Miracle-dz/UniFAR-FTUs huggingface dataset:
Harmonic Analysis of Separately Excited DC Motor Drive

Power Rectifiers are mainly employed in power system to convert AC power into controlled DC power. Since DC motors are very commonly broadly used in several industrial areas due to their simple and adjustable characteristics. With help of power electronics controllers, its regulation and handling become very simple and accurate. But contrary on other hand due to the over integration of these devices, problems regarding power factor and harmonic distortion (T.H.D) turn out to be a prominent concern. In this paper, AC power fed to DC motor drive is controlled and regulated by several controllers which includes thyristor- based converters with different pulse width modulation technique along various filters to reduce total harmonic distortions.
Online Parameter Identification for Surface-Mounted Permanent Magnet Synchronous Machine Based on Affine Projection Algorithm

Surface-mounted permanent magnet synchronous machines (SPMSMs) are widely used in electrified railway, aerospace and other fields. With the bad operating conditions and high precision control requirements, accurate motor parameters are needed for control algorithms. This paper provides an online parameter identification method for SPMSMs based on affine projection algorithm (APA). The stator inductance and permanent magnet flux are estimated in real time with rich enough operation data to reduce the unfavorable influence of temperature change and magnetic saturation on motor control and improve the control performance.
Design and Modeling of Ferrite Core Geometry for Inductive Wireless Chargers of Electric Vehicles

In order to maximize the inductive link efficiency in wireless electric vehicle chargers, a ferrite layer is added to improve the coupling performance and reduce the leakage of flux to the surrounding ferrous materials. The geometry of ferrite directly affects the self and mutual inductance of the primary and secondary coils and accordingly their coupling factor. Three ferrite geometries are investigated in this work and their coupling behavior is studied and compared to that of a ferrite sheet. Based on the simulation results, the geometry in which ferrite bars are aligned perpendicular to the current conduction path provides the highest coupling factor and highest inductive link efficiency at perfect alignment conditions over a range of air gaps from 10 cm to 30 cm, while having the smallest ferrite surface area. Hence, this geometry is recommended to be used for closely aligned rectangular EV coupling coils.
Body Part Labelling with Minkowski Networks

Labelling body parts in depth images is useful for a wide variety of tasks. Many approaches use skeleton-based labelling, which is not robust when there is a partial view of the figure. In this work we show that Minkowski networks, which have recently been developed for 3D point cloud labelling of scenes, can be used to label point clouds with body part categories, achieving 85.6% accuracy with a full view of the figure, and 82.1% with partial views. These results are limited by a small sample size of our training data, but there is evidence that some of these ‘misclassifications’ may be correcting mistakes in the reference labelling. Overall, we demonstrate that Minkowski networks are effective for body part labelling in point clouds, and are robust to occlusion.
Deep Residual Attention Reinforcement Learning

Making decisions based more on the crucial objects which are closely connected to the reward in a given visual input is advantageous in reinforcement learning. In this work, we incorporate an attention-based structure into the network structure of Importance Weighted Actor-Learner Architecture (IMPALA) to help the model find out the crucial objects and propose Deep Residual Attention Reinforcement Learning (DRARL). Experiments in Atari games and special environments which have more irrelevant objects than usual demonstrate the superiority of DRARL in the multi-objects environment compared to the original IMPALA. Furthermore, the visualization of trained agents’ attention indicates that the additional attention mechanism helps IMPALA concentrate on the crucial objects and therefore improves the performance of IMPALA.
Research on Mechanism and Key Technology of Intelligent Vehicles Brake By Wire system

Brake By Wire system is the development trend of the brake system and also one of the key technologies of Intelligent Vehicles. Firstly the paper introduces two types of Brake By Wire system, including electronic hydraulic brake system and electronic mechanical brake system, and compares its mechanism and energy state chain. Then it introduces the existing Brake By Wire systems and brake actuator of auto companies. Finally it predicts the research direction of the key technologies of Brake By Wire system.
Correlation - between Social Relation Value and Search Pattern in Social Networks

Social Network Service (SNS) is a popular and powerful Web service to connect and/or find friends, and the tendency of a user's interest often affects his/her friends who have similar interests. We can 'Collective Intelligence and Social Big Data' through the SNS. If we can track users' preferences in certain boundaries in terms of Web searching, we can we can improve search efficiency and reliability in view of users. In this paper, we analyze correlation - between Social Relation Value and Search Pattern of web users to improve search efficiency and. Social Relation Value indicates degree of relation on latent characteristic among web users who exist in Social Networks. We calculate Social Relation Value in granting association between 'M' topics and 'L' Attributes that are applicable to 'N' Factors(Similarity, Adjacency, Etc.). And then, we analyze the Correlation - between Social Relation Value and Search Pattern by comparing the search result of each web users correspond to input 'Query' based on Social Relation Value calculated according to topics. Consequently, we find that Search Pattern is similar to that of web users who have high Social Relation Value. Namely, web users who have similar characteristic and interact with each other with activity in (Online) Social Networks have high Correlation. Thus, we are able to improve search efficiency and reliability when the correlation is applied to search.
Energy Prediction for Energy Management Method of Sensor Node Powered by Temperature Difference Between Air and Shallow Underground Soil

