Miracle-dz/UniFAR-FTUs huggingface dataset:
Chaotic particle swarm optimization using a rotation transformation based on two best solutions

In this paper, we discuss the particle swarm optimization method (PSO) for global optimization, especially, a PSO using a perturbation-based chaotic updating system called PSO-SDPC. In this method, it is easy to select appropriate parameter values for effective search, and numerical experiments showed its good search ability. However, the search of the PSO-SDPC is not rotation-invariant because the perturbation terms of the chaotic updating system are added along the coordinate system of the standard basis, and the component-wise selection from the chaotic and the standard PSO updating systems for a particle’s position deeply depends on the coordinate systemTherefore, in this paper, we improve the PSO-SDPC: the perturbations are added along a new coordinate system that is selected according to two best solutions, and all components of each particle’s position are updated by the same system, which is selected from the two updating systems. Moreover, we show that the proposed method can be regarded as the rotation-invariant and keeps a high search ability for many problems through numerical experiments.
A fiber bundle fabrication from crystals of the AgBr - TlBr0.46I0.54 system for laser technology

The article is devoted to the manufacture of fiber bundle for the mid-infrared range by extrusion from crystals of the AgBr - TlBr0.46I0. 54 system. The fabricated bundle have improved spectral transmittance, optical loss, and spatial resolution characteristics - the main fiber bundle performance indicators. The basic optical properties of the developed bundles have been studied, allowing us to talk about their applicability in various industries, including as channels for delivering laser radiation.
Effect of surface modification of nanomaterial on insulation performance for HVDC transformer

Nanomaterials have provided their superior properties on the insulation performance of HVDC transformer, which is helpful to ensure the safe and stable operation of the ultra-large-scale transmission system effectively. Especially, nanomaterials conduce to reduce the size and cost of the HVDC transformer. Their effectiveness is related to the microscopic properties of nanomaterials. However, the influences of nanomaterial microscopic properties on insulation performance are lacking. This paper focuses on the effect of nanomaterial surface modification on the insulation performance of transformer oil. Titanium dioxide nanomaterials with different surface modification (acetic acid, caproic acid and oleic acid) were prepared and added into the transformer oil evenly. The breakdown property and discharge streamer propagation of transformer oil and nanofluids under positive lightning impulse voltage were measured. The results show that the surface modification of nanomaterials has a significant impact on the lightning breakdown performance of transformer oil. Compared with transformer oil, the breakdown voltage and time to breakdown of nanofluid with acetic acid modifier are increased by 37.4% and 67.7%, respectively. The breakdown voltage of nanofluid increases with the decrease of the chain length of carboxylic acid molecule. Moreover, As the chain length of carboxylic acid molecule of the nanomaterial modifier decreases, the streamer branches are denser and the development speed is slower. Therefore, the nanomaterials with acetic acid modifier inhibit the development of the streamer propagation more obviously, thereby generating a better performance on the breakdown voltage of transformer.
Research of Cybernic Intelligent Mobility System With Recognition for Approaching Targets and Physiological Management Function

Care recipients use a wheelchair or an electric wheelchair because they have difficulty in moving. To support the independent movement of Care recipients indoors, it is necessary that an electric wheelchair performs autonomous movement in narrow spaces and approaches a target whose position frequently changes, such as a chair and observe the physical condition such as the heart function of the care recipients. The purpose of this research is to develop cybernic intelligent mobility system as an autonomous mobile robot wheelchair that has an object recognition function, an autonomous movement function, and a approaching function to correspond the position change of the target object and a vital sensing function to check(detect and manage) the physiological condition of care recipients and to confirm the effectiveness of this mobility system through the basic experiment. The mobility system has a function to find and approach a target such as a desk or chair in a room based on the calculated relative position, even if the initial position of the target is changed. Moreover, a vital sensor was equipped to constantly perform physiological management. To confirm the basic ability of the developed mobility system, we carried out an experiment conducted in a simple living environment assuming a real environment where electric wheelchairs are used. The developed mobility system found the target that was placed in a different position from the initial position in the environmental map. And the mobility system moved autonomously into the area near the target avoiding objects such as walls or furniture. Furthermore, we confirmed basic function to operate the developed mobility system depending on physiological condition. We developed intelligent mobility system that has an object recognition function, an autonomous movement function based on relative position estimation, and a function to find and approach a target. And through the basic experiment, we confirmed the effectiveness of this mobility system.
Prediction of Power Transformer Oil Chromatography based on LSTM and RF Model

