implemented, and partially tested scripts which can be used to do the classification. I was also asked to consider directions for future development of the VRC. I think that the VRC can use XML to advantage. XML is a markup language with designer tags that can be used to build meaning into documents. An investigation as to how CORBA, an object-oriented object request broker included with JDK 1.2, might be used also seems justified.

The Iterated Classification Game: A New Model of the Cultural Transmission of Language

PubMed Central

Swarup, Samarth; Gasser, Les

2010-01-01

The Iterated Classification Game (ICG) combines the Classification Game with the Iterated Learning Model (ILM) to create a more realistic model of the cultural transmission of language through generations. It includes both learning from parents and learning from peers. Further, it eliminates some of the chief criticisms of the ILM: that it does not study grounded languages, that it does not include peer learning, and that it builds in a bias for compositional languages. We show that, over the span of a few generations, a stable linguistic system emerges that can be acquired very quickly by each generation, is compositional, and helps the agents to solve the classification problem with which they are faced. The ICG also leads to a different interpretation of the language acquisition process. It suggests that the role of parents is to initialize the linguistic system of the child in such a way that subsequent interaction with peers results in rapid convergence to the correct language. PMID:20190877

Investigating the Potential of Deep Neural Networks for Large-Scale Classification of Very High Resolution Satellite Images

NASA Astrophysics Data System (ADS)

Postadjian, T.; Le Bris, A.; Sahbi, H.; Mallet, C.

2017-05-01

Semantic classification is a core remote sensing task as it provides the fundamental input for land-cover map generation. The very recent literature has shown the superior performance of deep convolutional neural networks (DCNN) for many classification tasks including the automatic analysis of Very High Spatial Resolution (VHR) geospatial images. Most of the recent initiatives have focused on very high discrimination capacity combined with accurate object boundary retrieval. Therefore, current architectures are perfectly tailored for urban areas over restricted areas but not designed for large-scale purposes. This paper presents an end-to-end automatic processing chain, based on DCNNs, that aims at performing large-scale classification of VHR satellite images (here SPOT 6/7). Since this work assesses, through various experiments, the potential of DCNNs for country-scale VHR land-cover map generation, a simple yet effective architecture is proposed, efficiently discriminating the main classes of interest (namely buildings, roads, water, crops, vegetated areas) by exploiting existing VHR land-cover maps for training.

Explanation and Prediction: Building a Unified Theory of Librarianship, Concept and Review.

ERIC Educational Resources Information Center

McGrath, William E.

2002-01-01

Develops a comprehensive, unified, explanatory theory of librarianship by first making an analogy to the unification of the fundamental forces of nature. Topics include dependent and independent variables; publishing; acquisitions; classification and organization of knowledge; storage, preservation, and collection management; collections; and…

46 CFR 116.300 - Structural design.

Code of Federal Regulations, 2013 CFR

2013-10-01

... Structure § 116.300 Structural design. Except as otherwise allowed by this subpart, a vessel must comply... the vessel. (a) Steel hull vessels: (1) Rules and Regulations for the Classification of Yachts and Small Craft, Lloyd's Register of Shipping (Lloyd's); or (2) Rules for Building and Classing Steel...

46 CFR 116.300 - Structural design.

Code of Federal Regulations, 2012 CFR

2012-10-01

... Structure § 116.300 Structural design. Except as otherwise allowed by this subpart, a vessel must comply... the vessel. (a) Steel hull vessels: (1) Rules and Regulations for the Classification of Yachts and Small Craft, Lloyd's Register of Shipping (Lloyd's); or (2) Rules for Building and Classing Steel...

46 CFR 116.300 - Structural design.

Code of Federal Regulations, 2010 CFR

2010-10-01

... Structure § 116.300 Structural design. Except as otherwise allowed by this subpart, a vessel must comply... the vessel. (a) Steel hull vessels: (1) Rules and Regulations for the Classification of Yachts and Small Craft, Lloyd's Register of Shipping (Lloyd's); or (2) Rules for Building and Classing Steel...

46 CFR 116.300 - Structural design.

Code of Federal Regulations, 2011 CFR

2011-10-01

... Structure § 116.300 Structural design. Except as otherwise allowed by this subpart, a vessel must comply... the vessel. (a) Steel hull vessels: (1) Rules and Regulations for the Classification of Yachts and Small Craft, Lloyd's Register of Shipping (Lloyd's); or (2) Rules for Building and Classing Steel...

46 CFR 116.300 - Structural design.

Code of Federal Regulations, 2014 CFR

2014-10-01

... Structure § 116.300 Structural design. Except as otherwise allowed by this subpart, a vessel must comply... the vessel. (a) Steel hull vessels: (1) Rules and Regulations for the Classification of Yachts and Small Craft, Lloyd's Register of Shipping (Lloyd's); or (2) Rules for Building and Classing Steel...

Using Enviro-Pod low altitude imagery to inventory building surface materials for an acid rain study - A Baltimore example

NASA Technical Reports Server (NTRS)

Ellefsen, Richard; Coffland, Bruce

1987-01-01

Low altitude, oblique and vertical color photography taken from EPA's Enviro-Pod Ka 85 camera system has provided the data for taking an inventory of building surface materials in a test area of downtown Baltimore. Photography was acquired from a gridded flight plan to provide views of all sides of buildings. Color, texture, and linear detail are employed in the photo interpretation aided by contextual reference to a classification of building construction type developed in an earlier study. The work could potentially support a materials inventory initiated by the National Acid Precipitation Assessment Program (NAPAP) by scientists from EPA, Geological Survey, and the Department of Energy. Initial results show the method to be viable. Discrete surface materials such as brick, both bare and painted, stone, and metal are identified.

30 CFR 761.5 - Definitions.

Code of Federal Regulations, 2010 CFR

2010-07-01

...— Cemetery means any area of land where human bodies are interred. Community or institutional building means... temporary basis for human habitation. Public building means any structure that is owned or leased, and...

30 CFR 761.5 - Definitions.

Code of Federal Regulations, 2011 CFR

2011-07-01

...— Cemetery means any area of land where human bodies are interred. Community or institutional building means... temporary basis for human habitation. Public building means any structure that is owned or leased, and...

Reassessing the Formation of CK7 Northwest Africa (NWA) 8186

NASA Technical Reports Server (NTRS)

Srinivasan, P.; McCubbin, F. M.; Lapen, T. J.; Righter, M.; Agee, C. B.

2017-01-01

The classification of meteorites is commonly determined using isotopes, modal mineralogy, and bulk compositions [1]. Bulk rare earth elements (REEs) in meteorites are additionally utilized to understand parent body processes. Numerous authors have shown that chondritic groups exhibit REE patterns that may be attributable to their parent bodies [e.g. 2-4], and variations in abundances and concentrations of REEs may reflect early nebular processes, thermal metamorphism, and aqueous alteration on the parent body [5-6].

LPT. Elevations of low power test building (TAN640 and 641). ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

LPT. Elevations of low power test building (TAN-640 and -641). West and south elevations show stepped shield wall. South and east elevations show pumice block passageway on south side. Reactor cell walls are concrete. One-story parts are pumice block. Metal rollup doors. Ralph M. Parsons 1229-12 ANP/GE-7-640-A-2. November 1956. Approved by INEEL Classification Office for public release. INEEL index code no. 038-0640-00-693-107275 - Idaho National Engineering Laboratory, Test Area North, Scoville, Butte County, ID

A method for classification of multisource data using interval-valued probabilities and its application to HIRIS data

NASA Technical Reports Server (NTRS)

Kim, H.; Swain, P. H.

1991-01-01

A method of classifying multisource data in remote sensing is presented. The proposed method considers each data source as an information source providing a body of evidence, represents statistical evidence by interval-valued probabilities, and uses Dempster's rule to integrate information based on multiple data source. The method is applied to the problems of ground-cover classification of multispectral data combined with digital terrain data such as elevation, slope, and aspect. Then this method is applied to simulated 201-band High Resolution Imaging Spectrometer (HIRIS) data by dividing the dimensionally huge data source into smaller and more manageable pieces based on the global statistical correlation information. It produces higher classification accuracy than the Maximum Likelihood (ML) classification method when the Hughes phenomenon is apparent.

Lean waste classification model to support the sustainable operational practice

NASA Astrophysics Data System (ADS)

Sutrisno, A.; Vanany, I.; Gunawan, I.; Asjad, M.

2018-04-01

Driven by growing pressure for a more sustainable operational practice, improvement on the classification of non-value added (waste) is one of the prerequisites to realize sustainability of a firm. While the use of the 7 (seven) types of the Ohno model now becoming a versatile tool to reveal the lean waste occurrence. In many recent investigations, the use of the Seven Waste model of Ohno is insufficient to cope with the types of waste occurred in industrial practices at various application levels. Intended to a narrowing down this limitation, this paper presented an improved waste classification model based on survey to recent studies discussing on waste at various operational stages. Implications on the waste classification model to the body of knowledge and industrial practices are provided.

From Genome-Wide Association Study to Phenome-Wide Association Study: New Paradigms in Obesity Research.

PubMed

Zhang, Y-P; Zhang, Y-Y; Duan, D D

2016-01-01

Obesity is a condition in which excess body fat has accumulated over an extent that increases the risk of many chronic diseases. The current clinical classification of obesity is based on measurement of body mass index (BMI), waist-hip ratio, and body fat percentage. However, these measurements do not account for the wide individual variations in fat distribution, degree of fatness or health risks, and genetic variants identified in the genome-wide association studies (GWAS). In this review, we will address this important issue with the introduction of phenome, phenomics, and phenome-wide association study (PheWAS). We will discuss the new paradigm shift from GWAS to PheWAS in obesity research. In the era of precision medicine, phenomics and PheWAS provide the required approaches to better definition and classification of obesity according to the association of obese phenome with their unique molecular makeup, lifestyle, and environmental impact. Copyright © 2016 Elsevier Inc. All rights reserved.

Automatic topic identification of health-related messages in online health community using text classification.

