3,404 research outputs found

    Non-destructive soluble solids content determination for โ€˜Rochaโ€™ Pear Based on VIS-SWNIR spectroscopy under โ€˜Real Worldโ€™ sorting facility conditions

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    In this paper we report a method to determine the soluble solids content (SSC) of 'Rocha' pear (Pyrus communis L. cv. Rocha) based on their short-wave NIR reflectance spectra (500-1100 nm) measured in conditions similar to those found in packinghouse fruit sorting facilities. We obtained 3300 reflectance spectra from pears acquired from different lots, producers and with diverse storage times and ripening stages. The macroscopic properties of the pears, such as size, temperature and SSC were measured under controlled laboratory conditions. For the spectral analysis, we implemented a computational pipeline that incorporates multiple pre-processing techniques including a feature selection procedure, various multivariate regression models and three different validation strategies. This benchmark allowed us to find the best model/preproccesing procedure for SSC prediction from our data. From the several calibration models tested, we have found that Support Vector Machines provides the best predictions metrics with an RMSEP of around 0.82 โˆ˜ Brix and 1.09 โˆ˜ Brix for internal and external validation strategies respectively. The latter validation was implemented to assess the prediction accuracy of this calibration method under more 'real world-like' conditions. We also show that incorporating information about the fruit temperature and size to the calibration models improves SSC predictability. Our results indicate that the methodology presented here could be implemented in existing packinghouse facilities for single fruit SSC characterization.Funding Agency CEOT strategic project UID/Multi/00631/2019 project OtiCalFrut ALG-01-0247-FEDER-033652 Ideias em Caixa 2010, CAIXA GERAL DE DEPOSITOS Fundacao para a Ciencia e a Tecnologia (Ciencia)info:eu-repo/semantics/publishedVersio

    Seepage criteria based optimal design of water retaining structures with reliability quantification utilizing surrogate model linked simulation-optimization approach

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    The safety of hydraulic water retaining structures (HWRS) is an important issue as many instances of HWRS failure have been reported. Failure of HWRS may lead to catastrophic events, especially those associated with seepage failures. Therefore, seepage safety factors recommended for HWRS design are generally very conservative. These safety factors have been developed based on approximation calculations, unreliable assumptions, and ideal experimental conditions, which are rarely replicated in real field situations. However, with the development of the numerical methods, and high speed processors, more accurate seepage analysis has become possible, even for complex flow domains, different scenarios of boundary conditions, and varied hydraulic conductivity. On the other hand, because construction of HWRS requires a significant amount of construction material and engineering effort, the construction cost efficiency of HWRS is an issue that must be considered in design of HWRS. This study aims to determine the minimum cost design of HWRS constructed on permeable soils, incorporating numerical solutions of a seepage system related to HWRS, utilizing linked a simulationโ€“optimization (S-O) model. Due to the complexity and inefficacy of directly linking a simulation model to the optimization model, the numerical simulation model was replaced by trained surrogate models. These surrogate models can be trained based on numerically simulated data sets. Therefore, trained surrogate models expeditiously and accurately provide predicted responses relating to seepage characteristics pertaining to HWRS. The optimization model based on the linked S-O technique incorporated different safety factors and hydraulic structure design requirements as constraints. The majority of these constraints and objective function(s) were affected by the responses of predicted seepage characteristics based on the developed surrogate models. To improve the safety of HWRS design, the effect of non-homogenous and anisotropic hydraulic conductivity were incorporated in the S-O model. Obtained solution results demonstrated that considering stratification of the flow domain due to different hydraulic conductivity values or anisotropic ratios can significantly change the optimum design of HWRS. Low hydraulic conductivity and anisotropic ratios resulted in more critical seepage characteristics. Consequently, the minimum construction cost increased due to an increase of dimensions of involved seepage protection design variables. Furthermore, uncertainty in estimating hydraulic conductivity is incorporated in the S-O model. The reliability based optimal design (RBOD) framework based on the multi-realization optimization technique was implemented using the S-O model. The uncertainty in seepage quantities due to uncertainty of hydraulic conductivity was represented using many stochastic ensemble surrogate models. Each ensemble model included many surrogate models trained in utilizing inputโ€“ output data sets simulated with different scenarios of hydraulic conductivity drawn from diverse random fields based on different log-normal distributions. Obtained results of this approach demonstrated substantial consequences of considering uncertainty in hydraulic conductivity. Also, the deterministic safety factors, especially for those pertaining to the exit gradient, were insufficient to provide prescribed safety in the long term. Although surrogate models are utilized in S-O approaches, each run of the S-O model takes a long time as developed S-O models are applied to complex and large scale problems. Hence, efficiency of the S-O model was a key factor to successfully implement the methodology. Three main techniques were utilized to increase the efficiency of the S-O technique: using parallel computing, utilizing nested function technique, and using a vectorised formulation system. These strategies substantially boosted efficiency of implementing the S-O model. The S-O models were implemented for many hypothetical scenarios for different purposes. In general, results demonstrated that optimum design of the seepage protection system relating to HWRS design must include two end cut-offs with an apron between them. The dimensions of these components were augmented with an increase of upstream water head, and reduction of anisotropic ratios or hydraulic conductivity value. The main role of the downstream cut-off was to decrease the actual exit gradient value. This impact is more pronounced if the inclination angle of the cut-off is toward the downstream side (>90 degrees). The role of the upstream cut-off was to decrease uplift pressure values on the HWRS base. Consequently, this partially contributed to decreasing the exit gradient value. The effect of the upstream cut-off in reducing the uplift pressure was more when the inclination angle was toward the upstream side (<90 degrees). Moreover, the apron (floor) width helped to increase the stability of HWRS. This variable provided the required weight to improve HWRS resistance to external hydraulic forces and to uplift pressure. Incorporating the weight of water (hydrostatic pressure) at the upstream side in counterbalancing momentum and hydraulic forces showed improvement in the safety of the HWRS. Also, all conditions and safety factors pertaining to HWRS design were satisfied. The exit gradient safety factor was the most important critical factor affecting optimum design as obtained optimum solutions satisfied the minimum permissible values of the exit gradient safety factor, i.e., at the minimum permissible value. Also, the eccentric load condition played a crucial role in resulting optimum solutions. Finally, applying the S-O model to obtain reliable and safe design of HWRS at minimum cost was successfully implemented for performance evaluation purposes. This technique may be extended to incorporate more complex scenarios in HWRS design where the impact of dynamic and seismic load could be incorporated. The effect of unsteady state seepage system could be another interesting direction for future studies. Further, incorporating more sources of the uncertainty associated with design parameters could achieve a more accurate estimation of actual safety for the HWRS design

