Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on the anti-wind performance of transmission tower and early warning model under typhoon atmosphere

With the frequency and intensity of typhoon growing recent years, transmission lines in coastal areas are facing more severe challenges, such as tower collapse accidents which cause terrible consequences and are hard to recover. In that case, early-warning based on the typhoon prediction and tower-line dynamical system is positive to avoid these accidents. The collapsed tower in typhoon “Mujigae” were studied using finite element method to evaluate the anti-typhoon performance of the transmission tower structure for providing reasonable early warnings. Wind field of typhoon “Mujigae” was established according to yanmeng wind field theory and Shiyuan fluctuating wind speed spectrum. The wind field of typhoon can estimate wind velocity with time in a few hours and updated with newest observed typhoon data. Several average velocities were picked up as testing wind speed sections from natural wind speed to the maximum speed tower suffered in the established wind field. The mechanical analysis simulation was carried out under different wind speed sections and directions. The results show that the structural stress of the collapsed tower model exceeds the bearing range under applied typhoon wind field, which is consistent with the real accident. The relationship between maximum structural stress of tower components and wind field parameters can be concluded. Based on observed typhoon data and wind field theory, the key parameters of the future typhoon wind field can be estimated. Therefore, we can predict tower collapse accidents caused by typhoon and give early warning by analyzing the meteorological data of typhoon and the structure of the tower-line system according to the FEM simulation results.
Movement Artifact-Robust Mental Workload Assessment During Physical Activity Using Multi-Sensor Fusion

Mental workload assessment is of great importance for safety critical applications, especially in situations that involve physical demands, such as with first responders (e.g., paramedics, firefighters, or police officers). Advancements in physiological signal monitoring with wearable sensors have made way for real-time mental workload assessment using physiological signals. However, these models have typically been conducted in controlled laboratory settings and rely on a single physiological modality. As a result, such models often experience a drop in performance due to movement artifacts introduced in real-life conditions. In this paper, we demonstrate that a multi-modal mental workload model not only improves measurement accuracy, but can also increase robustness against physical activity artifacts. To this end, an experiment was conducted where mental workload and physical activity levels were modulated simultaneously while physiological data was collected from 48 participants using off-the-shelf wearable devices. Results show improved mental workload assessment with multi-modal fusion under varying physical activity conditions.
Light-Weight Platform for Attack Validation in LTE Network

Within the scope of Long Term Evolution (LTE) security research, open-source implementations of LTE stack are generally used to validate attacks. Since analysis of the code requires advanced understanding of both specifications and programming, validating attacks is tiring and time-consuming. To address the difficulty, we develop LTE Evaluation Assistant Platform (LEAP), an extension of OpenAirInterface (OAI). It provides a Python interface to monitor and manipulate traffic inside OAI. LEAP adopts a novel architecture to reduce complexity. We also investigated the tradeoff between high level customization, usability, and latency. The evaluation result shows LEAP could effectively simplify attack validation process.
A Fault Classification Method for Medium Voltage Networks with a high Penetration of Photovoltaic Systems using Artificial Neural Networks

With the rapid advancement of power electronic technologies and the reduction of photovoltaic cell price, the share of solar energy in the total power production has been booming recently. On the one hand, the increase in the amount of power delivered by solar energy can be beneficial in many economic and environmental aspects. On the other hand, this can cause various technical challenges to network operators. One of these issues is related to classifying faults located in distribution networks with high penetration of photovoltaic systems. Although many studies have paid significant attention to developing new algorithms applicable for a more active today distribution networks, there is still space for other improvements. Hence, after reviewing state-of-the-art researches, this paper was intended to develop a fault classification that is based on artificial neural networks. In particular, a technique so-called Multiplayer Perceptron Classifier was selected for the proposed algorithm. First, the authors generated a data set for the study by modeling and simulating a real distribution network with practical parameters provided by a local utility in the environment software PowerFactory/DigSILENT. Multiple fault scenarios were simulated. Second, a part of the generated data collection was used for network learning. Finally, the performance of the proposed methodology was demonstrated via testing on the remaining number of generated data.
Modeling the Relationship Between Cognitive State and Task Performance in Passive BCIs using Cross-Dataset Learning

