Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Dynamic State Estimation Method for Distribution Networks with Enhanced Robustness

In order to improve the robustness and accuracy of dynamic state estimation for uncertain systems, a dynamic state estimation method for distribution network based on adaptive mixed Kalman/H∞ filtering is proposed. The filtering gain of this method is obtained by weighted sum of the gain of extended Kalman filtering (EKF) and H∞ filtering. By mixing the two algorithms, the method achieves the performance balance of the two filters. Different from the traditional estimator, this method uses the performance index of Kalman filtering to adjust the weights of both filters, so as to improve the tracking ability of state of the dynamic system represented by the distribution networks. At the same time, a scaling factor is introduced to enlarge the performance bound of H∞ filter to reduce the bad influence of exceedingly conservative external factor assumptions in traditional H∞ filter on the accuracy of the result. Experimental results on various states of distribution networks further verified the feasibility of the proposed dynamic state estimation method.
A Modified Smith Predictor For Processes with Variable Delay

The present work shows the design of a new control scheme of two degrees of freedom based on the Smith predictor structure. This new proposal is compared with the Filtered Predictive Proportional and Integral controller and with the two degrees of freedom scheme of W. Zhang using the Integral Square Error and Total Variations of Control Efforts performance indexes. The results show that the new proposal allows to improve the performance and robustness when the process has variable delay.
Would it be Profitable Enough to Re-adapt Algorithmic Thinking for Parallelism Paradigm

A lot of progress in computing systems components are devoted today to grant more support for parallelism. This, is likely affording much opportunities for High Performance Computing (HPC) applications developers who become now able to accelerate run-times progressively. Adapting algorithmic writings for parallelism paradigm likely leads to additional improvement in run-times. This paper deals with this matter. We carry an empirical measures to assess how interesting is to re-adapt algorithmic thinking for parallel processing context. We provide thorough comparisons of achievable accelerations among a number of different sorting algorithm kinds. We use a proprietary framework meant previously to serve as a front-end kernel in an automatic parallelization compiler and we populated it with interpolation to make performance predictions in large-scale parallelization. Sequential, semi parallel and parallel algorithms for sorting problem are all involved in the empiric tests considering different distributions for randomized input records. The results let to estimate how much the innovation of specific parallel algorithms could be more profitable than parallelization of serial programs.
Scalable GPU Communication with Code Generation on Stencil Applications

Clusters with GPUs are mainstream in HPC as shown by the last edition of the Top500 list, increasing the demand for GPU capable scientific computing software. Programming large scale GPU systems in an efficient and future-proof way present numerous challenges, such as optimizations for a variety of GPUs and interconnect hardware, hiding communication overhead with computation and efficient domain partitioning. We present an improvement to the CUDA-based communication of stencil applications in the WALBERLA framework, achieving scalability while supporting different GPUs and communication infrastructures. We utilize the lattice Boltzmann Method for fluid flows as a representative of stencil-based scientific computing and implement a communication hiding strategy that is capable of adjusting to a system's computing and communication capabilities. We compare the use of CUDAMemCopy with the use of customized pack/unpack kernels and show that packing achieves almost linear weak scaling behavior in the Santos Dumont supercomputer with up to 128 GPUs. We also show that the proposed approach is not sensitive to the direction of the domain partitioning, one of the biggest challenges when communicating 3D domains in GPU-based stencil simulations.
On Herb Compatibility Rule of Insomnia Based on Machine Learning Approaches

Recent research in machine learning has led to significant progress in various research fields. Especially, the knowledge discovery using this method in Traditional Chinese Medicine (TCM) has been becoming a hot topic. In this paper, we studied on the herb compatibility rule of insomnia using some machine learning approaches. We have extracted insomnia data set with 807 samples from the real-world Electronic Medical Records (EMRs). After cleaning and selecting the theme data referring to the prescriptions and their herbs, we constructed the herb network analysis model using the theory of complex network. In order to explore the hidden relationships among the herbs, we trained each herb node in network to obtain the herb embeddings using the Skip-Gram model in word embedding theory. After acquiring the vocabulary of herbs with the formation of vectors, we calculated the similarity among any two herb embeddings, and clustered these herb embeddings into seven communities using the Spectral Clustering (SC) algorithm. The experimental results shed light on that the methodologies used in this paper can objectively and effectively discover the relationships among herbs, and reveal the herb compatibility and herb clusters for clinical treatment research of insomnia.
Attentive Context-Aware Music Recommendation

