Miracle-dz/UniFAR-FTUs huggingface dataset:
A Two-Stage Deep Learning-Based Approach for Automatic Reading of Analog Meters

In many factories in Japan, there are still thousands of analog meters to monitor and control the fabrication environments. Human inspectors must walk around, read the meters, record the values, and input them into the computer for further analysis. This process is prone to human errors. To solve the problem, we may consider to replace all analog meters with digital ones, but this is not practical due to the high replacement cost. In this study, we propose a system to automate the meter reading process. Using this system, an inspector just takes a short video of the meter using a smart phone, sends the video to a server, and the server can read the meter value and record the results automatically. To make the system practically useful, we introduce three techniques. The first one is a two-stage deep learning-based method for detecting the meter and its pointer; the second one is homography transform to normalize the view point for capturing the video; and the third is a multi-frame-based method for improving the reading accuracy. Experimental results with circular meters show that the accuracy of the proposed system is acceptable. This paves a way for us to automate the reading process of other types of analog meters.
Gradient-Based Optimizer for Parameter Extraction in Photovoltaic Models

Solar radiation is increasingly used as a clean energy source, and photovoltaic (PV) panels that contain solar cells (SCs) transform solar energy into electricity. The current-voltage characteristics for PV models is nonlinear. Due to a lack of data on the manufacturer’s datasheet for PV models, there are several unknown parameters. It is necessary to accurately design the PV systems by defining the intrinsic parameters of the SCs. Various methods have been proposed to estimate the unknown parameters of PV cells. However, their results are often inaccurate. In this article, a gradient-based optimizer (GBO) was applied as an efficient and accurate methodology to estimate the parameters of SCs and PV modules. Three common SC models, namely, single-diode models (SDMs), double-diode models (DDMs), and three-diode models (TDMs) were used to demonstrate the capacity of the GBO to estimate the parameters of SCs. The proposed GBO algorithm for estimating the optimal values of the parameters for various SCs models are applied on the real data of a 55 mm diameter commercial R.T. C-France SC. Comparison between the GBO and other algorithms are performed for the same data set. The smallest value of the error between the experimental and the simulated data is achieved by the proposed GBO. Also, high closeness between the simulated P-V and I-V curves is achieved by the proposed GBO compared with the experimental.
Electrosynthesis of a polyaniline/zeolite nanocomposite coating on copper in a three-step process and the effect of current density on its corrosion protection performance

A polyaniline/zeolite nanocomposite (PZN) was successfully electrosynthesized at a copper (Cu) electrode in a sodium oxalate solution to generate a homogeneous and adherent coating. During the formation of the PZN coatings, three stages (electro-adsorption of monomer and electrolyte and initiation of formation of the passive film, growth and impingement of the passive film and decomposition of the latter and formation of composite coatings) are observed and three times related to these stages are reported. The coatings were characterized by SEM, XRD, FTIR and UV-vis spectroscopy. The corrosion behavior of copper with the PZN coatings in 3.5 wt% NaCl solution was investigated by potentiodynamic polarization and electrochemical impedance spectroscopy techniques. The effect of the applied current density on the protective properties of the PZN coatings has been investigated, and it was shown that the protection efficiency depends on the applied current density. The corrosion current density decreased from 43.56 μA cm−2 for un-coated copper to 0.34 μA cm−2 for PZN-coated copper under optimal conditions. It was observed that the PZN coating provided efficient protection (99.2%) to copper in 3.5 wt% NaCl solution. The corrosion rate for the PZN-coated copper was significantly lower than the bare copper (about 130 times).
Drug-target interactions on a single living cell: an approach by optical microspectroscopy

Optical microspectroscopic techniques - both powerful and nondestructive - are particularly suited to the study within a single living cell of molecular interactions between drugs (anticancer agents or xenobiotics) and their pharmacological targets. These techniques include vibrational spectroscopy : Raman spectroscopy (conventional resonance SERS) and Fourier transform infrared microspectroscopy together with fluorescence microspectroscopy. Applications are given Ci) in the subcompartmental quantitative analysis of anthracyclines and ymca-alkaloids and (ii) in the study within the cell of the molecular interactions of these compounds with their target molecules. Among these techniques laser microspectrofluorometry is easy to operate and allows the study of intracellular drug pharmacokinetics related to biological characteristics such as cell resistance or differentiation.
On the Performance of MIMO Full-Duplex Relaying System With SWIPT Under Outdated CSI

