Modeling And Simulation Of a Continious Folding Process Of An Origami Pattern

The engineering applications of origami has gathered tremendous attention in recent years. Various aspects of origami have different characteristics based on its application. The shape changing aspect is used in areas where size is a constraint. The structural rigidity aspect is utilized where strength is needed with a minimal increase in weight. When polymer or metal sheets are processed to have origami creases, they exhibit an improvement in mechanical properties. The sheets which create a specific local texture by means of tessellated folds patterns are called folded textured sheets[1]. These sheets are utilized to create fold cores. These light-weight sandwiched structures are heavily used in the aerospace industry, due to its ability to prevent moisture accumulation on the aeronautical structures at higher altitudes. The objective of the current research is to explore a new method for the continuous production of these folded textured sheets. The method uses a laser etching setup to mark the sheet with the origami pattern. The pattern is then formed by dies and passes through a conveyor system which is specifically arranged like a funnel to complete the final stage of the forming process. A simulation approach is utilized to evaluate the method. Results show the feasibility of the process along with its limitations. The design is made to be feasible for scaling up for large scale manufactur

EXTENDING ORIGAMI TECHNIQUE TO FOLD FORMING OF SHEET METAL PRODUCTS

This dissertation presents a scientific based approach for the analysis of folded sheet metal products. Such analysis initializes the examination in terms of topological exploration using set of graph modeling and traversal algorithms. The geometrical validity and optimization are followed by utilizing boundary representation and overlapping detection during a geometrical analysis stage, in this phase the optimization metrics are established to evaluate the unfolded sheet metal design in terms of its manufacturability and cost parameters, such as nesting efficiency, total welding cost, bend lines orientation, and maximum part extent, which aides in handling purposes. The proposed approach evaluates the design in terms of the stressed-based behavior to indicate initial stress performance by utilizing a structural matrix analysis while developing modification factors for the stiffness matrix to cope with the stress-based differences of the diverse flat pattern designs. The outcome from the stressed-based ranking study is mainly the axial stresses as exerted on each element of folded geometry; this knowledge leads to initial optimizing the flat pattern in terms of its stress-based behavior. Furthermore, the sheet folding can also find application in composites manufacturing. Thus, this dissertation optimizes fiber orientation based on the elasticity theory principles, and the best fiber alignment for a flat pattern is determined under certain stresses along with the peel shear on adhesively bonded edges. This study also explores the implementation of the fold forming process within the automotive production lines. This is done using a tool that adopts Quality Function Deployment (QFD) principle and Analytical Hierarchy Process (AHP) methodology to structure the reasoning logic for design decisions. Moreover, the proposed tool accumulates all the knowledge for specific production line and parts design inside an interactive knowledge base. Thus, the system is knowledge-based oriented and exhibits the ability to address design problems as changes occur to the product or the manufacturing process options. Additionally, this technique offers two knowledge bases; the first holds the production requirements and their correlations to essential process attributes, while the second contains available manufacturing processes options and their characteristics to satisfy the needs to fabricate Body in White (BiW) panels. Lastly, the dissertation showcases the developed tools and mathematics using several case studies to verify the developed system\u27s functionality and merits. The results demonstrate the feasibility of the developed methodology in designing sheet metal products via folding

