Miracle-dz/UniFAR-FTUs huggingface dataset:
An Adaptive Attitude Controller for A Quadrotor Transporting the Payload With Unknown Mass

An adaptive control approach is presented in this article to deal with the impacts of carrying a payload with unknown mass by a quadrotor. This approach can effectively control the quadrotor’s attitude stability. The paper commences by presenting the dynamic model of the quadrotor, followed by the development of a controller to manage its attitude control. Through Lyapunov analysis, the stability of the controller is validated. Ultimately, the proposed controller and a backstepping controller are used to controlling the attitude of the quadrotor, respectively, and experimental outcomes substantiate the efficiency and feasibility of the introduced controller.
Automatic Load Management in Active Distribution Grids Using Reinforcement Learning

Demand side management (DSM) is a key component for integrating volatile and intermittent renewable energy sources in power grids. In this paper, we propose and study DSM in the form of indirect load control where an agent automatically controls household devices based on real-time electricity prices. The major challenge is the limited available information that includes only past and current values of the household’s net consumption and the electricity prices. To handle this issue, DSM is performed using a reinforcement learning-based approach. The results show that the decision agent performs close to optimal for input data similar to the training data. For input data that diverges from the training data, satisfactory performance is achieved when changing the building parameters, but not when the price curves are shifted. An alternative training approach is suggested that solves this issue.
Synchronous Machine Elements Heating in Dynamic Modes

The distribution of local heating of the rotor elements in the dynamic modes of operation of synchronous machines used for the electric drive of gas compressor units is researched based on the analysis of the electromagnetic field and the data on energy losses as heat sources. The influence of forced convection after the start-up mode on the degree of temperature field equalization in massive rotor assemblies is analyzed. The modeling is carried out using the numerical finite element method. Comparison of calculated and experimental data on the heating of a small tooth of the rotor of a full-scale electric motor in a non-stationary mode under load is performed. It is shown that the numerical model satisfactorily agrees with real practice.
High-Accuracy Readout Circuits for High-Resolution Active Matrix Resistive Sensor Arrays

The active matrix resistive sensor arrays are widely used in a variety of fields because they can eliminate the crosstalk current problem found in passive matrix resistive sensor arrays. However, the ON-resistance of the pixel transistor used in the active matrix resistive sensor array causes voltage drop and reduces the sensing accuracy. Increasing the width of the pixel transistor can reduce the ON-resistance and improve accuracy, but it increases the required circuit area, resulting in a decrease in the integration density and spatial resolution of the array. To solve the problem of inaccuracy and large circuit area found in conventional active matrix resistive sensor arrays, we propose two types of readout circuits based on an additional feedback path. With the additional feedback path between the sensor and the column amplifier, the effect of the voltage drop on the pixel transistor can be eliminated. The measurement results show that the proposed Type I and Type II readout circuits achieve less than 0.1% of the maximum error, remarkably reduced from 997.4% of the conventional counterpart.
Cross-Layer Optimizations for Ferroelectric Neuromorphic Computing

Hardware paradigms for neuromorphic computing mimic the functionalities of brain primitives in order to replicate similar area and energy advantages of biological nervous systems. Discovery of ferroelectricity in doped Hafnia has thrust Ferroelectronics, specifically Ferroelectric Field-effect transistors (FeFETs), at the forefront of realizing such device platforms for future data-centric applications. Despite the utility afforded by its intrinsic properties, e.g. CMOS-compatibility and scalability, harnessing their full potential requires a cross-layer design approach combining devices, circuits, and algorithms. In this paper, we review the recent developments looking at FeFETs and their application to enable low-power on-chip learning. We outline the unique opportunities emerging from device characterization and modelling results, that ultimately translate in novel algorithms and system-level benefits.
SPAD Based CMOS Monolithic PPG Sensor

This paper presents a novel scalable CMOS single photon avalanche diode (SPAD) based PPG sensor integrated with direct light to digital conversion using simple CMOS logic gates on a single chip. With single photon detection capability, a SPAD provides much higher light sensitivity than a photodiode (PD) used in typical PPG sensors, reducing LED driving current significantly for the same receiving current than that with a PD. Moreover, the proposed PPG sensor eliminates the typical power and area intensive analog front end readout electronics associated with conventional PPG sensing, which makes it an attractive option for wearable consumer electronics. For this prototype, a 2 X 2 SPAD based pixel array has been implemented in a standard 180 nm CMOS process. Four SPAD based subpixels have been combined to reduce dead time at the pixel level resulting in increased photon detection capability.
Robust Price-Based Demand Response Framework for Flexibility Provision in Distribution Systems

