Nullors, and Nullor Circuits;There Applications in Symbolic Circuit Analysis and Design

The objective in this presentation is to introduce some of the unique properties and applications of nullors in active circuit analysis and designs. The emphasis is to discuss the role nullors can play in symbolic representation of transfer functions. To show this we adopt the topological platform for the circuit analysis and use a recently developed Admittance Method (AM) to achieve the Sum of Tree Products (STP), replacing the determinant and cofactors of the Nodal Admittance Matrix (NAM) of the circuit. To construct a transfer function, we start with a given active circuit and convert all its controlled sources and I/O-ports to nullors. Now, with a solid nullor circuit (passive elements and nullors) we first eliminate the passive elements through AM operations. This produces the STPs. Second, the all-nullor circuit is then used to find the signs or the STPs. Finally, the transfer function (in symbolic, if chosen) is obtained from the ratio between the STPs.

Interplay between topology and localization on superconducting circuits

Topological insulators occupy a prominent position in the realm of condensed matter physics. Nevertheless, the presence of strong disorder has the potential to disrupt the integrity of topological states, leading to the localization of all states.This study delves into the intricate interplay between topology and localization within the one-dimensional Su–Schrieffer–Heeger(SSH) model, which incorporates controllable off-diagonal quasi-periodic modulations on superconducting circuits.Through the application of external alternating current(ac) magnetic fluxes, each transmon undergoes controlled driving,enabling independent tuning of all coupling strengths. Within a framework of this model, we construct comprehensive phase diagrams delineating regions characterized by extended topologically nontrivial states, critical localization, and coexisting topological and critical localization phases. The paper also addresses the dynamics of qubit excitations, elucidating distinct quantum state transfers resulting from the intricate interplay between topology and localization. Additionally, we propose a method for detecting diverse quantum phases utilizing existing experimental setups.

Quantum algorithm for minimum dominating set problem with circuit design

Using quantum algorithms to solve various problems has attracted widespread attention with the development of quantum computing.Researchers are particularly interested in using the acceleration properties of quantum algorithms to solve NP-complete problems.This paper focuses on the well-known NP-complete problem of finding the minimum dominating set in undirected graphs.To expedite the search process,a quantum algorithm employing Grover’s search is proposed.However,a challenge arises from the unknown number of solutions for the minimum dominating set,rendering direct usage of original Grover’s search impossible.Thus,a swap test method is introduced to ascertain the number of iterations required.The oracle,diffusion operators,and swap test are designed with achievable quantum gates.The query complexity is O(1.414^(n))and the space complexity is O(n).To validate the proposed approach,qiskit software package is employed to simulate the quantum circuit,yielding the anticipated results.

Neuromorphic circuits based on memristors: endowing robots with a human-like brain

Robots are widely used,providing significant convenience in daily life and production.With the rapid development of artificial intelligence and neuromorphic computing in recent years,the realization of more intelligent robots through a pro-found intersection of neuroscience and robotics has received much attention.Neuromorphic circuits based on memristors used to construct hardware neural networks have proved to be a promising solution of shattering traditional control limita-tions in the field of robot control,showcasing characteristics that enhance robot intelligence,speed,and energy efficiency.Start-ing with introducing the working mechanism of memristors and peripheral circuit design,this review gives a comprehensive analysis on the biomimetic information processing and biomimetic driving operations achieved through the utilization of neuro-morphic circuits in brain-like control.Four hardware neural network approaches,including digital-analog hybrid circuit design,novel device structure design,multi-regulation mechanism,and crossbar array,are summarized,which can well simulate the motor decision-making mechanism,multi-information integration and parallel control of brain at the hardware level.It will be definitely conductive to promote the application of memristor-based neuromorphic circuits in areas such as intelligent robotics,artificial intelligence,and neural computing.Finally,a conclusion and future prospects are discussed.

