Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-Specific Low Back Pain: An Inductive Exploratory Analysis through Factor Analysis and Deep Learning for Better Clustering

Non-specific low back pain (NSLBP) is a significant and pervasive public health issue in contemporary society. Despite the widespread prevalence of NSLBP, our understanding of its underlying causes, as well as our capacity to provide effective treatments, remains limited due to the high diversity in the population that does not respond to generic treatments. Clustering the NSLBP population based on shared characteristics offers a potential solution for developing personalized interventions. However, the complexity of NSLBP and the reliance on subjective categorical data in previous attempts present challenges in achieving reliable and clinically meaningful clusters. This study aims to explore the influence and importance of objective, continuous variables related to NSLBP and how to use these variables effectively to facilitate the clustering of NSLBP patients into meaningful subgroups. Data were acquired from 46 subjects who performed six simple movement tasks (back extension, back flexion, lateral trunk flexion right, lateral trunk flexion left, trunk rotation right, and trunk rotation left) at two different speeds (maximum and preferred). High-density electromyography (HD EMG) data from the lower back region were acquired, jointly with motion capture data, using passive reflective markers on the subject’s body and clusters of markers on the subject’s spine. An exploratory analysis was conducted using a deep neural network and factor analysis. Based on selected variables, various models were trained to classify individuals as healthy or having NSLBP in order to assess the importance of different variables. The models were trained using different subsets of data, including all variables, only anthropometric data (e.g., age, BMI, height, weight, and sex), only biomechanical data (e.g., shoulder and lower back movement), only neuromuscular data (e.g., HD EMG activity), or only balance-related data. The models achieved high accuracy in categorizing individuals as healthy or having NSLBP (full model: 93.30%, anthropometric model: 94.40%, biomechanical model: 84.47%, neuromuscular model: 88.07%, and balance model: 74.73%). Factor analysis revealed that individuals with NSLBP exhibited different movement patterns to healthy individuals, characterized by slower and more rigid movements. Anthropometric variables (age, sex, and BMI) were significantly correlated with NSLBP components. In conclusion, different data types, such as body measurements, movement patterns, and neuromuscular activity, can provide valuable information for identifying individuals with NSLBP. To gain a comprehensive understanding of NSLBP, it is crucial to investigate the main domains influencing its prognosis as a cohesive unit rather than studying them in isolation. Simplifying the conditions for acquiring dynamic data is recommended to reduce data complexity, and using back flexion and trunk rotation as effective options should be further explored.
Robot Imitation Learning From Image-Only Observation Without Real-World Interaction

Learning from observation (LfO) prompts the robot to imitate actions from experts' states via deep reinforcement learning (RL), achieving satisfactory results in simulation environments through hundreds of thousands of robot–environment interactions. While in the real world, constrained by the expensive and potentially dangerous interaction between the real robot and the environment, LfO is still challenging to be popularized. Therefore, reducing the number of interactions in LfO during training in the real world remains a hot research topic. Although significant progress has been made, the interaction is still inevitable. Hence, this article proposes the LION net (Learning from Image-only Observation net), which learns action from image-only demonstrations, e.g., a video of a human demonstrating a task, and reduces the number of the robot–environment interaction to zero in the real world. It is expected to be a realistic solution for real-world robot LfO. The LION net comprises two modules: 1) a domain transfer module that bridges the gap between the simulator and the real world and an RL-based control module that utilizes images as input to learning a task. The LION net affords the robot to imitate an action from the image-only human demonstration in the simulator and perform the learned action in the real world without additional training. The proposed method is evaluated on three real-life tasks: 1) pouring water, 2) washing cup, and 3) stacking cube, deployed to a real-world robot, demonstrating state-of-the-art performance.
Multilevel Volt/VAR Control of Active Distribution Networks with Solar PV and BESS

Volt/VAR control (VVC) is a significant component of the distribution network management system that employs the optimal voltage and VAR control techniques to fulfil operational requirements. However, the incorporation of highly stochastic distributed energy resources (DERs) into the grid offers significant challenges to traditional VVC approaches. This paper proposes a novel multi-objective, multi-level VVC approach that coordinates between centralized and local controllers at various time scales of operation to reduce network voltage violations and power loss. For centralized control, an hourly Volt/VAR optimization (VVO) based on day-ahead and intra-hour scheduling is developed as a linearized AC-OPF model. The objective of this model is to reduce line losses and the control action of discrete control devices. During real-time operation, however, a modified adaptive-based Volt/VAR droop (VVD) control technique is developed for the local controllers to moderate the fast voltage fluctuations caused by uncertainty in solar power production and load demand. To validate the accuracy and applicability of the proposed solution, a real-time co-simulation framework was built by interfacing the MATLAB and the real-time digital simulator (RTDS). The outcomes of the study on the IEEE 33-bus distribution network indicate that significant reductions in losses and voltage variations have been accomplished while satisfying the system’s constraints.
Automatic Detection of Cardiac Arrhythmia Using Deep Transfer Learning based Approach on ECG