This paper proposes a robust optimization frame-work of price-based demand response (PBDR) programs to procure flexibility from distributed energy resources (DERs) in distribution systems. The proposed method uses an uncertain budget to accommodate worst-case scenarios of available photovoltaic (PV) generation and determines an aggregated feasible flexibility range based on flexibility requirements. To solve the robust optimization problem effectively, AC optimal power flow is formulated using DistFlow equations, which are convex relaxed by second-order cone (SOC) relaxation and further linearized by polyhedral approximation techniques. An optimal solution is found using a column-and-constraint generation (C&CG) algorithm by decomposing the multi-stage robust optimization problem. The errors resulting from the SOC relaxation of the DistFlow equations are corrected using the exactness recovering procedure. The effectiveness of the proposed method is verified by case studies using an IEEE 33-bus test system with prosumers.
Observational needs for estimating Alaskan soil carbon stocks under current and future climate

Representing land surface spatial heterogeneity when designing observation networks is a critical scientific challenge. Here we present a geospatial approach that utilizes the multivariate spatial heterogeneity of soil‐forming factors—namely, climate, topography, land cover types, and surficial geology—to identify observation sites to improve soil organic carbon (SOC) stock estimates across the State of Alaska, USA. Standard deviations in existing SOC samples indicated that 657, 870, and 906 randomly distributed pedons would be required to quantify the average SOC stocks for 0–1 m, 0–2 m, and whole‐profile depths, respectively, at a confidence interval of 5 kg C m−2. Using the spatial correlation range of existing SOC samples, we identified that 309, 446, and 484 new observation sites are needed to estimate current SOC stocks to 1 m, 2 m, and whole‐profile depths, respectively. We also investigated whether the identified sites might change under future climate by using eight decadal (2020–2099) projections of precipitation, temperature, and length of growing season for three representative concentration pathway (RCP 4.5, 6.0, and 8.5) scenarios of the Intergovernmental Panel on Climate Change. These analyses determined that 12 to 41 additional sites (906 + 12 to 41; depending upon the emission scenarios) would be needed to capture the impact of future climate on Alaskan whole‐profile SOC stocks by 2100. The identified observation sites represent spatially distributed locations across Alaska that captures the multivariate heterogeneity of soil‐forming factors under current and future climatic conditions. This information is needed for designing monitoring networks and benchmarking of Earth system model results.
Block Order Bids Prediction for Day-Ahead Market

Prediction of the day ahead market variables is of critical importance to the energy market participants, in order to formulate offers in the form of bids in day ahead energy markets. As block order bids comprise a significant part of the traded energy, prior knowledge of the accepted block order bids provides valuable information to traders. Furthermore, since the day ahead energy prices are determined by these bids, the inclusion of the predicted accepted number and quantities of block order bids as input features to energy price prediction, using machine learning models, has the potential to increase the models’ performance. The focus of this paper is to predict the hourly time series of the accepted number of block order bids and energy quantities in the Greek day ahead electricity market, using machine learning methodologies. Publicly available data from the Greek and European markets are used. A significant section of this paper is devoted to preprocessing these data, in order to generate the most valuable features from already existing ones (feature generation) and to select the optimal feature combination (feature selection).
Hybrid Light Field Image Denoising Network using 4D-DCT Separated Transform

This paper proposes a novel hybrid light field (LF) denoising method which is based on a convolutional neural network (CNN) designed to reflect the characteristic of LF image in both pixel and frequency domains. Noting that the image noise usually has much high-frequency energy, the proposed network is designed to operate in a transform domain in two stages. At the first stage, energy compaction of spatial-angular information of LF image is sought by 4D-DCT separated transform which can achieve better energy compaction than 2D-DCT applied separately in the spatial and angular domain. The transformed LF is decomposed into different frequency components and each frequency component is recovered progressively. Subsequently, we reshape and convert different frequency components into pixel domain to perform the next refinement step for which a residual spatial-angular block (RSAB) is proposed to handle the 4D LF structure in the pixel domain. Extensive experimental results on different noisy datasets confirm the effectiveness of our proposed method compared to state-of-the-art methods in both objective and subjective quality.
Mitigate Gender Bias in Construction: Fusion of Deep Reinforcement Learning-Based Contract Theory and Blockchain

With the remarkable progress in teleoperation, physical fitness-based gender bias has become negligible within the construction sector. Nonetheless, the labor market remains male-dominated, posing tremendous unfairness toward females. In light of this, we developed a two-phase recruitment framework that utilizes blockchain, zero-knowledge proofs (ZKPs), deep reinforcement learning (DRL), and contract theory, aiming to enhance fairness, transparency, and automation. First, we devised a resume screening approach independent of gender to ensure fairness and alleviate gender bias in candidate assessment, by leveraging blockchain and ZKPs. In the second phase, we introduce a recruitment process that combines blockchain and DRL-based contract theory. This integration successfully mitigates gender bias that may arise from the self-disclosure property of contract theory. To evaluate the effectiveness of our proposed approach, we conducted comprehensive simulations from various dimensions. The results demonstrated the robustness and superiority of our method.
An Underwater Gas Leakage Measurement Method Based on Object Detection