We present an energy prediction model for the energy management of wireless sensor networks powered by temperature differences between near-surface air and shallow underground soil (TDASU). Energy harvesting (EH) using TDASU differs from other EH technologies as follows: (i) TDASU occurs throughout the day. (ii) TDASU has half-day periodicity in addition to the one-day periodicity common for solar energy. Our energy prediction model, denoted the TDASU-characterized moving average (TCMA), considers these characteristics of TDASU and refines the conventional prediction model in terms of half- and one-day forecasting. To assess the performance of our proposed solution, we used real TDASU data collected from fields in Japan and India. A comparative performance evaluation of TCMA and the conventional prediction method has shown that the former can reduce prediction errors by up to 25%.
Comparative Study of Model Optimization Techniques in Fine-Tuned CNN Models

Deep learning & especially Convolutional Neural Networks (CNNs) are taking a different shape in the area of Image Recognition & Classification. Number of different CNN architectures are found in the literature. Performance of any CNN model depends on various parameters such as size of the dataset, number of classes, weights of the model, hypermeters and optimizers so on. Transfer learning or Fine tuning a pretrained model has become very common during present time because of its advantages. Several model optimization techniques have been discussed in the literature. Stochastic Gradient Descent (SGD), Adam & RMS Prop model optimizers are more commonly used in CNN model optimization.This paper focusses on the effect of above three optimizers on well-known CNN models namely, ResNet50 and InceptionV3. Above optimizers are used to train the Fine-tuned CNN models for 15 Epochs on cat vs dog dataset created by hand picking hundreds of images of cats & dogs from Kaggle cat vs dog dataset. For this experimentation, learning rate of 0.001 is used, Categorical Cross Entropy is used to calculate the training & validation loss. Comparative analysis is made between optimizers by plotting training loss vs epochs & training accuracy vs epochs. Results showed that, SGD optimizer outperforms other two for ResNet50. Training accuracy of approximately 99% is observed for ResNet50 with 500 training & 100 validation images.
Comparison and Analysis of Control Strategies for Hybrid Electric Vehicles

The design of a hybrid powertrain is a complex task, which calls for experts from various fields. Their fuel saving potential greatly depends on their use and the sizing of their components. Several optimal control methods are proposed in the literature like Dynamic Programming (DP) and Pontryaguinâ€™s Minimum Principle (PMP). In this work, they are both compared to an optimized rule- based strategy (RBS) on an extended hybrid powertrain model. A complete analysis of the most efficient control strategy (torque split, engine state and gear shift) is then conducted. Finally, the commandâ€™s sensitivity to different powertrain configurations and applications is assessed.
CoRA: Decomposing and Describing Tangled Code Changes for Reviewer

Code review is an important mechanism for code quality assurance both in open source software and industrial software. Reviewers usually suffer from numerous, tangled and loosely related code changes that are bundled in a single commit, which makes code review very difficult. In this paper, we propose CoRA (Code Review Assistant), an automatic approach to decompose a commit into different parts and generate concise descriptions for reviewers. More specifically, CoRA can decompose a commit into independent parts (e.g., bug fixing, new feature adding, or refactoring) by code dependency analysis and tree-based similar-code detection, then identify the most important code changes in each part based on the PageRank algorithm and heuristic rules. As a result, CoRA can generate a concise description for each part of the commit. We evaluate our approach in seven open source software projects and 50 code commits. The results indicate that CoRA can improve the accuracy of decomposing code changes by 6.3% over the state-ofart practice. At the same time, CoRA can identify the important part from the fine-grained code changes with a mean average precision (MAP) of 87.7%. We also conduct a human study with eight participants to evaluate the performance and usefulness of CoRA, the user feedback indicates that CoRA can effectively help reviewers.
Distributed Fog Computing for Latency and Reliability Guaranteed Swarm of Drones

