Sampling Rate Research Articles

Effect of groove textures on wear of friction pair between groove side and scraper under different factor combinations

Three-body wear is the primary wear mechanism in friction pair between groove side and scraper. This study quantitatively investigates the effect of groove textures on three-body wear under different factor combinations through abrasive wear experiments. The results indicate that groove textures significantly reduce wear of samples. The wear reduction rate of sample reaches 41% under the optimal factor combination. Width, depth, and tilt angle of groove textures significantly influence wear loss. Among these factors, width exhibits a negative effect on the change of wear loss, while depth and tilt angle show positive effects. Additionally, there are interactive effects on the change of wear loss between width and depth, as well as width and tilt angle. The effect process of groove textures to three-body wear was qualitatively analyzed by discrete element method (DEM) simulation. Combined with wear morphologies, the wear-resistant mechanism of groove textures was explored. The results indicate that groove textures can mitigate the severity of three-body abrasive wear, reduce the contact area between particles and samples, alter particles movement and enhance wear resistance. Meanwhile, groove textures can serve as a guiding mechanism to modify particles movement and direction and transform the sliding motion of coal particles into rolling motion, converting the continuous “face-face” contact of three-body wear into intermittent “point-face” and “line-face” contacts. This study provides novel study ideas and theoretical foundations for wear-resistant designs in mining machinery and three-body wear equipment.

On the use of thermomechanical processing to enhance the strength-ductility-toughness balance of plain low-carbon steel

This study aimed to fabricate a dual-phase (DP) steel with a combination of high strength-ductility-toughness by thermomechanical processing for industrial applications. Accordingly, the effects of 40 % cold deformation and intercritical annealing temperature on the microstructural evolution and tensile properties of low-carbon steel were studied. The microstructure of dual-phase steels consisted of ferrite (α) and martensite (α'), however, the morphology of martensite was different. With increasing the intercritical annealing temperature, the martensite fraction increased gradually from 0.41 at 770 °C to 0.51 at 860 °C. Also, the increase of martensite fraction from 0.41 to 0.51 caused a decrease in the martensite carbon concentration from 0.174 to 0.142 wt%. All dual-phase steels had a larger hardness than the as-received steel. When the temperature of annealing increased from 770 °C to 860 °C, the yield strength enhanced from 370.4 to 496.3 MPa, the ultimate tensile strength improved from 642.0 to 809.2 MPa, the total elongation decreased slightly from 31.8 % to 29.8 %, and the energy absorption increased from 190.0 to 217.6 J/cm3. The work hardening rate of all DP samples was considerably higher than other samples. Unlike initial, quenched, and rolled samples, the DP steels exhibited three-stage strain hardening behavior with increasing the true strain. For all dual-phase steels, a perfect ductile fracture was observed, with numerous uniform deep and coarse dimples. The dominant mechanism in the fabricated dual-phase steels was interface decohesion.

What's Missing in Environmental Self-Monitoring: Evidence from Strategic Shutdowns of Pollution Monitors

Abstract Regulators often rely on regulated entities to self-monitor compliance, creating strategic incentives for endogenous monitoring. This paper builds a framework to detect whether local governments skip air pollution monitoring when they expect air quality to deteriorate. The core of our method tests whether the timing of monitor shutdowns coincides with the counties' air quality alerts – public advisories based on local governments' own pollution forecasts. Applying the method to a monitor in Jersey City, NJ, suspected of a deliberate shutdown during the 2013 “Bridgegate” traffic jam, we find a 33% reduction of this monitor's sampling rate on pollution-alert days. Building on large-scale inference tools, we then apply the method to test more than 1,300 monitors across the U.S., finding 14 metro areas with clusters of monitors showing similar strategic behavior. We assess geometric imputation and remote-sensing technologies as potential solutions to deter future strategic monitoring.