The current stage of marine carbon sequestration technology is at risk of leakage and there is a growing need for leakage monitoring. In this paper, we use underwater optical observation methods to measure the volume of underwater in-situ gas leakage. We propose the measuring-after-detecting method, which divides the estimation of the leakage volume into two parts. The first part is a multi-bubble separation method based on detection and tracking. This part separates each bubble image in the background environment from the other bubbles. The second part is a bubble measurement method based on a single bubble image. After extracting the contour information of the bubbles, we estimated the volume of bubbles based on the area of pixels of each bubble. We set up experiments in a tank, using a camera to monitor underwater gas leakage. A dataset of the underwater bubbles images was built and used to verify the availability of the proposed method. The bubble detection results in validated data were accurate and the measured volume of the gas leakage is close to ground truth, which illustrates the proposed gas leakage measurement method is available and reliable.
Inverse Design of Electromagnetic Induced Transparency Metamaterials Based on Generative Adversarial Network

The unique properties of electromagnetic induced transparency (EIT) metamaterials have caused a lot of concern in the field of terahertz wave regulation, but the traditional design methods of metamaterials have the problems of long design cycle and high trial and error cost. Applying deep learning method to the inverse design process of terahertz metamaterials can greatly reduce the design complexity, so that the EIT metamaterial structure can be quickly designed according to the requirements. In this paper, a generative adversarial network (GAN) model for EIT metamaterial structure design is constructed, which realizes the mapping relationship between target spectrum and metamaterial structure parameters. The proposed GAN model can accurately predict structure parameters of the EIT metamaterial according to the target spectrum, and the error between the generated and the real parameters is less than 1μm. Moreover, by introducing the fuzzy processing, the proposed GAN model can accurately generate multiple sets of metamaterial structure according to the same target spectrum, which provides more options for designers. This model offers a novel and efficient design method for EIT metamaterials.
Decision Model of Walk-Over-Weighing System (WoW) for Support the Fattening Process of Beef Cattle Farm

This study presents the implementation and evaluation of the Walk Over Weighing (WoW) system for monitoring the weight of beef cattle. The study results indicate that the WoW system improves efficiency in managing the beef cattle weight monitoring process. The system allows farmers to weigh, track, and process weight data for their cattle remotely, providing valuable insights to aid in identifying trends and improving livestock farming management. Furthermore, the system supports decision-making in the fattening process for beef cattle by offering data on stable rate growth performance, readiness for sale to the market, high growth rate performance, and low rate growth performance, accounting for uncontrollable differences in cattle age. The results of measuring the body weight of beef cattle showed a different correlation of cattle growth, which is associated with the cow's health with a correlation value (r) of growth of more than 0.900, while a poorly healthy beef castle has a lower correlation coefficient of growth. Overall, this study provides evidence for the utility of the WoW system in supporting decision-making in the fattening process for beef cattle.
Head Movement Analysis in Differentiating Children with ASD from TD under Various Sensory Stimuli Conditions: A Machine Learning Approach

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by behavioral symptoms in sensory processing, social interaction, and motor skills. The present study aimed to conceptually validate whether participants’ head movements could differentiate children with ASD from their typically developing peers under various sensory stimuli conditions. Four sensory stimulation conditions were included: visual, auditory, tactile, and olfactory. Data were collected from 40 individuals who were exposed to four different sensory stimuli, resulting in the construction of datasets comprising a total of 160 head movement data entries. Machine learning algorithms were utilized for classification purposes. Results revealed that under olfactory stimulation conditions, the decision tree model achieved the highest accuracy (90.0%) employing the rotation range in Z axis (ZRR) and the angular rotation per minute in Z axis (ZARPM) features. For visual conditions, both the decision tree with 2 features and the neural network with 3 features exhibited optimal performance (87.5%). Overall, classifiers demonstrated robust performance across sensory conditions, indicating potential for early ASD diagnosis.
Mulation Analysis of a Reinforcement-Learning-Based Warehouse Dispatching Method Considering due Date and Travel Distance

As the adoption of autonomous mobile robots in warehouses and other industrial environments continues to increase, there is a need for methods that can effectively dispatch robots to meet system demand. Real-time dispatching of autonomous mobile robots can be very complex, but simple rule-based methods are typically used for this task. In this paper, a reinforcement-learning-based dispatching method for intralogistics (RLDI) is proposed. RLDI is warehouse layout independent and takes into consideration task due dates and the travel distance. The algorithm is trained and tested in a simulation environment that represents a small warehouse. Monte Carlo simulation analysis is used to explore the capabilities and limitations of the established RLDI. The performance of the method is compared to the shortest distance dispatching rule in single and multi-agent environments under various levels of due date tightness. Experimental results demonstrate the potential for using reinforcement learning methods for warehouse dispatching.
Combining Mental States Recognition and Machine Learning for Neurorehabilitation

