形状記憶合金線維を応用した大動脈拍動補助装置の開発研究

要約のみTohoku University山家智之課

Synthesis and Development of One-part Rock-based Geopolymers for Well Cementing Applications

A one-part or Just Add Water (JAW) geopolymer system is an environmental- and user-friendly alternative to Ordinary Portland Cement (OPC) and conventional geopolymers. It helps with producing lower CO2 emissions avoiding OPC processing and eliminating the need for unnecessary liquid transportation for conventional geopolymers. A one-part geopolymer is ideal for large-scale deployment of geopolymers in well-cementing applications. It can help with quality control processes and reduce the need for extensive end-user knowledge of the chemistry involved. Accordingly, this study is to design a successful one-part naturally occurring (i.e., unprocessed nor pre-processed) granite-based geopolymer formulation. It aims to understand all possible impacts on the performance of these geopolymers of the utilized components including precursors, activators, water content and chemical admixtures. It is also to take into account petroleum engineering standards. This research is part of the SafeRock Project which is in collaboration between the University of Stavanger and operator and service companies. Additionally, it is done with a tight collaboration with academic institutes including Delft University of Technology (TU Delft, Netherlands), Universidade Federal do Rio de Janeiro (UFRJ, Brazil) and University of Oklahoma (OU, United States). The outcomes of this thesis have been published in seven scientific articles: four journal papers, two peer-reviewed conferences, one SPE conference, and a filed patent application in Norway and PCT application in Europe. This section is composed of a brief description of the published articles and their scientific findings. The outcomes of this research can be summarized as the following: Paper I shows the normalization of a Norwegian grounded granite in the solid phase by slag, microsilica, potassium silicates and alkali-metal hydroxides. It illustrates the synthesis of a one-part granite-based geopolymer for well-cementing applications (JAW). It reveals the chemical composition of granite needs normalization. Fluid-state properties at 50 ℃ and solid-state properties at 70 ℃ are investigated. These investigations include pumpability, strength development, mineralogy, and morphology. Papers II and III present monitoring and screening of the modified JAW mixes after the utilisation of various chemical admixtures to improve the early-age strength development at 70 ℃ of bottom-hole static temperature. Mechanical, chemical and mineralogical properties of the one-part geopolymers are investigated. Papers IV and V study the influence of different superplasticizers on the rheology of these granite-based geopolymers at 20 and 50 ℃ of bottom-hole circulating temperatures. Papers VI and VII illustrate various early-age characteristics of the screened and developed samples including their chemical, physical, mechanical, mineralogical, and morphological properties at 20 and 50 ℃ at bottom-hole circulating temperatures. Additionally, Paper VII further investigates and characterizes the aged JAW properties at 20 and 50 ℃ at bottom-hole circulating temperatures for up to two months of curing. Appendix 10 is a filed patent in Norway and Europe titled ONE-PART GEOPOLYMER COMPOSITION – ref. P31562NO – June 2022

