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Residential photovoltaic (PV) battery systems are key technology in the design of low-carbon and resilient energy systems, however, limited research has assessed their profitability. This study aims to evaluate the economic performance of PV battery systems for end-users. The evaluation takes geological, technological, and socio-economic factors into consideration, thereby making the evaluation more comprehensive. We used PV power generation data and power consumption data of more than 40,000 all-electric houses in Japan. We performed scenario analyses with a sensitivity analysis. The results showed that residential PV battery systems were highly profitable when their storage battery operation modes were appropriately utilized at the end of the purchase period for solar power generation in Japan’s feed-in-tariff (FIT) scheme. The profitability, however, varied across regions. The results also indicated that the PV self-consumption rate was more than 50% when charging the battery with surplus power. The results of the sensitivity analysis suggested that the unit prices of grid electricity and the purchasing price of surplus power after the FIT scheme had a significant effect on the profitability of residential PV battery systems.

Low-carbon technologies will play a vital role in the realization of environmentally sustainable economies. However, uncertainties remain as to the feasibility of their development and implementation. Therefore, possible scenarios for the potential of these technologies should be considered to allow for flexible decision-making with respect to long-term energy strategies in Japan. This study evaluates the role of hydrogen in future energy systems in Japan using a MARKet ALlocation (MARKAL) model. A range of uncertainties are considered for nuclear power generation and carbon capture and storage (CCS) from fossil power generation. Our results suggest that an 80% reduction of CO2 emissions from the 2013 level by 2050 requires emissions from the electricity sector to decrease to nearly zero. Hydrogen power must play a functional role in future energy systems in Japan, but its contribution should depend on nuclear power and CCS.

This paper aims to identify psychological characteristics of potential investors of socially responsible investment (SRI) in Japan to explain its possible motivation by economic experiments. We asked subjects to make decisions regarding stock investments on the basis of three attributes of return, variance, and corporate social responsibility (CSR). We also conducted a dictator game and two lottery-choice experiments to measure subjects' psychological characteristics: altruism, risk aversion, and time discount rate. Applying a conditional logit model as well as mixed logit model, we found that people who have a higher time discount rate tend to be SRI investors.

Appearance is often key to the success of consumer-oriented products. This paper investigates the influence of visual appeal of renewable energy systems on consumer preference in a case study of residential solar panels. Two surveys of US residents were conducted with 194 and 350 responses, respectively. The first focused on aspects of solar panel appearance, including color, surface pattern and frame, while the second examined the tradeoffs between solar panel appearance, functional performance and price. Respondents were presented with two types of visual representations: solar panel images alone or solar panel images shown in the context of use. Results showed that respondents in general preferred black solar panels that had solar cells with rounded corners and even surfaces, and preferred solar panels that matched the color of the roof. It was also found that respondents were willing to pay a higher price for better-looking solar systems. This effect was found to be stronger with contextualized images of solar panels on roofs.

A rough interface seems to be one of the possible reasons for low channel mobility (conductivity) in SiC MOSFETs. To evaluate the mobility by interface roughness, we drew a boundary line between amorphous insulator and crystalline 4H-SiC in a cross-sectional image obtained by a transmission electron microscope (TEM), by using the deep learning approach of convolutional neural network (CNN). We show that the CNN model recognizes the interface very well, even when the interface is too rough to draw the boundary line manually. Power spectral density of interface roughness was calculated., Comment: 16 pages, 8 figures

Maintenance plays a critical role in reducing operating cost and maximizing reliability of a complex engineering system. This paper proposes a novel maintenance-focused, system-level design framework that attempts to capture the interactions between maintenance strategies and system-level design parameters overlooked in current modeling approaches. The goal of this maintenance-focused approach is to help designers better understand the interconnectedness of system architecture, choice of maintenance strategy, and uncertainties in a design. Application of the proposed design framework is demonstrated through a case example of a power plant condenser system. Results show that using an integrated approach can reveal the many nonobvious interactions between subsystems, and produce system designs that have lower life-cycle cost compared to traditional sequential design approaches.

The design of complex systems involves selecting design parameters to satisfy the required constraints while meeting desired performance objectives. These parameters are often coupled and their relationships not easily understood. This paper presents results of an experiment to understand how designers solve parameter design problems, in the context of desalination systems. Subjects with different desalination expertise were asked to complete design tasks involving seawater reverse osmosis plants. Results confirmed that designers had difficulties understanding the sensitivity of coupled variables. More desalination knowledge was linked to better performance and designers with limited desalination knowledge tended to perform the worst due to having partial or incorrect domain knowledge. These findings have implications in design tool development and education.

