Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling and control of nonlinear nitrogen oxide decomposition process

This paper presents a modeling and predictive control approach for a non-stationary nonlinear nitrogen oxide (NO/sub x/) decomposition process whose dynamics depend on the time-varying working-points and may be locally linearized. An off-line identified hybrid pseudo-linear ARX model (RBF-ARX model), which is composed of Gaussian radial basis function (RBF) networks and linear ARX model structure, is utilized to describe the process behavior. On the basis of the RBF-ARX model, a long range predictive control (RBF-ARX-MPC) strategy that does not require on-line parameter estimation is investigated for this kind of nonlinear process. Stability of the controller proposed under certain condition is discussed. Particularly, an industrial experiment result is also given to show satisfactory modeling precision and control performance obtained by the proposed approach in real industrial application.
The Development of a Factory Simulation System Using Actual Operating Data

A Simulation Process was developed at the El Segundo Division of Hughes Aircraft company whereby actual shop operating data contained in an existing data processing system is used as input to an IBM 7090 computer for simulation of the El Segundo Fabrication Shop. The Simulation Process was used as a study tool to evaluate the effects of various management decisions on the operation of the Fabrication Shop. "Management Technology", ISSN 0542-4917, was published as a separate journal from 1960 to 1964. In 1965 it was merged into Management Science.
The Active Control of Altitude over Differing Texture

Earlier passive judgment studies showed that detection of loss in altitude was more accurate over texture consisting of stripes parallel to the direction of flight, than over texture consisting of perpendicular stripes or a combination of both, i.e., a square pattern. The current experiment required the participants to actively control and maintain a constant altitude over similar texture in the presence of a pseudorandom windgust. Results corresponded to those of the earlier judgment study with altitude being better controlled when flight took place over parallel texture, than over perpendicular or square texture. Theoretical and applied implications of these findings are discussed.
Evaluating Review Content for Book Selection: An Analysis of American History Reviews in Choice , American Historical Review , and Journal of American History

This study applies content analysis to compare assessments of 153 titles in American history, each reviewed in Choice , American Historical Review (AHR) , and Journal of American History (JAH) between 1988 and 1995. A numerical rating system is used to quantify reviewers’ overall opinions and their use of specific evaluative criteria. Study findings include: most books were praised in all three journals; the mean overall rating was highest in Choice and lowest in JAH ; little agreement existed in identifying either outstanding titles or inferior titles; reviews in Choice contained the fewest evaluative criteria, but appeared 8.5 to 10.6 months prior to reviews of the same titles in JAH and AHR .
Deep Neural Networks in Computational Neuroscience

The goal of computational neuroscience is to find mechanistic explanations of how the nervous system processes information to give rise to cognitive function and behaviour. At the heart of the field are its models, i.e. mathematical and computational descriptions of the system being studied, which map sensory stimuli to neural responses and/or neural to behavioural responses. These models range from simple to complex. Recently, deep neural networks (DNNs) have come to dominate several domains of artificial intelligence (AI). As the term “neural network” suggests, these models are inspired by biological brains. However, current DNNs neglect many details of biological neural networks. These simplifications contribute to their computational efficiency, enabling them to perform complex feats of intelligence, ranging from perceptual (e.g. visual object and auditory speech recognition) to cognitive tasks (e.g. machine translation), and on to motor control (e.g. playing computer games or controlling a robot arm). In addition to their ability to model complex intelligent behaviours, DNNs excel at predicting neural responses to novel sensory stimuli with accuracies well beyond any other currently available model type. DNNs can have millions of parameters, which are required to capture the domain knowledge needed for successful task performance. Contrary to the intuition that this renders them into impenetrable black boxes, the computational properties of the network units are the result of four directly manipulable elements: input statistics, network structure, functional objective, and learning algorithm. With full access to the activity and connectivity of all units, advanced visualization techniques, and analytic tools to map network representations to neural data, DNNs represent a powerful framework for building task-performing models and will drive substantial insights in computational neuroscience.
A review of European code-validation studies in high-enthalpy flow

The paper focuses on the experiments performed in the European high–enthalpy facilities during the crewed space flight hypersonic ground testing technology programme. Emphasis is placed on simple configuration testing enabling validation of the physical modelling within non–equilibrium Navier–Stokes codes. The high–enthalpy facilities used are the ONERA hot shot F4 and the DLR Stalker tube HEG. The simple configurations are the Nozzle, the Electre blunt code, the hyperboloid flare, and the 70 degree blunt cone. In addition to these axisymmetric configurations, the Halis/Orbiter configuration was extensively tested and numerically computed to validate the ground to flight extrapolation methodology. Real gas effects on aerodynamic forces and in particular on pitching moment for the Halis have been reproduced in the F4 and compared with flight. The paper concludes with a series of recommendations for additional improvements in nozzle calibrations using non–intrusive methods and for an increased use of CFD for the design of experiments, the definition of the test environment, the interpretation of the results and finally the extrapolation to flight.
How Do We Know E-Learning Works? Or Does it?

