A high resolution 16 k multi-channel analyzer PC add-on card

International Nuclear Information System (INIS)

Kulkarni, C.P.; Paulson, Molly; Vaidya, P.P.

2001-01-01

This paper describes the system details of a 16 K channel resolution Multi-Channel Analyzer (MCA) developed at Electronics Division, BARC, which is used in high resolution nuclear spectroscopy systems for pulse height analysis. The high resolution data acquisition PC add-on card is architectured using a state of the art digital circuit design technology which makes use of a Field Programmable Gate Array (FPGA), and some of the most modern and advanced analog counterparts like low power, high speed and high precision comparators, Op-amps, ADCs and DACs etc. The 16 K MCA card gives an economic, compact, and low power alternative for nuclear pulse spectroscopy use. (author)

Image Fusion-Based Land Cover Change Detection Using Multi-Temporal High-Resolution Satellite Images

Directory of Open Access Journals (Sweden)

Biao Wang

2017-08-01

Full Text Available Change detection is usually treated as a problem of explicitly detecting land cover transitions in satellite images obtained at different times, and helps with emergency response and government management. This study presents an unsupervised change detection method based on the image fusion of multi-temporal images. The main objective of this study is to improve the accuracy of unsupervised change detection from high-resolution multi-temporal images. Our method effectively reduces change detection errors, since spatial displacement and spectral differences between multi-temporal images are evaluated. To this end, a total of four cross-fused images are generated with multi-temporal images, and the iteratively reweighted multivariate alteration detection (IR-MAD method—a measure for the spectral distortion of change information—is applied to the fused images. In this experiment, the land cover change maps were extracted using multi-temporal IKONOS-2, WorldView-3, and GF-1 satellite images. The effectiveness of the proposed method compared with other unsupervised change detection methods is demonstrated through experimentation. The proposed method achieved an overall accuracy of 80.51% and 97.87% for cases 1 and 2, respectively. Moreover, the proposed method performed better when differentiating the water area from the vegetation area compared to the existing change detection methods. Although the water area beneath moderate and sparse vegetation canopy was captured, vegetation cover and paved regions of the water body were the main sources of omission error, and commission errors occurred primarily in pixels of mixed land use and along the water body edge. Nevertheless, the proposed method, in conjunction with high-resolution satellite imagery, offers a robust and flexible approach to land cover change mapping that requires no ancillary data for rapid implementation.

Objective Tuning of Model Parameters in CAM5 Across Different Spatial Resolutions

Science.gov (United States)

Bulaevskaya, V.; Lucas, D. D.

2014-12-01

Parameterizations of physical processes in climate models are highly dependent on the spatial and temporal resolution and must be tuned for each resolution under consideration. At high spatial resolutions, objective methods for parameter tuning are computationally prohibitive. Our work has focused on calibrating parameters in the Community Atmosphere Model 5 (CAM5) for three spatial resolutions: 1, 2, and 4 degrees. Using perturbed-parameter ensembles and uncertainty quantification methodology, we have identified input parameters that minimize discrepancies of energy fluxes simulated by CAM5 across the three resolutions and with respect to satellite observations. We are also beginning to exploit the parameter-resolution relationships to objectively tune parameters in a high-resolution version of CAM5 by leveraging cheaper, low-resolution simulations and statistical models. We will present our approach to multi-resolution climate model parameter tuning, as well as the key findings. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under contract DE-AC52-07NA27344 and was supported from the DOE Office of Science through the Scientific Discovery Through Advanced Computing (SciDAC) project on Multiscale Methods for Accurate, Efficient, and Scale-Aware Models of the Earth System.

Land Cover Change Detection in Urban Lake Areas Using Multi-Temporary Very High Spatial Resolution Aerial Images

Directory of Open Access Journals (Sweden)

Wenyuan Zhang

2018-01-01

Full Text Available The availability of very high spatial resolution (VHR remote sensing imagery provides unique opportunities to exploit meaningful change information in detail with object-oriented image analysis. This study investigated land cover (LC changes in Shahu Lake of Wuhan using multi-temporal VHR aerial images in the years 1978, 1981, 1989, 1995, 2003, and 2011. A multi-resolution segmentation algorithm and CART (classification and regression trees classifier were employed to perform highly accurate LC classification of the individual images, while a post-classification comparison method was used to detect changes. The experiments demonstrated that significant changes in LC occurred along with the rapid urbanization during 1978–2011. The dominant changes that took place in the study area were lake and vegetation shrinking, replaced by high density buildings and roads. The total area of Shahu Lake decreased from ~7.64 km2 to ~3.60 km2 during the past 33 years, where 52.91% of its original area was lost. The presented results also indicated that urban expansion and inadequate legislative protection are the main factors in Shahu Lake’s shrinking. The object-oriented change detection schema presented in this manuscript enables us to better understand the specific spatial changes of Shahu Lake, which can be used to make reasonable decisions for lake protection and urban development.

Multi-group transport methods for high-resolution neutron activation analysis

International Nuclear Information System (INIS)

Burns, K. A.; Smith, L. E.; Gesh, C. J.; Shaver, M. W.

2009-01-01

The accurate and efficient simulation of coupled neutron-photon problems is necessary for several important radiation detection applications. Examples include the detection of nuclear threats concealed in cargo containers and prompt gamma neutron activation analysis for nondestructive determination of elemental composition of unknown samples. In these applications, high-resolution gamma-ray spectrometers are used to preserve as much information as possible about the emitted photon flux, which consists of both continuum and characteristic gamma rays with discrete energies. Monte Carlo transport is the most commonly used modeling tool for this type of problem, but computational times for many problems can be prohibitive. This work explores the use of multi-group deterministic methods for the simulation of neutron activation problems. Central to this work is the development of a method for generating multi-group neutron-photon cross-sections in a way that separates the discrete and continuum photon emissions so that the key signatures in neutron activation analysis (i.e., the characteristic line energies) are preserved. The mechanics of the cross-section preparation method are described and contrasted with standard neutron-gamma cross-section sets. These custom cross-sections are then applied to several benchmark problems. Multi-group results for neutron and photon flux are compared to MCNP results. Finally, calculated responses of high-resolution spectrometers are compared. Preliminary findings show promising results when compared to MCNP. A detailed discussion of the potential benefits and shortcomings of the multi-group-based approach, in terms of accuracy, and computational efficiency, is provided. (authors)

Joint Multi-scale Convolution Neural Network for Scene Classification of High Resolution Remote Sensing Imagery

Directory of Open Access Journals (Sweden)

ZHENG Zhuo

2018-05-01

Full Text Available High resolution remote sensing imagery scene classification is important for automatic complex scene recognition, which is the key technology for military and disaster relief, etc. In this paper, we propose a novel joint multi-scale convolution neural network (JMCNN method using a limited amount of image data for high resolution remote sensing imagery scene classification. Different from traditional convolutional neural network, the proposed JMCNN is an end-to-end training model with joint enhanced high-level feature representation, which includes multi-channel feature extractor, joint multi-scale feature fusion and Softmax classifier. Multi-channel and scale convolutional extractors are used to extract scene middle features, firstly. Then, in order to achieve enhanced high-level feature representation in a limit dataset, joint multi-scale feature fusion is proposed to combine multi-channel and scale features using two feature fusions. Finally, enhanced high-level feature representation can be used for classification by Softmax. Experiments were conducted using two limit public UCM and SIRI datasets. Compared to state-of-the-art methods, the JMCNN achieved improved performance and great robustness with average accuracies of 89.3% and 88.3% on the two datasets.

Multi-dimensional analysis of high resolution {gamma}-ray data

Energy Technology Data Exchange (ETDEWEB)

Flibotte, S.; Huettmeier, U.J.; France, G. de; Haas, B.; Romain, P.; Theisen, Ch.; Vivien, J.P.; Zen, J. [Strasbourg-1 Univ., 67 (France). Centre de Recherches Nucleaires

1992-12-31

A new generation of high resolution {gamma}-ray spectrometers capable of recording high-fold coincidence events with a large efficiency will soon be available. Algorithms are developed to analyze high-fold {gamma}-ray coincidences. As a contribution to the software development associated with the EUROGAM spectrometer, the performances of computer codes designed to select multi-dimensional gates from 3-, 4- and 5-fold coincidence databases were tested. The tests were performed on events generated with a Monte Carlo simulation and also on real experimental triple data recorded with the 8{pi} spectrometer and with a preliminary version of the EUROGAM array. (R.P.) 14 refs.; 3 figs.; 3 tabs.

Multi-dimensional analysis of high resolution γ-ray data

International Nuclear Information System (INIS)

Flibotte, S.; Huettmeier, U.J.; France, G. de; Haas, B.; Romain, P.; Theisen, Ch.; Vivien, J.P.; Zen, J.

1992-01-01

A new generation of high resolution γ-ray spectrometers capable of recording high-fold coincidence events with a large efficiency will soon be available. Algorithms are developed to analyze high-fold γ-ray coincidences. As a contribution to the software development associated with the EUROGAM spectrometer, the performances of computer codes designed to select multi-dimensional gates from 3-, 4- and 5-fold coincidence databases were tested. The tests were performed on events generated with a Monte Carlo simulation and also on real experimental triple data recorded with the 8π spectrometer and with a preliminary version of the EUROGAM array. (R.P.) 14 refs.; 3 figs.; 3 tabs

A novel approach for multiple mobile objects path planning: Parametrization method and conflict resolution strategy

International Nuclear Information System (INIS)

Ma, Yong; Wang, Hongwei; Zamirian, M.

2012-01-01

We present a new approach containing two steps to determine conflict-free paths for mobile objects in two and three dimensions with moving obstacles. Firstly, the shortest path of each object is set as goal function which is subject to collision-avoidance criterion, path smoothness, and velocity and acceleration constraints. This problem is formulated as calculus of variation problem (CVP). Using parametrization method, CVP is converted to time-varying nonlinear programming problems (TNLPP) and then resolved. Secondly, move sequence of object is assigned by priority scheme; conflicts are resolved by multilevel conflict resolution strategy. Approach efficiency is confirmed by numerical examples. -- Highlights: ► Approach with parametrization method and conflict resolution strategy is proposed. ► Approach fits for multi-object paths planning in two and three dimensions. ► Single object path planning and multi-object conflict resolution are orderly used. ► Path of each object obtained with parameterization method in the first phase. ► Conflict-free paths gained by multi-object conflict resolution in the second phase.

ROBUST MOTION SEGMENTATION FOR HIGH DEFINITION VIDEO SEQUENCES USING A FAST MULTI-RESOLUTION MOTION ESTIMATION BASED ON SPATIO-TEMPORAL TUBES

OpenAIRE

Brouard , Olivier; Delannay , Fabrice; Ricordel , Vincent; Barba , Dominique

2007-01-01

4 pages; International audience; Motion segmentation methods are effective for tracking video objects. However, objects segmentation methods based on motion need to know the global motion of the video in order to back-compensate it before computing the segmentation. In this paper, we propose a method which estimates the global motion of a High Definition (HD) video shot and then segments it using the remaining motion information. First, we develop a fast method for multi-resolution motion est...

Adaptive optics plug-and-play setup for high-resolution microscopes with multi-actuator adaptive lens

Science.gov (United States)

Quintavalla, M.; Pozzi, P.; Verhaegen, Michelle; Bijlsma, Hielke; Verstraete, Hans; Bonora, S.

2018-02-01

Adaptive Optics (AO) has revealed as a very promising technique for high-resolution microscopy, where the presence of optical aberrations can easily compromise the image quality. Typical AO systems however, are almost impossible to implement on commercial microscopes. We propose a simple approach by using a Multi-actuator Adaptive Lens (MAL) that can be inserted right after the objective and works in conjunction with an image optimization software allowing for a wavefront sensorless correction. We presented the results obtained on several commercial microscopes among which a confocal microscope, a fluorescence microscope, a light sheet microscope and a multiphoton microscope.

A multi-channel data acquisition system with high resolution based on microcomputer

International Nuclear Information System (INIS)

An Qi; Wang Yanfang; Xing Tao

1995-01-01

The paper introduces the principle of a multi-channel data acquisition system with high resolution based on the microcomputer.The system consists of five parts.They are analog-to-digital converter, data buffer area, trigger logic circuit, control circuit, and digital-to-analog converter

Object-oriented Method of Hierarchical Urban Building Extraction from High-resolution Remote-Sensing Imagery

Directory of Open Access Journals (Sweden)

TAO Chao

2016-02-01

Full Text Available An automatic urban building extraction method for high-resolution remote-sensing imagery,which combines building segmentation based on neighbor total variations with object-oriented analysis,is presented in this paper. Aimed at different extraction complexity from various buildings in the segmented image,a hierarchical building extraction strategy with multi-feature fusion is adopted. Firstly,we extract some rectangle buildings which remain intact after segmentation through shape analysis. Secondly,in order to ensure each candidate building target to be independent,multidirectional morphological road-filtering algorithm is designed which can separate buildings from the neighboring roads with similar spectrum. Finally,we take the extracted buildings and the excluded non-buildings as samples to establish probability model respectively,and Bayesian discriminating classifier is used for making judgment of the other candidate building objects to get the ultimate extraction result. The experimental results have shown that the approach is able to detect buildings with different structure and spectral features in the same image. The results of performance evaluation also support the robustness and precision of the approach developed.

High resolution multi-scalar drought indices for Iberia

Science.gov (United States)

Russo, Ana; Gouveia, Célia; Trigo, Ricardo; Jerez, Sonia

2014-05-01

The Iberian Peninsula has been recurrently affected by drought episodes and by adverse associated effects (Gouveia et al., 2009), ranging from severe water shortages to losses of hydroelectricity production, increasing risk of forest fires, forest decline and triggering processes of land degradation and desertification. Moreover, Iberia corresponds to one of the most sensitive areas to current and future climate change and is nowadays considered a hot spot of climate change with high probability for the increase of extreme events (Giorgi and Lionello, 2008). The spatial and temporal behavior of climatic droughts at different time scales was analyzed using spatially distributed time series of multi-scalar drought indicators, such as the Standardized Precipitation Evapotranspiration Index (SPEI) (Vicente-Serrano et al., 2010). This new climatic drought index is based on the simultaneous use of precipitation and temperature fields with the advantage of combining a multi-scalar character with the capacity to include the effects of temperature variability on drought assessment. Moreover, reanalysis data and the higher resolution hindcasted databases obtained from them are valuable surrogates of the sparse observations and widely used for in-depth characterizations of the present-day climate. Accordingly, this work aims to enhance the knowledge on high resolution drought patterns in Iberian Peninsula, taking advantage of high-resolution (10km) regional MM5 simulations of the recent past (1959-2007) over Iberia. It should be stressed that these high resolution meteorological fields (e.g. temperature, precipitation) have been validated for various purposes (Jerez et al., 2013). A detailed characterization of droughts since the 1960s using the 10 km resolution hidncasted simulation was performed with the aim to explore the conditions favoring drought onset, duration and ending, as well as the subsequent short, medium and long-term impacts affecting the environment and the

Fallspeed measurement and high-resolution multi-angle photography of hydrometeors in freefall

OpenAIRE

T. J. Garrett; C. Fallgatter; K. Shkurko; D. Howlett

2012-01-01

We describe here a new instrument for imaging hydrometeors in freefall. The Multi-Angle Snowflake Camera (MASC) captures high resolution photographs of hydrometeors from three angles while simultaneously measuring their fallspeed. Based on the stereoscopic photographs captured over the two months of continuous measurements obtained at a high altitude location within the Wasatch Front in Utah, we derive statistics for fallspeed, hydrometeor size, shape, orientation and aspect ratio. From a sel...

Super-resolution imaging applied to moving object tracking

Science.gov (United States)

Swalaganata, Galandaru; Ratna Sulistyaningrum, Dwi; Setiyono, Budi

2017-10-01

Moving object tracking in a video is a method used to detect and analyze changes that occur in an object that being observed. Visual quality and the precision of the tracked target are highly wished in modern tracking system. The fact that the tracked object does not always seem clear causes the tracking result less precise. The reasons are low quality video, system noise, small object, and other factors. In order to improve the precision of the tracked object especially for small object, we propose a two step solution that integrates a super-resolution technique into tracking approach. First step is super-resolution imaging applied into frame sequences. This step was done by cropping the frame in several frame or all of frame. Second step is tracking the result of super-resolution images. Super-resolution image is a technique to obtain high-resolution images from low-resolution images. In this research single frame super-resolution technique is proposed for tracking approach. Single frame super-resolution was a kind of super-resolution that it has the advantage of fast computation time. The method used for tracking is Camshift. The advantages of Camshift was simple calculation based on HSV color that use its histogram for some condition and color of the object varies. The computational complexity and large memory requirements required for the implementation of super-resolution and tracking were reduced and the precision of the tracked target was good. Experiment showed that integrate a super-resolution imaging into tracking technique can track the object precisely with various background, shape changes of the object, and in a good light conditions.

A High-resolution Multi-wavelength Simultaneous Imaging System with Solar Adaptive Optics

Energy Technology Data Exchange (ETDEWEB)

Rao, Changhui; Zhu, Lei; Gu, Naiting; Rao, Xuejun; Zhang, Lanqiang; Bao, Hua; Kong, Lin; Guo, Youming; Zhong, Libo; Ma, Xue’an; Li, Mei; Wang, Cheng; Zhang, Xiaojun; Fan, Xinlong; Chen, Donghong; Feng, Zhongyi; Wang, Xiaoyun; Wang, Zhiyong, E-mail: gunaiting@ioe.ac.cn [The Key Laboratory on Adaptive Optics, Chinese Academy of Sciences, P.O. Box 350, Shuangliu, Chengdu 610209, Sichuan (China)

2017-10-01

A high-resolution multi-wavelength simultaneous imaging system from visible to near-infrared bands with a solar adaptive optics system, in which seven imaging channels, including the G band (430.5 nm), the Na i line (589 nm), the H α line (656.3 nm), the TiO band (705.7 nm), the Ca ii IR line (854.2 nm), the He i line (1083 nm), and the Fe i line (1565.3 nm), are chosen, is developed to image the solar atmosphere from the photosphere layer to the chromosphere layer. To our knowledge, this is the solar high-resolution imaging system with the widest spectral coverage. This system was demonstrated at the 1 m New Vaccum Solar Telescope and the on-sky high-resolution observational results were acquired. In this paper, we will illustrate the design and performance of the imaging system. The calibration and the data reduction of the system are also presented.

Flexible feature-space-construction architecture and its VLSI implementation for multi-scale object detection

Science.gov (United States)

Luo, Aiwen; An, Fengwei; Zhang, Xiangyu; Chen, Lei; Huang, Zunkai; Jürgen Mattausch, Hans

2018-04-01

Feature extraction techniques are a cornerstone of object detection in computer-vision-based applications. The detection performance of vison-based detection systems is often degraded by, e.g., changes in the illumination intensity of the light source, foreground-background contrast variations or automatic gain control from the camera. In order to avoid such degradation effects, we present a block-based L1-norm-circuit architecture which is configurable for different image-cell sizes, cell-based feature descriptors and image resolutions according to customization parameters from the circuit input. The incorporated flexibility in both the image resolution and the cell size for multi-scale image pyramids leads to lower computational complexity and power consumption. Additionally, an object-detection prototype for performance evaluation in 65 nm CMOS implements the proposed L1-norm circuit together with a histogram of oriented gradients (HOG) descriptor and a support vector machine (SVM) classifier. The proposed parallel architecture with high hardware efficiency enables real-time processing, high detection robustness, small chip-core area as well as low power consumption for multi-scale object detection.

Multi objective multi refinery optimization with environmental and catastrophic failure effects objectives

Science.gov (United States)

Khogeer, Ahmed Sirag

2005-11-01

Petroleum refining is a capital-intensive business. With stringent environmental regulations on the processing industry and declining refining margins, political instability, increased risk of war and terrorist attacks in which refineries and fuel transportation grids may be targeted, higher pressures are exerted on refiners to optimize performance and find the best combination of feed and processes to produce salable products that meet stricter product specifications, while at the same time meeting refinery supply commitments and of course making profit. This is done through multi objective optimization. For corporate refining companies and at the national level, Intea-Refinery and Inter-Refinery optimization is the second step in optimizing the operation of the whole refining chain as a single system. Most refinery-wide optimization methods do not cover multiple objectives such as minimizing environmental impact, avoiding catastrophic failures, or enhancing product spec upgrade effects. This work starts by carrying out a refinery-wide, single objective optimization, and then moves to multi objective-single refinery optimization. The last step is multi objective-multi refinery optimization, the objectives of which are analysis of the effects of economic, environmental, product spec, strategic, and catastrophic failure. Simulation runs were carried out using both MATLAB and ASPEN PIMS utilizing nonlinear techniques to solve the optimization problem. The results addressed the need to debottleneck some refineries or transportation media in order to meet the demand for essential products under partial or total failure scenarios. They also addressed how importing some high spec products can help recover some of the losses and what is needed in order to accomplish this. In addition, the results showed nonlinear relations among local and global objectives for some refineries. The results demonstrate that refineries can have a local multi objective optimum that does not

Underwater video enhancement using multi-camera super-resolution

Science.gov (United States)

Quevedo, E.; Delory, E.; Callicó, G. M.; Tobajas, F.; Sarmiento, R.

2017-12-01

Image spatial resolution is critical in several fields such as medicine, communications or satellite, and underwater applications. While a large variety of techniques for image restoration and enhancement has been proposed in the literature, this paper focuses on a novel Super-Resolution fusion algorithm based on a Multi-Camera environment that permits to enhance the quality of underwater video sequences without significantly increasing computation. In order to compare the quality enhancement, two objective quality metrics have been used: PSNR (Peak Signal-to-Noise Ratio) and the SSIM (Structural SIMilarity) index. Results have shown that the proposed method enhances the objective quality of several underwater sequences, avoiding the appearance of undesirable artifacts, with respect to basic fusion Super-Resolution algorithms.

3D high- and super-resolution imaging using single-objective SPIM.

Science.gov (United States)

Galland, Remi; Grenci, Gianluca; Aravind, Ajay; Viasnoff, Virgile; Studer, Vincent; Sibarita, Jean-Baptiste

2015-07-01

Single-objective selective-plane illumination microscopy (soSPIM) is achieved with micromirrored cavities combined with a laser beam-steering unit installed on a standard inverted microscope. The illumination and detection are done through the same objective. soSPIM can be used with standard sample preparations and features high background rejection and efficient photon collection, allowing for 3D single-molecule-based super-resolution imaging of whole cells or cell aggregates. Using larger mirrors enabled us to broaden the capabilities of our system to image Drosophila embryos.

DEEP SPACE: High Resolution VR Platform for Multi-user Interactive Narratives

Science.gov (United States)

Kuka, Daniela; Elias, Oliver; Martins, Ronald; Lindinger, Christopher; Pramböck, Andreas; Jalsovec, Andreas; Maresch, Pascal; Hörtner, Horst; Brandl, Peter

DEEP SPACE is a large-scale platform for interactive, stereoscopic and high resolution content. The spatial and the system design of DEEP SPACE are facing constraints of CAVETM-like systems in respect to multi-user interactive storytelling. To be used as research platform and as public exhibition space for many people, DEEP SPACE is capable to process interactive, stereoscopic applications on two projection walls with a size of 16 by 9 meters and a resolution of four times 1080p (4K) each. The processed applications are ranging from Virtual Reality (VR)-environments to 3D-movies to computationally intensive 2D-productions. In this paper, we are describing DEEP SPACE as an experimental VR platform for multi-user interactive storytelling. We are focusing on the system design relevant for the platform, including the integration of the Apple iPod Touch technology as VR control, and a special case study that is demonstrating the research efforts in the field of multi-user interactive storytelling. The described case study, entitled "Papyrate's Island", provides a prototypical scenario of how physical drawings may impact on digital narratives. In this special case, DEEP SPACE helps us to explore the hypothesis that drawing, a primordial human creative skill, gives us access to entirely new creative possibilities in the domain of interactive storytelling.

Built-Up Area Detection from High-Resolution Satellite Images Using Multi-Scale Wavelet Transform and Local Spatial Statistics

Science.gov (United States)

Chen, Y.; Zhang, Y.; Gao, J.; Yuan, Y.; Lv, Z.

2018-04-01

Recently, built-up area detection from high-resolution satellite images (HRSI) has attracted increasing attention because HRSI can provide more detailed object information. In this paper, multi-resolution wavelet transform and local spatial autocorrelation statistic are introduced to model the spatial patterns of built-up areas. First, the input image is decomposed into high- and low-frequency subbands by wavelet transform at three levels. Then the high-frequency detail information in three directions (horizontal, vertical and diagonal) are extracted followed by a maximization operation to integrate the information in all directions. Afterward, a cross-scale operation is implemented to fuse different levels of information. Finally, local spatial autocorrelation statistic is introduced to enhance the saliency of built-up features and an adaptive threshold algorithm is used to achieve the detection of built-up areas. Experiments are conducted on ZY-3 and Quickbird panchromatic satellite images, and the results show that the proposed method is very effective for built-up area detection.

Exploiting High Resolution Multi-Seasonal Textural Measures and Spectral Information for Reedbed Mapping

Directory of Open Access Journals (Sweden)

Alex Okiemute Onojeghuo

2016-02-01

Full Text Available Reedbeds across the UK are amongst the most important habitats for rare and endangered birds, wildlife and organisms. However, over the past century, this valued wetland habitat has experienced a drastic reduction in quality and spatial coverage due to pressures from human related activities. To this end, conservation organisations across the UK have been charged with the task of conserving and expanding this threatened habitat. With this backdrop, the study aimed to develop a methodology for accurate reedbed mapping through the combined use of multi-seasonal texture measures and spectral information contained in high resolution QuickBird satellite imagery. The key objectives were to determine the most effective single-date (autumn or summer and multi-seasonal QuickBird imagery suitable for reedbed mapping over the study area; to evaluate the effectiveness of combining multi-seasonal texture measures and spectral information for reedbed mapping using a variety of combinations; and to evaluate the most suitable classification technique for reedbed mapping from three selected classification techniques, namely maximum likelihood classifier, spectral angular mapper and artificial neural network. Using two selected grey-level co-occurrence textural measures (entropy and angular second moment, a series of experiments were conducted using varied combinations of single-date and multi-seasonal QuickBird imagery. Overall, the results indicate the multi-seasonal pansharpened multispectral bands (eight layers combined with all eight grey level co-occurrence matrix texture measures (entropy and angular second moment computed using windows 3 × 3 and 7 × 7 produced the optimal reedbed (76.5% and overall classification (78.1% accuracies using the maximum likelihood classifier technique. Using the optimal 16 layer multi-seasonal pansharpened multispectral and texture combined image dataset, a total reedbed area of 9.8 hectares was successfully mapped over the

Delineation of wetland areas from high resolution WorldView-2 data by object-based method

International Nuclear Information System (INIS)

Hassan, N; Hamid, J R A; Adnan, N A; Jaafar, M

2014-01-01

Various classification methods are available that can be used to delineate land cover types. Object-based is one of such methods for delineating the land cover from satellite imageries. This paper focuses on the digital image processing aspects of discriminating wetland areas via object-based method using high resolution satellite multispectral WorldView-2 image data taken over part of Penang Island region. This research is an attempt to improve the wetland area delineation in conjunction with a range of classification techniques which can be applied to satellite data with high spatial and spectral resolution such as World View 2. The intent is to determine a suitable approach to delineate and map these wetland areas more appropriately. There are common parameters to take into account that are pivotal in object-based method which are the spatial resolution and the range of spectral channels of the imaging sensor system. The preliminary results of the study showed object-based analysis is capable of delineating wetland region of interest with an accuracy that is acceptable to the required tolerance for land cover classification

High-resolution observation of phase contrast at 1MeV. Amorphous or crystalline objects

International Nuclear Information System (INIS)

Bourret, A.; Desseaux, J.

1975-01-01

Many authors have stressed the possibilities of high voltage to improve resolution, but owing to numerous experimental difficulties the resolution limit at 1MeV, which lies around 1A for conventional lenses, has so far been unattainable. Thus the phase contrast at 1MeV has not been studied on evaporated objects. On the other hand the fringes of crystal planes have been observed at 1MeV. the CEN-G microscope having been considerably modified it has been possible to observe the phase contrast of amorphous or crystalline objects [fr

A fast mass spring model solver for high-resolution elastic objects

Science.gov (United States)

Zheng, Mianlun; Yuan, Zhiyong; Zhu, Weixu; Zhang, Guian

2017-03-01

Real-time simulation of elastic objects is of great importance for computer graphics and virtual reality applications. The fast mass spring model solver can achieve visually realistic simulation in an efficient way. Unfortunately, this method suffers from resolution limitations and lack of mechanical realism for a surface geometry model, which greatly restricts its application. To tackle these problems, in this paper we propose a fast mass spring model solver for high-resolution elastic objects. First, we project the complex surface geometry model into a set of uniform grid cells as cages through *cages mean value coordinate method to reflect its internal structure and mechanics properties. Then, we replace the original Cholesky decomposition method in the fast mass spring model solver with a conjugate gradient method, which can make the fast mass spring model solver more efficient for detailed surface geometry models. Finally, we propose a graphics processing unit accelerated parallel algorithm for the conjugate gradient method. Experimental results show that our method can realize efficient deformation simulation of 3D elastic objects with visual reality and physical fidelity, which has a great potential for applications in computer animation.

Accurate and precise 40Ar/39Ar dating by high-resolution, multi-collection, mass spectrometry

DEFF Research Database (Denmark)

Storey, Michael; Rivera, Tiffany; Flude, Stephanie

New generation, high resolution, multi-collector noble gas mass spectrometers equipped with ion-counting electron multipliers provide opportunities for improved accuracy and precision in 40Ar/39Ar dating. Here we report analytical protocols and age cross-calibration studies using a NU-Instruments......New generation, high resolution, multi-collector noble gas mass spectrometers equipped with ion-counting electron multipliers provide opportunities for improved accuracy and precision in 40Ar/39Ar dating. Here we report analytical protocols and age cross-calibration studies using a NU......-Instruments multi-collector Noblesse noble gas mass spectrometer configured with a faraday detector and three ion-counting electron multipliers. The instrument has the capability to measure several noble gas isotopes simultaneously and to change measurement configurations instantaneously by the use of QUAD lenses...... (zoom optics). The Noblesse offer several advantages over previous generation noble gas mass spectrometers and is particularly suited for single crystal 40Ar/39Ar dating because of: (i) improved source sensitivity (ii) ion-counting electron multipliers, which have much lower signal to noise ratios than...

METHOD FOR OPTIMAL RESOLUTION OF MULTI-AIRCRAFT CONFLICTS IN THREE-DIMENSIONAL SPACE

Directory of Open Access Journals (Sweden)

Denys Vasyliev

2017-03-01

Full Text Available Purpose: The risk of critical proximities of several aircraft and appearance of multi-aircraft conflicts increases under current conditions of high dynamics and density of air traffic. The actual problem is a development of methods for optimal multi-aircraft conflicts resolution that should provide the synthesis of conflict-free trajectories in three-dimensional space. Methods: The method for optimal resolution of multi-aircraft conflicts using heading, speed and altitude change maneuvers has been developed. Optimality criteria are flight regularity, flight economy and the complexity of maneuvering. Method provides the sequential synthesis of the Pareto-optimal set of combinations of conflict-free flight trajectories using multi-objective dynamic programming and selection of optimal combination using the convolution of optimality criteria. Within described method the following are defined: the procedure for determination of combinations of aircraft conflict-free states that define the combinations of Pareto-optimal trajectories; the limitations on discretization of conflict resolution process for ensuring the absence of unobservable separation violations. Results: The analysis of the proposed method is performed using computer simulation which results show that synthesized combination of conflict-free trajectories ensures the multi-aircraft conflict avoidance and complies with defined optimality criteria. Discussion: Proposed method can be used for development of new automated air traffic control systems, airborne collision avoidance systems, intelligent air traffic control simulators and for research activities.

A multi-objective constraint-based approach for modeling genome-scale microbial ecosystems.

Science.gov (United States)

Budinich, Marko; Bourdon, Jérémie; Larhlimi, Abdelhalim; Eveillard, Damien

2017-01-01

Interplay within microbial communities impacts ecosystems on several scales, and elucidation of the consequent effects is a difficult task in ecology. In particular, the integration of genome-scale data within quantitative models of microbial ecosystems remains elusive. This study advocates the use of constraint-based modeling to build predictive models from recent high-resolution -omics datasets. Following recent studies that have demonstrated the accuracy of constraint-based models (CBMs) for simulating single-strain metabolic networks, we sought to study microbial ecosystems as a combination of single-strain metabolic networks that exchange nutrients. This study presents two multi-objective extensions of CBMs for modeling communities: multi-objective flux balance analysis (MO-FBA) and multi-objective flux variability analysis (MO-FVA). Both methods were applied to a hot spring mat model ecosystem. As a result, multiple trade-offs between nutrients and growth rates, as well as thermodynamically favorable relative abundances at community level, were emphasized. We expect this approach to be used for integrating genomic information in microbial ecosystems. Following models will provide insights about behaviors (including diversity) that take place at the ecosystem scale.

High-resolution multi-band imaging for validation and characterization of small Kepler planets

International Nuclear Information System (INIS)

Everett, Mark E.; Silva, David R.; Barclay, Thomas; Howell, Steve B.; Ciardi, David R.; Horch, Elliott P.; Crepp, Justin R.

2015-01-01

High-resolution ground-based optical speckle and near-infrared adaptive optics images are taken to search for stars in close angular proximity to host stars of candidate planets identified by the NASA Kepler Mission. Neighboring stars are a potential source of false positive signals. These stars also blend into Kepler light curves, affecting estimated planet properties, and are important for an understanding of planets in multiple star systems. Deep images with high angular resolution help to validate candidate planets by excluding potential background eclipsing binaries as the source of the transit signals. A study of 18 Kepler Object of Interest stars hosting a total of 28 candidate and validated planets is presented. Validation levels are determined for 18 planets against the likelihood of a false positive from a background eclipsing binary. Most of these are validated at the 99% level or higher, including five newly validated planets in two systems: Kepler-430 and Kepler-431. The stellar properties of the candidate host stars are determined by supplementing existing literature values with new spectroscopic characterizations. Close neighbors of seven of these stars are examined using multi-wavelength photometry to determine their nature and influence on the candidate planet properties. Most of the close neighbors appear to be gravitationally bound secondaries, while a few are best explained as closely co-aligned field stars. Revised planet properties are derived for each candidate and validated planet, including cases where the close neighbors are the potential host stars.

A new omni-directional multi-camera system for high resolution surveillance

Science.gov (United States)

Cogal, Omer; Akin, Abdulkadir; Seyid, Kerem; Popovic, Vladan; Schmid, Alexandre; Ott, Beat; Wellig, Peter; Leblebici, Yusuf

2014-05-01

Omni-directional high resolution surveillance has a wide application range in defense and security fields. Early systems used for this purpose are based on parabolic mirror or fisheye lens where distortion due to the nature of the optical elements cannot be avoided. Moreover, in such systems, the image resolution is limited to a single image sensor's image resolution. Recently, the Panoptic camera approach that mimics the eyes of flying insects using multiple imagers has been presented. This approach features a novel solution for constructing a spherically arranged wide FOV plenoptic imaging system where the omni-directional image quality is limited by low-end sensors. In this paper, an overview of current Panoptic camera designs is provided. New results for a very-high resolution visible spectrum imaging and recording system inspired from the Panoptic approach are presented. The GigaEye-1 system, with 44 single cameras and 22 FPGAs, is capable of recording omni-directional video in a 360°×100° FOV at 9.5 fps with a resolution over (17,700×4,650) pixels (82.3MP). Real-time video capturing capability is also verified at 30 fps for a resolution over (9,000×2,400) pixels (21.6MP). The next generation system with significantly higher resolution and real-time processing capacity, called GigaEye-2, is currently under development. The important capacity of GigaEye-1 opens the door to various post-processing techniques in surveillance domain such as large perimeter object tracking, very-high resolution depth map estimation and high dynamicrange imaging which are beyond standard stitching and panorama generation methods.

High-resolution SPECT for small-animal imaging

International Nuclear Information System (INIS)

Qi Yujin

2006-01-01

This article presents a brief overview of the development of high-resolution SPECT for small-animal imaging. A pinhole collimator has been used for high-resolution animal SPECT to provide better spatial resolution and detection efficiency in comparison with a parallel-hole collimator. The theory of imaging characteristics of the pinhole collimator is presented and the designs of the pinhole aperture are discussed. The detector technologies used for the development of small-animal SPECT and the recent advances are presented. The evolving trend of small-animal SPECT is toward a multi-pinhole and a multi-detector system to obtain a high resolution and also a high detection efficiency. (authors)

Raft cultivation area extraction from high resolution remote sensing imagery by fusing multi-scale region-line primitive association features

Science.gov (United States)

Wang, Min; Cui, Qi; Wang, Jie; Ming, Dongping; Lv, Guonian

2017-01-01

In this paper, we first propose several novel concepts for object-based image analysis, which include line-based shape regularity, line density, and scale-based best feature value (SBV), based on the region-line primitive association framework (RLPAF). We then propose a raft cultivation area (RCA) extraction method for high spatial resolution (HSR) remote sensing imagery based on multi-scale feature fusion and spatial rule induction. The proposed method includes the following steps: (1) Multi-scale region primitives (segments) are obtained by image segmentation method HBC-SEG, and line primitives (straight lines) are obtained by phase-based line detection method. (2) Association relationships between regions and lines are built based on RLPAF, and then multi-scale RLPAF features are extracted and SBVs are selected. (3) Several spatial rules are designed to extract RCAs within sea waters after land and water separation. Experiments show that the proposed method can successfully extract different-shaped RCAs from HR images with good performance.

Angular difference feature extraction for urban scene classification using ZY-3 multi-angle high-resolution satellite imagery

Science.gov (United States)

Huang, Xin; Chen, Huijun; Gong, Jianya

2018-01-01

Spaceborne multi-angle images with a high-resolution are capable of simultaneously providing spatial details and three-dimensional (3D) information to support detailed and accurate classification of complex urban scenes. In recent years, satellite-derived digital surface models (DSMs) have been increasingly utilized to provide height information to complement spectral properties for urban classification. However, in such a way, the multi-angle information is not effectively exploited, which is mainly due to the errors and difficulties of the multi-view image matching and the inaccuracy of the generated DSM over complex and dense urban scenes. Therefore, it is still a challenging task to effectively exploit the available angular information from high-resolution multi-angle images. In this paper, we investigate the potential for classifying urban scenes based on local angular properties characterized from high-resolution ZY-3 multi-view images. Specifically, three categories of angular difference features (ADFs) are proposed to describe the angular information at three levels (i.e., pixel, feature, and label levels): (1) ADF-pixel: the angular information is directly extrapolated by pixel comparison between the multi-angle images; (2) ADF-feature: the angular differences are described in the feature domains by comparing the differences between the multi-angle spatial features (e.g., morphological attribute profiles (APs)). (3) ADF-label: label-level angular features are proposed based on a group of urban primitives (e.g., buildings and shadows), in order to describe the specific angular information related to the types of primitive classes. In addition, we utilize spatial-contextual information to refine the multi-level ADF features using superpixel segmentation, for the purpose of alleviating the effects of salt-and-pepper noise and representing the main angular characteristics within a local area. The experiments on ZY-3 multi-angle images confirm that the proposed

Project overview of OPTIMOS-EVE: the fibre-fed multi-object spectrograph for the E-ELT

NARCIS (Netherlands)

Navarro, R.; Chemla, F.; Bonifacio, P.; Flores, H.; Guinouard, I.; Huet, J.-M.; Puech, M.; Royer, F.; Pragt, J.H.; Wulterkens, G.; Sawyer, E.C.; Caldwell, M.E.; Tosh, I.A.J.; Whalley, M.S.; Woodhouse, G.F.W.; Spanò, P.; Di Marcantonio, P.; Andersen, M.I.; Dalton, G.B.; Kaper, L.; Hammer, F.

2010-01-01

OPTIMOS-EVE (OPTical Infrared Multi Object Spectrograph - Extreme Visual Explorer) is the fibre fed multi object spectrograph proposed for the European Extremely Large Telescope (E-ELT), planned to be operational in 2018 at Cerro Armazones (Chile). It is designed to provide a spectral resolution of

Information Extraction of High Resolution Remote Sensing Images Based on the Calculation of Optimal Segmentation Parameters

Science.gov (United States)

Zhu, Hongchun; Cai, Lijie; Liu, Haiying; Huang, Wei

2016-01-01

Multi-scale image segmentation and the selection of optimal segmentation parameters are the key processes in the object-oriented information extraction of high-resolution remote sensing images. The accuracy of remote sensing special subject information depends on this extraction. On the basis of WorldView-2 high-resolution data, the optimal segmentation parameters methodof object-oriented image segmentation and high-resolution image information extraction, the following processes were conducted in this study. Firstly, the best combination of the bands and weights was determined for the information extraction of high-resolution remote sensing image. An improved weighted mean-variance method was proposed andused to calculatethe optimal segmentation scale. Thereafter, the best shape factor parameter and compact factor parameters were computed with the use of the control variables and the combination of the heterogeneity and homogeneity indexes. Different types of image segmentation parameters were obtained according to the surface features. The high-resolution remote sensing images were multi-scale segmented with the optimal segmentation parameters. Ahierarchical network structure was established by setting the information extraction rules to achieve object-oriented information extraction. This study presents an effective and practical method that can explain expert input judgment by reproducible quantitative measurements. Furthermore the results of this procedure may be incorporated into a classification scheme. PMID:27362762

Novel Super-Resolution Approach to Time-Resolved Volumetric 4-Dimensional Magnetic Resonance Imaging With High Spatiotemporal Resolution for Multi-Breathing Cycle Motion Assessment

International Nuclear Information System (INIS)

Li, Guang; Wei, Jie; Kadbi, Mo; Moody, Jason; Sun, August; Zhang, Shirong; Markova, Svetlana; Zakian, Kristen; Hunt, Margie; Deasy, Joseph O.

2017-01-01

Purpose: To develop and evaluate a super-resolution approach to reconstruct time-resolved 4-dimensional magnetic resonance imaging (TR-4DMRI) with a high spatiotemporal resolution for multi-breathing cycle motion assessment. Methods and Materials: A super-resolution approach was developed to combine fast 3-dimensional (3D) cine MRI with low resolution during free breathing (FB) and high-resolution 3D static MRI during breath hold (BH) using deformable image registration. A T1-weighted, turbo field echo sequence, coronal 3D cine acquisition, partial Fourier approximation, and SENSitivity Encoding parallel acceleration were used. The same MRI pulse sequence, field of view, and acceleration techniques were applied in both FB and BH acquisitions; the intensity-based Demons deformable image registration method was used. Under an institutional review board–approved protocol, 7 volunteers were studied with 3D cine FB scan (voxel size: 5 × 5 × 5 mm"3) at 2 Hz for 40 seconds and a 3D static BH scan (2 × 2 × 2 mm"3). To examine the image fidelity of 3D cine and super-resolution TR-4DMRI, a mobile gel phantom with multi-internal targets was scanned at 3 speeds and compared with the 3D static image. Image similarity among 3D cine, 4DMRI, and 3D static was evaluated visually using difference image and quantitatively using voxel intensity correlation and Dice index (phantom only). Multi-breathing-cycle waveforms were extracted and compared in both phantom and volunteer images using the 3D cine as the references. Results: Mild imaging artifacts were found in the 3D cine and TR-4DMRI of the mobile gel phantom with a Dice index of >0.95. Among 7 volunteers, the super-resolution TR-4DMRI yielded high voxel-intensity correlation (0.92 ± 0.05) and low voxel-intensity difference (<0.05). The detected motion differences between TR-4DMRI and 3D cine were −0.2 ± 0.5 mm (phantom) and −0.2 ± 1.9 mm (diaphragms). Conclusion: Super-resolution TR-4DMRI has been

Novel Super-Resolution Approach to Time-Resolved Volumetric 4-Dimensional Magnetic Resonance Imaging With High Spatiotemporal Resolution for Multi-Breathing Cycle Motion Assessment

Energy Technology Data Exchange (ETDEWEB)

Li, Guang, E-mail: lig2@mskcc.org [Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York (United States); Wei, Jie [Department of Computer Science, City College of New York, New York, New York (United States); Kadbi, Mo [Philips Healthcare, MR Therapy Cleveland, Ohio (United States); Moody, Jason; Sun, August; Zhang, Shirong; Markova, Svetlana; Zakian, Kristen; Hunt, Margie; Deasy, Joseph O. [Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York (United States)

2017-06-01

Purpose: To develop and evaluate a super-resolution approach to reconstruct time-resolved 4-dimensional magnetic resonance imaging (TR-4DMRI) with a high spatiotemporal resolution for multi-breathing cycle motion assessment. Methods and Materials: A super-resolution approach was developed to combine fast 3-dimensional (3D) cine MRI with low resolution during free breathing (FB) and high-resolution 3D static MRI during breath hold (BH) using deformable image registration. A T1-weighted, turbo field echo sequence, coronal 3D cine acquisition, partial Fourier approximation, and SENSitivity Encoding parallel acceleration were used. The same MRI pulse sequence, field of view, and acceleration techniques were applied in both FB and BH acquisitions; the intensity-based Demons deformable image registration method was used. Under an institutional review board–approved protocol, 7 volunteers were studied with 3D cine FB scan (voxel size: 5 × 5 × 5 mm{sup 3}) at 2 Hz for 40 seconds and a 3D static BH scan (2 × 2 × 2 mm{sup 3}). To examine the image fidelity of 3D cine and super-resolution TR-4DMRI, a mobile gel phantom with multi-internal targets was scanned at 3 speeds and compared with the 3D static image. Image similarity among 3D cine, 4DMRI, and 3D static was evaluated visually using difference image and quantitatively using voxel intensity correlation and Dice index (phantom only). Multi-breathing-cycle waveforms were extracted and compared in both phantom and volunteer images using the 3D cine as the references. Results: Mild imaging artifacts were found in the 3D cine and TR-4DMRI of the mobile gel phantom with a Dice index of >0.95. Among 7 volunteers, the super-resolution TR-4DMRI yielded high voxel-intensity correlation (0.92 ± 0.05) and low voxel-intensity difference (<0.05). The detected motion differences between TR-4DMRI and 3D cine were −0.2 ± 0.5 mm (phantom) and −0.2 ± 1.9 mm (diaphragms). Conclusion: Super-resolution TR-4

Multi-stage robust scheme for citrus identification from high resolution airborne images

Science.gov (United States)

Amorós-López, Julia; Izquierdo Verdiguier, Emma; Gómez-Chova, Luis; Muñoz-Marí, Jordi; Zoilo Rodríguez-Barreiro, Jorge; Camps-Valls, Gustavo; Calpe-Maravilla, Javier

2008-10-01

Identification of land cover types is one of the most critical activities in remote sensing. Nowadays, managing land resources by using remote sensing techniques is becoming a common procedure to speed up the process while reducing costs. However, data analysis procedures should satisfy the accuracy figures demanded by institutions and governments for further administrative actions. This paper presents a methodological scheme to update the citrus Geographical Information Systems (GIS) of the Comunidad Valenciana autonomous region, Spain). The proposed approach introduces a multi-stage automatic scheme to reduce visual photointerpretation and ground validation tasks. First, an object-oriented feature extraction process is carried out for each cadastral parcel from very high spatial resolution (VHR) images (0.5m) acquired in the visible and near infrared. Next, several automatic classifiers (decision trees, multilayer perceptron, and support vector machines) are trained and combined to improve the final accuracy of the results. The proposed strategy fulfills the high accuracy demanded by policy makers by means of combining automatic classification methods with visual photointerpretation available resources. A level of confidence based on the agreement between classifiers allows us an effective management by fixing the quantity of parcels to be reviewed. The proposed methodology can be applied to similar problems and applications.

The FALCON Concept: Multi-Object Spectroscopy Combined with MCAO in Near-IR

Science.gov (United States)

Hammer, François; Sayède, Frédéric; Gendron, Eric; Fusco, Thierry; Burgarella, Denis; Cayatte, Véronique; Conan, Jean-Marc; Courbin, Frédéric; Flores, Hector; Guinouard, Isabelle; Jocou, Laurent; Lançon, Ariane; Monnet, Guy; Mouhcine, Mustapha; Rigaud, François; Rouan, Daniel; Rousset, Gérard; Buat, Véronique; Zamkotsian, Frédéric

A large fraction of the present-day stellar mass was formed between z=0.5 and z˜ 3 and our understanding of the formation mechanisms at work at these epochs requires both high spatial and high spectral resolution: one shall simultaneously obtain images of objects with typical sizes as small as 1-2 kpc (˜ 0".1), while achieving 20-50 km/s (R≥ 5000) spectral resolution. In addition, the redshift range to be considered implies that most important spectral features are redshifted in the near-infrared. The obvious instrumental solution to adopt in order to tackle the science goal is therefore a combination of multi-object 3D spectrograph with multi-conjugate adaptive optics in large fields. A very promising way to achieve such a technically challenging goal is to relax the conditions of the traditional full adaptive optics correction. A partial, but still competitive correction shall be prefered, over a much wider field of view. This can be done by estimating the turbulent volume from sets of natural guide stars, by optimizing the correction to several and discrete small areas of few arcsec 2 selected in a large field (Nasmyth field of 25 arcmin) and by correcting up to the 6th, and eventually, up to the 60 th Zernike modes. Simulations on real extragalactic fields, show that for most sources (> 80%), the recovered resolution could reach 0".15-0".25 in the J and H bands. Detection of point-like objects is improved by factors from 3 to ≥10, when compared with an instrument without adaptive correction. The proposed instrument concept, FALCON, is equipped with deployable mini-integral field units (IFUs), achieving spectral resolutions between R=5000 and 20000. Its multiplex capability, combined with high spatial and spectral resolution characteristics, is a natural ground based complement to the next generation of space telescopes. Galaxy formation in the early Universe is certainly a main science driver. We describe here how FALCON shall allow to answer puzzling

Fall speed measurement and high-resolution multi-angle photography of hydrometeors in free fall

OpenAIRE

T. J. Garrett; C. Fallgatter; K. Shkurko; D. Howlett

2012-01-01

We describe here a new instrument for imaging hydrometeors in free fall. The Multi-Angle Snowflake Camera (MASC) captures high-resolution photographs of hydrometeors from three angles while simultaneously measuring their fall speed. Based on the stereoscopic photographs captured over the two months of continuous measurements obtained at a high altitude location within the Wasatch Front in Utah, we derive statistics for fall speed, hydrometeor size, shape, orientation and asp...

High-resolution time-frequency representation of EEG data using multi-scale wavelets

Science.gov (United States)

Li, Yang; Cui, Wei-Gang; Luo, Mei-Lin; Li, Ke; Wang, Lina

2017-09-01

An efficient time-varying autoregressive (TVAR) modelling scheme that expands the time-varying parameters onto the multi-scale wavelet basis functions is presented for modelling nonstationary signals and with applications to time-frequency analysis (TFA) of electroencephalogram (EEG) signals. In the new parametric modelling framework, the time-dependent parameters of the TVAR model are locally represented by using a novel multi-scale wavelet decomposition scheme, which can allow the capability to capture the smooth trends as well as track the abrupt changes of time-varying parameters simultaneously. A forward orthogonal least square (FOLS) algorithm aided by mutual information criteria are then applied for sparse model term selection and parameter estimation. Two simulation examples illustrate that the performance of the proposed multi-scale wavelet basis functions outperforms the only single-scale wavelet basis functions or Kalman filter algorithm for many nonstationary processes. Furthermore, an application of the proposed method to a real EEG signal demonstrates the new approach can provide highly time-dependent spectral resolution capability.

Mobile and embedded fast high resolution image stitching for long length rectangular monochromatic objects with periodic structure

Science.gov (United States)

Limonova, Elena; Tropin, Daniil; Savelyev, Boris; Mamay, Igor; Nikolaev, Dmitry

2018-04-01

In this paper we describe stitching protocol, which allows to obtain high resolution images of long length monochromatic objects with periodic structure. This protocol can be used for long length documents or human-induced objects in satellite images of uninhabitable regions like Arctic regions. The length of such objects can reach notable values, while modern camera sensors have limited resolution and are not able to provide good enough image of the whole object for further processing, e.g. using in OCR system. The idea of the proposed method is to acquire a video stream containing full object in high resolution and use image stitching. We expect the scanned object to have straight boundaries and periodic structure, which allow us to introduce regularization to the stitching problem and adapt algorithm for limited computational power of mobile and embedded CPUs. With the help of detected boundaries and structure we estimate homography between frames and use this information to reduce complexity of stitching. We demonstrate our algorithm on mobile device and show image processing speed of 2 fps on Samsung Exynos 5422 processor

Non-convex multi-objective optimization

CERN Document Server

Pardalos, Panos M; Žilinskas, Julius

2017-01-01

Recent results on non-convex multi-objective optimization problems and methods are presented in this book, with particular attention to expensive black-box objective functions. Multi-objective optimization methods facilitate designers, engineers, and researchers to make decisions on appropriate trade-offs between various conflicting goals. A variety of deterministic and stochastic multi-objective optimization methods are developed in this book. Beginning with basic concepts and a review of non-convex single-objective optimization problems; this book moves on to cover multi-objective branch and bound algorithms, worst-case optimal algorithms (for Lipschitz functions and bi-objective problems), statistical models based algorithms, and probabilistic branch and bound approach. Detailed descriptions of new algorithms for non-convex multi-objective optimization, their theoretical substantiation, and examples for practical applications to the cell formation problem in manufacturing engineering, the process design in...

Multi-objective optimization of linear multi-state multiple sliding window system

International Nuclear Information System (INIS)

Konak, Abdullah; Kulturel-Konak, Sadan; Levitin, Gregory

2012-01-01

This paper considers the optimal element sequencing in a linear multi-state multiple sliding window system that consists of n linearly ordered multi-state elements. Each multi-state element can have different states: from complete failure up to perfect functioning. A performance rate is associated with each state. The failure of type i in the system occurs if for any i (1≤i≤I) the cumulative performance of any r i consecutive elements is lower than w i . The element sequence strongly affects the probability of any type of system failure. The sequence that minimizes the probability of certain type of failure can provide high probability of other types of failures. Therefore the optimization problem for the multiple sliding window system is essentially multi-objective. The paper formulates and solves the multi-objective optimization problem for the multiple sliding window systems. A multi-objective Genetic Algorithm is used as the optimization engine. Illustrative examples are presented.

Multi-GPU Accelerated Admittance Method for High-Resolution Human Exposure Evaluation.

Science.gov (United States)

Xiong, Zubiao; Feng, Shi; Kautz, Richard; Chandra, Sandeep; Altunyurt, Nevin; Chen, Ji

2015-12-01

A multi-graphics processing unit (GPU) accelerated admittance method solver is presented for solving the induced electric field in high-resolution anatomical models of human body when exposed to external low-frequency magnetic fields. In the solver, the anatomical model is discretized as a three-dimensional network of admittances. The conjugate orthogonal conjugate gradient (COCG) iterative algorithm is employed to take advantage of the symmetric property of the complex-valued linear system of equations. Compared against the widely used biconjugate gradient stabilized method, the COCG algorithm can reduce the solving time by 3.5 times and reduce the storage requirement by about 40%. The iterative algorithm is then accelerated further by using multiple NVIDIA GPUs. The computations and data transfers between GPUs are overlapped in time by using asynchronous concurrent execution design. The communication overhead is well hidden so that the acceleration is nearly linear with the number of GPU cards. Numerical examples show that our GPU implementation running on four NVIDIA Tesla K20c cards can reach 90 times faster than the CPU implementation running on eight CPU cores (two Intel Xeon E5-2603 processors). The implemented solver is able to solve large dimensional problems efficiently. A whole adult body discretized in 1-mm resolution can be solved in just several minutes. The high efficiency achieved makes it practical to investigate human exposure involving a large number of cases with a high resolution that meets the requirements of international dosimetry guidelines.

A multi-objective constraint-based approach for modeling genome-scale microbial ecosystems.

