| publications-4681 |
article |
2019 |
Jiang, Pingyu and Leng, Jiewu and Leng, Jiewu and Leng, Jiewu and Zhang, Hao and Zhang, Hao and Yan, Douxi and Yan, Douxi and Liu, Qiang and Liu, Qiang and Liu, Qiang and Liu, Qiang and Chen, Xin and Chen, Xin and Chen, Xin and Chen, Xin and Ding, Zhizhong and Zhang, Ding |
Digital twin-driven manufacturing cyber-physical system for parallel controlling of smart workshop |
Journal of Ambient Intelligence and Humanized Computing |
10.1007/s12652-018-0881-5 |
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With increasing diverse product demands, the manufacturing paradigm has been transformed into a mass-individualized one, among which one bottleneck is to achieve the interoperability between physical world and the digital world of manufacturing system for the intelligent organizing of resources. This paper presents a digital twin-driven manufacturing cyber-physical system (MCPS) for parallel controlling of smart workshop under mass individualization paradigm. By establishing cyber-physical connection via decentralized digital twin models, various manufacturing resources can be formed as dynamic autonomous system to co-create personalized products. Clarification on the MCPS concept, characteristics, architecture, configuration, operating mechanism and key enabling technologies are elaborated, respectively. A demonstrative implementation of the digital twin-driven parallel controlling of board-type product smart manufacturing workshop is also presented. It addresses a bi-level online intelligence in proactive decision making for the organization and operation of manufacturing resources. |
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| publications-4682 |
article |
2019 |
Liu, Qiang and Liu, Qiang and Liu, Qiang and Liu, Qiang and Zhang, Hao and Zhang, Hao and Jiang, Pingyu and Leng, Jiewu and Leng, Jiewu and Leng, Jiewu and Chen, Xin and Chen, Xin and Chen, Xin and Chen, Xin |
Digital twin-driven rapid individualised designing of automated flow-shop manufacturing system |
International Journal of Production Research |
10.1080/00207543.2018.1471243 |
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Under a mass individualisation paradigm, the individualised design of manufacturing systems is difficult as it involves adaptive integrating both new and legacy machines for the formation of part f... |
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| publications-4683 |
article |
0 |
Nauges, CΓ©line and Nauges, CΓ©line and Thomas, Alban and Thomas, Alban |
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Environmental and Resource Economics |
10.1023/a:1025673318692 |
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| publications-4684 |
article |
1972 |
Geldreich, Edwin E. and Geldreich, Edwin E. and Nash, Harry D. and Nash, Harry D. and Reasoner, Donald J. and Reasoner, Donald J. and Taylor, Raymond H. and Taylor, Raymond H. |
The Necessity of Controlling Bacterial Populations in Potable Waters: Community Water Supply |
Journal American Water Works Association |
10.1002/j.1551-8833.1972.tb02753.x |
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The waters in some of the community water-supply systems in the US often contain a myriad of microorganisms that carry past the disinfection barrier. Although the majority of those that survive and flourish are not pathogenic, the situation presents a potential danger. Here is an article on the sort of organisms that contribute to the trouble, with a description of factors relating to propagation of the species. |
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| publications-4685 |
article |
1976 |
Eggener, Charles L. and Eggener, Charles L. and Polkowski, Lawrence B. and Polkowski, Lawrence B. |
Network Models and the Impact of Modeling Assumptions |
Journal American Water Works Association |
10.1002/j.1551-8833.1976.tb02385.x |
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Particular emphasis is given to the interest in network simulation expedients (skeletonization, load consolidation, and assumed pipe resistance factors). An approach to fulfilling the research need is presented which consists of building detailed models of actual but representative grid systems, verifying the performance of the models, and using them as research tools to investigate the impact of various simplifying assumptions often made in network modeling. With increased emphasis on digital computer modeling of the hydraulic performance of water-distribution networks for designand automatic operational control, there has been a recent upsurge of interest in the impact of expedients commonly employed for network simulation, i.e., skeletonization, load consolidation, and C value allocation. This article offers a historical explanation of the recent upsurge of interest, presents one approach to studying the impact of simulation expedients, demonstrates the value of the approach with a case study, and discusses the merits of the approach in terms of ultimately being able to generalize about the degree of input data refinement necessary to model grid systems adequately. The basic technique for hydraulic network balancing based on Hardy-Cross theory adapted to the digital computer has been available in the US since 1957.1 The first applications of the technology were for design of extensions and reinforcements to large distribution systems serving hundreds of thousands of customers.2 These systems were the first ones studied for two reasons. First, they represented the most critical problems facing network designers to which the technology could be applied. Second, digital computers were most available in the population centers. It is important to note that many of the early systems studied and reported in the journals were characterized by looped |