E-learning is an accepted and commonly used component in tertiary education. However, success would appear to remain variable. Effective e-learning is a concept which sometimes eludes even the most reputable of online educators. It is an issue which plagues both the corporate and education fields and which is frequently aggravated by the numerous, often contradictory, studies on the subject. This article seeks to yield the merits of these studies in order to decipher some of the better means of effectively evaluating, designing and managing e-learning programmes and to accurately envisage what the future may hold for the development of online education in the tertiary education sector. It attempts to merge the experiences of the business and education sectors into an effective approach to be used in the design of such programmes and to present guidelines concerning the future of e-learning in tertiary education.
Towards generic image classification: an extensive empirical study

This paper considers the general problem of image classification without using any prior knowledge about image classes. We study variants of a method based on supervised learning whose common steps are the extraction of random subwindows described by raw pixel intensity values and the use of ensemble of extremely randomized trees to directly classify images or to learn image features. The influence of method parameters and variants is thoroughly evaluated so as to provide baselines and guidelines for future studies. Detailed results are provided on 80 publicly available datasets that depict very diverse types of images (more than 3800 image classes and over 1.5 million images).
Virtual prototype and experimental research on spatial kinematics of telescopic robotic excavator

In order to achieve autonomous operation, the kinematics model and experiment system of telescopic excavator are studied. Firstly, the operation space of the robotic excavator is classified and mapping models between different spaces are established based on the D-H method and geometrical relationship method. Then, the virtual prototype of the telescopic robotic excavator is built based on the SimMechanics software, leveling operation and digging operation are simulated; kinematics model and virtual prototype are tested and verified. Finally, experimental system is developed and trajectory tracking experiment is carried out. By comparing with the simulation results, the validity of the proposed control scheme is verified. The research of this article laid foundations for further study and application.
The Kalman Filter for the Equalization of a Digital Communications Channel

Consideration is given to the use of the discrete Kalman filter as an equalizer for digital binary transmission in the presence of noise and intersymbol interference. When the channel is modeled as an n -tap transversal filter, the Kalman filter assumes a similar form with "feed forward and feedback. " It is shown how the Kalman filter can be used to estimate both the tap weights and the binary signal. Computer results on a fixed 6-tap channel show that use of the 6-tap Kalman filter yields a considerably smaller error probability than when a conventional transversal equalizer with 15 taps is used. Limited computer studies on the same channel, assumed to be initially unknown, suggest that the Kalman filter is capable of converging rapidly in the adaptive mode. Though these results are very encouraging, much work remains in the study and optimization of performance in the adaptive mode.
Modelling and simulation of a pulse position modulation (PPM) communications system over urban multipath channels

The authors discuss the performance pulse position modulation (PPM) in an urban multipath environment. A computer simulation using the Signal Processing Worksystem (SPW) was developed that implements an urban multipath channel and a PPM modem. Using SPW, the communications performance of the PPM waveform for this particular channel was examined. By conducting Monte Carlo simulations with the SPW computer model, the average bit error performance of a PPM signal for the urban multipath channel was investigated with additive white Gaussian noise. The bit error performance of PPM was compared with and without multipath. Simulation runs were conducted using different values for the average inter-arrival time and amplitude of the multipath signals, and the signal-to-noise ratio. <<ETX>>
Neural-net-based receiver structures for single- and multiamplitude bandlimited signals in CCI and ACI channels

This paper presents analysis and performance-evaluation results for several neural-network-based nondecision-feedback receiver structures, which improve the performance of bandlimited single- and multiamplitude signals transmitted over additive interference channels, such as cochannel interference (CCI) and adjacent channel interference (ACI). In particular, we propose, analyze, and evaluate a training algorithm for Nyquist-filtered single- and multiamplitude signals, based upon a novel nonuniform signal-sampling technique. We also introduce a novel nonlinear activation function for multiamplitude signals and evaluate its performance via computer simulation and in conjunction with various bandlimited signaling formats, detection techniques, and neural-network structures. Bit-error rate (BER) performance-evaluation results of the proposed neural-network receivers for coherent and noncoherent detection of Nyquist- and Butterworth-filtered single- and multiamplitude signals have shown performance improvements in the presence of CCI and ACI.
Using Prezi In Higher Education

