Miracle-dz/UniFAR-FTUs huggingface dataset:
Power Supply Restoration of Active Distribution Systems considering Distributed Renewable Generation

With the increasing maturity of renewable energy (e.g., wind energy, solar energy) power generation technology, distributed power generation based on renewable energy receiving increasing attention. However, the penetration of DGs increases the fault levels of the power distribution system, and hence increases the operational risks to power supply reliability and security. This paper presented a power supply restoration solution of active distribution systems considering distributed renewable generation. The proposed solution is assessed through simulation experiments based on the case study of a 53-bus test network for a range of operational scenarios and the numerical results demonstrated the effectiveness and feasibility of the proposed solution.
An SSD-Based Accelerator for Singular Value Decomposition Recommendation Algorithm on Edge

Recommender system (RS) is widely used in social networks, computational advertising, video platforms and many other Internet applications. Most RSs are based on the cloud-to-edge framework. Recommended item lists are computed in the cloud server and then transmitted to the edge device. Network bandwidth and latency between cloud server and edge may cause the delays in recommendation. Edge computing could help obtain user's real-time preferences and thus improve the performance of recommendation. However, the increasing complexity of rec-ommendation algorithms and data scales cause challenges to real-time recommendation on edge. To solve these problems, in this paper, we mainly focus on the Jacobi-based singular value decomposition (SVD) algorithm because of its high parallel processing potential and cost effective NVM-storage. We propose an SSD-based accelerator for the one-sided Jacobi transformation algorithm. We implement a hardware prototype on a real Xilinx FPGA development board. Experimental results show that the proposed SVD engine can achieve 3.4x speedup to 5.8x speedup compared with software SVD solvers such as MATLAB running on a high-performance CPU.
Reliability of Computational Analysis of Plasticity Problems

Abstract : The paper addresses the reliability of computational analysis of plastically problems in light of the experimental work and its mathematical formulation. The paper also addresses some basic constitutive laws used in engineering and proposes one which leads to a well posed mathematical problem and agrees well with the experimental data. The data of the experimental project for 5454 aluminum alloy in the H32 condition for periodic and random strains are presented. Various constitutive laws are assessed in light of the experimental data. Outstanding problems related to the reliability of the analysis are mentioned.
Traffic Light Management Systems Using Reinforcement Learning

While reducing traffic congestion and decrease the number of traffic accidents in the intersections, most of the traffic light management approaches cannot adapt well to fast changing traffic dynamics and growing demands of the intersections with modern world developments. To overcome this problem, adaptive traffic controllers are developed, and detectors and sensors are added to systems to enable adoption and dynamism. Recently, reinforcement learning has shown its capability to learn the dynamics of complex environments, such as urban traffic. Although it was studied in single junction systems, one of the problems was the lack of consistency with how the real world system works. Most of the systems assume that the environment is fully observable or actions would be freely executed using simulators. This study aims to merge usefulness of reinforcement learning methods with real-world traffic constraints. Comparative performance evaluations show that the reinforcement learning algorithm (Advantage Actor-Critic (A2C)) converges well while staying stable under changing traffic dynamics.
Classification of Solder Quality in Through-Hole Devices by Convolutional Neural Networks

Solder inspection has an important place in electronic board production. Deep learning methods can be used to control the quality of solders. In this paper, a comparative study on the performance of deep learning methods for the classification of solder quality of through-hole devices is presented. The dataset used contains 7320 pieces of data. Soldering quality will be classified using three different networks based on ResNet, Inception-v4 and Inception-ResNet, which were created in this study. The test results obtained will be compared.
A 6.78 MHz Current Detector Using PCB Transformer for Responsible Wireless Power Transfer

In this study, a current detector for the transmitter of responsible wireless power transfer(RWPT) was designed. The designed current detector aims to detect a change in load impedance by detecting a phase of an output signal of a power amplifier(PA). For the compact size of the entire system, the current detector was designed using a PCB. In addition, a technique for detecting the phase of the output signal with high accuracy by connecting a capacitor is proposed. The detected current was converted into a voltage through a resistor to enable appropriate signal processing at the next stage of the current detector. In order to verify the feasibility of the proposed technique, the PCB-based current detector was designed to confirm the characteristics of the output signal according to the load impedance change. As a result of the measurement, the current detector detects an original current phase with a phase error of up to 18° in the reactance range of $-j138\Omega$ to $j92\Omega$.
Algorithm of Informative Electrode Selection in Person Based Emotion Detection with Hjorth Parameters Obtained from EEG Signals