Quantum circuit-based proxy blind signatures:A novel approach and experimental evaluation on the IBM quantum cloud platform

This paper presents a novel approach to proxy blind signatures in the realm of quantum circuits,aiming to enhance security while safeguarding sensitive information.The main objective of this research is to introduce a quantum proxy blind signature(QPBS)protocol that utilizes quantum logical gates and quantum measurement techniques.The QPBS protocol is constructed by the initial phase,proximal blinding message phase,remote authorization and signature phase,remote validation,and de-blinding phase.This innovative design ensures a secure mechanism for signing documents without revealing the content to the proxy signer,providing practical security authentication in a quantum environment under the assumption that the CNOT gates are securely implemented.Unlike existing approaches,our proposed QPBS protocol eliminates the need for quantum entanglement preparation,thus simplifying the implementation process.To assess the effectiveness and robustness of the QPBS protocol,we conduct comprehensive simulation studies in both ideal and noisy quantum environments on the IBM quantum cloud platform.The results demonstrate the superior performance of the QPBS algorithm,highlighting its resilience against repudiation and forgeability,which are key security concerns in the realm of proxy blind signatures.Furthermore,we have established authentic security thresholds(82.102%)in the presence of real noise,thereby emphasizing the practicality of our proposed solution.

Analysis of multiple-faults of high-voltage circuit breakers based on non-negative matrix decomposition

High-voltage circuit breakers are the core equipment in power networks,and to a certain extent,are related to the safe and reliable operation of power systems.However,their core components are prone to mechanical faults.This study proposes a component separation method to detect multiple mechanical faults in circuit breakers that can achieve online real-time monitoring.First,a model and strategy are presented for obtaining mechanical voiceprint signals from circuit breakers.Subsequently,the component separation method was used to decompose the voiceprint signals of multiple faults into individual component signals.Based on this,the recognition of the features of a single-fault voiceprint signal can be achieved.Finally,multiple faults in high-voltage circuit breakers were identified through an experimental simulation and verification of the circuit breaker voiceprint signals collected from the substation site.The research results indicate that the proposed method exhibits excellent performance for multiple mechanical faults,such as spring structures and loose internal components of circuit breakers.In addition,it provides a reference method for the real-time online monitoring of high-voltage circuit breakers.

Understanding the correlation between energy-state mismatching and open-circuit voltage loss in bulk heterojunction solar cells

Photoinduced intermolecular charge transfer(PICT)determines the voltage loss in bulk heterojunction(BHJ)organic photovoltaics(OPVs),and this voltage loss can be minimized by inducing efficient PICT,which requires energy-state matching between the donor and acceptor at the BHJ interfaces.Thus,both geometrically and energetically accessible delocalized state matching at the hot energy level is crucial for achieving efficient PICT.In this study,an effective method for quantifying the hot state matching of OPVs was developed.The degree of energy-state matching between the electron donor and acceptor at BHJ interfaces was quantified using a mismatching factor(MF)calculated from the modified optical density of the BHJ.Furthermore,the correlation between the open-circuit voltage(Voc)of the OPV device and energy-state matching at the BHJ interface was investigated using the calculated MF.The OPVs with small absolute MF values exhibited high Voc values.This result clearly indicates that the energy-state matching between the donor and acceptor is crucial for achieving a high Voc in OPVs.Because the MF indicates the degree of energy-state matching,which is a critical factor for suppressing energy loss,it can be used to estimate the Voc loss in OPVs.

Electromagnetic Performance Analysis of Variable Flux Memory Machines with Series-magnetic-circuit and Different Rotor Topologies

In this paper,the electromagnetic performance of variable flux memory(VFM)machines with series-magnetic-circuit is investigated and compared for different rotor topologies.Based on a V-type VFM machine,five topologies with different interior permanent magnet(IPM)arrangements are evolved and optimized under same constrains.Based on two-dimensional(2-D)finite element(FE)method,their electromagnetic performance at magnetization and demagnetization states is evaluated.It reveals that the iron bridge and rotor lamination region between constant PM(CPM)and variable PM(VPM)play an important role in torque density and flux regulation(FR)capabilities.Besides,the global efficiency can be improved in VFM machines by adjusting magnetization state(MS)under different operating conditions.