With the frequency of cardiovascular issues rising, the automated categorization analysis of electrocardiogram signals (ECG) has become important in the medical assessment of disorders. A convolutional neural network (CNN)-based method is recommended for classifying ECG signals. Convolutional neural networks (CNNs) used in the deep learning technique may automatically detect ECG data without the need for simple manual feature extractions, but they need a lot of attention. In this work, AlexNet and ResNet-18, two pre-trained deep learning networks, were utilized to identify and detect cardiac arrhythmia, and their performance was evaluated across a number of metrics. There were some modifications within the layers of the two proposed CNN models for classifying cardiac arrhythmias, and spectrograms were employed as the model's input images. The practical findings demonstrate that the suggested method outperforms various well-used ECG classification methods, achieving promising classification accuracy, sensitivity, and precision of greater than 99%. Although, along with different learning rates and other modifications, the epoch sizes and minimum batch sizes were hyperparameters that were adjusted, the results were still impressive.
Semantic and Moving Object Segmentation-assisted LiDAR Odometry and Mapping

For autonomous vehicles, Simultaneous localization and mapping (SLAM) is one of the fundamental capabilities. Accurate and reliable SLAM are important for autonomous vehicles. In this work, we propose a novel LiDAR odometry and mapping method assisted by semantic segmentation and moving object segmentation. First, to acquire semantic information of point clouds and distinguish moving objects, a framework for segmenting LiDAR point clouds is proposed. Then an effective method for integrating semantic information and moving object information into feature-based LiDAR SLAM is proposed. With the assistance of semantic information and moving object information, moving points are filtered out, and semantic constrains are added in feature extraction and pose estimation to improve the localization accuracy. The experiment results on public datasets show that, compared to the baseline, the average relative pose estimation error of our proposed method is reduced by21.4% in rotation and 29.4% in translation.
Survey on AI and Machine Learning Techniques for Microgrid Energy Management Systems

In the era of an energy revolution, grid decentralization has emerged as a viable solution to meet the increasing global energy demand by incorporating renewables at the distributed level. Microgrids are considered a driving component for accelerating grid decentralization. To optimally utilize the available resources and address potential challenges, there is a need to have an intelligent and reliable energy management system (EMS) for the microgrid. The artificial intelligence field has the potential to address the problems in EMS and can provide resilient, efficient, reliable, and scalable solutions. This paper presents an overview of existing conventional and AI-based techniques for energy management systems in microgrids. We analyze EMS methods for centralized, decentralized, and distributed microgrids separately. Then, we summarize machine learning techniques such as ANNs, federated learning, LSTMs, RNNs, and reinforcement learning for EMS objectives such as economic dispatch, optimal power flow, and scheduling. With the incorporation of AI, microgrids can achieve greater performance efficiency and more reliability for managing a large number of energy resources. However, challenges such as data privacy, security, scalability, explainability, etc., need to be addressed. To conclude, the authors state the possible future research directions to explore AI-based EMS's potential in real-world applications.
A Comprehensive Study of AI-based Optimal Potential Point Tracking for Solar PV Frameworks

Industrialization and other technological growth require power as a mandatory source. Generating power without harming the environment makes much sense, so that it creates an eco-friendly environment and reserves fossil fuels for the next generation. Artificial Intelligence AI technology is evolving everywhere. Making use of AI technology in renewable energy generation techniques creates better benefits for society. The power demand of each and every country is growing rapidly. In order to convert the available natural solar energy into electrical energy, solar photovoltaic (PV) systems are used. Although its efficiency is theoretically 86.8%, practically, for varying reasons, it is only 68.7%. To bridge the practical efficiency gap, enormous focus is needed in different parts of the system. The proposed investigation will provide insights on various AI technologies that are used to track maximum power points (MPPs) from the solar PV.
Reinforcement Learning-Based Energy Management for Plug-in Hybrid Electric Vehicles

In this work, two reinforcement-learning-based energy management strategies are investigated for plug-in hybrid electric vehicles (PHEVs). The strategies are developed based on the Toyota Prius model, for which the dynamics are firstly established. Then, both the deep Q-learning (DQN) and deep deterministic policy gradient (DDPG) algorithms are formulated based on the vehicle model. Furthermore, to verify the effectiveness, the reinforcement-learning-based strategies are also benchmarked against the dynamic programming under the New European Driving Cycle (NEDC). The simulation results indicate that the DQN and DDPG algorithms lag the global optimizationbased DP by around 9% and 4% in fuel efficiency, respectively.
Lung Segmentation in CT scans with Residual Convolutional and Attention Learning-based U-Net

