Miracle-dz/UniFAR-FTUs huggingface dataset:
Diabetes Predicting mHealth Application Using Machine Learning

With the advancement of information technologies, mobile health (mHealth) technologies can be leveraged for patient self-management, patient diagnosis and determining the probability of being affected by some disease. Diabetes mellitus is a chronic and lifestyle disease and millions of people from all over the world fall victim to it. Although there are some mobile apps keeping track of calories, sugar taken, medicine doses, lifestyle, blood glucose, blood pressure, weight of individuals and giving suggestion about food, exercises to prevent or control diabetes, no application has been found that was explicitly developed to analyze the risk of being a diabetic patient. Therefore, the objective of this paper is to develop an intelligent mHealth application based on machine learning to assess his/her possibility of being diabetic, prediabetic or nondiabetic without the assistance of any doctor or medical tests.
Improving the Accuracy of Second-Order Cone AC Optimal Power Flow by Convex Approximations

We propose three convex approximation methods to improve the performance of second-order cone AC optimal power flow (SOC-ACOPF) model. These methods are formulated in the proposed SOC-ACOPF models, correspondingly, ConicLA, ConicRe, and ConicTA. The principle is to drop the original assumptions of the SOC-ACOPF model by using improved approximation methods. The nonconvex constraints in ACOPF are expressed by using quadratic equivalence. These quadratic constraints are then approximated in two different ways: ConicLA uses controllable tangent lines as linear approximations; ConicRe uses rotated cones. Furthermore, the assumption of small voltage phase angle difference (along each line) which is used in many power flow models is dropped in the ConicTA model. ConicTA approximates sinusoidal function by its expanded Taylor series and then re-applies approximation methods developed in ConicLA and ConicRe. The advantages of our approximation methods are that the proposed SOC-ACOPF models are convex and more accurate. Using Power System Analysis Toolbox (PSAT) as the benchmark, numerical results from various IEEE test cases show that our models outperform the original SOC-ACOPF model prominently especially for power distribution networks.
EEG-Based Detection of Epileptic Seizures Through the Use of a Directed Transfer Function Method

This paper aims to explore the automatic detection method of epileptic seizures to improve the treatment and diagnosis of medically refractory epilepsy patients. A new algorithm based on directed transfer function (DTF) method was proposed for epileptic seizure detection. First, the sliding window technique was used to segment electroencephalogram (EEG) recordings, and the cerebral functional connectivity was calculated by the DTF algorithm. Then, the total information outflow based on the DTF-derived connectivity was calculated by adding up the information flow from a single EEG channel to other channels. Finally, the information outflow was assigned as the features of support vector machine (SVM) classifier to discriminate interictal and ictal EEG segments. For 10 epilepsy patients, the proposed algorithm provided the mean correct rate of 98.45%, the mean selectivity of 64.43%, the mean sensitivity of 93.36%, the mean specificity of 98.42%, and the average detection rate of 95.89%. By applying the statistical analysis, the superiority of DTF-based method was statistically significant when compared with other algorithms in terms of five assessment criteria. Our results indicated that the DTF-derived connectivity could characterize the dynamic causal interaction patterns between brain areas during seizure states, and the proposed method was suitable for the detection of epileptic seizures.
A Review of Operation Methods and Simulation Requirements for Future Smart Distribution Grids

The increasing penetration of active elements at lower voltage levels introduces new challenges for efficient and reliable operation of distribution grids. Reverse power flow and the addition of interconnections between feeders not only make relay coordination harder, it requires development and testing of new control algorithms to ensure voltages are kept withing safe operating limits. Simulation tools that provide a good compromise between detail and computational complexity are necessary. This paper summarizes the requirements and characteristics for operating and simulating future smart distribution grids. Special focus is put on existing simulation tools that allow testing of complex algorithms involving a combination of energy sources and loads, i.e., distributed generation (DG), flexible load (FL) and energy storage. Finally, the importance of co-simulation of power and communications systems is discussed.
Decentralized P2P Energy Trading Under Network Constraints in a Low-Voltage Network

The increasing uptake of distributed energy resources in distribution systems and the rapid advance of technology have established new scenarios in the operation of low-voltage networks. In particular, recent trends in cryptocurrencies and blockchain have led to a proliferation of peer-to-peer (P2P) energy trading schemes, which allow the exchange of energy between the neighbors without any intervention of a conventional intermediary in the transactions. Nevertheless, far too little attention has been paid to the technical constraints of the network under this scenario. A major challenge to implementing P2P energy trading is ensuring network constraints are not violated during the energy exchange. This paper proposes a methodology based on sensitivity analysis to assess the impact of P2P transactions on the network and to guarantee an exchange of energy that does not violate network constraints. The proposed method is tested on a typical U.K. low-voltage network. The results show that our method ensures that energy is exchanged between users under the P2P scheme without violating the network constraints, and that users can still capture the economic benefits of the P2P architecture.
Tail Redundancy and its Characterization of Compression of Memoryless Sources

