Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative analysis of the mixed performance of two transformer oils in different proportions

As a good liquid insulating medium, mineral transformer oil is widely used in electrical equipment represented by transformers. This article selects two oils of different origins as the research object, mixes the two transformer oils in five ratios of 0: 1, 1: 3, 1: 1, 3: 1, 1: 0, and compares the different ratios of mixed oils. The physical and chemical properties and electrical properties of the oil, and the discharge characteristics and insulation characteristics of the mixed oil under different electric field distributions were studied through simulation methods to comprehensively evaluate the application of various proportions of transformer oil in high-voltage current transformers from technical performance, economic cost, etc applicability. The research shows that the loss factor of 5 oil samples varies with temperature. The increase increases exponentially and the volume resistivity decreases exponentially with increasing temperature; the breakdown voltage and partial discharge inception voltage of the same transformer oil at the same interval are affected by the electrode form, in which the needle-plate electrode has the lowest breakdown voltage,; the breakdown voltage under the plate-plate electrode is the highest, and the partial discharge initiation voltage of the needle tip-hemisphere is higher than that of the tip-plate electrode; the partial discharge initiation voltage of oil produced in A is higher than that of the oil produced in B. The starting voltage is lower. At the same voltage, the discharge amount of oil produced in A is greater than that produced in B.
Evaluation Method of Arcing Contacts Erosion State of SF6 Circuit Breakers

Due to arc erosion, the arcing contacts of highvoltage SF6 circuit breakers gradually deteriorate throughout the working life, and in severe cases will lead to failure to break and cause significant property damage. Therefore, it is important to timely and accurately evaluate the state of contact erosion. Extracting characteristic parameters based on the dynamic resistance curve of the SF6 circuit breakers are an effective technique for evaluating the erosion state of arcing contacts. However, the traditional characteristic parameters have a small application range and the evaluation effect is poor. In this paper, the characteristic parameter FN is extracted from the dynamic resistance curve by the frechet distance method, which is used to evaluate the erosion state of arcing contacts and classify it. The results show that the characteristic parameter FN has a high fault tolerance rate, a wide range of application, and an excellent evaluation effect. FN can be combined with traditional characteristic parameters to jointly evaluate the erosion state of arcing contacts, making the dynamic resistance method more complete.
Hybrid AI-enabled Method for UAS and Bird Detection and Classification

The advancement of UAS (Unmanned Aircraft Systems) technologies, and the rise in potential misuse of these vehicle platforms, and in particular small UAS (sUAS), has highlighted the demand for a robust and reliable solution for detection and classification of the aircraft (commonly referred to by Drone)vs. other flying objects. Most of the existing research addresses this problem either by extracting micro-Doppler from radar data or features from visual data. But these solutions do not perform well in all weather conditions and beyond a particular distance. To solve the problem of classifying small UASs and differentiating them from the birds, we propose a novel approach by merging classical Interactive Multiple Model tracking (IMM) filter and state of the art Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) unit. The IMM extracts the kinematic features of the target flight trajectory and the RNN with LSTM learn the complex sequence of flight maneuvers. We generated synthetic trajectories emulating birds and drones flights using 3D kinematic models for training. The paper demonstrates that the classification accuracy of 99.3% was achieved with five-fold cross-validation on a network with convolutional layers, LSTM layers, and the dense output layer.
Learning Timescales in Gated and Adaptive Continuous Time Recurrent Neural Networks

Recurrent neural networks that can capture temporal characteristics on multiple timescales are a key architecture in machine learning solutions as well as in neurocognitive models. A crucial open question is how these architectures can adopt both multi-term dependencies and systematic fluctuations from the data or from sensory input, similar to the adaptation and abstraction capabilities of the human brain. In this paper, we propose an extension of the classic Continuous Time Recurrent Neural Network (CTRNN) by allowing it to learn to gate its timescale characteristic during activation and thus dynamically change the timescales in processing sequences. This mechanism is simple but bio-plausible as it is motivated by the modulation of oscillation modes between neural populations. We test how the novel Gating Adaptive CTRNNs can solve difficult synthetic sequence prediction problems and explore the development of the timescale characteristics as well as the interplay of multiple timescales. As a particularly interesting finding, we report that timescale distributions emerge, which simultaneously capture systematic patterns as well as spontaneous fluctuations. Our extended architecture is interesting for cognitive models that aim to investigate the development of specific timescale characteristic under temporally complex perception and action, and vice versa.
Seamless, Bi-directional Transitions along the Reality-Virtuality Continuum: A Conceptualization and Prototype Exploration

