Experimental Design: Design Experimentation

This paper was selected for publication in MIT’s Design Issues. The research takes an original approach by positioning experimentation as a comprehensive design methodology, rather than using the traditional industrial design approach of employing experimentation as a problem-solving tool within a standard design model. It is an evolution of design thinking on non-linear design methods first developed by Hall and presented to the ‘International Association of Societies of Design Research Conference’, Seoul, South Korea (2009), and in a paper entitled ‘Innovation design engineering: Non-linear progressive education for diverse intakes’ presented at the ‘International Conference on Engineering and Product Design Education’, University of Brighton, UK, which offered a non-linear pedagogy (Hall and Childs 2009) that uniquely supports a diverse interdisciplinary intake. Experimental design is well known in the science domain but very little evidence has been recorded of experimentation in industrial design and its position in relation to work in other science and research domains. Connections are made with theories on research methods, an analysis of case studies and comparisons of literature on experimentation from science disciplines, especially that of Kuhn (1962), Galison (1987), Pasteur’s quadrant for scientific research in Stokes (1997) and Borgdorff (2007). Hall makes significant claims in exploring and articulating a model of design experimentation that highlights the differences between scientific and design experimentation. This work was original in describing an experimental design model for the increasing activity in early phases of design development by recording and enhancing knowledge in this important area for future design research and practice. The methods researched in the paper were later used in experimental design workshops in Daegu, South Korea (2011) and Busan, South Korea (2012)

Experimental Design of Electrocoagulation and Magnetic Technology for Enhancing Suspended Solids Removal From Synthetic Wastewater

Design of experiments (DOE) is one of the statistical method that is used as a tool to enhance and improve experimental quality. The changes to the variables of a process or system is supposed to give the optimal result (response) and quite satisfactory. Experimental design can defined as a test or series of test series by varying the input variables (factors) of a process that can known to cause changes in output (response). This paper presents the results of experimental design of wastewater treatment by electrocoagulation (EC) technique. A combined magnet and electrocoagulation (EC) technology were designed to increase settling velocity and to enhance suspended solid removal efficiencies from wastewater samples. In this experiment, a synthetic wastewater samples were prepared by mixing 700 mg of the milk powder in one litre of water and treated by using an acidic buffer solution. The monopolar iron (Fe) plate anodes and cathodes were employed as electrodes. Direct current was varied in a range of between 0.5 and 1.1 A, and flowrate in a range of between 1.00 to 3.50 mL/s. One permanent magnets namely AlNiCo with a magnetic strength of 0.16T was used in this experiment. The results show that the magnetic field and the flowrate have major influences on suspended solids removal. The efficiency removals of suspended solids, turbidity and COD removal efficiencies at optimum conditions were found to be more than 85%, 95%, and 75%, respectively

Experimental Design and Optimization of Conical Horn of Ultrasonic Amplitude

Based on the basic principle of particles and the simple mechanical vibration system, then according to the wave equation and the traditional design theory of the amplitude transformer, we design an amplitude transformer commonly used in the equipment of Ultrasonic machining. Then, the structure is analyzed by the finite element analysis software ANSYS in the modal and harmonic response module and further optimized to obtain the design parameters of the amplitude transformer with good performances. Finally, the amplitude transformer is made according to optimized parameters and later it is analyzed by the impedance analyzer. And then the designed transformer is further modified to achieve better performance