Image and model based methods for mechanical phenotyping of cells in 3D

Cells and tissues are constantly exposed to mechanical forces. Understanding how these forces act on cells to regulate essential processes like development, differentiation, tissue homeostasis and how their alteration is related to disease requires the characterization of their mechanical properties. Several methods have been developed to study mechanical properties of cells and nuclei. However, most of the established methods are not com- patible with culturing of cells in 3D substrates, a factor which plays an essential role in defining the structural and mechanical behavior of cells naturally existing in 3D environ- ments. In this work, image and model based methods have been developed to approach this problem and enable the characterization of the cells mechanical phenotype in 3D. On a first step, a previously developed method to measure the compressibility of the nuclear interior was enhanced to enable statistical significant measurements of nuclei to perform comparative analyses between phenotypes. Optimization of both the experi- mental, as well as the image processing methods led to a robust framework that served to measure an increase in nuclear compressibility in nuclei of LMNA−/− mouse embryonic fibroblasts. This study served as a proof of principle for this contact free method, which in a subsequent step was adapted to work for cells embedded in 3D substrates. Aiming to develop a method, in which specific forces could be applied and relate to cellular deformations, the second part of this work was centered in the development of the 3D substrate stretcher. This involved identifying and implementing the needs of the experimental and image analysis framework to ensure the required environment for the cells, while at the same time enabling the acquisition of suitable data for the mechanical analysis. The resulting experimental and analysis framework enables for the first time application and quantification of strains on cells embedded in 3D substrates. Motivation of the 3D-culture based methods was the analysis of epithelial-mesenchymal transition (EMT) in hepatocytes. These epithelial cells undergoing dedifferentiation upon treatment with TGF-β serve not only as a preeminent example of the need of 3D cell cultures in the characterization of mechanical properties, but also as a model of malignant transformation in fibrotic diseases and cancer. Quantification of previously unobserved morphological and structural properties led to the mechanical phenotyping of these cells, where a decrease in the compressibility of the nuclear interior, an enhanced resistance to deformation and a better anchorage of the nuclei inside the cells was observed after EMT

Design and implementation of robust systems for secure malware detection

Malicious software (malware) have significantly increased in terms of number and effectiveness during the past years. Until 2006, such software were mostly used to disrupt network infrastructures or to show coders’ skills. Nowadays, malware constitute a very important source of economical profit, and are very difficult to detect. Thousands of novel variants are released every day, and modern obfuscation techniques are used to ensure that signature-based anti-malware systems are not able to detect such threats. This tendency has also appeared on mobile devices, with Android being the most targeted platform. To counteract this phenomenon, a lot of approaches have been developed by the scientific community that attempt to increase the resilience of anti-malware systems. Most of these approaches rely on machine learning, and have become very popular also in commercial applications. However, attackers are now knowledgeable about these systems, and have started preparing their countermeasures. This has lead to an arms race between attackers and developers. Novel systems are progressively built to tackle the attacks that get more and more sophisticated. For this reason, a necessity grows for the developers to anticipate the attackers’ moves. This means that defense systems should be built proactively, i.e., by introducing some security design principles in their development. The main goal of this work is showing that such proactive approach can be employed on a number of case studies. To do so, I adopted a global methodology that can be divided in two steps. First, understanding what are the vulnerabilities of current state-of-the-art systems (this anticipates the attacker’s moves). Then, developing novel systems that are robust to these attacks, or suggesting research guidelines with which current systems can be improved. This work presents two main case studies, concerning the detection of PDF and Android malware. The idea is showing that a proactive approach can be applied both on the X86 and mobile world. The contributions provided on this two case studies are multifolded. With respect to PDF files, I first develop novel attacks that can empirically and optimally evade current state-of-the-art detectors. Then, I propose possible solutions with which it is possible to increase the robustness of such detectors against known and novel attacks. With respect to the Android case study, I first show how current signature-based tools and academically developed systems are weak against empirical obfuscation attacks, which can be easily employed without particular knowledge of the targeted systems. Then, I examine a possible strategy to build a machine learning detector that is robust against both empirical obfuscation and optimal attacks. Finally, I will show how proactive approaches can be also employed to develop systems that are not aimed at detecting malware, such as mobile fingerprinting systems. In particular, I propose a methodology to build a powerful mobile fingerprinting system, and examine possible attacks with which users might be able to evade it, thus preserving their privacy. To provide the aforementioned contributions, I co-developed (with the cooperation of the researchers at PRALab and Ruhr-Universität Bochum) various systems: a library to perform optimal attacks against machine learning systems (AdversariaLib), a framework for automatically obfuscating Android applications, a system to the robust detection of Javascript malware inside PDF files (LuxOR), a robust machine learning system to the detection of Android malware, and a system to fingerprint mobile devices. I also contributed to develop Android PRAGuard, a dataset containing a lot of empirical obfuscation attacks against the Android platform. Finally, I entirely developed Slayer NEO, an evolution of a previous system to the detection of PDF malware. The results attained by using the aforementioned tools show that it is possible to proactively build systems that predict possible evasion attacks. This suggests that a proactive approach is crucial to build systems that provide concrete security against general and evasion attacks