In this paper, we propose a full-duplex (FD) relaying system with multi-input multi-output (MIMO) with simultaneous wireless information and power transfer (SWIPT), where the communication from source to destination is assisted by a full-duplex decode-and-forward (DF) relay. The time switching (TS) protocol is used at the relay to harvest the energy of radio-frequency (RF) signals transmitted from the source. To improve both system performance and the amount of harvested energy, multiple antennas are used at source and destination. Transmit antenna selection (TAS) is employed at the source with an assumption that the feedback of channel state information (CSI) is outdated. Meanwhile, both selection combining (SC) and maximal ratio combining (MRC) techniques are applied at the destination. The closed-form expressions of the outage probability (OP), optimal throughput, and symbol error probability (SEP) are derived subject to the outdated CSI over Rayleigh fading channels. We also make a comparison between the performance of the proposed MIMO-FD relaying system with SWIPT and that of MIMO half-duplex (HD) relaying systems with and without SWIPT. The validity of derived mathematical expressions is verified by Monte-Carlo simulations. Numerical results show that the TAS/MRC scheme gives better system performance than the TAS/SC scheme. Moreover, higher energy harvesting time is needed to maximize the throughput than to minimize the OP.
Protection Relay for Secondary Ferroresonance Suppression in Coupling Capacitor Voltage Transformers

A new protection relay to detect, suppress and mitigate secondary ferroresonance in CCVTs is presented in this paper. The new protection relay uses frequency response characteristic to detect and characterized secondary ferroresonance and an electronic switch with back to back thyristor to introduce a commutable load to the CCVT’s secondary circuit for secondary ferroresonance suppression. The proposed protection relay has been verified with computer simulations and hardware experiments, which demonstrated a successful performance regarding three design aspects, namely, detection, suppression and security.
A Framework for Analyzing, Designing, and Visualizing Spiking Neural Networks Part I: Linear Response Surfaces

In this first of two closely related papers, we set the foundation for a new framework, called Response Surfaces (RSs), to address fundamental problems of analyzing, designing, and visualizing spiking neurons and networks. An RS is a plot of the direct transfer function between input and output firing times of a spiking neuron and shows all the patterns of input spike times that fire a neuron at a given time. Thus, an RS is a graphical tool for visualizing, analyzing, and designing spiking neural networks. In this paper, we develop a linear RS framework based on triangular, post-synaptic-potential waveforms and apply it to the following problems: graphing the transfer function of a linear spiking neuron, designing an efficient spiking-XOR gate, analyzing phase tracking, and calculating spike times of arbitrarily large recurrent spiking networks. As another fundamental result of the linear RS framework, we show that the output firing time of a spiking neuron is equivalent to the center-of-mass of input spike times (acting as positions) and weights (acting as masses) plus a fixed delay. In the second paper, we explore the possibilities of more biologically realistic post-synaptic-potential waveforms and create a nonlinear RS framework as an extension of the linear RS framework presented here. Although our application examples in both papers focus on design and analysis, we touch on why the RS framework is helpful for understanding the effects of weight changes in learning algorithms.
Structures and electronic properties of ultrathin titanium nanowires

The structures of free-standing titanium nanowires are studied by using a genetic algorithm with a tight-binding potential. Helical multi-walled cylindrical structures are obtained and pentagonal packing is found for these thin wires with diameters from 0.747 to 1.773 nm. The angular correlation functions and vibrational properties of nanowires are discussed. We have further calculated the electronic structures of the titanium nanowires with the plane-wave pseudopotential method. Bulk-like continuous electronic bands are found in the Ti wires thicker than 1 nm. The vibrational and electronic properties of titanium nanowire are significantly dependent on the multi-walled structure of the nanowire.
Indoor localization with correction and validation

This paper proposes a hybrid system for indoor localization. It is composed of a core system and a validation subsystem. The core system is range-based which uses a lateration algorithm based on RSSI. This component also has a correction mechanism for the attenuation coefficient (Path Loss of the environment). This mechanism operates based on the feedback received from the known positions of the static devices located nearby. The correction mechanism is not enough to make the core system invulnerable to environmental noises. To ensure the robustness of the proposed hybrid system, a validation subsystem is used. It consists of an accelerometer and a magnetometer. This combination of sensors can provide movement tracking. It is used to validate the core's decisions. This subsystem is also able to take control when the range-based system doesn't have enough data to make a decision. The validation subsystem doesn't have the same problems as the core system because it is not RSSI based, but it has its own flaws. The main purpose of the hybrid system is to mitigate the disadvantages of each technology using their complementary advantages. The proposed system is accurate, robust in different environments and does not require any input from the user.
Propagation of Supraharmonics Generated by PMSG Wind Power Plants into Transmission Systems

