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  • 1.
    Carling, Kenneth
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Meng, Xiangli
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Confidence in heuristic solutions?2015Ingår i: Journal of Global Optimization, ISSN 0925-5001, E-ISSN 1573-2916, Vol. 63, nr 2, s. 381-399Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Solutions to combinatorial optimization problems frequently rely on heuristics to minimize an objective function. The optimum is sought iteratively and pre-setting the number of iterations dominates in operations research applications, which implies that the quality of the solution cannot be ascertained. Deterministic bounds offer a mean of ascertaining the quality, but such bounds are available for only a limited number of heuristics and the length of the interval may be difficult to control in an application. A small, almost dormant, branch of the literature suggests using statistical principles to derive statistical bounds for the optimum. We discuss alternative approaches to derive statistical bounds. We also assess their performance by testing them on 40 test p-median problems on facility location, taken from Beasley’s OR-library, for which the optimum is known. We consider three popular heuristics for solving such location problems; simulated annealing, vertex substitution, and Lagrangian relaxation where only the last offers deterministic bounds. Moreover, we illustrate statistical bounds in the location of 71 regional delivery points of the Swedish Post. We find statistical bounds reliable and much more efficient than deterministic bounds provided that the heuristic solutions are sampled close to the optimum. Statistical bounds are also found computationally affordable.

  • 2.
    Cortas Nordlander, Maria
    Université de Reims Champagne Ardenne, Ecole Doctorale Sciences Exactes et Biologie, France.
    Calcul par tranches pour les équations différentielles à variable temps à caractère explosif2005Doktorsavhandling, monografi (Övrigt vetenskapligt)
    Abstract [en]

    The aim of this work is to propose a numerical method for solving different types of partial and ordinary differential equations. The equations share the same common property for their solutions to become infinite (blow up behaviour) or to become null (extinction behaviour) in finite time. This type of equations is solved using a sliced time computing technique, combined with rescaling both the variable time and the solution of the differential system. The main criterion under which the slice of time is defined, consists in imposing that the rescaled solution should not be greater than a preset cut off value. Another selection criterion for the method is based on the invariance and similarity conditions, enforced on the rescaled model in each of the time slices

  • 3.
    Han, Mengjie
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Computational study of the step size parameter of the subgradient optimization methodManuskript (preprint) (Övrigt vetenskapligt)
    Abstract [en]

    The subgradient optimization method is a simple and flexible linear programming iterative algorithm. It is much simpler than Newton's method and can be applied to a wider variety of problems. It also converges when the objective function is non-differentiable. Since an efficient algorithm will not only produce a good solution but also take less computing time, we always prefer a simpler algorithm with high quality. In this study a series of step size parameters in the subgradient equation is studied. The performance is compared for a general piecewise function and a specific p-median problem. We examine how the quality of solution changes by setting five forms of step size parameter.

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    Computational study of the step size parameter of the subgradient optimization method
  • 4.
    Meng, Xiangli
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Optimization heuristic solutions, how good can they be?: With empirical applications in location problems2015Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [sv]