Directory of Open Access Journals (Sweden)

Marko Budinich

Full Text Available Interplay within microbial communities impacts ecosystems on several scales, and elucidation of the consequent effects is a difficult task in ecology. In particular, the integration of genome-scale data within quantitative models of microbial ecosystems remains elusive. This study advocates the use of constraint-based modeling to build predictive models from recent high-resolution -omics datasets. Following recent studies that have demonstrated the accuracy of constraint-based models (CBMs for simulating single-strain metabolic networks, we sought to study microbial ecosystems as a combination of single-strain metabolic networks that exchange nutrients. This study presents two multi-objective extensions of CBMs for modeling communities: multi-objective flux balance analysis (MO-FBA and multi-objective flux variability analysis (MO-FVA. Both methods were applied to a hot spring mat model ecosystem. As a result, multiple trade-offs between nutrients and growth rates, as well as thermodynamically favorable relative abundances at community level, were emphasized. We expect this approach to be used for integrating genomic information in microbial ecosystems. Following models will provide insights about behaviors (including diversity that take place at the ecosystem scale.

Multi-scale Analysis of High Resolution Topography: Feature Extraction and Identification of Landscape Characteristic Scales

Science.gov (United States)

Passalacqua, P.; Sangireddy, H.; Stark, C. P.

2015-12-01

With the advent of digital terrain data, detailed information on terrain characteristics and on scale and location of geomorphic features is available over extended areas. Our ability to observe landscapes and quantify topographic patterns has greatly improved, including the estimation of fluxes of mass and energy across landscapes. Challenges still remain in the analysis of high resolution topography data; the presence of features such as roads, for example, challenges classic methods for feature extraction and large data volumes require computationally efficient extraction and analysis methods. Moreover, opportunities exist to define new robust metrics of landscape characterization for landscape comparison and model validation. In this presentation we cover recent research in multi-scale and objective analysis of high resolution topography data. We show how the analysis of the probability density function of topographic attributes such as slope, curvature, and topographic index contains useful information for feature localization and extraction. The analysis of how the distributions change across scales, quantified by the behavior of modal values and interquartile range, allows the identification of landscape characteristic scales, such as terrain roughness. The methods are introduced on synthetic signals in one and two dimensions and then applied to a variety of landscapes of different characteristics. Validation of the methods includes the analysis of modeled landscapes where the noise distribution is known and features of interest easily measured.

Statistical Projections for Multi-resolution, Multi-dimensional Visual Data Exploration and Analysis

Energy Technology Data Exchange (ETDEWEB)

Nguyen, Hoa T. [Univ. of Utah, Salt Lake City, UT (United States); Stone, Daithi [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Bethel, E. Wes [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

2016-01-01

An ongoing challenge in visual exploration and analysis of large, multi-dimensional datasets is how to present useful, concise information to a user for some specific visualization tasks. Typical approaches to this problem have proposed either reduced-resolution versions of data, or projections of data, or both. These approaches still have some limitations such as consuming high computation or suffering from errors. In this work, we explore the use of a statistical metric as the basis for both projections and reduced-resolution versions of data, with a particular focus on preserving one key trait in data, namely variation. We use two different case studies to explore this idea, one that uses a synthetic dataset, and another that uses a large ensemble collection produced by an atmospheric modeling code to study long-term changes in global precipitation. The primary findings of our work are that in terms of preserving the variation signal inherent in data, that using a statistical measure more faithfully preserves this key characteristic across both multi-dimensional projections and multi-resolution representations than a methodology based upon averaging.

Extraction of Terraces on the Loess Plateau from High-Resolution DEMs and Imagery Utilizing Object-Based Image Analysis

Directory of Open Access Journals (Sweden)

Hanqing Zhao

2017-05-01

Full Text Available Abstract: Terraces are typical artificial landforms on the Loess Plateau, with ecological functions in water and soil conservation, agricultural production, and biodiversity. Recording the spatial distribution of terraces is the basis of monitoring their extent and understanding their ecological effects. The current terrace extraction method mainly relies on high-resolution imagery, but its accuracy is limited due to vegetation coverage distorting the features of terraces in imagery. High-resolution topographic data reflecting the morphology of true terrace surfaces are needed. Terraces extraction on the Loess Plateau is challenging because of the complex terrain and diverse vegetation after the implementation of “vegetation recovery”. This study presents an automatic method of extracting terraces based on 1 m resolution digital elevation models (DEMs and 0.3 m resolution Worldview-3 imagery as auxiliary information used for object-based image analysis (OBIA. A multi-resolution segmentation method was used where slope, positive and negative terrain index (PN, accumulative curvature slope (AC, and slope of slope (SOS were determined as input layers for image segmentation by correlation analysis and Sheffield entropy method. The main classification features based on DEMs were chosen from the terrain features derived from terrain factors and texture features by gray-level co-occurrence matrix (GLCM analysis; subsequently, these features were determined by the importance analysis on classification and regression tree (CART analysis. Extraction rules based on DEMs were generated from the classification features with a total classification accuracy of 89.96%. The red band and near-infrared band of images were used to exclude construction land, which is easily confused with small-size terraces. As a result, the total classification accuracy was increased to 94%. The proposed method ensures comprehensive consideration of terrain, texture, shape, and

High performance pseudo-analytical simulation of multi-object adaptive optics over multi-GPU systems

KAUST Repository

Abdelfattah, Ahmad; Gendron, É ric; Gratadour, Damien; Keyes, David E.; Ltaief, Hatem; Sevin, Arnaud; Vidal, Fabrice

2014-01-01

Multi-object adaptive optics (MOAO) is a novel adaptive optics (AO) technique dedicated to the special case of wide-field multi-object spectrographs (MOS). It applies dedicated wavefront corrections to numerous independent tiny patches spread over a large field of view (FOV). The control of each deformable mirror (DM) is done individually using a tomographic reconstruction of the phase based on measurements from a number of wavefront sensors (WFS) pointing at natural and artificial guide stars in the field. The output of this study helps the design of a new instrument called MOSAIC, a multi-object spectrograph proposed for the European Extremely Large Telescope (E-ELT). We have developed a novel hybrid pseudo-analytical simulation scheme that allows us to accurately simulate in detail the tomographic problem. The main challenge resides in the computation of the tomographic reconstructor, which involves pseudo-inversion of a large dense symmetric matrix. The pseudo-inverse is computed using an eigenvalue decomposition, based on the divide and conquer algorithm, on multicore systems with multi-GPUs. Thanks to a new symmetric matrix-vector product (SYMV) multi-GPU kernel, our overall implementation scores significant speedups over standard numerical libraries on multicore, like Intel MKL, and up to 60% speedups over the standard MAGMA implementation on 8 Kepler K20c GPUs. At 40,000 unknowns, this appears to be the largest-scale tomographic AO matrix solver submitted to computation, to date, to our knowledge and opens new research directions for extreme scale AO simulations. © 2014 Springer International Publishing Switzerland.

Object-based vegetation classification with high resolution remote sensing imagery

Science.gov (United States)

Yu, Qian

Vegetation species are valuable indicators to understand the earth system. Information from mapping of vegetation species and community distribution at large scales provides important insight for studying the phenological (growth) cycles of vegetation and plant physiology. Such information plays an important role in land process modeling including climate, ecosystem and hydrological models. The rapidly growing remote sensing technology has increased its potential in vegetation species mapping. However, extracting information at a species level is still a challenging research topic. I proposed an effective method for extracting vegetation species distribution from remotely sensed data and investigated some ways for accuracy improvement. The study consists of three phases. Firstly, a statistical analysis was conducted to explore the spatial variation and class separability of vegetation as a function of image scale. This analysis aimed to confirm that high resolution imagery contains the information on spatial vegetation variation and these species classes can be potentially separable. The second phase was a major effort in advancing classification by proposing a method for extracting vegetation species from high spatial resolution remote sensing data. The proposed classification employs an object-based approach that integrates GIS and remote sensing data and explores the usefulness of ancillary information. The whole process includes image segmentation, feature generation and selection, and nearest neighbor classification. The third phase introduces a spatial regression model for evaluating the mapping quality from the above vegetation classification results. The effects of six categories of sample characteristics on the classification uncertainty are examined: topography, sample membership, sample density, spatial composition characteristics, training reliability and sample object features. This evaluation analysis answered several interesting scientific questions

Super-resolution mapping using multi-viewing CHRIS/PROBA data

Science.gov (United States)

Dwivedi, Manish; Kumar, Vinay

2016-04-01

High-spatial resolution Remote Sensing (RS) data provides detailed information which ensures high-definition visual image analysis of earth surface features. These data sets also support improved information extraction capabilities at a fine scale. In order to improve the spatial resolution of coarser resolution RS data, the Super Resolution Reconstruction (SRR) technique has become widely acknowledged which focused on multi-angular image sequences. In this study multi-angle CHRIS/PROBA data of Kutch area is used for SR image reconstruction to enhance the spatial resolution from 18 m to 6m in the hope to obtain a better land cover classification. Various SR approaches like Projection onto Convex Sets (POCS), Robust, Iterative Back Projection (IBP), Non-Uniform Interpolation and Structure-Adaptive Normalized Convolution (SANC) chosen for this study. Subjective assessment through visual interpretation shows substantial improvement in land cover details. Quantitative measures including peak signal to noise ratio and structural similarity are used for the evaluation of the image quality. It was observed that SANC SR technique using Vandewalle algorithm for the low resolution image registration outperformed the other techniques. After that SVM based classifier is used for the classification of SRR and data resampled to 6m spatial resolution using bi-cubic interpolation. A comparative analysis is carried out between classified data of bicubic interpolated and SR derived images of CHRIS/PROBA and SR derived classified data have shown a significant improvement of 10-12% in the overall accuracy. The results demonstrated that SR methods is able to improve spatial detail of multi-angle images as well as the classification accuracy.

High resolution integral holography using Fourier ptychographic approach.

Science.gov (United States)

Li, Zhaohui; Zhang, Jianqi; Wang, Xiaorui; Liu, Delian

2014-12-29

An innovative approach is proposed for calculating high resolution computer generated integral holograms by using the Fourier Ptychographic (FP) algorithm. The approach initializes a high resolution complex hologram with a random guess, and then stitches together low resolution multi-view images, synthesized from the elemental images captured by integral imaging (II), to recover the high resolution hologram through an iterative retrieval with FP constrains. This paper begins with an analysis of the principle of hologram synthesis from multi-projections, followed by an accurate determination of the constrains required in the Fourier ptychographic integral-holography (FPIH). Next, the procedure of the approach is described in detail. Finally, optical reconstructions are performed and the results are demonstrated. Theoretical analysis and experiments show that our proposed approach can reconstruct 3D scenes with high resolution.

A Multi-instrument and Multi-wavelength High Angular Resolution Study of MWC 614: Quantum Heated Particles Inside the Disk Cavity

Science.gov (United States)

Kluska, Jacques; Kraus, Stefan; Davies, Claire L.; Harries, Tim; Willson, Matthew; Monnier, John D.; Aarnio, Alicia; Baron, Fabien; Millan-Gabet, Rafael; Ten Brummelaar, Theo; Che, Xiao; Hinkley, Sasha; Preibisch, Thomas; Sturmann, Judit; Sturmann, Laszlo; Touhami, Yamina

2018-03-01

High angular resolution observations of young stellar objects are required to study the inner astronomical units of protoplanetary disks in which the majority of planets form. As they evolve, gaps open up in the inner disk regions and the disks are fully dispersed within ∼10 Myr. MWC 614 is a pretransitional object with a ∼10 au radius gap. We present a set of high angular resolution observations of this object including SPHERE/ZIMPOL polarimetric and coronagraphic images in the visible, Keck/NIRC2 near-infrared (NIR) aperture masking observations, and Very Large Telescope Interferometer (AMBER, MIDI, and PIONIER) and Center for High Angular Resolution Astronomy (CLASSIC and CLIMB) long-baseline interferometry at infrared wavelengths. We find that all the observations are compatible with an inclined disk (i ∼ 55° at a position angle of ∼20°–30°). The mid-infrared data set confirms that the disk inner rim is at 12.3 ± 0.4 au from the central star. We determined an upper mass limit of 0.34 M ⊙ for a companion inside the cavity. Within the cavity, the NIR emission, usually associated with the dust sublimation region, is unusually extended (∼10 au, 30 times larger than the theoretical sublimation radius) and indicates a high dust temperature (T ∼ 1800 K). As a possible result of companion-induced dust segregation, quantum heated dust grains could explain the extended NIR emission with this high temperature. Our observations confirm the peculiar state of this object where the inner disk has already been accreted onto the star, exposing small particles inside the cavity to direct stellar radiation. Based on observations made with the Keck observatory (NASA program ID N104N2) and with ESO telescopes at the Paranal Observatory (ESO program IDs 073.C-0720, 077.C-0226, 077.C-0521, 083.C-0984, 087.C-0498(A), 190.C-0963, 095.C-0883) and with the Center for High Angular Resolution Astronomy observatory.

A multi-resolution approach for an automated fusion of different low-cost 3D sensors.

Science.gov (United States)

Dupuis, Jan; Paulus, Stefan; Behmann, Jan; Plümer, Lutz; Kuhlmann, Heiner

2014-04-24

The 3D acquisition of object structures has become a common technique in many fields of work, e.g., industrial quality management, cultural heritage or crime scene documentation. The requirements on the measuring devices are versatile, because spacious scenes have to be imaged with a high level of detail for selected objects. Thus, the used measuring systems are expensive and require an experienced operator. With the rise of low-cost 3D imaging systems, their integration into the digital documentation process is possible. However, common low-cost sensors have the limitation of a trade-off between range and accuracy, providing either a low resolution of single objects or a limited imaging field. Therefore, the use of multiple sensors is desirable. We show the combined use of two low-cost sensors, the Microsoft Kinect and the David laserscanning system, to achieve low-resolved scans of the whole scene and a high level of detail for selected objects, respectively. Afterwards, the high-resolved David objects are automatically assigned to their corresponding Kinect object by the use of surface feature histograms and SVM-classification. The corresponding objects are fitted using an ICP-implementation to produce a multi-resolution map. The applicability is shown for a fictional crime scene and the reconstruction of a ballistic trajectory.

Approximating multi-objective scheduling problems

NARCIS (Netherlands)

Dabia, S.; Talbi, El-Ghazali; Woensel, van T.; Kok, de A.G.

2013-01-01

In many practical situations, decisions are multi-objective by nature. In this paper, we propose a generic approach to deal with multi-objective scheduling problems (MOSPs). The aim is to determine the set of Pareto solutions that represent the interactions between the different objectives. Due to

An Object-Based Image Analysis Approach for Detecting Penguin Guano in very High Spatial Resolution Satellite Images

OpenAIRE

Chandi Witharana; Heather J. Lynch

2016-01-01

The logistical challenges of Antarctic field work and the increasing availability of very high resolution commercial imagery have driven an interest in more efficient search and classification of remotely sensed imagery. This exploratory study employed geographic object-based analysis (GEOBIA) methods to classify guano stains, indicative of chinstrap and Adélie penguin breeding areas, from very high spatial resolution (VHSR) satellite imagery and closely examined the transferability of knowle...

A Multi-stage Method to Extract Road from High Resolution Satellite Image

International Nuclear Information System (INIS)

Zhijian, Huang; Zhang, Jinfang; Xu, Fanjiang

2014-01-01

Extracting road information from high-resolution satellite images is complex and hardly achieves by exploiting only one or two modules. This paper presents a multi-stage method, consisting of automatic information extraction and semi-automatic post-processing. The Multi-scale Enhancement algorithm enlarges the contrast of human-made structures with the background. The Statistical Region Merging segments images into regions, whose skeletons are extracted and pruned according to geometry shape information. Setting the start and the end skeleton points, the shortest skeleton path is constructed as a road centre line. The Bidirectional Adaptive Smoothing technique smoothens the road centre line and adjusts it to right position. With the smoothed line and its average width, a Buffer algorithm reconstructs the road region easily. Seen from the last results, the proposed method eliminates redundant non-road regions, repairs incomplete occlusions, jumps over complete occlusions, and reserves accurate road centre lines and neat road regions. During the whole process, only a few interactions are needed

Tuneable resolution as a systems biology approach for multi-scale, multi-compartment computational models.

Science.gov (United States)

Kirschner, Denise E; Hunt, C Anthony; Marino, Simeone; Fallahi-Sichani, Mohammad; Linderman, Jennifer J

2014-01-01

The use of multi-scale mathematical and computational models to study complex biological processes is becoming increasingly productive. Multi-scale models span a range of spatial and/or temporal scales and can encompass multi-compartment (e.g., multi-organ) models. Modeling advances are enabling virtual experiments to explore and answer questions that are problematic to address in the wet-lab. Wet-lab experimental technologies now allow scientists to observe, measure, record, and analyze experiments focusing on different system aspects at a variety of biological scales. We need the technical ability to mirror that same flexibility in virtual experiments using multi-scale models. Here we present a new approach, tuneable resolution, which can begin providing that flexibility. Tuneable resolution involves fine- or coarse-graining existing multi-scale models at the user's discretion, allowing adjustment of the level of resolution specific to a question, an experiment, or a scale of interest. Tuneable resolution expands options for revising and validating mechanistic multi-scale models, can extend the longevity of multi-scale models, and may increase computational efficiency. The tuneable resolution approach can be applied to many model types, including differential equation, agent-based, and hybrid models. We demonstrate our tuneable resolution ideas with examples relevant to infectious disease modeling, illustrating key principles at work. © 2014 The Authors. WIREs Systems Biology and Medicine published by Wiley Periodicals, Inc.

High-resolution wave number spectrum using multi-point measurements in space – the Multi-point Signal Resonator (MSR technique

Directory of Open Access Journals (Sweden)

Y. Narita

2011-02-01

Full Text Available A new analysis method is presented that provides a high-resolution power spectrum in a broad wave number domain based on multi-point measurements. The analysis technique is referred to as the Multi-point Signal Resonator (MSR and it benefits from Capon's minimum variance method for obtaining the proper power spectral density of the signal as well as the MUSIC algorithm (Multiple Signal Classification for considerably reducing the noise part in the spectrum. The mathematical foundation of the analysis method is presented and it is applied to synthetic data as well as Cluster observations of the interplanetary magnetic field. Using the MSR technique for Cluster data we find a wave in the solar wind propagating parallel to the mean magnetic field with relatively small amplitude, which is not identified by the Capon spectrum. The Cluster data analysis shows the potential of the MSR technique for studying waves and turbulence using multi-point measurements.

The edge-preservation multi-classifier relearning framework for the classification of high-resolution remotely sensed imagery

Science.gov (United States)

Han, Xiaopeng; Huang, Xin; Li, Jiayi; Li, Yansheng; Yang, Michael Ying; Gong, Jianya

2018-04-01

In recent years, the availability of high-resolution imagery has enabled more detailed observation of the Earth. However, it is imperative to simultaneously achieve accurate interpretation and preserve the spatial details for the classification of such high-resolution data. To this aim, we propose the edge-preservation multi-classifier relearning framework (EMRF). This multi-classifier framework is made up of support vector machine (SVM), random forest (RF), and sparse multinomial logistic regression via variable splitting and augmented Lagrangian (LORSAL) classifiers, considering their complementary characteristics. To better characterize complex scenes of remote sensing images, relearning based on landscape metrics is proposed, which iteratively quantizes both the landscape composition and spatial configuration by the use of the initial classification results. In addition, a novel tri-training strategy is proposed to solve the over-smoothing effect of relearning by means of automatic selection of training samples with low classification certainties, which always distribute in or near the edge areas. Finally, EMRF flexibly combines the strengths of relearning and tri-training via the classification certainties calculated by the probabilistic output of the respective classifiers. It should be noted that, in order to achieve an unbiased evaluation, we assessed the classification accuracy of the proposed framework using both edge and non-edge test samples. The experimental results obtained with four multispectral high-resolution images confirm the efficacy of the proposed framework, in terms of both edge and non-edge accuracy.

The LUVOIR Ultraviolet Multi-Object Spectrograph (LUMOS): instrument definition and design

Science.gov (United States)

France, Kevin; Fleming, Brian; West, Garrett; McCandliss, Stephan R.; Bolcar, Matthew R.; Harris, Walter; Moustakas, Leonidas; O'Meara, John M.; Pascucci, Ilaria; Rigby, Jane; Schiminovich, David; Tumlinson, Jason

2017-08-01

The Large Ultraviolet/Optical/Infrared Surveyor (LUVOIR) is one of four large mission concepts currently undergoing community study for consideration by the 2020 Astronomy and Astrophysics Decadal Survey. LUVOIR is being designed to pursue an ambitious program of exoplanetary discovery and characterization, cosmic origins astrophysics, and planetary science. The LUVOIR study team is investigating two large telescope apertures (9- and 15-meter primary mirror diameters) and a host of science instruments to carry out the primary mission goals. Many of the exoplanet, cosmic origins, and planetary science goals of LUVOIR require high-throughput, imaging spectroscopy at ultraviolet (100 - 400 nm) wavelengths. The LUVOIR Ultraviolet Multi-Object Spectrograph, LUMOS, is being designed to support all of the UV science requirements of LUVOIR, from exoplanet host star characterization to tomography of circumgalactic halos to water plumes on outer solar system satellites. LUMOS offers point source and multi-object spectroscopy across the UV bandpass, with multiple resolution modes to support different science goals. The instrument will provide low (R = 8,000 - 18,000) and medium (R = 30,000 - 65,000) resolution modes across the far-ultraviolet (FUV: 100 - 200 nm) and nearultraviolet (NUV: 200 - 400 nm) windows, and a very low resolution mode (R = 500) for spectroscopic investigations of extremely faint objects in the FUV. Imaging spectroscopy will be accomplished over a 3 × 1.6 arcminute field-of-view by employing holographically-ruled diffraction gratings to control optical aberrations, microshutter arrays (MSA) built on the heritage of the Near Infrared Spectrograph (NIRSpec) on the James Webb Space Telescope (JWST), advanced optical coatings for high-throughput in the FUV, and next generation large-format photon-counting detectors. The spectroscopic capabilities of LUMOS are augmented by an FUV imaging channel (100 - 200nm, 13 milliarcsecond angular resolution, 2 × 2

Mutual information registration of multi-spectral and multi-resolution images of DigitalGlobe's WorldView-3 imaging satellite

Science.gov (United States)

Miecznik, Grzegorz; Shafer, Jeff; Baugh, William M.; Bader, Brett; Karspeck, Milan; Pacifici, Fabio

2017-05-01

WorldView-3 (WV-3) is a DigitalGlobe commercial, high resolution, push-broom imaging satellite with three instruments: visible and near-infrared VNIR consisting of panchromatic (0.3m nadir GSD) plus multi-spectral (1.2m), short-wave infrared SWIR (3.7m), and multi-spectral CAVIS (30m). Nine VNIR bands, which are on one instrument, are nearly perfectly registered to each other, whereas eight SWIR bands, belonging to the second instrument, are misaligned with respect to VNIR and to each other. Geometric calibration and ortho-rectification results in a VNIR/SWIR alignment which is accurate to approximately 0.75 SWIR pixel at 3.7m GSD, whereas inter-SWIR, band to band registration is 0.3 SWIR pixel. Numerous high resolution, spectral applications, such as object classification and material identification, require more accurate registration, which can be achieved by utilizing image processing algorithms, for example Mutual Information (MI). Although MI-based co-registration algorithms are highly accurate, implementation details for automated processing can be challenging. One particular challenge is how to compute bin widths of intensity histograms, which are fundamental building blocks of MI. We solve this problem by making the bin widths proportional to instrument shot noise. Next, we show how to take advantage of multiple VNIR bands, and improve registration sensitivity to image alignment. To meet this goal, we employ Canonical Correlation Analysis, which maximizes VNIR/SWIR correlation through an optimal linear combination of VNIR bands. Finally we explore how to register images corresponding to different spatial resolutions. We show that MI computed at a low-resolution grid is more sensitive to alignment parameters than MI computed at a high-resolution grid. The proposed modifications allow us to improve VNIR/SWIR registration to better than ¼ of a SWIR pixel, as long as terrain elevation is properly accounted for, and clouds and water are masked out.

Dynamic high resolution imaging of rats

International Nuclear Information System (INIS)

Miyaoka, R.S.; Lewellen, T.K.; Bice, A.N.

1990-01-01

A positron emission tomography with the sensitivity and resolution to do dynamic imaging of rats would be an invaluable tool for biological researchers. In this paper, the authors determine the biological criteria for dynamic positron emission imaging of rats. To be useful, 3 mm isotropic resolution and 2-3 second time binning were necessary characteristics for such a dedicated tomograph. A single plane in which two objects of interest could be imaged simultaneously was considered acceptable. Multi-layered detector designs were evaluated as a possible solution to the dynamic imaging and high resolution imaging requirements. The University of Washington photon history generator was used to generate data to investigate a tomograph's sensitivity to true, scattered and random coincidences for varying detector ring diameters. Intrinsic spatial uniformity advantages of multi-layered detector designs over conventional detector designs were investigated using a Monte Carlo program. As a result, a modular three layered detector prototype is being developed. A module will consist of a layer of five 3.5 mm wide crystals and two layers of six 2.5 mm wide crystals. The authors believe adequate sampling can be achieved with a stationary detector system using these modules. Economical crystal decoding strategies have been investigated and simulations have been run to investigate optimum light channeling methods for block decoding strategies. An analog block decoding method has been proposed and will be experimentally evaluated to determine whether it can provide the desired performance

Multi-example feature-constrained back-projection method for image super-resolution

Institute of Scientific and Technical Information of China (English)

Junlei Zhang; Dianguang Gai; Xin Zhang; Xuemei Li

2017-01-01

Example-based super-resolution algorithms,which predict unknown high-resolution image information using a relationship model learnt from known high- and low-resolution image pairs, have attracted considerable interest in the field of image processing. In this paper, we propose a multi-example feature-constrained back-projection method for image super-resolution. Firstly, we take advantage of a feature-constrained polynomial interpolation method to enlarge the low-resolution image. Next, we consider low-frequency images of different resolutions to provide an example pair. Then, we use adaptive k NN search to find similar patches in the low-resolution image for every image patch in the high-resolution low-frequency image, leading to a regression model between similar patches to be learnt. The learnt model is applied to the low-resolution high-frequency image to produce high-resolution high-frequency information. An iterative back-projection algorithm is used as the final step to determine the final high-resolution image.Experimental results demonstrate that our method improves the visual quality of the high-resolution image.

Geo-oculus: high resolution multi-spectral earth imaging mission from geostationary orbit

Science.gov (United States)

Vaillon, L.; Schull, U.; Knigge, T.; Bevillon, C.

2017-11-01

Geo-Oculus is a GEO-based Earth observation mission studied by Astrium for ESA in 2008-2009 to complement the Sentinel missions, the space component of the GMES (Global Monitoring for Environment & Security). Indeed Earth imaging from geostationary orbit offers new functionalities not covered by existing LEO observation missions, like real-time monitoring and fast revisit capability of any location within the huge area in visibility of the satellite. This high revisit capability is exploited by the Meteosat meteorogical satellites, but with a spatial resolution (500 m nadir for the third generation) far from most of GMES needs (10 to 100 m). To reach such ground resolution from GEO orbit with adequate image quality, large aperture instruments (> 1 m) and high pointing stability (challenges of such missions. To address the requirements from the GMES user community, the Geo-Oculus mission is a combination of routine observations (daily systematic coverage of European coastal waters) with "on-demand" observation for event monitoring (e.g. disasters, fires and oil slicks). The instrument is a large aperture imaging telescope (1.5 m diameter) offering a nadir spatial sampling of 10.5 m (21 m worst case over Europe, below 52.5°N) in a PAN visible channel used for disaster monitoring. The 22 multi-spectral channels have resolutions over Europe ranging from 40 m in UV/VNIR (0.3 to 1 μm) to 750 m in TIR (10-12 μm).

A chronometric exploration of high-resolution 'sensitive TMS masking' effects on subjective and objective measures of vision.

Science.gov (United States)

de Graaf, Tom A; Herring, Jim; Sack, Alexander T

2011-03-01

Transcranial magnetic stimulation (TMS) can induce masking by interfering with ongoing neural activity in early visual cortex. Previous work has explored the chronometry of occipital involvement in vision by using single pulses of TMS with high temporal resolution. However, conventionally TMS intensities have been high and the only measure used to evaluate masking was objective in nature. Recent studies have begun to incorporate subjective measures of vision, alongside objective ones. The current study goes beyond previous work in two regards. First, we explored both objective vision (an orientation discrimination task) and subjective vision (a stimulus visibility rating on a four-point scale), across a wide range of time windows with high temporal resolution. Second, we used a very sensitive TMS-masking paradigm: stimulation was at relatively low TMS intensities, with a figure-8 coil, and the small stimulus was difficult to discriminate already at baseline level. We hypothesized that this should increase the effective temporal resolution of our paradigm. Perhaps for this reason, we are able to report a rather interesting masking curve. Within the classical-masking time window, previously reported to encompass broad SOAs anywhere between 60 and 120 ms, we report not one, but at least two dips in objective performance, with no masking in-between. The subjective measure of vision did not mirror this pattern. These preliminary data from our exploratory design suggest that, with sensitive TMS masking, we might be able to reveal visual processes in early visual cortex previously unreported.

Large-Area, High-Resolution Tree Cover Mapping with Multi-Temporal SPOT5 Imagery, New South Wales, Australia

Directory of Open Access Journals (Sweden)

Adrian Fisher

2016-06-01

Full Text Available Tree cover maps are used for many purposes, such as vegetation mapping, habitat connectivity and fragmentation studies. Small remnant patches of native vegetation are recognised as ecologically important, yet they are underestimated in remote sensing products derived from Landsat. High spatial resolution sensors are capable of mapping small patches of trees, but their use in large-area mapping has been limited. In this study, multi-temporal Satellite pour l’Observation de la Terre 5 (SPOT5 High Resolution Geometrical data was pan-sharpened to 5 m resolution and used to map tree cover for the Australian state of New South Wales (NSW, an area of over 800,000 km2. Complete coverages of SPOT5 panchromatic and multispectral data over NSW were acquired during four consecutive summers (2008–2011 for a total of 1256 images. After pre-processing, the imagery was used to model foliage projective cover (FPC, a measure of tree canopy density commonly used in Australia. The multi-temporal imagery, FPC models and 26,579 training pixels were used in a binomial logistic regression model to estimate the probability of each pixel containing trees. The probability images were classified into a binary map of tree cover using local thresholds, and then visually edited to reduce errors. The final tree map was then attributed with the mean FPC value from the multi-temporal imagery. Validation of the binary map based on visually assessed high resolution reference imagery revealed an overall accuracy of 88% (±0.51% standard error, while comparison against airborne lidar derived data also resulted in an overall accuracy of 88%. A preliminary assessment of the FPC map by comparing against 76 field measurements showed a very good agreement (r2 = 0.90 with a root mean square error of 8.57%, although this may not be representative due to the opportunistic sampling design. The map represents a regionally consistent and locally relevant record of tree cover for NSW, and

Automatic Multi-Level Thresholding Segmentation Based on Multi-Objective Optimization

Directory of Open Access Journals (Sweden)

L. DJEROU,

2012-01-01

Full Text Available In this paper, we present a new multi-level image thresholding technique, called Automatic Threshold based on Multi-objective Optimization "ATMO" that combines the flexibility of multi-objective fitness functions with the power of a Binary Particle Swarm Optimization algorithm "BPSO", for searching the "optimum" number of the thresholds and simultaneously the optimal thresholds of three criteria: the between-class variances criterion, the minimum error criterion and the entropy criterion. Some examples of test images are presented to compare our segmentation method, based on the multi-objective optimization approach with Otsu’s, Kapur’s and Kittler’s methods. Our experimental results show that the thresholding method based on multi-objective optimization is more efficient than the classical Otsu’s, Kapur’s and Kittler’s methods.

Aerodynamic multi-objective integrated optimization based on principal component analysis

Directory of Open Access Journals (Sweden)

Jiangtao HUANG

2017-08-01

Full Text Available Based on improved multi-objective particle swarm optimization (MOPSO algorithm with principal component analysis (PCA methodology, an efficient high-dimension multi-objective optimization method is proposed, which, as the purpose of this paper, aims to improve the convergence of Pareto front in multi-objective optimization design. The mathematical efficiency, the physical reasonableness and the reliability in dealing with redundant objectives of PCA are verified by typical DTLZ5 test function and multi-objective correlation analysis of supercritical airfoil, and the proposed method is integrated into aircraft multi-disciplinary design (AMDEsign platform, which contains aerodynamics, stealth and structure weight analysis and optimization module. Then the proposed method is used for the multi-point integrated aerodynamic optimization of a wide-body passenger aircraft, in which the redundant objectives identified by PCA are transformed to optimization constraints, and several design methods are compared. The design results illustrate that the strategy used in this paper is sufficient and multi-point design requirements of the passenger aircraft are reached. The visualization level of non-dominant Pareto set is improved by effectively reducing the dimension without losing the primary feature of the problem.

USING A MICRO-UAV FOR ULTRA-HIGH RESOLUTION MULTI-SENSOR OBSERVATIONS OF ANTARCTIC MOSS BEDS

Directory of Open Access Journals (Sweden)

A. Lucieer

2012-07-01

Full Text Available This study is the first to use an Unmanned Aerial Vehicle (UAV for mapping moss beds in Antarctica. Mosses can be used as indicators for the regional effects of climate change. Mapping and monitoring their extent and health is therefore important. UAV aerial photography provides ultra-high resolution spatial data for this purpose. We developed a technique to extract an extremely dense 3D point cloud from overlapping UAV aerial photography based on structure from motion (SfM algorithms. The combination of SfM and patch-based multi-view stereo image vision algorithms resulted in a 2 cm resolution digital terrain model (DTM. This detailed topographic information combined with vegetation indices derived from a 6-band multispectral sensor enabled the assessment of moss bed health. This novel UAV system has allowed us to map different environmental characteristics of the moss beds at ultra-high resolution providing us with a better understanding of these fragile Antarctic ecosystems. The paper provides details on the different UAV instruments and the image processing framework resulting in DEMs, vegetation indices, and terrain derivatives.

High spectral resolution observations of the H2 2.12 micron line in Herbig-Haro objects

International Nuclear Information System (INIS)

Zinnecker, H.; Mundt, R.; Geballe, T.R.; Zealey, W.J.

1989-01-01

High-spectral-resolution Fabry-Perot observations of the H 2 2.12-micron line emissions of several Herbig-Haro (HH) objects are discussed. It is shown that H 2 emission by the shock heating of external molecular gas in the wings of the bow shock associated with the working surface of a high-velocity jet may occur for HH objects associated with the jet's end. The shock heating of external molecular gas entrained in the flow by internal shocks occurring in the jet itself and/or in its boundary layer may be the H 2 emission mechanism for HH objects observed along the flow axis. 59 refs

A high-resolution, multi-stop, time-to-digital converter for nuclear time-of-flight measurements

International Nuclear Information System (INIS)

Spencer, D.F.; Cole, J.; Drigert, M.; Aryaeinejad, R.

2006-01-01

A high-resolution, multi-stop, time-to-digital converter (TDC) was designed and developed to precisely measure the times-of-flight (TOF) of incident neutrons responsible for induced fission and capture reactions on actinide targets. The minimum time resolution is ±1 ns. The TDC design was implemented into a single, dual-wide CAMAC module. The CAMAC bus is used for command and control as well as an alternative data output. A high-speed ECL interface, compatible with LeCroy FERA modules, was also provided for the principle data output path. An Actel high-speed field programmable gate array (FPGA) chip was incorporated with an external oscillator and an internal multiple clock phasing system. This device implemented the majority of the high-speed register functions, the state machine for the FERA interface, and the high-speed counting circuit used for the TDC conversion. An external microcontroller was used to monitor and control system-level changes. In this work we discuss the performance of this TDC module as well as its application

An object-oriented classification method of high resolution imagery based on improved AdaTree

International Nuclear Information System (INIS)

Xiaohe, Zhang; Liang, Zhai; Jixian, Zhang; Huiyong, Sang

2014-01-01

With the popularity of the application using high spatial resolution remote sensing image, more and more studies paid attention to object-oriented classification on image segmentation as well as automatic classification after image segmentation. This paper proposed a fast method of object-oriented automatic classification. First, edge-based or FNEA-based segmentation was used to identify image objects and the values of most suitable attributes of image objects for classification were calculated. Then a certain number of samples from the image objects were selected as training data for improved AdaTree algorithm to get classification rules. Finally, the image objects could be classified easily using these rules. In the AdaTree, we mainly modified the final hypothesis to get classification rules. In the experiment with WorldView2 image, the result of the method based on AdaTree showed obvious accuracy and efficient improvement compared with the method based on SVM with the kappa coefficient achieving 0.9242

Sparse PDF Volumes for Consistent Multi-Resolution Volume Rendering

KAUST Repository

Sicat, Ronell Barrera; Kruger, Jens; Moller, Torsten; Hadwiger, Markus

2014-01-01

This paper presents a new multi-resolution volume representation called sparse pdf volumes, which enables consistent multi-resolution volume rendering based on probability density functions (pdfs) of voxel neighborhoods. These pdfs are defined

Digital fabrication of multi-material biomedical objects

Energy Technology Data Exchange (ETDEWEB)

Cheung, H H; Choi, S H, E-mail: shchoi@hku.h [Department of Industrial and Manufacturing Systems Engineering, University of Hong Kong, Pokfulam Road (Hong Kong)

2009-12-15

This paper describes a multi-material virtual prototyping (MMVP) system for modelling and digital fabrication of discrete and functionally graded multi-material objects for biomedical applications. The MMVP system consists of a DMMVP module, an FGMVP module and a virtual reality (VR) simulation module. The DMMVP module is used to model discrete multi-material (DMM) objects, while the FGMVP module is for functionally graded multi-material (FGM) objects. The VR simulation module integrates these two modules to perform digital fabrication of multi-material objects, which can be subsequently visualized and analysed in a virtual environment to optimize MMLM processes for fabrication of product prototypes. Using the MMVP system, two biomedical objects, including a DMM human spine and an FGM intervertebral disc spacer are modelled and digitally fabricated for visualization and analysis in a VR environment. These studies show that the MMVP system is a practical tool for modelling, visualization, and subsequent fabrication of biomedical objects of discrete and functionally graded multi-materials for biomedical applications. The system may be adapted to control MMLM machines with appropriate hardware for physical fabrication of biomedical objects.

Digital fabrication of multi-material biomedical objects

International Nuclear Information System (INIS)

Cheung, H H; Choi, S H

2009-01-01

This paper describes a multi-material virtual prototyping (MMVP) system for modelling and digital fabrication of discrete and functionally graded multi-material objects for biomedical applications. The MMVP system consists of a DMMVP module, an FGMVP module and a virtual reality (VR) simulation module. The DMMVP module is used to model discrete multi-material (DMM) objects, while the FGMVP module is for functionally graded multi-material (FGM) objects. The VR simulation module integrates these two modules to perform digital fabrication of multi-material objects, which can be subsequently visualized and analysed in a virtual environment to optimize MMLM processes for fabrication of product prototypes. Using the MMVP system, two biomedical objects, including a DMM human spine and an FGM intervertebral disc spacer are modelled and digitally fabricated for visualization and analysis in a VR environment. These studies show that the MMVP system is a practical tool for modelling, visualization, and subsequent fabrication of biomedical objects of discrete and functionally graded multi-materials for biomedical applications. The system may be adapted to control MMLM machines with appropriate hardware for physical fabrication of biomedical objects.

Object-based Morphological Building Index for Building Extraction from High Resolution Remote Sensing Imagery

Directory of Open Access Journals (Sweden)

LIN Xiangguo

2017-06-01

Full Text Available Building extraction from high resolution remote sensing images is a hot research topic in the field of photogrammetry and remote sensing. In this article, an object-based morphological building index (OBMBI is constructed based on both image segmentation and graph-based top-hat reconstruction, and OBMBI is used for building extraction from high resolution remote sensing images. First, bidirectional mapping relationship between pixels, objects and graph-nodes are constructed. Second, the OBMBI image is built based on both graph-based top-hat reconstruction and the above mapping relationship. Third, a binary thresholding is performed on the OBMBI image, and the binary image is converted into vector format to derive the building polygons. Finally, the post-processing is made to optimize the extracted building polygons. Two images, including an aerial image and a panchromatic satellite image, are used to test both the proposed method and classic PanTex method. The experimental results suggest that our proposed method has a higher accuracy in building extraction than the classic PanTex method. On average, the correctness, the completeness and the quality of our method are respectively 9.49%, 11.26% and 14.11% better than those of the PanTex.

A Concept of Multi-Mode High Spectral Resolution Lidar Using Mach-Zehnder Interferometer

Directory of Open Access Journals (Sweden)

Jin Yoshitaka

2016-01-01

Full Text Available In this paper, we present the design of a High Spectral Resolution Lidar (HSRL using a laser that oscillates in a multi-longitudinal mode. Rayleigh and Mie scattering components are separated using a Mach-Zehnder Interferometer (MZI with the same free spectral range (FSR as the transmitted laser. The transmitted laser light is measured as a reference signal with the same MZI. By scanning the MZI periodically with a scanning range equal to the mode spacing, we can identify the maximum Mie and the maximum Rayleigh signals using the reference signal. The cross talk due to the spectral width of each laser mode can also be estimated.

High spatial resolution distributed fiber system for multi-parameter sensing based on modulated pulses.

Science.gov (United States)

Zhang, Jingdong; Zhu, Tao; Zhou, Huan; Huang, Shihong; Liu, Min; Huang, Wei

2016-11-28

We demonstrate a cost-effective distributed fiber sensing system for the multi-parameter detection of the vibration, the temperature, and the strain by integrating phase-sensitive optical time domain reflectometry (φ-OTDR) and Brillouin optical time domain reflectometry (B-OTDR). Taking advantage of the fast changing property of the vibration and the static properties of the temperature and the strain, both the width and intensity of the laser pulses are modulated and injected into the single-mode sensing fiber proportionally, so that three concerned parameters can be extracted simultaneously by only one photo-detector and one data acquisition channel. A data processing method based on Gaussian window short time Fourier transform (G-STFT) is capable of achieving high spatial resolution in B-OTDR. The experimental results show that up to 4.8kHz vibration sensing with 3m spatial resolution at 10km standard single-mode fiber can be realized, as well as the distributed temperature and stress profiles along the same fiber with 80cm spatial resolution.

Quasielastic high-resolution time-of-flight spectrometers employing multi-disk chopper cascades for spallation sources

International Nuclear Information System (INIS)

Lechner, R.E.

2001-01-01

The design of multi-disk chopper time-of-flight (MTOF) spectrometers for high-resolution quasielastic and low-energy inelastic neutron scattering at spallation sources is discussed in some detail. A continuously variable energy resolution (1 μeV to 10 meV), and a large dynamic range (1 μeV to 100 meV), are outstanding features of this type of instrument, which are easily achieved also at a pulsed source using state-of-the-art technology. The method of intensity-resolution optimization of MTOF spectrometers at spallation sources is treated on the basis of the requirement of using (almost) 'all the neutrons of the pulse', taking into account the constant, but wavelength-dependent duration of the source pulse. It follows, that the optimization procedure (which is slightly different from that employed in the steady-state source case) should give priority to the highest resolution, whenever such a choice becomes necessary. This leads to long monochromator distances (L l2 ) of the order of 50 m, for achieving resolutions now available at reactor sources. A few examples of spectrometer layout and corresponding design parameters for large-angle and for small-angle quasielastic scattering instruments are given. In the latter case higher energy resolution than for large-angle scattering is required and achieved. The use of phase-space transformers, neutron wavelength band-pass filters and multichromatic operation for the purpose of intensity-resolution optimization are discussed. This spectrometer can be designed to make full use of the pulsed source peak flux. Therefore, and because of a number of improvements, high resolution will be available at high intensity: for any given resolution the total intensity at the detectors, when placed at one of the planned new spallation sources (SNS, JSNS, ESS, AUSTRON) will be larger by at least three orders of magnitude than the total intensity of any of the presently existing instruments of this type in routine operation at steady

High-resolution inverse Raman and resonant-wave-mixing spectroscopy

Energy Technology Data Exchange (ETDEWEB)

Rahn, L.A. [Sandia National Laboratories, Livermore, CA (United States)

1993-12-01

These research activities consist of high-resolution inverse Raman spectroscopy (IRS) and resonant wave-mixing spectroscopy to support the development of nonlinear-optical techniques for temperature and concentration measurements in combustion research. Objectives of this work include development of spectral models of important molecular species needed to perform coherent anti-Stokes Raman spectroscopy (CARS) measurements and the investigation of new nonlinear-optical processes as potential diagnostic techniques. Some of the techniques being investigated include frequency-degenerate and nearly frequency-degenerate resonant four-wave-mixing (DFWM and NDFWM), and resonant multi-wave mixing (RMWM).

Object-oriented classification of land use in urban areas applying very high resolution satellite data

International Nuclear Information System (INIS)

Bauer, T.B.

2001-08-01

The availability of the new very high resolution satellite imagery will offer a wide range of new applications in the field of remote sensing. Information about actual land use is an important task for the management and planning in urban areas. High resolution satellite data will be an alternative to aerial photographs for updating and maintaining cartographic and geographic databases at reduced costs. The aim of the research is to formalize the visual interpretation procedure in order to automate the whole process. The assumption underlying this approach is that the land use functions can be distinguished on the basis of the differences in spatial distribution and pattern of land cover forms. Therefore a two-stage classification procedure is applied. In a first stage a land cover map is produced. In a second stage the morphological properties and spatial patterns of the land cover objects are analyzed with the structural analyzing and mapping system leading to a characterization and description of distinct urban land use categories. This information is then used for building a rule system that is implemented in a new commercial software tool called eCognition. An object-oriented classifier applies the rules to the land cover objects resulting in the required land use map. The potential of this method is demonstrated in a case study using IKONOS data covering a part of the metropolitan area of Vienna. (author)

Effect of objective function on multi-objective inverse planning of radiation therapy

International Nuclear Information System (INIS)

Li Guoli; Wu Yican; Song Gang; Wang Shifang

2006-01-01

There are two kinds of objective functions in radiotherapy inverse planning: dose distribution-based and Dose-Volume Histogram (DVH)-based functions. The treatment planning in our days is still a trial and error process because the multi-objective problem is solved by transforming it into a single objective problem using a specific set of weights for each object. This work investigates the problem of objective function setting based on Pareto multi-optimization theory, and compares the effect on multi-objective inverse planning of those two kinds of objective functions including calculation time, converge speed, etc. The basis of objective function setting on inverse planning is discussed. (authors)

Recent advances in evolutionary multi-objective optimization

CERN Document Server

Datta, Rituparna; Gupta, Abhishek

2017-01-01

This book covers the most recent advances in the field of evolutionary multiobjective optimization. With the aim of drawing the attention of up-andcoming scientists towards exciting prospects at the forefront of computational intelligence, the authors have made an effort to ensure that the ideas conveyed herein are accessible to the widest audience. The book begins with a summary of the basic concepts in multi-objective optimization. This is followed by brief discussions on various algorithms that have been proposed over the years for solving such problems, ranging from classical (mathematical) approaches to sophisticated evolutionary ones that are capable of seamlessly tackling practical challenges such as non-convexity, multi-modality, the presence of multiple constraints, etc. Thereafter, some of the key emerging aspects that are likely to shape future research directions in the field are presented. These include:< optimization in dynamic environments, multi-objective bilevel programming, handling high ...

A HIGH-RESOLUTION, MULTI-EPOCH SPECTRAL ATLAS OF PECULIAR STARS INCLUDING RAVE, GAIA , AND HERMES WAVELENGTH RANGES

International Nuclear Information System (INIS)

Tomasella, Lina; Munari, Ulisse; Zwitter, Tomaz

2010-01-01

We present an Echelle+CCD, high signal-to-noise ratio, high-resolution (R = 20,000) spectroscopic atlas of 108 well-known objects representative of the most common types of peculiar and variable stars. The wavelength interval extends from 4600 to 9400 A and includes the RAVE, Gaia, and HERMES wavelength ranges. Multi-epoch spectra are provided for the majority of the observed stars. A total of 425 spectra of peculiar stars, which were collected during 56 observing nights between 1998 November and 2002 August, are presented. The spectra are given in FITS format and heliocentric wavelengths, with accurate subtraction of both the sky background and the scattered light. Auxiliary material useful for custom applications (telluric dividers, spectrophotometric stars, flat-field tracings) is also provided. The atlas aims to provide a homogeneous database of the spectral appearance of stellar peculiarities, a tool useful both for classification purposes and inter-comparison studies. It could also serve in the planning and development of automated classification algorithms designed for RAVE, Gaia, HERMES, and other large-scale spectral surveys. The spectrum of XX Oph is discussed in some detail as an example of the content of the present atlas.

Development of an Objective High Spatial Resolution Soil Moisture Index

Science.gov (United States)

Zavodsky, B.; Case, J.; White, K.; Bell, J. R.

2015-12-01

Drought detection, analysis, and mitigation has become a key challenge for a diverse set of decision makers, including but not limited to operational weather forecasters, climatologists, agricultural interests, and water resource management. One tool that is heavily used is the United States Drought Monitor (USDM), which is derived from a complex blend of objective data and subjective analysis on a state-by-state basis using a variety of modeled and observed precipitation, soil moisture, hydrologic, and vegetation and crop health data. The NASA Short-term Prediction Research and Transition (SPoRT) Center currently runs a real-time configuration of the Noah land surface model (LSM) within the NASA Land Information System (LIS) framework. The LIS-Noah is run at 3-km resolution for local numerical weather prediction (NWP) and situational awareness applications at select NOAA/National Weather Service (NWS) forecast offices over the Continental U.S. (CONUS). To enhance the practicality of the LIS-Noah output for drought monitoring and assessing flood potential, a 30+-year soil moisture climatology has been developed in an attempt to place near real-time soil moisture values in historical context at county- and/or watershed-scale resolutions. This LIS-Noah soil moisture climatology and accompanying anomalies is intended to complement the current suite of operational products, such as the North American Land Data Assimilation System phase 2 (NLDAS-2), which are generated on a coarser-resolution grid that may not capture localized, yet important soil moisture features. Daily soil moisture histograms are used to identify the real-time soil moisture percentiles at each grid point according to the county or watershed in which the grid point resides. Spatial plots are then produced that map the percentiles as proxies to the different USDM categories. This presentation will highlight recent developments of this gridded, objective soil moisture index, comparison to subjective

Fall speed measurement and high-resolution multi-angle photography of hydrometeors in free fall

Directory of Open Access Journals (Sweden)

T. J. Garrett

2012-11-01

Full Text Available We describe here a new instrument for imaging hydrometeors in free fall. The Multi-Angle Snowflake Camera (MASC captures high-resolution photographs of hydrometeors from three angles while simultaneously measuring their fall speed. Based on the stereoscopic photographs captured over the two months of continuous measurements obtained at a high altitude location within the Wasatch Front in Utah, we derive statistics for fall speed, hydrometeor size, shape, orientation and aspect ratio. From a selection of the photographed hydrometeors, an illustration is provided for how the instrument might be used for making improved microwave scattering calculations. Complex, aggregated snowflake shapes appear to be more strongly forward scattering, at the expense of reduced back-scatter, than heavily rimed graupel particles of similar size.

Automatic Centerline Extraction of Coverd Roads by Surrounding Objects from High Resolution Satellite Images

Science.gov (United States)

Kamangir, H.; Momeni, M.; Satari, M.

2017-09-01

This paper presents an automatic method to extract road centerline networks from high and very high resolution satellite images. The present paper addresses the automated extraction roads covered with multiple natural and artificial objects such as trees, vehicles and either shadows of buildings or trees. In order to have a precise road extraction, this method implements three stages including: classification of images based on maximum likelihood algorithm to categorize images into interested classes, modification process on classified images by connected component and morphological operators to extract pixels of desired objects by removing undesirable pixels of each class, and finally line extraction based on RANSAC algorithm. In order to evaluate performance of the proposed method, the generated results are compared with ground truth road map as a reference. The evaluation performance of the proposed method using representative test images show completeness values ranging between 77% and 93%.

Surrogate Based Uni/Multi-Objective Optimization and Distribution Estimation Methods

Science.gov (United States)

Gong, W.; Duan, Q.; Huo, X.

2017-12-01

Parameter calibration has been demonstrated as an effective way to improve the performance of dynamic models, such as hydrological models, land surface models, weather and climate models etc. Traditional optimization algorithms usually cost a huge number of model evaluations, making dynamic model calibration very difficult, or even computationally prohibitive. With the help of a serious of recently developed adaptive surrogate-modelling based optimization methods: uni-objective optimization method ASMO, multi-objective optimization method MO-ASMO, and probability distribution estimation method ASMO-PODE, the number of model evaluations can be significantly reduced to several hundreds, making it possible to calibrate very expensive dynamic models, such as regional high resolution land surface models, weather forecast models such as WRF, and intermediate complexity earth system models such as LOVECLIM. This presentation provides a brief introduction to the common framework of adaptive surrogate-based optimization algorithms of ASMO, MO-ASMO and ASMO-PODE, a case study of Common Land Model (CoLM) calibration in Heihe river basin in Northwest China, and an outlook of the potential applications of the surrogate-based optimization methods.

Wireless Sensor Network Optimization: Multi-Objective Paradigm.

Science.gov (United States)

Iqbal, Muhammad; Naeem, Muhammad; Anpalagan, Alagan; Ahmed, Ashfaq; Azam, Muhammad

2015-07-20

Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks.

Wireless Sensor Network Optimization: Multi-Objective Paradigm

Science.gov (United States)

Iqbal, Muhammad; Naeem, Muhammad; Anpalagan, Alagan; Ahmed, Ashfaq; Azam, Muhammad

2015-01-01

Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks. PMID:26205271

Geo-Parcel Based Crop Identification by Integrating High Spatial-Temporal Resolution Imagery from Multi-Source Satellite Data

Directory of Open Access Journals (Sweden)

Yingpin Yang

2017-12-01

Full Text Available Geo-parcel based crop identification plays an important role in precision agriculture. It meets the needs of refined farmland management. This study presents an improved identification procedure for geo-parcel based crop identification by combining fine-resolution images and multi-source medium-resolution images. GF-2 images with fine spatial resolution of 0.8 m provided agricultural farming plot boundaries, and GF-1 (16 m and Landsat 8 OLI data were used to transform the geo-parcel based enhanced vegetation index (EVI time-series. In this study, we propose a piecewise EVI time-series smoothing method to fit irregular time profiles, especially for crop rotation situations. Global EVI time-series were divided into several temporal segments, from which phenological metrics could be derived. This method was applied to Lixian, where crop rotation was the common practice of growing different types of crops, in the same plot, in sequenced seasons. After collection of phenological features and multi-temporal spectral information, Random Forest (RF was performed to classify crop types, and the overall accuracy was 93.27%. Moreover, an analysis of feature significance showed that phenological features were of greater importance for distinguishing agricultural land cover compared to temporal spectral information. The identification results indicated that the integration of high spatial-temporal resolution imagery is promising for geo-parcel based crop identification and that the newly proposed smoothing method is effective.

Objective high Resolution Analysis over Complex Terrain with VERA

Science.gov (United States)

Mayer, D.; Steinacker, R.; Steiner, A.

