Cycle-Based Robot Drive Train Optimization Utilizing SVD Analysis

Y-stiffened panel multi-objective optimization using genetic algorithm

Various optimization problems have been studied for the purpose of structural morphogenesis by many researchers. Structural optimization problems can be classified into three different sub-problems, namely size, shape and topology optimization problem. Size optimization problem is consisted of small sets of design variables. The shape and topology of a structure are defined by a set of design variables, and these design variables are adjusted to achieve given objectives, such as minimum volume. Such optimization problems can be solved iteratively, using gradient-based techniques. Introducing more design variables increases the complexity of the optimization problem. Therefore, it becomes difficult to solve the optimization problem by using MP techniques with large sets of design variables. Heuristics can be improved the difficulty. There are many studies using heuristics like genetic algorithm and simulated annealing. An effective method for structural morphogenesis inspired by self-organization phenomena is presented in this paper. Self-organization is a phenomenon that an entire structure gradually emerges by interaction between elements of the structure, and the elements are affected by the entire structure. Since the self-organization algorithm consists of simple calculation iteration, it can be applied to problems with large number of design variables. Proposed method is applied to problems of uniform member length, minimization of strain energy and cross-section design for frame structures, and the effectiveness is demonstrated through some examples. On the other hand, the number of projects incorporating computational design has increased in recent year, and designs with complicated forms composed of free surfaces are also increasing. In order to design such a shape, the designer needs to consider the rationality of the structure from the initial stage. So, it seems necessary to develop simple software for the structural morphogenesis. Therefore, we developed components of Grasshopper that works within Rhinoceros, so that it will be possible to reassemble algorithms in an intuitive way for designers who have never experienced programming. Grasshopper is one of Graphical Algorithm Editor (GAE), and can be visually constructed an algorithm by connecting components that is a function of modeling. Analysis by gradient-based MP techniques often involves jumping of solutions and analytical instability peculiar to non-linear problems, and it is difficult to apply to the computer aided systems as described above. Analysis algorithms based on self-organizing algorithms are considered to be suitable for the above system due to their high robustness.

The role of subsurface temperature variability in modulating El Niño–Southern Oscillation (ENSO) properties is examined using an intermediate coupled model (ICM), consisting of an intermediate dynamic ocean model and a sea surface temperature (SST) anomaly model. An empirical procedure is used to parameterize the temperature of subsurface water entrained into the mixed layer (Te) from sea level (SL) anomalies via a singular value decomposition (SVD) analysis for use in simulating sea surface temperature anomalies (SSTAs). The ocean model is coupled to a statistical atmospheric model that estimates wind stress anomalies also from an SVD analysis. Using the empirical Te models constructed from two subperiods, 1963–79 (T63–79e) and 1980–96 (T80–96e), the coupled system exhibits strikingly different properties of interannual variability (the oscillation period, spatial structure, and temporal evolution). For the T63–79e model, the system features a 2-yr oscillation and westward propagation of SSTAs on the equator, while for the T80–96e model, it is characterized by a 5-yr oscillation and eastward propagation. These changes in ENSO properties are consistent with the behavior shift of El Niño observed in the late 1970s. Heat budget analyses further demonstrate a controlling role played by the vertical advection of subsurface temperature anomalies in determining the ENSO properties.

Cardiac fibrillation is a major clinical and societal burden. Rotors may drive fibrillation in many cases, but their role and patterns are often masked by complex propagation. We used Singular Value Decomposition (SVD), which ranks patterns of activation hierarchically, together with Wiener-Granger causality analysis (WGCA), which analyses direction of information among observations, to investigate the role of rotors in cardiac fibrillation. We hypothesized that combining SVD analysis with WGCA should reveal whether rotor activity is the dominant driving force of fibrillation even in cases of high complexity. Optical mapping experiments were conducted in neonatal rat cardiomyocyte monolayers (diameter, 35 mm), which were genetically modified to overexpress the delayed rectifier K+ channel IKr only in one half of the monolayer. Such monolayers have been shown previously to sustain fast rotors confined to the IKr overexpressing half and driving fibrillatory-like activity in the other half. SVD analysis of the optical mapping movies revealed a hierarchical pattern in which the primary modes corresponded to rotor activity in the IKr overexpressing region and the secondary modes corresponded to fibrillatory activity elsewhere. We then applied WGCA to evaluate the directionality of influence between modes in the entire monolayer using clear and noisy movies of activity. We demonstrated that the rotor modes influence the secondary fibrillatory modes, but influence was detected also in the opposite direction. To more specifically delineate the role of the rotor in fibrillation, we decomposed separately the respective SVD modes of the rotor and fibrillatory domains. In this case, WGCA yielded more information from the rotor to the fibrillatory domains than in the opposite direction. In conclusion, SVD analysis reveals that rotors can be the dominant modes of an experimental model of fibrillation. Wiener-Granger causality on modes of the rotor domains confirms their preferential driving influence on fibrillatory modes.