PubMed

Lu, Yingjie

2013-01-01

To facilitate patient involvement in online health community and obtain informative support and emotional support they need, a topic identification approach was proposed in this paper for identifying automatically topics of the health-related messages in online health community, thus assisting patients in reaching the most relevant messages for their queries efficiently. Feature-based classification framework was presented for automatic topic identification in our study. We first collected the messages related to some predefined topics in a online health community. Then we combined three different types of features, n-gram-based features, domain-specific features and sentiment features to build four feature sets for health-related text representation. Finally, three different text classification techniques, C4.5, Naïve Bayes and SVM were adopted to evaluate our topic classification model. By comparing different feature sets and different classification techniques, we found that n-gram-based features, domain-specific features and sentiment features were all considered to be effective in distinguishing different types of health-related topics. In addition, feature reduction technique based on information gain was also effective to improve the topic classification performance. In terms of classification techniques, SVM outperformed C4.5 and Naïve Bayes significantly. The experimental results demonstrated that the proposed approach could identify the topics of online health-related messages efficiently.

Breast density characterization using texton distributions.

PubMed

Petroudi, Styliani; Brady, Michael

2011-01-01

Breast density has been shown to be one of the most significant risks for developing breast cancer, with women with dense breasts at four to six times higher risk. The Breast Imaging Reporting and Data System (BI-RADS) has a four class classification scheme that describes the different breast densities. However, there is great inter and intra observer variability among clinicians in reporting a mammogram's density class. This work presents a novel texture classification method and its application for the development of a completely automated breast density classification system. The new method represents the mammogram using textons, which can be thought of as the building blocks of texture under the operational definition of Leung and Malik as clustered filter responses. The new proposed method characterizes the mammographic appearance of the different density patterns by evaluating the texton spatial dependence matrix (TDSM) in the breast region's corresponding texton map. The TSDM is a texture model that captures both statistical and structural texture characteristics. The normalized TSDM matrices are evaluated for mammograms from the different density classes and corresponding texture models are established. Classification is achieved using a chi-square distance measure. The fully automated TSDM breast density classification method is quantitatively evaluated on mammograms from all density classes from the Oxford Mammogram Database. The incorporation of texton spatial dependencies allows for classification accuracy reaching over 82%. The breast density classification accuracy is better using texton TSDM compared to simple texton histograms.

CAMUR: Knowledge extraction from RNA-seq cancer data through equivalent classification rules.

PubMed

Cestarelli, Valerio; Fiscon, Giulia; Felici, Giovanni; Bertolazzi, Paola; Weitschek, Emanuel

2016-03-01

Nowadays, knowledge extraction methods from Next Generation Sequencing data are highly requested. In this work, we focus on RNA-seq gene expression analysis and specifically on case-control studies with rule-based supervised classification algorithms that build a model able to discriminate cases from controls. State of the art algorithms compute a single classification model that contains few features (genes). On the contrary, our goal is to elicit a higher amount of knowledge by computing many classification models, and therefore to identify most of the genes related to the predicted class. We propose CAMUR, a new method that extracts multiple and equivalent classification models. CAMUR iteratively computes a rule-based classification model, calculates the power set of the genes present in the rules, iteratively eliminates those combinations from the data set, and performs again the classification procedure until a stopping criterion is verified. CAMUR includes an ad-hoc knowledge repository (database) and a querying tool.We analyze three different types of RNA-seq data sets (Breast, Head and Neck, and Stomach Cancer) from The Cancer Genome Atlas (TCGA) and we validate CAMUR and its models also on non-TCGA data. Our experimental results show the efficacy of CAMUR: we obtain several reliable equivalent classification models, from which the most frequent genes, their relationships, and the relation with a particular cancer are deduced. dmb.iasi.cnr.it/camur.php emanuel@iasi.cnr.it Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.

Physical fitness of secondary school adolescents in relation to the body weight and the body composition: classification according to Bioelectrical Impedance Analysis. Part II.

PubMed

Chwałczyńska, Agnieszka; Jędrzejewski, Grzegorz; Lewandowski, Zdzisław; Jonak, Wiesława; Sobiech, Krzysztof A

2017-03-01

Underweight and obesity are important factors affecting the level of physical fitness. The aim of this study was to assess physical fitness of lower secondary school adolescents in relation to BMI. Two-hundred students, aged 14-16, were examined. Respondents were divided into 4 groups according to BMI classification. The body height and weight were determined. Physical fitness was assessed on the basis Zuchora's ISF tests. The body weight deficiency occurred in 3% of girls and 5% of boys, overweight was noted in 14% of both groups, and obesity in 6% and 12% accordingly. Statistically significant differences were determined in the components of physical fitness. They were noted in both genders between the group of children with standard body weight and overweight as well as obese children. Significant negative correlations were determined between and the components of physical fitness. More significant correlations giving evidence to the decrease of Zuchora's ISF score along with the increase of BMI were more significant in girls. Statistically significant differences between the boys and girls were determined in all five Zuchora's tests. The highest scores in physical fitness were achieved by the boys and girls with weight deficiency.

Physical fitness of secondary school adolescents in relation to the body weight and the body composition: classification according to World Health Organization. Part I.

PubMed

Chwałczyńska, Agnieszka; Jędrzejewski, Grzegorz; Socha, Małgorzata; Jonak, Wiesława; Sobiech, Krzysztof A

2017-03-01

Underweight and obesity are important factors affecting the level of physical fitness. The aim of this study was to assess physical fitness of lower secondary school adolescents in relation to BMI. Two-hundred students, aged 14-16, were examined. Respondents were divided into 4 groups according to BMI classification. The body height and weight were determined. Physical fitness was assessed on the basis Zuchora's ISF tests. The body weight deficiency occurred in 3% of girls and 5% of boys, overweight was noted in 14% of both groups, and obesity in 6% and 12% accordingly. Statistically significant differences were determined in the components of physical fitness. They were noted in both genders between the group of children with standard body weight and overweight as well as obese children. Significant negative correlations were determined between and the components of physical fitness. More significant correlations giving evidence to the decrease of Zuchora's ISF score along with the increase of BMI were more significant in girls. Statistically significant differences between the boys and girls were determined in all five Zuchora's tests. The highest scores in physical fitness were achieved by the boys and girls with weight deficiency.

Professional classifications of American nurses, 1910 to 1935.

PubMed

Lusk, B

1997-04-01

Nursing's claim to professional status is debatable. The purpose of this historical study is to describe the official classifications of American nurses as professionals or nonprofessionals, from 1910 to 1935. Labor legislation before World War I, military decisions during that war, and federal mandates during the Great Depression resulted in differing professional classifications of nurses. Although nurse leaders aspired to traditional criteria of professionalism, such as individual responsibility and a deep, distinct body of knowledge, these criteria were subsumed by political, financial, and gender issues. This study demonstrates that professional status cannot be assured by attainment of professional criteria alone, but is defined by more diverse and complex issues.

SVMs for Vibration-Based Terrain Classification

NASA Astrophysics Data System (ADS)

Weiss, Christian; Stark, Matthias; Zell, Andreas

When an outdoor mobile robot traverses different types of ground surfaces, different types of vibrations are induced in the body of the robot. These vibrations can be used to learn a discrimination between different surfaces and to classify the current terrain. Recently, we presented a method that uses Support Vector Machines for classification, and we showed results on data collected with a hand-pulled cart. In this paper, we show that our approach also works well on an outdoor robot. Furthermore, we more closely investigate in which direction the vibration should be measured. Finally, we present a simple but effective method to improve the classification by combining measurements taken in multiple directions.

Proteolytic digestion of bacterial inclusion body proteins during dynamic transition between soluble and insoluble forms.

PubMed

Carrió, M M; Corchero, J L; Villaverde, A

1999-09-14

Inclusion bodies formed by two closely related hybrid proteins, namely VP1LAC and LACVP1, have been compared during their building in Escherichia coli. Features of these proteins are determinant of aggregation rates and protein composition of the bodies, generating insoluble particles with distinguishable volume evolution. Interestingly, in LACVP1 and less perceptibly in VP1LAC bodies, an important fraction of the aggregated polypeptide is lost at a given stage of body construction. Stable degradation intermediates of the more fragile LACVP1 are concomitantly found embedded in the bodies. When recombinant protein synthesis is arrested in growing cells, the amount of aggregated protein drops while the amount of soluble protein undergoes a sudden rise before proteolysis. This indicates an architectural plasticity during the in vivo building of the studied inclusion bodies by a dynamic transition between soluble and insoluble forms of the recombinant proteins involved. During this transition, protease-sensitive polypeptides can suffer an efficient proteolytic attack and the resulting fragments further aggregate as inclusion body components.

Sensitivity and specificity of criteria for classifying body mass index in adolescents.

PubMed

Farias Júnior, José Cazuza de; Konrad, Lisandra Maria; Rabacow, Fabiana Maluf; Grup, Susane; Araújo, Valbério Candido

2009-02-01

To estimate the prevalence of overweight among adolescents using different body mass index (BMI) classification criteria, and to determine sensitivity and specificity values for these criteria. Weight, height, and tricipital and subscapular skinfolds in 934 adolescents (462 males and 472 females) aged 14-18 years (mean age 16.2; SD=1.0) of the city of Florianópolis, Southern Brazil, in 2001. Percent fat estimated based on skinfold measurements (> or =25% in males and > or =30% in females) was used as a gold-standard for determining specificity and sensitivity of BMI classification criteria among adolescents. The different cutoff points used for classifying BMI in general resulted in similar prevalence of overweight (p>0.05). Sensitivity of the evaluated criteria was high for males (85.4% to 91.7%) and low for females (33.8 to 52.8%). Specificity of all criteria was high for both sexes (83.6% to 98.8%). Estimates of prevalence of obesity among adolescents using different BMI classification criteria were similar and highly specific for both sexes, but sensitivity for females was low.

Contextual Classification of Point Cloud Data by Exploiting Individual 3d Neigbourhoods

NASA Astrophysics Data System (ADS)

Weinmann, M.; Schmidt, A.; Mallet, C.; Hinz, S.; Rottensteiner, F.; Jutzi, B.