Brain-computer interfaces are widely used to control machines using Electroencephalography (EEG) signals. Several low-cost electroencephalographs are available on the market that achieves good-quality EEG signals. One of the most intriguing issues for developing biofeedback systems is classifying users' emotional states using EEG signals and Machine Learning (ML) methods. In our study, we propose a novel ML-based biofeedback tool using a BCI to detect two different users' mental states: Focus, and Relaxation. We compared several ML algorithms achieving an average accuracy on the Test Set of 0.90 by using SVM. Finally, we propose a prototype for music generation according to the classification output that could be adopted in neurorehabilitation scenarios.
Cardiac Arrhythmias Classification Using Machine Learning and Single-Lead ECG

Cardiac arrhythmias are associated with an elevated risk of myocardial infarction, stroke, and other abnormalities. Accurate and timely identification of the type of arrhythmia enables cardiologists to provide prompt and appropriate treatment. This paper presents an accurate approach to detect 6 types of heart rhythms (Sinus Bradycardia (SB), Sinus ventricular Tachycardia (SVT), Atrial Fibrillation (AFIB), Sinus Tachycardia (ST), Sinus Irregularity (SI), and Sinus Rhythm (SR)) from single-lead ECG. ECG recordings are de-noised by the Butterworth filter, Non-Local Mean (NLM) smoothing, and robust LOESS. A wide range of amplitude, spectral, nonlinear, entropy, and heart rate variability features are selected based on feature permutation importance and fed to various machine learning models to classify the type of rhythm. The single-lead models are compared to the 12-lead and evaluated using 5-fold cross-validation and testing on 2277 patients. The Histogram Gradient Boosting (HGB) model trained on lead II ECG data offers the best results of 97.6% accuracy among single lead-trained models with fewer features. The highest accuracy of 98.4% is also obtained from the HGB model trained on 12-lead ECG data. The proposed single-lead detection with a smaller set of features, i.e. 35, is a promising solution for low-power and low-resource environments.
Enhancing Click-Through Rate Prediction by User Multi-Type Behavior Sequences

In the area of recommendation systems, prevailing models for click-through rates prediction heavily depend on users' historical click behavior sequences as their primary input. However, this approach poses challenges in acquiring a comprehensive representation of user interests, consequently compromising the model's accuracy. To overcome this limitation, we propose a the Enhancing Click-Through Rate Prediction Model through User Multi-Type Behavior Sequences (UMBS). In order to mitigate noise in implicit feedback, UMBS designed a Fourier-enhanced Transformer Layer(FTL), effectively alleviating the impact of noisy behaviors on the model's performance. In order to modeling the influence of user historical preferences on candidate items more comprehensively, the UMBS model integrates a User-Perceived Similarity Enhancement Attention Layer(USEA). Within this module, a user-perceived adaptive preference-weighted enhancement is applied to the multi-head attention mechanism, thereby achieving a more precise modeling of user interests. To evaluate the performance of the UMBS model, comparative experiments were conducted on the KuaiRand-Pure and KuaiRand-1K datasets within the short video recommendation domain. The results demonstrate that, when compared to other advanced baseline models, the UMBS model achieves a notable improvement in Area Under the Curve (AUC) by 0.95% and 0.58%, respectively.
Development of a Quantitative Evaluation System for Bradykinesia Using MediaPipe Hands

Parkinson's disease is a neurodegenerative disease characterized by slow progression and is never cured. Its symptoms include motor impairments such as bradykinesia, muscle stiffness, tremor at rest, and postural dysreflexia, as well as non-motor impairments such as depression and autonomic neuropathy. One measure to evaluate Parkinson's disease is the Unified Parkinson's Disease Rating Scale. One of the items in this scale that evaluate bradykinesia, one of the motor symptoms, is the finger tapping test. However, many of the expressions used there are subjective, and the evaluation criteria are ambiguous. Therefore, the purpose of this study was to develop a quantitative hand movement evaluation system using MediaPipe Hands, to analyze finger tapping test, and to evaluate the effectiveness of the system. With this developed system, hand movements can be analyzed by using a webcam. Hand motion was analyzed by calculating the length between hand joints, the speed of joint movement, and the angle between joints. To evaluate the accuracy of the system, we first measured the length between the joints of the fingers as the hand was moved away from and closer to the camera, and then examined the error. Then, the finger tapping test was conducted using the developed system to evaluate the bradykinesia. Accuracy evaluations have shown that MediaPipe Hands can detect hand movements with high accuracy. The finger tapping test also revealed that there was reproducibility in the magnitude of each finger movement, but not in the speed of the movement.