Listening to and singing music online have become two popular forms of entertainment for people. Online music service providers are devoted to recommend personalized music to users, so that more users can be attracted by spending more money on their music service platform. Hence, an accurate recommendation for users is one of the most important tasks for the music service companies. This paper proposes a novel recommender system for recommending music. According to the preliminary measurement, we find that the interest of users on music changes over time. Furthermore, we notice that the time interval between two consecutive listened/sung musics can reflect the temporal interests of users. Based on these findings, we propose MusicRecLSTM, which leverages the attention mechanism and a modified LSTM network to learn the embeddings of both the user and the music based on the sequential data and the temporal context. MusicRecLSTM also learns whether a user would tend to listen to a newly released music at a specific time. Evaluations over three real-world datasets show that the proposed MusicRecLSTM can provide high precision, compared to several state-of-the-art recommender systems, with at least 20% improvement on the recall and mean reciprocal metrics.
An Examination of the Anaerobic Capacity of Children Using Maximal Accumulated Oxygen Deficit

The purpose of this study was to determine the anaerobic capacity of children using the maximal accumulated oxygen deficit technique (AOD). Eighteen healthy children (9 boys, 9 girls) with a mean age of 10.6 years volunteered as subjects. Peak oxygen uptake and submaximal steady-state oxygen uptake tests were conducted against progressive constant work rates on a Cybex cycle ergometer. Supramaximal work rates were predicted from the linear regression of submaximal steady-state work rates and oxygen uptakes to equal 110, 130, and 150% of peak oxygen uptake. Results indicated a significant interaction in the responses of both sexes when the accumulated oxygen deficit data were expressed in both absolute and relative terms. The profile of accumulated oxygen deficits across the three intensities indicated a downward shift in the girls responses between the 110 and 150% supramaximal tests. This trend was not evident in the boys’ responses. Intraclass correlations conducted on test-retest data indicated that co.
Parallel Approaches in the Heavy Ball GMRES Method

Linear system is a useful mathematical model that is used in many research areas. This paper presents a parallelism study that considers a recent iterative method to solve large sparse linear systems, called Heavy Ball Generalized Minimal Residual (HBGMRES) method. This method is based on the classical restarted GMRES and tries to add some history in each iteration. Our parallelism study is made with the OpenMP API, based on the shared memory paradigm. We proposed three different parallel approaches. Our experimental results show a maximum speedup of 2.11 on a four-core processor.
The FPGA-Based Problem-Oriented On-Board Processor

The FPGA-based problem-oriented processor for the implementation of robust algorithms for estimating the state vector and for the control of the small spacecraft is developed. Comparison and analysis of calculation results of new ellipsoid center obtained for the C-program model as well as the developed core of problem-oriented processor for the estimation of ellipsoidal state and for control algorithms are performed.
A Survey: Cloud Data Security Based on Blockchain Technology

The development of cloud computing technology is becoming more and more mature, but its security problems are always accompanied by it. As an emerging technology, blockchain has the characteristics of decentralization, distributed fault tolerance, and trustworthiness. Applying blockchain technology to solve cloud security problems is a very promising direction, and it is used for protecting cloud data security currently. This paper first analyzes the security threats in the cloud, then briefly introduces the blockchain technology, summarizes the classification of the current blockchain technology to solve the cloud data security problems and defects existed. Finally, a new cloud forensic storage architecture model based on blockchain technology is proposed to protect the cloud data, which lays a foundation for our future research.
An Extraction Method of Handwritten Characters on Printed Documents by Maxout Filter Networks

This paper proposes an extraction method of handwritten characters written on printed documents using maxout filter networks. The extraction of handwritten characters from document images, e.g. printed text with handwritten annotation, plays important roles in the field of the document analysis and recognition. In this paper, the maxout filters that are composed with the maxout activation functions are trained and applied to extraction of the handwritten characters from the documents. The maxout filter for the extraction is an extension of the morphological closing filter, which can eliminate the valleys of intensity surface of images. For extraction of the handwritten characters, the parameters of the maxout filters are trained to eliminate only the printed characters, which appear as the valleys of the intensity. In order to handle multiple writers, a batch learning that trains parameters with averaging the errors of each writer is introduced. Moreover, the novel network configuration that has the skipping path is introduced to improve the learning rate and the quality of the extracted images. In the experiments, the extraction of the handwritten annotation from the document images is demonstrated. The results of the comparison with the conventional network shows the advantage of the proposed network.
A Compact Wideband Phase Shifter Using Slotted Substrate Integrated Waveguide