With head mounted displays, consumers are able to transition from the real world to virtual realities. However, this requires frequent transitions between the two realities to maintain their physical integrity and awareness of the real world while in the virtual space. We completed two consecutive studies to investigate the dimensions of a system that supports seamless transition between realities without requiring the user to remove the headset. Our results are twofold: First, based on the the analysis of structured interviews (n=20), we present a conceptualization of existing solutions (n=37) and novel ideas (n=9) in the form of a design space. Second, we present the results of a user study (n=36) in which we tested two exemplary prototypes that evolved from the design space, called “Sky Portal” and “Virtual Phone. ” Our exploration shows that our “Virtual Phone” metaphor has the potential to support HMD users in completing bidirectional transitions along Milgram’s reality-virtuality continuum. Users are also enabled to complete micro-interactions across the realities, even without performance loss.
Impact Analysis of Double Fed Induction Generator Low-Voltage Ride-Through on the Switches of the Back-to-Back Converters

The expansion of wind energy conversion systems (WECS) is largely due to the advancements of power electronics devices, especially in terms of processed power levels as well as robustness in grid fault situations. In this sense, the power converters participate in the conditioning and control of the power delivered by the generators, requiring an analysis of the impact caused by situations that lead to possible damages or lifetime shortening due to abnormal operations. Considering this problem, in this paper a case study is performed in order to evaluate the impact of the double fed induction generator (DFIG) low-voltage ride-through (LVRT) on the switches of the back-to-back converters. For this purpose, modeling of the DFIG-based WECS has been performed in MatLab/Simulink, including a linear model for the converter switches, which are of insulated gate bipolar transistor (IGBT) type. The analysis presents the main impacts on the IGBT internal currents and voltages under symmetrical and asymmetrical grid faults, making possible to guide the application of methods and techniques to minimize the impacts caused to the converters.
Agent Coordination in Air Combat Simulation using Multi-Agent Deep Reinforcement Learning

Simulation-based training has the potential to significantly improve training value in the air combat domain. However, synthetic opponents must be controlled by high-quality behavior models, in order to exhibit human-like behavior. Building such models by hand is recognized as a very challenging task. In this work, we study how multi-agent deep reinforcement learning can be used to construct behavior models for synthetic pilots in air combat simulation. We empirically evaluate a number of approaches in two air combat scenarios, and demonstrate that curriculum learning is a promising approach for handling the high-dimensional state space of the air combat domain, and that multi-objective learning can produce synthetic agents with diverse characteristics, which can stimulate human pilots in training.
Tunable Second Harmonic Generation in AlGaAs Metasurface Through Reconfigurable Liquid Crystal

Optical modulators are essential in many optoelectronics applications such as energy harvesting, sensor and imaging apparatuses. In this context, nonlinear photon conversion processes represent a valid opportunity to increase the logic capabilities of these devices. Here, we study the directionality of the second harmonic (SH) signal emitted by a dielectric metasurface made of AlGaAs nanodisks embedded into a liquid crystal matrix. Notably, we numerically demonstrate the modulation of the SH total power and the emission pattern signal coming from the proposed metasurface as a function of the liquid crystal orientation.
Video Coding for Machines with Feature-Based Rate-Distortion Optimization

Common state-of-the-art video codecs are optimized to deliver a low bitrate by providing a certain quality for the final human observer, which is achieved by rate-distortion optimization (RDO). But, with the steady improvement of neural networks solving computer vision tasks, more and more multimedia data is not observed by humans anymore, but directly analyzed by neural networks. In this paper, we propose a standard-compliant feature-based RDO (FRDO) that is designed to increase the coding performance, when the decoded frame is analyzed by a neural network in a video coding for machine scenario. To that extent, we replace the pixel-based distortion metrics in conventional RDO of VTM-8.0 with distortion metrics calculated in the feature space created by the first layers of a neural network. Throughout several tests with the segmentation network Mask R-CNN and single images from the Cityscapes dataset, we compare the proposed FRDO and its hybrid version HFRDO with different distortion measures in the feature space against the conventional RDO. With HFRDO, up to 5.49% bitrate can be saved compared to the VTM-8.0 implementation in terms of Bjøntegaard Delta Rate and using the weighted average precision as quality metric. Additionally, allowing the encoder to vary the quantization parameter results in coding gains for the proposed HFRDO of up 9.95% compared to conventional VTM.
Automatic Labeling for Hierarchical Topics with NETL

Hierarchical topic model is the method used in considering topics with hierarchical relationships. Neural embedding topic labelling (NETL) is a method utilized to label topics with neural embedding, even though it labels topics without topic relationships. The labels of hierarchical topics should have hierarchical relationship with other labels. This study proposes a method for labeling hierarchical topics with hierarchical relationships, and uses NETL to generate candidate labels for bottom topics. Moreover, our proposed method calculates how small the overlap of the candidate labels compared with other sibling topics. To label the upper topics, our method adds the label of the bottom topics and generate labels in the same way as the bottom topics recursively. Our method succeeded label hierarchical topics with labels which is more qualitative labels to consider hierarchical relationship of topics.
Automatic Gain Control for Enhanced HDR Performance on Audio

