Electric Current Signal Research Articles

A novel experimental procedure for lock-in thermography on solar cells

<abstract> <p>The occurrence of defects in solar cells is intrinsically related to a reduction in the efficiency and reliability of these devices. Therefore, monitoring techniques, such as lock-in thermography, electroluminescence and the I-V characteristic curve are adopted in order to evaluate the integrity of the solar cells. In the present work, a novel experimental procedure for the lock-in thermography of solar cells is proposed, aiming to improve the detection capability of the assay. Conventional techniques use pulse width modulation to operate the cell at a fixed point on the I-V curve. Instead, we propose a methodology based on a sinusoidal electric current excitation in order to extend the range of operational points that are close to the maximum power point as the cell operates in the field. Some traditional image processing techniques (principal component analysis, the fast Fourier transform and the four-step phase-shifting method) have been used to analyze the thermal images captured by an infrared camera during steady-state operation mode of the solar cells using both sinusoidal electric current signal and standard pulse width modulation procedures. Comparison between the results of both procedures found that this novel approach provides smoother and clearer delimitation of the defects. Furthermore, the contrast of the phase images was found to exhibit significant changes between the defective and non-defective regions for different modulation frequencies and types of defects. From the achieved results, it was possible to obtain a satisfactory characterization of the existing defects.</p> </abstract>

Velocity measurement of pneumatically conveyed particles via a simple current signal technique and the influence of electrostatic charge

Particle velocity in pulse feeding pneumatic conveying was measured utilizing current signal analysis and video imaging. Glass beads and silica particles were conveyed in a conductive tube (ID of 4.57 × 10 −3 m) at gas velocities of 7, 15, and 30 m/s. To measure particle velocity, electrical current signal method was used with a new approach that uses raw current data and eliminates signal conditioning and cross-correlation analysis. This technique was successful and results were comparable to video imaging results. The conveyed particles' charge was measured to determine its influence on particle velocity. 30 m/s gas velocity resulted in a turbulent flow and greater charge generation causing more deviations between particle and gas velocity due to the enhancement of particles' radial motion. Finally, the comparison of results with predictions of existing models revealed that further research is necessary to better understand the effect of particles' motion and charge on their conveying velocity. Video imaging and current signal measurement techniques and the acquired electrostatic charge during pneumatic conveying. • Particle velocity in pneumatic conveying was measured based on particles charge. • A simplified technique based on electrical current signal method was proposed. • Influence of particles electrostatic charge on their velocity was investigated. • Existing models overestimated particle velocity in the presence of electrostatics. • Existing models predictions were not in agreement to the measurements at high gas velocities.

Deep Scattering Spectrum Germaneness for Fault Detection and Diagnosis for Component-Level Prognostics and Health Management (PHM)

Most methodologies for fault detection and diagnosis in prognostics and health management (PHM) systems use machine learning (ML) or deep learning (DL), in which either some features are extracted beforehand (in the case of typical ML approaches) or the filters are used to extract features autonomously (in the case of DL) to perform the critical classification task. In particular, in the fault detection and diagnosis of industrial robots where the primary sources of information are electric current, vibration, or acoustic emissions signals that are rich in information in both the temporal and frequency domains, techniques capable of extracting meaningful information from non-stationary frequency-domain signals with the ability to map the signals into their constituent components with compressed information are required. This has the potential to minimise the complexity and size of traditional ML- and DL-based frameworks. The deep scattering spectrum (DSS) is one of the approaches that use the Wavelet Transform (WT) analogy for separating and extracting information embedded in a signal's various temporal and frequency domains. Therefore, the primary focus of this work is the investigation of the efficacy and applicability of the DSS's feature domain relative to fault detection and diagnosis for the mechanical components of industrial robots. For this, multiple industrial robots with distinct mechanical faults were studied. Data were collected from these robots under different fault conditions and an approach was developed for classifying the faults using DSS's low-variance features extracted from input signals. The presented approach was implemented on the practical test benches and demonstrated satisfactory performance in fault detection and diagnosis for simple and complex classification problems with a classification accuracy of 99.7% and 88.1%, respectively. The results suggest that, similarly to other ML techniques, the DSS offers significant potential in addressing fault classification challenges, especially for cases where the data are in the form of signals.