Total ionizing dose effect modeling method for CMOS digital-integrated circuit

Simulating the total ionizing dose(TID)of an electrical system using transistor-level models can be difficult and expensive,particularly for digital-integrated circuits(ICs).In this study,a method for modeling TID effects in complementary metaloxide semiconductor(CMOS)digital ICs based on the input/output buffer information specification(IBIS)was proposed.The digital IC was first divided into three parts based on its internal structure:the input buffer,output buffer,and functional area.Each of these three parts was separately modeled.Using the IBIS model,the transistor V-I characteristic curves of the buffers were processed,and the physical parameters were extracted and modeled using VHDL-AMS.In the functional area,logic functions were modeled in VHDL according to the data sheet.A golden digital IC model was developed by combining the input buffer,output buffer,and functional area models.Furthermore,the golden ratio was reconstructed based on TID experimental data,enabling the assessment of TID effects on the threshold voltage,carrier mobility,and time series of the digital IC.TID experiments were conducted using a CMOS non-inverting multiplexer,NC7SZ157,and the results were compared with the simulation results,which showed that the relative errors were less than 2%at each dose point.This confirms the practicality and accuracy of the proposed modeling method.The TID effect model for digital ICs developed using this modeling technique includes both the logical function of the IC and changes in electrical properties and functional degradation impacted by TID,which has potential applications in the design of radiation-hardening tolerance in digital ICs.

Fluid-chemical modeling of the near-cathode sheath formation process in a high current broken in DC air circuit breaker

When the contacts of a medium-voltage DC air circuit breaker(DCCB) are separated, the energy distribution of the arc is determined by the formation process of the near-electrode sheath. Therefore, the voltage drop through the near-electrode sheath is an important means to build up the arc voltage, which directly determines the current-limiting performance of the DCCB. A numerical model to describe the near-electrode sheath formation process can provide insight into the physical mechanism of the arc formation, and thus provide a method for arc energy regulation. In this work, we establish a two-dimensional axisymmetric time-varying model of a medium-voltage DCCB arc when interrupted by high current based on a fluid-chemical model involving 16 kinds of species and 46 collision reactions. The transient distributions of electron number density, positive and negative ion number density, net space charge density, axial electric field, axial potential between electrodes, and near-cathode sheath are obtained from the numerical model. The computational results show that the electron density in the arc column increases, then decreases, and then stabilizes during the near-cathode sheath formation process, and the arc column's diameter gradually becomes wider. The 11.14 V–12.33 V drops along the17 μm space charge layer away from the cathode(65.5 k V/m–72.5 k V/m) when the current varies from 20 k A–80 k A.The homogeneous external magnetic field has little effect on the distribution of particles in the near-cathode sheath core,but the electron number density at the near-cathode sheath periphery can increase as the magnetic field increases and the homogeneous external magnetic field will lead to arc diffusion. The validity of the numerical model can be proven by comparison with the experiment.

基于MSPA-MCR-CIRCUIT的山西省运城市景观生态网络构建

山西省运城市位于黄河中下游三省交界处,其景观要素成分复杂,对运城景观生态网络斑块与廊道的研究有利于黄河生态系统稳定性的保护。基于形态空间格局理论(MSPA)确定连通性强的核心斑块,识别核心生态源地,以最小累计阻力模型(MCR)为基础,叠加7种阻力因子构建综合阻力面,根据重力模型划分生态廊道等级。以基于电路理论(CIRCUIT)的Linkage Mapper工具识别生态夹点和障碍点作为生态节点,综合构建源地(面)-廊道(线)-节点(点)的运城市景观生态网络。结果表明:1)运城市核心生态源地有9处,综合重力模型和实际建设需求划分13条重要生态廊道和10条一般生态廊道,识别出关键生态节点17个,一般生态节点34个;2)现有重要生态廊道集中于运城南部,南北方向生态联系较弱,可优先加强贯穿稷山县、新绛县、闻喜县的重要生态廊道建设;3)MSPA模型与电路理论的综合运用可提升景观生态网络构建的科学性和准确性,有利于确定生态修复关键区域,为生态修复项目的布局提供有力支撑。

Design of Cold-Junction Compensation and Disconnection Detection Circuits of Various Thermocouples and Implementation of Multi-Channel Interfaces Using Them-A Secondary Publication

