Which of the following is true of personal ethics?
People stop growing in their understanding of ethical behavior once they become adults.
A person's genes, cultural background, and upbringing have no influence on ethical understanding.
Different people view complex situations differently based on their own ethical understandings.
People act unethically on their own accord without any external influences.
Business and marketing ethics have an overbearing influence on personal ethics.
Moul, a diaper manufacturer, is developing a media plan that involves trying to expose its target audience to its new advertisement about ten times. Moul is trying to increase its advertisement's.
Saleye Pharmaceuticals develops cheaper alternatives to proprietary drugs. Its mission statement states that it wants to create affordable medicine for everyone and create a healthier world. Saleye releases a new and improved version of its pain-relieving drug, Fento. Within a week, Saleye receives many complaints stating that the drug is inducing hallucinations and in some cases triggering certain anxiety disorders. Saleye was caught off-guard as its animal and human trials did not reveal any side effects. Saleye deliberated over the decision to recall the drug for over a week and by the time it eventually did, it had lost millions in stocks. Where did Saleye fail?
It did not have an ethical mission statement.
It did not have control measures in place.
It did not consider the target market before creating its drug.
It prioritized profits over effective drugs.
It did not conduct large-scale human trials.
In a(n) search for information , a buyer examines his or her own memory and knowledge about a product or service, gathered through past experiences.
Which of the following aspects is directly responsible for determining the zone of tolerance of customers?
The brand awareness of a product
The breadth of the product mix
The importance of each service quality dimension
The complexity of the product or service
The type of labeling used
If a company claims to be fair toward its customers, it would imply that:
the company gives back to the community through volunteerism.
the company offers discounts to its regular customers.
the company does not engage in price fixing or ""bait and switch"" tactics.
the company makes an effort to improve the satisfaction of customers.
the company does not accept criticism from its stakeholders other than
Diversification refers to the marketing strategy of
increasing sales of current products in current markets.
selling current products to new markets.
selling new products to new markets.
selling new products to current markets.
selling the same brands in both current and new markets.
Customer relationship management refers to
the view that organizations should satisfy the needs of consumers in a way that provides for society’s well-being.
the process of identifying prospective buyers, understanding them intimately, and developing favorable long-term perceptions of the organization and its offerings so that buyers will choose them in the marketplace.
the idea that an organization should (1) strive to satisfy the needs of consumers
(2) while also trying to achieve the organization’s goals.
the links an organization has to its individual customers, employees, suppliers, and other partners for their mutual long-term benefit.
the cluster of benefits that an organization promises customers to satisfy their needs.
In an administered vertical marketing system,:
there are contractual relationships between all parties.
there is common ownership of goods.
one member can directs the actions of another member.
there are franchise relationships between channel members.
there is no dominant member; all members have equal power.
Cuppa, a coffee-mug manufacturer, invests money in procuring equipment to produce custom prints on coffee mugs. Cuppa also releases a new line of eco- friendly porcelain mugs priced at $20 each. Cuppa spends $24,000 per month on its production, including employees' salaries. The cost of producing and packaging each mug is $12. Cuppa has a target profit of $8,000 a month. How many mugs should Cuppa sell to gain this profit?
Which of the following is true of the in-depth interview method?
In-depth interviews can be used for sentiment mining.
The results of in-depth interviews can be used to make quick changes to the product roll.
In-depth interviews are relatively less time consuming.
The results of in-depth interviews can be used to develop surveys.
In-depth interviews cannot be used to establish an historical context.
Which of the following is true of postpurchase cognitive dissonance?
It is likely for products that work as intended.
It is not likely for products that are widely available.
It is likely for products that are associated with low levels of risk.
It is likely for products that are frequently purchased.
It is likely for expensive products.
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Holger Schmidt (photo by C. Lagatttuta)
Holger Schmidt, the Kapany Professor of Optoelectronics at UC Santa Cruz, has been named a Fellow of the Institute of Electrical and Electronics Engineers (IEEE).
Elevation to the grade of fellow is a prestigious honor conferred by the IEEE Board of Directors on members with an outstanding record of accomplishments. The total number selected in any one year cannot exceed one-tenth of one percent of the total voting membership.
