Miracle-dz/UniFAR-FTUs huggingface dataset:
LOW SPEED ROLLING BEARING DIAGNOSTICS USING ACOUSTIC EMISSION AND HIGHER ORDER STATISTICS TECHNIQUES

Diagnostics in low speed rolling element bearings is difficult. Not only are normal frequency domain diagnostics methods not appropriate for this application, but the bearing response signals are usually immersed in background noise which make it difficult to detect these faults. Higher order statistics (HOS) techniques have been available for decades but have not been widely applied to machine condition monitoring with the exceptions of skewness and kurtosis. There is however reason to believe that these HOS techniques could play an important role in acoustic emission (AE) based condition monitoring of rolling element bearings at low speeds provided appropriate care is taken. To explore this hypothesis, AE signals at low bearing rotational speeds of 70, 80, 90 and 100 rpm respectively were used in this work for the monitoring of tapered roller bearings. In addition to the well-established statistical parameters such as mean, standard deviation, skewness and kurtosis, higher moments such as hyper flatness are considered in this study. A novel diagnostic method is proposed for fault extraction based on hyperflatness, combined with Kullback-Leibler divergence, and an indicator formula derived with the use of Lempel-Ziv Complexity is given. The Kullback-Leibler divergence is used together with the skewness and hyperflatness to obtain the Kullback-Leibler information Wave (KLW)with which the analysis is performed, and better results obtained as compared to conventional frequency domain analysis.
Multidimensional Computerized Adaptive Testing Using Non-Compensatory Item Response Theory Models

Current use of multidimensional computerized adaptive testing (MCAT) has been developed in conjunction with compensatory multidimensional item response theory (MIRT) models rather than with non-compensatory ones. In recognition of the usefulness of MCAT and the complications associated with non-compensatory data, this study aimed to develop MCAT algorithms using non-compensatory MIRT models and to evaluate their performance. For the purpose of the study, three item selection methods were adapted and compared, namely, the Fisher information method, the mutual information method, and the Kullback–Leibler information method. The results of a series of simulations showed that the Fisher information and mutual information methods performed similarly, and both outperformed the Kullback–Leibler information method. In addition, it was found that the more stringent the termination criterion and the higher the correlation between the latent traits, the higher the resulting measurement precision and test reliability. Test reliability was very similar across the dimensions, regardless of the correlation between the latent traits and termination criterion. On average, the difficulties of the administered items were found to be at a lower level than the examinees’ abilities, which shed light on item bank construction for non-compensatory items.
A Hybrid Model for Document Retrieval Systems.

A methodology for the design of document retrieval systems is presented. First, a composite index term weighting model is developed based on term frequency statistics, including document frequency, relative frequency within document and relative frequency within collection, which can be adjusted by selecting various coefficients to fit into different indexing environments. Then, a composite retrieval model is proposed to process a user's information request in a weighted Phrase-Oriented Fixed-Level Expression (POFLE), which may apply more than Boolean operators, through two phases. That is, we have a search for documents which are topically relevant to the information request by means of a descriptor matching mechanism, which incorporate a partial matching facility based on a structurally-restricted relationship imposed by indexing model, and is more general than matching functions of the traditional Boolean model and vector space model, and then we have a ranking of these topically relevant documents, by means of two types of heuristic-based selection rules and a knowledge-based evaluation function, in descending order of a preference score which predicts the combined effect of user preference for quality, recency, fitness and reachability of documents.
Automatic implementation and platform adaptation of discrete filtering and wavelet algorithms

Moore's law, with the doubling of the transistor count every 18 months, poses serious challenges to high-performance numerical software designers: how to stay close to the maximum achievable performance on ever-changing and ever-faster hardware technologies? Up-to-date numerical libraries are usually maintained by large teams of expert programmers who hand-tune the code to a specific class of computer platforms, sacrificing portability for performance. Every new generation of processors reopens the cycle of implementing, tuning, and debugging. The SPIRAL system addresses this problem by automatically generating and implementing algorithms for DSP numerical kernels and searching for the best solution on the platform of interest. Using search, SPIRAL adapts code to take optimal advantage of the available platform features, such as the architecture of the memory hierarchy and register banks. As a result, SPIRAL generates high-performance implementations for DSP transforms that are competitive with the best hand-coded numerical libraries provided by hardware vendors. In this thesis, we focus on automatic implementation and platform adaptation of filtering and wavelet kernels, which are at the core of many performance-critical DSP applications. We formulate many well-known algorithms for FIR filters and discrete wavelet transforms (DWT) using a concise and flexible symbolic mathematical language and integrate it in the SPIRAL system. This enables automatic generation and search over the comprehensive space of competitive algorithms, often leading to complex solutions that are hardly ever considered by a human programmer. Experimental results show that our automatically generated and tuned code for FIR filters and DWTs is competitive and sometimes even outperforms hand-coded numerical libraries provided by hardware vendors. This implies that the richness and the extent of the automatically generated search space can match human ingenuity in achieving high performance. Our system generates high-quality code for digital filtering and wavelet kernels across most current and compatible future computer platforms and frees software developers from tedious and time-consuming coding at the machine level.
DFT Based Quantum Chemical Descriptors of 1-Substituted THβC, DHβC, βC Derivatives

