Miracle-dz/UniFAR-FTUs huggingface dataset:
Output Feedback Disturbance Rejection Control for DC-DC Buck Converter-DC Motor System Subject to Unmatched Load Torques

This paper investigates the angular velocity trajectory tracking problem for a DC permanent magnet motor with a DC-DC buck power converter based smooth starter. In practice, the composite control system is inevitably effected by unknown, exogenous, time-varying load torque disturbances. To this end, this paper specifically puts forward a disturbance rejection control approach. Under the proposed method, a novel disturbance observer is constructed to estimate the differences between actual states and the steady-state values of states and control. Therefore, an output feedback composite controller is designed. A key idea in the proposed control strategy is that the unmatched disturbances are transformed into matched ones ingeniously. The proposed control method is tested through simulation studies, where the results obtained validate the superiority of the proposed method.
Study of Node Arrival in Evolution of Disease Network

Protein-protein interaction network(PPI) is a biological network which represents interaction between proteins and these interactions change over time. Our aim is to study how these networks change from normal state to disease state. The normal network transforms into a disease network by new proteins joining the network or existing proteins leaving the network. To model this process, Graph kernel measures are used. By comparing pairs of graphs with different arrival sequences of nodes, we try to discover the arrival sequence that produces a smooth transition during transformation of a normal network into a disease network. Also the nodes that produce significant changes in the network are studied from the perspective of influential nodes theory in social networks. The network evolution is modelled using Duplication-mutation with complementarity (DMC) model. Results at a global level matching of network properties are satisfactory. But the actual matching of links between the simulated network and the original is poor and this issue needs to be investigated further.
Gesture-Based Object Localization for Robot Applications in Intelligent Environments

Drawing attention to objects and their localization in the environment are essential building blocks for domestic robot applications, e.g. fetch-and-delivery or navigation tasks. For this purpose, human pointing gestures turned out to be a natural and intuitive interaction method to transfer the spatial data of an object from human to robot. Current approaches only use the robot's on-board sensors to perceive gesture-based instructions, which restricts them to the field of view of the robot's camera. The integration of mobile robots into intelligent environments, such as smart homes, opens new possibilities to overcome this limitation by utilizing components of the surrounding environment as additional sensors. We take advantage of these new possibilities and propose a multi-stage object localization system based on human pointing gestures that considers the whole intelligent environment as interaction partner. Our experimental results show that our multi-stage approach successfully refines the position initially proposed by a human pointing gesture by employing a distributed camera network integrated into the environment for object localization.
Robustness Margin for Leader-based Multi-agent Consensus Systems in Presence of Parametric Uncertainty

In this paper, the use of a class of robust control technique as a tool to compute the robustness margin for a closed loop multi-agent system is discussed. Our approach is based on a leader/follower structure of the multiple robot systems. It is shown that, for multiple agent system with cyclic, chain and balanced topology of information exchange, the result is achieved. A Lyapunov based robust control design for coordination and trajectory tracking of multiple agent systems with parametric uncertainty is presented. The results are illustrated with examples and simulations.
Adaptive Protection for Preserving Microgrid Security

The low inertia renders renewable-based microgrids (<inline-formula> <tex-math notation="LaTeX">$\mu$ </tex-math></inline-formula>Gs) more susceptible to incipient faults and makes it more difficult for distributed systems to maintain a reasonable margin for dynamic security. This paper studies the <inline-formula> <tex-math notation="LaTeX">$\mu\text{G}$ </tex-math></inline-formula> security using a three-stage approach to the power system protection. The first stage considers offline analyses of synchronous generator-based distributed energy resources (SGBDERs) and inverter-based distributed energy resources (IBDERs) for establishing dynamic security models in <inline-formula> <tex-math notation="LaTeX">$\mu$ </tex-math></inline-formula>Gs. The required data and settings are also determined at this stage. The second stage uses the first stage models for the online calculation of equilibrium points, regions of attraction, and protection zones for SGBDERs and IBDERs operations. This stage adapts to different conditions for <inline-formula> <tex-math notation="LaTeX">$\mu\text{G}$ </tex-math></inline-formula> operations. The third stage is responsible for the real-time protection of <inline-formula> <tex-math notation="LaTeX">$\mu$ </tex-math></inline-formula>Gs. This stage uses the real-time data for the fast detection of dynamic security status and protection of <inline-formula> <tex-math notation="LaTeX">$\mu$ </tex-math></inline-formula>Gs. Simulation results presented in the paper demonstrate the adaptive features of the proposed scheme.
Current Sensor-less Control for Boost DC-DC Converter Based on Switched Observer

