Miracle-dz/UniFAR-FTUs huggingface dataset:
A Robust Control Scheme to Improve the Network Stability of WECS-based Microgrids

This paper proposes a control scheme based on event triggered sliding mode control (ETSMC) to improve the network stability of WECS-based microgrids during bus voltage excursions. The ETSMC scheme is designed to effectively regulate the bus voltage near its rated value with the reactive current support from a static synchronous compensator (STATCOM) such that the overall network stability of the microgrid remains unhampered. Reliance on an event triggered approach enables the control input to be updated only when certain pre-established conditions (triggering instants) are violated. Thus, the communication channel is only utilized during these instances and remains free otherwise. Thus, resulting in reduced communication channel bandwidth and computational power as well as significant reduction in the chattering phenomena that is typically associated with SMC. The proposed approach was validated using a WECS-based test microgrid subject to network instabilities and mismatched uncertainties. The obtained results confirmed an effective regulation of the bus voltage and improved network stability as well as limited communication between the controller and the STATCOM actuator unit.
ClustVariants: An Approach for Schema Variants Extraction from JSON Document Collections

The use of NoSQL Document Stores has grown in recent years as it offers the potential for increased scalability, flexibility, and consistency to store a massive collection of varied structured data in JSON format. Although the document stores do not impose any structural constraint on the data, the lack of schema information challenges efficient data processing, data management, and data integration. Hence, extant research focussed on identifying the global schema for a collection. Nevertheless, it comes at the cost of losing essential benefits of schema such as a detailed structural description of data, query optimization, etc. To address the specific research gap, we propose ClustVariants, a novel approach for discovering the exact schema variants available in a collection. While the complex structure of large heterogeneous JSON data can not be analyzed directly, we resolve this limitation by systematically extract the structure of data, analyze the fields, and cluster the homogeneous documents. We apply a distributed Formal Concept Analysis algorithm, using Apache Spark, to identify the schema variants from a large cluster of JSON documents. The experimental study on real datasets prove that ClustVariants is efficient in inferring exact schema variants of JSON document collections.
Exploring Sex Differences in Key Frequency Bands and Channel Connections for EEG-based Emotion Recognition

Previous studies have demonstrated the existence of sex differences in emotion recognition by comparing the performance of same-sex and cross-sex training strategies. However, the EEG properties behind the sex differences have not been fully explored. To fill this research gap, we aim to investigate the sex differences in key frequency bands and channel connections of EEG signals. The single-modality attentive simple graph convolutional network (ASGC) is applied to three datasets SEED, SEED-IV and SEED-V under subject-dependence conditions. The classification rates are $90.86\ \pm 4.84\%,\ 83.14\ \pm\ 8.84{\%}$ and $78.33\pm 7.83{\%}$, respectively. The adjacency matrices learned by ASGC indicate that females and males have similar channel-connection patterns, but the degree of importance of channel connections varies by sex. Additionally, by comparing the classification results of 5 frequency bands, we find that males and females represent similar frequency band characteristics, i.e., high-frequency bands achieve better performance, indicating that these frequency bands are more related to emotion processing. Finally, we conduct the cross-subject experiment using ASGC and find that the same-sex strategy outperforms the cross-sex strategy, which is consistent with previous studies. The results also imply that males may be more suitable for sex generalization. However, this finding needs the support of more samples and advanced algorithms.
Quantifying Motor and Cognitive Function of the Upper Limb Using Mixed Reality Smartglasses

In our aging world, the need to measure and evaluate motor and cognitive functions and to automate physical and occupational therapy will increase in the future. Many studies on VR-based rehabilitation systems are already underway. However, there are some issues such as the risk of falling or crashing due to the complete blockage of visual information, VR sickness, and lack of reality. The purpose of this research is to develop a system that simultaneously measures and evaluates multiple abilities and functions, such as motor function, cognitive function, and prediction ability, by using mixed reality (MR) smartglasses technology that enables interaction with spatially arranged objects while maintaining real-world information. In this study, we focused on the motor function of the upper limbs and cognitive function, and measured finger and gaze movements during a reaching task. In addition, we developed a game-based task for occupational therapy in a MR environment and reported the results.
Multi-Chain Semi-Markov Analysis of Intrapartum Cardiotocography

