Miracle-dz/UniFAR-FTUs huggingface dataset:
Feature Engineering with Microarray Gene Expression Techniques for Asymptomatic Disease Classification

The microarray gene expression patterns (MGEP) allow gene microarray technology to classify cancers at a very early stage. Thus, cancer categorization gene expression profile studies can improve Computer Aided Diagnosis (CAD) systems. However, designing the CAD using MGEP is a challenging research problem. This paper presents a systematic analysis of the present solutions for processing MGEP for disease identification. The recent studies mainly worked on complex MGEP data, pre-processing, important feature selection, and classification. State-of-the-arts were explored followed by the investigation of commonly used MGEP feature selection methods using publicly available datasets. This work designs and investigates feature selection approaches such as Fisher score (FS), Relief F (RF), Signal Noise Ratio (SNR), and Information gain (IG). We used Artificial Neural Network (ANN), Support Vector Machine (SVM), Naive Bayes (NB), and Adaboost classifiers to analyze the results. The outcome of this paper demonstrates the different challenges of existing methods.
Prescribed performance output feedback controller design for MEMS gyroscopes using adaptive integral terminal SMC and high-order sliding mode observer

this paper presents a new robust finite time stable output feedback control method for MEMS gyroscopes by considering uncertainties and external disturbances and assuming the unavailability of velocity information. In order to achieve finite time stable tracking and guarantee the good transient performance of the control system, the combination of the integral terminal sliding mode and the prescribed performance control method has been used as the central part of the control system. Also, a third-order sliding mode observer has been used to simultaneously estimate external disturbances, dynamic uncertainties, and velocity. The results of the simulation of the proposed method and its comparison with the methods of previous researches were presented, which shows the favorable performance of the proposed method in terms of tracking accuracy and fast convergence, high robustness, reducing chattering in control signals and improving the transient response of the system by ensuring that the tracking errors are within a predefined domain.
Optimal Batching Under Computation Budget

Batching methods operate by dividing data into batches and conducting inference by aggregating estimates from batched data. These methods have been used extensively in simulation output analysis and, among other strengths, an advantage is the light computation cost when using a small number of batches. However, under computation budget constraints, it is open to our knowledge which batching approach among the range of alternatives is statistically optimal, which is important in guiding procedural configuration. We show that standard batching, but also certain carefully designed schemes using uneven-size batches or overlapping batches, are large-sample optimal in the sense of so-called uniformly most accurate unbiasedness from a dual view of hypothesis testing.
Electrode Selection Based on FDA techniques for EEG Signal Classification

We propose a completely data driven two pass electrode selection technique based on functional data analysis for EEG classification. In the first pass, electrodes are selected based on their activity and in the second pass, selected electrodes are retained based on their discriminant activity. Electrodes are ranked and based on their connectivity, informative clusters are extracted. We test our method on a face familiarity task and demonstrate the efficacy of our channel selection method over all channels and commonly used channels in face familiarity task.
Trends and Heterogeneity in Electric Vehicle Economics: A French Case Study

The total cost of ownership of electric vehicles depends on both the behavior and economic context of the driver as well as the level of government support offered for electrified vehicles. In this work, we study both of these effects as they shift private vehicle costs and emissions benefits, focused on a case study of the faculty and staff commuters to the Cité Scientifique campus of the University of Lille. The modeling approach considers the net costs and emissions from switching each driver from an internal combustion engine vehicle to three electrified options: a hybrid electric, a plug-in hybrid, and a pure battery electric. Increasing government support or technology-induced cost declines of electric vehicles both improve the economics of electrified options and shift more drivers towards battery electrics as the preferred alternative to thermal vehicles.
Conceptual Design and Characteristics of a New Wrist Rehabilitation Exoskeleton Based on Systematic Approach

In order to speed up the recovery process of stroke or spinal cord injury patients with impaired wrist movement, rehabilitation exercises are recommended. The use of exoskeleton robots for wrist rehabilitation has been highly regarded by researchers because they offer excellent maneuverability, can be controlled separately by each joint, and can interact with the patient well due to their wearability. Creating cost-effective, lightweight, and comfortable devices would enhance their suitability for home-based use, significantly reducing the patient’s treatment duration. In this study, the aim is to design a rehabilitation exoskeleton with a simple structure, a lightweight design, and a maximum level of comfort. This robot has been designed using a systematic approach in order to obtain the most optimal solution for all functional requirements and design specifications. In this rehabilitation robot, there are three active degrees of freedom (DOF), which allow it to perform pronation/supination (P/S), abduction/adduction (AB/AD), flexion/extension (F/E) movements, and two passive DOF, which allow the instantaneous center of rotation to be changed, as well as the rotation axis to adapt to two DOFs, AB/AD and F/E. Lastly, the forward kinematics of the robot has been examined using the Denavit-Hartenberg method in order to observe the workspace of the robot and ensure its kinematic performance. Based on the results, the designed robot covers 94.9% of its maximum range in the F/E DOF and can fully utilize its movement capabilities (100%) in two DOFs P/S and AB/AD. Its maximum range of motion in all DOFs is wider than the range required for patients to perform their daily activities, so this robot can perform rehabilitation exercises fully.
A Deep Learning Based Lightweight Human Activity Recognition System Using Reconstructed WiFi CSI