We formalize the tail redundancy of a collection <inline-formula> <tex-math notation="LaTeX">${\mathcal{ P}}$ </tex-math></inline-formula> of distributions over a countably infinite alphabet, and show that this fundamental quantity characterizes the asymptotic per-symbol minimax redundancy of universally compressing sequences generated <italic>i.i. d. </italic> from <inline-formula> <tex-math notation="LaTeX">${\mathcal{ P}}$ </tex-math></inline-formula>. Contrary to the worst case formulations of universal compression, finite single letter minimax (average case) redundancy of <inline-formula> <tex-math notation="LaTeX">${\mathcal{ P}}$ </tex-math></inline-formula> does not automatically imply that the expected minimax redundancy of describing length-<inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> strings sampled <italic>i.i. d. </italic> from <inline-formula> <tex-math notation="LaTeX">${\mathcal{ P}}$ </tex-math></inline-formula> grows sublinearly with <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>. Instead, we prove that universal compression of length-<inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> <italic>i.i. d. </italic> sequences from <inline-formula> <tex-math notation="LaTeX">${\mathcal{ P}}$ </tex-math></inline-formula> is characterized by how well the tails of distributions in <inline-formula> <tex-math notation="LaTeX">${\mathcal{ P}}$ </tex-math></inline-formula> can be universally described, showing that the asymptotic per-symbol redundancy of <italic>i.i. d. </italic> strings is equal to the tail redundancy.
Dual-Comparison One-Cycle Control for Single-Phase Bidirectional Power Converters

Dual-comparison one-cycle control for the bidirectional power flow in single-phase grid-connected converters is proposed in this paper. Advantages of dual-comparison one-cycle control when compared to conventional one-cycle control is that it does not give any steady-state dc offset, no light-load instability, and implements unipolar pulsewidth modulation without having to sense the grid voltage. Addition of a fictitious current term, which is generated from the gating signal of one of the active devices, using a bandpass filter, enables the bidirectional power flow. The quality factor of the bandpass filter is shown to impact the total harmonic distortion in current, fundamental displacement angle, and steady-state dc offset. The optimal value of a quality factor, obtained with aid of simulations, gives a superior performance. The current drawn or injected into the grid is seen to lag the grid voltage. The reason for a nonunity displacement power factor is analyzed and a compensation to achieve a unity displacement power factor is also proposed in this paper. Detailed simulation and experimental studies are carried out to validate the proposed control and the high performance that can be obtained.
Is Convolutional Network Competitive for Vision Method in the Furniture Dowel Quality Control?

The paper presents a method of the furniture dowel quality control based on the convolutional neural network. We applied AlexNet, testing the ability of transfer learning. The paper presents details of the method and experiments performed to justify our solution on images datasets collected from real dowels production. The outcomes are compared with the results of baseline vision method. The experiments show that both methods have their strengths and weaknesses, but they complement each other.
Increased Gibberellins and Light Levels Promotes Cell Wall Thickness and Enhance Lignin Deposition in Xylem Fibers

Light intensity and hormones (gibberellins; GAs) alter plant growth and development. A fine regulation triggered by light and GAs induces changes in stem cell walls (CW). Cross-talk between light-stimulated and GAs-induced processes as well as the phenolic compounds metabolism leads to modifications in lignin formation and deposition on cell walls. How these factors (light and GAs) promote changes in lignin content and composition. In addition, structural changes were evaluated in the stem anatomy of tobacco plants. GA3 was sprayed onto the leaves and paclobutrazol (PAC), a GA biosynthesis inhibitor, via soil, at different irradiance levels. Fluorescence microscopy techniques were applied to detect lignin, and electron microscopy (SEM and TEM) was used to obtain details on cell wall structure. Furthermore, determination of total lignin and monomer contents were analyzed. Both light and GAs induces increased lignin content and CW thickening as well as greater number of fiber-like cells but not tracheary elements. The assays demonstrate that light exerts a role in lignification under GA3 supplementation. In addition, the existence of an exclusive response mechanism to light was detected, that GAs are not able to replace.
Event-Triggered Multi-equilibrium Control of Dynamical Networks

