Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of a surrogate model for wind farm control

We present a method to derive a surrogate model for wind farm control. The procedure is based on a stochastic approach using generalized polynomial chaos (PC) and high-fidelity simulations. The turbine control law and the incoming wind conditions, such as speed and directions, are treated as uncertain variables. Wind farm power production is viewed as the random process depending on these uncertain variables. Thus, polynomial chaos expansion is used to obtain a response function that provides the wind farm power production as a function of the turbine control parameters and the wind speed and direction. The response function is obtained by using a finite set of deterministic realizations, which consist in high-fidelity simulations for certain values of wind speed, direction and control parameters, interpolated by polynomials. In PC, the interpolating polynomial basis and the set of realizations are selected according to the probability density function of the uncertain parameters. This allows using a limited number of realizations to obtain an accurate response function and provides uncertainty bounds on the model. Thus, a mapping of the optimal control settings is obtained for any wind speed and direction to be employed for real-time wind farm operations. In this work, the procedure is validated against field measurements in a real wind farm in north Texas. The surrogate model is obtained by performing 64 simulations with our in-house code interpolated by 7th -order Hermite polynomials. The energy production computed with the surrogate model is accurate within 2% of the measured SCADA data. Once the response function has been obtained, an optimization problem is solved to find the control parameters maximizing the wind farm power production.
Short Packet Communications in Large-Scale Wireless Powered Networks

Wireless power transfer and short packet communications are two technologies ideal for next-generation low-power wireless networks such as the Internet of Things. In this paper, we consider the employment of wireless powered short packet communications and investigate its performance from a macroscopic point-of-view. Specifically, we consider a terminal randomly deployed in a single-cell, which communicates with the cell's access point (AP) over finite block-lengths. The terminal uses the energy harvested from the AP's transmitted signals to transmit back to the AP the required information bits. We derive analytical closed-form expressions for the average error probability and delay, in terms of the terminal's possible locations in the cell. Our results provide important insights behind the performance of wireless powered short packet communications in large-scale networks.
A Study on Slippage and Tip-over Stability for an Omnidirectional Mobile Robot with Longitudinal MY-wheels

This paper presents an omnidirectional mobile robot (OMR) with longitudinal MY-wheels, and because of the switching effect of the contact radius which is the distance between the robot center and the contact point with the ground, the real model of this OMR is a discontinuous model. In order to study the influence of different approximate continuous models on the performance of the OMR, a parameterized model based on the cycloidal curve is established. A solving method of the reaction force from the ground using the movement state of the OMR is proposed, and with the solved reaction force and the force-angle stability measurement (FASM) the slippage and tip-over stability of the OMR can be evaluated. Finally, the simulation is performed, and the influence of different models on the slippage and tip-over stability of the OMR is analyzed.
Complexity factors affecting research and development projects duration

Research and development (R&D) has become important to the survival and performance of many organizations in terms of market share, increased profitability, technological advancement and competitive advantage. Therefore, the management of these projects is crucial in ensuring that they achieve the project goals and objectives. However, due to their high uncertainties and complexities, R&D projects are often beset with time delays that result in termination before achieving the project objectives. A case study was conducted on a R&D project team in an engineering organization to identify the complexities of R&D project management process and their impact on project duration. The results of the study show that different factors contribute to R&D project complexity and the delay in these projects is due to ineffective management of R&D project management process.
Self-Weighting Grading Biomarker Based on Graph-Guided Information Propagation for the Prediction of Mild Cognitive Impairment Conversion

Mild cognitive impairment (MCI) represents a transitional stage between normal aging and Alzheimer’s disease (AD), with a higher risk to convert to AD. The information of AD and normal control (NC) subjects can aid the classification between progressive MCI and stable MCI. In this paper, we develop an effective biomarker by combining the auxiliary information of AD and NC subjects with the relationship of brain regions of MCI subject, which makes best of auxiliary information and improves the prediction accuracy of MCI-to-AD conversion. Specifically, a projection vector is first obtained for each MCI subject via graph-guided information propagation. Next, the information of projection vector is integrated using a self-weighting grading method to acquire the novel biomarker. Finally, the self-weighting grading biomarkers derived from multiple morphological features are combined to provide more accurate prediction of MCI-to-AD conversion. Experimental results on the Alzheimer’s Disease Neuroimaging Initiative database demonstrate the effectiveness of the proposed biomarkers for the prediction of MCI conversion.
A Novel Capture-Aware TDMA-Based MAC Protocol for Safety Messages Broadcast in Vehicular Ad Hoc Networks

