Miracle-dz/UniFAR-FTUs huggingface dataset:
Going Green with Lightweight Robots: Energy Optimal Programming of Lightweight Robots

Industries worldwide are shifting their focus toward implementing sustainable practices. Robotics technology has become essential in this transformation due to its ability to enhance productivity and precision while minimizing material and energy waste. Lightweight industrial robots (LIRs), also known as collaborative robots (cobots), have gained significance for their cooperative and efficient work with human operators. This study explores energy optimization techniques tailored specifically for LIRs. Unlike conventional industrial robots (IRs), LIRs have unique energy characteristics, requiring unique optimization strategies. We introduce three energy-minimizing techniques for LIRs: optimal manufacturer commands, optimal motion time determination, and dissipative energy reduction. Each technique can significantly impact the energy efficiency of LIRs. Case studies demonstrate that these strategies can result in energy savings of up to 31% for a manipulator. This study contributes to the advancement of energy-optimized industrial robotics, providing valuable insights for enhancing sustainability in modern manufacturing environments.
Numerical optimization of an electro-adhesion actuator used in the application of electrostatic sorting of metal and polymer particles

The electrostatic adhesion technique is recently used in the electrostatic separation of granular metal/polymer (Copper/PVC) materials. In order to understand the behavior of conductive and dielectric particles on an electrostatic adhesion actuator, it is essential to study the force that appears above the surface of the actuator, when applying the electrical voltage. The main objective of this work is to evaluate the effect of the electro-adhesion force on the conductive and dielectric particles. Based on Maxwell's stress tensor formulation, a numerical simulation is carried out to study the properties of the electro-adhesive force in the presence of conductive and dielectric particles, for different electrical and geometrical parameters. The electro-adhesion force is evaluated numerically by the electrical voltage, the waveform impact, frequency and the electrodes geometrical parameters, the width and the spacing effects in particular. The conclusions of this analyze show that the best compromise between electro-adhesion and separation is favored by using an alternative waveform and electrode width and spacing corresponding to particles size.
Power System Transient Security Assessment using Unsupervised Probabilistic Deep Bayesian Neural Network

This paper introduces an unsupervised deep Bayesian network, built upon normalizing flow and deep Bayesian network principles, to precisely evaluate the transient security condition of a power system. The proposed approach can capture locational and temporal features using an imbalanced dataset, is noise-model-free, and can handle unlabeled data. It can learn interdependencies between different signals and understand high-dimensional signals in power systems. To validate its effectiveness, the proposed method is studied using the New England power system and shows accuracy and reliability in comparison with state-of-the-art deep networks (convolutional neural network (CNN) and long short-term memory (LSTM)) and shallow networks (support vector machine (SVM) and artificial neural network (ANN)).
Disturbance Observer Based Control for Torque Ripple Mitigation in PMSG-Based Wind Turbine During Unbalanced Grid Voltages

This paper presents a composite current controller for Back-to-Back (BTB) converters in wind energy conversion systems (WECSs) to cope with balanced as well as unbalanced grid voltages. The composite controller consists of a combination of a state feedback controller with a disturbance observer (DO). The idea is to use an observer to estimate the offset, representing the mismatch between the actual system and the nominal model including unknown disturbances, which is then canceled by the state-feedback controller. The proposed controller is designed for both machine-side converter (MSC) and grid-side converter (GSC) to control the power flow injected into the grid under both balanced and unbalanced grid voltages. The proposed controllers for MSC and GSC are coordinated to control the power exchange between the grid and the permanent magnet synchronous generator (PMSG) with good power quality. More precisely, such a control scheme is used to mitigate the effect of the second harmonic oscillation in dc bus voltage that arises during unbalanced grid voltages. Simulation tests were performed to test the efficacy of the proposed controller considering both balanced and unbalanced grid voltages. The obtained results provide evidence to the effectiveness of the proposed controller to maintain a good quality current for both PMSG and the host grid even in the presence of voltage unbalance.
Optimization of Aluminum Electrolytic Process Parameters Based on Parallel Tumbleweed Algorithm

In order to enhance the optimization capability of process parameters in aluminum electrolysis production, this paper proposes a Parallel Tumbleweed Algorithm (PTA) based on parallel computing. The Tumbleweed Algorithm(TA) is prone to local convergence during the iteration process. The PTA algorithm achieves parallel processing between populations through two communication strategies. First, the optimal values are exchanged between two subpopulations. Second, in a three-subpopulation scenario, a portion of individuals in one subpopulation is replaced with the best individuals from the other two subpopulations. These two types of exchanges allow for the transfer and sharing of the optimal values among the subpopulations. Applying PTA to solve the CEC2017 test functions and comparing it with various intelligent optimization algorithms. The results show that this algorithm has better optimization performance and competitiveness. Finally, applying the PTA algorithm to the aluminum electrolysis parameter optimization problem validates the practicality of the algorithm.
Partial Discharge Separation by Using Pulses Cross-Correlation

