Optimal Retailer Load Estimates Using Stochastic Dynamic Programming

In this paper, we describe a stochastic dynamic programming methodology for determining optimal forward load estimates for electric power retailers to their suppliers. This work describes both a model as well as results based on real data for the Electric Reliability Council of Texas market and provides insights useful for planning purposes for electric power retailers in the face of uncertain market prices and end-user loads.     

Robust/Optimal Temperature Profile Control of a High-Speed Aerospace Vehicle Using Neural Networks

An approximate dynamic programming (ADP)-based suboptimal neurocontroller to obtain desired temperature for a high-speed aerospace vehicle is synthesized in this paper. A 1-D distributed parameter model of a fin is developed from basic thermal physics principles. ldquoSnapshotrdquo solutions of the dynamics are generated with a simple dynamic inversion-based feedback controller. Empirical basis functions are designed using the ldquoproper orthogonal decompositionrdquo (POD) technique and the snapshot solutions. A low-order nonlinear lumped parameter system to characterize the infinite dimensional system is obtained by carrying out a Galerkin projection. An ADP-based neurocontroller with a dual heuristic programming (DHP) formulation is obtained with a single-network-adaptive-critic (SNAC) controller for this approximate nonlinear model. Actual control in the original domain is calculated with the same POD basis functions through a reverse mapping. Further contribution of this paper includes development of an online robust neurocontroller to account for unmodeled dynamics and parametric uncertainties inherent in such a complex dynamic system. A neural network (NN) weight update rule that guarantees boundedness of the weights and relaxes the need for persistence of excitation (PE) condition is presented. Simulation studies show that in a fairly extensive but compact domain, any desired temperature profile can be achieved starting from any initial temperature profile. Therefore, the ADP and NN-based controllers appear to have the potential to become controller synthesis tools for nonlinear distributed parameter systems.     

Exact and numerically stable closed-form expressions for potential coefficients of rectangular conductors

Existing exact closed-form expressions for the scalar mutual and self-potential coefficients for rectangular conductors may be ill-conditioned for certain geometries. We propose new, exact, closed-form expressions for potential coefficients that are much better-conditioned. The basic idea is to express all potential coefficients as weighted sums of mutual and self-potential coefficients of suitably defined virtual plates. Experimental results are presented to demonstrate the improved numerical stability of the new formulas.     

Unusual persistent photoconductivity in the InAs/AlSb quantum well

Unusual features of persistent photoconductivity are reported for the InAs/AlSb quantum-well (QW) structure with a backgate. A negative persistent photoconductivity made it possible to decrease the electron concentration by an order of magnitude from 6 × 10¹¹ cm^?2. This is the largest variation in the electron concentration for this effect. In addition to a pronounced negative persistent photoconductivity, the relaxation of the structural resistance was bistable under exposure of the structure to visible light. These phenomena are attributed to the effect of a thin Ge film deposited on the structure surface prior to photolithography. This film forms a region in the GaSb layer in which the holes are accumulated from the sequence of the Ge/GaSb/AlSb layers located above the QW. IR radiation initiates beats of Shubnikov-de Haas oscillations in the region of weak magnetic fields. These beats are believed to be caused by spin splitting in a zero magnetic field due to the asymmetry of a potential profile of the QW. This asymmetry is induced by prolonged illumination of the structure.     

The influence of component inclination on surface finish evaluation using digital image processing

Many researchers have so far used Machine Vision and digital image processing for grabbing images of machined surfaces, improving their quality by pre-processing and then analysed them for evaluation of surface finish with a reasonable success. In the conventional mechanical stylus method used for roughness evaluation, many of the fundamental requirements are taken care of during measurement which includes alignment of component with the stylus pick up movement, tracing length, filter cut off length, etc. Practical use of Machine Vision for surface roughness estimation faces many challenges, as in this case only image is used for evaluation and not the component. For example, if the component is kept at an angle during imaging, there is a possibility of getting distorted information and the consistency of evaluation/quantification would become a problem. Therefore, there is a need to ensure that the measured surface is kept horizontal when the image is being taken. In this work, estimation of the surface roughness has been done and analysed using digital images of machined surfaces obtained by a Machine Vision system deliberately maintained at varying angles. The optical surface finish values (G_a) estimated in all such cases using Machine Vision approach are compared with that obtained using conventional stylus method (R_a). An artificial neural network (ANN) is trained and tested to arrive at the R_a values using the input obtained from the digital images of inclined surfaces which include optical roughness parameters estimated and angle of inclination of test parts. The experimental result indicates that the surface roughness could be estimated/predicted with a reasonable accuracy using Machine Vision and ANN, respectively. In addition, a shadow removal algorithm is used to improve the quality of the images of inclined surfaces and then the optical roughness parameter is estimated. All the results are compared with that obtained using stylus method and analysed in this paper.     

Assessment of surface roughness based on super resolution reconstruction algorithm

In this work, an attempt has been made to use a super resolution image processing algorithm for preprocessing the images over and above existing image quality enhancement techniques. The improved quality images processed using a machine vision system have been used to assess the quality of the surfaces. To ensure the validity of the approach the roughness values quantified using these images are then compared with widely accepted standard mechanical stylus instrument values. Quantification of digital images for surface roughness is performed using two Fourier transform parameters (major peak frequency and principal component magnitude squared value) and the standard deviation of gray level intensity values. Then the group method of data handling (GMDH) technique was used to obtain an analytical relationship of the roughness parameters calculated using the digital surface image and the stylus instrument values. We present in this paper an analysis based on the comparison to make sure that the present approach of estimation of surface finish based on the digital processed image could be implemented in practice.     

Variable Time Impulse System Optimization with Continuous Control and Impulse Control

Optimal control of a variable time impulse system is considered in this study. Besides impulse control, the system is assumed to operate with continuous control also. Necessary conditions for the optimization of such system are derived using the calculus of variations. A reinforcement based solution technique called a single network adaptive critic (SNAC) method is developed in this paper to obtain an optimal solution. Details of the SNAC‐based algorithm are presented. Simulation examples are also presented for illustrative purposes.     

Chemical,antioxidant, functional and thermal properties of rice bran proteins after yeast and natural fermentations

The paper focuses on the chemical, antioxidant, functional and thermal properties of rice bran proteins after yeast, natural fermentations and unfermented rice bran. Protein content of yeast‐fermented rice bran protein concentrate (YFRBPC), naturally fermented rice bran protein concentrate (NFRBPC) and unfermented rice protein concentrate (UFRBPC) were 72.50%, 68.92% and 65.73%, respectively, while ash content were 4.72%, 4.61% and 3.04%, respectively. The total amino acids of YFRBPC, NFRBPC and UFRBPC were 123.16, 118.45 and 99.39, respectively. DPPH radical inhibition of YFRBPC, NFRBPC and UFRBPC were 58.62%, 55.29% and 47.14%, respectively, while ferric reducing ability power were 0.73, 0.58 and 0.41 mmol TE per gram, respectively. The highest foam capacity of UFRBPC (57.56%), NFRBPC (64.15%) and YFRBPC (76.00%) was observed at pH 9.0. YFRBPC and NFRBPC were lighter in colour than UFRBPC. YFRBPC had higher denaturation temperature and enthalpy value than NFRBPC and UFRBPC. The β‐sheets structures were more in YFRBPC and NFRBPC than UNFBPC.     

Integrated approach for assembly tolerance analysis

In mechanical design, tolerance assignment is a critical but complex task since the designer will not only have to consider the associated cost to achieve a certain tolerance level, but also the cost due to failure in assembling task. These associated tolerance costs as well as the failure rate are fuzzy in nature. This paper presents an integrated approach to incorporate manufacturing costs of certain tolerance specifications into design stages for automatic tolerance assignment and design. Tolerance design is interdisciplinary in nature and is characterized by a highly uncertain environment. In recent years, fuzzy logic has appeared as a credible alternative for tolerance design. A fuzzy based tolerance representation scheme is presented to model three dimensional (3D) tolerances. With this representation, relative assembly tolerance constraints can be expressed. A fuzzy tolerance generation and assignment process for assembly is discussed. Fuzzy tolerance equations are generated for 3D assembly considerations. Manufacturing process information, along with uncertain cost information modelled in fuzzy terms, is added to the system to arrive at a cost-optimal tolerance assignment.