Miracle-dz/UniFAR-FTUs huggingface dataset:
Predictive Analytics Based Knowledge-Defined Orchestration in a Hybrid Optical/Electrical Datacenter Network Testbed

For datacenter networks (DCNs), it is always important to have an effective network orchestration scheme that can coordinate the usages of IT and bandwidth resources timely. In this paper, we consider the hybrid optical/electrical DCNs (HOE-DCNs) and propose a knowledge-defined network orchestration (KD-NO) system for them. The KD-NO system follows the predictive analytics in human behaviors, which includes forecasting based on memory and decision making based on knowledge. To explain the design of our KD-NO system, we first discuss how to fetch low-level knowledge from the telemetry data about the resource utilization in an HOE-DCN. Then, we describe how to optimize the HOE-DCN's configuration for network orchestration. Specifically, we design an online scheme based on deep reinforcement learning, and make sure that it can extract high-level knowledge from the low-level input and come up with optimal HOE-DCN configurations on-the-fly. We prototype the proposed KD-NO system and demonstrate it in an HOE-DCN testbed. The experiments run Hadoop applications in the testbed and show that our KD-NO system can make timely and correct decisions in different experimental schemes by leveraging the two-level knowledge, maintain a high matching degree between the HOE-DCN's configuration and the applications running in it, and thus, effectively reduce the job completion time.
Machine Learning Techniques for Screening and Diagnosis of Diabetes: a Survey

Diabetes has become one of the major causes of national disease and death in most countries. By 2015, diabetes had affected more than 415 million people worldwide. According to the International Diabetes Federation report, this figure is expected to rise to more than 642 million in 2040, so early screening and diagnosis of diabetes patients have great significance in detecting and treating diabetes on time. Diabetes is a multifactorial metabolic disease, its diagnostic criteria is difficult to cover all the ethology, damage degree, pathogenesis and other factors, so there is a situation for uncertainty and imprecision under various aspects of medical diagnosis process. With the development of Data mining, researchers find that machine learning is playing an increasingly important role in diabetes research. Machine learning techniques can find the risky factors of diabetes and reasonable threshold of physiological parameters to unearth hidden knowledge from a huge amount of diabetes-related data, which has a very important significance for diagnosis and treatment of diabetes. So this paper provides a survey of machine learning techniques that has been applied to diabetes data screening and diagnosis of the disease. In this paper, conventional machine learning techniques are described in early screening and diagnosis of diabetes, moreover deep learning techniques which have a significance of biomedical effect are also described.
Evaluation of hemiplegia caused by stroke by using joint detection of depth sensors—case of SIAS

Stroke Impairment Analysis Set (SIAS) is a set of evaluation methods of the hemiplegia body caused by stroke. The score of the test is made by visual examiners. However, the decisions are often different between them. Thus, the purpose of this study is to make an automatic evaluation system for the SIAS by using depth sensors. Kinect and Leap Motion are used to evaluate the proximal and distal upper extremity tests and the proximal lower extremity test in SIAS. The evaluation of these tests is based on the time difference of left and right cases, and the smoothness. If both of left and right motions are roughly the same and the motion is smooth enough, the score is 5, otherwise the score is decreased to between 4 and 0. The perfect match ratio between the system output and therapist judged is 65‐75% and it can be higher than this if similar judgments are partially added to the score. In experimental results, this system has been shown to yield reasonable scores for the knee‐mouth test, the finger test and the hip flexion test in motor function of SIAS.
Optical MEMS accelerometer sensor relying on a micro-ring resonator and an elliptical disk

Here, a novel optical micro-electro-mechanical systems (MEMS) accelerometer sensor based on a micro-ring resonator and an elliptical disk is proposed. The designed optical MEMS accelerometer is then analysed to obtain its functional characteristics. The proposed optical MEMS sensor presents an optical sensitivity of 0.0025 nm/g, a mechanical sensitivity of 1.56 nm/g, a linear measurement range of ±22 g, a first resonance frequency of 13.02 kHz, and a footprint of 34 μm × 50 μm. Furthermore, the achieved functional characteristics of the proposed accelerometer are compared to several recent contributions in the related field. According to this comparison study, the present optical MEMS accelerometer can be a suitable device for many applications ranging from consumer electronics to inertial measurement units.
Comparative Study on Two Modular Spoke-Type PM Machines for In-Wheel Traction Applications