Research on complex system optimization has focused on areas including algorithms, coordination strategies, and communication tools. This paper considers optimization from the perspective of information coordination during the solution process. This work aims to determine the potential impact of factors including the role of a system facilitator in managing system-level tradeoffs, the influence of an ideal information scenario, the choice of system variables, and the amount and form of passed information. The goal is not to develop the “best” communication structure or optimization algorithm, but to understand the significance of these factors. A traditional multidisciplinary design optimization coordination framework, a game theoretic approach, and a modified game theory approach are used to mimic design team behaviors. Results suggest these factors can influence the coordination process outcomes: The choice of system variable plays a significant role in the optimality of the final design; perfect inform...

Environmental problems must be solved urgently, and sustainable production activities are desired. This study focuses on environmental finance, which is a method of promoting sustainable corporation activities. Environmental finance allows socially responsible investment to directly contribute to corporate activities and sustainable production activities. To clarify the mechanism of eco-friendly investment decision making, 4,843 respondents took a questionnaire survey on investment decision making, based on the framework of prospect theory. The results showed that prospect theory did not always work for environment issues and that people’s attitudes when they decide on eco-friendly investments could be classified to four clusters.

We empirically investigate the relationship between environmental management and economic performance using such data as environmental rating, credit rating, and accumulated earnings ratio of companies listed in the manufacturing sector on the first section of the Tokyo Stock Exchange. We performed a path analysis using two types of structural equation modeling: Model A with a terminal economic performance path and Model B with a terminal environmental management path. As a result, we found that Model B has better goodness of fit in the path analysis than does Model A. This is because it is natural to place the economic performance path before the environment management path since companies pursue profits for stockholders. We also confirmed that in Model B, the path from the accumulated earnings rate to environmental management has a significantly negative coefficient. This suggests that two groups of enterprises exist: profit-oriented and environment-oriented companies. Since our results are based on Japanese companies, it is advisable to investigate further to generalize the results worldwide.

Peptidylarginine deiminase (PAD; EC 3.5.3.15) is a post-translational modification enzyme that catalyzes the conversion of protein-bound arginine to citrulline (deimination) in a calcium ion dependent manner. Although PADI genes are widely conserved among vertebrates, their function in the chicken is poorly understood. Here, we cloned and sequenced three chicken PADI cDNAs and analyzed the expression of their proteins in various tissues. Immunoblotting analysis showed that chicken PAD1 and PAD3 were present in cells of several central neuron system tissues including the retina; the chicken PAD2 protein was not detected in any tissue. We expressed recombinant chicken PADs in insect cells and characterized their enzymatic properties. The chicken PAD1 and PAD3 recombinant proteins required calcium ions as an essential cofactor for their catalytic activity. The two recombinant proteins showed similar substrate specificities toward synthetic arginine derivatives. By contrast to them, chicken PAD2 did not show any activity. We found that one of the conserved active centers in mammalian PADs had been altered in chicken PAD2; we prepared a reverse mutant but we did not detect an activity. We conclude that chicken PAD1 and PAD3 might play specific roles in the nervous system, but that chicken PAD2 might not be functional under normal physiological conditions.

The process of selecting among design alternatives is an important activity in the early stages of design. A designer is said to express design preferences when assigning priorities to a set of possible design choices. However, the assignment of preferences becomes more challenging on both a practical and theoretical level when performed by a group. This paper presents a probabilistic approach for estimating a team’s overall preference-related information known as preferential probabilities that extracts information from the natural language used in team discussion transcripts without aggregation of individual team member opinions. Assessment of the method is conducted by surveying a design team to obtain quantitative ratings of alternatives. Two different approaches are applied to convert these ratings into values that may be compared to the results of transcript analysis: the application of a modified Logit model and simulation based on the principle of maximum entropy. The probabilistic approach proposed in the paper represents how likely a choice is to be “most preferred” by a design team over a given period of time. A preliminary design selection experiment was conducted as an illustrative case example of the method. Correlations were found between the preferential probabilities estimated from transcripts and those computed from the surveyed preferences. The proposed methods may provide a formal way to understand and represent informal, unstructured design information using a low overhead information extraction method.

