Miracle-dz/UniFAR-FTUs huggingface dataset:
High-Performance Time Series Prediction With Predictive Error Compensated Wavelet Neural Networks

Machine learning (ML) algorithms have gained prominence in time series prediction problems. Depending on the nature of the time series data, it can be difficult to build an accurate ML model with the proper structure and hyperparameters. In this study, we propose a predictive error compensation wavelet neural network model (PEC-WNN) for improving the prediction accuracy of chaotic and stochastic time series data. In the proposed model, an additional network is used for the prediction of the main network error to compensate the overall prediction error. The main network takes as inputs the time series data through moving frames in multiple-scales. The same structure and hyperparameter sets are applied for quite distinct four types of problems for verification of the robustness and accuracy of the proposed model. Specifically, the Mackey-Glass, Box-Jenkins, and Lorenz Attractor benchmark problems, as well as drought forecasting are used to characterize the performance of the model for chaotic and stochastic data cases. The results show that the PEC-WNN provides significantly more accurate predictions for all compared benchmark problems with respect to conventional machine learning and time series prediction methods without changing any hyperparameter or the structure. In addition, the time and space complexity of the PEC-WNN model is less than all other compared ML methods, including long short-term memory (LSTM) and convolutional neural networks (CNNs).
PID Controller Design based on IMC Using Model Order Reduction and Modern Control Approach: Application to Single Area Load Frequency Control Problem

In this paper, a new approach for the design of PID based Internal model control (IMC) controller is proposed. The proposed design scheme incorporates model order reduction technique and an advanced state space control technique, i.e., linear quadratic regulator (LQR) in the classical IMC control scheme. A systematic algorithm is developed for determining the tuning parameters. Subsequently, the IMC controller is converted into a proportional-integral-derivative (PID) type controller. Further, the proposed scheme is applied for single area load frequency control of the power system. The proposed approach is compared with the existing techniques. The simulation results indicate that the proposed control scheme shows an improved disturbance rejection capability and better transient response in comparison to existing design techniques.
Power Packet Dispatching System and Router for Bi-directional Dispatching

Power packet dispatching system is proposed as one of the advanced dc power distribution systems. In order to manage the flow of power packet in the distribution system according to the tag information attached to the power packet, the router, which dispatches the packets, a key element of the system. In this paper, a router is invented to dispatch the packets by-directionally. The laboratory prototype router applies SiC MOSFET for the power switches. The bi-directional router enables us to select the setting points to connect power sources and loads in the system more flexibly.
Unsupervised Noise Detection in Unstructured data for Automatic Parsing

The telecommunications industry makes extensive use of data extracted from logs, alarms, traces, diagnostics, and other monitoring devices. Analyzing the generated data requires that the data be parsed, re-structured, and re-formatted. Developing custom parsers for each input format is labor-intensive and requires domain knowledge. In this paper, we describe a novel unsupervised text processing pipeline to automatically detect and label relevant data and eliminate noise using Levenshtein similarity and Agglomerative clustering. We experiment with different similarity and clustering algorithms on a selection of common data formats to verify the accuracy of the proposed technique. The results suggest that the proposed methodology has higher accuracy.
Is Business Analytics Education Sufficient in Business Schools? The Case in Turkish Business Schools

Undergraduate and graduate programs conducted with the names of Business Analytics, Data Analytics, Big Data Analytics and Business Intelligence have become one of the most discussed topics both in academia and in the market novadays. This current discussion is due to the increasing importance of the management and analysis of Big Data, which has emerged as a result of developments in Information Technology and science. In order to meet the needs of both academy and the companies about how to take advantage of Big Data, many universities in Europe and USA have established analytics related graduate programs. This paper investigates recent analytics related programs in Turkey and determines whether these programs can meet the needs and demands of the companies by considering the literature on analytics education and assessing a maturity score based on the offered analytics related classes.
Optimal Photovoltaic Array Dynamic Reconfiguration Strategy Based on Direct Power Evaluation

In actual operation, photovoltaic arrays inevitably encounter shadow problems, resulting in a power output decline and multiple peaks in the output characteristics. The effect of partial shade can be satisfactorily reduced by photovoltaic array reconfiguration. Almost all current reconfiguration techniques are based on the irradiance equalization principle. However, by analyzing the output characteristics of photovoltaic array, it is found that the irradiance equalization principle improves the output power by increasing only the minimum row current without considering the effect of the voltage, so the reconfiguration techniques based on this principle cannot obtain the global optimal configuration under some partial shading conditions. In order to maximize the output power of photovoltaic array under any partial shaded condition, this paper proposes a reconfiguration strategy based on direct power evaluation. In this approach, the reconfiguration problem is formulated as a 0–1 multi-knapsack problem, and a novel mathematical model is established to directly evaluate the maximum output power of photovoltaic array. Then, the optimal reconfiguration scheme is determined by solving the mathematical model. Finally, the effectiveness of the proposed reconfiguration strategy is proved in theory and simulations.
MMSPP: Multimodal Social Media Popularity Prediction