This paper focuses on the comparative analysis of modular spoke-type permanent magnet machines with two magnetization modes, which are referred to as M-I and M-II types. The analytical models of the proposed machines are built based on the simple magneto-motive-force-permeance method. With the help of finite element analysis and the analytical models, magnetic fields in machines with different magnetization modes are compared. Then, taking as a base an existing commercial in-wheel machine used in an electric motorcycle, two proposed machines with different magnetization modes are designed as in-wheel traction machines and compared with respect to electromagnetic torque, flux-weakening performance, over-load capability, etc. The machines are prototyped and experimentally tested to verify the prediction that the M-II machines exhibit a higher torque output whereas the M-I machines have a wider speed range.
Fraud Detection in Electric Power Distribution: An Approach That Maximizes the Economic Return

The detection of non-technical losses (NTL) is a very important economic issue for power utilities. Diverse machine learning strategies have been proposed to support electric power companies tackling this problem. Methods performance is often measured using standard cost-insensitive metrics, such as the accuracy, true positive ratio, AUC, or F1. In contrast, we propose to design a NTL detection solution that maximizes the effective economic return. To that end, both the income recovered and the inspection cost are considered. Furthermore, the proposed framework can be used to design the infrastructure of the division in charge of performing customers inspections. Then, assisting not only short term decisions, e.g., which customer should be inspected first, but also the elaboration of long term strategies, e.g., planning of NTL company budget. The problem is formulated in a Bayesian risk framework. Experimental validation is presented using a large dataset of real users from the Uruguayan utility. The results obtained show that the proposed method can boost companies profit and provide a highly efficient and realistic countermeasure to NTL. Moreover, the proposed pipeline is general and can be easily adapted to other practical problems.
Magnetically Levitated Parallel Actuated Dual-Stage (Maglev-PAD) System for Six-Axis Precision Positioning

This paper presents a magnetically levitated parallel actuated dual-stage (maglev-PAD) motion system for six-axis precision positioning. This dual-stage motion system consists of a six-degrees-of-freedom (6-DOF) maglev primary stage for coarse positioning and a 2-DOF planar motion flexure-based secondary stage for fine positioning. Its coarse-fine positioning is realized through a parallel actuation concept. Unlike the conventional designs, the secondary stage in this dual-stage system is serially connected to the primary stage via the flexure hinges while the actuation coils are laid underneath the primary and secondary stages. This design concept largely minimized the size and weight of the levitated platform, thus improving the dynamic characteristics. Furthermore, the primary and secondary stages are truly contactless and most importantly cableless since no power cable is attached to the secondary stage. Design, analysis, and modeling of the Maglev-PAD motion system are presented in this paper. A prototype was developed and demonstrated that the proposed concept largely improves the static positioning errors and significantly compensates the dynamic tracking errors beyond the performance of the primary maglev stage.
Lane Keeping Control of Autonomous Vehicles With Prescribed Performance Considering the Rollover Prevention and Input Saturation

This paper investigates the lane keeping control of autonomous ground vehicles (AGVs) considering the rollover prevention and input saturation. An enhanced state observer-based sliding mode control (SMC) strategy is proposed to achieve the control purpose and maintain the lane keeping errors as well as the roll angle within the prescribed performance boundaries. Three contributions are made in this paper. First, a prescribed performance function (PPF) is proposed in the controller design, aiming to implement the error transformation so as to constrain the controlled variables within the prescribed performance boundaries. Second, a modified sliding surface is developed incorporating two nonlinear functions, whose specialities and benefits are taken advantage of: one is a barrier function to restrict the load transfer ratio (LTR) in a safe boundary to guarantee the roll stability; another is a monotonely decreasing function to adaptively change the damping ratio of the closed-loop system to improve the transient performance, including reducing the transient overshoots and steady-state errors. Third, a modified multivariable adaptive SMC controller is proposed to achieve the integrated lane-keeping and roll control in the presence of the input saturation and bound-unknown disturbances. The stability of the closed-loop system is rigorously proved via the Lyapunov function. Finally, the effectiveness of the proposed control strategy is verified with a high-fidelity and full-car model via the CarSim platform.
Bridgeless Triple-Mode Resonant AC–DC Converter: Dynamic Modeling and Controls