Study on signal characteristics of burst tendency coal under different loading rates

In order to study the mechanics, acoustic emission (AE) and electromagnetic emission (EME) response law of bursting liability coal at different loading rates, uniaxial compression tests were carried out on coal mass from Konggu Coal Mine. The corresponding relations among mechanical properties, AE and EME signals in the process of coal failure under loading were analyzed, and the energy evolution law of coal failure with bursting liability under loading rate was discussed. The results show that within a certain range of loading rate, the higher the loading rate, the higher the compressive strength and peak load of bursting liability coal, and the shorter the time for coal to reach the peak load. Under different loading rates, the mechanics, AE and EME signals of coal samples can be well corresponded. When the loading rate is low, the number of blocks destroyed of coal sample is large and the block size is relatively small, and the blocks are mainly scattered around the test platform. When the loading rate is high, the number of damaged blocks is relatively small and the block size is relatively large, and the blocks are far away from the test bench. When loading at a low rate, the internal cracks in coal can be fully developed and connected, and the energy release rate is relatively uniform in the process of loading and failure of coal sample. In the case of high loading rate, the energy release rate of coal sample in the loading process is much smaller than that in the moment of failure. Combining the above test results with the actual situation of the working face, it can be concluded that the total energy stored in the coal of fast mining increases and the threshold of impact decreases compared with that of slow mining. Therefore, under the disturbance of external dynamic load, rapid mining is more likely to induce rock burst.

Support Vector Machine-Based Fault Diagnosis under Data Imbalance with Application to High-Speed Train Electric Traction Systems

The safety and reliability of high-speed train electric traction systems are crucial. However, the operating environment for China Railway High-speed (CRH) trains is challenging, with severe working conditions. Dataset imbalance further complicates fault diagnosis. Therefore, conducting fault diagnosis for high-speed train electric traction systems under data imbalance is not only theoretically important but also crucial for ensuring vehicle safety. Firstly, when addressing the data imbalance issue, the fault diagnosis mechanism based on support vector machines tends to prioritize the majority class when constructing the classification hyperplane. This frequently leads to a reduction in the recognition rate of minority-class samples. To tackle this problem, a self-tuning support vector machine is proposed in this paper by setting distinct penalty factors for each class based on sample information. This approach aims to ensure equal misclassification costs for both classes and achieve the objective of suppressing the deviation of the classification hyperplane. Finally, simulation experiments are conducted on the Traction Drive Control System-Fault Injection Benchmark (TDCS-FIB) platform using three different imbalance ratios to address the data imbalance issue. The experimental results demonstrate consistent misclassification costs for both the minority- and majority-class samples. Additionally, the proposed self-tuning support vector machine effectively mitigates hyperplane deviation, further confirming the effectiveness of this fault diagnosis mechanism for high-speed train electric traction systems.

Thickness evaluation of organic coating using active long-pulse transmission thermography

The thickness of the optically translucent coating was evaluated using the transmission thermography. The transmitted temperature of a two-layer structure was theoretically analysed based on the equation of 1D heat transfer in the depth direction, and accordingly, the method for the measurement of coating thickness in a two-layer structure was established based on a long-pulse transmission thermography. The coating thickness was determined based on the characteristic time at the maximum half-rise temperature. Then, the proposed method was experimentally validated and calibrated by coating specimen with a different coating thickness. The thickness measurement method was further applied to measure an uneven coating specimen fabricated by mechanical grinding. The measurements were compared with a 3D digital image correlation method and the averaged relative error was less than 4%. Finally, thermal excitation, sampling rate of thermography and translucence of organic coating were discussed for accurate measurement.

Compressive stochastic subspace identification: A modal parameter identification method suitable for compressive sampling

