Miracle-dz/UniFAR-FTUs huggingface dataset:
Mapping the Field and Terminology

We give into temptation and say that over the past 100 years, the means by which we learn has changed out of all recognition. According to the utopian view, in the developed world education has changed from being classroom‐based and teacher‐led, to life‐long learning that is learner‐focused and capable of being delivered where and when the learner wants. Training has evolved from being a one‐on‐one activity where an expert demonstrated his or her skills to a novice, to a flexible, packaged delivery of targeted training at the point and time of need. It is learning technology that has made this revolution possible. There are several problems with this utopian view. First, the majority of learning technol­ ogists live in the developed world and even in the developed world most of the education system still relies on classroom‐based, teacher‐led learning. In developing countries, with a few notable exceptions, there has been little change in traditional educational practices. In the world of training, classroom‐ and workshop‐based, instructor‐led sessions are still the norm rather than the exception. Second (and more optimistically), this view is but a snapshot of a world in which change is accelerating. It would be naïve to assume that the point we have reached now represents the pinnacle of achievement for learning technology. Nils Bohr is quoted as saying, “Prediction is very difficult, especially if it’s about the future. ” and the only thing that we might say with any confidence is that after the next hundred years education and training will (probably) look very different. St Jude, who lived in the first century AD and who was martyred in Persia or Syria around 65 AD, is the patron saint of impossible causes. Many have adopted him as the patron saint of learning technology. They feel that although learning technology has so much to offer to the process of learning and performance improvement, its promise has Mapping the Field and Terminology
Dependence of Ultimate Bending Moment of Box Girders on Panel’s Slenderness

The structural behavior of box-girders under pure bending moment is analyzed and discussed on the basis of the results of experiments performed by the author. The box girders are geometrically similar but made of different materials and having different stiffener’s geometries. The influence of the main parameters that influence the ultimate strength of box-girders under pure bending moment is analyzed and discussed. Practical design formulas are derived and presented, allowing for a fast evaluation of the performance of the boxes under pure bending. The achievements may be extrapolated to the analysis of the hull girder of ship and used as a basis for the structural codes of ship design.
Dynamical Systems Approach To Turbulence

Thank you very much for downloading dynamical systems approach to turbulence. Maybe you have knowledge that, people have search hundreds times for their chosen novels like this dynamical systems approach to turbulence, but end up in harmful downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they juggled with some infectious virus inside their laptop. dynamical systems approach to turbulence is available in our book collection an online access to it is set as public so you can download 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 dynamical systems approach to turbulence is universally compatible with any devices to read.
The Effect of Internal Environmental Factors on Operational Excellence of Manufacturing Industry: A Pilot Study

This paper examines internal environmental factors that influence the operational excellence of SMEs manufacturing sector in Malaysia. The objective of this paper is to measure the reliability and validity of internal environmental factors and its construct. A pilot study was conducted among 30 SMEs company in Terengganu which represented by the managerial level who are closely linked to the manufacturing operations. This study adopted a survey method by using simple random sampling method for the data collection. The reliability and validity of the instrument were examined through content validity, face validity and reliability based on expert assessment and the data was analyzed using the statistical software SPSS version 22. The result shows that the survey instrument is reliable and valid. Therefore, this study expected will help the Malaysian SMEs manufacturing sector in determining the effect of internal environmental factors on operational excellence.
Infrastructureless Pedestrian Positioning (Infrastrukturloser Fußgängerpositionierung)

