Loop Detectors Research Articles

Josephson diode effect in monolithic dc-SQUIDs based on 3D Dayem nanobridges

It was recently experimentally proved that the superconducting counterpart of a diode, i.e., a device that realizes nonreciprocal Cooper pairs transport, can be realized by breaking the spatial and time-reversal symmetry of a system simultaneously. Here, we report the theory, fabrication, and operation of a monolithic dc superconducting quantum interference device that embedding three-dimensional Dayem nanobridges as weak links realizes an efficient and magnetic flux-tunable supercurrent diode. The device is entirely realized in Al and achieves a maximum rectification efficiency of ∼20%, which stems from the high harmonic content of its current-to-phase relation only without the need of any sizable screening current caused by a finite loop inductance. Our interferometer can be easily integrated with state-of-the-art superconducting electronics and since it does not require a finite loop inductance to provide large rectification, its downsizing is not limited by the geometrical constraints of the superconducting ring.

Energy efficient half-flux-quantum circuit aiming at milli-kelvin stage operation

Half-flux-quantum (HFQ) circuits are based on 0–π superconducting quantum interference devices (SQUIDs) and is one of the energy-efficient superconductor digital circuits. The bit energy is determined by the critical current I cn of 0–π SQUID, which can be easily tuned with the loop inductance and junction critical current. In this work, an alternative π–π–π SQUID is adopted to demonstrate HFQ circuits to simplify the fabrication process and enhance circuit energy efficiency. The properties of superconductor/ferromagnet/insulator/superconductor Josephson junctions (π-JJs) are measured with temperature dependence from 4.2 K down to 10 mK. HFQ toggle flip-flops (TFFs) are successfully demonstrated at frequencies of up to 6.7 GHz and 44.5 GHz at temperatures of 4.2 K and 10 mK, respectively. Comparing the HFQ TFF with its rapid single-flux quantum counterpart under the same fabrication process, it is anticipated that the HFQ TFF will exhibit approximately 70% reduction in both static and dynamic energy dissipation. This research establishes the foundation for developing cryogenic interface control and readout circuits for large-scale quantum computing in the future.

Physics-informed deep learning for traffic state estimation based on the traffic flow model and computational graph method

Traffic state estimation (TSE) is a critical task for intelligent transportation systems. However, it is extremely challenging because the traffic data quality is often affected by the installation position of devices, data collection frequency, interference during the transmission process, etc., thus causing the problem of data sparsity or data missing. To address the issue of traffic state estimation under the scenario of data sparsity, we propose a TSE model that combines the computational graph with physics-informed deep learning (PIDL) methods. Firstly, we apply the computational graph method to determine the parameters of the traffic fundamental diagram. These parameters are embedded into the computational graph framework, and their values are determined through the forward propagation of variables and the backward propagation of errors. Next, we employ the PIDL method to realize TSE (taking the LWR model based on the Greenshields fundamental diagram as an example). The PIDL leverages the advantages of data-driven and model-driven approaches to achieve accurate traffic state estimation. Case studies are conducted using the NGSIM dataset under two sparse data scenarios: loop detectors and probe vehicles. Experimental results demonstrate that PIDL can accurately reconstruct the traffic state of the entire road segment based on partially observed data. Furthermore, compared to pure deep learning methods and other baseline models, PIDL performs better in situations with sparse data, thereby proving the feasibility of integrating domain knowledge with deep learning frameworks. This paper fully acknowledges the issue of data sparsity in TSE and effectively addresses it by applying the PIDL method to achieve precise TSE, which holds significant implications for the control and management of real traffic flow.