Human activity recognition (HAR) is a key technology in the field of human–computer interaction. Unlike systems using sensors or special devices, the WiFi channel state information (CSI)-based HAR systems are noncontact and low cost, but they are limited by high computational complexity and poor cross-domain generalization performance. In order to address the above problems, a reconstructed WiFi CSI tensor and deep learning based lightweight HAR system (Wisor-DL) is proposed, which firstly reconstructs WiFi CSI signals with a sparse signal representation algorithm, and a CSI tensor construction and decomposition algorithm. Then, gated temporal convolutional network with residual connections is designed to enhance and fuse the features of the reconstructed WiFi CSI signals. Finally, dendrite network makes the final decision of activity instead of the traditional dense layer. Experimental results show that Wisor-DL is a lightweight HAR system with high recognition accuracy and satisfactory cross-domain generalization ability.
The Impacts of Spatially Distributed Demand Growth and Load Modelling on Distribution Network Operation

The continuous demand growth in the power systems will change the load composition and load models. This paper illustrates the impact of load growth and load models on the steady-state operation of the distribution network by conducting a range of case studies. The load growth was modelled by increasing the loads at particular geographical locations in the network and load types to 230% or until the load flow (LF) simulation stopped converging. The analysed load models included different static load models, such as polynomial and constant power load models. The LF simulations were conducted in DIgSILENT PowerFactory. The simulation results demonstrated how load models affect the network’s ability to withstand demand increase before the LF simulation collapses. The results also showed how the loads’ sizes and locations of synchronous generators impact the line loadings, line active power losses, and bus voltages.
Dynamic State Estimation based Cyber Attack Detection scheme to Supervise Distance Relay Operation in Transmission line

This paper addresses the growing challenge of false tripping of distance relay caused by cyber attacks in power systems. To prevent significant disruptions in the power system, it is crucial to avoid false tripping of protective relays. In this study, a Dynamic State Estimation (DSE) technique is proposed to identify cyber attacks on distance relays and prevent false tripping. By utilizing previous bus data, the electrical quantities are estimated and compared with the actual bus data of the network to detect cyber attacks. The effectiveness of the proposed cyber attack detection method is demonstrated using the IEEE 9 bus system.
Generating a General Culturing Microorganism Pattern Using a Delta Parallel Robot and Cam-in-Hand Calibration Method

Robots can play a vital role in laboratory tasks, especially in culturing microorganisms. Currently, many of these operations are performed manually, which leads to biased and irreproducible results. This paper explores a calibration method for minimizing errors of a performing two certain laboratory tasks of swabbing and pipetting. This leads to enhanced accuracy and productivity. This research intends to culture microorganisms in a petri-dish in a specific pattern, measuring the coordinates of each object in the output image so the object location in the real world can be calculated. This research investigates the Cam-in-Hand method for a Delta Parallel Robot, meaning that the camera is mounted on the End-Effector. In this approach, a method of translating image coordinates into real-world coordinates is introduced. It is essential to provide an additional coordinate (in this case $z -$value) before the conversion, given that the camera outputs a 2D image. A vector-based image, in SVG format, as input to the algorithm generates a set of coordinates which determines the main points for swabbing or pipetting operation. Using this data, an array of coordinates is linearly interpolated in 3D space for the swabbing operation. Conversely, the trajectory generated for pipetting uses the 4-5-6-7 interpolating polynomial. The robot then adheres to following the interpolated array of coordinates as a function of time, using a PID controller. The pipetting device is built with a 3D printer using PLA materials. The calibration is done in different heights. The Camin-Hand method leads to ±1 cm precision as a result. Minimum, maximum, and mean errors are 0.092,1.61 and 0.76 respectively for the central point.
Design of BDC for Multi-Level Inverter based VSI for ESS-STATCOM Applications

