Miracle-dz/UniFAR-FTUs huggingface dataset:
A Tailpipe NOxSensor Decoupling Algorithm for Integrated SCR and AMOX Systems

Urea-based selective catalytic reduction (SCR) system coupled with a downstream ammonia oxidation catalyst (AMOX), has become a standard NOxreduction device for Diesel engines. On-board diagnostics of tailpipe NOxand ammonia (NH3) emissions and closed-loop controls using only tailpipe NOxsensor, are critical for SCR systems to achieve high NOxconversion efficiency and low tailpipe NH3 slip. However, due to commercial NOxsensor NH3 cross-sensitivity issue, the tailpipe NH3 emissions can be misinterpreted as NOxemissions, which may lead to erratic diagnostics and unstable control systems. The purpose of this study is to develop a high-fidelity control-oriented SCR-AMOX model and a model-based estimation algorithm using extended Kalman filter (EKF) for effectively decoupling NOxand NH3 concentrations from the mixed tailpipe NOxsensor signals for an SCR-AMOX system. The proposed model and EKF-based decoupling algorithm were validated using the experimental data collected from a Diesel engine platform during both steady-state and transient driving cycles. Experimental verification results demonstrated high accuracy of the control-oriented model and proved the efficacy of the proposed decoupling algorithm in meeting the preset thresholds. The robustness of the decoupling algorithm against the uncertainty from catalyst aging was also demonstrated and verified. Such EKF-based robust decoupling algorithm can be instrumental in the diagnostics and controls of urea-based SCR systems.
TT-Net: Topic Transfer-Based Neural Network for Conversational Reading Comprehension

Conversational machine reading comprehension (MRC) is a new question answering task, which is more challenging compared to traditional single-turn MRC since it requires a better understanding of conversation history. In this paper, a novel neural network model for conversational reading comprehension, namely TT-Net, is proposed, which is capable of capturing topic transfer features using temporal convolutional network (TCN) in the dialog. The TT-Block packaged by the BiLSTM, TCN and Self-attention mechanism is presented to extract topic transfer features between questions. Our model is evaluated on the CoQA benchmark dataset compared with several baseline models including the strong baseline model named FlowQA. The results show that the model outperforms the baseline models: BiDAF++ by 7.6% and FlowQA by 0.7%, especially in children’s story domain our model promotes FlowQA’s performance by 3.9%, which indicates that the TT-Net contributes to a decent promotion for conversational reading comprehension.
Online Learning of the Inverse Dynamics with Parallel Drifting Gaussian Processes: Implementation of an Approach for Feedforward Control of a Parallel Kinematic Industrial Robot

The present paper deals with an online approach to learn the inverse dynamics of any robot. This is realized by the use of Gaussian Processes drifting parallel along the system data. An extension by a database enables the efficient use of data points from the past. The central component of this work is the implementation of such a method in a controller in order to achieve the actual goal: the feedforward control of an industrial robot by means of machine learning. This is done by splitting the procedure into two threads running parallel so that the prediction is decoupled from the computing-intensive training of the models. Experiments show that the method reduces the tracking errors more clearly than an elaborately identified rigid body model including friction. For a defined trajectory, the squared areas of the tracking errors of all axes are reduced by more than 54% compared to motion without pre-control. In addition, a highly dynamic pick-and-place experiment is used to investigate the possible changes in system dynamics. Compared to an offline trained model, the approximation error of the proposed online approach is smaller for the remaining time of the experiment after an initial phase. Furthermore, this error is smaller throughout the experiment for online learning with parallel drifting Gaussian Processes than when using a single one.
Fast Lidar - Camera Fusion for Road Detection by CNN and Spherical Coordinate Transformation

A fast lidar-camera fusion method is proposed to detect road in autonomous vehicles. The height data of lidar is transformed to spherical coordinate system to increase the data density. The RGB data of camera is also transformed to spherical coordinate system to match with the lidar data. The amount of data is greatly reduced by spherical coordinate transformation, which results in the fast running time. The transformed images of height, red, green, and blue are input to the CNN. A dilated convolution structure is newly proposed to improve the learning accuracy by expanding the receptive field of CNN. The experimental results using the KITTI data set are finally presented to show the usefulness of the proposed method.
Speech Emotion Recognition Based on Multi-Task Learning

The complexity of emotion generation, expression, and data annotation make emotion recognition very challenging. As a kind of transfer learning, multi-task learning can aggregate multiple related corpora to achieve data sharing, and achieve the feature level sharing by utilizing the correlation of tasks, improving the training efficiency and accuracy. In this paper, we investigate the application of multi-task learning in the field of speech emotion recognition, including the model analysis, the database selection and the feature extraction. And the key research points of the research are proposed.
A Complex Fiber Sensor System for Three Parameters Measurement Simultaneously