This paper introduces the bridgeless triple-mode resonant ac–dc converter and presents its dynamic model and control technique. The proposed converter operates in triple mode: discontinuous-conduction mode (DCM), continuous-conduction mode (CCM), and missing resonance mode (MRM). It has the advantages of small number of required components, medium power capacity, and high efficiency, but it is difficult to control because DCM, CCM, and MRM have their own dynamic characteristics. To solve this control problem, the mode boundaries are first identified and, corresponding to each mode, the dynamic model is developed to design the controller. In each mode, we propose to add a repetitive controller (RC) with a phase-lead compensator to the conventional duty ratio plus feedback controller. The RC is used to suppress the deterministic periodic disturbances and the phase-lead compensator is used to compensate for the incurred phase lag. A 1-kW digitally controlled converter prototype is built and used to demonstrate the validity of the proposed modeling and control approach.
Energy efficiency based on relay station deployment and sleep mode activation of eNBs for 4G LTE-A network

ABSTRACT The energy efficiency is considered as a major issue due to large power consumption of eNBs in heterogeneous cellular networks. In this paper, a novel relay station (RS) deployment scheme and base station (BS) sleep mode algorithm is proposed to minimize the power consumption of eNBs. Initially, the RSs are deployed to cover the entire area of a cell. It is due to the purpose of providing service when the BS is in sleep mode. Then the network traffic of each cell is measured with Erlang B and C probability measure. If the network traffic is low, then the BS is decided to put into sleep mode which reduces the power consumption. For that, the corresponding RS are selected to handover the active mobile users (MUs). Then, the network traffic is estimated for each RS and the RS without MU becomes sleep mode in order to reduce power consumption further. The proposed model of cellular network reduces the power consumption by applying sleep mode algorithm for both BS and RS based on the measured network traffic. The power consumed by the entire network is measured and compared with the network without sleep mode. The evaluation results show the efficiency of our proposed work.
Alleviating sparsity by specificity‐aware ontology‐based clustering for improving web service recommendation

The rapid development of the Internet in recent years has led to a vast increase in the numbers of Web services, which challenge users' capability to find their favorite services quickly and accurately. There is thus an urgent demand for service recommendations that help discover applicable services. Although the collaborative filtering technique is one of the most successful recommendation system technologies, it suffers from data sparsity and cold‐start problems, which in turn lead to inaccurate results. In this article, we deal with these issues by applying a novel ontology‐based clustering approach that uses domain specificity and service similarity for ontology generation. This clustering approach can easily and effectively increase the data density of the user‐service dataset by assuming blank user preferences according to the history of user‐favored domain(s). Then, user ratings are predicted by calculating the trust value between users. The experimental results indicate that the proposed approach can effectively alleviate the data sparsity and cold‐start problems with lower prediction error with the best recommendation performance, and our method performed better than baseline approaches in terms of accuracy and novelty. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
IPSO based SVC-PID for angle stability enhancement

This paper presents a new optimization approach for tuning conventional PID controller parameters of a static var compensator (SVC) which controls a synchronous machine. SVC is designed and implemented to improve the damping of a synchronous generator. For tuning three SVC-PID parameters, a new Particle Swarm Optimization (PSO) technique called Iteration PSO (IPSO) is proposed. In this method, a new iteration best index is implemented into conventional PSO in order to enhance the quality of the solution. Based on eigenvalues and damping ratio results, it is confirmed that the proposed technique is more efficient than conventional PSO in improving the angle stability of the system. The study involves the optimization of proportional gain, KP, interval gain, KI and derivative gain, KD of PID controller. Comparison between IPSO, PSO and Evolutionary Programming (EP) optimization techniques showed that the proposed computation approach give better solution and faster computation time.
PMSM Open-Phase Fault-Tolerant Control Strategy Based on Four-Leg Inverter

Open-phase fault is a common failure in permanent magnet synchronous motor (PMSM), which would degrade motor performance and increase its loss due to the unbalanced phase currents. However, conventional fault-tolerant strategies suffer from the tracking problem of sinusoidal current references. In this paper, different from previous fault-tolerant strategies, a new fault-tolerant method for open-phase PMSM is proposed by designing a novel transformation matrix for current/voltage references. With the new transformation matrix, the voltage/current references in the d–q frame can be transformed to the phase voltage/current references of the two remaining phases. With the proposed method, open-phase fault-tolerant implementation can be much easier (proportional-integral controller is enough) and its performance can be much higher. Besides, as the tolerant method is designed based on the four-leg inverter, stronger fault-tolerance capability can be obtained compared with the three-phase inverter-based method. Finally, the experimental results confirm the validity of the proposed fault-tolerant method.
A set of audio features for the morphological description of vocal imitations