Development of an improved automatic control algorithm for combined RCB burners and carbonaceous material injectors of a flexible modular furnace

The control algorithm for combined ceramic regenerative burners (Regenerative Ceramic Burner, RCB) and carbon-containing material injectors of a flexible modular arc steel-smelting furnace DSP-1 operating at one of the domestic metallurgical enterprises is considered. It was revealed that in the concept adopted in production, the decision to switch the RCB burner from the burner mode to the oxygen lance mode, as well as to turn on the carbon-containing material injectors, is made directly by the operator of the furnace plant based on a set of empirical features characteristic of one or another technological stage of melting. In this regard, an improved control algorithm for RCB burners and carbon-containing material injectors has been developed and proposed for implementation, which will ensure the adoption of the above-mentioned decisions in automatic mode. This algorithm is based on the analysis of the slag coefficient, the value of which directly depends on the level of higher harmonics in the spectrum of electric arc current signals, as well as physical processes occurring inside the furnace bath. The implementation of the proposed algorithm enables to achieve the most timely adaptation of operating modes of combined burners and carbon-containing material injectors, which in turn will increase the overall energy efficiency of the steelmaking complex as a result of an increase in the total thermal efficiency of electric arcs and the optimal operating mode of the burners.This work was supported financially by the Ministry of Science and Higher Education of the Russian Federation (Project No. FZRU-2020-0011).

A study of interfacial electrical contact resistances of thermoelectric generators for hypersonic vehicles by experimental measurements and two-scale numerical simulations

An enormous amount of aerodynamic heat is produced on the surface of hypersonic vehicles, and the thermoelectric generator (TEG) technique holds the potential to convert aerodynamic heat into electric energy. In a TEG, the micrometer-scale roughness on TE legs and electrodes surfaces leads to the constriction of interfacial electric current and brings about electrical contact resistance (ECR). The heat transfer and conversion processes of a TEG are greatly influenced by such type of ECR. In this work, a TEG includes two ceramic substrates and a middle TE layer is established, and the TE layer consists of insulation material, N- and P-type TE legs and electrodes to connect the TE legs. The ECR and its effects on the TEG performance are studied by two-scale numerical simulations and experimental measurements. First, the practical topography of rough surfaces for TE legs and electrodes is measured, and the micrometer-scale ECR prediction numerical models are established based on the reconstructed topography. An ECR measurement platform is developed and the platform consists of a pressure device, power supply and data acquisition system which are used to provide loading pressures, voltage, and acquisition of electric current and voltage signals, respectively. The ECRs under different temperatures, loading pressures and gap mediums are then predicted and the numerical models are validated by a series of measurements. Second, the TE conversion process of TEG is simulated by a millimeter-scale model including insulation materials, TE legs, electrodes, and equivalent interfacial layers of ECR. The influence of ECR on the TE performance of TEG for the hypersonic vehicle is thus clarified.

Induction motors fault diagnosis using a stacked sparse auto-encoder deep neural network

In this article, deep neural network and stacked sparse auto-encoder deep neural network performances in fault diagnosis are compared. Methods are employed experimentally for the detection and isolation of an induction motor’s condition (healthy, bearing outer race fault, stator winding short circuit, and rotor broken bar) in the presence of unbalanced power supply and pump dry running disturbances. Pre-processing and de-noising is performed on three-phase electrical current signals using fast Fourier transform and independence component analysis algorithm, respectively. Experimental results show that sparse auto-encoder deep neural network method has outperformed and diagnosed the aforementioned faults in the presence of disturbances with a highly reliable accuracy rate of 90.65%.