Visual assessment of the evolution of fetal heart rate (FHR) and uterine pressure (UP) patterns is the standard of care in the intrapartum period. Unfortunately, this assessment has high levels of intra- and inter-observer variability. This study processed and analyzed FHR and UP patterns using computerized pattern recognition tools. The goal was to evaluate differences in FHR and UP patterns between fetuses with normal outcomes and those who developed hypoxic-ischemic encephalopathy (HIE). For this purpose, we modeled the sequence of FHR patterns and uterine contractions using Multi-Chain Semi-Markov models (MCSMMs). These models estimate the probability of transitioning between FHR or UP patterns and the dwell time of each pattern. Our results showed that in comparison to the control group, the HIE group had: (1) more frequent uterine contractions during the last 12 hours before birth; (2) more frequent FHR decelerations during the last 12 hours before birth; (3) longer decelerations during the last eight hours before birth; and (4) shorter baseline durations during the last five hours before birth. These results demonstrate that the fetuses in the HIE group were subject to a more stressful environment than those in the normal group. Clinical Relevance— Our results revealed statistically significant differences in FHR/UP patterns between the normal and HIE groups in the hours before birth. This indicates that features derived using MCSMMs may be useful in a machine learning framework to detect infants at increased risk of developing HIE allowing preventive interventions
Transient Intervals of Significantly Different Whole Brain Connectivity Predict Recovery vs. Progression from Mild Cognitive Impairment: New Insights from Interpretable LSTM Classifiers

The high dimensionality and complexity of time-varying measures of functional brain connectivity have created an environment in which a very rich transformation of the data remains difficult to map into disease states without some form of reduction (averaging, clustering, statistical blindness to the multivariate interactions between features that modulate their contributions). In this work, employing a recently developed architecture for long short-term memory classifiers that supports use of gradient-based model interpretability techniques, we predict progression or recovery from mild cognitive impairment (MCI) from an instantaneous (windowless) wavelet-based measure of dynamic functional network connectivity. This time-attention LSTM (TA-LSTM) model achieves 0.79 AUC on the task of predicting which MCI patients who will recover (RMCI) vs. those who will progress (PMCI) to AZD within a three-year timeframe. Using a common gradient-based model interpretation technique, saliency analysis, on this TA-LSTM points to potentially important predictive dynamic biomarkers, including the duration of the highly salient time intervals and the average connectivity patterns within these highly salient intervals.
LEADs: A Swarm Behavioral Decision-making System for Different Tasks

Swarm robots perform better at complex tasks than single robots because they have exceptional performance that solo robots do not. The behavioral decision-making of swarm robots, in particular, is a very important study area that still has a lot of unresolved problems, such as how to integrate the existing swarm behaviors to adapt to different kinds of jobs. In order to achieve a unified control framework for swarm robots, which can be commanded to perform tasks by simply changing the fitness function, this study proposes a system for generating swarm behavior control strategies. The system creates a behavior library by combining the current swarm behaviors and using the K-means clustering algorithm to reduce the dimension of the swarm state space. Then, find a better solution by the greedy algorithm that can finish the job. The experimental findings demonstrate that the system not only scales better in terms of the number of swarm robots but also more effectively combines basic behavior, giving the swarm a high degree of adaptability and scalability across a variety of tasks.
Analysis of the skin conductance and pupil signals for evaluation of emotional elicitation by images and sounds

Many studies in the literature attempt recognition of emotions through the use of videos or images, but very few have explored the role that sounds have in evoking emotions. In this study we have devised an experimental protocol for elicitation of emotions by using, separately and jointly, images and sounds from the widely used International Affective Pictures System and International Affective Digital Sounds databases. During the experiments we have recorded the skin conductance and pupillary signals and processed them with the goal of extracting indices linked to the autonomic nervous system, thus revealing specific patterns of behavior depending on the different stimulation modalities. Our results show that skin conductance helps discriminate emotions along the arousal dimension, whereas features derived from the pupillary signal are able to discriminate different states along both valence and arousal dimensions. In particular, the pupillary diameter was found to be significantly greater at increasing arousal and during elicitation of negative emotions in the phases of viewing images and images with sounds. In the sound-only phase, on the other hand, the power calculated in the high and very high frequency bands of the pupillary diameter were significantly greater at higher valence (valence ratings > 5). Clinical relevance— This study demonstrates the ability of physiological signals to assess specific emotional states by providing different activation patterns depending on the stimulation through images, sounds and images with sounds. The approach has high clinical relevance as it could be extended to evaluate mood disorders (e.g. depression, bipolar disorders, or just stress), or to use physiological patterns found for sounds in order to study whether hearing aids can lead to increased emotional perception.
S-band Band-pass Filter by Using Integrated Passive Device Technology