New research and development efforts are highlighting the ways in which electroencephalogram-based (EEG) brain-computer interface (BCI) technology can be used to improve the quality of life for healthy individuals. One such application incorporates cognitive state monitoring into passive BCI (pBCI) systems. Among the challenges facing this development, a significant barrier to adoption is the time-intensive calibration typically needed to tune the system to account for variations in neural activity patterns. An open research question is understanding the relationship between underlying user state and user performance in real-world situations and environments. However, user states are often derived and defined as a function of observed user performance for a particular analysis. Understanding the relationship between user state and user performance ideally requires the definition of user state be independent of observed user performance. This work represents our initial steps towards this goal by using cross-dataset learning, where we define user state from a dataset recorded in a highly-controlled experiment, building a subject-independent pBCI model to predict user state using deep learning approaches, and applying this pBCI model to analyze user performance in a new, unobserved dataset. We show that user performance varies smoothly across a continuum of pBCI model outputs. Our results highlight a promising approach for dealing with one of the major hurdles in the development of BCI systems for healthy users.
A Study on the Impact of Training Data in CNN-Based Super-Resolution for Low Bitrate End-to-End Video Coding

In this study, the effectiveness of Super Resolution (SR) methods based on Convolutional Neural Network (CNN) in low bitrate video coding, with a focus on the Versatile Video Coding Standard (VVC), is investigated. Video transmission over networks with limited bandwidth is a common challenge for different applications. One solution is to adopt SR methods where the main principle is to spatially down-sample the input sequence prior to the encoding, then up-sampling the decoded sequence before displaying it. For a fixed target bandwidth, a finer quantization is applied on the low-resolution sequence compared to high-resolution, so that the high quality reconstructed pixels help in retrieving the lost information. However, most CNN-based SR methods are designed for single images and merely focus on the original input signal. Therefore, their trained networks lack understanding of compression artifacts. In this study, we test a hypothesis that training CNN-based SR methods with compressed sequences outperforms training with uncompressed ones. The assumption is that such training allows the SR methods to learn compression artifacts and differentiate them from actual texture information. To this end, state-of-the-art CNN-based SR methods are tested with compressed and uncompressed training set. Experiments show that the use of compressed training data brings, on average, an additional bitrate saving of 6%, in terms of BD-Rate.
Deep Learning Ensemble Based Model for Time Series Forecasting Across Multiple Applications

Time series prediction has been challenging topic in several application domains. In this paper, an ensemble of two top performing deep learning architectures across different applications such as fresh produce (FP) yield prediction, FP price prediction and crude oil price prediction is proposed. First, the input data is trained on an array of different machine learning architectures, the top two performers are then combined using a stacking ensemble. The top two performers across the three tested applications are found to be Attention CNN-LSTM (AC-LSTM) and Attention ConvLSTM (ACV-LSTM). Different ensemble techniques, mean prediction, Linear Regression (LR) and Support vector Regression (SVR), are then utilized to come up with the best prediction. An aggregated measure that combines the results of mean absolute error (MAE), mean squared error (MSE) and R2 coefficient of determination (R2) is used to evaluate model performance. The experiment results show that across the various examined applications, the proposed model which is a stacking ensemble of the AC-LSTM and ACV-LSTM using a linear SVR is the best performing based on the aggregated measure.
Distributed Secondary Frequency Control for Microgrids Under Weak Communication Conditions

Consensus-based control is a typical distributed control method that has been widely applied in microgrids. As a distributed control method, the performance of consensus-based control depends greatly on reliable communication networks. In practical systems, weak communication conditions, e.g. low communication bandwidths and communication failures, will significantly deteriorate the performance of consensus-based control and even affect the microgrid’s stability. This paper focuses on consensus-based secondary frequency control of microgrids under weak communication conditions. In order to mitigate the influence of weak communication conditions, a fully decentralized event-triggered strategy and a time-varying control gain are introduced, and a distributed secondary frequency control method with finite-time convergence performance is developed. A detailed performance analysis of the proposed control is presented, which provides theoretical guidance for its practical application. Simulation based on MATLAB/Simulink verifies the effectiveness of the proposed method.
Research on the Application and Development of Intelligent Inspection System

With the development of internet and artificial intelligence, Internet+ and intelligence become the leading development direction of inspection. As the value chain extension and the driving force to reform the testing industry, the intelligent inspection system plays a more and more important role. This paper analyzes the research and application of intelligent inspection systems and studies the relevant macro-environment from four aspects based on the PEST method. Then, combined with the analysis of the upstream and downstream industries, the development prospect of the intelligent inspection system is discussed.
SOLR Calibration Using Planar Offset Short in Free-space Material Measurement