This work introduces a new bidirectional converter for a voltage source inverter based on a multi-level inverter for an energy storage system static compensator application. The proposed BDC has the inherent property of balanced output voltage, low input current ripple, and high voltage conversion gain ratio with low component counts compared to the conventional BDC topologies. The different stages of the converter derivation have been explained, followed by the converter operation principle. Finally, with the aid of the simulation results, the operation of the proposed converter’s design is validated.
A 37nW, All-in-One Trim-Free Voltage/Current Reference Without Using Resistors and Amplifiers

This paper presents a trim-free, low-power, and high-precision voltage/current reference for IoT and biomedical applications. The voltage reference is generated by the proper addition of CTAT and PTAT voltages, whereas, the current reference is generated using a MOSFET in the active region, which acts like a resistor and helps in lowering power consumption without increasing the form factor. Implemented in the 90nm CMOS process, the proposed voltage/current reference outputs a voltage and current of 820mV and 3.23nA, respectively. Without trimming, the process variation of the proposed voltage/current reference is 1.34% (σ / μ) / 1.75% (σ / μ) and the temperature coefficient of the voltage/current reference is 62ppm/°C / 332ppm/°C over the temperature range of -40°C to 100°C. The line sensitivity of the voltage/current reference is 0.296%/V / 0.414%/V for a wide supply range of 1V - 3.5V. The area occupied by the total circuit is 0.0112mm2, while the total power consumption of the design is 37nW at the typical corner of 27°C and 1V supply.
Optimal Operation of Energy Storage Units with Efficiency Improvement and State of Charge Balance

This paper proposes an optimal energy storage units (ESUs) operation strategy with efficiency improvement and state of charge (SoC) balance by considering converter characters and network loss. First, the optimal power-sharing ratio considering minimized power loss of paralleled ESUs is obtained with the Lagrange Multiplier Method. Second, the optimal power-sharing ratio with SoC balance of ESUs is obtained. On the basis of it, the optimal operation strategy of paralleled ESUs with efficiency improvement and SoC balance is proposed. The power-sharing ratio of ESUs changes actively according to system operation status. Under the proposed ESUs SoC balance and efficiency improvement control strategy, the difference in each paralleled energy storage unit SoC can be controlled within a desired range. Meanwhile, the system efficiency considering converter loss and cable loss can be improved. In that case, the system's overall performance can be improved. The proposed control strategy effectiveness is verified through the real-time simulation with RT-Lab. The proposed control strategy can enhance ESU operation performance with efficiency improvement and SoC balance.
Reinforcement Learning-based Frame-level Bit Allocation for VVC

As frame-level bit allocation is a dependent sequential decision-making problem, it can be modeled as a Markov Decision Process (MDP) and solved by reinforcement learning (RL). Existing reports using RL for coding mainly have two problems: unrepresentative handcrafted features and limited interaction efficiency. In this paper, to address these two problems, we first propose to use a deep neural network to extract features from the state, which is designed as a combination of raw pixel-level information and handcrafted features. The network is able to extract information necessary for the following decision-making neural network automatically. We then propose an efficient training scheme. By designing a parallel interaction algorithm and using a simplified video encoder, our proposed scheme can train on a sophisticated encoder such as VTM. To the best of our knowledge, this is the first RL algorithm implemented for bit allocation in VTM. The experimental results show that our scheme can learn interpretable frame-level bit allocation and achieves a better rate-distortion (R-D) performance compared with VTM.
Stability Assessment for Mercury Emissions Downstream Wet FGD of a Lignite-Fired Power Unit

Since the adoption of the conclusions for the best available techniques (BATC) for large combustion plants (LCP) by the EU, a new requirement for monitoring and reporting the mercury (Hg) emissions was established. This study investigates the total mercury emissions concentrations at the outlet of a wet flue-gas desulphurization system (FGD) of a Bulgarian lignite-fired power unit and evaluates the stability of the obtained results via EPA Method 30B.
Robust and Adaptive Online State-of-Health and State-of-Charge Estimation of Li-ion Battery Cell

The proposed algorithm aims to estimate the instantaneous capacity of an electrical battery. The battery capacity is a crucial parameter of the battary management system and the knowledge of its value is necessary for the state of charge, state of function and remaining useful life estimators. Moreover, operation of lithium batteries raises safety issues, and the very accurate estimation of the battery capacity makes it possible to always guarantee its use in its Safe Operating Area (SOA). As a starting point, the classical Coulomb counting relation is taken as an observation equation in a Kalman framework based on a state equation of the capacity evolution. The observation equation is a biased linear relation with the variables of interest being affected by errors and corrupted by outlier samples. A random consensus is thus applied to reject these outliers and a pre-estimation of the ordinate at the origin of the affine function is performed. The bias reduction is performed thanks to a modified hough-transform. This hough-transform uses the a-priori prediction of the capacity and a multi-resolution recursive approach is used to optimize the bias reduction while limiting the computational complexity. The algorithm is robust to the presence of a high level of outlier and the Kalman approach makes the estimation adaptive. Moreover a statistical test is realized to determine whether the estimated capacity should be directly injected back in the SOC estimator or not. Our algorithm is tested on a database delivered by the NASA DashLink plateform. Statistical simulations confirm the robustness and the adaptive nature of the method. The obtained RMS of the relative errors over the entire ageing cycles are mostly below 3%.
YOLOv7-CBAM for Similar Casting Inspection

