Miracle-dz/UniFAR-FTUs huggingface dataset:
Artificial Neural Network (ANN)-based Crack Identification in Aluminum Plates with Lamb Wave Signals:

An inverse analysis based on the artificial neural network technique is introduced for effective identification of crack damage in aluminum plates. The concepts of digital damage fingerprints and damage parameter database, which are prerequisites for neural network developing and training, are presented. Parameterized modeling for finite element analysis and an information mapping approach are applied to constitute the damage parameter database cost-effectively. The generalization performance of the neural network is examined by a process of `leave-one-out' cross-validation and diverse factors are discussed, based on which the optimization of the neural network architecture is evaluated. The capability of this inverse approach is assessed by two crack cases from experiments, with good accuracy obtained in damage parameters (central position, size, and orientation).
Range-only fuzzy Voronoi-enhanced localization of mobile robots in wireless sensor networks

SUMMARY Wireless Sensor Network (WSN) localization has shown a growing research interest, thanks to the expected proliferation of WSN applications. This work is focused on indoor localization of a mobile robot in a WSN using only inter-node range measurements, which are estimated by radio frequency signal strength attenuation. These measurements are affected by different sources of uncertainty that make them highly noisy and unreliable. The proposed approach makes use of fuzzy logic for modeling and dealing with such uncertain information. Besides, the position estimation is enhanced using a rough description of indoor environment. The experiments show that the proposed localization approach (i) is fault-tolerant, (ii) results feasible in low-density WSNs, and (iii) provides better position estimations than well-known localization methods when the position measurements are affected by high uncertainty.
Wavelet-based Illumination Compensation and Application to Face Recognition Using Eigenface Method

Engineered for human face recognition and based on Wavelet transform,a novel algorithm for the illumination compensation is proposed.By removing the low frequency components in the face image and reconstructing the image using only the high frequency components,the undesired effects of lightings on face image recognition can be greatly reduced.Experiments are carried out using the Yale face database and our own face database,and the testing results show that,for the lighting-compensated images,the recognition rate using the Eigenface method is increased significantly.
Seeing or Doing? - Pitch Recognition of Batters versus Pitchers: A Preliminary Report

This study was designed to examine function of the vastus lateralis muscle (VL) fibers during maximal voluntary contraction (MVC) in knee extension which was exerted based solely on the kinesthesia acquired from repeating the MVC movements. Fifteen men performed 10 consecutive isokinetic knee extensions comprising 7 passive contractions and 3 MVCs, which was repeated for 7 sets. In the first 3 sets, subjects were instructed to perform MVCs immediately a light cue appeared when the leg reached 60 deg knee joint angle in the 3rd, 6th, and 9th extensions; in the next 4 sets, subjects tried to maintain the timing of MVC repetitions without the light cue. VL electromyographic activity was monitored. The point where a fascicle arose from the deep aponeurosis and the pennation angle were measured on VL ultrasonic images. Subjects classified their MVC performance (force and timing) into 5 grades after each set. Based solely on kinesthesia (without the light cue), the VL fibers contracted tightly to a point where the fascicle arises from the deep aponeurosis, and it appeared to compensate for a delay in reaction time to start MVC. However, the subject’s self-evaluation remained unchanged despite the changes in muscle behavior during MVC. In the 4th set only, when the light cue was not used for the first time, did their self-evaluation tend to decrease and VL pre-activity was significantly increased. These results suggest that kinesthesia does not always correspond to actual muscle activity.
A model for supporting inter-organizational relations in the supply chain

The construction industry is highly fragmented and adversarial in nature, which has resulted in it being criticized for its poor project performance and lack of innovation. To improve performance, particularly inter‐organizational relations, organizations need to consider the formation of alliances with their project partners. Some construction organ‐ izations are beginning to initiate short‐term alliances with their customers and suppliers as part of a supply chain management strategy. However, such short‐term alliances inhibit feedback, which in turn supports learning and the development of mutual trust and cooperation. It is proffered that construction organizations should consider developing long‐term alliances, so as to enable parties to form learning alliances. The implications of forming different types of strategic alliances/partnering in construction are discussed. An inter‐organizational model that can be used to support learning and is founded on the principles of total quality management (TQM) is described. A case study is used to demonstrate that cooperative relationships can be used to cultivate a culture for reflective learning and mutual trust, beyond merely project‐specific performance improvements.
Robust implementation of generalized impedance control for robot manipulators