Impedance Response of Electrochemical Interfaces: Part IV─Low-Frequency Inductive Loop for a Single-Electron Reaction

Impedance Response of Electrochemical Interfaces: Part IV─Low-Frequency Inductive Loop for a Single-Electron Reaction

Deep Reinforcement Learning Two-Way Transit Signal Priority Algorithm for Optimizing Headway Adherence and Speed

Transit Signal Priority (TSP) is a broadly used traffic signal control strategy designed for reducing transit delays at signalized intersections. Although recent TSP systems began to consider more objectives, TSPs that addressed transit reliability issues commonly focused on improving schedule adherence and were only able to reduce schedule delays by expediting buses. Buses running ahead of the schedule were not considered. This paper proposed a dual-objective two-way TSP algorithm (D2 TSP) using Deep Reinforcement Learning (DRL). D2 TSP concurrently optimizes transit delays and reliability (i.e., headway adherence) by expediting late buses or delaying early buses. Further, the DRL agents were enhanced with a coordination algorithm for an optimized solution balancing opposite directions. This D2 TSP reacts adaptively and efficiently to real-time bus performance using data provided by readily available technology (loop detector) at low communication frequencies. We trained and tested this algorithm in a stochastic microsimulation environment in Aimsun Next that modelled a transit route segment with reliability issues in the City of Toronto. The performance of D2 TSP was compared with four baseline scenarios, one without TSP, one with the current TSP algorithm used in the field in the City of Toronto, one conditional TSP with an arrival prediction model, and one using DRL agents with a First-Come-First-Served logic. D2 TSP demonstrated its advantages in providing an efficient and balanced solution in reducing headway variability and travel time for both directions.

Analysis of the Effect of 0-π SQUIDs for Enhancing Mutual Coupling

This study shows that we can obtain an enhanced mutual coupling by using a superconducting quantum interference device (SQUID) containing a π-phase shift Josephson junction (π-junction). The π-junction can increase the current flowing in the loop inductance of the SQUID, thereby enhancing the magnetic flux generated in the inductance. Therefore, the effective self-inductance of the SQUID can increase, resulting in an increase in the mutual coupling. We use a 0-π SQUID formed by a conventional Josephson junction (0-junction) and a π-junction. Our numerical analysis for a simplified structure revealed that the ratio of effective to original self-inductance increased to 50. To confirm this, we measured the modulation patterns of critical current in dc-SQUIDs in which part of the loop inductance was replaced with a series of 0-π SQUIDs. The observed modulation pattern revealed that the effective self-inductance became more than 10-fold larger than the original self-inductance under the conditions that the applied current is a few μA while the critical current of the 0-junction in 0-π SQUIDs is 35 μA.

Qubit energy tuner based on single flux quantum circuits

A device called the qubit energy tuner (QET), based on single flux quantum (SFQ) circuits, has been proposed for Z control of superconducting qubits. The QET is created by improving flux digital-to-analog converters (flux DACs). It can set the energy levels or frequencies of qubits, particularly flux-tunable transmons, and perform gate operations requiring Z control. The circuit structure of the QET is elucidated, consisting of an inductor loop and flux bias units for coarse or fine-tuning. The key feature of the QET is analyzed to understand how SFQ pulses change the inductor loop current, which provides external flux for qubits. Three simulations were performed to verify QET functionality. The first simulation verified the responses of the inductor loop current to SFQ pulses, showing a relative deviation of approximately 4.259% between the analytical solutions of the inductor loop current and the solutions from the WRSpice time-domain simulation. The second and third simulations, using QuTip, demonstrated how to perform a Z gate and an iSWAP gate using the QET, respectively, with corresponding fidelities of 99.99884% and 99.93906% for only one gate operation to specific initial states. These simulations indicate that the SFQ-based QET could act as an efficient component of SFQ-based quantum–classical interfaces for digital Z control of large-scale superconducting quantum computers.

Comparison of Double Relaxation Oscillation SQUIDs and DC-SQUIDs of Large Stewart-McCumber Parameter