In this study, it is aimed to compose a method for the selection of electrodes that provides information in order to produce a test device that uses a small number of electrodes for emotion detection with electroencephalography (EEG) signals. In order to prevent data pollution and excessive cost in studies on the brain, as few electrodes as possible have been tried to be chosen as informative. An emotion dataset in the literature was created by recording various biomedical signals that occurred while participants were watching music videos and then voting on their triggered emotions. In the first stage of the study, the brain waves were obtained by filtering the EEG signals for the selected videos from this dataset, and then the Hjorth parameters of the brain waves were calculated and used for analysis. Among these parameters, the activity parameter was used to determine the users used in the analysis, and the mobility parameter was used to determine the informative electrodes. The EEG dataset used in the second stage was recorded for this study by asking the participants questions that triggered their emotions. Using the information obtained in the first stage, the electrode selection for the second stage dataset was tested and observed to be compatible with detecting triggered emotions. As a result, the electrode selection algorithm, which provides emotion detection on a person-based basis, is provided for the datasets used.
A Rasorber with a Selective in-Band Transmission Response between Wide Absorption Bands

A polarization-insensitive frequency selective rasorber structure with a highly selective transmission band is presented in this paper. The unit cell of the proposed design comprises a top lossy layer and a bottom lossless layer resulting in broadband absorption and narrow-band transmission responses, respectively. The novelty of the topology lie in its selective in-band transmission response at 9.3 GHz (with a 3-dB bandwidth of 13%) appearing between two wide absorption bands, spreading between 3.6 to 8.10 GHz and 10.50 to 13.30 GHz (having above 80% absorptivity). The structure has several other advantages, such as low footprint, angular stability, and wide adaptability. An equivalent circuit model and several parametric variations are also carried out to explain the operating principle of the geometry. Finally, a sample prototype has been fabricated and experimentally demonstrated for validation.
Indicator of Safety and Comfort Performance: Quantifying Different Driving Styles for Automated Vehicles

Quantitative assessment of driving performances and analysis of different driving styles are significantly critical for the mass employment of automated vehicles (AVs). There-fore, this paper presents a synthetical indicator to quantify AV s' driving performance with regard to dynamic safety and comfort and then classifies quantitatively different driving styles based on the proposed indicator. All dynamic signals are analyzed based on vehicle global dynamics modeling, and we develop rigorous performance indicators mathematically. Furthermore, the distribution of our proposed indicator is investigated to reveal different driving styles including normal, aggressive, and conservative driving. Experimental results illustrate the proposed indicator can efficiently evaluate driving performance and can be used to classify different driving styles. These findings suggest our research has great potential to provide a foundation for the design of AVs' planning and control methods.
Reversing FPGA architectures for speeding up fault injection: does it pay?

Although initially considered for fast system prototyping, Field Programmable Gate Arrays (FPGAs) are gaining interest for implementing final products thanks to their inherent reconfiguration capabilities. As they are susceptible to soft errors in their configuration memory, the dependability of FPGA-based designs must be accurately evaluated to be used in critical systems. In recent years, research has focused on speeding up fault injection in FPGA-based systems by parallelising experimentation, reducing the injection time, and decreasing the number of experiments. Going a step further requires delving into the FPGA architecture, i.e. precisely determining which components are implementing the considered design (mapping) and which are exercised by the considered workload (profiling). After that, fault injection campaigns can focus on those components actually used to identify critical ones, i.e. those leading the target system to fail. Some manufacturers, like Xilinx, identify those bits in the FPGA configuration memory that may change the implemented design when affected by a soft error. However, their correspondence to particular components of the FPGA fabric and their relationship with the implementation-level model are yet unknown. This paper addresses whether the effort of reversing an FPGA architecture to filter out redundant and unused essential bits pays in terms of experimental time. Since the work of reversing the complete architecture of an FPGA is titanic, as the first step towards this ambitious goal, this paper focuses on those elements in charge of implementing the combinational logic of the design (Look-Up Tables). The experimental results that support this study derive from implementing three soft-core processors on a Zynq SoC FPGA and show the interest of the proposal.
Distributed Repository for Software Packages Using Blockchain

A package repository is an essential piece of a software ecosystem. In FOSS, the software repositories are oftentimes hosted using limited donations, given the technical solutions adopted in the implementation. This work proposes a package repository using Blockchains with experiments and statistics based on a real-world scenario. The Blockchain described has its consensus algorithm crafted to befit the purpose of a package repository without financial appeal; Also, the proposed Blockchain keeps a compatible layer with the traditional repositories, easing its adoption. Furthermore, this work also presents a package search over peer-to-peer, computed on untrusted nodes, yet guaranteeing that the results are trusted. Finally, we present a functional Blockchain that cohesively exposes the PyPi catalog.
SEMISIMPLICITY OF HECKE AND (WALLED) BRAUER ALGEBRAS