In this paper, a current sensor-less control strategy for boost dc/dc converter is proposed. A switched observer is first designed for switched model of boost converter to estimate the inductor current and output voltage under arbitrary switching. Then based on the estimation of inductor current and output voltage, a two-loop control structure is employed for the average model of boost converter which consists of voltage loop (outer loop) and current loop (inner loop). In the inner loop, a second order sliding mode (SOSM) controller based on a super-twisting algorithm (STA) is established to drive the estimated inductor current towards its reference. The proportional-integral (PI) controller is applied in the outer loop to regulate the output voltage. Finally simulation results are provided to testify the effectiveness of the presented approach.
Tracking Design of Omnidirectional Drive Service Robot Using Hierarchical Adaptive Finite-Time Control

Owing to advantageous feature of omnidirectional drive service robot (ODSR), i.e., simultaneous translation and rotation, the human-robot collaborations are more reliable and satisfactory. The path tracking model of an ODSR includes mechanism kinematics, mechanical dynamics with friction of each wheel and transformation between ODSR and world coordinates, and motor dynamics. To track the desired pose of ODSR in finite time, the adaptive finite-time virtual desired path (AFTVDP) is designed. To make the direct output track the AFTVDP in finite time, an adaptive finite-time control (AFTTC) is designed to execute high frequency motions. In summary, the proposed hierarchical adaptive finite-time control (HAFTC) contains AFTVDP and AFTTC. Finally, experiment validates the effectiveness of the proposed control.
Achieving Real-Time Quality of Service in Software Defined Networks

Software defined networks (SDNs) have emerged as a promising paradigm by separating control plane from data plane. In this paper, we study the problem of achieving realtime quality of services in a SDN for both centralized control plane and distributed control plane. In the first place, we propose a system prototype that handles real-time end to end services. Then, we formulate the problem for a centralized control plane as a linear programming and apply prime-dual technique to derive an efficient algorithm. We also discuss about the method to achieve load balancing between link resources and node resources. Finally, we extend the algorithm to a distributed control plane and present efficient algorithms to achieve good competitive ratio.
Personalized Student Assessment based on Learning Analytics and Recommender Systems

This paper presents a process based on learning analytics and recommender systems with the objective of analyzing student assessment in order to provide clues that can help teachers in scaffolding the students’ performance. For this, a set of tests was used to evaluate students’ competence in direct current circuits. The tests had multiple versions and to solve them each student had to use multiple approaches. The results indicate a better performance in calculus and simulations approaches when compared with hands-on and remote laboratories approaches. The analyses also provide support for the recommendation step allowing the configuration of a knowledge base. The process as a whole is consistent in what regards its ability to make suggestions to the students as they complete a given test and to provide teachers with information that can help them formulate strategies to positively impact students’ learning.
Multi-ellipse Fitting with PEARL and a Multi-Objective Genetic Algorithm

This paper deals with the multi-ellipse fitting problem, which consist in identifying and fitting an unknown number of ellipses in presence of noise and outliers from data points in R2. This problem is an active research area with many applications in engineering and biology. Numerous studies attempted to solve it by detecting (fitting and extracting) the ellipses in a one by one approach from the data points. Although the one by one approach is effective and useful for many applications, recent studies show that this approach is ill posed and new methods like PEARL have been proposed. PEARL is an energy based multi-model fitting algorithm that considers multiple model instances in the final solution. The PEARL algorithm requires to be initialized with random solutions (models constructed from the minimal number of data points). These solutions are taken by the algorithm and they are refined until obtain a local optimum. Depending on the data points complexity (number of models, level of noise, outliers, and model's spatial support) the number of initial solutions required increases considerably. In this paper we propose the use of a multi-objective genetic algorithm to initialize PEARL. We aim to generate a reduced and high quality initial solutions set to allow PEARL to solve challenging multi-ellipse fitting problem instances. With our approach we are able to solve challenging data points instances, with high amount of noise, up to 100% percentage, and also with overlapping and nested spatial supports.
Fire detection of Unmanned Aerial Vehicle in a Mixed Reality-based System

This paper presents a system that combines robotic operating system (ROS) and computer vision techniques for fire detection in a mixed reality environment. We have collected video streams from a mini camera on an Unmanned Aerial Vehicle (UAV), where the navigation data relied on state-of-the-art Simultaneous Localization and Mapping (SLAM) system. The data collected onboard are communicated to the ground station and processed through the robotic operating system. A robust and efficient re-localisation SLAM was performed to recover from tracking failure and frame lost in the received data. The fire detection algorithm was developed based on the colour, movement attributes, temporal variation of fire intensity and its accumulation around a point. A mixed reality environment was used to visualise and test the proposed system. The observation and data analysis confirmed that the UAV could successfully detect fire and flame, fly towards and hover around it.
An Improved Speed and Position Estimator for Transient Performance of Back-Emf Self-Sensing for IPMSM

