Miracle-dz/UniFAR-FTUs huggingface dataset:
Some Analytical Remarks on Sliding Mode Control for Multi-Input Multi-Output Nonlinear Systems

More and more research has been developed on model based control techniques in recent times. These complicated control systems have the foundations laid on theories such as: Model Predictive Control, Feedback Linearization, Adaptive and Robust Control. The last approach is studied here, in particular the Sliding Mode (SM) Control is investigated. While a huge number of papers rotates around the description and application of SM control on single-input single-output (SISO) systems, the general theory of multi-input multi-output (MIMO) systems is not analyzed so thoroughly. Therefore, the goal of this paper is to identify the conditions necessary to realize the SM control for MIMO systems. In addition, in order to obtain the desired performance from the control system, a method for its parameter tuning is illustrated. In the end, a mere example is shown with the purpose of comparing the performance of the proposed approach with the same controller conventionally tuned.
Mapping Three-Dimensional Structures of Forest Canopy Using UAV Stereo Imagery: Evaluating Impacts of Forward Overlaps and Image Resolutions With LiDAR Data as Reference

The application of aerial stereo imagery on the measurement of forest three-dimensional structures is growing in recent years due to the rapid development of unmanned aerial vehicle platforms and automatic processing algorithms of stereo images. Yet there is still no clear knowledge about how the description of forest three-dimensional structures is affected by settings of critical acquisition parameters of stereo images. This study systematically addressed the impacts of image resolutions and forward overlaps over a broad range by using LiDAR data as reference. The different combinations of image resolutions and forward overlaps used in this study are produced by image average downsampling and subsetting. Their performances were evaluated from four aspects, including computation loads, point densities, estimation of canopy height indices at forest stand level, and the vertical distribution of point clouds over forest stands and along forest transects with different levels of canopy closure. Results showed that the coupling between image resolutions and forward overlaps in the data processing should be given full consideration. Generally, finer image resolutions require higher forward overlaps; otherwise, much of the area could not be detected and sparse trees were easily missed. It was a better choice to degrade image resolutions while keeping forward overlap in data processing if more blank areas appeared or sparse trees could not be detected. The good match between image resolutions and forward overlaps could dramatically reduce the computation load while keeping the estimation accuracy.
Economic Impact Analysis of Load Shifting in a Smart Household

The global energy consumption has been showing a remarkable growth particularly in the last two decades in parallel to the arising environmental concerns. In consequence of researching alternative solutions to ensure a more sustainable end environmental friendly energy issue, the smart grid vision has emerged. The demand side participation through demand response (DR) strategies plays a crucial role in the smart grid concept. This paper presents a pricing based DR scheme oriented simulation study of an experimental smart home, which is located in Yildiz Technical University in Istanbul, Turkey. All types of controllable and non-controllable household electric appliances are modeled in MATLAB/Simulink® by using data from real measurements. In order to provide an energy management concept, an optimization algorithm is developed to minimize electricity bills and reduce peak load demand by controlling the shiftable loads.
Towards Realistic High-Speed Train Channels at 5G Millimeter-Wave Band—Part II: Case Study for Paradigm Implementation

In this paper, we present two case studies for generating realistic high-speed train (HST) channels at fifth-generation (5G) millimeter-wave (mmWave) band. The first one is the tunnel environment at relatively low 5G mmWave band, 30 GHz band, whereas the second one is the outdoor HST environment at relatively high 5G mmWave band, 90 GHz band. Both case studies include the following steps: ray-tracing simulations, stochastic channel modeling and realization, verification with ray-tracing simulations, and validation with a reduced set of measurements. A profound and insightful conclusion is reached that by employing the proposed paradigm, realistic channels can be realized for the design and evaluation of 5G mmWave communication systems in high-speed railways, even without the support of sufficient channel sounding data.
Multi-Criteria Planning Tool for a Net Zero Energy Village

The integration of volatile renewable energy sources, such as wind and sun, into the electric power system is a big challenge. Many solutions have been investigated in recent years. Among them, the use of flexibility options, such as energy storage systems or active consumers, and the concept of multienergy systems have attracted the attention of many researchers. Within this study, a methodology for planning net zero energy systems has been developed. It considers a residential multienergy system in which electricity, heat and transportation have been contemplated simultaneously. Technical, economic and social analysis have been performed to evaluate the size of the components (generation) and the investment costs necessary.
Foreground Detection based on Co-occurrence Background Model with Hypothesis on Degradation Modification in Background Changes

