Miracle-dz/UniFAR-FTUs huggingface dataset:
Intelligent Load Monitoring in Beam Structures

Li, Junping Intelligent load monitoring in beam structures. (Under the direction of Dr. F. G. Yuan.) A robust approach for identifying impact load location and impact force history in beam structures is presented in this thesis. Beam strain transients propagating from the impact site can be inverted to yield the impact location and force history. Solving the inverse problem consists of three parts: a transient wave model, an impact location determination, and then an impact history identification. The classical Euler-Bernoulli beam theory (EBT) is used to obtain the dynamic models of a simply-supported beam. The simulated measured strain data are utilized from the transient wave model derived from finite element method and state variable approach. A wavelet analysis using Gabor basis function is employed to determine the impact load location from strain measurements resulting from a pair of sensors which are located on the opposite side of the impact site. The information on traveling dispersive waves is described by the timefrequency representation of the transient strain data analyzed using wavelet transform. A Radial Basis Function Neural (RBFN) networks is then employed to efficiently reconstruct the impact load history from a single strain sensor. The back-propagation algorithm in conjunction with Levenberg-Marquardt method is adopted to update the parameters of RBFN networks by minimizing the difference between model prediction and the sensor measurements, where the transient wave model is embedded in the RBFN networks to guide and speed up the training process. The effect of noise on the identification of impact site and loading history is also investigated in detail. The denoising technique using discrete wavelet transform (DWT) reasonably performed on the noisy data improves the accuracy of the predicted impact load history. Several examples demonstrate the effectiveness of the approach. A close agreement shows the effectiveness and the validity of the proposed method INTELLIGENT LOAD MONITORING IN BEAM STRUCTURES
Analyzing the extent of standardization in Shared Service Centers - Literature review and Case study of Region Skåne

The purpose of this thesis is to give an overview of the current state of academic research regarding Shared Service Centers. Moreover, it provides a theoretical frame of reference to evaluate the extent of standardization of Shared Service Centers as well as the corresponding potential for improvement. The presented theoretical framework is applied to the case of Region Skane’s Shared Service Center.
Sensitive Chaotic Fragile Watermarking Technique for Binary Images Verification

A new fragile watermarking scheme is presented. Which is capable of authentication, tamper-proofing and integrity verification of binary text images. By defining concept of LSPB(Least Significant Pixel Block), let the values of non-watermarked pixels be mapped to the initial values of the chaotic mapping. The watermark, which obtained by iterating several times of the chaotic mapping, is embedded into the central pixel of LSPB. Experimental results demonstrate that, those watermarked binary images have good visual quality, the algorithm can detect and localize precisely the tampers on the watermarked image. It is a sensitive and complete blind watermark scheme.
A Comparative Study of Virtual Communities in Bangladesh and the Usa

The objective of this study is to compare the differences between Virtual Communities in Bangladesh and the USA in terms of website usability, marketing, languages and norms. The two countries were compared because the USA is a more advanced country compared to Bangladesh. Studying their differences would identify possible gaps which can be bridged to improve Virtual Communities of Bangladesh and perhaps other developing countries. Thirty-three Virtual Communities sites from both countries were randomly selected and evaluated based on a set of criteria relating to the above aspects. The data were analyzed using statistical tests to determine any significant difference between Virtual Communities of the two countries. The study found significant differences in the website usability aspects i.e. the use of multimedia; in marketing aspects i.e. the use of electronic catalogs and Web advertisements; and in languages and norms i.e. the inclusion of language options and the use of religious, collectivistic, and structured messages. As a conclusion, useful recommendations were given to Web developers, marketers, and Bangladeshi government to improve Web usability, marketing, and cultural environment of Bangladeshi Virtual Communities. Future studies can be conducted by replicating the study to other developing countries such as China and India.
DoS detection model base on alive entropy

An alive entropy model is proposed for detecting increasingly serious Denial of Service(DoS) attacks.The model is based on the theory of active communication that combines the information entropy and related sessions of network flow.The model detects DoS attacks through the analysis of the variation of the network flow's alive entropy.Experiment result show that the alive entropy is stable under normal network flow,and when attack occurs it fluctuates obviously.Compared with other methods based on the static entropy model,the proposed model is more accurate and more effective in detecting unknown DoS attack.
Language Is a Complex Network