The process of classifying industrial castings and putting them into boxes heavily relies on human vision for judgment. When castings share similar colors, shapes, and sizes, manual classification often leads to misjudgments and inefficiencies, significantly impacting economic outcomes and product reputation. Consequently, rapid, accurate, and automated inspection and identification of industrial castings have become pivotal tasks. This study focuses on the arc-shaped pipe castings primarily used in firefighting applications. Leveraging the distinguishing feature of varying identification strings on similar castings, the research employs object detection techniques from computer vision. It integrates cameras for inspection and recognition, thereby considerably reducing costs and enhancing product efficiency. The main research work is to integrate the spatial and channel attention module (CBAM), which can improve the representation and differentiation of features, into the YOLOv7 framework, to better focus on feature information, capable of enhancing feature representation and discriminability. Moreover, to enhance the generalization and robustness of the model, data enhancement is carried out. At the same time, to strengthen the feature differentiation, the study innovatively introduces a dual-label annotation approach tailored to cases where similar castings only differ in identification strings. Comparative experiments demonstrate that CBAM-enhanced YOLOv7 achieves a MAP@0.5 of 99.91%, with a frames-per-second (FPS) rate of 33.41, it fully meets the requirements of accuracy and speed of casting inspection. Exceptional on-site performance of the model in factory tests validates the proposed method's effectiveness, offering valuable insights and strategies for addressing analogous challenges in industrial environments.
Cost Optimization to Enhance Profit to both Consumers and Providers in EV Charging Stations

The transportation sector significantly contributes to global CO2 emissions, heavily relying on fossil fuels. Full decarbonization in road transportation is complex due to evolving EV technology and fuel economy regulations. A potential solution is local energy flexibility markets that maximize self-generated green energy, benefiting EV owners and distribution network operators. This paper aims to maximize the utilization of solar energy for EV charging, considering factors like solar power generation, EV charging needs, and electricity tariffs. Reinforcement Learning (RL) integrates user preferences and continuous learning in demand response applications, while Deep Reinforcement Learning techniques handle complex residential resource scheduling considering user preferences and PV power self-consumption. The proposed DQN EV optimal charging policy reduces electricity bills by 18.5% on average and achieves 89.4% average solar power utilization.
Research on High-Precision Cloth Animation Synthesis Method Based on Deep Learning

Aiming at the shortcomings of long time-consuming and poor real-time performance in cloth simulation method based on physical model, a high-precision cloth animation synthesis method based on deep learning is proposed. Firstly, high-precision cloth animation is obtained by physical method simulation; Then each frame grid is converted into a “geometric image” (RGB image storing vertex displacement), and then down-sampled into a low-resolution geometric image. Using these image pairs, an image super-resolution network is trained, which takes $2\mathrm{N}+1$ continuous low-resolution geometric images as input to predict the central high-resolution geometric image. Finally, the predicted high-resolution geometric image is reconstructed to obtain a three-dimensional mesh model.
Anti-Jamming Method of Near-Field Underwater Acoustic Detection Based on WGAN

This paper analyzes the issue of artificial interference encountered in underwater near-field detection. We briefly examines three common types of artificial jamming signal and their mechanisms. Taking inspiration from the application of Generative Adversarial Networks in speech signal enhancement, this study employs Wasserstein GAN and integrates the characteristics of detection signals. L2 loss is added to the generator's loss function in WGAN to enhance training stability. Simulation analysis demonstrates that the trained WGAN generator effectively combats the three types of artificial jamming.
Three-dimensional Environmentally Sustainable Neuromorphic Computing System Based on Natural Organic Memristor

Three-dimensional environmentally sustainable neuromorphic computing system based on natural organic honey-memristor is proposed in this paper. The experimental results indicates the proposed systems have high inference accuracy over 90 % with device variation and nonlinearity. What is more, four conductance drift scenarios, ADC (Analog-to-Digital Converter) quantization effects, and different algorithms (VGG8 and DenseNet-40) are considered to further verify the proposed systems.