2015-03-01

The fully automated analysis of 3D point clouds is of great importance in photogrammetry, remote sensing and computer vision. For reliably extracting objects such as buildings, road inventory or vegetation, many approaches rely on the results of a point cloud classification, where each 3D point is assigned a respective semantic class label. Such an assignment, in turn, typically involves statistical methods for feature extraction and machine learning. Whereas the different components in the processing workflow have extensively, but separately been investigated in recent years, the respective connection by sharing the results of crucial tasks across all components has not yet been addressed. This connection not only encapsulates the interrelated issues of neighborhood selection and feature extraction, but also the issue of how to involve spatial context in the classification step. In this paper, we present a novel and generic approach for 3D scene analysis which relies on (i) individually optimized 3D neighborhoods for (ii) the extraction of distinctive geometric features and (iii) the contextual classification of point cloud data. For a labeled benchmark dataset, we demonstrate the beneficial impact of involving contextual information in the classification process and that using individual 3D neighborhoods of optimal size significantly increases the quality of the results for both pointwise and contextual classification.

[Bodybuilding: hypokalemia and hypophosphatemia].

PubMed

Britschgi, F; Zünd, G

1991-08-17

In preparing for competitive body building, body builders--in addition to continuous and hard muscle training--engage in stringent dietetic manipulations: the first few months of hypercaloric nutrition, rich in proteins, are devoted to the build-up of muscle mass. A second phase of reduced caloric intake is designed reduce subcutaneous fat, while, during the last week of preparations, extreme carbohydrate intake aims at loading muscles with glycogen. Simultaneously, sodium and water restriction results in extracellular and therefore subcutaneous volume deficit and better "definition" of muscle contours and structure. In the course of these dietetic manipulations a young body builder develops hypokalemia, hypophosphatemia, rhabdomyolysis and flaccid tetraparesis. The disturbances are pathophysiologically predictable.

Perceived somatotype and stereotypes of physique among Nigerian schoolchildren.

PubMed

Salokun, S O; Toriola, A L

1985-11-01

The influence of perceived somatotype on stereotypes of behavior associated with body build was investigated among 160 male and 140 female Nigerian children in secondary school. In both groups, the perception of subjects' own physiques and discrepancy between their perceived and preferred physiques significantly explained the variance in the character trait scores attributed to body types. In general, the subjects attributed positive character traits to their perceived somatotypes and undesirable traits to the physiques with which they were dissatisfied. Thus, the perception of somatotype and discrepancy between perceived and preferred physique could significantly differentiate the character traits attributed to body build among male and female children.

Recognition and characterization of networks of water bodies in the Arctic ice-wedge polygonal tundra using high-resolution satellite imagery

NASA Astrophysics Data System (ADS)

Skurikhin, A. N.; Gangodagamage, C.; Rowland, J. C.; Wilson, C. J.

2013-12-01

Arctic lowland landscapes underlain by permafrost are often characterized by polygon-like patterns such as ice-wedge polygons outlined by networks of ice wedges and complemented with polygon rims, troughs, shallow ponds and thermokarst lakes. Polygonal patterns and corresponding features are relatively easy to recognize in high spatial resolution satellite imagery by a human, but their automated recognition is challenging due to the variability in their spectral appearance, the irregularity of individual trough spacing and orientation within the patterns, and a lack of unique spectral response attributable to troughs with widths commonly between 1 m and 2 m. Accurate identification of fine scale elements of ice-wedge polygonal tundra is important as their imprecise recognition may bias estimates of water, heat and carbon fluxes in large-scale climate models. Our focus is on the problem of identification of Arctic polygonal tundra fine-scale landscape elements (as small as 1 m - 2 m width). The challenge of the considered problem is that while large water bodies (e.g. lakes and rivers) can be recognized based on spectral response, reliable recognition of troughs is more difficult. Troughs do not have unique spectral signature, their appearance is noisy (edges are not strong), their width is small, and they often form connected networks with ponds and lakes, and thus they have overlapping spectral response with other water bodies and surrounding non-water bodies. We present a semi-automated approach to identify and classify Arctic polygonal tundra landscape components across the range of spatial scales, such as troughs, ponds, river- and lake-like objects, using high spatial resolution satellite imagery. The novelty of the approach lies in: (1) the combined use of segmentation and shape-based classification to identify a broad range of water bodies, including troughs, and (2) the use of high-resolution WorldView-2 satellite imagery (with resolution of 0.6 m) for this identification. The approach starts by segmenting water bodies from an image, which are then categorized using shape-based classification. Segmentation uses combination of pan sharpened multispectral bands and is based on the active contours without edges technique. The segmentation is robust to noise and can detect objects with weak boundaries that is important for extraction of troughs. We then categorize the segmented regions via shape based classification. Because segmentation accuracy is the main factor impacting the quality of the shape-based classification, for segmentation accuracy assessment we created reference image using WorldView-2 satellite image of ice-wedge polygonal tundra. Reference image contained manually labelled image regions which cover components of drainage networks, such as troughs, ponds, rivers and lakes. The evaluation has shown that the approach provides a good accuracy of segmentation and reasonable classification results. The overall accuracy of the segmentation is approximately 95%, the segmentation user's and producer's accuracies are approximately 92% and 97% respectively.

Cancer

MedlinePlus

Cancer begins in your cells, which are the building blocks of your body. Normally, your body forms ... be benign or malignant. Benign tumors aren't cancer while malignant ones are. Cells from malignant tumors ...

Access for Disabled People to School Buildings: Management and Design Guide. Building Bulletin 91.

ERIC Educational Resources Information Center

Wood, Sue

England's Department for Education and Employment provides construction standards with regard to access to school buildings for people with disabilities. This bulletin gives supplementary nonstatutory guidance for school governors and commissioning bodies, seeking to promote a general understanding of the issues and providing guidelines for the…

Case definition and classification of leukodystrophies and leukoencephalopathies.

PubMed

Vanderver, Adeline; Prust, Morgan; Tonduti, Davide; Mochel, Fanny; Hussey, Heather M; Helman, Guy; Garbern, James; Eichler, Florian; Labauge, Pierre; Aubourg, Patrick; Rodriguez, Diana; Patterson, Marc C; Van Hove, Johan L K; Schmidt, Johanna; Wolf, Nicole I; Boespflug-Tanguy, Odile; Schiffmann, Raphael; van der Knaap, Marjo S

2015-04-01

An approved definition of the term leukodystrophy does not currently exist. The lack of a precise case definition hampers efforts to study the epidemiology and the relevance of genetic white matter disorders to public health. Thirteen experts at multiple institutions participated in iterative consensus building surveys to achieve definition and classification of disorders as leukodystrophies using a modified Delphi approach. A case definition for the leukodystrophies was achieved, and a total of 30 disorders were classified under this definition. In addition, a separate set of disorders with heritable white matter abnormalities but not meeting criteria for leukodystrophy, due to presumed primary neuronal involvement and prominent systemic manifestations, was classified as genetic leukoencephalopathies (gLE). A case definition of leukodystrophies and classification of heritable white matter disorders will permit more detailed epidemiologic studies of these disorders. Copyright © 2015 Elsevier Inc. All rights reserved.

Supervised versus unsupervised categorization: two sides of the same coin?

PubMed

Pothos, Emmanuel M; Edwards, Darren J; Perlman, Amotz

2011-09-01

Supervised and unsupervised categorization have been studied in separate research traditions. A handful of studies have attempted to explore a possible convergence between the two. The present research builds on these studies, by comparing the unsupervised categorization results of Pothos et al. ( 2011 ; Pothos et al., 2008 ) with the results from two procedures of supervised categorization. In two experiments, we tested 375 participants with nine different stimulus sets and examined the relation between ease of learning of a classification, memory for a classification, and spontaneous preference for a classification. After taking into account the role of the number of category labels (clusters) in supervised learning, we found the three variables to be closely associated with each other. Our results provide encouragement for researchers seeking unified theoretical explanations for supervised and unsupervised categorization, but raise a range of challenging theoretical questions.

Job and industry classifications associated with sarcoidosis in A Case-Control Etiologic Study of Sarcoidosis (ACCESS).

PubMed

Barnard, Juliana; Rose, Cecile; Newman, Lee; Canner, Martha; Martyny, John; McCammon, Chuck; Bresnitz, Eddy; Rossman, Milt; Thompson, Bruce; Rybicki, Benjamin; Weinberger, Steven E; Moller, David R; McLennan, Geoffrey; Hunninghake, Gary; DePalo, Louis; Baughman, Robert P; Iannuzzi, Michael C; Judson, Marc A; Knatterud, Genell L; Teirstein, Alvin S; Yeager, Henry; Johns, Carol J; Rabin, David L; Cherniack, Reuben

2005-03-01

To determine whether specific occupations and industries may be associated with sarcoidosis. A Case Control Etiologic Study of Sarcoidosis (ACCESS) obtained occupational and environmental histories on 706 newly diagnosed sarcoidosis cases and matched controls. We used Standard Industrial Classification (SIC) and Standard Occupational Classification (SOC) to assess occupational contributions to sarcoidosis risk. Univariable analysis identified elevated risk of sarcoidosis for workers with industrial organic dust exposures, especially in Caucasian workers. Workers for suppliers of building materials, hardware, and gardening materials were at an increased risk of sarcoidosis as were educators. Work providing childcare was negatively associated with sarcoidosis risk. Jobs with metal dust or metal fume exposures were negatively associated with sarcoidosis risk, especially in Caucasian workers. In this study, we found that exposures in particular occupational settings may contribute to sarcoidosis risk.

Building a Library Network from Scratch: Eric & Veronica's Excellent Adventure.

ERIC Educational Resources Information Center

Sisler, Eric; Smith, Veronica

2000-01-01

Describes library automation issues during the planning and construction of College Hill Library (Colorado), a joint-use facility shared by a community college and a public library. Discuses computer networks; hardware selection; public access to catalogs and electronic resources; classification schemes and bibliographic data; children's…

A Framework for Concept-Based Digital Course Libraries

ERIC Educational Resources Information Center

Dicheva, Darina; Dichev, Christo

2004-01-01

This article presents a general framework for building conceptbased digital course libraries. The framework is based on the idea of using a conceptual structure that represents a subject domain ontology for classification of the course library content. Two aspects, domain conceptualization, which supports findability and ontologies, which support…

Industrial Landscapes: Perception and Classification as Learning Activities

ERIC Educational Resources Information Center

Peters, Gary; Larkin, Robert P.