Many real networks are made up of few clusters and different clusters often complete different functions. In this paper, the function of each cluster is seemed as an equilibrium point of the isolated dynamics of each node. When the nodes in different clusters achieve different equilibrium points, the network achieves a multi-equilibrium point, i.e. the network is becoming multi-stable. We study here how to control such a network by using event-triggering functions. By adopting a distributed event-triggered control approach, in which piecewise continuous control laws and event-triggering functions are proposed and combined. More precisely, the suggested piecewise continuous control laws only involve local measurements updating at infrequent and inequitably event-triggered instants that are individually determined, and contain partial control actions that are kept constant during updating intervals until the next updating instant. These event-triggered updating instants are determined individually according to given event-triggered functions about the state differences in between the nodes and their corresponding desired equilibrium points. Exponential stability in the closed-loop networks configured via the proposed control strategies is examined by employing the Lyapunov method. Numerical simulations are performed to illustrate the main results.
Fractional Order Adaptive Sliding Mode Control for the Deployment of Space Tethered System With Input Limitation

This paper develops a novel sliding mode control strategy for the deployment of space tethered system with consideration of constrained input. The simplified nonlinear dynamic model of space tethered system in elliptical orbits is first modeled by using the Euler–Lagrange mechanical equation. Considering the flexibility of tether, the compression or any component of shear forces for the tether is assumed to be input limitation. By introducing fractional order operator and saturation function into the sliding surface, a new adaptive fractional order sliding mode control strategy is introduced based on the proposed nonlinear dynamic model. Compared with classical sliding mode methods, a faster deployment time without overshoot and chattering-reduced performance can be achieved. Finally, numerical simulations are illustrated to validate the effectiveness of our methods.
Seamless Handoff Management in IEEE 802.11 Networks Using SDN

The implementation of IEEE 802.11 wireless networks has grown notably in recent years. Handoff management in wireless networks, i.e., transferring an active data session from one cell defined by an access point to another, is a critical function. In IEEE 802.11 networks, this function interrupts mobile users’ connection to the network, ultimately resulting in service disruption. Currently, the Software Defined Networking (SDN) paradigm presents innovations and abstractions in the network architecture that can improve network management in wireless networks. SDN, through Light Virtual Access Point abstraction, enables the network to take control of the handoff process in IEEE 802.11 without network disconnection (seamless handoff) which is not possible in IEEE 802.11 traditional networks. In this paper, we show through experimentation over real network devices that SDN enables to perform the seamless handoff process in IEEE 802.11 without modification to the end user’ s mobile station and completely controlled by the network. We show that it is possible to reduce the handoff delay from one second or more in traditional networks to milliseconds in SDN networks by means of the LVAP abstraction.
Gaussian Processes for Learning and Control: A Tutorial with Examples

Many challenging real-world control problems require adaptation and learning in the presence of uncertainty. Examples of these challenging domains include aircraft adaptive control under uncertain disturbances [1], [2], multiple-vehicle tracking with space-dependent uncertain dynamics [3], [4], robotic-arm control [5], blimp control [6], [7], mobile robot tracking and localization [8], [9], cart-pole systems and unicycle control [10], gait optimization in legged robots [11] and snake robots [12], and any other system whose dynamics are uncertain and for which limited data are available for model learning. Classical model reference adaptive control (MRAC) [13]-[15] and reinforcement learning (RL) methods [16]-[23] have been developed to address these challenges and rely on parametric adaptive elements or control policies whose number of parameters or features are fixed and determined a priori. One example of such an adaptive model are radial basis function networks (RBFNs), with RBF centers pre-allocated based on expected operating domains [24], [25].
Rapidly Sampling-Based Trajectory Planning for Spacecraft Proximity

A suitably automatic rapid planning approach based on sampling-based algorithms is investigated for impulsive spacecraft closely proximity. Integrated with deterministic sampling and rewiring strategies, the enhanced sweeping Rapidly-Exploring Dense Trees (sweep-RDT*) are lazily extracted from the graph-based roadmaps for generating an asymptotically optimal trajectory, which is then smoothed using linearly homotopic superposition. The proposed method is finally validated and analyzed through various numerical simulations with different sample counts as well as exploring cost thresholds. Results indicate the ability for rapidly safe trajectory planning in agile spacecraft proximity scenarios.
Design and Performance Analysis of Nonlinear Photonic Crystals Based THz Modulator for Ultra-High Speed Communication

The Terahertz (THz) wireless over fibre is an emerging technology for ultra-high speed communication. It is still to be realized due to the lack of effective THz modulators. The nonlinear photonic crystals based THz modulator is designed and analyzed with Finite Difference Time Domain (FDTD) technique. This proposed structure is investigated with the two nonlinear characteristics of materials for enhancing the efficiency of THz modulator. The simulation results show that the nonlinear characteristics of Gallium Arsenide (GaAs) material based THz modulator has very low insertion loss, large modulation rate and high extinction ratio compared to Polyaniline material based THz modulator.
Multiobjective Design of a Line-Start PM Motor Using the Taguchi Method