We show how to use Jantzen’s sum formula for Weyl modules to prove semisimplicity criteria for endomorphism algebras of $\mathbf{U}_{q}$ -tilting modules (for any field $\mathbb{K}$ and any parameter $q\in \mathbb{K}-\{0,-1\}$ ). As an application, we recover the semisimplicity criteria for the Hecke algebras of types $\mathbf{A}$ and $\mathbf{B}$ , the walled Brauer algebras and the Brauer algebras from our more general approach.
Offline Reinforcement Learning for Autonomous Driving with Real World Driving Data

Since traditional reinforcement learning (RL) approaches need active online interaction with the environment, previous works are mainly investigated in the simulation environment rather than the real world environment, especially for safety-critical applications. Offline RL has recently emerged as a promising data-driven learning paradigm to learn a policy from offline dataset directly. It seems that offline RL is well suited for autonomous driving, as it is feasible to collect offline naturalized driving dataset. However, it remains unclear how to deploy offline RL with real world driving dataset only including observation data, and whether current offline RL algorithms work well to learn a driving policy than imitation learning? In this paper, we provide an offline RL benchmark for autonomous driving including the dataset, baselines, and a data driven simulator11Code: https://github.com/weiaiF/offiineRL-INTERACTION. First, we summarize and introduce the popular offline RL baseline methods. Then, we construct an offline RL dataset for the car following task based on the real world driving dataset INTERACTION. A data driven simulator is applied to obtain augmented data and test the driving policy. Further, we deploy four popular offline algorithms and analyze their performances under different datasets including real world driving data and augmented data. Finally, related conclusions and discussions are given to analyze the critical challenge for offline RL in autonomous driving.
Modeling Taxi Departure System and Optimizing Vehicle Dispatch at Airport Hubs

At present, most taxi systems at airport hubs generally have the problems of unreasonable vehicle resource allocation with lower level-of-service and higher operational cost. In order to improve the level of taxi transfer service and increase the operational efficiency of taxi system at airport hubs, we focus on optimizing the resource allocation of the taxi departure system at airport hubs. Firstly, the optimization model of passenger departure process in taxi pick-up area is proposed, and a genetic algorithm is designed to solve the model. In addition, aiming at the problem of insufficient taxi capacity resources, we formulate the taxi dispatching model for airport hubs to obtain the taxi dispatching plan under the optimal passenger departure scheme. Finally, taking the Beijing Capital International Airport as an example, we carry out a simulation study by the proposed models. The optimization results show that the taxi pick-up area can meet the travel needs of passengers in peak hours, and the total cost is reduced by 25.2%. The taxi dispatching model can dynamically dispatch taxis to enter or leave the storage pool according to the number of taxis in the storage pool and the supplementary arrival rate of taxis. Our research results can provide the reference for optimizing the taxi system at other large-scale passenger transportation hubs.
The Rise of the Digital RFIC Era: An Overview of Past and Present Digital RFIC Advancements

It has been a mere 130 years since the first widely known wireless telegraphy apparatus was demonstrated by Guglielmo Marconi, transmitting radio signals to a distance of about 2.4 km. In today’s world, as illustrated by <xref ref-type="fig" rid="fig1">Figure 1</xref>, we are using a plethora of radios for an ever-increasing number of different applications. In fact, observing the data rate of wireless standards published by bodies like the 3rd Generation Partnership Project (3GPP) and the Wi-Fi alliance <xref ref-type="bibr" rid="ref1">[1]</xref>, <xref ref-type="bibr" rid="ref2">[2]</xref>, shown in <xref ref-type="fig" rid="fig1">Figure 1</xref>, it can be readily seen that they increase by an order of magnitude every five years.
Energy-Optimal RAN Configurations for SWIPT IoT

Internet of Things (IoT) devices often have batteries of limited capacity, which are not easily replaced or recharged. This implies very short device lifetimes, and calls for a very careful device configuration to achieve the optimal trade-off between performance and power consumption. SWIPT (Simultaneous Wireless Information and Power Transfer) deals with this problem by harvesting energy at IoT devices from the received RF signals. Studying the efficiency of SWIPT in dealing with the energy and data transfer demands of IoT nodes leads to a number of open issues. In this paper, we devise an analytical model based on stochastic geometry for a SWIPT radio access network with a dense population of IoT users. With our model, it is possible to accurately study the impact of the system parameters on the key system performance indicators, while accounting in a realistic manner for device performance, and for the statistics of time scheduling at base stations. This allows us to understand (not without some surprise) what are the most effective strategies to minimize energy consumption in a SWIPT network, and what is their potential for energy savings.
High Gain Omnidirectional Array Antenna Using SIW Technology