Electrical properties of materials are requisite to analysis and design electromagnetic (EM) devices and systems. Free-space material measurement method, where the measurand is the free-space scattering parameters of an MUT (Material Under Test) located at the middle of transmit (Tx)/receive (Rx) antennas, is suitable for non-destructively testing the MUT without prior machining and physical contact in high frequencies. This paper proposes a free-space SOLR (Short-Open-Load-Reciprocal or unknown thru) calibration method using three planar offset shorts with the respective offset of (0, λ/6, 2λ/6) for calibrating a free-space material measurement system and measurement results of a glass plate of 4.775 mm thickness are shown in W-band (75–110 GHz).
Excited States Analysis of Polyethylene Molecule with Carbonyl Defects Based on Time-dependent Density Function Theory

Although degradation process of polyethylene is correlated with chemical defect, our knowledge of how these special structures influent insulation degradation has to be further accumulated. In recent years, quantum chemical and first principle calculations have been widely used in various field including condensed matter physics, theoretical chemical to get a microscopic and intrinsic understanding of different material and chemical reaction. In this paper, we focused on the influence of carbonyl in polyethylene on the conductivity from the aspect of excited states. It turns out that the presence of carbonyl group decreases the excitation energy dramatically. In addition, it is implied that some excited states exhibit charge transfer characteristics which contributes to charge transfer in polyethylene materials.
Torque ripple minimization in PMSM drive with non-sinusoidal back EMF using model predictive control

A conventional field oriented control (FOC) relies on park transform and sinusoidal back EMF, in order to tread harmonic signals as constants. As a consequence, resulting control structure based on linear PI controllers is very simple and well tested. However if the back EMF of the machine is not sinusoidal, single coordinated dq system fails to adequately describe the motor, which negatively impacts the control performance and causes additional torque ripple and joule losses in the motor. A commonly used methods for compensation of negative effects of non-sinusoidal back EMF significantly increase the complexity of the algorithm. In this paper we use popular version of predictive control called FCS-MPC which does not relies on the park transform, and thus it allows to address this issue in a simple way. The algorithm has been tested in simulations using the real data of target permanent magnet synchronous motor (PMSM) drive of rated power 100kW.
Privacy-Preserving Similarity Search With Efficient Updates in Distributed Key-Value Stores

Privacy-preserving similarity search plays an essential role in data analytics, especially when very large encrypted datasets are stored in the cloud. Existing mechanisms on privacy-preserving similarity search were not able to support secure updates (addition and deletion) efficiently when frequent updates are needed. In this article, we propose a new mechanism to support parallel privacy-preserving similarity search in a distributed key-value store in the cloud, with a focus on efficient addition and deletion operations, both executed with sublinear time complexity. If search accuracy is the top priority, we further leverage Yao's garbled circuits and the homomorphic property of Hash-ElGamal encryption to build a secure evaluation protocol, which can obtain the top-<inline-formula><tex-math notation="LaTeX">$R$</tex-math><alternatives><mml:math><mml:mi>R</mml:mi></mml:math><inline-graphic xlink:href="lin-ieq1-3042695.gif"/></alternatives></inline-formula> most accurate results without extensive client-side post-processing. We have formally analyzed the security strength of our proposed approach, and performed an extensive array of experiments to show its superior performance as compared to existing mechanisms in the literature. In particular, we evaluate the performance of our proposed protocol with respect to the time it takes to build the index and perform similarity queries. Extensive experimental results demonstrated that our protocol can speedup the index building process by up to 800× with 2 threads and the similarity queries by up to <inline-formula><tex-math notation="LaTeX">$\sim $</tex-math><alternatives><mml:math><mml:mo>∼</mml:mo></mml:math><inline-graphic xlink:href="lin-ieq2-3042695.gif"/></alternatives></inline-formula>7× with comparable accuracy, as compared to the state-of-the-art in the literature.
CNN Based iPS Cell Formation Stage Classifier for Human iPS Cell Growth Status Prediction Using Time-lapse Microscopy Images