The insulation strength of the power transformer depends on the oil-immersed of the transformer. The oil of the transformer becomes contaminated with moisture and the dissolved gases increase and the insulation gets weak with the time. Thus, it is necessary to predict the concentration of dissolved gases to avoid internal insulation failure of the power transformer. The behavior of dissolved gases is non-linear therefore, machine learning models is the best way out to predict the concentration of dissolved gases in the transformer. The proposed approach used AI algorithm to detect the inceptive failure of transformer early, using the time series Long Short-term Memory (LSTM) model and Random Forest algorithm. The approach uses online real time data that is acquire from a dissolved gas analysis (DGA) online monitoring system. The data is preprocess to obtain highly correlated variable using statistical correlation and regression square output. The time series model of random forest and LSTM model used to train the model, both techniques perform effectively during testing. Finally, comparison of two methods has been made, the results indicate the Random forest method captivate a better forecast the dissolved gases content in power transformer to avoid insulation failure.
Time-Optimal and Collision-Free Path Planning for Dual-Manipulator 3D Printer

To meet the demand for high additive manufacturing efficiency, we are working on an FDM (Fused Deposition Modeling) printer called DEXTER which has dual Selective Compliance Articulated Robot Arms (SCARA). We develop a path planner to produce time-optimal motion paths for each of DEXTER’s arms while guaranteeing that the arms do not collide with one another. We present a collision-free path planner for DEXTER’s arms using an improved Sampling-Based Model Predictive Optimization (SBMPO) based on A* type optimization by adding efficient collision determination and a new type of cost function. The simulation results show that the improved SBMPO can be used to efficiently generate smooth collision-free paths and trajectories with bounded velocity and acceleration.
Light Weight Dilated CNN for Time Series Classification and Prediction

Time series data is available from a diverse set of sensors in real life. It is of prime importance in the domain of machine learning and artificial intelligence to analyze such data and identify outliers or anomalies, characteristic of the underlying activities and predict the future. Traditionally, time-series analysis involves identifying features using exploratory data analysis and using statistical approaches for classification and prediction. However, with the advent of convolutional neural networks (CNN), our ability to extract features automatically has substantially improved. In this paper, we propose a novel lightweight deep learning architecture of dilated CNN for classification and predicting time series data sets. We evaluate our model on a real-world human activity recognition time series data set and a synthetically crafted pseudo-realistic dataset for human intent recognition. Our model outperforms the state-of-the-art models and is light-weight.
Simulation of SI-SFCL for HVDC System: Loss Analysis in System Level

This paper presents a system-level loss study of the saturated iron-core superconducting fault current limiter (SI-SFCL) used in the voltage source converter-based direct current (DC) system. A multi-terminal VSC-based DC system equipped with SI-SFCLs was simulated in PSCAD, and its short-circuit fault process was modelled. The results of pole-to-ground fault current in the multi-terminal VSC-based DC system, with or without the SI-SFCL were compared. The data of fault current with SI-SFCLs was exported to the FEM model of SI-SFCL. Then, the instantaneous dissipation on the DC bias superconducting coil was investigated. Results show that the power loss of that DC system SI-SFCL during the pole-to-ground fault could be up to several kilowatts.
Language Acquisition with Echo State Networks: Towards Unsupervised Learning

The modeling of children language acquisition with robots is a long quest paved with pitfalls. Recently a sentence parsing model learning in cross-situational conditions has been proposed: it learns from the robot visual representations. The model, based on random recurrent neural networks (i.e. reservoirs), can achieve significant performance after few hundreds of training examples, more quickly that what a theoretical model could do. In this study, we investigate the developmental plausibility of such model: (i) if it can learn to generalize from single-object sentence to double-object sentence; (ii) if it can use more plausible representations: (ii.a) inputs as sequence of phonemes (instead of words) and (ii.b) outputs fully independent from sentence structure (in order to enable purely unsupervised cross-situational learning). Interestingly, tasks (i) and (ii.a) are solved in a straightforward fashion, whereas task (ii.b) suggest that that learning with tensor representations is a more difficult task
Non-intrusive Load Monitoring Using Factorial Hidden Markov Model Based on Gaussian Mixture Model

Load disaggregation is a difficult problem in non-intrusive load monitoring. This paper proposes a load disaggregation method by the combination of supervised learning and unsupervised learning. Firstly, the proposed model uses the sum of squared errors to determine the optimal clustering number. Secondly, Gaussian mixture model is applied to achieve robustness to noise and outliers. Thirdly, the hidden Markov model and factorial hidden Markov model are established for each load and total loads, respectively. By disaggregating the window of state change, the computational complexity is effectively reduced. The simulation experiment is carried out with the AMPds data set. The effect of changing the clustering numbers of the third type load on the disaggregation accuracy is studied. Moreover, the proposed model has been compared with other models. The simulation results show that the proposed model can not only reduce the computational complexity but also effectively improve decomposition accuracy.
Plasmonic Control of Analyte Motion

