Miracle-dz/UniFAR-FTUs huggingface dataset:
A PM2.5 Concentration Prediction in High-Cost and Low-Cost Wireless Sensor Networks Using Neural Networks

Due to its increasing impact on human health, air pollution is becoming a progressively important topic in modern society. Particulate matter with a diameter of 2.5 μm is cited as one of the main air pollutants. Thus, prediction of concentration of these particles presents a very important research topic. Therefore, in this paper, we observed a Deep Learning based spatial prediction of this concentration realized by using the installed high-cost sensors and/or low-cost sensors, which are simulated. Based on the obtained analysis results, a proposal was made to employ the low-cost sensors, completely or partially, with distributed Deep Learning Neural Networks, instead of the currently used high-cost sensors in the wireless sensor network for PM2.5 concentration measuring. It is shown that in this way we can lower complexity, datasets and time for training without the loss (or even with the gain) in the prediction quality.
Enhancing Wind Turbine Reliability through Proactive High Speed Bearing Prognosis Based on Adaptive Threshold and Gated Recurrent Unit Networks

High-speed bearings play a vital role in the functionality of modern wind turbines. Accurately estimating impending failure and predicting the remaining useful life (RUL) of bearings brings significant benefits in scheduling maintenance strategies and preventing sudden turbine shutdowns. This paper discusses a reliability enhancement prognosis framework for high-speed wind turbine bearings based on a measured representative vibration signature. A health indicator is constructed using a sliding window adaptive threshold (SWAT) to map the degradation behavior of the bearings into RUL. The Gated recurrent unit (GRU)-based deep learning algorithm shows promising predictive performance in terms of mean absolute error (MAE) and relative accuracy (RA).
A Double Layer Laxity-Based Approach to Queue Management for Multipart EV Charging

With the increasing penetration of plug-in electric vehicles (PEV s), the availability of public PEV charging stations (PEVCS) becomes a major concern. Indeed, in a PEVCS with a limited number of chargers, the waiting time may be quite large, particularly during peak hours, given that each PEV requires a considerable amount of time to charge. This could lead to discontent with the service and a high customer attrition rate. In this paper, we propose a portable multi-port system (PMS), which is a patent-protected technology, that optimises the investment cost of the charging station while improving the accessibility of the chargers. The system allows multiple vehicles to be connected to the same charger and be automatically charged according to an optimized scheduling scheme. To ensure greater accessibility, a double layer laxity-based approach (DLBA) has been designed to assign the most appropriate charging spot for each vehicle according to their charging urgency. The proposed solution is shown to outperform the single layer laxity-based allocation method in terms of the required number of chargers meeting PEVs charging needs.
Smart Health Monitoring System Considering Privacy Without Using Camera

In an aging society, the demand for technology to monitor the behavior of people in nursing homes and hospitals is on the rise. Meanwhile, the use of cameras for monitoring is not commonplace due to concerns regarding the privacy of occupants. In this study, we report the development of a behavior detection technology equivalent to a camera while protecting privacy using a millimeter-wave sensor. Conventional millimeter-wave sensors can only detect large movements, such as standing and supine positions, whereas the developed pose estimation technology can estimate human skeleton coordinates in three dimensions from millimeter-wave signal data and can detect the movements of the arms and legs in detail. The system provides immediate alerts when a person encounters a dangerous situation, such as a fall. We also conduct demonstration tests using the technology at a hospital to promote peaceful and healthy living conditions for people living in nursing homes and hospitals.
Load Change Assessment-Based Feedforward Compensation for FCS-MPCC Used in PMSMs Considering Load Disturbances

This paper presents a load change assessment-based feedforward compensation method for finite control set model predictive current control (FCS-MPCC) in permanent magnet synchronous motors (PMSMs). The objective is to address the adverse effects of load disturbances on FCS-MPCC, which can lead to deteriorated control performance and system instability. To mitigate these effects, a novel feedforward compensation mechanism is proposed by integrating a load change assessment mechanism within the FCS-MPCC framework. The mechanism enables real-time estimation of load changes by accurately capturing their rate and direction. A sliding mode torque observer (SMTO) is developed to ensure accurate load estimation, characterized by fast response and strong robustness. The stability of the SMTO is analyzed using a Lyapunov function. Furthermore, a technique is proposed to generate feedforward compensation values based on the load change assessment, specifically applied to the q-axis reference current. Comparative simulation results verify the effectiveness of the proposed strategies.
Battery Life Prediction Using LSTM-Based Model for Tire Pressure Monitoring System

