Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning Based Visual Automated Sorting System for Remanufacturing

Remanufacturing is a crucial component of the circular economy concept which emphasises sustainable consumption habits. This study proposes a novel automated sorting system for remanufacturing which is based on deep convolutional neural networks(CNN). To demonstrate its applicability, the proposed deep learning (DL) system was used to distinguish among dry, wet, oily and defected surfaces. The test was conducted on four locally sourced 3” x 6 ” plates. Sample image data were captured using a USB webcam. The network training was done with 75% of the data while the balance data were used for testing. In this preliminary study, the DCNN classified the features with up to 99.74% accuracy on validation data and above 96% accuracy on live video feed; demonstrating that it can accurately sort components. This study is the first to propose a low-cost sorting system for remanufacturing based on the deep CNN and logic gates. The results show that the method is an accurate, reliable, cost-effective and fast technique that can potentially outperform existing sorting systems in the remanufacturing industry.
Breakdown Characteristics of Vacuum Gaps with Geometrically Similar Structures under Lightning Impulse Voltage

In the insulation design inside the vacuum interrupters, a traditional method is to evaluate the dielectric strength through the experiments. For the design of the vacuum interrupter with a large size, the experimental method requires lots of cost and time. The objective of this paper is to determine the breakdown characteristics of the vacuum gaps with geometrically similar structures under the lightning impulse voltage. A negative standard lightning impulse voltage was applied to a pair of copper sphere-plane electrodes. The diameter of sphere electrode varied from 14 to 30 mm. The vacuum gaps with geometrically similar structures were achieved by keeping the ratio of the diameter of sphere electrode to the gap distance at 9.0. The results revealed that, when the sphere diameter varied as 14, 18, 24 and 30 mm, the 50% breakdown voltage of the vacuum gap with a geometrically similar structure was 62.84, 71.46, 92.13, 116.6 kV, respectively. With increasing the size of the similar structure, the maximum electric field strength decreased, while the geometric enhancement factor was nearly constant. The breakdown voltage of a large-sized vacuum gap could be well estimated by using the breakdown field strength of a small-sized gap with a similar structure.
Optimizing the Charging Parameters of Linear Motor Flux Pump with BP Neural Network and Genetic Algorithm

Charging superconducting coils with linear motor flux pump is affected by many parameters. In order to pump the maximum charging current, we accumulated a large amount of data in the experiment. Due to the principle of linear flux pump is not entirely clear, in this article, we mine the functional relationship between three important parameters (AC current, DC current, frequency) and charging current based on historical data, and adopt the method of BP neural network to predict an optimal charging scheme of flux pump by using the genetic algorithm to optimize the BP neural network parameters. Experimental results show that this method is more efficient than the manual random method, which provides a direction for flux pump design and charging parameter adjustment.
Stereo Visual SLAM for Autonomous Vehicles: A Review

Simultaneous Localization and Mapping (SLAM) problem, where an autonomous vehicle moving in an unknown environment attempts to sense and map its surroundings while recognizing its own location and trajectory within the map, has always been a notable and popular research topic in the field of computer vision, robotics and artificial intelligence. Among the various types of solutions relying on different sensor modalities such as the global positioning system (GPS), radio signals, lidar, etc., vision-based solutions are of major interest nowadays because most cameras are low-cost and rich information gathering, especially for the stereo cameras. In this paper, different technologies of visual SLAM, where the main sensors are cameras, are surveyed with an emphasis on methodologies using stereo cameras. Some state-of-the-art open-source stereo visual SLAM frameworks are also discussed and compared. Finally, a general discussion of the challenges in terms of accuracy, processing time, cost, etc. is provided. The main purpose of this review is to provide a comprehensive overview of public available stereo visual SLAM frameworks and their corresponding pros and cons in different real-world scenarios.
Vulnerability Assessment in Power Systems: A Review and Representing Novel Perspectives

Due to environmental impacts and high cost of transmission network expansion, power systems are loaded closer to their limits, which increases their vulnerability and possible blackouts. In this paper, the recent literature in the steady-state vulnerability assessment (SSVA) and dynamic vulnerability assessment (DVA) are classified and reviewed. After finding the research gaps arising from the current literature review, several systematic ideas are presented in terms of designing a modular framework with a generalized index to determine the vulnerable areas of system, integrated T&D system vulnerability assessment, and vulnerability of PMUs to cyber-attacks.
Validating SuperHuman Automated Driving Performance