Budget Feasible Mechanisms for Experimental Design

In the classical experimental design setting, an experimenter E has access to a population of $n$ potential experiment subjects $i\in \{1,...,n\}$, each associated with a vector of features $x_i\in R^d$. Conducting an experiment with subject $i$ reveals an unknown value $y_i\in R$ to E. E typically assumes some hypothetical relationship between $x_i$'s and $y_i$'s, e.g., $y_i \approx \beta x_i$, and estimates $\beta$ from experiments, e.g., through linear regression. As a proxy for various practical constraints, E may select only a subset of subjects on which to conduct the experiment. We initiate the study of budgeted mechanisms for experimental design. In this setting, E has a budget $B$. Each subject $i$ declares an associated cost $c_i >0$ to be part of the experiment, and must be paid at least her cost. In particular, the Experimental Design Problem (EDP) is to find a set $S$ of subjects for the experiment that maximizes V(S) = \log\det(I_d+\sum_{i\in S}x_i\T{x_i}) under the constraint $\sum_{i\in S}c_i\leq B$; our objective function corresponds to the information gain in parameter $\beta$ that is learned through linear regression methods, and is related to the so-called $D$-optimality criterion. Further, the subjects are strategic and may lie about their costs. We present a deterministic, polynomial time, budget feasible mechanism scheme, that is approximately truthful and yields a constant factor approximation to EDP. In particular, for any small $\delta > 0$ and $\epsilon > 0$, we can construct a (12.98, $\epsilon$)-approximate mechanism that is $\delta$-truthful and runs in polynomial time in both $n$ and $\log\log\frac{B}{\epsilon\delta}$. We also establish that no truthful, budget-feasible algorithms is possible within a factor 2 approximation, and show how to generalize our approach to a wide class of learning problems, beyond linear regression

Experimental Design for the LATOR Mission

This paper discusses experimental design for the Laser Astrometric Test Of Relativity (LATOR) mission. LATOR is designed to reach unprecedented accuracy of 1 part in 10^8 in measuring the curvature of the solar gravitational field as given by the value of the key Eddington post-Newtonian parameter \gamma. This mission will demonstrate the accuracy needed to measure effects of the next post-Newtonian order (~G^2) of light deflection resulting from gravity's intrinsic non-linearity. LATOR will provide the first precise measurement of the solar quadrupole moment parameter, J2, and will improve determination of a variety of relativistic effects including Lense-Thirring precession. The mission will benefit from the recent progress in the optical communication technologies -- the immediate and natural step above the standard radio-metric techniques. The key element of LATOR is a geometric redundancy provided by the laser ranging and long-baseline optical interferometry. We discuss the mission and optical designs, as well as the expected performance of this proposed mission. LATOR will lead to very robust advances in the tests of Fundamental physics: this mission could discover a violation or extension of general relativity, or reveal the presence of an additional long range interaction in the physical law. There are no analogs to the LATOR experiment; it is unique and is a natural culmination of solar system gravity experiments.Comment: 16 pages, 17 figures, invited talk given at ``The 2004 NASA/JPL Workshop on Physics for Planetary Exploration.'' April 20-22, 2004, Solvang, C

Two polynomial representations of experimental design

In the context of algebraic statistics an experimental design is described by a set of polynomials called the design ideal. This, in turn, is generated by finite sets of polynomials. Two types of generating sets are mostly used in the literature: Groebner bases and indicator functions. We briefly describe them both, how they are used in the analysis and planning of a design and how to switch between them. Examples include fractions of full factorial designs and designs for mixture experiments.Comment: 13 page

Nonlinear Matroid Optimization and Experimental Design

We study the problem of optimizing nonlinear objective functions over matroids presented by oracles or explicitly. Such functions can be interpreted as the balancing of multi-criteria optimization. We provide a combinatorial polynomial time algorithm for arbitrary oracle-presented matroids, that makes repeated use of matroid intersection, and an algebraic algorithm for vectorial matroids. Our work is partly motivated by applications to minimum-aberration model-fitting in experimental design in statistics, which we discuss and demonstrate in detail

Contest Design: An Experimental Investigation

This paper experimentally compares the performance of four simultaneous lottery contests: a grand contest, two multiple prize settings (equal and unequal prizes), and a contest which consists of two subcontests. Consistent with the theory, the grand contest generates the highest effort levels among all simultaneous contests. In multi-prize settings, equal prizes produce lower efforts than unequal prizes. The results also support the argument that joint contests generate higher efforts than an equivalent number of subcontests. Contrary to the theory, there is significant over-dissipation. This over-dissipation can be partially explained by strong endowment size effects. Subjects who receive higher endowments tend to over-dissipate, while such over-dissipation disappears when the endowments are lower. This behavior is consistent with the predictions of a quantal response equilibrium. We also find that less risk-averse subjects over-dissipate more.rent-seeking, contest, contest design, experiments, risk aversion, over-dissipation