MS

thesisIn this investigation detailed experimentation has been carried out to establish a relationship for mill scale-up using a linear population balance model in dry and wet grinding systems. Data were obtained in three different sizes of mill (10, 15 and 30 inches in diameter) for limestone grinding. In each case the selection and breakage parameters of the population balance model were determined. Analysis of these data showed that the selection functions are proportional to the specific power draft of the mill (S^ = S^(P/H)) for both wet and dry grinding. In addition the breakage functions were found to be independent of mill size, the same for wet and dry grinding. For dry grinding the specific selection functions (S?) were found to be independent of fineness of grind. While for wet grinding the specific selection functions varied with fineness of grind. These relationships were found to constitute a basis for mill scale-up. By incorporating the specific selection functions and breakage functions into the linear population balance model it was possible to accurately predict dry product size distributions in the larger mills from data obtained in the 10-inch diameter mill. Equally accurate predictions were achieved for wet grinding by employing a linearization procedure termed as the "similar fineness of grind approach"

Effect of material properties on ductility factor of singly rc beam sections

Ductility may be defined as the ability to undergo deformations without a substantial reduction in the flexural capacity of the member. The ductility of reinforced concrete beams depends mainly on the shape of the moment-curvature relationship of the sections. The constituents of reinforced concrete are very complex due to its mechanical properties. The stress-strain behavior of concrete is considered parabolic and that of the steel is elastic plastic. Concrete and reinforcing steel are represented by separate material models that are combined together to describe the behavior of the reinforced concrete sections. The end displacements of the steel element are assumed to be compatible with the boundary displacements of the concrete element which implied perfect bond between them. The curvature ductility factor of singly reinforced concrete rectangular beams is derived taking into account the possible nonlinear behavior of the unconfined compressed concrete and reinforcing steel. Effects of material properties such as concrete compressive strength, reinforcement ratio and yield strength of reinforcement on the curvature ductility factors are derived analytically. From the analyses it is observed that an increasing steel content decreases the curvature ductility of a singly reinforced concrete section and this pattern is valid for any concrete strength. On the other hand, for the same reinforcement content curvature ductility increases as the concrete strength is increased

Spectrum Sharing Methods in Coexisting Wireless Networks

Radio spectrum, the fundamental basis for wireless communication, is a finite resource. The development of the expanding range of radio based devices and services in recent years makes the spectrum scarce and hence more costly under the paradigm of extensive regulation for licensing. However, with mature technologies and with their continuous improvements it becomes apparent that tight licensing might no longer be required for all wireless services. This is from where the concept of utilizing the unlicensed bands for wireless communication originates. As a promising step to reduce the substantial cost for radio spectrum, different wireless technology based networks are being deployed to operate in the same spectrum bands, particularly in the unlicensed bands, resulting in coexistence. However, uncoordinated coexistence often leads to cases where collocated wireless systems experience heavy mutual interference. Hence, the development of spectrum sharing rules to mitigate the interference among wireless systems is a significant challenge considering the uncoordinated, heterogeneous systems. The requirement of spectrum sharing rules is tremendously increasing on the one hand to fulfill the current and future demand for wireless communication by the users, and on the other hand, to utilize the spectrum efficiently. In this thesis, contributions are provided towards dynamic and cognitive spectrum sharing with focus on the medium access control (MAC) layer, for uncoordinated scenarios of homogeneous and heterogeneous wireless networks, in a micro scale level, highlighting the QoS support for the applications. This thesis proposes a generic and novel spectrum sharing method based on a hypothesis: The regular channel occupation by one system can support other systems to predict the spectrum opportunities reliably. These opportunities then can be utilized efficiently, resulting in a fair spectrum sharing as well as an improving aggregated performance compared to the case without having special treatment. The developed method, denoted as Regular Channel Access (RCA), is modeled for systems specified by the wireless local resp. metropolitan area network standards IEEE 802.11 resp. 802.16. In the modeling, both systems are explored according to their respective centrally controlled channel access mechanisms and the adapted models are evaluated through simulation and results analysis. The conceptual model of spectrum sharing based on the distributed channel access mechanism of the IEEE 802.11 system is provided as well. To make the RCA method adaptive, the following enabling techniques are developed and integrated in the design: a RSS-based (Received Signal Strength based) detection method for measuring the channel occupation, a pattern recognition based algorithm for system identification, statistical knowledge based estimation for traffic demand estimation and an inference engine for reconfiguration of resource allocation as a response to traffic dynamics. The advantage of the RCA method is demonstrated, in which each competing collocated system is configured to have a resource allocation based on the estimated traffic demand of the systems. The simulation and the analysis of the results show a significant improvement in aggregated throughput, mean delay and packet loss ratio, compared to the case where legacy wireless systems coexists. The results from adaptive RCA show its resilience characteristics in case of dynamic traffic. The maximum achievable throughput between collocated IEEE 802.11 systems applying RCA is provided by means of mathematical calculation. The results of this thesis provide the basis for the development of resource allocation methods for future wireless networks particularly emphasized to operate in current unlicensed bands and in future models of the Open Spectrum Alliance