Reconfigurable Kirigami Optics and Chiral Phonons

All atoms and chemical bonds vibrate at natural frequencies associated with their physical properties. Scientists therefore use resonance-based spectroscopy to investigate the structural characteristics and dynamics of molecules in several research areas. However, it has been arduous to observe and identify complex vibrational modes of biomolecules and tissues with excitations in the terahertz (THz) range. In this work, we report on the development of THz circular dichroism spectroscopy enabled by kirigami polarization modulator and their applications for probing mesoscale chiral architectures and vibrations from the (bio) materials. Also, we show that hyperspectral THz chiroptical spectroscopy enables registration and attribution of chiral phonons in microcrystals of numerous amino acids and dipeptides. Terahertz circular dichroism (TCD) offers multifaceted spectroscopic capabilities for understanding of biomaterials, biomolecules, and pharmaceuticals because the energy of THz photons enables probing the ‘soft’ oscillatory vibrations of biomolecules with distinct chirality. However, the lack of dynamic polarization modulators is impeding the proliferation of TCD spectroscopy. In the Chapter 3 of this dissertation, we show that tunable optical elements fabricated from patterned plasmonic sheets with periodic kirigami cuts make possible polarization modulation of THz radiation under application of mechanical strain. A herringbone pattern of microscale metal stripes enables dynamic range of polarization modulation exceeding 80 degree repeatable over thousands of cycles. Upon out-of-plane buckling, the plasmonic stripes function as reconfigurable semi-helices of variable pitch aligned along the THz propagation direction. Several biomaterials, exemplified by elytra of Chrysina gloriosa beetles, revealed distinct TCD fingerprints associated with the helical substructure in the bio-composites. Chiral phonons, complex lattice vibrations modes with mirror asymmetry, have been known only for a small number of low-dimensional inorganic nanostructures. Abundant chiral phonon modes can also be expected for crystals of many biomolecules but experimental and theoretical toolbox on their observation and identification is unknown. Besides much larger variety of vibrational modes, chiral phonons in biological crystals can also be medically relevant. In the Chapter 4 of the dissertation, we show that terahertz absorption (TA), circular dichroism (TCD), and optical rotation dispersion (TORD) provide effective tools for the registration and identification of chiral phonons in micro-crystals of 20 proteinogenic L- and D-amino acids (AAs). Theoretical predictions and molecular dynamics simulations of chiral phonon in AA crystals provided direct evidence for the molecular origins of TCD and TORD spectra, which are dominated by collective motions of AA molecules. Generality of these findings can be highlighted by the observation of chiral phonons in crystals of dipeptides cystine and carnosine, which also demonstrates direct relevance of chiral phonons for medical and pharmaceutical applications.PHDMaterials Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/168046/1/wonjchoi_1.pd