This paper investigates the use of the Taguchi method for the design of line-start permanent magnet synchronous motors (LS-PMSMs). To address the inherent limitation of the Taguchi method in solving multiresponse optimization problems, an improved regression rate methodology and a weighted factor multiobjective technique are incorporated to form a Taguchi method based multiobjective design framework. To validate the proposed method, a prototype machine has been designed, constructed, and experimentally evaluated. It shows that the proposed method can effectively take into account both steady-state and transient synchronization performances in the design of LS-PMSMs.
Analysis and Development of Instructional Design on Moodle Learning Management System in a Flipped Classroom

Flipped classroom is one of the learning methods that flip activities between inside and outside the classroom. One of important factors to determine how good this method is is an individual learning which is conducted outside the classroom. Nowadays, most of the studies about flipped classroom focus on how to implement it with independent tools, without using learning management system (LMS). Therefore, an instructional design prepared on a LMS based on this method is needed. The instructional design was designed based on the learning process at Faculty of Computer Science, University of Indonesia as a case study through interview and literature review. Consequently, the outcome of this research is an instructional design of flipped classroom in Moodle-based LMS within mock up form. The proposed design will become a milestone for future research in flipped classroom. Moreover, this method was expected to increase student's motivation in learning, hence the use of Moodle-based LMS becomes more optimal.
Self-adaptive De-noising Technique based on DWT for PD Measurements and Self-healing Networks

Partial Discharge (PD) detection and measurement are considered as reliable source of information related to early signs of insulation degradation in order to avoid complete breakdown and longer power outages. During the last decade, the online PD measurements and self healing networks gained significant interest over the periodic maintenance. However, online measured signals are suppressed by high frequency noise, therefore, de-noising of measurements is of paramount importance to get reliable information about a fault. An adaptive de-noising technique based on discrete wavelet transform (DWT), capable of de-noising the measured signals automatically without any human intervention is presented in this paper. The major challenges in using DWT such as wavelet function selection and reconstruction of de-noised signals are addressed. A simple criteria about selection of decomposition coefficients based on dominant frequency and amplitude is used in the presented algorithm. The de-noising performance is evaluated by comparing with other techniques such as correlation based wavelet selection and energy based wavelet selection methods. The results prove that adaptive de-noising is simple, effective and reliable in terms of run time which makes it more useful for online monitoring application.
Zero-Voltage Switching Full-Bridge T-Type DC/DC Converter with Wide Input Voltage Range and Balanced Switch Currents

With the development of silicon carbide (SiC) power devices, the circuit structure of power converter for high-voltage applications would be simpler and more compact because the two-level-based topologies become applicable. This paper proposes a full-bridge (FB) T-type isolated dc/dc converter for the applications with high input voltage and wide input voltage range. The proposed converter is composed of four main power switches with high voltage stress (SiC mosfet) and four auxiliary power switches with low voltage stress (Si mosfet). Comparing with the conventional diode clamped FB three-level isolated dc/dc converters, the proposed FB T-type converter has simpler circuit structure and higher efficiency. A corresponding control strategy including two working patterns is proposed, which can not only realize the zero-voltage switching for the main and auxiliary power switches but also satisfy the wide input voltage range. More importantly, the proposed corresponding control strategy has the ability of balancing the currents among the power switches, which would balance the power losses, thermal stresses among the power switches, and thus improve the reliability of the converter. The characteristics and performances of the proposed converter with the corresponding control strategy are analyzed in detail and verified by the simulation and experimental results.
Model Predictive Direct Flux Vector Control of Multi-three-Phase Induction Motor Drives

A model predictive control scheme for multiphase induction machines, configured as multi-three-phase structures, is proposed in this paper. The predictive algorithm uses a direct flux vector control scheme based on a multi-three-phase approach, where each three-phase winding set is independently controlled. In this way, the fault-tolerant behavior of the drive system is improved. The proposed solution has been tested with a multimodular power converter feeding a six-phase asymmetrical induction machine (10 kW, 6000 r/min). Complete details about the predictive control scheme and adopted flux observer are included. The experimental validation in both generation and motoring modes is reported, including open-winding postfault operations. The experimental results demonstrate full drive controllability, including deep flux-weakening operation.
Applications of the Coanda Effect in Aeronautics

The Coandă effect is present in both external and internal flows, from which practical applications are yielded. In this paper four possible applications of the Coandă effect are presented: jet on a Coandă surface with a thin channel, jet blown over the flap of an airfoil, the thrust vectoring by means of a secondary jet in the presence of a Coandă surface (for internal flow) and the propulsion of the so-called "flying saucers". The aim of the research is to identify the efficiency and limits of the application of this effect and also methods to enhance the Coandă effect. The flow analysis is done by CFD.