1977-01-01

Suggests a high school or college level program of subjective perception and evaluation of industrial landscapes. Slides of local or national industrial sites can be rated and classified as pleasing or unpleasing in terms of variables such as architectural style of building, smokestacks, age, and visible pollution. (AV)

Automatic Term Class Construction Using Relevance--A Summary of Work in Automatic Pseudoclassification.

ERIC Educational Resources Information Center

Salton, G.

1980-01-01

Summarizes studies of pseudoclassification, a process of utilizing user relevance assessments of certain documents with respect to certain queries to build term classes designed to retrieve relevant documents. Conclusions are reached concerning the effectiveness and feasibility of constructing term classifications based on human relevance…

Good Health: The Power of Power

ERIC Educational Resources Information Center

Corbin, Charles B.; Janz, Kathleen F.; Baptista, Fátima

2017-01-01

Power has long been considered to be a skill-related fitness component. However, based on recent evidence, a strong case can be made for the classification of power as a health-related fitness component. Additionally, the evidence indicates that performing physical activities that build power is associated with the healthy development of bones…

10 CFR 1045.53 - Appeal of denial of mandatory declassification review requests.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 10 Energy 4 2012-01-01 2012-01-01 false Appeal of denial of mandatory declassification review requests. 1045.53 Section 1045.53 Energy DEPARTMENT OF ENERGY (GENERAL PROVISIONS) NUCLEAR CLASSIFICATION... and Security Officer, HS-1/Forrestal Building, Department of Energy, 1000 Independence Avenue SW...

Building Process Improvement Business Cases Using Bayesian Belief Networks and Monte Carlo Simulation

DTIC Science & Technology

2009-07-01

simulation. The pilot described in this paper used this two-step approach within a Define, Measure, Analyze, Improve, and Control ( DMAIC ) framework to...networks, BBN, Monte Carlo simulation, DMAIC , Six Sigma, business case 15. NUMBER OF PAGES 35 16. PRICE CODE 17. SECURITY CLASSIFICATION OF

10 CFR 1045.53 - Appeal of denial of mandatory declassification review requests.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 10 Energy 4 2010-01-01 2010-01-01 false Appeal of denial of mandatory declassification review requests. 1045.53 Section 1045.53 Energy DEPARTMENT OF ENERGY (GENERAL PROVISIONS) NUCLEAR CLASSIFICATION... and Security Officer, HS-1/Forrestal Building, Department of Energy, 1000 Independence Avenue SW...

10 CFR 1045.53 - Appeal of denial of mandatory declassification review requests.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 10 Energy 4 2013-01-01 2013-01-01 false Appeal of denial of mandatory declassification review requests. 1045.53 Section 1045.53 Energy DEPARTMENT OF ENERGY (GENERAL PROVISIONS) NUCLEAR CLASSIFICATION... and Security Officer, HS-1/Forrestal Building, Department of Energy, 1000 Independence Avenue SW...

10 CFR 1045.53 - Appeal of denial of mandatory declassification review requests.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 10 Energy 4 2011-01-01 2011-01-01 false Appeal of denial of mandatory declassification review requests. 1045.53 Section 1045.53 Energy DEPARTMENT OF ENERGY (GENERAL PROVISIONS) NUCLEAR CLASSIFICATION... and Security Officer, HS-1/Forrestal Building, Department of Energy, 1000 Independence Avenue SW...

10 CFR 1045.53 - Appeal of denial of mandatory declassification review requests.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 10 Energy 4 2014-01-01 2014-01-01 false Appeal of denial of mandatory declassification review requests. 1045.53 Section 1045.53 Energy DEPARTMENT OF ENERGY (GENERAL PROVISIONS) NUCLEAR CLASSIFICATION... and Security Officer, HS-1/Forrestal Building, Department of Energy, 1000 Independence Avenue SW...

Building Collections: Folklore

ERIC Educational Resources Information Center

Krapp, JoAnn Vergona

2005-01-01

Folklore, the oldest form of storytelling, reflects the culture of a country, hence its nonfiction classification. Through these tales, one senses the values, the humor, and the lifestyles of its peoples. A powerful genre, folklore is the foundation on which high fantasy is created, epic films are produced, and a single story is passed from one…

Backyard Botany: Using GPS Technology in the Science Classroom

ERIC Educational Resources Information Center

March, Kathryn A.

2012-01-01

Global Positioning System (GPS) technology can be used to connect students to the natural world and improve their skills in observation, identification, and classification. Using GPS devices in the classroom increases student interest in science, encourages team-building skills, and improves biology content knowledge. Additionally, it helps…

21 CFR 866.5100 - Antinuclear antibody immunological test system.

Code of Federal Regulations, 2012 CFR

2012-04-01

...), rheumatoid arthritis, Sjogren's syndrome (arthritis with inflammation of the eye, eyelid, and salivary glands), and systemic sclerosis (chronic hardening and shrinking of many body tissues). (b) Classification...

21 CFR 866.5100 - Antinuclear antibody immunological test system.

Code of Federal Regulations, 2011 CFR

2011-04-01

...), rheumatoid arthritis, Sjogren's syndrome (arthritis with inflammation of the eye, eyelid, and salivary glands), and systemic sclerosis (chronic hardening and shrinking of many body tissues). (b) Classification...

Employing ASHRAE Standard 62-1989 in urban building environments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Meckler, M.

1991-01-01

Indoor air quality (IAQ) is a result of a complex relationship between the contamination sources in a building, the ventilation rate, and the dilution of the indoor air contaminant concentrations with outdoor air. This complex relationship is further complicated by outdoor sources used for dilution air and pollution sinks in a building which may modify or remove contaminants. This paper reports that the factors influencing IAQ in a building are: emissions from indoor contamination sources, dilution rate of outdoor ventilation air, quality of the outdoor dilution air, and systems and materials in a building that change the concentrations of contaminants.more » Emissions from contaminant sources in a building are the primary determinant of IAQ. They include building materials, consumer products, cleaners, furnishings, combustion appliances and processes, biological growth from standing water and damp surfaces and building occupants. These factors combined with the emissions from indoor air contamination sources such as synthetic building materials, modern office equipment, and cleaning and biological agents are believed to increase the levels of indoor air contamination. The physiological reactions to these contaminants, coupled with the psychosocial stresses of the modern office environment, and the wide range of human susceptibility to indoor air contaminants led to the classification of acute building sicknesses: sick building syndrome (SBS), building-related illness (BRI), and multiple chemical sensitivity (MCS).« less

Vitamin D

MedlinePlus

... body needs to grow and develop normally. Vitamin D helps your body absorb calcium. Calcium is one ... building blocks of bone. A lack of vitamin D can lead to bone diseases such as osteoporosis ...

A Psychometric Measure of Working Memory Capacity for Configured Body Movement

PubMed Central

Wu, Ying Choon; Coulson, Seana

2014-01-01

Working memory (WM) models have traditionally assumed at least two domain-specific storage systems for verbal and visuo-spatial information. We review data that suggest the existence of an additional slave system devoted to the temporary storage of body movements, and present a novel instrument for its assessment: the movement span task. The movement span task assesses individuals' ability to remember and reproduce meaningless configurations of the body. During the encoding phase of a trial, participants watch short videos of meaningless movements presented in sets varying in size from one to five items. Immediately after encoding, they are prompted to reenact as many items as possible. The movement span task was administered to 90 participants along with standard tests of verbal WM, visuo-spatial WM, and a gesture classification test in which participants judged whether a speaker's gestures were congruent or incongruent with his accompanying speech. Performance on the gesture classification task was not related to standard measures of verbal or visuo-spatial working memory capacity, but was predicted by scores on the movement span task. Results suggest the movement span task can serve as an assessment of individual differences in WM capacity for body-centric information. PMID:24465437

Detection of goat body fat adulteration in pure ghee using ATR-FTIR spectroscopy coupled with chemometric strategy.

PubMed

Upadhyay, Neelam; Jaiswal, Pranita; Jha, Shyam Narayan

2016-10-01

Ghee forms an important component of the diet of human beings due to its rich flavor and high nutritive value. This high priced fat is prone to adulteration with cheaper fats. ATR-FTIR spectroscopy coupled with chemometrics was applied for determining the presence of goat body fat in ghee (@1, 3, 5, 10, 15 and 20% level in the laboratory made/spiked samples). The spectra of pure (ghee and goat body fat) and spiked samples were taken in the wavenumber range of 4000-500 cm -1 . Separated clusters of pure ghee and spiked samples were obtained on applying principal component analysis at 5% level of significance in the selected wavenumber range (1786-1680, 1490-919 and 1260-1040 cm -1 ). SIMCA was applied for classification of samples and pure ghee showed 100% classification efficiency. The value of R 2 was found to be >0.99 for calibration and validation sets using partial least square method at all the selected wavenumber range which indicate that the model was well developed. The study revealed that the spiked samples of goat body fat could be detected even at 1% level in ghee.

Obesity classification in military personnel: a comparison of body fat, waist circumference, and body mass index measurements.

PubMed

Heinrich, Katie M; Jitnarin, Nattinee; Suminski, Richard R; Berkel, LaVerne; Hunter, Christine M; Alvarez, Lisa; Brundige, Antionette R; Peterson, Alan L; Foreyt, John P; Haddock, C Keith; Poston, Walker S C

2008-01-01

The purpose of this study was to evaluate obesity classifications from body fat percentage (BF%), body mass index (BMI), and waist circumference (WC). A total of 451 overweight/obese active duty military personnel completed all three assessments. Most were obese (men, 81%; women, 98%) using National Institutes of Health (NIH) BF% standards (men, >25%; women, >30%). Using the higher World Health Organization (WHO) BF >35% standard, 86% of women were obese. BMI (55.5% and 51.4%) and WC (21.4% and 31.9%) obesity rates were substantially lower for men and women, respectively (p < 0.05). BMI/WC were accurate discriminators for BF% obesity (theta for all comparisons >0.75, p < 0.001). Optimal cutoff points were lower than NIH/WHO standards; WC = 100 cm and BMI = 29 maximized sensitivity and specificity for men, and WC = 79 cm and BMI = 25.5 (NIH) or WC = 83 cm and BMI = 26 (WHO) maximized sensitivity and specificity for women. Both WC and BMI measures had high rates of false negatives compared to BF%. However, at a population level, WC/BMI are useful obesity measures, demonstrating fair-to-high discriminatory power.

