Size optimization of MEMS gyroscopes using substructuring

Formulation of the problem. Teaching the design of the environment, as well as practical achievements in this field of artistic creativity of teachers, students and graduates of Lviv National akademy of arts, have a thorough basis. Lviv school design of interiors has a long history, and the chair of design of the environment of Lviv National akademy of arts is the heir to the traditions and achievements of several generations of Lviv artists. It is important to reveal the process of formation in Lviv artistic design education, the theoretical foundations of the artistic organization of the subject environment surrounding a person, to identify and compare historical levels in the development of works, their technological and stylistic changes in time, investigate the relationship between different artistic techniques and options design at different stages. Analysis of recent research and publications. Important is the study of Alexander Noha on the theme of the Lviv design school. In his book 2001 «Ukrainian art-industrial ceramics in Galicia (1840-1940)» is considered a period of birth and development of industrial design in Lviv [10]. The activities of the architect Ivan Levynskyi and his school, especially in the art of ceramics important for the design of the environment, were covered in collective monographs: 2019 «Ivan Levynskyi. Impulse» [6] and 2020 «Ceramic code of Ivan Levynskyi» [7]. Architecture and design of public spaces in the structure of historical residential building of Lviv on the verge of the XIX-XX centuries became the object of scientific research in articles and in the candidate’s thesis Olga Lysenko [8]. Similar issues (secession design of the entrance spaces of Lviv buildings) are partially solved Yulia Bogdanova and Zhanna Komar in their 2014 book-album «Secession in Lviv» [24]. The analysis of the source base revealed that to date no comprehensive study of the development of the Lviv school of subject-spatial environment design has been carried out. This problem requires a multifaceted and synthetic approach. The purpose of the article: to consider the forming factors, the theoretical principles of the Lviv design of the environment and their practical implementation in 1872-1918, introduce new facts to scientific space, surnames of artists and their works. Study Results. For the first time there is a comprehensive scientific study of the stylistic evolution of the Lviv design of the subject-spatial environment – from the neo-styles of historicism to secession (art nouveau), modernized empire and early art déco. The universal essence of the secession provided it gradually, but continuously growing coverage of various types of creativity and became one of the main formative factors of a subject-spatial design in the capital of Galicia at the beginning of the twentieth century. The article reveals the formative factors of the design of the environment, illuminates the regularities of the emergence and establishment of artistic and design education and practical activities in this area of the creativity. The emergence of theoretical principles of artistic design in Lviv is shown, starting with articles and public speeches of Julian Zachariewicz. The history of Lviv design of the environment is systematized, in many aspects the works of individual artists of different nationalities are analyzed, their previously unknown works, which are introduced into scientific circulation for the first time, are revealed. In particular, the creative work in the field of design of W. Sadłowski, A. Zachariewicz, I. Levynskyi and his followers, T. Obmiński, M. Olszewski is studied. Getting to know the achievements of leading European art centers, Lviv design school has maintained its independence and originality. The peculiarity of its representatives was the complexity of the polyethnic structure, the multilayer of the topics and motives (the sources of which were diverse aspects of national life and the tradition of everyday life) and in the use of the treasury of regional cultural achievements. Conclusions. The analysis of creativity of artists of the Lviv school design of the environment showed its artistic integrity, revealed a large socio-cultural and artistic significance of objects of subject-spatial design of the period of 1872-1918.The historical change of styles in the design of public and residential interiors of Lviv, the formative factors of design, the achievements of teachers and graduates of the Artistic and industrial school are considered. Until recently, the existing idea of the Lviv art of design was significantly supplemented, and new facts and works were introduced into scientific circulation.

Visual communication design and space design is discussed in this paper the complementary relationship between, it is worth mentioning that the visual communication design can be a good blend of collision and space design, so the analysis of the blend mode in the design of visual communication design elements in space, and space visual design elements of law, more reasonable and effective use of visual communication design elements in space design activities for better design services, become a carrier capacity and environmental design in visual communication design concept, therefore, in the design of the two classify, between the two cannot be away from each other, can only promote better design complement each other. As is known to all, the most direct contact between human beings in this world is vision, also known as sight and sight, followed by hearing, smelling and tasting. Therefore, the more intuitive feelings of human beings will be conveyed by vision. In 1960, the term ＂visual communication＂ popular in the world design congress held in Japan, including printed newspapers and magazines, posters and other promotional material design, and television, movies, electronic billboards and other media, through the eyes to convey to the brain for modelling of expressive design is widely referred to as visual communication design, there's a phrase can express: show people design, design. Moreover, in the design of contemporary exhibition space, visual communication design elements are often used. These design elements not only have the function of transmitting information, but also have unlimited commercial value and propaganda effect. Left the concept of positioning, space design is only the so-called functional platter; Ignore the visual communication of visual language, no matter how good the environment will be due to graphic design style gap and left a failure. As we know, one of the most important functions of visual communication design elements is to help present the most complete visual effect in the spatial display. The organic connection between visual communication design and display space design not only harmonizes the relationship between two-dimensional plane and three-dimensional space environment, but also expands the cooperation between subjects and strengthens the research space between the two design disciplines. Only in this way can designers solve complex and comprehensive problems and help designers make full use of their advantages to create better works.