2012-04-01

VERA (Vienna Enhanced Resolution Analysis) is a model independent, high resolution objective analysis of meteorological fields over complex terrain. This system consists of a special developed quality control procedure and a combination of an interpolation and a downscaling technique. Whereas the so called VERA-QC is presented at this conference in the contribution titled "VERA-QC, an approved Data Quality Control based on Self-Consistency" by Andrea Steiner, this presentation will focus on the method and the characteristics of the VERA interpolation scheme which enables one to compute grid point values of a meteorological field based on irregularly distributed observations and topography related aprior knowledge. Over a complex topography meteorological fields are not smooth in general. The roughness which is induced by the topography can be explained physically. The knowledge about this behavior is used to define the so called Fingerprints (e.g. a thermal Fingerprint reproducing heating or cooling over mountainous terrain or a dynamical Fingerprint reproducing positive pressure perturbation on the windward side of a ridge) under idealized conditions. If the VERA algorithm recognizes patterns of one or more Fingerprints at a few observation points, the corresponding patterns are used to downscale the meteorological information in a greater surrounding. This technique allows to achieve an analysis with a resolution much higher than the one of the observational network. The interpolation of irregularly distributed stations to a regular grid (in space and time) is based on a variational principle applied to first and second order spatial and temporal derivatives. Mathematically, this can be formulated as a cost function that is equivalent to the penalty function of a thin plate smoothing spline. After the analysis field has been divided into the Fingerprint components and the unexplained part respectively, the requirement of a smooth distribution is applied to the

TOPOGRAPHIC LOCAL ROUGHNESS EXTRACTION AND CALIBRATION OVER MARTIAN SURFACE BY VERY HIGH RESOLUTION STEREO ANALYSIS AND MULTI SENSOR DATA FUSION

Directory of Open Access Journals (Sweden)

J. R. Kim

2012-08-01

Full Text Available The planetary topography has been the main focus of the in-orbital remote sensing. In spite of the recent development in active and passive sensing technologies to reconstruct three dimensional planetary topography, the resolution limit of range measurement is theoretically and practically obvious. Therefore, the extraction of inner topographical height variation within a measurement spot is very challengeable and beneficial topic for the many application fields such as the identification of landform, Aeolian process analysis and the risk assessment of planetary lander. In this study we tried to extract the topographic height variation over martian surface so called local roughness with different approaches. One method is the employment of laser beam broadening effect and the other is the multi angle optical imaging. Especially, in both cases, the precise pre processing employing high accuracy DTM (Digital Terrain Model were introduced to minimise the possible errors. Since a processing routine to extract very high resolution DTMs up to 0.5–4m grid-spacing from HiRISE (High Resolution Imaging Science Experiment and 20–10m DTM from CTX (Context Camera stereo pair has been developed, it is now possible to calibrate the local roughness compared with the calculated height variation from very high resolution topographic products. Three testing areas were chosen and processed to extract local roughness with the co-registered multi sensor data sets. Even though, the extracted local roughness products are still showing the strong correlation with the topographic slopes, we demonstrated the potentials of the height variations extraction and calibration methods.

Sparse PDF Volumes for Consistent Multi-Resolution Volume Rendering

KAUST Repository

Sicat, Ronell Barrera

2014-12-31

This paper presents a new multi-resolution volume representation called sparse pdf volumes, which enables consistent multi-resolution volume rendering based on probability density functions (pdfs) of voxel neighborhoods. These pdfs are defined in the 4D domain jointly comprising the 3D volume and its 1D intensity range. Crucially, the computation of sparse pdf volumes exploits data coherence in 4D, resulting in a sparse representation with surprisingly low storage requirements. At run time, we dynamically apply transfer functions to the pdfs using simple and fast convolutions. Whereas standard low-pass filtering and down-sampling incur visible differences between resolution levels, the use of pdfs facilitates consistent results independent of the resolution level used. We describe the efficient out-of-core computation of large-scale sparse pdf volumes, using a novel iterative simplification procedure of a mixture of 4D Gaussians. Finally, our data structure is optimized to facilitate interactive multi-resolution volume rendering on GPUs.

Stochastic multi-period multi-product multi-objective Aggregate Production Planning model in multi-echelon supply chain

Directory of Open Access Journals (Sweden)

Kaveh Khalili-Damghani

2017-07-01

Full Text Available In this paper a multi-period multi-product multi-objective aggregate production planning (APP model is proposed for an uncertain multi-echelon supply chain considering financial risk, customer satisfaction, and human resource training. Three conflictive objective functions and several sets of real constraints are considered concurrently in the proposed APP model. Some parameters of the proposed model are assumed to be uncertain and handled through a two-stage stochastic programming (TSSP approach. The proposed TSSP is solved using three multi-objective solution procedures, i.e., the goal attainment technique, the modified ε-constraint method, and STEM method. The whole procedure is applied in an automotive resin and oil supply chain as a real case study wherein the efficacy and applicability of the proposed approaches are illustrated in comparison with existing experimental production planning method.

Adaptive multi-resolution Modularity for detecting communities in networks

Science.gov (United States)

Chen, Shi; Wang, Zhi-Zhong; Bao, Mei-Hua; Tang, Liang; Zhou, Ji; Xiang, Ju; Li, Jian-Ming; Yi, Chen-He

2018-02-01

Community structure is a common topological property of complex networks, which attracted much attention from various fields. Optimizing quality functions for community structures is a kind of popular strategy for community detection, such as Modularity optimization. Here, we introduce a general definition of Modularity, by which several classical (multi-resolution) Modularity can be derived, and then propose a kind of adaptive (multi-resolution) Modularity that can combine the advantages of different Modularity. By applying the Modularity to various synthetic and real-world networks, we study the behaviors of the methods, showing the validity and advantages of the multi-resolution Modularity in community detection. The adaptive Modularity, as a kind of multi-resolution method, can naturally solve the first-type limit of Modularity and detect communities at different scales; it can quicken the disconnecting of communities and delay the breakup of communities in heterogeneous networks; and thus it is expected to generate the stable community structures in networks more effectively and have stronger tolerance against the second-type limit of Modularity.

Extraction Method for Earthquake-Collapsed Building Information Based on High-Resolution Remote Sensing

International Nuclear Information System (INIS)

Chen, Peng; Wu, Jian; Liu, Yaolin; Wang, Jing

2014-01-01

At present, the extraction of earthquake disaster information from remote sensing data relies on visual interpretation. However, this technique cannot effectively and quickly obtain precise and efficient information for earthquake relief and emergency management. Collapsed buildings in the town of Zipingpu after the Wenchuan earthquake were used as a case study to validate two kinds of rapid extraction methods for earthquake-collapsed building information based on pixel-oriented and object-oriented theories. The pixel-oriented method is based on multi-layer regional segments that embody the core layers and segments of the object-oriented method. The key idea is to mask layer by layer all image information, including that on the collapsed buildings. Compared with traditional techniques, the pixel-oriented method is innovative because it allows considerably rapid computer processing. As for the object-oriented method, a multi-scale segment algorithm was applied to build a three-layer hierarchy. By analyzing the spectrum, texture, shape, location, and context of individual object classes in different layers, the fuzzy determined rule system was established for the extraction of earthquake-collapsed building information. We compared the two sets of results using three variables: precision assessment, visual effect, and principle. Both methods can extract earthquake-collapsed building information quickly and accurately. The object-oriented method successfully overcomes the pepper salt noise caused by the spectral diversity of high-resolution remote sensing data and solves the problem of same object, different spectrums and that of same spectrum, different objects. With an overall accuracy of 90.38%, the method achieves more scientific and accurate results compared with the pixel-oriented method (76.84%). The object-oriented image analysis method can be extensively applied in the extraction of earthquake disaster information based on high-resolution remote sensing

The evolution of active Lavina di Roncovetro landslides by multi-temporal high-resolution topographic data

Science.gov (United States)

Isola, Ilaria; Fornaciai, Alessandro; Favalli, Massimiliano; Gigli, Giovanni; Nannipieri, Luca; Mucchi, Lorenzo; Intrieri, Emanuele; Pizziolo, Marco; Bertolini, Giovanni; Trippi, Federico; Casagli, Nicola; Schina, Rosa; Carnevale, Ennio

2017-04-01

High-resolution topographic data has been collected over the Lavina di Roncovetro active landslide (Reggio Emilia, Italy) for about 3 years by using various methods and technologies. Tha Lavina di Roncovetro landslide can be considered as a fluid-viscous mudflow, which can reach a down flow maximum rate of 10 m/day. The landslide started between the middle and the end of the XIX century and since then it has had a rapid evolution mainly characterized by the rapid retrogression of the crown to the extent that now reaches the top of Mount Staffola. In the frame of EU Wireless Sensor Network for Ground Instability Monitoring - Wi-GIM project (LIFE12ENV/IT/001033) the Lavina di Roncovetro landslide has been periodically tracked using technologies that span from the LiDAR, both terrestrial and aerial, to the Structure from Motion (SfM) photogrammetry method based on Unmanned Aerial Vehicle (UAV) and aerial survey. These data are used to create six high-resolution Digital Terrain Models (DEMs), which imaged the landslide surface on March 2014, October 2014, June 2015, July 2015, January 2016 and December 2016. Multi-temporal high-resolution topographic data have been used for qualitative and quantitative morphometric analysis and topographic change detection of the landslide with the aim to estimate and map the volume of removed and/or accumulated material, the average rates of vertical and horizontal displacement and the deformation structures affecting the landslide over the investigated period.

Multi-Sensor Fusion of Infrared and Electro-Optic Signals for High Resolution Night Images

Directory of Open Access Journals (Sweden)

Victor Lawrence

2012-07-01

Full Text Available Electro-optic (EO image sensors exhibit the properties of high resolution and low noise level at daytime, but they do not work in dark environments. Infrared (IR image sensors exhibit poor resolution and cannot separate objects with similar temperature. Therefore, we propose a novel framework of IR image enhancement based on the information (e.g., edge from EO images, which improves the resolution of IR images and helps us distinguish objects at night. Our framework superimposing/blending the edges of the EO image onto the corresponding transformed IR image improves their resolution. In this framework, we adopt the theoretical point spread function (PSF proposed by Hardie et al. for the IR image, which has the modulation transfer function (MTF of a uniform detector array and the incoherent optical transfer function (OTF of diffraction-limited optics. In addition, we design an inverse filter for the proposed PSF and use it for the IR image transformation. The framework requires four main steps: (1 inverse filter-based IR image transformation; (2 EO image edge detection; (3 registration; and (4 blending/superimposing of the obtained image pair. Simulation results show both blended and superimposed IR images, and demonstrate that blended IR images have better quality over the superimposed images. Additionally, based on the same steps, simulation result shows a blended IR image of better quality when only the original IR image is available.

Multi-Objective Nonlinear Model Predictive Control: Lexicographic Method

OpenAIRE

Zheng, Tao; Wu, Gang; Liu, Guang-Hong; Ling, Qing

2010-01-01

In this chapter, to avoid the disadvantages of weight coefficients in multi-objective dynamic optimization, lexicographic (completely stratified) and partially stratified frameworks of multi-objective controller are proposed. The lexicographic framework is absolutely prioritydriven and the partially stratified framework is a modification of it, they both can solve the multi-objective control problem with the concept of priority for objective’s relative importance, while the latter one is mo...

Adaptive multi-resolution 3D Hartree-Fock-Bogoliubov solver for nuclear structure

Science.gov (United States)

Pei, J. C.; Fann, G. I.; Harrison, R. J.; Nazarewicz, W.; Shi, Yue; Thornton, S.

2014-08-01

Background: Complex many-body systems, such as triaxial and reflection-asymmetric nuclei, weakly bound halo states, cluster configurations, nuclear fragments produced in heavy-ion fusion reactions, cold Fermi gases, and pasta phases in neutron star crust, are all characterized by large sizes and complex topologies in which many geometrical symmetries characteristic of ground-state configurations are broken. A tool of choice to study such complex forms of matter is an adaptive multi-resolution wavelet analysis. This method has generated much excitement since it provides a common framework linking many diversified methodologies across different fields, including signal processing, data compression, harmonic analysis and operator theory, fractals, and quantum field theory. Purpose: To describe complex superfluid many-fermion systems, we introduce an adaptive pseudospectral method for solving self-consistent equations of nuclear density functional theory in three dimensions, without symmetry restrictions. Methods: The numerical method is based on the multi-resolution and computational harmonic analysis techniques with a multi-wavelet basis. The application of state-of-the-art parallel programming techniques include sophisticated object-oriented templates which parse the high-level code into distributed parallel tasks with a multi-thread task queue scheduler for each multi-core node. The internode communications are asynchronous. The algorithm is variational and is capable of solving coupled complex-geometric systems of equations adaptively, with functional and boundary constraints, in a finite spatial domain of very large size, limited by existing parallel computer memory. For smooth functions, user-defined finite precision is guaranteed. Results: The new adaptive multi-resolution Hartree-Fock-Bogoliubov (HFB) solver madness-hfb is benchmarked against a two-dimensional coordinate-space solver hfb-ax that is based on the B-spline technique and a three-dimensional solver

An Object-Oriented Classification Method on High Resolution Satellite Data

National Research Council Canada - National Science Library

Xiaoxia, Sun; Jixian, Zhang; Zhengjun, Liu

2004-01-01

.... Thereby only the spectral information is used for the classification. High spatial resolution sensors involves a general increase of spatial information and the accuracy of results may decrease on a per-pixel basis...

A multi-method high-resolution geophysical survey in the Machado de Castro museum, central Portugal

International Nuclear Information System (INIS)

Grangeia, Carlos; Matias, Manuel; Hermozilha, Hélder; Figueiredo, Fernando; Carvalho, Pedro; Silva, Ricardo

2011-01-01

Restoration of historical buildings is a delicate operation as they are often built over more ancient and important structures. The Machado de Castro Museum, Coimbra, Central Portugal, has suffered several interventions in historical times and lies over the ancient Roman forum of Coimbra. This building went through a restoration project. These works were preceded by an extensive geophysical survey that aimed at investigating subsurface stratigraphy, including archeological remains, and the internal structure of the actual walls. Owing to the needs of the project, geophysical data interpretation required not only integration but also high resolution. The study consisted of data acquisition over perpendicular planes and different levels that required detailed survey planning and integration of data from different locations that complement images of the surveyed area. Therefore a multi-method, resistivity imaging and a 3D ground probing radar (GPR), high-resolution geophysical survey was done inside the museum. Herein, radargrams are compared with the revealed stratigraphy so that signatures are interpreted, characterized and assigned to archeological structures. Although resistivity and GPR have different resolution capabilities, their data are overlapped and compared, bearing in mind the specific characteristics of this survey. It was also possible to unravel the inner structure of the actual walls, to establish connections between walls, foundations and to find older remains with the combined use and spatial integration of the GPR and resistivity imaging data

Multi-objective Transmission Planning Paper

DEFF Research Database (Denmark)

Xu, Zhao; Dong, Zhao Yang; Wong, Kit Po

2009-01-01

This paper describes a transmission expansion planning method based on multi-objective optimization (MOOP). The method starts with constructing a candidate pool of feasible expansion plans, followed by selection of the best candidates through MOOP, of which multiple objectives are tackled...

A multi-sample based method for identifying common CNVs in normal human genomic structure using high-resolution aCGH data.

Directory of Open Access Journals (Sweden)

Chihyun Park

Full Text Available BACKGROUND: It is difficult to identify copy number variations (CNV in normal human genomic data due to noise and non-linear relationships between different genomic regions and signal intensity. A high-resolution array comparative genomic hybridization (aCGH containing 42 million probes, which is very large compared to previous arrays, was recently published. Most existing CNV detection algorithms do not work well because of noise associated with the large amount of input data and because most of the current methods were not designed to analyze normal human samples. Normal human genome analysis often requires a joint approach across multiple samples. However, the majority of existing methods can only identify CNVs from a single sample. METHODOLOGY AND PRINCIPAL FINDINGS: We developed a multi-sample-based genomic variations detector (MGVD that uses segmentation to identify common breakpoints across multiple samples and a k-means-based clustering strategy. Unlike previous methods, MGVD simultaneously considers multiple samples with different genomic intensities and identifies CNVs and CNV zones (CNVZs; CNVZ is a more precise measure of the location of a genomic variant than the CNV region (CNVR. CONCLUSIONS AND SIGNIFICANCE: We designed a specialized algorithm to detect common CNVs from extremely high-resolution multi-sample aCGH data. MGVD showed high sensitivity and a low false discovery rate for a simulated data set, and outperformed most current methods when real, high-resolution HapMap datasets were analyzed. MGVD also had the fastest runtime compared to the other algorithms evaluated when actual, high-resolution aCGH data were analyzed. The CNVZs identified by MGVD can be used in association studies for revealing relationships between phenotypes and genomic aberrations. Our algorithm was developed with standard C++ and is available in Linux and MS Windows format in the STL library. It is freely available at: http://embio.yonsei.ac.kr/~Park/mgvd.php.

Fuzzy Multi-objective Linear Programming Approach

Directory of Open Access Journals (Sweden)

Amna Rehmat

2007-07-01

Full Text Available Traveling salesman problem (TSP is one of the challenging real-life problems, attracting researchers of many fields including Artificial Intelligence, Operations Research, and Algorithm Design and Analysis. The problem has been well studied till now under different headings and has been solved with different approaches including genetic algorithms and linear programming. Conventional linear programming is designed to deal with crisp parameters, but information about real life systems is often available in the form of vague descriptions. Fuzzy methods are designed to handle vague terms, and are most suited to finding optimal solutions to problems with vague parameters. Fuzzy multi-objective linear programming, an amalgamation of fuzzy logic and multi-objective linear programming, deals with flexible aspiration levels or goals and fuzzy constraints with acceptable deviations. In this paper, a methodology, for solving a TSP with imprecise parameters, is deployed using fuzzy multi-objective linear programming. An example of TSP with multiple objectives and vague parameters is discussed.

Interactive Approach for Multi-Level Multi-Objective Fractional Programming Problems with Fuzzy Parameters

Directory of Open Access Journals (Sweden)

M.S. Osman

2018-03-01

Full Text Available In this paper, an interactive approach for solving multi-level multi-objective fractional programming (ML-MOFP problems with fuzzy parameters is presented. The proposed interactive approach makes an extended work of Shi and Xia (1997. In the first phase, the numerical crisp model of the ML-MOFP problem has been developed at a confidence level without changing the fuzzy gist of the problem. Then, the linear model for the ML-MOFP problem is formulated. In the second phase, the interactive approach simplifies the linear multi-level multi-objective model by converting it into separate multi-objective programming problems. Also, each separate multi-objective programming problem of the linear model is solved by the ∊-constraint method and the concept of satisfactoriness. Finally, illustrative examples and comparisons with the previous approaches are utilized to evince the feasibility of the proposed approach.

MR-CDF: Managing multi-resolution scientific data

Science.gov (United States)

Salem, Kenneth

1993-01-01

MR-CDF is a system for managing multi-resolution scientific data sets. It is an extension of the popular CDF (Common Data Format) system. MR-CDF provides a simple functional interface to client programs for storage and retrieval of data. Data is stored so that low resolution versions of the data can be provided quickly. Higher resolutions are also available, but not as quickly. By managing data with MR-CDF, an application can be relieved of the low-level details of data management, and can easily trade data resolution for improved access time.

Multi-Objective Optimization of Hybrid Renewable Energy System Using an Enhanced Multi-Objective Evolutionary Algorithm

Directory of Open Access Journals (Sweden)

Mengjun Ming

2017-05-01

Full Text Available Due to the scarcity of conventional energy resources and the greenhouse effect, renewable energies have gained more attention. This paper proposes methods for multi-objective optimal design of hybrid renewable energy system (HRES in both isolated-island and grid-connected modes. In each mode, the optimal design aims to find suitable configurations of photovoltaic (PV panels, wind turbines, batteries and diesel generators in HRES such that the system cost and the fuel emission are minimized, and the system reliability/renewable ability (corresponding to different modes is maximized. To effectively solve this multi-objective problem (MOP, the multi-objective evolutionary algorithm based on decomposition (MOEA/D using localized penalty-based boundary intersection (LPBI method is proposed. The algorithm denoted as MOEA/D-LPBI is demonstrated to outperform its competitors on the HRES model as well as a set of benchmarks. Moreover, it effectively obtains a good approximation of Pareto optimal HRES configurations. By further considering a decision maker’s preference, the most satisfied configuration of the HRES can be identified.

Comparison Effectiveness of Pixel Based Classification and Object Based Classification Using High Resolution Image In Floristic Composition Mapping (Study Case: Gunung Tidar Magelang City)

Science.gov (United States)

Ardha Aryaguna, Prama; Danoedoro, Projo

2016-11-01

Developments of analysis remote sensing have same way with development of technology especially in sensor and plane. Now, a lot of image have high spatial and radiometric resolution, that's why a lot information. Vegetation object analysis such floristic composition got a lot advantage of that development. Floristic composition can be interpreted using a lot of method such pixel based classification and object based classification. The problems for pixel based method on high spatial resolution image are salt and paper who appear in result of classification. The purpose of this research are compare effectiveness between pixel based classification and object based classification for composition vegetation mapping on high resolution image Worldview-2. The results show that pixel based classification using majority 5×5 kernel windows give the highest accuracy between another classifications. The highest accuracy is 73.32% from image Worldview-2 are being radiometric corrected level surface reflectance, but for overall accuracy in every class, object based are the best between another methods. Reviewed from effectiveness aspect, pixel based are more effective then object based for vegetation composition mapping in Tidar forest.

A 4.5 km resolution Arctic Ocean simulation with the global multi-resolution model FESOM 1.4

Science.gov (United States)

Wang, Qiang; Wekerle, Claudia; Danilov, Sergey; Wang, Xuezhu; Jung, Thomas

2018-04-01

In the framework of developing a global modeling system which can facilitate modeling studies on Arctic Ocean and high- to midlatitude linkage, we evaluate the Arctic Ocean simulated by the multi-resolution Finite Element Sea ice-Ocean Model (FESOM). To explore the value of using high horizontal resolution for Arctic Ocean modeling, we use two global meshes differing in the horizontal resolution only in the Arctic Ocean (24 km vs. 4.5 km). The high resolution significantly improves the model's representation of the Arctic Ocean. The most pronounced improvement is in the Arctic intermediate layer, in terms of both Atlantic Water (AW) mean state and variability. The deepening and thickening bias of the AW layer, a common issue found in coarse-resolution simulations, is significantly alleviated by using higher resolution. The topographic steering of the AW is stronger and the seasonal and interannual temperature variability along the ocean bottom topography is enhanced in the high-resolution simulation. The high resolution also improves the ocean surface circulation, mainly through a better representation of the narrow straits in the Canadian Arctic Archipelago (CAA). The representation of CAA throughflow not only influences the release of water masses through the other gateways but also the circulation pathways inside the Arctic Ocean. However, the mean state and variability of Arctic freshwater content and the variability of freshwater transport through the Arctic gateways appear not to be very sensitive to the increase in resolution employed here. By highlighting the issues that are independent of model resolution, we address that other efforts including the improvement of parameterizations are still required.

Multi Camera Multi Object Tracking using Block Search over Epipolar Geometry

Directory of Open Access Journals (Sweden)

Saman Sargolzaei

2000-01-01

Full Text Available We present strategy for multi-objects tracking in multi camera environment for the surveillance and security application where tracking multitude subjects are of utmost importance in a crowded scene. Our technique assumes partially overlapped multi-camera setup where cameras share common view from different angle to assess positions and activities of subjects under suspicion. To establish spatial correspondence between camera views we employ an epipolar geometry technique. We propose an overlapped block search method to find the interested pattern (target in new frames. Color pattern update scheme has been considered to further optimize the efficiency of the object tracking when object pattern changes due to object motion in the field of views of the cameras. Evaluation of our approach is presented with the results on PETS2007 dataset..

Object-Based Change Detection in Urban Areas from High Spatial Resolution Images Based on Multiple Features and Ensemble Learning

Directory of Open Access Journals (Sweden)

Xin Wang

2018-02-01

Full Text Available To improve the accuracy of change detection in urban areas using bi-temporal high-resolution remote sensing images, a novel object-based change detection scheme combining multiple features and ensemble learning is proposed in this paper. Image segmentation is conducted to determine the objects in bi-temporal images separately. Subsequently, three kinds of object features, i.e., spectral, shape and texture, are extracted. Using the image differencing process, a difference image is generated and used as the input for nonlinear supervised classifiers, including k-nearest neighbor, support vector machine, extreme learning machine and random forest. Finally, the results of multiple classifiers are integrated using an ensemble rule called weighted voting to generate the final change detection result. Experimental results of two pairs of real high-resolution remote sensing datasets demonstrate that the proposed approach outperforms the traditional methods in terms of overall accuracy and generates change detection maps with a higher number of homogeneous regions in urban areas. Moreover, the influences of segmentation scale and the feature selection strategy on the change detection performance are also analyzed and discussed.

Structural damage detection-oriented multi-type sensor placement with multi-objective optimization

Science.gov (United States)

Lin, Jian-Fu; Xu, You-Lin; Law, Siu-Seong

2018-05-01

A structural damage detection-oriented multi-type sensor placement method with multi-objective optimization is developed in this study. The multi-type response covariance sensitivity-based damage detection method is first introduced. Two objective functions for optimal sensor placement are then introduced in terms of the response covariance sensitivity and the response independence. The multi-objective optimization problem is formed by using the two objective functions, and the non-dominated sorting genetic algorithm (NSGA)-II is adopted to find the solution for the optimal multi-type sensor placement to achieve the best structural damage detection. The proposed method is finally applied to a nine-bay three-dimensional frame structure. Numerical results show that the optimal multi-type sensor placement determined by the proposed method can avoid redundant sensors and provide satisfactory results for structural damage detection. The restriction on the number of each type of sensors in the optimization can reduce the searching space in the optimization to make the proposed method more effective. Moreover, how to select a most optimal sensor placement from the Pareto solutions via the utility function and the knee point method is demonstrated in the case study.

Benchmarks for dynamic multi-objective optimisation

CSIR Research Space (South Africa)

Helbig, M

2013-06-01

Full Text Available When algorithms solve dynamic multi-objective optimisation problems (DMOOPs), benchmark functions should be used to determine whether the algorithm can overcome specific difficulties that can occur in real-world problems. However, for dynamic multi...

Method of Obtaining High Resolution Intrinsic Wire Boom Damping Parameters for Multi-Body Dynamics Simulations

Science.gov (United States)

Yew, Alvin G.; Chai, Dean J.; Olney, David J.

2010-01-01

The goal of NASA's Magnetospheric MultiScale (MMS) mission is to understand magnetic reconnection with sensor measurements from four spinning satellites flown in a tight tetrahedron formation. Four of the six electric field sensors on each satellite are located at the end of 60- meter wire booms to increase measurement sensitivity in the spin plane and to minimize motion coupling from perturbations on the main body. A propulsion burn however, might induce boom oscillations that could impact science measurements if oscillations do not damp to values on the order of 0.1 degree in a timely fashion. Large damping time constants could also adversely affect flight dynamics and attitude control performance. In this paper, we will discuss the implementation of a high resolution method for calculating the boom's intrinsic damping, which was used in multi-body dynamics simulations. In summary, experimental data was obtained with a scaled-down boom, which was suspended as a pendulum in vacuum. Optical techniques were designed to accurately measure the natural decay of angular position and subsequently, data processing algorithms resulted in excellent spatial and temporal resolutions. This method was repeated in a parametric study for various lengths, root tensions and vacuum levels. For all data sets, regression models for damping were applied, including: nonlinear viscous, frequency-independent hysteretic, coulomb and some combination of them. Our data analysis and dynamics models have shown that the intrinsic damping for the baseline boom is insufficient, thereby forcing project management to explore mitigation strategies.

Optimisation of chromatographic resolution using objective functions including both time and spectral information.

Science.gov (United States)

Torres-Lapasió, J R; Pous-Torres, S; Ortiz-Bolsico, C; García-Alvarez-Coque, M C

2015-01-16

The optimisation of the resolution in high-performance liquid chromatography is traditionally performed attending only to the time information. However, even in the optimal conditions, some peak pairs may remain unresolved. Such incomplete resolution can be still accomplished by deconvolution, which can be carried out with more guarantees of success by including spectral information. In this work, two-way chromatographic objective functions (COFs) that incorporate both time and spectral information were tested, based on the peak purity (analyte peak fraction free of overlapping) and the multivariate selectivity (figure of merit derived from the net analyte signal) concepts. These COFs are sensitive to situations where the components that coelute in a mixture show some spectral differences. Therefore, they are useful to find out experimental conditions where the spectrochromatograms can be recovered by deconvolution. Two-way multivariate selectivity yielded the best performance and was applied to the separation using diode-array detection of a mixture of 25 phenolic compounds, which remained unresolved in the chromatographic order using linear and multi-linear gradients of acetonitrile-water. Peak deconvolution was carried out using the combination of orthogonal projection approach and alternating least squares. Copyright © 2014 Elsevier B.V. All rights reserved.

High resolution optical DNA mapping

Science.gov (United States)

Baday, Murat

Many types of diseases including cancer and autism are associated with copy-number variations in the genome. Most of these variations could not be identified with existing sequencing and optical DNA mapping methods. We have developed Multi-color Super-resolution technique, with potential for high throughput and low cost, which can allow us to recognize more of these variations. Our technique has made 10--fold improvement in the resolution of optical DNA mapping. Using a 180 kb BAC clone as a model system, we resolved dense patterns from 108 fluorescent labels of two different colors representing two different sequence-motifs. Overall, a detailed DNA map with 100 bp resolution was achieved, which has the potential to reveal detailed information about genetic variance and to facilitate medical diagnosis of genetic disease.

Classification of high-resolution remote sensing images based on multi-scale superposition

Science.gov (United States)

Wang, Jinliang; Gao, Wenjie; Liu, Guangjie

2017-07-01

Landscape structures and process on different scale show different characteristics. In the study of specific target landmarks, the most appropriate scale for images can be attained by scale conversion, which improves the accuracy and efficiency of feature identification and classification. In this paper, the authors carried out experiments on multi-scale classification by taking the Shangri-la area in the north-western Yunnan province as the research area and the images from SPOT5 HRG and GF-1 Satellite as date sources. Firstly, the authors upscaled the two images by cubic convolution, and calculated the optimal scale for different objects on the earth shown in images by variation functions. Then the authors conducted multi-scale superposition classification on it by Maximum Likelyhood, and evaluated the classification accuracy. The results indicates that: (1) for most of the object on the earth, the optimal scale appears in the bigger scale instead of the original one. To be specific, water has the biggest optimal scale, i.e. around 25-30m; farmland, grassland, brushwood, roads, settlement places and woodland follows with 20-24m. The optimal scale for shades and flood land is basically as the same as the original one, i.e. 8m and 10m respectively. (2) Regarding the classification of the multi-scale superposed images, the overall accuracy of the ones from SPOT5 HRG and GF-1 Satellite is 12.84% and 14.76% higher than that of the original multi-spectral images, respectively, and Kappa coefficient is 0.1306 and 0.1419 higher, respectively. Hence, the multi-scale superposition classification which was applied in the research area can enhance the classification accuracy of remote sensing images .

Ultra high resolution tomography

Energy Technology Data Exchange (ETDEWEB)

Haddad, W.S.

1994-11-15

Recent work and results on ultra high resolution three dimensional imaging with soft x-rays will be presented. This work is aimed at determining microscopic three dimensional structure of biological and material specimens. Three dimensional reconstructed images of a microscopic test object will be presented; the reconstruction has a resolution on the order of 1000 A in all three dimensions. Preliminary work with biological samples will also be shown, and the experimental and numerical methods used will be discussed.

High-resolution imaging of solar system objects

International Nuclear Information System (INIS)

Goldberg, B.A.

1988-01-01

The strategy of this investigation has been to develop new high-resolution imaging capabilities and to apply them to extended observing programs. These programs have included Io's neutral sodium cloud and comets. The Io observing program was carried out at Table Mountain Observatory (1976 to 1981), providing a framework interpreting Voyager measurements of the Io torus, and serving as an important reference for studying asymmetries and time variabilities in the Jovian magnetosphere. Comet observations made with the 3.6 m Canada-France-Hawaii Telescope and 1.6 m AMOS telescope (1984 to 1987) provide basis for studying early coma development in Halley, the kinematics of its nucleus, and the internal and external structure of the nucleus. Images of GZ from the ICE encounter period form the basis for unique comparisons with in situ magnetic field and dust impact measurements to determine the ion tail and dust coma structure, respectively

Large-Scale Multi-Resolution Representations for Accurate Interactive Image and Volume Operations

KAUST Repository

Sicat, Ronell B.

2015-11-25

The resolutions of acquired image and volume data are ever increasing. However, the resolutions of commodity display devices remain limited. This leads to an increasing gap between data and display resolutions. To bridge this gap, the standard approach is to employ output-sensitive operations on multi-resolution data representations. Output-sensitive operations facilitate interactive applications since their required computations are proportional only to the size of the data that is visible, i.e., the output, and not the full size of the input. Multi-resolution representations, such as image mipmaps, and volume octrees, are crucial in providing these operations direct access to any subset of the data at any resolution corresponding to the output. Despite its widespread use, this standard approach has some shortcomings in three important application areas, namely non-linear image operations, multi-resolution volume rendering, and large-scale image exploration. This dissertation presents new multi-resolution representations for large-scale images and volumes that address these shortcomings. Standard multi-resolution representations require low-pass pre-filtering for anti- aliasing. However, linear pre-filters do not commute with non-linear operations. This becomes problematic when applying non-linear operations directly to any coarse resolution levels in standard representations. Particularly, this leads to inaccurate output when applying non-linear image operations, e.g., color mapping and detail-aware filters, to multi-resolution images. Similarly, in multi-resolution volume rendering, this leads to inconsistency artifacts which manifest as erroneous differences in rendering outputs across resolution levels. To address these issues, we introduce the sparse pdf maps and sparse pdf volumes representations for large-scale images and volumes, respectively. These representations sparsely encode continuous probability density functions (pdfs) of multi-resolution pixel

Mapping High-Resolution Soil Moisture over Heterogeneous Cropland Using Multi-Resource Remote Sensing and Ground Observations

Directory of Open Access Journals (Sweden)

Lei Fan

2015-10-01

Full Text Available High spatial resolution soil moisture (SM data are crucial in agricultural applications, river-basin management, and understanding hydrological processes. Merging multi-resource observations is one of the ways to improve the accuracy of high spatial resolution SM data in the heterogeneous cropland. In this paper, the Bayesian Maximum Entropy (BME methodology is implemented to merge the following four types of observed data to obtain the spatial distribution of SM at 100 m scale: soil moisture observed by wireless sensor network (WSN, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER-derived soil evaporative efficiency (SEE, irrigation statistics, and Polarimetric L-band Multi-beam Radiometer (PLMR-derived SM products (~700 m. From the poor BME predictions obtained by merging only WSN and SEE data, we observed that the SM heterogeneity caused by irrigation and the attenuating sensitivity of the SEE data to SM caused by the canopies result in BME prediction errors. By adding irrigation statistics to the merged datasets, the overall RMSD of the BME predictions during the low-vegetated periods can be successively reduced from 0.052 m3·m−3 to 0.033 m3·m−3. The coefficient of determination (R2 and slope between the predicted and in situ measured SM data increased from 0.32 to 0.64 and from 0.38 to 0.82, respectively, but large estimation errors occurred during the moderately vegetated periods (RMSD = 0.041 m3·m−3, R = 0.43 and the slope = 0.41. Further adding the downscaled SM information from PLMR SM products to the merged datasets, the predictions were satisfactorily accurate with an RMSD of 0.034 m3·m−3, R2 of 0.4 and a slope of 0.69 during moderately vegetated periods. Overall, the results demonstrated that merging multi-resource observations into SM estimations can yield improved accuracy in heterogeneous cropland.

Combining simulation and multi-objective optimisation for equipment quantity optimisation in container terminals

OpenAIRE

Lin, Zhougeng

2013-01-01

This thesis proposes a combination framework to integrate simulation and multi-objective optimisation (MOO) for container terminal equipment optimisation. It addresses how the strengths of simulation and multi-objective optimisation can be integrated to find high quality solutions for multiple objectives with low computational cost. Three structures for the combination framework are proposed respectively: pre-MOO structure, integrated MOO structure and post-MOO structure. The applications of ...

Multi-Object Spectroscopy in the Next Decade: A Conference Summary

NARCIS (Netherlands)

Trager, S. C.; Skillen, I.; Barcells, M.

2016-01-01

I present a highly-biased summary of the conference "Multi-Object Spectroscopy in the Next Decade: Big Questions, Large Surveys, and Wide Fields," held 2-6 March 2015 in Santa Cruz de la Palma, Spain. I focus on four issues in this summary: (1) complexity in objects, physics, and instruments is

Power magnetic devices a multi-objective design approach

CERN Document Server

Sudhoff, Scott D

2014-01-01

Presents a multi-objective design approach to the many power magnetic devices in use today Power Magnetic Devices: A Multi-Objective Design Approach addresses the design of power magnetic devices-including inductors, transformers, electromagnets, and rotating electric machinery-using a structured design approach based on formal single- and multi-objective optimization. The book opens with a discussion of evolutionary-computing-based optimization. Magnetic analysis techniques useful to the design of all the devices considered in the book are then set forth. This material is then used for ind

A comparative analysis of pixel- and object-based detection of landslides from very high-resolution images

Science.gov (United States)

Keyport, Ren N.; Oommen, Thomas; Martha, Tapas R.; Sajinkumar, K. S.; Gierke, John S.

2018-02-01

A comparative analysis of landslides detected by pixel-based and object-oriented analysis (OOA) methods was performed using very high-resolution (VHR) remotely sensed aerial images for the San Juan La Laguna, Guatemala, which witnessed widespread devastation during the 2005 Hurricane Stan. A 3-band orthophoto of 0.5 m spatial resolution together with a 115 field-based landslide inventory were used for the analysis. A binary reference was assigned with a zero value for landslide and unity for non-landslide pixels. The pixel-based analysis was performed using unsupervised classification, which resulted in 11 different trial classes. Detection of landslides using OOA includes 2-step K-means clustering to eliminate regions based on brightness; elimination of false positives using object properties such as rectangular fit, compactness, length/width ratio, mean difference of objects, and slope angle. Both overall accuracy and F-score for OOA methods outperformed pixel-based unsupervised classification methods in both landslide and non-landslide classes. The overall accuracy for OOA and pixel-based unsupervised classification was 96.5% and 94.3%, respectively, whereas the best F-score for landslide identification for OOA and pixel-based unsupervised methods: were 84.3% and 77.9%, respectively.Results indicate that the OOA is able to identify the majority of landslides with a few false positive when compared to pixel-based unsupervised classification.

Development of high speed integrated circuit for very high resolution timing measurements

International Nuclear Information System (INIS)

Mester, Christian

2009-10-01

A multi-channel high-precision low-power time-to-digital converter application specific integrated circuit for high energy physics applications has been designed and implemented in a 130 nm CMOS process. To reach a target resolution of 24.4 ps, a novel delay element has been conceived. This nominal resolution has been experimentally verified with a prototype, with a minimum resolution of 19 ps. To further improve the resolution, a new interpolation scheme has been described. The ASIC has been designed to use a reference clock with the LHC bunch crossing frequency of 40 MHz and generate all required timing signals internally, to ease to use within the framework of an LHC upgrade. Special care has been taken to minimise the power consumption. (orig.)

Development of high speed integrated circuit for very high resolution timing measurements

Energy Technology Data Exchange (ETDEWEB)

Mester, Christian

2009-10-15

A multi-channel high-precision low-power time-to-digital converter application specific integrated circuit for high energy physics applications has been designed and implemented in a 130 nm CMOS process. To reach a target resolution of 24.4 ps, a novel delay element has been conceived. This nominal resolution has been experimentally verified with a prototype, with a minimum resolution of 19 ps. To further improve the resolution, a new interpolation scheme has been described. The ASIC has been designed to use a reference clock with the LHC bunch crossing frequency of 40 MHz and generate all required timing signals internally, to ease to use within the framework of an LHC upgrade. Special care has been taken to minimise the power consumption. (orig.)

Object-Based Change Detection Using High-Resolution Remotely Sensed Data and GIS

Science.gov (United States)

Sofina, N.; Ehlers, M.

2012-08-01

High resolution remotely sensed images provide current, detailed, and accurate information for large areas of the earth surface which can be used for change detection analyses. Conventional methods of image processing permit detection of changes by comparing remotely sensed multitemporal images. However, for performing a successful analysis it is desirable to take images from the same sensor which should be acquired at the same time of season, at the same time of a day, and - for electro-optical sensors - in cloudless conditions. Thus, a change detection analysis could be problematic especially for sudden catastrophic events. A promising alternative is the use of vector-based maps containing information about the original urban layout which can be related to a single image obtained after the catastrophe. The paper describes a methodology for an object-based search of destroyed buildings as a consequence of a natural or man-made catastrophe (e.g., earthquakes, flooding, civil war). The analysis is based on remotely sensed and vector GIS data. It includes three main steps: (i) generation of features describing the state of buildings; (ii) classification of building conditions; and (iii) data import into a GIS. One of the proposed features is a newly developed 'Detected Part of Contour' (DPC). Additionally, several features based on the analysis of textural information corresponding to the investigated vector objects are calculated. The method is applied to remotely sensed images of areas that have been subjected to an earthquake. The results show the high reliability of the DPC feature as an indicator for change.

Distribution Network Expansion Planning Based on Multi-objective PSO Algorithm

DEFF Research Database (Denmark)

Zhang, Chunyu; Ding, Yi; Wu, Qiuwei

2013-01-01

This paper presents a novel approach for electrical distribution network expansion planning using multi-objective particle swarm optimization (PSO). The optimization objectives are: investment and operation cost, energy losses cost, and power congestion cost. A two-phase multi-objective PSO...... algorithm was proposed to solve this optimization problem, which can accelerate the convergence and guarantee the diversity of Pareto-optimal front set as well. The feasibility and effectiveness of both the proposed multi-objective planning approach and the improved multi-objective PSO have been verified...

An Object-Based Image Analysis Approach for Detecting Penguin Guano in very High Spatial Resolution Satellite Images

Directory of Open Access Journals (Sweden)

Chandi Witharana

2016-04-01

Full Text Available The logistical challenges of Antarctic field work and the increasing availability of very high resolution commercial imagery have driven an interest in more efficient search and classification of remotely sensed imagery. This exploratory study employed geographic object-based analysis (GEOBIA methods to classify guano stains, indicative of chinstrap and Adélie penguin breeding areas, from very high spatial resolution (VHSR satellite imagery and closely examined the transferability of knowledge-based GEOBIA rules across different study sites focusing on the same semantic class. We systematically gauged the segmentation quality, classification accuracy, and the reproducibility of fuzzy rules. A master ruleset was developed based on one study site and it was re-tasked “without adaptation” and “with adaptation” on candidate image scenes comprising guano stains. Our results suggest that object-based methods incorporating the spectral, textural, spatial, and contextual characteristics of guano are capable of successfully detecting guano stains. Reapplication of the master ruleset on candidate scenes without modifications produced inferior classification results, while adapted rules produced comparable or superior results compared to the reference image. This work provides a road map to an operational “image-to-assessment pipeline” that will enable Antarctic wildlife researchers to seamlessly integrate VHSR imagery into on-demand penguin population census.

Variability Extraction and Synthesis via Multi-Resolution Analysis using Distribution Transformer High-Speed Power Data

Energy Technology Data Exchange (ETDEWEB)

Chamana, Manohar [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Mather, Barry A [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

2017-10-19

A library of load variability classes is created to produce scalable synthetic data sets using historical high-speed raw data. These data are collected from distribution monitoring units connected at the secondary side of a distribution transformer. Because of the irregular patterns and large volume of historical high-speed data sets, the utilization of current load characterization and modeling techniques are challenging. Multi-resolution analysis techniques are applied to extract the necessary components and eliminate the unnecessary components from the historical high-speed raw data to create the library of classes, which are then utilized to create new synthetic load data sets. A validation is performed to ensure that the synthesized data sets contain the same variability characteristics as the training data sets. The synthesized data sets are intended to be utilized in quasi-static time-series studies for distribution system planning studies on a granular scale, such as detailed PV interconnection studies.

High-resolution flood modeling of urban areas using MSN_Flood

Directory of Open Access Journals (Sweden)

Michael Hartnett

2017-07-01

Full Text Available Although existing hydraulic models have been used to simulate and predict urban flooding, most of these models are inadequate due to the high spatial resolution required to simulate flows in urban floodplains. Nesting high-resolution subdomains within coarser-resolution models is an efficient solution for enabling simultaneous calculation of flooding due to tides, surges, and high river flows. MSN_Flood has been developed to incorporate moving boundaries around nested domains, permitting alternate flooding and drying along the boundary and in the interior of the domain. Ghost cells adjacent to open boundary cells convert open boundaries, in effect, into internal boundaries. The moving boundary may be multi-segmented and non-continuous, with recirculating flow across the boundary. When combined with a bespoke adaptive interpolation scheme, this approach facilitates a dynamic internal boundary. Based on an alternating-direction semi-implicit finite difference scheme, MSN_Flood was used to hindcast a major flood event in Cork City resulting from the combined pressures of fluvial, tidal, and storm surge processes. The results show that the model is computationally efficient, as the 2-m high-resolution nest is used only in the urban flooded region. Elsewhere, lower-resolution nests are used. The results also show that the model is highly accurate when compared with measured data. The model is capable of incorporating nested sub-domains when the nested boundary is multi-segmented and highly complex with lateral gradients of elevation and velocities. This is a major benefit when modelling urban floodplains at very high resolution.

Non-periodic multi-slit masking for a single counter rotating 2-disc chopper and channeling guides for high resolution and high intensity neutron TOF spectroscopy

Energy Technology Data Exchange (ETDEWEB)

Bartkowiak, M.; Hofmann, T.; Stüßer, N.

2017-02-01

Energy resolution is an important design goal for time-of-flight instruments and neutron spectroscopy. For high-resolution applications, it is required that the burst times of choppers be short, going down to the µs-range. To produce short pulses while maintaining high neutron flux, we propose beam masks with more than two slits on a counter-rotating 2-disc chopper, behind specially adapted focusing multi-channel guides. A novel non-regular arrangement of the slits ensures that the beam opens only once per chopper cycle, when the masks are congruently aligned. Additionally, beam splitting and intensity focusing by guides before and after the chopper position provide high intensities even for small samples. Phase-space analysis and Monte Carlo simulations on examples of four-slit masks with adapted guide geometries show the potential of the proposed setup.

Single Image Super-Resolution Based on Multi-Scale Competitive Convolutional Neural Network.

Science.gov (United States)

Du, Xiaofeng; Qu, Xiaobo; He, Yifan; Guo, Di

2018-03-06

Deep convolutional neural networks (CNNs) are successful in single-image super-resolution. Traditional CNNs are limited to exploit multi-scale contextual information for image reconstruction due to the fixed convolutional kernel in their building modules. To restore various scales of image details, we enhance the multi-scale inference capability of CNNs by introducing competition among multi-scale convolutional filters, and build up a shallow network under limited computational resources. The proposed network has the following two advantages: (1) the multi-scale convolutional kernel provides the multi-context for image super-resolution, and (2) the maximum competitive strategy adaptively chooses the optimal scale of information for image reconstruction. Our experimental results on image super-resolution show that the performance of the proposed network outperforms the state-of-the-art methods.

Knowledge Guided Disambiguation for Large-Scale Scene Classification With Multi-Resolution CNNs

Science.gov (United States)

Wang, Limin; Guo, Sheng; Huang, Weilin; Xiong, Yuanjun; Qiao, Yu

2017-04-01

Convolutional Neural Networks (CNNs) have made remarkable progress on scene recognition, partially due to these recent large-scale scene datasets, such as the Places and Places2. Scene categories are often defined by multi-level information, including local objects, global layout, and background environment, thus leading to large intra-class variations. In addition, with the increasing number of scene categories, label ambiguity has become another crucial issue in large-scale classification. This paper focuses on large-scale scene recognition and makes two major contributions to tackle these issues. First, we propose a multi-resolution CNN architecture that captures visual content and structure at multiple levels. The multi-resolution CNNs are composed of coarse resolution CNNs and fine resolution CNNs, which are complementary to each other. Second, we design two knowledge guided disambiguation techniques to deal with the problem of label ambiguity. (i) We exploit the knowledge from the confusion matrix computed on validation data to merge ambiguous classes into a super category. (ii) We utilize the knowledge of extra networks to produce a soft label for each image. Then the super categories or soft labels are employed to guide CNN training on the Places2. We conduct extensive experiments on three large-scale image datasets (ImageNet, Places, and Places2), demonstrating the effectiveness of our approach. Furthermore, our method takes part in two major scene recognition challenges, and achieves the second place at the Places2 challenge in ILSVRC 2015, and the first place at the LSUN challenge in CVPR 2016. Finally, we directly test the learned representations on other scene benchmarks, and obtain the new state-of-the-art results on the MIT Indoor67 (86.7\\%) and SUN397 (72.0\\%). We release the code and models at~\\url{https://github.com/wanglimin/MRCNN-Scene-Recognition}.

Beyond RGB: Very high resolution urban remote sensing with multimodal deep networks

Science.gov (United States)

Audebert, Nicolas; Le Saux, Bertrand; Lefèvre, Sébastien

2018-06-01

In this work, we investigate various methods to deal with semantic labeling of very high resolution multi-modal remote sensing data. Especially, we study how deep fully convolutional networks can be adapted to deal with multi-modal and multi-scale remote sensing data for semantic labeling. Our contributions are threefold: (a) we present an efficient multi-scale approach to leverage both a large spatial context and the high resolution data, (b) we investigate early and late fusion of Lidar and multispectral data, (c) we validate our methods on two public datasets with state-of-the-art results. Our results indicate that late fusion make it possible to recover errors steaming from ambiguous data, while early fusion allows for better joint-feature learning but at the cost of higher sensitivity to missing data.

Accuracy assessment of tree crown detection using local maxima and multi-resolution segmentation

International Nuclear Information System (INIS)

Khalid, N; Hamid, J R A; Latif, Z A

2014-01-01

Diversity of trees forms an important component in the forest ecosystems and needs proper inventories to assist the forest personnel in their daily activities. However, tree parameter measurements are often constrained by physical inaccessibility to site locations, high costs, and time. With the advancement in remote sensing technology, such as the provision of higher spatial and spectral resolution of imagery, a number of developed algorithms fulfil the needs of accurate tree inventories information in a cost effective and timely manner over larger forest areas. This study intends to generate tree distribution map in Ampang Forest Reserve using the Local Maxima and Multi-Resolution image segmentation algorithm. The utilization of recent worldview-2 imagery with Local Maxima and Multi-Resolution image segmentation proves to be capable of detecting and delineating the tree crown in its accurate standing position

High-Resolution PET Detector. Final report

International Nuclear Information System (INIS)

Karp, Joel

2014-01-01

The objective of this project was to develop an understanding of the limits of performance for a high resolution PET detector using an approach based on continuous scintillation crystals rather than pixelated crystals. The overall goal was to design a high-resolution detector, which requires both high spatial resolution and high sensitivity for 511 keV gammas. Continuous scintillation detectors (Anger cameras) have been used extensively for both single-photon and PET scanners, however, these instruments were based on NaI(Tl) scintillators using relatively large, individual photo-multipliers. In this project we investigated the potential of this type of detector technology to achieve higher spatial resolution through the use of improved scintillator materials and photo-sensors, and modification of the detector surface to optimize the light response function.We achieved an average spatial resolution of 3-mm for a 25-mm thick, LYSO continuous detector using a maximum likelihood position algorithm and shallow slots cut into the entrance surface

Object recognition through a multi-mode fiber

Science.gov (United States)

Takagi, Ryosuke; Horisaki, Ryoichi; Tanida, Jun

2017-04-01

We present a method of recognizing an object through a multi-mode fiber. A number of speckle patterns transmitted through a multi-mode fiber are provided to a classifier based on machine learning. We experimentally demonstrated binary classification of face and non-face targets based on the method. The measurement process of the experimental setup was random and nonlinear because a multi-mode fiber is a typical strongly scattering medium and any reference light was not used in our setup. Comparisons between three supervised learning methods, support vector machine, adaptive boosting, and neural network, are also provided. All of those learning methods achieved high accuracy rates at about 90% for the classification. The approach presented here can realize a compact and smart optical sensor. It is practically useful for medical applications, such as endoscopy. Also our study indicated a promising utilization of artificial intelligence, which has rapidly progressed, for reducing optical and computational costs in optical sensing systems.

Object-based methods for individual tree identification and tree species classification from high-spatial resolution imagery

Science.gov (United States)

Wang, Le

2003-10-01

Modern forest management poses an increasing need for detailed knowledge of forest information at different spatial scales. At the forest level, the information for tree species assemblage is desired whereas at or below the stand level, individual tree related information is preferred. Remote Sensing provides an effective tool to extract the above information at multiple spatial scales in the continuous time domain. To date, the increasing volume and readily availability of high-spatial-resolution data have lead to a much wider application of remotely sensed products. Nevertheless, to make effective use of the improving spatial resolution, conventional pixel-based classification methods are far from satisfactory. Correspondingly, developing object-based methods becomes a central challenge for researchers in the field of Remote Sensing. This thesis focuses on the development of methods for accurate individual tree identification and tree species classification. We develop a method in which individual tree crown boundaries and treetop locations are derived under a unified framework. We apply a two-stage approach with edge detection followed by marker-controlled watershed segmentation. Treetops are modeled from radiometry and geometry aspects. Specifically, treetops are assumed to be represented by local radiation maxima and to be located near the center of the tree-crown. As a result, a marker image was created from the derived treetop to guide a watershed segmentation to further differentiate overlapping trees and to produce a segmented image comprised of individual tree crowns. The image segmentation method developed achieves a promising result for a 256 x 256 CASI image. Then further effort is made to extend our methods to the multiscales which are constructed from a wavelet decomposition. A scale consistency and geometric consistency are designed to examine the gradients along the scale-space for the purpose of separating true crown boundary from unwanted

Kite aerial photography for low-cost, ultra-high spatial resolution multi-spectral mapping of intertidal landscapes.

Directory of Open Access Journals (Sweden)

Mitch Bryson

Full Text Available Intertidal ecosystems have primarily been studied using field-based sampling; remote sensing offers the ability to collect data over large areas in a snapshot of time that could complement field-based sampling methods by extrapolating them into the wider spatial and temporal context. Conventional remote sensing tools (such as satellite and aircraft imaging provide data at limited spatial and temporal resolutions and relatively high costs for small-scale environmental science and ecologically-focussed studies. In this paper, we describe a low-cost, kite-based imaging system and photogrammetric/mapping procedure that was developed for constructing high-resolution, three-dimensional, multi-spectral terrain models of intertidal rocky shores. The processing procedure uses automatic image feature detection and matching, structure-from-motion and photo-textured terrain surface reconstruction algorithms that require minimal human input and only a small number of ground control points and allow the use of cheap, consumer-grade digital cameras. The resulting maps combine imagery at visible and near-infrared wavelengths and topographic information at sub-centimeter resolutions over an intertidal shoreline 200 m long, thus enabling spatial properties of the intertidal environment to be determined across a hierarchy of spatial scales. Results of the system are presented for an intertidal rocky shore at Jervis Bay, New South Wales, Australia. Potential uses of this technique include mapping of plant (micro- and macro-algae and animal (e.g. gastropods assemblages at multiple spatial and temporal scales.

Kite aerial photography for low-cost, ultra-high spatial resolution multi-spectral mapping of intertidal landscapes.

Science.gov (United States)

Bryson, Mitch; Johnson-Roberson, Matthew; Murphy, Richard J; Bongiorno, Daniel

2013-01-01

Intertidal ecosystems have primarily been studied using field-based sampling; remote sensing offers the ability to collect data over large areas in a snapshot of time that could complement field-based sampling methods by extrapolating them into the wider spatial and temporal context. Conventional remote sensing tools (such as satellite and aircraft imaging) provide data at limited spatial and temporal resolutions and relatively high costs for small-scale environmental science and ecologically-focussed studies. In this paper, we describe a low-cost, kite-based imaging system and photogrammetric/mapping procedure that was developed for constructing high-resolution, three-dimensional, multi-spectral terrain models of intertidal rocky shores. The processing procedure uses automatic image feature detection and matching, structure-from-motion and photo-textured terrain surface reconstruction algorithms that require minimal human input and only a small number of ground control points and allow the use of cheap, consumer-grade digital cameras. The resulting maps combine imagery at visible and near-infrared wavelengths and topographic information at sub-centimeter resolutions over an intertidal shoreline 200 m long, thus enabling spatial properties of the intertidal environment to be determined across a hierarchy of spatial scales. Results of the system are presented for an intertidal rocky shore at Jervis Bay, New South Wales, Australia. Potential uses of this technique include mapping of plant (micro- and macro-algae) and animal (e.g. gastropods) assemblages at multiple spatial and temporal scales.