This paper deals with joint penalization and material selection in frame topology optimization. The models used in this study are frame structures with flexible joints. The problem considered is to find the frame design which fulfills a stiffness requirement at the lowest structural weight. To support topological change of joints, each joint is modelled as a set of subelements. A set of design variables are applied to each beam and joint subelement. Two kinds of design variables are used. One of these variables is an area-type design variable used to control the global element size and support a topology change. The other variables are length ratio variables controlling the cross section of beams and internal stiffness properties of the joints. This paper presents two extensions to classical frame topology optimization. Firstly, penalization of structural joints is presented. This introduces the possibility of finding a topology with less complexity in terms of the number of beam connections. Secondly, a material interpolation scheme is introduced to support mixed material design.

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

In this paper we develop an as-yet-missing theoretical framework as well as a general methodology for model-based robust design. At the outset, a distinction is made between three sets: the set of design variables, grouped in the n-dimensional vector x, which are to be assigned values as an outcome of the design job; the set of design-environment parameters (DEP), grouped in the v-dimensional vector p, over which the designer has no control; and the set of performance functions, arrayed in the m-dimensional vector f, representing the functional relations among performance, design variables and DEP. Resorting to the mathematical model available for the object under design, an m × v design performance matrix F, mapping the space of relative variations of p into that of relative variations of f, is derived. Moreover, two pertinent concepts are introduced: the design sensitivity matrix, which plays a major role in the transmission of the variations of p into variations off, and its associated bandwidth, defined as the logarithm of the square root of the ratio between the maximum to the minimum singular values of the design performance matrix, measured in decades. A result stating the relation between the bandwidth of a matrix and its inverse is shown. Consequently, the aforementioned bandwidth represents an index for evaluating the robustness of a design. To demonstrate our approach, case studies are included

Discussion of this paper is invited. Three copies of any discussion should be sent to the Society of Petroleum Engineers office. Such discussion may be presented at the above meeting and, with the paper, may be considered for publication in one of the two SPE magazines. Abstract This paper explains how 9 5/8" turbine diamond bit designs for the North Sea were optimized through the combined results of practical field developments and analytical practical field developments and analytical design studies. Various mechanical characteristics such as bit face contour, diamond size, and diamond pattern were analyzed with the aid of the diamond drilling theory, taking into account the particular conditions encountered in North Sea drilling. The mathematical model for rotary and turbine diamond drilling as used in this study was developed during recent years, and is now considered an effective aid to design optimization. The drilling theory is based on previously reported studies on the cutting actions of single diamonds in rocks, now expanded to encompass the total effect of all diamonds on the cutting surfaces of the bit. Using specific drilling conditions and rock properties, expressions for bit torque, properties, expressions for bit torque, penetration rate, rotary power and other penetration rate, rotary power and other performance characteristics are obtained in performance characteristics are obtained in terms of bit weight, rotary speed and the design parameters, like diamond size, fluid handling characteristics and geometric factors. The evolution of the bit designs leads to a bit style which, within the known limitations on diamonds, has best theoretical performance and best field performance. Introduction In view of the expanding use and importance of turbo diamond drilling techniques, particularly in modern offshore development, particularly in modern offshore development, it is important to study the way in which diamond bits work under the specific conditions imposed by turbines, and to use this knowledge to improve the performance of the diamond tools. In this study, several practical turbo diamond bit design approaches are practical turbo diamond bit design approaches are studied analytically in an effort to isolate the best performing or "optimum" combination of bit design variables. The goal of optimization is to find the particular set of design variables which yields the highest penetration rate under the selected operating conditions, without violating any of the assumed practical limitations on loading and application of the diamonds, the diamond bits and the turbines.

