Optimal design and verification of temporal and spatial filters using second-order cone programming approach

11 Introduction The filters are widely used in many applications of signal processing. Filter design is an important research problem in many diverse application areas. The filters we usually refer to are temporal filters, which pass the frequency components of interest and attenuate the others. A spatial filter passes the signal radiating from a specific location and attenuates signals from other locations. Beamformer that widely used in radar, sonar,and wireless communications is a kind of …     

Investigating designers’ and users’ cognitive representations of products to assist inclusive interaction design

There is strong evidence of the importance of good interaction design in the creation of intuitive-use products. However, there is also a strong indication, both in the literature and in the study with designers documented in this paper, that despite this evidence designers get little support in adequately representing, analysing and comparing design and user information. Since designers require a practical and relatively easy-to-use support tool that would enable them to better understand cognitive processes of users and evaluate the accessibility and usability of different product features, this paper proposes the Goals-Actions-Beliefs-Objects (GABO) modelling approach that can form the basis of such a tool for designers. The four distinct stages of the GABO approach are designed to assess and compare designers and users’ understanding and usage of everyday products. The evaluation results of the GABO approach with eight product designers have indicated that designers find it useful and effective in identifying the key similarities and differences in the understanding of designers and users.     

Interactive fuzzy stochastic multi-level 0–1 programming using tabu search and probability maximization

In this paper, we consider interactive fuzzy programming for multi-level 0–1 programming problems involving random variable coefficients both in objective functions and constraints. Following the probability maximization model together with the concept of chance constraints, the formulated stochastic multi-level 0–1 programming problems are transformed into deterministic ones. Taking into account vagueness of judgments of the decision makers, we present interactive fuzzy programming. In the proposed interactive method, after determining the fuzzy goals of the decision makers at all levels, a satisfactory solution is derived efficiently by updating satisfactory levels of the decision makers with considerations of overall satisfactory balance among all levels. For solving the transformed deterministic problems efficiently, we also introduce novel tabu search for general 0–1 programming problems. A numerical example for a three-level 0–1 programming problem is provided to illustrate the proposed method.     

Grading in interaction design education using design practitioners’ conceptions of process quality

The designed product is often assessed in interaction design education, but there are also courses that focus on learning the design process. It is then necessary to develop criteria for grading in such courses. To make a successful transfer from theory to practice, students also need to learn the criteria practitioners use, rather than the criteria that academically oriented teachers use. To do this, one approach is to align criteria with the conceptions practicing interaction designers have of process quality in design. Therefore, the research questions for this study are what those conceptions are, and how they can be utilized in grading criteria for interaction design projects in education. Interviews were made with 10 interaction designers. The interviews were qualitatively analyzed. The results demonstrate that practicing interaction designers conceptualize the quality of the design process in three ways: it is good if established methods are used and the design is managed within resource constraints, and within organizational and technological limitations, while also meeting stated objectives; it is even better if the design has a thought-through rationale; and ideally, the design should also be inspirational. These conceptions were transferred to points on a criteria-referenced grading scale which was used to develop course specific grading criteria. The criteria were evaluated in terms of comprehensibility and reliability. The evaluation showed that most of the students who also attended lectures understood the criteria. A high and significant covariation and a high level of agreement between the two teachers who graded the projects were shown. Further, the developed criteria should be generalizable to other process-centered interaction design courses and to assessment in other design disciplines.     

A non-linear programming approach to the computer-aided design of regulators using a linear-quadratic formulation†

A design technique is proposed for linear regulators in which a feedback controller of fixed structure is chosen to minimize an integral quadratic objective function subject to the satisfaction of integral quadratic constraint functions. Application of a non-linear programming algorithm to this mathematically tractable formulation results in an efficient and useful computer-aided design tool. Particular attention is paid to computational efficiency and various recommendations are made. Two design examples illustrate the flexibility of the approach and highlight the special insight afforded to the designer.     

A university-timetabling problem and its solution using Benders’ partitioning—a case study

In this paper, we address a university-timetabling problem and present a methodology that relies on Benders’ partitioning for its solution. This partitioning results from the special nature of the underlying integer programming formulation for this problem. We have used our methodology to schedule courses offered by the College of Engineering as well as to those offered university-wide at Virginia Tech. The results clearly depict an improvement in the quality of course schedules obtained by our methodology over those currently used, when the performance of a timetable is measured by the total distance traveled by the faculty members from their offices in respective departments to the classrooms, where the courses are offered.     