PowerPoint can be viewed as boring and commonplace (Craig & Amernic, 2006). While it is a great tool, using a more dynamic presentation editor may better capture the attention of a class or any other group of people. Having an editor that is cloud-based allows for more flexibility and collaboration than is possible with PowerPoint (Settle, Abrams, & Baker, 2011). Prezi is a cloudbased presentation editor that allows the user to create presentations that zoom. The goal is to make presentations more dynamic in nature and not as linear in structure. Being able to access your presentation from the internet also allows for a much more flexible working environment. Presentations can be edited at any place and any time. A link is available that will allow others to view the presentation as well. Also, any changes made to a presentation will be reflected in what a viewer sees. Students can use Prezi to much more easily work collaboratively on projects.
Capture-recapture Estimation for Conflict Data and Hierarchical Models for Program Impact Evaluation

A relatively recent increase in the popularity of evidence-based activism has created a higher demand for statisticians to work on human rights and economic development projects. The statistical challenges of revealing patterns of violence in armed conflict require efficient use of the data, and careful consideration of the implications of modeling decisions on estimates. Impact evaluation of a complex economic development project requires a careful consideration of causality and transparency to donors and beneficiaries. In this dissertation, I compare marginal and conditional models for capture recapture, and develop new hierarchical models that accommodate challenges in data from the armed conflict in Colombia, and more generally, in many other capture recapture settings. Additionally, I propose a study design for a non-randomized impact evaluation of the Millennium Villages Project (MVP), to be carried out during my postdoctoral fellowship. The design includes small area estimation of baseline variables, propensity score matching, and hierarchical models for causal inference.
Modeling Path Dependent Derivatives Using CUDA Parallel Platform

The pricing of derivative securities with path dependence is governed by stochastic diﬀerential equations which rarely have a closed-form, analytic solution. These complex derivatives can be valued used simulation methods known as Monte Carlo methods, which converge slowly and thus require signiﬁcant computational cost. This thesis demonstrates how the use of the GPU (Graphics Process Unit) can drastically lower the computational cost of these methods. The Longstaﬀ-Schwartz Least Squares Monte Carlo Method is implemented to price American options, and suggestions are made for improving the eﬃciency of the algorithm. A model for valuing Guaranteed Lifetime Withdrawal Beneﬁt (GLWB) options using Monte Carlo methods is also proposed and implemented in CUDA’s parallel environment. Finally, the sensitivity of the GLWB option to various factors and the ramiﬁcations for insurance companies who sell this guarantee is discussed.
ENHANCING THE COVERAGE OF INDOOR RADIO LOCALIZATION BY DISTRIBUTED COMPUTATIONS

The prevalent evaluation criterion for indoor local positioning systems (ILPS) is the achievable accuracy in terms of Euclidean distance between estimated and true position. Systems relying on received signal strength (RSS) ranging often use a distributed collection of RSS sensor data at reference nodes and a centralized position estimation. For this direct remote positioning, the accuracy is dependent on the reference node density and thus, is indirect proportional to the achievable coverage. To split up the dependency between these two criteria, we propose a distributed weighted centroid localization (dWCL) strategy with a hierarchical sensor data field bus. Accuracy and coverage of centralized and distributed WCL algorithms are compared for a one-dimensional tracking simulation and 196 reference nodes, arranged in up to 28 gateway segments. Using distributed computations, the localization system’s coverage is increased by factor ten while the location estimation error increases only slightly.
Optimization of Algorithms in Relation to iBeacon

The boom of portable electronics and high-speed wireless networks has brought changes throughout society, including development in positioning systems. Indoor localization is more and more important. With modern technology, we are able to track people in shopping complexes and offer them discounts for surrounding goods. The following text deals with the design and description of methods to determine user’s position based on fingerprint technology. The text focuses on the description of algorithms in relation to the iBeacon. Three main algorithms were described in the text. The following text describes the implementation of Knn algorithm. The main goal of this paper is to clearly describe the basic positioning algorithms for the readers, introduce implementation of the Knn algorithm and its usage in real environment.
Design and implementation of a hands-free electrolarynx device controlled by neck strap muscle electromyographic activity

Total laryngectomy surgery results in a loss of human voice, and a corresponding deficit for normal speech production. A key disadvantage of available voice replacement prostheses is the inconvenience of using one hand for operating the device when speaking. To address this problem, a hands-free portable electrolarynx (EL) was developed. This wearable device transforms neck muscle electromyographic (EMG) activity into control signals for initiation and termination of voicing. Using the new system (EMG-EL), seven normal subjects produced voice initiation that was as fast as with a commercial hand-held EL, and faster than with normal voice. The EMG-EL was also tested in a laryngectomy subject, who produced voice initiation that was slower than with the commercial EL, but faster than with tracheo-esophageal voice. Voice termination with the EMG-EL was slower than with the other voice sources for all subjects.
Energy-efficient high-speed packet access in WCDMA systems with smart packet dispatching: performance with TCP/IP based applications