Cold-junction compensation(CJC)and disconnection detection circuit design of various thermocouples(TC)and multi-channel TC interface circuits were designed.The CJC and disconnection detection circuit consists of a CJC semiconductor device,an instrumentation amplifier(IA),two resistors,and a diode for disconnection detection.Based on the basic circuit,a multi-channel interface circuit was also implemented.The CJC was implemented using compensation semiconductor and IA,and disconnection detection was detected by using two resistors and a diode so that IA input voltage became-0.42 V.As a result of the experiment using R-type TC,the error of the designed circuit was reduced from 0.14 mV to 3μV after CJC in the temperature range of 0°C to 1400°C.In addition,it was confirmed that the output voltage of IA was saturated from 88 mV to-14.2 V when TC was disconnected from normal.The output voltage of the designed circuit was 0 V to 10 V in the temperature range of 0°C to 1400°C.The results of the 4-channel interface experiment using R-type TC were almost identical to the CJC and disconnection detection results for each channel.The implemented multi-channel interface has a feature that can be applied equally to E,J,K,T,R,and S-type TCs by changing the terminals of CJC semiconductor devices and adjusting the IA gain.

Thermodynamic Performance of Three-Terminal Hybrid Quantum Dot Thermoelectric Devices

We propose four different models of three-terminal quantum dot thermoelectric devices. From general thermodynamic laws, we examine the rew;rsible efficiencies of the four different models. Based on the master equation, the expressions for the efficiency and power output are derived and the corresponding working regions are determined. Moreover, we particularly analyze the performance of a three-terminal hybrid quantum dot refrigerator. The performance characteristic curves and the optimal performance parameters are obtained. Finally, we discuss the influence of the nonradiative effects on the optimal performance parameters in detail.

Emerging MoS_(2)Wafer-Scale Technique for Integrated Circuits

As an outstanding representative of layered materials,molybdenum disulfide(MoS_(2))has excellent physical properties,such as high carrier mobility,stability,and abundance on earth.Moreover,its reasonable band gap and microelectronic compatible fabrication characteristics makes it the most promising candidate in future advanced integrated circuits such as logical electronics,flexible electronics,and focal-plane photodetector.However,to realize the all-aspects application of MoS_(2),the research on obtaining high-quality and large-area films need to be continuously explored to promote its industrialization.Although the MoS_(2)grain size has already improved from several micrometers to sub-millimeters,the high-quality growth of wafer-scale MoS_(2)is still of great challenge.Herein,this review mainly focuses on the evolution of MoS_(2)by including chemical vapor deposition,metal–organic chemical vapor deposition,physical vapor deposition,and thermal conversion technology methods.The state-of-the-art research on the growth and optimization mechanism,including nucleation,orientation,grain,and defect engineering,is systematically summarized.Then,this review summarizes the wafer-scale application of MoS_(2)in a transistor,inverter,electronics,and photodetectors.Finally,the current challenges and future perspectives are outlined for the wafer-scale growth and application of MoS_(2).

A Three-Terminal Quantum Well Heat Engine with Heat Leakage

We propose a model for a three-terminal quantum well heat engine with heat leakage. According to the Landauer formula, the expressions for the charge current, the heat current, the power output and the efficiency are derived in the linear-response regime. The curves of the power output and the efficiency versus the positions of energy levels and the bias voltage are plotted by numerical calculation. Moreover, we obtain the maximum power output and the corresponding efficiency, and analyze the influence of the heat leakage factor, the positions of energy levels and the bias voltage on these performance parameters.

Data-driven short circuit resistance estimation in battery safety issues

Developing precise and fast methods for short circuit detection is crucial for preventing or mitigating the risk of safety issues of lithium-ion batteries(LIBs).In this paper,we developed a Convolutional Neural Networks(CNN)based model that can quickly and precisely predict the short circuit resistance of LIB cells during various working conditions.Cycling tests of cells with an external short circuit(ESC)are produced to obtain the database and generate the training/testing samples.The samples are sequences of voltage,current,charging capacity,charging energy,total charging capacity,total charging energy with a length of 120 s and frequency of 1 Hz,and their corresponding short circuit resistances.A big database with~6×10^(5)samples are generated,covering various short circuit resistances(47~470Ω),current loading modes(Constant current-constant voltage(CC-CV)and drive cycle),and electrochemical states(cycle numbers from 1 to 300).Results show that the average relative absolute error of five random sample splits is 6.75%±2.8%.Further parametric analysis indicates the accuracy estimation benefits from the appropriate model setups:the optimized input sequence length(~120 s),feature selection(at least one total capacity-related variable),and rational model design,using multiple layers with different kernel sizes.This work highlights the capabilities of machine learning algorithms and data-driven methodologies in real-time safety risk prediction for batteries.