People post and share news on social media in daily lives. Popular posts get attention and further influence the society. It’s helpful to know in advance if certain posts will become popular. In order to achieve this, several challenges need to be resolved: (i) more posts become multi-modal – smartphones make it easy to capture and share images and videos with texts in posts, and (ii) the popularity of posts depends not only on the quality of the content, but also on the popularity of the post author themselves. To this end, we propose MMSPP, a multimodal social media popularity prediction system incorporating image, text and author social features of a post to predict its retweet counts and favorite counts in Twitter as the measurements for popularity. Specifically, two novel multimodal fusion methods are proposed. We evaluate MMSPP with the two fusion methods in a Tweeter dataset with 3448 image-text pairs of posts. MMSPP is able to beat three baselines by at least 12.50% and 7.69% MSE for retweet counts and favorite counts respectively.
From Injectivity to Integrity Studies of CO2 Geological Storage - Chemical Alteration Effects on Carbonates Petrophysical and Geomechanical Properties

The technical and economical success of a CO2 geological storage project requires the preservation of the site injectivity and integrity properties over its lifetime. Unlike conventional hydrocarbon gas injection, CO2 injection implies geochemical reactions between the reactive brine and the in situ formations (reservoir and cap rock) leading to modifications of their petrophysical and geomechanical properties. This paper underlines the experimental difficulties raised by the low permeability of samples representative either of the cap rock itself or at least of transition zones between the reservoir and the effective cap rock. Acidification effects induced by CO2 injection have been studied using an experimental procedure of chemical alteration, which ensures a homogeneous dissolution pattern throughout the rock sample and especially avoids any wormholing process that would lead to erroneous measurements at the core scale. Porosity, permeability and geomechanical properties of outcrop and field carbonate samples of various permeability levels have been measured under their native state and different levels of alteration. The present work has been conducted within the framework of ANR GeoCarbone-INJECTIVITY and GeoCarbone-INTEGRITY projects. Each experimental step: chemical alteration, petrophysical measurements and geomechanical testing, is considered from the point of view of injectivity and integrity issues. The obtained experimental data show clear trends of chemically induced mechanical weakening.
Reduction of Parity Overhead in a Subset of Orthogonal Latin Square Codes

Radiation may cause unexpected effects in electronics, in particular in harsh environments such as Space. Error correcting codes can be used to mitigate radiation effects in these scenarios. Orthogonal Latin Square (OLS) codes are a type of error-correction codes (ECC) capable of correcting multiple bit errors. They can be decoded with low delay and complexity. Therefore, they are of interest in communications and memory applications. Their main drawback is the overhead required in terms of parity bits needed to protect the information. Thus, a relevant goal is to reduce the number of parity bits while keeping the simplicity and delay properties. This paper presents a method to limit this overhead with similar delay and complexity characteristics with respect to the original code.
Trajectory Tracking Control of Four-Wheel Independent Drive Electric Vehicles*

A two layered control scheme is proposed to solve the trajectory tracking problem for a four-wheel independent drive electric vehicle (4WIDEV) in this paper. The first layered control is a control strategy producing front steering wheel angle and yaw moment that is designed by model predictive control (MPC) approach. The second layered control is a torque optimal distribution control algorithm to achieve yaw moment distribution to four tires. CarSim-Simulink joint simulation results demonstrate the effectiveness of the proposed control scheme and advantage over the traditional PID.
WFNet: A Wider and Finer Network for Salient Object Detection

Multi-layer feature integration has demonstrated its superiority in salient object detection. However, the salient regions generated by most models still suffer from inconsistencies and coarse region boundaries. In this paper, to solve these two problems, we proposed a wider and finer network named WFNet. Firstly, a wider feature enhancement module (WFE) is designed to expand the receptive fields of deep semantic features, which makes the network look wider and more accurate while locating salient regions. Secondly, to improve the regional continuity and reduce background noise, we introduce a finer feature fusion module (F3M) which consists of scale-invariant average pooling and detailed feature integration module with channel-wise attention. Finally, we propose an edge-region complementary strategy (ERC) and an edge-focused loss (EL), which can supplement the diluted deep semantics and let the network pay more attention to boundary pixels of salient objects. Benefit from rich deep semantics and more detailed edge features, WFNet can predict saliency maps with clear boundaries under the guidance of edge-focused loss. Experimental results prove that the proposed method outperforms state-of-the-art methods on five benchmarks without any post-processing.
An Improved Deadbeat Predictive Current Control Scheme for Open-Winding Permanent Magnet Synchronous Motors Drives With Disturbance Observer