We introduce a method to enhance the performance of the high dynamic range (HDR) technique on audio signals by automatically controlling the gains of the individual signal channels. Automatic gain control (AGC) compensates the receiver’s dynamic range by ensuring that the incoming signal is contained within the desired range while the HDR utilizes these multi-channel gains to extend the dynamic range of the composited signal. The results validate that the benefits given by each method are compounded when they are used together. In effect, we produce a dynamic high dynamic range (DHDR) composite signal. The HDR AGC method is simulated to show performance gains under various conditions. The method is then implemented using a custom PCB and a microcontroller to show feasibility in real-world and real-time applications.
Distributed Newton-Based Voltage Control Method for Distribution Networks with High PV Penetration

Due to the integration of large-scale distributed photovoltaic (PV) and the access of electronic devices, distribution networks are suffering from more and more serious voltage risks. Inherent uncertainty of PV generation poses significant challenges to voltage control. By using appropriate methods, PV inverters can autonomously regulate reactive power output in a distributed manner to improve voltage profile in networks. In this paper, a distributed Newton-based voltage control method is presented to realize distributed coordination of PV generation, which can fast respond to voltage mismatch and realize voltage profiles optimization. Exhibiting a more efficient, reliable and flexible performance than existing decentralized methods in case studies, the proposed method is demonstrated to be effectively to organize and control PV generation to provide corresponding reactive power support and exert “friendly” effect on distribution networks.
A Comparative Assessment of Dimensionality Reduction Techniques for Diagnosing Faults in Smart Grids

Data-driven diagnostic frameworks for large-scale power grid networks usually deal with a large number of features collected by means of sparse measuring devices. As a pre-processing task, dimensionality reduction methods can improve the efficiency of data-driven diagnostic methods by extracting sets of informative and relevant features from the raw data through appropriate transformations. This work is devoted to studying the applicability of various well-known dimensionality reduction techniques in combination with four classification models in diagnosing open circuit faults in smart grids. By providing a comparative study, this work aims at finding the best combination of dimensionality reduction techniques and classification models for diagnosing faults under normal, high signal-to-noise-ratio, low sampling rate, and high fault-resistance conditions. Various fault scenarios have been simulated on the IEEE 39-bus system and a rigorous analysis of the attained results is fulfilled so as to determine the best combinations under different conditions.
The Impact of Social Media Technologies on Supplier and Customer Relationship Management: An Empirical Analysis in the U.S.

Social media has received considerable research attention in various academic disciplines but is still in its infancy in operations management. In particular, the existing literature does not provide sufficient empirical evidence on how social media technologies affect a company’s supply chain relationships. Thus, this study conceptualized the role of social media in forming supply chain networks between a focal company, suppliers, and customers, based on social network theory. This study also empirically investigated the impact of social media technologies on supplier and customer relationship management using sample data from U.S. industrial organizations. Hypotheses were verified through the structural equation modeling analysis. Sample analysis has proven that social media has a significant positive impact on the operational efficiency of supplier and customer relationship management. In addition, the evidence from this study suggests that social media networks built into supply chain relationships function as a source of knowledge resources that can create a competitive advantage, especially for focal companies. This study also contributes to research methodology by introducing alternative mitigation methods for common method biases that often occur in survey-based data collection. Finally, we discussed the implications of the study findings for practice and research, along with limitations and future research directions.
Profit-Maximized Task Offloading with Simulated-annealing-based Migrating Birds Optimization in Hybrid Cloud-Edge Systems

As an emerging framework, edge computing achieves Internet of Things by providing computing, storage and network resources. It moves computation to edge devices located near users. Nevertheless, nodes in the edge often own limited resources and constrained energy capacities. It is impossible to entirely execute tasks in the edge due to their unsatisfied quality of service. Cloud data centers (CDCs) own almost unlimited resources yet they might cause large transmission delay and high resource cost. Consequently, it is highly needed to intelligently offload tasks between CDC and edge. This work proposes a task offloading algorithm for hybrid cloud-edge systems to achieve profit maximization of a system provider with response time bound assurance. It comprehensively investigates CPU, memory and bandwidth limits of nodes in the edge, and constraints of available energy and servers in CDC. These factors are integrated into a single-objective constrained optimization problem, which is solved by a simulated-annealing-based migrating birds optimization algorithm to yield a close-to-optimal offloading policy between CDC and the edge. Real-life data-driven experimental results show that its profit outperforms its four typical peers.
Key Point Agnostic Frequency-Selective Mesh-to-Grid Image Resampling using Spectral Weighting