Multi-objective congestion management by modified augmented ε-constraint method

International Nuclear Information System (INIS)

Esmaili, Masoud; Shayanfar, Heidar Ali; Amjady, Nima

2011-01-01

Congestion management is a vital part of power system operations in recent deregulated electricity markets. However, after relieving congestion, power systems may be operated with a reduced voltage or transient stability margin because of hitting security limits or increasing the contribution of risky participants. Therefore, power system stability margins should be considered within the congestion management framework. The multi-objective congestion management provides not only more security but also more flexibility than single-objective methods. In this paper, a multi-objective congestion management framework is presented while simultaneously optimizing the competing objective functions of congestion management cost, voltage security, and dynamic security. The proposed multi-objective framework, called modified augmented ε-constraint method, is based on the augmented ε-constraint technique hybridized by the weighting method. The proposed framework generates candidate solutions for the multi-objective problem including only efficient Pareto surface enhancing the competitiveness and economic effectiveness of the power market. Besides, the relative importance of the objective functions is explicitly modeled in the proposed framework. Results of testing the proposed multi-objective congestion management method on the New-England test system are presented and compared with those of the previous single objective and multi-objective techniques in detail. These comparisons confirm the efficiency of the developed method. (author)

Multi-objective compared to single-objective optimization with application to model validation and uncertainty quantification

Energy Technology Data Exchange (ETDEWEB)

Schulze-Riegert, R.; Krosche, M.; Stekolschikov, K. [Scandpower Petroleum Technology GmbH, Hamburg (Germany); Fahimuddin, A. [Technische Univ. Braunschweig (Germany)

2007-09-13

History Matching in Reservoir Simulation, well location and production optimization etc. is generally a multi-objective optimization problem. The problem statement of history matching for a realistic field case includes many field and well measurements in time and type, e.g. pressure measurements, fluid rates, events such as water and gas break-throughs, etc. Uncertainty parameters modified as part of the history matching process have varying impact on the improvement of the match criteria. Competing match criteria often reduce the likelihood of finding an acceptable history match. It is an engineering challenge in manual history matching processes to identify competing objectives and to implement the changes required in the simulation model. In production optimization or scenario optimization the focus on one key optimization criterion such as NPV limits the identification of alternatives and potential opportunities, since multiple objectives are summarized in a predefined global objective formulation. Previous works primarily focus on a specific optimization method. Few works actually concentrate on the objective formulation and multi-objective optimization schemes have not yet been applied to reservoir simulations. This paper presents a multi-objective optimization approach applicable to reservoir simulation. It addresses the problem of multi-objective criteria in a history matching study and presents analysis techniques identifying competing match criteria. A Pareto-Optimizer is discussed and the implementation of that multi-objective optimization scheme is applied to a case study. Results are compared to a single-objective optimization method. (orig.)

Sparse PDF maps for non-linear multi-resolution image operations

KAUST Repository

Hadwiger, Markus

2012-11-01

We introduce a new type of multi-resolution image pyramid for high-resolution images called sparse pdf maps (sPDF-maps). Each pyramid level consists of a sparse encoding of continuous probability density functions (pdfs) of pixel neighborhoods in the original image. The encoded pdfs enable the accurate computation of non-linear image operations directly in any pyramid level with proper pre-filtering for anti-aliasing, without accessing higher or lower resolutions. The sparsity of sPDF-maps makes them feasible for gigapixel images, while enabling direct evaluation of a variety of non-linear operators from the same representation. We illustrate this versatility for antialiased color mapping, O(n) local Laplacian filters, smoothed local histogram filters (e.g., median or mode filters), and bilateral filters. © 2012 ACM.

A robust object-based shadow detection method for cloud-free high resolution satellite images over urban areas and water bodies

Science.gov (United States)

Tatar, Nurollah; Saadatseresht, Mohammad; Arefi, Hossein; Hadavand, Ahmad

2018-06-01

Unwanted contrast in high resolution satellite images such as shadow areas directly affects the result of further processing in urban remote sensing images. Detecting and finding the precise position of shadows is critical in different remote sensing processing chains such as change detection, image classification and digital elevation model generation from stereo images. The spectral similarity between shadow areas, water bodies, and some dark asphalt roads makes the development of robust shadow detection algorithms challenging. In addition, most of the existing methods work on pixel-level and neglect the contextual information contained in neighboring pixels. In this paper, a new object-based shadow detection framework is introduced. In the proposed method a pixel-level shadow mask is built by extending established thresholding methods with a new C4 index which enables to solve the ambiguity of shadow and water bodies. Then the pixel-based results are further processed in an object-based majority analysis to detect the final shadow objects. Four different high resolution satellite images are used to validate this new approach. The result shows the superiority of the proposed method over some state-of-the-art shadow detection method with an average of 96% in F-measure.

Multi-objective optimization using genetic algorithms: A tutorial

International Nuclear Information System (INIS)

Konak, Abdullah; Coit, David W.; Smith, Alice E.

2006-01-01

Multi-objective formulations are realistic models for many complex engineering optimization problems. In many real-life problems, objectives under consideration conflict with each other, and optimizing a particular solution with respect to a single objective can result in unacceptable results with respect to the other objectives. A reasonable solution to a multi-objective problem is to investigate a set of solutions, each of which satisfies the objectives at an acceptable level without being dominated by any other solution. In this paper, an overview and tutorial is presented describing genetic algorithms (GA) developed specifically for problems with multiple objectives. They differ primarily from traditional GA by using specialized fitness functions and introducing methods to promote solution diversity

Object-based assessment of burn severity in diseased forests using high-spatial and high-spectral resolution MASTER airborne imagery

Science.gov (United States)

Chen, Gang; Metz, Margaret R.; Rizzo, David M.; Dillon, Whalen W.; Meentemeyer, Ross K.

2015-04-01

Forest ecosystems are subject to a variety of disturbances with increasing intensities and frequencies, which may permanently change the trajectories of forest recovery and disrupt the ecosystem services provided by trees. Fire and invasive species, especially exotic disease-causing pathogens and insects, are examples of disturbances that together could pose major threats to forest health. This study examines the impacts of fire and exotic disease (sudden oak death) on forests, with an emphasis on the assessment of post-fire burn severity in a forest where trees have experienced three stages of disease progression pre-fire: early-stage (trees retaining dried foliage and fine twigs), middle-stage (trees losing fine crown fuels), and late-stage (trees falling down). The research was conducted by applying Geographic Object-Based Image Analysis (GEOBIA) to MASTER airborne images that were acquired immediately following the fire for rapid assessment and contained both high-spatial (4 m) and high-spectral (50 bands) resolutions. Although GEOBIA has gradually become a standard tool for analyzing high-spatial resolution imagery, high-spectral resolution data (dozens to hundreds of bands) can dramatically reduce computation efficiency in the process of segmentation and object-based variable extraction, leading to complicated variable selection for succeeding modeling. Hence, we also assessed two widely used band reduction algorithms, PCA (principal component analysis) and MNF (minimum noise fraction), for the delineation of image objects and the subsequent performance of burn severity models using either PCA or MNF derived variables. To increase computation efficiency, only the top 5 PCA and MNF and top 10 PCA and MNF components were evaluated, which accounted for 10% and 20% of the total number of the original 50 spectral bands, respectively. Results show that if no band reduction was applied the models developed for the three stages of disease progression had relatively

Global Multi-Resolution Topography (GMRT) Synthesis - Recent Updates and Developments

Science.gov (United States)

Ferrini, V. L.; Morton, J. J.; Celnick, M.; McLain, K.; Nitsche, F. O.; Carbotte, S. M.; O'hara, S. H.

2017-12-01

The Global Multi-Resolution Topography (GMRT, http://gmrt.marine-geo.org) synthesis is a multi-resolution compilation of elevation data that is maintained in Mercator, South Polar, and North Polar Projections. GMRT consists of four independently curated elevation components: (1) quality controlled multibeam data ( 100m res.), (2) contributed high-resolution gridded bathymetric data (0.5-200 m res.), (3) ocean basemap data ( 500 m res.), and (4) variable resolution land elevation data (to 10-30 m res. in places). Each component is managed and updated as new content becomes available, with two scheduled releases each year. The ocean basemap content for GMRT includes the International Bathymetric Chart of the Arctic Ocean (IBCAO), the International Bathymetric Chart of the Southern Ocean (IBCSO), and the GEBCO 2014. Most curatorial effort for GMRT is focused on the swath bathymetry component, with an emphasis on data from the US Academic Research Fleet. As of July 2017, GMRT includes data processed and curated by the GMRT Team from 974 research cruises, covering over 29 million square kilometers ( 8%) of the seafloor at 100m resolution. The curated swath bathymetry data from GMRT is routinely contributed to international data synthesis efforts including GEBCO and IBCSO. Additional curatorial effort is associated with gridded data contributions from the international community and ensures that these data are well blended in the synthesis. Significant new additions to the gridded data component this year include the recently released data from the search for MH370 (Geoscience Australia) as well as a large high-resolution grid from the Gulf of Mexico derived from 3D seismic data (US Bureau of Ocean Energy Management). Recent developments in functionality include the deployment of a new Polar GMRT MapTool which enables users to export custom grids and map images in polar projection for their selected area of interest at the resolution of their choosing. Available for both

Evaluating Climate Causation of Conflict in Darfur Using Multi-temporal, Multi-resolution Satellite Image Datasets With Novel Analyses

Science.gov (United States)

Brown, I.; Wennbom, M.

2013-12-01

Climate change, population growth and changes in traditional lifestyles have led to instabilities in traditional demarcations between neighboring ethic and religious groups in the Sahel region. This has resulted in a number of conflicts as groups resort to arms to settle disputes. Such disputes often centre on or are justified by competition for resources. The conflict in Darfur has been controversially explained by resource scarcity resulting from climate change. Here we analyse established methods of using satellite imagery to assess vegetation health in Darfur. Multi-decadal time series of observations are available using low spatial resolution visible-near infrared imagery. Typically normalized difference vegetation index (NDVI) analyses are produced to describe changes in vegetation ';greenness' or ';health'. Such approaches have been widely used to evaluate the long term development of vegetation in relation to climate variations across a wide range of environments from the Arctic to the Sahel. These datasets typically measure peak NDVI observed over a given interval and may introduce bias. It is furthermore unclear how the spatial organization of sparse vegetation may affect low resolution NDVI products. We develop and assess alternative measures of vegetation including descriptors of the growing season, wetness and resource availability. Expanding the range of parameters used in the analysis reduces our dependence on peak NDVI. Furthermore, these descriptors provide a better characterization of the growing season than the single NDVI measure. Using multi-sensor data we combine high temporal/moderate spatial resolution data with low temporal/high spatial resolution data to improve the spatial representativity of the observations and to provide improved spatial analysis of vegetation patterns. The approach places the high resolution observations in the NDVI context space using a longer time series of lower resolution imagery. The vegetation descriptors

A scalable coevolutionary multi-objective particle swarm optimizer

Directory of Open Access Journals (Sweden)

Xiangwei Zheng

2010-11-01

Full Text Available Multi-Objective Particle Swarm Optimizers (MOPSOs are easily trapped in local optima, cost more function evaluations and suffer from the curse of dimensionality. A scalable cooperative coevolution and ?-dominance based MOPSO (CEPSO is proposed to address these issues. In CEPSO, Multi-objective Optimization Problems (MOPs are decomposed in terms of their decision variables and are optimized by cooperative coevolutionary subswarms, and a uniform distribution mutation operator is adopted to avoid premature convergence. All subswarms share an external archive based on ?-dominance, which is also used as a leader set. Collaborators are selected from the archive and used to construct context vectors in order to evaluate particles in a subswarm. CEPSO is tested on several classical MOP benchmark functions and experimental results show that CEPSO can readily escape from local optima and optimize both low and high dimensional problems, but the number of function evaluations only increases linearly with respect to the number of decision variables. Therefore, CEPSO is competitive in solving various MOPs.

Reconstruction of high resolution MLC leaf positions using a low resolution detector for accurate 3D dose reconstruction in IMRT

NARCIS (Netherlands)

Visser, R; Godart, J; Wauben, D J L; Langendijk, J A; Van't Veld, A A; Korevaar, E W

2016-01-01

In pre-treatment dose verification, low resolution detector systems are unable to identify shifts of individual leafs of high resolution multi leaf collimator (MLC) systems from detected changes in the dose deposition. The goal of this study was to introduce an alternative approach (the shutter

Multi-criteria objective based climate change impact assessment for multi-purpose multi-reservoir systems

Science.gov (United States)

Müller, Ruben; Schütze, Niels

2014-05-01

Water resources systems with reservoirs are expected to be sensitive to climate change. Assessment studies that analyze the impact of climate change on the performance of reservoirs can be divided in two groups: (1) Studies that simulate the operation under projected inflows with the current set of operational rules. Due to non adapted operational rules the future performance of these reservoirs can be underestimated and the impact overestimated. (2) Studies that optimize the operational rules for best adaption of the system to the projected conditions before the assessment of the impact. The latter allows for estimating more realistically future performance and adaption strategies based on new operation rules are available if required. Multi-purpose reservoirs serve various, often conflicting functions. If all functions cannot be served simultaneously at a maximum level, an effective compromise between multiple objectives of the reservoir operation has to be provided. Yet under climate change the historically preferenced compromise may no longer be the most suitable compromise in the future. Therefore a multi-objective based climate change impact assessment approach for multi-purpose multi-reservoir systems is proposed in the study. Projected inflows are provided in a first step using a physically based rainfall-runoff model. In a second step, a time series model is applied to generate long-term inflow time series. Finally, the long-term inflow series are used as driving variables for a simulation-based multi-objective optimization of the reservoir system in order to derive optimal operation rules. As a result, the adapted Pareto-optimal set of diverse best compromise solutions can be presented to the decision maker in order to assist him in assessing climate change adaption measures with respect to the future performance of the multi-purpose reservoir system. The approach is tested on a multi-purpose multi-reservoir system in a mountainous catchment in Germany. A

Application of multi-scale wavelet entropy and multi-resolution Volterra models for climatic downscaling

Science.gov (United States)

Sehgal, V.; Lakhanpal, A.; Maheswaran, R.; Khosa, R.; Sridhar, Venkataramana

2018-01-01

This study proposes a wavelet-based multi-resolution modeling approach for statistical downscaling of GCM variables to mean monthly precipitation for five locations at Krishna Basin, India. Climatic dataset from NCEP is used for training the proposed models (Jan.'69 to Dec.'94) and are applied to corresponding CanCM4 GCM variables to simulate precipitation for the validation (Jan.'95-Dec.'05) and forecast (Jan.'06-Dec.'35) periods. The observed precipitation data is obtained from the India Meteorological Department (IMD) gridded precipitation product at 0.25 degree spatial resolution. This paper proposes a novel Multi-Scale Wavelet Entropy (MWE) based approach for clustering climatic variables into suitable clusters using k-means methodology. Principal Component Analysis (PCA) is used to obtain the representative Principal Components (PC) explaining 90-95% variance for each cluster. A multi-resolution non-linear approach combining Discrete Wavelet Transform (DWT) and Second Order Volterra (SoV) is used to model the representative PCs to obtain the downscaled precipitation for each downscaling location (W-P-SoV model). The results establish that wavelet-based multi-resolution SoV models perform significantly better compared to the traditional Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) based frameworks. It is observed that the proposed MWE-based clustering and subsequent PCA, helps reduce the dimensionality of the input climatic variables, while capturing more variability compared to stand-alone k-means (no MWE). The proposed models perform better in estimating the number of precipitation events during the non-monsoon periods whereas the models with clustering without MWE over-estimate the rainfall during the dry season.

High-resolution imaging of expertise reveals reliable object selectivity in the fusiform face area related to perceptual performance.

Science.gov (United States)

McGugin, Rankin Williams; Gatenby, J Christopher; Gore, John C; Gauthier, Isabel

2012-10-16

The fusiform face area (FFA) is a region of human cortex that responds selectively to faces, but whether it supports a more general function relevant for perceptual expertise is debated. Although both faces and objects of expertise engage many brain areas, the FFA remains the focus of the strongest modular claims and the clearest predictions about expertise. Functional MRI studies at standard-resolution (SR-fMRI) have found responses in the FFA for nonface objects of expertise, but high-resolution fMRI (HR-fMRI) in the FFA [Grill-Spector K, et al. (2006) Nat Neurosci 9:1177-1185] and neurophysiology in face patches in the monkey brain [Tsao DY, et al. (2006) Science 311:670-674] reveal no reliable selectivity for objects. It is thus possible that FFA responses to objects with SR-fMRI are a result of spatial blurring of responses from nonface-selective areas, potentially driven by attention to objects of expertise. Using HR-fMRI in two experiments, we provide evidence of reliable responses to cars in the FFA that correlate with behavioral car expertise. Effects of expertise in the FFA for nonface objects cannot be attributed to spatial blurring beyond the scale at which modular claims have been made, and within the lateral fusiform gyrus, they are restricted to a small area (200 mm(2) on the right and 50 mm(2) on the left) centered on the peak of face selectivity. Experience with a category may be sufficient to explain the spatially clustered face selectivity observed in this region.

Multi-objective differential evolution with adaptive Cauchy mutation for short-term multi-objective optimal hydro-thermal scheduling

Energy Technology Data Exchange (ETDEWEB)

Qin Hui [College of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074 (China); Zhou Jianzhong, E-mail: jz.zhou@hust.edu.c [College of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074 (China); Lu Youlin; Wang Ying; Zhang Yongchuan [College of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074 (China)

2010-04-15

A new multi-objective optimization method based on differential evolution with adaptive Cauchy mutation (MODE-ACM) is presented to solve short-term multi-objective optimal hydro-thermal scheduling (MOOHS) problem. Besides fuel cost, the pollutant gas emission is also optimized as an objective. The water transport delay between connected reservoirs and the effect of valve-point loading of thermal units are also taken into account in the presented problem formulation. The proposed algorithm adopts an elitist archive to retain non-dominated solutions obtained during the evolutionary process. It modifies the DE's operators to make it suit for multi-objective optimization (MOO) problems and improve its performance. Furthermore, to avoid premature convergence, an adaptive Cauchy mutation is proposed to preserve the diversity of population. An effective constraints handling method is utilized to handle the complex equality and inequality constraints. The effectiveness of the proposed algorithm is tested on a hydro-thermal system consisting of four cascaded hydro plants and three thermal units. The results obtained by MODE-ACM are compared with several previous studies. It is found that the results obtained by MODE-ACM are superior in terms of fuel cost as well as emission output, consuming a shorter time. Thus it can be a viable alternative to generate optimal trade-offs for short-term MOOHS problem.

A high-throughput, multi-channel photon-counting detector with picosecond timing

Science.gov (United States)

Lapington, J. S.; Fraser, G. W.; Miller, G. M.; Ashton, T. J. R.; Jarron, P.; Despeisse, M.; Powolny, F.; Howorth, J.; Milnes, J.

2009-06-01

High-throughput photon counting with high time resolution is a niche application area where vacuum tubes can still outperform solid-state devices. Applications in the life sciences utilizing time-resolved spectroscopies, particularly in the growing field of proteomics, will benefit greatly from performance enhancements in event timing and detector throughput. The HiContent project is a collaboration between the University of Leicester Space Research Centre, the Microelectronics Group at CERN, Photek Ltd., and end-users at the Gray Cancer Institute and the University of Manchester. The goal is to develop a detector system specifically designed for optical proteomics, capable of high content (multi-parametric) analysis at high throughput. The HiContent detector system is being developed to exploit this niche market. It combines multi-channel, high time resolution photon counting in a single miniaturized detector system with integrated electronics. The combination of enabling technologies; small pore microchannel plate devices with very high time resolution, and high-speed multi-channel ASIC electronics developed for the LHC at CERN, provides the necessary building blocks for a high-throughput detector system with up to 1024 parallel counting channels and 20 ps time resolution. We describe the detector and electronic design, discuss the current status of the HiContent project and present the results from a 64-channel prototype system. In the absence of an operational detector, we present measurements of the electronics performance using a pulse generator to simulate detector events. Event timing results from the NINO high-speed front-end ASIC captured using a fast digital oscilloscope are compared with data taken with the proposed electronic configuration which uses the multi-channel HPTDC timing ASIC.

A high-throughput, multi-channel photon-counting detector with picosecond timing

International Nuclear Information System (INIS)

Lapington, J.S.; Fraser, G.W.; Miller, G.M.; Ashton, T.J.R.; Jarron, P.; Despeisse, M.; Powolny, F.; Howorth, J.; Milnes, J.

2009-01-01

High-throughput photon counting with high time resolution is a niche application area where vacuum tubes can still outperform solid-state devices. Applications in the life sciences utilizing time-resolved spectroscopies, particularly in the growing field of proteomics, will benefit greatly from performance enhancements in event timing and detector throughput. The HiContent project is a collaboration between the University of Leicester Space Research Centre, the Microelectronics Group at CERN, Photek Ltd., and end-users at the Gray Cancer Institute and the University of Manchester. The goal is to develop a detector system specifically designed for optical proteomics, capable of high content (multi-parametric) analysis at high throughput. The HiContent detector system is being developed to exploit this niche market. It combines multi-channel, high time resolution photon counting in a single miniaturized detector system with integrated electronics. The combination of enabling technologies; small pore microchannel plate devices with very high time resolution, and high-speed multi-channel ASIC electronics developed for the LHC at CERN, provides the necessary building blocks for a high-throughput detector system with up to 1024 parallel counting channels and 20 ps time resolution. We describe the detector and electronic design, discuss the current status of the HiContent project and present the results from a 64-channel prototype system. In the absence of an operational detector, we present measurements of the electronics performance using a pulse generator to simulate detector events. Event timing results from the NINO high-speed front-end ASIC captured using a fast digital oscilloscope are compared with data taken with the proposed electronic configuration which uses the multi-channel HPTDC timing ASIC.

Development of an improved high resolution mass spectrometry based multi-residue method for veterinary drugs in various food matrices.

Science.gov (United States)

Kaufmann, A; Butcher, P; Maden, K; Walker, S; Widmer, M

2011-08-26

Multi-residue methods for veterinary drugs or pesticides in food are increasingly often based on ultra performance liquid chromatography (UPLC) coupled to high resolution mass spectrometry (HRMS). Previous available time of flight (TOF) technologies, showing resolutions up to 15,000 full width at half maximum (FWHM), were not sufficiently selective for monitoring low residue concentrations in difficult matrices (e.g. hormones in tissue or antibiotics in honey). The approach proposed in this paper is based on a single stage Orbitrap mass spectrometer operated at 50,000 FWHM. Extracts (liver and kidney) which were produced according to a validated multi-residue method (time of flight detection based) could not be analyzed by Orbitrap because of extensive signal suppression. This required the improvement of established extraction and clean-up procedures. The introduced, more extensive deproteinzation steps and dedicated instrumental settings successfully eliminated these detrimental suppression effects. The reported method, covering more than 100 different veterinary dugs, was validated according to the EU Commission Decision 2002/657/EEC. Validated matrices include muscle, kidney, liver, fish and honey. Significantly better performance parameters (e.g. linearity, reproducibility and detection limits) were obtained when comparing the new method with the older, TOF based method. These improvements are attributed to the higher resolution (50,000 versus 12,000 FWHM) and the superior mass stability of the of the Orbitrap over the previously utilized TOF instrument. Copyright © 2010 Elsevier B.V. All rights reserved.

Multi-objective optimization of inverse planning for accurate radiotherapy

International Nuclear Information System (INIS)

Cao Ruifen; Pei Xi; Cheng Mengyun; Li Gui; Hu Liqin; Wu Yican; Jing Jia; Li Guoli

2011-01-01

The multi-objective optimization of inverse planning based on the Pareto solution set, according to the multi-objective character of inverse planning in accurate radiotherapy, was studied in this paper. Firstly, the clinical requirements of a treatment plan were transformed into a multi-objective optimization problem with multiple constraints. Then, the fast and elitist multi-objective Non-dominated Sorting Genetic Algorithm (NSGA-II) was introduced to optimize the problem. A clinical example was tested using this method. The results show that an obtained set of non-dominated solutions were uniformly distributed and the corresponding dose distribution of each solution not only approached the expected dose distribution, but also met the dose-volume constraints. It was indicated that the clinical requirements were better satisfied using the method and the planner could select the optimal treatment plan from the non-dominated solution set. (authors)

High-Resolution Near-Infrared Spectroscopy of FU Orionis Objects

Science.gov (United States)

Hartmann, Lee; Hinkle, Kenneth; Calvet, Nuria

2004-07-01

We present an analysis of recent near-infrared, high-resolution spectra of the variable FU Ori objects. During a phase of rapid fading in optical brightness during 1997, V1057 Cyg exhibited shell absorption in first-overtone (v''-v'=2-0) CO lines, blueshifted by about 50 km s-1 from the system velocity. This shell component had not been seen previously, nor was it present in 1999, although some blueshifted absorption asymmetry is seen at the latter epoch. The appearance of this CO absorption shell is connected with the roughly contemporaneous appearance of blueshifted, low-excitation optical absorption lines with comparable low velocities; we suggest that this shell was also responsible for some of the peculiar emission features seen in red-optical spectra of V1057 Cyg. FU Ori continues to exhibit broad CO lines, with some evidence for the double-peaked profiles characteristic of an accretion disk; the line profiles are consistent with previous observations. Both FU Ori and V1057 Cyg continue to exhibit lower rotational broadening at 2.3 μm than at optical wavelengths, in agreement with the prediction of differentially rotating disk models; we have a marginal detection of the same effect in V1515 Cyg. The relative population of the first-overtone CO rotational levels in the FU Ori objects suggests low excitation temperatures. We compare disk models to the observations and find agreement with overall line strengths and rotational broadening, but the observed line profiles are generally less double-peaked than predicted. We suggest that the discrepancy in line profiles is due to turbulent motions in FU Ori disks, an effect qualitatively predicted by recent simulations of the magnetorotational instability in vertically stratified accretion disks. Based on observations obtained at the Gemini Observatory, which is operated by the Association of Universities for Research in Astronomy (AURA), Inc., under a cooperative agreement with the NSF, on behalf of the Gemini

Effectiveness of meta-models for multi-objective optimization of centrifugal impeller

Energy Technology Data Exchange (ETDEWEB)

Bellary, Sayed Ahmed Imran; Samad, Abdus [Indian Institute of Technology Madras, Chennai (India); Husain, Afzal [Sultan Qaboos University, Al-Khoudh (Oman)

2014-12-15

The major issue of multiple fidelity based analysis and optimization of fluid machinery system depends upon the proper construction of low fidelity model or meta-model. A low fidelity model uses responses obtained from a high fidelity model, and the meta-model is then used to produce population of solutions required for evolutionary algorithm for multi-objective optimization. The Pareto-optimal front which shows functional relationships among the multiple objectives can produce erroneous results if the low fidelity models are not well-constructed. In the present research, response surface approximation and Kriging meta-models were evaluated for their effectiveness for the application in the turbomachinery design and optimization. A high fidelity model such as CFD technique along with the metamodels was used to obtain Pareto-optimal front via multi-objective genetic algorithm. A centrifugal impeller has been considered as case study to find relationship between two conflicting objectives, viz., hydraulic efficiency and head. Design variables from the impeller geometry have been chosen and the responses of the objective functions were evaluated through CFD analysis. The fidelity of each metamodel has been discussed in context of their predictions in entire design space in general and near optimal region in particular. Exploitation of the multiple meta-models enhances the quality of multi-objective optimization and provides the information pertaining to fidelity of optimization model. It was observed that the Kriging meta-model was better suited for this type of problem as it involved less approximation error in the Pareto-optimal front.

Effectiveness of meta-models for multi-objective optimization of centrifugal impeller

International Nuclear Information System (INIS)

Bellary, Sayed Ahmed Imran; Samad, Abdus; Husain, Afzal

2014-01-01

The major issue of multiple fidelity based analysis and optimization of fluid machinery system depends upon the proper construction of low fidelity model or meta-model. A low fidelity model uses responses obtained from a high fidelity model, and the meta-model is then used to produce population of solutions required for evolutionary algorithm for multi-objective optimization. The Pareto-optimal front which shows functional relationships among the multiple objectives can produce erroneous results if the low fidelity models are not well-constructed. In the present research, response surface approximation and Kriging meta-models were evaluated for their effectiveness for the application in the turbomachinery design and optimization. A high fidelity model such as CFD technique along with the metamodels was used to obtain Pareto-optimal front via multi-objective genetic algorithm. A centrifugal impeller has been considered as case study to find relationship between two conflicting objectives, viz., hydraulic efficiency and head. Design variables from the impeller geometry have been chosen and the responses of the objective functions were evaluated through CFD analysis. The fidelity of each metamodel has been discussed in context of their predictions in entire design space in general and near optimal region in particular. Exploitation of the multiple meta-models enhances the quality of multi-objective optimization and provides the information pertaining to fidelity of optimization model. It was observed that the Kriging meta-model was better suited for this type of problem as it involved less approximation error in the Pareto-optimal front.

Object-based land cover classification and change analysis in the Baltimore metropolitan area using multitemporal high resolution remote sensing data

Science.gov (United States)

Weiqi Zhou; Austin Troy; Morgan Grove

2008-01-01

Accurate and timely information about land cover pattern and change in urban areas is crucial for urban land management decision-making, ecosystem monitoring and urban planning. This paper presents the methods and results of an object-based classification and post-classification change detection of multitemporal high-spatial resolution Emerge aerial imagery in the...

The spectrometer of the High-Resolution Multi position Thomson Scattering Diagnostic for TJ-II

International Nuclear Information System (INIS)

Herranz, J.; Barth, C. J.; Castejon, F.; Lopez-Sanchez, A.; Mirones, E.; Pastor, I.; Perez, D.; Rodriguez, C.

2001-01-01

Since 1998, a high-resolution multiposition thompson scattering system is in operation at the stellarator TJ-II, combining high accuracy and excellent spatial resolution. A description of the diagnostic spectrometer is presented. The main characteristics of the spectrometer that allow YJ-II Thomson scattering diagnostic to have high spatial and spectral resolution are described in this paper. (Author)

Energy thermal management in commercial bread-baking using a multi-objective optimisation framework

International Nuclear Information System (INIS)

Khatir, Zinedine; Taherkhani, A.R.; Paton, Joe; Thompson, Harvey; Kapur, Nik; Toropov, Vassili

2015-01-01

In response to increasing energy costs and legislative requirements energy efficient high-speed air impingement jet baking systems are now being developed. In this paper, a multi-objective optimisation framework for oven designs is presented which uses experimentally verified heat transfer correlations and high fidelity Computational Fluid Dynamics (CFD) analyses to identify optimal combinations of design features which maximise desirable characteristics such as temperature uniformity in the oven and overall energy efficiency of baking. A surrogate-assisted multi-objective optimisation framework is proposed and used to explore a range of practical oven designs, providing information on overall temperature uniformity within the oven together with ensuing energy usage and potential savings. - Highlights: • A multi-objective optimisation framework to design commercial ovens is presented. • High fidelity CFD embeds experimentally calibrated heat transfer inputs. • The optimum oven design minimises specific energy and bake time. • The Pareto front outlining the surrogate-assisted optimisation framework is built. • Optimisation of industrial bread-baking ovens reveals an energy saving of 637.6 GWh

A multi-scale tensor voting approach for small retinal vessel segmentation in high resolution fundus images.

Science.gov (United States)

Christodoulidis, Argyrios; Hurtut, Thomas; Tahar, Houssem Ben; Cheriet, Farida

2016-09-01

Segmenting the retinal vessels from fundus images is a prerequisite for many CAD systems for the automatic detection of diabetic retinopathy lesions. So far, research efforts have concentrated mainly on the accurate localization of the large to medium diameter vessels. However, failure to detect the smallest vessels at the segmentation step can lead to false positive lesion detection counts in a subsequent lesion analysis stage. In this study, a new hybrid method for the segmentation of the smallest vessels is proposed. Line detection and perceptual organization techniques are combined in a multi-scale scheme. Small vessels are reconstructed from the perceptual-based approach via tracking and pixel painting. The segmentation was validated in a high resolution fundus image database including healthy and diabetic subjects using pixel-based as well as perceptual-based measures. The proposed method achieves 85.06% sensitivity rate, while the original multi-scale line detection method achieves 81.06% sensitivity rate for the corresponding images (p<0.05). The improvement in the sensitivity rate for the database is 6.47% when only the smallest vessels are considered (p<0.05). For the perceptual-based measure, the proposed method improves the detection of the vasculature by 7.8% against the original multi-scale line detection method (p<0.05). Copyright © 2016 Elsevier Ltd. All rights reserved.

Convex hull ranking algorithm for multi-objective evolutionary algorithms

NARCIS (Netherlands)

Davoodi Monfrared, M.; Mohades, A.; Rezaei, J.

2012-01-01

Due to many applications of multi-objective evolutionary algorithms in real world optimization problems, several studies have been done to improve these algorithms in recent years. Since most multi-objective evolutionary algorithms are based on the non-dominated principle, and their complexity

Multi-objective experimental design for (13)C-based metabolic flux analysis.

Science.gov (United States)

Bouvin, Jeroen; Cajot, Simon; D'Huys, Pieter-Jan; Ampofo-Asiama, Jerry; Anné, Jozef; Van Impe, Jan; Geeraerd, Annemie; Bernaerts, Kristel

2015-10-01

(13)C-based metabolic flux analysis is an excellent technique to resolve fluxes in the central carbon metabolism but costs can be significant when using specialized tracers. This work presents a framework for cost-effective design of (13)C-tracer experiments, illustrated on two different networks. Linear and non-linear optimal input mixtures are computed for networks for Streptomyces lividans and a carcinoma cell line. If only glucose tracers are considered as labeled substrate for a carcinoma cell line or S. lividans, the best parameter estimation accuracy is obtained by mixtures containing high amounts of 1,2-(13)C2 glucose combined with uniformly labeled glucose. Experimental designs are evaluated based on a linear (D-criterion) and non-linear approach (S-criterion). Both approaches generate almost the same input mixture, however, the linear approach is favored due to its low computational effort. The high amount of 1,2-(13)C2 glucose in the optimal designs coincides with a high experimental cost, which is further enhanced when labeling is introduced in glutamine and aspartate tracers. Multi-objective optimization gives the possibility to assess experimental quality and cost at the same time and can reveal excellent compromise experiments. For example, the combination of 100% 1,2-(13)C2 glucose with 100% position one labeled glutamine and the combination of 100% 1,2-(13)C2 glucose with 100% uniformly labeled glutamine perform equally well for the carcinoma cell line, but the first mixture offers a decrease in cost of $ 120 per ml-scale cell culture experiment. We demonstrated the validity of a multi-objective linear approach to perform optimal experimental designs for the non-linear problem of (13)C-metabolic flux analysis. Tools and a workflow are provided to perform multi-objective design. The effortless calculation of the D-criterion can be exploited to perform high-throughput screening of possible (13)C-tracers, while the illustrated benefit of multi-objective

Enhanced Multi-Objective Energy Optimization by a Signaling Method

OpenAIRE

Soares, João; Borges, Nuno; Vale, Zita; Oliveira, P.B.

2016-01-01

In this paper three metaheuristics are used to solve a smart grid multi-objective energy management problem with conflictive design: how to maximize profits and minimize carbon dioxide (CO2) emissions, and the results compared. The metaheuristics implemented are: weighted particle swarm optimization (W-PSO), multi-objective particle swarm optimization (MOPSO) and non-dominated sorting genetic algorithm II (NSGA-II). The performance of these methods with the use of multi-dimensi...

MOPSO-based multi-objective TSO planning considering uncertainties

DEFF Research Database (Denmark)

Wang, Qi; Zhang, Chunyu; Ding, Yi

2014-01-01

factors, i.e. load growth, generation capacity and line faults, and aims to enhance the transmission system via the multi-objective TSO planning (MOTP) approach. The proposed MOTP approach optimizes three objectives simultaneously, namely the probabilistic available transfer capability (PATC), investment...... cost and power outage cost. A two-phase MOPSO algorithm is employed to solve this optimization problem, which can accelerate the convergence and guarantee the diversity ofPareto-optimal front set as well. The feasibility and effectiveness of both the proposed multi-objective planning approach...

A procedure for multi-objective optimization of tire design parameters

Directory of Open Access Journals (Sweden)

Nikola Korunović

2015-04-01

Full Text Available The identification of optimal tire design parameters for satisfying different requirements, i.e. tire performance characteristics, plays an essential role in tire design. In order to improve tire performance characteristics, formulation and solving of multi-objective optimization problem must be performed. This paper presents a multi-objective optimization procedure for determination of optimal tire design parameters for simultaneous minimization of strain energy density at two distinctive zones inside the tire. It consists of four main stages: pre-analysis, design of experiment, mathematical modeling and multi-objective optimization. Advantage of the proposed procedure is reflected in the fact that multi-objective optimization is based on the Pareto concept, which enables design engineers to obtain a complete set of optimization solutions and choose a suitable tire design. Furthermore, modeling of the relationships between tire design parameters and objective functions based on multiple regression analysis minimizes computational and modeling effort. The adequacy of the proposed tire design multi-objective optimization procedure has been validated by performing experimental trials based on finite element method.

Connected Component Model for Multi-Object Tracking.

Science.gov (United States)

He, Zhenyu; Li, Xin; You, Xinge; Tao, Dacheng; Tang, Yuan Yan

2016-08-01

In multi-object tracking, it is critical to explore the data associations by exploiting the temporal information from a sequence of frames rather than the information from the adjacent two frames. Since straightforwardly obtaining data associations from multi-frames is an NP-hard multi-dimensional assignment (MDA) problem, most existing methods solve this MDA problem by either developing complicated approximate algorithms, or simplifying MDA as a 2D assignment problem based upon the information extracted only from adjacent frames. In this paper, we show that the relation between associations of two observations is the equivalence relation in the data association problem, based on the spatial-temporal constraint that the trajectories of different objects must be disjoint. Therefore, the MDA problem can be equivalently divided into independent subproblems by equivalence partitioning. In contrast to existing works for solving the MDA problem, we develop a connected component model (CCM) by exploiting the constraints of the data association and the equivalence relation on the constraints. Based upon CCM, we can efficiently obtain the global solution of the MDA problem for multi-object tracking by optimizing a sequence of independent data association subproblems. Experiments on challenging public data sets demonstrate that our algorithm outperforms the state-of-the-art approaches.

Robust multi-objective calibration strategies – possibilities for improving flood forecasting

Directory of Open Access Journals (Sweden)

G. H. Schmitz

2012-10-01

Full Text Available Process-oriented rainfall-runoff models are designed to approximate the complex hydrologic processes within a specific catchment and in particular to simulate the discharge at the catchment outlet. Most of these models exhibit a high degree of complexity and require the determination of various parameters by calibration. Recently, automatic calibration methods became popular in order to identify parameter vectors with high corresponding model performance. The model performance is often assessed by a purpose-oriented objective function. Practical experience suggests that in many situations one single objective function cannot adequately describe the model's ability to represent any aspect of the catchment's behaviour. This is regardless of whether the objective is aggregated of several criteria that measure different (possibly opposite aspects of the system behaviour. One strategy to circumvent this problem is to define multiple objective functions and to apply a multi-objective optimisation algorithm to identify the set of Pareto optimal or non-dominated solutions. Nonetheless, there is a major disadvantage of automatic calibration procedures that understand the problem of model calibration just as the solution of an optimisation problem: due to the complex-shaped response surface, the estimated solution of the optimisation problem can result in different near-optimum parameter vectors that can lead to a very different performance on the validation data. Bárdossy and Singh (2008 studied this problem for single-objective calibration problems using the example of hydrological models and proposed a geometrical sampling approach called Robust Parameter Estimation (ROPE. This approach applies the concept of data depth in order to overcome the shortcomings of automatic calibration procedures and find a set of robust parameter vectors. Recent studies confirmed the effectivity of this method. However, all ROPE approaches published so far just identify

New approach for solving intuitionistic fuzzy multi-objective ...

Indian Academy of Sciences (India)

SANKAR KUMAR ROY

2018-02-07

Feb 7, 2018 ... Transportation problem; multi-objective decision making; intuitionistic fuzzy programming; interval programming ... MOTP under multi-choice environment using utility func- ... theory is an intuitionistic fuzzy set (IFS), which was.

Ring artifact correction for high-resolution micro CT

International Nuclear Information System (INIS)

Kyriakou, Yiannis; Prell, Daniel; Kalender, Willi A

2009-01-01

In high-resolution micro CT using flat detectors (FD), imperfect or defect detector elements may cause concentric-ring artifacts due to their continuous over- or underestimation of attenuation values, which often disturb image quality. We here present a dedicated image-based ring artifact correction method for high-resolution micro CT, based on median filtering of the reconstructed image and working on a transformed version of the reconstructed images in polar coordinates. This post-processing method reduced ring artifacts in the reconstructed images and improved image quality for phantom and in in vivo scans. Noise and artifacts were reduced both in transversal and in multi-planar reformations along the longitudinal axis. (note)

A multi-resolution approach to heat kernels on discrete surfaces

KAUST Repository

Vaxman, Amir; Ben-Chen, Mirela; Gotsman, Craig

2010-01-01

process - limits this type of analysis to 3D models of modest resolution. We show how to use the unique properties of the heat kernel of a discrete two dimensional manifold to overcome these limitations. Combining a multi-resolution approach with a novel

Applying multi-resolution numerical methods to geodynamics

Science.gov (United States)

Davies, David Rhodri

Computational models yield inaccurate results if the underlying numerical grid fails to provide the necessary resolution to capture a simulation's important features. For the large-scale problems regularly encountered in geodynamics, inadequate grid resolution is a major concern. The majority of models involve multi-scale dynamics, being characterized by fine-scale upwelling and downwelling activity in a more passive, large-scale background flow. Such configurations, when coupled to the complex geometries involved, present a serious challenge for computational methods. Current techniques are unable to resolve localized features and, hence, such models cannot be solved efficiently. This thesis demonstrates, through a series of papers and closely-coupled appendices, how multi-resolution finite-element methods from the forefront of computational engineering can provide a means to address these issues. The problems examined achieve multi-resolution through one of two methods. In two-dimensions (2-D), automatic, unstructured mesh refinement procedures are utilized. Such methods improve the solution quality of convection dominated problems by adapting the grid automatically around regions of high solution gradient, yielding enhanced resolution of the associated flow features. Thermal and thermo-chemical validation tests illustrate that the technique is robust and highly successful, improving solution accuracy whilst increasing computational efficiency. These points are reinforced when the technique is applied to geophysical simulations of mid-ocean ridge and subduction zone magmatism. To date, successful goal-orientated/error-guided grid adaptation techniques have not been utilized within the field of geodynamics. The work included herein is therefore the first geodynamical application of such methods. In view of the existing three-dimensional (3-D) spherical mantle dynamics codes, which are built upon a quasi-uniform discretization of the sphere and closely coupled

Towards multi-resolution global climate modeling with ECHAM6-FESOM. Part II: climate variability

Science.gov (United States)

Rackow, T.; Goessling, H. F.; Jung, T.; Sidorenko, D.; Semmler, T.; Barbi, D.; Handorf, D.

2018-04-01

This study forms part II of two papers describing ECHAM6-FESOM, a newly established global climate model with a unique multi-resolution sea ice-ocean component. While part I deals with the model description and the mean climate state, here we examine the internal climate variability of the model under constant present-day (1990) conditions. We (1) assess the internal variations in the model in terms of objective variability performance indices, (2) analyze variations in global mean surface temperature and put them in context to variations in the observed record, with particular emphasis on the recent warming slowdown, (3) analyze and validate the most common atmospheric and oceanic variability patterns, (4) diagnose the potential predictability of various climate indices, and (5) put the multi-resolution approach to the test by comparing two setups that differ only in oceanic resolution in the equatorial belt, where one ocean mesh keeps the coarse 1° resolution applied in the adjacent open-ocean regions and the other mesh is gradually refined to 0.25°. Objective variability performance indices show that, in the considered setups, ECHAM6-FESOM performs overall favourably compared to five well-established climate models. Internal variations of the global mean surface temperature in the model are consistent with observed fluctuations and suggest that the recent warming slowdown can be explained as a once-in-one-hundred-years event caused by internal climate variability; periods of strong cooling in the model (`hiatus' analogs) are mainly associated with ENSO-related variability and to a lesser degree also to PDO shifts, with the AMO playing a minor role. Common atmospheric and oceanic variability patterns are simulated largely consistent with their real counterparts. Typical deficits also found in other models at similar resolutions remain, in particular too weak non-seasonal variability of SSTs over large parts of the ocean and episodic periods of almost absent

Land use/land cover mapping using multi-scale texture processing of high resolution data

Science.gov (United States)

Wong, S. N.; Sarker, M. L. R.

2014-02-01

Land use/land cover (LULC) maps are useful for many purposes, and for a long time remote sensing techniques have been used for LULC mapping using different types of data and image processing techniques. In this research, high resolution satellite data from IKONOS was used to perform land use/land cover mapping in Johor Bahru city and adjacent areas (Malaysia). Spatial image processing was carried out using the six texture algorithms (mean, variance, contrast, homogeneity, entropy, and GLDV angular second moment) with five difference window sizes (from 3×3 to 11×11). Three different classifiers i.e. Maximum Likelihood Classifier (MLC), Artificial Neural Network (ANN) and Supported Vector Machine (SVM) were used to classify the texture parameters of different spectral bands individually and all bands together using the same training and validation samples. Results indicated that texture parameters of all bands together generally showed a better performance (overall accuracy = 90.10%) for land LULC mapping, however, single spectral band could only achieve an overall accuracy of 72.67%. This research also found an improvement of the overall accuracy (OA) using single-texture multi-scales approach (OA = 89.10%) and single-scale multi-textures approach (OA = 90.10%) compared with all original bands (OA = 84.02%) because of the complementary information from different bands and different texture algorithms. On the other hand, all of the three different classifiers have showed high accuracy when using different texture approaches, but SVM generally showed higher accuracy (90.10%) compared to MLC (89.10%) and ANN (89.67%) especially for the complex classes such as urban and road.

Land use/land cover mapping using multi-scale texture processing of high resolution data

International Nuclear Information System (INIS)

Wong, S N; Sarker, M L R

2014-01-01

Land use/land cover (LULC) maps are useful for many purposes, and for a long time remote sensing techniques have been used for LULC mapping using different types of data and image processing techniques. In this research, high resolution satellite data from IKONOS was used to perform land use/land cover mapping in Johor Bahru city and adjacent areas (Malaysia). Spatial image processing was carried out using the six texture algorithms (mean, variance, contrast, homogeneity, entropy, and GLDV angular second moment) with five difference window sizes (from 3×3 to 11×11). Three different classifiers i.e. Maximum Likelihood Classifier (MLC), Artificial Neural Network (ANN) and Supported Vector Machine (SVM) were used to classify the texture parameters of different spectral bands individually and all bands together using the same training and validation samples. Results indicated that texture parameters of all bands together generally showed a better performance (overall accuracy = 90.10%) for land LULC mapping, however, single spectral band could only achieve an overall accuracy of 72.67%. This research also found an improvement of the overall accuracy (OA) using single-texture multi-scales approach (OA = 89.10%) and single-scale multi-textures approach (OA = 90.10%) compared with all original bands (OA = 84.02%) because of the complementary information from different bands and different texture algorithms. On the other hand, all of the three different classifiers have showed high accuracy when using different texture approaches, but SVM generally showed higher accuracy (90.10%) compared to MLC (89.10%) and ANN (89.67%) especially for the complex classes such as urban and road

Approximate ideal multi-objective solution Q(λ) learning for optimal carbon-energy combined-flow in multi-energy power systems

International Nuclear Information System (INIS)

Zhang, Xiaoshun; Yu, Tao; Yang, Bo; Zheng, Limin; Huang, Linni

2015-01-01

Highlights: • A novel optimal carbon-energy combined-flow (OCECF) model is firstly established. • A novel approximate ideal multi-objective solution Q(λ) learning is designed. • The proposed algorithm has a high convergence stability and reliability. • The proposed algorithm can be applied for OCECF in a large-scale power grid. - Abstract: This paper proposes a novel approximate ideal multi-objective solution Q(λ) learning for optimal carbon-energy combined-flow in multi-energy power systems. The carbon emissions, fuel cost, active power loss, voltage deviation and carbon emission loss are chosen as the optimization objectives, which are simultaneously optimized by five different Q-value matrices. The dynamic optimal weight of each objective is calculated online from the entire Q-value matrices such that the greedy action policy can be obtained. Case studies are carried out to evaluate the optimization performance for carbon-energy combined-flow in an IEEE 118-bus system and the regional power grid of southern China.

Multiple utility constrained multi-objective programs using Bayesian theory

Science.gov (United States)

Abbasian, Pooneh; Mahdavi-Amiri, Nezam; Fazlollahtabar, Hamed

2018-03-01

A utility function is an important tool for representing a DM's preference. We adjoin utility functions to multi-objective optimization problems. In current studies, usually one utility function is used for each objective function. Situations may arise for a goal to have multiple utility functions. Here, we consider a constrained multi-objective problem with each objective having multiple utility functions. We induce the probability of the utilities for each objective function using Bayesian theory. Illustrative examples considering dependence and independence of variables are worked through to demonstrate the usefulness of the proposed model.

Multi-dimensional water quality assessment of an urban drinking water source elucidated by high resolution underwater towed vehicle mapping.

Science.gov (United States)

Lock, Alan; Spiers, Graeme; Hostetler, Blair; Ray, James; Wallschläger, Dirk

2016-04-15

Spatial surveys of Ramsey Lake, Sudbury, Ontario water quality were conducted using an innovative underwater towed vehicle (UTV) equipped with a multi-parameter probe providing real-time water quality data. The UTV revealed underwater vent sites through high resolution monitoring of different spatial chemical characteristics using common sensors (turbidity, chloride, dissolved oxygen, and oxidation/reduction sensors) that would not be feasible with traditional water sampling methods. Multi-parameter probe vent site identification is supported by elevated alkalinity and silica concentrations at these sites. The identified groundwater vent sites appear to be controlled by bedrock fractures that transport water from different sources with different contaminants of concern. Elevated contaminants, such as, arsenic and nickel and/or nutrient concentrations are evident at the vent sites, illustrating the potential of these sources to degrade water quality. Copyright © 2016 Elsevier Ltd. All rights reserved.

A study of disrupted carotid plaque using high-resolution MRI

International Nuclear Information System (INIS)

Yu Wei; Zhang Zhaoqi; Underhill, H.; Hatsukami, T.S.; Chun, Y.

2008-01-01

Objective: To evaluate distribution features of disrupted carotid plaque. Methods: Forty-three subjects with duplex ultrasound evidence of 50% to 99% stenosis were retrospectively analyzed. Plaques were categorized as disrupted if there was MRI evidence of fibrous cap rupture. Quantity measured areas of the lumen (LA), wall (WA), and plaque components. The morphological parameters used were total vessel area, vessel burden index, eccentricity index. Mann-Whitney test and Chi-square test appropriate used SPSS (v. 12.0). Results: There were 17 disrupted and 26 undisrupted lesions identified for comparison. Disrupted plaques showed a predominance of longer longitudinal length of large lip nucleus along the vessel wall (6 mm vs. 0 mm, U=126, P 2 vs. 30.18 mm 2 U=138 P<0.05) and a longer segment of stenosis when compared with the intact plaques. Conclusions: Disrupted plaques have significantly different characteristics in terms of both axial and longitudinal distribution. A combination of multi-plane and multi-contrast high resolution MRI may provide valuable information about overall lesion morphology and its association to vulnerability. (authors)

Novel ultrahigh resolution data acquisition and image reconstruction for multi-detector row CT

International Nuclear Information System (INIS)

Flohr, T. G.; Stierstorfer, K.; Suess, C.; Schmidt, B.; Primak, A. N.; McCollough, C. H.

2007-01-01

We present and evaluate a special ultrahigh resolution mode providing considerably enhanced spatial resolution both in the scan plane and in the z-axis direction for a routine medical multi-detector row computed tomography (CT) system. Data acquisition is performed by using a flying focal spot both in the scan plane and in the z-axis direction in combination with tantalum grids that are inserted in front of the multi-row detector to reduce the aperture of the detector elements both in-plane and in the z-axis direction. The dose utilization of the system for standard applications is not affected, since the grids are moved into place only when needed and are removed for standard scanning. By means of this technique, image slices with a nominal section width of 0.4 mm (measured full width at half maximum=0.45 mm) can be reconstructed in spiral mode on a CT system with a detector configuration of 32x0.6 mm. The measured 2% value of the in-plane modulation transfer function (MTF) is 20.4 lp/cm, the measured 2% value of the longitudinal (z axis) MTF is 21.5 lp/cm. In a resolution phantom with metal line pair test patterns, spatial resolution of 20 lp/cm can be demonstrated both in the scan plane and along the z axis. This corresponds to an object size of 0.25 mm that can be resolved. The new mode is intended for ultrahigh resolution bone imaging, in particular for wrists, joints, and inner ear studies, where a higher level of image noise due to the reduced aperture is an acceptable trade-off for the clinical benefit brought about by the improved spatial resolution

Multi-objective superstructure-free synthesis and optimization of thermal power plants

International Nuclear Information System (INIS)

Wang, Ligang; Lampe, Matthias; Voll, Philip; Yang, Yongping; Bardow, André

2016-01-01

The merits of superstructure-free synthesis are demonstrated for bi-objective design of thermal power plants. The design of thermal power plants is complex and thus best solved by optimization. Common optimization methods require specification of a superstructure which becomes a tedious and error-prone task for complex systems. Superstructure specification is avoided by the presented superstructure-free approach, which is shown to successfully solve the design task yielding a high-quality Pareto front of promising structural alternatives. The economic objective function avoids introducing infinite numbers of units (e.g., turbine, reheater and feedwater preheater) as favored by pure thermodynamic optimization. The number of feasible solutions found per number of mutation tries is still high even after many generations but declines after introducing highly-nonlinear cost functions leading to challenging MINLP problems. The identified Pareto-optimal solutions tend to employ more units than found in modern power plants indicating the need for cost functions to reflect current industrial practice. In summary, the multi-objective superstructure-free synthesis framework is a robust approach for very complex problems in the synthesis of thermal power plants. - Highlights: • A generalized multi-objective superstructure-free synthesis framework for thermal power plants is presented. • The superstructure-free synthesis framework is comprehensively evaluated by complex bi-objective synthesis problems. • The proposed framework is effective to explore the structural design space even for complex problems.

A multi-objective framework to predict flows of ungauged rivers within regions of sparse hydrometeorologic observation

Science.gov (United States)

Alipour, M.; Kibler, K. M.

2017-12-01

Despite advances in flow prediction, managers of ungauged rivers located within broad regions of sparse hydrometeorologic observation still lack prescriptive methods robust to the data challenges of such regions. We propose a multi-objective streamflow prediction framework for regions of minimum observation to select models that balance runoff efficiency with choice of accurate parameter values. We supplement sparse observed data with uncertain or low-resolution information incorporated as `soft' a priori parameter estimates. The performance of the proposed framework is tested against traditional single-objective and constrained single-objective calibrations in two catchments in a remote area of southwestern China. We find that the multi-objective approach performs well with respect to runoff efficiency in both catchments (NSE = 0.74 and 0.72), within the range of efficiencies returned by other models (NSE = 0.67 - 0.78). However, soil moisture capacity estimated by the multi-objective model resonates with a priori estimates (parameter residuals of 61 cm versus 289 and 518 cm for maximum soil moisture capacity in one catchment, and 20 cm versus 246 and 475 cm in the other; parameter residuals of 0.48 versus 0.65 and 0.7 for soil moisture distribution shape factor in one catchment, and 0.91 versus 0.79 and 1.24 in the other). Thus, optimization to a multi-criteria objective function led to very different representations of soil moisture capacity as compared to models selected by single-objective calibration, without compromising runoff efficiency. These different soil moisture representations may translate into considerably different hydrological behaviors. The proposed approach thus offers a preliminary step towards greater process understanding in regions of severe data limitations. For instance, the multi-objective framework may be an adept tool to discern between models of similar efficiency to select models that provide the "right answers for the right reasons

EFFECTIVE MULTI-RESOLUTION TRANSFORM IDENTIFICATION FOR CHARACTERIZATION AND CLASSIFICATION OF TEXTURE GROUPS

Directory of Open Access Journals (Sweden)

S. Arivazhagan

2011-11-01

Full Text Available Texture classification is important in applications of computer image analysis for characterization or classification of images based on local spatial variations of intensity or color. Texture can be defined as consisting of mutually related elements. This paper proposes an experimental approach for identification of suitable multi-resolution transform for characterization and classification of different texture groups based on statistical and co-occurrence features derived from multi-resolution transformed sub bands. The statistical and co-occurrence feature sets are extracted for various multi-resolution transforms such as Discrete Wavelet Transform (DWT, Stationary Wavelet Transform (SWT, Double Density Wavelet Transform (DDWT and Dual Tree Complex Wavelet Transform (DTCWT and then, the transform that maximizes the texture classification performance for the particular texture group is identified.