Epiplasmins and epiplasm in paramecium: the building of a submembraneous cytoskeleton.

PubMed

Aubusson-Fleury, Anne; Bricheux, Geneviève; Damaj, Raghida; Lemullois, Michel; Coffe, Gérard; Donnadieu, Florence; Koll, France; Viguès, Bernard; Bouchard, Philippe

2013-07-01

In ciliates, basal bodies and associated appendages are bound to a submembrane cytoskeleton. In Paramecium, this cytoskeleton takes the form of a thin dense layer, the epiplasm, segmented into regular territories, the units where basal bodies are inserted. Epiplasmins, the main component of the epiplasm, constitute a large family of 51 proteins distributed in 5 phylogenetic groups, each characterized by a specific molecular design. By GFP-tagging, we analyzed their differential localisation and role in epiplasm building and demonstrated that: 1) The epiplasmins display a low turnover, in agreement with the maintenance of an epiplasm layer throughout the cell cycle; 2) Regionalisation of proteins from different groups allows us to define rim, core, ring and basal body epiplasmins in the interphase cell; 3) Their dynamics allows definition of early and late epiplasmins, detected early versus late in the duplication process of the units. Epiplasmins from each group exhibit a specific combination of properties. Core and rim epiplasmins are required to build a unit; ring and basal body epiplasmins seem more dispensable, suggesting that they are not required for basal body docking. We propose a model of epiplasm unit assembly highlighting its implication in structural heredity in agreement with the evolutionary history of epiplasmins. Copyright © 2013 Elsevier GmbH. All rights reserved.

Comprehensive assessment of the efficiency of high-rise construction projects in the form of urban blocks

NASA Astrophysics Data System (ADS)

Orlov, Alexandr; Chubarkina, Irina

2018-03-01

The paper is dedicated to main modern trends in the area of high-rise construction. The classification of buildings and structures by height is given. Functional distribution by the height of buildings is presented. A review of positive and negative aspects of high-rise construction from the economic point of view is given. On the basis of the data obtained, it is proposed to build up residential microdistricts in the form of urban blocks. A plan of microdistricts development is presented. It takes into account urban blocks and includes their main characteristics. An economic and mathematical model was developed to carry out a comprehensive assessment of the effectiveness of high-rise construction projects.

Recommendations for Safe Separation Distances from the Kennedy Space Center (KSC) Vehicle Assembly Building (VAB) Using a Heat-Flux-Based Analytical Approach (Abridged)

NASA Technical Reports Server (NTRS)

Cragg, Clinton H.; Bowman, Howard; Wilson, John E.

2011-01-01

The NASA Engineering and Safety Center (NESC) was requested to provide computational modeling to support the establishment of a safe separation distance surrounding the Kennedy Space Center (KSC) Vehicle Assembly Building (VAB). The two major objectives of the study were 1) establish a methodology based on thermal flux to determine safe separation distances from the Kennedy Space Center's (KSC's) Vehicle Assembly Building (VAB) with large numbers of solid propellant boosters containing hazard division 1.3 classification propellants, in case of inadvertent ignition; and 2) apply this methodology to the consideration of housing eight 5-segment solid propellant boosters in the VAB. The results of the study are contained in this report.

The classification of body dysmorphic disorder symptoms in male and female adolescents.

PubMed

Schneider, Sophie C; Baillie, Andrew J; Mond, Jonathan; Turner, Cynthia M; Hudson, Jennifer L

2018-01-01

Body dysmorphic disorder (BDD) was categorised in DSM-5 within the newly created 'obsessive-compulsive and related disorders' chapter, however this classification remains subject to debate. Confirmatory factor analysis was used to test competing models of the co-occurrence of symptoms of BDD, obsessive-compulsive disorder, unipolar depression, anxiety, and eating disorders in a community sample of adolescents, and to explore potential sex differences in these models. Self-report questionnaires assessing disorder symptoms were completed by 3149 Australian adolescents. The fit of correlated factor models was calculated separately in males and females, and measurement invariance testing compared parameters of the best-fitting model between males and females. All theoretical models of the classification of BDD had poor fit to the data. Good fit was found for a novel model where BDD symptoms formed a distinct latent factor, correlated with affective disorder and eating disorder latent factors. Metric non-invariance was found between males and females, and the majority of factor loadings differed between males and females. Correlations between some latent factors also differed by sex. Only cross-sectional data were collected, and the study did not assess a broad range of DSM-5 defined eating disorder symptoms or other disorders in the DSM-5 obsessive-compulsive and related disorders chapter. This study is the first to statistically evaluate competing models of BDD classification. The findings highlight the unique features of BDD and its associations with affective and eating disorders. Future studies examining the classification of BDD should consider developmental and sex differences in their models. Copyright © 2017. Published by Elsevier B.V.

Early classification of pathological heartbeats on wireless body sensor nodes.

PubMed

Braojos, Rubén; Beretta, Ivan; Ansaloni, Giovanni; Atienza, David

2014-11-27

Smart Wireless Body Sensor Nodes (WBSNs) are a novel class of unobtrusive, battery-powered devices allowing the continuous monitoring and real-time interpretation of a subject's bio-signals, such as the electrocardiogram (ECG). These low-power platforms, while able to perform advanced signal processing to extract information on heart conditions, are usually constrained in terms of computational power and transmission bandwidth. It is therefore essential to identify in the early stages which parts of an ECG are critical for the diagnosis and, only in these cases, activate on demand more detailed and computationally intensive analysis algorithms. In this work, we present a comprehensive framework for real-time automatic classification of normal and abnormal heartbeats, targeting embedded and resource-constrained WBSNs. In particular, we provide a comparative analysis of different strategies to reduce the heartbeat representation dimensionality, and therefore the required computational effort. We then combine these techniques with a neuro-fuzzy classification strategy, which effectively discerns normal and pathological heartbeats with a minimal run time and memory overhead. We prove that, by performing a detailed analysis only on the heartbeats that our classifier identifies as abnormal, a WBSN system can drastically reduce its overall energy consumption. Finally, we assess the choice of neuro-fuzzy classification by comparing its performance and workload with respect to other state-of-the-art strategies. Experimental results using the MIT-BIH Arrhythmia database show energy savings of as much as 60% in the signal processing stage, and 63% in the subsequent wireless transmission, when a neuro-fuzzy classification structure is employed, coupled with a dimensionality reduction technique based on random projections.

Early Classification of Pathological Heartbeats on Wireless Body Sensor Nodes

PubMed Central

Braojos, Rubén; Beretta, Ivan; Ansaloni, Giovanni; Atienza, David

2014-01-01

Smart Wireless Body Sensor Nodes (WBSNs) are a novel class of unobtrusive, battery-powered devices allowing the continuous monitoring and real-time interpretation of a subject's bio-signals, such as the electrocardiogram (ECG). These low-power platforms, while able to perform advanced signal processing to extract information on heart conditions, are usually constrained in terms of computational power and transmission bandwidth. It is therefore essential to identify in the early stages which parts of an ECG are critical for the diagnosis and, only in these cases, activate on demand more detailed and computationally intensive analysis algorithms. In this work, we present a comprehensive framework for real-time automatic classification of normal and abnormal heartbeats, targeting embedded and resource-constrained WBSNs. In particular, we provide a comparative analysis of different strategies to reduce the heartbeat representation dimensionality, and therefore the required computational effort. We then combine these techniques with a neuro-fuzzy classification strategy, which effectively discerns normal and pathological heartbeats with a minimal run time and memory overhead. We prove that, by performing a detailed analysis only on the heartbeats that our classifier identifies as abnormal, a WBSN system can drastically reduce its overall energy consumption. Finally, we assess the choice of neuro-fuzzy classification by comparing its performance and workload with respect to other state-of-the-art strategies. Experimental results using the MIT-BIH Arrhythmia database show energy savings of as much as 60% in the signal processing stage, and 63% in the subsequent wireless transmission, when a neuro-fuzzy classification structure is employed, coupled with a dimensionality reduction technique based on random projections. PMID:25436654

Two-body problem in scalar-tensor theories as a deformation of general relativity: An effective-one-body approach

NASA Astrophysics Data System (ADS)

Julié, Félix-Louis; Deruelle, Nathalie

2017-06-01

In this paper we address the two-body problem in massless scalar-tensor (ST) theories within an effective-one-body (EOB) framework. We focus on the first building block of the EOB approach, that is, mapping the conservative part of the two-body dynamics onto the geodesic motion of a test particle in an effective external metric. To this end, we first deduce the second post-Keplerian (2PK) Hamiltonian of the two-body problem from the known 2PK Lagrangian. We then build, by means of a canonical transformation, a ST deformation of the general relativistic EOB Hamiltonian that allows us to incorporate the scalar-tensor (2PK) corrections to the currently best available general relativity EOB results. This EOB-ST Hamiltonian defines a resummation of the dynamics that may provide information on the strong-field regime, in particular, the ISCO location and associated orbital frequency, and can be compared to, other, e.g., tidal, corrections.

Technology for Building Systems Integration and Optimization – Landscape Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Goetzler, William; Guernsey, Matt; Bargach, Youssef

BTO's Commercial Building Integration (CBI) program helps advance a range of innovative building integration and optimization technologies and solutions, paving the way for high-performing buildings that could use 50-70% less energy than typical buildings. CBI’s work focuses on early stage technology innovation, with an emphasis on how components and systems work together and how whole buildings are integrated and optimized. This landscape study outlines the current body of knowledge, capabilities, and the broader array of solutions supporting integration and optimization in commercial buildings. CBI seeks to support solutions for both existing buildings and new construction, which often present very differentmore » challenges.« less

Land cover classification in multispectral imagery using clustering of sparse approximations over learned feature dictionaries

DOE PAGES

Moody, Daniela I.; Brumby, Steven P.; Rowland, Joel C.; ...