Aircraft manufacturing companies have to consider multiple derivatives to satisfy various market requirements. They modify or extend an existing aircraft to meet new market demands while keeping the development time and cost to a minimum. Many researchers have studied the derivative design process, but these research efforts consider baseline and derivative designs together, while using the whole set of design variables. Therefore, an efficient process that can reduce cost and time for aircraft derivative design is needed. In this research, a more efficient design process is proposed which obtains global changes from local changes in aircraft design in order to develop aircraft derivatives efficiently. Sensitivity analysis was introduced to remove unnecessary design variables that have a low impact on the objective function. This prevented wasting computational effort and time on low priority variables for design requirements and objectives. Additionally, uncertainty from the fidelity of analysis tools was considered in design optimization to increase the probability of optimization results. The Reliability Based Design Optimization (RBDO) and Possibility Based Design Optimization (PBDO) methods were proposed to handle the uncertainty in aircraft conceptual design optimization. In this paper, Collaborative Optimization (CO) based framework with RBDO and PBDO was implemented to consider uncertainty. The proposed method was applied for civil jet aircraft derivative design that increases cruise range and the number of passengers. The proposed process provided deterministic design optimization, RBDO, and PBDO results for given requirements.

A singular value decomposition (SVD) analysis between the Northern Hemisphere winter-mean 500-hPa geopotential height (Z500) and near-global surface temperature (land-surface air temperature + sea surface temperature) fields was conducted for the recent three decades. The leading mode, referred to as Polar/Eurasia (PEA), is considered as a hemispheric-wide atmospheric internal variability rather than a local teleconnection pattern. Winter mean Z500 of the leading mode is closely related to Eurasian surface air temperature from autumn to spring throughout the period. Both interannual and interdecadal time scales are dominant during the three decades in the time scales. No linear relationship between the tropical SST anomalies is found in this mode. The second and third modes can be identified as the Pacific/North American (PNA) and Western Pacific (WP)-type teleconnection patterns, respectively. Although they are also considered as internal modes of atmospheric variabilities, their interannual variabilities are also influenced by the tropical Pacific SST anomalies. These results are compared with the more popular SVDanalysis between geopotential height and SST, for which the leading mode exhibits the so-called "ENSO (El Niño/Southern Oscillation) related" winter circulation pattern. It is shown that this ENSO-related mode can be reconstructed from the linear combination of the second (PNA) and third (WP) modes of the present SVD mentioned above. These results suggest that inter-El Niño differences of atmospheric circulation in midlatitudes can be interpreted as the different strengths of the PNA and WP patterns on one hand, and on the other hand by the different magnitude of the PEA, which is linearly independent of ENSO. The characteristics of the vertical structure of the atmospheric variability were further examined by conducting a SVD analysis of 100-hPa temperature and Z500. The PEA and PNA patterns in the Z500 field extracted as the first and second SVD modes, respectively, exhibit contrasting features in association with lower stratospheric temperature. The PNA pattern is confined to the troposphere, while PEA extends further into the stratosphere. This suggests they are produced by different mechanisms.

Traditionally, computational design optimization of commercial aircraft is performed by considering a small number of representative operating conditions. These conditions are based on the design Mach number, altitude, payload, and range for which the aircraft will be flown. However, the design also influences which routes and mission parameters are optimal, and so there is coupling that is ignored when using the traditional approach. Here, the aircraft design, mission profiles, and the allocation of aircraft to routes in an airline network are simultaneously optimized. This is a mixed-integer nonlinear programming problem that is reformulated as a nonlinear programming problem because of the large number of design variables. The reformulated problem is solved using a gradient-based optimization approach with a parallel computational framework that facilitates the multidisciplinary analysis and the derivative computation. A surrogate model is used for the computational fluid dynamics analysis that is retrained in each optimization iteration given the new set of shape design variables. The resulting optimization problem contains over 4,000 design variables and close to 14,000 constraints. The optimization results show a 2% increase in airline profit compared with the traditional multipoint optimization approach. The wing area increases to the upper bound, enabling a higher cruise altitude that improves propulsive efficiency. This study finds that simultaneously optimizing the allocation, mission, and design to maximize airline profit results in a different optimized wing design from that resulting from the multipoint optimization approach.