The miniaturization of sensors and batteries, along with the development of cloud systems, have made Internet-of-Things (IoT) technologies more accessible. Simultaneously, recent developments in machine learning have brought about a variety of predictive technologies that benefit our lives. Several life-prediction models for rechargeable batteries have recently been proposed and studied to replace coin-cell batteries, which are often replaced to avoid data loss, as primary energy supplies. This paper presents a battery life prediction method for a tire pressure monitoring system (TPMS) that uses coin batteries in severe environments, such as those characterized by high temperatures and pressures, where data can only be measured at fixed points. Although the measured data were irregular, noisy, and sparse, the sample size exceeded several millions. We approached limited data as time-series data, extracted features from the data, and predicted battery life from the features using a recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU). Predictive performance was evaluated using accuracy, recall, F1-value, and a confusion matrix to experimentally demonstrate the effectiveness of our method.
Digital Predistortion Implemented in Software Defined Radios

Digital predistortion (DPD) is recognized as an effective solution for power amplifier (PA) linearization. The DPD provides conformity of wireless infrastructure to telecommunication standard requirements (regarding bit error rate (BER), transmit spectrum mask (TSM), error vector magnitude (EVM) …) by making PA more linear, while at the same time reduces the running cost of the telecommunication infrastructure. Different PA models were used for simulation of DPD algorithm: Memoryless, Complex Valued Memory Polynomial model, Hammerstein and Wiener. The simulations results are provided in the paper as well the measured results obtained by Class-J GaN HEMPT amplifier linearization.
Investigation of loudspeaker array layout for sound field reproduction by room-mode matching in rectangular rooms

Sound field reproduction, a technique for reproducing a wide range of sound fields, has several practical issues, such as the degradation of spatial reproduction accuracy due to room reflections. For reproducing the sound field in the low-frequency range where room modes are prominent, mode matching has already been proposed using the room mode as a basis function. In this paper, as an extension of room mode matching, we propose an approach to extend the desired sound field using a discrete cosine transform by approximating the eigenfunction of a rectangular closed space as a cosine function since most rooms can be regarded as rectangular rooms at low frequencies. We have investigated loudspeaker array geometries suitable for room-mode driving in simulations and have shown that a rectangular loudspeaker array surrounding the entire room is the most effective.
Engraved Character Recognition Using Computer Vision To Recognize Engine Number With Environmental Condition Setup

Optical Character Recognition (OCR) systems have been developed successfully to recognize printed characters. Serial number logging by hand is a time-consuming and tedious task in many industries. The distinct engine and chassis numbers that are inscribed on machinery-like vehicles are an example of such an application. The study explains how engine and chassis numbers inscribed on two and four-wheeled vehicles can be read using computer vision and optical character recognition. The proposed system is reliable in dimly lit areas, and the environmental parameters are adjusted to optimize output efficiency, especially in poorly illuminated surroundings. Over twenty-eight photos of automobile engines, with various levels of ambient lighting are used to test the system. The engine number was kept in focus as these pictures were being taken, and a grid attached to the camera's experimental configuration was used to maintain a steady angle of view and distance from the vehicle. Pre-processing of these photos was done using various common image processing algorithms. The proposed OCR system is then fed to the enhanced resulting images. The system is effective overall and is used in real-time applications.
Anthropomorphism for improving user tolerance to system errors in environmental behavior change

Technologies that support behavior change are often implemented in systems such as health advice and social support in daily life. However, users might distrust the systems and eventually cease using them if there is a measurement or judgment error, which would go against the goal of behavior improvement. Based on the assumption that failures are inevitable, this study proposes a strategy to prevent withdrawal from the system by making the system reliable and empathetic. An anthropomorphic garbage-disposal system that has the capability of revealing vulnerability is introduced, to increase the users’ awareness of trash reduction.
Comparative Study of Origami Enabled Helical and Curlicue Reconfigurable Antennas

The field of reconfigurable antennas has recently received major interest from the research community. The Origami based reconfigurable antennas provide enhanced capabilities to advanced applications. However, it is essential to compare performance of different types of Origami antennas to understand the specific benefits. In the current article, the performance of an Origami helical antenna and an Origami curlicue antenna is compared. Both the antennas are designed such that they are reconfigurable in nature, and for the antenna construction, aluminium is used as the material. Extensive experiments are conducted for validating and comparing the performance of the designed antennas. The experiment results demonstrate that the antennas operate between frequency range of 0.85-1.3 GHz. Further, the helical antenna operates appropriately in unfolded state whereas, the curlicue antenna operates efficiently in both, folded state and unfolded state.
Analysis of Voltage Drop and Low-Accuracy Position Estimation Issues for High-Speed PMMs Used in Electric Submersible Pumps