Elements of the theory and methods of parametric regulation of national economy’s evolution using discrete dynamic stochastic models

For the class of discreet stochastic dynamic systems with additive noise, advances of the theory of parametric regulation were presented. Efficiency of their use was demonstrated by way of the example of a stochastic computable model of general equilibrium of the major industries.     

More work cooperation or relationship harmony in mass customization? The impacts of personalized design,modular design,and automated processes

Information Technology and Management - Mass customization (MC) is recognized as a new manufacturing paradigm for market competition, and several empirical studies have discussed elements of...     

Integration of panchromatic and SAR features into multispectral SPOT images using the ‘à trous’ wavelet decomposition

A method is described for integrating panchromatic (P) and synthetic aperture radar (SAR) features into multispectral (XS) images using conjointly the modified Brovey transform (MBT) and the ‘à trous’ wavelet decomposition (ATDW). The MBT is based on the local modulation of each multispectral image by the ratio of the new and initial intensity components to produce new multispectral images directly. The ATWD allows extraction of features from P and SAR images, which are combined through a feature selection rule to integrate into the initial intensity component. For evaluating the effect of each feature selection on new XS images, experimental results are conducted on SPOT (XS, P) and Radarsat (SAR) images using both visual inspection and many refined statistical measures.     

Application of mathematical programming in the design of optimal control systems†

The problem of applying the various computational methods of mathematical programming in the design of an optimal control system is discussed. A general case of non-linear, non-autonomous, state equations, subject to inequality constraints on both state and control variables, is considered. Both continuous and discrete time systems are investigated. In case of discrete time systems, the sampling intervals are assumed generally unequal and aperiodic, with inequality constraints imposed upon them.

Systems like these impose considerable computational difficulties when treated by the maximum principle or dynamie programming. Using mathematical programming, one may simplify a wide class of those computational problems.

Several examples of applying mathematical programming to particular control problems are presented.     

Modelling damping ratio and shear modulus of sand–mica mixtures using genetic programming

This study presents two Genetic Programming (GP) models for damping ratio and shear modulus of sand–mica mixtures based on experimental results. The experimental database used for GP modelling is based on a laboratory study of dynamic properties of saturated coarse rotund sand and mica mixtures with various mix ratios under different effective stresses. In the tests, shear modulus, and damping ratio of the geomaterials have been measured for a strain range of 0.001% up to 0.1% using a Stokoe resonant column testing apparatus. The input variables in the developed NN models are the mica content, effective stress and strain, and the outputs are damping ratio and shear modulus. The performance of accuracies of proposed NN models are quite satisfactory (R² = 0.95 for damping ratio and R² = 0.98 for shear modulus).     

Optimal control for stochastic linear quadratic singular Takagi–Sugeno fuzzy delay system using genetic programming

In this paper, optimal control for stochastic linear singular Takagi–Sugeno (T–S) fuzzy delay system with quadratic performance is obtained using genetic programming (GP). To obtain the optimal control, the solution of matrix Riccati differential equation (MRDE) is computed by solving differential algebraic equation (DAE) using a novel and nontraditional GP approach. The GP solution is equivalent or very close to the exact solution of the problem. Accuracy of the GP solution to the problem is qualitatively better. The solution of this novel method is compared with the traditional Runge Kutta (RK) method. An illustrative numerical example is presented for the proposed method.     

Development and correlation analysis of non-dominated sorting buffalo optimization NSBUF II using Taguchi’s design coupled gray relational analysis and ANN

African Buffalo Optimization (ABO) is a latest bio-inspired optimization technique in the domain of evolutionary optimization, which mimics the migratory behaviour of the buffalo foraging for food across the African plains and forests. The ABO is, by now, recognized as a single-objective optimization algorithm, comprising the ability to solve both, the continuous and discrete optimization problems. However, a multi-objective version of ABO could be more useful for industrial problems. An aim is made in this article to develop the multi-objective variant of ABO, namely NSBUF II, which incorporates Pareto search for non-dominated solutions in the state space and a local search module for faster convergence. Selection of parameters for the NSBUF II is extremely sensitive to the obtained Pareto fronts. Thus, a Grey Relational Analysis (GRA) coupled with Taguchi’s L₁₆ orthogonal array is adopted, which efficiently obtains the best set of parameters for the NSBUF II. Initially the proposed NSBUF II is tested using utilization based bi-objective production cell design problem and compared with published Multi-Objective Particle Swarm Optimization (MOPSO), and Non-dominated Sorting Genetic Algorithm (NSGA II) successfully. To analyse the performance of the NSBUF II, Self-Organizing Map (SOM) is applied, which is a powerful tool for visualizing the high-dimensional data in low dimensional maps. Applied SOM visually reveals the hidden correlational structure among the design parameters and the objective space. The performance of the NSBUF II is validated statistically NSBUF II is further verified with a real-world case obtained based on the Abrasive Water Jet Machining (AWJM) process. Validation test proves the competence of the proposed NSBUF II for real-world problem solving. The contribution of this paper is threefold. First, a novel multi-objective NSBUF II algorithm is developed. Second, a SOM based visual analysis is proposed to visualize the correlation among design parameters and Pareto fronts. Third, the NSBUF II is employed to solve a combinatorial production cell design problem followed by a real-world industrial problem.     