Lung segmentation is considered as prerequisite step in medical image analysis, particularly for the diagnosis formulation and treatment plan of lung diseases. Hence, we are proposing a residual convolutional and attention learning-based U-Net model for precise and proficient lung segmentation in CT scans. The proposed model incorporates a residual convolutional learning block in place of conventional convolutional layer that is utilized in encoder and decoder and an attention mechanism implemented in skip connections of the conventional U-Net architecture, which resulted in augmenting feature representational capability and advancing the discriminative competence of the model. The model is trained and evaluated on a very well-known public dataset named Lung Image Database Consortium (LIDC) dataset and a private dataset taken from a hospital. Experimental outcomes reveal that the presented model accomplishes state-of-the-art performance in terms of Dice Similarity Coefficient as 0.981 for LIDC and 0.987 for private dataset and outperforms several existing methods. The proposed model has the capability to be employed in various clinical applications including lung disease diagnosis and treatment planning and hence, can assist radiologists in enhancing patient survival rate.
Photometric Properties of the Variable Star LZ Herculis

We present BV photometric results of the variable star LZ Her. Frolov & Irkaev classified it as a field SX Phe–type pulsating star, but our observations clearly show that LZ Her is not an SX Phe star, but rather a W UMa–type eclipsing binary, as reported by Garrido et al. The orbital period is derived to be 0.33173646 days by combining our data with that in the literature. The eclipsing light‐curve solution from the Wilson‐Devinney code shows that LZ Her belongs to the W subgroup of W UMa stars; the primary component is less massive and hotter, with a spectral type of F9–G0, and the secondary is more massive and cooler, with a spectral type G5–G6. The spectral types were estimated from the photometric light‐curve solution. Asymmetric light curves could be explained by assuming a cool spot on the secondary star. In the observing field of LZ Her, we found a new eclipsing binary, 2MASS 17500367+2928020, with an orbital period of 0.3056 days.
An Artificial Neural SLAM Framework for Event-Based Vision

The SLAM problem for autonomous robots can be greatly improved by using event-based cameras. Compared to others, event-based cameras consume very low power while providing great temporal resolution and dynamic range. In this study, we propose a convolutional neural SLAM framework based solely on the event data. Event-based cameras generate events for pixels whose brightness changes. Therefore, the event data is rich in motion and edge information. The purpose of the proposed framework is to make all estimations using encoded information in event data. The proposed solution is in the form of keyframe-based visual SLAM, consisting of three neural networks that can estimate the relative camera pose, log-depth and features for loop closure detection. In the study, network architectures and learning curves for the trained networks are presented and it is shown that networks can learn the problems successfully. The proposed method has been developed and tested on a new dataset generated by the CARLA simulator. It has been shown that the proposed method is a SLAM solution and it can keep global drift under control with loop closure estimations. Evaluation metrics for estimations, evaluation of the global model and an analysis of run-time performance are also presented.
Numerically Efficient Model of Voltage-Source Converters for Power Systems Transient Studies

Voltage source converters (VSCs) enable the vast integration of renewable energy resources in modern power systems. Planning and analysis of VSC-based power systems require numerically efficient and accurate models of VSCs. The traditional discrete switching models of VSCs are computationally burdensome; therefore, the average-value models (AVMs) are indispensable for system-level studies. Conventionally, the AVMs of VSCs are interfaced with external subsystems using dependent voltage and current sources in nodal analysis-based programs. This classical type of indirect interfacing of AVM (IDI-AVM) requires a one-time step delay inherently which can cause numerical inaccuracy and/or instability. Recently, a directly-interfaced AVM (DI-AVM) was proposed for VSCs in which the interfacing delay is avoided. This was achieved by formulating the new AVM as a conductance matrix that merges with the overall network nodal equations so that it can be solved with the external subsystems simultaneously and without inducing latency. In this paper, the computational performance of the DI-AVM is investigated against the IDI-AVM for a large-scale VSC-based wind generation system. It is verified that the DI-AVM outperforms the IDI-AVM of VSCs in terms of numerical accuracy as well as efficiency by enabling large simulation time steps.
Modeling of Series Resonant Dual Active Bridge with Grid-Connected Inverter for Vehicle-to-Everything Applications