Increasing the flux-to-voltage transfer coefficient of the SQUID output voltage is necessary to release the constraint on the preamplifier input noise. As one of the approaches to increase the transfer coefficient, we have been using a double relaxation oscillation SQUID (DROS) having a transfer coefficient of about 10 times larger than those of DC-SQUIDs. Traditionally, DC-SQUIDs were operated in the non-hysteretic condition with the Stewart-McCumber parameter (β <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sub> ) less than 1. In this study, we fabricated hysteretic DC-SQUIDs with the β <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sub> ranging from 2.5 and compared the transfer coefficient and the system noise of DC-SQUIDs and DROSs. Both DROS and DC-SQUID have the same structure and design parameters for the SQUID loop inductance, resonance-damping circuits in the SQUID loop, and input coil. All the SQUIDs were fabricated in the same wafer using the Nb/AlO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> /Nb junction process. The transfer coefficients of DROSs were about 1 mV/Φ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> , regardless of SQUID parameters. For DC-SQUIDs, the transfer coefficients were sensitive to the β <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sub> and the operating margin was narrower than that of DROS. The white noise of both SQUIDs in the flux-locked loop mode was around 1.5 μΦ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> /√Hz at 100 Hz, measured with a preamplifier of input voltage noise of 1 nV/√Hz. DC-SQUIDs had mostly a narrower operating margin against the bias current, and the modulation voltage was smaller than DROS. In a near-optimized operating condition, both DROS and hysteretic DC-SQUID showed comparable noise performance.

Design of Ultracompact Gate Driver Integrated With Current Sensor and Commutation Path for a 211-kW Three-Level SiC Aircraft Propulsion Inverter

The aviation industry is increasingly interested in high-efficiency and high-density electric propulsion systems enabled by high-power Silicon Carbide (SiC) modules. However, designing a high-power SiC-based multi-level inverter for aircraft faces several challenges, including high loop inductance, bulky current sensors, high-bandwidth sampling of device current, partial discharge (PD) at high altitudes, and shorter short-circuit withstanding time of SiC MOSFETs. To address these challenges, this paper proposes an ultra-compact multi-functional gate driver that integrates the driving circuit, Rogowski coil current sensor (RCCS), and commutation path for a 211-kW SiC-based three-level (3-L) propulsion inverter. The optimized commutation path and RCCS provide multiple functions, including suppressing overshoot voltage, ultra-fast short-circuit protection, and ac load current sampling. The gate driver’s electric-field can be controlled by PCB-based shielding and shaping to eliminate the PD at high altitudes, thereby improving power density and reducing insulation risk. The paper presents the design methodology of the multi-functional ultra-compact gate-driver and the board-level to system-level common-mode (CM) noise modeling and suppression. The performance of the gate driver is verified on a 211-kW 3-L altitude-ready SiC propulsion inverter system, achieving a power density of 19.5 kW/kg and 7.1 kW/L.

Optimization of Half-Flux-Quantum Circuits Composed of π-Shift and Conventional Josephson Junctions

We report optimization of half-flux-quantum (HFQ) circuits composed of SQUIDs containing conventional Josephson junctions and π-shift magnetic Josephson junctions. The SQUID can act as a Josephson junction with an extremely small critical current and be easily switched by a weak driving force with the assistance of the circulating current that is induced due to the π-shift and quantization conditions. It leads to a drastic reduction of both static and dynamic power consumption. However, the parameter margins of the HFQ circuits obtained so far is narrow compared with conventional single-flux-quantum circuits. Since HFQ circuits use many circuit elements and require the two junctions in the SQUID to switch alternately and symmetrically, circuit parameter optimization is much more difficult. In this study, we developed an optimization tool for HFQ circuits, including operation judgment and margin calculation. We improved the critical margin of the HFQ Josephson transmission line to 39%. We also optimized the HFQ splitter, confluence buffer, and D flip-flop. We discuss design guidelines for HFQ circuits based on our systematic exploration of the optimized circuit parameter sets. We suggest that the optimized loop inductances are around 0.5 and 1.5 of Φ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> /2 for data transmission and storage, respectively.