This paper is focused on the propagation of supraharmonics into power transmission grids introduced by wind power plants (WPP) based on permanent magnet synchronous generator (PMSG) wind turbines. The methodology is based in steady state and time domain simulations performed by MATLAB Simulink®. As results steady state power flow and time domain voltages and currents are obtained and supraharmonic indicators are proposed for analysis. In order to simulate the electrical power system, transmission lines are modelled by Pi equivalent circuit based on distributed parameters and linear models of transformers are considered. Furthermore, wind power plants are modelled as aggregations of several wind turbines. A study case based on the west side of the Brazilian state of Rio Grande do Sul is proposed to validate the modelling and simulation methodology. Results show that the level of supraharmonics emission from WPP is straightly related to the PWM modulation of power inverters. Furthermore, the propagation of supraharmonics into transmission grids follows a linear tendency of decreasing according to the distance in transmission lines to the point of common coupling (PCC) of the wind power plants. Anyway, intrinsic characteristics at each point of the grid such as load profile, generation and presence of capacitor banks also influence the behavior of supraharmonic propagation.
Modeling for residence preference in local city considering heterogeneous households

The purpose of this study is to model the residence preference area in local areas with a huge disaster risk considering the heterogeneity of households. In addition to indicators such as convenience in transportation, living comfort, and safety and security, it is assumed that the familiarity to community and the living near family are also considered as factors for residence preference. Therefore, we conduct the internet questionnaire survey in local areas, using the subject as a person who is planning to relocate. Using survey data, we analyze the characteristics of the various indicators and their weights for the residence preference. Therefore, we model the residence preference according to the household characteristics with hierarchical Bayesian modeling. Based on the result, we also analyze the relationship between household characteristics and the weight of various factors. As the result, it can be mentioned that household characteristics should be considered to model residence preference in local city with a huge disaster risk.
Feature Selection and Ensemble Learning Techniques in One-Class Classifiers: An Empirical Study of Two-Class Imbalanced Datasets

Class imbalance learning is an important research problem in data mining and machine learning. Most solutions including data levels, algorithm levels, and cost sensitive approaches are derived using multi-class classifiers, depending on the number of classes to be classified. One-class classification (OCC) techniques, in contrast, have been widely used for anomaly or outlier detection where only normal or positive class training data are available. In this study, we treat every two-class imbalanced dataset as an anomaly detection problem, which contains a larger number of data in the majority class, i.e. normal or positive class, and a very small number of data in the minority class. The research objectives of this paper are to understand the performance of OCC classifiers and examine the level of performance improvement when feature selection is considered for pre-processing the training data in the majority class and ensemble learning is employed to combine multiple OCC classifiers. Based on 55 datasets with different ranges of class imbalance ratios and one-class support vector machine, isolation forest, and local outlier factor as the representative OCC classifiers, we found that the OCC classifiers are good at high imbalance ratio datasets, outperforming the C4.5 baseline. In most cases, though, performing feature selection does not improve the performance of the OCC classifiers in most. However, many homogeneous and heterogeneous OCC classifier ensembles do outperform the single OCC classifiers, with some specific combinations of multiple OCC classifiers, both with and without feature selection, performing similar to or better than the baseline combination of SMOTE and C4.5.
Quartz Crystal Microbalance Study: Ionic Motion Across Conducting Polymers

In situ monitoring of mass change was performed during electrochemical growth and redox cycling of a conducting polymer(polypyrrole) on a quartz crystal microbalance. For the polypyrrole films grown with large polymeric anions(poly(4-styrenesulfonate) and polyvinylsulfonate), mostly cations and solvent molecules were inserted and removed to compensate charge in polypyrrole. The films formed with medium-sized anions (tosylate) showed both apparent anion and cation motion during the redox process. The films prepared in the presence of small anions (ClO 4 − and BF 4 − ) showed mostly anion motion, but apparent cation motion also became significant for higher oxidation and/or reduction state
A Convolutional Neural Network Method for Self-Driving Cars*

The profound impacts of driverless vehicles technology could change to our society remarkably, not to mention the significant enhancements they could bring to the overall safety, efficiency, and convenience of transportation and transit systems. This paper employs deep neural networks to predict the steering angle and throttle values for an autonomous vehicle by obtained images taken from different viewpoints. The proposed convolutional neural network is able to extract the features from the images and find the dependencies for forecasting the steering angle and the speed to keep the vehicle running at the center of the lane automatically. The synthetic images used in our work is generated from Udacity platform.
Piezoresistive Electronic-Skin Sensors Produced With Self-Channeling Laser Microstructured Silicon Molds

Highly sensitive and cost-effective flexible piezoresistive sensors are pursued as essential components of electronic skin (e-skin) for a variety of applications such as soft robotics and body prosthesis. A common strategy for achieving high sensitivity of these sensors is to fabricate their electrode material with complex 3-D microstructures, but manufacturing cost-effective micro-structured sensors with high reliability and reproducibility is a challenge. Herein, a robust approach for producing high-reproductive microstructured polydimethylsiloxane (PDMS) films composing the sensors is proposed, based on silicon molds with a honeycomb-like architecture fabricated rapidly and cost-effectively at a standoff distance by femtosecond laser pulses in the self-channeling regime. Single-walled carbon nanotubes (SWNTs) are embedded in the micro-structured PDMS films composing sandwich-structural flexible piezoresistive sensors. The sensing devices exhibit good performance with high sensitivity, fast response time, and excellent stability and are used with success for the detection of hand motion pressure and blood pressure at the wrist, thus showing a great potential as components of e-skin for detection of various human motions.
Energy Harvesting through Analog Network Coding for the Wireless Body Area Network