Schmidt, who directs the W. M. Keck Center for Nanoscale Optofluidics at UC Santa Cruz, was honored "for contributions to optofluidics and integrated photonics." He has authored or coauthored over 380 publications and several book chapters in various fields of optics, and he is coeditor of the Handbook of Optofluidics (CRC Press). Schmidt and his collaborators have developed technologies for optical analysis of samples on integrated chip-based platforms, with applications in areas such as biological sensors, virus detection, and chemical analysis. Diagnostic instruments based on these "optofluidic chips" could provide a rapid, low-cost, and portable option for identifying specific disease-related molecules or viruses.
Schmidt joins ten other UCSC engineering faculty who are IEEE Fellows. He earned his M.S. in physics from the University of Stuttgart, Germany, and M.S. and Ph.D. degrees in electrical and computer engineering at UC Santa Barbara. He joined the faculty of the Baskin School of Engineering at UC Santa Cruz in 2001.
The IEEE is the world's leading professional association dedicated to the advancement of technology. The IEEE publishes 30 percent of the world's literature in the electrical and electronics engineering and computer science fields, and has developed more than 1300 active industry standards.
SALT LAKE CITY--(BUSINESS WIRE)--
SC16, the 28th annual international conference of high performance computing, networking, storage and analysis, celebrated the contributions of researchers and scientists - from those just starting their careers to those whose contributions have made lasting impacts.
This Smart News Release features multimedia. View the full release here:
The conference drew more than 11,100 registered attendees and featured a technical program spanning six days. The exhibit hall featured 349 exhibitors from industry, academia and research organizations from around the world.
“There has never been a more important time for high performance computing, networking and data analysis,” said SC16 General Chair John West from the Texas Advanced Computing Center. “But it is also an acute time for growing our workforce and expanding diversity in the industry. SC16 was the perfect blend of research, technological advancement, career recognition and improving the ways in which we attract and retain that next generation of scientists.”
According to Trey Breckenridge, SC16 Exhibits Chair from Mississippi State University, the SC16 Exhibition was the largest in the history of the conference. The overall size of the exhibition was 150,000 net square feet (breaking the 2015 record of 141,430). The 349 industry and research-focused exhibits included 44 first-timers and 120 organizations from 25 countries outside the United States.
During the conference, Salt Lake City also became the hub for the world’s fastest computer network: SCinet, SC16’s custom-built network which delivered 3.15 terabits per second in bandwidth. The network featured 56 miles of fiber deployed throughout the convention center and $32 million in loaned equipment. It was all made possible by 200 volunteers representing global organizations spanning academia, government and industry.
For the third year, SC featured an opening “HPC Matters” plenary that this year focused on Precision Medicine, which examined what the future holds in this regard and how advances are only possible through the power of high performance computing and big data. Leading voices from the frontlines of clinical care, medical research, HPC system evolution, pharmaceutical R&D and public policy shared diverse perspectives on the future of precision medicine and how it will impact society.
The Technical Program again offered the highest quality original HPC research. The SC workshops set a record with more than 2,500 attendees. There were 14 Best Paper Finalists and six Gordon Bell Finalists. These submissions represent the best of the best in a wide variety of research topics in HPC.
Overall Stats on Tech Program Tracks:
• 81 Papers
• 12 Panels
• 138 Posters
• 37 Tutorials
• 37 Workshops
• 12 Invited Speakers
• 12 Doctoral Showcase Presentations
• 13 Emerging Technology Presentations
• 55 Birds-of-a-Feather
“These awards are very important for the SC Conference Series. They celebrate the best and the brightest in high performance computing,” said Satoshi Matsuoka, SC16 Awards Chair from Tokyo Institute of Technology. “These awards are not just plaques or certificates. They define excellence. They set the bar for the years to come and are powerful inspiration for both early career and senior researchers.”
Following is the list of Technical Program awards presented at SC16:
SC16 received 442 paper submissions, of which 81 were accepted (18.3 percent acceptance rate). Of those, 13 were selected as finalists for the Best Paper (six) and Best Student Paper (seven) awards.
The Best Paper Award went to “Daino: A High-Level Framework for Parallel and Efficient AMR on GPUs” by Mohamed Wahib Attia and Naoya Maruyama, RIKEN; and Takayuki Aoki, Tokyo Institute of Technology.