High Performance SiC Power Module Based on Repackaging of Discrete SiC Devices

Increased adoption of electric vehicles (EV), photovoltaic (PV), and battery energy storage systems (BESS) is driving the need for high-current SiC power modules. The state-of-the-art multichip module is substantially more expensive than the IGBT module. This paper proposes a cost-effective packaging methodology for high-power SiC intelligent power modules (IPMs) with discrete SiC devices. The proposed IPM integrates the gate drivers, decoupling capacitors, snubbers, discrete SiC devices, direct bond copper (DBC), base plate, temperature sensors, and overcurrent protection circuits, achieving very low loop inductance and thermal resistance, and more than 50% cost reduction. A 1200V/480A/2.15mΩ half-bridge IPM is presented for comparison with commercial counterparts. A 1200V/240A/4.3mΩ six-phase IPM is developed, and its performance is experimentally verified.

Effects of loop detector position on the macroscopic fundamental diagram

Loop detectors are probably the widest-used technology for traffic state estimation. Previous research has shown that loop detector positions within the link significantly affect the estimation of the macroscopic fundamental diagram (MFD) of a given network. This paper examines the biases produced by the positioning of loop detectors on the MFD, using both analytical and simulation methods, as well as empirical data from UTD19. We confirm earlier results that a uniform distribution of loop detector positions reduces the bias. We discovered that: (i) subsets of the MFD determined by the loop detector position can help estimate whether the loop detector MFD will have a bias; (ii) non-uniform distribution of loop detectors is more likely to cause a discrepancy in the position subsets of the MFD, particularly if detectors in the network are positioned more downstream with a greater variation; and (iii) a lower ratio of link length to green signal time increases the possibility of bias in loop detector MFD, while the impact of the aggregation interval was found to be negligible. This research opens the possibility for the bias of MFD induced by the loop detector data to be corrected by only using itself.

Eupatorium adenophorum Spreng leaves extract/potassium iodide as a highly sustainable inhibitor for the corrosion protection of steel in citric acid solution

Plants extract inhibitor has the standout merits of low-cost, eco-friendly and rich natural raw resources. The synergism between Eupatorium adenophorum Spreng leaves extract (EASLE) and KI for the corrosion of cold rolled steel (CRS) in citric acid (H3C6H5O7) solution was tested in detail by weight loss and electrochemical techniques. The micro-morphology of CRS surface was fully characterized by both scanning electron microscope (SEM) and atomic force microscope (AFM). Fourier transform infrared spectrometer (FTIR) and high performance liquid chromatography (HPLC) were employed to deeply analyze the main bio-active components in EASLE. The results show that either EASLE or KI can moderately inhibits the corrosion of CRS, and their maximum inhibition efficiency values are below 75%. As EASLE is combined with KI, their mixture has a better inhibition of over 90%. There is a synergism between EASLE and KI, and the corresponding synergism parameters are all higher than unity with a highest value of 3.5 for 400 mg L-1 EASLE/ 100 mg L-1 KI at 60 °C. The adsorption of EASLE, KI or EASLE/KI mixture follows Langmuir isotherm, and the adsorption is strengthened for the synergistic mixture. EASLE mainly retards the anodic reaction, while EASLE/KI mixture drastically inhibits both cathodic and anodic reactions simultaneously with a low corrosion current densities of 17.8 µA cm−2. Nyquist graph appears a depressed capacitive loop at high frequencies followed by an inductive loop at low frequencies. EASLE/KI mixture has larger charge transfer resistance (Rt) comparing with either EASLE or KI. Rt values for 500 mg L-1 EASLE, 100 mg L-1 KI and 500 mg L-1 EASLE/100 mg L-1 KI mixture are 464.9, 382.9 and 1068 Ω cm2, respectively. SEM and AFM micro-images clearly confirm that the corrosion extent as well as surface roughness of CRS surface were sharply dropped after adding EASLE/KI mixture. The main bio-active components in EASLE are phenylpropanoids like chlorogenic acid, caffeic acid and ferulic acid.

Travel Time Prediction Utilizing Hybrid Deep Learning Models

Travel time prediction is vital to the development and maintainence of advanced intelligent transportation system technologies. The travel time on a road segment is dependent on various factors like dynamic traffic demands, incidents, weather conditions, and geometric factors. However, uncertainties associated with prediction performance consistency may reduce the effectiveness of such systems. To tackle these challenges, this paper proposes a hybrid deep learning algorithm-based methodology by integrating variational mode decomposition, multivariate long short-term memory, and quantile regression to predict estimates of travel time ranges instead of single-point predictions. Travel time data collected from loop detectors on motorways near the city of Dublin, Republic of Ireland were modeled. The proposed method was evaluated using various design scenarios and was found to perform efficiently in comparison with conventional deep learning algorithms.