As engineering systems grow in complexity, it becomes more challenging to achieve system-level designs that effectively balance the trade-offs among subsystems. Lewis and others have developed a well-known, traditional game-theoretic approach for formally modeling complex systems that can locate a Nash equilibrium design with a minimum of information sharing in the form of a point design. This paper builds on Lewis’ work by proposing algorithms that are capable of converging to Pareto-optimal system-level designs by increasing cooperation among subsystems through additional passed information. This paper investigates several forms for this additional passed information, including both quadratic and eigen-based formulations. Such forms offer guidance to designers on how they should change parameter values to better suit the overall system by providing information on directionality and curvature. Strategies for representing passed information are examined in three case studies of 2- and 3-player scenarios that cover a range of system complexity. Depending on the scenario, findings suggest that passing more information generally leads to convergence to a Pareto-optimal set. However, more iterations may be required to reach the Pareto set than if using a traditional game-theoretic approach.

User-centered approaches to design can guide teams toward an understanding of users and aid teams in better posing design problems. This paper investigates the role of user-centered design approaches in design process and outcome within the context of design team projects. The value of interaction with users is examined at several stages throughout the design process. The influence of user-centered design on the performance of design teams is also explored. Results suggest that the quantity of interactions with users and time spent interacting with users alone is not linked with better design outcome, but that iterative evaluation of concepts by users may be of particular value to design prototypes. Suggestions are made based on the reflections from the authors after conducting this study.

Gathering user feedback on provisional design concepts early in the design process has the potential to reduce time-to-market and create more satisfying products. Among the parameters that shape user response to a product, this paper investigates how design experts use sketches, physical prototypes, and computer-aided design (CAD) to generate and represent ideas, as well as how these tools are linked to design attributes and multiple measures of design quality. Eighteen expert designers individually addressed a 2 hr design task using only sketches, foam prototypes, or CAD. It was found that prototyped designs were generated more quickly than those created using sketches or CAD. Analysis of 406 crowdsourced responses to the resulting designs showed that those created as prototypes were perceived as more novel, more aesthetically pleasing, and more comfortable to use. It was also found that designs perceived as more novel tended to fare poorly on all other measured qualities.

In this work, an estimate of the elevation of airborne lead concentrations via the lead solder contained in domestic electricity installations and an investigation into the effects of human exposure were conducted using a simulation method. The elevation of airborne lead concentrations due to incineration was calculated to be 0.001 µg/m3 maximum. The simulation results of the calculations indicated that blood lead concentrations could reach as high as 10−5 µg/dl. These concentrations could primarily be attributed to the inhalation of airborne lead particles. In addition, we discuss the influences of lead solder disposal on the ecosystem via soil and water.

Life cycle impact assessment for woodfree papers containing nonwood pulp (bagasse pulp and kenaf pulp) or deinked pulp (DIP) was investigated by eight kinds of environmental impact methods such as EPS 1992, EPS 2000, Eco-Point 1993, Eco-Point 1997, Eco-Indicator 95, Eco-Indicator 99, Panel method and Distance to Target method, based on the results of LCI (Life Cycle Inventory) analysis.Considering the environmental impact caused by CO2 emission from biomass, the environmental impact of woodfree paper containing DIP was smaller than that of other kinds of woodfree paper. Not considering the environmental impact caused by CO2 emission from biomass, the environmental impact of woodfree paper made from 100% wood pulp was smaller than that of woodfree paper containing DIP except Eco-Indicator 99. It was evaluated by eight kinds of environmental impact methods that the environmental impact of woodfree paper containing kenaf pulp was the largest in four kinds of woodfree paper. In the comparison of the environmental impacts calculated by eight kinds of environmental impact methods for four kinds of woodfree paper, the ratio of total environmental impact of the kenaf pulp and DIP was less than 1.8 in seven kinds of environmental impact methods except Eco-Indicator 99. It was confirmed that the environmental impact caused by CO2 emission was evaluated larger than that caused by other substances in eight kinds of environmental impact methods, especially accounting for about 70% in six environmental impact methods except Eco-Point 1993 and Eco-Indicator 99

