A. James Clark School of Engineering
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The collections in this community comprise faculty research works, as well as graduate theses and dissertations.
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Item Novel Statistical Pattern Recognition and 3D Machine Vision Technologies for Automated Food Quality Inspection(2008-12-02) Zhu, Bin; Tao, Yang; Fischell Department of Bioengineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Machine vision technologies have received a lot of attention for automated food quality inspection. This dissertation describes three techniques developed to improve the quality inspection of apple and poultry products. First, a Gabor feature-based kernel principal component analysis (PCA) method was introduced by combining Gabor wavelet representation of apple images and the kernel PCA method for apple quality inspection using near-infrared (NIR) imaging. Gabor wavelet decomposition was employed to extract appropriate Gabor features of whole apple NIR images. Then, the kernel PCA method with polynomial kernels was applied in the Gabor feature space to handle nonlinear separable features. The experimental results showed the effectiveness of the Gabor-based kernel PCA method. Using the proposed Gabor kernel PCA eliminated the need for local feature segmentation and also resolved the nonlinear separable problem in the Gabor feature space. An overall 90.5% detection rate was achieved. Second, a novel 3D-based apple near-infrared (NIR) data analysis strategy was utilized so that the apple stem-end/calyx could be identified, and hence differentiated from defects and normal tissue according to their different 3D shapes. Two automated 3D data processing approaches were developed in this research: 1) A 3D quadratic facet model fitting, which employed a small concave 3D patch to fit the 3D apple surface and the best fit could be found around stem-end/calyx area; and 2) A 3D shape enhanced transform (SET), which enhanced the apple stem-end/calyx area and made it easily detectable because of the 3D surface gradient difference between the stem-end/calyx and the apple surface. An overall 92.6% accuracy was achieved. Third, high resolution on-line laser 3D imaging was investigated for improving the 3D profile recovery for thickness compensation purposes. Parallel processing and memory management were also considered to improve the processing speed of the detection system. Multiple-lane coverage was fulfilled such that a wider conveyor could be used and overall throughput would be increased. To further improve the detection performance of the dual X-ray and laser imaging system, a dynamic thresholding approach was introduced to suppress the errors and noise involved by the imaging system. Unlike the traditional single threshold method, dynamic thresholding monitored the responses of the region of interest under a set of thresholds to determine the true physical contaminants, making it more tolerant to the noise than the single threshold method. An overall 98.6% detection rate was achieved.Item Predicting Water Table Fluctuations Using Artificial Neural Network(2008-11-17) Wu, Chung-Yu; Shirmohammadi, Adel; Fischell Department of Bioengineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Correctly forecasting groundwater level fluctuations can assist water resource managers and engineers in efficient allocation of the regional water needs. Modeling such systems based on satellite remotely sensed data may be a viable option to predict water table fluctuations. Two types of water table prediction models based on Artificial Neural Network (ANN) technology were developed to simulate the water table fluctuations at two well sites in Maryland. One was based on the relationship between the variations of brightness temperature and water table depth. The other one was based on the relationship between the changes of soil moisture and water table depth. Water table depths recorded at these two wells, brightness temperature retrieved from the Advanced Microwave Scanning Radiometer, and soil moisture data produced by the Land Data Assimilation System were used to train and validate the models. Three models were constructed and they all performed well in predicting water table fluctuations. The root mean square errors of the water table depth forecasts for 12 months were between 0.043m and 0.047m for these three models. The results of sensitivity test showed that the models were more sensitive to the uncertainty in water table depth than to that in brightness temperature or in soil moisture content. This suggests that for situations where high resolution remotely sensed data is not available, an ANN water table prediction model still can be built if the trend of the time series of the data, such as brightness temperature or soil moisture, over the study site correlates well with the trend of the time series of the ground measurement at the study site. An extension of the study to a regional scale was also performed at 12 available well sites in Piedmont Plateau, Maryland. Hydrologic soil types, LDAS soil moistures, and water table depths at these locations were used in the ANN modeling. The root mean square error of one month long water table depth forecast was 0.142m. However, the accuracy of the monthly forecast decreases with the increase of time. A further study to improve the accuracy of long-term water table fluctuation forecast is recommended.Item INTEGRATED ECONOMIC DECISION SUPPORT SYSTEM MODEL FOR DETERMINING IRRIGATION APPLICATION AND PROJECTED AGRICULTURAL WATER DEMAND ON A WATERSHED SCALE(2006-11-27) Hanna, Kalim; Shirmohammadi, Adel; Biological