Compliant manipulation requires the robot to follow a motion trajectory and to exert a force profile while making compliant contact with a dynamic environment. For this purpose, a generalized impedance in the task space consisting of a second-order function relating motion errors and interaction force errors is introduced such that force tracking can be achieved. Using variable structure model reaching control, the generalized impedance is realized in the presence of parametric uncertainties. The proposed control method is applied to a multi-d.o. f. robot for an assembly task of inserting a printed circuit board into an edge connector socket. It is suggested that an assembly strategy which involves a sequence of planned target generalized impedances can enable the task to be executed in a desirable manner. The effectiveness of this approach is illustrated through experiments by comparing the results with those obtained using a model-based control implementation.
Sensorless Control of Permanent Magnet Synchronous Motor

In order to resolve the various defects for using mechanical sensors, sensorless control technology become a research hotspot. The various methods of the estimation about the position and speed of PMSM rotor are presented, and pointed out the advantages and disadvantages of them. The status and the development trend of the research about the sensorless are given.
Local control strategies for groups of mobile autonomous agents

The problem is studied of achieving a specified formation among a group of mobile autonomous agents by distributed control. If convergence to a point is feasible, then more general formations are achievable too, so the focus is on convergence to a point (the agreement problem). Three formation strategies are studied and convergence is proved under certain conditions. Also, motivated by the question of whether collisions occur, formation evolution is studied.
CLIMATE CHANGE AND FLOODS IN PARANÁ RIVER BASIN

The Paraná River basin is a major river system in Latin America, with a total drainage area of about 800.000 km2, until the confluence with the Iguaçu river. It is situated over six Brazilian states (Minas Gerais, São Paulo, Goiás, Mato Grosso do Sul, Paraná and Distrito Federal), being especially important for hydropower generation (including Itaipu dam), agriculture and human water supply. These water resources uses depends on the rivers and climate issues at the basin. Therefore for better water resources management it is important the knowledge of hydrological processes and its possible responses to climate changes. This study presents the use of the MGB-IPH distributed hydrological model to assess the impact of climate changes on maximum discharges for several control points across the Paraná River basin. Projections of climate variables, from four members of the ETA-CPTEC Regional Climate Model (RCM), under A1B emission scenario, were used to run the hydrological model. The simulated annual maximum discharges (floods) were analyzed throughout four 30-year’s time intervals (1960-1990, 2011-2040, 2041-2070 and 2071-2099). Results show in most control points an increase of simulated maximum floods. However considerable discrepancies are showed by different model members, for different return periods and control point locations. Considerations are made about uncertainties associated with data and employed methods.
Mining Driving Preferences and Efficient Personalized Routing

Current navigation systems only support limited personalized settings and typically only provide the shortest or fastest route to a target without considering other features in the road network. Proposing routes based on personal preferences, such as avoiding traffic lights or preferring tunnels over bridges, is desirable. These preferences towards different features can be determined from previous driving behavior, instead of being explicitly specified by each driver. We propose a general framework for personalized routing optimized for query speed. The personalization is achieved by using Customizable Contraction Hierarchies with a personalized cost function extracted from a data set consisting of 203 million GPS records distributed across 183 drivers. The records of each driver are separated into meaningful trips and map matched to roads in OpenStreetMap to obtain accurate GPS trajectories and features for each trip. Gradient Descent in TensorFlow is used to find the preference vector for each driver, i.e. to what degree the driver favors or disfavors each feature. Customizable Contraction Hierarchies is utilized to propose personalized routes based on the personal preference vectors. Analysis of the preference vectors indicate that drivers in general have similar preferences. However, we show that further improvement is possible when using personalized routing for the individual user compared to the general preferences of all users. We also achieve efficient personalized routing, with runtime performance of up to 173 times faster than traditional shortest path algorithms. We also show that performance of computing preferences is increased by one order of magnitude when using GPU computation.
A study of biped balance control using proportional feedback of ground reaction forces