To date, continuous monitoring of power grid elements susceptible to aging processes is essential to increase the reliability of these systems and avoid failures. In the field of energy transmission, an index of the state of a cable insulation system is partial discharge activity, which is influenced by the presence of structural defects (e.g. voids within the material). In High-Voltage-Direct-Current applications, given the dependence of dielectric conductivity on temperature, this phenomenon is also influenced by the load. Thus, partial discharge activity is strongly related to the operating condition of the line and not only by the aging of materials. Given the importance of partial discharges monitoring, it is necessary to develop techniques for analyzing them that allow the identification of different types of discharges. The lack of a reference standard prompts researchers to propose different approaches to achieve these goals. This paper describes a comparison of two separation algorithms both based on cross-correlation. Data used for comparison have been obtained through a partial discharges measurement, under DC voltage stress, on XLPE model cable. The first algorithm evaluates the similarity among the pulses and separates them into clusters following the acquisition order. The second one, on the other hand, uses the correlation matrix as input data, which must be calculated before running the algorithm. The results show that both algorithms succeed in identifying the same phenomena. The former with less accuracy but employing less computation time than the latter. The latter, on the other hand, provides higher accuracy but requires longer computation time. The choice in using either algorithm can thus be traced back to the desired accuracy/time ratio.
Coordination of Competing Supply Chains: Wholesale Pricing vs. Two-Part Tariff

In recent years, market competition is gradually changing from interactions of enterprises to that of competing supply chains. And this chain-to-chain competition raises a new problem: how to internally coordinate one chain to cope with the competition of another chain. To address this problem the paper investigates two pricing strategies: the wholesale pricing strategy and the two-part tariff, and compares the performance of the two strategies by employment of Nash bargaining model. The paper obtains new insights as follows: First, previous studies based on single-chain argue that two-part tariff can better coordinate the supply chain compared to wholesale pricing strategy. This is not always the case in chain-to-chain competition: two-part tariff is better only when competition intensity is relatively low. In contrast, wholesale pricing strategy is more adaptive when competition exceeds a certain threshold. Second, the choice of pricing strategy is also related to the power structure of supply chain. As the bargaining power of retailers increases, the incentive to implement a wholesale pricing strategy is increasing and the two-part tariff is decreasing.
CNN-Conformer: Conformer in Channel Mapping Based Convolutional Neural Network for Stereophonic Acoustic Echo Cancellation

In acoustic echo cancellation (AEC), conventional methods estimate the path of the microphone and loudspeaker to achieve clear communication. However, for stereophonic AEC (SAEC), the problem of non-uniqueness arises because of a high correlation between far-end signals. This paper proposes a convolutional neural network (CNN) with a conformer module to remove echoes and background noise. First, the generalized cross-correlation (GCC) is utilized to align the far-end signals with the echoes in the microphone signal. After that, the complex spectral features are fed into the CNN-Conformer, where the encoder module is used to extract the frequency information, the conformer module explores the channel relationship, and the temporal convolutional network (TCN) module performs time modeling. Finally, a joint loss function in the time domain and frequency domain is designed to train the network. The experiments show that the proposed approach presents a good generalization capability and performs better than previous neural network-based approaches in SAEC.
Large-Scale Group Emergency Decision-Making: A Literature Review

In dealing with emergencies, rational decisions are needed with limited time to reduce losses to death. Emergencies, which affect many public interests, require the participation of decision-makers with different perspectives to mask the lack of knowledge and experience of one decision-maker or a smaller group. Large-scale group decision-making (LSGDM) has become an exciting research topic in the last decade. Previous researchers have widely discussed the development and application of the LSGDM model. Time constraints and rational solutions are the main challenges for LSGDM in emergencies. This literature study explores LSGDM research trends in emergencies and provides new insights and opportunities for determining future research.
Development and Validation of a Wearable System for Multi-Parametric Stress Level Assessment

The present study aims to develop and validate a multi-parametric wearable platform for the estimation of continuous quantitative stress level using physiological signals. Thirty healthy volunteers were involved in a dedicated acquisition protocol, including rest and task-induced stress phases, and asked to qualitatively evaluate their stress level using the VAS scale as reference. From each acquired physiological signal (GSR; ECG; respiration), statistically significant features were identified. A feasibility study allowed to evaluate the physiological response to stress with classification models aiming to discriminate between rest and stress phases, both in absolute and differential terms. Absolute analysis allowed 2-class classification (no stress; stress) with an accuracy of 90% and 3-class classification (no stress; moderate stress; high stress) with an accuracy of 73%. Differential analysis allowed 3-class classification (baseline; decrease; increase) with an accuracy of 80%. In conclusion, the proposed study could represent a groundwork for future applications aiming to apply stress level monitoring with physiological signals also in daily life scenarios.
Constructing MDS Array Codes with Small Repair Bandwidth under Sub-packetization Two