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.

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.

Our goal in this work is to develop analytical tools to support the definition of balanced and compatible sets of vehicle specifications in the early stages of vehicle development. In this paper, we discuss the development and application of a Technical Feasibility Model (TFM) that may be used in preliminary design to assess the technical feasibility and optimality of specified combinations of vehicle performance targets. For this paper, we have exercised the TFM specifically to explore the relationships between vehicle mass, vehicle performance measures, (such as acceleration, fuel efficiency, and interior roominess), and high-level vehicle design parameters (such as overall exterior dimensions, occupant positions, and selection of a powertrain). The TFM is developed by first applying a MultiObjective Genetic Algorithm to a multidisciplinary design framework to generate a set of Pareto-optimal design solutions, then applying response surface methods to generate a smooth mathematical representation of the Pareto set, and finally using geometric construction to analyze the position of a test point relative to the representation of the Pareto set. Results of this analysis include an assessment of the feasibility and optimality of the test point as well as a variety of projections from the test point to the representation of the Pareto set that may be used to identify opportunities for refining, relaxing, improving, or prioritizing performance specifications. The mapping between performance space and design space has been preserved, allowing for investigation of relationships between performance specifications and design variable settings.

Identification of design solutions for a built environment that caters to the human needs at all levels, and more specifically, to the needs of the clients and the society, is the main task addressed by architectural design. Architectural design is a prime example of a design task that is characterized by a high degree of complexity. Architectural design problems by definition entail relationships between decisions and objectives that are all but transparent. For the decision-maker to be able to guide design towards fulfilling objectives, a ‘closed-loop’ approach where variations in design solutions are generated and evaluated in an iterative process is employed. Due to the sheer number of alternative solutions to problems of even a moderate scale (due to combinatorial explosion), it is only feasible to iterate over a minuscule fraction of possible solutions. Design intuition of the professionals involved in design is a strong driving force behind the identification of design direction, in which alternatives are explored as part of the preliminary design process. This is an approach that depends on the human cognitive capabilities to navigate the design space and identify potentially promising solutions. Regardless, the complexity associated with architectural design often poses significant challenges to human cognition. Human cognition, while formidable in its ability to flexibly and efficiently navigate challenging environments, is faced with difficulties in addressing the complexity factors outlined previously, namely: the excessive (combinatorially explosive) number of potential solutions to architectural problems, the complex and non-linear relations between objects and their properties and the conflicting nature of design goals that architectural design entails. Thus, design professionals are often faced with the real threat that their decisions may be biased due to the natural limitations of human cognition acting in complex environments.<br/><br/>Due to the reasons highlighted above, a systematic approach to design space exploration must be undertaken, to maximize the potential for discovering optimal solutions to design problems. Due to the nature of such problems that entail multiple conflicting objectives, a single best solution is generally not attainable. Nonetheless, best-tradeoff solutions are distinguished and highly desirable for such multi-objective design problems. The field of Computational Intelligence, and within that in particular Evolutionary Computation-based (EC) intelligent approaches, offer a lucrative option as decision-support tools in design, as they are able to efficiently address the aforementioned proponents of design complexity. EC approaches are able to navigate the design space efficiently and systematically, considering multiple conflicting objectives and hard constraints, and being able to deal with arbitrary relations between design decision variables and design objectives. <br/><br/>In today's setting, products of architecture must lead the way to a sustainable and environmentally friendlier society. As such, the performance of buildings has become the main driving force behind the design process, being referred to as ``performance-driven design''. This initiative emphasizes the quantitative evaluation of a design's function in accordance with established design objectives, related to aspects such as energy performance, visual and thermal comfort, cost and environmental footprint, etc. Simulation-based tools that enable accurate design evaluation are gaining ground and offering valuable insight into the performance of buildings. Nonetheless, making decisions in this multi-objective environment is not trivial, and, as stipulated above, may be challenging to human cognition. Thus, in today’s setting where the quantitative performance of buildings keeps gaining ground, the research on the application of EC in architectural design is high on the scientific agenda.