Tool Condition Monitoring Using Machine Tool Spindle Electric Current and Multiscale Analysis while Milling Steel Alloy

In the metal cutting process, the tool condition directly affects the quality of the machined component. To control the quality of the cutting tool and avoid equipment downtime, it is essential to monitor its condition during the machining process. The primary purpose is to send a warning before tool wear reaches a certain level, which could influence product quality. In this paper, tool condition is monitored using fractal analysis of the spindle electric current signal. The current study analyzes the monitoring of the cutting tool when milling AISI 5140 steel with a four-flute solid carbide end mill cutter to develop monitoring techniques for wear classification of metal cutting processes. The spindle electric current signal is acquired using the machine tool internal sensor, which meets industrial constraints in their operating conditions. As a new approach, the fractal theory is referred to analyze the spindle electric current signal and then assess the tool wear condition during the metal cutting process. Fractal parameters were defined to extract significant characteristic features of the signal. This research provides a proof of concept for the use of fractal analysis as a decision-making strategy in monitoring tool condition.

Modification of BIOFISH 300 HIS for Determination of Histamine in Fish and Fish Products: AOAC Performance Tested MethodSM 051604.

The BIOFISH 300 HIS method for the quantification of histamine in fish and fish products was validated by the AOAC Research Institute and granted Performance Tested MethodSM certification in 2016. The method is based on the use of an electrochemical reader (BIOFISH 300 device) together with disposable gold electrodes (Biotest) modified with a specific enzyme that oxidizes the histamine molecules and produces a detectable and quantifiable electric current signal. It was validated for raw fish (tuna, mackerel, sardine, and anchovy), cooked fish (tuna), canned fish (tuna in water, tuna in oil, mackerel in tomato sauce, and pickled sardine), and salted fish (canned salted anchovy). The validated ranges were 30-150 mg/kg histamine for canned salted anchovy and 10-200 mg/kg histamine for all other matrices. The objective of the present report is to validate some method modifications, namely the use of a new reader (BIOFISH 3000), the inclusion of new quantification ranges, new matrixes in the certified claims, a histamine reference solution (Verifying Solution) that will enable the user to perform a system verification, and also the inclusion of the kit ASC1-10 for the elimination of ascorbic acid from sample extract. New quantification ranges can be obtained by diluting the sample appropriately into the measurement cuvette, thereby adjusting the concentration to the internal calibration range of the device. New matrixes such as fish meal, preserved salted anchovy, and fish with added ascorbic acid were validated. The new digitalized reader enables new functionalities such as the use of an intuitive app and cloud storage of the results, with no changes in the data acquisition and processing. As the use of the new reader does not imply any change at the electrochemical readout level, all assays were performed using the BIOFISH 3000 device. The method was shown to be linear in all the quantitation ranges configured. Accuracy and recovery studies over a wide range of matrixes showed that the method yields comparable results to the reference method used. Stability and product consistency testing of kit components demonstrated that the system produces accurate results when expiration dates from the manufacturer are met. All modifications made to the BIOFISH 300 HIS method, PTM 051604, have been validated satisfactorily, under criteria of the AOAC Research Institute. The use of the BIOFISH 300/3000 HIS method is ideal as a routine histamine control method for the fish industry. After a simple aqueous extraction of the sample, results are obtained in less than 3 minutes. All data generated by the new BIO3000 biosensor are sent to a web platform, allowing new functionalities to the user. All these technical capabilities represent a competitive advantage over other existing technologies.

Tool condition monitoring based on the fractal analysis of current and cutting force signals during CFRP trimming