Multivariate time-varying Kalman filter approach for cycle-based maximum queue length estimation

Real-time queue length is a crucial information, increasingly needed for signal operation and signal optimization purposes. This paper proposes a novel multivariate time-varying Kalman filter approach to estimate the cycle-based maximum queue lengths in real-time by only using high-resolution vehicle loop detector data and signal timing data. The modeling framework builds on state changing points that can be identified at detector site and shockwave theory that models the propagation of traffic states in the space–time domain. The times at which traffic states change at the detector site, known as break points (e.g., free flow to jam condition, jam to saturation condition, etc.) are identified using high resolution detector data and the end of the queue is estimated using linear models that build on the shockwave theory. Nevertheless, this deterministic approach, which is previously proposed in the literature, does not account for measurements errors associated with traffic state changing points or the assumptions in the shockwave theory. Therefore, we propose a multivariate time-varying Kalman filter approach to produce a robust estimate for the cycle-based maximum queue length in both undersaturated and oversaturated conditions. Further, the developed algorithm can estimate the residual queue length and inform its occurrence in case of oversaturation. The proposed methodology can easily be adapted in practice, as it relies solely on measurements from a single loop detector that may be located close to the stop line.

A flexible and scalable single-level framework for OD matrix inference using IoT data

This study proposes a flexible and scalable single-level framework for origin–destination matrix (ODM) inference using data from IoT (Internet of Things) and other sources. The framework allows the analyst to integrate information from multiple data sources, while controlling for differences in data quality across sources. We assess the effectiveness of the framework through a real-world experiment in Greater Adelaide (GA), Australia. We infer car OD flows within the region using four separate data sources: site-level traffic counts from loop detectors, vehicle trajectories recorded by roadside Bluetooth sensors, partial OD flows based on data from in to vehicle navigation systems, and journey-to-work OD data collected by the Australian Census. We compare our OD inferences with those from the current version of the Strategic Adelaide Model (SAM), calibrated using data from traditional household travel surveys. We find remarkable consistency between our inferences and those from SAM, despite differences in input data and methodologies. For example, for the morning peak period, we predict the total number of trips made within GA to be equal to 556,000, while the corresponding prediction from SAM is equal to 484,000. The two predictions are within 15 per cent of each other. When we compare the spatial distribution of trips, in terms of origins and destinations, we find that our inferred OD matrix has an 86 per cent cosine similarity to the corresponding SAM matrix. Travel time predictions based on our OD inferences correspond closely to those predicted by the Google Directions API, lending further confidence in our inferences. In summary, our results show that the proposed framework can produce highly comparable ODM, trip production and trip attraction patterns to those inferred from traditional household travel survey-based transportation demand modelling methods.

Analytical formulation for explaining the variations in traffic states: A fundamental diagram modeling perspective with stochastic parameters

Despite the simplicity and practicality of (deterministic) fundamental diagram models in highway traffic flow theory, the wide scattering effect observed in empirical data remains highly controversial, particularly for explaining traffic state variations. Owing to the analytical properties of the fundamental diagram modeling approach, in this study, we proposed an analytical and quantitative method for analyzing traffic state variations. We investigated the scattering effect in the fundamental diagram and proposed two stochastic fundamental diagram (SFD) models with lognormal and skew-normal distributions to explain the variations in traffic states. The first SFD model assumes that the scattering effect results from stochasticity in both the free-flow speed and the speed at critical density. Both random variables were assumed to follow the lognormal distribution. In the second SFD model, an integrated error term that was assumed to follow the skew-normal distribution over different density ranges was appended to the deterministic fundamental diagram. The properties of these two SFD models were analyzed and compared, and the parameters in these SFD models were calibrated using real-world loop detector data. The observed scatters from the empirical data were reproduced well by the simulated fundamental diagram model, indicating the validity of the proposed SFD models for explaining traffic state variations. Using these two analytical SFD models, we can analyze the stochastic capacity of freeways with closed forms. More importantly, the sources of stochasticity in freeway capacity can be traced in terms of randomly distributed parameters in fundamental diagram models.