Swarm of drones, as an intensely significant category of swarm robots, is widely used in various fields, e.g., search and rescue, detection missions, military, etc. Because of the limitation of computing resource of drones, dealing with computation-intensive tasks locally is difficult. Hence, the cloud-based computation offloading is widely adopted, nevertheless, for some latency-sensitive tasks, e.g., object recognition, path planning, etc., the cloud-based manner is inappropriate due to the excessive delay. Even in some harsh environments, e.g., disaster area, battlefield, etc., there is no wireless infrastructure existed to combine the drones and cloud center. Thus, to solve the problem encountered by cloud-based computation offloading, in this paper, Fog Computing aided Swarm of Drones (FCSD) architecture is proposed. Considering the uncertainty factors in harsh environments which may threaten the success of FCSD processing tasks, not only the latency model, but also the reliability model of FCSD is constructed to guarantee the high reliability of task completion. Moreover, in view of the limited battery life of the drone, we formulated the problem as the task allocation problem which minimized the energy consumption of FCSD under the constraints of latency and reliability. Furthermore, to speed up the process of the optimization problem solving to improve the practicality, relying on the recent advances in distributed convex optimization, we develop a fast Proximal Jacobi Alternating Direction Method of Multipliers (ADMM) based distributed algorithm. Finally, simulation results validate the effectiveness of our proposed scheme.
An algorithm for routing heterogeneous vehicles in robotized warehouses

Over the past decades, warehouses and storage facilities have been using robotic vehicles in order to lower the cost of production and increase overall efficiency. Many researchers have attempted to solve the internal routing problem of a warehouse, and as a result, there are a number of routing algorithms that could potentially be used in a warehouse. However, most of them are static and cannot take multiple robots and their characteristics into account. This paper proposes an innovative algorithm to solve this problem. The algorithm utilizes multiple robots, tailoring the path for each robot based on its specifications, i.e., speed, type and current position in order to reduce the delivery time and overall increase the efficiency of the warehouse. We have performed an experimental evaluation of the algorithm in multiple artificially synthetic warehouse environments and demonstrated that the algorithm can find a solution in all environments within reasonable time frame.
Feature Extraction for Bearing Fault Diagnosis in Noisy Environment: A Study

Operating induction machine throughout the year is vital, thus maintenance and diagnosing within stipulated time is very essential. Else, this leads to complete shutdown of the systems and huge production losses. Thus a robust diagnosing scheme is essential for mechanical fault detection at incipient stages, as the probability of mechanical fault occurrences is high. In this work normal bearing, natural cage defective bearing and natural outer race defective bearings are considered. Prior to classification, the presence of fault is validated using spectra of current and vibration signals. This work demonstrates a simple fault diagnosing scheme using 3 different classifiers. 12 statistical time domain features are extracted from vibration and current signals. Feature plots of all conditions are investigated in detail. Experimental results showed that features slope sign changes, variance and standard deviation are performing good irrespective of the signal recorded and NB classifier performs well irrespective of the signal recorded and the feature employed.
Humanoid: A Deep Learning-Based Approach to Automated Black-box Android App Testing

Automated input generators must constantly choose which UI element to interact with and how to interact with it, in order to achieve high coverage with a limited time budget. Currently, most black-box input generators adopt pseudo-random or brute-force searching strategies, which may take very long to find the correct combination of inputs that can drive the app into new and important states. We propose Humanoid, an automated black-box Android app testing tool based on deep learning. The key technique behind Humanoid is a deep neural network model that can learn how human users choose actions based on an app's GUI from human interaction traces. The learned model can then be used to guide test input generation to achieve higher coverage. Experiments on both open-source apps and market apps demonstrate that Humanoid is able to reach higher coverage, and faster as well, than the state-of-the-art test input generators. Humanoid is open-sourced at https://github.com/yzygitzh/Humanoid and a demo video can be found at https://youtu.be/PDRxDrkyORs.
MapLite: Autonomous Intersection Navigation Without a Detailed Prior Map

In this work, we present MapLite: a one-click autonomous navigation system capable of piloting a vehicle to an arbitrary desired destination point given only a sparse publicly available topometric map (from OpenStreetMap). The onboard sensors are used to segment the road region and register the topometric map in order to fuse the high-level navigation goals with a variational path planner in the vehicle frame. This enables the system to plan trajectories that correctly navigate road intersections without the use of an external localization system such as GPS or a detailed prior map. Since the topometric maps already exist for the vast majority of roads, this solution greatly increases the geographical scope for autonomous mobility solutions. We implement MapLite on a full-scale autonomous vehicle and exhaustively test it on over 15 km of road including over 100 autonomous intersection traversals. We further extend these results through simulated testing to validate the system on complex road junction topologies such as traffic circles.
Marking the City: Interactions in Multiple Space Scales in Virtual Reality

This article shows an overview of an immersive VR application to explore, to analyze and to mark large-scale photogrammetric 3D models. The objective is to interrogate the effectiveness of the proposed navigation process and annotation tools for spatial understanding. Due to the amount of interactions, different kinds of menus were necessary: desktop coordination menu, radial menu attached to the controller and 3D spatial menu for creating markers. Besides the menus, the navigation tasks through different perception space scales required a great number of interactions metaphors, patterns and techniques displaying the complexity of the user experience in Virtual Reality for understanding and analyzing urban digital twins. Those interactions allowed by the user interface were then analysed and classifyed according to a theoretical background and were experimented in preliminary tests with end users. Although designed for particular needs of the army, the tools and interactions can be adapted for city models explorations and urban planning. For future steps of the research, a usability study is going to be performed to test the performance of the interface and to have more end users feedback.
Random-Byzantine Attack Mitigation in Cognitive Radio Networks using a Multi-Hidden Markov Model System