CNN classifiers using microscopy images to pick up induced pluripotent stem (iPS) cells from a lot of non-iPS cells ware proposed to improves efficiency of human iPS cell production. By selecting iPS-forming cells at early culture stage, it should be more promising to reduce a considerable amount of work. This paper proposes a CNN reprogramming stage classifier trained by respective types (grown into high-, medium, and low-growth status of iPS-like cells) of time-lapse images taken from the completed cell culturing process. These trained CNNs are used to classify a time-lapse image taken in cell culturing as one stage (a number of consecutive frames) of a respective set of trained time-lapse images. The differences between the actual stage with respective classified stages are than calculated to decide which stage of the completed cell this time-step image taken in cell culturing is near to, then to predict the iPS cell growth status that the culturing cells finally grow into. The method achieved the 85% accuracy to predict the growth status of culturing cells two days later, thus can be applicable for early selection suggestion of good iPS-forming cells.
Optimization Design of GIL Three Post Insulators Based on A Collaborative Expert System

In order to reduce the stress concentration of three post insulators, based on a 550kV GIL three post insulator structure, a collaborative expert optimization method is proposed. Firstly, taking the stress of the post base and the field strength of the surface synthesis as the characteristic parameters, the expert judgment rule and the collaborative judgment rule are determined, and the collaborative expert system of the post base structure is established. Then, the collaborative expert system is coupled with the 550kV Gil three post insulator simulation model, and the new post base structure satisfying the conditions is calculated. The simulation results show that the stress concentration area of the three post insulator is at the joint of epoxy resin and the insert. Under the same excitation, the optimized post base structure alleviates the stress concentration phenomenon of the original structure. The maximum internal stress is 32.7MPa, which is 20.4% lower than the original structure. ased on the analysis of the electric field distribution of the two kinds of structure insulators, it is found that the optimized composite field distribution on the surface of the insulator is improved, and the maximum field strength at the joint of the post base insert is 10.5kV/mm, which is 9.8% lower than the original structure.
Pseudo- Randomization in Automating Robot Behaviour during Human-Robot Interaction

Automating robot behavior in a specific situation is an active area of research. There are several approaches available in the literature of robotics to cater for the automatic behavior of a robot. However, when it comes to humanoids or human-robot interaction in general, the area has been less explored. In this paper, a pseudo-randomization approach has been introduced to automatize the gestures and facial expressions of an interactive humanoid robot called ROBIN based on its mental state. A significant number of gestures and facial expressions have been implemented to allow the robot more options to perform a relevant action or reaction based on visual stimuli. There is a display of noticeable differences in the behaviour of the robot for the same stimuli perceived from an interaction partner. This slight autonomous behavioural change in the robot clearly shows a notion of automation in behaviour. The results from experimental scenarios and human-centered evaluation of the system help validate the approach.
Cognitive Agents and Ethical Behavior in Collaborative Teams

This paper discusses the problem of having cognitive agents display an ethical behavior when simulating humans engaged in collaborative activities. It reviews briefly the notion of ethics for humans and its implementation in systems of cognitive agents, reviews some research approaches, and lists the factors to take into account for simulation of collaborative work. An example of training a medical leader constituting a preliminary approach, is used to illustrate the discourse.
Enhancing Visitor Experience or Hindering Docent Roles: Attentional Issues in Augmented Reality Supported Installations

Studies using augmented reality (AR) technology have suggested that users focus excessively on the virtual content in the AR environment at the expense of the physical world around them. This has implications related to the design of installations that aim to incorporate the user’s physical environment as part of the AR experience. To better understand how user attention is managed in an AR environment, we present an observational study of Rewild Our Planet, a multi-modal installation that combined video, audio, a human docent and mobile AR to promote awareness about environmental issues. We found that, while AR was successful in engaging visitors, it drew attention away from other modalities within the installation. This impacts the work of the human docent and affects how visitors absorb information presented in the installation. Based on these observations, we present guidelines to inform the design of future AR-supported installations with the aim of minimizing or taking advantage of the observed attentional issues.
Analysis of Error Correction for Backward Grounding resistance measurement method with short lead wire Considering Shunt Phenomenon

In order to ensure the safe and stable operation of the substation, it is necessary to test and evaluate the substation grounding grid. In the actual measurement process, since the overhead ground wire is difficult to disconnect from the ground network, in order to obtain accurate measurement results, it is necessary to consider the influence of the shunt phenomenon on the grounding resistance measurement of the grounding network. In recent years, the resistance measurement method with short lead wire has gained wide attention due to the advantages brought by its shorter measurement leads. Based on the backward measurement method with short lead wire, this paper considers the influence of the shunt coefficient on its measurement results, proposes a correction formula through theoretical derivation, and proposes an improved method of it, which is verified by simulation. The improved method proposed in this paper improves its accuracy while preserving advantages of the original method, and it has certain significance for the measurement of large-scale grounding grids.