The ground reaction force is informative for maintaining a biped balance with respect to unknown external forces. From this point of view, we proposed a new control method for static balancing that containing the integral feedback of the ground reaction forces in our previous papers. As the result, the adaptive posture changes are achieved for constant external forces. However, the convergence to the equilibrium posture is not so good because of the integral property of this control method. To improve this problem, the direct, i.e., proportional feedback of ground reaction forces is introduced in this paper. Using the linearization and the transformation to the discrete system, the stability as well as the speed of the convergence is evaluated. The analysis using the root locus method is effectively used to obtain the range of proportional feedback gain of the ground reaction forces for the given parameters of a model. Finally, numerical analyses reveal that the proportional feedback of ground reaction forces slightly improve the speed of the convergence. In conclusion, it is difficult to say that the drastic improvement of the convergence is expected by the proportional feedback of ground reaction forces.
On the Reynolds-Averaged Navier-Stokes Equations

This paper attempts to clarify an long-standing issue about the number of unknowns in the Reynolds-Averaged Navier-Stokes equations (RANS). This study shows that all perspectives regarding the numbers of unknowns in the RANS stem from the misinterpretation of the Reynolds stress tensor. The current literature consider that the Reynolds stress tensor has six unknown components; however, this study shows that the Reynolds stress tensor actually has only three unknown components, namely the three components of fluctuation velocity. This understanding might shed a light to understand the well-known closure problem of turbulence.
Electromagnetically Functionalized Colloidal Dispersions and Microfluidic Reconfiguration Mechanisms for Phase-Reconfigurable Reflectarray Elements

This work presents an application of a unique antenna reconfiguration mechanism that exploits the bidirectional, pressure-driven flow of electromagnetically functionalized colloidal dispersions (EFCDs) through a coaxial stub microfluidic impedance transformer (COSMIX). The COSMIX used in this work has one coaxial RF port that connects to the antenna and two fluid ports (inlet and outlet); the coaxial structure is electrically capped at the end and terminated by an open circuit (recessed inner conductor). The COSMIX provides a wide range of reactive tuning by adjusting the volume fraction of colloidal material in the EFCD. As an integrated mechanism in a microstrip patch reflectarray element, this translates to more than a full cycle of phase shift with nominal losses. Simulated and measured results of a COSMIX using an EFCD composed of 400 nm colloidal barium strontium titanate dispersed in low-loss silicon oil, and its integrated performance with reconfigurablephase reflectarray element, are presented for a 3 GHz design of microstrip patch reflectarray element.
nderstanding public support for eugenic policies : Results rom survey data

Little published empirical research has investigated public support for eugenic policies. To add to this literature, a survey on attitudes about eugenic policies was conducted of participants from Amazon Mechanical Turk who indicated residence in the United States (N > 400). Survey items assessed the levels and correlates of support for policies that, among other things, encourage lower levels of reproduction among the poor, the unintelligent, and people who have committed serious crimes and encourage higher levels of reproduction among the wealthy and the intelligent. Analyses of responses indicated nontrivial support for most of the eugenic policies asked about, such as at least 40% support for policies encouraging lower levels of reproduction among poor people, unintelligent people, and people who have committed serious crimes. Support for the eugenic policies often associated with feelings about the target group and with the perceived heritability of the distinguishing trait of the target group. To the extent that this latter association reflects a causal effect of perceived heritability, increased genetic attributions among the public might produce increased public support for eugenic policies and increase the probability that such policies are employed. © 2019 Western Social Science Association. Published by Elsevier Inc. All rights reserved.
A predictability-distinctiveness trade-off in the historical emergence of word forms

It has been proposed that language evolves under the joint constraints of communicative expressivity and cognitive ease. We explore this idea in the historical emergence of word forms. We hypothesize that new word forms that enter the lexicon should reflect a trade-off between predictability and distinctiveness. An emergent word form can be highly predictable if it efficiently reuses elements from the existing word forms, resulting in low cognitive load. An emergent word form should also be sufficiently distinctive from the existing lexicon, facilitating communicative expressivity. We test our hypothesis by examining the properties of 34,478 emergent word forms over the past 200 years of Modern English. We show how word forms at future time t + 1 are bounded statistically between n-gram generated word forms (highly predictable) and slang words that are outside the standard lexicon (highly distinctive) at time t. Our work supports the view of cognitive economy in lexical emergence.
Constructive Learning Modulo Theories

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Site 1 Site 2 C Cache AC Cache Resilient Cloud-of-Clouds Storage Data B Metadata d 1 d 2 A B C D CHARON CHARON Public Cloud