Many methods for pedestrian positioning exist. In outdoor environments, global satellite navigation systems such as GPS can give satisfactory positioning performance in many circumstances encountered by pedestrians. Pre-installed outdoor communication infrastructure, such as cellular networks or TV broadcast signals, can be leveraged for pedestrian uses. Specialized RF, ultrasound or light ranging beacons can also be installed indoors for positioning in spaces as small as individual rooms and networks of transponders can cover large installations. However, all these systems use transmitted signals that are subject to attenuation, blocking, reflection and diffraction effects, all of which can greatly reduce the accuracy and availability of range information. In contrast, Inertial Navigation Systems (INS) are “sourceless” in that they do not rely on any external transmitted signals. This explains their great utility in highend land, air, marine and space guidance, navigation and control systems, where depending on external signals for aiding purposes might be impractical or risky. Unfortunately, for pedestrian navigation, unaided traditional INSs are of limited use. If the upper limit to the position error is set to some reasonable value, say a few metres after some 10s of minutes of self-contained navigation, either a very accurate navigation-grade INS or very frequent zero velocity updates (ZUPTs) with a tacticalgrade system are required. These realities, plus the fact that navigation-grade INSs will remain large, costly and power-hungry for at least another 10 years, means that traditional mechanization schemes for self-contained, personal navigation are currently impractical. The overall objective of this thesis is to investigate how low-grade, low-cost, and low-power INSs can be exploited for pedestrian positioning and in particular for first responder scenarios. To begin, a thorough bibliography of past research permits the identification of the relative merits of various technologies that have been proposed for emergency, rescue and military operations. Next, an extension to the well-studied occurrential pedestrian dead reckoning (PDR) technique using headgear-mounted motion sensors is described and good distance over ground (DoG) estimation performance is demonstrated. Since it is not a simple matter to apply occurrential techniques to a large class of locomotion patterns, the foot-inertial technique is then explored as an alternative. With an IMU (Inertial Measurement Unit) attached to (and in the future, mounted in) footwear, simplified strapdown inertial navigation techniques allow for omnidirectional motion patterns, very good DoG estimates, and vertical excursion characterization. Unfortunately, large heading jumps occur indoors, caused by magnetic disturbances and by the use of a generic orientation filter. It is shown how these heading errors can be modeled and then mitigated via map filtering techniques running over minimal a priori building geometry information.
ANYexo: A Versatile and Dynamic Upper-Limb Rehabilitation Robot

This letter presents a versatile upper-limb exoskeleton based on low-impedance torque controllable series elastic actuators. This experimental platform is designed to validate novel algorithms and hardware concepts for more autonomous therapy of moderately and severely affected patients with a neural impairment. The design is optimized to achieve a large range of motion (ROM) and robust interaction force control to best mimic the compliant and accurate haptic interaction of therapists. The presented robot covers the relevant ROM required for activities of daily life (ADL) particularly, including poses close to the torso, head, and behind the back. The kinematics are optimized for high manipulability during ADL and low inertia. We use modified modular series elastic actuators that provide the required power and torque control performance. We demonstrate highly transparent behavior up to speeds of 11 rad/s with a feed-forward torque controller based on an accurate dynamic model. The presented robot unites a large ROM with optimized manipulability, high nominal power to weight ratio (111 W/kg), accurate torque control at speeds sufficient for unconstrained recovery of patients, and versatility for a broad variety of experiments in one device. To our knowledge, no other device is tailored to such an extent for this application.
Sampling-Based Path Planning for Cooperative Autonomous Maritime Vehicles to Reduce Uncertainty in Range-Only Localization

The following letter presents an adaptive path planning algorithm for cooperative localization in the maritime environment. It considers the scenario where an Autonomous Surface Vehicle (ASV) acts as a Communication and Navigation Aid (CNA) to support Autonomous Underwater Vehicles (AUVs) with range measurements. As AUVs have no access to GPS while submerged, range measurements can bind the otherwise continuously growing navigational error. This can be done by methods such as range-only Extended Kalman Filter (EKF). In such methods, the resulting uncertainty and positional error depends on the geometry between transmitter (CNA) and receiver (AUV). This letter proposes a planning algorithm that combines priority based expansion of a search tree with random sampling-based exploration to position the CNA at strategic positions to transmit ranging messages at optimal times to reduce the uncertainty and error at the AUVs’ position. The approach is validated and shows an increased confidence for AUVs’ localization in simulated environments as well as real experiments using a dataset gathered from AUV Sirius.
Testing and Validation of Automotive Point-Cloud Sensors in Adverse Weather Conditions

Light detection and ranging sensors (LiDARS) are the most promising devices for range sensing in automated cars and therefore, have been under intensive development for the last five years. Even though various types of resolutions and scanning principles have been proposed, adverse weather conditions are still challenging for optical sensing principles. This paper investigates proposed methods in the literature and adopts a common validation method to perform both indoor and outdoor tests to examine how fog and snow affect performances of different LiDARs. As suspected, the performance degraded with all tested sensors, but their behavior was not identical.
A Design of PH3 Monitoring System Based on NB-IoT

This paper describes a phosphine gas monitor based on NB IoT, which is designed by us and can be used to remotely measure the concentration of phosphine gas released during grain fumigation. The monitor uses the STM32 to control the sensor work, and after the measurement, upload the data to the network server through the NB IoT, and further data analysis can be performed on the server to achieve high accuracy, low power consumption (it can work for two years), low packet loss rate (less than 0.1%) and so on.
Adaptive neural network‐based fault‐tolerant trajectory‐tracking control of unmanned surface vessels with input saturation and error constraints