The Best Student Paper Award went to “Flexfly: Enabling a Reconfigurable Dragonfly Through Silicon Photonics” by Ke Wen, Payman Samadi, Sebastien Rumley, Christine P. Chen, Yiwen Shen, Meisam Bahadori, and Karen Bergman, Columbia University and Jeremiah Wilke, Sandia National Laboratories.
ACM Gordon Bell Prize:
The ACM Gordon Bell Prize is awarded for outstanding team achievement in high performance computing and tracks the progress of parallel computing.
This year, the prize was awarded to a 12-member Chinese team for their research project, “10M-Core Scalable Fully-Implicit Solver for Nonhydrostatic Atmospheric Dynamics.” The winning team presented a solver (method for calculating) atmospheric dynamics.
In the abstract of their presentation, the winning team writes, “On the road to the seamless weather-climate prediction, a major obstacle is the difficulty of dealing with various spatial and temporal scales. The atmosphere contains time-dependent multi-scale dynamics that support a variety of wave motions.”
To simulate the vast number of variables inherent in a weather system developing in the atmosphere, the winning group presents a highly scalable fully implicit solver for three-dimensional nonhydrostatic atmospheric simulations governed by fully compressible Euler equations. Euler equations are a set of equations frequently used to understand fluid dynamics (liquids and gasses in motion).
Winning team members are Chao Yang, Chinese Academy of Sciences; Wei Xue, Weimin Zheng, Guangwen Yang, Ping Xu, and Haohuan Fu, Tsinghua University; Hongtao You, National Research Center of Parallel Computer Engineering and Technology; Xinliang Wang, Beijing Normal University; Yulong Ao and Fangfang Liu, Chinese Academy of Sciences, Lin Gan, Tsinghua University; Lanning Wang, Beijing Normal University.
This year, SC received 172 detailed poster submissions that went through a rigorous review process. In the end, 112 posters were accepted and five finalists were selected for the Best Poster Award. As part of its research poster activities, SC16 also hosted the ACM Student Research Competition for both undergraduate and graduate students. In all 63 submissions were received, 26 Student Research Competition posters were accepted – 14 in the graduate category and 12 in the undergraduate category.
The Best Poster Award went to “A Fast Implicit Solver with Low Memory Footprint and High Scalability for Comprehensive Earthquake Simulation System” with Kohei Fujita from RIKEN as the lead author.
The 2016 Undergraduate Student Research Award recipients were:
First Place: “Touring Dataland? Automated Recommendations for the Big Data Traveler” by Willian Agnew and Michael Fischer, Advisors: Kyle Chard and Ian Foster.
Second Place: “Analysis of Variable Selection Methods on Scientific Cluster Measurement Data” by Jonathan Wang, Advisors: Wucherl Yoo and Alex Sim.
Third Place: “Discovering Energy Usage Patterns on Scientific Clusters” by Matthew Bae, Advisors: Wucherl Yoo, Alex Sim and Kesheng Wu.
The 2016 Graduate Student Research Award recipients were:
First Place: “Job Startup at Exascale: Challenges and Solutions” by Sourav Chakroborty, Advisor: Dhabaleswar K. Panda.
Second Place: “Performance Modeling and Engineering with Kerncraft,” by Julian Hammer, Advisors: Georg Hager and Gerhard Wellein.
Third Place: “Design and Evaluation of Topology-Aware Scatter and AllGather Algorithms for Dragonfly Networks” by Nathanael Cheriere, Advisor: Matthieu Dorier.
Scientific Visualization and Data Analytics Showcase:
The Scientific Visualization and Data Analytics Award featured six finalists. The award went to “Visualization and Analysis of Threats from Asteroid Ocean Impacts” with John Patchett as the lead author.
SC16 Student Cluster Competition:
The Student Cluster Competition returned for its 10th year. The competition which debuted at SC07 in Reno and has since been replicated in Europe, Asia and Africa, is a real-time, non-stop, 48-hour challenge in which teams of six undergraduates assemble a small cluster at SC16 and race to complete a real-world workload across a series of scientific applications, demonstrate knowledge of system architecture and application performance, and impress HPC industry judges. The students partner with vendors to design and build a cutting-edge cluster from commercially available components, not to exceed a 3120-watt power limit and work with application experts to tune and run the competition codes.