Large-scale desalination plants are complex systems with many inter-disciplinary interactions and different levels of sub-system hierarchy. Advanced complex systems design tools have been shown to have a positive impact on design in aerospace and automotive, but have generally not been used in the design of water systems. This work presents a multi-disciplinary design optimization approach to desalination system design to minimize the total water production cost of a 30,000m3/day capacity reverse osmosis plant situated in the Middle East, with a focus on comparing monolithic with distributed optimization architectures. A hierarchical multi-disciplinary model is constructed to capture the entire system’s functional components and subsystem interactions. Three different multi-disciplinary design optimization (MDO) architectures are then compared to find the optimal plant design that minimizes total water cost. The architectures include the monolithic architecture multidisciplinary feasible (MDF), individual disciplinary feasible (IDF) and the distributed architecture analytical target cascading (ATC). The results demonstrate that an MDF architecture was the most efficient for finding the optimal design, while a distributed MDO approach such as analytical target cascading is also a suitable approach for optimal design of desalination plants, but optimization performance may depend on initial conditions.Copyright © 2014 by ASME

To minimize the design cost of a complex system and maximize performance, a design team ideally must be able to quantify reliability and mitigate risk at the earliest phases of the design process, where 80% of the cost is committed. This paper demonstrates the capabilities of a new System Reliability Exploration Tool based on the improved simulation capabilities of a system called Fault-Augmented Modelica Extension (FAME). This novel tool combines concepts from FMEA, traditional Reliability Analysis, and Quality Engineering to identify, gain insight, and quantify the impact of component failure modes through time evolution of a system’s lifecycle. We illustrate how to use the FAME System Reliability Exploration Tool through a vehicle design case study.Copyright © 2014 by ASME

This paper aims, through laboratory-based economic experiments, to shed light on the decision-making process in SRI and how it differs from decision making by other types of investors. We asked subjects to make decisions regarding stock investments on the basis of the three attributes of return, variance, and CSR, and we estimated their utility function to classify the types of investors. We also conducted a dictator game and lottery-choice experiments to measure subjects f heterogeneity in regard to three psychological factors: altruism, risk aversion, and time preference. We used psychological factors to apply a latent class model and we examined whether these factors affect investment in the stock of companies that actively promote CSR. The main finding of this study is that there is heterogeneity among investors, even among SRI investors. With the latent class model, we found conventional investors, SRI investors, and risk-loving SRI investors among our subjects. We hypothesised three psychological factors behind this heterogeneity. While the results support our altruistic hypothesis, the results under the risk-averse hypothesis and lower time preference hypothesis depend on which investor group is treated as SRI investors and also on the model specification. Although previous studies also found heterogeneity among SRI investors, no studies have tried to reveal the psychological background of the heterogeneity.

This paper addresses the problem of describing the decision-making process of a committee of engineers based upon their verbalized linguistic appraisals of alternatives. First, we show a way to model an individual’s evaluation of an alternative through natural language based on the Systemic-Functional Linguistics system of APPRAISAL. The linguistic model accounts for both the degree of intensity and the uncertainty of expressed evaluations. Second, this multi-dimensional linguistic model is converted into a scalar to represent the degree of intensity and a probability distribution function for the stated evaluation. Finally, we present a Markovian model to calculate the time-varying change in preferential probability, the probability that an alternative is the most preferred alternative. We further demonstrate how preferential probability toward attributes of alternatives correspond to preferential probability toward alternatives. We illustrate the method on two case studies to highlight the time-variant dynamics of preferences toward alternatives and attributes. This research contributes to process tracing in descriptive decision science to understand how engineers actually take decisions., National Science Foundation (U.S.) (Award CMMI-0900255)