The micro integration of passive components such as filters is one of the important problems faced by system in package (SiP). Thin-film integrated passive device (IPD) technology provides an effective solution for the minimization. In this paper, the design, fabrication and measurement of the S-band band-pass filter has been investigated by GaAs based thin-film IPD technology. Firstly, by using lumped circuit model, it can be determined the topology structure of the filter and obtain the schematic diagram to meet the specific requirements of the filter. Secondly, the filter is fabricated by using thin film IPD-PI processing, which includes the Wafer Front Side process and the Wafer Backside process on GaAs substrate. Finally, the performance of the filter is tested by using the probe and vector network analyzer. The maximum insert loss in band is 3.0 dB at 3GHz, the lower suppression out of band is 34.7dB at 1.2GHz and the upper suppression out of band is 28.6dB at 3.6GHz, which meets the requirements of designed s-band band-pass filter. The test results and simulations are very close which means the filter designed and fabricated by thin-film IPD technology has quite good accuracy. The S-band band-pass filter by IPD technology greatly reduces the size and weight compared with traditional discrete devices, improves the system integration, and provides the basis for the implementation of highly integrated RF front-end.
Improved Inductive Coupled Wireless Power Transfer System with Square Coils for Odd Harmonic Reduction in Electric Vehicles

This paper proposes a Double-Sided LCL compensation topology with a planar spiral square coil structure. This structure helps in the reduction of odd harmonics in a wireless power transfer system. The square coil has a higher self and mutual inductance and an improved coupling in comparison with the conventional circular coil geometry. The higher the inductance, the higher is the input impedance at odd harmonic frequencies. This will reduce the odd harmonic currents and in turn the magnetic fields. Thus, the Electro- Magnetic Interference is reduced. The benefit of the proposed square coil WPT system is proved by MATLAB scripts programming and simulation. Furthermore, the effect of air gap distance on coupling coefficient, mutual inductance and transmission efficiency is studied. The resonant operating frequency of the proposed Double Sided-LCL square coil system of dimension 600x600mm on both transmitter and receiver sides is taken to be 85KHz.
Integration of Surrogate Huxley Muscle Model into Finite Element Solver for Simulation of the Cardiac Cycle

Clinicians can use biomechanical simulations of cardiac functioning to evaluate various real and fictional events. Our present understanding of the molecular processes behind muscle contraction has inspired Huxley-like muscle models. Huxley-type muscle models, unlike Hill-type muscle models, are capable of modeling non-uniform and unstable contractions. Huxley's computational requirements, on the other hand, are substantially higher than those of Hill-type models, making large-scale simulations impractical to use. We created a data-driven surrogate model that acts similarly to the original Huxley muscle model but requires substantially less processing power in order to make the Huxley muscle models easier to use in computer simulations. We gathered data from multiple numerical simulations and trained a deep neural network based on gated-recurrent units. Once we accomplished satisfying precision, we integrated the surrogate model into our finite element solver and simulated a full cardiac cycle. Clinical Relevance— This enables clinicians to track the effects of changes in muscles at the microscale to the cardiac contraction (macroscale)
Characterization of a Power Substation for Electric Vehicle Charging Stations based on Voltage Transformers for Auxiliary Services

The definition of goals aimed at reducing CO2 emissions has catapulted the emergence of innovative solutions that promote the development of an infrastructure for charging electric vehicles (EV) and add confidence among users in the adoption of electric mobility designs. It is natural that this confidence will grow while decreasing the user’s anxiety regarding the use of the EV in the same conditions as with fossil fuel vehicles, that is, the availability of the EV battery to perform long distances, a short charging time and an infrastructure with dimension and deployment in the field. In order to obtain uniform responses from European countries, Transmission System Operators must play an important role in a successful transition, contributing with knowledge and providing the necessary energy for charging stations. This article presents a solution that intends to overcome some of the challenges presented through the characterization of substations that supply charging stations for electric vehicles, providing high power from a network without constraints.
Actuator fault detection and isolation system for multirotor unmanned aerial vehicles

This article presents a new actuator fault detection and isolation method for multi rotor unmanned aerials (UAVs). The UAV community raises the need to develop a highly efficient classifier capable of early detection of failures and fully inde-pendent of other systems. This paper presents the entire process of preparing the method discussed and its implementation in the embedded system. The measurements of four accelerometers were digitally processed data, the main element of which was the frequency domain analysis. The feature vectors prepared in this way were used to train the artificial neural network. The network model has been implemented on a microcontroller. The tests were carried out using data collected during actual flight in various configurations of damaged propellers. The overall accuracy of the proposed method was 98.08 % without the presence of false alarms. The total processing time was also tested, demonstrating real-time classification capability onboard the autonomous flying robot. The efficiency results were compared with the random forest method.
Designing an Intelligent Parcel Management System using IoT & Machine Learning