Language is a dynamic complex system,which can analyze and study language with the complex network technology. Complex network analysis method can study the overall characteristics of language as a system of relations and the emergence phenomenon of language through an empirical approach based on large- scale real corpus.From the inter- discipline and disciplinary integration perspective,this study will be not only helpful for us to better understand the structure and organization of language,but also conducive to understanding the universality and specialty of language,understanding the knowledge network of language and promoting the scientization and internationalization of linguistic research as well.
AN EFFICIENT INDEXED BASED CLASSIFICATION APPROACH FOR FEATURE EVOLVING DATA STREAMS

Today, rapid growth in hardware technology has provided a means to generate huge volume of data continuously. In most of the real time data stream application data usually reach very rapidly that flows continuously in real time environment . This incoming data streams comprises of several important and interesting patterns underneath. However, mining an essential data out from this data stream, has some major challenges such as infinite length, concept evolution and concept-drift . Earlier studies, carried till now, have been mainly focusing on building accurate classification model. But, keeping glance over the huge amount of classifiers while prediction, require more response time, which makes ensemble approach being impractical for many real-world times critical data stream applications. In order to over this problem, we propose a new enhanced indexing structure that organizes all classifiers of ensemble in order to get fast prediction response in lesser amount of time . In addition to this, ensemble model is updated continuously by integrating new classifiers, while adapting to new trends. Experimental results and theoretical analysis on both real-worlds as well as synthetic data streams exhibit the better performance of our method over the existing techniques.
Preliminary study on nanoparticle sizes under the APEC technology cooperative framework

Despite the fact that there exist several techniques capable of characterizing nanoparticle sizes, their measurement results from the same sample often deviate from each other by an amount that is considered significant on the nanometre scale. In the absence of international standards, or worldwide recognized protocols dealing with nanoparticle characterization, an APEC-led preliminary interlaboratory comparison on nanoparticle size characterization was carried out among ten laboratories from six member economies. Test samples of nanoparticles of 20 nm and 100 nm nominal sizes were distributed for measurements by dynamic light scattering (DLS), transmission electron microscopy (TEM), scanning electron microscopy (SEM), atomic force microcopy (AFM) and differential mobility analyser (DMA). The comparison result showed fair agreement among the measurements on the certified reference materials (CRM) of 20 nm and 100 nm nanoparticles. Greater deviation was observed on the 20 nm nanosilver colloid sample. This comparison was regarded as a preliminary study on the measurement consistency among various nanoscale measurement techniques.
An Empirical Study of the Impact of Knowledge Management on Organizational Performance

In recent years, the area of knowledge management (KM) and organizational learning has received much attention by both researchers and practitioners. The complexity of KM stems from the need for organizations to have a systematic and integrated approach to build the learning processes, culture, technology infrastructure and performance management needed to define, use and develop knowledge-related assets. The integrated approach, once in place, can have a great impact on the overall organizational systems and performance. This study explores this impact based on an empirical survey of Kuwaiti organizations. The study looks at both the concepts of KM and learning organizations. Several interesting findings are reported, together with detailed discussions based on the literature of their implications for research and practice.
Spoken Language Translator: First-Year Report

This document is the first-year report for a project whose long-term goal is the construction of a practically useful system capable of translating continuous spoken language within a restricted domain. The main deliverable resulting from the first year is a prototype, the Spoken Language Translator (SLT), which can translate queries from spoken English to spoken Swedish in the domain of air travel planning. The system was developed by SRI International, the Swedish Institute of Computer Science, and Telia Research AB. Most of it is constructed from previously existing pieces of software, which have been adapted for use in the speech translation task with as few changes as possible. The main components are connected together in a pipelined sequence as follows. The input signal is processed by SRI''s DECIPHER(TM), a speaker-independent continuous speech recognition system. It produces a set of speech hypotheses which is passed to the English-language processor, the SRI Core Language Engine (CLE), a general natural- language processing system. The CLE grammar associates each speech hypothesis with a set of possible logical-form-like representations, typically producing 5 to 50 logical forms per hypothesis. A preference component is then used to give each of them a numerical score reflecting its linguistic plausibility. When the preference component has made its choice, the highest-scoring logical form is passed to the transfer component, which uses a set of simple non-deterministic recursive pattern-matching rules to rewrite it into a set of possible corresponding Swedish representations. The preference component is now invoked again, to select the most plausible transferred logical form. The result is fed to a second copy of the CLE, which uses a Swedish- language grammar and lexicon developed at SICS to convert the form into a Swedish string and an associated syntax tree. Finally, the string and tree are passed to the Telia Prophon speech synthesizer, which utilizes polyphone synthesis to produce the spoken Swedish utterance. The system''s current performance figures, measured on previously unseen test data, are as follows. For sentences of length 12 words and under, 65% of all utterances are such that the top-scoring speech hypothesis is an acceptable one. If the speech hypothesis is correct, then a translation is produced in 80% of the cases; and 90% of all translations produced are acceptable. Nearly all incorrect translations are incorrect due to their containing errors in grammar or naturalness of expression, with errors due to divergence in meaning between the source and target sentences accounting for less than 1% of all translations. Making fairly conservative extrapolations from the current SLT prototype, we believe that simply continuing the basic development strategy could within three to five years produce an enhanced version, which recognized about 90% of the short sentences (12 words or less) in a specific domain, and produced accepta
Finite-element method for obtaining the regularized photon Green function in lossy material

