Miracle-dz/UniFAR-FTUs huggingface dataset:
Multitouch Pressure Sensing With Soft Optical Time-of-Flight Sensors

This article reports on optical time-of-flight (ToF) as a detection mode for soft optical deformation sensors. In this work, low-cost miniature light detection and ranging (LiDAR) chips recently developed for consumer mobile device applications perform real-time optical reflectometry at cm scales in soft optical waveguide networks. Our methods complement existing work by capturing spatial information that is lost with amplitude-only measurements. We demonstrate its application in a multitouch button system capable of extracting displacement values from soft optical switches with average error in the range of 100–200 microns on 1.75 mm of the switch travel. Because making reliable electronic connections to soft materials is a challenging task, we focus on minimizing connector count. In a tracking application, we find the centroid of an object on a three-point supported plate using three optical pressure switches connected to a single LiDAR sensor.
A Review on EEG based Epileptic Seizures Detection using Deep Learning Techniques

Magnetic Resonance Imaging (MRI) and Electroencephalography (EEG) modalities have been used in several screening procedures to diagnose epileptic seizures with a high-level accuracy. over the last two decades; significant contributions have been made to automate the procedure of EEG tests to diagnose epilepsy and seizures detection. Because manual seizure detection will take more time for a doctor as well as for technician to put their efforts into recognizing the type of seizure within less time. Deep Learning (DL) helps in automatic feature extraction and classification techniques. In the Medical field, adopting these approaches has resulted in the introduction of these approaches has led to a significant breakthrough in substantial advancements including the identification of epileptic seizures. This paper presents a comprehensive summary of each section in depth and methods used to diagnose epilepsy from a variety of methods. In addition, they are highlighted because they are best suited for applied medicine, hardware execution, and cloud-based tasks. This paper provides a review of novel seizure detection applications and previous studies. The research concludes with some fresh concepts for seizure detection utilizing DL techniques which are becoming increasingly popular.
Synchronous Rectification for LCLC Resonant Converter

The synchronous rectifiers are implemented to enhance the converter efficiency and its power density. A Synchronous rectification scheme for LCLC resonant converter is proposed in this paper. Several synchronous rectification methods for LLC resonant converters are discussed in literature such as on state drain-source voltage $(v_{dson})$ sensing, current transformers (CT’s) based methods to sense the rectifier current, adaptive technique, and other alternative methods. Resonant Capacitor Voltage (RCV) sensing method is one of the attractive methods discussed in the literature for LLC resonant converter. This method eliminates the requirement of CT’s and it is least affected by the noise injected by parasitic components unlike vdson sensing technique. The typical RCV method cannot be used for LCLC resonant converter due to resonant tank equivalent parallel branch inductance $(L_{peq})$ variation with switching frequency. A control technique for the synchronous rectification of the LCLC resonant converter is proposed to compensate for the change in Lpeq value. The proposed estimation block in the control technique improves the control performance and facilitates digital implementation. Detailed analysis is carried out for the proposed control scheme and the performance is evaluated in simulations for a LCLC resonant converter.
Short-Term Electricity Consumption Forecasting Based On Long Short-Term Memory Network

High-precision electricity consumption forecasting can not only ensure the safe and reliable operation of the power system, but also bring considerable economic benefits. Aiming at the contradiction between the demand for high-precision electricity consumption forecasting and the insufficiency of traditional forecasting methods, this paper uses a short-term electricity consumption forecasting model based on long-term short-term memory network (LSTM) to forecast daily electricity consumption on an hourly scale in a Wanda shopping mall in Guangdong. First, analyze the characteristics of the total electricity consumption of the mall to obtain its dominant influence factors. Secondly, the LSTM-based short-term electricity consumption prediction model is used to predict the total electricity consumption of the mall on an hourly scale. At the same time, in order to improve the accuracy of the prediction, the comfortable temperature range and time characteristics are modified in the model. Finally, in order to qualitatively and quantitatively evaluate the effectiveness of the model in this paper, the classic BP and Elman models are selected as comparison benchmarks for comparison experiments. The experimental results show that the prediction model used in this paper has higher prediction accuracy than traditional prediction models.
The correlation between the 30-15 intermittent fitness test and a novel test of running performance

Intermittent training is frequently used by team sport players to improve performance (2,3,8,9). It has been shown that the running velocity sustained during intermittent training has a significant effect on the oxygen uptake (VO2) and that the increase in aerobic capacity after a period of intermittent training is greatly affected by the intensity of training (10). Recently, Buchheit (5) developed the 30-15 intermittent fitness test (30-15IFT) to determine the optimal or maximal running speed (MRS) for team sport athletes to train at during intermittent exercise. However this test is somewhat difficult to administer to athletes who are unaccustomed to it. Athletes require specific instructions on how to perform the test and the test itself can last for greater than 20 minutes. Team sport athletes and coaches need time efficient tests that are easy to administer that will not have a significant impact on total training time. Recently a new intermittent shuttle run test (ISRT) has been used to determine MRS. The aim of this study was to investigate the relationship between the MRS determined by the 30-15IFT and the MRS determined by the new ISRT.
Transmission Line Fault Location Based on the Stacked Sparse Auto-Encoder Deep Neural Network