<br/><br/>Recognizing the impact design complexity has on architectural design and the potential that EC-based approaches offer in addressing it, this thesis proposes a comprehensive computational intelligence decision support system that combines components based on intelligence with ones based on cognition, with the ultimate aim of enabling decision-makers manage design complexity and improve decision making. In particular, this thesis adopts the theoretical standpoint that efficient navigation of an unknown environment assumes a fusion of intelligence and cognition. In this sense, and given the already widespread adoption of intelligent approaches (such as EC mentioned above), the main contribution of this thesis is to endow the intelligent approach with cognitive facilities, so as to improve its efficiency to the point that it is readily applicable to the early stages of the architectural design process.<br/><br/>Fusion of intelligent with cognitive approaches, as outlined in the approach proposed by this thesis, offers the unique advantage of a decision support approach that is both powerful, owing to the extensive capabilities of intelligent search algorithms, and flexible, owing to the extensive knowledge modeling capabilities of cognitive approaches. As such, it is uniquely suited to the early conceptual design stage where the need to explore large design spaces, flexibly redefine the design problem, and satisfy preferences that are not included in the primary design goals, are all paramount.<br/><br/>Thus, the word ``comprehensive'' as it appears on this thesis' title obtains a twofold meaning: On one hand comprehension as in the combination of computational intelligence and cognition in a single approach; on the other hand, as in \\textit{comprehension} of the environment, the result of an intelligent and cognitive approach to understanding.<br/><br/>Firstly, it seeks to address the excessive computational burden associated with the use of modern high-fidelity simulation software in architecture, to render computational optimization more approachable. There is a clear trend in modern design practice to employ accurate simulation-based performance assessment tools from the very early stages of design. The use of such tools provides a valuable advantage to the decision-maker, in endowing objective awareness regarding the performance of a design solution. On the other hand, such tools are associated with a heavy computational burden, which may limit their application to the conceptual design stage. There exist methods to alleviate the computational burden through the use of computational cognitive machine learning tools, also known as surrogate modeling. However, training of surrogate models can be time-consuming itself, thus limiting the application. This thesis proposes a surrogate model that is modular in that it considers each space of the building in question as a separate entity, encoded through generic variables, and as such promotes model reuse in different design cases.<br/><br/>Secondly, it seeks to advance the state of the art on post-Pareto decision support by proposing a cognitive machine-learning based approach that enables the decision-maker to combine near-optimality with preferences regarding concrete features of the design solution. Post-Pareto decision making is an important step of the decision-making process, that seeks to identify a best-tradeoff solution among the possible ones that best matches the decision-maker's preferences in terms of performance. Such preferences are termed second-order because they follow design objectives in terms of importance. Nonetheless, it is often in architectural design that preferences are expressed in terms of design properties and not performance. Due to the non-linearity between the objective function space and the decision variable space that dictates object properties, it is challenging to exercise decision making using second-order preferences. Here the contribution of this thesis is a machine cognitive approach that learns the underlying relationships between object properties, distinguishing those that are relevant when the object is optimal with respect to design objectives. In other words, only imposing relations that are relevant to achieve optimality, it enables the expression of preferences by the decision-maker that are minimally constrained.<br/><br/>The main output of this thesis is a comprehensive decision support framework; it is a framework, in the sense that it comprises a set of methods and implemented tools that seek to augment decision making in architectural design; it is termed comprehensive in that it employs computational cognition and machine learning to augment the intelligent decision support capabilities throughout the design decision support process. It is also generic and applicable as-is to a wide spectrum of architectural design problems. In the context of this thesis, validation of the proposed approach is performed mainly in case studies relevant to facade design, recognizing this design topic as a complexity-exhibiting exemplar in architectural design practice.