In the area of electric submersible pumps (ESPs), permanent magnet motor (PMM) drives are drawing increasing attention due to the advantages of high energy efficiency and high-power density. However, there are few studies focusing on the specific issues of the ESP PMM drives. To solve the issue, this paper emphasizes the voltage drop and position estimation inaccuracy issues of the system. First, the voltage drops caused by the LC filter, transformer, and long cables are analyzed, providing guidelines for designing the appropriate PMM drives. Second, the impacts of system structure on position and speed estimation accuracy are explained at length, posing necessity of developing improved sensorless control strategies. Finally, simulation is conducted on a PMM drive to verify the effectiveness of the analytical results.
High Peak-to-Average-Ratio Pulsed Power Suppression Based on Ripple Voltage Compensation

Bulky electrolytic capacitors are equipped on the conventional stand-alone high peak-to-average-ratio (PAPR) pulsed power system. Although there are some active schemes have been proposed to reduce the capacitance, they also have the negative impact on system efficiency. Therefore, in this article, a topology adopted for pulsed power suppression based on ripple voltage compensation and partial power conversion is introduced, in which a direct power path is constructed between the high voltage direct current (HVDC) bus and the energy storage capacitor (ESC) to improve the system efficiency, the output series structure compensates for voltage fluctuations in the ESC, which stimulates the energy storage potential of the capacitors. The structure and operating principle of the topology is introduced, the power flow and distribution are analyzed. Then, the design consideration and control strategy are explained, Finally, a simulation model is built in PSIM and an experimental prototype is built in the lab to verify the accuracy of theoretical analysis and the effectiveness of the topology.
Power Quality Improvement in a 7 level Inverter using Selective Harmonic Elimination

Multilevel Inverters are preferred in low and medium power applications. Multilevel inverters have advantage of ease of control, and therefore they are widely used in Industries and Electric Vehicles. The output voltage of these inverters contains too many harmonics which causes heating and ultimately breakdown of the entire system and that leads to huge financial losses. The work carried out here describes a method to reduce the magnitude of lower order harmonics using a technique called Selective Harmonic Elimination (SHE). Genetic Algorithm (GA) is applied to calculate the firing angles for the switches in the single phase 7-level inverter. The behavior of the inverter is tested on a single phase Induction motor.
An Optimal Modulation Schemes for a Family of Single-Stage Three Switch Leg Multi-Port Boost DC-AC Converter

This paper presents an optimal modulation schemes for a family of single-stage three switch leg multi-port boost (TSLMPB) DC-AC converters. The proposed multi-port DC-AC converter topologies and associated modulation scheme exchange power between multiple DC and AC ports with high voltage gain. Furthermore, the DC ports functioned with diverse voltage levels, while the AC ports can be operated at varying voltage magnitudes and frequencies. The feasibility of the modulation schemes for the TSLMPB DC-AC converters are evaluated in the MATLAB/ Simulink environment, and the results are presented. To validate the performance and efficiency of the TSLMPB DC-AC converters, a 300 W laboratory prototype was developed, and experimental results are presented and discussed.
False Data Injection Attack Detection for Control Systems Based on Correlation Analysis

The correlation of control system data is characterized by its intrinsic dynamics, which is pretty hard to forge by the attacker. In this paper, false data injection detection problem for linear time-invariant systems is studied from a perspective of correlation analysis. Two detection methods are proposed based on targeted data correction construction and analysis. First, a noise encryption-based correlation enhancement mechanism and the optimization-based attack detection method are proposed. Second, a coding-based data correlation construction mechanism is designed and analyzed, and the corresponding detection scheme is proposed. The effectiveness and performance are illustrated by simulation. The proposed correlation-based detection schemes require no control performance sacrifice and can be implemented easily.
Design and Implementation of Air Conditioning Monitoring System Based on Heterogeneous Wireless Communication

Now, a lot of companies that use the Supervisory Control and Data Acquisition (SCADA), and it can help companies to monitor and manage the air-condition system. The most common structure of SCADA is to use PLC (Programmable Logic Controller) on the field side to remote control, status monitoring, and data collection of peripheral components. In the traditional way, most of the transmission and connection methods used are mainly based on physical lines. There is a lot of cost to build or maintain. This study main considers on the implementation of the wireless air-conditioning monitoring system, which uses LoRa, Zigbee, and Wi-Fi wireless communication modules, and Micro Control Units (MCU) are also installed on these modules. Using a decentralized architecture shortens the construction time and reduces the difficulty of maintenance and debugging. This study's results can provide many organizations and companies with an alternative system on the Node-RED free development platform, which can be applied in the future with low cost and security.
Secondary Reserve Marginal Band Price Prediction with Classical and Machine Learning Based Techniques