A compact phase shifter using substrate integrated waveguide (SIW) with two slots is proposed in this letter. The wideband phase shift is mainly caused by the opposite phase shift slopes produced by the slots and the microstrip delay lines over a wide frequency range. Furthermore, the wide phase shift range (−90° to 90°) can be achieved and be with the same reference line. Compared with the state of the arts, the proposed one gives consideration of the wide bandwidth, wide phase shift range, simple and compact size, and easy-to-make structure, while the SIW counterparts just consider part of the above features. Five prototypes covering the frequency of 26 GHz are designed with the operating bandwidth for 90° ± 5° (−45° ± 2.5°, 45° ± 2.5°, and 90° ± 5°) phase shifter of 26.6% (29.2%, 35.8%, and 39.3%) and the size of <inline-formula> <tex-math notation="LaTeX">$2.29\lambda _{g}^{2}$ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">$2.42\lambda _{g}^{2}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$2.71\lambda _{g}^{2}$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$2.86\lambda _{g}^{2}$ </tex-math></inline-formula>; <inline-formula> <tex-math notation="LaTeX">$\lambda _{g}$ </tex-math></inline-formula> is the guided wavelength at the center frequency <inline-formula> <tex-math notation="LaTeX">$f_{0}$ </tex-math></inline-formula>).
Theoretical and Experimental Investigation of an All-Fiber Waveguide Coupled Surface Plasmon Resonance Sensor With Au–ZnO–Au Sandwich Structure

In this paper, we proposed an all-fiber waveguide coupled surface Plasmon resonance (SPR) sensor which was designed and fabricated with Au-ZnO-Au sandwich multi-layer structure. Based on the transfer matrix method (TMM), the theoretical modeling of the proposed sensor structure was investigated. The influence of the layer thickness on the transmission spectrum of the sensor was studied numerically, in order to optimize each layer thickness, leading to sensitivity enhancement in the infrared spectral range. Meanwhile, an experimental testbed for the refractive index measurement was implemented based on the proposed all-fiber waveguide coupled SPR sensor. Using this waveguide coupled SPR sensor, the refractive index sensing was realized with a sensitivity enhancement of 7%—131% compared with conventional SPR sensors.
Electroencephalography Based Motor Imagery Classification Using Unsupervised Feature Selection

The major challenge in Brain Computer Interface (BCI) is to obtain reliable classification accuracy of motor imagery (MI) task. This paper mainly focuses on unsupervised feature selection for electroencephalography (EEG) classification leading to BCI implementation. The multichannel EEG signal is decomposed into a number of subband signals. The features are extracted from each subband by applying spatial filtering technique. The features are combined into a common feature space to represent the effective event MI classification. It may inevitably include some irrelevant features yielding the increase of dimension and mislead the classification system. The unsupervised discriminative feature selection (UDFS) is employed here to select the subset of extracted features. It effectively selects the dominant features to improve classification accuracy of motor imagery task acquired by EEG signals. The classification of MI tasks is performed by support vector machine. The performance of the proposed method is evaluated using publicly available dataset obtained from BCI Competition III (IVA). The experimental results show that the performance of this method is better than that of the recently developed algorithms.
Application of Love Wave Sensing With Mesoporous Thin Film

Acoustic wave sensors are given special attention since a few decades as they offer a good sensitivity for detection applications. Their ability to operate in liquid and gas media make them a very interesting device. Nevertheless, to further improve their sensitivity in term of “mass loading effect”, the increasing of the surface to volume ratio of the associated sensitive layer is a promising strategy which could overcome many of the challenges faced by the in situ detection of low mass biomolecules. This paper presents an overview of the latest works carried out on the association of Love wave transducers with SiO2 and TiO2 porous layers at the IMS Laboratory. The principal results are developed, and the current investigations and perspective are exposed.
Fast and Accurate Model for Optimization-based Design of Fractional-Slot Surface PM Machines

This paper presents the development and validation of a fast, accurate, and high dimensional Multiphysics analytical model for the optimization-based design of fractional-slot surface permanent magnet (PM) machines. The approach is non-iterative and high dimensional, i.e. considers a high number of input parameters. The resulting model takes an average of 0.03 seconds to run on a standard PC, and its accuracy is verified by both Finite Element (FE) analysis and experimental tests. Due to its accuracy and speed, the model can be easily integrated within a design optimization environment.
Velocity Profile optimization of an Electric Vehicle (EV) with Battery Constraint Using Pontryagin’s Minimum Principle (PMP)