The unmanned surface vessel (USV) plays an important role in smart ocean. This study proposes an adaptive fault-tolerant tracking control for USVs in the presence of input saturations and error constraints. A tan-type barrier Lyapunov function is utilised for the error constraints and the neural networks are employed to treat the model uncertainty. Moreover, the adaptive technique combined with the backstepping method not only enables the actuator fault-tolerant controller to address the fault effects but also handles the external disturbances and input saturations. The proposed control approach can track the desired trajectory with error constraints and the system is guaranteed to be uniformly bounded under certain actuator failure. Numerical simulation is carried out to verify the effectiveness of this control strategy.
An Improved Direction-preserving Trajectory Simplification Algorithm

There is a lot of valuable information in the trajectory data, but the sheer volume of data creates challenges for storing and analyzing data. Therefore, the simplification of the trajectory data is particularly important. The directional information of the trajectory contains a large amount of semantic information, and the trajectory contour can be well maintained based on the directional simplification. The algorithm in this paper, is based on the improvement of the classical direction-preserving simplification algorithm DPTS. The directed weighting graph is used to obtain the unique path according to the shortest path algorithm. The experimental evaluation shows that the average direction error is declined under the same compression ratio.
Voting by Confidence

The method of voting by confidence exploits not only the label but also the confidence outputted by base classifiers. The bound of training error rate is drawn as well as that of expected error rate. It is shown that error-independent base classifiers should been employed in order to minimize the bound of expected error rate. And voting by confidence can achieve expected error rate that decreases exponentially fast with the decreasing of base classifiers’ error rate. Also, the bound will decrease with the increasing of voting times if the base classifiers’ error rate is not too high. For minimizing the bound of training error rate, a scheme that assigns weights to base classifiers is introduced. An ensemble algorithm named CAB is obtained when applying the scheme to AB, namely a kind of Bagging. Experiments with UCI datasets show the validity of CAB.
The context of neighbourhood regeneration in Western Europe. A comparative study of nine neighbourhoods undergoing physical and social economic regeneration

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Wearable Hearing Assist System to Provide Hearing-Dog Functionality

This study developed a wearable hearing-assist system that can identify the direction of a sound source while using short-term interaural time differences (ITDs) of sound pressure and convey the sound source direction to a hearing-impaired person via vibrators that are attached to his or her shoulders. This system, which is equipped with two microphones, could dynamically detect and convey the direction of front, side, and even rear sound sources. A male subject was able to turn his head toward continuous or intermittent sound sources within approximately 2.8 s when wearing the developed system. The sound source direction is probably overestimated when the interval between the two ears is smaller. When the subject can utilize vision, this may help in tracking the location of the target sound source, especially if the target comes into view, and it may shorten the tracking period.
Fuzzy proportional-integral speed control of switched reluctance motor with MATLAB/Simulink and programmable logic controller communication

In this study, the auto-tuning proportional-integral controller is used to control the speed of a switched reluctance motor. The control algorithm is executed by the programmable logic controller. The proportional integral gains are determined via fuzzy logic. Fuzzy logic is executed on a separate computer via MATLAB/Simulink software. The data exchange between the programmable logic controller and MATLAB/Simulink is done with object linking embedding/component for the process. The fuzzy proportional integral control algorithm is compared with the conventional proportional integral controller. We reduced the load on the programmable logic controller via executing fuzzy logic in a separate computer and at the same time eliminated the disadvantages of the conventional proportional-integral controller. With the proposed method, the engine reached the reference speed value in a short time and the overshoots were eliminated in variable conditions such as different load and different speed conditions.
Identification of Secondary Crash Risk Factors using Penalized Logistic Regression Model

Secondary crashes (SCs) have increasingly been recognized as a major problem leading to reduced capacity and additional traffic delays. However, the limited knowledge on the nature and characteristics of SCs has largely impeded their mitigation strategies. There are two main issues with analyzing SCs. First, relevant variables are unknown, but, at the same time, most of the variables considered in the models are highly correlated. Second, only a small proportion of incidents results in SCs, making it an imbalanced classification problem. This study developed a reliable SC risk prediction model using the Least Absolute Shrinkage and Selection Operator (LASSO) penalized logistic regression model with Synthetic Minority Oversampling TEchnique-Nominal Continuous (SMOTE-NC). The proposed model is considered to improve the predictive accuracy of the SC risk model because it accounts for the asymmetric nature of SCs, performs variable selection, and removes highly correlated variables. The study data were collected on a 35-mi I-95 section for 3 years in Jacksonville, Florida. SCs were identified based on real-time speed data. The results indicated that real-time traffic variables and primary incident characteristics significantly affect the likelihood of SCs. The most influential variables included mean of detector occupancy, coefficient of variation of equivalent hourly volume, mean of speed, primary incident type, percentage of lanes closed, incident occurrence time, shoulder blocked, number of responding agencies, incident impact duration, incident clearance duration, and roadway alignment. The study results can be used by agencies to develop SC mitigation strategies, and therefore improve the operational and safety performance of freeways.
Distance-Based Cooperative Relative Localization for Leader-Following Control of MAVs