    Kombinatoriska optimeringsproblem, är en av de viktigaste typerna av problem i operationsanalys (OR). Heuristiska och metaheuristiska algoritmer tillämpas allmänt för att hitta lösningar med hög kvalitet. Ett vanligt problem är dock att dessa algoritmer inte garanterar optimala lösningar och sålunda kan det finnas osäkerhet i kvaliteten på lösningar på tillämpade operationsanalytiska problem. Huvudsyftet med denna avhandling är att undersöka användbarheten av statistiska konfidensintervall för att utvärdera kvaliteten på heuristiska lösningar då de tillämpas på stora kombinatoriska optimeringsproblem. Bidragen från denna avhandling är både metodologiska och empiriska. Ur metodologisk synvinkel har nyttan av statistiska konfidensintervall för ett lokaliseringsproblem (p-median problemet) undersökts. Statistiska konfidensintervall fungerar väl för att tillhandahålla information om lösningens kvalitet vid rätt implementering av problemen. Statistiska konfidensintervall överträffar även intervallen som erhålls vid den vanligen använda Lagrange-relaxation. I avhandlingen visas även på att metoden med statistiska konfidensintervall är fungerar väl jämfört med många andra deterministiska intervall i ett mer komplexa optimeringsproblem som det kvadratiska tilldelningsproblemet. P-median problemet och de statistiska konfidensintervallen har implementerats empiriskt för att beräkna och jämföra e-handelns och traditionell handels CO2-utsläpp från transporter, vilken visar att ehandel medför betydligt mindre CO2-utsläpp. Ett annat lokaliseringsproblem som analyserats empiriskt är hur förändringar av den regionala administrativa indelningen av Sverige, vilket är en aktuell och pågående samhällsdiskussion, påverkar medborgarnas tillgänglighet till offentlig service. Analysen visar att regionala administrativa iv gränserna lett till en suboptimal placering av offentliga tjänster. Därmed finns en risk att den samhällsekonomiska nyttan av dessa tjänster är suboptimerad.

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  • 5. Nassif, Nabil R.
    et al.
    Fayyad, Dolly
    Cortas Nordlander, Maria
    Lebanese University, Beirut, Lebanon.
    Sliced-Time Computations with Re-scaling for Blowing-Up Solutions to Initial Value Differential Equations2005Ingår i: Computational Science – ICCS 2005 / [ed] Sunderam, Vaidy S.; van Albada, Geert Dick; Sloot, Peter M. A.; Dongarra, Jack J., Springer Berlin/Heidelberg, 2005, s. 58-65Konferensbidrag (Refereegranskat)
    Abstract [en]

    In this paper, we present a new approach to simulate time-dependent initial value differential equations which solutions have a common property of blowing-up in a finite time. For that purpose, we introduce the concept of “sliced-time computations”, whereby, a sequence of time intervals (slices) {[Tn − 1, Tn]| n ≥ 1} is defined on the basis of a change of variables (re-scaling), allowing the generation of computational models that share symbolically or numerically “similarity” criteria. One of these properties is to impose that the re-scaled solution computed on each slice do not exceed a well-defined cut-off value (or threshold) S. In this work we provide fundamental elements of the method, illustrated on a scalar ordinary differential equation y′ = f(y) where f(y) verifies $\int_0^\infty {f(y)dy} < \infty$. Numerical results on various ordinary and partial differential equations are available in [7], some of which will be presented in this paper.

  • 6. Nassif, Nabil R.
    et al.
    Fayyad, Dolly
    Cortas Nordlander, Maria
    Lebanese University, Beirut, Lebanon.
    Sliced-Time Computations with Re-scaling for Blowing-Up Solutions to Initial Value Differential Equations2005Konferensbidrag (Refereegranskat)
  • 7.
    Valyukh, Iryna
    Högskolan Dalarna, Akademin Industri och samhälle, Materialteknik. Uppsala universitet, Institutionen för teknikvetenskaper.
    Optical Characterization and Optimization of Display Components: Some Applications to Liquid-Crystal-Based and Electrochromics-Based Devices2009Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    This dissertation is focused on theoretical and experimental studies of optical properties of materials and multilayer structures composing liquid crystal displays (LCDs) and electrochromic (EC) devices.

    By applying spectroscopic ellipsometry, we have determined the optical constants of thin films of electrochromic tungsten oxide (WOx) and nickel oxide (NiOy), the films’ thickness and roughness. These films, which were obtained at spattering conditions possess high transmittance that is important for achieving good visibility and high contrast in an EC device.

    Another application of the general spectroscopic ellipsometry relates to the study of a photo-alignment layer of a mixture of azo-dyes SD-1 and SDA-2. We have found the optical constants of this mixture before and after illuminating it by polarized UV light. The results obtained confirm the diffusion model to explain the formation of the photo-induced order in azo-dye films.

    We have developed new techniques for fast characterization of twisted nematic LC cells in transmissive and reflective modes. Our techniques are based on the characteristics functions that we have introduced for determination of parameters of non-uniform birefringent media. These characteristic functions are found by simple procedures and can be utilised for simultaneous determination of retardation, its wavelength dispersion, and twist angle, as well as for solving associated optimization problems.