The importance of prototyping in the design process has been widely recognized, but less research emphasis has been placed on the appropriate timing and detail of so-called "throwaway" prototyping during the preliminary design phase. Based on a study of mid-career professional graduate students, statistically significant correlations were found between the time such prototypes were created and design outcome. Building prototypes early on in the design process, or performing additional rounds of benchmarking and user interaction later on during the project (in addition to the typical early stage efforts), correlated with better design outcome, although the total time spent on these activities did not. The correlation between project presentations and reviewer scores are also touched upon. These findings suggest that the timing of design activities is more important than the time spent on them. INTRODUCTION It is widely recognized that decisions made in the early [9]stages of the design process have great bearing on the outcome of the design in terms of design success, cost of manufacturing and time to market. The later in the design process problems are identified and changes made to the design, the more costly it becomes [1-3]. The question then is, how should designers go about exploring the design space in the most efficient way possible, find a desirable design direction, and execute a successful design? on effective ways to conduct different design activities This research examines the timing of prototyping related activities in the early stages of the design process. Research has been conducted on prototyping in the early stages of the design process, with some arguing for early and frequent prototyping as a way to test ideas early [4, 5] and to help build confidence in design concepts in a team [6]. There are several different design activities that have been recognized as important to design outcome and have been widely researched. These range from prototyping and sketching to benchmarking and collecting input from end-users. Prototyping is often thought of in terms of the particular technology or materials involved in creating the prototype, such as 3D printing or aluminum stock. This paper considers prototypes from a different perspective, that of as an artifact that design teams use to evaluate potential design concepts before further development [7, 8]. These early stage prototypes are created with the express understanding that they will be discarded after evaluation, and are thus "throwaway" prototypes. Using this strategy encourages building the "cheapest" prototype that can still provide needed information, meaning that such prototypes may be made of relatively inexpensive materials and are fast to fabricate. This approach to fabrication has been expressed as "fail early to succeed sooner,"popularized by the design firm IDEO and has been widely cited as a strategy for early stage design[4].Although there has been a significant amount of research

The optimal maintenance scheduling of systems with degrading components is highly coupled with the design of the system and various uncertainties associated with the system, including the operating conditions, the interaction of different degradation profiles of various system components, and the ability to measure and predict degradation using prognostics and health management (PHM) technologies. Due to this complexity, designers need to understand the correlations and feedback between the design variables and lifecycle parameters to make optimal decisions. A framework is proposed for the high level integration of design, component degradation, and maintenance decisions. The framework includes constructing screening models for rapid design evaluation, defining a multi-objective robust optimization problem, and using sensitivity studies to compare trade-offs between different design and maintenance strategies. A case example of power plant condenser is used to illustrate the proposed framework and advise how designers can make informed comparisons between different design concepts and maintenance strategies under highly uncertain lifecycle conditions.Copyright © 2013 by ASME

This paper investigates ways to obtain consumer preferences for technology products to help designers identify the key attributes that contribute to a product's market success. A case study of residential photovoltaic panels is performed in the context of the California, USA, market within the 2007–2011 time span. First, interviews are conducted with solar panel installers to gain a better understanding of the solar industry. Second, a revealed preference method is implemented using actual market data and technical specifications to extract preferences. The approach is explored with three machine learning methods: Artificial neural networks (ANN), Random Forest decision trees, and Gradient Boosted regression. Finally, a stated preference self-explicated survey is conducted, and the results using the two methods compared. Three common critical attributes are identified from a pool of 34 technical attributes: power warranty, panel efficiency, and time on market. From the survey, additional nontechnical attributes are identified: panel manufacturer's reputation, name recognition, and aesthetics. The work shows that a combination of revealed and stated preference methods may be valuable for identifying both technical and nontechnical attributes to guide design priorities., Center for Scalable and Integrated Nanomanufacturing

Consumer preferences can serve as an effective basis for determining key product attributes necessary for market success, allowing firms to optimally allocate time and resources toward the development of these critical attributes. However, identification of consumer preferences can be challenging, particularly for technology-push products that are still early on in the technology diffusion S-curve, which need an additional push to appeal to the early majority. This paper presents a method for revealing preferences from actual market data and technical specifications. The approach is explored using three machine learning methods: Artificial Neural Networks, Random Forest decision trees, and Gradient Boosted regression applied on the residential photovoltaic panel industry in California, USA. Residential solar photovoltaic installation data over a period of 5 years from 2007–2011 obtained from the California Solar Initiative is analyzed, and 3 critical attributes are extracted from a pool of 34 technical attributes obtained from panel specification sheets. The work shows that machine learning methods, when used carefully, can be an inexpensive and effective method of revealing consumer preferences and guiding design priorities.

Large-scale engineering systems require design teams to balance complex sets of considerations using a wide range of design and decision-making skills. Formal, computational approaches for optimizing complex systems offer strategies for arriving at optimal solutions in situations where system integration and design optimization are well-formulated. However, observation of design practice suggests engineers may be poorly prepared for this type of design. Four graduate student teams completed a distributed, complex system design task. Analysis of the teams' design histories suggests three categories of suboptimal approaches: global rather than local searches, optimizing individual design parameters separately, and sequential rather than concurrent optimization strategies. Teams focused strongly on individual subsystems rather than system-level optimization, and did not use the provided system gradient indicator to understand how changes in individual subsystems impacted the overall system. This suggests the need for curriculum to teach engineering students how to appropriately integrate systems as a whole., National Science Foundation (U.S.) (Award CMMI-0830134), National Science Foundation (U.S.) (Award CMMI-0900255), Ford Foundation (Predoctoral Fellowship)