Many applications in image processing require re-sampling of arbitrarily located samples onto regular grid positions. This is important in frame-rate up-conversion, super-resolution, and image warping among others. A state-of-the-art high quality model-based resampling technique is frequency-selective mesh-to-grid resampling which requires pre-estimation of key points. In this paper, we propose a new key point agnostic frequency-selective mesh-to-grid resampling that does not depend on pre-estimated key points. Hence, the number of data points that are included is reduced drastically and the run time decreases significantly. To compensate for the key points, a spectral weighting function is introduced that models the optical transfer function in order to favor low frequencies more than high ones. Thereby, resampling artefacts like ringing are supressed reliably and the resampling quality increases. On average, the new AFSMR is conceptually simpler and gains up to 1.2 dB in terms of PSNR compared to the original mesh-to-grid resampling while being approximately 14.5 times faster.
A Comparative Study of Orientation Support Tools in Virtual Reality Environments with Virtual Teleportation

Movement-compensating interactions like teleportation are commonly deployed techniques in virtual reality environments. Although practical, they tend to cause disorientation while navigating. Previous studies show the effectiveness of orientation-supporting tools, such as trails, in reducing such disorientation and reveal different strengths and weaknesses of individual tools. However, to date, there is a lack of a systematic comparison of those tools when teleportation is used as a movement-compensating technique, in particular under consideration of different tasks. In this paper, we compare the effects of three orientation-supporting tools, namely minimap, trail, and heatmap. We conducted a quantitative user study with 48 participants to investigate the accuracy and efficiency when executing four exploration and search tasks. As dependent variables, task performance, completion time, space coverage, amount of revisiting, retracing time, and memorability were measured. Overall, our results indicate that orientation-supporting tools improve task completion times and revisiting behavior. The trail and heatmap tools were particularly useful for speed-focused tasks, minimal revisiting, and space coverage. The minimap increased memorability and especially supported retracing tasks. These results suggest that virtual reality systems should provide orientation aid tailored to the specific tasks of the users.
Learning-based Model Predictive Control for Path Tracking Control of Autonomous Vehicle

Path tracking controller of Autonomous Vehicles (AVs) plays an important role in improving the dynamic behaviour of the vehicle. Model Predictive Control (MPC) is one the most capable controllers that can handle multiple optimisation objectives, and accommodate the physical limits of the actuators and vehicle states to ensure safety and the other desired behaviour. As a high-potential solution, learning cost function from human demonstration can be integrated into an MPC. By learning the cost function from human demonstrations, extensive parameters tuning can be avoided, and more importantly, the controllers can be adjusted to provide desired control actions which are more natural to the human. In this study, an innovative Inverse Optimal Control (IOC) algorithm is proposed to learn a suitable cost function for the control task using collected data from human demonstration. The objective is to design a controller that generates motion which matches specific features of human-generated motion. These features include lateral acceleration, lateral velocity and deviation from the center of the lane. From the results, it is observed that the designed controller is capable of learning the desired features of human driving and implementing them while generating the appropriate control actions.
Towards Eyeglass-style Holographic Near-eye Displays with Statically

Holography is perhaps the only method demonstrated so far that can achieve a wide field of view (FOV) and a compact eyeglass-style form factor for augmented reality (AR) near-eye displays (NEDs). Unfortunately, the eyebox of such NEDs is impractically small ($\sim \lt$ 1 mm). In this paper, we introduce and demonstrate a design for holographic NEDs with a practical, wide eyebox of $\sim$ 10 mm and without any moving parts, based on holographic lenslets. In our design, a holographic optical element (HOE) based on a lenslet array was fabricated as the image combiner with expanded eyebox. A phase spatial light modulator (SLM) alters the phase of the incident laser light projected onto the HOE combiner such that the virtual image can be perceived at different focus distances, which can reduce the vergence-accommodation conflict (VAC). We have successfully implemented a bench-top prototype following the proposed design. The experimental results show effective eyebox expansion to a size of $\sim$ 10 mm. With further work, we hope that these design concepts can be incorporated into eyeglass-size NEDs.
Research on Magnetic Source Topologies Effect for A High Power Factor Flux-Modulated PM Motor

In this paper, the magnetic source topologies effect of the flux-modulated motor is investigated from the perspective of power factor, which aims to improve the motor performances. Firstly, the power factor of the motor with the principle of flux-modulated is derived, which is closely related to the magnetic source and pole slot of the motor. Secondly, the effects of different permanent magnet (PM) magnetic source topologies and pole slot combination on the power factor of the motor are investigated and compared. The analysis results show that the surface-embedded flux-modulated with the pole slot combination of 18/28 can offer high power factor and high output torque.