Bu calisma ile dogal ya da sentetik olarak elde edilen, N-1 substitueli βCCM turevlerinin kimyasal davranislari incelenmistir. Antitumor ajani olan bu bilesikler, antiviral ve anti sitma aktivitelerinin yani sira, insan kanser hucreleri icin de onemli bir role sahiptirler. Geometri optimizasyonlari farkli temel settler ile 10 farkli cozucu ortamda DFT kullanilarak yapilmistir. Kimyasal denge ve buna bagli ozelliklerin cozucuye bagli olarak nasil degistigini degerlendirmek icin Polarize Kontinuum Model’in Isodensity versiyonu kullanilmistir. Kimyasal davranis ve aromatiklik arasinda ki iliskiyi elde etmek icin kuresel aktiflik tanimlayicilarinin kullanilabilecegini onerebiliriz: 2 no’lu temel yapi ve substitueli turevleri, diger molekullerden daha aromatik olduklari icin, termodinamiksel olarak daha kararlidirlar. Antrasen 9-yl substitueli her bir molekulun elektrostatik potansiyel degerleri 2A ( - 9.696e -2 ) < 0A ( - 9.689e -2 ) < 1A ( - 9.343e -2 ) olarak degisirken, substitue grup icermeyen temel molekullerin elektrostatik potansiyelleri 6311++g(d,p) temel set ve sulu fazda 2 ( - 0.128) < 0 ( - 0.123) < 1 ( - 0.114) olarak hesaplanmistir. Bu calismanin sonuclarinin, kanser tedavisinde kullanilan antitumor ilaclarinin kimyasal ozelliklerinin aciklanmasi ve degerlendirilmesi acisindan onemli bilgiler saglayacagini umuyoruz.
Features and Fluents for Logic Programming: Non-simulative Algebraic Semantics

A Non-simulative Algebraic Semantics is defined and its range of applicability is proven to be the K-RACi class of the Features and Fluents framework. The comparative assessment reveals the semantics epistemologically equivalent and ontologically stronger than the Abductive Logic Programming, the Action Description Language A and the PMON entailment. The semantics is shown to be decidable.
Reflection of the strahl within the foot of the Earth's bow shock

Abstract. The reflection of a fraction of the solar wind at the bow shock to some extent defines the physical properties of what is known as the foreshock, the region where the interplanetary magnetic field has a direct connection to the bow shock. Both ion and electron reflection have been observed and together form a significant source of free energy that is responsible for many of the instabilities observed in this region. In this paper we concentrate on the reflection of electrons at the shock and report two significant findings: the first is that the strahl, the field-aligned component of the electron solar wind distribution, appears to be fully reflected at the bow shock; the second finding is that the reflection is observed to occur in the foot of the shock and not in the shock ramp. This latter observation implies that mirroring in these examples is not the primary determinant of the electron reflection process.
A new combined classification method for land covering using AVHRR data supervised by TM data

High resolution satellite images with wide area coverage are usually expensive and rarely obtained, but the precision of classification basically depends on the resolution of the satellite images. In this paper, a new combined classification method using low-resolution satellite data (NOAA AVHRR data) supervised by high-resolution satellite data (LANDSAT TM data) was proposed. In this method, some sample areas of TM data were classified by unsupervised cluster at first, and then the likelihood functions were estimated for each class according to the "multi-single" spatial correspondence between TM and AVHRR pixels. Finally, the whole AVHRR image can be classified with these likelihood functions by maximum likelihood classification method. The result of our experiments shown that, the accuracy of the classification greatly depended on where and which the sample areas were selected, the precision of the classification would be equal to those classified by high resolution satellite images directly if the sample areas were selected properly.
Evaluating the Effects of Automatic Speech Recognition Word Accuracy

Automatic Speech Recognition (ASR) research has been primarily focused towards large-scale systems and industry, while other areas that require attention are often over-looked by researchers. For this reason, this research looked at automatic speech recognition at the consumer level. Many individual consumers will purchase and use automatic software recognition for a different purpose than that of the military or commercial industries, such as telecommunications. Consumers who purchase the software for personal use will mainly use ASR for dictation of correspondences and documents. Two ASR dictation software packages were used to conduct the study. The research examined the relationships between (1) speech recognition software training and word accuracy, (2) errorcorrection time by the user and word accuracy, and (3) correspondence type and word accuracy. The correspondences evaluated were those that resemble Personal, Business, and Technical Correspondences. Word accuracy was assessed after initial system training, five minutes of error-correction time, and ten minutes of error-correction time. Results indicated that word recognition accuracy achieved does affect user satisfaction. It was also found that with increased error-correction time, word accuracy results improved. Additionally, the results found that Personal Correspondence achieved the highest mean word accuracy rate for both systems and that Dragon Systems achieved the highest mean word accuracy recognition for the Correspondences explored in this research. Results were discussed in terms of subjective and objective measures, advantages and disadvantages of speech input, and design recommendations were provided.
Network based Framework for Author Name Disambiguation Applications