    Advanced Sensing, Fault Diagnostics, and Structural Health Management

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    Advanced sensing, fault diagnosis, and structural health management are important parts of the maintenance strategy of modern industries. With the advancement of science and technology, modern structural and mechanical systems are becoming more and more complex. Due to the continuous nature of operation and utilization, modern systems are heavily susceptible to faults. Hence, the operational reliability and safety of the systems can be greatly enhanced by using the multifaced strategy of designing novel sensing technologies and advanced intelligent algorithms and constructing modern data acquisition systems and structural health monitoring techniques. As a result, this research domain has been receiving a significant amount of attention from researchers in recent years. Furthermore, the research findings have been successfully applied in a wide range of fields such as aerospace, manufacturing, transportation and processes

    Advances in Evolutionary Algorithms

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    With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field

    Robust Geotechnical Design - Methodology and Applications

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    This dissertation is aimed at developing a novel robust geotechnical design methodology and demonstrating this methodology for the design of geotechnical systems. The goal of a robust design is to make the response of a system insensitive to, or robust against, the variation of uncertain geotechnical parameters (termed noise factors in the context of robust design) by carefully adjusting design parameters (those that can be controlled by the designer such as geometry of the design). Through an extensive investigation, a robust geotechnical design methodology that considers explicitly safety, robustness, and cost is developed. Various robustness measures are considered in this study, and the developed methodology is implemented with a multi-objective optimization scheme, in which safety is considered as a constraint and cost and robustness are treated as the objectives. Because the cost and the robustness are conflicting objectives, the robust design optimization does not yield a single best solution. Rather, a Pareto front is obtained, which is a collection of non-dominated optimal designs. The Pareto front reveals a trade-off relationship between cost and robustness, which enables the engineer to make an informed design decision according to a target level of cost or robustness. The significance and versatility of the new design methodology are illustrated with multiple geotechnical applications, including the design of drilled shafts, shallow foundations, and braced excavations

    ์œ ์ „ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ด์šฉํ•œ ๋‹ค์ค‘์Šค์ผ€์ผ/๋‹ค๋ชฉ์  ๊ณต๊ฐ„๊ณ„ํš ์ตœ์ ํ™”๋ชจ๋ธ ๊ตฌ์ถ•