This paper presents a robust background model for foreground detection in background changes, such as illumination changes and background motions. By utilizing the co-occurrence “pixel to block” structure, our model, which is named as Co-occurrence Pixel-Block Pairs (CPB) can learn the change information of the background and extract the spatial-temporal information of each pixel in the scene, and build a prospective background model for each pixel. Then, we employ an efficient evaluation strategy to identify the current state of each pixel to classify the foreground and background, which is named as correlation dependent decision function. A key contribution of this work is it offers a robust background subtraction against the dynamically changing background. Furthermore, we also introduce a Hypothesis on Degradation Modification (HoD) into CPB structure to reinforce the robustness of CPB, and it also further improves the performance of our algorithm. This observation is robust to illumination changes and background motions. Experimental results obtained from the datasets under different challenges prove that our algorithm has a good performance for foreground detection.
Sources and Impact of Media Thermal Fluctuations in Heat-Assisted Magnetic Recording

Optical and thermal modeling is used to understand and quantify the contribution of different sources of temperature fluctuations in heat-assisted magnetic recording. The dominant sources of temperature fluctuations are spatial variations of the absorption in the recording layer and spatial variations of the underlayer effective thermal boundary resistance. They result in an increased transition jitter that is proportional to the amplitude of the source variations. Besides, the transition jitter amplitude strongly depends on the length scale of the source fluctuations: fluctuations with smaller periods lead to reduced jitter as a result of in-plane heat spreading within the medium layers. Temperature fluctuations are also proportional to the input laser power or to the average temperature gradient. Consequently, transition jitter arising from temperature fluctuations does not change with improved temperature gradients, in contrast to jitter resulting from Curie temperature distributions. Interface roughness is also investigated as it is a practical source of both optical absorption fluctuations and thermal property fluctuations. It is found to lead to sizeable jitter, whose amplitude is proportional to the roughness amplitude and reduces at small roughness wavelengths.
A Novel Data-Driven DSSE Method to Achieve Water Sustainability for Farmers in Madurai District, India

The potential of Internet-of-Things (IoT) for useful large-scale applications like water security requires new methods for the engineering of ‘human-information’ systems. The method views the entire region systemically in support of effective resource management (e.g. water, food, transportation). We propose a novel ‘Digital Service Systems Engineering’(DSSE) method and demonstrate how this real world data-driven method can target actions to address water-security related ‘As-Is’ problems locally. This leads to a more sustainable ‘To-Be’ state with IoT technology. The paper presents the effectiveness of the proposed DSSE method and its application motivating further research in the area.
An Integrated Motor-Drive and Battery-Charging System Based on Split-Field-Winding Doubly Salient Electromagnetic Machine

In this paper, an integrated motor-drive and battery-charging (IMB) system with a doubly salient electromagnetic machine (DSEM) is proposed to be implemented for electric vehicles. In the driving mode, the front-end dc/dc converter is employed to boost the battery voltage to improve the speed range and acceleration ability of the DSEM. The field windings distribution of a 12/10-pole DSEM is arranged for the constant self-inductance, and adopted as the inductance of the front-end dc/dc converter to eliminate the traditional filter inductance and excitation loss of the machine. In the charging mode, the windings of DSEM could be exploited as the filter inductances to obtain the unity power factor operation and constant current charging ability. The simulations and experiments have been accomplished to verify the feasibility of the independent field current control method for the proposed IMB system.
Robot Leg Design: A Constructive Framework

Most robot leg designs are either based on biomimetics of humans’ or animals’ leg morphologies or on being mechanically optimized for specific task(s). In the first approach, differences between the actuation of the mechanical leg and the original biological structure usually result in inefficiencies and control malfunction, and legs in the second group are often limited to good performance for a single task and would fail when used for others. In this paper, we present a constructive framework for robot leg design, which tries to take advantage of the positive factors of both aforementioned approaches. For this purpose, we first, through selection of a template whose biological relevance for a wide range of tasks has been proven, establish a foundation on which mechanical design can be built. Then, we present a general theorem for designing a mechanism based on a template in order to maximize efficiency. In the final step and once the mechanism is designed, we address the problem of selecting the actuators and formulate it as a constrained optimization problem. In a case study with experimental walking data, we show how the mechanism design theorem and the formulated optimization problem can be used together to improve the walking energy efficiency by more than 50%. The proposed three-step approach is not limited to any template and should provide a more structured procedure for leg design, result in optimal energy economy, and maintain important bioinspired factors vital for control and versatility of legged robots.
A Meta-Analysis of the Association Between Patients’ Early Treatment Outcome Expectation and Their Posttreatment Outcomes