Power Prediction of VLSI Circuits Using Machine Learning

The difference between circuit design stage and time requirements has broadened with the increasing complexity of the circuit.A big database is needed to undertake important analytical work like statistical method,heat research,and IR-drop research that results in extended running times.This unit focuses on the assessment of test strength.Because of the enormous number of successful designs for currentmodels and the unnecessary time required for every test,maximum energy ratings with all tests cannot be achieved.Nevertheless,test safety is important for producing trustworthy findings to avoid loss of output and harm to the chip.Generally,effective power assessment is only possible in a limited sample of pre-selected experiments.Thus,a key objective is to find the experiments that might give the worst situations again for testing power.It offers a machine-based circuit power estimation(MLCPE)system for the selection of exams.Two distinct techniques of predicting are utilized.Firstly,to find testings with power dissipation,it forecasts the behavior of testing.Secondly,the changemovement and energy data are linked to the semiconductor design,identifying small problem areas.Several types of algorithms are utilized.In particular,the methods compared.The findings show great accuracy and efficiency in forecasting.That enables such methods suitable for selecting the worst scenario.

Fault Diagnosis of Power Electronic Circuits Based on Adaptive Simulated Annealing Particle Swarm Optimization

In the field of energy conversion,the increasing attention on power electronic equipment is fault detection and diagnosis.A power electronic circuit is an essential part of a power electronic system.The state of its internal components affects the performance of the system.The stability and reliability of an energy system can be improved by studying the fault diagnosis of power electronic circuits.Therefore,an algorithm based on adaptive simulated annealing particle swarm optimization(ASAPSO)was used in the present study to optimize a backpropagation(BP)neural network employed for the online fault diagnosis of a power electronic circuit.We built a circuit simulation model in MATLAB to obtain its DC output voltage.Using Fourier analysis,we extracted fault features.These were normalized as training samples and input to an unoptimized BP neural network and BP neural networks optimized by particle swarm optimization(PSO)and the ASAPSO algorithm.The accuracy of fault diagnosis was compared for the three networks.The simulation results demonstrate that a BP neural network optimized with the ASAPSO algorithm has higher fault diagnosis accuracy,better reliability,and adaptability and can more effectively diagnose and locate faults in power electronic circuits.

Optimal Performance Analysis of a Three-Terminal Thermoelectric Refrigerator with Ideal Tunneling Quantum Dots

The model of a three-terminal thermoelectric refrigerator with ideal tunneling quantum dots is established. It consists of a cavity connected to two quantum dots embedded between two electron reservoirs at different temperatures and chemical potentials. According to the Landauer formula the expressions for the heat current, the cooling rate and the coefficient of performance （COP） are derived analytically. The performance characteristic curves of the cooling rate versus the coefficient of performance are plotted with numerical calculation. The optimal regions of the cooling rate and the COP are determined. Moreover, we optimize the cooling rate and the COP with respect to the position of energy level of the right quantum dot, respectively. The influence of the width of energy level and the temperature ratio on performance of the three-terminal thermoelectric refrigerator is analyzed. Lastly, when the width of energy level is small enough, the optimal performance of the refrigerator is discussed in detail.

A Fault Feature Extraction Model in Synchronous Generator under Stator Inter-Turn Short Circuit Based on ACMD and DEO3S

This paper proposed a new diagnosis model for the stator inter-turn short circuit fault in synchronous generators.Different from the past methods focused on the current or voltage signals to diagnose the electrical fault,the sta-tor vibration signal analysis based on ACMD(adaptive chirp mode decomposition)and DEO3S(demodulation energy operator of symmetrical differencing)was adopted to extract the fault feature.Firstly,FT(Fourier trans-form)is applied to the vibration signal to obtain the instantaneous frequency,and PE(permutation entropy)is calculated to select the proper weighting coefficients.Then,the signal is decomposed by ACMD,with the instan-taneous frequency and weighting coefficient acquired in the former step to obtain the optimal mode.Finally,DEO3S is operated to get the envelope spectrum which is able to strengthen the characteristic frequencies of the stator inter-turn short circuit fault.The study on the simulating signal and the real experiment data indicates the effectiveness of the proposed method for the stator inter-turn short circuit fault in synchronous generators.In addition,the comparison with other methods shows the superiority of the proposed model.