As a consequence of the continuous growth being experienced by renewable energy systems, the role of the different ancillary services is becoming essential for the reliable operation of the electric system. This paper develops a methodology for estimating the secondary reserve marginal band price in the Iberian electricity market using four forecasting techniques: two classical models (ARIMAX and SARIMAX) and two machine learning models (Random Forest and Support Vector Regression). The methodology involves a Pearson correlation analysis and a Sequential Forward Selection algorithm to select the relevant model inputs and exogenous variables. A statistical data analysis is conducted to examine the temporal characteristics of the target variable and a data-preprocessing is performed for the proper implementation of the models. A grid search and a sequential division cross-validation are applied to determine the optimal parameters of the models. The performance is evaluated using three statistical metrics. The results show that the Random Forest model outperforms the other models, achieving the lowest MAE (2.40 €/MW) and RMSE (3.40 €/MW) values.
Photovoltaic Maximum Power Point Tracking Based on Bayesian Optimization Neural Network

This paper uses the neural network to realize a photovoltaic (PV) system's maximum power point tracking. After Bayesian optimization of hyperparameters, the model can accurately determine the maximum power point voltage according to solar irradiance and temperature. The optimized PV system not only adapts to different operating conditions but also achieves the optimal power output under uniform irradiance, static irradiance shading, and dynamic shading, ensuring the maximum efficiency of the PV system. Moreover, the optimized model is superior to the unoptimized model and the traditional perturbation and observation method in terms of accuracy. The improved model enhances the practicality and reliability of the PV system, helping to improve PV efficiency and reduce energy loss.
A Video Object Detection Method of ECNet Based on Frame Difference and Grid Cell Confidence

In recent years, real-time video processing has advanced thanks to dramatic improvements in object detection algorithms. This has increased the demand for video object detection by various edge devices. An example is the use case of analyzing video captured by CCTV cameras. Therefore, there is a need to perform object detection on the edge within a limited time, but complex object detection models cannot be deployed on the edge with limited resources. For the task of image classification, the Edge-Cloud Net (ECNet) is proposed to achieve both transmission cost and accuracy by sharing processing between the edge and cloud sides using an offload controller. In this study, we have applied ECNet to video object detection. We propose a method to implement a frame differencing mechanism before edge inference and use the confidence value of each grid cell as an offload criterion for cloud transmission. Furthermore, depending on the confidence value, the image is masked to reduce the amount of information sent to the cloud. Our method can reduce the amount of transmission while maintaining the accuracy of video object detection, especially when the data size is small.
Auto-Tuned Model Predictive Control-Based Neural Network Controller for Modular Multilevel Converters

This paper investigates the control of the Modular Multilevel Converter (MMC), a versatile converter topology. The MMC offers advantages such as modularity, scalability, and fault- tolerance capabilities. To address the challenges of controlling the MMC, various methods including Model Predictive Control (MPC) have been proposed. This paper focuses on online weighting factor selection for MPC in Direct MPC and Indirect MPC and emulating these control techniques with neural network controllers. Dynamic equations of the MMC considering a novel method for including the discrete-time common model voltage are derived, and direct MPC (DMPC) and indirect MPC (IMPC) with online weighting factor selection are implemented. Auto-tuned DMPC and IMPC are used to extract data for training neural networks, which emulate the auto-tuned MPC controllers.The optimal neural network structure is selected and trained. Comparisons of steady-state and transient performance among auto-tuned DMPC, IMPC, and neural network-based controllers reveal that neural network-based controllers perform similarly to conventional auto-tuned MPC controllers, but with reduced computational burden. These controllers exhibit improved robustness to parameter mismatches and unpredictable converter performance, indicating their potential as viable replacements for conventional auto-tuned MPC controllers in MMC control, offering enhanced efficiency and reduced calculation burden.
Two-Beam Slot Antenna for Power Harvesting Applications

In this paper a new design of a double-slot antenna fed by microstrip line is proposed for power harvesting applications at Ka band. The antenna consists of two parallel slot radiators connected with CPW line in the middle. Radiation pattern of this antenna is optimized to exhibit the maximum gain of 9 dBi in two directions that are offset 40 degrees in respect to the antenna broadside. Proposed antenna is suitable for powering wireless sensor nodes and passive RF identification tags with known RF source when the frequency and direction of arrival of the field are known.