Patients’ outcome expectation (OE) represents their belief about the mental health consequences of participating in psychotherapy. A previous meta-analysis of 46 independent samples receiving the treatment of at least 3 sessions revealed a significant association between more optimistic baseline, or early treatment, OE and more adaptive posttreatment outcomes (weighted r = .12 or d = .24; Constantino, Glass, Arnkoff, Ametrano, & Smith, 2011). The present study represents an update to that meta-analysis. To be included, articles published through June 2017 had to (a) include a clinical sample, (b) include a therapist-delivered treatment of at least 3 sessions, (c) include a measure of patients’ own OE, (d) include at least 1 posttreatment mental health outcome not explicitly referenced as a follow-up occasion, and (e) report a statistical test of the OE−outcome association. The updated meta-analysis was conducted on 81 independent samples (extracted from 72 references) with 12,722 patients. The overall weighted effect size was r = . 18, p < . 001, or d = . 36, with high heterogeneity (I2 = 76%) and no evidence of publication bias. Several variables (patient age, measure type, and treatment manual used) moderated the OE−outcome association. These robust, replicated meta-analytic findings are discussed in light of methodological limitations and with regard to their practice implications.
On Occupancy Based Randomized Routing Schemes in Large Systems of Shared Servers

Recently there has been a great interest in randomized load balancing schemes for large systems of parallel servers. Various policies such as SQ(d) (shortest queue amongst d randomly sampled), threshold based policies etc have been studied via a mean-field approach. All these policies are special cases of occupancy based routing decisions. In this paper we present a unified mean-field approach that holds for any routing scheme that only depends on the server occupancy in a system with a large numbers of processor sharing servers as an archetype of shared resource systems. The mean-field equations we obtain hold for general job length distributions unlike most recent works that assume exponentially distributed job lengths. We then show that the probability measure of occupancy defined on the set of non-negative integers Z_+ obtained from a fixed-point of the mean-field also satisfies the stationary mean-field equations under the assumption that the job lengths are exponential with the same average length. If the mean-field under exponential case has unique fixed-point, then the fixed point is insensitive to the job length distribution. The approach is via a measure-valued Markov process approach.
Characterization of a Prototype Visible Light Communication System

In this study, a prototype visible light communication system between a stationary transmitter and mobile receiver was designed, assembled and characterized. A makeshift traffic light post with an assembly of white light emitting diodes(LED) was used as the stationary transmitter, while a remote-controlled toy train with photo-diodes, necessary electronics circuit and micro-controller was used as the mobile receiver. The system was characterized by measuring the distance range and field of view in terms of bit error rate. The distance range and field of view corresponding to an assembly of three white LEDs have been found to be 8 cm to 21.5 cm and 50.39° while the distance range and field of view corresponding to an assembly of twelve white LEDs have been found to be 8.5 cm to 23.4 cm and 55.96°. Both the distance range as well as the field of view have been measured using a threshold bit error rate of $5\times 10^{-3}$.
Smart Grid and Microgrid Cooperation in a Real Distribution Network Under Emergency Conditions

The paper presents a feasibility study regarding the ability of an existing rural microgrid to supply an islanded portion of the MV distribution network. The microgrid is currently off-grid, but in the near future will be connected to the MV distribution grid. Based on load and generation measurements in a six-month period, a statistical analysis is carried out with the aim to check the ability of the microgrid to guarantee such a service whenever requested. Steady state simulations, also taking into account a proposed control strategy of the microgrid energy resources during islanding operation, show that for a significant share of cases the islanding operation is feasible.
Synthetic to Real World Image Translation Using Generative Adversarial Networks

In this paper we perform synthetic to real world image translation using Cycle Consistent Adversarial Networks and further provide insights as to how the translation can be utilized to build a framework that can solve a plethora of domain adaptation tasks by reducing the same to a supervised problem via image generation and label transfer from a source domain (synthetic images) to a target domain (real world images) thereby eliminating the need for a labeled target domain dataset. We run experiments taking the (GTA) dataset as the source domain and the Cityscape dataset as the target domain and provide a comprehensive evaluation of the experiments. We also provide analysis and visualization of existing domain adaptation techniques and evaluate their pros and cons. A potential application of the concept is in training of self-driving cars as it is difficult to obtain an extensive labeled dataset for every geographical region.
Incorporating User Willingness in Contract-Based Incentive Mechanism for D2D Cooperative Data Forwarding