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ํ™˜๊ฒฝ๋Œ€ํ•™์› ํ˜‘๋™๊ณผ์ • ์กฐ๊ฒฝํ•™์ „๊ณต, 2019. 2. ์ด๋™๊ทผ.๊ณต๊ฐ„๊ณ„ํš ๊ณผ์ •์—์„œ ๋‹ค์–‘ํ•œ ์ดํ•ด๊ด€๊ณ„์ž์™€ ๊ฒฐ๋ถ€๋œ ๋ชฉํ‘œ์™€ ์ œ์•ฝ ์š”๊ฑด์„ ๋งŒ์กฑ์‹œํ‚ค๋Š” ๊ฒƒ์€ ๋ณต์žกํ•œ ๋น„์„ ํ˜•์  ๋ฌธ์ œ๋กœ์„œ ํ•ด๊ฒฐํ•˜๊ธฐ ์–ด๋ ค์šด ๊ฒƒ์œผ๋กœ ์•Œ๋ ค์ ธ ์™”๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ตœ๊ทผ ์ด๋Ÿฌํ•œ ๋ฌธ์ œ์— ์œ ์ „ ์•Œ๊ณ ๋ฆฌ์ฆ˜ (genetic algorithms), ๋‹ด๊ธˆ์งˆ ๊ธฐ๋ฒ• (simulated annealing), ๊ฐœ๋ฏธ ๊ตฐ์ง‘ ์ตœ์ ํ™” (ant colony optimization) ๋“ฑ์˜ ๋‹ค๋ชฉ์  ์ตœ์ ํ™” ์•Œ๊ณ ๋ฆฌ์ฆ˜์ด ์‘์šฉ๋˜๊ณ  ์žˆ์œผ๋ฉฐ, ๊ด€๋ จ ์—ฐ๊ตฌ ์—ญ์‹œ ๊ธ‰์ฆํ•˜๊ณ  ์žˆ๋‹ค. ์ด ์ค‘ ์œ ์ „ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ๊ณต๊ฐ„ ์ตœ์ ํ™” ๋ถ€๋ฌธ์— ๊ฐ€์žฅ ๋นˆ๋„ ๋†’๊ฒŒ ์ ์šฉ๋œ ์ตœ์ ํ™” ์•Œ๊ณ ๋ฆฌ์ฆ˜์œผ๋กœ exploration๊ณผ exploitation์˜ ๊ท ํ˜•์œผ๋กœ ํ•ฉ๋ฆฌ์ ์ธ ์‹œ๊ฐ„ ๋‚ด์— ์ถฉ๋ถ„ํžˆ ์ข‹์€ ๊ณ„ํš์•ˆ์„ ์ œ์‹œํ•  ์ˆ˜ ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ณต๊ฐ„ ์ตœ์ ํ™” ์—ฐ๊ตฌ๊ฐ€ ๋ณด์—ฌ์ค€ ์ข‹์€ ์„ฑ๊ณผ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ๋Œ€๋ถ€๋ถ„์˜ ์—ฐ๊ตฌ๊ฐ€ ํŠน์ • ์šฉ๋„ ํ˜น์€ ์‹œ์„ค์˜ ๋ฐฐ์น˜์— ์ง‘์ค‘๋˜์–ด ์žˆ์œผ๋ฉฐ, ๊ธฐํ›„๋ณ€ํ™” ์ ์‘, ์žฌํ•ด ๊ด€๋ฆฌ, ๊ทธ๋ฆฐ์ธํ”„๋ผ ๊ณ„ํš๊ณผ ๊ฐ™์€ ์ตœ๊ทผ์˜ ํ™˜๊ฒฝ ์ด์Šˆ๋ฅผ ๋‹ค๋ฃฌ ์‚ฌ๋ก€๋Š” ๋งค์šฐ ๋ฏธํกํ•˜๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์œ ์ „ ์•Œ๊ณ ๋ฆฌ์ฆ˜๊ณผ ๋น„์ง€๋ฐฐ ์ •๋ ฌ ์œ ์ „ ์•Œ๊ณ ๋ฆฌ์ฆ˜ (non-dominated sorting genetic algorithm II)์— ๊ธฐ์ดˆํ•˜์—ฌ ๊ธฐํ›„๋ณ€ํ™” ์ ์‘, ์žฌํ•ด ๊ด€๋ฆฌ, ๋„์‹œ์˜ ๋…น์ง€ ๊ณ„ํš ๋“ฑ๊ณผ ๊ฐ™์€ ํ™˜๊ฒฝ ์ด์Šˆ๋ฅผ ๊ณต๊ฐ„๊ณ„ํš์— ๋ฐ˜์˜ํ•  ์ˆ˜ ์žˆ๋Š” ์ผ๋ จ์˜ ๊ณต๊ฐ„ ์ตœ์ ํ™” ๋ชจ๋ธ์„ ์ œ์‹œํ•˜์˜€๋‹ค. ๊ฐœ๋ณ„ ํ™˜๊ฒฝ ์ด์Šˆ์— ๋”ฐ๋ผ ๊ณต๊ฐ„ ํ•ด์ƒ๋„, ๋ชฉ์ , ์ œ์•ฝ์š”๊ฑด์ด ๋‹ค๋ฅด๊ฒŒ ๊ตฌ์„ฑํ•˜์˜€์œผ๋ฉฐ, ๊ณต๊ฐ„์  ๋ฒ”์œ„๊ฐ€ ์ข์•„์ง€๊ณ  ๊ณต๊ฐ„ํ•ด์ƒ๋„๋Š” ๋†’์•„์ง€๋Š” ์ˆœ์„œ๋Œ€๋กœ ๋‚˜์—ดํ•˜์˜€๋‹ค. ๋…ผ๋ฌธ์˜ ์ฒซ๋ฒˆ์งธ ์žฅ์—์„œ๋Š” ํ–‰์ •๊ตฌ์—ญ ๋„ ๊ทœ๋ชจ (province scale, ํ•ด์ƒ๋„ 1ใŽข)์—์„œ ๋ฏธ๋ž˜์˜ ๊ธฐํ›„๋ณ€ํ™”์— ์ ์‘ํ•˜๊ธฐ ์œ„ํ•œ ํ† ์ง€์ด์šฉ ์‹œ๋‚˜๋ฆฌ์˜ค๋ฅผ ๋ชจ์˜ํ•  ์ˆ˜ ์žˆ๋Š” ๊ณต๊ฐ„ ์ตœ์ ํ™” ๋ชจ๋ธ์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ๊ธฐํ›„๋ณ€ํ™”๊ฐ€ ๋จผ ๋ฏธ๋ž˜๊ฐ€ ์•„๋‹Œ, ํ˜„์žฌ ์ด๋ฏธ ์ง„ํ–‰๋˜๊ณ  ์žˆ์œผ๋ฉฐ ๊ด€๋ จํ•œ ๋‹ค์ˆ˜์˜ ํ”ผํ•ด๊ฐ€ ๊ด€์ฐฐ๋˜๊ณ  ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ๊ณต๊ฐ„์  ๊ด€์ ์—์„œ ๊ธฐํ›„๋ณ€ํ™”์— ๋Œ€ํ•œ ์ ์‘์˜ ํ•„์š”์„ฑ์ด ์ง€์ ๋˜์–ด ์™”๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ตฌ์ฒด์ ์œผ๋กœ ๊ธฐํ›„์— ๋Œ€ํ•œ ํšŒ๋ณต ํƒ„๋ ฅ์„ฑ์„ ํ–ฅ์ƒ์‹œํ‚ค๊ธฐ ์œ„ํ•˜์—ฌ ํ† ์ง€์ด์šฉ์˜ ๊ณต๊ฐ„์  ๊ตฌ์„ฑ์„ ์–ด๋–ป๊ฒŒ ๋ณ€ํ™”์‹œ์ผœ์•ผ ํ• ์ง€์— ๋Œ€ํ•œ ๋ฐฉ๋ฒ•๋ก  ์ œ์‹œ๋Š” ๋ฏธํกํ•˜๋‹ค. ์ง€์—ญ๊ณ„ํš์—์„œ ๊ธฐํ›„๋ณ€ํ™” ์˜ํ–ฅ์„ ๊ณ ๋ คํ•œ ํ† ์ง€์ด์šฉ ๋ฐฐ๋ถ„์€ ๋งค์šฐ ์œ ์šฉํ•œ, ๊ธฐ๋ณธ์ ์ธ ์ค‘์žฅ๊ธฐ ์ ์‘ ์ „๋žต์— ํ•ด๋‹นํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋‹ค๋ชฉ์  ์œ ์ „ ์•Œ๊ณ ๋ฆฌ์ฆ˜ (MOGA, multi-objective genetic algorithm)์— ๊ธฐ์ดˆํ•˜์—ฌ 9,982ใŽข์— 350๋งŒ์˜ ์ธ๊ตฌ๊ฐ€ ๊ฑฐ์ฃผํ•˜๋Š” ํ•œ๊ตญ์˜ ์ถฉ์ฒญ๋‚จ๋„ ๋ฐ ๋Œ€์ „๊ด‘์—ญ์‹œ ์ผ๋Œ€๋ฅผ ๋Œ€์ƒ์œผ๋กœ ๊ธฐํ›„๋ณ€ํ™” ์ ์‘์„ ์œ„ํ•œ ํ† ์ง€์ด์šฉ ์‹œ๋‚˜๋ฆฌ์˜ค๋ฅผ ์ œ์‹œํ•˜์˜€๋‹ค. ์ง€์—ญ์ ์ธ ๊ธฐํ›„๋ณ€ํ™” ์˜ํ–ฅ๊ณผ ๊ฒฝ์ œ์  ์—ฌ๊ฑด์„ ๊ณ ๋ คํ•˜์—ฌ ์žฌํ•ด ํ”ผํ•ด ๋ฐ ์ „ํ™˜๋Ÿ‰์˜ ์ตœ์†Œํ™”, ๋ฒผ ์ƒ์‚ฐ๋Ÿ‰, ์ข… ํ’๋ถ€๋„ ๋ณด์ „, ๊ฒฝ์ œ์  ๊ฐ€์น˜์˜ ์ตœ๋Œ€ํ™” ๋“ฑ ๋‹ค์„ฏ ๊ฐ€์ง€์˜ ๋ชฉ์ ์„ ์„ ํƒํ•˜์˜€๋‹ค. ๊ฐ ๋ชฉ์  ๋ณ„ ๊ฐ€์ค‘์น˜๋ฅผ ๋ณ€ํ™”์‹œํ‚ค๋ฉฐ ์—ฌ์„ฏ ๊ฐ€์ง€ ๊ฐ€์ค‘์น˜ ์กฐํ•ฉ์— ๋Œ€ํ•œ 17๊ฐœ์˜ ํŒŒ๋ ˆํ†  ์ตœ์  ํ† ์ง€์ด์šฉ ์‹œ๋‚˜๋ฆฌ์˜ค๋ฅผ ์ƒ์„ฑํ•˜์˜€๋‹ค. ๋Œ€๋ถ€๋ถ„์˜ ์‹œ๋‚˜๋ฆฌ์˜ค๋Š” ์ •๋„์˜ ์ฐจ์ด๋Š” ์žˆ์œผ๋‚˜ ํ˜„์žฌ์˜ ํ† ์ง€์ด์šฉ์— ๋น„ํ•ด ๊ธฐํ›„๋ณ€ํ™” ์ ์‘ ๋ถ€๋ถ„์—์„œ ๋” ์ข‹์€ ํผํฌ๋จผ์Šค๋ฅผ ๋ณด์˜€์œผ๋ฏ€๋กœ, ๊ธฐํ›„๋ณ€ํ™”์— ๋Œ€ํ•œ ํšŒ๋ณตํƒ„๋ ฅ์„ฑ์ด ๊ฐœ์„ ํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ํŒ๋‹จํ•˜์˜€๋‹ค. ๋˜ํ•œ ๊ณต๊ฐ„ ์ตœ์ ํ™” ๋ชจ๋ธ์˜ ์œ ์—ฐํ•œ ๊ตฌ์กฐ๋ฅผ ๊ณ ๋ คํ•˜์˜€์„ ๋•Œ, ์ง€์—ญ์˜ ์‹ค๋ฌด์ž ์—ญ์‹œ ๊ฐ€์ค‘์น˜์™€ ๊ฐ™์€ ๋ชจ๋ธ์˜ ํŒŒ๋ผ๋ฏธํ„ฐ, ๊ธฐํ›„๋ณ€ํ™” ์˜ํ–ฅ ํ‰๊ฐ€์™€ ๊ฐ™์€ ์ž…๋ ฅ์ž๋ฃŒ๋ฅผ ๋ณ€๊ฒฝํ•จ์œผ๋กœ์จ ํšจ์œจ์ ์œผ๋กœ ์ƒˆ๋กœ์šด ์‹œ๋‚˜๋ฆฌ์˜ค๋ฅผ ์ƒ์„ฑ ๋ฐ ์„ ํƒํ•˜๋Š” ๊ฒƒ์ด ๊ฐ€๋Šฅํ•  ๊ฒƒ์œผ๋กœ ์˜ˆ์ƒํ•˜์˜€๋‹ค. ๋…ผ๋ฌธ์˜ ๋‘ ๋ฒˆ์งธ ์žฅ์—์„œ๋Š” ํ–‰์ •๊ตฌ์—ญ ๊ตฐ ๊ทœ๋ชจ (local scale, ํ•ด์ƒ๋„ 100m)์—์„œ ๊ธฐํ›„๋ณ€ํ™”์— ๋”ฐ๋ฅธ ์žฌํ•ด ํ”ผํ•ด๋ฅผ ๊ด€๋ฆฌํ•˜๊ธฐ ์œ„ํ•œ ํ† ์ง€์ด์šฉ ์‹œ๋‚˜๋ฆฌ์˜ค๋ฅผ ๋ชจ์˜ํ•  ์ˆ˜ ์žˆ๋Š” ๊ณต๊ฐ„ ์ตœ์ ํ™” ๋ชจ๋ธ์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์‚ฐ์•…์ง€ํ˜•์—์„œ ํญ์šฐ๋กœ ์ธํ•œ ์‚ฐ์‚ฌํƒœ๋Š” ์ธ๋ช…๊ณผ ์žฌ์‚ฐ์— ์‹ฌ๊ฐํ•œ ํ”ผํ•ด๋ฅผ ์ดˆ๋ž˜ํ•  ์ˆ˜ ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ์•Œ๋ ค์ ธ ์žˆ๋‹ค. ๋”์šฑ์ด ๊ธฐํ›„๋ณ€ํ™”์— ๋”ฐ๋ฅธ ๊ฐ•์šฐ์˜ ๋ณ€๋™์„ฑ ์ฆ๊ฐ€๋กœ ์ด๋Ÿฌํ•œ ์‚ฐ์‚ฌํƒœ ๋นˆ๋„ ๋ฐ ๊ฐ•๋„ ์—ญ์‹œ ์ฆ๋Œ€๋  ๊ฒƒ์œผ๋กœ ์˜ˆ์ƒ๋œ๋‹ค. ์ผ๋ฐ˜์ ์œผ๋กœ ์‚ฐ์‚ฌํƒœ ๋ฆฌ์Šคํฌ๊ฐ€ ๋†’์€ ์ง€์—ญ์„ ํ”ผํ•ด ๊ฐœ๋ฐœ์ง€์—ญ์„ ๋ฐฐ์น˜ํ•˜๋Š” ๊ฒƒ์ด ํ”ผํ•ด๋ฅผ ์ €๊ฐ ํ˜น์€ ํšŒํ”ผํ•  ์ˆ˜ ์žˆ๋Š” ๊ฐ€์žฅ ํšจ๊ณผ์ ์ธ ์ „๋žต์œผ๋กœ ์•Œ๋ ค์ ธ ์žˆ์œผ๋‚˜, ์‹ค์ œ๊ณต๊ฐ„์—์„œ์˜ ๊ณ„ํš์€ ๋งค์šฐ ๋ณต์žกํ•œ ๋น„์„ ํ˜•์˜ ๋ฌธ์ œ๋กœ์„œ ์ด๊ฒƒ์„ ์‹คํ˜„ํ•˜๋Š” ๋ฐ ์–ด๋ ค์›€์ด ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋น„์ง€๋ฐฐ ์ •๋ ฌ ์œ ์ „ ์•Œ๊ณ ๋ฆฌ์ฆ˜ II์— ๊ธฐ์ดˆํ•˜์—ฌ ์‚ฐ์‚ฌํƒœ ๋ฆฌ์Šคํฌ ๋ฐ ์ „ํ™˜๋Ÿ‰, ํŒŒํŽธํ™”์˜ ์ตœ์†Œํ™” ๋“ฑ์˜ ๋‹ค์–‘ํ•œ ๋ชฉ์ ์„ ๋งŒ์กฑ์‹œํ‚ค๋Š” ์ข…ํ•ฉ์ ์ธ ํ† ์ง€์ด์šฉ ๋ฐฐ๋ถ„ ๊ณ„ํš์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ๋Œ€์ƒ์ง€๋Š” 2018๋…„ ๋™๊ณ„์˜ฌ๋ฆผํ”ฝ ๊ฐœ์ตœ์ง€์ธ ํ•œ๊ตญ์˜ ํ‰์ฐฝ๊ตฐ์œผ๋กœ์„œ 2006๋…„์— ์‚ฐ์‚ฌํƒœ๋กœ ์ธํ•œ ๋Œ€๊ทœ๋ชจ์˜ ํ”ผํ•ด๋ฅผ ๊ฒฝํ—˜ํ•˜์˜€์œผ๋‚˜, ์˜ฌ๋ฆผํ”ฝ ํŠน์ˆ˜ ๋“ฑ์˜ ๊ฐœ๋ฐœ์••๋ ฅ์œผ๋กœ ์ธํ•œ ๋‚œ๊ฐœ๋ฐœ์ด ์šฐ๋ ค๋˜๋Š” ์ง€์—ญ์ด๋‹ค. ์ตœ์ข…์ ์œผ๋กœ ํ•œ๋ฒˆ์˜ ๋ชจ์˜๋ฅผ ํ†ตํ•ด ํ˜„์žฌ์˜ ํ† ์ง€์ด์šฉ ๋ณด๋‹ค ์ ์–ด๋„ ํ•œ๊ฐ€์ง€ ์ด์ƒ์˜ ๋ชฉ์ ์—์„œ ์ข‹์€ ํผํฌ๋จผ์Šค๋ฅผ ๋ณด์ด๋Š” 100๊ฐœ์˜ ํŒŒ๋ ˆํ†  ์ตœ์  ๊ณ„ํš์•ˆ์„ ์ƒ์„ฑํ•˜์˜€๋‹ค. ๋˜ํ•œ 5๊ฐœ์˜ ๋Œ€ํ‘œ์ ์ธ ๊ณ„ํš์•ˆ์„ ์„ ์ •ํ•˜์—ฌ ์‚ฐ์‚ฌํƒœ๋ฆฌ์Šคํฌ ์ตœ์†Œํ™”์™€ ์ „ํ™˜๋Ÿ‰ ์ตœ์†Œํ™” ๊ฐ„์— ๋ฐœ์ƒํ•˜๋Š” ์ƒ์‡„ ํšจ๊ณผ๋ฅผ ์„ค๋ช…ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋Š” ๊ธฐํ›„๋ณ€ํ™”์™€ ๊ด€๋ จ๋œ ๊ณต๊ฐ„ ์ ์‘ ์ „๋žต์˜ ์ˆ˜๋ฆฝ, ๋ณด๋‹ค ํ–ฅ์ƒ๋œ ๊ฐœ๋ฐœ๊ณ„ํš์„ ์œ„ํ•œ ์˜์‚ฌ๊ฒฐ์ •์„ ํšจ๊ณผ์ ์œผ๋กœ ์ง€์›ํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ์˜ˆ์ƒํ•˜์˜€๋‹ค. ๋…ผ๋ฌธ์˜ ์„ธ ๋ฒˆ์งธ ์žฅ์—์„œ๋Š” ๋ธ”๋ก ๊ทœ๋ชจ(neighborhood scale, 2m)์—์„œ ๋„์‹œ ๋‚ด ๋…น์ง€๊ณ„ํš์•ˆ์„ ๋ชจ์˜ํ•  ์ˆ˜ ์žˆ๋Š” ๊ณต๊ฐ„ ์ตœ์ ํ™” ๋ชจ๋ธ์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ๋…น์ง€ ๊ณต๊ฐ„์€ ๋„์‹œ๋ฏผ์˜ ์‚ถ์˜ ์งˆ์— ๊ฒฐ์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์น˜๊ธฐ ๋•Œ๋ฌธ์— ๋‹ค์–‘ํ•œ ๋„์‹œ ์žฌ์ƒ ๋ฐ ๊ฐœ๋ฐœ๊ณ„ํš์—๋Š” ๋…น์ง€์™€ ์ง ๊ฐ„์ ‘์ ์œผ๋กœ ๊ด€๋ จ๋œ ์ „๋žต์ด ํฌํ•จ๋œ๋‹ค. ๋…น์ง€ ๊ณต๊ฐ„์€ ๋„์‹œ์ง€์—ญ ๋‚ด์—์„œ ์—ด์„ฌ ํ˜„์ƒ ์™„ํ™”, ์œ ์ถœ๋Ÿ‰ ์ €๊ฐ, ์ƒํƒœ ๋„คํŠธ์›Œํฌ ์ฆ์ง„ ๋“ฑ ๋‹ค์–‘ํ•œ ๊ธ์ •์  ํšจ๊ณผ๊ฐ€ ์žˆ์Œ์ด ์•Œ๋ ค์ ธ ์žˆ์œผ๋‚˜, ๊ณต๊ฐ„ ๊ณ„ํš์˜ ๊ด€์ ์—์„œ ์ด๋Ÿฌํ•œ ๋‹ค์–‘ํ•œ ํšจ๊ณผ๋ฅผ ์ข…ํ•ฉ์ , ์ •๋Ÿ‰์ ์œผ๋กœ ๊ณ ๋ ค๋œ ์‚ฌ๋ก€๋Š” ๋งค์šฐ ๋ฏธํกํ•˜๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋น„์ง€๋ฐฐ ์ •๋ ฌ ์œ ์ „ ์•Œ๊ณ ๋ฆฌ์ฆ˜ II์— ๊ธฐ์ดˆํ•˜์—ฌ ๋…น์ง€์˜ ์ƒํƒœ์  ์—ฐ๊ฒฐ์„ฑ ์ฆ์ง„, ์—ด์„ฌ ํšจ๊ณผ ์™„ํ™”์™€ ๊ฐ™์€ ๋‹ค์–‘ํ•œ ํšจ๊ณผ์™€ ์„ค์น˜์— ๋”ฐ๋ฅด๋Š” ๋น„์šฉ์„ ์ข…ํ•ฉ์ ์œผ๋กœ ๊ณ ๋ คํ•˜์—ฌ ์ ์ ˆํ•œ ๋…น์ง€์˜ ์œ ํ˜•๊ณผ ์œ„์น˜๋ฅผ ๊ฒฐ์ •ํ•œ ๋…น์ง€๊ณ„ํš์•ˆ์„ ์ œ์‹œํ•˜์˜€๋‹ค. ๋ธ”๋ก ๊ทœ๋ชจ์˜ ๊ฐ€์ƒ์˜ ๋Œ€์ƒ์ง€์— ๋ณธ ์ตœ์ ํ™” ๋ชจ๋ธ์„ ์ ์šฉํ•จ์œผ๋กœ์จ 30๊ฐœ์˜ ํŒŒ๋ ˆํ†  ์ตœ์  ๋…น์ง€๊ณ„ํš์•ˆ์„ ์ƒ์„ฑํ•˜์˜€์œผ๋ฉฐ, ๊ฐ ๋ชฉ์  ๊ฐ„ ํผํฌ๋จผ์Šค๋ฅผ ๋น„๊ตํ•˜์—ฌ ๋…น์ง€์˜ ์—ด์„ฌ ์™„ํ™” ํšจ๊ณผ์™€ ์ƒํƒœ์  ์—ฐ๊ฒฐ์„ฑ ์ฆ์ง„ ํšจ๊ณผ ๊ฐ„์˜ ์ƒ์Šน ๊ด€๊ณ„ (synergistic relationship), ์ด๋Ÿฌํ•œ ๊ธ์ •์  ํšจ๊ณผ์™€ ๋น„์šฉ ์ ˆ๊ฐ ๊ฐ„์˜ ์ƒ์‡„ ํšจ๊ณผ (trade-off relationship)๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋˜ํ•œ ๋‹ค์–‘ํ•œ ๊ณ„ํš์•ˆ ์ค‘ ๋Œ€ํ‘œ์ ์ธ ํŠน์„ฑ์„ ์ง€๋‹ˆ๋Š” ๊ณ„ํš์•ˆ, ๋‹ค์ˆ˜์˜ ๊ณ„ํš์•ˆ์—์„œ ๊ณตํ†ต์ ์œผ๋กœ ๋…น์ง€ ์„ค์น˜๋ฅผ ์œ„ํ•ด ์„ ํƒ๋œ ์ฃผ์š” ํ›„๋ณด์ง€์—ญ ์—ญ์‹œ ๊ทœ๋ช…ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ ์ œ์‹œ๋œ ๋ชจ๋ธ์€ ๊ณ„ํš์•ˆ์˜ ์ˆ˜์ •์—์„œ๋ถ€ํ„ฐ ์ •๋Ÿ‰์  ํ‰๊ฐ€, ๊ณ„ํš์•ˆ ์„ ํƒ์— ์ด๋ฅด๋Š” ์ผ๋ จ์˜ ๊ธ์ •์ ์ธ ํ”ผ๋“œ๋ฐฑ ๊ณผ์ •์„ ์ˆ˜์—†์ด ๋ฐ˜๋ณตํ•จ์œผ๋กœ์จ ๊ธฐ์กด์˜ ๋…น์ง€๊ณ„ํš ๊ณผ์ •์„ ๊ฐœ์„ ํ•˜๋Š” ๋ฐ ๊ธฐ์—ฌํ•  ์ˆ˜ ์žˆ์„ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๋ชจ๋ธ์˜ ๊ฒฐ๊ณผ ์—ญ์‹œ ๋‹ค์ž๊ฐ„ ํ˜‘๋ ฅ์  ๋””์ž์ธ (co-design)์„ ์œ„ํ•œ ์ดˆ์•ˆ์œผ๋กœ์„œ ํ™œ์šฉ๋  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ์˜ˆ์ƒํ•˜์˜€๋‹ค.The meeting of heterogeneous goals while staying within the constraints of spatial planning is a nonlinear problem that cannot be solved by linear methodologies. Instead, this problem can be solved using multi-objective optimization algorithms such as genetic algorithms (GA), simulated annealing (SA), ant colony optimization (ACO), etc., and research related to this field has been increasing rapidly. GA, in particular, are the most frequently applied spatial optimization algorithms and are known to search for a good solution within a reasonable time period by maintaining a balance between exploration and exploitation. However, despite its good performance and applicability, it has not adequately addressed recent urgent issues such as climate change adaptation, disaster management, and green infrastructure planning. It is criticized for concentrating on only the allocation of specific land use such as urban and protected areas, or on the site selection of a specific facility. Therefore, in this study, a series of spatial optimizations are proposed to address recent urgent issues such as climate change, disaster management, and urban greening by supplementing quantitative assessment methodologies to the spatial planning process based on GA and