For the first-time ever, the team that won top honors also won the award for achieving highest performance for the Linpack benchmark application. The team “SwanGeese” is from the University of Science and Technology of China. In traditional Chinese culture, the rare Swan Goose stands for teamwork, perseverance and bravery. This is the university’s third appearance in the competition.
Also, an ACM SIGHPC Certificate of Appreciation is presented to the authors of a recent SC paper to be used for the SC16 Student Cluster Competition Reproducibility Initiative. The selected paper was “A Parallel Connectivity Algorithm for de Bruijn Graphs in Metagenomic Applications” by Patrick Flick, Chirag Jain, Tony Pan and Srinivas Aluru from Georgia Institute of Technology.
ACMIEEE George Michael Memorial HPC Fellowship:
The George Michael Memorial HPC Fellowship honors exceptional Ph.D. students. The first recipient is Johann Rudi from the Institute for Computational Engineering and Sciences at the University of Texas at Austin for his project, “Extreme-Scale Implicit Solver for Nonlinear, Multiscale, and Heterogeneous Stokes Flow in the Earth’s Mantle.”
The second recipient is Axel Huebl from Helmholtz-Zentrum Dresden-Rossendorf at the Technical University of Dresden for his project, “Scalable, Many-core Particle-in-cell Algorithms to Stimulate Next Generation Particle Accelerators and Corresponding Large-scale Data Analytics.”
The SC Conference Series also serves as the venue for recognizing leaders in the HPC community for their contributions during their careers. Here are the career awards presented at SC16:
IEEE-CS Seymour Cray Computer Engineering Award:
The IEEE-CS Seymour Cray Computer Engineering Award recognizes innovative contributions to high performance computing systems that best exemplify the creative spirit demonstrated by Seymour Cray. The 2016 IEEE-CS Seymour Cray Computer Engineering Award was presented to William J. Camp of Los Alamos National Laboratory “for visionary leadership of the Red Storm project, and for decades of leadership of the HPC community.” Camp previously served as Intel’s Chief Supercomputing Architect and directed Intel’s Exascale R&D efforts.
ACMIEEE-CS Ken Kennedy Award:
Established in memory of Ken Kennedy, the founder of Rice University's nationally ranked computer science program and one of the world's foremost experts on high-performance computing, the ACMIEEE-CS Ken Kennedy Award recognizes outstanding contributions to programmability or productivity in high-performance computing together with significant community service or mentoring contributions.
The 2016 Ken Kennedy Award was presented to William D. Gropp “for highly influential contributions to the programmability of high-performance parallel and distributed computers, and extraordinary service to the profession.” Gropp Is the Acting Director of the National Center for Supercomputing Applications and Director, Parallel Computing Institute, Thomas M. Siebel Chair in Computer Science at the University of Illinois Urbana-Champaign.
IEEE-CS Sidney Fernbach Memorial Award:
The IEEE-CS Sidney Fernbach Memorial Award is awarded for outstanding contributions in the application of high performance computers using innovative approaches. The 2016 IEEE-CS Sidney Fernbach Memorial Award was presented to Vipin Kumar “for foundational work on understanding scalability, and highly scalable algorithms for graph positioning, sparse linear systems and data mining.” Kumar is a Regents Professor at the University of Minnesota.
SC Conference Test of Time Award:
The Supercomputing Conference Test of Time Award recognizes an outstanding paper that has appeared at the SC conference and has deeply influenced the HPC discipline. It is a mark of historical impact and recognition that the paper has changed HPC trends. The winning paper is “Automatically Tuned Linear Algebra Software” by Clint Whaley from University of Tennessee and Jack Dongarra from University of Tennessee and Oak Ridge National Laboratory.
IEEE TCSC Award for Excellence in Scalable Computing for Early Career Researchers:
The IEEE TCHPC Award for Excellence in Scalable Computing for Early Career Researchers recognizes individuals who have made outstanding and potentially long-lasting contributions to the field within five years of receiving their Ph.D. The 2016 awards were presented to Kyle Chard, Computation Institute , University of Chicago and Argonne National Laboratory; Sunita Chandrassekaran, University of Delaware; and Seyong Lee, Oak Ridge National Laboratory.