Ultra high resolution soft x-ray tomography

International Nuclear Information System (INIS)

Haddad, W.S.; Trebes, J.E.; Goodman, D.M.

1995-01-01

Ultra high resolution three dimensional images of a microscopic test object were made with soft x-rays using a scanning transmission x-ray microscope. The test object consisted of two different patterns of gold bars on silicon nitride windows that were separated by ∼5μm. A series of nine 2-D images of the object were recorded at angles between -50 to +55 degrees with respect to the beam axis. The projections were then combined tomographically to form a 3-D image by means of an algebraic reconstruction technique (ART) algorithm. A transverse resolution of ∼1000 Angstrom was observed. Artifacts in the reconstruction limited the overall depth resolution to ∼6000 Angstrom, however some features were clearly reconstructed with a depth resolution of ∼1000 Angstrom. A specially modified ART algorithm and a constrained conjugate gradient (CCG) code were also developed as improvements over the standard ART algorithm. Both of these methods made significant improvements in the overall depth resolution bringing it down to ∼1200 Angstrom overall. Preliminary projection data sets were also recorded with both dry and re-hydrated human sperm cells over a similar angular range

Ultra high resolution soft x-ray tomography

International Nuclear Information System (INIS)

Haddad, W.S.; Trebes, J.E.; Goodman, D.M.; Lee, H.R.; McNulty, I.; Zalensky, A.O.

1995-01-01

Ultra high resolution three dimensional images of a microscopic test object were made with soft x-rays using a scanning transmission x-ray microscope. The test object consisted of two different patterns of gold bars on silicon nitride windows that were separated by ∼5 microm. A series of nine 2-D images of the object were recorded at angles between -50 to +55 degrees with respect to the beam axis. The projections were then combined tomographically to form a 3-D image by means of an algebraic reconstruction technique (ART) algorithm. A transverse resolution of ∼ 1,000 angstrom was observed. Artifacts in the reconstruction limited the overall depth resolution to ∼ 6,000 angstrom, however some features were clearly reconstructed with a depth resolution of ∼ 1,000 angstrom. A specially modified ART algorithm and a constrained conjugate gradient (CCG) code were also developed as improvements over the standard ART algorithm. Both of these methods made significant improvements in the overall depth resolution, bringing it down to ∼ 1,200 angstrom overall. Preliminary projection data sets were also recorded with both dry and re-hydrated human sperm cells over a similar angular range

Subcortical White Matter Changes with Normal Aging Detected by Multi-Shot High Resolution Diffusion Tensor Imaging.

Directory of Open Access Journals (Sweden)

Sheng Xie

Full Text Available Subcortical white matter builds neural connections between cortical and subcortical regions and constitutes the basis of neural networks. It plays a very important role in normal brain function. Various studies have shown that white matter deteriorates with aging. However, due to the limited spatial resolution provided by traditional diffusion imaging techniques, microstructural information from subcortical white matter with normal aging has not been comprehensively assessed. This study aims to investigate the deterioration effect with aging in the subcortical white matter and provide a baseline standard for pathological disorder diagnosis. We apply our newly developed multi-shot high resolution diffusion tensor imaging, using self-feeding multiplexed sensitivity-encoding, to measure subcortical white matter changes in regions of interest of healthy persons with a wide age range. Results show significant fractional anisotropy decline and radial diffusivity increasing with age, especially in the anterior part of the brain. We also find that subcortical white matter has more prominent changes than white matter close to the central brain. The observed changes in the subcortical white matter may be indicative of a mild demyelination and a loss of myelinated axons, which may contribute to normal age-related functional decline.

Multi-Objective Optimization of Start-up Strategy for Pumped Storage Units

Directory of Open Access Journals (Sweden)

Jinjiao Hou

2018-05-01

Full Text Available This paper proposes a multi-objective optimization method for the start-up strategy of pumped storage units (PSU for the first time. In the multi-objective optimization method, the speed rise time and the overshoot during the process of the start-up are taken as the objectives. A precise simulation platform is built for simulating the transient process of start-up, and for calculating the objectives based on the process. The Multi-objective Particle Swarm Optimization algorithm (MOPSO is adopted to optimize the widely applied start-up strategies based on one-stage direct guide vane control (DGVC, and two-stage DGVC. Based on the Pareto Front obtained, a multi-objective decision-making method based on the relative objective proximity is used to sort the solutions in the Pareto Front. Start-up strategy optimization for a PSU of a pumped storage power station in Jiangxi Province in China is conducted in experiments. The results show that: (1 compared with the single objective optimization, the proposed multi-objective optimization of start-up strategy not only greatly shortens the speed rise time and the speed overshoot, but also makes the speed curve quickly stabilize; (2 multi-objective optimization of strategy based on two-stage DGVC achieves better solution for a quick and smooth start-up of PSU than that of the strategy based on one-stage DGVC.

Advantages of Task-Specific Multi-Objective Optimisation in Evolutionary Robotics.

Science.gov (United States)

Trianni, Vito; López-Ibáñez, Manuel

2015-01-01

The application of multi-objective optimisation to evolutionary robotics is receiving increasing attention. A survey of the literature reveals the different possibilities it offers to improve the automatic design of efficient and adaptive robotic systems, and points to the successful demonstrations available for both task-specific and task-agnostic approaches (i.e., with or without reference to the specific design problem to be tackled). However, the advantages of multi-objective approaches over single-objective ones have not been clearly spelled out and experimentally demonstrated. This paper fills this gap for task-specific approaches: starting from well-known results in multi-objective optimisation, we discuss how to tackle commonly recognised problems in evolutionary robotics. In particular, we show that multi-objective optimisation (i) allows evolving a more varied set of behaviours by exploring multiple trade-offs of the objectives to optimise, (ii) supports the evolution of the desired behaviour through the introduction of objectives as proxies, (iii) avoids the premature convergence to local optima possibly introduced by multi-component fitness functions, and (iv) solves the bootstrap problem exploiting ancillary objectives to guide evolution in the early phases. We present an experimental demonstration of these benefits in three different case studies: maze navigation in a single robot domain, flocking in a swarm robotics context, and a strictly collaborative task in collective robotics.

Advantages of Task-Specific Multi-Objective Optimisation in Evolutionary Robotics.

Directory of Open Access Journals (Sweden)

Vito Trianni

Full Text Available The application of multi-objective optimisation to evolutionary robotics is receiving increasing attention. A survey of the literature reveals the different possibilities it offers to improve the automatic design of efficient and adaptive robotic systems, and points to the successful demonstrations available for both task-specific and task-agnostic approaches (i.e., with or without reference to the specific design problem to be tackled. However, the advantages of multi-objective approaches over single-objective ones have not been clearly spelled out and experimentally demonstrated. This paper fills this gap for task-specific approaches: starting from well-known results in multi-objective optimisation, we discuss how to tackle commonly recognised problems in evolutionary robotics. In particular, we show that multi-objective optimisation (i allows evolving a more varied set of behaviours by exploring multiple trade-offs of the objectives to optimise, (ii supports the evolution of the desired behaviour through the introduction of objectives as proxies, (iii avoids the premature convergence to local optima possibly introduced by multi-component fitness functions, and (iv solves the bootstrap problem exploiting ancillary objectives to guide evolution in the early phases. We present an experimental demonstration of these benefits in three different case studies: maze navigation in a single robot domain, flocking in a swarm robotics context, and a strictly collaborative task in collective robotics.

Improving lateral resolution and image quality of optical coherence tomography by the multi-frame superresolution technique for 3D tissue imaging.

Science.gov (United States)

Shen, Kai; Lu, Hui; Baig, Sarfaraz; Wang, Michael R

2017-11-01

The multi-frame superresolution technique is introduced to significantly improve the lateral resolution and image quality of spectral domain optical coherence tomography (SD-OCT). Using several sets of low resolution C-scan 3D images with lateral sub-spot-spacing shifts on different sets, the multi-frame superresolution processing of these sets at each depth layer reconstructs a higher resolution and quality lateral image. Layer by layer processing yields an overall high lateral resolution and quality 3D image. In theory, the superresolution processing including deconvolution can solve the diffraction limit, lateral scan density and background noise problems together. In experiment, the improved lateral resolution by ~3 times reaching 7.81 µm and 2.19 µm using sample arm optics of 0.015 and 0.05 numerical aperture respectively as well as doubling the image quality has been confirmed by imaging a known resolution test target. Improved lateral resolution on in vitro skin C-scan images has been demonstrated. For in vivo 3D SD-OCT imaging of human skin, fingerprint and retina layer, we used the multi-modal volume registration method to effectively estimate the lateral image shifts among different C-scans due to random minor unintended live body motion. Further processing of these images generated high lateral resolution 3D images as well as high quality B-scan images of these in vivo tissues.

High resolution muon computed tomography at neutrino beam facilities

International Nuclear Information System (INIS)

Suerfu, B.; Tully, C.G.

2016-01-01

X-ray computed tomography (CT) has an indispensable role in constructing 3D images of objects made from light materials. However, limited by absorption coefficients, X-rays cannot deeply penetrate materials such as copper and lead. Here we show via simulation that muon beams can provide high resolution tomographic images of dense objects and of structures within the interior of dense objects. The effects of resolution broadening from multiple scattering diminish with increasing muon momentum. As the momentum of the muon increases, the contrast of the image goes down and therefore requires higher resolution in the muon spectrometer to resolve the image. The variance of the measured muon momentum reaches a minimum and then increases with increasing muon momentum. The impact of the increase in variance is to require a higher integrated muon flux to reduce fluctuations. The flux requirements and level of contrast needed for high resolution muon computed tomography are well matched to the muons produced in the pion decay pipe at a neutrino beam facility and what can be achieved for momentum resolution in a muon spectrometer. Such an imaging system can be applied in archaeology, art history, engineering, material identification and whenever there is a need to image inside a transportable object constructed of dense materials

A hybrid evolutionary algorithm for multi-objective anatomy-based dose optimization in high-dose-rate brachytherapy

International Nuclear Information System (INIS)

Lahanas, M; Baltas, D; Zamboglou, N

2003-01-01

Multiple objectives must be considered in anatomy-based dose optimization for high-dose-rate brachytherapy and a large number of parameters must be optimized to satisfy often competing objectives. For objectives expressed solely in terms of dose variances, deterministic gradient-based algorithms can be applied and a weighted sum approach is able to produce a representative set of non-dominated solutions. As the number of objectives increases, or non-convex objectives are used, local minima can be present and deterministic or stochastic algorithms such as simulated annealing either cannot be used or are not efficient. In this case we employ a modified hybrid version of the multi-objective optimization algorithm NSGA-II. This, in combination with the deterministic optimization algorithm, produces a representative sample of the Pareto set. This algorithm can be used with any kind of objectives, including non-convex, and does not require artificial importance factors. A representation of the trade-off surface can be obtained with more than 1000 non-dominated solutions in 2-5 min. An analysis of the solutions provides information on the possibilities available using these objectives. Simple decision making tools allow the selection of a solution that provides a best fit for the clinical goals. We show an example with a prostate implant and compare results obtained by variance and dose-volume histogram (DVH) based objectives

High Resolution Radar Imaging using Coherent MultiBand Processing Techniques

NARCIS (Netherlands)

Dorp, Ph. van; Ebeling, R.P.; Huizing, A.G.

2010-01-01

High resolution radar imaging techniques can be used in ballistic missile defence systems to determine the type of ballistic missile during the boost phase (threat typing) and to discriminate different parts of a ballistic missile after the boost phase. The applied radar imaging technique is 2D

Evaluation of Medium Spatial Resolution BRDF-Adjustment Techniques Using Multi-Angular SPOT4 (Take5) Acquisitions

OpenAIRE

Claverie, Martin; Vermote, Eric; Franch, Belen; He, Tao; Hagolle, Olivier; Kadiri, Mohamed; Masek, Jeff

2015-01-01

High-resolution sensor Surface Reflectance (SR) data are affected by surface anisotropy but are difficult to adjust because of the low temporal frequency of the acquisitions and the low angular sampling. This paper evaluates five high spatial resolution Bidirectional Reflectance Distribution Function (BRDF) adjustment techniques. The evaluation is based on the noise level of the SR Time Series (TS) corrected to a normalized geometry (nadir view, 45° sun zenith angle) extracted from the multi-...

Pareto-optimal multi-objective dimensionality reduction deep auto-encoder for mammography classification.

Science.gov (United States)

Taghanaki, Saeid Asgari; Kawahara, Jeremy; Miles, Brandon; Hamarneh, Ghassan

2017-07-01

Feature reduction is an essential stage in computer aided breast cancer diagnosis systems. Multilayer neural networks can be trained to extract relevant features by encoding high-dimensional data into low-dimensional codes. Optimizing traditional auto-encoders works well only if the initial weights are close to a proper solution. They are also trained to only reduce the mean squared reconstruction error (MRE) between the encoder inputs and the decoder outputs, but do not address the classification error. The goal of the current work is to test the hypothesis that extending traditional auto-encoders (which only minimize reconstruction error) to multi-objective optimization for finding Pareto-optimal solutions provides more discriminative features that will improve classification performance when compared to single-objective and other multi-objective approaches (i.e. scalarized and sequential). In this paper, we introduce a novel multi-objective optimization of deep auto-encoder networks, in which the auto-encoder optimizes two objectives: MRE and mean classification error (MCE) for Pareto-optimal solutions, rather than just MRE. These two objectives are optimized simultaneously by a non-dominated sorting genetic algorithm. We tested our method on 949 X-ray mammograms categorized into 12 classes. The results show that the features identified by the proposed algorithm allow a classification accuracy of up to 98.45%, demonstrating favourable accuracy over the results of state-of-the-art methods reported in the literature. We conclude that adding the classification objective to the traditional auto-encoder objective and optimizing for finding Pareto-optimal solutions, using evolutionary multi-objective optimization, results in producing more discriminative features. Copyright © 2017 Elsevier B.V. All rights reserved.

Multi-Label Object Categorization Using Histograms of Global Relations

DEFF Research Database (Denmark)

Mustafa, Wail; Xiong, Hanchen; Kraft, Dirk

2015-01-01

In this paper, we present an object categorization system capable of assigning multiple and related categories for novel objects using multi-label learning. In this system, objects are described using global geometric relations of 3D features. We propose using the Joint SVM method for learning......). The experiments are carried out on a dataset of 100 objects belonging to 13 visual and action-related categories. The results indicate that multi-label methods are able to identify the relation between the dependent categories and hence perform categorization accordingly. It is also found that extracting...

Multi-objective optimization in computer networks using metaheuristics

CERN Document Server

Donoso, Yezid

2007-01-01

Metaheuristics are widely used to solve important practical combinatorial optimization problems. Many new multicast applications emerging from the Internet-such as TV over the Internet, radio over the Internet, and multipoint video streaming-require reduced bandwidth consumption, end-to-end delay, and packet loss ratio. It is necessary to design and to provide for these kinds of applications as well as for those resources necessary for functionality. Multi-Objective Optimization in Computer Networks Using Metaheuristics provides a solution to the multi-objective problem in routing computer networks. It analyzes layer 3 (IP), layer 2 (MPLS), and layer 1 (GMPLS and wireless functions). In particular, it assesses basic optimization concepts, as well as several techniques and algorithms for the search of minimals; examines the basic multi-objective optimization concepts and the way to solve them through traditional techniques and through several metaheuristics; and demonstrates how to analytically model the compu...

High resolution mid-infrared spectroscopy based on frequency upconversion

DEFF Research Database (Denmark)

Dam, Jeppe Seidelin; Hu, Qi; Tidemand-Lichtenberg, Peter

2013-01-01

signals can be analyzed. The obtainable frequency resolution is usually in the nm range where sub nm resolution is preferred in many applications, like gas spectroscopy. In this work we demonstrate how to obtain sub nm resolution when using upconversion. In the presented realization one object point...... high resolution spectral performance by observing emission from hot water vapor in a butane gas burner....

Multi-objective convex programming problem arising in multivariate ...

African Journals Online (AJOL)

user

Multi-objective convex programming problem arising in ... However, although the consideration of multiple objectives may seem a novel concept, virtually any nontrivial ..... Solving multiobjective programming problems by discrete optimization.

Combined multi-plane phase retrieval and super-resolution optical fluctuation imaging for 4D cell microscopy

Science.gov (United States)

Descloux, A.; Grußmayer, K. S.; Bostan, E.; Lukes, T.; Bouwens, A.; Sharipov, A.; Geissbuehler, S.; Mahul-Mellier, A.-L.; Lashuel, H. A.; Leutenegger, M.; Lasser, T.

2018-03-01

Super-resolution fluorescence microscopy provides unprecedented insight into cellular and subcellular structures. However, going `beyond the diffraction barrier' comes at a price, since most far-field super-resolution imaging techniques trade temporal for spatial super-resolution. We propose the combination of a novel label-free white light quantitative phase imaging with fluorescence to provide high-speed imaging and spatial super-resolution. The non-iterative phase retrieval relies on the acquisition of single images at each z-location and thus enables straightforward 3D phase imaging using a classical microscope. We realized multi-plane imaging using a customized prism for the simultaneous acquisition of eight planes. This allowed us to not only image live cells in 3D at up to 200 Hz, but also to integrate fluorescence super-resolution optical fluctuation imaging within the same optical instrument. The 4D microscope platform unifies the sensitivity and high temporal resolution of phase imaging with the specificity and high spatial resolution of fluorescence microscopy.

Searching for the Pareto frontier in multi-objective protein design.

Science.gov (United States)

Nanda, Vikas; Belure, Sandeep V; Shir, Ofer M

2017-08-01

The goal of protein engineering and design is to identify sequences that adopt three-dimensional structures of desired function. Often, this is treated as a single-objective optimization problem, identifying the sequence-structure solution with the lowest computed free energy of folding. However, many design problems are multi-state, multi-specificity, or otherwise require concurrent optimization of multiple objectives. There may be tradeoffs among objectives, where improving one feature requires compromising another. The challenge lies in determining solutions that are part of the Pareto optimal set-designs where no further improvement can be achieved in any of the objectives without degrading one of the others. Pareto optimality problems are found in all areas of study, from economics to engineering to biology, and computational methods have been developed specifically to identify the Pareto frontier. We review progress in multi-objective protein design, the development of Pareto optimization methods, and present a specific case study using multi-objective optimization methods to model the tradeoff between three parameters, stability, specificity, and complexity, of a set of interacting synthetic collagen peptides.

Multi-objective optimization of GENIE Earth system models.

Science.gov (United States)

Price, Andrew R; Myerscough, Richard J; Voutchkov, Ivan I; Marsh, Robert; Cox, Simon J

2009-07-13

The tuning of parameters in climate models is essential to provide reliable long-term forecasts of Earth system behaviour. We apply a multi-objective optimization algorithm to the problem of parameter estimation in climate models. This optimization process involves the iterative evaluation of response surface models (RSMs), followed by the execution of multiple Earth system simulations. These computations require an infrastructure that provides high-performance computing for building and searching the RSMs and high-throughput computing for the concurrent evaluation of a large number of models. Grid computing technology is therefore essential to make this algorithm practical for members of the GENIE project.

A 30 ps Timing Resolution for Single Photons with Multi-pixel Burle MCP-PMT

Energy Technology Data Exchange (ETDEWEB)

Va' vra, J.; Benitez, J.; Coleman, J.; Leith, D.W.G.S.; Mazaheri, G.; Ratcliff, B.; Schwiening, J.; /SLAC

2006-07-05

We have achieved {approx}30 psec single-photoelectron and {approx}12ps for multi-photoelectron timing resolution with a new 64 pixel Burle MCP-PMT with 10 micron microchannel holes. We have also demonstrated that this detector works in a magnetic field of 15kG, and achieved a single-photoelectron timing resolution of better than 60 psec. The study is relevant for a new focusing DIRC RICH detector for particle identification at future Colliders such as the super B-factory or ILC, and for future TOF techniques. This study shows that a highly pixilated MCP-PMT can deliver excellent timing resolution.

Study on the performance of large area MRPC with high position resolution

Energy Technology Data Exchange (ETDEWEB)

Yue Qian, E-mail: yueq@mail.tsinghua.edu.cn [Department of Engineering Physics, Tsinghua University, Beijing 100084 (China); Key Laboratory of Particle and Radiation Imaging, Tsinghua University, Ministry of Education (China); Wu Yucheng; Li Yuanjing; Ye Jin; Cheng Jianping; Wang Yi; Li Jin [Department of Engineering Physics, Tsinghua University, Beijing 100084 (China); Key Laboratory of Particle and Radiation Imaging, Tsinghua University, Ministry of Education (China)

2012-01-01

Multi-gap resistive plate chamber (MRPC), which is mostly developed in high energy physics domain with excellent time resolution, is also highlighted in imaging applications. A set of 50 cm Multiplication-Sign 50 cm large area MRPC with high position resolution was successfully developed by our group and different experiments have been done to test its performances. Cosmic ray muons were used to do the test and proper high voltage and working gas were chosen. Data analysis indicates its good detection efficiency and good position resolution, which encourages further study of its application in RPC-PET and muon tomography.

Resolution improvement and noise reduction in human pinhole SPECT using a multi-ray approach and the SHINE method

International Nuclear Information System (INIS)

Seret, A.; Vanhove, C.; Defrise, M.

2009-01-01

Purpose: This work aimed at quantifying the gains in spatial resolution and noise that could be achieved when using resolution modelling based on a multi-ray approach and additionally the Statistical and Heuristic Noise Extraction (SHINE) method in human pinhole single photon emission tomography (PH-SPECT). Methods: PH-SPECT of two line phantoms and one homogeneous cylinder were recorded using parameters suited for studies of the human neck area. They were reconstructed using pinhole dedicated ordered subsets expectation maximisation algorithm including a resolution recovery technique based on 7 or 21 rays. Optionally, the SPECT data were SHINE pre-processed. Transverse and axial full widths at half-maximum (FWHM) were obtained from the line phantoms. The noise was quantified using the coefficient of variation (COV) derived from the uniform phantom. Two human PH-SPECT studies of the thyroid (a hot nodule and a very low uptake) were processed with the same algorithms. Results: Depending on the number of iterations, FWHM decreased by 30 to 50% when using the multi-ray approach in the reconstruction process. The SHINE method did not affect the resolution but decreased the COV by at least 20% and by 45% when combined with the multi-ray method. The two human studies illustrated the gain in spatial resolution and the decrease in noise afforded both by the multi-ray reconstruction and the SHINE method. Conclusion: Iterative reconstruction with resolution modelling allows to obtain high resolution human PH-SPECT studies with reduced noise content. The SHINE method affords an additional noise reduction without compromising the resolution. (orig.)

Reference resolution in multi-modal interaction: Preliminary observations

NARCIS (Netherlands)

González González, G.R.; Nijholt, Antinus

2002-01-01

In this paper we present our research on multimodal interaction in and with virtual environments. The aim of this presentation is to emphasize the necessity to spend more research on reference resolution in multimodal contexts. In multi-modal interaction the human conversational partner can apply

Reference Resolution in Multi-modal Interaction: Position paper

NARCIS (Netherlands)

Fernando, T.; Nijholt, Antinus

2002-01-01

In this position paper we present our research on multimodal interaction in and with virtual environments. The aim of this presentation is to emphasize the necessity to spend more research on reference resolution in multimodal contexts. In multi-modal interaction the human conversational partner can

Characterising Dynamic Instability in High Water-Cut Oil-Water Flows Using High-Resolution Microwave Sensor Signals

Science.gov (United States)

Liu, Weixin; Jin, Ningde; Han, Yunfeng; Ma, Jing

2018-06-01

In the present study, multi-scale entropy algorithm was used to characterise the complex flow phenomena of turbulent droplets in high water-cut oil-water two-phase flow. First, we compared multi-scale weighted permutation entropy (MWPE), multi-scale approximate entropy (MAE), multi-scale sample entropy (MSE) and multi-scale complexity measure (MCM) for typical nonlinear systems. The results show that MWPE presents satisfied variability with scale and anti-noise ability. Accordingly, we conducted an experiment of vertical upward oil-water two-phase flow with high water-cut and collected the signals of a high-resolution microwave resonant sensor, based on which two indexes, the entropy rate and mean value of MWPE, were extracted. Besides, the effects of total flow rate and water-cut on these two indexes were analysed. Our researches show that MWPE is an effective method to uncover the dynamic instability of oil-water two-phase flow with high water-cut.

Concept of dual-resolution light field imaging using an organic photoelectric conversion film for high-resolution light field photography.

Science.gov (United States)

Sugimura, Daisuke; Kobayashi, Suguru; Hamamoto, Takayuki

2017-11-01

Light field imaging is an emerging technique that is employed to realize various applications such as multi-viewpoint imaging, focal-point changing, and depth estimation. In this paper, we propose a concept of a dual-resolution light field imaging system to synthesize super-resolved multi-viewpoint images. The key novelty of this study is the use of an organic photoelectric conversion film (OPCF), which is a device that converts spectra information of incoming light within a certain wavelength range into an electrical signal (pixel value), for light field imaging. In our imaging system, we place the OPCF having the green spectral sensitivity onto the micro-lens array of the conventional light field camera. The OPCF allows us to acquire the green spectra information only at the center viewpoint with the full resolution of the image sensor. In contrast, the optical system of the light field camera in our imaging system captures the other spectra information (red and blue) at multiple viewpoints (sub-aperture images) but with low resolution. Thus, our dual-resolution light field imaging system enables us to simultaneously capture information about the target scene at a high spatial resolution as well as the direction information of the incoming light. By exploiting these advantages of our imaging system, our proposed method enables the synthesis of full-resolution multi-viewpoint images. We perform experiments using synthetic images, and the results demonstrate that our method outperforms other previous methods.

3D High Resolution Mesh Deformation Based on Multi Library Wavelet Neural Network Architecture

Science.gov (United States)

Dhibi, Naziha; Elkefi, Akram; Bellil, Wajdi; Amar, Chokri Ben

2016-12-01

This paper deals with the features of a novel technique for large Laplacian boundary deformations using estimated rotations. The proposed method is based on a Multi Library Wavelet Neural Network structure founded on several mother wavelet families (MLWNN). The objective is to align features of mesh and minimize distortion with a fixed feature that minimizes the sum of the distances between all corresponding vertices. New mesh deformation method worked in the domain of Region of Interest (ROI). Our approach computes deformed ROI, updates and optimizes it to align features of mesh based on MLWNN and spherical parameterization configuration. This structure has the advantage of constructing the network by several mother wavelets to solve high dimensions problem using the best wavelet mother that models the signal better. The simulation test achieved the robustness and speed considerations when developing deformation methodologies. The Mean-Square Error and the ratio of deformation are low compared to other works from the state of the art. Our approach minimizes distortions with fixed features to have a well reconstructed object.

Recent applications of gas chromatography with high-resolution mass spectrometry.

Science.gov (United States)

Špánik, Ivan; Machyňáková, Andrea

2018-01-01

Gas chromatography coupled to high-resolution mass spectrometry is a powerful analytical method that combines excellent separation power of gas chromatography with improved identification based on an accurate mass measurement. These features designate gas chromatography with high-resolution mass spectrometry as the first choice for identification and structure elucidation of unknown volatile and semi-volatile organic compounds. Gas chromatography with high-resolution mass spectrometry quantitative analyses was previously focused on the determination of dioxins and related compounds using magnetic sector type analyzers, a standing requirement of many international standards. The introduction of a quadrupole high-resolution time-of-flight mass analyzer broadened interest in this method and novel applications were developed, especially for multi-target screening purposes. This review is focused on the development and the most interesting applications of gas chromatography coupled to high-resolution mass spectrometry towards analysis of environmental matrices, biological fluids, and food safety since 2010. The main attention is paid to various approaches and applications of gas chromatography coupled to high-resolution mass spectrometry for non-target screening to identify contaminants and to characterize the chemical composition of environmental, food, and biological samples. The most interesting quantitative applications, where a significant contribution of gas chromatography with high-resolution mass spectrometry over the currently used methods is expected, will be discussed as well. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

High resolution X-ray spectromicroscopy of laser produced plasmas

Energy Technology Data Exchange (ETDEWEB)

Faenov, A.Ya. [Multi-charged Ions Spectra Data Center of VNIIFTRI (MISDC), Mendeleevo, Moscow region, (Russian Federation)

2000-01-01

In recent years new classes of X-ray spectroscopic instruments possessing both dispersive and focusing properties have been manufactured. Their principal advantage over more traditional instruments is that they combine very high luminosity with high spatial resolution, while preserving the highest possible spectral resolution of their dispersive elements. These instruments opened up the registration of plasmas in new regimes and surroundings. The measurements delivered new information about the properties of even previously studied traditional plasma objects (e.g. ns-laser produced plasmas). Also the detailed investigation of relatively new plasma laboratory sources with very small dimensions and low energy content (e.g. mJ fs-laser pulses) became possible. The purpose of this report is to give a short review of the experimental and theoretical results obtained in the past few years by MISDC (Multi-charged Ions Spectra Data Center) research team in the field of X-ray spectroscopy of a laser-produced plasma. Experimental spectra have been obtained at various laser installations with nanosecond, sub-nanosecond, picosecond and sub-picosecond pulses interacting with solid, gaseous or cluster targets in collaborations with research teams from Russia, USA, Germany, France, Poland, Belgium, Italy, China and Israel. Practically all results have been obtained with the help of spectrographs with spherically bent mica crystals operating in FSSR-1D, 2D schemes. (author)

TH-EF-BRA-11: Feasibility of Super-Resolution Time-Resolved 4DMRI for Multi-Breath Volumetric Motion Simulation in Radiotherapy Planning

Energy Technology Data Exchange (ETDEWEB)

Li, G; Zakian, K; Deasy, J [Memorial Sloan Kettering Cancer Center, New York, NY (United States); Wei, J [City College of New York, New York, NY (United States); Hunt, M [Mem Sloan-Kettering Cancer Ctr, New York, NY (United States)

2016-06-15

Purpose: To develop a novel super-resolution time-resolved 4DMRI technique to evaluate multi-breath, irregular and complex organ motion without respiratory surrogate for radiotherapy planning. Methods: The super-resolution time-resolved (TR) 4DMRI approach combines a series of low-resolution 3D cine MRI images acquired during free breathing (FB) with a high-resolution breath-hold (BH) 3DMRI via deformable image registration (DIR). Five volunteers participated in the study under an IRB-approved protocol. The 3D cine images with voxel size of 5×5×5 mm{sup 3} at two volumes per second (2Hz) were acquired coronally using a T1 fast field echo sequence, half-scan (0.8) acceleration, and SENSE (3) parallel imaging. Phase-encoding was set in the lateral direction to minimize motion artifacts. The BH image with voxel size of 2×2×2 mm{sup 3} was acquired using the same sequence within 10 seconds. A demons-based DIR program was employed to produce super-resolution 2Hz 4DMRI. Registration quality was visually assessed using difference images between TR 4DMRI and 3D cine and quantitatively assessed using average voxel correlation. The fidelity of the 3D cine images was assessed using a gel phantom and a 1D motion platform by comparing mobile and static images. Results: Owing to voxel intensity similarity using the same MRI scanning sequence, accurate DIR between FB and BH images is achieved. The voxel correlations between 3D cine and TR 4DMRI are greater than 0.92 in all cases and the difference images illustrate minimal residual error with little systematic patterns. The 3D cine images of the mobile gel phantom preserve object geometry with minimal scanning artifacts. Conclusion: The super-resolution time-resolved 4DMRI technique has been achieved via DIR, providing a potential solution for multi-breath motion assessment. Accurate DIR mapping has been achieved to map high-resolution BH images to low-resolution FB images, producing 2Hz volumetric high-resolution 4DMRI

High-resolution tree canopy mapping for New York City using LIDAR and object-based image analysis

Science.gov (United States)

MacFaden, Sean W.; O'Neil-Dunne, Jarlath P. M.; Royar, Anna R.; Lu, Jacqueline W. T.; Rundle, Andrew G.

2012-01-01

Urban tree canopy is widely believed to have myriad environmental, social, and human-health benefits, but a lack of precise canopy estimates has hindered quantification of these benefits in many municipalities. This problem was addressed for New York City using object-based image analysis (OBIA) to develop a comprehensive land-cover map, including tree canopy to the scale of individual trees. Mapping was performed using a rule-based expert system that relied primarily on high-resolution LIDAR, specifically its capacity for evaluating the height and texture of aboveground features. Multispectral imagery was also used, but shadowing and varying temporal conditions limited its utility. Contextual analysis was a key part of classification, distinguishing trees according to their physical and spectral properties as well as their relationships to adjacent, nonvegetated features. The automated product was extensively reviewed and edited via manual interpretation, and overall per-pixel accuracy of the final map was 96%. Although manual editing had only a marginal effect on accuracy despite requiring a majority of project effort, it maximized aesthetic quality and ensured the capture of small, isolated trees. Converting high-resolution LIDAR and imagery into usable information is a nontrivial exercise, requiring significant processing time and labor, but an expert system-based combination of OBIA and manual review was an effective method for fine-scale canopy mapping in a complex urban environment.

Design of a high-resolution high-stability positioning mechanism for crystal optics

International Nuclear Information System (INIS)

Shu, D.; Toellner, T. S.; Alp, E. E.

1999-01-01

The authors present a novel miniature multi-axis driving structure that will allow positioning of two crystals with better than 50-nrad angular resolution and nanometer linear driving sensitivity.The precision and stability of this structure allow the user to align or adjust an assembly of crystals to achieve the same performance as does a single channel-cut crystal, so they call it an artificial channel-cut crystal. In this paper, the particular designs and specifications, as well as the test results,for a two-axis driving structure for a high-energy-resolution artificial channel-cut crystal monochromator are presented

High-resolution tomography of positron emitters with clustered pinhole SPECT

Energy Technology Data Exchange (ETDEWEB)

Goorden, Marlies C; Beekman, Freek J [Section of Radiation Detection and Medical Imaging, Applied Sciences, Delft University of Technology, Mekelweg 15, 2629 JB Delft (Netherlands)], E-mail: m.c.goorden@tudelft.nl

2010-03-07

State-of-the-art small-animal single photon emission computed tomography (SPECT) enables sub-half-mm resolution imaging of radio-labelled molecules. Due to severe photon penetration through pinhole edges, current multi-pinhole SPECT is not suitable for high-resolution imaging of photons with high energies, such as the annihilation photons emitted by positron emitting tracers (511 keV). To deal with this edge penetration, we introduce here clustered multi-pinhole SPECT (CMP): each pinhole in a cluster has a narrow opening angle to reduce photon penetration. Using simulations, CMP is compared with (i) a collimator with traditional pinholes that is currently used for sub-half-mm imaging of SPECT isotopes (U-SPECT-II), and (ii), like (i) but with collimator thickness adapted to image high-energy photons (traditional multi-pinhole SPECT, TMP). At 511 keV, U-SPECT-II is able to resolve the 0.9 mm rods of an iteratively reconstructed Jaszczak-like capillary hot rod phantom, and while TMP only leads to small improvements, CMP can resolve rods as small as 0.7 mm. Using a digital tumour phantom, we show that CMP resolves many details not assessable with standard USPECT-II and TMP collimators. Furthermore, CMP makes it possible to visualize uptake of positron emitting tracers in sub-compartments of a digital mouse striatal brain phantom. This may open up unique possibilities for analysing processes such as those underlying the function of neurotransmitter systems. Additional potential of CMP may include (i) the imaging of other high-energy single-photon emitters (e.g. I-131) and (ii) localized imaging of positron emitting tracers simultaneously with single photon emitters, with an even better resolution than coincidence PET.

High-spatial-resolution electron density measurement by Langmuir probe for multi-point observations using tiny spacecraft

Science.gov (United States)

Hoang, H.; Røed, K.; Bekkeng, T. A.; Trondsen, E.; Clausen, L. B. N.; Miloch, W. J.; Moen, J. I.

2017-11-01

A method for evaluating electron density using a single fixed-bias Langmuir probe is presented. The technique allows for high-spatio-temporal resolution electron density measurements, which can be effectively carried out by tiny spacecraft for multi-point observations in the ionosphere. The results are compared with the multi-needle Langmuir probe system, which is a scientific instrument developed at the University of Oslo comprising four fixed-bias cylindrical probes that allow small-scale plasma density structures to be characterized in the ionosphere. The technique proposed in this paper can comply with the requirements of future small-sized spacecraft, where the cost-effectiveness, limited space available on the craft, low power consumption and capacity for data-links need to be addressed. The first experimental results in both the plasma laboratory and space confirm the efficiency of the new approach. Moreover, detailed analyses on two challenging issues when deploying the DC Langmuir probe on a tiny spacecraft, which are the limited conductive area of the spacecraft and probe surface contamination, are presented in the paper. It is demonstrated that the limited conductive area, depending on applications, can either be of no concern for the experiment or can be resolved by mitigation methods. Surface contamination has a small impact on the performance of the developed probe.

Fabrication of MTF measurement system for a mobile phone lens using multi-square objects

Science.gov (United States)

Hong, Sung Mok; Jo, Jae Heung; Lee, Hoi Youn; Yang, Ho Soon; Lee, Yun Woo; Lee, In Won

2007-12-01

The mobile phone market grows rapidly and the performance estimation about camera module is required. Accordingly, we fabricate the MTF measurement system for a mobile phone lens having extremely small diameter and large f-number. The objective lens with the magnification of X20 for MTF measurement for high resolution lens and a detector of CCD that is pixel size of 7.4 um are adapted to the system. Also, the CCD is translated by using a linear motor to reduce measurement errors. The measurement lens is placed at the most suitable imaging point by a precise auto-focusing motor. The measuring equipment which we developed for off-axis MTF measurement of a mobile phone lens used the multi-square objects. The square objects of measuring equipment are arranged a unit in the on-axis and total 12 units (0.3 field: 4 units, 0.5 field: 4 units, 0.7 field: 4 units) in the off-axis. When the measurement is started, the linear motors of signal detection part are transferred from on-axis to off-axis. And a detected signals from the each square objects are used for MTF measurement. System driver and MTF measure are using application program that developed us. This software can be measure the on-axis and the off-axis sequentially. In addition to that it did optimization of motor transfer for measurement time shortening.

Study of a high spatial resolution {sup 10}B-based thermal neutron detector for application in neutron reflectometry: the Multi-Blade prototype

Energy Technology Data Exchange (ETDEWEB)

Piscitelli, F; Buffet, J C; Clergeau, J F; Cuccaro, S; Guérard, B; Khaplanov, A; Manna, Q La; Rigal, J M; Esch, P Van, E-mail: piscitelli@ill.fr [Institut Laue-Langevin (ILL), 6, Jules Horowitz, 38042, Grenoble (France)

2014-03-01

Although for large area detectors it is crucial to find an alternative to detect thermal neutrons because of the {sup 3}He shortage, this is not the case for small area detectors. Neutron scattering science is still growing its instruments' power and the neutron flux a detector must tolerate is increasing. For small area detectors the main effort is to expand the detectors' performances. At Institut Laue-Langevin (ILL) we developed the Multi-Blade detector which wants to increase the spatial resolution of {sup 3}He-based detectors for high flux applications. We developed a high spatial resolution prototype suitable for neutron reflectometry instruments. It exploits solid {sup 10}B-films employed in a proportional gas chamber. Two prototypes have been constructed at ILL and the results obtained on our monochromatic test beam line are presented here.

Design of UHECR telescope with 1 arcmin resolution and 50 deg. field of view

CERN Document Server

Sasaki, M; Asaoka, Y

2002-01-01

A new telescope design based on Baker-Nunn optics is proposed for observation of ultra-high-energy cosmic rays (UHECRs). The optical system has an image resolution better than 0.02 deg. within a wide field of view of 50 deg. angular diameter. When combined with a high-quality imaging device, the proposed design enables the directions of UHECRs and high-energy neutrinos to be determined with an accuracy better than 1 arcmin. The outstanding resolution of this telescope allows charge-separated cosmic-rays to be resolved and the source to be determined accurately. This marked improvement in angular resolution will allow the multi-wavelength and 'multi-particle' observations of astronomical objects through collaboration with established astronomical observations.

Multi-Resolution Multimedia QoE Models for IPTV Applications

Directory of Open Access Journals (Sweden)

Prasad Calyam

2012-01-01

Full Text Available Internet television (IPTV is rapidly gaining popularity and is being widely deployed in content delivery networks on the Internet. In order to proactively deliver optimum user quality of experience (QoE for IPTV, service providers need to identify network bottlenecks in real time. In this paper, we develop psycho-acoustic-visual models that can predict user QoE of multimedia applications in real time based on online network status measurements. Our models are neural network based and cater to multi-resolution IPTV applications that include QCIF, QVGA, SD, and HD resolutions encoded using popular audio and video codec combinations. On the network side, our models account for jitter and loss levels, as well as router queuing disciplines: packet-ordered and time-ordered FIFO. We evaluate the performance of our multi-resolution multimedia QoE models in terms of prediction characteristics, accuracy, speed, and consistency. Our evaluation results demonstrate that the models are pertinent for real-time QoE monitoring and resource adaptation in IPTV content delivery networks.

Deep learning for classification of islanding and grid disturbance based on multi-resolution singular spectrum entropy

Science.gov (United States)

Li, Tie; He, Xiaoyang; Tang, Junci; Zeng, Hui; Zhou, Chunying; Zhang, Nan; Liu, Hui; Lu, Zhuoxin; Kong, Xiangrui; Yan, Zheng

2018-02-01

Forasmuch as the distinguishment of islanding is easy to be interfered by grid disturbance, island detection device may make misjudgment thus causing the consequence of photovoltaic out of service. The detection device must provide with the ability to differ islanding from grid disturbance. In this paper, the concept of deep learning is introduced into classification of islanding and grid disturbance for the first time. A novel deep learning framework is proposed to detect and classify islanding or grid disturbance. The framework is a hybrid of wavelet transformation, multi-resolution singular spectrum entropy, and deep learning architecture. As a signal processing method after wavelet transformation, multi-resolution singular spectrum entropy combines multi-resolution analysis and spectrum analysis with entropy as output, from which we can extract the intrinsic different features between islanding and grid disturbance. With the features extracted, deep learning is utilized to classify islanding and grid disturbance. Simulation results indicate that the method can achieve its goal while being highly accurate, so the photovoltaic system mistakenly withdrawing from power grids can be avoided.

Forest Stand Segmentation Using Airborne LIDAR Data and Very High Resolution Multispectral Imagery

Science.gov (United States)

Dechesne, Clément; Mallet, Clément; Le Bris, Arnaud; Gouet, Valérie; Hervieu, Alexandre

2016-06-01

Forest stands are the basic units for forest inventory and mapping. Stands are large forested areas (e.g., ≥ 2 ha) of homogeneous tree species composition. The accurate delineation of forest stands is usually performed by visual analysis of human operators on very high resolution (VHR) optical images. This work is highly time consuming and should be automated for scalability purposes. In this paper, a method based on the fusion of airborne laser scanning data (or lidar) and very high resolution multispectral imagery for automatic forest stand delineation and forest land-cover database update is proposed. The multispectral images give access to the tree species whereas 3D lidar point clouds provide geometric information on the trees. Therefore, multi-modal features are computed, both at pixel and object levels. The objects are individual trees extracted from lidar data. A supervised classification is performed at the object level on the computed features in order to coarsely discriminate the existing tree species in the area of interest. The analysis at tree level is particularly relevant since it significantly improves the tree species classification. A probability map is generated through the tree species classification and inserted with the pixel-based features map in an energetical framework. The proposed energy is then minimized using a standard graph-cut method (namely QPBO with α-expansion) in order to produce a segmentation map with a controlled level of details. Comparison with an existing forest land cover database shows that our method provides satisfactory results both in terms of stand labelling and delineation (matching ranges between 94% and 99%).

Use of Aerial high resolution visible imagery to produce large river bathymetry: a multi temporal and spatial study over the by-passed Upper Rhine

Science.gov (United States)

Béal, D.; Piégay, H.; Arnaud, F.; Rollet, A.; Schmitt, L.

2011-12-01

Aerial high resolution visible imagery allows producing large river bathymetry assuming that water depth is related to water colour (Beer-Bouguer-Lambert law). In this paper we aim at monitoring Rhine River geometry changes for a diachronic study as well as sediment transport after an artificial injection (25.000 m3 restoration operation). For that a consequent data base of ground measurements of river depth is used, built on 3 different sources: (i) differential GPS acquisitions, (ii) sounder data and (iii) lateral profiles realized by experts. Water depth is estimated using a multi linear regression over neo channels built on a principal component analysis over red, green and blue bands and previously cited depth data. The study site is a 12 km long reach of the by-passed section of the Rhine River that draws French and German border. This section has been heavily impacted by engineering works during the last two centuries: channelization since 1842 for navigation purposes and the construction of a 45 km long lateral canal and 4 consecutive hydroelectric power plants of since 1932. Several bathymetric models are produced based on 3 different spatial resolutions (6, 13 and 20 cm) and 5 acquisitions (January, March, April, August and October) since 2008. Objectives are to find the optimal spatial resolution and to characterize seasonal effects. Best performances according to the 13 cm resolution show a 18 cm accuracy when suspended matters impacted less water transparency. Discussions are oriented to the monitoring of the artificial reload after 2 flood events during winter 2010-2011. Bathymetric models produced are also useful to build 2D hydraulic model's mesh.

Application of evolutionary algorithms for multi-objective optimization in VLSI and embedded systems

CERN Document Server

2015-01-01

This book describes how evolutionary algorithms (EA), including genetic algorithms (GA) and particle swarm optimization (PSO) can be utilized for solving multi-objective optimization problems in the area of embedded and VLSI system design. Many complex engineering optimization problems can be modelled as multi-objective formulations. This book provides an introduction to multi-objective optimization using meta-heuristic algorithms, GA and PSO, and how they can be applied to problems like hardware/software partitioning in embedded systems, circuit partitioning in VLSI, design of operational amplifiers in analog VLSI, design space exploration in high-level synthesis, delay fault testing in VLSI testing, and scheduling in heterogeneous distributed systems. It is shown how, in each case, the various aspects of the EA, namely its representation, and operators like crossover, mutation, etc. can be separately formulated to solve these problems. This book is intended for design engineers and researchers in the field ...

A Multi-Objective Trade-Off Model in Sustainable Construction Projects

Directory of Open Access Journals (Sweden)

Guangdong Wu

2017-10-01

Full Text Available Based on the consideration of the relative importance of sustainability-related objectives and the inherent nature of sustainable construction projects, this study proposes that the contractor can balance the levels of efforts and resources used to improve the overall project sustainability. A multi-objective trade-off model using game theory was established and verified through simulation and numerical example under a moral hazard situation. Results indicate that effort levels of the contractor on sustainability-related objectives are positively related to the outcome coefficient while negatively to the coefficients of effort cost of the relevant objectives. High levels of the relative importance of sustainability-related objectives contribute to high levels of effort of the contractor. With the variation in effort levels and the coefficient of benefit allocation, the project net benefit increases before declining. The function of project benefit has a marked peak value, with an inverted “U” shape. An equilibrium always exists as for the given relative importance and coefficients of the effort costs of sustainability-related objectives. Under this condition, the owner may offer the contractor a less intense incentive and motivate the contractor reasonably arranging input resources. The coefficient of benefit allocation is affected by the contractor characteristic factors and the project characteristic factors. The owner should balance these two types of factors and select the most appropriate incentive mechanism to improve the project benefit. Meanwhile, the contractor can balance the relative importance of the objectives and arrange the appropriate levels of effort and resources to achieve a sustainability-related objective. Very few studies have emphasized the effects of the relative importance of sustainability-related objectives on the benefits of sustainable construction projects. This study therefore builds a multi-objective trade

EIT image regularization by a new Multi-Objective Simulated Annealing algorithm.

Science.gov (United States)

Castro Martins, Thiago; Sales Guerra Tsuzuki, Marcos

2015-01-01

Multi-Objective Optimization can be used to produce regularized Electrical Impedance Tomography (EIT) images where the weight of the regularization term is not known a priori. This paper proposes a novel Multi-Objective Optimization algorithm based on Simulated Annealing tailored for EIT image reconstruction. Images are reconstructed from experimental data and compared with images from other Multi and Single Objective optimization methods. A significant performance enhancement from traditional techniques can be inferred from the results.

Considering Decision Variable Diversity in Multi-Objective Optimization: Application in Hydrologic Model Calibration

Science.gov (United States)

Sahraei, S.; Asadzadeh, M.

2017-12-01

Any modern multi-objective global optimization algorithm should be able to archive a well-distributed set of solutions. While the solution diversity in the objective space has been explored extensively in the literature, little attention has been given to the solution diversity in the decision space. Selection metrics such as the hypervolume contribution and crowding distance calculated in the objective space would guide the search toward solutions that are well-distributed across the objective space. In this study, the diversity of solutions in the decision-space is used as the main selection criteria beside the dominance check in multi-objective optimization. To this end, currently archived solutions are clustered in the decision space and the ones in less crowded clusters are given more chance to be selected for generating new solution. The proposed approach is first tested on benchmark mathematical test problems. Second, it is applied to a hydrologic model calibration problem with more than three objective functions. Results show that the chance of finding more sparse set of high-quality solutions increases, and therefore the analyst would receive a well-diverse set of options with maximum amount of information. Pareto Archived-Dynamically Dimensioned Search, which is an efficient and parsimonious multi-objective optimization algorithm for model calibration, is utilized in this study.

Automated Generation of the Alaska Coastline Using High-Resolution Satellite Imagery

Science.gov (United States)

Roth, G.; Porter, C. C.; Cloutier, M. D.; Clementz, M. E.; Reim, C.; Morin, P. J.

2015-12-01

Previous campaigns to map Alaska's coast at high resolution have relied on airborne, marine, or ground-based surveying and manual digitization. The coarse temporal resolution, inability to scale geographically, and high cost of field data acquisition in these campaigns is inadequate for the scale and speed of recent coastal change in Alaska. Here, we leverage the Polar Geospatial Center (PGC) archive of DigitalGlobe, Inc. satellite imagery to produce a state-wide coastline at 2 meter resolution. We first select multispectral imagery based on time and quality criteria. We then extract the near-infrared (NIR) band from each processed image, and classify each pixel as water or land with a pre-determined NIR threshold value. Processing continues with vectorizing the water-land boundary, removing extraneous data, and attaching metadata. Final coastline raster and vector products maintain the original accuracy of the orthorectified satellite data, which is often within the local tidal range. The repeat frequency of coastline production can range from 1 month to 3 years, depending on factors such as satellite capacity, cloud cover, and floating ice. Shadows from trees or structures complicate the output and merit further data cleaning. The PGC's imagery archive, unique expertise, and computing resources enabled us to map the Alaskan coastline in a few months. The DigitalGlobe archive allows us to update this coastline as new imagery is acquired, and facilitates baseline data for studies of coastal change and improvement of topographic datasets. Our results are not simply a one-time coastline, but rather a system for producing multi-temporal, automated coastlines. Workflows and tools produced with this project can be freely distributed and utilized globally. Researchers and government agencies must now consider how they can incorporate and quality-control this high-frequency, high-resolution data to meet their mapping standards and research objectives.

The Dynamic Multi-objective Multi-vehicle Covering Tour Problem

Science.gov (United States)

2013-06-01

144 [38] Coello, Carlos A. Coello, Gary B Lamont, and David A Van Veldhuizen . Evolutionary Algorithms for Solving Multi-Objective Problems. Springer...Traveling Repairperson Problem (DTRP) Policies Proposed by Bertsimas and Van Ryzin. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.3...queuing theory perspective. Table 3.2: DTRP Policies Proposed by Bertsimas and Van Ryzin. Name Description First Come First Serve (FCFS) vehicles

Using high resolution satellite multi-temporal interferometry for landslide hazard detection in tropical environments: the case of Haiti

Science.gov (United States)

Wasowski, Janusz; Nutricato, Raffaele; Nitti, Davide Oscar; Bovenga, Fabio; Chiaradia, Maria Teresa; Piard, Boby Emmanuel; Mondesir, Philemon

2015-04-01

Synthetic aperture radar (SAR) multi-temporal interferometry (MTI) is one of the most promising satellite-based remote sensing techniques for fostering new opportunities in landslide hazard detection and assessment. MTI is attractive because it can provide very precise quantitative information on slow slope displacements of the ground surface over huge areas with limited vegetation cover. Although MTI is a mature technique, we are only beginning to realize the benefits of the high-resolution imagery that is currently acquired by the new generation radar satellites (e.g., COSMO-SkyMed, TerraSAR-X). In this work we demonstrate the potential of high resolution X-band MTI for wide-area detection of slope instability hazards even in tropical environments that are typically very harsh (eg. coherence loss) for differential interferometry applications. This is done by presenting an example from the island of Haiti, a tropical region characterized by dense and rapidly growing vegetation, as well as by significant climatic variability (two rainy seasons) with intense precipitation events. Despite the unfavorable setting, MTI processing of nearly 100 COSMO-SkyMed (CSK) mages (2011-2013) resulted in the identification of numerous radar targets even in some rural (inhabited) areas thanks to the high resolution (3 m) of CSK radar imagery, the adoption of a patch wise processing SPINUA approach and the presence of many man-made structures dispersed in heavily vegetated terrain. In particular, the density of the targets resulted suitable for the detection of some deep-seated and shallower landslides, as well as localized, very slow slope deformations. The interpretation and widespread exploitation of high resolution MTI data was facilitated by Google EarthTM tools with the associated high resolution optical imagery. Furthermore, our reconnaissance in situ checks confirmed that MTI results provided useful information on landslides and marginally stable slopes that can represent a

Development of high-resolution multi-scale modelling system for simulation of coastal-fluvial urban flooding

Science.gov (United States)

Comer, Joanne; Indiana Olbert, Agnieszka; Nash, Stephen; Hartnett, Michael

2017-02-01

Urban developments in coastal zones are often exposed to natural hazards such as flooding. In this research, a state-of-the-art, multi-scale nested flood (MSN_Flood) model is applied to simulate complex coastal-fluvial urban flooding due to combined effects of tides, surges and river discharges. Cork city on Ireland's southwest coast is a study case. The flood modelling system comprises a cascade of four dynamically linked models that resolve the hydrodynamics of Cork Harbour and/or its sub-region at four scales: 90, 30, 6 and 2 m. Results demonstrate that the internalization of the nested boundary through the use of ghost cells combined with a tailored adaptive interpolation technique creates a highly dynamic moving boundary that permits flooding and drying of the nested boundary. This novel feature of MSN_Flood provides a high degree of choice regarding the location of the boundaries to the nested domain and therefore flexibility in model application. The nested MSN_Flood model through dynamic downscaling facilitates significant improvements in accuracy of model output without incurring the computational expense of high spatial resolution over the entire model domain. The urban flood model provides full characteristics of water levels and flow regimes necessary for flood hazard identification and flood risk assessment.

Spatio-Temporal Super-Resolution Reconstruction of Remote-Sensing Images Based on Adaptive Multi-Scale Detail Enhancement.