2014-12-09

We present results from an ongoing effort to extend neuromimetic machine vision algorithms to multispectral data using adaptive signal processing combined with compressive sensing and machine learning techniques. Our goal is to develop a robust classification methodology that will allow for automated discretization of the landscape into distinct units based on attributes such as vegetation, surface hydrological properties, and topographic/geomorphic characteristics. We use a Hebbian learning rule to build spectral-textural dictionaries that are tailored for classification. We learn our dictionaries from millions of overlapping multispectral image patches and then use a pursuit search to generate classification features. Land cover labelsmore » are automatically generated using unsupervised clustering of sparse approximations (CoSA). We demonstrate our method on multispectral WorldView-2 data from a coastal plain ecosystem in Barrow, Alaska. We explore learning from both raw multispectral imagery and normalized band difference indices. We explore a quantitative metric to evaluate the spectral properties of the clusters in order to potentially aid in assigning land cover categories to the cluster labels. In this study, our results suggest CoSA is a promising approach to unsupervised land cover classification in high-resolution satellite imagery.« less

3D texture analysis for classification of second harmonic generation images of human ovarian cancer

NASA Astrophysics Data System (ADS)

Wen, Bruce; Campbell, Kirby R.; Tilbury, Karissa; Nadiarnykh, Oleg; Brewer, Molly A.; Patankar, Manish; Singh, Vikas; Eliceiri, Kevin. W.; Campagnola, Paul J.

2016-10-01

Remodeling of the collagen architecture in the extracellular matrix (ECM) has been implicated in ovarian cancer. To quantify these alterations we implemented a form of 3D texture analysis to delineate the fibrillar morphology observed in 3D Second Harmonic Generation (SHG) microscopy image data of normal (1) and high risk (2) ovarian stroma, benign ovarian tumors (3), low grade (4) and high grade (5) serous tumors, and endometrioid tumors (6). We developed a tailored set of 3D filters which extract textural features in the 3D image sets to build (or learn) statistical models of each tissue class. By applying k-nearest neighbor classification using these learned models, we achieved 83-91% accuracies for the six classes. The 3D method outperformed the analogous 2D classification on the same tissues, where we suggest this is due the increased information content. This classification based on ECM structural changes will complement conventional classification based on genetic profiles and can serve as an additional biomarker. Moreover, the texture analysis algorithm is quite general, as it does not rely on single morphological metrics such as fiber alignment, length, and width but their combined convolution with a customizable basis set.

Advances in Spectral-Spatial Classification of Hyperspectral Images

NASA Technical Reports Server (NTRS)

Fauvel, Mathieu; Tarabalka, Yuliya; Benediktsson, Jon Atli; Chanussot, Jocelyn; Tilton, James C.

2012-01-01

Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper. Several techniques are investigated for combining both spatial and spectral information. Spatial information is extracted at the object (set of pixels) level rather than at the conventional pixel level. Mathematical morphology is first used to derive the morphological profile of the image, which includes characteristics about the size, orientation and contrast of the spatial structures present in the image. Then the morphological neighborhood is defined and used to derive additional features for classification. Classification is performed with support vector machines using the available spectral information and the extracted spatial information. Spatial post-processing is next investigated to build more homogeneous and spatially consistent thematic maps. To that end, three presegmentation techniques are applied to define regions that are used to regularize the preliminary pixel-wise thematic map. Finally, a multiple classifier system is defined to produce relevant markers that are exploited to segment the hyperspectral image with the minimum spanning forest algorithm. Experimental results conducted on three real hyperspectral images with different spatial and spectral resolutions and corresponding to various contexts are presented. They highlight the importance of spectral-spatial strategies for the accurate classification of hyperspectral images and validate the proposed methods.

An unsupervised classification method for inferring original case locations from low-resolution disease maps.

PubMed

Brownstein, John S; Cassa, Christopher A; Kohane, Isaac S; Mandl, Kenneth D

2006-12-08

Widespread availability of geographic information systems software has facilitated the use of disease mapping in academia, government and private sector. Maps that display the address of affected patients are often exchanged in public forums, and published in peer-reviewed journal articles. As previously reported, a search of figure legends in five major medical journals found 19 articles from 1994-2004 that identify over 19,000 patient addresses. In this report, a method is presented to evaluate whether patient privacy is being breached in the publication of low-resolution disease maps. To demonstrate the effect, a hypothetical low-resolution map of geocoded patient addresses was created and the accuracy with which patient addresses can be resolved is described. Through georeferencing and unsupervised classification of the original image, the method precisely re-identified 26% (144/550) of the patient addresses from a presentation quality map and 79% (432/550) from a publication quality map. For the presentation quality map, 99.8% of the addresses were within 70 meters (approximately one city block length) of the predicted patient location, 51.6% of addresses were identified within five buildings, 70.7% within ten buildings and 93% within twenty buildings. For the publication quality map, all addresses were within 14 meters and 11 buildings of the predicted patient location. This study demonstrates that lowering the resolution of a map displaying geocoded patient addresses does not sufficiently protect patient addresses from re-identification. Guidelines to protect patient privacy, including those of medical journals, should reflect policies that ensure privacy protection when spatial data are displayed or published.

21 CFR 880.5300 - Medical absorbent fiber.

Code of Federal Regulations, 2012 CFR

2012-04-01

...'s body surface. Absorbent fibers intended solely for cosmetic purposes are not included in this generic device category. (b) Classification. Class I (general controls). The device is exempt from the...

21 CFR 880.5300 - Medical absorbent fiber.

Code of Federal Regulations, 2013 CFR

2013-04-01

...'s body surface. Absorbent fibers intended solely for cosmetic purposes are not included in this generic device category. (b) Classification. Class I (general controls). The device is exempt from the...

21 CFR 880.5300 - Medical absorbent fiber.

Code of Federal Regulations, 2014 CFR

2014-04-01

...'s body surface. Absorbent fibers intended solely for cosmetic purposes are not included in this generic device category. (b) Classification. Class I (general controls). The device is exempt from the...

Febuxostat

MedlinePlus

... Gout is a type of arthritis in which uric acid, a naturally occurring substance in the body, builds ... inhibitors. It works by decreasing the amount of uric acid that is made in the body. Febuxostat is ...

Technical Support Document: Development of the Advanced Energy Design Guide for Large Hospitals - 50% Energy Savings

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bonnema, E.; Leach, M.; Pless, S.

2013-06-01

This Technical Support Document describes the process and methodology for the development of the Advanced Energy Design Guide for Large Hospitals: Achieving 50% Energy Savings Toward a Net Zero Energy Building (AEDG-LH) ASHRAE et al. (2011b). The AEDG-LH is intended to provide recommendations for achieving 50% whole-building energy savings in large hospitals over levels achieved by following Standard 90.1-2004. The AEDG-LH was created for a 'standard' mid- to large-size hospital, typically at least 100,000 ft2, but the strategies apply to all sizes and classifications of new construction hospital buildings. Its primary focus is new construction, but recommendations may be applicablemore » to facilities undergoing total renovation, and in part to many other hospital renovation, addition, remodeling, and modernization projects (including changes to one or more systems in existing buildings).« less

Culto: AN Ontology-Based Annotation Tool for Data Curation in Cultural Heritage

NASA Astrophysics Data System (ADS)

Garozzo, R.; Murabito, F.; Santagati, C.; Pino, C.; Spampinato, C.

2017-08-01

This paper proposes CulTO, a software tool relying on a computational ontology for Cultural Heritage domain modelling, with a specific focus on religious historical buildings, for supporting cultural heritage experts in their investigations. It is specifically thought to support annotation, automatic indexing, classification and curation of photographic data and text documents of historical buildings. CULTO also serves as a useful tool for Historical Building Information Modeling (H-BIM) by enabling semantic 3D data modeling and further enrichment with non-geometrical information of historical buildings through the inclusion of new concepts about historical documents, images, decay or deformation evidence as well as decorative elements into BIM platforms. CulTO is the result of a joint research effort between the Laboratory of Surveying and Architectural Photogrammetry "Luigi Andreozzi" and the PeRCeiVe Lab (Pattern Recognition and Computer Vision Lab) of the University of Catania,

Build platform that provides mechanical engagement with additive manufacturing prints

DOE Office of Scientific and Technical Information (OSTI.GOV)

Elliott, Amelia M.

A build platform and methods of fabricating an article with such a platform in an extrusion-type additive manufacturing machine are provided. A platform body 202 includes features 204 that extend outward from the body 202. The features 204 define protrusive areas 206 and recessive areas 208 that cooperate to mechanically engage the extruded material that forms the initial layers 220 of an article when the article is being fabricated by a nozzle 12 of the additive manufacturing machine 10.

Identification of Cichlid Fishes from Lake Malawi Using Computer Vision

PubMed Central

Joo, Deokjin; Kwan, Ye-seul; Song, Jongwoo; Pinho, Catarina; Hey, Jody; Won, Yong-Jin

2013-01-01

Background The explosively radiating evolution of cichlid fishes of Lake Malawi has yielded an amazing number of haplochromine species estimated as many as 500 to 800 with a surprising degree of diversity not only in color and stripe pattern but also in the shape of jaw and body among them. As these morphological diversities have been a central subject of adaptive speciation and taxonomic classification, such high diversity could serve as a foundation for automation of species identification of cichlids. Methodology/Principal Finding Here we demonstrate a method for automatic classification of the Lake Malawi cichlids based on computer vision and geometric morphometrics. For this end we developed a pipeline that integrates multiple image processing tools to automatically extract informative features of color and stripe patterns from a large set of photographic images of wild cichlids. The extracted information was evaluated by statistical classifiers Support Vector Machine and Random Forests. Both classifiers performed better when body shape information was added to the feature of color and stripe. Besides the coloration and stripe pattern, body shape variables boosted the accuracy of classification by about 10%. The programs were able to classify 594 live cichlid individuals belonging to 12 different classes (species and sexes) with an average accuracy of 78%, contrasting to a mere 42% success rate by human eyes. The variables that contributed most to the accuracy were body height and the hue of the most frequent color. Conclusions Computer vision showed a notable performance in extracting information from the color and stripe patterns of Lake Malawi cichlids although the information was not enough for errorless species identification. Our results indicate that there appears an unavoidable difficulty in automatic species identification of cichlid fishes, which may arise from short divergence times and gene flow between closely related species. PMID:24204918

Metabolic Syndrome

MedlinePlus

... cause of metabolic syndrome. The cause might be insulin resistance. Insulin is a hormone your body produces to help ... into energy for your body. If you are insulin resistant, too much sugar builds up in your ...