We proposed and demonstrated a complex sensor, which combined a long period fiber grating and two fiber Bragg gratings. We used the proposed fiber sensor to detect the surrounding temperature, water level and refractive index of an unknown liquid. The measured parameter range of the temperature, water level and refractive index were 2 0-45°C, 40–140 cm 1.335-1.375, respectively. The measured results have linearly trend with high sensitivity.
Evaluation of Full-SiC-Based Integrated Inverter Prototype

This paper introduces the design and some experimental results of a high power-density integrated inverter prototype based on full-SiC devices. The inverter prototype is a 2-level voltage source inverter, and the whole system is cooled by force air. In this inverter prototype, we integrated a DC voltage sensor and three AC current sensors with the main circuit. We applied thin-film capacitors paralleled to decrease stray inductance of main circuit to nearly 10nH. The volume of the prototype system is about 9.3dm3 and the weight is about 36.25kg. Experimental performance shows that the rated output power of the prototype inverter can up to 132kW, which is larger than the conventional IGBT inverter by 18.2%. In this case, the power density of the proposed inverter is 14.19kW/dm3 or 3.64kW/kg and the efficiency of which can up to 98%.
Pan-genomic analysis of Corynebacterium amycolatum gives insights into molecular mechanisms underpinning the transition to a pathogenic phenotype

Corynebacterium amycolatum is a nonlipophilic coryneform which is increasingly being recognized as a relevant human and animal pathogen showing multidrug resistance to commonly used antibiotics. However, little is known about the molecular mechanisms involved in transition from colonization to the MDR invasive phenotype in clinical isolates. In this study, we performed a comprehensive pan-genomic analysis of C. amycolatum, including 26 isolates from different countries. We obtained the novel genome sequences of 8 of them, which are multidrug resistant clinical isolates from Spain and Tunisia. They were analyzed together with other 18 complete or draft C. amycolatum genomes retrieved from GenBank. The species C. amycolatum presented an open pan-genome (α = 0.854905), with 3,280 gene families, being 1,690 (51.52%) in the core genome, 1,121 related to accessory genes (34.17%), and 469 related to unique genes (14.29%). Although some classic corynebacterial virulence factors are absent in the species C. amycolatum, we did identify genes associated with immune evasion, toxin, and antiphagocytosis among the predicted putative virulence factors. Additionally, we found genomic evidence for extensive acquisition of antimicrobial resistance genes through genomic islands.
uSendfile: A User-Space Sendfile Verb Based on Flash and RDMA

Flash and RDMA (Remote Direct Memory Access) provide extremely high performance in storage and network hardware. However, a gap between distributed system and new hardware exists. Although RDMA speeds up memory access between two nodes, there are many serious problems to be solved when sending data or command from one flash to another flash. In this paper, we propose a distributed system on flash and RDMA, and implement a User-space Sendfile verb based on it. Experimental results show that the RPC in DSFR outperforms the traditional RPC mechanism by dozens of times, and the uSendfile reduces time overhead significantly.
Stochastic Model Predictive Control: uncertainty impact on wind farm power tracking

Active power control for wind farms is needed to provide ancillary services. One of these services is to track a power reference signal with a wind farm by dynamically de- and uprating the turbines. Due to the stochastic nature of the wind, it is necessary to take this stochastic behavior into account when evaluating control signals. In this paper we present a closed-loop stochastic wind farm controller that evaluates thrust coefficients providing power tracking under uncertain wind speed measurements. The controller is evaluated in a high-fidelity wind farm model simulating a 9-turbine wind farm to demonstrate the stochastic controller under different uncertainty levels on the wind speed measurement and different controller settings. Results illustrate that a stochastic controller provides better tracking performance with respect to its deterministic variant.
Environment-aware Development of Robust Vision-based Cooperative Perception Systems

Autonomous vehicles need a complete and robust perception of their environment to correctly understand the surrounding traffic scene and come to the right decisions. Making use of vehicle-to-vehicle (V2V) communication can improve the perception capabilities of autonomous vehicles by extending the range of their own local sensors. For the development of robust cooperative perception systems it is necessary to include varying environmental conditions to the scenarios used for validation. In this paper we present a new approach to investigate a cooperative perception pipeline within simulation under varying rain conditions. We demonstrate our approach on the example of a complete vision-based cooperative perception pipeline. Scenarios with a varying number of cooperative vehicles under different synthetically generated rain variations are used to show the influence of rain on local and cooperative perception.
Capturing Real World Derived Information Relevancies In XML