Science.gov (United States)

Zhu, Hong; Tang, Xinming; Xie, Junfeng; Song, Weidong; Mo, Fan; Gao, Xiaoming

2018-02-07

There are many problems in existing reconstruction-based super-resolution algorithms, such as the lack of texture-feature representation and of high-frequency details. Multi-scale detail enhancement can produce more texture information and high-frequency information. Therefore, super-resolution reconstruction of remote-sensing images based on adaptive multi-scale detail enhancement (AMDE-SR) is proposed in this paper. First, the information entropy of each remote-sensing image is calculated, and the image with the maximum entropy value is regarded as the reference image. Subsequently, spatio-temporal remote-sensing images are processed using phase normalization, which is to reduce the time phase difference of image data and enhance the complementarity of information. The multi-scale image information is then decomposed using the L ₀ gradient minimization model, and the non-redundant information is processed by difference calculation and expanding non-redundant layers and the redundant layer by the iterative back-projection (IBP) technique. The different-scale non-redundant information is adaptive-weighted and fused using cross-entropy. Finally, a nonlinear texture-detail-enhancement function is built to improve the scope of small details, and the peak signal-to-noise ratio (PSNR) is used as an iterative constraint. Ultimately, high-resolution remote-sensing images with abundant texture information are obtained by iterative optimization. Real results show an average gain in entropy of up to 0.42 dB for an up-scaling of 2 and a significant promotion gain in enhancement measure evaluation for an up-scaling of 2. The experimental results show that the performance of the AMED-SR method is better than existing super-resolution reconstruction methods in terms of visual and accuracy improvements.

Spatio-Temporal Super-Resolution Reconstruction of Remote-Sensing Images Based on Adaptive Multi-Scale Detail Enhancement

Science.gov (United States)

Zhu, Hong; Tang, Xinming; Xie, Junfeng; Song, Weidong; Mo, Fan; Gao, Xiaoming

2018-01-01

There are many problems in existing reconstruction-based super-resolution algorithms, such as the lack of texture-feature representation and of high-frequency details. Multi-scale detail enhancement can produce more texture information and high-frequency information. Therefore, super-resolution reconstruction of remote-sensing images based on adaptive multi-scale detail enhancement (AMDE-SR) is proposed in this paper. First, the information entropy of each remote-sensing image is calculated, and the image with the maximum entropy value is regarded as the reference image. Subsequently, spatio-temporal remote-sensing images are processed using phase normalization, which is to reduce the time phase difference of image data and enhance the complementarity of information. The multi-scale image information is then decomposed using the L0 gradient minimization model, and the non-redundant information is processed by difference calculation and expanding non-redundant layers and the redundant layer by the iterative back-projection (IBP) technique. The different-scale non-redundant information is adaptive-weighted and fused using cross-entropy. Finally, a nonlinear texture-detail-enhancement function is built to improve the scope of small details, and the peak signal-to-noise ratio (PSNR) is used as an iterative constraint. Ultimately, high-resolution remote-sensing images with abundant texture information are obtained by iterative optimization. Real results show an average gain in entropy of up to 0.42 dB for an up-scaling of 2 and a significant promotion gain in enhancement measure evaluation for an up-scaling of 2. The experimental results show that the performance of the AMED-SR method is better than existing super-resolution reconstruction methods in terms of visual and accuracy improvements. PMID:29414893

Optical system design with wide field of view and high resolution based on monocentric multi-scale construction

Science.gov (United States)

Wang, Fang; Wang, Hu; Xiao, Nan; Shen, Yang; Xue, Yaoke

2018-03-01

With the development of related technology gradually mature in the field of optoelectronic information, it is a great demand to design an optical system with high resolution and wide field of view(FOV). However, as it is illustrated in conventional Applied Optics, there is a contradiction between these two characteristics. Namely, the FOV and imaging resolution are limited by each other. Here, based on the study of typical wide-FOV optical system design, we propose the monocentric multi-scale system design method to solve this problem. Consisting of a concentric spherical lens and a series of micro-lens array, this system has effective improvement on its imaging quality. As an example, we designed a typical imaging system, which has a focal length of 35mm and a instantaneous field angle of 14.7", as well as the FOV set to be 120°. By analyzing the imaging quality, we demonstrate that in different FOV, all the values of MTF at 200lp/mm are higher than 0.4 when the sampling frequency of the Nyquist is 200lp/mm, which shows a good accordance with our design.

High-resolution monitoring across the soil-groundwater interface - Revealing small-scale hydrochemical patterns with a novel multi-level well

Science.gov (United States)

Gassen, Niklas; Griebler, Christian; Stumpp, Christine

2016-04-01

Biogeochemical turnover processes in the subsurface are highly variable both in time and space. In order to capture this variability, high resolution monitoring systems are required. Particular in riparian zones the understanding of small-scale biogeochemical processes is of interest, as they are regarded as important buffer zones for nutrients and contaminants with high turnover rates. To date, riparian research has focused on influences of groundwater-surface water interactions on element cycling, but little is known about processes occurring at the interface between the saturated and the unsaturated zone during dynamic flow conditions. Therefore, we developed a new type of high resolution multi-level well (HR-MLW) that has been installed in the riparian zone of the Selke river. This HR-MLW for the first time enables to derive water samples both from the unsaturated and the saturated zone across one vertical profile with a spatial vertical resolution of 0.05 to 0.5 m to a depth of 4 m b.l.s. Water samples from the unsaturated zone are extracted via suction cup sampling. Samples from the saturated zone are withdrawn through glass filters and steel capillaries. Both, ceramic cups and glass filters, are installed along a 1" HDPE piezometer tube. First high resolution hydrochemical profiles revealed a distinct depth-zonation in the riparian alluvial aquifer. A shallow zone beneath the water table carried a signature isotopically and hydrochemically similar to the nearby river, while layers below 1.5 m were influenced by regional groundwater. This zonation showed temporal dynamics related to groundwater table fluctuations and microbial turnover processes. The HR-MLW delivered new insight into mixing and turnover processes between riverwater and groundwater in riparian zones, both in a temporal and spatial dimension. With these new insights, we are able to improve our understanding of dynamic turnover processes at the soil - groundwater interface and of surface

Multi-objective hierarchical genetic algorithms for multilevel redundancy allocation optimization

Energy Technology Data Exchange (ETDEWEB)

Kumar, Ranjan [Department of Aeronautics and Astronautics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto 606-8501 (Japan)], E-mail: ranjan.k@ks3.ecs.kyoto-u.ac.jp; Izui, Kazuhiro [Department of Aeronautics and Astronautics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto 606-8501 (Japan)], E-mail: izui@prec.kyoto-u.ac.jp; Yoshimura, Masataka [Department of Aeronautics and Astronautics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto 606-8501 (Japan)], E-mail: yoshimura@prec.kyoto-u.ac.jp; Nishiwaki, Shinji [Department of Aeronautics and Astronautics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto 606-8501 (Japan)], E-mail: shinji@prec.kyoto-u.ac.jp

2009-04-15

Multilevel redundancy allocation optimization problems (MRAOPs) occur frequently when attempting to maximize the system reliability of a hierarchical system, and almost all complex engineering systems are hierarchical. Despite their practical significance, limited research has been done concerning the solving of simple MRAOPs. These problems are not only NP hard but also involve hierarchical design variables. Genetic algorithms (GAs) have been applied in solving MRAOPs, since they are computationally efficient in solving such problems, unlike exact methods, but their applications has been confined to single-objective formulation of MRAOPs. This paper proposes a multi-objective formulation of MRAOPs and a methodology for solving such problems. In this methodology, a hierarchical GA framework for multi-objective optimization is proposed by introducing hierarchical genotype encoding for design variables. In addition, we implement the proposed approach by integrating the hierarchical genotype encoding scheme with two popular multi-objective genetic algorithms (MOGAs)-the strength Pareto evolutionary genetic algorithm (SPEA2) and the non-dominated sorting genetic algorithm (NSGA-II). In the provided numerical examples, the proposed multi-objective hierarchical approach is applied to solve two hierarchical MRAOPs, a 4- and a 3-level problems. The proposed method is compared with a single-objective optimization method that uses a hierarchical genetic algorithm (HGA), also applied to solve the 3- and 4-level problems. The results show that a multi-objective hierarchical GA (MOHGA) that includes elitism and mechanism for diversity preserving performed better than a single-objective GA that only uses elitism, when solving large-scale MRAOPs. Additionally, the experimental results show that the proposed method with NSGA-II outperformed the proposed method with SPEA2 in finding useful Pareto optimal solution sets.

Multi-objective hierarchical genetic algorithms for multilevel redundancy allocation optimization

International Nuclear Information System (INIS)

Kumar, Ranjan; Izui, Kazuhiro; Yoshimura, Masataka; Nishiwaki, Shinji

2009-01-01

Multilevel redundancy allocation optimization problems (MRAOPs) occur frequently when attempting to maximize the system reliability of a hierarchical system, and almost all complex engineering systems are hierarchical. Despite their practical significance, limited research has been done concerning the solving of simple MRAOPs. These problems are not only NP hard but also involve hierarchical design variables. Genetic algorithms (GAs) have been applied in solving MRAOPs, since they are computationally efficient in solving such problems, unlike exact methods, but their applications has been confined to single-objective formulation of MRAOPs. This paper proposes a multi-objective formulation of MRAOPs and a methodology for solving such problems. In this methodology, a hierarchical GA framework for multi-objective optimization is proposed by introducing hierarchical genotype encoding for design variables. In addition, we implement the proposed approach by integrating the hierarchical genotype encoding scheme with two popular multi-objective genetic algorithms (MOGAs)-the strength Pareto evolutionary genetic algorithm (SPEA2) and the non-dominated sorting genetic algorithm (NSGA-II). In the provided numerical examples, the proposed multi-objective hierarchical approach is applied to solve two hierarchical MRAOPs, a 4- and a 3-level problems. The proposed method is compared with a single-objective optimization method that uses a hierarchical genetic algorithm (HGA), also applied to solve the 3- and 4-level problems. The results show that a multi-objective hierarchical GA (MOHGA) that includes elitism and mechanism for diversity preserving performed better than a single-objective GA that only uses elitism, when solving large-scale MRAOPs. Additionally, the experimental results show that the proposed method with NSGA-II outperformed the proposed method with SPEA2 in finding useful Pareto optimal solution sets

Crack Identification in CFRP Laminated Beams Using Multi-Resolution Modal Teager–Kaiser Energy under Noisy Environments

Science.gov (United States)

Xu, Wei; Cao, Maosen; Ding, Keqin; Radzieński, Maciej; Ostachowicz, Wiesław

2017-01-01

Carbon fiber reinforced polymer laminates are increasingly used in the aerospace and civil engineering fields. Identifying cracks in carbon fiber reinforced polymer laminated beam components is of considerable significance for ensuring the integrity and safety of the whole structures. With the development of high-resolution measurement technologies, mode-shape-based crack identification in such laminated beam components has become an active research focus. Despite its sensitivity to cracks, however, this method is susceptible to noise. To address this deficiency, this study proposes a new concept of multi-resolution modal Teager–Kaiser energy, which is the Teager–Kaiser energy of a mode shape represented in multi-resolution, for identifying cracks in carbon fiber reinforced polymer laminated beams. The efficacy of this concept is analytically demonstrated by identifying cracks in Timoshenko beams with general boundary conditions; and its applicability is validated by diagnosing cracks in a carbon fiber reinforced polymer laminated beam, whose mode shapes are precisely acquired via non-contact measurement using a scanning laser vibrometer. The analytical and experimental results show that multi-resolution modal Teager–Kaiser energy is capable of designating the presence and location of cracks in these beams under noisy environments. This proposed method holds promise for developing crack identification systems for carbon fiber reinforced polymer laminates. PMID:28773016

Identifying Spatial Units of Human Occupation in the Brazilian Amazon Using Landsat and CBERS Multi-Resolution Imagery

Directory of Open Access Journals (Sweden)

Maria Isabel Sobral Escada

2012-01-01

Full Text Available Every spatial unit of human occupation is part of a network structuring an extensive process of urbanization in the Amazon territory. Multi-resolution remote sensing data were used to identify and map human presence and activities in the Sustainable Forest District of Cuiabá-Santarém highway (BR-163, west of Pará, Brazil. The limits of spatial units of human occupation were mapped based on digital classification of Landsat-TM5 (Thematic Mapper 5 image (30m spatial resolution. High-spatial-resolution CBERS-HRC (China-Brazil Earth Resources Satellite-High-Resolution Camera images (5 m merged with CBERS-CCD (Charge Coupled Device images (20 m were used to map spatial arrangements inside each populated unit, describing intra-urban characteristics. Fieldwork data validated and refined the classification maps that supported the categorization of the units. A total of 133 spatial units were individualized, comprising population centers as municipal seats, villages and communities, and units of human activities, such as sawmills, farmhouses, landing strips, etc. From the high-resolution analysis, 32 population centers were grouped in four categories, described according to their level of urbanization and spatial organization as: structured, recent, established and dependent on connectivity. This multi-resolution approach provided spatial information about the urbanization process and organization of the territory. It may be extended into other areas or be further used to devise a monitoring system, contributing to the discussion of public policy priorities for sustainable development in the Amazon.

Interactive Preference Learning of Utility Functions for Multi-Objective Optimization

OpenAIRE

Dewancker, Ian; McCourt, Michael; Ainsworth, Samuel

2016-01-01

Real-world engineering systems are typically compared and contrasted using multiple metrics. For practical machine learning systems, performance tuning is often more nuanced than minimizing a single expected loss objective, and it may be more realistically discussed as a multi-objective optimization problem. We propose a novel generative model for scalar-valued utility functions to capture human preferences in a multi-objective optimization setting. We also outline an interactive active learn...

Multi-objective evolutionary algorithms for fuzzy classification in survival prediction.

Science.gov (United States)

Jiménez, Fernando; Sánchez, Gracia; Juárez, José M

2014-03-01

This paper presents a novel rule-based fuzzy classification methodology for survival/mortality prediction in severe burnt patients. Due to the ethical aspects involved in this medical scenario, physicians tend not to accept a computer-based evaluation unless they understand why and how such a recommendation is given. Therefore, any fuzzy classifier model must be both accurate and interpretable. The proposed methodology is a three-step process: (1) multi-objective constrained optimization of a patient's data set, using Pareto-based elitist multi-objective evolutionary algorithms to maximize accuracy and minimize the complexity (number of rules) of classifiers, subject to interpretability constraints; this step produces a set of alternative (Pareto) classifiers; (2) linguistic labeling, which assigns a linguistic label to each fuzzy set of the classifiers; this step is essential to the interpretability of the classifiers; (3) decision making, whereby a classifier is chosen, if it is satisfactory, according to the preferences of the decision maker. If no classifier is satisfactory for the decision maker, the process starts again in step (1) with a different input parameter set. The performance of three multi-objective evolutionary algorithms, niched pre-selection multi-objective algorithm, elitist Pareto-based multi-objective evolutionary algorithm for diversity reinforcement (ENORA) and the non-dominated sorting genetic algorithm (NSGA-II), was tested using a patient's data set from an intensive care burn unit and a standard machine learning data set from an standard machine learning repository. The results are compared using the hypervolume multi-objective metric. Besides, the results have been compared with other non-evolutionary techniques and validated with a multi-objective cross-validation technique. Our proposal improves the classification rate obtained by other non-evolutionary techniques (decision trees, artificial neural networks, Naive Bayes, and case

Multi-objective optimization of bioethanol production during cold enzyme starch hydrolysis in very high gravity cassava mash.

Science.gov (United States)

Yingling, Bao; Li, Chen; Honglin, Wang; Xiwen, Yu; Zongcheng, Yan

2011-09-01

Cold enzymatic hydrolysis conditions for bioethanol production were optimized using multi-objective optimization. Response surface methodology was used to optimize the effects of α-amylase, glucoamylase, liquefaction temperature and liquefaction time on S. cerevisiae biomass, ethanol concentration and starch utilization ratio. The optimum hydrolysis conditions were: 224 IU/g(starch) α-amylase, 694 IU/g(starch) glucoamylase, 77°C and 104 min for biomass; 264 IU/g(starch) α-amylase, 392 IU/g(starch) glucoamylase, 60°C and 85 min for ethanol concentration; 214 IU/g(starch) α-amylase, 398 IU/g(starch) glucoamylase, 79°C and 117 min for starch utilization ratio. The hydrolysis conditions were subsequently evaluated by multi-objectives optimization utilizing the weighted coefficient methods. The Pareto solutions for biomass (3.655-4.380×10(8)cells/ml), ethanol concentration (15.96-18.25 wt.%) and starch utilization ratio (92.50-94.64%) were obtained. The optimized conditions were shown to be feasible and reliable through verification tests. This kind of multi-objective optimization is of potential importance in industrial bioethanol production. Copyright © 2011 Elsevier Ltd. All rights reserved.

A VIRTUAL GLOBE-BASED MULTI-RESOLUTION TIN SURFACE MODELING AND VISUALIZETION METHOD

Directory of Open Access Journals (Sweden)

X. Zheng

2016-06-01

Full Text Available The integration and visualization of geospatial data on a virtual globe play an significant role in understanding and analysis of the Earth surface processes. However, the current virtual globes always sacrifice the accuracy to ensure the efficiency for global data processing and visualization, which devalue their functionality for scientific applications. In this article, we propose a high-accuracy multi-resolution TIN pyramid construction and visualization method for virtual globe. Firstly, we introduce the cartographic principles to formulize the level of detail (LOD generation so that the TIN model in each layer is controlled with a data quality standard. A maximum z-tolerance algorithm is then used to iteratively construct the multi-resolution TIN pyramid. Moreover, the extracted landscape features are incorporated into each-layer TIN, thus preserving the topological structure of terrain surface at different levels. In the proposed framework, a virtual node (VN-based approach is developed to seamlessly partition and discretize each triangulation layer into tiles, which can be organized and stored with a global quad-tree index. Finally, the real time out-of-core spherical terrain rendering is realized on a virtual globe system VirtualWorld1.0. The experimental results showed that the proposed method can achieve an high-fidelity terrain representation, while produce a high quality underlying data that satisfies the demand for scientific analysis.

A novel VLSI processor for high-rate, high resolution spectroscopy

CERN Document Server

Pullia, Antonio; Gatti, E; Longoni, A; Buttler, W

2000-01-01

A novel time-variant VLSI shaper amplifier, suitable for multi-anode Silicon Drift Detectors or other multi-element solid-state X-ray detection systems, is proposed. The new read-out scheme has been conceived for demanding applications with synchrotron light sources, such as X-ray holography or EXAFS, where both high count-rates and high-energy resolutions are required. The circuit is of the linear time-variant class, accepts randomly distributed events and features: a finite-width (1-10 mu s) quasi-optimal weight function, an ultra-low-level energy discrimination (approx 150 eV), and a full compatibility for monolithic integration in CMOS technology. Its impulse response has a staircase-like shape, but the weight function (which is in general different from the impulse response in time-variant systems) is quasi trapezoidal. The operation principles of the new scheme as well as the first experimental results obtained with a prototype of the circuit are presented and discussed in the work.

A Bayesian alternative for multi-objective ecohydrological model specification

Science.gov (United States)

Tang, Yating; Marshall, Lucy; Sharma, Ashish; Ajami, Hoori

2018-01-01

Recent studies have identified the importance of vegetation processes in terrestrial hydrologic systems. Process-based ecohydrological models combine hydrological, physical, biochemical and ecological processes of the catchments, and as such are generally more complex and parametric than conceptual hydrological models. Thus, appropriate calibration objectives and model uncertainty analysis are essential for ecohydrological modeling. In recent years, Bayesian inference has become one of the most popular tools for quantifying the uncertainties in hydrological modeling with the development of Markov chain Monte Carlo (MCMC) techniques. The Bayesian approach offers an appealing alternative to traditional multi-objective hydrologic model calibrations by defining proper prior distributions that can be considered analogous to the ad-hoc weighting often prescribed in multi-objective calibration. Our study aims to develop appropriate prior distributions and likelihood functions that minimize the model uncertainties and bias within a Bayesian ecohydrological modeling framework based on a traditional Pareto-based model calibration technique. In our study, a Pareto-based multi-objective optimization and a formal Bayesian framework are implemented in a conceptual ecohydrological model that combines a hydrological model (HYMOD) and a modified Bucket Grassland Model (BGM). Simulations focused on one objective (streamflow/LAI) and multiple objectives (streamflow and LAI) with different emphasis defined via the prior distribution of the model error parameters. Results show more reliable outputs for both predicted streamflow and LAI using Bayesian multi-objective calibration with specified prior distributions for error parameters based on results from the Pareto front in the ecohydrological modeling. The methodology implemented here provides insight into the usefulness of multiobjective Bayesian calibration for ecohydrologic systems and the importance of appropriate prior

Robust Hydrological Forecasting for High-resolution Distributed Models Using a Unified Data Assimilation Approach

Science.gov (United States)

Hernandez, F.; Liang, X.

2017-12-01

Reliable real-time hydrological forecasting, to predict important phenomena such as floods, is invaluable to the society. However, modern high-resolution distributed models have faced challenges when dealing with uncertainties that are caused by the large number of parameters and initial state estimations involved. Therefore, to rely on these high-resolution models for critical real-time forecast applications, considerable improvements on the parameter and initial state estimation techniques must be made. In this work we present a unified data assimilation algorithm called Optimized PareTo Inverse Modeling through Inverse STochastic Search (OPTIMISTS) to deal with the challenge of having robust flood forecasting for high-resolution distributed models. This new algorithm combines the advantages of particle filters and variational methods in a unique way to overcome their individual weaknesses. The analysis of candidate particles compares model results with observations in a flexible time frame, and a multi-objective approach is proposed which attempts to simultaneously minimize differences with the observations and departures from the background states by using both Bayesian sampling and non-convex evolutionary optimization. Moreover, the resulting Pareto front is given a probabilistic interpretation through kernel density estimation to create a non-Gaussian distribution of the states. OPTIMISTS was tested on a low-resolution distributed land surface model using VIC (Variable Infiltration Capacity) and on a high-resolution distributed hydrological model using the DHSVM (Distributed Hydrology Soil Vegetation Model). In the tests streamflow observations are assimilated. OPTIMISTS was also compared with a traditional particle filter and a variational method. Results show that our method can reliably produce adequate forecasts and that it is able to outperform those resulting from assimilating the observations using a particle filter or an evolutionary 4D variational

NEULAND at R{sup 3}B: Multi-neutron response and resolution of the novel neutron detector

Energy Technology Data Exchange (ETDEWEB)

Kresan, Dmytro; Aumann, Thomas [Technische Universitaet Darmstadt, Darmstadt (Germany); Boretzky, Konstanze; Bertini, Denis; Heil, Michael; Rossi, Dominic; Simon, Haik [GSI Helmholtzzentrum fuer Schwerionenforschung, Darmstadt (Germany)

2012-07-01

NEULAND (New Large Area Neutron Detector) will serve for the detection of fast neutrons (200 - 1000 MeV) in the R3B experiment at the future FAIR. A high detection efficiency (> 90%), a high resolution (down to 20 keV) and a large multi-neutron-hit resolving power ({>=}5 neutrons) are demanded. The detector concept foresees a fully active and highly granular design of plastic scintillators. We present the detector capabilities, based on simulations performed within the FairRoot framework. The relevance of calorimetric properties for the multi-hit recognition is discussed, and exemplarily the performance for specific physics cases is presented.

Optimization of multi-objective micro-grid based on improved particle swarm optimization algorithm

Science.gov (United States)

Zhang, Jian; Gan, Yang

2018-04-01

The paper presents a multi-objective optimal configuration model for independent micro-grid with the aim of economy and environmental protection. The Pareto solution set can be obtained by solving the multi-objective optimization configuration model of micro-grid with the improved particle swarm algorithm. The feasibility of the improved particle swarm optimization algorithm for multi-objective model is verified, which provides an important reference for multi-objective optimization of independent micro-grid.

Refinement procedure for the image alignment in high-resolution electron tomography

International Nuclear Information System (INIS)

Houben, L.; Bar Sadan, M.

2011-01-01

High-resolution electron tomography from a tilt series of transmission electron microscopy images requires an accurate image alignment procedure in order to maximise the resolution of the tomogram. This is the case in particular for ultra-high resolution where even very small misalignments between individual images can dramatically reduce the fidelity of the resultant reconstruction. A tomographic-reconstruction based and marker-free method is proposed, which uses an iterative optimisation of the tomogram resolution. The method utilises a search algorithm that maximises the contrast in tomogram sub-volumes. Unlike conventional cross-correlation analysis it provides the required correlation over a large tilt angle separation and guarantees a consistent alignment of images for the full range of object tilt angles. An assessment based on experimental reconstructions shows that the marker-free procedure is competitive to the reference of marker-based procedures at lower resolution and yields sub-pixel accuracy even for simulated high-resolution data. -- Highlights: → Alignment procedure for electron tomography based on iterative tomogram contrast optimisation. → Marker-free, independent of object, little user interaction. → Accuracy competitive with fiducial marker methods and suited for high-resolution tomography.

Application of Multi-Objective Human Learning Optimization Method to Solve AC/DC Multi-Objective Optimal Power Flow Problem

Science.gov (United States)

Cao, Jia; Yan, Zheng; He, Guangyu

2016-06-01

This paper introduces an efficient algorithm, multi-objective human learning optimization method (MOHLO), to solve AC/DC multi-objective optimal power flow problem (MOPF). Firstly, the model of AC/DC MOPF including wind farms is constructed, where includes three objective functions, operating cost, power loss, and pollutant emission. Combining the non-dominated sorting technique and the crowding distance index, the MOHLO method can be derived, which involves individual learning operator, social learning operator, random exploration learning operator and adaptive strategies. Both the proposed MOHLO method and non-dominated sorting genetic algorithm II (NSGAII) are tested on an improved IEEE 30-bus AC/DC hybrid system. Simulation results show that MOHLO method has excellent search efficiency and the powerful ability of searching optimal. Above all, MOHLO method can obtain more complete pareto front than that by NSGAII method. However, how to choose the optimal solution from pareto front depends mainly on the decision makers who stand from the economic point of view or from the energy saving and emission reduction point of view.

Genetic Particle Swarm Optimization–Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection

Science.gov (United States)

Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

2016-01-01

In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm. PMID:27483285

Genetic Particle Swarm Optimization-Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection.

Science.gov (United States)

Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

2016-07-30

In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm.

Detectors for high resolution dynamic pet

International Nuclear Information System (INIS)

Derenzo, S.E.; Budinger, T.F.; Huesman, R.H.

1983-05-01

This report reviews the motivation for high spatial resolution in dynamic positron emission tomography of the head and the technical problems in realizing this objective. We present recent progress in using small silicon photodiodes to measure the energy deposited by 511 keV photons in small BGO crystals with an energy resolution of 9.4% full-width at half-maximum. In conjunction with a suitable phototube coupled to a group of crystals, the photodiode signal to noise ratio is sufficient for the identification of individual crystals both for conventional and time-of-flight positron tomography

INTERACTIVE CHANGE DETECTION USING HIGH RESOLUTION REMOTE SENSING IMAGES BASED ON ACTIVE LEARNING WITH GAUSSIAN PROCESSES

Directory of Open Access Journals (Sweden)

H. Ru

2016-06-01

Full Text Available Although there have been many studies for change detection, the effective and efficient use of high resolution remote sensing images is still a problem. Conventional supervised methods need lots of annotations to classify the land cover categories and detect their changes. Besides, the training set in supervised methods often has lots of redundant samples without any essential information. In this study, we present a method for interactive change detection using high resolution remote sensing images with active learning to overcome the shortages of existing remote sensing image change detection techniques. In our method, there is no annotation of actual land cover category at the beginning. First, we find a certain number of the most representative objects in unsupervised way. Then, we can detect the change areas from multi-temporal high resolution remote sensing images by active learning with Gaussian processes in an interactive way gradually until the detection results do not change notably. The artificial labelling can be reduced substantially, and a desirable detection result can be obtained in a few iterations. The experiments on Geo-Eye1 and WorldView2 remote sensing images demonstrate the effectiveness and efficiency of our proposed method.

An improved fast and elitist multi-objective genetic algorithm-ANSGA-II for multi-objective optimization of inverse radiotherapy treatment planning

International Nuclear Information System (INIS)

Cao Ruifen; Li Guoli; Song Gang; Zhao Pan; Lin Hui; Wu Aidong; Huang Chenyu; Wu Yican

2007-01-01

Objective: To provide a fast and effective multi-objective optimization algorithm for inverse radiotherapy treatment planning system. Methods: Non-dominated Sorting Genetic Algorithm-NSGA-II is a representative of multi-objective evolutionary optimization algorithms and excels the others. The paper produces ANSGA-II that makes use of advantage of NSGA-II, and uses adaptive crossover and mutation to improve its flexibility; according the character of inverse radiotherapy treatment planning, the paper uses the pre-known knowledge to generate individuals of every generation in the course of optimization, which enhances the convergent speed and improves efficiency. Results: The example of optimizing average dose of a sheet of CT, including PTV, OAR, NT, proves the algorithm could find satisfied solutions in several minutes. Conclusions: The algorithm could provide clinic inverse radiotherapy treatment planning system with selection of optimization algorithms. (authors)

Scalable and practical multi-objective distribution network expansion planning

NARCIS (Netherlands)

Luong, N.H.; Grond, M.O.W.; Poutré, La J.A.; Bosman, P.A.N.

2015-01-01

We formulate the distribution network expansion planning (DNEP) problem as a multi-objective optimization (MOO) problem with different objectives that distribution network operators (DNOs) would typically like to consider during decision making processes for expanding their networks. Objectives are

OBJECT-SPACE MULTI-IMAGE MATCHING OF MOBILE-MAPPING-SYSTEM IMAGE SEQUENCES

Directory of Open Access Journals (Sweden)

Y. C. Chen

2012-07-01

Full Text Available This paper proposes an object-space multi-image matching procedure of terrestrial MMS (Mobile Mapping System image sequences to determine the coordinates of an object point automatically and reliably. This image matching procedure can be applied to find conjugate points of MMS image sequences efficiently. Conventional area-based image matching methods are not reliable to deliver accurate matching results for this application due to image scale variations, viewing angle variations, and object occlusions. In order to deal with these three matching problems, an object space multi-image matching is proposed. A modified NCC (Normalized Cross Correlation coefficient is proposed to measure the similarity of image patches. A modified multi-window matching procedure will also be introduced to solve the problem of object occlusion. A coarse-to-fine procedure with a combination of object-space multi-image matching and multi-window matching is adopted. The proposed procedure has been implemented for the purpose of matching terrestrial MMS image sequences. The ratio of correct matches of this experiment was about 80 %. By providing an approximate conjugate point in an overlapping image manually, most of the incorrect matches could be fixed properly and the ratio of correct matches was improved up to 98 %.

High Fidelity Multi-Objective Design Optimization of a Downscaled Cusped Field Thruster

Directory of Open Access Journals (Sweden)

Thomas Fahey

2017-11-01

Full Text Available The Cusped Field Thruster (CFT concept has demonstrated significantly improved performance over the Hall Effect Thruster and the Gridded Ion Thruster; however, little is understood about the complexities of the interactions and interdependencies of the geometrical, magnetic and ion beam properties of the thruster. This study applies an advanced design methodology combining a modified power distribution calculation and evolutionary algorithms assisted by surrogate modeling to a multi-objective design optimization for the performance optimization and characterization of the CFT. Optimization is performed for maximization of performance defined by five design parameters (i.e., anode voltage, anode current, mass flow rate, and magnet radii, simultaneously aiming to maximize three objectives; that is, thrust, efficiency and specific impulse. Statistical methods based on global sensitivity analysis are employed to assess the optimization results in conjunction with surrogate models to identify key design factors with respect to the three design objectives and additional performance measures. The research indicates that the anode current and the Outer Magnet Radius have the greatest effect on the performance parameters. An optimal value for the anode current is determined, and a trend towards maximizing anode potential and mass flow rate is observed.

Multi-objective evacuation routing optimization for toxic cloud releases

International Nuclear Information System (INIS)

Gai, Wen-mei; Deng, Yun-feng; Jiang, Zhong-an; Li, Jing; Du, Yan

2017-01-01

This paper develops a model for assessing the risks associated with the evacuation process in response to potential chemical accidents, based on which a multi-objective evacuation routing model for toxic cloud releases is proposed taking into account that the travel speed on each arc will be affected by disaster extension. The objectives of the evacuation routing model are to minimize travel time and individual evacuation risk along a path respectively. Two heuristic algorithms are proposed to solve the multi-objective evacuation routing model. Simulation results show the effectiveness and feasibility of the model and algorithms presented in this paper. And, the methodology with appropriate modification is suitable for supporting decisions in assessing emergency route selection in other cases (fires, nuclear accidents). - Highlights: • A model for assessing and visualizing the risks is developed. • A multi-objective evacuation routing model is proposed for toxic cloud releases. • A modified Dijkstra algorithm is designed to obtain an solution of the model. • Two heuristic algorithms have been developed as the optimization tool.

Pipelining Computational Stages of the Tomographic Reconstructor for Multi-Object Adaptive Optics on a Multi-GPU System

KAUST Repository

Charara, Ali; Ltaief, Hatem; Gratadour, Damien; Keyes, David E.; Sevin, Arnaud; Abdelfattah, Ahmad; Gendron, Eric; Morel, Carine; Vidal, Fabrice

2014-01-01

called MOSAIC has been proposed to perform multi-object spectroscopy using the Multi-Object Adaptive Optics (MOAO) technique. The core implementation of the simulation lies in the intensive computation of a tomographic reconstruct or (TR), which is used

A multi-objective decision framework for lifecycle investment

NARCIS (Netherlands)

Timmermans, S.H.J.T.; Schumacher, J.M.; Ponds, E.H.M.

2017-01-01

In this paper we propose a multi-objective decision framework for lifecycle investment choice. Instead of optimizing individual strategies with respect to a single-valued objective, we suggest evaluation of classes of strategies in terms of the quality of the tradeoffs that they provide. The

Cancer microarray data feature selection using multi-objective binary particle swarm optimization algorithm

Science.gov (United States)

Annavarapu, Chandra Sekhara Rao; Dara, Suresh; Banka, Haider

2016-01-01

Cancer investigations in microarray data play a major role in cancer analysis and the treatment. Cancer microarray data consists of complex gene expressed patterns of cancer. In this article, a Multi-Objective Binary Particle Swarm Optimization (MOBPSO) algorithm is proposed for analyzing cancer gene expression data. Due to its high dimensionality, a fast heuristic based pre-processing technique is employed to reduce some of the crude domain features from the initial feature set. Since these pre-processed and reduced features are still high dimensional, the proposed MOBPSO algorithm is used for finding further feature subsets. The objective functions are suitably modeled by optimizing two conflicting objectives i.e., cardinality of feature subsets and distinctive capability of those selected subsets. As these two objective functions are conflicting in nature, they are more suitable for multi-objective modeling. The experiments are carried out on benchmark gene expression datasets, i.e., Colon, Lymphoma and Leukaemia available in literature. The performance of the selected feature subsets with their classification accuracy and validated using 10 fold cross validation techniques. A detailed comparative study is also made to show the betterment or competitiveness of the proposed algorithm. PMID:27822174

Multi-objective possibilistic model for portfolio selection with transaction cost

Science.gov (United States)

Jana, P.; Roy, T. K.; Mazumder, S. K.

2009-06-01

In this paper, we introduce the possibilistic mean value and variance of continuous distribution, rather than probability distributions. We propose a multi-objective Portfolio based model and added another entropy objective function to generate a well diversified asset portfolio within optimal asset allocation. For quantifying any potential return and risk, portfolio liquidity is taken into account and a multi-objective non-linear programming model for portfolio rebalancing with transaction cost is proposed. The models are illustrated with numerical examples.

An Evolutionary Approach for Bilevel Multi-objective Problems

Science.gov (United States)

Deb, Kalyanmoy; Sinha, Ankur

Evolutionary multi-objective optimization (EMO) algorithms have been extensively applied to find multiple near Pareto-optimal solutions over the past 15 years or so. However, EMO algorithms for solving bilevel multi-objective optimization problems have not received adequate attention yet. These problems appear in many applications in practice and involve two levels, each comprising of multiple conflicting objectives. These problems require every feasible upper-level solution to satisfy optimality of a lower-level optimization problem, thereby making them difficult to solve. In this paper, we discuss a recently proposed bilevel EMO procedure and show its working principle on a couple of test problems and on a business decision-making problem. This paper should motivate other EMO researchers to engage more into this important optimization task of practical importance.

High resolution, high speed ultrahigh vacuum microscopy

International Nuclear Information System (INIS)

Poppa, Helmut

2004-01-01

The history and future of transmission electron microscopy (TEM) is discussed as it refers to the eventual development of instruments and techniques applicable to the real time in situ investigation of surface processes with high resolution. To reach this objective, it was necessary to transform conventional high resolution instruments so that an ultrahigh vacuum (UHV) environment at the sample site was created, that access to the sample by various in situ sample modification procedures was provided, and that in situ sample exchanges with other integrated surface analytical systems became possible. Furthermore, high resolution image acquisition systems had to be developed to take advantage of the high speed imaging capabilities of projection imaging microscopes. These changes to conventional electron microscopy and its uses were slowly realized in a few international laboratories over a period of almost 40 years by a relatively small number of researchers crucially interested in advancing the state of the art of electron microscopy and its applications to diverse areas of interest; often concentrating on the nucleation, growth, and properties of thin films on well defined material surfaces. A part of this review is dedicated to the recognition of the major contributions to surface and thin film science by these pioneers. Finally, some of the important current developments in aberration corrected electron optics and eventual adaptations to in situ UHV microscopy are discussed. As a result of all the path breaking developments that have led to today's highly sophisticated UHV-TEM systems, integrated fundamental studies are now possible that combine many traditional surface science approaches. Combined investigations to date have involved in situ and ex situ surface microscopies such as scanning tunneling microscopy/atomic force microscopy, scanning Auger microscopy, and photoemission electron microscopy, and area-integrating techniques such as x-ray photoelectron

A multi-resolution HEALPix data structure for spherically mapped point data

Directory of Open Access Journals (Sweden)

Robert W. Youngren

2017-06-01

Full Text Available Data describing entities with locations that are points on a sphere are described as spherically mapped. Several data structures designed for spherically mapped data have been developed. One of them, known as Hierarchical Equal Area iso-Latitude Pixelization (HEALPix, partitions the sphere into twelve diamond-shaped equal-area base cells and then recursively subdivides each cell into four diamond-shaped subcells, continuing to the desired level of resolution. Twelve quadtrees, one associated with each base cell, store the data records associated with that cell and its subcells.HEALPix has been used successfully for numerous applications, notably including cosmic microwave background data analysis. However, for applications involving sparse point data HEALPix has possible drawbacks, including inefficient memory utilization, overwriting of proximate points, and return of spurious points for certain queries.A multi-resolution variant of HEALPix specifically optimized for sparse point data was developed. The new data structure allows different areas of the sphere to be subdivided at different levels of resolution. It combines HEALPix positive features with the advantages of multi-resolution, including reduced memory requirements and improved query performance.An implementation of the new Multi-Resolution HEALPix (MRH data structure was tested using spherically mapped data from four different scientific applications (warhead fragmentation trajectories, weather station locations, galaxy locations, and synthetic locations. Four types of range queries were applied to each data structure for each dataset. Compared to HEALPix, MRH used two to four orders of magnitude less memory for the same data, and on average its queries executed 72% faster. Keywords: Computer science

Enhanced Multi-Objective Optimization of Groundwater Monitoring Networks

DEFF Research Database (Denmark)

Bode, Felix; Binning, Philip John; Nowak, Wolfgang

Drinking-water well catchments include many sources for potential contaminations like gas stations or agriculture. Finding optimal positions of monitoring wells for such purposes is challenging because there are various parameters (and their uncertainties) that influence the reliability...... and optimality of any suggested monitoring location or monitoring network. The goal of this project is to develop and establish a concept to assess, design, and optimize early-warning systems within well catchments. Such optimal monitoring networks need to optimize three competing objectives: (1) a high...... be reduced to a minimum. The method is based on numerical simulation of flow and transport in heterogeneous porous media coupled with geostatistics and Monte-Carlo, wrapped up within the framework of formal multi-objective optimization. In order to gain insight into the flow and transport physics...

CGLXTouch: A multi-user multi-touch approach for ultra-high-resolution collaborative workspaces

KAUST Repository

Ponto, Kevin; Doerr, Kai; Wypych, Tom; Kooker, John; Kuester, Falko

2011-01-01

multi-touch tablet and phone devices, which can be added to and removed from the system on the fly. Events from these devices are tagged with a device identifier and are synchronized with the distributed display environment, enabling multi-user support

Optimal Waste Load Allocation Using Multi-Objective Optimization and Multi-Criteria Decision Analysis

Directory of Open Access Journals (Sweden)

L. Saberi

2016-10-01

Full Text Available Introduction: Increasing demand for water, depletion of resources of acceptable quality, and excessive water pollution due to agricultural and industrial developments has caused intensive social and environmental problems all over the world. Given the environmental importance of rivers, complexity and extent of pollution factors and physical, chemical and biological processes in these systems, optimal waste-load allocation in river systems has been given considerable attention in the literature in the past decades. The overall objective of planning and quality management of river systems is to develop and implement a coordinated set of strategies and policies to reduce or allocate of pollution entering the rivers so that the water quality matches by proposing environmental standards with an acceptable reliability. In such matters, often there are several different decision makers with different utilities which lead to conflicts. Methods/Materials: In this research, a conflict resolution framework for optimal waste load allocation in river systems is proposed, considering the total treatment cost and the Biological Oxygen Demand (BOD violation characteristics. There are two decision-makers inclusive waste load discharges coalition and environmentalists who have conflicting objectives. This framework consists of an embedded river water quality simulator, which simulates the transport process including reaction kinetics. The trade-off curve between objectives is obtained using the Multi-objective Particle Swarm Optimization Algorithm which these objectives are minimization of the total cost of treatment and penalties that must be paid by discharges and a violation of water quality standards considering BOD parameter which is controlled by environmentalists. Thus, the basic policy of river’s water quality management is formulated in such a way that the decision-makers are ensured their benefits will be provided as far as possible. By using MOPSO

Active x-ray optics for high resolution space telescopes

Science.gov (United States)

Doel, Peter; Atkins, Carolyn; Brooks, D.; Feldman, Charlotte; Willingale, Richard; Button, Tim; Rodriguez Sanmartin, Daniel; Meggs, Carl; James, Ady; Willis, Graham; Smith, Andy

2017-11-01

The Smart X-ray Optics (SXO) Basic Technology project started in April 2006 and will end in October 2010. The aim is to develop new technologies in the field of X-ray focusing, in particular the application of active and adaptive optics. While very major advances have been made in active/adaptive astronomical optics for visible light, little was previously achieved for X-ray optics where the technological challenges differ because of the much shorter wavelengths involved. The field of X-ray astronomy has been characterized by the development and launch of ever larger observatories with the culmination in the European Space Agency's XMM-Newton and NASA's Chandra missions which are currently operational. XMM-Newton uses a multi-nested structure to provide modest angular resolution ( 10 arcsec) but large effective area, while Chandra sacrifices effective area to achieve the optical stability necessary to provide sub-arc second resolution. Currently the European Space Agency (ESA) is engaged in studies of the next generation of X-ray space observatories, with the aim of producing telescopes with increased sensitivity and resolution. To achieve these aims several telescopes have been proposed, for example ESA and NASA's combined International X-ray Observatory (IXO), aimed at spectroscopy, and NASA's Generation-X. In the field of X-ray astronomy sub 0.2 arcsecond resolution with high efficiency would be very exciting. Such resolution is unlikely to be achieved by anything other than an active system. The benefits of a such a high resolution would be important for a range of astrophysics subjects, for example the potential angular resolution offered by active X-ray optics could provide unprecedented structural imaging detail of the Solar Wind bowshock interaction of comets, planets and similar objects and auroral phenomena throughout the Solar system using an observing platform in low Earth orbit. A major aim of the SXO project was to investigate the production of thin

Research of building information extraction and evaluation based on high-resolution remote-sensing imagery

Science.gov (United States)

Cao, Qiong; Gu, Lingjia; Ren, Ruizhi; Wang, Lang

2016-09-01

Building extraction currently is important in the application of high-resolution remote sensing imagery. At present, quite a few algorithms are available for detecting building information, however, most of them still have some obvious disadvantages, such as the ignorance of spectral information, the contradiction between extraction rate and extraction accuracy. The purpose of this research is to develop an effective method to detect building information for Chinese GF-1 data. Firstly, the image preprocessing technique is used to normalize the image and image enhancement is used to highlight the useful information in the image. Secondly, multi-spectral information is analyzed. Subsequently, an improved morphological building index (IMBI) based on remote sensing imagery is proposed to get the candidate building objects. Furthermore, in order to refine building objects and further remove false objects, the post-processing (e.g., the shape features, the vegetation index and the water index) is employed. To validate the effectiveness of the proposed algorithm, the omission errors (OE), commission errors (CE), the overall accuracy (OA) and Kappa are used at final. The proposed method can not only effectively use spectral information and other basic features, but also avoid extracting excessive interference details from high-resolution remote sensing images. Compared to the original MBI algorithm, the proposed method reduces the OE by 33.14% .At the same time, the Kappa increase by 16.09%. In experiments, IMBI achieved satisfactory results and outperformed other algorithms in terms of both accuracies and visual inspection

A high-throughput, multi-channel photon-counting detector with picosecond timing

CERN Document Server

Lapington, J S; Miller, G M; Ashton, T J R; Jarron, P; Despeisse, M; Powolny, F; Howorth, J; Milnes, J

2009-01-01

High-throughput photon counting with high time resolution is a niche application area where vacuum tubes can still outperform solid-state devices. Applications in the life sciences utilizing time-resolved spectroscopies, particularly in the growing field of proteomics, will benefit greatly from performance enhancements in event timing and detector throughput. The HiContent project is a collaboration between the University of Leicester Space Research Centre, the Microelectronics Group at CERN, Photek Ltd., and end-users at the Gray Cancer Institute and the University of Manchester. The goal is to develop a detector system specifically designed for optical proteomics, capable of high content (multi-parametric) analysis at high throughput. The HiContent detector system is being developed to exploit this niche market. It combines multi-channel, high time resolution photon counting in a single miniaturized detector system with integrated electronics. The combination of enabling technologies; small pore microchanne...

Conflicting Multi-Objective Compatible Optimization Control

OpenAIRE

Xu, Lihong; Hu, Qingsong; Hu, Haigen; Goodman, Erik

2010-01-01

Based on ideas developed in addressing practical greenhouse environmental control, we propose a new multi-objective compatible control method. Several detailed algorithms are proposed to meet the requirements of different kinds of problem: 1) A two-layer MOCC framework is presented for problems with a precise model; 2) To deal with situations

A multi-resolution approach to heat kernels on discrete surfaces

KAUST Repository

Vaxman, Amir

2010-07-26

Studying the behavior of the heat diffusion process on a manifold is emerging as an important tool for analyzing the geometry of the manifold. Unfortunately, the high complexity of the computation of the heat kernel - the key to the diffusion process - limits this type of analysis to 3D models of modest resolution. We show how to use the unique properties of the heat kernel of a discrete two dimensional manifold to overcome these limitations. Combining a multi-resolution approach with a novel approximation method for the heat kernel at short times results in an efficient and robust algorithm for computing the heat kernels of detailed models. We show experimentally that our method can achieve good approximations in a fraction of the time required by traditional algorithms. Finally, we demonstrate how these heat kernels can be used to improve a diffusion-based feature extraction algorithm. © 2010 ACM.

Note: Tandem Kirkpatrick-Baez microscope with sixteen channels for high-resolution laser-plasma diagnostics

Science.gov (United States)

Yi, Shengzhen; Zhang, Zhe; Huang, Qiushi; Zhang, Zhong; Wang, Zhanshan; Wei, Lai; Liu, Dongxiao; Cao, Leifeng; Gu, Yuqiu

2018-03-01

Multi-channel Kirkpatrick-Baez (KB) microscopes, which have better resolution and collection efficiency than pinhole cameras, have been widely used in laser inertial confinement fusion to diagnose time evolution of the target implosion. In this study, a tandem multi-channel KB microscope was developed to have sixteen imaging channels with the precise control of spatial resolution and image intervals. This precise control was created using a coarse assembly of mirror pairs with high-accuracy optical prisms, followed by precise adjustment in real-time x-ray imaging experiments. The multilayers coated on the KB mirrors were designed to have substantially the same reflectivity to obtain a uniform brightness of different images for laser-plasma temperature analysis. The study provides a practicable method to achieve the optimum performance of the microscope for future high-resolution applications in inertial confinement fusion experiments.

Uncertain and multi-objective programming models for crop planting structure optimization

Directory of Open Access Journals (Sweden)

Mo LI,Ping GUO,Liudong ZHANG,Chenglong ZHANG

2016-03-01

Full Text Available Crop planting structure optimization is a significant way to increase agricultural economic benefits and improve agricultural water management. The complexities of fluctuating stream conditions, varying economic profits, and uncertainties and errors in estimated modeling parameters, as well as the complexities among economic, social, natural resources and environmental aspects, have led to the necessity of developing optimization models for crop planting structure which consider uncertainty and multi-objectives elements. In this study, three single-objective programming models under uncertainty for crop planting structure optimization were developed, including an interval linear programming model, an inexact fuzzy chance-constrained programming (IFCCP model and an inexact fuzzy linear programming (IFLP model. Each of the three models takes grayness into account. Moreover, the IFCCP model considers fuzzy uncertainty of parameters/variables and stochastic characteristics of constraints, while the IFLP model takes into account the fuzzy uncertainty of both constraints and objective functions. To satisfy the sustainable development of crop planting structure planning, a fuzzy-optimization-theory-based fuzzy linear multi-objective programming model was developed, which is capable of reflecting both uncertainties and multi-objective. In addition, a multi-objective fractional programming model for crop structure optimization was also developed to quantitatively express the multi-objective in one optimization model with the numerator representing maximum economic benefits and the denominator representing minimum crop planting area allocation. These models better reflect actual situations, considering the uncertainties and multi-objectives of crop planting structure optimization systems. The five models developed were then applied to a real case study in Minqin County, north-west China. The advantages, the applicable conditions and the solution methods

Latest Results from the Multi-Object Keck Exoplanet Tracker

Science.gov (United States)

Van Eyken, Julian C.; Ge, J.; Wan, X.; Zhao, B.; Hariharan, A.; Mahadevan, S.; DeWitt, C.; Guo, P.; Cohen, R.; Fleming, S. W.; Crepp, J.; Warner, C.; Kane, S.; Leger, F.; Pan, K.

2006-12-01

The W. M. Keck Exoplanet Tracker is a precision Doppler radial velocity instrument based on dispersed fixed-delay interferometry (DFDI) which takes advantage of the new technique to allow multi-object RV surveying. Installed at the 2.5m Sloan telescope at Apache Point Observatory, the combination of Michelson interferometer and medium resolution spectrograph allows design for simultaneous Doppler measurements of up to 60 targets, while maintaining high instrument throughput. Using a single-object prototype of the instrument at the Kitt Peak National Observatory 2.1m telescope, we previously discovered a 0.49MJup planet, HD 102195b (ET-1), orbiting with a 4.11d period, and other interesting targets are being followed up. From recent trial observations, the Keck Exoplanet Tracker now yields 59 usable simultaneous fringing stellar spectra, of a quality sufficient to attempt to detect short period hot-Jupiter type planets. Recent engineering improvements reduced errors by a factor of 2, and typical photon limits for stellar data are now at the 30m/s level for magnitude V 10.5 (depending on spectral type and v sin i), with a best value of 6.9m/s at V=7.6. Preliminary RMS precisions from solar data (daytime sky) are around 10m/s over a few days, with some spectra reaching close to their photon limit of 6-7m/s on the short term ( 1 hour). A number of targets showing interesting RV variability are currently being followed up independently. Additional engineering work is planned which should make for further significant gains in Doppler precision. Here we present the latest results and updates from the most recent engineering and observing runs with the Keck ET.

Multi-objective optimal power flow with FACTS devices

International Nuclear Information System (INIS)

Basu, M.

2011-01-01

This paper presents multi-objective differential evolution to optimize cost of generation, emission and active power transmission loss of flexible ac transmission systems (FACTS) device-equipped power systems. In the proposed approach, optimal power flow problem is formulated as a multi-objective optimization problem. FACTS devices considered include thyristor controlled series capacitor (TCSC) and thyristor controlled phase shifter (TCPS). The proposed approach has been examined and tested on the modified IEEE 30-bus and 57-bus test systems. The results obtained from the proposed approach have been compared with those obtained from nondominated sorting genetic algorithm-II, strength pareto evolutionary algorithm 2 and pareto differential evolution.

High Frequency High Spectral Resolution Focal Plane Arrays for AtLAST

Science.gov (United States)

Baryshev, Andrey

2018-01-01

Large collecting area single dish telescope such as ATLAST will be especially effective for medium (R 1000) and high (R 50000) spectral resolution observations. Large focal plane array is a natural solution to increase mapping speed. For medium resolution direct detectors with filter banks (KIDs) and or heterodyne technology can be employed. We will analyze performance limits of comparable KID and SIS focal plane array taking into account quantum limit and high background condition of terrestrial observing site. For large heterodyne focal plane arrays, a high current density AlN junctions open possibility of large instantaneous bandwidth >40%. This and possible multi frequency band FPSs presents a practical challenge for spatial sampling and scanning strategies. We will discuss phase array feeds as a possible solution, including a modular back-end system, which can be shared between KID and SIS based FPA. Finally we will discuss achievable sensitivities and pixel co unts for a high frequency (>500 GHz) FPAs and address main technical challenges: LO distribution, wire counts, bias line multiplexing, and monolithic vs. discrete mixer component integration.

Penalized likelihood and multi-objective spatial scans for the detection and inference of irregular clusters

Directory of Open Access Journals (Sweden)

Fonseca Carlos M

2010-10-01

detection of moderately irregularly shaped clusters. The multi-objective cohesion scan is most effective for the detection of highly irregularly shaped clusters.

HIGH-RESOLUTION IMAGING OF THE ATLBS REGIONS: THE RADIO SOURCE COUNTS

Energy Technology Data Exchange (ETDEWEB)

Thorat, K.; Subrahmanyan, R.; Saripalli, L.; Ekers, R. D., E-mail: kshitij@rri.res.in [Raman Research Institute, C. V. Raman Avenue, Sadashivanagar, Bangalore 560080 (India)

2013-01-01

The Australia Telescope Low-brightness Survey (ATLBS) regions have been mosaic imaged at a radio frequency of 1.4 GHz with 6'' angular resolution and 72 {mu}Jy beam{sup -1} rms noise. The images (centered at R.A. 00{sup h}35{sup m}00{sup s}, decl. -67 Degree-Sign 00'00'' and R.A. 00{sup h}59{sup m}17{sup s}, decl. -67 Degree-Sign 00'00'', J2000 epoch) cover 8.42 deg{sup 2} sky area and have no artifacts or imaging errors above the image thermal noise. Multi-resolution radio and optical r-band images (made using the 4 m CTIO Blanco telescope) were used to recognize multi-component sources and prepare a source list; the detection threshold was 0.38 mJy in a low-resolution radio image made with beam FWHM of 50''. Radio source counts in the flux density range 0.4-8.7 mJy are estimated, with corrections applied for noise bias, effective area correction, and resolution bias. The resolution bias is mitigated using low-resolution radio images, while effects of source confusion are removed by using high-resolution images for identifying blended sources. Below 1 mJy the ATLBS counts are systematically lower than the previous estimates. Showing no evidence for an upturn down to 0.4 mJy, they do not require any changes in the radio source population down to the limit of the survey. The work suggests that automated image analysis for counts may be dependent on the ability of the imaging to reproduce connecting emission with low surface brightness and on the ability of the algorithm to recognize sources, which may require that source finding algorithms effectively work with multi-resolution and multi-wavelength data. The work underscores the importance of using source lists-as opposed to component lists-and correcting for the noise bias in order to precisely estimate counts close to the image noise and determine the upturn at sub-mJy flux density.

Multi-objective engineering design using preferences

Science.gov (United States)

Sanchis, J.; Martinez, M.; Blasco, X.

2008-03-01

System design is a complex task when design parameters have to satisy a number of specifications and objectives which often conflict with those of others. This challenging problem is called multi-objective optimization (MOO). The most common approximation consists in optimizing a single cost index with a weighted sum of objectives. However, once weights are chosen the solution does not guarantee the best compromise among specifications, because there is an infinite number of solutions. A new approach can be stated, based on the designer's experience regarding the required specifications and the associated problems. This valuable information can be translated into preferences for design objectives, and will lead the search process to the best solution in terms of these preferences. This article presents a new method, which enumerates these a priori objective preferences. As a result, a single objective is built automatically and no weight selection need be performed. Problems occuring because of the multimodal nature of the generated single cost index are managed with genetic algorithms (GAs).