The Vehicle-to-Everything (V2X) application requires a DC-DC converter coupled with an inverter to supply energy demands. Series Resonant Dual Active Bridge (SRDAB) DC-DC converters are gaining popularity owing to their efficiency, miniature size, and high power density. SRDAB is designed to provide a stiff DC source and improve the dynamic performance of three-phase two-level Grid-Connected Inverters (GCI) at the output of SRDAB. These converters are generally time-varying and non-linear because of their switching operation. This paper derives a linear, time-invariant (LTI) model by using generalized state-space averaging. The dynamics are studied by modeling the combined system. The derivation of open-loop models is done analytically, whereas the transfer functions are solved numerically using MATLAB. The bode plots of the derived model and the time-domain simulation using PLECS is compared with each other. A MATLAB/Simulink simulation is done for the combined system with 48 V DC input voltage and 230 V, three-phase AC output voltage to see the effects of disturbances of GCI on the DC link capacitor voltage of SRDAB.
A Survey of On-Body Antenna Arrays: Future improvements, new designs, and lessons learned [Bioelectromagnetics]

This manuscript presents a survey of the existing literature on on-body antenna arrays. They can be used to enable wireless communication to and from different on-body nodes in a wireless body area network (WBAN) and are also used in medical applications. The design process and the required characteristics of on-body antenna arrays are discussed. On-body antenna arrays should be small, lightweight, and low profile to ensure user comfort. The different types of on-body antenna arrays are discussed as well. On-body antenna arrays differ in the type of antenna element and in the geometric configuration of the array. The gain normalized to the area for the antenna arrays is compared, and it is found that, in the current state of the art, this quantity reduces with increasing frequency from 36 to 0.7 dBi. From our literature review, we draw important lessons that can be used to develop future on-body antenna arrays.
Electric Vehicle Fast Charging Station Location and Capacity Planning Model Using Butterfly Optimization Algorithm

Carbon emissions from the transportation network surpass those from other energy sectors. Thus, electric vehicles (EVs) have gained extensive awareness in society as this can scale down greenhouse gas emissions. Putting charging stations in a growing charging infrastructure network is important for EVs to work and be easy to get. The location and size of fast- charging stations (FCS) are crucial for the distribution network. This article concerns where and how big FCS should be in the electrical network. Therefore, the cost of electric energy loss in the electrical network and the cost of transportation energy loss for charging the battery are considered as decision factors to find the best place and capacity for the FCSs. Also, PV power integration is encouraged for the best placement and capacity of FCSs to decrease the need for the grid to handle more charging. A meta-heuristic butterfly optimization technique is suggested to obtain the optimal results of charging infrastructure. The given IEEE-34 electrical network is chosen to demonstrate the optimal location with the capacity of charging stations. In addition, active power demand from the power grid and voltage profile of the electrical network are also analysed after the integration of EV charging and PV sources.
Modelling of a MIMO Non-Linear System using a Double Internal Loop Recurrent Neural Network

A Double Internal Loop Recurrent Neural Network (DILRNN) model is proposed for Multiple Input Multiple Output (MIMO) system to obtain improved output using Gradient Descent based Back Propagation Algorithm. In the modelling of DILRNN, three feedback loops are taken i.e., the first feedback is taken from the unit delay of the output to the hidden layer, the second feedback is taken from the previous value of hidden layer to itself and the last feedback is taken from the previous value of output to output. The weight is updated with the help of the algorithm after every iteration. The output of the proposed DILRNN model reduces error significantly vis-à-vis other Recurrent Neural Network (RNN) models for constant value of iterations and epochs. The statistical parameters such as RMSE and TMAE for the proposed model are greatly in comparison to other models and thereby enhances the accuracy of the model.
A Sparse Sextuplet 13-Level Switched-Capacitor DCAC Converter for Renewable Energy Integration

Low components count multilevel inverters (MLI) with boosting capabilities are being explored extensively to integrate distributed renewable energy-based sources with stand-alone loads and the grid. Switched-Capacitor multilevel inverters (SCMLI) provide such capabilities. Here, a novel sparse 13-level inverter is presented, which provides sextuple-boosted output voltage concerning the single DC source employed. It employs only fourteen switches, four capacitors and two diodes. All capacitor’s voltages maintain desired magnitudes during the corresponding inverter states. The voltage magnitude of the capacitors is developed by paralleling them with two of them across the DC-source and the rest two across the combination of the DC-source along with the capacitors. A fair comparison is performed with recent 13-level SCMLIs exhibits lowest cost factor of 2.461 among the six times boosted MLIs. The nearest level control is applied for the modulation of the presented topology. The topology is verified in various operational conditions, and its efficacy is validated in a Hardware-in-the-Loop environment.