From pixels to people : recovering location, shape and pose of humans in images

Humans are at the centre of a significant amount of research in computer vision. Endowing machines with the ability to perceive people from visual data is an immense scientific challenge with a high degree of direct practical relevance. Success in automatic perception can be measured at different levels of abstraction, and this will depend on which intelligent behaviour we are trying to replicate: the ability to localise persons in an image or in the environment, understanding how persons are moving at the skeleton and at the surface level, interpreting their interactions with the environment including with other people, and perhaps even anticipating future actions. In this thesis we tackle different sub-problems of the broad research area referred to as "looking at people", aiming to perceive humans in images at different levels of granularity. We start with bounding box-level pedestrian detection: We present a retrospective analysis of methods published in the decade preceding our work, identifying various strands of research that have advanced the state of the art. With quantitative exper- iments, we demonstrate the critical role of developing better feature representations and having the right training distribution. We then contribute two methods based on the insights derived from our analysis: one that combines the strongest aspects of past detectors and another that focuses purely on learning representations. The latter method outperforms more complicated approaches, especially those based on hand- crafted features. We conclude our work on pedestrian detection with a forward-looking analysis that maps out potential avenues for future research. We then turn to pixel-level methods: Perceiving humans requires us to both separate them precisely from the background and identify their surroundings. To this end, we introduce Cityscapes, a large-scale dataset for street scene understanding. This has since established itself as a go-to benchmark for segmentation and detection. We additionally develop methods that relax the requirement for expensive pixel-level annotations, focusing on the task of boundary detection, i.e. identifying the outlines of relevant objects and surfaces. Next, we make the jump from pixels to 3D surfaces, from localising and labelling to fine-grained spatial understanding. We contribute a method for recovering 3D human shape and pose, which marries the advantages of learning-based and model- based approaches. We conclude the thesis with a detailed discussion of benchmarking practices in computer vision. Among other things, we argue that the design of future datasets should be driven by the general goal of combinatorial robustness besides task-specific considerations.Der Mensch steht im Zentrum vieler Forschungsanstrengungen im Bereich des maschinellen Sehens. Es ist eine immense wissenschaftliche Herausforderung mit hohem unmittelbarem Praxisbezug, Maschinen mit der Fähigkeit auszustatten, Menschen auf der Grundlage von visuellen Daten wahrzunehmen. Die automatische Wahrnehmung kann auf verschiedenen Abstraktionsebenen erfolgen. Dies hängt davon ab, welches intelligente Verhalten wir nachbilden wollen: die Fähigkeit, Personen auf der Bildfläche oder im 3D-Raum zu lokalisieren, die Bewegungen von Körperteilen und Körperoberflächen zu erfassen, Interaktionen einer Person mit ihrer Umgebung einschließlich mit anderen Menschen zu deuten, und vielleicht sogar zukünftige Handlungen zu antizipieren. In dieser Arbeit beschäftigen wir uns mit verschiedenen Teilproblemen die dem breiten Forschungsgebiet "Betrachten von Menschen" gehören. Beginnend mit der Fußgängererkennung präsentieren wir eine Analyse von Methoden, die im Jahrzehnt vor unserem Ausgangspunkt veröffentlicht wurden, und identifizieren dabei verschiedene Forschungsstränge, die den Stand der Technik vorangetrieben haben. Unsere quantitativen Experimente zeigen die entscheidende Rolle sowohl der Entwicklung besserer Bildmerkmale als auch der Trainingsdatenverteilung. Anschließend tragen wir zwei Methoden bei, die auf den Erkenntnissen unserer Analyse basieren: eine Methode, die die stärksten Aspekte vergangener Detektoren kombiniert, eine andere, die sich im Wesentlichen auf das Lernen von Bildmerkmalen konzentriert. Letztere übertrifft kompliziertere Methoden, insbesondere solche, die auf handgefertigten Bildmerkmalen basieren. Wir schließen unsere Arbeit zur Fußgängererkennung mit einer vorausschauenden Analyse ab, die mögliche Wege für die zukünftige Forschung aufzeigt. Anschließend wenden wir uns Methoden zu, die Entscheidungen auf Pixelebene betreffen. Um Menschen wahrzunehmen, müssen wir diese sowohl praezise vom Hintergrund trennen als auch ihre Umgebung verstehen. Zu diesem Zweck führen wir Cityscapes ein, einen umfangreichen Datensatz zum Verständnis von Straßenszenen. Dieser hat sich seitdem als Standardbenchmark für Segmentierung und Erkennung etabliert. Darüber hinaus entwickeln wir Methoden, die die Notwendigkeit teurer Annotationen auf Pixelebene reduzieren. Wir konzentrieren uns hierbei auf die Aufgabe der Umgrenzungserkennung, d. h. das Erkennen der Umrisse relevanter Objekte und Oberflächen. Als nächstes machen wir den Sprung von Pixeln zu 3D-Oberflächen, vom Lokalisieren und Beschriften zum präzisen räumlichen Verständnis. Wir tragen eine Methode zur Schätzung der 3D-Körperoberfläche sowie der 3D-Körperpose bei, die die Vorteile von lernbasierten und modellbasierten Ansätzen vereint. Wir schließen die Arbeit mit einer ausführlichen Diskussion von Evaluationspraktiken im maschinellen Sehen ab. Unter anderem argumentieren wir, dass der Entwurf zukünftiger Datensätze neben aufgabenspezifischen Überlegungen vom allgemeinen Ziel der kombinatorischen Robustheit bestimmt werden sollte

Taking a Deeper Look at Pedestrians

In this paper we study the use of convolutional neural networks (convnets) for the task of pedestrian detection. Despite their recent diverse successes, convnets historically underperform compared to other pedestrian detectors. We deliberately omit explicitly modelling the problem into the network (e.g. parts or occlusion modelling) and show that we can reach competitive performance without bells and whistles. In a wide range of experiments we analyse small and big convnets, their architectural choices, parameters, and the influence of different training data, including pre-training on surrogate tasks. We present the best convnet detectors on the Caltech and KITTI dataset. On Caltech our convnets reach top performance both for the Caltech1x and Caltech10x training setup. Using additional data at training time our strongest convnet model is competitive even to detectors that use additional data (optical flow) at test time

Design of a Web-Based Appointment for Patient of Optometry Department at El-Beida Hospital, Libya.