Closed-loop validation of autonomous vehicles is an open problem, significantly influencing development and adoption of this technology. The main contribution of this paper is a novel approach to reproducible, scenario-based validation that decouples the problem into several sub-problems, while avoiding to brake the crucial couplings. First, a realistic scenario is generated from the real urban traffic. Second, human participants, drive in a virtual scenario (in a driving simulator), based on the real traffic. Third, human and automated driving trajectories are reproduced and compared in the real vehicle on an empty track without traffic. Thus, benefits of automation with respect to safety, efficiency and comfort can be clearly benchmarked in a reproducible manner. Presented approach is used to benchmark performance of SBOMP planner in one scenario and validate SuperHuman driving performance.
Calculation and optimization of four splitting spacers and fittings in ± 800kV HVDC-Flexible low voltage valve hall

Fittings like spacers are vital parts in the valve hall which could connect lines and buses. However, spacers without proper arrangement and structure would result in unexpected flashover or discharge, thus to risk the safety of the whole valve hall. In this paper, the finite element simulation model of ± 800kV HVDC-flexible low voltage valve hall was established in the software Comsol. The overall electric field distribution was calculated and analyzed. In addition, e-field of several kinds of splitting spacer structure were also studied and compared, with experiments accomplished to verify the simulation results. The results could provide with evidence on designing fittings in the valve hall.
Over Thermal Decomposition Characteristics of Environmental Insulating Medium $\mathrm{C}_{5}\mathrm{F}_{10}\mathrm{O}$ at Different Concentrations

$\mathrm{C}_{5}\mathrm{F}_{10}\mathrm{O}$ gas insulating medium has received universal attention by electrical industry for its superior environmental and insulating characteristics. As to the industrial application of $\mathrm{C}_{5}\mathrm{F}_{10}\mathrm{O}$, due to the limit of saturated vapor pressure characteristic, it must be applied mixed with background gases in order to remain gaseous state in normal temperature, so its necessary to research how the ratio of $\mathrm{C}_{5}\mathrm{F}_{10}\mathrm{O}$ and background gas influence the physical, chemical and insulating properties. If different ratios of $\mathrm{C}_{5}\mathrm{F}_{10}\mathrm{O}$ itself can remain stable under long term over thermal failure is a significant factor of the industrial application. In this paper, we apply systematic researches on the existing gas insulating partial over thermal experimental platform, figure out the decomposition characteristics of 2%, 5% and 8% $\mathrm{C}_{5}\mathrm{F}_{10}\mathrm{O}$ with background gas He under several hours of highest temperature of $500^{\circ}\mathrm{C}$. Then a quantitative detection of the concentration changes of seven decomposition products is done, before analysis of their generation patterns. At last, we process the data and obtain total decomposition product and the proportion of total decomposition product, to characterize the thermal stability and degree of decomposition under different ratios. The production amount and gas production rate of each product vary differently with $\mathrm{C}_{5}\mathrm{F}_{10}\mathrm{O}$ ratio, but the proportion of total decomposition product reaches the peak when $\mathrm{C}_{5}\mathrm{F}_{10}\mathrm{O}$ is 5%, which means it has the worst thermal stability. The research results of this paper have laid a foundation for the industrial application of $\mathrm{C}_{5}\mathrm{F}_{10}\mathrm{O}$ gas.
Natural Scene Statistics for Detecting Adversarial Examples in Deep Neural Networks

The deep neural networks (DNNs) have been adopted in a wide spectrum of applications. However, it has been demonstrated that their are vulnerable to adversarial examples (AEs): carefully-crafted perturbations added to a clean input image. These AEs fool the DNNs which classify them incorrectly. Therefore, it is imperative to develop a detection method of AEs allowing the defense of DNNs. In this paper, we propose to characterize the adversarial perturbations through the use of natural scene statistics. We demonstrate that these statistical properties are altered by the presence of adversarial perturbations. Based on this finding, we design a classifier that exploits these scene statistics to determine if an input is adversarial or not. The proposed method has been evaluated against four prominent adversarial attacks and on three standards datasets. The experimental results have shown that the proposed detection method achieves a high detection accuracy, even against strong attacks, while providing a low false positive rate.
Decoding Reward Information from Local Field Potential and Spikes in Medial Prefrontal Cortex of Rats

Reinforcement learning (RL)-based brain-machine interfaces (BMIs) obtain the mapping between neural activities and the subject’s intention using reward. The advantage is to allow subjects to learn to control the external device without real limb movements. Internal-reward-based RL-BMIs train the decoder based on the reward information extracted from neural activities, which is a step towards autonomous BMI design. Studies have used medial prefrontal cortex (mPFC) activity to extract the internal reward when rodents are in the learning process. However, the reward and non-reward classification using single neuron spikes is noisy. In this paper, we explore the reward interpretation ability of local field potentials (LFPs) in the mPFC area of SD rats, especially on the high-frequency bands. We also investigate whether LFPs contain extra information over spikes by using support vector machine (SVM) as the classifier to distinguish the rewarding and non-rewarding trials. We find that among the three bands, namely, gamma (30–80Hz), high-gamma (80–200Hz) and bhfLFP (200–400Hz), the bhfLFP band has the highest decoding accuracy (86.97% for a high lever task and 79% for a low lever task). Compared with the spike only, the integrated LFP-spike feature has comparable or better classification performance. It potentially provides more stable internal reward for RL-based BMIs.
An Exploratory Study for Designing Social Experience of Watching VR Movies Based on Audience’s Voice Comments