We present CHARON, a cloud-backed storage system capable of storing and sharing big data in a secure, reliable, and efficient way using multiple cloud providers and storage repositories to comply with the legal requirements of sensitive personal data. CHARON implements three distinguishing features: (1) it does not require trust on any single entity, (2) it does not require any client-managed server, and (3) it efficiently deals with large files over a set of geo-dispersed storage services. Besides that, we developed a novel Byzantine-resilient data-centric leasing protocol to avoid write-write conflicts between clients accessing shared repositories. We evaluate CHARON using micro and application-based benchmarks simulating representative workflows from bioinformatics, a prominent big data domain. The results show that our unique design is not only feasible but also presents an end-to-end performance of up to 2.5× better than other cloud-backed solutions.
AN ACCOUNT OF STUDENTS ’ LEARNING EXPERIENCE IN AN ENGINEERING PROBLEM-BASED LEARNING SUBJECT : IMPLICATIONS FOR IMPLEMENTATION

This paper is based on research conducted to investigate the learning experiences of Engineering students who are encountering PBL for the first time. Data sources include online questionnaire surveys, class observations and group and individual interviews. While analysis of the findings indicates that the response of these students towards certain activities was not markedly different from what was discovered in some earlier research studies in the non-engineering disciplines, an interesting point that emerged was that quite a significant number of students were able to cope well with the PBL process despite the fact that this was their first exposure and they did not go through intensive formal PBL preparatory training. They were able to find strategies to cope with their learning even though facilitators did not provide direct assistance. Implications of many other findings for instructional practice are also discussed.
The Role of Language Policy Documents in the Internationalisation of Multilingual Higher Education: An Exploratory Corpus-Based Study

Using corpus methods, this study explores the role of Language Policy (LP) documents in the internationalisation process of Spanish universities. It aims at understanding how non-Anglophone universities integrate English and local languages in the functions of education, research, and administration. Content analysis was used for the identification of key themes, and discourse analysis examined how those themes were textually expressed. Consistent with previous literature, this study shows that relevant strategic areas of LP deal with training, regulation, accreditation, and support measures. Results also highlight the role played by institutions in LP and the presence of language hierarchies between English and local languages. The discussion of these findings leads to further inquiry of mismatches between top-down institutional expectations and bottom-up realities regarding the design and implementation of institutional policies.
ON SYSTEM IDENTIFICATION IN POWER GENERATION SYSTEMS

Power generation systems with multiple input-multiple output have a wide operating range and may not be fully defined by a fixed model due to highorder nonlinear dynamics. As the parameters of the conventional excitation and speed governor controllers are determined by the system model which is linearized around one operating point, the performances of the controllers at different operating points can be reduced. Large disturbances encountered in the system can cause the controllers to operate outside the linear region. In addition, when the plant's operating structure changes with time or with changing environmental conditions, it is necessary to readjust the controller parameters. This readjustment is needed because the controller parameters that are set to provide the best performance at one operating point may not provide the same performance when the operating points change. In order to avoid the degradation in controller performance, system identification can be performed so that the controller parameters will have an adaptive structure. At the same time, it will be possible to make predictive maintenance, determine optimum operating points, diagnose faults and estimate performance by means of the power plant model built on the basis of system identification. In order to meet these requirements, system identification methods used in power generation systems have been examined throughout this review study and the performances obtained as a result of the changes made in the controllers have been compared.
Prediction of random sequences and universal coding

The prediction is represented as a set of probability estimates of possible continuations of the stochastic process. The prediction problem is solved in two settings: (1) given that the sequence is computable or (2) given that the sequence is stationary. In mathematical terms, the problem is related to coding theory and its solution accordingly relies on known information-theoretical results. Problems of Information Transmission, 1988, 24:2, 87–96
ACode : Web-based System for Automatic Evaluation of Java Code

Most current approaches for automatic evaluation of source code use input/output testing to validate student-submitted solutions. However, very few use software engineering metrics to analyze source code. With the limitations of related work in mind, we present, in this paper, our 4-stage approach for automatic evaluation of source code: i) the code is first compiled and checked for any errors; ii) the compiled code is then tested against a set of JUnit tests provided by the teacher; iii) a set of software engineering metrics is used to compare the student's solution against the teacher's solution; iv) and finally, based on the previous stages, feedback is provided to the students so they can selfevaluate and identify the areas in which they need further study.