21 CFR 874.1800 - Air or water caloric stimulator.

Code of Federal Regulations, 2010 CFR

2010-04-01

... vestibular function testing of a patient's body balance system. The vestibular stimulation of the...) Classification. Class I (general controls). The device is exempt from the premarket notification procedures in...

21 CFR 866.5100 - Antinuclear antibody immunological test system.

Code of Federal Regulations, 2014 CFR

2014-04-01

...), rheumatoid arthritis, Sjögren's syndrome (arthritis with inflammation of the eye, eyelid, and salivary glands), and systemic sclerosis (chronic hardening and shrinking of many body tissues). (b) Classification...

21 CFR 866.5100 - Antinuclear antibody immunological test system.

Code of Federal Regulations, 2013 CFR

2013-04-01

...), rheumatoid arthritis, Sjögren's syndrome (arthritis with inflammation of the eye, eyelid, and salivary glands), and systemic sclerosis (chronic hardening and shrinking of many body tissues). (b) Classification...

Organophosphate and phthalate esters in indoor air: a comparison between multi-storey buildings with high and low prevalence of sick building symptoms.

PubMed

Bergh, Caroline; Magnus Åberg, K; Svartengren, Magnus; Emenius, Gunnel; Östman, Conny

2011-07-01

An extensive study has been conducted on the prevalence of organophosphorous flame retardants/plasticizers and phthalate ester plasticizers in indoor air. The targeted substances were measured in 45 multi-storey apartment buildings in Stockholm, Sweden. The apartment buildings were classified as high or low risk with regard to the reporting of sick building symptoms (SBS) within the project Healthy Sustainable Houses in Stockholm (3H). Air samples were taken from two to four apartments per building (in total 169 apartments) to facilitate comparison within and between buildings. Association with building characteristics has been examined as well as association with specific sources by combining chemical analysis and exploratory uni- and multivariate data analysis. The study contributes to the overall perspective of levels of organophosphate and phthalate ester in indoor air enabling comparison with other studies. The results indicated little or no difference in the concentrations of the target substances between the two risk classifications of the buildings. The differences between the apartments sampled within (intra) buildings were greater than the differences between (inter) buildings. The concentrations measured in air ranged up to 1200 ng m(-3) for organophosphate esters and up to 11 000 ng m(-3) for phthalate esters. Results in terms of sources were discerned e.g. PVC flooring is a major source of benzylbutyl phthalate in indoor air.

Micro-bias and macro-performance.

PubMed

Seaver, S M D; Moreira, A A; Sales-Pardo, M; Malmgren, R D; Diermeier, D; Amaral, L A N

2009-02-01

We use agent-based modeling to investigate the effect of conservatism and partisanship on the efficiency with which large populations solve the density classification task - a paradigmatic problem for information aggregation and consensus building. We find that conservative agents enhance the populations' ability to efficiently solve the density classification task despite large levels of noise in the system. In contrast, we find that the presence of even a small fraction of partisans holding the minority position will result in deadlock or a consensus on an incorrect answer. Our results provide a possible explanation for the emergence of conservatism and suggest that even low levels of partisanship can lead to significant social costs.

Topological classification of periodic orbits in the Kuramoto-Sivashinsky equation

NASA Astrophysics Data System (ADS)

Dong, Chengwei

2018-05-01

In this paper, we systematically research periodic orbits of the Kuramoto-Sivashinsky equation (KSe). In order to overcome the difficulties in the establishment of one-dimensional symbolic dynamics in the nonlinear system, two basic periodic orbits can be used as basic building blocks to initialize cycle searching, and we use the variational method to numerically determine all the periodic orbits under parameter ν = 0.02991. The symbolic dynamics based on trajectory topology are very successful for classifying all short periodic orbits in the KSe. The current research can be conveniently adapted to the identification and classification of periodic orbits in other chaotic systems.

Towards automatic music transcription: note extraction based on independent subspace analysis

NASA Astrophysics Data System (ADS)

Wellhausen, Jens; Hoynck, Michael

2005-01-01

Due to the increasing amount of music available electronically the need of automatic search, retrieval and classification systems for music becomes more and more important. In this paper an algorithm for automatic transcription of polyphonic piano music into MIDI data is presented, which is a very interesting basis for database applications, music analysis and music classification. The first part of the algorithm performs a note accurate temporal audio segmentation. In the second part, the resulting segments are examined using Independent Subspace Analysis to extract sounding notes. Finally, the results are used to build a MIDI file as a new representation of the piece of music which is examined.

Towards automatic music transcription: note extraction based on independent subspace analysis

NASA Astrophysics Data System (ADS)

Wellhausen, Jens; Höynck, Michael

2004-12-01

Due to the increasing amount of music available electronically the need of automatic search, retrieval and classification systems for music becomes more and more important. In this paper an algorithm for automatic transcription of polyphonic piano music into MIDI data is presented, which is a very interesting basis for database applications, music analysis and music classification. The first part of the algorithm performs a note accurate temporal audio segmentation. In the second part, the resulting segments are examined using Independent Subspace Analysis to extract sounding notes. Finally, the results are used to build a MIDI file as a new representation of the piece of music which is examined.

Establishing a Framework for a Natural Area Taxonomy.

PubMed

Ebach, Malte C; Michaux, Bernard

2017-09-01

The identification of areas of endemism is essential in building an area classification, but plays little role in how natural areas are discovered. Rather area monophyly, derived from cladistics, is essential in the discovery of natural area classifications or area taxonomy. We propose Area Taxonomy to be a new sub-discipline of historical biogeography, one that can be revised and debated, and which has its own area nomenclature. Separately to area taxonomy, we outline how natural areas may be discovered by transcribing the concepts of homology and monophyly from biological systematics to historical biogeography, in the form of area homologues, area homologies and area monophyly.

Nursing Classification Systems

PubMed Central

Henry, Suzanne Bakken; Mead, Charles N.

1997-01-01

Abstract Our premise is that from the perspective of maximum flexibility of data usage by computer-based record (CPR) systems, existing nursing classification systems are necessary, but not sufficient, for representing important aspects of “what nurses do.” In particular, we have focused our attention on those classification systems that represent nurses' clinical activities through the abstraction of activities into categories of nursing interventions. In this theoretical paper, we argue that taxonomic, combinatorial vocabularies capable of coding atomic-level nursing activities are required to effectively capture in a reproducible and reversible manner the clinical decisions and actions of nurses, and that, without such vocabularies and associated grammars, potentially important clinical process data is lost during the encoding process. Existing nursing intervention classification systems do not fulfill these criteria. As background to our argument, we first present an overview of the content, methods, and evaluation criteria used in previous studies whose focus has been to evaluate the effectiveness of existing coding and classification systems. Next, using the Ingenerf typology of taxonomic vocabularies, we categorize the formal type and structure of three existing nursing intervention classification systems—Nursing Interventions Classification, Omaha System, and Home Health Care Classification. Third, we use records from home care patients to show examples of lossy data transformation, the loss of potentially significant atomic data, resulting from encoding using each of the three systems. Last, we provide an example of the application of a formal representation methodology (conceptual graphs) which we believe could be used as a model to build the required combinatorial, taxonomic vocabulary for representing nursing interventions. PMID:9147341

Comprehensive 4-stage categorization of bicuspid aortic valve leaflet morphology by cardiac MRI in 386 patients.

PubMed

Murphy, I G; Collins, J; Powell, A; Markl, M; McCarthy, P; Malaisrie, S C; Carr, J C; Barker, A J

2017-08-01

Bicuspid aortic valve (BAV) disease is heterogeneous and related to valve dysfunction and aortopathy. Appropriate follow up and surveillance of patients with BAV may depend on correct phenotypic categorization. There are multiple classification schemes, however a need exists to comprehensively capture commissure fusion, leaflet asymmetry, and valve orifice orientation. Our aim was to develop a BAV classification scheme for use at MRI to ascertain the frequency of different phenotypes and the consistency of BAV classification. The BAV classification scheme builds on the Sievers surgical BAV classification, adding valve orifice orientation, partial leaflet fusion and leaflet asymmetry. A single observer successfully applied this classification to 386 of 398 Cardiac MRI studies. Repeatability of categorization was ascertained with intraobserver and interobserver kappa scores. Sensitivity and specificity of MRI findings was determined from operative reports, where available. Fusion of the right and left leaflets accounted for over half of all cases. Partial leaflet fusion was seen in 46% of patients. Good interobserver agreement was seen for orientation of the valve opening (κ = 0.90), type (κ = 0.72) and presence of partial fusion (κ = 0.83, p < 0.0001). Retrospective review of operative notes showed sensitivity and specificity for orientation (90, 93%) and for Sievers type (73, 87%). The proposed BAV classification schema was assessed by MRI for its reliability to classify valve morphology in addition to illustrating the wide heterogeneity of leaflet size, orifice orientation, and commissural fusion. The classification may be helpful in further understanding the relationship between valve morphology, flow derangement and aortopathy.

Updated United Nations Framework Classification for reserves and resources of extractive industries

USGS Publications Warehouse

Ahlbrandt, T.S.; Blaise, J.R.; Blystad, P.; Kelter, D.; Gabrielyants, G.; Heiberg, S.; Martinez, A.; Ross, J.G.; Slavov, S.; Subelj, A.; Young, E.D.