With the rapid development of digital libraries, name disambiguation becomes more and more important technique to distinguish authors with same names from physical persons. Many algorithms have been developed to accomplish the task. However, they are usually based on some restricted preconditions and rarely concern how to be incorporated into a practical application. In this paper, name disambiguation is regarded as the technique of learning module integrated with a knowledge base. A network is defined for the modeling of publication information, which facilitates the representation of differenttypes of relations among the attributes. The knowledge base component serves as the user interface for domain knowledge input. Furthermore, this paper exploits a random walk with restart algorithm and affinity propagation clustering algorithm to finally output name disambiguation results.
Short-term Prediction of Stock Index Based on EMD and SVMs

In allusion to the prediction of the stock return rate, this paper has proposed nonlinear combined prediction method based on EMD (Empirical Mode Decomposition) and SVMs (Support Vector Machines). The method has divided stock return rate series into several components of different frequencies using EMD technology, getting three new sequences by grouping superposition of each component according to the frequency, which represents items of market volatility, major events, and trend respectively. Based on these three sequences, prediction is made by constructing different SVMs models to obtain the predicted value of each sequence. With SVMs, a combined model is built on the basis of predictive value of each sequence to obtain the final predicted value of stock return rate. Using CSI 300 Index, the validity of this method is verified, and the results show that the proposed model is better than the other models presented in this paper on forecasting CSI 300 Index.
Managing Provenance for Knowledge Discovery and Reuse

Serving as a record of what happened during a scientific process, often computational, provenance has become an important piece of computing. The importance of archiving not only data and results but also the lineage of these entities has led to a variety of systems that capture provenance as well as models and schemas for this information. Despite significant work focused on obtaining and modeling provenance, there has been litte work on managing and using this information. Using the provenance from past work, it is possible to mine common computational structure or determine differences between executions. Such information can be used to suggest possible completions for partial workflows, summarize a set of approaches, or extend past work in new directions. These applications require infrastructure to support efficient queries and accessible reuse. In order to support knowledge discovery and reuse from provenance information, the management of those data is important. One component of provenance is the specification of the computations; workflows provide structured abstractions of code and are commonly used for complex tasks. Using change-based provenance, it is possible to store large numbers of similar workflows compactly. This storage also allows efficient computation of differences between specifications. However, querying for specific structure across a large collection of workflows is difficult because comparing graphs depends on computing subgraph isomorphism which is NP-Complete. Graph indexing methods identify features that help distinguish graphs of a collection to filter results for a subgraph containment query and reduce the number of subgraph isomorphism computations. For provenance, this work extends these methods to work for more exploratory queries and collections with significant overlap. However, comparing workflow or provenance graphs may not require exact equality; a match between two graphs may allow paired nodes to be similar yet not equivalent. This work presents techniques to better correlate graphs to help summarize collections. Using this infrastructure, provenance can be reused so that users can learn from their own and others' history. Just as textual search has been augmented with suggested completions based on past or common queries, provenance can be used to suggest how computations can be completed or which steps might connect to a given subworkflow. In addition, provenance can help further science by accelerating publication and reuse. By incorporating provenance into publications, authors can more easily integrate their results, and readers can more easily verify and repeat results. However, reusing past computations requires maintaining stronger associations with any input data and underlying code as well as providing paths for migrating old work to new hardware or algorithms. This work presents a framework for maintaining data and code as well as supporting upgrades for workflow computations.
Speech recognition using discriminative classifiers