Resources Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This study involves the development of an irrigation economic model used to determine the estimated net benefit of various irrigation systems when used in temperate zones. The model processes SWAT (Soil and Water Assessment Tool) output data together with user supplied economic data as a basis for identifying agricultural fields likely to result in the greatest economic return for irrigation installations, based on irrigation installation costs, water costs, and the expected revenue from increased yields due to applied water. The model is capable of not only identifying those agricultural fields within the area of interest likely to result in the greatest net benefit, but is able to prescribe the most profitable irrigation system from an array of possible systems, based on user supplied economic and performance data. The model can also be used to determine the optimal average monthly irrigation volume to be applied to a given field, by balancing the expected revenue due to the estimated yield increase as a result of irrigation application verses the cost of water. The model is applied in this study to a range of water cost levels and crop types from which general conclusions about the use of irrigation in temperate zones are made. The primary product of this study is an irrigation economic tool capable of determining the profitability of irrigation installations verses non-irrigated systems for a wide range of hydrological and environmental conditions. The project included the collection and compilation of required data on land-use, topography, and soil properties, into a GIS project, used as a data input basis for the SWAT model. For demonstration purposes the model is applied to the Pocomoke River basin located in the Coastal Plain of Maryland's Eastern Shore. Input data for the model is taken from multiple SWAT simulations for various crops, modeled with a statistically generated artificial weather pattern typical of the region. Further analysis is conducted on the environmental impact of irrigation, using SWAT model simulations over a range of irrigation application levels. General conclusions are drawn on the effects of irrigation on water quality parameters and the nutrient/sediment transport processes involved.Item MULTI-SCALE INVERSE MODELING IN BIOLOGICAL MASS TRANSPORT PROCESSES(2006-11-24) SADEGH ZADEH, KOUROUSH; MONTAS, HUBERT J; Fischell Department of Bioengineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)A state-of-the-art inverse modeling strategy was developed, analyzed, and applied in two different biological mass transport processes. The strategy was developed in the framework of the nonlinear optimization problem in which model parameters were estimated by minimizing an appropriate objective function which represents the discrepancy between the observed and predicted responses of the biological systems. The forward problems were solved numerically using the mass conservative Galerkin based linear finite element and finite difference methods. Before incorporating in the framework of the inverse code, the numerical simulators were validated with either analytical or reference solutions. In the inverse code, the Osborne- Moré extended version of the Levenberg- Marquardt algorithm was used to determine the search direction. The Jacobian matrix was constructed using partial derivatives of the state variables with respect to model parameters by one and two-sided finite difference approximations. A mixed termination criterion was used to end the optimization. The strategy was applied to parameter identification problem in Fluorescence Recovery after Photobleaching (FRAP) protocol to estimate the optimized values of the mass transport and binding rate parameters for GFP-tagged glucocorticoid receptor. Results indicate that the protocol provides enough information to uniquely estimate one parameter. It also provides enough information to uniquely estimate the individual values of the binding rate coefficients given the value of the molecular diffusion coefficient is known. However, the protocol provides insufficient information for unique simultaneous estimation of three parameters (diffusion coefficient and binding rate parameters) owing to the high intercorrelation between the molecular diffusion coefficient and pseudo-association rate parameter. Attempts to estimate macromolecule mass transport and binding rate parameters simultaneously from FRAP data result in misleading conclusions regarding concentrations of free macromolecule and bound complex inside the cell, average binding time per vacant site, average time for diffusion of macromolecules from one site to the next, and slow or rapid mobility of biomolecules in cells. To obtain unique values for molecular diffusion coefficient and binding rate parameters of biomolecule, two FRAP experiments should be conducted on the same class of macromolecule and cell. One experiment should be used to measure the molecular diffusion coefficient independently of binding in an effective diffusion regime and the other should be conducted in a reaction dominant or reaction-diffusion regime to quantify the binding rate parameters. The inverse modeling strategy was also successfully used to identify hydraulic parameters for both single and multi-objective optimization problems in homogeneous and heterogeneous variably saturated soils. Incorporating both soil water content information and soil water pressure head data in the framework of the multi-objective parameter optimization, produced excellent result for both soil water content and pressure head profiles.