In the recent healthcare medium, the wireless body area network (WBAN) technologies provide suitability between doctors and people receiving medical treatment. The WBANs technologies are one of the essential technologies of the healthcare system, as all sick people are observed utilizing some set of nodes of sensors that are light as well as with no high power. In this paper, we proposed Energy Harvesting-based Analog Network Coding (EH-ANC). The outage probability is derived for the proposed algorithm. Finally, the proposed algorithm shows better performance compared with existing work.
Power Reserve Dispatch to Mitigate Variability of Generation Output due to Wind Ramps

Flexibility in power systems with high penetration of wind power is the most important factor required for maintaining generation capacity and load balance. Generating reserves play a key role in this new scenario. These must be available not only in terms of quantity, but particularly in their ability to provide a suitable response rate compatible with variations in wind generation, as well as with the phenomenon of wind ramps. The generating units responsible for the AGC may not be able to compensate this variation in generating power. Taking into account, reserves must be redispatched to keep the power balance. However, since reserves have different costs and response ramps, our problem is to find out which reserves have to be redispatched at minimum cost. This paper addresses the question of the optimal dispatch of reserves against wind power ramp events by taking account of operational constraints including interruptible load contracts.
Sampled-data Tracking and Disturbance Compensation of Nonlinear Systems

This paper solves the tracking problem for feedback linearizable sampled data nonlinear systems. The conversion of a continuous system into its sampled data equivalent has been discussed and the proposed control law has been derived. In classical control theory, high gain observers based feedback control has been adopted for stabilizing a nonlinear system and state estimation to track a varying reference, but it compromises the main design purpose. The proposed scheme uses augmented state estimation technique to find the additional compensation to be applied at input channel to minimize the tracking error. The results are shown through an illustrative example of an inverted pendulum system that verifies the effectiveness of the proposed scheme.
Characterization and Architecture of Monolithic N⁺P-CMOS-SiPM Array for ToF Measurements

This article focuses on the characterization and architecture of a new mixed design approach to monolithic CMOS-silicon-photomultiplier (SiPM), where the mixed-signal circuitry as well as the logical concepts are implemented at the die level. The new architecture contains two key areas: 1) a method for sampling, filtering, and converting the electrical signal generated by the SiPM to the digital domain by taking advantage of the nature of the SiPM as an “optical-to-digital converter” and 2) a fully digital configurable method, which requires just one clock generator, enabling sampling at an adjustable time window (gating method) and reduces background noise. We report the design, simulation, and measurement results. The system was fabricated at a low-cost <inline-formula> <tex-math notation="LaTeX">$0.18~\mathrm {[{\mu {\mathrm{ m}}}]}0.18$ </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process yet achieves a clock rate of 1 GHz, high timing and photon counting resolution, and low jitter. The reported demonstrator may be applied to 3-D range measurements, light detection and ranging (LIDAR), time-of-flight (ToF) system, and gaming systems.
Performance Evaluation of Pre-trained Convolutional Neural Network for Milkfish Freshness Classification

Milkfish are the top five fish of aquaculture products in Indonesia with high sales in traditional markets. Hence, the Indonesian people should recognize the freshness of the fish in the traditional market. An automated system to recognize the freshness of milkfish based on the eye using Convolutional Neural Network (CNN) deep learning requires vast image data in training sessions. For our small dataset, we performed transfer learning with fine-tuning pre-trained CNNs. In this study, we evaluate several pre-trained CNN models to classify milkfish eye freshness. The dataset consists of 234 milkfish eye images and three freshness class. The experiments and analysis results show that NasNet Mobile and Densenet 121 outperform state-of-the-art with the best performance on training, validation, and testing data.
Cybersecurity for Smart Substation

We are currently undergoing an incredible digital transformation in our power distribution substations with the implementation of intelligent electronic devices (IEDs) in TCP / IP network in accordance with the standards established by IEC 61850 for substation protection and control. The use of information sharing and interoperability among IEDS are technical characteristics in smart substations. With the advent of this digital technology, which bring us a huge framework of opportunities for improvement in its operation and maintenance, we also have a weakness to be considered and studied that is cybersecurity, as already occurs in the corporate network of companies. The proposal of this project was to develop a diagnostics, studies, benchmarking analysis and the application of a cybersecurity action for distribution substations protection and control system.