XML data dependencies play important roles in eliminating XML redundancies and normalizing XML schemas. The main existing XML data dependency models can be classified into two categories: the tree tuple based models and the path based models. The main considerations in these models are the structural relevancies of XML nodes but not the information relevancies in XML data which derive from the real world. Therefore, on the one hand, they cannot capture the essential XML data dependencies faithfully and completely; on the other hand, they have expressive deficiencies and semantic deficiencies in expressing XML data dependencies. In this paper, we adopt a novel notion and propose an XML data dependency model (XDDM) based on entity segments. Our main objective is to make XML data dependencies in XDDM match the information relevancies derived from the real world. Firstly, we summarize these information relevancies into three categories and propose the completeness condition of XML data dependency model. And then, the XML attribute dependency and the XML entity dependency are defined to compose the XDDM. Finally, we analyze the capabilities of XDDM in fulfilling the completeness condition and eliminating expressive deficiencies and semantic deficiencies.
IoT Based Smart Plug-Load Energy Conservation and Management System

In the last decades, energy and energy management has grown as an important research area. Energy consumption can be significantly reduced by making people aware of their behavior as energy consumers. In this project we implement a Smart and Sustainable technology by adapting IoT based methodologies using Smart plugs for capturing data which is transferred through the Wireless-gateway to the central database [1]. The system is designed in way that the Smart plugs can be switched on/off when not in use from the central server. Due to this facility a system has been enabled to turn off the smart plugs apart from office hours. Moreover we have also performed a deep analysis on the amount of energy saved by implementing our system by comparing it with the total energy consumed before implementing the system. Using the data obtained from the smart plug various approaches are adapted to identify the consumer behaviour and take necessary effort in order to minimize the total energy consumption. A Time-series based analysis has been implemented and Forecast of total energy consumption has been obtained [2], [3].
Multi-source power system based on PV-Batteries and diesel generator for Micro-grid Applications

This article deals with the three level space vector pulse width modulation (SVPWM) technique to control the Neutral Point Clamped (NPC) inverter for energy management in a multi-source system based on a variable speed diesel generator, a PV generator, and a pack of the lithium-batteries for the micro grid applications. The contribution of the paper is focused on the multi-source system energy management using the frequency components distribution method between the linked sources according to the dynamic response capability of the each source.
Kinematics Modeling and Workspace Analysis of a 5-DOF Hydraulic Manipulator

This paper focused on the kinematic modeling and workspace analysis of a 5 degrees of freedom(DOF) heavy-duty hydraulic manipulator system, which consists of four linear hydraulic cylinder and one rotary hydraulic cylinder. The kinematics model of the hydraulic manipulator is analyzed by modified D-H method (I-type kinematics) and geometric mapping algorithm (II-type kinematics) and is validated with simulation comparison of kinematic response predicted by theoretical kinematic modeling. Then, the workspace of the hydraulic manipulator is analyzed through random point method in SimMechanics. Lastly, The influence of cylinder stroke upon available workspace place is presented.
Remaining Useful Life Estimation by Empirical Mode Decomposition and Ensemble Deep Convolution Neural Networks

Bearing remaining useful life (RUL) prediction plays a key role in guaranteeing safe operation and reducing maintenance costs. In this paper, we present a novel deep learning method for RUL estimation approach through time Empirical Mode Decomposition (EMD) and Convolutional Neural Network (CNN). EMD can reveal the nonstationary property of bearing degradation signals effectively. After acquiring time-series degradation signals, namely Intrinsic Mode Functions (IMF), we can utilize the featured information as the input of Convolution layer of models. Here, we introduce an EMD-CNN model structure, which keeps the global and local information synchronously compared to a traditional CNN. In order to get a more accurate prediction, an ensemble model with several weighting methods are proposed, where the experiment indicates an improvement of performance.
Novel Multiantenna Reader Design for Multi-Tag Backscattered Throughput Fairness Maximization

The green low power networking potential of backscatter communication technology can be practically realized by efficiently using a multiantenna reader for tackling the hardware constraints of the underlying tags. We address this timely requirement by investigating the optimal transceiver designs for the multiantenna reader to maximize the minimum backscattered throughput among the single-antenna semi-passive tags. Novel analytical insights on the jointly-optimal precoding vector and detector matrix at the reader are also provided by exploring the asymptotically-optimal designs. Lastly, the numerical results corroborate the nontrivial analytical proposals and demonstrate a significant 47% increase in achievable common-backscattered-throughput among tags as compared to the relevant benchmarks.
Evaluation of Accuracy of the Infra-Red Traffic Logger Under Mix Traffic Condition