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| publications-4686 |
article |
1978 |
Burtless, Gary and Burtless, Gary and Hausman, Jerry A. and Hausman, Jerry A. |
The Effect of Taxation on Labor Supply: Evaluating the Gary Negative Income Tax Experiment |
Journal of Political Economy |
10.1086/260730 |
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A model of labor supply is formulated which takes explicit account of nonlinearities in the budget set which arise because the net, after-tax wage depends on hours worked. These nonlinearities may lead to a convex budget set due to the effect of progressive marginal tax rates, or they may lead to a nonconvex budget set due to the effect of government transfer programs such as AFDC or a negative income tax. The nonlinearities affect both the marginal wage and the "virtual" nonlabor income which the individual faces. The model is estimated on a sample of prime-age males from the Gary negative income tax experiment. |
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| publications-4687 |
article |
1984 |
BollobΓ΅s, BΓ©la and BollobΓ΅s, BΓ©la |
The evolution of random graphs |
Transactions of the American Mathematical Society |
10.1090/s0002-9947-1984-0756039-5 |
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According to a fundamental result of ErdΓ¶s and RΓ©nyi, the structure of a random graph <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="upper G Subscript upper M"> <mml:semantics> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mi>G</mml:mi> <mml:mi>M</mml:mi> </mml:msub> </mml:mrow> <mml:annotation encoding="application/x-tex">{G_M}</mml:annotation> </mml:semantics> </mml:math> </inline-formula> changes suddenly when <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="upper M tilde n slash 2"> <mml:semantics> <mml:mrow> <mml:mi>M</mml:mi> <mml:mo>∼<!-- ∼ --></mml:mo> <mml:mi>n</mml:mi> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>/</mml:mo> </mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> <mml:annotation encoding="application/x-tex">M \sim n/2</mml:annotation> </mml:semantics> </mml:math> </inline-formula>: if <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="upper M equals left floor c n right floor"> <mml:semantics> <mml:mrow> <mml:mi>M</mml:mi> <mml:mo>=</mml:mo> <mml:mrow> <mml:mo>β_x008c__x008a_</mml:mo> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi>c</mml:mi> <mml:mi>n</mml:mi> </mml:mrow> <mml:mo>β_x008c_‹</mml:mo> </mml:mrow> </mml:mrow> <mml:annotation encoding="application/x-tex">M = \left \lfloor {cn} \right \rfloor</mml:annotation> </mml:semantics> </mml:math> </inline-formula> and <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="c greater-than one half"> <mml:semantics> <mml:mrow> <mml:mi>c</mml:mi> <mml:mo>></mml:mo> <mml:mfrac> <mml:mn>1</mml:mn> <mml:mn>2</mml:mn> </mml:mfrac> </mml:mrow> <mml:annotation encoding="application/x-tex">c > \frac {1}{2}</mml:annotation> </mml:semantics> </mml:math> </inline-formula> then a.e. random graph of order <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="n"> <mml:semantics> <mml:mi>n</mml:mi> <mml:annotation encoding="application/x-tex">n</mml:annotation> </mml:semantics> </mml:math> </inline-formula> and since <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="upper M"> <mml:semantics> <mml:mi>M</mml:mi> <mml:annotation encoding="application/x-tex">M</mml:annotation> </mml:semantics> </mml:math> </inline-formula> is such that its largest component has <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="upper O left-parenthesis log n right-parenthesis"> <mml:semantics> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:mi>log</mml:mi> <mml:mo><!-- --></mml:mo> <mml:mi>n</mml:mi> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> <mml:annotation encoding="application/x-tex">O(\log n)</mml:annotation> </mml:semantics> </mml:math> </inline-formula> vertices, but for <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="c greater-than one half"> <mml:semantics> <mml:mrow> <mml:mi>c</mml:mi> <mml:mo>></mml:mo> <mml:mfrac> <mml:mn>1</mml:mn> <mml:mn>2</mml:mn> </mml:mfrac> </mml:mrow> <mml:annotation encoding="application/x-tex">c > \frac {1}{2}</mml:annotation> </mml:semantics> </mml:math> </inline-formula> a.e. <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="upper G Subscript upper M"> <mml:semantics> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mi>G</mml:mi> <mml:mi>M</mml:mi> </mml:msub> </mml:mrow> <mml:annotation encoding="application/x-tex">{G_M}</mml:annotation> </mml:semantics> </mml:math> </inline-formula> has a giant component: a component of order <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="left-parenthesis 1 minus alpha Subscript c Baseline plus o left-parenthesis 1 right-parenthesis right-parenthesis n"> <mml:semantics> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mn>1</mml:mn> <mml:mo>−<!