Erasure Codes are widely implemented in distributed storage systems to achieve fault tolerance with high storage efficiency. Reed-Solomon code is commonly deployed in data centers due to its optimal storage efficiency, but it requires massive repair bandwidth for node repair. Minimum Storage Regenerating (MSR) code and Locally Repairable (LR) code are proposed to reduce repair bandwidth. However, MSR code usually carries a heavy disk I/O burden and LR code is not optimal in storage efficiency. In this paper, we take disk I/O, storage efficiency, repair bandwidth, sub-packetization level, and repair degree (number of helper nodes) into consideration together, and propose explicit construction of MDS array code (named LDIO code) with low disk I/O and reduced repair bandwidth, where the sub-packetization level is 2. Besides the low disk I/O consumption, LDIO code also achieves a small repair bandwidth of ${\bar \gamma _{sys}} = \frac{1}{2} + \frac{1}{{4(r - 1)}}$ for systematic nodes with repair degree d = k+1. Normally, LDIO code can provide bandwidth savings of 25% to 50% for the repair of systematic nodes compared with RS code, and almost savings of 6% compared with piggybacking code. For an LDIO code of length n and redundancy r, the size of the finite field needed is Θ(nr+1).
A Ship Radar Cross Section Recognition Method Based on Bi-directional Gated Recurrent Unit Net

Using the Radar cross section (RCS) to independently recognize the ship targets can strengthen the autonomous decision-making ability of the combat platform and promote the unmanned combat OODA cycle. In order to mine the deep features of RCS sequence data and realize high-precision and efficient ship RCS recognition, Bi-directional gated recurrent unit net (BiGRU-Net) was established, which reduces the impact of long-term dependency problems on recurrent neural network, while ensuring recognition efficiency. The experimental results show that compared with other recognition models, the recognition accuracy of BiGRU-Net is increased by 1.43%, 14.37%, which can effectively improve the recognition accuracy of RCS under different noise environments while ensuring the recognition efficiency.
Safe Force Control of Multirotor Unmanned Aerial Manipulator

This article studies the problem of applying normal forces using an underactuated aerial vehicle equipped with a two degree-of-freedom SCARA robotic arm. Given known desired location for force exertion, proper initial configuration for the entire aerial manipulation system is computed. The necessary conditions for generating forces along the desired axis are investigated, resulting in exact solutions for the motion of the aerial vehicle and the robot arm joints. A position controller is devised for the vehicle, guaranteeing desired end-effector pose can be achieved during interaction. For the interest of safety, potential fields are used for the contact surface, such that force interaction can only occur under predefined desired conditions. Simulations are presented to demonstrate the force exertion stability and safe recovery against large disturbances.
Slab dehydration and earthquake distribution beneath southwestern and central Japan based on three‐dimensional thermal modeling

We developed a 3‐D thermal convection model to estimate the thermal regime, water content distribution, and slab dehydration beneath southwestern and central Japan, where deep tectonic tremors and short‐term slow slip events (S‐SSEs) are frequently observed on the plate interface extending from the western Shikoku to the Tokai district. The results showed that the pressure‐temperature (P‐T) conditions for the S‐SSEs, deep tectonic tremors, and regular earthquakes indicate that they probably originated from slab dehydration in association with subduction, resulting in a phase transformation from prehnite‐actinolite/lawsonite blueschist to amphibolite with a large thermal gradient. Slab dehydration and the thermal gradient in the dip direction are considered key factors for controlling the seismogenesis of slow and regular earthquakes in the Philippine Sea plate beneath southwestern and central Japan, although the deep tectonic tremors are more complicated because they are likely favored by a comparatively low temperature gradient in southwestern Japan.
RegFormer: A Local–Nonlocal Regularization-Based Model for Sparse-View CT Reconstruction