2004-01-01

The United Nations have studied how the oil and gas resource classification developed jointly by the SPE, the World Petroleum Congress (WPC) and the American Association of Petroleum Geologists (AAPG) could be harmonized with the United Nations Framework Classification (UNFC) for Solid Fuel and Mineral Resources (1). The United Nations has continued to build on this and other works, with support from many relevant international organizations, with the objective of updating the UNFC to apply to the extractive industries. The result is the United Nations Framework Classification for Energy and Mineral Resources (2) that this paper will present. Reserves and resources are categorized with respect to three sets of criteria: ??? Economic and commercial viability ??? Field project status and feasibility ??? The level of geologic knowledge The field project status criteria are readily recognized as the ones highlighted in the SPE/WPC/AAPG classification system of 2000. The geologic criteria absorb the rich traditions that form the primary basis for the Russian classification system, and the ones used to delimit, in part, proved reserves. Economic and commercial criteria facilitate the use of the classification in general, and reflect the commercial considerations used to delimit proved reserves in particular. The classification system will help to develop a common understanding of reserves and resources for all the extractive industries and will assist: ??? International and national resources management to secure supplies; ??? Industries' management of business processes to achieve efficiency in exploration and production; and ??? An appropriate basis for documenting the value of reserves and resources in financial statements.

Single-trial EEG RSVP classification using convolutional neural networks

NASA Astrophysics Data System (ADS)

Shamwell, Jared; Lee, Hyungtae; Kwon, Heesung; Marathe, Amar R.; Lawhern, Vernon; Nothwang, William

2016-05-01

Traditionally, Brain-Computer Interfaces (BCI) have been explored as a means to return function to paralyzed or otherwise debilitated individuals. An emerging use for BCIs is in human-autonomy sensor fusion where physiological data from healthy subjects is combined with machine-generated information to enhance the capabilities of artificial systems. While human-autonomy fusion of physiological data and computer vision have been shown to improve classification during visual search tasks, to date these approaches have relied on separately trained classification models for each modality. We aim to improve human-autonomy classification performance by developing a single framework that builds codependent models of human electroencephalograph (EEG) and image data to generate fused target estimates. As a first step, we developed a novel convolutional neural network (CNN) architecture and applied it to EEG recordings of subjects classifying target and non-target image presentations during a rapid serial visual presentation (RSVP) image triage task. The low signal-to-noise ratio (SNR) of EEG inherently limits the accuracy of single-trial classification and when combined with the high dimensionality of EEG recordings, extremely large training sets are needed to prevent overfitting and achieve accurate classification from raw EEG data. This paper explores a new deep CNN architecture for generalized multi-class, single-trial EEG classification across subjects. We compare classification performance from the generalized CNN architecture trained across all subjects to the individualized XDAWN, HDCA, and CSP neural classifiers which are trained and tested on single subjects. Preliminary results show that our CNN meets and slightly exceeds the performance of the other classifiers despite being trained across subjects.

A New Direction of Cancer Classification: Positive Effect of Low-Ranking MicroRNAs.

PubMed

Li, Feifei; Piao, Minghao; Piao, Yongjun; Li, Meijing; Ryu, Keun Ho

2014-10-01

Many studies based on microRNA (miRNA) expression profiles showed a new aspect of cancer classification. Because one characteristic of miRNA expression data is the high dimensionality, feature selection methods have been used to facilitate dimensionality reduction. The feature selection methods have one shortcoming thus far: they just consider the problem of where feature to class is 1:1 or n:1. However, because one miRNA may influence more than one type of cancer, human miRNA is considered to be ranked low in traditional feature selection methods and are removed most of the time. In view of the limitation of the miRNA number, low-ranking miRNAs are also important to cancer classification. We considered both high- and low-ranking features to cover all problems (1:1, n:1, 1:n, and m:n) in cancer classification. First, we used the correlation-based feature selection method to select the high-ranking miRNAs, and chose the support vector machine, Bayes network, decision tree, k-nearest-neighbor, and logistic classifier to construct cancer classification. Then, we chose Chi-square test, information gain, gain ratio, and Pearson's correlation feature selection methods to build the m:n feature subset, and used the selected miRNAs to determine cancer classification. The low-ranking miRNA expression profiles achieved higher classification accuracy compared with just using high-ranking miRNAs in traditional feature selection methods. Our results demonstrate that the m:n feature subset made a positive impression of low-ranking miRNAs in cancer classification.

21 CFR 884.6110 - Assisted reproduction catheters.

Code of Federal Regulations, 2014 CFR

2014-04-01

... procedures to introduce or remove gametes, zygote(s), preembryo(s), and/or embryo(s) into or from the body..., and component parts. (b) Classification. Class II (special controls) (mouse embryo assay information...

21 CFR 884.6110 - Assisted reproduction catheters.

Code of Federal Regulations, 2012 CFR

2012-04-01

... procedures to introduce or remove gametes, zygote(s), preembryo(s), and/or embryo(s) into or from the body..., and component parts. (b) Classification. Class II (special controls) (mouse embryo assay information...

21 CFR 884.6110 - Assisted reproduction catheters.

Code of Federal Regulations, 2010 CFR

2010-04-01

... procedures to introduce or remove gametes, zygote(s), preembryo(s), and/or embryo(s) into or from the body..., and component parts. (b) Classification. Class II (special controls) (mouse embryo assay information...

21 CFR 884.6110 - Assisted reproduction catheters.

Code of Federal Regulations, 2013 CFR

2013-04-01

... procedures to introduce or remove gametes, zygote(s), preembryo(s), and/or embryo(s) into or from the body..., and component parts. (b) Classification. Class II (special controls) (mouse embryo assay information...

21 CFR 884.6110 - Assisted reproduction catheters.

Code of Federal Regulations, 2011 CFR

2011-04-01

... procedures to introduce or remove gametes, zygote(s), preembryo(s), and/or embryo(s) into or from the body..., and component parts. (b) Classification. Class II (special controls) (mouse embryo assay information...

Extremely low frequency magnetic field measurements in buildings with transformer stations in Switzerland.

PubMed

Röösli, Martin; Jenni, Daniela; Kheifets, Leeka; Mezei, Gabor

2011-08-15

The aim of this study was to evaluate an exposure assessment method that classifies apartments in three exposure categories of extremely low frequency magnetic fields (ELF-MF) based on the location of the apartment relative to the transformer room. We completed measurements in 39 apartments in 18 buildings. In each room of the apartments ELF-MF was concurrently measured with 5 to 6 EMDEX II meters for 10 min. Measured arithmetic mean ELF-MF was 0.59 μT in 8 apartments that were fully adjacent to a transformer room, either directly above the transformer or touching the transformer room wall-to-wall. In apartments that only partly touched the transformer room at corners or edges, average ELF-MF level was 0.14 μT. Average exposure in the remaining apartments was 0.10 μT. Kappa coefficient for exposure classification was 0.64 (95%-CI: 0.45-0.82) if only fully adjacent apartments were considered as highly exposed (>0.4 μT). We found a distinct ELF-MF exposure gradient in buildings with transformer. Exposure classification based on the location of the apartment relative to the transformer room appears feasible. Such an approach considerably reduces effort for exposure assessment and may be used to eliminate selection bias in future epidemiologic studies. Copyright © 2011 Elsevier B.V. All rights reserved.

Assessment of land use and land cover change using spatiotemporal analysis of landscape: case study in south of Tehran.

PubMed

Sabr, Abutaleb; Moeinaddini, Mazaher; Azarnivand, Hossein; Guinot, Benjamin

2016-12-01

In the recent years, dust storms originating from local abandoned agricultural lands have increasingly impacted Tehran and Karaj air quality. Designing and implementing mitigation plans are necessary to study land use/land cover change (LUCC). Land use/cover classification is particularly relevant in arid areas. This study aimed to map land use/cover by pixel- and object-based image classification methods, analyse landscape fragmentation and determine the effects of two different classification methods on landscape metrics. The same sets of ground data were used for both classification methods. Because accuracy of classification plays a key role in better understanding LUCC, both methods were employed. Land use/cover maps of the southwest area of Tehran city for the years 1985, 2000 and 2014 were obtained from Landsat digital images and classified into three categories: built-up, agricultural and barren lands. The results of our LUCC analysis showed that the most important changes in built-up agricultural land categories were observed in zone B (Shahriar, Robat Karim and Eslamshahr) between 1985 and 2014. The landscape metrics obtained for all categories pictured high landscape fragmentation in the study area. Despite no significant difference was evidenced between the two classification methods, the object-based classification led to an overall higher accuracy than using the pixel-based classification. In particular, the accuracy of the built-up category showed a marked increase. In addition, both methods showed similar trends in fragmentation metrics. One of the reasons is that the object-based classification is able to identify buildings, impervious surface and roads in dense urban areas, which produced more accurate maps.

Hemochromatosis

MedlinePlus

Hemochromatosis is a disease in which too much iron builds up in your body. Your body needs iron but too much of it ... types of hemochromatosis. Primary hemochromatosis is an inherited disease. Secondary hemochromatosis is usually the result of something ...

A bioassay experience and lessons learned on the internal contamination of (131)I during a maintenance period in a Korean nuclear power plant.

PubMed

Kim, Hee Geun; Kong, Tae Young

2012-08-01

During a maintenance period at a Korean nuclear power plant, internal exposure of radiation workers occurred by the inhalation of (131)I that was released into the reactor building from a primary system opening due to defective fuels. The internal activity in radiation workers contaminated by (131)I was immediately measured using a whole body counter (WBC). A whole body counting was performed again a few days later, considering the factors of equilibrium in the body. The intake and the committed effective dose were estimated based on the WBC results. The intake was also calculated by hand, based on both the entrance records to the reactor building, and the counted results of the air concentration for (131)I were compared with the whole body counting results.

Raman spectroscopic signature of vaginal fluid and its potential application in forensic body fluid identification.

PubMed

Sikirzhytskaya, Aliaksandra; Sikirzhytski, Vitali; Lednev, Igor K

2012-03-10

Traces of human body fluids, such as blood, saliva, sweat, semen and vaginal fluid, play an increasingly important role in forensic investigations. However, a nondestructive, easy and rapid identification of body fluid traces at the scene of a crime has not yet been developed. The obstacles have recently been addressed in our studies, which demonstrated the considerable potential of Raman spectroscopy. In this study, we continued to build a full library of body fluid spectroscopic signatures. The problems concerning vaginal fluid stain identification were addressed using Raman spectroscopy coupled with advanced statistical analysis. Calculated characteristic Raman and fluorescent spectral components were used to build a multidimensional spectroscopic signature of vaginal fluid, which demonstrated good specificity and was able to handle heterogeneous samples from different donors. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.