Social experience is important when audience are watching movies. Virtual reality (VR) movies engage audience through immersive environment and interactive narrative. However, VR headsets restrict audience to an individual experience, which disrupt the potential for shared social realities. In our study, we propose an approach to design an asynchronous social experience that allows the participant to receive other audiences’ voice comments (such as their opinions, impressions or emotional reactions) in VR movies. We measured the participants’ feedback on their engagement levels, recall abilities and social presence. The results showed that in VR-Voice Comment (VR-VC) movie, the audience’s voice comments could affect participant’s engagement and the recall of information in the scenes. The participants obtained social awareness and enjoyment at the same time. A few of them were worried mainly because of the potential auditory clutter that resulted from unpredictable voice comments. We discuss the design implications for this and directions for future research. Overall, we observe a positive tendency in watching VR-VC movie, which could be adapted for future VR movie experience.
Distributed Multi-Area Economic Dispatch Considering Reactive Power Using Critical Region Projection

The growing tight interconnections between regional power systems brings about the critical need to coordinate multi-area economic dispatch. Because of the limitations on private data exchange and model management, it is suitable to address the multi-area economic dispatch problem in a distributed manner. In this paper, the linearized AC power flow model is used to consider the reactive power to make the dispatch more accurate. The dispatch model is decomposed into regional sub-problems and the coordinator’s problem by using the boundary phase angles and voltage amplitudes as coupling variables. In order to handle the multi-parametric programming, a critical region projection method is used to obtain high-quality solutions with finite convergence. Numerical tests on multi-area interconnected systems are conducted to show the excellent computational performance and the more accurate solution of the proposed method.
Adaptive Fault-Tolerant Control of Nonlinear Time-Delay Systems With Prescribed Performance

An adaptive fault-tolerant control method considering actuator fault is proposed for a class of strict-feedback nonlinear time-delay systems. The prescribed performance is introduced by error transformation, which guarantees the transient performance of the system. Pade approximation and intermediate variables are used to eliminate the effect of input delay on the system performance. The universal approximation nature of fuzzy logic systems is used to approximate the unknown function in the system. A general fault model is introduced to describe the partial fault and stuck fault that may occur during the operation of the systems. The controller based on backstepping can ensure that the system operates normally with actuator fault. Through the Lyapunov function, all signals in the designed closed-loop system can be proved to be semi-globally uniformly ultimately bounded, and the tracking error can quickly converge to a compact set near the origin. Compared with the non-fault-tolerant control system, the simulation results show the effectiveness of the proposed control strategy.
An Intelligent Fault Diagnosis Method for Open-Circuit Faults in Power-Electronics Energy Conversion System

Applying the fault diagnosis techniques to power electronic equipments is beneficial to improve the stability and reliability of renewable energy system, because power electronic equipments are a key component of renewable energy system that largely defines their performance. This paper presents a novel intelligent fault diagnosis method for a three-phase power-electronics energy conversion system based on knowledge-based and data-driven methods. Firstly, the three-phase AC currents of the power-electronics energy conversion system are collected and used to analyze. Secondly, the feature transformation, a knowledge-based method, is utilized to preprocess the fault data. After feature transformation, the slopes of current trajectories (transformed features) are not affected by different loads. And then random forests algorithms (RFs), a data-driven method, are adopted to train the fault diagnosis classifier with the processed fault data. Finally, the proposed method is implemented online on an actual three-phase PWM rectifier platform. The results show that the proposed fault diagnosis method can successfully detect and locate the open-circuit faults in IGBTs of the three-phase PWM rectifier. In addition, the proposed method is suitable for most of three-phase power-electronics energy conversion systems.
Quantitative Analysis of 12CO2, 13CO2, 12CH4 and 13CH4 Gases by Raman Spectroscopy

A method of quantitative analysis of <sup>12</sup>CO<inf>2</inf>, <sup>13</sup>CO<inf>2</inf>, <sup>12</sup>CH<inf>4</inf> and <sup>13</sup>CH<inf>4</inf> gases based on Raman spectroscopy is developed. The Raman spectra of isotopically enriched (>99.5%) gases are measured and compared. The concentration detection limit and the measurement uncertainty are estimated.
Siamese KG-LSTM: A deep learning model for enriching UMLS Metathesaurus synonymy