Carbon fiber–reinforced plastic (CFRP) is becoming more popular in the aerospace industry due to its high strength-to-weight ratio and low weight. Machining CFRP to achieve the required surface quality, on the other hand, remains a challenge. High temperature in the cutting zone area affects the tool life and surface quality of the machined part. A thermally affected matrix makes an inaccurate interpretation of the surface quality. Then, the roughness parameters cannot be an appropriate indicator for surface evaluation of the CFRP. In the aerospace industry, however, ensuring the acceptable surface quality of a part is essential. Minimizing and controlling tool wear are necessary to avoid degrading the finished surface and losing the dimensional accuracy of the final part. Early detection of tool wear and appropriate surface quality in finishing operations can be achieved using online tool condition monitoring. Cutting forces and electric current signals related to the spindle during machining are very responsive to cutting conditions and can accurately represent tool condition changes. Fractal analysis, as a new approach in the online tool condition monitoring, can assess the tool condition during machining. This research investigates the fractal analysis of the spindle electric current signal and the total cutting force signal while trimming CFRP using a CVD end mill through three different tool life conditions, e.g. new tool, moderately worn tool, and severely worn tool. The empirical fractal index is also introduced to assess the tool condition and ensure the acceptable surface qualities in the finishing operations. The effectiveness of fractal analysis as a decision-making method in the tool condition monitoring was successfully proven in this study.

Optical absorption tomography for temperature and H2O concentration imaging by a compact photoelectric detecting system combined with twice adaptive algebraic algorithm

Tunable diode laser absorption spectroscopy is a robust method for simultaneous measurements of temperature and gas species. Different from commercial detectors integrated with photodiode receivers and amplifiers as the detecting system, a custom-made photoelectric detecting system comprised of Ge detectors and separated amplifiers was designed. First, Ge detectors received optical power from laser beams and then converted it into electrical current signals. The trans-impedance amplifiers were used to convert the current signals to voltage signals. The second stage amplifiers further amplified the voltage signals. All amplifiers that integrated the electronic circuits were packed in a portable case. Finally, the amplified voltage signals were received by data acquisition card. In this way, detectors could be arranged more compact and are easier to facilitate expandability. In addition, absorption data were reconstructed using twice adaptive algebraic reconstruction technique (AART) algorithm. During the reconstruction, after obtaining the temperature distribution through AART algorithm, AART algorithm was adopted for the second time for H2O concentration distribution. This system was validated with measurements of one-dimensional average temperature in a high temperature tube furnace and the system combined with twice adaptive algebraic algorithm demonstrated in a square flow field containing McKenna burners for simultaneous measurements of temperature and H2O distribution. The results indicated the viability and potential of this system combined with twice adaptive algebraic algorithm for sensitive temperature and H2O distribution tomography.

Identification of DNA nucleotides by conductance and tunnelling current variation through borophene nanogaps.

Rapid and inexpensive DNA sequencing is critical to biomedical research and healthcare for the accomplishment of personalized medicine. Solid-state nanopores and nanogaps have marshalled themselves in the fascinating paradigm of nano-research since the advent of its application in DNA sequencing by analyzing the quantum conductance and electric current signals. In this study, the feasibility of the considered borophene nanogaps for DNA sequencing purposes via the electronic tunnelling current approach was investigated by utilizing combined density functional theory with non-equilibrium Green's function (DFT-NEGF) techniques. The interaction energy (Ei) and the charge density difference (CDD) plots exploit the charge modulation around the nanogap edges due to the presence of each nucleotide. Our results revealed a distinct variation in the tunnelling conductance, as a characteristic fingerprint of each nucleotide at the Fermi level. The calculated tunnelling current variation across the nanogap under an applied bias voltage was also significant due to the effective coupling of nucleotides with the electrode edges. The current was in the picoampere (pA) range, which was fairly higher than the electrical background noise and also experimentally detectable by the canning tunnelling microscopy (STM) technique. Our findings demonstrated that in the borophene nanopore vs. nanogap scenario, the nanogap has several advantages and is a more promising nanobiosensor. Moreover, we also compared our results with various previous experimental and theoretical reports on nanogaps as well as nanopores for gaining better insights.