In order to meet people’s diverse, complex, and changeable living requirements and aesthetic requirements, it is necessary to rationalize the design of indoor three-dimensional space. The optimization of the indoor environmental space layout is affected by the energy consumption and rationality of functional areas, resulting in indoor environmental space The performance of the layout optimization system deteriorates. In order to improve the performance of the indoor environment spatial layout and meet the living needs of more people, this paper proposes an optimal design of a spatial layout based on multi-intelligence decision-making. The research results of the article show that (1) the average confidence and the average recognition time of logo set B are both smaller than those of logo set A , indicating that logo set B is easier to be recognized by users in the same commercial building environment and can be more quickly recognized. In terms of discrimination, the time used to correct the misrecognition of identification set B is shorter than that of identification set A , which means that the different types of identifications in identification set B are easier to distinguish. (2) The spatial positioning algorithm proposed in this paper has good reliability and practicability, high positioning accuracy, and small error for indoor spatial coordinate positioning. Compared with the traditional method, the projection method of this paper has a significantly smaller error, and the maximum error is only 0.272, which makes the indoor space design more rational. (3) Comparing the energy consumption coefficients of the three spatial layout optimization systems, it can be seen that the indoor environmental spatial layout optimization system based on multi-intelligence decision-making has the lowest energy consumption when optimizing the indoor environmental spatial layout, because the system is in the design process. The indoor environment space layout model is designed, which effectively reduces the energy consumption of the indoor environment space layout optimization. When the indoor environment space layout optimization system based on multi-intelligence decision-making is used to optimize the indoor environment space layout, the average optimization accuracy is 98.32%, while the average indoor environment space layout optimization accuracy of the space layout optimization system with curved shading space layout optimization is 42.2% %, the layout optimization of binocular stereo vision space. When optimizing the indoor space layout, the average optimization accuracy is 70.87%.

Design Space Exploration (DSE) techniques for complex embedded systems must cope with a huge variety of applications and target architectures as well as a wide spectrum of objectives and constraints. In particular, existing design automation approaches are either problem-independent, in that they do not exploit any knowledge about the optimization problem at hand, or are tailored to specific a priori assumptions about the problem and/or a specific set of design objectives. While the latter are only applicable within a very limited scope of design problems, the former may struggle to deliver high-quality solutions for problems with large design spaces and/or complex design objectives. As a remedy, we propose Importance-Guided Order Rearrangement (IGOR) as a novel approach for DSE of embedded systems. Instead of relying on an a priori problem knowledge, IGOR uses a machine-learning-inspired technique to dynamically analyze the importance of design decisions, i.e., the impact that these decisions—within the specific problem that is being optimized—have on the quality of explored problem solutions w.r.t. the given design objectives. Throughout the DSE, IGOR uses this information to guide the optimization towards the most promising regions of the design space. Experimental results for a variety of applications from different domains of embedded computing and for different optimization scenarios give evidence that the proposed approach is both scalable and adaptable, as it can be used for the optimization of systems described by several thousands constraints, where it outperforms both problem-specific and problem-independent optimization approaches and achieves ε-dominance improvements of up to 95%.

Presents a finite element formulation of the layout optimization and design sensitivity applied to doubly‐curved shells of revolution. The objectives of the optimization are to maximize buckling pressures and first‐ply‐failure pressures. The problem is formulated and solved with the use of geometrically non‐linear transverse shear shell theory. However, the optimization method proposed limits the sensitivity analysis to a geometrically linear problem. Focuses special attention on the formulation of the optimization problem taking into account various factors, such as the form of geometrical and physical relations, types of design variables and the finite element discretization. Demonstrates several numerical examples to illustrate the capability of the proposed optimization procedures.

This paper explores a new metamodeling framework that potentially collapses the computational explosion that characterizes the modeling of complex systems under a multiobjective setting. Under the new framework, a pseudo response surface is constructed for each design objective. This pseudo response surface has the unique property of being highly accurate in Pareto optimal regions, while being intentionally inaccurate in all other regions. In short, the response surface for each design objective is accurate only where it matters. Because the pseudo response surface is not accurate in all regions of the design space, the computational cost to construct it is dramatically reduced. An important distinguishing feature of the new framework is that the response surfaces for all the design objectives are constructed simultaneously, in order to identify Pareto regions for the multiobjective problem. The new framework supports the puzzling notion that it is possible to obtain more accuracy and radically more design space exploration capability by actually reducing the computation effort. This counterintuitive metamodeling paradigm shift makes it possible to identify highly competitive products and systems that are well beyond today’s state of the art.

This paper presents a design of thermally actuated dual mass MEMS gyroscope with 2-DoF drive mode oscillator. A complete process based simulation of the proposed design following design rules of commercially available low cost standard MEMS process; MetalMUMPs is included in this paper. Properties of electroplated Nickel are incorporated while modeling and simulations of proposed device. Complete modal analysis as well as dynamic transient analysis is carried out to verify design parameters of the device. Simulations indicate drive amplitude of 5.5 mum at 0.5 Vac making chevron an efficient actuator for gyroscopes instead of traditional electrostatic actuators. Active-passive mass configuration is used to achieve mechanical amplification of the sensing mass drive-mode amplitude increasing the sensitivity of the device. This configuration scheme also helped to achieve a wide bandwidth of 2.11 kHz to enhance our device robustness against structural parameters variations.