Parcels delivery is a critical activity in railways. More importantly, each parcel must be thoroughly checked and sorted according to its destination address. We require an efficient and robust IoT system capable of doing all of these tasks with great precision and minimal human interaction. This paper discusses, We created a fully-fledged solution using IoT and machine learning to assist trains in performing this operation efficiently. In this study, we covered the product, which consists mostly of two phases. Scanning is the first step, followed by sorting. During the scanning process, the parcel will be passed through three scanners that will look for explosives, drugs, and any dangerous materials in the parcel and will trash it if any of the tests fail. When the scanning step is over, the parcel moves on to the sorting phase, where we use Qr codes to retrieve the details of the parcels and sort them properly. The simulation of the system is done using the blender software. Our research shows that our procedure significantly improves accuracy as well as the assessment of cutting-edge technology and existing techniques.
An HTTP Anomaly Detection Architecture Based on the Internet of Intelligence

The prompt expansion of the Internet of Things (IoT) and its wide application in smart homes and transportation has brought tremendous convenience to people’s lives. However, the increase of IoT devices has also brought huge security problems, threatening people’s information and property security. This paper designs a new anomaly detection architecture based on the concept of the “Internet of intelligence”. It is a general architecture that can be applied to different IoT anomaly detection methods. The architecture effectively combines the blockchain and the IoT anomaly detection method, which can overcome the problems of data resource sharing and collective learning. At the same time, we propose a novel method for detecting abnormal HTTP traffic in IoT. It combines clustering and Autoencoder method to efficiently and exactly detect abnormal HTTP traffic in IoT devices. In addition, we propose an optimized feature extraction method, which is favorable to enhance the detection effect. Simulation results show the proposed architecture and method can enhance the detection performance of abnormal HTTP traffic in IoT and address the challenges of existing approaches.
Robust Control of a Duocopter by Discrete-Time Sliding Mode Techniques and an Extended Kalman Filter for Disturbance Compensation

This paper proposes a discrete-time cascade control for a Duocopter which represents a two-rotor helicopter com-bined with a guiding mechanism: A nonlinear MIMO sliding mode control loop is responsible for the positioning of the Duocopter, while its rotation angle is controlled in a linear inner control loop. Feedforward control counteracts the nominal coupling forces related to the guidance. Moreover, an extended Kalman filter (EKF) estimates selected states and remaining errors concerning the nominal coupling forces. The sum of the feedforward part and the estimates can be used to robustly and accurately compensate for the impact of the guiding mechanism on the motion of the Duocopter. Thereby, an excellent tracking performance is achieved.
Voltage Balancing in MMC Operated at Reduced Switching Frequency without using Sensors

The most futuristic converter for medium and high power applications is Modular Multilevel Converter (MMC). There are two measure problems related to MMC. Firstly, the use of excessive capacitor voltage sensors in MMC will increase control complexity and overall cost. Secondly, the increase in Sub-Module (SM) switching frequency increases the switching losses. This paper proposes a two-stage solution technique. In first stage, estimate capacitor voltages instead of sensing using State Observer (SO). In the second stage, a capacitor balancing strategy is proposed to reduce the SM switching frequency using those estimated capacitor voltages. Using the proposed technique, the state observer estimated voltage reach to measured voltage in 0.02 sec. The error between measured and estimated capacitor voltages is found to be 0.08%. The simulations are performed in MATLAB/SIMULINK environment, and the results are compared with various benchmark techniques.
ADAN: An Adversarial Domain Adaptation Neural Network for Early Gastric Cancer Prediction

Gastric cancer is a highly prevalent cancer world-wide. Accurate diagnosis of Early Gastric Cancer (EGC) is of great significance to improve the treatment and survival rate of patients. However, EGC and gastric ulcers have similar en-doscopic image characteristics, resulting in a high misdiagnosis rate. Most existing deep learning and machine learning models for EGC recognition have the disadvantages of cumbersome pre-processing steps and high leakage ratios. To address the above challenges, we propose an end-to-end Adversarial Do-main Adaptation Neural network (ADAN) for EGC prediction on endoscopic images. ADAN network consists of a source domain feature extractor, a source domain classifier, two target domain feature extractors, a target domain classifier, and a domain discriminator. A source domain feature extractor is designed to train the model on public gastrointestinal datasets, which effectively solves the problem of insufficient training data. In addition, an adaptive source-target domain mapping classifier is added to each target domain feature extractor for automatically adjusting the number of classification categories in the target domain. Experimental results show that the proposed ADAN network is superior to the most advanced methods and can accurately predict EGC in clinical practice. Clinical relevance-In this study, the EGC diagnosis model based on the adversarial domain adaptive construction will be more applicable to the real clinical scenario, with higher accuracy and sensitivity and assist the endoscopist to make more accurate diagnosis for EGC and reduce the workload.