Given the current situation that a large amount of data is available for transmission line faults, and supervised learning is mainly used to extract fault features, a fault location method based on Stacked Sparse Auto-Encoder (SSAE) is proposed. Firstly, the maximal information coefficient is used to reduce the dimension of the data. The data interval with good information density is selected, the deep network is used to extract features, and the network parameters are initialized by greedy layer-wise unsupervised pretraining. Finally, the linear regression layer is connected and fine-tuned with the fault distance data. The simulation data show that, compared with the traditional BP neural network method, the proposed fault location method has higher accuracy and obvious feature extraction ability.
A Fault Ride-Through Method of Paralleled Synchronous and Virtual Synchronous Generators in Islanded Microgrids

With the development of distributed power generation technologies, parallel operations of virtual synchronous generators (VSGs) and synchronous generators (SGs) become increasingly common in a microgrid. Due to the low overcurrent capacity of VSGs, fault ride-through (FRT) is necessary for microgrids, which makes the transient stability of SG-VSG-based microgrids be different from the traditional power system. In this paper, a new abnormal overcurrent phenomenon caused by FRT is discovered in SG-VSG-based islanded microgrids. Then, characteristics of the output current of the VSG which uses different parameters are compared and analyzed. Furthermore, an optimized FRT method is proposed, which suppresses the abnormal overcurrent. Finally, a simulation is performed to verify the effectiveness of the proposed method on the PSCAD/EMTDC.
A Cyber Risk Assessment and Detection Method for Power Systems Against Data Integrity Attack on Load Frequency Control

Modern power system is a typical cyber-physical system due to the coupling of primary and secondary systems. The development of renewable energy and smart grids make many new application scenarios. Various new communication technologies make the system complex. Cyber systems and complex network environments have introduced a series of security issues. All of which make them easy to become ideal targets for cyberattacks. Therefore, it is of great significance to the research of cyber security in cyber-physical systems. This paper selects a scenario of centralized LFC, considers the linearized discrete system model under partial observation with Gaussian disturbance. Based on the known and un-changed characteristics of the physical model of the control system, dynamic watermarking is used to detect data integrity attacks from malicious sensors.
Accommodation of Distributed Photovoltaic Energy with Charging Strategy Optimization of Electric Vehicles

Electric vehicles offer the potential to locally integrate the energy of distributed photovoltaic (PV) to decrease carbon emissions of the power system. In this paper, the charging strategy for PV storage charging station is formulated as a multi-objective constrained optimization problem. The energy scheduling and charging states are optimized to meet the charging requirement and to decrease carbon emissions. The constraints of the problem are charging duration, battery SOC and power balance. The objectives of the problem are total cost and battery lifetime. A charging scheduling approach based on particle swarm optimization is proposed to obtain the optimized result. Multiple metrics are considered with a hybrid fitness function to achieve the best overall performance. Simulation results show the feasibility of the proposed approach.
Optimal Operation of Gas-Electricity Integrated Energy Systems with Demand Response

The environment-friendly gas-fired power plants have intensified the interdependence and economic efficiency of gas-electricity integrated energy systems (GEIES). This paper proposes an optimal operation of constrained electricity units and pipeline-constrained natural gas units which are conducted to minimize the total operational cost of GEIES. Demand response program (DRP) has been viewed as flexible assets in smart grid, which is expected to alleviate the volatile renewable energy sources (RESs) penetration. The gains of DR implementation with GEIES are investigated in this research. A numerical case study combining the IEEE RTS 24-bus system and Belgian 20-node natural gas network has validated the advantages of DRP in GEIES. The electricity cost decreases by 8.73% compared with non-participation DRP. The total cost for GEIES reduces by 2.93% under 20% DRP penetration level. The simulation results validate that a higher DR penetration level causes less volatility of load demand and total cost payment.
Design of Optimal Multilevel Thresholding based Segmentation with AlexNet Model for Plant Leaf Disease Diagnosis