In this paper, we propose a method of genetic algorithm (GA) for information retrieval (IR) based on Singular Value Decomposition and Principal Component Analysis. The main difficulty in GA based IR system is processing of high dimensional input strings, as affects the performance in terms of retrieval time. In proposed work, we tried to reduce the high dimensional input data to low dimensional in order to improve retrieval time. Singular Value Decomposition (SVD) and Principal Component Analysis (PCA) computations are done on input data streams to produce reduced rank matrix approximation and orthogonal transformations. With this reduced matrix we got promising results in terms of computation time when GA is applied for IR. Experiments were performed on sample dataset of 2500 input documents and weighted vectors are generated. Information retrieval is done using two techniques PCA-GA and SVD-GA with all classical matching functions. It is found that PCA-GA performs well as compared to SVD-GA in terms of computational time.

A method is defined and tested for the characterization of agricultural land from multi-spectral imagery, based on singular value decomposition (SVD) and key vector analysis. The SVD technique, which bears a close resemblance to multivariate statistic techniques, has previously been successfully applied to problems of signal extraction for marine data and forestry species classification. In this study the SVD technique is used as a classifier for agricultural regions, using airborne Daedalus ATM data, with 1 m resolution. The specific region chosen is an experimental research farm in Bavaria, Germany. This farm has a large number of crops, within a very small region and hence is not amenable to existing techniques. There are a number of other significant factors which render existing techniques such as the maximum likelihood algorithm less suitable for this area. These include a very dynamic terrain and tessellated pattern soil differences, which together cause large variations in the growth characteristics of the crops. The SVD technique is applied to this data set using a multi-stage classification approach, removing unwanted land-cover classes one step at a time. Typical classification accuracy's for SVD are of the order of 85-100%. Preliminary results indicate that it is a fast and efficient classifier with the ability to differentiate between crop types such as wheat, rye, potatoes and clover. The results of characterizing 3 sub-classes of Winter Wheat are also shown.

To examine the mechanisms of electron injection to TiO2 in retinoic acid (RA) and carotenoic acids (CAs), including RA5, CA6, CA7, CA8, CA9, and CA11 having the number of conjugated double bonds n = 5, 6, 7, 8, 9, and 11, respectively, their subpicosecond time-resolved absorption spectra were recorded free in solution and bound to TiO2 nanoparticles in suspension. The time-resolved spectra were analyzed by singular-value decomposition (SVD) followed by global fitting based on an energy diagram consisting of the 3A(g)(-), 1B(u)(-), 1B(u)(+), and 2A(g)(-) singlet excited states, whose energies had been determined as functions of 1/(2n + 1) by the use of carotenoids with n = 9-13. It was found that electron injection took place from both the 1B(u)(+) and 2A(g)(-) states in RA5, CA6, CA7, and CA8, whereas only from the 1B(u)(+) state in CA9 and CA11. The electron-injection efficiencies were determined, by the use of the relevant time constants determined by the SVD and global-fitting analyses, to be in the following order: RA5 approximately CA6 < CA7 > CA8 > CA9 > CA11. To determine the mechanism of charge recombination via the T(1) state, submicrosecond time-resolved absorption spectra of RA5, CA6, CA7, and CA8 bound to TiO2 nanoparticles in suspension were recorded. The SVD and global-fitting analyses lead us to a new scheme, which includes the formation of the D(0)(*+) - T(1) complex followed by transformation to both the D(0)(*+) and T(1) states. On the other hand, their one-electron oxidation potentials were determined, and their singlet and triplet levels were scaled to the conduction band edge (CBE) of TiO2. The T(1) level was lower than, but closest to, the CBE in RA5, and it became lower in the order RA5, CA6, CA7, and CA8. Consistent with the energy gap between the CBE and the T(1) levels, the generation of the T(1) state (or in other words, charge recombination) decreased in the order RA5 > CA6 > CA7 > CA8.

Low-rank prior-based Fast-RPCA for clutter filtering and noise suppression in non-contrast ultrasound microvascular imaging