Walsh–Hadamard Domain-Based Intelligent Online Fault Diagnosis of Broken Rotor Bars in Induction Motors

Mechanical failures are the most common defects in induction motors (IMs). They derive from the continuous starts and stops with high-load torque. They can cause interruptions in production lines with great economic losses. Therefore, online monitoring systems, allowing a continuous monitoring for fault diagnosis in early stages, have been intended for many researches in last years. Broken rotor bars (BRBs) are common failures, which are among the most difficult to detect since the induction motor works with apparent normality without giving any signal about the defect. Several methodologies have been proposed to detect BRBs in IMs. However, they require high-computational-complexity algorithms for parameterizing the current signal, making it difficult to achieve an online implementation of the corresponding monitoring system. Hence, aiming to overcome the limitations provoked by complex parameterizations of the current signal and to improve the fault classification accuracy, in this work, a novel methodology, based on a hardware processing system implemented on a field-programmable gate array (FPGA), for electric current signal analysis in the Walsh–Hadamard domain is proposed to achieve online intelligent BRB diagnosis. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -nearest neighbors ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -NN) algorithm is used for classifying different operational conditions from the Walsh–Hadamard transform (WHT) of the electric-current signal. Experimentally obtained results from applying the proposed method on acquired real data demonstrate that the introduced approach reaches a remarkable high efficiency on detecting and classifying BRBs.

Dielectric Manipulation of Polymer Translocation Dynamics in Engineered Membrane Nanopores.

The alteration of the dielectric membrane properties by membrane engineering techniques such as carbon nanotube (CNT) coating opens the way to novel molecular transport strategies for biosensing purposes. In this article, we predict a macromolecular transport mechanism enabling the dielectric manipulation of the polymer translocation dynamics in dielectric membrane pores confining mixed electrolytes. In the giant permittivity regime of these engineered membranes governed by attractive polarization forces, multivalent ions adsorbed by the membrane nanopore trigger a monovalent ion separation and set an electroosmotic counterion flow. The drag force exerted by this flow is sufficiently strong to suppress and invert the electrophoretic velocity of anionic polymers and also to generate the mobility of neutral polymers whose speed and direction can be solely adjusted by the charge and concentration of the added multivalent ions. These features identify the dielectrically generated transport mechanism as an efficient means to drive overall neutral or weakly charged analytes that cannot be controlled by an external voltage. We also reveal that, in anionic polymer translocation, multivalent cation addition into the monovalent salt solution amplifies the electric current signal by several factors. The signal amplification is caused by the electrostatic many-body interactions replacing the monovalent polymer counterions by the multivalent cations of higher electric mobility. The strength of this electrokinetic charge discrimination points out the potential of multivalent ions as current amplifiers capable of providing boosted resolution in nanopore-based biosensing techniques.

Technique for modeling random disturbances of the electric arcs lengths for tuning a nonlinear P-controller of impedance

There is a common disadvantage for most modern electric mode control systems of electric arc furnaces and ladle-furnaces, such as HI-REG, Q-REG, ARCOS, Simelt, Melt Expert, Ferrotron, Meltnet, Arcelec, EMPERE, Digitarc. This disadvantage is associated with the absence of technical solutions aimed at complete linearization of the electric and hydraulic circuit of the furnace as a control object. As a result, the system cannot be tuned to the technical optimum over the entire range of working arc lengths. One of the ways to solve this problem is the use of a nonlinear adaptive P-impedance controller, which provides complete linearization of the electrical and hydraulic circuits using special linearizing characteristics. In order to achieve the technical effect, expressed in reducing the dispersion of electric arc currents on different melting stages, it is necessary to select the optimal value of the gain of the adaptive controller. In practice, this problem may be solved iteratively using a mathematical model that makes it possible to form random disturbances along the length of an electric arc, corresponding in their statistical characteristics to real disturbances. Obviously, the signal of the the electric arc current effective value is not stationary and ergodic throughout the entire melt due to constantly changing conditions (for example, furnace transformer tap, reactor tap, the setting of the control parameter, the stage of melting, etc.). As a result, the task of identifying stationary and ergodic sections as part of the electric arc current signal is of particular relevance. Within the framework of the research carried out, a method has been developed for modeling random disturbances of the length for optimal tuning of the impedance non-linear controller.The research was conducted under financial support of the RF Ministry of Science&nbsp;and High Education (the project No. FZRU-2020-0011).

