181 research outputs found
Σύγχρονος σχεδιασμός της μεταλλικής κατασκευής πλοίων bulk carrier
174 σ.Στο πρώτο κεφάλαιο της διπλωματικής με τίτλο «σύγχρονος σχεδιασμός της μεταλλικής κατασκευής πλοίων bulk carrier», γίνεται προσπάθεια να εξοικειωθεί ο αναγνώστης με την έννοια της σχεδίασης της μεταλλικής του bulk carrier. Το δεύτερο κεφάλαιο αποτυπώνει τις βασικές αρχές που ακολουθούνται στο σχεδιασμό της μεταλλικής κατασκευής των χώρων φορτίου, ξεκινώντας από την περιγραφή της διαδικασίας, που είναι υποσύνολο της σπειροειδούς διαδικασίας κατά τη μελέτη του πλοίου.Το τρίτο κεφάλαιο είναι αφιερωμένο στον προσδιορισμό των φορτίων που επηρεάζουν την κατασκευή του πλοίου. Στο τέταρτο κεφάλαιο της διπλωματικής, γίνεται η παρουσίαση των υπολογισμών, σύμφωνα με τους κανονισμούς του Αμερικανικού Νηογνώμονα (ABS), για τη διαστασιολόγηση των στοιχείων που απαρτίζουν τη μεταλλική κατασκευή του bulk carrier, στην περιοχή της μέσης τομής. Το πέμπτο κεφάλαιο ασχολείται με την ανάλυση της αντοχής του πλοίου. Το έκτο κεφάλαιο επικεντρώνει στο συνολικό σχεδιασμό της μεταλλικής κατασκευής στην περιοχή των αμπαριών, όπως αυτή επηρεάζεται από τα λειτουργικά χαρακτηριστικά και τις ανάγκες που παρουσιάζονται κατά την επιχειρησιακή λειτουργία του πλοίου. Τέλος, στο έβδομο κεφάλαιο παρουσιάζονται όλα τα εναλλακτικά σχέδια της μορφής της μεταλλικής κατασκευής των bulk carrier που έχουν προταθεί τα περασμένα χρόνια, ενώ καταγράφονται και οι κυριότερες περιοχές καινοτομιών που μελετώνται και είναι υπό εξέταση η εφαρμογή τους στη λειτουργία του πλοίου.In the first chapter of this thesis an effort is being made to familiarize the reader on the concept of the bulk carrier design. The second chapter outlines the design principles followed in the structural design of the cargo spaces of a bulk carrier, starting from the structural design process, that is part of the design spiral. The third chapter mentions the determination of loads affecting the hull structure.
In the fourth chapter, the calculations for the structural components in the midship section area of a bulk carrier are presented, whereas the fifth chapter of this study copes with the strength analysis of the hull structure.
The sixth chapter copes with the overall design of the hold area in a bulk carrier, seen from an operational point of view. Finally, the seventh chapter outlines the main alternative designs that have been implemented last decades in bulk carrier structural design, whereas the major areas of concern for the design of the future are also listed.Σπανολιός Παντελεήμω
Classification SINGLE-LEAD ECG by using conventional neural network algorithm
Cardiac disease, including atrial fibrillation (AF), is one of the biggest causes of morbidity and mortality in the world, accounting for one third of all deaths. Cardiac modelling is now a well-established field. The Convolutional Neural Network (CNN) algorithm offer a valuable way of gaining insight into the dynamic behaviors of the heart, in normal and pathological conditions. Great efforts have been put into modelling the ventricles, whilst the atria have received less focus. This research therefore concentrates on developing models of the heart ECG atria using deep learning. The research developed an experimental result on MIT-BIH dataset for modelling myocyte electrophysiology and excitation waves in 1D & 2D tissues. It includes optimizations such as adaptive stimulus protocols. As examples of application, it is used to investigate effects of a novel anion bearing current on heart atrial excitation and the effect of remodeling on atrial myocyte electrical heterogeneity. A computationally efficient CNN anatomically based model of the heart atria is constructed. The 3D-CNN model includes heterogeneous, biophysically detailed electrophysiology and conduction anisotropy. The full model activates in 121 ms in heart rhythm, in close agreement with clinical ECG data. The model is used, with the toolkit, to investigate the function effects of S140G mutation in MIT-BIH dataset which is associated with familial. The 3D-CNN model forms the core of a boundary element model of the P-wave Body Surface Potential (BSP). The CNN model incorporates representations of the heart blood masses. Generated ECGs show qualitative agreement with clinical data. Their morphology is as expected for a healthy person, with a lead duration of 103 ms. The CNN model is used to verify an existing algorithm for focal atrial tachycardia location and in providing explanation for a novel clinical phenomenon, using CNN with 99.27% accuracy. Models of the human atria and body surface potential are constructed. The models are validated against both experimental and clinical data. These models are suitable to use as the platform for further research
The Construction of Nonseparable Wavelet Bi-Frames and Associated Approximation Schemes
Wavelet analysis and its fast algorithms are widely used in many fields of applied mathematics such as in signal and image processing. In the present thesis, we circumvent the restrictions of orthogonal and biorthogonal wavelet bases by constructing wavelet frames. They still allow for a stable decomposition, and so-called wavelet bi-frames provide a series expansion very similar to those of pairs of biorthogonal wavelet bases. Contrary to biorthogonal bases, primal and dual wavelets are no longer supposed to satisfy any geometrical conditions, and the frame setting allows for redundancy. This provides more flexibility in their construction. Finally, we construct families of optimal wavelet bi-frames in arbitrary dimensions with arbitrarily high smoothness. Then we verify that the n-term approximation can be described by Besov spaces and we apply the theoretical findings to image denoising
Evolutionary Computation
This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field
Corporate Governance Reforms in the Philippines: An Ethnographic Approach