This paper presents a performance analysis of high-speed downlink packet access (HSDPA) for TCP/IP applications in WCDMA systems with smart packet dispatching (SPD). The focus is on energy efficiency over burst-error correlated fading channels. The fading-channel characterization thereof is due to low user mobility of the primarily targeted environments for HSDPA applications. Using theoretical analysis and simulation, we show that in burst-error correlated fading channels adapting the time instant of scheduling/dispatching retransmission to fading-channel conditions: (i) significantly reduces the number of retransmissions; and (ii) helps closing the gap of bad channel states allowing the system to operate with much lower fade margin or transmit-power. It thus improves the efficiency of resource utilization in HSDPA. This method is referred to as SPD. In our modeling, we study the impact of fading based on fade duration statistics. The effects of diversity, mobility, and QoS constraints (such as timer for discarding out-of-date packets) are taken into account. The results are used to develop simple and energy-efficient adaptive SPD mechanisms.
Discourse Management in Three Modalities

The strategies adopted by dyads engaged in decision-making interactions are compared in face-to-face, synchronous computer mediated, and asynchronous computer mediated (email) communication environments. The results show that the decisions required to plan an awards ceremony were encoded in similar, routine sequences of discourse functions in all three modalities, but the sequences were packaged very differently in the synchronous and asynchronous environments. In face-to-face interaction, short turns rarely encoded more than one or two functions and did not combine decisionmaking functions and other discourse management functions in the same turn. Many messages in the synchronous computer-mediated environment had the same structure, although some dyads in the synchronous computer-mediated environment interacted using long messages that encoded several functions and combined decision-making functions with other managerial functions. In the email interactions, in contrast, messages were routinely structured so that decision-making functions were preceded and followed by other discourse management functions. Turn-taking is found to be impacted by decision management, transmission management, interpersonal management, and modality. Moreover, the language behaviors that emerged in the asynchronous environment suggest that many features associated with use of the written word are more appropriately understood as characteristic of asynchronous communication.
The relationship between employee initiatives and occupational expertise throughout the career in small- and medium-sized enterprises in The Netherlands

The present study describes the relationship between three individual predictor variables and the degree of occupational expertise of middle and higher-level employees in three different career stages working in small and medium-sized enterprises (SMEs). Occupational expertise is operationalised by means of five dimensions, i.e. knowledge, meta-cognitive knowledge, skills, social recognition and growth and flexibility. The predictors in question are: the degree of participation in social networks, the degree of participation in training and development programmes and the degree of initiatives that are taken by the individual employee. Hypotheses have been tested with original survey data from 233 higher-level employees and 217 direct supervisors.
The Effect of Item Parameter Estimation Error on Decisions Made Using the Sequential Probability Ratio Test

Abstract : A series of computer simulations were performed in order to observe the effects of item response theory(IRT)item parameter estimation error on decisions made using an IRT-based sequential probability ratio test. Specifically, the effects of such error on misclassification rates and the average number of items required for either a mastery(pass)or nonmastery(fail) decision were observed under varied SPRT conditions. These conditions include the a priori or nominal type I(alpha)and typeII(beta)error rates, the simple hypotheses tested by the SPRT procedure, and the composition of the item pool(specifically the a, b, and c parameters which characterized the items according to a three-parameter logistic model)used to administer the SPRT. The results of these simulations showed that these SPRT decisions are not greatly affected by this particular level of error in parameter estimates modeled in this study. Misclassification error rates were slightly greater when estimation error in the item parameters was present, but such differences appear to be negligible. Keywords: Adaptive testing.
Spatio-Temporal Interpolation using gstat

We present new spatio-temporal geostatistical modelling and interpolation capabilities of the R package gstat. Various spatio-temporal covariance models have been implemented, such as the separable, product-sum, metric and sum-metric models. In a real-world application we compare spatiotemporal interpolations using these models with a purely spatial kriging approach. The target variable of the application is the daily mean PM 10 concentration measured at rural air quality monitoring stations across Germany in 2005. R code for variogram fitting and interpolation is presented in this paper to illustrate the workflow of spatio-temporal interpolation using gstat. We conclude that the system works properly and that the extension of gstat facilitates and eases spatio-temporal geostatistical modelling and prediction for R users