SC17 will be held next November 12-17 in Denver, Colorado. For more details, go to
SC16, sponsored by the IEEE Computer Society and ACM (Association for Computing Machinery), offers a complete technical education program and exhibition to showcase the many ways high performance computing, networking, storage and analysis lead to advances in scientific discovery, research, education and commerce. This premier international conference includes a globally attended technical program, workshops, tutorials, a world-class exhibit area, demonstrations and opportunities for hands-on learning. For more information on SC16,
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1 A Novel Approach on Virtual Systems Prototyping Based on a Validated, Hierarchical, Modular Library Prof.Dr. Herbert Palm, Jörg Holzmann, University of Applied Sciences, Lothstrasse 64, Munich, Germany Robert Klein, Dr. Stefan-Alexander Schneider BMW AG, Petuelring 130, Munich, Germany Prof.Dr. Dieter Gerling Universitaet der Bundeswehr Muenchen, Werner-Heisenberg-Weg 39, Neubiberg, Germany Abstract Development of highly innovative systems requires adaptation of standard processes in order to reflect its specific characteristics. Unknown best solutions with respect to system requirements or their efficacy at development start is one of those most common characteristics in innovation projects. In those cases adequate tailoring or revision of system development processes may increase quality of the technical solution while at the same time reducing the involved project risks and costs. This paper describes a novel methodology to establish Virtual Prototyping for multi-domain system development by Design Space Exploration (DSE) based on Virtual Systems Prototyping (VSP). VSP comprises four elementary steps to systematically build up the space of potential solutions. It offers a structural and dynamic insight view to validate performance indicators against a set of requirements. Our approach allows choosing the best solution within the design phase while simultaneously providing a high confidence level of its efficacy prior to implementation. VSP, therefore, is a powerful instrument for increasing the quality confidence level of innovative systems while reducing risk and cost of their implementation. VSP complements the method of prototyping on system design level inherently by a tool chain based on a multi-domain validated, hierarchical and modular library. The set of tool supported process steps makes DSE based on VSP a valuable methodology for effectively and efficiently developing innovative systems. The authors demonstrate their new approach on the basis of an automotive example in the context of novel fully-electric powertrain car architectures. 1) Macro Procedural Models: V-Model for Systems Development System developments usually follow a reference model providing a template sequence of generic activities. Two kinds of reference models have been established: a) Procedural models and b) Phase models. While procedural models describe the logical (conditional) sequence of activities, phase models, in addition, reveal a directed time path. From that point of view phase models may be considered as instantiated procedural models. Driven by German Government and independently by Hughes Aircraft the V-Model  has been established since early 1980s as one of the most common procedural reference models for system development. A sample representation of the V-Model is shown in figure 1. From a Systems Engineering point of view the V-Model includes two basic principles: A system is supposed to be designed top-down within its hierarchical structure while it has to be implemented bottom-up.
2 Figure 1: The V-Model Referring to the nature of any procedural model the referenced generic activities follow a logical rather than any timely sequence. Loops, therefore, are allowed, for instance when working results as achieved during component design trigger changes of the hierarchically upward module design. 2) Phase Models as derived from Procedural Models When asking for the timely sequence of activities within a development the underlying procedural model has to be transferred into a corresponding phase model. Phase models instantiate macro procedural models by a specified content and a time-ordered sequence. As the number of individual activities may grow significantly it is common practice to cluster activities based on specific interim results. Standard macro elements as a cluster of micro elements are listed in table 1. Milestones, thereby, mark significant events or results, respectively ( i.e. major decisions andor deliverables). A phase model may consist of only one single macro phase (typically System Build ), thereby defining a Single-Phase Model. Consequently, a sequence of more than one macro phase forms a Multi- Phase Model. Figure 2 illustrates schematically how the V-Model may be instantiated by Single- or Multi-Phase Implementations. Macro element Result (defining milestone) Comment System Build - System Version Vn.m Built to go-live study PCM - Knowledge of basic solution alternatives - Decision on solution alternative to follow Hierarchical structure: Pre-study PCM, Main study PCM,.. Prototype - Realization of a subset of system aspects real or virtual Pilot - Realization within an artificial environment Allows risk reduction Table 1: Macro Elements clustering activity sequence sets A Single-Phase Implementation of the V-Model obviously corresponds to the shortest path of a sequence activity from top-left to top-right without any loops. Based on this linear activity flow we may also call the Single-Phase Implementation a Waterfall-Approach . This Waterfall-Approach, however, is linked to a significant drawback: The maximum system feedback time between definition of a system and knowledge about features and behavior of its realization (if possible at all).