Accurate and earlier detection of plant leaf diseases is important for precision agriculture, and also to avoid financial loss and improve crop productivity. Many of the plant leaf diseases exhibit visible symptoms, it is hard to manually identify the plant leaf diseases. Therefore, image processing based computer aided tools is essential for prompt and accurate plant disease detection. This study presents an optimal segmentation with Alexnet based feature extraction for plant leaf disease diagnosis (OSAFEM-PLDD) technique. The proposed OSAFEM-PLDD technique intends to determine and classify various kinds of plant diseases using leaf images. The OSAFEM-PLDD technique involves fuzzy filtering (FF) based pre-processing to remove the noise and improve the image quality. In addition, chicken swarm optimization (CSO) with Kapur's thresholding based segmentation approach was employed for the detection of affected leaf regions. Moreover, AlexNet method was utilized for deriving a helpful group of feature vectors and finally, functional link neural network (FLNN) based classification model is used to perform effective plant disease diagnosis. To showcase the enhanced performance of the OSAFEM-PLDD technique, a wide range of simulations are carried out on benchmark plant leaf disease datasets and the experimental outcomes highlighted the enhanced performance of the OSAFEM-PLDD technique over the recent methods.
Investigating Accurate Electrochemical Battery Models for Intermittent Discharge Applications

Energy dense Lithium-ion batteries are used in most consumer electronics devices and also Electric Vehicles. Producing robust Battery Management Systems to ensure battery health and safety has been the primary research goal for scientists working in the energy domain. Presently, approximate equivalent battery models are used for online State-of-Charge estimations. These approximate models do not represent all the electrochemical side reactions happening within a cell. In this paper, we investigate the performance of a fast solver by using the Doyle Fuller Newman (DFN) model. We focus on emulating precise battery behaviour for intermittent discharge applications. This approach is especially useful for developing accurate representation of batteries as all electrochemical reactions are factored into the battery model. Evaluations were done at 1C, 4C and 5C rates to test the performance of this approach.
Non-intrusive television recognition based on harmonic current characteristics

Non-intrusive load monitoring (NILM) is an effective means to realize detailed user energy monitoring. As a kind of appliances which is widely used and can intuitively reflect the user's behavior, the recognition of television (TV) is of great significance for NILM application. However, it is often difficult to recognize TV in the aggregated load data because of its relatively poor power-range distinction and complex power consumption characteristics. In this paper, the harmonic current characteristics or the fundamental-third harmonic current space trajectory is analyzed, and they are combined with the random power fluctuation characteristics during TV operation and other prior knowledge as multi-dimensional load signatures of TV. Based on this, an unsupervised non-intrusive TV recognition framework and method is proposed. Experimental analysis on collected real-world dataset shows that the proposed method can detect and recognize TV with fluctuating operation characteristics accurately and effectively.
Cooperative Wind Farm Control with Hybrid-Model-Based Deep Deterministic Policy Gradient and Model Selection

Using deep reinforcement learning to control the wind farm is difficult to practically apply in the real environment due to high learning costs. Model-based reinforcement learning utilizes the data-driven model to simulate the environment and accelerate the learning speed. But the model needs a large amount of data to achieve an accurate result. Therefore, hybrid-model-based reinforcement learning is applied to address this problem by combining the knowledge-driven model with the model-based reinforcement learning framework. The Hybrid-Model-based Deep Deterministic Policy Gradient (HM-DDPG) algorithm with model selection method is proposed. Three scenarios in WFSim are built as the test environment. The simulation results show that the proposed method can accelerate the wind farm learning process and is more suitable for sizeable state-action space situations.
Multi-Objective Optimal Dispatching Algorithm for Park Integrated Energy System Considering Starkberg Game and Energy Cascade Utilization

With the development of integrated energy system and the proposal of sustainable development goals, the participation of multiple stakeholders in the park integrated energy system (PIES) and the demand for efficient cascade utilization have brought huge challenges to the optimal dispatching of PIES, Therefore, for the PIES with heterogeneous energy flows of cold, heat, electricity and gas, an optimal dispatching algorithm considering Starkberg game and energy cascade utilization is proposed. Firstly, the architecture of PIES is proposed. Secondly, the PIES and users of the park are modeled, and the exergy efficiency target is expounded, and the Starkberg game equilibrium model was obtained. Finally, an example is given to illustrate the rationality of the proposed algorithm.
Impact of PWM and Duty Ratio Control on Voltage for Sic Fed Three Phase BLDC Motor Drive

This paper presents an analysis of phase voltage and dc bus utilization for a three-phase Brushless DC motor (BLDC). Both pulse-amplitude (PAM) and pulse-width modulation (PWM) is employed to control the phase voltage and line current by adjusting the duty cycle. In this paper an analytical expression is derived for six-step-commutation, Bipolar PWM, HPWM-L-ON, and PWM-ON. Through analytical expression and experimentation a comparative analysis has been performed for the aforementioned PWM techniques. Experimental results on a three-phase star-connected Sic-based BLDC drive are provided to substantiate the analysis on phase voltage, and DC bus utilization.
Research on Influencing Factors of Airconditioning Electricity Demand Flexibility for Nearly Zero Energy Commercial Buildings