    Cholesteric LCD that possesses some unique properties, such as bistability and good selective scattering, however, has a disadvantage – relatively high driving voltage (tens of volts). The way we propose to reduce the driving voltage consists of applying a stack of thin (~1µm) LC layers.

    We have studied the ability of a layer of a surface stabilized ferroelectric liquid crystal coupled with several retardation plates for birefringent color generation. We have demonstrated that in order to accomplish good color characteristics and high brightness of the display, one or two retardation plates are sufficient.

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    FULLTEXT01
  • 8. Wang, Zhenwu
    et al.
    Xue, Liang
    Guo, Yinan
    Han, Mengjie
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Liang, Shangchao
    Solving dynamic multi-objective optimization problems via quantifying intensity of environment changes and ensemble learning-based prediction strategies2024Ingår i: Applied Soft Computing, ISSN 1568-4946, E-ISSN 1872-9681, Vol. 154, artikel-id 111317Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Algorithms designed to solve dynamic multi-objective optimization problems (DMOPs) need to consider all of themultiple conflicting objectives to determine the optimal solutions. However, objective functions, constraints orparameters can change over time, which presents a considerable challenge. Algorithms should be able not only toidentify the optimal solution but also to quickly detect and respond to any changes of environment. In order toenhance the capability of detection and response to environmental changes, we propose a dynamic multiobjectiveoptimization (DMOO) algorithm based on the detection of environment change intensity andensemble learning (DMOO-DECI&EL). First, we propose a method for detecting environmental change intensity,where the change intensity is quantified and used to design response strategies. Second, a series of responsestrategies under the framework of ensemble learning are given to handle complex environmental changes.Finally, a boundary learning method is introduced to enhance the diversity and uniformity of the solutions.Experimental results on 14 benchmark functions demonstrate that the proposed DMOO-DECI&EL algorithmachieves the best comprehensive performance across three evaluation criteria, which indicates that DMOODECI&EL has better robustness and convergence and can generate solutions with better diversity compared tofive other state-of-the-art dynamic prediction strategies. In addition, the application of DMOO-DECI&EL to thereal-world scenario, namely the economic power dispatch problem, shows that the proposed method caneffectively handle real-world DMOPs.

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  • 9. Wang, Zhenwu
    et al.
    Zhou, Yang
    Han, Mengjie
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Guo, Yinan
    Interpreting convolutional neural network by joint evaluation of multiple feature maps and an improved NSGA-II algorithm2024Ingår i: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 255, artikel-id 124489Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The ’black box’ characteristics of Convolutional Neural Networks (CNNs) present significant risks to their application scenarios, such as reliability, security, and division of responsibilities. Addressing the interpretability of CNN emerges as an urgent and critical issue in the field of machine learning. Recent research on CNN interpretability has either yielded unstable or inconsistent interpretations, or produced coarse-scale interpretable heatmaps, limiting their applicability in various scenarios. In this work, we propose a novel method of CNNs interpretation by incorporating a joint evaluation of multiple feature maps and employing multi-objective optimization (JE&MOO-CAM). Firstly, a method of joint evaluation for all feature maps is proposed to preserve the complete object instances and improve the overall activation values. Secondly, an interpretation method of CNNs under the MOO framework is proposed to avoid the instability and inconsistency of interpretation. Finally, the operators of selection, crossover, and mutation, along with the method of population initialization in NSGA-II, are redesigned to properly express the characteristics of CNNs. The experimental results, including both qualitative and quantitative assessments along with a sanity check conducted on three classic CNN models—VGG16, AlexNet, and ResNet50—demonstrate the superior performance of the proposed JE&MOO-CAM model. This model not only accurately pinpoints the instances within the image requiring explanation but also preserves the integrity of these instances to the greatest extent possible. These capabilities signify that JE&MOO-CAM surpasses six other leading state-of-the-art methods across four established evaluation criteria.

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