Non-dominated Sorting Genetic Algorithm II (NSGA II). This optimization model needs to be understood as a tool for providing a draft plan that quantitatively meets the essential requirements so that the stakeholders can collaborate smoothly in the planning process. Three types of spatial planning optimization models are classified according to urgent issues. Spatial resolution, planning objectives, and constraints were also configured differently according to relevant issues. Each spatial planning optimization model was arranged in the order of increasing spatial resolution. In the first chapter, the optimization model was proposed to simulate land use scenarios to adapt to climate change on a provincial scale. As climate change is an ongoing phenomenon, many recent studies have focused on adaptation to climate change from a spatial perspective. However, little is known about how changing the spatial composition of land use could improve resilience to climate change. Consideration of climate change impacts when spatially allocating land use could be a useful and fundamental long-term adaptation strategy, particularly for regional planning. Here climate adaptation scenarios were identified on the basis of existing extents of three land use classes using Multi-objective Genetic Algorithms (MOGA) for a 9,982 km2 region with 3.5 million inhabitants in South Korea. Five objectives were selected for adaptation based on predicted climate change impacts and regional economic conditions: minimization of disaster damageand existing land use conversionmaximization of rice yieldprotection of high-species-richness areasand economic value. The 17 Pareto land use scenarios were generated by six weighted combinations of the adaptation objectives. Most scenarios, although varying in magnitude, showed better performance than the current spatial land use composition for all adaptation objectives, suggesting that some alteration of current land use patterns could increase overall climate resilience. Given the flexible structure of the optimization model, it is expected that regional stakeholders would efficiently generate other scenarios by adjusting the model parameters (weighting combinations) or replacing the input data (impact maps) and selecting a scenario depending on their preference or a number of problem-related factors. In the second chapter, the optimization model was proposed to simulate land use scenarios for managing disaster damage due to climate change on local scale. Extreme landslides triggered by rainfall in hilly regions frequently lead to serious damage, including casualties and property loss. The frequency of landslides may increase under climate change, because of the increased variability of precipitation. Developing urban areas outside landslide risk zones is the most effective method of reducing or preventing damageplanning in real life is, however, a complex and nonlinear problem. For such multi-objective problems, GA may be the most appropriate optimization tool. Therefore, comprehensive land use