The Unified Medical Language System, or UMLS, is a repository of medical terminology developed by the U.S. National Library of Medicine for improving the computer system’s ability of understanding the biomedical and health languages. The UMLS Metathesaurus is one of the three UMLS knowledge sources, containing medical terms and their relationships. Due to the rapid increase in the number of medical terms recently, the current construction of UMLS Metathesaurus, which heavily depends on lexical tools and human editors, is error-prone and time-consuming. This paper takes advantages of the emerging deep learning models for learning to predict the synonyms and non-synonyms between the pairs of biomedical terms in the Metathesaurus. Our learning approach focuses a subset of specific terms instead of the whole Metathesaurus corpus. Particularly, we train the models with biomedical terms from the Disorders semantic group. To strengthen the models, we enrich the inputs with different strategies, including synonyms and hierarchical relationships from source vocabularies. Our deep learning model adopts the Siamese KG-LSTM (Siamese Knowledge Graph - Long Short-Term Memory) in the architecture. The experimental results show that this approach yields excellent performance when handling the task of synonym detection for Disorders semantic group in the Metathesaurus. This shows the potential of applying machine learning techniques in the UMLS Metathesaurus construction process. Although the work in this paper focuses only on specific semantic group of Disorders, we believe that the proposed method can be applied to other semantic groups in the UMLS Metathesaurus.
A Planning Platform for Droop-based Isolated Microgrids

Isolated microgrids are operated based on droop characteristics in terms of frequency-active power and voltage-reactive power to evenly share active and reactive powers among their dispatchable DGs. Although active powers are accurately distributed among the different DGs, there is an inherent error in the sharing of reactive powers. This error is a function of the DGs’ locations, and accordingly, should be addressed in the planning. This paper presents a planning platform to simultaneously site and size DGs and capacitor banks (CBs) for isolated microgrids that takes into account the operational characteristics at the planning stage. For this aim, a suitable power flow that considers the specific features of isolated microgrids is employed along with an additional constraint that is introduced to the planning. Loads’ and DGs’ intermittent behaviors are modelled through a probabilistic model. To validate the effectiveness of the proposed planning approach, the PG& E 69-bus system has been used.
Camera-based Driver Drowsiness State Classification Using Logistic Regression Models

Drowsiness at the wheel is a major problem for traffic road safety. A drowsy driver suffers from decreased vigilance, increased reaction time and degraded decision-making ability, all of which have a huge impact on the driving performance. A driver monitoring system that warns the driver of his or her critical drowsiness state is a worthwhile contribution to traffic road safety. A drowsy driver typically exhibits some observable behaviors, such as eye blinking and head movements, that can be tracked using a camera. In this study, we analyze the potential of eye closure and head rotation signals, provided by a driver camera, to classify the driver’s drowsiness state using logistic regression models. This analysis is based on a large dataset collected from 71 subjects in driving simulator experiments. A reliable and independent reference for drowsiness, however, is required in order to perform this analysis. For this purpose, we devise a methodology that merges several drowsiness monitoring approaches to construct a reliable reference for drowsiness. Furthermore, we describe our approach to extract eye blink and head rotation features. Ultimately, we design logistic regression classifiers and combine them using the one-vs-one binarization technique. Our approach achieves a global balanced validation accuracy of 72.7% on a three-class classification problem (awake, questionable and drowsy) by adopting a strict and rigorous evaluation scheme (i.e., leave-one-drive-out cross-validation).
TEARS: A temperature-aware real-time scheduler for heterogeneous multi-core systems

Nowadays, multi-core processing systems have to perform complex functionalities on densely packed multi-million gate platforms, which makes such systems prone to uncontrolled surges in core temperatures, if not effectively controlled. Increasing temperature above specified limits not only leads to high cost of cooling but also results in higher dissipation of leakage power together with a reduction in performance and lower system life expectancy. In this work, we propose a two-level low-overhead proportional fair resource allocation strategy called TEARS: A temperature-aware real-time scheduler for heterogeneous multi-core systems, for scheduling of periodic tasks with bounded number of migrations and context-switches. The proposed algorithm’s first level divides time into distinct windows based on deadlines of tasks, so that exact proportional fairness is maintained at all window boundaries. The second level accomplishes intra-window scheduling with the aim of maximising the use of resources while not breaching a specified thermal threshold. Our experimental analysis shows that the presented strategy not only improves upon the state-of-the-art in terms of resource utilisation (as high as 16.09%) but also reduces average temperatures of cores in the system.