Cognitive Radio Networks (CRN) are opportunistic networks which aim to harness the white space in the television frequency spectrum, on a need-to-need basis, without interfering the incumbent, called the Primary User (PU). Cognitive radios (CR) that sense the spectrum periodically for sensing the PU activity, are called Secondary Users (SU). CRNs are susceptible to two major attacks, Byzantine attacks and Primary User Emulation Attack (PUEA). Both the attacks are capable of rendering a CRN useless, by either interfering with the PU itself or capturing the entire channel for themselves. Byzantine attacks detection and mitigation is an important security issue in CRN. Hence, the current work proposes using a multi-Hidden Markov Model system with an aim to detect different types of random-Byzantine attacks. Simulation results show good detection rate across all the attacks.
Monitoring Particle Sedimentation in Conductive Suspensions with Moving Electrode Impedance Spectroscopy

We devised a method to monitor the sedimentation of particles from conductive solutions by means of moving electrode electrochemical impedance spectroscopy. Measuring impedance spectra at different electrode spacings allows an isolated view on the electrolyte properties while simultaneously studying sedimentation processes. We present an experimental setup and propose application, for instance, to monitor crystallization processes in ionic liquids for the use of zeolite synthesis.
Motion Estimation on Homogenous Surface for Around View Monitoring System

Around View Monitoring (AVM) system uses multiple input cameras mounted on different positions of a vehicle to display 360° bird-eye-view around the vehicle that is not readily visible to the driver. The development of this system will contribute to the reduction of parking accidents by monitoring its surroundings, detecting lanes and identifying obstacles. With AVM, we can significantly decrease the number of minor accidents. AVM will not only be used for parking assistance but can also assist navigation in the narrow path area. Conventional AVM systems developed in the market using four or six cameras and requires an additional sensor for detection in order to minimise stitching error or to reduce the time to calibrate the output display image. The procedure is time-consuming and increases the cost of development. We propose to develop two ultra-wide-angle cameras located on the front and rear vehicle integrated with the motion estimation (ME) algorithm to produce a parking bird eye view and forward/backward trajectory lines. From our ablative analysis, optical flow is not suitable to be used for real-time ADAS systems as it fails at least 25.5% of the time. However, block matching algorithm based on normalized cross-correlation (CCORR NORMED) and normalised correlation coefficient (CCOEFF NORMED) were able to detect all templates correctly with 0% of false detection on our dataset.
Sensor Decals: a Minimum Effort Approach to Sensors on Unconventional Substrates and Surfaces

In this contribution we present a minimum effort approach to sensors for physical quantities like, e.g., force, pressure, strain, and temperature without the need for cost-intensive lab equipment. Only adhesive tape, carbon black, some kind of cutting tool, and a heat source are needed to fabricate sensors that can easily be affixed also to unusual and curved surfaces. We demonstrate the ease of fabrication and usage at the example of a rather generic sensor structure that can be used to measure strain, force/pressure, and temperature at virtually any chosen point of interest.
Implementation of Space-time Coding and Decoding Algorithms for MIMO Communication System Based on DSP and FPGA

Multiple input multiple output (MIMO) communication technology will be widely used in future mobile communication system. And the space-time coding and decoding algorithms are key algorithms in MIMO communication technology. In this paper the space-time coding and decoding principle and algorithms of Alamouti scheme are analyzed. To verify the algorithms the MIMO communication experimental platform is designed. The baseband part of the platform is based on DSP and FPGA architecture. FPGA is mainly used to realize bus interface between modules. And DSP is used to accomplish the space-time coding and decoding algorithms and execute the data exchange scheduling strategy. All these ensure the effective implementation of the algorithms and efficient scheduling of data. The system has high real-time performance and flexibility.
Implementation of Particle Swarm Optimization for Maximum Power Absorption From Photovoltaic System Using Energy Extraction Circuit

The focus of this paper, improvements in universal rules have seen the thrust for larger use of non-conventional energy sector. To absorb the maximum power output from the solar photovoltaic (PV) panel, Particle Swarm Optimization (PSO) based Maximum Power Point tracking (MPPT) control algorithm is suggested. At a specific operating point, PV panel can harvest maximum power called Maximum Power Point (MPP) in which the solar PV panel must operate at this MPP to give maximum efficiency. In this paper, the capability of the PV panel to track the MPP using PSO under great variation of insolation has been modelled and analyzed in MATLAB/SIMULINK.