This paper proposes an improved PLL-type estimator for self-sensing control based on back-electromotive force (back-EMF)estimation in an interior permanent magnet synchronous motor (IPMSM). Although the position and speed of rotor are accurately estimated by PLL-type estimator in steady state, the estimated information in transient state may be unstable. To analyze the transient performance of rotor position estimators, the structure design and proposed methods of estimator are introduced. And, the effect of proposed method has been figured out that the estimated speed error and torque ripple are decreased in transient state. The feasibility of the introduced estimator effect are verified through simulation results.
Formal Modeling and Security Analysis for OpenFlow-Based Networks

We present a formal OpenFlow-based network programming language (OF) including various flow rules, which can not only describe the behaviors of an individual switch, but also support to model a network of switches connected in the point-to-point topology. Besides, a topology-oriented operational semantics of the proposed language is explored to specify how the packet is processed and delivered in the OpenFlow-based networks. Based on the formal framework, we also propose an approach to detect potential security threats caused by the conflict of dynamic flow rules imposed by dynamic OpenFlow applications.
Tracking Control Method Considering Obstacle Avoidance by Reflective Motion for Mobile Robot

This paper proposes a tracking control method considering an obstacle avoidance by a reflective motion for a mobile robot. In the mobile robot, a laser range finder is utilized to recognize environmental information. The obstacle avoid motion is created by using environmental information and the virtual manipulators mounted on the mobile robot. In the conventional researches, the flexible obstacle avoidance method by using the kinematic relation between the virtual manipulators and environmental information was reported. This method has the advantage for the calculation cost and flexibility like reflective motion. However, the robot may not follow the desired trajectory after the obstacle avoidance, since this conventional method is based on the velocity control. In order to solve this problem, this paper proposes a combination method of the tracking control and the obstacle avoidance. Considering the velocity limits of the tracking control, it is possible to modify the priority of the trajectory tracking and the obstacle avoidance. The effectiveness of the proposed method was confirmed from the experimental results.
Preemptive Diagnosis of Diabetes Mellitus Using Machine Learning

Diabetes Mellitus (DM) is one of the most prevalent chronic diseases in the world with around 150 million patients. Patients with chronic diseases are highly susceptible to deterioration in their physical and mental health; consequently, hindering their independence, restricting their daily activities imposing a large financial burden on them and the government. If not discovered early, chronic diseases may lead to serious health complications or in extreme cases, death. Diagnostic solutions have been explored using intelligent methods, however, different ethnic groups have variant factors leading to the development of a disease. Therefore, the proposed system aims to preemptively diagnose DM in a region never explored before. Data are retrieved from King Fahd University Hospital (KFUH) in Khobar, Saudi Arabia. Data undergoes preprocessing to identify relevant features and prepare for identification/classification process. Experimental results show that ANN outperformed SVM, Naïve Bayes, and K-Nearest Neighbor with the testing accuracy of 77.5%.
Electronic System for Graphical Representation on Cooking Surfaces of Domestic Induction Hobs

LED lighting technology provides new functionalities to the systems in which it is incorporated. In the particular case of induction cooktops, the light elements have been used to transmit information to the user and have generally been placed outside the area occupied by the inductor. Several interference and temperature issues have arisen when electronic components have been placed on areas nearby the inductor. In this paper, the design of a panel of configurable LEDs that is capable of representing information in a domestic induction cooker placed in the cooking surface is presented. The presence of electronics devices in this part of the inductor cooker can present two main problems, thermal stress as well as electromagnetic interference. Minimization of electromagnetic interference was addressed by describing this effect from the simulation point of view in order to propose solutions based on the optimized routing of the printed circuit board of the electronics. Finally, a fully functional prototype has been built.
Analyses of Aerodynamic Characteristics and Boundary Layer Features of Airfoils at Low Reynolds Numbers and Moderate Turbulence Intensities

Research studies on low Reynolds number are continuously gaining technical interest aiming to contribute to the advancement of small aerial vehicles and wind turbines. Despite this, there are issues though concerning the sensitivity of the boundary layer on the airfoil surface due to their low-speed operating condition and the unstable environment where they are situated in. In order to enhance the performance of these machines, aerodynamic analysis of the airfoils employed for these kinds of applications needs to be essentially conducted. Therefore, this study aims to investigate the aerodynamic characteristics of several airfoils suitable for small-scale horizontal-axis wind turbines. Numerical results show that the effects of Reynolds number and turbulence intensity are somewhat similar in terms of lift-to-drag ratio and chordwise behavior of separation bubbles. Likewise, different structures exhibited by the flow on the airfoil upper surface were captured as functions of angle of attack, Reynolds number and turbulence intensity.
Deep Structure Learning for Fraud Detection