3 Only at the very end of a Waterfall-Approach development process a system can be validated against system level requirements of the design phase. As a consequence, we recommend a Single-Phase Figure 2: Single vs. Multi-Phase Implementation Implementation of the V-Model only in those cases where the following prerequisites are met: All requirements are known and stable The best solution approach may easily be picked amongst alternatives that are all known The chosen solution to be realized is known and proven to work (on all hierarchical levels) Building the entire system is expected to be a first-time-right Under the listed prerequisites a Waterfall-Approach offers the advantage of the shortest possible way from a problem to its solution within a V-Model approach. Adding phases may be considered a tradeoff between reducing risk of failure versus increasing time and cost requirements. Considering the value of an additional loop it should be kept in mind that severeness of a failure strongly depends on its occurrence-to-observation stage of the development gap. Cost to extract defects as a function of a system s life cycle phase has been investigated by the INCOSE (International Council of Systems Engineering) and is shown in figure 3 (according to ). It clearly indicates the value of an as early as possible preventive risk reduction in system developments. Figure 3: Benefits of avoiding failures according to 
4 Multi-Phase Models allow reducing system development risks by providing feedback loops. Loops may provide information on solution alternatives and their capabilities. They allow the clarification and stabilization of requirements. Loops offer the ability of incremental learning in cycles and to define and reduce the risk of a go-life. These benefits have to be considered versus increasing overall development time and effort. Table 2 shows macro elements and their specific benefit. Macro element Useful if.. Comment System Build study PCM Prototype.. a full system version is required andor.. learning cycles are required.. best solution approach is not obvious.. proof of concept is missing andor.. requirements are unclear or unstable Sequencing leads to spiral model  Pick the best instead of the first that comes along solution Applicable during design or implementation branch Pilot.. risk of first-time-right implementation is too high Allows risk reduction Table 2: Macro elements versus their useful integration within a V-Model. 3) System development in the context of disruptive innovations remental, evolutionary system changes usually allow reuse of an existing technology base. In contrast, major changes of system requirements often require a fundamental change of the underlying base. Development processes for this kind of disruptive innovations usually reveal the following characteristics: A part of the solution space (set of potential solutions) is unknown due to a missing proof of concept The best solution approach cannot be immediately identified, typically even not on top system level Reflecting table 2 for this context the application of Virtual Prototyping   in combination with a Pre- study PCM  is suggested. The combination of these two macro elements allows a significant risk reduction at the earliest possible stage of a V-Process based development: Identification of all potential solutions as part of the pre-study PCM will ensure not to miss the best one System feedback as part of the Virtual Prototyping will ensure a proof of concept Besides the question of which sequence of generic actions (method) has to be applied a corresponding tool (technique or technology) supporting the method needs to be identified. The combination of method and tooltechnology will be referred to as methodology. Aerospace industry certainly is more than many other industry segments driven by requirements of disruptive innovation. New products require fundamentally improved or even new features and behavior. Technology changes between new generations frequently reveal major rather than minor steps. In this area of aerospace system development the methodology of Design Space Evaluation (DSE) has emerged addressing all of the characteristics mentioned above. The methodology is described in detail elsewhere ( ) but its basic process steps may be briefly mentioned and reflected in figure 4: 0) Definition of major top-level requirements 1) Systematic built-up of potential solutions (indicated as yellow dots in figure 4) 2) Selection of those potential solutions meeting top-level requirements (blue circle in figure 4) 3) Trade-off quantification of selected solutions based on Virtual Prototyping (xy-graph in figure 4)