It has shown that the advent of World Wide Web has revolutionized the business processes and assists the information dissemination especially in hospital all over the world. This project research on the problem that is currently facing the patients of optometry department in El-Beidal hospital Libya where patients are finding it difficult to receive medical care due to the old method of medical treatment that are in practice. A prototype is developed to help the patients in communicating and booking appointment with medical officers in Optometry Department of El-Beidal hospital Libya irrespective of time and their location. The system was tested by the prospective users and found that it is easy to use

Knowledge Transfer Portal For Bachelor And Master Of IT Course Students

In spite of the fact that the field of applying knowledge research in education is fairly young it is already quite broad and fuzzy. The set of technologies used and developed there have roots in a variety of diverse areas of information and pedagogical sciences. To facilitate the process of scientific and scholastic search the domain needs to be structured. This paper presents an overview of a knowledge transfer portal for Education field and an initial report on the development of a web portal providing a single network place, where instructors, students, and practitioners can find information about available research projects and courses and successful practices in this field

Neural Body Fitting: Unifying Deep Learning and Model-Based Human Pose and Shape Estimation

Direct prediction of 3D body pose and shape remains a challenge even for highly parameterized deep learning models. Mapping from the 2D image space to the prediction space is difficult: perspective ambiguities make the loss function noisy and training data is scarce. In this paper, we propose a novel approach (Neural Body Fitting (NBF)). It integrates a statistical body model within a CNN, leveraging reliable bottom-up semantic body part segmentation and robust top-down body model constraints. NBF is fully differentiable and can be trained using 2D and 3D annotations. In detailed experiments, we analyze how the components of our model affect performance, especially the use of part segmentations as an explicit intermediate representation, and present a robust, efficiently trainable framework for 3D human pose estimation from 2D images with competitive results on standard benchmarks. Code will be made available at http://github.com/mohomran/neural_body_fittingComment: 3DV 201

Aging and Temperature Effects on the Performance of Sustainable One-Part Geopolymers Developed for Well-Cementing Applications

This study elucidates the effects of aging and temperature over the performance of one-part “just add water” (JAW) granite-based geopolymers for application in well cementing and well abandonment. Additionally, the investigation delves into the fluid-state and early-age solid-state properties of these geopolymers, with a particular emphasis on their performance after aging. The aging process extended up to 56 days for assessing mechanical properties and up to 28 days for evaluating hydraulic sealability through dedicated tests. The obtained results unveil a nonlinear correlation between the designated temperature and pumping duration. Notably, the issue of fluid loss emerged as a significant concern for these geopolymers. The early-age strength development of the mix design containing zinc demonstrates adherence to industry norms by achieving minimal strength requirements within 24 hours of curing. Zinc plays a pivotal role as a strength enhancer during the initial curing stages of geopolymers, both under ambient conditions and at elevated temperatures (70℃). However, upon extended curing at elevated temperatures, zinc’s impact slightly diminishes compared with the unmodified mix design. After around 30 days of curing, a consecutive reaction occurs in both the unmodified and zinc-modified mix designs. Aging leads to a decline in the material’s hydraulic sealability that was initially established during the early stages of curing.publishedVersio

Design and Early Age Performance of Sustainable One-Part Geopolymers for Well Cementing

One-part geopolymers, known as “just add water” (JAW), alkali-activated formulation is presented in this work. This work reveals the design and development of short-term properties of JAW geopolymers for use in oilwell cementing and well abandonment. Granite-based mix designs normalized with a byproduct slag and a small amount of microsilica as precursors were developed. The solid activator is composed of potassium silicate and potassium hydroxide, which are mixed with the precursors to synthesize the JAW formulation. Zinc oxide is used as a strength booster admixture. The cementing properties of the developed granite-based mix designs were characterized by investigating reaction phases and mechanical properties. Dissolution, heat evolution, pumpability, strength development, and mineralogy are also studied. The results show that a positive correlation among all the given analyses for the final geopolymeric product is quite observable. Zinc oxide is favorable to be added for optimizing the given precursor mix design to enhance the solubility and leads to much higher heat evolutions. Furthermore, it develops early strength up to 16 MPa without any negative effect on the investigated one-part geopolymer slurries.publishedVersio

Selective video encryption algorithm based on H.264/AVC and AES

H.264/AVC is an industry standard that has been designed to address different technical solutions such as broadcast applications, interactive or serial storage. conversational services, Video on Demand or multimedia streaming services [1]. The main objective behind the H.264 [2][3] development was to build a high peIformance video coding standard by adopting a back to basics approach with a simple and straightforward design using well known blocks. H.264/AVC is based on the conventional block motion compensated video coding the same way as the existing standard