5,562化合物のハイスループットスクリーニングによる肺高血圧症治療薬としてのエメチンの同定

要約のみTohoku University下川　宏明課

Enhancement of Local Buckling Behaviour of Steel Structures Retrofitted Through Bonding GFRP Plates

An effective technique involving the use of glass fiber reinforced polymer (GFRP) plates to enhance the local buckling behaviour of steel plates, beams, and moment resisting frames is presented in this Thesis. The enhancement in buckling capacity is achieved by bonding GFRP plates to the steel sections. These steel/GFRP joints have the advantages of ease of application, low cost, high strength-to-weight ratio, and resistance to corrosion. An interface element that simulates the behaviour of the adhesive bonding the steel and GFRP elements is developed and is implemented into an in-house developed finite element model to represent steel/GFRP joints. The model is based on a powerful nonlinear shell element that is capable of simulating both thin and thick-walled structures. The strength and stiffness of both the GFRP and the adhesive used in the model are based on values obtained from previously conducted tests. The enhancement in buckling capacity of retrofitted steel/GFRP plates is studied by bonding GFRP plates to steel plates having different aspect and slenderness ratios. The study also considers the effect of initial geometric imperfections on both the elastic and inelastic buckling capacities of retrofitted plates. Better improvement in load capacity is predicted for slender steel plates. The strength of the adhesive is shown to play an important role in defining the mode of failure and in determining the capacity of the retrofitted plates. The improvement in buckling behaviour of retrofitted steel/GFRP beams is then studied considering various thicknesses of GFRP plates. The conducted analysis covers a range of slenderness ratios of steel beams and assesses the effect of plastic modulus of steel, initial geometric imperfection, and residual stresses of the steel section on the load-deflection behaviour of steel beams. The lateral behaviour of moment resisting steel frames retrofitted with GFRP plates is studied to assess their capacity improvement in seismic regions. Nonlinear static pushover analyses are carried out for frames retrofitted at their beams’ flanges with different thickness of GFRP plates. The global capacity curves for the retrofitted frames are compared with their corresponding original frames to assess the improvement in seismic performance of the frames. Finally, an experimental investigation is carried out to assess the strength and stiffness properties of adhesively bonded steel/GFRP joints under cyclic loading. A number of shear lap tests are conducted and the obtained results are used to determine the characteristics of spring systems that simulate the shear and peel behaviour of the adhesive. Comparison is made between the stiffness and strength capacity under cyclic loading to the corresponding values under monotonic loading

Study and Observation of the Variations of Accuracies for Handwritten Digits Recognition with Various Hidden Layers and Epochs using Neural Network Algorithm