A dynamic self-improving ramp metering algorithm based on multi-agent deep reinforcement learning

ABSTRACT We present a novel ramp metering algorithm that incorporates multi-agent deep reinforcement learning (DRL) techniques, which utilizes monitoring data from loop detectors. Our proposed approach employed a multi-agent DRL framework to generate optimized ramp metering schedules for each ramp meter in real-time, enhancing the operational efficiency of urban freeways with less investment. To simplify the implementation and training of the algorithm, we developed a simulation platform based on SUMO microscopic traffic simulator. We conducted a series of simulation experiments, including local and coordinated ramp metering scenarios with various traffic demands profiles. The simulation results indicate that the proposed DRL-based algorithm outperforms the state-of-the-practice ramp metering methods, considering a comprehensive evaluation index encompassing mainstream speed at the bottleneck and queue length on ramp. Additionally, the method exhibits robustness, scalability, and the potential for further improvement through online learning during implementation.

An Effective Imputation Method Using Data Enrichment for Missing Data of Loop Detectors in Intelligent Traffic Control Systems

In intelligent traffic control systems, the features extracted by loop detectors are insufficient to accurately impute missing data. Most of the existing imputation methods use only these extracted features, which leads to the construction of data models that cannot fulfill the required accuracy. This deficiency is the main motivation to propose an enrichment imputation method for loop detectors namely EIM-LD, in which the imputation accuracy is increased for different missing patterns and ratios by introducing a data enrichment technique using statistical multi-class labeling. It first enriches the clean data by adding a statistical multi-class label, including C1…Cn classes. Then, the class of samples in the missed-volume data is labeled using the best data model constructed from the labeled clean data by five different classifiers. Experts of the traffic control department in Isfahan city determined classes of the statistical multi-class label for n = 5 (class labels), and we also developed subclass labels (n = 20) since the number of samples in the subclass labels was sufficient. Next, the enriched data are divided into n datasets, each of them is imputed independently using various imputation methods, and their results are finally merged. To evaluate the impact of using the proposed method, the original data, including missing volumes, are first imputed without our enrichment method. Then, the proposed method’s accuracy is evaluated by considering two class labels and subclass labels. The experimental and statistical results prove that the proposed EIM-LD method can enrich the real data collected by loop detectors, by which the comparative imputation methods construct a more accurate data model. In addition, using subclass labels further enhances the imputation method’s accuracy.

Calibration of a Microscopic Traffic Simulation in an Urban Scenario Using Loop Detector Data

Travel demand is an essential input for the creation of traffic models. However, estimating travel demand to accurately represent traffic behaviour usually requires the collection of extensive sets of data on traffic behaviour. Traffic counts are a comparably cost effective and reproducible source of information on travel demand. The utilisation of traffic counts to estimate demand is commonly found in the literature as the static and dynamic O-D estimation problem. A variety of approaches have been developed over recent decades to tackle this problem. Usually initial estimates of the O-D matrix are calibrated by utilising traffic counts and considering different assignment models. Other approaches for the estimation of travel demand solely based on traffic measurements can be found in the simulation software SUMO. The present work demonstrates the systematic development of a network model in SUMO in the inner city of Munich. In a sample network the estimation of travel demand through the tools flowrouter and routeSampler is tested by utilising flow measurements from induction loop detectors. The tests delivered unsatisfactory results, which is proven through observations of traffic flows in the resulting simulations as well as comparisons to historic traffic counts. The lack of sufficient detector data and the complexity of the sample network are discussed as the main reasons for the results. It is concluded that the applied tools should be tested in future studies with a more extensive dataset to perform a more comprehensive review of both tools. Therefore, we deliver specific requirements based on the network example of Munich.