allocation plans were suggested using the NSGA II to overcome multi-objective problems, including the minimization of landslide risk, minimization of change, and maximization of compactness. The study area is Pyeongchang-gun, the host city of the 2018 Winter Olympics in Korea, where high development pressure has resulted in an urban sprawl into the hazard zone that experienced a large-scale landslide in 2006. We obtained 100 Pareto plans that are better than the actual land use data for at least one objective, with five plans that explain the trade-offs between meeting the first and the second objectives mentioned above. The results can be used by decision makers for better urban planning and for climate change-related spatial adaptation. In the third chapter, the optimization model was proposed to simulate urban greening plans on a neighborhood scale. Green space is fundamental to the good quality of life of residents, and therefore urban planning or improvement projects often include strategies directly or indirectly related to greening. Although green spaces generate positive effects such as cooling and reduction of rainwater runoff, and are an ecological corridor, few studies have examined the comprehensive multiple effects of greening in the urban planning context. To fill this gap in this fields literature, this study seeks to identify a planning model that determines the location and type of green cover based on its multiple effects (e.g., cooling and enhancement of ecological connectivity) and the implementation cost using NSGA II. The 30 Pareto-optimal plans were obtained by applying our model to a hypothetical landscape on a neighborhood scale. The results showed a synergistic relationship between cooling and enhancement of connectivity, as well as a trade-off relationship between greenery effects and implementation cost. It also defined critical lots for urban greening that are commonly selected in various plans. This model is expected to contribute to the improvement of existing planning processes by repeating the positive feedback loop: from plan modification to quantitative evaluation and selection of better plans. These optimal plans can also be considered as options for co-design by related stakeholders.1. INTRODUCTION 2. CHAPTER 1: Modelling Spatial Climate Change Land use Adaptation with Multi-Objective Genetic Algorithms to Improve Resilience for Rice Yield and Species Richness and to Mitigate Disaster Risk 2.1. Introduction 2.2. Study area 2.3. Methods 2.4. Results 2.5. Discussion 2.6. References 2.7. Supplemental material 3. CHAPTER 2: Multi-Objective Land-Use Allocation Considering Landslide Risk under Climate Change: Case Study in Pyeongchang-gun, Korea 3.1. Introduction 3.2. Material and Methods 3.3. Results 3.4. Discussion 3.5. Conclusion 3.6. References 4. CHAPTER 3: Multi-Objective Planning Model for Urban Greening based on Optimization Algorithms 3.1. Introduction 3.2. Methods 3.3. Results 3.4. Discussion 3.5. Conclusion 3.6. References 3.7. Appendix 5. CONCLUSION REFERENCESDocto