Traffic data collection under heterogeneous traffic conditions is one of the major challenges faced by highway agencies, policy makers as well as researchers. The Infrared Traffic Logger is a highly advanced, non-intrusive traffic surveillance system which can detect different classes of vehicles based on wheel base size and axle spacing. The fundamental macroscopic traffic parameters: flow, speed and composition under different traffic conditions and weather condition were checked against to base line condition. It was found that overall accuracy of vehicle counts from infrared traffic logger is more than 90% except for two-wheelers under heavy traffic condition and for heavy vehicles under medium and heavy traffic condition. Speed detection accuracy of infrared traffic logger also more than 90% and accuracy of vehicle classification also more than 95% except multi axle vehicles. Multi axle vehicles are misclassify as number of two-axle vehicles under medium or high traffic conditions. Correction factors are suggested to adjust the accuracy level for over counting and under counting situations.
Enhancement of range of creation of foot sole tactile illusion by vibration stimulation of the foot instep*

We previously developed a method for augmenting tactile sensation on the toepad. By presenting vibration to the toenail while the toepad contacts a surface, an illusory tactile sensation can be induced on the toepad. Importantly, this method does not require users to attach a rigid actuator to the sole. However, the illusory sensation is restricted to a limited area. In the current study, we tested a proposed method for extending the range of the illusory tactile sensation in the foot sole by applying vibration stimulation to the foot instep. In a preliminary experiment, we identified that stimulating the first and fifth metatarsal tips induced a tactile illusory sensation on the foot sole. In the first experiment, the probability of occurrence of tactile illusion was determined, and the effectiveness of our presentation method was confirmed. In the second experiment, we determined the load conditions under which the illusion occurs. The results suggested the feasibility of use during walking. Our findings indicated that the range of the illusory tactile sensation on the foot sole can be expanded by stimulating the first and fifth metatarsal tips in addition to the toenail.
Perceptual Limits of Visual-Haptic Simultaneity in Virtual Reality Interactions

The goal of this work is to establish the range of visual-haptic asynchronies that go unnoticed when touching an object. To perform a psychophysical study, however, we would need asynchronous visual-haptic stimuli, but because the contact of the finger with a real object inevitably creates synchronized haptic feedback, here we employ instead a virtual reproduction of the interaction. Participants immersed in a realistic Virtual Reality environment tapped on a virtual object with their index while viewing a fully articulated representation of their hand. Upon tapping, they received haptic feedback in the form of vibration at their fingertip. After each tap, participants judged whether they perceived the view of the contact and the haptic signal to be synchronous or asynchronous and they also reported which of the two seemed to happen first. Despite the difference between the two judgments, results indicate that none of the 19 participants could reliably detect the asynchrony if haptic feedback was presented less than 50ms after the view of the contact with an object. The asynchrony tolerated for haptic before visual feedback was instead only 15ms. These findings can be used as guidelines for haptic feedback in hand-based interactions in Virtual Reality.
RDCGAN: Unsupervised Representation Learning With Regularized Deep Convolutional Generative Adversarial Networks

In Recent years, Representation learning as one of the information extraction and data mapping methods in machine learning systems has received huge attention. Artificial deep neural networks are considered as one of the basic structures capable of representation learning. However, a large number of standard representation learning methods are supervised and requires a lot of labeled data. In this paper, we introduce an unsupervised representation learning by designing and implementing deep neural networks (DNNs) in combination with Generative Adversarial Networks (GANs). The main idea behind the proposed method, which causes the superiority of this method over others is representation learning via the generative models and encoder networks altogether. In this research, encoders are utilized in addition to the generative models to help the more features to be extracted. It is shown that the proposed method not only help feature extraction but accelerate and improve the performance of the learning in GANs which lead to better feature extraction. The results confirm the superiority of the proposed approach regarding classification accuracy by 2% to 6% improvement over other unsupervised feature learning methods.
An improved SURF algorithm based on gradient and amplitude pre-computation

This paper presents an improved SURF algorithm based on gradient and amplitude pre-computation. This method is based on the traditional SURF algorithm and replaces the complex Gauss calculation with integral graph search. There are extreme value features and high frequency noise describing the edge and sharpness of the image. In order to measure the relationship between them, it is necessary to introduce the signal-to-noise ratio measurement index to highlight the effective features or their components. When constructing Hessian matrix with SURF algorithm, Hessian algorithm is sensitive to gradient change. It can describe the gradient of image well. The method of calculating gradient magnitude before Hessian can effectively improve the effect of existing SURF algorithm. This method solves the problem of small number of feature points and non-uniform feature points in traditional SURF algorithm. It has the advantages of high accuracy of feature points extraction and better noise suppression. It can be introduced into underwater three-dimensional reconstruction, which can effectively improve the accuracy and quality of underwater target three-dimensional reconstruction and provide strong support for underwater observation and operation of underwater vehicles.