This research investigates the impact of corporate governance reforms in Philippine institutions and firms. Literature reviewed looks at the macro and micro view of corporate governance theories and the prevailing business environment. The methodology of this research applies an ethnographic approach combining both deductive and inductive inquiries with a triangulated method using interviews, media articles and participant observation. Collection of the interview data occurred over four intensive months whilst in situ in Manila, Philippines. The data was then analysed using the NVivo qualitative analysis computer program. The results of my data collection and analysis are explained and distilled in six chapters. They are: - Chapter 4: Business and Corporate Governance Environment - Chapter 5: The Regulators - Chapter 6: Business Groups: The Owners of Companies - Chapter 7: The Board and Management: The Controllers of the Company - Chapter 8: The Government Financial Institutions - Chapter 9: Corruption The thesis concludes with a chapter on a summary of the research findings and recommendations for policy and practice
The possibility of super-somnolent mentation: A new information-processing approach to sleep-onset acceleration and insomnia exemplified by serial diverse imagining
This paper proposes a new conceptual framework and techniques for sleep-onset acceleration: the somnolent mentation framework. It distinguishes between somnolent, asomnolent and insomnolent mentation. Somnolent mentation inherently accelerates sleep onset (SO). Insomnolent mentation (e.g., deliberating, ruminating or focusing on one’s arousal) interferes with SO. Deliberate mentation approaches to insomnia attempt to influence the participant’s mentation at SO. They may prescribe somnolent or counter-insomnolent mentation. Existing deliberate mentation approaches attempt mainly to counter insomnolent mentation (e.g., thought control through imagery distraction). Thus they are at best counter-insomnolent. Super-somnolent mentation is both somnolent and counter-insomnolent. Extended SO (E-SO) is defined as the period just before SO (P-SO) combined with SO. A scientific challenge is to correctly classify features of mentation as somnolent, asomnolent and insomnolent. This classification should be done both from a phenomena-based perspective—e.g., the empirical study of E-SO mentation— and from a designer-based perspective (in terms of a theory of the architecture of the human mind). This paper proposes a secondary hypothesis: the E-SO mentation emulation hypothesis. To emulate somnolent features of P-SO mentation is somnolent. This paper proposes also that some types of incoherent mentation are super-somnolent.
This paper presents no new empirical data. However, from the new conjectures, several predictions can be derived, new treatments developed, and new possibilities investigated. From the incoherent mentation principle the serial diverse imagining (SDI) family of techniques is derived. From this and related considerations SDI is expected to be super-somnolent
Recommended from our members
Compiler and system for resilient distributed heterogeneous graph analytics
Graph analytics systems are used in a wide variety of applications including health care, electronic circuit design, machine learning, and cybersecurity. Graph analytics systems must handle very large graphs such as the Facebook friends graph, which has more than a billion nodes and 200 billion edges. Since machines have limited main memory, distributed-memory clusters with sufficient memory and computation power are required for processing of these graphs. In distributed graph analytics, the graph is partitioned among the machines in a cluster, and communication between partitions is implemented using a substrate like MPI. However, programming distributed-memory systems are not easy and the recent trend towards the processor heterogeneity has added to this complexity. To simplify the programming of graph applications on such platforms, this dissertation first presents a compiler called Abelian that translates shared-memory descriptions of graph algorithms written in the Galois programming model into efficient code for distributed-memory platforms with heterogeneous processors. An important runtime parameter to the compiler-generated distributed code is the partitioning policy. We present an experimental study of partitioning strategies for distributed work-efficient graph analytics applications on different CPU architecture clusters at large scale (up to 256 machines). Based on the study we present a simple rule of thumb to select among myriad policies. Another challenge of distributed graph analytics that we address in this dissertation is to deal with machine fail-stop failures, which is an important concern especially for long-running graph analytics applications on large clusters. We present a novel communication and synchronization substrate called Phoenix that leverages the algorithmic properties of graph analytics applications to recover from faults with zero overheads during fault-free execution and show that Phoenix is 24x faster than previous state-of-the-art systems. In this dissertation, we also look at the new opportunities for graph analytics on massive datasets brought by a new kind of byte-addressable memory technology with higher density and lower cost than DRAM such as intel Optane DC Persistent Memory. This enables the design of affordable systems that support up to 6TB of randomly accessible memory. In this dissertation, we present key runtime and algorithmic principles to consider when performing graph analytics on massive datasets on Optane DC Persistent Memory as well as highlight ideas that apply to graph analytics on all large-memory platforms. Finally, we show that our distributed graph analytics infrastructure can be used for a new domain of applications, in particular, embedding algorithms such as Word2Vec. Word2Vec trains the vector representations of words (also known as word embeddings) on large text corpus and resulting vector embeddings have been shown to capture semantic and syntactic relationships among words. Other examples include Node2Vec, Code2Vec, Sequence2Vec, etc (collectively known as Any2Vec) with a wide variety of uses. We formulate the training of such applications as a graph problem and present GraphAny2Vec, a distributed Any2Vec training framework that leverages the state-of-the-art distributed heterogeneous graph analytics infrastructure developed in this dissertation to scale Any2Vec training to large distributed clusters. GraphAny2Vec also demonstrates a novel way of combining model gradients during training, which allows it to scale without losing accuracyComputer Science
- …