Fraud detection is of great importance because fraudulent behaviors may mislead consumers or bring huge losses to enterprises. Due to the lockstep feature of fraudulent behaviors, fraud detection problem can be viewed as finding suspicious dense blocks in the attributed bipartite graph. In reality, existing attribute-based methods are not adversarially robust, because fraudsters can take some camouflage actions to cover their behavior attributes as normal. More importantly, existing structural information based methods only consider shallow topology structure, making their effectiveness sensitive to the density of suspicious blocks. In this paper, we propose a novel deep structure learning model named DeepFD to differentiate normal users and suspicious users. DeepFD can preserve the non-linear graph structure and user behavior information simultaneously. Experimental results on different types of datasets demonstrate that DeepFD outperforms the state-of-the-art baselines.
Male‒male marriage in Sinophone and Anglophone Harry Potter danmei and slash

ABSTRACT The aim of this study is to compare Sinophone and Anglophone fan fiction consisting of female-oriented male‒male romance: danmei and slash, respectively. To increase comparability, we analysed Harry Potter fan fiction in which the characters Harry and Draco are married. Male‒male marriage was selected because our online Sinophone and Anglophone BL fandom surveys indicate this to be the most popular story element of the nine options we provided. We analysed five stories originally written in Chinese and five originally written in English which subsequently had been fan-translated into Chinese. Using thematic analysis, we found some robust patterns. In contrast to the Anglophone fiction, the Sinophone tended to: stress the importance of family approval for the marriage; incorporate a wedding ceremony; employ clearly gendered roles between partners; utilise extended, as opposed to nuclear, families; and showed the couple to produce children, particularly boys. Hence, the stories mirror the relative social conservatism and social liberalism of their cultures of origin. However, in reading and writing such danmei young Chinese women are still pushing at the boundaries of the traditional family.
Partial Multi-view Clustering via Consistent GAN

Multi-view clustering, as one of the most important methods to analyze multi-view data, has been widely used in many real-world applications. Most existing multi-view clustering methods perform well on the assumption that each sample appears in all views. Nevertheless, in real-world application, each view may well face the problem of the missing data due to noise, or malfunction. In this paper, a new consistent generative adversarial network is proposed for partial multi-view clustering. We learn a common low-dimensional representation, which can both generate the missing view data and capture a better common structure from partial multi-view data for clustering. Different from the most existing methods, we use the common representation encoded by one view to generate the missing data of the corresponding view by generative adversarial networks, then we use the encoder and clustering networks. This is intuitive and meaningful because encoding common representation and generating the missing data in our model will promote mutually. Experimental results on three different multi-view databases illustrate the superiority of the proposed method.
Overcoming design challenges in low voltage GaN based PSFB battery charger

This paper highlights the specific design challenges faced while implementing a GaN based phase shifted full bridge (PSFB) converter for battery charger applications. Inherent with the PSFB is the voltage ringing across the secondary side devices. This effect becomes even more severe, when fast switching GaN devices are used. Especially the effects of parasitics in the circuit become a major problem and need to be considered. Effective solutions to mitigate the overvoltages on the secondary side devices are theoretically derived and practically implemented in a 1.4kW GaN based PSFB. The effectiveness of the different solutions is presented.
Online Dictionary Learning with Confidence

Online dictionary learning has received intensive attention in signal processing field with streaming or dynamic data. Different from classical online dictionary learning methods that treat all atoms equally, in this paper, we present a novel online dictionary learning with a confidence parameter introduced on each of atoms. The confidence indicates the scale of the update of atoms during online learning; frequently estimated atoms are thus supposed to be updated less aggressively than those of low usage frequency. This updating mechanism is beneficial for learning with dirty examples. As a result, the outliers would be prevented from influencing much on the frequently used atoms that have been well estimated through the clean examples. In detail, we employ variance of the atoms to measure the confidence on the quality of the learned atoms. And frequently-used atoms are supposed to have smaller variances (i.e. more confidence) than those of low usage frequency. Our algorithm consists of two main parts, namely, confidence-weighted sparse coding and dictionary update with confidence fine-tuning, each of which can be solved efficiently by our designed optimization methods. In addition, due to the introduced confidence, our algorithm does not have to depend on the widely-used ℓ_1 norm in order to realize the robustness against the outliers, which is usually computationally expensive in the training cost. Experimental results on synthetic and benchmark datasets demonstrate the imposed confidence on atoms in the dictionary can improve the performance of the learned dictionary. The quality of the obtained dictionary is comparable to that of state-of-the-art robust online dictionary learning methods, however, our method is much faster than those ℓ_1 norm based ones.