Despite the fact that analyte manipulation can have great applications in different research fields, most techniques used for this still present some limitations. Plasmonic nanostructures can be used to try and overcome this, but they lack the ability to manipulate objects over long distances. In this paper we therefore present different geometries for Brownian ratchet devices that are able to not only optically trap, but also to manipulate over long distances, dielectric nanometer-sized beads by periodically modulating an external light source.
An Online Energy Flow Control Strategy for Regional Integrated Energy System

In this paper, we propose an online strategy for optimal energy flow control based on Lyapunov method in regional integrated energy system. First, consider the changes in specific events. e.g., basic load, electricity price, queue length of controllable load, an event triggering mechanism is established to automatically execute the scheduling signals. Without relying on a priori knowledge, Lyapunov optimization method is then used to control virtual energy queue of active load and the system stability in real time. Finally, the remaining renewable energy resources are allocated in a pre-defined priority. The theoretical deduction proves the feasibility of the Lyapunov optimization algorithm. The numerical results show that the method reduces the cost by transferring load during peak period of electricity price and effectively improves the utilization of renewable energy source.
Comparative Study on Dielectric Performance of Thermal Aged Silicone Oil and Mineral Oil

The dielectric spectrums properties of silicone oil and mineral oil with different aging status were experimentally studied for comparison in this paper. Frequency domain spectroscopy as a non-destructive diagnosis method to evaluate the insulation status of oil-immersed paper insulation. Study the dielectric spectroscopy difference between thermal aged silicone oil and mineral oil can provide principles for evaluating the insulation status of silicone oil. Samples are conditioned to the same initial conditions through the heating at a temperature of 100 °C for 24 hours at first. Then silicone oil samples and mineral oil samples were thermal aged at 140°C in hermetical bottles with paper insulation sheets inside. Dielectric measurement was done after aging time of 0 hour, 500 hours and 1000 hours. The results show that the relative permittivity of the silicone oil is a little lager than that of mineral oil, which can improve the electric distribution inside the power apparatus with oil-impregnated paper insulation system. With the increasing of aging duration, the relative permittivity of silicone oil almost remains constant, while the mineral oil's relative permittivity shows low frequency dispersion phenomenon and becomes higher. The thermal stability of silicone oil is obviously better than that of mineral oil.
Probability distribution model of DC basin insulator flashover

The flashover of the DC basin insulator is the limiting factor for the safe and stable operation of DC-GIL and DC-GIS. It is of great significance to study the probability distribution model of the flashover. In this paper, the DC voltage experiment platform is built for the basin insulator in DC-GIL. The relationship between the flashover voltage and the flashover time of the basin insulator is obtained through experimental measurements. The probability distribution of flashover is established by using the Weibull distribution. The experimental results show that the flashover probability is related to the flashover voltage and flashover time. The higher the applied voltage, the greater the flashover probability, and the longer the applied time, the greater the flashover probability, and the steady state flashover voltage of this basin insulator is obtained. U0.1% is 300kV.
Condition Monitoring Based on Partial Discharge Diagnostics Using Machine Learning Methods: A Comprehensive State-of-the-Art Review

This paper presents a state-of-the-art review on machine learning (ML) based intelligent diagnostics that have been applied for partial discharge (PD) detection, localization, and pattern recognition. ML techniques, particularly those developed in the last five years, are examined and classified as conventional ML or deep learning (DL). Important features of each method, such as types of input signal, sampling rate, core methodology, and accuracy, are summarized and compared in detail. Advantages and disadvantages of different ML algorithms are discussed. Moreover, technical roadblocks preventing intelligent PD diagnostics from being applied to industry are identified, such as insufficient/imbalanced dataset, data inconsistency, and difficulties in cost-effective real-time deployment. Finally, potential solutions are proposed, and future research directions are suggested.
Motor Habituation: Theory and Experiment

Habituation is the phenomenon that responses to a stimulus weaken over repetitions. Because habituation is selective to the stimulus, it can be used to assess infant perception and cognition. Novelty preference is observed as dishabituation to stimuli that are sufficiently different from the stimulus to which an infant was first habituated. In many cases, there is also evidence for familiarity preference observed early during habituation. In motor development, perseveration, selecting a previously experienced movement over a novel one, is commonly observed. Perseveration may be thought of as analogous to familiarity preference. Is there also habituation to movement and does it induce novelty preference, observed as motor dishabituation? We apply the experimental paradigm of habituation to a motor task and provide experimental evidence for motor habituation, disha-bituation and Spencer-Thompson dishabituation. We account for this data in a neural dynamic model that unifies previous neural dynamic accounts for habituation and perseveration.
Increasing 4-D Imaging Radar Calibration Accuracy Using Compact Antenna Test Range