Method for assessing the current and additional load on the parallel kinematic structure mechanisms electric drive system

Introduction. The problem of the load on an electric drive system in a parallel kinematic structure is considered. The task of developing a fault-tolerant system that provides performing a given process in case of a failure of one or more drives is described. The work objective is to create a method for estimating the current and additional load on each drive of the mechanism of a parallel kinematic structure. The solution enables to correct the operating mode when performing a given process without compromising serviceable drives.Materials and Methods. Previously, a diagnostic method was developed. It is based on the calculation and analysis of the coefficients of straight lines that approximate the envelopes of the values of the wavelet transform coefficients of electric motor current signals, taking into account the characteristic scales. This makes it possible to determine the current technical condition of the electric motor and find malfunctions. The logical continuation of this approach is the proposed method for assessing the current and additional load. It provides finding the current load on the drive based on the coefficients of the lines approximating the envelopes of the wavelet coefficients of the current signal. To calculate the additional load, the number and location of faulty drives are taken into account.Results. For each scale of the wavelet coefficients, the relative coefficients and the current load on each drive are determined. The possibility of redistributing the load to two adjacent jacks was checked; the behavior of the system in this case was investigated. The load moved by the faulty jack is redistributed to two adjacent jacks in equal shares — 14.76 % each. The total load on the drives is 44.28 %, which is safe for the servo. The load on the drive of the fourth jack does not change (29.52 %). The drives have a sufficient safety margin. It is established that all three operating modes are acceptable for the studied servo drive, and they do not cause dynamic overloads and premature failure.Discussion and Conclusions. The experimental studies on the method of assessing the current and additional load have shown its adequacy and high efficiency. It was found that when the drives were disconnected from one of the racks of the mechanism, the system performed a load redistribution on the drives. Thus, it was possible to avoid their dynamic overloads and premature failure. This means that the solution is able to ensure the reliable functioning of the complex at the time of renovation work.

Electronic Conductance and Current Modulation through Graphdiyne Nanopores for DNA Sequencing

Solid-state nanopore devices have emerged as one of the most promising technologies that may decode the sequence of DNA nucleobases by reading electronic conductance and electric current signals. Herein we study the potential of atomically thick graphdiyne (GDY) to act as an all-electronic nanopore (NP)-based DNA sequencing device. The first-principles density functional theory method is used to study the structural properties, interaction energy (Ei) values, translocation time (τ), charge transfer [Q(e)] values, charge density difference [Δρ(r)], molecular orbital (MO) positions, and electronic density of states (DOS) properties of the GDYNP and GDYNP + nucleobase (nucleobases: adenine (A), guanine (G), thymine (T), and cytosine (C)) systems. Our results suggest that the GDYNP system could be a suitable device for the detection of nucleobases due to their specific translocation times inside the GDYNP device. The Δρ(r) results reveal that the charge fluctuates around the GDYNP edges close to the target nucleobase. This charge reorganization causes the modulation of charge transport in the GDYNP device. Furthermore, the GDYNP device is used to read-out the conductance and current signals of each DNA nucleobase while located inside the GDYNP by using nonequilibrium Green’s functions formalism. Our findings show a change in conductance with respect to the reference bare “GDYNP” device for energy values below/above the Fermi energy. The conductance as well as current sensitivity (%) values (C > G > T > A) indicates that the nucleobases could possibly be sequenced through the GDYNP device. Further, each nucleobase transmits unique current signals when transported through the GDYNP device. A distinction between purine- and pyrimidine-type nucleobases seems possible due to their different current signals. Thus, our study highlights the potentials that would motivate research toward the development of the GDYNP device, which may be even a better DNA nucleobase detector compared to graphene-based devices.