To improve the performance of open-winding permanent magnet synchronous motors (OW-PMSMs) with a common dc bus, a novel control method that can simultaneously eliminate parameter mismatch in the d-axis, q-axis, and zero-sequence loop (ZSL) is proposed. First, the parameter mismatches are analyzed. Second, an extended state observer (ESO), which can predict the current in the next instant and the disturbance caused by parameter mismatch, is established. By combining the ESO and the deadbeat predictive current control (DPCC) in the d-axis and q-axis, replacing the sampled current in the DPCC with the predictive current in the ESO, and considering predictive disturbance as a voltage reference feedforward compensation, one-step delay and the disturbance caused by parameter mismatch are addressed. Then, in the ZSL, the predicted zero-sequence disturbance is considered as a compensation for the reference zero-sequence voltage (ZSV). The ZSV is obtained using the zero-voltage vector redistribution strategy in alternate subhexagonal center pulsewidth modulation strategy. The proposed method enhances the robustness of OW-PMSM, against parameter mismatch in the d-axis, q-axis, or ZSL. To verify the effectiveness of the proposed method, simulation and experimental results obtained using the traditional DPCC method and the ESO+DPCC method are presented herein.
Robust Traffic Light Detection and Classification Under Day and Night Conditions

Recently, traffic light detection and classification systems have been studied and developed to build an autonomous car by many research institutes, universities, and companies. However, the results of existing traffic light detection systems are still not stable under day and night conditions. It is difficult to detect the location of traffic light due to their small size. Moreover, traffic lights’ shapes are also similar to advertisement lights in a city road. Therefore, this paper proposed a new approach to improve the performance of existing traffic light detection systems by using the benefits of hand-crafted features and deep learning techniques. Experimental results show that the proposed system obtained the detection rate of 80% under night conditions, while the color-based density method only got the detection rate of 50.43% under night conditions.
Gold Nanoparticles Embedded Microrobots for Real-time X-ray Imaging

Microrobots have been demonstrated to have the potential in medical approaches, such as for targeted drug delivery. However, real-time tracking and imaging are still one of the major issues faced by microrobots. Here, we fabricated helical microrobots based on self-folding technique, and we performed double coating of gold nanoparticles (AuNPs) to the surface of microrobots to enhance the image quality under X-ray. The result shows that, we successfully coated the AuNPs into the self-rolled microrobots, and it increases the image quality when compared to non-coated microrobots. Despite the fact that there still remain some challenges to achieve successful applications of microrobots, we believe that the potential of this study can contribute to reduce the challenges faces in the medical microrobot fields.
Analytical-Based Iron Loss Assessment in the SPM Slotless Machine Stator Core

The use of slotless machines has acquired growing interest when features such as low vibrations and noise, along with low losses at high speed, are required. However, a careful assessment of the iron losses in the stator core will be relevant already in the design or pre-design phase. Standard methodologies for the iron losses estimation are typically FE analysis with constant loss coefficients, which can lead to weak extrapolations with huge errors. In this paper, an analytical method for iron losses prediction is derived. It is based on the extension of the field solution in the stator iron core, where an accurate 2-D field solution of the slotless machine topology is found. The analytical solution is combined with a variable loss coefficients model, which can be efficiently computed by vectorized post-processing. The case study proves that the approach can predict all of the loss phenomena over a wide operating range with improved precision.
No-Load Loss Separation of High-Speed Electric Motors for Electrically-Assisted Turbochargers

This article presents a testing procedure for the no-load loss separation of high-speed electric machines designed for Electrically-Assisted Turbochargers. The main contribution of this paper is to introduce a loss separation method suitable for low power (several kW) high-speed electric motors. The paper begins by presenting the environmental context of the study, the overall system and motivations to develop such a method. Then, the experimental procedure using several back-to-back (B2B) configurations supported by an air bearing is proposed to identify the no-load losses including: no-load iron, air bearing and windage losses. Finally, the method is applied to three stator topologies to compare the no-load iron losses levels. The presented principle enables a no-load loss separation for very high-speed machines in the range of several kilowatts without the use of a torque meter. In addition, the identification of the air bearing losses makes possible the evaluation of the motor efficiency on the presented B2B test bench without any major modifications.
Security Analysis of Wireless Sensor Networks Using SIEM and Multi-agent Approach