In this paper, a novel omnidirectional array antenna based on Substrate Integrated Waveguide (SIW) technology is proposed. The proposed antenna is realized by removing some vias on one side of the narrow wall of a conventional SIW which results a slot antenna. The gain of an antenna with 8 slots is 9.5 dBi with a gain variation of 2 dB over 360°. Besides, the antenna polarization is horizontal with the cross-polarization level of less than −40 dB in comparison with the co-polarization level. The bandwidth of 80 MHz at 6.66 GHz is achieved with the antenna's dimension of 300×22.86×1.575 mm3. The measurement results of the antenna have a good agreement with the full-wave simulation results. The proposed antenna is well suited for 6 GHz wireless access applications due to the low cost, low complexity, simple integration, and high gain.
Traffic dynamics with slowdown vehicles based on the traffic wave theory

In this paper, we study the macroscopic traffic dynamics and propose models of traffic mean speed and density of each lane under speed drop of connected and autonomous vehicles (CAVs) in three-lane freeway scene in wave propagation perspective. The propagation of macroscopic traffic waves in mixed streams is analyzed to derive functions of macroscopic traffic flow parameters of each lane in different evolution process. Three superposition principles of traffic wave are proposed to analyze compound disturbances in different propagation periods. The experiments are performed in simulation of urban mobility SUMO platform under various CAVs penetration, speed drop of CAV, the proposed model is capable of adequately describing the evolution process of mixed traffic flow after speed drop,
Certifiable Evaluation for Autonomous Vehicle Perception Systems using Deep Importance Sampling (Deep IS)

Evaluating the performance of autonomous vehicles (AV) and their complex AI-driven functionalities to high precision under naturalistic conditions remains a challenge, especially when the failure or dangerous cases are rare. Rarity does not only require an enormous sample size for a naive method to achieve high confidence residual risk estimation, but it can also cause serious risk underestimation issues that is hard to detect. Meanwhile, the state-of-the-art rare safety-critical event evaluation approach that comes with a correctness guarantee can compute an upper bound for the true risk under certain conditions, which limits its practical uses. In this work, we propose Deep Importance Sampling (Deep IS) framework that utilizes a deep neural network to obtain an efficient less biased risk estimate, with an efficiency that is on par with that of the state-of-the-art method. In the numerical experiment evaluating the misclassification rate of a traffic sign classifier, Deep IS only needs 1/40-th of the samples required by a naive sampling method to achieve 10% relative error. Furthermore, the estimate produced by Deep IS is 10 times less conservative compared to the risk upper bound and only off by at most 10% difference to the true target. This efficient deep-learning-based IS procedure promises a highly efficient method to deal with often high-dimensional functional safety problems with rare naturalistic failure cases that are prevalent in AV domains.
Variations of pressure data from insoles in normal population during the 10meter walking test and an appropriate parameters’ extraction algorithm: a pilot study

Our objective was to explore variations of pressure data from insole pressure sensors after a walking session and to produce an algorithm which extracts spatiotemporal gait parameters in close real time. In this study we performed a 10-meter stand up and walking test on 25 normal individuals. All subjects were walking at their comfortable speed during the test. The subjects were wearing in-soles equipped with 9 pressure sensors on each foot (the W-inshoe system by Medicapteurs). The W-inshoe system is simple, relatively cheap and reliable. System’s software is quite outdated, has multiple bugs and does not produce the appropriate numerical gait parameters, so we developed an algorithm to process the raw data from the insoles using MATLAB. The developed algorithm can successfully process the pressure data, remove noise, and produce the appropriate gait parameters accurately. Using the algorithm on our sampling pool, we procured the following results: Average peak pressure range was 644 to 1555 gram force (average: 1000gf, average stance period range was 0.64 to 0.85 seconds (average: 0. 76s) and double support period range was 0.17 to 22s (average: 0. 18s). Even though there were only 9 sensors in every insole, the data were sufficient to develop the algorithm. In the future, the algorithm could be used on a cloud-based application to extract pressure and spatiotemporal gait parameters from a simple and reliable insole system.
Online convex optimization in wireless networks and beyond: The feedback-performance trade-off

The high degree of variability present in current and emerging mobile wireless networks calls for mathematical tools and techniques that transcend classical (convex) optimization paradigms. The aim of this short survey paper is to provide a gentle introduction to online learning and optimization algorithms that are able to provably cope with this variability and provide policies that are asymptotically optimal in hindsight-a property known as no regret. The focal point of this survey will be to delineate the trade-off between the information available as feedback to the learner, and the achievable regret guarantees starting with the case of gradient-based (first-order) feedback, then moving on to value-based (zeroth-order) feedback, and, ultimately, pushing the envelope to the extreme case of a single bit of feedback. We illustrate our theoretical analysis with a series of practical wireless network examples that highlight the potential of this elegant toolbox.