Electric vehicles (EVs) powered by electric motors and batteries have solved the issues related to the environmental pollution. The major concern with electric vehicles is their short range, which is due to the limited capacity of a battery installed in the system. Limited range issue can be solved or minimized through eco-driving; an optimization technique in which a driver is advised to follow a velocity profile that consumes minimum energy. In the recent past, eco-driving problem with an online requirement using the static model of a battery has been discussed. Eco-driving problem has been formulated as an optimal control problem, where the goal is to minimize vehicle power consumption only. In this work, the goal is extended to increase the life cycle of battery along with power consumption. For this purpose, battery dynamics are included along with vehicle dynamics. To increase the battery’s life cycle constraints are applied to avoid the battery from getting fully charged and discharged. Problem is solved using pontryagin’s minimum principle (PMP), where initial and final position of a vehicle, initial velocity of a vehicle and total trip time is fixed. Optimal torque, optimized velocity trajectory and constrained state of charge during a trip are generated that in turn minimize the energy consumption of the battery. The results have shown that constrained case is approximately 7 percent more efficient in terms of energy consumption than an unconstrained case. Reduction in energy consumption, coupled with battery constraints result in proper charging and discharging of the battery thereby extending battery life.
Online k-step PNARX identification for nonlinear engine systems

Real systems are often nonlinear, and accounting for the nonlinearity can be essential for the attainable closed-loop performance in model-based control. This is especially true for many engine related control problems, as the achievable tradeoff between different targets, e.g. emissions and consumption, depends strongly on the model quality. However, engine systems tend to change over time, and it would be beneficial to be able to track these changes in the model. Against this background, we propose here a novel recursive algorithm for online adaptive system identification aimed to estimate an approximating parametric nonlinear model (polynomial NARX) of systems. This model structure has been used earlier e.g. for the air path control. The presented identification scheme is also suitable for parameter estimation in a closed-loop setting, provided that the data is sufficiently exciting. The main contribution of this paper is a recursive algorithm minimizing the k-step ahead prediction error for updating the model parameters in a computationally efficient way. We show its effectiveness by means of simulation examples of a nonlinear case study system and real data of a Diesel engine air path.
A Weighted Tensor Factorization Method for Low-Rank Tensor Completion

Recently, low-rank tensor completion has attracted increasing attention in recovering incomplete tensor whose elements are missing. The basic assumption is that the underlying tensor is a low-rank tensor, and therefore tensor nuclear norm minimization can be applied to recover such tensor. By taking color images as third-order tensors, it has been shown that these tensors are not necessary to be low-rank. The main aim of this paper is to propose and develop a weighted tensor factorization method for low-rank tensor completion. The main idea is to determine a suitable weight tensor such that the multiplication of the weight tensor to the underlying tensor can be low-rank or can be factorized into a product of low-rank tensors. Fast iterative minimization method can be designed to solve for the weight tensor and the underlying tensor very efficiently. We make use of color images as examples to illustrate the proposed approach. A series of experiments are conducted on various incomplete color images to demonstrate the superiority of our proposed low-rank tensor factorization method by comparing with the state-of-the-art methods in color image completion performance.
Smart Resource Configuration and Task Offloading with Ultra-Dense Edge Computing

The ongoing increasing number of mobile devices (MDs) with innovative applications yields unprecedented demands for user experience and network capacity expansion. The combination of ultra-dense network (UDN) and mobile edge computing (MEC) has been envisioned as a promising futuristic technology. It can remarkably improve the capacity of system and extend cloud-computing capabilities to proximate edge servers, by which the growing computation requirements of MDs will be met. However, what is not addressed well is how to optimize the configuration of computing resources with varying computation demands of MDs being satisfied, aiming at maximizing the operating earnings of operators while decreasing the cost of MDs. Considering the diverse computation demands in different regions and variational computing resources of edge servers, it is hard to solve this problem by traditional methods. To address this issue, an effective solution is generating an optimal computing resource configuration strategy and task offloading profile in time-varying UDN scenarios. Toward this end, a deep Q-network based scheme is proposed to achieve maximum long-term weighted network utility in such ever-changing environments. Simulation results validate the significant performance improvement of our scheme in weighted network utility and task offloading compared to conventional game-theoretical solution.
Windowing for Speech Emotion Recognition

In this paper we present window time length analysis for speech emotion recognition. We performed our tests on spectrograms calculated from Hamming window of different length in time and trained our Convolutional Neural Network (CNN) architecture with two convolutional layers and one fully-connect layer. The goal was to find out which resolution of spectrograms, whether time or frequency one is better to classify speech signal into seven emotional classes successfully. According to our results, right choice of window length is important for Speech Emotion Recognition (SER). Accuracy depends on window length and overlap of consecutive frames. The results show that preprocessing of speech signal in time domain plays important role for SER system, even with usage of CNN as classifier.