The paper addresses the issue of providing information security to wireless sensor networks using Security Information and Event Management (SIEM) methodology along with multi-agent approach. The concept of wireless sensor networks and providing their information security, including construction of SIEM system architecture, SIEM analysis methodologies and its main features, are considered. The proposed approach is to integrate SIEM system methodology with a multi-agent architecture which includes data collecting agents, coordinating agent (supervisor) and local Intrusion Detection Systems (IDSs) based on artificial immune system mechanisms. Each IDS is used as an agent that performs a primary analysis and sends information about suspicious activity to the server. The server performs correlation analysis, identifies the most significant incidents, and helps to prioritize the incident response. The presented results of computational experiments confirm the effectiveness of the proposed approach.
Integration of DC Power Distribution System and Digital Transmission System

This paper presents the integration of DC power distribution system and digital transmission system using switching power converter. The proposed concept can be referred to as digital power distribution system. The aim is to deliver electric power and to provide digital information for residential, commercial and industrial customers. Both digital information and electric power are transmitted through the same wireline channel at the same time. In other words, the distributed DC electric power carry outs digital input signal. This paper is focused on digital DC power distribution system based on buck converter topology. The proposed digital power distribution system has specification of 800 V dc input voltage, 2 Mbps data rate with unipolar NRZ (Non-Return to Zero) line coding and three point of loads in the range of 2.2 – 3.9 kW. The voltage and current stresses of semiconductor devices will be analyzed in this paper. In addition, the behavior of current and voltage waveforms will be simulated and discussed. The dynamic performance analysis of the converter in CCM (Continuous Conduction Mode) operation based on digital input signal is also given. The method to separate DC output power from digital output signal is also provided. Simulation results validate that the digital output signal with data rate of 2 Mbps is the same as the digital input signal, and the output power can be extracted to the loads without introducing harmful functions.
Overview on SDN and NFV based architectures for IoT environments: challenges and solutions

The Internet of Things (IoT) is a novel paradigm that ensures connections between the real and virtual world. Generally, connected things form the real world where components are equipped with wireless interfaces. Accordingly, a huge number of smart devices can interact and communicate directly or through a network infrastructure. As a result, a big amount of data will be generated. However, IoT based networks are not adapted to support data management. Thus, a new architecture design seems to be an efficient solution to deal with the fast growth of data in an IoT environment. In this sens, Software-Defined Networking (SDN) and Network Function Virtualization (NFV) are two techniques which can help to overcome these IoT challenges. The first one enables centralized control of the network. The second one separates the software and hardware of the network function. This paper gives an overview of SDN and NFV based architectures for IoT environments with a focus on challenges and solution.
Minimum-energy Control of Nearly-controllable Discrete-time Bilinear Systems with Nondiagonalizable Structure

In this paper, a class of discrete-time bilinear systems which are uncontrollable but can be nearly-controllable is considered. A necessary and sufficient condition for near-controllability of such systems was obtained and the corresponding control inputs to achieve the transition of the systems between any given pair of states are computable, but there are infinite groups of such control inputs. Therefore, this paper proposes a new algorithm, which attempts to find a minimum-energy control sequence to achieve the state transition and improves the previous algorithm for computing the control inputs. Analysis and examples are given to illustrate the proposed algorithm.
Predictive Power Control for Electric Vehicle Charging Applications

This paper presents a direct predictive power control (DPPC) design for vehicle charging applications. The proposed control design allows working in the Park’s reference frame avoiding the usage of the phase-lock loops, which help increasing the reliability of the system. Direct power control allows defining active and reactive power references as function of the control objectives independently. In the case of the active, it is defined as function of the battery current or state-of-charge desired profiles, while reactive power can be projected as function of the grid requirements. Numerical results show that the proposed DPPC allows controlling active and reactive power regardless with minimum steady-state errors (er ≤ 1%); in addition, the state-of-charge and the battery currents are controlled to evidence the applicability of the proposed DPPC design for tracking different desired references. All the numerical test are performed in MATLAB/simulink.
Vulnerability Time Series Prediction Based on Multivariable LSTM

Vulnerabilities have been widely exploited to launch various cyberattacks, and become one of the most popular network security problems. Several kinds of research on vulnerabilities have been carried out, such as, mining vulnerability, tracing vulnerability, forecasting vulnerability, and so on. Vulnerability forecasting models make the prediction of zero-day vulnerabilities possible, hence they can help us learn more about the number of vulnerabilities in future days and take defence measures in advance. However, most of these models stem from statistics, which cannot adapt to our application scenario very well. Unlike traditional statistical methods, we propose a vulnerability forecasting method based on multivariable LSTM and carry on experiments on the NVD data set. In comparison with ARIMA, our method perform better in number prediction of vulnerabilities.