The main objective of this project is to design a MEMS Vibratory gyroscope. A gyroscope is a device used for measuring or maintaining orientation, based on the principle of angular momentum. MEMS gyroscopes are gaining popularity because of their high accuracy and low manufacturing costs in large quantities. Gyroscopes can also be used for detecting and monitoring Gait phase detection system (GPDS). In case of Gait phase movement asymmetry is one of the motor symptoms. Improper Gait phase is the disorder of the central nervous system due to Structural defect walking pattern. Gait phase of a person is important to know which plays a number of important physiological roles. Once Improper Gait phase detected at an early stage by the use of MEMS vibrating gyroscopes, patients are alerted with signals in order to prevent the fall.

In a typical engineering design, there are often many design parameters to consider. Also, there are multiple competing requirements and objectives to meet. Manual approach of adjusting the parameters to achieve specific objectives is not optimal especially as the design becomes complex. In the quest for optimizing complex engineering systems, the exploration of the design space becomes imperative, especially when dealing with multi-objective systems characterized by an array of independent variables. This paper presents a comprehensive study on the design space mapping of complex engineering systems, utilizing a turboshaft engine as a case study. The initial phase of our methodology employs a physics-based model to generate synthetic dataset, reflecting the intricate interplay of various system parameters underpinning the engine's operation. This synthesized data serves as a foundation for the subsequent development of a Machine Learning or Deep Learning based surrogate model. The surrogate AI model, will be crafted to encapsulate multiple inputs and outputs inherent in the turboshaft engine's functioning, thereby facilitating an efficient and accurate exploration of the design space. Through this investigation, we will evaluate the efficacy of combining physics-based models with AI-driven techniques in mapping the design space of multi-objective systems. The core of our investigation revolves around the utilization of the AI surrogate model for achieving multi-objective optimization. This optimization process is not only focused on enhancing specific performance metrics but is also geared towards identifying a comprehensive family of feasible design solutions. Such an approach enables the delineation of the entire design space, offering invaluable insights into the trade-offs and synergies among different design objectives. Through this methodology, our goal is to uncover a wide spectrum of viable design alternatives, thereby providing a robust framework for decision-making in the engineering design process.

Adaptive antenna applications demand high-performance, control and data intensive processing, and are deployed in energy constrained envi-ronments. Heterogeneous embedded systems that integrate several components such as reconfigurable devices, general purpose processors or DSPs, and memory onto a single system are attractive targets for implementing such applications. Such systems support various capabilities such as reconfiguration, dynamic voltage and frequency scaling, and low-power operating states. Such choices during application design result in a large design space that must be traversed efficiently. We propose an embedded system design environment that enables efficient traversal of a large design space. The environment supports modeling of adaptive antenna applications, candidate hardware architectures, mapping information, performance requirements, and design constraints. Using the information captured in the models, the environment facilitates rapid design space exploration using optimization heuristics and hierarchical simulation. The design environment can be used to identify energy and latency efficient mappings for a given application and target hardware, or, if a set of hardware choices are given, select the most energy efficient hardware and the corresponding mapping. We illustrate the advantages of the design environment through efficient hardware selection for, and mapping of, an adaptive minimum variance distortionless response beamformer.KeywordsDesign SpaceSmart AntennaDesign Space ExplorationDynamic Voltage ScalingAutomate Target RecognitionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Mathematical models can be used as an integral part of the quality by design (QbD) concept throughout the product lifecycle for variety of purposes, including appointment of the design space and control strategy, continual improvement and risk assessment. Examples of different mathematical modeling techniques (mechanistic, empirical and hybrid) in the pharmaceutical development and process monitoring or control are provided in the presented review. In the QbD context, mathematical models are predominantly used to support design space and/or control strategies. Considering their impact to the final product quality, models can be divided into the following categories: high, medium and low impact models. Although there are regulatory guidelines on the topic of modeling applications, review of QbD-based submission containing modeling elements revealed concerns regarding the scale-dependency of design spaces and verification of models predictions at commercial scale of manufacturing, especially regarding real-time release (RTR) models. Authors provide critical overview on the good modeling practices and introduce concepts of multiple-unit, adaptive and dynamic design space, multivariate specifications and methods for process uncertainty analysis. RTR specification with mathematical model and different approaches to multivariate statistical process control supporting process analytical technologies are also presented.