The automotive industry perceives high-resolution radar sensors as one of the backbones of autonomous driving technology. With human safety being at stake, the topic of calibration is of the utmost importance. Yet, realizing possibly large volumes of accurate measurements of devices at 77 / 79 GHz, with 15 cm radiation aperture or more, is a challenge. Applying the Fraunhofer formula in such case results in a prohibitive direct far-field range length of 11.5 m. To address that issue, this paper introduces a new measurement system, consisting of a short-size focal length offset-fed compact antenna test range (CATR), interfaced with an analog echo generator. With a chamber size of only 0.9 m × 2 m × 1.6 m, the setup is designed to test apertures up to 30 cm size. Measurements are presented which involve a 4-D imaging radar on chip (RoC). Results obtained in the CATR and in a reference 7 m far-field range are compared and show excellent agreement. Reducing the cost and size of the test environment offers the possibility to measure more, and in particular to perform full angular calibrations, with the target being swept through the complete azimuth and elevation region. Such procedure is compared to a typically used diagonal calibration approach and is demonstrated to bring significant improvement in the compensation of sensor biases, with a 10 dB increase in peak-to-side-lobe ratio.
High-Temperature Polypropylene Nanocomposite with Different Surface-Modified Nanoparticles for HVDC Cables

As a promising insulation material for HVDC cable, polypropylene (PP)-based nanocomposite shows excellent electrical properties under high operating temperature. The coupling agent plays an important role in the nanocomposite and may affect the high-temperature performance. In this paper, octyltrimethoxysilane and octadecyltrimethoxysilane were used to surface-modify MgO nanoparticles respectively. The PP/MgO nanocomposites were prepared and the surface-modification effect on electrical properties under 30 °C and 90 °C were investigated. The results show that deep traps were introduced in the nanocomposites by surface-modified MgO, whose density is proportional to the alkyl length of silane. Both PP/MgO nanocomposites show better electrical properties than pure PP. The nanocomposite with octadecyl-modified MgO shows the highest DC breakdown strength and lowest DC leakage current under 30 °C. Whereas octyl-modified PP/MgO nanocomposite performs better on these electrical properties under 90 °C. The reason of them may be correlated with the chemical stability of silane under high electric field and temperature. The work can be useful for selecting coupling agents in PP-based nanocomposite cable insulation considering high operating temperature.
Uneven Inter-turn Voltage Distribution among Windings of Medium-voltage Medium/High-frequency Transformers

Working under PWM pulse voltage, medium voltage medium/high-frequency transformers may face unevenly distributed electrical stress between inter-turns. This could increase the probability of partial discharge in the winding insulation and thus the risk of premature insulation failure would be higher. To investigate this issue, an equivalent circuit model of a transformer's winding is built. Based on that, simulations on voltage distribution between inter-turns with different voltage rise times, frequencies, material relative permittivity, insulation distance, and number of turns are conducted. The results show that uneven voltage distribution exists in the winding of transformers operating under PWM pulse voltage, with the first two turns suffering from the highest voltage drop. Also, the influence of the above-mentioned parameters on the inter-turn voltage distribution is revealed.
Athlete 3D pose estimation from a monocular TV sports video using pre-trained temporal convolutional networks

Our goal is to estimate athlete 3D pose from monocular TV sports video with a lower cost of collecting training data. To achieve this goal, we utilize a pre-trained deep neural network as a 3D pose estimator for estimating human 3D pose from 2D joint locations of the person in each image. Each image in popular datasets used for training such 3D pose estimator is obtained from a camera whose axis is parallel to the ground. On the other hand, since an image in TV sports video is generally taken from a bird’s eye view, joint locations of a human is distorted in the lower part of the image. Therefore, it is not appropriate to give 2D joint locations of the person directly to the pre-trained 3D pose estimator. To resolve this problem, we propose to correct 2D joint locations in an image of TV sports video by a homography transformation that maps the points in the image of TV sports video to the corresponding points in the image taken by the camera that captures training data for the 3D pose estimator. Experimental results show that the proposed method can estimate athlete 3D pose from monocular TV sports video.
A Study of Using Language Models to Detect Sarcasm

Organizations that provide online services solicit users to provide feedback to the services that their users receive. This feedback enables the organization to gauge user-satisfaction. To automatically process users’ feedback, many organizations have used natural language processing and machine learning techniques to analyze users’ sentiment from their feedback. In recent years, many language models have been developed for carrying out sentiment analysis. In this paper, we evaluate the effectiveness of several recently developed language models for carrying out sentiment analysis. The result of this research should help organizations in choosing an effective language model for analyzing feedback to monitor user-satisfaction rate.