A cloud mask methodology for high resolution remote sensing data combining information from high and medium resolution optical sensors

Science.gov (United States)

Sedano, Fernando; Kempeneers, Pieter; Strobl, Peter; Kucera, Jan; Vogt, Peter; Seebach, Lucia; San-Miguel-Ayanz, Jesús

2011-09-01

This study presents a novel cloud masking approach for high resolution remote sensing images in the context of land cover mapping. As an advantage to traditional methods, the approach does not rely on thermal bands and it is applicable to images from most high resolution earth observation remote sensing sensors. The methodology couples pixel-based seed identification and object-based region growing. The seed identification stage relies on pixel value comparison between high resolution images and cloud free composites at lower spatial resolution from almost simultaneously acquired dates. The methodology was tested taking SPOT4-HRVIR, SPOT5-HRG and IRS-LISS III as high resolution images and cloud free MODIS composites as reference images. The selected scenes included a wide range of cloud types and surface features. The resulting cloud masks were evaluated through visual comparison. They were also compared with ad-hoc independently generated cloud masks and with the automatic cloud cover assessment algorithm (ACCA). In general the results showed an agreement in detected clouds higher than 95% for clouds larger than 50 ha. The approach produced consistent results identifying and mapping clouds of different type and size over various land surfaces including natural vegetation, agriculture land, built-up areas, water bodies and snow.

Optimal Golomb Ruler Sequences Generation for Optical WDM Systems: A Novel Parallel Hybrid Multi-objective Bat Algorithm

Science.gov (United States)

Bansal, Shonak; Singh, Arun Kumar; Gupta, Neena

2017-02-01

In real-life, multi-objective engineering design problems are very tough and time consuming optimization problems due to their high degree of nonlinearities, complexities and inhomogeneity. Nature-inspired based multi-objective optimization algorithms are now becoming popular for solving multi-objective engineering design problems. This paper proposes original multi-objective Bat algorithm (MOBA) and its extended form, namely, novel parallel hybrid multi-objective Bat algorithm (PHMOBA) to generate shortest length Golomb ruler called optimal Golomb ruler (OGR) sequences at a reasonable computation time. The OGRs found their application in optical wavelength division multiplexing (WDM) systems as channel-allocation algorithm to reduce the four-wave mixing (FWM) crosstalk. The performances of both the proposed algorithms to generate OGRs as optical WDM channel-allocation is compared with other existing classical computing and nature-inspired algorithms, including extended quadratic congruence (EQC), search algorithm (SA), genetic algorithms (GAs), biogeography based optimization (BBO) and big bang-big crunch (BB-BC) optimization algorithms. Simulations conclude that the proposed parallel hybrid multi-objective Bat algorithm works efficiently as compared to original multi-objective Bat algorithm and other existing algorithms to generate OGRs for optical WDM systems. The algorithm PHMOBA to generate OGRs, has higher convergence and success rate than original MOBA. The efficiency improvement of proposed PHMOBA to generate OGRs up to 20-marks, in terms of ruler length and total optical channel bandwidth (TBW) is 100 %, whereas for original MOBA is 85 %. Finally the implications for further research are also discussed.

PARETO OPTIMAL SOLUTIONS FOR MULTI-OBJECTIVE GENERALIZED ASSIGNMENT PROBLEM

Directory of Open Access Journals (Sweden)

S. Prakash

2012-01-01

Full Text Available

ENGLISH ABSTRACT: The Multi-Objective Generalized Assignment Problem (MGAP with two objectives, where one objective is linear and the other one is non-linear, has been considered, with the constraints that a job is assigned to only one worker – though he may be assigned more than one job, depending upon the time available to him. An algorithm is proposed to find the set of Pareto optimal solutions of the problem, determining assignments of jobs to workers with two objectives without setting priorities for them. The two objectives are to minimise the total cost of the assignment and to reduce the time taken to complete all the jobs.

AFRIKAANSE OPSOMMING: ‘n Multi-doelwit veralgemeende toekenningsprobleem (“multi-objective generalised assignment problem – MGAP” met twee doelwitte, waar die een lineêr en die ander nielineêr is nie, word bestudeer, met die randvoorwaarde dat ‘n taak slegs toegedeel word aan een werker – alhoewel meer as een taak aan hom toegedeel kan word sou die tyd beskikbaar wees. ‘n Algoritme word voorgestel om die stel Pareto-optimale oplossings te vind wat die taaktoedelings aan werkers onderhewig aan die twee doelwitte doen sonder dat prioriteite toegeken word. Die twee doelwitte is om die totale koste van die opdrag te minimiseer en om die tyd te verminder om al die take te voltooi.

A multi-resolution envelope-power based model for speech intelligibility

DEFF Research Database (Denmark)

Jørgensen, Søren; Ewert, Stephan D.; Dau, Torsten

2013-01-01

The speech-based envelope power spectrum model (sEPSM) presented by Jørgensen and Dau [(2011). J. Acoust. Soc. Am. 130, 1475-1487] estimates the envelope power signal-to-noise ratio (SNRenv) after modulation-frequency selective processing. Changes in this metric were shown to account well...... to conditions with stationary interferers, due to the long-term integration of the envelope power, and cannot account for increased intelligibility typically obtained with fluctuating maskers. Here, a multi-resolution version of the sEPSM is presented where the SNRenv is estimated in temporal segments...... with a modulation-filter dependent duration. The multi-resolution sEPSM is demonstrated to account for intelligibility obtained in conditions with stationary and fluctuating interferers, and noisy speech distorted by reverberation or spectral subtraction. The results support the hypothesis that the SNRenv...

Multi-Objective Flight Control for Drag Minimization and Load Alleviation of High-Aspect Ratio Flexible Wing Aircraft

Science.gov (United States)

Nguyen, Nhan; Ting, Eric; Chaparro, Daniel; Drew, Michael; Swei, Sean

2017-01-01

As aircraft wings become much more flexible due to the use of light-weight composites material, adverse aerodynamics at off-design performance can result from changes in wing shapes due to aeroelastic deflections. Increased drag, hence increased fuel burn, is a potential consequence. Without means for aeroelastic compensation, the benefit of weight reduction from the use of light-weight material could be offset by less optimal aerodynamic performance at off-design flight conditions. Performance Adaptive Aeroelastic Wing (PAAW) technology can potentially address these technical challenges for future flexible wing transports. PAAW technology leverages multi-disciplinary solutions to maximize the aerodynamic performance payoff of future adaptive wing design, while addressing simultaneously operational constraints that can prevent the optimal aerodynamic performance from being realized. These operational constraints include reduced flutter margins, increased airframe responses to gust and maneuver loads, pilot handling qualities, and ride qualities. All of these constraints while seeking the optimal aerodynamic performance present themselves as a multi-objective flight control problem. The paper presents a multi-objective flight control approach based on a drag-cognizant optimal control method. A concept of virtual control, which was previously introduced, is implemented to address the pair-wise flap motion constraints imposed by the elastomer material. This method is shown to be able to satisfy the constraints. Real-time drag minimization control is considered to be an important consideration for PAAW technology. Drag minimization control has many technical challenges such as sensing and control. An initial outline of a real-time drag minimization control has already been developed and will be further investigated in the future. A simulation study of a multi-objective flight control for a flight path angle command with aeroelastic mode suppression and drag

A Multi-Objective Approach to Evolving Platooning Strategies in Intelligent Transportation Systems

NARCIS (Netherlands)

van Willigen, W; Haasdijk, E; Kester, Leon

2013-01-01

The research in this paper is inspired by a vision of intelligent vehicles that autonomously move along motorways: they join and leave trains of vehicles (platoons), overtake other vehicles, etc. We propose a multi-objective evolutionary algorithm based on NEAT and SPEA2 that evolves high-level

A multi-objective multi-memetic algorithm for network-wide conflict-free 4D flight trajectories planning

Institute of Scientific and Technical Information of China (English)

Su YAN; Kaiquan CAI

2017-01-01

Under the demand of strategic air traffic flow management and the concept of trajectory based operations (TBO),the network-wide 4D flight trajectories planning (N4DFTP) problem has been investigated with the purpose of safely and efficiently allocating 4D trajectories (4DTs) (3D position and time) for all the flights in the whole airway network.Considering that the introduction of large-scale 4DTs inevitably increases the problem complexity,an efficient model for strategic level conflict management is developed in this paper.Specifically,a bi-objective N4DFTP problem that aims to minimize both potential conflicts and the trajectory cost is formulated.In consideration of the large-scale,high-complexity,and multi-objective characteristics of the N4DFTP problem,a multi-objective multi-memetic algorithm (MOMMA) that incorporates an evolutionary global search framework together with three problem-specific local search operators is implemented.It is capable of rapidly and effectively allocating 4DTs via rerouting,target time controlling,and flight level changing.Additionally,to balance the ability of exploitation and exploration of the algorithm,a special hybridization scheme is adopted for the integration of local and global search.Empirical studies using real air traffic data in China with different network complexities show that the pro posed MOMMA is effective to solve the N4DFTP problem.The solutions achieved are competitive for elaborate decision support under a TBO environment.

A multi-objective multi-memetic algorithm for network-wide conflict-free 4D flight trajectories planning

Directory of Open Access Journals (Sweden)

Su YAN

2017-06-01

Full Text Available Under the demand of strategic air traffic flow management and the concept of trajectory based operations (TBO, the network-wide 4D flight trajectories planning (N4DFTP problem has been investigated with the purpose of safely and efficiently allocating 4D trajectories (4DTs (3D position and time for all the flights in the whole airway network. Considering that the introduction of large-scale 4DTs inevitably increases the problem complexity, an efficient model for strategic-level conflict management is developed in this paper. Specifically, a bi-objective N4DFTP problem that aims to minimize both potential conflicts and the trajectory cost is formulated. In consideration of the large-scale, high-complexity, and multi-objective characteristics of the N4DFTP problem, a multi-objective multi-memetic algorithm (MOMMA that incorporates an evolutionary global search framework together with three problem-specific local search operators is implemented. It is capable of rapidly and effectively allocating 4DTs via rerouting, target time controlling, and flight level changing. Additionally, to balance the ability of exploitation and exploration of the algorithm, a special hybridization scheme is adopted for the integration of local and global search. Empirical studies using real air traffic data in China with different network complexities show that the proposed MOMMA is effective to solve the N4DFTP problem. The solutions achieved are competitive for elaborate decision support under a TBO environment.

Moderate resolution spectrophotometry of high redshift quasars

Science.gov (United States)

Schneider, Donald P.; Schmidt, Maarten; Gunn, James E.

1991-01-01

A uniform set of photometry and high signal-to-noise moderate resolution spectroscopy of 33 quasars with redshifts larger than 3.1 is presented. The sample consists of 17 newly discovered quasars (two with redshifts in excess of 4.4) and 16 sources drawn from the literature. The objects in this sample have r magnitudes between 17.4 and 21.4; their luminosities range from -28.8 to -24.9. Three of the 33 objects are broad absorption line quasars. A number of possible high redshift damped Ly-alpha systems were found.

Joint Conditional Random Field Filter for Multi-Object Tracking

Directory of Open Access Journals (Sweden)

Luo Ronghua

2011-03-01

Full Text Available Object tracking can improve the performance of mobile robot especially in populated dynamic environments. A novel joint conditional random field Filter (JCRFF based on conditional random field with hierarchical structure is proposed for multi-object tracking by abstracting the data associations between objects and measurements to be a sequence of labels. Since the conditional random field makes no assumptions about the dependency structure between the observations and it allows non-local dependencies between the state and the observations, the proposed method can not only fuse multiple cues including shape information and motion information to improve the stability of tracking, but also integrate moving object detection and object tracking quite well. At the same time, implementation of multi-object tracking based on JCRFF with measurements from the laser range finder on a mobile robot is studied. Experimental results with the mobile robot developed in our lab show that the proposed method has higher precision and better stability than joint probabilities data association filter (JPDAF.

Multi-User Domain Object Oriented (MOO) as a High School Procedure for Foreign Language Acquisition.

Science.gov (United States)

Backer, James A.

Foreign language students experience added difficulty when they are isolated from native speakers and from the culture of the target language. It has been posited that MOO (Multi-User Domain Object Oriented) may help overcome the geographical isolation of these students. MOOs are Internet-based virtual worlds in which people from all over the real…

Cramer-Rao Lower Bound for Support-Constrained and Pixel-Based Multi-Frame Blind Deconvolution (Postprint)

National Research Council Canada - National Science Library

Matson, Charles; Haji, Aiim

2006-01-01

Multi-frame blind deconvolution (MFBD) algorithms can be used to reconstruct a single high-resolution image of an object from one or more measurement frames of that are blurred and noisy realizations of that object...

Spatial Ensemble Postprocessing of Precipitation Forecasts Using High Resolution Analyses

Science.gov (United States)

Lang, Moritz N.; Schicker, Irene; Kann, Alexander; Wang, Yong

2017-04-01

Ensemble prediction systems are designed to account for errors or uncertainties in the initial and boundary conditions, imperfect parameterizations, etc. However, due to sampling errors and underestimation of the model errors, these ensemble forecasts tend to be underdispersive, and to lack both reliability and sharpness. To overcome such limitations, statistical postprocessing methods are commonly applied to these forecasts. In this study, a full-distributional spatial post-processing method is applied to short-range precipitation forecasts over Austria using Standardized Anomaly Model Output Statistics (SAMOS). Following Stauffer et al. (2016), observation and forecast fields are transformed into standardized anomalies by subtracting a site-specific climatological mean and dividing by the climatological standard deviation. Due to the need of fitting only a single regression model for the whole domain, the SAMOS framework provides a computationally inexpensive method to create operationally calibrated probabilistic forecasts for any arbitrary location or for all grid points in the domain simultaneously. Taking advantage of the INCA system (Integrated Nowcasting through Comprehensive Analysis), high resolution analyses are used for the computation of the observed climatology and for model training. The INCA system operationally combines station measurements and remote sensing data into real-time objective analysis fields at 1 km-horizontal resolution and 1 h-temporal resolution. The precipitation forecast used in this study is obtained from a limited area model ensemble prediction system also operated by ZAMG. The so called ALADIN-LAEF provides, by applying a multi-physics approach, a 17-member forecast at a horizontal resolution of 10.9 km and a temporal resolution of 1 hour. The performed SAMOS approach statistically combines the in-house developed high resolution analysis and ensemble prediction system. The station-based validation of 6 hour precipitation sums

Optimization of Fuel Consumption and Emissions for Auxiliary Power Unit Based on Multi-Objective Optimization Model

Directory of Open Access Journals (Sweden)

Yongpeng Shen

2016-02-01

Full Text Available Auxiliary power units (APUs are widely used for electric power generation in various types of electric vehicles, improvements in fuel economy and emissions of these vehicles directly depend on the operating point of the APUs. In order to balance the conflicting goals of fuel consumption and emissions reduction in the process of operating point choice, the APU operating point optimization problem is formulated as a constrained multi-objective optimization problem (CMOP firstly. The four competing objectives of this CMOP are fuel-electricity conversion cost, hydrocarbon (HC emissions, carbon monoxide (CO emissions and nitric oxide (NO x emissions. Then, the multi-objective particle swarm optimization (MOPSO algorithm and weighted metric decision making method are employed to solve the APU operating point multi-objective optimization model. Finally, bench experiments under New European driving cycle (NEDC, Federal test procedure (FTP and high way fuel economy test (HWFET driving cycles show that, compared with the results of the traditional fuel consumption single-objective optimization approach, the proposed multi-objective optimization approach shows significant improvements in emissions performance, at the expense of a slight drop in fuel efficiency.

A procedure for multi-objective optimization of tire design parameters

OpenAIRE

Nikola Korunović; Miloš Madić; Miroslav Trajanović; Miroslav Radovanović

2015-01-01

The identification of optimal tire design parameters for satisfying different requirements, i.e. tire performance characteristics, plays an essential role in tire design. In order to improve tire performance characteristics, formulation and solving of multi-objective optimization problem must be performed. This paper presents a multi-objective optimization procedure for determination of optimal tire design parameters for simultaneous minimization of strain energy density at two distinctive zo...

Simulating the Agulhas system in global ocean models - nesting vs. multi-resolution unstructured meshes

Science.gov (United States)

Biastoch, Arne; Sein, Dmitry; Durgadoo, Jonathan V.; Wang, Qiang; Danilov, Sergey

2018-01-01

Many questions in ocean and climate modelling require the combined use of high resolution, global coverage and multi-decadal integration length. For this combination, even modern resources limit the use of traditional structured-mesh grids. Here we compare two approaches: A high-resolution grid nested into a global model at coarser resolution (NEMO with AGRIF) and an unstructured-mesh grid (FESOM) which allows to variably enhance resolution where desired. The Agulhas system around South Africa is used as a testcase, providing an energetic interplay of a strong western boundary current and mesoscale dynamics. Its open setting into the horizontal and global overturning circulations also requires global coverage. Both model configurations simulate a reasonable large-scale circulation. Distribution and temporal variability of the wind-driven circulation are quite comparable due to the same atmospheric forcing. However, the overturning circulation differs, owing each model's ability to represent formation and spreading of deep water masses. In terms of regional, high-resolution dynamics, all elements of the Agulhas system are well represented. Owing to the strong nonlinearity in the system, Agulhas Current transports of both configurations and in comparison with observations differ in strength and temporal variability. Similar decadal trends in Agulhas Current transport and Agulhas leakage are linked to the trends in wind forcing.

Multi-objective and multi-physics optimization methodology for SFR core: application to CFV concept

International Nuclear Information System (INIS)

Fabbris, Olivier

2014-01-01

Nuclear reactor core design is a highly multidisciplinary task where neutronics, thermal-hydraulics, fuel thermo-mechanics and fuel cycle are involved. The problem is moreover multi-objective (several performances) and highly dimensional (several tens of design parameters).As the reference deterministic calculation codes for core characterization require important computing resources, the classical design method is not well suited to investigate and optimize new innovative core concepts. To cope with these difficulties, a new methodology has been developed in this thesis. Our work is based on the development and validation of simplified neutronics and thermal-hydraulics calculation schemes allowing the full characterization of Sodium-cooled Fast Reactor core regarding both neutronics performances and behavior during thermal hydraulic dimensioning transients.The developed methodology uses surrogate models (or meta-models) able to replace the neutronics and thermal-hydraulics calculation chain. Advanced mathematical methods for the design of experiment, building and validation of meta-models allows substituting this calculation chain by regression models with high prediction capabilities.The methodology is applied on a very large design space to a challenging core called CFV (French acronym for low void effect core) with a large gain on the sodium void effect. Global sensitivity analysis leads to identify the significant design parameters on the core design and its behavior during unprotected transient which can lead to severe accidents. Multi-objective optimizations lead to alternative core configurations with significantly improved performances. Validation results demonstrate the relevance of the methodology at the pre-design stage of a Sodium-cooled Fast Reactor core. (author) [fr

3D OBJECT COORDINATES EXTRACTION BY RADARGRAMMETRY AND MULTI STEP IMAGE MATCHING

Directory of Open Access Journals (Sweden)

A. Eftekhari

2013-09-01

Full Text Available Nowadays by high resolution SAR imaging systems as Radarsat-2, TerraSAR-X and COSMO-skyMed, three-dimensional terrain data extraction using SAR images is growing. InSAR and Radargrammetry are two most common approaches for removing 3D object coordinate from SAR images. Research has shown that extraction of terrain elevation data using satellite repeat pass interferometry SAR technique due to atmospheric factors and the lack of coherence between the images in areas with dense vegetation cover is a problematic. So the use of Radargrammetry technique can be effective. Generally height derived method by Radargrammetry consists of two stages: Images matching and space intersection. In this paper we propose a multi-stage algorithm founded on the combination of feature based and area based image matching. Then the RPCs that calculate for each images use for extracting 3D coordinate in matched points. At the end, the coordinates calculating that compare with coordinates extracted from 1 meters DEM. The results show root mean square errors for 360 points are 3.09 meters. We use a pair of spotlight TerraSAR-X images from JAM (IRAN in this article.

Optimal thickness of a monocrystal line object in atomic plane visualization on its image in a high-resolution electron microscope

International Nuclear Information System (INIS)

Grishina, T.A.; Sviridova, V.Yu.

1983-01-01

Theoretical and experimental investigation of the influence of the FCC-lattice crystal (gold, nickel) thickness on conditions of visulization of atomic plane projections (APP) on the crystal image in a transmission high-resolution electron microscope (THREM) is reported. Results of electron diffraction theory are used for theoretical investigation. Calculation analysis of the influence of the monocrystal thickness and orientation on conitions of visualization of APP and atomic columns in monocrystal images formed in THREM in multibeam regimes with inclined and axial illumination is conducted. It is shown that, to visualize the atomic column projections in a crystal image formed in the multibeam regime with axial illumination, optimal are the thicknesses from 0.1 xisub(min) to 0.25 xisub(min) and at some object orientations also the thicknesses from 0.8 xisub(min) to 0.9 xisub(min), where xisub(min) is the extinction length minimum for the given orientation. It is shown that, to realize the ultimate resolutions in multibeam regimes both with inclined and axial illumination the optimal thickness of the object is 0.63 xisub(min). Satisfactory coincidence of theoretical and experimental data is obtained

High-resolution wavefront shaping with a photonic crystal fiber for multimode fiber imaging

NARCIS (Netherlands)

Amitonova, L. V.; Descloux, A.; Petschulat, J.; Frosz, M. H.; Ahmed, G.; Babic, F.; Jiang, X.; Mosk, A. P.; Russell, P. S. J.; Pinkse, P.W.H.

2016-01-01

We demonstrate that a high-numerical-aperture photonic crystal fiber allows lensless focusing at an unparalleled res- olution by complex wavefront shaping. This paves the way toward high-resolution imaging exceeding the capabilities of imaging with multi-core single-mode optical fibers. We analyze

A Survey of Multi-Objective Sequential Decision-Making

OpenAIRE

Roijers, D.M.; Vamplew, P.; Whiteson, S.; Dazeley, R.

2013-01-01

Sequential decision-making problems with multiple objectives arise naturally in practice and pose unique challenges for research in decision-theoretic planning and learning, which has largely focused on single-objective settings. This article surveys algorithms designed for sequential decision-making problems with multiple objectives. Though there is a growing body of literature on this subject, little of it makes explicit under what circumstances special methods are needed to solve multi-obj...

Multi-objective trajectory optimization of Space Manoeuvre Vehicle using adaptive differential evolution and modified game theory

Science.gov (United States)

Chai, Runqi; Savvaris, Al; Tsourdos, Antonios; Chai, Senchun

2017-07-01

Highly constrained trajectory optimization for Space Manoeuvre Vehicles (SMV) is a challenging problem. In practice, this problem becomes more difficult when multiple mission requirements are taken into account. Because of the nonlinearity in the dynamic model and even the objectives, it is usually hard for designers to generate a compromised trajectory without violating strict path and box constraints. In this paper, a new multi-objective SMV optimal control model is formulated and parameterized using combined shooting-collocation technique. A modified game theory approach, coupled with an adaptive differential evolution algorithm, is designed in order to generate the pareto front of the multi-objective trajectory optimization problem. In addition, to improve the quality of obtained solutions, a control logic is embedded in the framework of the proposed approach. Several existing multi-objective evolutionary algorithms are studied and compared with the proposed method. Simulation results indicate that without driving the solution out of the feasible region, the proposed method can perform better in terms of convergence ability and convergence speed than its counterparts. Moreover, the quality of the pareto set generated using the proposed method is higher than other multi-objective evolutionary algorithms, which means the newly proposed algorithm is more attractive for solving multi-criteria SMV trajectory planning problem.

3D high-resolution radar imaging of small body interiors

Science.gov (United States)

Sava, Paul; Asphaug, Erik

2017-10-01

Answering fundamental questions about the origin and evolution of small planetary bodies hinges on our ability to image their interior structure in detail and at high resolution (Asphaug, 2009). We often infer internal structure from surface observations, e.g. that comet 67P/Churyumov-Gerasimenko is a primordial agglomeration of cometesimals (Massironi et al., 2015). However, the interior structure is not easily accessible without systematic imaging using, e.g., radar transmission and reflection data, as suggested by the CONSERT experiment on Rosetta. Interior imaging depends on observations from multiple viewpoints, as in medical tomography.We discuss radar imaging using methodology adapted from terrestrial exploration seismology (Sava et al., 2015). We primarily focus on full wavefield methods that facilitate high quality imaging of small body interiors characterized by complex structure and large contrasts of physical properties. We consider the case of a monostatic system (co-located transmitters and receivers) operated at two frequency bands, centered around 5 and 15 MHz, from a spacecraft in slow polar orbit around a spinning comet nucleus. Assuming that the spin period is significantly (e.g. 5x) faster than the orbital period, this configuration allows repeated views from multiple directions (Safaeinili et al., 2002)Using realistic numerical experiments, we argue that (1) the comet/asteroid imaging problem is intrinsically 3D and conventional SAR methodology does not satisfy imaging, sampling and resolution requirements; (2) imaging at different frequency bands can provide information about internal surfaces (through migration) and internal volumes (through tomography); (3) interior imaging can be accomplished progressively as data are being acquired through successive orbits around the studied object; (4) imaging resolution can go beyond the apparent radar frequency band by deconvolution of the point-spread-function characterizing the imaging system; and (5

Resolution Enhancement of Multilook Imagery

Energy Technology Data Exchange (ETDEWEB)

Galbraith, Amy E. [Univ. of Arizona, Tucson, AZ (United States)

2004-07-01

This dissertation studies the feasibility of enhancing the spatial resolution of multi-look remotely-sensed imagery using an iterative resolution enhancement algorithm known as Projection Onto Convex Sets (POCS). A multi-angle satellite image modeling tool is implemented, and simulated multi-look imagery is formed to test the resolution enhancement algorithm. Experiments are done to determine the optimal con guration and number of multi-angle low-resolution images needed for a quantitative improvement in the spatial resolution of the high-resolution estimate. The important topic of aliasing is examined in the context of the POCS resolution enhancement algorithm performance. In addition, the extension of the method to multispectral sensor images is discussed and an example is shown using multispectral confocal fluorescence imaging microscope data. Finally, the remote sensing issues of atmospheric path radiance and directional reflectance variations are explored to determine their effect on the resolution enhancement performance.

Visual short-term memory for high resolution associations is impaired in patients with medial temporal lobe damage.

Science.gov (United States)

Koen, Joshua D; Borders, Alyssa A; Petzold, Michael T; Yonelinas, Andrew P

2017-02-01

The medial temporal lobe (MTL) plays a critical role in episodic long-term memory, but whether the MTL is necessary for visual short-term memory is controversial. Some studies have indicated that MTL damage disrupts visual short-term memory performance whereas other studies have failed to find such evidence. To account for these mixed results, it has been proposed that the hippocampus is critical in supporting short-term memory for high resolution complex bindings, while the cortex is sufficient to support simple, low resolution bindings. This hypothesis was tested in the current study by assessing visual short-term memory in patients with damage to the MTL and controls for high resolution and low resolution object-location and object-color associations. In the location tests, participants encoded sets of two or four objects in different locations on the screen. After each set, participants performed a two-alternative forced-choice task in which they were required to discriminate the object in the target location from the object in a high or low resolution lure location (i.e., the object locations were very close or far away from the target location, respectively). Similarly, in the color tests, participants were presented with sets of two or four objects in a different color and, after each set, were required to discriminate the object in the target color from the object in a high or low resolution lure color (i.e., the lure color was very similar or very different, respectively, to the studied color). The patients were significantly impaired in visual short-term memory, but importantly, they were more impaired for high resolution object-location and object-color bindings. The results are consistent with the proposal that the hippocampus plays a critical role in forming and maintaining complex, high resolution bindings. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Time-Frequency Feature Representation Using Multi-Resolution Texture Analysis and Acoustic Activity Detector for Real-Life Speech Emotion Recognition

Directory of Open Access Journals (Sweden)

Kun-Ching Wang

2015-01-01

Full Text Available The classification of emotional speech is mostly considered in speech-related research on human-computer interaction (HCI. In this paper, the purpose is to present a novel feature extraction based on multi-resolutions texture image information (MRTII. The MRTII feature set is derived from multi-resolution texture analysis for characterization and classification of different emotions in a speech signal. The motivation is that we have to consider emotions have different intensity values in different frequency bands. In terms of human visual perceptual, the texture property on multi-resolution of emotional speech spectrogram should be a good feature set for emotion classification in speech. Furthermore, the multi-resolution analysis on texture can give a clearer discrimination between each emotion than uniform-resolution analysis on texture. In order to provide high accuracy of emotional discrimination especially in real-life, an acoustic activity detection (AAD algorithm must be applied into the MRTII-based feature extraction. Considering the presence of many blended emotions in real life, in this paper make use of two corpora of naturally-occurring dialogs recorded in real-life call centers. Compared with the traditional Mel-scale Frequency Cepstral Coefficients (MFCC and the state-of-the-art features, the MRTII features also can improve the correct classification rates of proposed systems among different language databases. Experimental results show that the proposed MRTII-based feature information inspired by human visual perception of the spectrogram image can provide significant classification for real-life emotional recognition in speech.

Irrigation water allocation optimization using multi-objective evolutionary algorithm (MOEA) - a review

Science.gov (United States)

Fanuel, Ibrahim Mwita; Mushi, Allen; Kajunguri, Damian

2018-03-01

This paper analyzes more than 40 papers with a restricted area of application of Multi-Objective Genetic Algorithm, Non-Dominated Sorting Genetic Algorithm-II and Multi-Objective Differential Evolution (MODE) to solve the multi-objective problem in agricultural water management. The paper focused on different application aspects which include water allocation, irrigation planning, crop pattern and allocation of available land. The performance and results of these techniques are discussed. The review finds that there is a potential to use MODE to analyzed the multi-objective problem, the application is more significance due to its advantage of being simple and powerful technique than any Evolutionary Algorithm. The paper concludes with the hopeful new trend of research that demand effective use of MODE; inclusion of benefits derived from farm byproducts and production costs into the model.

Multi-Objective Weather Routing of Sailing Vessels

Directory of Open Access Journals (Sweden)

Życzkowski Marcin

2017-12-01

Full Text Available The paper presents a multi-objective deterministic method of weather routing for sailing vessels. Depending on a particular purpose of sailboat weather routing, the presented method makes it possible to customize the criteria and constraints so as to fit a particular user’s needs. Apart from a typical shortest time criterion, safety and comfort can also be taken into account. Additionally, the method supports dynamic weather data: in its present version short-term, mid-term and long-term term weather forecasts are used during optimization process. In the paper the multi-objective optimization problem is first defined and analysed. Following this, the proposed method solving this problem is described in detail. The method has been implemented as an online SailAssistance application. Some representative examples solutions are presented, emphasizing the effects of applying different criteria or different values of customized parameters.

Using object-based image analysis to conduct high-resolution conifer extraction at regional spatial scales

Science.gov (United States)

Coates, Peter S.; Gustafson, K. Benjamin; Roth, Cali L.; Chenaille, Michael P.; Ricca, Mark A.; Mauch, Kimberly; Sanchez-Chopitea, Erika; Kroger, Travis J.; Perry, William M.; Casazza, Michael L.

2017-08-10

The distribution and abundance of pinyon (Pinus monophylla) and juniper (Juniperus osteosperma, J. occidentalis) trees (hereinafter, "pinyon-juniper") in sagebrush (Artemisia spp.) ecosystems of the Great Basin in the Western United States has increased substantially since the late 1800s. Distributional expansion and infill of pinyon-juniper into sagebrush ecosystems threatens the ecological function and economic viability of these ecosystems within the Great Basin, and is now a major contemporary challenge facing land and wildlife managers. Particularly, pinyon-juniper encroachment into intact sagebrush ecosystems has been identified as a primary threat facing populations of greater sage-grouse (Centrocercus urophasianus; hereinafter, "sage-grouse"), which is a sagebrush obligate species. Even seemingly innocuous scatterings of isolated pinyon-juniper in an otherwise intact sagebrush landscape can negatively affect survival and reproduction of sage-grouse. Therefore, accurate and high-resolution maps of pinyon-juniper distribution and abundance (indexed by canopy cover) across broad geographic extents would help guide land management decisions that better target areas for pinyon-juniper removal projects (for example, fuel reduction, habitat improvement for sage-grouse, and other sagebrush species) and facilitate science that further quantifies ecological effects of pinyon-juniper encroachment on sage-grouse populations and sagebrush ecosystem processes. Hence, we mapped pinyon-juniper (referred to as conifers for actual mapping) at a 1 × 1-meter (m) high resolution across the entire range of previously mapped sage-grouse habitat in Nevada and northeastern California.We used digital orthophoto quad tiles from National Agriculture Imagery Program (2010, 2013) as base imagery, and then classified conifers using automated feature extraction methodology with the program Feature Analyst™. This method relies on machine learning algorithms that extract features from

High Resolution Tracking Devices Based on Capillaries Filled with Liquid Scintillator

CERN Multimedia

Bonekamper, D; Vassiltchenko, V; Wolff, T

2002-01-01

%RD46 %title\\\\ \\\\The aim of the project is to develop high resolution tracking devices based on thin glass capillary arrays filled with liquid scintillator. This technique provides high hit densities and a position resolution better than 20 $\\mu$m. Further, their radiation hardness makes them superior to other types of tracking devices with comparable performance. Therefore, the technique is attractive for inner tracking in collider experiments, microvertex devices, or active targets for short-lived particle detection. High integration levels in the read-out based on the use of multi-pixel photon detectors and the possibility of optical multiplexing allow to reduce considerably the number of output channels, and, thus, the cost for the detector.\\\\ \\\\New optoelectronic devices have been developed and tested: the megapixel Electron Bombarded CCD (EBCCD), a high resolution image-detector having an outstanding capability of single photo-electron detection; the Vacuum Image Pipeline (VIP), a high-speed gateable pi...

Multi-objective optimization of p-xylene oxidation process using an improved self-adaptive differential evolution algorithm

Institute of Scientific and Technical Information of China (English)

Lili Tao; Bin Xu; Zhihua Hu; Weimin Zhong

2017-01-01

The rise in the use of global polyester fiber contributed to strong demand of the Terephthalic acid (TPA). The liquid-phase catalytic oxidation of p-xylene (PX) to TPA is regarded as a critical and efficient chemical process in industry [1]. PX oxidation reaction involves many complex side reactions, among which acetic acid combustion and PX combustion are the most important. As the target product of this oxidation process, the quality and yield of TPA are of great concern. However, the improvement of the qualified product yield can bring about the high energy consumption, which means that the economic objectives of this process cannot be achieved simulta-neously because the two objectives are in conflict with each other. In this paper, an improved self-adaptive multi-objective differential evolution algorithm was proposed to handle the multi-objective optimization prob-lems. The immune concept is introduced to the self-adaptive multi-objective differential evolution algorithm (SADE) to strengthen the local search ability and optimization accuracy. The proposed algorithm is successfully tested on several benchmark test problems, and the performance measures such as convergence and divergence metrics are calculated. Subsequently, the multi-objective optimization of an industrial PX oxidation process is carried out using the proposed immune self-adaptive multi-objective differential evolution algorithm (ISADE). Optimization results indicate that application of ISADE can greatly improve the yield of TPA with low combustion loss without degenerating TA quality.

Comparison of four machine learning methods for object-oriented change detection in high-resolution satellite imagery

Science.gov (United States)

Bai, Ting; Sun, Kaimin; Deng, Shiquan; Chen, Yan

2018-03-01

High resolution image change detection is one of the key technologies of remote sensing application, which is of great significance for resource survey, environmental monitoring, fine agriculture, military mapping and battlefield environment detection. In this paper, for high-resolution satellite imagery, Random Forest (RF), Support Vector Machine (SVM), Deep belief network (DBN), and Adaboost models were established to verify the possibility of different machine learning applications in change detection. In order to compare detection accuracy of four machine learning Method, we applied these four machine learning methods for two high-resolution images. The results shows that SVM has higher overall accuracy at small samples compared to RF, Adaboost, and DBN for binary and from-to change detection. With the increase in the number of samples, RF has higher overall accuracy compared to Adaboost, SVM and DBN.

Pipelining Computational Stages of the Tomographic Reconstructor for Multi-Object Adaptive Optics on a Multi?GPU System

KAUST Repository

Charara, Ali

2014-05-04

European Extreme Large Telescope (E-ELT) is a high priority project in ground based astronomy that aims at constructing the largest telescope ever built. MOSAIC is an instrument proposed for E-ELT using Multi- Object Adaptive Optics (MOAO) technique for astronomical telescopes, which compensates for effects of atmospheric turbulence on image quality, and operates on patches across a large FoV.

Pipelining Computational Stages of the Tomographic Reconstructor for Multi-Object Adaptive Optics on a Multi?GPU System

KAUST Repository

Charara, Ali; Ltaief, Hatem; Gratadour, Damien; Keyes, David E.; Sevin, Arnaud; Abdelfattah, Ahmad; Gendron, Eric; Morel, Carine; Vidal, Fabrice

2014-01-01

European Extreme Large Telescope (E-ELT) is a high priority project in ground based astronomy that aims at constructing the largest telescope ever built. MOSAIC is an instrument proposed for E-ELT using Multi- Object Adaptive Optics (MOAO) technique for astronomical telescopes, which compensates for effects of atmospheric turbulence on image quality, and operates on patches across a large FoV.

Assessment of engineered surfaces roughness by high-resolution 3D SEM photogrammetry

Energy Technology Data Exchange (ETDEWEB)

Gontard, L.C., E-mail: lionelcg@gmail.com [Departamento de Ciencia de los Materiales e Ingeniería Metalúrgica y Química Inorgánica, Universidad de Cádiz, Puerto Real 11510 (Spain); López-Castro, J.D.; González-Rovira, L. [Departamento de Ciencia de los Materiales e Ingeniería Metalúrgica y Química Inorgánica, Escuela Superior de Ingeniería, Laboratorio de Corrosión, Universidad de Cádiz, Puerto Real 11519 (Spain); Vázquez-Martínez, J.M. [Departamento de Ingeniería Mecánica y Diseño Industrial, Escuela Superior de Ingeniería, Universidad de Cádiz, Puerto Real 11519 (Spain); Varela-Feria, F.M. [Servicio de Microscopía Centro de Investigación, Tecnología e Innovación (CITIUS), Universidad de Sevilla, Av. Reina Mercedes 4b, 41012 Sevilla (Spain); Marcos, M. [Departamento de Ingeniería Mecánica y Diseño Industrial, Escuela Superior de Ingeniería, Universidad de Cádiz, Puerto Real 11519 (Spain); and others

2017-06-15

Highlights: • We describe a method to acquire a high-angle tilt series of SEM images that is symmetrical respect to the zero tilt of the sample stage. The method can be applied in any SEM microscope. • Using the method, high-resolution 3D SEM photogrammetry can be applied on planar surfaces. • 3D models of three surfaces patterned with grooves are reconstructed with high resolution using multi-view freeware photogrammetry software as described in LC Gontard et al. Ultramicroscopy, 2016. • From the 3D models roughness parameters are measured • 3D SEM high-resolution photogrammetry is compared with two conventional methods used for roughness characetrization: stereophotogrammetry and contact profilometry. • It provides three-dimensional information with high-resolution that is out of reach for any other metrological technique. - Abstract: We describe a methodology to obtain three-dimensional models of engineered surfaces using scanning electron microscopy and multi-view photogrammetry (3DSEM). For the reconstruction of the 3D models of the surfaces we used freeware available in the cloud. The method was applied to study the surface roughness of metallic samples patterned with parallel grooves by means of laser. The results are compared with measurements obtained using stylus profilometry (PR) and SEM stereo-photogrammetry (SP). The application of 3DSEM is more time demanding than PR or SP, but it provides a more accurate representation of the surfaces. The results obtained with the three techniques are compared by investigating the influence of sampling step on roughness parameters.

High-resolution intravital microscopy.

Directory of Open Access Journals (Sweden)

Volker Andresen

Full Text Available Cellular communication constitutes a fundamental mechanism of life, for instance by permitting transfer of information through synapses in the nervous system and by leading to activation of cells during the course of immune responses. Monitoring cell-cell interactions within living adult organisms is crucial in order to draw conclusions on their behavior with respect to the fate of cells, tissues and organs. Until now, there is no technology available that enables dynamic imaging deep within the tissue of living adult organisms at sub-cellular resolution, i.e. detection at the level of few protein molecules. Here we present a novel approach called multi-beam striped-illumination which applies for the first time the principle and advantages of structured-illumination, spatial modulation of the excitation pattern, to laser-scanning-microscopy. We use this approach in two-photon-microscopy--the most adequate optical deep-tissue imaging-technique. As compared to standard two-photon-microscopy, it achieves significant contrast enhancement and up to 3-fold improved axial resolution (optical sectioning while photobleaching, photodamage and acquisition speed are similar. Its imaging depth is comparable to multifocal two-photon-microscopy and only slightly less than in standard single-beam two-photon-microscopy. Precisely, our studies within mouse lymph nodes demonstrated 216% improved axial and 23% improved lateral resolutions at a depth of 80 µm below the surface. Thus, we are for the first time able to visualize the dynamic interactions between B cells and immune complex deposits on follicular dendritic cells within germinal centers (GCs of live mice. These interactions play a decisive role in the process of clonal selection, leading to affinity maturation of the humoral immune response. This novel high-resolution intravital microscopy method has a huge potential for numerous applications in neurosciences, immunology, cancer research and

High-Resolution Intravital Microscopy

Science.gov (United States)

Andresen, Volker; Pollok, Karolin; Rinnenthal, Jan-Leo; Oehme, Laura; Günther, Robert; Spiecker, Heinrich; Radbruch, Helena; Gerhard, Jenny; Sporbert, Anje; Cseresnyes, Zoltan; Hauser, Anja E.; Niesner, Raluca

2012-01-01

Cellular communication constitutes a fundamental mechanism of life, for instance by permitting transfer of information through synapses in the nervous system and by leading to activation of cells during the course of immune responses. Monitoring cell-cell interactions within living adult organisms is crucial in order to draw conclusions on their behavior with respect to the fate of cells, tissues and organs. Until now, there is no technology available that enables dynamic imaging deep within the tissue of living adult organisms at sub-cellular resolution, i.e. detection at the level of few protein molecules. Here we present a novel approach called multi-beam striped-illumination which applies for the first time the principle and advantages of structured-illumination, spatial modulation of the excitation pattern, to laser-scanning-microscopy. We use this approach in two-photon-microscopy - the most adequate optical deep-tissue imaging-technique. As compared to standard two-photon-microscopy, it achieves significant contrast enhancement and up to 3-fold improved axial resolution (optical sectioning) while photobleaching, photodamage and acquisition speed are similar. Its imaging depth is comparable to multifocal two-photon-microscopy and only slightly less than in standard single-beam two-photon-microscopy. Precisely, our studies within mouse lymph nodes demonstrated 216% improved axial and 23% improved lateral resolutions at a depth of 80 µm below the surface. Thus, we are for the first time able to visualize the dynamic interactions between B cells and immune complex deposits on follicular dendritic cells within germinal centers (GCs) of live mice. These interactions play a decisive role in the process of clonal selection, leading to affinity maturation of the humoral immune response. This novel high-resolution intravital microscopy method has a huge potential for numerous applications in neurosciences, immunology, cancer research and developmental biology

Design of a centrifugal compressor impeller using multi-objective optimization algorithm

International Nuclear Information System (INIS)

Kim, Jin Hyuk; Husain, Afzal; Kim, Kwang Yong; Choi, Jae Ho

2009-01-01

This paper presents a design optimization of a centrifugal compressor impeller with hybrid multi-objective evolutionary algorithm (hybrid MOEA). Reynolds-averaged Navier-Stokes equations with shear stress transport turbulence model are discretized by finite volume approximations and solved on hexahedral grids for flow analyses. Two objectives, i.e., isentropic efficiency and total pressure ratio are selected with four design variables defining impeller hub and shroud contours in meridional contours to optimize the system. Non-dominated Sorting of Genetic Algorithm (NSGA-II) with ε-constraint strategy for local search coupled with Radial Basis Neural Network model is used for multi-objective optimization. The optimization results show that isentropic efficiencies and total pressure ratios of the five cluster points at the Pareto-optimal solutions are enhanced by multi-objective optimization.

Design of a centrifugal compressor impeller using multi-objective optimization algorithm

Energy Technology Data Exchange (ETDEWEB)

Kim, Jin Hyuk; Husain, Afzal; Kim, Kwang Yong [Inha University, Incheon (Korea, Republic of); Choi, Jae Ho [Samsung Techwin Co., Ltd., Changwon (Korea, Republic of)

2009-07-01

This paper presents a design optimization of a centrifugal compressor impeller with hybrid multi-objective evolutionary algorithm (hybrid MOEA). Reynolds-averaged Navier-Stokes equations with shear stress transport turbulence model are discretized by finite volume approximations and solved on hexahedral grids for flow analyses. Two objectives, i.e., isentropic efficiency and total pressure ratio are selected with four design variables defining impeller hub and shroud contours in meridional contours to optimize the system. Non-dominated Sorting of Genetic Algorithm (NSGA-II) with {epsilon}-constraint strategy for local search coupled with Radial Basis Neural Network model is used for multi-objective optimization. The optimization results show that isentropic efficiencies and total pressure ratios of the five cluster points at the Pareto-optimal solutions are enhanced by multi-objective optimization.

Hybrid Robust Multi-Objective Evolutionary Optimization Algorithm

Science.gov (United States)

2009-03-10

xfar by xint. Else, generate a new individual, using the Sobol pseudo- random sequence generator within the upper and lower bounds of the variables...12. Deb, K., Multi-Objective Optimization Using Evolutionary Algorithms, John Wiley & Sons. 2002. 13. Sobol , I. M., "Uniformly Distributed Sequences

Multi-scale climate modelling over Southern Africa using a variable-resolution global model

CSIR Research Space (South Africa)

Engelbrecht, FA

2011-12-01

Full Text Available -mail: fengelbrecht@csir.co.za Multi-scale climate modelling over Southern Africa using a variable-resolution global model FA Engelbrecht1, 2*, WA Landman1, 3, CJ Engelbrecht4, S Landman5, MM Bopape1, B Roux6, JL McGregor7 and M Thatcher7 1 CSIR Natural... improvement. Keywords: multi-scale climate modelling, variable-resolution atmospheric model Introduction Dynamic climate models have become the primary tools for the projection of future climate change, at both the global and regional scales. Dynamic...

Multi-Objective Particle Swarm Optimization Approach for Cost-Based Feature Selection in Classification.

Science.gov (United States)

Zhang, Yong; Gong, Dun-Wei; Cheng, Jian

2017-01-01

Feature selection is an important data-preprocessing technique in classification problems such as bioinformatics and signal processing. Generally, there are some situations where a user is interested in not only maximizing the classification performance but also minimizing the cost that may be associated with features. This kind of problem is called cost-based feature selection. However, most existing feature selection approaches treat this task as a single-objective optimization problem. This paper presents the first study of multi-objective particle swarm optimization (PSO) for cost-based feature selection problems. The task of this paper is to generate a Pareto front of nondominated solutions, that is, feature subsets, to meet different requirements of decision-makers in real-world applications. In order to enhance the search capability of the proposed algorithm, a probability-based encoding technology and an effective hybrid operator, together with the ideas of the crowding distance, the external archive, and the Pareto domination relationship, are applied to PSO. The proposed PSO-based multi-objective feature selection algorithm is compared with several multi-objective feature selection algorithms on five benchmark datasets. Experimental results show that the proposed algorithm can automatically evolve a set of nondominated solutions, and it is a highly competitive feature selection method for solving cost-based feature selection problems.

A Pareto-based multi-objective optimization algorithm to design energy-efficient shading devices

International Nuclear Information System (INIS)

Khoroshiltseva, Marina; Slanzi, Debora; Poli, Irene

2016-01-01

Highlights: • We present a multi-objective optimization algorithm for shading design. • We combine Harmony search and Pareto-based procedures. • Thermal and daylighting performances of external shading were considered. • We applied the optimization process to a residential social housing in Madrid. - Abstract: In this paper we address the problem of designing new energy-efficient static daylight devices that will surround the external windows of a residential building in Madrid. Shading devices can in fact largely influence solar gains in a building and improve thermal and lighting comforts by selectively intercepting the solar radiation and by reducing the undesirable glare. A proper shading device can therefore significantly increase the thermal performance of a building by reducing its energy demand in different climate conditions. In order to identify the set of optimal shading devices that allow a low energy consumption of the dwelling while maintaining high levels of thermal and lighting comfort for the inhabitants we derive a multi-objective optimization methodology based on Harmony Search and Pareto front approaches. The results show that the multi-objective approach here proposed is an effective procedure in designing energy efficient shading devices when a large set of conflicting objectives characterizes the performance of the proposed solutions.

A multi-objective approach for developing national energy efficiency plans

International Nuclear Information System (INIS)

Haydt, Gustavo; Leal, Vítor; Dias, Luís

2014-01-01

This paper proposes a new approach to deal with the problem of building national energy efficiency (EE) plans, considering multiple objectives instead of only energy savings. The objectives considered are minimizing the influence of energy use on climate change, minimizing the financial risk from the investment, maximizing the security of energy supply, minimizing investment costs, minimizing the impacts of building new power plants and transmission infrastructures, and maximizing the local air quality. These were identified through literature review and interaction with real decision makers. A database of measures is established, from which millions of potential EE plans can be built by combining measures and their respective degree of implementation. Finally, a hybrid multi-objective and multi-criteria decision analysis (MCDA) model is proposed to search and select the EE plans that best match the decision makers’ preferences. An illustration of the working mode and the type of results obtained from this novel hybrid model is provided through an application to Portugal. For each of five decision perspectives a wide range of potential best plans were identified. These wide ranges show the relevance of introducing multi-objective analysis in a comprehensive search space as a tool to inform decisions about national EE plans. - Highlights: • A multiple objective approach to aid the choice of national energy efficiency plans. • A hybrid multi-objective MCDA model is proposed to search among the possible plans. • The model identified relevant plans according to five different idealized DMs. • The approach is tested with Portugal

Single and Multiple Object Tracking Using a Multi-Feature Joint Sparse Representation.

Science.gov (United States)

Hu, Weiming; Li, Wei; Zhang, Xiaoqin; Maybank, Stephen

2015-04-01

In this paper, we propose a tracking algorithm based on a multi-feature joint sparse representation. The templates for the sparse representation can include pixel values, textures, and edges. In the multi-feature joint optimization, noise or occlusion is dealt with using a set of trivial templates. A sparse weight constraint is introduced to dynamically select the relevant templates from the full set of templates. A variance ratio measure is adopted to adaptively adjust the weights of different features. The multi-feature template set is updated adaptively. We further propose an algorithm for tracking multi-objects with occlusion handling based on the multi-feature joint sparse reconstruction. The observation model based on sparse reconstruction automatically focuses on the visible parts of an occluded object by using the information in the trivial templates. The multi-object tracking is simplified into a joint Bayesian inference. The experimental results show the superiority of our algorithm over several state-of-the-art tracking algorithms.

High-resolution 3D imaging of polymerized photonic crystals by lab-based x-ray nanotomography with 50-nm resolution

Science.gov (United States)

Yin, Leilei; Chen, Ying-Chieh; Gelb, Jeff; Stevenson, Darren M.; Braun, Paul A.

2010-09-01

High resolution x-ray computed tomography is a powerful non-destructive 3-D imaging method. It can offer superior resolution on objects that are opaque or low contrast for optical microscopy. Synchrotron based x-ray computed tomography systems have been available for scientific research, but remain difficult to access for broader users. This work introduces a lab-based high-resolution x-ray nanotomography system with 50nm resolution in absorption and Zernike phase contrast modes. Using this system, we have demonstrated high quality 3-D images of polymerized photonic crystals which have been analyzed for band gap structures. The isotropic volumetric data shows excellent consistency with other characterization results.

Multi objective decision making in hybrid energy system design

Science.gov (United States)

Merino, Gabriel Guillermo

The design of grid-connected photovoltaic wind generator system supplying a farmstead in Nebraska has been undertaken in this dissertation. The design process took into account competing criteria that motivate the use of different sources of energy for electric generation. The criteria considered were 'Financial', 'Environmental', and 'User/System compatibility'. A distance based multi-objective decision making methodology was developed to rank design alternatives. The method is based upon a precedence order imposed upon the design objectives and a distance metric describing the performance of each alternative. This methodology advances previous work by combining ambiguous information about the alternatives with a decision-maker imposed precedence order in the objectives. Design alternatives, defined by the photovoltaic array and wind generator installed capacities, were analyzed using the multi-objective decision making approach. The performance of the design alternatives was determined by simulating the system using hourly data for an electric load for a farmstead and hourly averages of solar irradiation, temperature and wind speed from eight wind-solar energy monitoring sites in Nebraska. The spatial variability of the solar energy resource within the region was assessed by determining semivariogram models to krige hourly and daily solar radiation data. No significant difference was found in the predicted performance of the system when using kriged solar radiation data, with the models generated vs. using actual data. The spatial variability of the combined wind and solar energy resources was included in the design analysis by using fuzzy numbers and arithmetic. The best alternative was dependent upon the precedence order assumed for the main criteria. Alternatives with no PV array or wind generator dominated when the 'Financial' criteria preceded the others. In contrast, alternatives with a nil component of PV array but a high wind generator component

Investigating multi-objective fluence and beam orientation IMRT optimization

Science.gov (United States)

Potrebko, Peter S.; Fiege, Jason; Biagioli, Matthew; Poleszczuk, Jan

2017-07-01

Radiation Oncology treatment planning requires compromises to be made between clinical objectives that are invariably in conflict. It would be beneficial to have a ‘bird’s-eye-view’ perspective of the full spectrum of treatment plans that represent the possible trade-offs between delivering the intended dose to the planning target volume (PTV) while optimally sparing the organs-at-risk (OARs). In this work, the authors demonstrate Pareto-aware radiotherapy evolutionary treatment optimization (PARETO), a multi-objective tool featuring such bird’s-eye-view functionality, which optimizes fluence patterns and beam angles for intensity-modulated radiation therapy (IMRT) treatment planning. The problem of IMRT treatment plan optimization is managed as a combined monolithic problem, where all beam fluence and angle parameters are treated equally during the optimization. To achieve this, PARETO is built around a powerful multi-objective evolutionary algorithm, called Ferret, which simultaneously optimizes multiple fitness functions that encode the attributes of the desired dose distribution for the PTV and OARs. The graphical interfaces within PARETO provide useful information such as: the convergence behavior during optimization, trade-off plots between the competing objectives, and a graphical representation of the optimal solution database allowing for the rapid exploration of treatment plan quality through the evaluation of dose-volume histograms and isodose distributions. PARETO was evaluated for two relatively complex clinical cases, a paranasal sinus and a pancreas case. The end result of each PARETO run was a database of optimal (non-dominated) treatment plans that demonstrated trade-offs between the OAR and PTV fitness functions, which were all equally good in the Pareto-optimal sense (where no one objective can be improved without worsening at least one other). Ferret was able to produce high quality solutions even though a large number of parameters

Classification of high resolution remote sensing image based on geo-ontology and conditional random fields

Science.gov (United States)

Hong, Liang

2013-10-01

The availability of high spatial resolution remote sensing data provides new opportunities for urban land-cover classification. More geometric details can be observed in the high resolution remote sensing image, Also Ground objects in the high resolution remote sensing image have displayed rich texture, structure, shape and hierarchical semantic characters. More landscape elements are represented by a small group of pixels. Recently years, the an object-based remote sensing analysis methodology is widely accepted and applied in high resolution remote sensing image processing. The classification method based on Geo-ontology and conditional random fields is presented in this paper. The proposed method is made up of four blocks: (1) the hierarchical ground objects semantic framework is constructed based on geoontology; (2) segmentation by mean-shift algorithm, which image objects are generated. And the mean-shift method is to get boundary preserved and spectrally homogeneous over-segmentation regions ;(3) the relations between the hierarchical ground objects semantic and over-segmentation regions are defined based on conditional random fields framework ;(4) the hierarchical classification results are obtained based on geo-ontology and conditional random fields. Finally, high-resolution remote sensed image data -GeoEye, is used to testify the performance of the presented method. And the experimental results have shown the superiority of this method to the eCognition method both on the effectively and accuracy, which implies it is suitable for the classification of high resolution remote sensing image.