In recent days, Artificial Neural Network (ANN) can be applied to a vast majority of fields including business, medicine, engineering, etc. The most popular areas where ANN is employed nowadays are pattern and sequence recognition, novelty detection, character recognition, regression analysis, speech recognition, image compression, stock market prediction, Electronic nose, security, loan applications, data processing, robotics, and control. The benefits associated with its broad applications leads to increasing popularity of ANN in the era of 21st Century. ANN confers many benefits such as organic learning, nonlinear data processing, fault tolerance, and self-repairing compared to other conventional approaches. The primary objective of this paper is to analyze the influence of the hidden layers of a neural network over the overall performance of the network. To demonstrate this influence, we applied neural network with different layers on the MNIST dataset. Also, another goal is to observe the variations of accuracies of ANN for different numbers of hidden layers and epochs and to compare and contrast among them.Comment: To be published in the 4th IEEE International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT 2018

ADBSCAN: Adaptive Density-Based Spatial Clustering of Applications with Noise for Identifying Clusters with Varying Densities

Density-based spatial clustering of applications with noise (DBSCAN) is a data clustering algorithm which has the high-performance rate for dataset where clusters have the constant density of data points. One of the significant attributes of this algorithm is noise cancellation. However, DBSCAN demonstrates reduced performances for clusters with different densities. Therefore, in this paper, an adaptive DBSCAN is proposed which can work significantly well for identifying clusters with varying densities.Comment: To be published in the 4th IEEE International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT 2018

Study and Observation of the Variation of Accuracies of KNN, SVM, LMNN, ENN Algorithms on Eleven Different Datasets from UCI Machine Learning Repository

Machine learning qualifies computers to assimilate with data, without being solely programmed [1, 2]. Machine learning can be classified as supervised and unsupervised learning. In supervised learning, computers learn an objective that portrays an input to an output hinged on training input-output pairs [3]. Most efficient and widely used supervised learning algorithms are K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Large Margin Nearest Neighbor (LMNN), and Extended Nearest Neighbor (ENN). The main contribution of this paper is to implement these elegant learning algorithms on eleven different datasets from the UCI machine learning repository to observe the variation of accuracies for each of the algorithms on all datasets. Analyzing the accuracy of the algorithms will give us a brief idea about the relationship of the machine learning algorithms and the data dimensionality. All the algorithms are developed in Matlab. Upon such accuracy observation, the comparison can be built among KNN, SVM, LMNN, and ENN regarding their performances on each dataset.Comment: To be published in the 4th IEEE International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT 2018

Developing a community of inquiry using an educational blog in higher education from the perspective of Bangladesh

Web 2.0 tools such as blogs, wikis, social networking, and podcasting have received attention in educational research over the last decade. Blogs enable students to reflect their learning experiences, disseminate ideas, and participate in analytical thinking. The Community of Inquiry (CoI) framework has been widely used in educational research to understand and enhance online and blended learning platforms. There is insufficient research evidence to demonstrate the impact of educational blogging using the CoI model as a framework. This article explores how blogs can be used to support collaborative learning and how such an interaction upholds CoI through enhancing critical thinking and meaningful learning in the context of higher education (HE). An exploratory sequential mixed-method approach has been followed in this study. A convenience sampling method was employed to choose 75 undergraduate students from Dhaka University for a 24-week blogging project. Every publication on the blog was segmented into meaningful units. Whole texts of posts and comments are extracted from the blog, and the transcripts are analyzed in a qualitative manner considering the CoI framework, more specifically, through the lens of cognitive, social, and teaching presence. In addition, the semi-structured questionnaire is used to collect data from students irrespective of whether blogging expedited students' learning or not. The research findings indicate that cognitive presence, namely, the exploration component, is dominant in blog-based learning activity. Moreover, this research has demonstrated that blogs build reliable virtual connections among students through exchanging ideas and information and by offering opportunities for reflective practice and asynchronous feedback. This study also revealed challenges related to blogging in the context of developing countries, including lack of familiarity with blogs, restricted internet connectivity, limited access to devices, and low levels of social interaction. It is recommended that different stakeholders including policymakers, curriculum developers, and teachers take the initiative to synchronize the utilization of educational blogs with the formal curriculum, guaranteeing that blog activities supplement and improve traditional teaching–learning activities