Since broadcast communication is considered the highly appropriate technique for the dissemination of safety messages in vehicular ad hoc networks (VANETs), it is vital to design a high-efficient medium access control (MAC) protocol for vehicles accessing wireless channel. However, the characteristics of VANETs, such as high-velocity vehicles, highly dynamic topology and very unstable communication link, lower the performance of existing MAC protocols. In this paper, we propose a novel Capture-aware TDMA-based MAC (CT-MAC) protocol, which can better utilize the channel resource than the existing MAC protocols by setting the optimal frame length with taking capture effect into account. To obtain the realistically optimal frame length, the closed form expression of probability of capture effect is derived under Nakagami- ${m}$ fading channel which is proper to model the small-scale fading channel in VANETs. Besides, a discrete Markov chain is introduced to analyze the impact of capture effect on channel utilization. The theoretical analyses and the simulation results show that the reliability of broadcast and the efficiency of channel utilization are significantly improved.
A Study on Breathing and Heartbeat Monitoring System During Sleeping Using Multi-Piezoelectric Elements

Incorporating real time health monitoring systems in a non-restrictive way, can significantly improve an individual’s quality of life in the treatment of sleep disorders. This paper suggests a novel system fornon-wearable, cost effective measurement of breathing and heart-beat during sleep state. The measurement system is composed of flexible multi-piezoelectric elements to acquire the pressure fluctuations persuade by respiratory movement and heart-beat when the subject is lying on it. By employing filters with predefined frequency bands, the breathing andheart-beat waveforms were extracted. The capturing of breathing & heart-beat counts was performed by normalizing the extracted waveforms using Wavelet Transform (WT) followed by Fuzzy Logic principles. Further, in the evaluation of nonbreathing time intervals, the amplitude changes in the extracted breathing waveforms were considered over its sleep event. Several experiments were conducted in order to evaluate the performance of the system on above criteria, along with a commercially available system. The experimental results suggest that the proposed method can measure and extract the related waveforms of breathing and heart-beat successfully.
Optimization-based Tactical Behavior Planning for Autonomous Freeway Driving in Favor of the Traffic Flow

In behavior planning for autonomous vehicles, other traffic participants have to be considered. This is typically achieved by using a prediction model to estimate their future behavior and by evaluating how it affects the planned actions of the ego vehicle. The inverse problem of how the ego's behavior may affect the traffic situation, is rarely looked at. This paper presents a novel planning approach which treats the whole traffic situation as the system to control. By means of an interaction-aware prediction, the consequences of the ego's actions for other vehicles are estimated. Via a cost function, an optimal outcome for the whole traffic situation is defined. The optimum is approached in an iterative way instead of using sampling-based methods. The planning combines lane change behavior and longitudinal acceleration. To narrow down the continuous solution space, the available actions are expressed by only four variables. The approach was designed for use on controlled-access highways, but the basic concept is transferable to other scenarios. Its usefulness is demonstrated in three interesting scenarios within a third-party traffic simulator.
Deep Learning-Based Parkinson’s Disease Classification Using Vocal Feature Sets

Parkinson’s Disease (PD) is a progressive neurodegenerative disease with multiple motor and non-motor characteristics. PD patients commonly face vocal impairments during the early stages of the disease. So, diagnosis systems based on vocal disorders are at the forefront on recent PD detection studies. Our study proposes two frameworks based on Convolutional Neural Networks to classify Parkinson’s Disease (PD) using sets of vocal (speech) features. Although, both frameworks are employed for the combination of various feature sets, they have difference in terms of combining feature sets. While the first framework combines different feature sets before given to 9-layered CNN as inputs, whereas the second framework passes feature sets to the parallel input layers which are directly connected to convolution layers. Thus, deep features from each parallel branch are extracted simultaneously before combining in the merge layer. Proposed models are trained with dataset taken from UCI Machine Learning repository and their performances are validated with Leave-One-Person-Out Cross Validation (LOPO CV). Due to imbalanced class distribution in our data, F-Measure and Matthews Correlation Coefficient metrics are used for the assessment along with accuracy. Experimental results show that the second framework seems to be very promising, since it is able to learn deep features from each feature set via parallel convolution layers. Extracted deep features are not only successful at distinguishing PD patients from healthy individuals but also effective in boosting up the discriminative power of the classifiers.
An Adaptive Path Tracking Controller Based on Reinforcement Learning with Urban Driving Application

Urban driving requires the autonomous vehicles to drive with smooth control and track the planned path accurately. However, most of the existing path-tracking controllers pay more attention to the tracking errors than the smoothness because of the difficulties to balance them. This paper proposes a learning-based method to achieve the trade-off between the smooth control and the tracking-error control. An Reinforcement Learning algorithm, which is called Proximal Policy Optimization, is used to train a neural model to tune the weights of a designed controller PP_PID (Pure-Pursuit_Proportional Integral Derivative). The successfully trained model will adaptively select the optimal weights for the Pure-Pursuit and $PID$ to guarantee the control smoothness and accuracy. Finally, the proposed controller will be tested in two path tracking scenarios. The results show that the proposed controller can change the weights adaptively to maintain a balance in the tracking error and lateral acceleration under the 35km/h.
Learning-Based Channel Estimation for Various Antenna Array Configurations