For nearly zero energy buildings, it is of great significance to make full use of the renewable energy of the air-conditioning system's electricity demand flexibility. The commercial building is the main body of the social electricity consumption, and its air-conditioning load alone can reach more than 50% of its peak load. It can be seen that the electricity load of the building air conditioning system is an important control target for commercial buildings to achieve nearly zero energy consumption. The main technology is to coordinate and control the flexibility of the air-conditioning system load and the intermittent power generation of renewable energy based on the integrated energy management digital platform, so as to achieve the greatest reduction in the fossil energy consumption of the building. This study selects five typical commercial buildings as the research objects, i.e., large, medium and small office buildings, super markets and general hotels. The influence of different parameters on the demand flexibility of commercial buildings is analyzed from three aspects: outdoor air parameters, thermal comfort parameters and thermal performance of envelope structure. Based on the digital technology and energy consumption analysis software (EnergyPlus), this study integrates energy efficiency assessment methods; establishes a commercial building air-conditioning energy consumption model; simulates and analyzes the impact of three types of parameter changes on the building load change rate and demand flexibility; thus the degree of influence of the three types of parameters on the building demand is obtained. This study established a typical model database of commercial buildings to support the follow-up research on the effect of other
Non-uniform Quantisation Current Differential Protection Algorithm Applied On Power Specific Integrated Circuit

An improved current differential protection algorithm, which actually based on non-uniform quantisation because of the dissimilar sampling demands between transient state and steady state of power system, is presented and mounted on the power specific integrated circuit (PSIC) named FUXI. This method reduces the computational pressure on the PSIC by non-uniformly quantizing the electrical quantity information sampled. This paper also verifies the feasibility of applying the improved algorithm to power-specific integrated circuits by comparing the correctness of the logic running on the chip and in simulation. It practically indicates the engineering application value of power specific integrated circuit in the relay protection.
A Novel Single Phase Three Level Triple Boost CG Switched-Capacitor Based Grid-Connected Transformerless PV Inverter

A novel three-level inverter has been proposed in this paper for PV applications. This inverter can boost the input photo-voltaic (PV) voltage up to three times in output without additional DC/DC converter. Two switched capacitor (SC) cells are connected in a cascade manner to boost the input PV voltage. Further, this topology can eliminate the leakage current completely, as it bypasses the parasitic capacitance by way of a common ground (CG) connection between the negative terminal of the PV panel and the neutral of the grid. Consequently, it does not need an electro-magnetic interference (EMI) filter for electromagnetic compatibility (EMC). The additional feature of the proposed inverter is reactive power handling capability. A proportional + resonant (PR) current controller has been implemented to regulate the active and reactive components of the grid current. The proposed modulation strategy makes the inverter capable of balancing the switched capacitors by charging during its inactive state. The inverter operating principle, operating modes, and modulation scheme have been analyzed in detail. The various claims about the proposed inverter have been verified in MATLABO simulation, and the results are presented.
Benchmarking Reinforcement Learning Algorithms on Island Microgrid Energy Management

Tremendous increase of electricity demand of coun-tries in Southeast Asia has been observed over the past few years and expected to sustain in the next decade. A large number of inhabited archipelagos in the Insular region eagerly await electrification. Compared to the traditional solutions relying on distributed diesel generators and grid extension, island micro-grids demonstrate advantages of cost effectiveness and reduced environmental impact. One of the key challenges in operating island microgrid is to optimize the power dispatch to achieve the lowest operation cost while ensuring the grid reliability. In this paper, we have adopted both deterministic cycle charging algorithm and state-of-the-art Deep Reinforcement Learning algorithms in an islanded microgrid model built based on data from Indonesia. Once the reinforcement learning models are implemented and tuned using the latest Stable Baselines 3 library, we benchmark the models' performance, which is measured by the total number of blackout occurred and the cumulative fuel consumption over a year. The result quantifies the improvement and highlights the efficiency of energy management optimization achieved by applying Deep Reinforcement Learning algorithms for islanded microgrid application.
Implementation of Intelligent Residential Fire Extinguisher System

A new generation Intelligent fire extinguishing systems have been proposed to rescue humans and important documents which would be flamed. The Automatic Fire Extinguisher system can be controlled by Smartphones through Internet of things. It is a hardware-based model, accustomed automatically to extinguish the fireplace. Instructions may be given to the robot concerning its movement, turning on its water pump. It starts to manoeuvre within the direction with reference to fire intensity once when it senses the heat. The motor speed is adjusted with regard to the temperature range to pump the water. So that this robot pumps the accurate amount of water which is required to extinguish the fireplace. Proteus is used for Simulation of Fire Extinguisher and Python is used for coding in the Proteus.