In multi-robot systems, the capability of each robot to relatively localize its neighbors is a crucial requirement, which needs to be resolved as a prerequisite for almost any distributed scheme of operation. Notably, this problem proves to be quite challenging in GPS-denied environments. In this letter, we investigate a problem of simultaneous relative localization and leader-following control of aerial robots by using only ranging and odometry sensors, waiving the need of external positioning systems. To tackle this challenge, we propose a cooperative estimation-control scheme where specialized agents called orbiters are tasked with maintaining persistently exciting trajectories to facilitate exponential convergence of both relative localization and tracking errors for itself and others. Numerical simulations and experiments on quadcopters in a GPS-denied environment are carried out to validate the theoretical findings.
Assessing Variable Sampling Time Controllers for Five-Phase Induction Motor Drives

Finite control set predictive controllers perform a direct digital control of power converters without a modulation stage such as pulsewidth modulation. Opposed to the inherent positive traits such as high flexibility and enhanced bandwidth, an increase in harmonic content is usually reported. The concept of variable sampling time (VST) has been recently introduced as a means to alleviate the harmonic content. The nonuniform sampling approach can be seen as an extra degree of freedom that is conferred to the controller, keeping its model-based feature. This paper goes beyond the state-of-the-art of VST controllers, introducing a new scheme for the computation of the sampling time. The resulting VST controller is experimentally assessed for a two-level five-phase induction motor drive, where steady-state and dynamic performance tests confirm the interest of the proposal.
Homotopically equivalent simple loops on 2-bridge spheres in Heckoid orbifolds for 2-bridge links (I)

In this paper and its sequel, we give a necessary and sufficient condition for two essential simple loops on a 2-bridge sphere in an even Heckoid orbifold for a 2-bridge link to be homotopic in the orbifold. We also give a necessary and sufficient condition for an essential simple loop on a 2-bridge sphere in an even Heckoid orbifold for a 2-bridge link to be peripheral or torsion in the orbifold. This paper treats the case when the 2-bridge link is a $(2,p)$-torus link, and its sequel will treat the remaining cases.
Concrete–Concrete Bond in Mode-I: A study on the Synergistic Effect of Surface Roughness and Fiber Reinforcement

Effectiveness and durability of interventions on deficient concrete structures remain a major concern, comprising the challenge of old-to-new concrete compatibility and bonding, as stress concentrations and microstructural flaws at the old-to-new concrete interface compromise structural integrity and create migration paths for harmful contaminants. Fiber reinforcement can be beneficial, but proper quantification and mastering of fundamental mechanisms is required before these are fully utilized. A study is presented on Mode-I crack growth resistance at the interface between two concretes (substrate and repair). Countered Double Cantilever Beam tests are performed, crack growth resistance curves calculated (Modified Linear Elastic Fracture Mechanics), and complemented with analysis of interfacial roughness and failure planes. Polyvinyl alcohol (8 and 12 mm length) and steel fibers (13 mm) are introduced in the repairs at 0.5% and 1% volume fractions. Results indicate that fibers improve fracture behavior of both the repair material and substrate-repair interface; correlations with interfacial roughness, crack deviation, and fracture parameters are discussed.
An evolutionary algorithm for global optimization based on self-organizing maps

In this article, a new population-based algorithm for real-parameter global optimization is presented, which is denoted as self-organizing centroids optimization (SOC-opt). The proposed method uses a stochastic approach which is based on the sequential learning paradigm for self-organizing maps (SOMs). A modified version of the SOM is proposed where each cell contains an individual, which performs a search for a locally optimal solution and it is affected by the search for a global optimum. The movement of the individuals in the search space is based on a discrete-time dynamic filter, and various choices of this filter are possible to obtain different dynamics of the centroids. In this way, a general framework is defined where well-known algorithms represent a particular case. The proposed algorithm is validated through a set of problems, which include non-separable problems, and compared with state-of-the-art algorithms for global optimization.