Non-intrusive load monitoring using artificial intelligence classifiers: Performance analysis of machine learning techniques

In recent years, strategies for load monitoring have been proposed to mitigate power consumption. It has been found, in several reported studies, that as more information is provided for consumers about their electricity consumption, more power energy conservation will occur. In this way, Non-Intrusive Load Monitoring (NILM) has been studied and applied in real-life applications. It consists of detecting and classifying appliances on/off states by measuring electrical signals only at one location of the residential consumer. Several studies have been made using different techniques to improve the accuracy of this strategy. In this paper electromagnetic transients are taking into account and, a performance analysis between cutting-edge artificial classifiers is made. It has been found that 1D convolutional neural networks perform better for this case and electrical current signals are more suitable for NILM, once it carries more features than voltage and power signals.

Detection of DNA Bases via Field Effect Transistor of Graphene Nanoribbon With a Nanopore: Semi-Empirical Modeling.

DNA sequencing techniques are critical in order to investigate genes' functions. Obtaining fast, accurate, and affordable DNA bases detection makes it possible to acquire personalized medicine. In this article, a semi-empirical technique is used to calculate the electron transport characteristics of the developed z-shaped graphene device to detect the DNA bases. The z-shaped transistor consists of a pair of zigzag graphene nanoribbon (ZGNR) connected through an armchair graphene nanoribbon (AGNR) channel with a nanopore where the DNA nucleobases are positioned. Non-equilibrium Green's function (NEGF) integrated with semi-empirical methodologies are employed to analyze the different electronic transport characteristics. The semi-empirical approach applied is an extension of the extended Hückel (EH) method integrated with self-consistent (SC) Hartree potential. By employing the NEGF+SC-EH, it is proved that each one of the four DNA nucleobases positioned within the nanopore, with the hydrogen passivated edge carbon atoms, results in a unique electrical signature. Both electrical current signal and transmission spectrum measurements of DNA nucleobases inside the device's pore are studied for the different bases with modification of their orientation and lateral translation. Moreover, the electronic noise effect of various factors is studied. The sensor sensitivity is improved by using nitrogen instead of hydrogen to passivate the nanopore and by adding a dual gate to surround the central semiconducting channel of the z-shaped graphene nanoribbon.

Dynamic performance of locomotive electric drive system under excitation from gear transmission and wheel-rail interaction

Development of railway transportation capabilities towards high-power, high-speed and heavy axle-load makes the dynamic interactions between the electric drive subsystem and the mechanical subsystem of a locomotive more intensive and complicated, which may threaten the locomotive operational safety. However, the included dynamics mechanism and influencing law have not been revealed completely. Based on the co-simulation method and vehicle-track coupling dynamics, a model which enables considering dynamic interactions between the electric drive subsystem and the mechanical subsystem of a locomotive through an electromagnetic coupling interface and motor control subsystem is developed in this paper. By using this co-simulation model, the dynamic responses of the mechanical subsystem and the electric drive subsystem are analysed under complicated dynamic excitation, such as the time-varying gear mesh stiffness and the track geometric irregularity. The results indicate an obvious interaction between the electric drive subsystem and the mechanical subsystem, where the vibration status of the major mechanical components can be reflected by the electric current signals. Besides, the characteristic frequencies of the gear transmission can be extracted from the frequency spectrum of the traction motor current signals, which provides the possibility for condition monitoring and fault diagnosis of the locomotive mechanical components through the electrical signals.

New Electronic Interface Circuits for Humidity Measurement Based on the Current Processing Technique

Abstract The paper describes a new electronic conditioning circuit based on the current-processing technique for accurate and reliable humidity measurement, without post-processing requirements. Pseudobrookite nanocrystalline (Fe2TiO5) thick film was used as capacitive humidity transducer in the proposed design. The interface integrated circuit was realized in TSMC 0.18 μm CMOS technology, but commercial devices were used for practical realization. The sensing principle of the sensor was obtained by converting the information on environment humidity into a frequency variable square-wave electric current signal. The proposed solution features high linearity, insensitivity to temperature, as well as low power consumption. The sensor has a linear function with relative humidity in the range of Relative Humidity (RH) 30-90 %, error below 1.5 %, and sensitivity 8.3 x 1014 Hz/F evaluated over the full range of changes. A fast recovery without the need of any refreshing methods was observed with a change in RH. The total power dissipation of readout circuitry was 1 mW.