The photon Green function (GF) is a vital and decisive factor in the field of quantum light-matter interaction. It is divergent with two equal space arguments in lossy structure and should be regularized. We introduce a finite-element method (FEM) for calculating the regularized GF in arbitrary-shaped lossy structure, which is expressed by the averaged radiation electric field over the finite size of a photon emitter. For an emitter located in a homogeneous lossy material, excellent agreement with the analytical results is found for both real and virtual cavity models. For an emitter located in a metal nano-sphere, the regularized scattered GF, which is the difference between the regularized GF and the analytical regularized one in homogeneous space, agrees well with the analytical scattered GF. Applying this method for an emitter located in a metal nano-rod where there is no analytical solution, we find that the scattering contribution to the enhancement of the spontaneous emission rate is nearly unrelated to the cavity radius while the homogeneous part depends heavily. Our results should be significant for novel photon sources within lossy structures.
Neighborhood Verification: A Strategy for Rewarding Close Forecasts

Abstract High-resolution forecasts may be quite useful even when they do not match the observations exactly. Neighborhood verification is a strategy for evaluating the “closeness” of the forecast to the observations within space–time neighborhoods rather than at the grid scale. Various properties of the forecast within a neighborhood can be assessed for similarity to the observations, including the mean value, fractional coverage, occurrence of a forecast event sufficiently near an observed event, and so on. By varying the sizes of the neighborhoods, it is possible to determine the scales for which the forecast has sufficient skill for a particular application. Several neighborhood verification methods have been proposed in the literature in the last decade. This paper examines four such methods in detail for idealized and real high-resolution precipitation forecasts, highlighting what can be learned from each of the methods. When applied to idealized and real precipitation forecasts from the Spatial Veri.
Decision Prevention Mechanism for FrequentPattern Mining Process

Security and privacy methods are used to protect the data values. Private data values are secured with confidentiality and integrity methods. Privacy model hides the individual identity over the public data values. Sensitive attributes are protected using anonymity methods. Discrimination is the prejudicial treatment of an individual based on their membership in a certain group or category. Antidiscrimination acts are designed to prevent discrimination on the basis of a number of attributes in various settings. Public data collections are used to train association/classification rules in view of making automated decisions. Data mining can be both a source of discrimination and a means for discovering discrimination. Automated data collection and data mining techniques such as classification rule mining are used to making automated decisions. Discriminations are divided into two types such as direct and indirect discriminations. Direct discrimination occurs when decisions are made based on sensitive attributes. Indirect discrimination occurs when decisions are made based on non sensitive attributes which are strongly correlated with biased sensitive ones. Discrimination discovery and prevention are used for anti-discrimination requirements. Direct and indirect discriminations prevention is applied on individually or both at the same time. The data values are cleaned to obtain direct and/or indirect discriminatory decision rules. Data transformation techniques are applied to prepare the data values for the discrimination prevention. Rule protection and rule generalization algorithm and direct and indirect discrimination prevention algorithm are used to protect discriminations. The discrimination prevention model is integrated with the differential privacy scheme to high privacy. Dynamic policy selection based discrimination prevention is adopted to generalize the systems for all regions. Data transformation technique is improved to increase the utility rate. Discrimination removal process is improved with rule hiding techniques.
MODELLING PERCEIVED QUALITY FOR URBAN TRANSPORT SYSTEMS USING WEIGHTED VARIABLES AND RANDOM PARAMETERS