A hybrid multi-objective evolutionary algorithm approach for ...

Indian Academy of Sciences (India)

V K MANUPATI

for handling sequence- and machine-dependent set-up times ... algorithm has been compared to that of multi-objective particle swarm optimization (MOPSO) and conventional ..... position and cognitive learning factor are considered for.

THE PRISM MULTI-OBJECT SURVEY (PRIMUS). II. DATA REDUCTION AND REDSHIFT FITTING

Energy Technology Data Exchange (ETDEWEB)

Cool, Richard J. [MMT Observatory, Tucson, AZ 85721 (United States); Moustakas, John [Department of Physics, Siena College, 515 Loudon Rd., Loudonville, NY 12211 (United States); Blanton, Michael R.; Hogg, David W. [Center for Cosmology and Particle Physics, Department of Physics, New York University, 4 Washington Place, New York, NY 10003 (United States); Burles, Scott M. [D.E. Shaw and Co. L.P, 20400 Stevens Creek Blvd., Suite 850, Cupertino, CA 95014 (United States); Coil, Alison L.; Aird, James; Mendez, Alexander J. [Department of Physics, Center for Astrophysics and Space Sciences, University of California, 9500 Gilman Dr., La Jolla, San Diego, CA 92093 (United States); Eisenstein, Daniel J. [Harvard-Smithsonian Center for Astrophysics, 60 Garden St, MS 20, Cambridge, MA 02138 (United States); Wong, Kenneth C. [Steward Observatory, The University of Arizona, 933 N. Cherry Ave., Tucson, AZ 85721 (United States); Zhu, Guangtun [Center for Astrophysical Sciences, Department of Physics and Astronomy, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218 (United States); Bernstein, Rebecca A. [Department of Astronomy and Astrophysics, UCA/Lick Observatory, University of California, 1156 High Street, Santa Cruz, CA 95064 (United States); Bolton, Adam S. [Department of Physics and Astronomy, University of Utah, Salt Lake City, UT 84112 (United States)

2013-04-20

The PRIsm MUlti-object Survey (PRIMUS) is a spectroscopic galaxy redshift survey to z {approx} 1 completed with a low-dispersion prism and slitmasks allowing for simultaneous observations of {approx}2500 objects over 0.18 deg{sup 2}. The final PRIMUS catalog includes {approx}130,000 robust redshifts over 9.1 deg{sup 2}. In this paper, we summarize the PRIMUS observational strategy and present the data reduction details used to measure redshifts, redshift precision, and survey completeness. The survey motivation, observational techniques, fields, target selection, slitmask design, and observations are presented in Coil et al. Comparisons to existing higher-resolution spectroscopic measurements show a typical precision of {sigma}{sub z}/(1 + z) = 0.005. PRIMUS, both in area and number of redshifts, is the largest faint galaxy redshift survey completed to date and is allowing for precise measurements of the relationship between active galactic nuclei and their hosts, the effects of environment on galaxy evolution, and the build up of galactic systems over the latter half of cosmic history.

Thermodynamic design of Stirling engine using multi-objective particle swarm optimization algorithm

International Nuclear Information System (INIS)

Duan, Chen; Wang, Xinggang; Shu, Shuiming; Jing, Changwei; Chang, Huawei

2014-01-01

Highlights: • An improved thermodynamic model taking into account irreversibility parameter was developed. • A multi-objective optimization method for designing Stirling engine was investigated. • Multi-objective particle swarm optimization algorithm was adopted in the area of Stirling engine for the first time. - Abstract: In the recent years, the interest in Stirling engine has remarkably increased due to its ability to use any heat source from outside including solar energy, fossil fuels and biomass. A large number of studies have been done on Stirling cycle analysis. In the present study, a mathematical model based on thermodynamic analysis of Stirling engine considering regenerative losses and internal irreversibilities has been developed. Power output, thermal efficiency and the cycle irreversibility parameter of Stirling engine are optimized simultaneously using Particle Swarm Optimization (PSO) algorithm, which is more effective than traditional genetic algorithms. In this optimization problem, some important parameters of Stirling engine are considered as decision variables, such as temperatures of the working fluid both in the high temperature isothermal process and in the low temperature isothermal process, dead volume ratios of each heat exchanger, volumes of each working spaces, effectiveness of the regenerator, and the system charge pressure. The Pareto optimal frontier is obtained and the final design solution has been selected by Linear Programming Technique for Multidimensional Analysis of Preference (LINMAP). Results show that the proposed multi-objective optimization approach can significantly outperform traditional single objective approaches

Multi-objective optimal strategy for generating and bidding in the power market

International Nuclear Information System (INIS)

Peng Chunhua; Sun Huijuan; Guo Jianfeng; Liu Gang

2012-01-01

Highlights: ► A new benefit/risk/emission comprehensive generation optimization model is established. ► A hybrid multi-objective differential evolution optimization algorithm is designed. ► Fuzzy set theory and entropy weighting method are employed to extract the general best solution. ► The proposed approach of generating and bidding is efficient for maximizing profit and minimizing both risk and emissions. - Abstract: Based on the coordinated interaction between units output and electricity market prices, the benefit/risk/emission comprehensive generation optimization model with objectives of maximal profit and minimal bidding risk and emissions is established. A hybrid multi-objective differential evolution optimization algorithm, which successfully integrates Pareto non-dominated sorting with differential evolution algorithm and improves individual crowding distance mechanism and mutation strategy to avoid premature and unevenly search, is designed to achieve Pareto optimal set of this model. Moreover, fuzzy set theory and entropy weighting method are employed to extract one of the Pareto optimal solutions as the general best solution. Several optimization runs have been carried out on different cases of generation bidding and scheduling. The results confirm the potential and effectiveness of the proposed approach in solving the multi-objective optimization problem of generation bidding and scheduling. In addition, the comparison with the classical optimization algorithms demonstrates the superiorities of the proposed algorithm such as integrality of Pareto front, well-distributed Pareto-optimal solutions, high search speed.

Identification of mutated driver pathways in cancer using a multi-objective optimization model.

Science.gov (United States)

Zheng, Chun-Hou; Yang, Wu; Chong, Yan-Wen; Xia, Jun-Feng

2016-05-01

New-generation high-throughput technologies, including next-generation sequencing technology, have been extensively applied to solve biological problems. As a result, large cancer genomics projects such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium are producing large amount of rich and diverse data in multiple cancer types. The identification of mutated driver genes and driver pathways from these data is a significant challenge. Genome aberrations in cancer cells can be divided into two types: random 'passenger mutation' and functional 'driver mutation'. In this paper, we introduced a Multi-objective Optimization model based on a Genetic Algorithm (MOGA) to solve the maximum weight submatrix problem, which can be employed to identify driver genes and driver pathways promoting cancer proliferation. The maximum weight submatrix problem defined to find mutated driver pathways is based on two specific properties, i.e., high coverage and high exclusivity. The multi-objective optimization model can adjust the trade-off between high coverage and high exclusivity. We proposed an integrative model by combining gene expression data and mutation data to improve the performance of the MOGA algorithm in a biological context. Copyright © 2016 Elsevier Ltd. All rights reserved.

Preparation of wholemount mouse intestine for high-resolution three-dimensional imaging using two-photon microscopy.

Science.gov (United States)

Appleton, P L; Quyn, A J; Swift, S; Näthke, I

2009-05-01

Visualizing overall tissue architecture in three dimensions is fundamental for validating and integrating biochemical, cell biological and visual data from less complex systems such as cultured cells. Here, we describe a method to generate high-resolution three-dimensional image data of intact mouse gut tissue. Regions of highest interest lie between 50 and 200 mum within this tissue. The quality and usefulness of three-dimensional image data of tissue with such depth is limited owing to problems associated with scattered light, photobleaching and spherical aberration. Furthermore, the highest-quality oil-immersion lenses are designed to work at a maximum distance of image at high-resolution deep within tissue. We show that manipulating the refractive index of the mounting media and decreasing sample opacity greatly improves image quality such that the limiting factor for a standard, inverted multi-photon microscope is determined by the working distance of the objective as opposed to detectable fluorescence. This method negates the need for mechanical sectioning of tissue and enables the routine generation of high-quality, quantitative image data that can significantly advance our understanding of tissue architecture and physiology.

A new multi-objective optimization model for preventive maintenance and replacement scheduling of multi-component systems

Science.gov (United States)

Moghaddam, Kamran S.; Usher, John S.

2011-07-01

In this article, a new multi-objective optimization model is developed to determine the optimal preventive maintenance and replacement schedules in a repairable and maintainable multi-component system. In this model, the planning horizon is divided into discrete and equally-sized periods in which three possible actions must be planned for each component, namely maintenance, replacement, or do nothing. The objective is to determine a plan of actions for each component in the system while minimizing the total cost and maximizing overall system reliability simultaneously over the planning horizon. Because of the complexity, combinatorial and highly nonlinear structure of the mathematical model, two metaheuristic solution methods, generational genetic algorithm, and a simulated annealing are applied to tackle the problem. The Pareto optimal solutions that provide good tradeoffs between the total cost and the overall reliability of the system can be obtained by the solution approach. Such a modeling approach should be useful for maintenance planners and engineers tasked with the problem of developing recommended maintenance plans for complex systems of components.

Effective multi-objective optimization of Stirling engine systems

International Nuclear Information System (INIS)

Punnathanam, Varun; Kotecha, Prakash

2016-01-01

Highlights: • Multi-objective optimization of three recent Stirling engine models. • Use of efficient crossover and mutation operators for real coded Genetic Algorithm. • Demonstrated supremacy of the strategy over the conventionally used algorithm. • Improvements of up to 29% in comparison to literature results. - Abstract: In this article we demonstrate the supremacy of the Non-dominated Sorting Genetic Algorithm-II with Simulated Binary Crossover and Polynomial Mutation operators for the multi-objective optimization of Stirling engine systems by providing three examples, viz., (i) finite time thermodynamic model, (ii) Stirling engine thermal model with associated irreversibility and (iii) polytropic finite speed based thermodynamics. The finite time thermodynamic model involves seven decision variables and consists of three objectives: output power, thermal efficiency and rate of entropy generation. In comparison to literature, it was observed that the used strategy provides a better Pareto front and leads to improvements of up to 29%. The performance is also evaluated on a Stirling engine thermal model which considers the associated irreversibility of the cycle and consists of three objectives involving eleven decision variables. The supremacy of the suggested strategy is also demonstrated on the experimentally validated polytropic finite speed thermodynamics based Stirling engine model for optimization involving two objectives and ten decision variables.

Multi-objective optimization for an automated and simultaneous phase and baseline correction of NMR spectral data

Science.gov (United States)

Sawall, Mathias; von Harbou, Erik; Moog, Annekathrin; Behrens, Richard; Schröder, Henning; Simoneau, Joël; Steimers, Ellen; Neymeyr, Klaus

2018-04-01

Spectral data preprocessing is an integral and sometimes inevitable part of chemometric analyses. For Nuclear Magnetic Resonance (NMR) spectra a possible first preprocessing step is a phase correction which is applied to the Fourier transformed free induction decay (FID) signal. This preprocessing step can be followed by a separate baseline correction step. Especially if series of high-resolution spectra are considered, then automated and computationally fast preprocessing routines are desirable. A new method is suggested that applies the phase and the baseline corrections simultaneously in an automated form without manual input, which distinguishes this work from other approaches. The underlying multi-objective optimization or Pareto optimization provides improved results compared to consecutively applied correction steps. The optimization process uses an objective function which applies strong penalty constraints and weaker regularization conditions. The new method includes an approach for the detection of zero baseline regions. The baseline correction uses a modified Whittaker smoother. The functionality of the new method is demonstrated for experimental NMR spectra. The results are verified against gravimetric data. The method is compared to alternative preprocessing tools. Additionally, the simultaneous correction method is compared to a consecutive application of the two correction steps.

FGP Approach for Solving Multi-level Multi-objective Quadratic Fractional Programming Problem with Fuzzy parameters

Directory of Open Access Journals (Sweden)

m. s. osman

2017-09-01

Full Text Available In this paper, we consider fuzzy goal programming (FGP approach for solving multi-level multi-objective quadratic fractional programming (ML-MOQFP problem with fuzzy parameters in the constraints. Firstly, the concept of the ?-cut approach is applied to transform the set of fuzzy constraints into a common deterministic one. Then, the quadratic fractional objective functions in each level are transformed into quadratic objective functions based on a proposed transformation. Secondly, the FGP approach is utilized to obtain a compromise solution for the ML-MOQFP problem by minimizing the sum of the negative deviational variables. Finally, an illustrative numerical example is given to demonstrate the applicability and performance of the proposed approach.

High-resolution X-ray television and high-resolution video recorders

International Nuclear Information System (INIS)

Haendle, J.; Horbaschek, H.; Alexandrescu, M.

1977-01-01

The improved transmission properties of the high-resolution X-ray television chain described here make it possible to transmit more information per television image. The resolution in the fluoroscopic image, which is visually determined, depends on the dose rate and the inertia of the television pick-up tube. This connection is discussed. In the last few years, video recorders have been increasingly used in X-ray diagnostics. The video recorder is a further quality-limiting element in X-ray television. The development of function patterns of high-resolution magnetic video recorders shows that this quality drop may be largely overcome. The influence of electrical band width and number of lines on the resolution in the X-ray television image stored is explained in more detail. (orig.) [de

A morphologically preserved multi-resolution TIN surface modeling and visualization method for virtual globes

Science.gov (United States)

Zheng, Xianwei; Xiong, Hanjiang; Gong, Jianya; Yue, Linwei

2017-07-01

Virtual globes play an important role in representing three-dimensional models of the Earth. To extend the functioning of a virtual globe beyond that of a "geobrowser", the accuracy of the geospatial data in the processing and representation should be of special concern for the scientific analysis and evaluation. In this study, we propose a method for the processing of large-scale terrain data for virtual globe visualization and analysis. The proposed method aims to construct a morphologically preserved multi-resolution triangulated irregular network (TIN) pyramid for virtual globes to accurately represent the landscape surface and simultaneously satisfy the demands of applications at different scales. By introducing cartographic principles, the TIN model in each layer is controlled with a data quality standard to formulize its level of detail generation. A point-additive algorithm is used to iteratively construct the multi-resolution TIN pyramid. The extracted landscape features are also incorporated to constrain the TIN structure, thus preserving the basic morphological shapes of the terrain surface at different levels. During the iterative construction process, the TIN in each layer is seamlessly partitioned based on a virtual node structure, and tiled with a global quadtree structure. Finally, an adaptive tessellation approach is adopted to eliminate terrain cracks in the real-time out-of-core spherical terrain rendering. The experiments undertaken in this study confirmed that the proposed method performs well in multi-resolution terrain representation, and produces high-quality underlying data that satisfy the demands of scientific analysis and evaluation.

Very high resolution UV and X-ray spectroscopy and imagery of solar active regions

Science.gov (United States)

Bruner, M.; Brown, W. A.; Haisch, B. M.

1987-01-01

A scientific investigation of the physics of the solar atmosphere, which uses the techniques of high resolution soft X-ray spectroscopy and high resolution UV imagery, is described. The experiments were conducted during a series of three sounding rocket flights. All three flights yielded excellent images in the UV range, showing unprecedented spatial resolution. The second flight recorded the X-ray spectrum of a solar flare, and the third that of an active region. A normal incidence multi-layer mirror was used during the third flight to make the first astronomical X-ray observations using this new technique.

High-Resolution Remote Sensing Image Building Extraction Based on Markov Model

Science.gov (United States)

Zhao, W.; Yan, L.; Chang, Y.; Gong, L.

2018-04-01

With the increase of resolution, remote sensing images have the characteristics of increased information load, increased noise, more complex feature geometry and texture information, which makes the extraction of building information more difficult. To solve this problem, this paper designs a high resolution remote sensing image building extraction method based on Markov model. This method introduces Contourlet domain map clustering and Markov model, captures and enhances the contour and texture information of high-resolution remote sensing image features in multiple directions, and further designs the spectral feature index that can characterize "pseudo-buildings" in the building area. Through the multi-scale segmentation and extraction of image features, the fine extraction from the building area to the building is realized. Experiments show that this method can restrain the noise of high-resolution remote sensing images, reduce the interference of non-target ground texture information, and remove the shadow, vegetation and other pseudo-building information, compared with the traditional pixel-level image information extraction, better performance in building extraction precision, accuracy and completeness.

High resolution satellite imagery : from spies to pipeline management

Energy Technology Data Exchange (ETDEWEB)

Adam, S. [Canadian Geomatic Solutions Ltd., Calgary, AB (Canada); Farrell, M. [TransCanada Transmission, Calgary, AB (Canada)

2000-07-01

The launch of Space Imaging's IKONOS satellite in September 1999 has opened the door for corridor applications. The technology has been successfully implemented by TransCanada PipeLines in mapping over 1500 km of their mainline. IKONOS is the world's first commercial high resolution satellite which collects data at 1-meter black/white and 4-meter multi-spectral. Its use is regulated by the U.S. government. It is the best source of high resolution satellite image data. Other sources include the Indian Space Agency's IRS-1 C/D satellite and the Russian SPIN-2 which provides less reliable coverage. In addition, two more high resolution satellites may be launched this year to provide imagery every day of the year. IKONOS scenes as narrow as 5 km can be purchased. TransCanada conducted a pilot study to determine if high resolution satellite imagery is as effective as ortho-photos for identifying population structures within a buffer of TransCanada's east line right-of-way. The study examined three unique segments where residential, commercial, industrial and public features were compared. It was determined that IKONOS imagery is as good as digital ortho-photos for updating structures from low to very high density areas. The satellite imagery was also logistically easier than ortho-photos to acquire. This will be even more evident when the IKONOS image archives begins to grow. 4 tabs., 3 figs.

Portfolio optimization using fundamental indicators based on multi-objective EA

CERN Document Server

Silva, Antonio Daniel; Horta, Nuno

2016-01-01

This work presents a new approach to portfolio composition in the stock market. It incorporates a fundamental approach using financial ratios and technical indicators with a Multi-Objective Evolutionary Algorithms to choose the portfolio composition with two objectives the return and the risk. Two different chromosomes are used for representing different investment models with real constraints equivalents to the ones faced by managers of mutual funds, hedge funds, and pension funds. To validate the present solution two case studies are presented for the SP&500 for the period June 2010 until end of 2012. The simulations demonstrates that stock selection based on financial ratios is a combination that can be used to choose the best companies in operational terms, obtaining returns above the market average with low variances in their returns. In this case the optimizer found stocks with high return on investment in a conjunction with high rate of growth of the net income and a high profit margin. To obtain s...

Convex Coverage Set Methods for Multi-Objective Collaborative Decision Making

NARCIS (Netherlands)

Roijers, D.M.; Lomuscio, A.; Scerri, P.; Bazzan, A.; Huhns, M.

2014-01-01

My research is aimed at finding efficient coordination methods for multi-objective collaborative multi-agent decision theoretic planning. Key to coordinating efficiently in these settings is exploiting loose couplings between agents. We proposed two algorithms for the case in which the agents need

A high time and spatial resolution MRPC designed for muon tomography

Science.gov (United States)

Shi, L.; Wang, Y.; Huang, X.; Wang, X.; Zhu, W.; Li, Y.; Cheng, J.

2014-12-01

A prototype of cosmic muon scattering tomography system has been set up in Tsinghua University in Beijing. Multi-gap Resistive Plate Chamber (MRPC) is used in the system to get the muon tracks. Compared with other detectors, MRPC can not only provide the track but also the Time of Flight (ToF) between two detectors which can estimate the energy of particles. To get a more accurate track and higher efficiency of the tomography system, a new type of high time and two-dimensional spatial resolution MRPC has been developed. A series of experiments have been done to measure the efficiency, time resolution and spatial resolution. The results show that the efficiency can reach 95% and its time resolution is around 65 ps. The cluster size is around 4 and the spatial resolution can reach 200 μ m.

Multi-objective and multi-criteria optimization for power generation expansion planning with CO2 mitigation in Thailand

Directory of Open Access Journals (Sweden)

Kamphol Promjiraprawat

2013-06-01

Full Text Available In power generation expansion planning, electric utilities have encountered the major challenge of environmental awareness whilst being concerned with budgetary burdens. The approach for selecting generating technologies should depend on economic and environmental constraint as well as externalities. Thus, the multi-objective optimization becomes a more attractive approach. This paper presents a hybrid framework of multi-objective optimization and multi-criteria decision making to solve power generation expansion planning problems in Thailand. In this paper, CO2 emissions and external cost are modeled as a multi-objective optimization problem. Then the analytic hierarchy process is utilized to determine thecompromised solution. For carbon capture and storage technology, CO2 emissions can be mitigated by 74.7% from the least cost plan and leads to the reduction of the external cost of around 500 billion US dollars over the planning horizon. Results indicate that the proposed approach provides optimum cost-related CO2 mitigation plan as well as external cost.

Multi-objective Search-based Mobile Testing

OpenAIRE

Mao, K.

2017-01-01

Despite the tremendous popularity of mobile applications, mobile testing still relies heavily on manual testing. This thesis presents mobile test automation approaches based on multi-objective search. We introduce three approaches: Sapienz (for native Android app testing), Octopuz (for hybrid/web JavaScript app testing) and Polariz (for using crowdsourcing to support search-based mobile testing). These three approaches represent the primary scientific and technical contributions of the thesis...

Multi-objective decision-making model based on CBM for an aircraft fleet

Science.gov (United States)

Luo, Bin; Lin, Lin

2018-04-01

Modern production management patterns, in which multi-unit (e.g., a fleet of aircrafts) are managed in a holistic manner, have brought new challenges for multi-unit maintenance decision making. To schedule a good maintenance plan, not only does the individual machine maintenance have to be considered, but also the maintenance of the other individuals have to be taken into account. Since most condition-based maintenance researches for aircraft focused on solely reducing maintenance cost or maximizing the availability of single aircraft, as well as considering that seldom researches concentrated on both the two objectives: minimizing cost and maximizing the availability of a fleet (total number of available aircraft in fleet), a multi-objective decision-making model based on condition-based maintenance concentrated both on the above two objectives is established. Furthermore, in consideration of the decision maker may prefer providing the final optimal result in the form of discrete intervals instead of a set of points (non-dominated solutions) in real decision-making problem, a novel multi-objective optimization method based on support vector regression is proposed to solve the above multi-objective decision-making model. Finally, a case study regarding a fleet is conducted, with the results proving that the approach efficiently generates outcomes that meet the schedule requirements.

The coupling of high-speed high resolution experimental data and LES through data assimilation techniques

Science.gov (United States)

Harris, S.; Labahn, J. W.; Frank, J. H.; Ihme, M.

2017-11-01

Data assimilation techniques can be integrated with time-resolved numerical simulations to improve predictions of transient phenomena. In this study, optimal interpolation and nudging are employed for assimilating high-speed high-resolution measurements obtained for an inert jet into high-fidelity large-eddy simulations. This experimental data set was chosen as it provides both high spacial and temporal resolution for the three-component velocity field in the shear layer of the jet. Our first objective is to investigate the impact that data assimilation has on the resulting flow field for this inert jet. This is accomplished by determining the region influenced by the data assimilation and corresponding effect on the instantaneous flow structures. The second objective is to determine optimal weightings for two data assimilation techniques. The third objective is to investigate how the frequency at which the data is assimilated affects the overall predictions. Graduate Research Assistant, Department of Mechanical Engineering.

Measuring large-scale social networks with high resolution.

Directory of Open Access Journals (Sweden)

Arkadiusz Stopczynski

Full Text Available This paper describes the deployment of a large-scale study designed to measure human interactions across a variety of communication channels, with high temporal resolution and spanning multiple years-the Copenhagen Networks Study. Specifically, we collect data on face-to-face interactions, telecommunication, social networks, location, and background information (personality, demographics, health, politics for a densely connected population of 1000 individuals, using state-of-the-art smartphones as social sensors. Here we provide an overview of the related work and describe the motivation and research agenda driving the study. Additionally, the paper details the data-types measured, and the technical infrastructure in terms of both backend and phone software, as well as an outline of the deployment procedures. We document the participant privacy procedures and their underlying principles. The paper is concluded with early results from data analysis, illustrating the importance of multi-channel high-resolution approach to data collection.

Multi-objective approach in thermoenvironomic optimization of a benchmark cogeneration system

International Nuclear Information System (INIS)

Sayyaadi, Hoseyn

2009-01-01

Multi-objective optimization for designing of a benchmark cogeneration system known as CGAM cogeneration system has been performed. In optimization approach, the exergetic, economic and environmental aspects have been considered, simultaneously. The thermodynamic modeling has been implemented comprehensively while economic analysis conducted in accordance with the total revenue requirement (TRR) method. The results for the single objective thermoeconomic optimization have been compared with the previous studies in optimization of CGAM problem. In multi-objective optimization of the CGAM problem, the three objective functions including the exergetic efficiency, total levelized cost rate of the system product and the cost rate of environmental impact have been considered. The environmental impact objective function has been defined and expressed in cost terms. This objective has been integrated with the thermoeconomic objective to form a new unique objective function known as a thermoenvironomic objective function. The thermoenvironomic objective has been minimized while the exergetic objective has been maximized. One of the most suitable optimization techniques developed using a particular class of search algorithms known as multi-objective evolutionary algorithms (MOEAs) has been considered here. This approach which is developed based on the genetic algorithm has been applied to find the set of Pareto optimal solutions with respect to the aforementioned objective functions. An example of decision-making has been presented and a final optimal solution has been introduced. The sensitivity of the solutions to the interest rate and the fuel cost has been studied

Object-Based Classification of Grasslands from High Resolution Satellite Image Time Series Using Gaussian Mean Map Kernels

Directory of Open Access Journals (Sweden)

Mailys Lopes

2017-07-01

Full Text Available This paper deals with the classification of grasslands using high resolution satellite image time series. Grasslands considered in this work are semi-natural elements in fragmented landscapes, i.e., they are heterogeneous and small elements. The first contribution of this study is to account for grassland heterogeneity while working at the object level by modeling its pixels distributions by a Gaussian distribution. To measure the similarity between two grasslands, a new kernel is proposed as a second contribution: the α -Gaussian mean kernel. It allows one to weight the influence of the covariance matrix when comparing two Gaussian distributions. This kernel is introduced in support vector machines for the supervised classification of grasslands from southwest France. A dense intra-annual multispectral time series of the Formosat-2 satellite is used for the classification of grasslands’ management practices, while an inter-annual NDVI time series of Formosat-2 is used for old and young grasslands’ discrimination. Results are compared to other existing pixel- and object-based approaches in terms of classification accuracy and processing time. The proposed method is shown to be a good compromise between processing speed and classification accuracy. It can adapt to the classification constraints, and it encompasses several similarity measures known in the literature. It is appropriate for the classification of small and heterogeneous objects such as grasslands.

Integrated production planning and control: A multi-objective optimization model

Directory of Open Access Journals (Sweden)

Cheng Wang

2013-09-01

Full Text Available Purpose: Production planning and control has crucial impact on the production and business activities of enterprise. Enterprise Resource Planning (ERP is the most popular resources planning and management system, however there are some shortcomings and deficiencies in the production planning and control because its core component is still the Material Requirements Planning (MRP. For the defects of ERP system, many local improvement and optimization schemes have been proposed, and improve the feasibility and practicality of the plan in some extent, but study considering the whole planning system optimization in the multiple performance management objectives and achieving better application performance is less. The purpose of this paper is to propose a multi-objective production planning optimization model Based on the point of view of the integration of production planning and control, in order to achieve optimization and control of enterprise manufacturing management. Design/methodology/approach: On the analysis of ERP planning system’s defects and disadvantages, and related research and literature, a multi-objective production planning optimization model is proposed, in addition to net demand and capacity, multiple performance management objectives, such as on-time delivery, production balance, inventory, overtime production, are considered incorporating into the examination scope of the model, so that the manufacturing process could be management and controlled Optimally between multiple objectives. The validity and practicability of the model will be verified by the instance in the last part of the paper. Findings: The main finding is that production planning management of manufacturing enterprise considers not only the capacity and materials, but also a variety of performance management objectives in the production process, and building a multi-objective optimization model can effectively optimize the management and control of enterprise

A method for generating high resolution satellite image time series

Science.gov (United States)

Guo, Tao

2014-10-01

There is an increasing demand for satellite remote sensing data with both high spatial and temporal resolution in many applications. But it still is a challenge to simultaneously improve spatial resolution and temporal frequency due to the technical limits of current satellite observation systems. To this end, much R&D efforts have been ongoing for years and lead to some successes roughly in two aspects, one includes super resolution, pan-sharpen etc. methods which can effectively enhance the spatial resolution and generate good visual effects, but hardly preserve spectral signatures and result in inadequate analytical value, on the other hand, time interpolation is a straight forward method to increase temporal frequency, however it increase little informative contents in fact. In this paper we presented a novel method to simulate high resolution time series data by combing low resolution time series data and a very small number of high resolution data only. Our method starts with a pair of high and low resolution data set, and then a spatial registration is done by introducing LDA model to map high and low resolution pixels correspondingly. Afterwards, temporal change information is captured through a comparison of low resolution time series data, and then projected onto the high resolution data plane and assigned to each high resolution pixel according to the predefined temporal change patterns of each type of ground objects. Finally the simulated high resolution data is generated. A preliminary experiment shows that our method can simulate a high resolution data with a reasonable accuracy. The contribution of our method is to enable timely monitoring of temporal changes through analysis of time sequence of low resolution images only, and usage of costly high resolution data can be reduces as much as possible, and it presents a highly effective way to build up an economically operational monitoring solution for agriculture, forest, land use investigation

Exploring the trade-off between competing objectives for electricity energy retailers through a novel multi-objective framework

International Nuclear Information System (INIS)

Charwand, Mansour; Ahmadi, Abdollah; Siano, Pierluigi; Dargahi, Vahid; Sarno, Debora

2015-01-01

Highlights: • Proposing a new stochastic multi-objective framework for an electricity retailer. • Proposing a MIP model for an electricity retailer problem. • Employing ε-constraint method to generate Pareto solution. - Abstract: Energy retailer is the intermediary between Generation Companies and consumers. In the medium time horizon, in order to gain market share, he has to minimize his selling price while looking at the profit, which is dependent on the revenues from selling and the costs to buy energy from forward contracts and participation in the market pool. In this paper, the two competing objectives are engaged proposing a new multi-objective framework in which a ε-constraint mathematical technique is used to produce the Pareto front (set of optimal solutions). The stochasticity of energy prices in the market and customer load demand are coped with the Lattice Monte Carlo Simulation (LMCS) and the method of the roulette wheel, which allow the stochastic multi-objective problem to be turned into a set of deterministic equivalents. The method performance is tested into some case studies

Use of interactive data visualization in multi-objective forest planning.

Science.gov (United States)

Haara, Arto; Pykäläinen, Jouni; Tolvanen, Anne; Kurttila, Mikko

2018-03-15

Common to multi-objective forest planning situations is that they all require comparisons, searches and evaluation among decision alternatives. Through these actions, the decision maker can learn from the information presented and thus make well-justified decisions. Interactive data visualization is an evolving approach that supports learning and decision making in multidimensional decision problems and planning processes. Data visualization contributes the formation of mental image data and this process is further boosted by allowing interaction with the data. In this study, we introduce a multi-objective forest planning decision problem framework and the corresponding characteristics of data. We utilize the framework with example planning data to illustrate and evaluate the potential of 14 interactive data visualization techniques to support multi-objective forest planning decisions. Furthermore, broader utilization possibilities of these techniques to incorporate the provisioning of ecosystem services into forest management and planning are discussed. Copyright © 2018 Elsevier Ltd. All rights reserved.

Beam-target interaction for high-dose, multi-pulse radiography

International Nuclear Information System (INIS)

DeVolder, B.G.; Kwan, T.J.T.; Snell, C.M.; Kares, R.J.; McLenithan, K.D.

1996-01-01

The conversion of an intense relativistic electron beam into x-rays for radiographic imaging is achieved through the bremsstrahlung process of electrons in a tantalum or tungsten target of some optimal thickness. A high-dose radiographic source with small spot size is needed to achieve desirable resolution for thick objects. Consequently, an extremely high brightness electron beam is used and a significant amount of electron beam energy can be deposited in a small area of the target. The authors describe a computational methodology used to model the beam-target interaction and the evolution of the resultant plasma. Several codes, including particle-in-cell (PIC), Monte Carlo transport, and magnetohydrodynamic (MHD) codes, contribute to simulate different parts of the problem in a linked fashion. Issues addressed by the calculations include: the effects of the time dependence of the energy profile deposited in the target; the influence of the external magnetic field on plasma expansion; the influence of the expanding plasma on the guide magnetic field; radiation effects; and multi-dimensional effects

High-resolution electron microscopy and its applications.

Science.gov (United States)

Li, F H

1987-12-01

A review of research on high-resolution electron microscopy (HREM) carried out at the Institute of Physics, the Chinese Academy of Sciences, is presented. Apart from the direct observation of crystal and quasicrystal defects for some alloys, oxides, minerals, etc., and the structure determination for some minute crystals, an approximate image-contrast theory named pseudo-weak-phase object approximation (PWPOA), which shows the image contrast change with crystal thickness, is described. Within the framework of PWPOA, the image contrast of lithium ions in the crystal of R-Li2Ti3O7 has been observed. The usefulness of diffraction analysis techniques such as the direct method and Patterson method in HREM is discussed. Image deconvolution and resolution enhancement for weak-phase objects by use of the direct method are illustrated. In addition, preliminary results of image restoration for thick crystals are given.

Sparse frequencies data inversion and the role of multi-scattered energy

KAUST Repository

Alkhalifah, Tariq Ali

2017-01-01

time to inject more energy of those frequencies at a reduced cost. Such additional energy is necessary to the recording of more multi-scattered events. The objective of this new paradigm is a high resolution model of the Earth.

Multi-frame super-resolution with quality self-assessment for retinal fundus videos.

Science.gov (United States)

Köhler, Thomas; Brost, Alexander; Mogalle, Katja; Zhang, Qianyi; Köhler, Christiane; Michelson, Georg; Hornegger, Joachim; Tornow, Ralf P

2014-01-01

This paper proposes a novel super-resolution framework to reconstruct high-resolution fundus images from multiple low-resolution video frames in retinal fundus imaging. Natural eye movements during an examination are used as a cue for super-resolution in a robust maximum a-posteriori scheme. In order to compensate heterogeneous illumination on the fundus, we integrate retrospective illumination correction for photometric registration to the underlying imaging model. Our method utilizes quality self-assessment to provide objective quality scores for reconstructed images as well as to select regularization parameters automatically. In our evaluation on real data acquired from six human subjects with a low-cost video camera, the proposed method achieved considerable enhancements of low-resolution frames and improved noise and sharpness characteristics by 74%. In terms of image analysis, we demonstrate the importance of our method for the improvement of automatic blood vessel segmentation as an example application, where the sensitivity was increased by 13% using super-resolution reconstruction.

Multi-objective optimization for generating a weighted multi-model ensemble

Science.gov (United States)

Lee, H.

2017-12-01

Many studies have demonstrated that multi-model ensembles generally show better skill than each ensemble member. When generating weighted multi-model ensembles, the first step is measuring the performance of individual model simulations using observations. There is a consensus on the assignment of weighting factors based on a single evaluation metric. When considering only one evaluation metric, the weighting factor for each model is proportional to a performance score or inversely proportional to an error for the model. While this conventional approach can provide appropriate combinations of multiple models, the approach confronts a big challenge when there are multiple metrics under consideration. When considering multiple evaluation metrics, it is obvious that a simple averaging of multiple performance scores or model ranks does not address the trade-off problem between conflicting metrics. So far, there seems to be no best method to generate weighted multi-model ensembles based on multiple performance metrics. The current study applies the multi-objective optimization, a mathematical process that provides a set of optimal trade-off solutions based on a range of evaluation metrics, to combining multiple performance metrics for the global climate models and their dynamically downscaled regional climate simulations over North America and generating a weighted multi-model ensemble. NASA satellite data and the Regional Climate Model Evaluation System (RCMES) software toolkit are used for assessment of the climate simulations. Overall, the performance of each model differs markedly with strong seasonal dependence. Because of the considerable variability across the climate simulations, it is important to evaluate models systematically and make future projections by assigning optimized weighting factors to the models with relatively good performance. Our results indicate that the optimally weighted multi-model ensemble always shows better performance than an arithmetic

Multi-element analysis of unidentified fallen objects from Tatale in ...

African Journals Online (AJOL)

A multi-element analysis has been carried out on two fallen objects, # 01 and # 02, using instrumental neutron activation analysis technique. A total of 17 elements were identified in object # 01 while 21 elements were found in object # 02. The two major elements in object # 01 were Fe and Mg, which together constitute ...

Computerized tomography using high resolution X-ray imaging system with a microfocus source

International Nuclear Information System (INIS)

Zaprazny, Z.; Korytar, D.; Konopka, P.; Ac, V.; Bielecki, J.

2011-01-01

In recent years there is an effort to image an internal structure of an object by using not only conventional 2D X-ray radiography but also using high resolution 3D tomography which is based on reconstruction of multiple 2D projections at various angular positions of the object. We have previously reported [1] the development and basic parameters of a high resolution x-ray imaging system with a microfocus source. We report the recent progress using this high resolution X-ray laboratory system in this work. These first findings show that our system is particularly suitable for light weight and nonmetallic objects such as biological objects, plastics, wood, paper, etc. where phase contrast helps to increase the visibility of the finest structures of the object. Phase-contrast X-ray Computerized Tomography is of our special interest because it is an emerging imaging technique that can be implemented at third generation synchrotron radiation sources and also in laboratory conditions using a microfocus X-ray tube or beam conditioning optics. (authors)

High-throughput isotropic mapping of whole mouse brain using multi-view light-sheet microscopy

Science.gov (United States)

Nie, Jun; Li, Yusha; Zhao, Fang; Ping, Junyu; Liu, Sa; Yu, Tingting; Zhu, Dan; Fei, Peng

2018-02-01

Light-sheet fluorescence microscopy (LSFM) uses an additional laser-sheet to illuminate selective planes of the sample, thereby enabling three-dimensional imaging at high spatial-temporal resolution. These advantages make LSFM a promising tool for high-quality brain visualization. However, even by the use of LSFM, the spatial resolution remains insufficient to resolve the neural structures across a mesoscale whole mouse brain in three dimensions. At the same time, the thick-tissue scattering prevents a clear observation from the deep of brain. Here we use multi-view LSFM strategy to solve this challenge, surpassing the resolution limit of standard light-sheet microscope under a large field-of-view (FOV). As demonstrated by the imaging of optically-cleared mouse brain labelled with thy1-GFP, we achieve a brain-wide, isotropic cellular resolution of 3μm. Besides the resolution enhancement, multi-view braining imaging can also recover complete signals from deep tissue scattering and attenuation. The identification of long distance neural projections across encephalic regions can be identified and annotated as a result.

(LMRG): Microscope Resolution, Objective Quality, Spectral Accuracy and Spectral Un-mixing

Science.gov (United States)

Bayles, Carol J.; Cole, Richard W.; Eason, Brady; Girard, Anne-Marie; Jinadasa, Tushare; Martin, Karen; McNamara, George; Opansky, Cynthia; Schulz, Katherine; Thibault, Marc; Brown, Claire M.

2012-01-01

The second study by the LMRG focuses on measuring confocal laser scanning microscope (CLSM) resolution, objective lens quality, spectral imaging accuracy and spectral un-mixing. Affordable test samples for each aspect of the study were designed, prepared and sent to 116 labs from 23 countries across the globe. Detailed protocols were designed for the three tests and customized for most of the major confocal instruments being used by the study participants. One protocol developed for measuring resolution and objective quality was recently published in Nature Protocols (Cole, R. W., T. Jinadasa, et al. (2011). Nature Protocols 6(12): 1929–1941). The first study involved 3D imaging of sub-resolution fluorescent microspheres to determine the microscope point spread function. Results of the resolution studies as well as point spread function quality (i.e. objective lens quality) from 140 different objective lenses will be presented. The second study of spectral accuracy looked at the reflection of the laser excitation lines into the spectral detection in order to determine the accuracy of these systems to report back the accurate laser emission wavelengths. Results will be presented from 42 different spectral confocal systems. Finally, samples with double orange beads (orange core and orange coating) were imaged spectrally and the imaging software was used to un-mix fluorescence signals from the two orange dyes. Results from 26 different confocal systems will be summarized. Time will be left to discuss possibilities for the next LMRG study.

Identifying Spatial Units of Human Occupation in the Brazilian Amazon Using Landsat and CBERS Multi-Resolution Imagery

OpenAIRE

Dal’Asta, Ana Paula; Brigatti, Newton; Amaral, Silvana; Escada, Maria Isabel Sobral; Monteiro, Antonio Miguel Vieira

2012-01-01

Every spatial unit of human occupation is part of a network structuring an extensive process of urbanization in the Amazon territory. Multi-resolution remote sensing data were used to identify and map human presence and activities in the Sustainable Forest District of Cuiabá-Santarém highway (BR-163), west of Pará, Brazil. The limits of spatial units of human occupation were mapped based on digital classification of Landsat-TM5 (Thematic Mapper 5) image (30m spatial resolution). High-spatial-...

The Partition of Multi-Resolution LOD Based on Qtm

Science.gov (United States)

Hou, M.-L.; Xing, H.-Q.; Zhao, X.-S.; Chen, J.

2011-08-01

The partition hierarch of Quaternary Triangular Mesh (QTM) determine the accuracy of spatial analysis and application based on QTM. In order to resolve the problem that the partition hierarch of QTM is limited by the level of the computer hardware, the new method that Multi- Resolution LOD (Level of Details) based on QTM will be discussed in this paper. This method can make the resolution of the cells varying with the viewpoint position by partitioning the cells of QTM, selecting the particular area according to the viewpoint; dealing with the cracks caused by different subdivisions, it satisfies the request of unlimited partition in part.

THE PARTITION OF MULTI-RESOLUTION LOD BASED ON QTM

Directory of Open Access Journals (Sweden)

M.-L. Hou

2012-08-01

Full Text Available The partition hierarch of Quaternary Triangular Mesh (QTM determine the accuracy of spatial analysis and application based on QTM. In order to resolve the problem that the partition hierarch of QTM is limited by the level of the computer hardware, the new method that Multi- Resolution LOD (Level of Details based on QTM will be discussed in this paper. This method can make the resolution of the cells varying with the viewpoint position by partitioning the cells of QTM, selecting the particular area according to the viewpoint; dealing with the cracks caused by different subdivisions, it satisfies the request of unlimited partition in part.

Multi-dimensional diagnostics of high power ion beams by Arrayed Pinhole Camera System

International Nuclear Information System (INIS)

Yasuike, K.; Miyamoto, S.; Shirai, N.; Akiba, T.; Nakai, S.; Imasaki, K.; Yamanaka, C.

1993-01-01

The authors developed multi-dimensional beam diagnostics system (with spatially and time resolution). They used newly developed Arrayed Pinhole Camera (APC) for this diagnosis. The APC can get spatial distribution of divergence and flux density. They use two types of particle detectors in this study. The one is CR-39 can get time integrated images. The other one is gated Micro-Channel-Plate (MCP) with CCD camera. It enables time resolving diagnostics. The diagnostics systems have resolution better than 10mrad divergence, 0.5mm spatial resolution on the objects respectively. The time resolving system has 10ns time resolution. The experiments are performed on Reiden-IV and Reiden-SHVS induction linac. The authors get time integrated divergence distributions on Reiden-IV proton beam. They also get time resolved image on Reiden-SHVS

Exergoeconomic multi objective optimization and sensitivity analysis of a regenerative Brayton cycle

International Nuclear Information System (INIS)

Naserian, Mohammad Mahdi; Farahat, Said; Sarhaddi, Faramarz

2016-01-01

Highlights: • Finite time exergoeconomic multi objective optimization of a Brayton cycle. • Comparing the exergoeconomic and the ecological function optimization results. • Inserting the cost of fluid streams concept into finite-time thermodynamics. • Exergoeconomic sensitivity analysis of a regenerative Brayton cycle. • Suggesting the cycle performance curve drawing and utilization. - Abstract: In this study, the optimal performance of a regenerative Brayton cycle is sought through power maximization and then exergoeconomic optimization using finite-time thermodynamic concept and finite-size components. Optimizations are performed using genetic algorithm. In order to take into account the finite-time and finite-size concepts in current problem, a dimensionless mass-flow parameter is used deploying time variations. The decision variables for the optimum state (of multi objective exergoeconomic optimization) are compared to the maximum power state. One can see that the multi objective exergoeconomic optimization results in a better performance than that obtained with the maximum power state. The results demonstrate that system performance at optimum point of multi objective optimization yields 71% of the maximum power, but only with exergy destruction as 24% of the amount that is produced at the maximum power state and 67% lower total cost rate than that of the maximum power state. In order to assess the impact of the variation of the decision variables on the objective functions, sensitivity analysis is conducted. Finally, the cycle performance curve drawing according to exergoeconomic multi objective optimization results and its utilization, are suggested.

Proposal for a semiconductor high resolution tracking detector

International Nuclear Information System (INIS)

Rehak, P.

1983-01-01

A 'new' concept for detection and tracking of charged particles in high energy physics experiments is proposed. It combines a well known high purity semiconductor diode detector (HPSDD) with a heterojunction structure (HJ) and a negative electron affinity (NEA) surface. The detector should be capable of providing a two dimensional view (few cm 2 ) of multi-track events with the following properties: a) position resolution down to a few μm (10 8 position elements); b) high density of information (10 2 -10 3 dots per mm of minimum ionizing track); c) high rate capabilities (few MHz); d) live operation with options to be triggered and/or the information from the detector can be used as an input for the decision to record an event. (orig.)

Mathematical modelling and numerical resolution of multi-phase compressible fluid flows problems

International Nuclear Information System (INIS)

Lagoutiere, Frederic

2000-01-01

This work deals with Eulerian compressible multi-species fluid dynamics, the species being either mixed or separated (with interfaces). The document is composed of three parts. The first parts devoted to the numerical resolution of model problems: advection equation, Burgers equation, and Euler equations, in dimensions one and two. The goal is to find a precise method, especially for discontinuous initial conditions, and we develop non dissipative algorithms. They are based on a downwind finite-volume discretization under some stability constraints. The second part treats of the mathematical modelling of fluids mixtures. We construct and analyse a set of multi-temperature and multi-pressure models that are entropy, symmetrizable, hyperbolic, not ever conservative. In the third part, we apply the ideas developed in the first part (downwind discretization) to the numerical resolution of the partial differential problems we have constructed for fluids mixtures in the second part. We present some numerical results in dimensions one and two. (author) [fr

Optical cryptography with biometrics for multi-depth objects.

Science.gov (United States)

Yan, Aimin; Wei, Yang; Hu, Zhijuan; Zhang, Jingtao; Tsang, Peter Wai Ming; Poon, Ting-Chung

2017-10-11

We propose an optical cryptosystem for encrypting images of multi-depth objects based on the combination of optical heterodyne technique and fingerprint keys. Optical heterodyning requires two optical beams to be mixed. For encryption, each optical beam is modulated by an optical mask containing either the fingerprint of the person who is sending, or receiving the image. The pair of optical masks are taken as the encryption keys. Subsequently, the two beams are used to scan over a multi-depth 3-D object to obtain an encrypted hologram. During the decryption process, each sectional image of the 3-D object is recovered by convolving its encrypted hologram (through numerical computation) with the encrypted hologram of a pinhole image that is positioned at the same depth as the sectional image. Our proposed method has three major advantages. First, the lost-key situation can be avoided with the use of fingerprints as the encryption keys. Second, the method can be applied to encrypt 3-D images for subsequent decrypted sectional images. Third, since optical heterodyning scanning is employed to encrypt a 3-D object, the optical system is incoherent, resulting in negligible amount of speckle noise upon decryption. To the best of our knowledge, this is the first time optical cryptography of 3-D object images has been demonstrated in an incoherent optical system with biometric keys.

Optimizing Energy and Modulation Selection in Multi-Resolution Modulation For Wireless Video Broadcast/Multicast

KAUST Repository

She, James

2009-11-01

Emerging technologies in Broadband Wireless Access (BWA) networks and video coding have enabled high-quality wireless video broadcast/multicast services in metropolitan areas. Joint source-channel coded wireless transmission, especially using hierarchical/superposition coded modulation at the channel, is recognized as an effective and scalable approach to increase the system scalability while tackling the multi-user channel diversity problem. The power allocation and modulation selection problem, however, is subject to a high computational complexity due to the nonlinear formulation and huge solution space. This paper introduces a dynamic programming framework with conditioned parsing, which significantly reduces the search space. The optimized result is further verified with experiments using real video content. The proposed approach effectively serves as a generalized and practical optimization framework that can gauge and optimize a scalable wireless video broadcast/multicast based on multi-resolution modulation in any BWA network.

Optimizing Energy and Modulation Selection in Multi-Resolution Modulation For Wireless Video Broadcast/Multicast

KAUST Repository

She, James; Ho, Pin-Han; Shihada, Basem

2009-01-01

Emerging technologies in Broadband Wireless Access (BWA) networks and video coding have enabled high-quality wireless video broadcast/multicast services in metropolitan areas. Joint source-channel coded wireless transmission, especially using hierarchical/superposition coded modulation at the channel, is recognized as an effective and scalable approach to increase the system scalability while tackling the multi-user channel diversity problem. The power allocation and modulation selection problem, however, is subject to a high computational complexity due to the nonlinear formulation and huge solution space. This paper introduces a dynamic programming framework with conditioned parsing, which significantly reduces the search space. The optimized result is further verified with experiments using real video content. The proposed approach effectively serves as a generalized and practical optimization framework that can gauge and optimize a scalable wireless video broadcast/multicast based on multi-resolution modulation in any BWA network.

Design for sustainability of industrial symbiosis based on emergy and multi-objective particle swarm optimization.

Science.gov (United States)

Ren, Jingzheng; Liang, Hanwei; Dong, Liang; Sun, Lu; Gao, Zhiqiu

2016-08-15

Industrial symbiosis provides novel and practical pathway to the design for the sustainability. Decision support tool for its verification is necessary for practitioners and policy makers, while to date, quantitative research is limited. The objective of this work is to present an innovative approach for supporting decision-making in the design for the sustainability with the implementation of industrial symbiosis in chemical complex. Through incorporating the emergy theory, the model is formulated as a multi-objective approach that can optimize both the economic benefit and sustainable performance of the integrated industrial system. A set of emergy based evaluation index are designed. Multi-objective Particle Swarm Algorithm is proposed to solve the model, and the decision-makers are allowed to choose the suitable solutions form the Pareto solutions. An illustrative case has been studied by the proposed method, a few of compromises between high profitability and high sustainability can be obtained for the decision-makers/stakeholders to make decision. Copyright © 2016 Elsevier B.V. All rights reserved.

High-Resolution Imaging of Colliding and Merging Galaxies

Science.gov (United States)

Whitmore, Brad

1991-07-01

We propose to obtain high-resolution images, using the WF/PC, of two colliding and merging galaxies (i.e., NGC 4038/4039 = "The Antennae" and NGC 7252 ="Atoms-for-Peace Galaxy". Our goal is to use HST to make critical observations of each object in order to gain a better understanding of the various phases of the merger process. Our primary objective is to determine whether globular clusters are formed during mergers\\?

A characteristics of East Asian climate using high-resolution regional climate model

Science.gov (United States)

Yhang, Y.

2013-12-01

Climate research, particularly application studies for water, agriculture, forestry, fishery and energy management require fine scale multi-decadal information of meteorological, oceanographic and land states. Unfortunately, spatially and temporally homogeneous multi-decadal observations of these variables in high horizontal resolution are non-existent. Some long term surface records of temperature and precipitation exist, but the number of observation is very limited and the measurements are often contaminated by changes in instrumentation over time. Some climatologically important variables, such as soil moisture, surface evaporation, and radiation are not even measured over most of East Asia. Reanalysis is one approach to obtaining long term homogeneous analysis of needed variables. However, the horizontal resolution of global reanalysis is of the order of 100 to 200 km, too coarse for many application studies. Regional climate models (RCMs) are able to provide valuable regional finescale information, especially in regions where the climate variables are strongly regulated by the underlying topography and the surface heterogeneity. In this study, we will provide accurately downscaled regional climate over East Asia using the Global/Regional Integrated Model system [GRIMs; Hong et al. 2013]. A mixed layer model is embedded within the GRIMs in order to improve air-sea interaction. A detailed description of the characteristics of the East Asian summer and winter climate will be presented through the high-resolution numerical simulations. The increase in horizontal resolution is expected to provide the high-quality data that can be used in various application areas such as hydrology or environmental model forcing.

An Improved Method for Producing High Spatial-Resolution NDVI Time Series Datasets with Multi-Temporal MODIS NDVI Data and Landsat TM/ETM+ Images

Directory of Open Access Journals (Sweden)

Yuhan Rao

2015-06-01

Full Text Available Due to technical limitations, it is impossible to have high resolution in both spatial and temporal dimensions for current NDVI datasets. Therefore, several methods are developed to produce high resolution (spatial and temporal NDVI time-series datasets, which face some limitations including high computation loads and unreasonable assumptions. In this study, an unmixing-based method, NDVI Linear Mixing Growth Model (NDVI-LMGM, is proposed to achieve the goal of accurately and efficiently blending MODIS NDVI time-series data and multi-temporal Landsat TM/ETM+ images. This method firstly unmixes the NDVI temporal changes in MODIS time-series to different land cover types and then uses unmixed NDVI temporal changes to predict Landsat-like NDVI dataset. The test over a forest site shows high accuracy (average difference: −0.0070; average absolute difference: 0.0228; and average absolute relative difference: 4.02% and computation efficiency of NDVI-LMGM (31 seconds using a personal computer. Experiments over more complex landscape and long-term time-series demonstrated that NDVI-LMGM performs well in each stage of vegetation growing season and is robust in regions with contrasting spatial and spatial variations. Comparisons between NDVI-LMGM and current methods (i.e., Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM, Enhanced STARFM (ESTARFM and Weighted Linear Model (WLM show that NDVI-LMGM is more accurate and efficient than current methods. The proposed method will benefit land surface process research, which requires a dense NDVI time-series dataset with high spatial resolution.

Multi-resolution simulation of focused ultrasound propagation through ovine skull from a single-element transducer

Science.gov (United States)

Yoon, Kyungho; Lee, Wonhye; Croce, Phillip; Cammalleri, Amanda; Yoo, Seung-Schik

2018-05-01

Transcranial focused ultrasound (tFUS) is emerging as a non-invasive brain stimulation modality. Complicated interactions between acoustic pressure waves and osseous tissue introduce many challenges in the accurate targeting of an acoustic focus through the cranium. Image-guidance accompanied by a numerical simulation is desired to predict the intracranial acoustic propagation through the skull; however, such simulations typically demand heavy computation, which warrants an expedited processing method to provide on-site feedback for the user in guiding the acoustic focus to a particular brain region. In this paper, we present a multi-resolution simulation method based on the finite-difference time-domain formulation to model the transcranial propagation of acoustic waves from a single-element transducer (250 kHz). The multi-resolution approach improved computational efficiency by providing the flexibility in adjusting the spatial resolution. The simulation was also accelerated by utilizing parallelized computation through the graphic processing unit. To evaluate the accuracy of the method, we measured the actual acoustic fields through ex vivo sheep skulls with different sonication incident angles. The measured acoustic fields were compared to the simulation results in terms of focal location, dimensions, and pressure levels. The computational efficiency of the presented method was also assessed by comparing simulation speeds at various combinations of resolution grid settings. The multi-resolution grids consisting of 0.5 and 1.0 mm resolutions gave acceptable accuracy (under 3 mm in terms of focal position and dimension, less than 5% difference in peak pressure ratio) with a speed compatible with semi real-time user feedback (within 30 s). The proposed multi-resolution approach may serve as a novel tool for simulation-based guidance for tFUS applications.