Sketching and prototyping of design concepts have long been valued as tools to support productive early stage design. This study investigates previous findings about the interplay between the use and timing of use of such design tools. This study evaluates such tools in the context of team design projects. General trends and statistically significant results about “sketchstorming” and prototyping suggest that, in certain constrained contexts, the focus should be on the quality of information rather than on the quantity of information generated, and that prototyping should begin as soon as possible during the design process. Ramifications of these findings are discussed in the context of educating future designers on the efficient use of design tools.

This report describes investors’ decision-making related to environmental Socially Responsible Investment (SRI) by administering a questionnaire with virtual stock-selection conditions and information related to both financial and environmental performance. Our findings are as follows: First, prospect theory is not valid with decision-making over environmental achievement. Second, a person’s level of environmental consciousness has little effect on decision-making related to environmental performance. Third, four clusters with each different decision-making property were found using cluster analysis. Finally, significant differences were found in behaviors related to negative and positive financial performance. These findings are expected to be an aid in managing SRI funds, which requires consideration of both financial and social objectives.

Preferences are a formal way to represent a designer’s choices when assigning priorities for a set of possible design alternatives within the context of the design process. A design team’s preferences can change over the life of project, and knowledge of this evolution can be useful for understanding a team’s rationale as well as its confidence in a decision. This paper presents a “sliding window” approach (SPPT) to the extraction of preference related information from transcribed design team discussion. The approach suggested in this paper can assess design preferences over time with a finer granularity than a previous approach known as PPT, and removes perturbations that occur when there is little design team discussion. Both SPPT and PPT were applied to a discussion transcript. Results show good consistency among SPPT, PPT and survey results. SPPT is also able to detect more changes in design team preference.

The process of designing large engineering systems can involve extensive sharing of resources among many competing subsystems. These resources may be represented as design parameters that are shared between the system level and each subsystem. Frameworks for understanding the role of the choice of system level design variables on overall system design may be valuable for the synthesis of complex engineering systems. This paper examines the outcome of Multidisciplinary Design Optimization (MDO) using three distinct combinations of system level variables on a satellite design example. The results of these sets of system level variables are compared on their convergence time and robustness., National Science Foundation (U.S.) (Award DMI- 0547629)

In design, as with many fields, the bases of decisions are generally not formally modeled but only talked or written about. The research problem addressed in this paper revolves around the problem of modeling the direct evaluation of design alternatives and their attributes as they are realized in linguistic communication. The question is what types of linguistic data provide the most reliable linguistic displays of preference and utility. The paper compares two formal methods for assessing a design team’s preferences for alternatives based on the team’s discussion: APPRAISAL and Preferential Probabilities from Transcripts (PPT). Results suggest that the two methods are comparable in their assessment of preferences. This paper also examines the nature of consistency in the way design teams consider the attributes of a design. Findings suggest that assessment of an attribute can change substantially over time.Copyright © 2010 by ASME

Frameworks for modeling the communication and coordination of subsystem stakeholders are valuable for the synthesis of large engineering systems. However, these frameworks can be resource intensive and challenging to implement. This paper compares three frameworks, Multidisciplinary Design Optimization (MDO), traditional Game Theory, and a Modified Game Theoretic approach on the form and flow of information passed between subsystems. This paper considers the impact of “complete” information sharing by determining the effect of merging subsystems. Comparisons are made of convergence time and robustness in a case study of the design of a satellite. Results comparing MDO in two- and three-player scenarios indicate that, when the information passed between subsystems is sufficiently linear, the two scenarios converge in statistically indifferent number of iterations, but additional “complete” information does reduce variability in the number of iterations. The Modified Game Theoretic approach converges to a smaller region of the Pareto set compared to MDO, but does so without a system facilitator. Finally, a traditional Game Theoretic approach converges to a limit cycle rather than a fixed point for the given initial design. There may also be a region of attraction for convergence for a traditional Game Theoretic approach.Copyright © 2010 by ASME

During the formative stage of the design cycle, teams engage in a variety of tasks to arrive at a design, including selecting among design alternatives. A key notion in design alternative selection is that of “preference” in which a designer assigns priorities to a set of design choices. This paper presents a preliminary approach for extracting a projection of aggregated design team preferences from design team discussion. This approach further takes into consideration how the design preferences of a team can evolve over time as the team changes its priorities based on new design information. Two initial models are given for representing the most probable and preferred design alternative from the transcriptions of design team discussion, and for predicting how preferences might change from one time interval to the next. These models are applied to an illustrative, real-world case example.