Recently, a neural-network-based method for massive MIMO uplink channel estimation was introduced. The derivations assumed a uniform linear array (ULA) with half-wavelength antenna spacing at the base station. In this work, we show that the estimator can also be used in case of ULAs and uniform rectangular arrays (URAs) with antenna spacings given by integer multiples of half the wavelength. We then investigate how the antenna spacing and certain parameters of the channel model influence the estimation performance.
Review of the Analytical Flow Model to Predict the Hydraulic Behaviour in Electrical Machine

This paper considers the analytical flow analysis for the through-flow ventilated electrical machine to predict the airflow rate in the different sections of the machine. This method can be applied to other types of air forced cooling electrical machine. For these purposes, the airflow paths are modeled by flow resistance network. The developing of the flow resistance network and calculating the flow resistances are described in details.
Promoting Higher Revenues for Both Crowdsourcer and Crowds in Crowdsourcing via Contest

Crowdsourcing emerges as a promising means of solution generation, which creates tremendous value by leveraging the intelligence of crowds in the web services. With the rise of the business of crowdsourcing services, both crowdsourcers and workers expect to gain better Quality of Experience, as well as more profits. But there is a contradiction between the incentives of the crowdsourcer and the quality of result of the crowds. In order to balance this conflict, the paper develops a profit optimization model for all parties in crowdsourcing by employing Tullock Contests. The model consists of two parts. Firstly, optimized incentives are provided by crowdsourcer to encourage workers to achieve a better quality of result. Secondly, an optimal fee schedule is provided as guidance to workers. The visualization of the equilibria of benefits is helpful to reach a win-win situation for crowdsourcer and crowds, which in turn impacts the development of crowdsourcing services. In addition, we simulate the acquisition of Nash-equilibrium as a repeated crowdsourcing task. The effectiveness of our model is testified by the experimental results.
Engineering Resilient Collaborative Edge-Enabled IoT

Novel scenarios like IoT and smart cities promote a vision of computational ecosystems whereby heterogeneous collectives of humans, devices and computing infrastructure interact to provide various services. There, autonomous agents with different capabilities are expected to cooperate towards global goals in dependable ways. This is challenging, as deployments are within unknown, changing and loosely connected environments characterized by lack of centralized control, where components may come and go, or disruption may be caused by failures. Key issues include (i) how to leverage, functionally and non-functionally, forms of opportunistic computing and locality that often underlie IoT scenarios; (ii) how to design and operate large-scale, resilient ecosystems through suitable assumptions, decentralized control, and adaptive mechanisms; and (iii) how to capture and enact "global" behaviors and properties, when the system consists of heterogeneous, autonomous entities. In this paper, we propose a model for resilient, collaborative edge-enabled IoT that leverages spatial locality, opportunistic agents, and coordinator nodes at the edge. The engineering approach is declarative and configurable, and works by dynamically dividing the environment into collaboration areas coordinated by edge devices. We provide an implementation as a collective, self-organizing workflow based on Aggregate Computing, provide evaluation by means of simulation, and finally discuss properties and general applicability of the approach.
Bellowband: A Pneumatic Wristband for Delivering Local Pressure and Vibration

In this paper we present the design and control of Bellowband, a pneumatic wristband for localized pressure and vibration haptic feedback. The wristband has eight equally spaced pneumatic bellows that extend into the wrist, constructed from layers of polyester thermoplastic polyurethane (TPU), resulting in a flexible, lightweight (11 g) band capable of rendering complex pressure and vibration cues to the user. Each bellow can withstand over 100 kPa, extend over 10 mm, and exert over 10 N of force at zero displacement. Quasi-static analysis is performed to estimate bellow force for a given input pressure and bellow displacement, and the dynamic response is examined experimentally. Finally, we demonstrate the wristband’s ability to deliver various haptic cues to the wrist, including uniform squeeze, uniform vibration, local force, and local vibration. Bellowband is a thin, soft, low-encumbrance wristband that can provide meaningful haptic feedback, making it ideal for AR/VR environments.
Redundant Anticipatory Control to Avoid Wind Turbine Failures Based on SCADA Data

Due to the characteristics of SCADA data, it is difficult, if not impossible, to use data-driven approach to predict wind turbine failures based on SCADA data. In this paper, we take a different tact. Resorting to unsupervised anomaly detection, we could detect suspicious turbine states, which might contain potential approaching failures. After that, a rule based decision maker could decide which redundant anticipatory operations, such as feathering, could eliminate the failures beforehand. Even if there is no failure actually, the redundant operations will not harm the equipment. To build such a system, we just use SCADA data, and do not need to modify the existing equipment. This paper presents the requirements and mechanism of the proposed system, as well as shows the feasibility using a case study about blade breakage.
Robust Filtering for Markov Jump Systems by Randomized Algorithm Approach