-- − --></mml:mo> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mi>Ξ±<!-- Ξ± --></mml:mi> <mml:mi>c</mml:mi> </mml:msub> </mml:mrow> <mml:mo>+</mml:mo> <mml:mi>o</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:mn>1</mml:mn> <mml:mo stretchy="false">)</mml:mo> <mml:mo stretchy="false">)</mml:mo> <mml:mi>n</mml:mi> </mml:mrow> <mml:annotation encoding="application/x-tex">(1-{\alpha _c}+o(1))n</mml:annotation> </mml:semantics> </mml:math> </inline-formula> where <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="alpha Subscript c Baseline greater-than 1"> <mml:semantics> <mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mi>Ξ±<!-- Ξ± --></mml:mi> <mml:mi>c</mml:mi> </mml:msub> </mml:mrow> <mml:mo>></mml:mo> <mml:mn>1</mml:mn> </mml:mrow> <mml:annotation encoding="application/x-tex">{\alpha _c} > 1</mml:annotation> </mml:semantics> </mml:math> </inline-formula>. The aim of this paper is to examine in detail the structure of a random graph <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="upper G Subscript upper M"> <mml:semantics> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mi>G</mml:mi> <mml:mi>M</mml:mi> </mml:msub> </mml:mrow> <mml:annotation encoding="application/x-tex">{G_M}</mml:annotation> </mml:semantics> </mml:math> </inline-formula> when <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="upper M"> <mml:semantics> <mml:mi>M</mml:mi> <mml:annotation encoding="application/x-tex">M</mml:annotation> </mml:semantics> </mml:math> </inline-formula> is close to <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="n slash 2"> <mml:semantics> <mml:mrow> <mml:mi>n</mml:mi> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>/</mml:mo> </mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> <mml:annotation encoding="application/x-tex">n/2</mml:annotation> </mml:semantics> </mml:math> </inline-formula>. Among others it is proved that if <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="upper M equals n slash 2 plus s"> <mml:semantics> <mml:mrow> <mml:mi>M</mml:mi> <mml:mo>=</mml:mo> <mml:mi>n</mml:mi> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>/</mml:mo> </mml:mrow> <mml:mn>2</mml:mn> <mml:mo>+</mml:mo> <mml:mi>s</mml:mi> </mml:mrow> <mml:annotation encoding="application/x-tex">M = n/2 + s</mml:annotation> </mml:semantics> </mml:math> </inline-formula>, <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="s equals o left-parenthesis n right-parenthesis"> <mml:semantics> <mml:mrow> <mml:mi>s</mml:mi> <mml:mo>=</mml:mo> <mml:mi>o</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:mi>n</mml:mi> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> <mml:annotation encoding="application/x-tex">s = o(n)</mml:annotation> </mml:semantics> </mml:math> </inline-formula> and <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="s greater-than-or-equal-to left-parenthesis log n right-parenthesis Superscript 1 slash 2 Baseline n Superscript 2 slash 3"> <mml:semantics> <mml:mrow> <mml:mi>s</mml:mi> <mml:mo>≥<!-- ≥ --></mml:mo> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo stretchy="false">(</mml:mo> <mml:mi>log</mml:mi> <mml:mo><!-- --></mml:mo> <mml:mi>n</mml:mi> <mml:msup> <mml:mo stretchy="false">)</mml:mo> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>1</mml:mn> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>/</mml:mo> </mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msup> <mml:mi>n</mml:mi> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>2</mml:mn> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>/</mml:mo> </mml:mrow> <mml:mn>3</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:mrow> <mml:annotation encoding="application/x-tex">s \geq {(\log n)^{1/2}}{n^{2/3}}</mml:annotation> </mml:semantics> </mml:math> </inline-formula> then the giant component has <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="left-parenthesis 4 plus o left-parenthesis 1 right-parenthesis right-parenthesis s"> <mml:semantics> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mn>4</mml:mn> <mml:mo>+</mml:mo> <mml:mi>o</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:mn>1</mml:mn> <mml:mo stretchy="false">)</mml:mo> <mml:mo stretchy="false">)</mml:mo> <mml:mi>s</mml:mi> </mml:mrow> <mml:annotation encoding="application/x-tex">(4 + o(1))s</mml:annotation> </mml:semantics> </mml:math> </inline-formula> vertices. Furthermore, rather precise estimates are given for the order of the <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="r"> <mml:semantics> <mml:mi>r</mml:mi> <mml:annotation encoding="application/x-tex">r</mml:annotation> </mml:semantics> </mml:math> </inline-formula>th largest component for every fixed <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="r"> <mml:semantics> <mml:mi>r</mml:mi> <mml:annotation encoding="application/x-tex">r</mml:annotation> </mml:semantics> </mml:math> </inline-formula>. |
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| publications-4688 |
article |
1985 |