Device-to-device (D2D) cooperative communication is regarded as a technology which can significantly improve the spectrum efficiency and network capacity. However, the high dependence of cooperative communication on user participation makes the design of incentive mechanism become an issue of great concern. In this paper, a contract-based benefit allocation mechanism is firstly proposed to optimize the benefits of sending users and cooperative users. In such mechanism, sending user is as a contract principal and cooperative user is as a contract agent, by formulating the utility function of both contract parties, maximum benefits between them can be obtained under the control of the base station (BS). What’s more, by considering the social relationships between sending users and cooperative users, user preference which describes subjective willingness with each other is further introduced in this paper. Finally, a stable matching algorithm based on the calculation of user satisfaction is proposed combining the benefits and preference of sending users and cooperative users. The simulation results show that the proposed mechanism can effectively encourage users to participate in cooperation, thus ensures high performances in terms of utility and user satisfaction.
A Theoretical Pre-Assessment of Solar Photovoltaic Electrical Production for Commercial Retail Centers

The successful implementation of solar photovoltaic technology in retail centers has enabled various consumers the ability to generate and consume electricity on the same premises, reducing electricity costs and dependency on currently utilities. The rooftop mounting solution consists of affixing the photovoltaic modules on existing roof structures of retail centres. Accelerated deployment of photovoltaic technologies in recent history (within developing countries) led to various logistical, technical and bureaucratic barriers arising. A comprehensive understanding of these barriers is required to ensure efficient and effective deployment of the technology in developing countries. This study investigates the assessment considerations for the pre-deployment phase of photovoltaic technologies in retail centres. Environmental-, policy-, technical-, and financial factors are identified as the main barriers affecting the financial viability and Return on Investment of such a project. The evolution of this technology within developing countries and the integration of PV and retail centres are investigated. Finally, various financial evaluation criteria are presented to which the viability of the technology is determined. Understanding of the various barriers ensures effective deployment of the technology in retail centres, commercial and residential sectors of an economy.
Deep Multiple Feature Fusion for Hyperspectral Image Classification

Hyperspectral images usually have high-dimensional and abundant spectral information for land-cover types classification. Research shows that multiple kinds of features would be helpful to the classification task. In this paper, a new feature fusion framework—deep multiple feature fusion (DMFF)—is proposed for the classification of the hyperspectral image. First, several different features are extracted for each pixel. Then, these features are fed to a deep random forest classifier. With a multiple-layer structure, the outputs of preceding layers will be used as the inputs of the subsequent layers. After the final layer, the classification probability will be computed. By introducing the information of neighboring pixels, the spectral–spatial information is combined effectively. Besides, the structure of the DMFF is easy to expand. Experimental results based on two widely used hyperspectral datasets (Indian pines image and Pavia University image) demonstrate that the proposed method can achieve a satisfactory classification performance compared with other multiple feature fusion methods.
A Noise-Resistant Stereo Matching Algorithm Integrating Regional Information

Focusing on the problem existing in stereo matching that low-SNR image, such as images collected at night, we propose a novel matching framework based on semi-global matching algorithm and AD-Census. This algorithm extends the original algorithms in two ways. First, image segmentation information as an additional constraint is added that solve the problem of incomplete path and improve the accuracy of cost calculation. Second, the matching cost volume is calculated with AD-SoftCensus measure that minimizes the impact of noise on the quality of matching by changing the pattern of census descriptor from binary to trinary. Results of Middlebury standard test data show that the algorithm significantly improves the precision of matching. In addition, a low-light binocular platform is built to test our method in night environment. Results show the disparity maps are more accurate compared to previous methods.
Identification of Noise Covariance Matrices to Improve Orientation Estimation by Kalman Filter

Magneto-inertial measurement units (MIMUs) are a promising way to perform human motion analysis outside the laboratory. To do so, in the literature, orientation provided by an MIMU is used to deduce body segment orientation. This is generally achieved by means of a Kalman filter that fuses acceleration, angular velocity, and magnetic field measures. A critical point when implementing a Kalman filter is the initialization of the covariance matrices that characterize mismodelling and input error from noisy sensors. The present study proposes a methodology to identify the initial values of these covariance matrices that optimize orientation estimation in the context of human motion analysis. The approach used was to apply motion to the sensor manually, and to compare the orientation obtained via the Kalman filter to a measurement from an optoelectronic system acting as a reference. Testing different sets of values for each parameter of the covariance matrices, and comparing each MIMU measurement with the reference measurement, enabled identification of the most effective values. Moreover, with these optimized initial covariance matrices, the orientation estimation was greatly improved. The method, as presented here, provides a unique solution to the problem of identifying the optimal covariance matrices values for Kalman filtering. However, the methodology should be improved in order to reduce the duration of the whole process.