5 The following chapter will elaborate in more detail how the sequence of DSE process steps may be integrated into a V-Model to form a Pre-study PCM (revealing the best suited solution within a systematically built-up set of potential solutions) with integrated Virtual Prototyping (providing a proofof concept for the selected best solution prior to implementation). 4) Systematic Approach of VSP Figure 4: Principles of VSP and DSE The methodology of Design Space Exploration (DSE) based on Virtual Systems Prototyping (VSP) is not limited to any special technology or engineering domain. The automotive example chosen in the following course of this paper, therefore, is of exemplary character only serving clarification of its use. Battery electric vehicles (BEV) are based on fundamentally new and disruptive technologies in the automotive industry. Vehicles based on an electrified power train offer a wide range of architectural alternatives compared to combustion cars. Only a few of these alternatives have been realized so far. The total amount of potential solutions that are able to proof their concept is low. Given an application scenario ( use cases and requirements) for a new car development engineers are confronted with a huge set of unknown solutions. When applying our newly proposed DSE based VSP methodology to the V-Model four basic steps have to be followed as indicated in figure 5 and described in the following. Figure 5: The 4 major steps of VSP
6 Step 0: Definition of top-level requirements Step 0 reflects top-level requirements based on specific use-cases and related top level expectations. In our example, a user requests a zero emission urban car (referring to the system use case) with maximum energy efficiency and reasonable acceleration behavior. The user may decide upon important quantifiable performance parameters such as energy consumption per km and time to speed-up from zero to 100 kmh. Step 1: Building up the Design Space Step 1 systematically builds up the set of potential solutions (also called design space ). In our example, without loss of generality, we limit ourselves to Battery Electric Vehicles (BEVs) and have to look at potential architectural solutions for their power train. A BEV power train may consist of one, two, three or four engines. It may integrate a motor within a wheel, close to a wheel or centrally. Energy may be provided by a high-energy storage (e.g. Lithium-Ion battery), a high-density storage (e.g. Supercaps) or a combination of both. Torque may be provided to wheels by a fixed, discretely variable or continuously variable transmission. Other top-level architectural alternatives may be built up accordingly. A top-level architectural alternative may be any combination of the architectural aspect variants mentioned. An appropriate tool to reflect them all is the Morphological Matrix (often referred to as Zwicky-Box )  shown in table 3. Each line of the matrix shows an architectural aspect while columns indicate options of it. Table 3 shows the 108 top-level architectural alternatives of our example. Architectural Aspect Option 1 Option 2 Option 3 Option 4 Number of engines Engine integration In-wheel Close-to-wheel Centrally - Energy storage High-energy High-power Combination - Transmission fixed Discretely variable Continuously variable - Table 3: Zwicky Matrix of the BEV power train example indicating 108 architectural top-level alternatives Step 2: Modeling & simulating potential solutions Step 2 analyses relevant features of individual alternatives based on Virtual Systems Prototyping (VSP). In our example we evaluate key performance indicators as defined in step 0, specifically acceleration time and energy consumption. The example shows typical VSP challenges that arise from their multi-domain character. Most tools for virtual prototyping are domain-specific, focusing on selected aspects (as relevant within the according domain) and are limited to a specific frequency range. Modeling and simulation of a multi-domain system, however, requires concurrency of all involved domains. Therefore, system prototyping requires overcoming of all multi-domain related challenges. Table 4 lists our solution approaches at technology level. Specifically, we would like to emphasize our approach of using Co-Simulation to integrate models implemented in different languages and their domain-specific authoring tools accordingly. This solution allows us to keep the strengths of all integrated languages   and tools. An integration tool combines all models used and - in the case of our automotive example adds a driver and an exterior environment.