The issue of probabilistic filtering for a class of Markov jump systems with random uncertain parameters is addressed. A scenario random approach is proposed for designing a probabilistic filter to achieve probabilistic stochastic stabilization, and satisfying an $L_{2}-L_{\infty}$ disturbance rejection performance requirement. In Particular, conditions on which the resulting filter based system is stochastically asymptotically stable with a confidence level are established.
Adaptive Backstepping Control Design for Active Suspension Systems with Output Feedback

The paper focuses on designing an output-feedback adaptive controller which maintains the comfort and safety of the vehicle body. The road disturbance is modelled as a finite sum of sinusoidal waves with unknown frequencies, amplitudes and phases. The road disturbance is parametrized and estimation of the derivative of the unknown road profile as well as the unmeasured system states are achieved. An adaptive controller is proposed using a backstepping approach. It is shown that the equilibrium of the closed loop system is exponentially stable. The effectiveness of the observer and controller designs is illustrated with a simulation of a road test.
Discrete Kalman Filter Design for Kuramoto-Sivashinsky Equation

Kuramoto-Sivashinsky partial differential equation (KSE) has attracted a lot of attention from academia and industry due to its ability to describe various physical phenomena associated with both wave and propagation wave front dynamics. This work addresses infinite-dimensional discrete-time Kalman filter design for KSE by applying a state-of-the-art Crank-Nicolson discretization framework which does not account for spatial approximation or order reduction of the underlying model. A novel infinite-dimensional discrete-time Crank-Nicolson discretization is provided and utilized for KSE discretization in time, which is amenable to the ensuing discrete Kalman filter design. In addition, a two-step infinite-dimensional discrete-time Kalman filter is developed for the state estimation of KSE model augmented with the state and measurement noises. Finally, the effectiveness of the presented discrete-time Kalman filter is investigated and validated by simulations.
Fusing Acoustic and Electroencephalographic Modalities for User-Independent Emotion Prediction

Search and retrieval of multimedia content based on the evoked emotion comprises an interesting scientific field with numerous applications. This paper proposes a method that fuses two heterogeneous modalities, i.e. music and electroencephalographic signals, both for predicting emotional dimensions in the valence-arousal plane and for addressing four binary classification tasks, namely i.e. high/low arousal, positive/negative valence, high/low dominance, high/low liking. The proposed solution exploits Mel-scaled and EEG spectrograms feeding a k-medoids clustering scheme based on canonical correlation analysis. A thorough experimental campaign carried out on a publicly available dataset confirms the efficacy of such an approach. Despite its low computational cost, it was able to surpass state of the art results, and most importantly, in a user-independent manner.
Short Term and Long Term Path Loss Estimation in Urban, SubUrban and Rural Areas

Planning and performance of any wireless channel in different environment is analysed by the path loss prediction models. In this paper the path-loss between the Base-Station (BS) and Mobile-Station (MS) for the Received-Signal-Strength (RSS) in different wireless environments is estimated. Non-isotropic antennas are used with different transmitter and receiver gains. A mathematical expression is proposed to compute path loss with antenna gains of 0.5 and 1 by varying the distance up to 1km. Then the path loss is optimized by introducing the path loss component that varies with environment. The path loss component is varied from 2 to 6. The shadowing effect is modelled by log-distance method. In this paper Gaussian random variable is considered with a mean zero and standard deviation is taken as 3dB. Simulation results show the path loss component increases by increasing the gain of antenna. The path loss is also calculated by IEEE 802.16d model and Hata-model. It is observed that as the gain of antenna decreases the path-loss increases and vice-versa. Path-loss also increases by distance and shadowing effects.
Finite element versus experimental Thermo-mechanical behaviour of prismatic Li-Ion cell

In electric vehicle design it is crucial to identify the batteries behaviour. To understand properly how batteries behave, proper characterization tests are needed. In the literature, many different models of various degree of complexity were developed to simulate the electrochemical, electrical and thermal behaviour of Li-Ion cells. In this work, the focus is on the cell thermal and mechanical aspects. To guarantee proper battery pack operations, it is crucial to pay attention on the thermal aspects to guarantee both safe working conditions and a homogeneous exploitation of the cells capabilities, useful for better performance of the whole battery pack in terms of number of cycles. In this work, the behaviour of a prismatic Li-Ion cell was investigated from a simulation prospective. A cell CAE model was implemented within the ANSYS environment and it was used to obtain numerical results under various discharging current rates. The resulting model was validated with experimental data and then it was coupled with a structural model in order to evaluate the thermal induced strain and stress.