Hamilton, Lawrence C. and Hamilton, Lawrence C. |
Self-Reported and Actual Savings in a Water Conservation Campaign: |
Environment and Behavior |
10.1177/0013916585173003 |
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Data from a survey questionnaire and from water utility billing records are used to compare self-reported and actual water savings for 471 households during a conservation campaign. Self-reports are only weakly related to actual changes in water consumption. Errors are widespread, and not wholly random: The accuracy of self-reports increases with household socioeconomic status and with the extent of conservation behavior. The large and nonrandom error component makes self-reports questionable as a proxy for objective measures of overall water savings in conservation research. Because knowledge about water use is both generally low and related to conservation behavior, informational feedback may be a particularly effective strategy for increasing conservation. The effectiveness of this feedback may increase with social class, however. |
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| publications-4689 |
article |
1986 |
Clark, Robert M. and Clark, Robert M. and Males, Richard M. and Males, Richard M. |
Developing and Applying the Water Supply Simulation Model |
Journal American Water Works Association |
10.1002/j.1551-8833.1986.tb05800.x |
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Passage of the Safe Drinking Water Act has intensified interest in problems related to water supply and water utility management. Analysis of the regulations to be promulgated under the act indicates that some water utilities, particularly small ones, may be adversely affected economically.1 An often-suggested option is that small systems combine with a larger system to form a regional water supply utility. It is assumed that the economies of scale associated with a regional water system would benefit the customers of small systems. A characteristic of many production and transportation problems is the trade-off between the cost of building and operating facilities to meet demands for a product and the cost of transportation.2 High transportation costs and low facility costs indicate decentralization; the reverse situation indicates a few large central facilities. These costs must be considered in planning, designing, constructing, and operating water supply systems. It is possible to separate the water supply system physically into two components: (1) the acquisition and treatment function, and (2) the delivery (transmission and distribution) system.3 Each of these components has a different cost function. The unit costs associated with treatment facilities are usually assumed to decrease as the quantity of service provided increases. The delivery system, however, is more directly affected by the characteristics of the area being served. The cost tradeoffs between the two components determine the cost of delivering water to any portion of the service area. Because few analytical instruments are available for study of the economics of water supply systems, the US Environmental Protection Agency's Drinking Water Supply Research Division initiated a program to develop techniques and methodologies to evaluate the economics of regional systems. This article describes the development of a simulation model designed to aid in such an evaluation. The model can also provide insights into other water-related economic issues, such as spatial pricing and costing, conservation policies, operating improvements versus increased capital expenditure, user class subsidization, and fire protection capacity. The model, called the Water Supply Simulation Model (WSSM), incorporates a series of submodels to describe the various economic, demographic, and hydraulic aspects of a water utility. |
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| publications-4690 |
article |
1987 |
Goulter, I. C. and Goulter, I. C. |
Current and future use of systems analysis in water distribution network design |
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10.1080/02630258708970484 |
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Abstract Computer use in the design of water distribution networks was inititated through the use of network analysis techniques to determine system performance in terms of heads and flows. The last fifteen years, however, have seen the introduction of systems analysis optimization techniques to the range of computer models available for network design purposes. These optimization models differ markedly from the ‘traditional’ network analysis models in that they ‘design’ systems for specified loading conditions rather than just analysing the performance of predetermined systems under given loading conditions. Cost was the primary or only objective in almost all these early optimization models. Water distribution network design has, however, a number of other important objectives, such as maximizing reliability. Issues related to reliability concern include probability of component failure, probability of actual demands being greater than design values, and the system redundancy inherent within the layout ... |
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