In our current project, vocal signal has to be used to drive sound synthesis. In order to study the mapping between voice and synthesis parameters, the inverse problem is first studied. A set of reference synthesizer sounds have been created and each sound has been imitated by a large number of people. Each reference synthesizer sound belongs to one of the six following morphological categories: "up" , "down" , "up/down" , "impulse" , "repetition" , "stable". The goal of this paper is to study the automatic estimation of these morphological categories from the vocal imitations. We propose three approaches for this. A base-line system is first introduced. It uses standard audio descriptors as inputs for a continuous Hidden Markov Model (HMM) and provides an accuracy of 55.1%. To improve this, we propose a set of slope descriptors which, converted into symbols, are used as input for a discrete HMM. This system reaches 70.8% accuracy. The recognition performance has been further increased by developing specific compact audio descriptors that directly highlight the morphological aspects of sounds instead of relying on HMM. This system allows reaching the highest accuracy: 83.6%.
Comprehensive Controller Implementation for Wind-PV-Diesel Based Standalone Microgrid

In this paper, a comprehensive controller of a standalone microgrid is implemented, which has three dispersed generation units based on a wind, solar photovoltaic (PV) array, and a diesel generator (DG). The power ratio variable step perturb and observe method is applied to achieve maximum power point tracking of a solar PV array and a variable speed wind turbine coupled a permanent magnet brushless dc generator without rotor/wind speed sensors. Moreover, to ensure perfect synchronization of a DG to the point of common coupling (PCC), a control algorithm is developed, which is based on in-phase and quadrature units. An active power control based on proportional–integral controller with anti-windup, is used for voltage and frequency regulation. The LCL filter based on virtual resistor, is used for power quality improvement at PCC. Simulation and test results are presented for the validation of the proposed system using a prototype of 2 kW in the laboratory.
Sampled-Data Modeling for Wireless Power Transfer Systems

In this paper, a dynamic model based on the sampled-data method is presented for a wireless power transfer (WPT) system. The orders of the derived model are equal to the orders of a system. The modeling is different from the existing dynamic modeling for WPT systems, which almost double the model's order. Furthermore, the systematic analysis process is suitable for any WPT topologies and without the higher harmonics neglected and low-frequency approximation. Meanwhile, the state variables are allowed to vary up to twice the switching frequency, since the half-period symmetry property is exploited. In this paper, the series–series (SS) compensated WPT system is chosen as an example and the large-signal model and the steady-state operation point are determined. The small-signal responses perturbed by the phase-shift angle, switching frequency, and input voltage are obtained. The derived model is more adequate with simulation and experimental validation.
Cricket Biomechanics Analysis of Skilled and Amateur Fast Bowling Techniques

Cricket fast bowling action involves complex three-dimensional (3D) motion of the body and poses a high risk of injury more so in schoolboys. It is not known how the bowling technique varies between skilled and less skilled fast bowlers. The aim of this study is to compare the differences in bowling technique between young sub-elite (skilled) and amateur university level cricketers. Twelve players, 6 skilled and six amateur, were attached with 35 retro-reflective markers using the full body Plug-in-Gait marker set and asked to bowl 6 deliveries at a good length. Their bowling action was captured with 12 Vicon 3D cameras and the ground reaction force was measured using AMTI force plates. The best delivery from each bowler was selected. Their bowling action types were classified and parameters like shoulder counter rotation (sCr), pelvicshoulder separation angle at back foot contact, trunk lateral flexion, front knee angle, front foot vertical ground reaction force (vGrF) and ball release speed were measured. The results were analyzed with Levene’s test for Equality of Variances and a t-test for equality of means. The skilled bowlers showed faster ball release speed and experienced larger vGrF while the other parameters did not show any significant differences.
Design, Evaluation, and Optimization of Communication Architectures for Smart Grids (Design, Evaluierung und Optimierung von Kommunikationsarchitekturen für Smart Grids)

Future electrical power grids (smart grids) use information and communication technology to improve power system control beyond what has been possible in the past. The objectives are, among others, to enable efficient use of renewable energy resources, to ensure security of electricity supply and to support future energy market structures. This requires data exchange between various legacy and future applications in the smart grid. To facilitate this, a flexible communication platform is required. A central question of this work is what such a communication platform should look like. Concepts for resilient communication, integration of legacy and future smart grid applications, and secure communication over untrusted infrastructures are designed, evaluated and optimized using simulations, analysis and prototypes. In addition, the communication dynamics of a trading platform for the future retail energy market are analyzed, and smart meters and their communication components (smart meter gateways) are investigated as further important building blocks for the smart grid.