In this article, an Ordered Logit model is proposed considering systematic and random variations in tastes. The methodology followed for the creation of this model consisted, in first place, in obtaining data using a revelled preferences survey. In the survey, each user had to evaluate, following a qualitative scale, each one of the attributes of the analysed transport system. The variables evaluated in the survey had been grouped into six groups, and for each group, users had to order the attributes belonging to the group, using a ranking based method, from the most important to de least important, and, in the same way, with the groups itself. Once the database is formed, a generic model have been created, establishing this model as a comparative base for the rest. Next, two more models have been estimated one considering systematic users variations and the other one combining the systematic variations with weighted variables. Additionally, three new models have been calculated as an evolution of the previous ones using random variables as representation of systematic and random variations in user’s tastes. The results shows that as model’s complexity increase, an improvement in model fit is achieved. DOI: http://dx.doi. org/10.4995/CIT2016.2016. 4072
A First Course In Numerical Analysis

Thank you for downloading a first course in numerical analysis. As you may know, people have look numerous times for their favorite readings like this a first course in numerical analysis, but end up in infectious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they juggled with some harmful bugs inside their computer. a first course in numerical analysis is available in our digital library an online access to it is set as public so you can get it instantly. Our books collection spans in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the a first course in numerical analysis is universally compatible with any devices to read.
AN IMPROVED BAYESIAN NETWORKS LEARNING ALGORITHM

Bayesian network structure learning based on model selection is an NP-hard problem. And none of the presented algorithms is perfectly successful in solving the problem of searching efficiency, especially for complex system learning. Presented in this paper is a new independent-MDL-based approach to learn Bayesian network structures. The proposed algorithm limits the searching space by using a set of lower order independence tests, thus executing the MDL-based searching algorithm B B-MDL to obtain the final graph. The precision analysis of algorithm is presented. And the problem of parameter design is also concerned. The result of the experiment shows that the new algorithm I-B B-MDL is more efficient in time consumption than B B-MDL algorithm.
Continuum-atomistic algorithms for dendritic formation

GREGORY HERSCHLAG: Continuum-atomistic algorithms for dendritic formation (Under the direction of Sorin Mitran) Nanoscale patterning of materials at scales of less than 20nm remains a challenging problem. Standard techniques, such as lithography, rely on electron and photon beams to shape materials, yet these methods are difficult to employ at the sub 50nm scale. We consider the possibility of employing natural interface instabilities during solidification in order to reliably produce desirable structures. Existing continuum methods for complicated materials may break down at these scales, while direct atomistic simulation is computationally infeasible. The primary objective of the thesis is to develop methods capable of efficiently resolving the atomistic computations which may eventually be solved concurrently with continuum scale approaches. In the present work we develop atomistic algorithms to furnish closures for continuum methods. We develop novel molecular techniques capable of resolving detail only about an interface with non-periodic boundaries and accelerated by GPUs. The new algorithms provide a method to determine interfacial dynamics based on local curvature, employing wedge shaped domains. These dynamics lead to continuum closures which are used to determine necessary physical parameters to enable dendritic patterning at the sub 20nm scale.
Haar Wavelet Data Compression Algorithm with Error Bound for Wireless Sensor Networks

Wireless sensor networks usually have limited energy and transmission capacity,and they can’t match the transmission of a great deal of data.So,it is necessary to approximate or aggregate raw data sampled by sensors in networks.By designing an error tree and solving the regression equations set,this paper proposes a data compression scheme with infinite norm error bound for wireless sensor networks.The algorithms in the scheme can simultaneously explore the temporal and multiple-streams correlations among the sensory data.The temporal correlation in one stream is captured by the 1D Haar wavelet transform.For multivariate monitoring sensor networks,some streams from one sensor are selected as the bases according to the correlation coefficient matrix,and the other streams from the same sensor node can be expressed with one of these bases using linear regression.Theoretically and experimentally,it is concluded that the proposed algorithms can effectively exploit the temporal and multiple-streams correlations on the same sensor node and achieve significant data reduction.
Compilation of the ELECTRE reactive into finite transition systems

We present in this paper an operational semantics for the ELECTRE reactive language (Roux et al., 1992). This language is based on an asynchronous approach to real-time systems. First basic concepts and intuitive semantics are introduced. Then we give rules to model dynamic semantics of ELECTRE programs: this constitutes an operational semantics for the ELECTRE language. This operational semantics is used to define a model of execution for ELECTRE programs: transition system. In addition, we prove, using structural induction on the operational semantics, that this transition system is a finite state transition system. Eventually, we extend the previous transition system so as to handle multiple-storage events: it is important since the asynchronous ELECTRE language deals with multiple memorized occurrences of the events. This result gives a means of compiling the ELECTRE language into a finite-state machine.