Sparse-view computed tomography (CT) is one of the primal means to reduce radiation risk. However, the reconstruction of sparse-view CT with the classic analytical method is usually contaminated by severe artifacts. By designing the regularization terms carefully, iterative reconstruction (IR) algorithms can produce promising results. Aided by the powerful deep learning techniques, learned regularization terms with convolution neural network (CNN) have attracted much attention and can further improve performance. In this article, to further enhance the performance of existing learnable regularization-based networks, we propose a learnable local–nonlocal regularization-based model called RegFormer for sparse-view CT reconstruction. Specifically, we unroll the iterative scheme into a neural network and replace the gradient of handcrafted regularization terms with learnable kernels. The convolution layers are used to learn the gradient of local regularization, resulting in excellent denoising performance. In addition, the transformer-based encoders and decoders incorporate the learned nonlocal prior into the model, preserving the structures and details. To enhance the ability to extract deep features, we propose an iteration transmission (IT) module that further improves the efficiency of each iteration. The experimental results show that our proposed RegFormer outperforms several state-of-the-art methods in artifact reduction and detail preservation.
Geometric Convolutional Neural Network for Point Cloud Object Classification

Point cloud object classification has many challenges related to the quality of the data used to perform the classification in real time applications like robotic navigation. The quality of the data is related to the sensor calibration, data acquisition methodology and noise or perturbances on the measurement process. It is required for real time practical applications a solution that performs well even with medium quality data. In this paper, a novel method is presented to design and implement a geometric convolutional system that works using the mathematical framework of conformal algebra to reduce the impact of having object point clouds that are incomplete, occluded, or noisy, which causes deformations on the objects to classify. We accomplish this by designing three preprocesing layers in our convolutional system, that use conformal geometric operations to perform transformations of the input cloud point: a mapping from 3D to conformal space, a search for the best object perspective and an adjustment of the distance between sampling planes that describe the object.
A point of view about the control of a reusable engine cluster

In the framework of studies on reusable liquid propulsion engines and reusable launchers, some control problematics are highlighted. These questions open the field of possibilities to control a multi-engine bay under maintainability, controllability and safety constraints. This complex new system imposes new needs such as for example high throttling capabilities, monitoring and maintenance tools. The strong interaction between the launcher Guidance Navigation & Control (GNC) system on one hand and the Engine Control System (ECS) with its monitoring on the other hand, while introducing the challenges of cost, reliability and stability, may lead to the deployment of new functions. These functions are either already evaluated but not deployed, or to be designed or even just imagined. This article will illustrate the associated issues, seen from an automatic control perspective. Topics covered include:
Reconfigurable Intelligent Surface-Assisted Grant-Free NOMA Transmission

Massive machine-type communications (mMTC) plays a crucial role in the fifth generation (5G) and beyond wireless networks. To tackle the massive connectivity challenge with limited radio resources, grant-free non-orthogonal multiple access (NOMA) has been proposed to support multiple devices access with the same spectrum. In grant-free NOMA, the devices can directly transmit data without going through the scheduling requests, so that the signaling overhead can be significantly reduced. Moreover, the reconfigurable intelligent surface (RIS) is deployed to assist grant-free NOMA transmission to further enable massive connectivity. In this paper, we investigate a RIS-assisted grant-free NOMA system with no direct link in which multiple devices transmit uplink signals to a base station via a RIS. In order to increase the number of the successfully accessed devices, we redesign the frame structure and optimize the RIS phase shifts. Hence, a novel frame structure is firstly proposed to enable RIS optimization based on channel state information. Then, we formulate a problem that optimizes the signal-to-interference-plus-noise-ratio of all devices while considering the fairness among devices, with optimizing the RIS phase shifts. To handle the non-convexity of the problem, we propose the semidefinite relaxation technique and bisection search method to solve the problem. Simulation results demonstrate that the proposed RIS-assisted grant-free NOMA scheme can substantially increase the minimum data rate by up to 44.3% and 623.1%, respectively, compared to: 1) the scheme where RIS is configured based on a priori positional information, and 2) the scheme where RIS is configured based on random phase.
INFLUENCE OF STORAGE CONDITIONS AND DNA EXTRACTION PROTOCOL ON THE

One of the key steps of the molecular identification of bark beetles is obtaining a sufficient quantity of high-quality DNA extract. In this study, we investigated the influence of different storage procedures for Ips typographus (L.) specimens and various DNA extraction protocols on the quantity and quality of DNA intended for use in molecular diagnostics. Adult beetles were frozen at -20 °C, either dry or in ethanol. We tested four different protocols for DNA extraction. We compared the quantity of extracted DNA and assessed its quality with PCR and Sanger sequencing. Different storage protocols had no significant effect on the quantity of DNA extracted. However, freezing specimens in ethanol provided higher-quality DNA for molecular applications. Only two of the extraction protocols produced sequenceable amplicons, and the difference in the amount of extracted DNA between them was not significant. We propose the optimal combination of storing specimens in ethanol at -20°C and using the Nucleospin Insect DNA extraction kit from Macherey Nagel, enabling a time-efficient identification process.