7 Multi-Domain VSP challenge ing tools and languages are domainspecific and heterogeneous Divergence of models (esp. component models) Different frequency ranges Not runtime-optimized Too complex for top level simulation No integration capability due to Missing standardized interface Non-consistent models Range of validity is unknown on Component level Architecture level Solution approach chosen in this paper Keep strength of authoring tools and create Co- Simulation environment Library elements of (controllable) Co-simulation using own solver Component level runtime-optimization Hierarchic behavior clustering Exchangeability and flexibility due to Use of standardized Functional Mock-up Interface (FMI)  Definition and guidance of interfaces through model templates and objectoriented class structure Only validated elements are used Validity range (confidence) marked Watchdog on architectural level Table 4: VSPs solutions for Virtual Prototyping Figure 6 illustrates our top level template of the library for BEV power train VSP to demonstrate the aspect of Definition and guidance of interfaces through model templates and object-oriented class structure. Individual sub-systems, such as front or rear axle drive, energy storage etc. reveal a hierarchic sub-structure underneath. The template structure allows and corresponds to the systematic build-up of the system design space as required for DSE and reflects the V-Model hierarchy. Figure 6: Top level template of the library for BEV power train VSP Step 3: Validating & identifying the best solution Step 3 quantifies potential solutions against each other with respect to requirement trade-offs by simulation and evaluation of all design space elements. When the design space has been built-up as described in step 1 based on a Zwicky Matrix the underlying simulation runs may also be automated. Figure 7 describes our tool chain for automatically building up models according to the design space, simulating them and evaluating key performance indicators.
8 Figure 7: Tool chain for automatically building up models, simulating them and evaluating key performance indicators. When all required simulation runs are finalized designers and product engineers may visualize performance indicator trade-offs between potential solutions. There are numerous ways for representing these trade-offs. A standard representation of DSE is shown in Figure 8. Individual solutions are marked by a bullet. Axes represent key indicators. Without loss of generality we restricted this example to two parameters while the evaluation tool (AVL Cameo) allows up to 30 dimensions to be handled. This seems well sufficient for any top-level system architecture trade-off decision. In our 2-dimensional sample we can clearly mark (blue dashed line) a so-called pareto front. Solutions corresponding to dots on this pareto front  are well superior to any other vertical or horizontal solution but represent a trade-off, in our case energy efficiency versus acceleration time. Figure 8: Result of the DSE based VSP optimization indicating a pronounced pareto front. 5) Conclusion In this paper we proposed a novel methodology to establish Virtual Prototyping for multi-domain system development by Design Space Exploration (DSE) based on Virtual Systems Prototyping (VSP). The method allows to systematically build up the space of potential solutions (design space)
9 and offers a structural and dynamic insight view to quantify performance indicator trade-offs. This allows choosing the best solution within the design phase while simultaneously providing a high confidence level of its efficacy prior to implementation. Based on an automotive example we demonstrated DSE based on VSP to be a powerful instrument for increasing the quality confidence level of innovative systems while reducing risk and cost of their implementation. On a tool base we proposed a novel approach to overcome multi-domain modeling and simulation challenges as required to systematically support DSE based VSP. The tool chain has been proven to work successfully. The methodology is not restricted to the selected automotive industry segment and may become a standard element in all V-Model based system developments based on disruptive innovation. 6) Acknowledgements The authors would like to mention and thank Eliane Fourgeau from Dassault Systemes, Johannes Gerl from Modelon, Bernhard Schick from IPG and Dr. Hans-Michael Kögeler from AVL for their tool and tool related consulting support. 7) References  B. Boehm, Guidelines for verifying and validating software requirements and design specifications, EURO IFIP 79, p ,  B. Boehm, Software Engineering Economics, Englewood Cliffs: Prentice Hall,  SE Handbook Working Group, Systems Engineering Handbook - A Guide for System Life Cycle Processes and Activities V3.2.2, International Council on Systems Engineering (INCOSE),  B. Boehm, A Spiral Model of Software Development and Enhancement, IEEE Computer, pp ,  L. Prowse-Fosler, Virtual Systems Prototyping Ensures Reusable Design Platforms, Vast Systems,  S.-A. Schneider, B. Schick and H. Palm, "Virtualization, Integration and Simulation in the Context of Vehicle Systems Engineering," Embedded World 2012 Exhibition & Conference Proceedings,  Haberfellner, Nagel, Becker, Büchel and v. Massow, Systems Engineering, 11. Auflage, Verlag Industrielle Organisation,  W. K. Hofstetter, A Framework for the Architecting of Aerospace Systems Portfolios with Commonality, Massachusetts: Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics,  F. Zwicky, Discovery, Invention, Research - Through the Morphological Approach, Toronto: The Macmillian Company,  P. Fritzson, Principles of Object-Oriented Modeling andsimulation with Modelica 2.1, IEEE Press, 2004.
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