    Particle Swarm Optimization

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    Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

    Evolutionary Computation 2020

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    Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms

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    This doctoral dissertation presents the most important concepts of multi-objective optimization and a systematic review of the most cited articles in the last years of this subject in mechanical engineering. The State of the Art shows a trend towards the use of metaheuristics and the use of a posteriori decision-making techniques to solve engineering problems. This fact increases the demand for algorithms, which compete to deliver the most accurate answers at the lowest possible computational cost. In this context, a new hybrid multi-objective metaheuristic inspired by lightning and Linchtenberg Figures is proposed. The Multi-objective Lichtenberg Algorithm (MOLA) is tested using complex test functions and explicit contrainted engineering problems and compared with other metaheuristics. MOLA outperformed the most used algorithms in the literature: NSGA-II, MOPSO, MOEA/D, MOGWO, and MOGOA. After initial validation, it was applied to two complex and impossible to be analytically evaluated problems. The first was a design case: the multi-objective optimization of CFRP isogrid tubes using the finite element method. The optimizations were made considering two methodologies: i) using a metamodel, and ii) the finite element updating. The last proved to be the best methodology, finding solutions that reduced at least 45.69% of the mass, 18.4% of the instability coefficient, 61.76% of the Tsai-Wu failure index and increased by at least 52.57% the natural frequency. In the second application, MOLA was internally modified and associated with feature selection techniques to become the Multi-objective Sensor Selection and Placement Optimization based on the Lichtenberg Algorithm (MOSSPOLA), an unprecedented Sensor Placement Optimization (SPO) algorithm that maximizes the acquired modal response and minimizes the number of sensors for any structure. Although this is a structural health monitoring principle, it has never been done before. MOSSPOLA was applied to a real helicopterโ€™s main rotor blade using the 7 best-known metrics in SPO. Pareto fronts and sensor configurations were unprecedentedly generated and compared. Better sensor distributions were associated with higher hypervolume and the algorithm found a sensor configuration for each sensor number and metric, including one with 100% accuracy in identifying delamination considering triaxial modal displacements, minimum number of sensors, and noise for all blade sections.Esta tese de doutorado traz os conceitos mais importantes de otimizaรงรฃo multi-objetivo e uma revisรฃo sistemรกtica dos artigos mais citados nos รบltimos anos deste tema em engenharia mecรขnica. O estado da arte mostra uma tendรชncia no uso de meta-heurรญsticas e de tรฉcnicas de tomada de decisรฃo a posteriori para resolver problemas de engenharia. Este fato aumenta a demanda sobre os algoritmos, que competem para entregar respostas mais precisas com o menor custo computacional possรญvel. Nesse contexto, รฉ proposta uma nova meta-heurรญstica hรญbrida multi-objetivo inspirada em raios e Figuras de Lichtenberg. O Algoritmo de Lichtenberg Multi-objetivo (MOLA) รฉ testado e comparado com outras metaheurรญsticas usando funรงรตes de teste complexas e problemas restritos e explรญcitos de engenharia. Ele superou os algoritmos mais utilizados na literatura: NSGA-II, MOPSO, MOEA/D, MOGWO e MOGOA. Apรณs validaรงรฃo, foi aplicado em dois problemas complexos e impossรญveis de serem analiticamente otimizados. O primeiro foi um caso de projeto: otimizaรงรฃo multi-objetivo de tubos isogrid CFRP usando o mรฉtodo dos elementos finitos. As otimizaรงรตes foram feitas considerando duas metodologias: i) usando um meta-modelo, e ii) atualizaรงรฃo por elementos finitos. A รบltima provou ser a melhor metodologia, encontrando soluรงรตes que reduziram pelo menos 45,69% da massa, 18,4% do coeficiente de instabilidade, 61,76% do TW e aumentaram em pelo menos 52,57% a frequรชncia natural. Na segunda aplicaรงรฃo, MOLA foi modificado internamente e associado a tรฉcnicas de feature selection para se tornar o Seleรงรฃo e Alocaรงรฃo รณtima de Sensores Multi-objetivo baseado no Algoritmo de Lichtenberg (MOSSPOLA), um algoritmo inรฉdito de Otimizaรงรฃo de Posicionamento de Sensores (SPO) que maximiza a resposta modal adquirida e minimiza o nรบmero de sensores para qualquer estrutura. Embora isto seja um princรญpio de Monitoramento da Saรบde Estrutural, nunca foi feito antes. O MOSSPOLA foi aplicado na pรก do rotor principal de um helicรณptero real usando as 7 mรฉtricas mais conhecidas em SPO. Frentes de Pareto e configuraรงรตes de sensores foram ineditamente geradas e comparadas. Melhores distribuiรงรตes de sensores foram associadas a um alto hipervolume e o algoritmo encontrou uma configuraรงรฃo de sensor para cada nรบmero de sensores e mรฉtrica, incluindo uma com 100% de precisรฃo na identificaรงรฃo de delaminaรงรฃo considerando deslocamentos modais triaxiais, nรบmero mรญnimo de sensores e ruรญdo para todas as seรงรตes da lรขmina

    Artificial Neural Networks in Agriculture

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    Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible
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