Developing Oregon’s renewable energy portfolio using fuzzy goal programming model

Renewable energy continues to be a hot topic in the United States affecting security and sustainability. A model to create renewable energy portfolio is established using guidelines drawn by Oregon’s Renewable Portfolio Standard (RPS) legislation with the objective of responding to a 25% of the state electricity demand by renewable resources in 2025. The fuzzy goal programming model is adaptable to accommodate changes in energy costs and future advances in technology maturity. It can also take into consideration the preferences of policy-makers and stakeholders. This model can help to reveal the costs and benefits of complex decisions regarding renewable energy.     

New approach to stochastic stability and controller design for networked control systems

This paper addresses the random time-delays and packet losses issues of networked control systems （NCS） within the framework of jump linear systems with mode-dependent time-delays. A new delay-dependent condition on the stochastic stability is proposed by a new stochastic Lyapunov-Krasovskii functional. The condition is formulated as a set of coupled linear matrix inequalities （LMIs）. As an example to verify the proposed method, an inverted-pendulum system with network is considered. The simulation results demonstrate the effectiveness of the method.     

Modelling and simulation of stochastic systems†

In the field of automatic control, systems with stochastic inputs are frequently encountered. The purpose of this paper is twofold: (I) to treat the problem of mathematically modelling a general class of stochastic systems, and (2) to simulate the optimal control for a special case of this class of stochastic systems, specifically a linear time-invariant system with stochastic inputs and stochastically perturbed observations. The general class of systems considered in the modelling includes systems having non-linear and time-varying state dependence and additive white noise which is added in a non-linear and time-varying manner.     

A data-driven based decomposition–integration method for remanufacturing cost prediction of end-of-life products

Remanufacturing cost prediction is conducive to visually judging the remanufacturability of end-of-life (EOL) products from economic perspective. However, due to the randomness, non-linearity of remanufacturing cost and the lack of sufficient data samples. The general method for predicting the remanufacturing cost of EOL products is very low precision. To this end, a data-driven based decomposition–integration method is proposed to predict remanufacturing cost of EOL products. The approach is based on historical remanufacturing cost data to build a model for prediction. First of all, the remanufacturing cost of individual EOL product is arranged as a time series in reprocessing order. The Improved Local Mean Decomposition (ILMD) is employed to decompose remanufacturing cost time series data into several components with smooth, periodic fluctuation and use this as input. BP neural network based on Particle Swarm Optimization (PSO-BP) algorithm is utilized to predict the cost of each component. Finally, the predicted components are added to obtain the final prediction result. To illustrate and verify the feasibility of the proposed method, the remanufacturing cost of DH220 excavator is applied as the sample data, and empirical results show that the proposed model is statistically superior to other benchmark models owing to its high prediction accuracy and less computation time. And proposed method can be utilized as an effective tool to analyze and predict remanufacturing cost of EOL products.     

Optimal control for stochastic linear quadratic singular neuro Takagi–Sugeno fuzzy system with singular cost using genetic programming

In this paper, optimal control for stochastic linear quadratic singular neuro Takagi–Sugeno (T-S) fuzzy system with singular cost is obtained using genetic programming(GP). To obtain the optimal control, the solution of matrix Riccati differential equation (MRDE) is computed by solving differential algebraic equation (DAE) using a novel and nontraditional GP approach. The obtained solution in this method is equivalent or very close to the exact solution of the problem. Accuracy of the solution computed by GP approach to the problem is qualitatively better. The solution of this novel method is compared with the traditional Runge–Kutta (RK) method. A numerical example is presented to illustrate the proposed method.