An engineer presented with a design challenge often creates a symmetric solution. For instance, consider a table (front-back and left-right symmetry), a car (left and right symmetry), a bridge (front-back and left-right symmetry), or the space shuttle (left-right) symmetry. These examples may not be 100% symmetric, but their overriding features are remarkably similar. The reasons for the design of symmetric structures is not always clear. In some cases, like the table, symmetry may be a tradition. Similarly, the symmetry may be for aesthetic reasons. However in automated design algorithms, especially stochastic techniques, the output is often largely asymmetric, One reason for this is that fitness functions are not rewarded for symmetry. A possible resolution to this is to add a reward function for symmetry. Unfortunately, this approach is computationally intractable as well as arbitrary. In this paper a Genetic Algorithm based method is presented that rewards re-use of parts. The method is applied to a simple, idealized situation as well as to real design case. The results show that in some situations, symmetry naturally emerges from the synthesis, but that it does not provide clear performance advantages over asymmetric configurations.Copyright © 2005 by ASME

The Method of Imprecision (MO I) is a multi-objective design method that maximizes the overall degree of both design and performance preferences. Sets of design variables are iteratively selected, and the corresponding performances are approximately computed. The designer’s judgment (expressed as preferences) are combined (aggregated) with the customer’s preferences, to determine the overall preference for sets of points in the design space. In addition to degrees of preference for values of the design and performance variables, engineering design problems also typically include uncertainties caused by uncontrolled variations, for example, measuring and fabrication limitations. This paper illustrates the computation of expected preference for cases where the uncertainties are uncorrelated, and also where the uncertainties are correlated. The result is a “best” set of design variable values for engineering problems, where the overall aggregated preference is maximized. As is illustrated by the examples shown here, where both preferences and uncontrolled variations are present, the presence of uncertainties can have an important effect on the choice of the overall best set of design variable values.Copyright © 2003 by ASME

The chemisorption of O2 on nanoscale n-doped CdS semiconductors is computed in terms of a Wolkenstein isotherm coupled to the Schrodinger Poisson equation. Present numerical results show the dependence of the chemisorbed charge and the differential capacitance on oxygen partial pressure. A comparison against the classical Poisson-Boltzmann approach shows a higher chemisorbed charge in the quantum model, but a greater differential capacitance in the classical case.

Detection of fraud, waste, and abuse (FWA) is an important yet challenging problem. In this article, we describe a system to detect suspicious activities in large healthcare datasets. Each healthcare dataset is viewed as a heterogeneous network consisting of millions of patients, hundreds of thousands of doctors, tens of thousands of pharmacies, and other entities. Graph analysis techniques are developed to find suspicious individuals, suspicious relationships between individuals, unusual changes over time, unusual geospatial dispersion, and anomalous network structure. The visualization interface, known as the Network Explorer, provides a good overview of data and enables users to filter, select, and zoom into network details on demand. The system has been deployed on multiple sites and datasets, both government and commercial, and identified many overpayments with a potential value of several million dollars per month.

This paper proposes a method that dynamically improves a statistical model of system degradation by incorporating uncertainty. The method is illustrated by a case example of fouling, or degradation, in a heat exchanger in a cogeneration desalination plant. The goal of the proposed method is to select the best model from several representative condenser fouling models including linear, falling rate, and asymptotic fouling, and to validate and improve model parameters over the duration of operation. Maximum likelihood estimation (MLE) was applied to obtain a stochastic distribution of condenser fouling. Akaike’s Information Criterion (AIC) and the Bayesian Information Criterion (BIC) were then computed at time intervals to assess the accuracy of the MLE results. The degradation model was further evaluated by estimating future prognoses and then cross-validating with real world fouling data. The results show the accuracy of a prognosis can be improved substantially by continuously updating fouling model parameters. The proposed method is a step toward facilitating prognosis of engineering systems in the early design stages by improving the prediction of future component degradation., Center for Clean Water and Clean Energy at MIT and KFUPM, Natural Sciences and Engineering Research Council of Canada