This work concerns the automatic speech recognition (ASR) problem, which roughly speaking, consists in converting digitized speech into text. More specifically, we study front ends and acoustic modeling, which together with language modeling and search, constitute a typical ASR system. The main approach is to investigate how ASR can benefit from recent advances in machine learning. We study feature selection and discriminative classifiers, such as support vector machines (SVM) and other kernel methods. These kernel classifiers achieved state-of-art results in many applications, but early experiments in ASR have exposed some difficulties. One of them is that the classifier's input in ASR is a variable-length vector. A second issue is that SVM and similar classifiers are restricted to binary (two-classes) problems. Another point is that the computational cost of training kernel classifiers can be prohibitive, given that relatively large datasets are used in ASR. We address all these three issues in this work. We use a hybrid framework, where discriminative classifiers are combined to hidden Markov models (HMM), such that the system is able to cope with a variable-length input. We discuss different architectures and expose their limitations. We propose a new architecture that is suitable for continuous speech recognition. We study error-correcting output code (ECOC) and improve existing bounds on the error rate of the multiclass classifier given the average binary distortion. We also present experimental results comparing several ECOC schemes, which bring new insights on ECOC performance. The proposed hybrid framework allows for adopting a heterogeneous set of features. When using only 25 selected features per SVM, the accuracy of the heterogeneous set was higher than when using a homogeneous set of 118 features based on a standard PLP front end. We also propose an algorithm to discriminatively train Gaussian mixture models (GMM), which is based on the extended Baum-Welch used for maximum mutual information estimation (MMIE) in ASR. Using this algorithm, we train a discriminative GMM classifier (DGMM) and compare its accuracy and sparsity to the ones obtained with kernel classifiers, such as SVM and the relevance vector machine.
Design of experiment development environment based on STC MCU

The hardware circuit's design of the experiment development environment based on STC89C516RD+MCU,the ISP program,the software example and the EMI of this system are introduced.The experiment development environment is used to help the students study the MCU technology and the experienced people develop and debug their product and lay a foundation for their application.
LA DISTRIBUTION DES BAUXITES SUR LE CRATON OUEST-AFRICAIN

RESUME - Des etudes anterieures sur les bauxites lateritiques nous permettent dedis.tingl. l! L~ci es originels d'alteration et des ~e~~(ives de degradation. La distribution de ces divers facies deba~~~lconque. D-;;;~lepaysage, Jeur-~t correspond a des mecanismes d'aplanissements ~~~himigues. A l'echelle ducraton ouest-africain, les difference§ d'altitude des gisements ~! - compte du bombemen_t. l! '! ~r! 1to! l-! _co urs~rtia! ~ --_. Laterite, Bauxite, Surface d'aplanissement, Craton ouest-africain Distribution of bauxites on the West African Craton
High dimensional modulation and MIMO techniques for access networks

Exploration of advanced modulation formats and multiplexing techniques for next generation optical access networks are of interest as promising solutions for delivering multiple services to end-users. This thesis addresses this from two different angles: high dimensionality carrierless amplitudephase (CAP) and multiple-input multiple-output (MIMO) radio-over-fiber (RoF) systems. High dimensionality CAP modulation has been investigated in optical fiber systems. In this project we conducted the first experimental demonstration of 3 and 4 dimensional CAP with bit rates up to 10 Gb/s. These results indicate the potentiality of supporting multiple users with converged services. At the same time, orthogonal division multiple access (ODMA) systems for multiple possible dimensions of CAP modulation has been demonstrated for user and service allocation in wavelength division multiplexing (WDM) optical access network. 2 x 2 MIMO RoF employing orthogonal frequency division multiplexing (OFDM) with 5.6 GHz RoF signaling over all-vertical cavity surface emitting lasers (VCSEL) WDM passive optical networks (PONs). We have employed polarization division multiplexing (PDM) to further increase the capacity per wavelength of the femto-cell network. Bit rate up to 1.59 Gbps with fiber-wireless transmission over 1 m air distance is demonstrated. The results presented in this thesis demonstrate the feasibility of high dimensionality CAP in increasing the number of dimensions and their potentially to be utilized for multiple service allocation to different users. MIMO multiplexing techniques with OFDM provides the scalability in increasing spectral effciency and bit rates for RoF systems. High dimensional CAP and MIMO multiplexing techniques are two promising solutions for supporting wired and hybrid wired-wireless access networks.
Spring and summer in the gulf of california - observations of surface thermal patterns

Une sequence d'images infrarouges des satellites NOAA-6 et TIROS-N du Golfe de Californie a ete obtenue, entre le 24 mars et le 22 aout 1981; ces photographies montrent une abondance de structures thermiques d'echelle moyenne, dont la distribution temporelle et spatiale manifeste la transition du regime des vents de printemps a celui d'ete. La structure la plus remarquable est une tache persistante d'eau froide entre l'ile Angel de la Guarda et la peninsule de la Basse-Californie, qui s'etend le long du Canal des Baleines. La presence de cette eau froide ne parait pas dependre du vent, mais resulte probablement du melange vertical induit par la forte maree dans le canal. Cette region est la plus froide du Golfe, delimitee par un systeme de fronts dont la configuration depend de la direction du vent. Au printemps, les vents de Nord-Ouest causent des remontees d'eau froide le long de la cote est du Golfe; la manifestation en surface de cet upwelling, clairement visible dans les images, s'etend jusqu'a la cote opposee. Une transition atmospherique s'opere fin mai et debut juin, et l'upwelling reapparait le long de la cote ouest du Golfe. L'upwelling d'ete est moins intense que celui de printemps