Shape Machine: shape embedding and rewriting in visual design

Shape grammar interpreters have been studied for more than forty years addressing several areas of design research including architectural, engineering, and product design. At the core of all these implementations, the operation of embedding – the ability of a shape grammar interpreter to search for subshapes in a geometry model even if they are not explicitly encoded in the database of the system – resists a general solution. It is suggested here that beyond a seemingly long list of technological hurdles, the implementation of shape embedding, that is, the implementation of the mathematical concept of the “part relation” between two shapes, or equivalently, between two drawings, or between a shape and a design, is the single major obstacle to take on. This research identifies five challenges underlying the implementation of shape embedding and shape grammar interpreters at large: 1) complex entanglement of the calculations required for shape embedding and a shape grammar interpreter at large, with those required by a CAD system for modeling and modifying geometry; 2) accumulated errors caused by the modeling processes of CAD systems; 3) accumulated errors caused by the complex calculations required for the derivation of affine, and mostly, perspectival transformations; 4) limited support for indeterminate shape embedding; 5) low performance of the current shape embedding algorithms for models consisting of a large number of shapes. The dissertation aims to provide a comprehensive engineering solution to all these five challenges above. More specifically, the five contributions of the dissertation are: 1) a new architecture to separate the calculations required for the shape embedding and replacement (appropriately called here Shape Machine) vs. the calculations required by a CAD system for the selection, instantiation, transformation, and combination of shapes in CAD modeling; 2) a new modeling calibration system to ensure the effective translation of geometrical types of shapes to their maximal representations without cumulative calculating errors; 3) a new dual-mode system of the derivation of transformations for shape embedding, including a geometric approach next to the known algebraic one, to implement the shape embedding relation under the full spectrum of linear transformations without the accumulated errors caused by the current algorithms; 4) a new multi-step mechanism that resolves all cases of indeterminate embeddings for shapes having fewer registration points than those required for a shape embedding under a particular type of transformation; and 5) a new data representation for hyperplane intersections, the registration point signature, to allow for the effective calculation of shape embeddings for complex drawings consisting of a large number of shapes. All modules are integrated into a common computational framework to test the model for a particular type of shapes – the shapes consisting of lines in the Euclidean plane in the algebra U12.Ph.D

Large bichromatic point sets admit empty monochromatic 4-gons

We consider a variation of a problem stated by Erd˝os and Szekeres in 1935 about the existence of a number fES(k) such that any set S of at least fES(k) points in general position in the plane has a subset of k points that are the vertices of a convex k-gon. In our setting the points of S are colored, and we say that a (not necessarily convex) spanned polygon is monochromatic if all its vertices have the same color. Moreover, a polygon is called empty if it does not contain any points of S in its interior. We show that any bichromatic set of n ≥ 5044 points in R2 in general position determines at least one empty, monochromatic quadrilateral (and thus linearly many).Postprint (published version

3D Object Recognition Based On Constrained 2D Views

The aim of the present work was to build a novel 3D object recognition system capable of classifying man-made and natural objects based on single 2D views. The approach to this problem has been one motivated by recent theories on biological vision and multiresolution analysis. The project's objectives were the implementation of a system that is able to deal with simple 3D scenes and constitutes an engineering solution to the problem of 3D object recognition, allowing the proposed recognition system to operate in a practically acceptable time frame. The developed system takes further the work on automatic classification of marine phytoplank- (ons, carried out at the Centre for Intelligent Systems, University of Plymouth. The thesis discusses the main theoretical issues that prompted the fundamental system design options. The principles and the implementation of the coarse data channels used in the system are described. A new multiresolution representation of 2D views is presented, which provides the classifier module of the system with coarse-coded descriptions of the scale-space distribution of potentially interesting features. A multiresolution analysis-based mechanism is proposed, which directs the system's attention towards potentially salient features. Unsupervised similarity-based feature grouping is introduced, which is used in coarse data channels to yield feature signatures that are not spatially coherent and provide the classifier module with salient descriptions of object views. A simple texture descriptor is described, which is based on properties of a special wavelet transform. The system has been tested on computer-generated and natural image data sets, in conditions where the inter-object similarity was monitored and quantitatively assessed by human subjects, or the analysed objects were very similar and their discrimination constituted a difficult task even for human experts. The validity of the above described approaches has been proven. The studies conducted with various statistical and artificial neural network-based classifiers have shown that the system is able to perform well in all of the above mentioned situations. These investigations also made possible to take further and generalise a number of important conclusions drawn during previous work carried out in the field of 2D shape (plankton) recognition, regarding the behaviour of multiple coarse data channels-based pattern recognition systems and various classifier architectures. The system possesses the ability of dealing with difficult field-collected images of objects and the techniques employed by its component modules make possible its extension to the domain of complex multiple-object 3D scene recognition. The system is expected to find immediate applicability in the field of marine biota classification