Stochastic subspace identification (SSI) and its variants are widely used in conventional structural health monitoring (SHM) owing to their high computation robustness and estimation accuracy. Conventional SHM is based on a Nyquist (high-rate and uniform) sampling dictated by Shannon–Nyquist sampling theorem. As data quantity of SHM is increasing with duration and equipment at an ever-growing rate and new applications of SHM with strict constricts on sampling are emerging one by one, sub-Nyquist sampling, also known as compressive sampling has increasingly been used in SHM in recent years. Therefore, despite the success of SSI method and its algorithmic variants, a significant drawback lies in their handling of sub-Nyquist data. To overcome this drawback, we set out to extend the applicable scenarios of SSI from Nyquist (high-rate/uniform) sampling to sub-Nyquist (low-rate/nonuniform) sampling. In this paper, we propose a compressive stochastic subspace identification (CSSI) which can identify modal parameters from sub-Nyquist/compressed data. Specifically, CSSI involves two aspects: covariance matrix reconstruction and system parameter identification. We find the complete covariance matrix in classic SSI can also be estimated/reconstructed by sub-Nyquist samples. More importantly, this reconstruction can be physically guaranteed through special sampling patterns without sparsity prior. Based on the above findings, we develop a compressed covariance sensing-based covariance matrix reconstruction technique and derive the optimal sub-Nyquist sampling pattern such that the reconstruction is hassle-free and efficient. Then, we perform modal parameter identification on the basis of the reconstructed covariance matrix. Finally, the effectiveness of CSSI is validated by simulations and experiments. In short, CSSI, as a new extension of SSI, can extract modal parameters at lower sampling rates than what is prescribed by the classic SSI. The implication of this is the possibility that CSSI for operational modal analysis will require fewer measurements thus it would reduce the storage requirement and the bandwidth for transmitting data.

Algorithms to Reduce the Data File Size and Improve the Write Rate for Storing Sensor Reading Values in Hard Disk Drives for Measurements with Exceptionally High Sampling Rates

This study aimed to enhance the data write performance in measurements with exceptionally high sampling rates, such as acoustic emission measurements. This is particularly crucial when employing conventional hard disk drives to store data. This study introduced algorithms for handling binary formats, thereby reducing the data file size, increasing write rates, and ultimately shortening data write times during measurements. The suggested approaches included utilizing specialized binary formats and implementing self-created buffers. These approaches resulted in a remarkable write time reduction of up to 40×. Furthermore, employing multiple drives for writing significantly enhanced performance compared with that of using a single drive. Therefore, the proposed algorithms offer promising results for managing large amounts of data in real time.

Assessing structural uncertainty of biochemical regulatory networks in metabolic pathways under varying data quality

Ordinary differential equation (ODE) models are powerful tools for studying the dynamics of metabolic pathways. However, key challenges lie in constructing ODE models for metabolic pathways, specifically in our limited knowledge about which metabolite levels control which reaction rates. Identification of these regulatory networks is further complicated by the limited availability of relevant data. Here, we assess the conditions under which it is feasible to accurately identify regulatory networks in metabolic pathways by computationally fitting candidate network models with biochemical systems theory (BST) kinetics to data of varying quality. We use network motifs commonly found in metabolic pathways as a simplified testbed. Key features correlated with the level of difficulty in identifying the correct regulatory network were identified, highlighting the impact of sampling rate, data noise, and data incompleteness on structural uncertainty. We found that for a simple branched network motif with an equal number of metabolites and fluxes, identification of the correct regulatory network can be largely achieved and is robust to missing one of the metabolite profiles. However, with a bi-substrate bi-product reaction or more fluxes than metabolites in the network motif, the identification becomes more challenging. Stronger regulatory interactions and higher metabolite concentrations were found to be correlated with less structural uncertainty. These results could aid efforts to predict whether the true metabolic regulatory network can be computationally identified for a given stoichiometric network topology and dataset quality, thus helping to identify optimal measures to mitigate such identifiability issues in kinetic model development.

Factors Associated with Anxiety and Depression among Elite Collegiate Badminton Players in Japan: Exploratory Analysis

This study focused on mental health and fatigue in elite university student-athletes competing in badminton at the national level, comparing them with control university students and examining related factors. Among university athletes, anxiety and depression showed a moderately negative correlation with life satisfaction outside of sports, as determined by partial correlation tests adjusted for fatigue. Athletes demonstrated a 47% rate of anxiety and a 30% rate of depression. These values showed no significant difference from those of the anxiety (29%) and depression (21%) rates in a non-athlete university student sample. In addition, this study suggested that the evaluation of fatigue using a checklist of individual strength might produce low reliability and validity for elite university student-athletes in badminton. These exploratory findings highlight the importance of focusing on athletes’ overall life satisfaction and suggest the potential need for effective mental health interventions beyond sports. However, because of the exploratory nature of this study and the small sample size, further research is necessary to confirm these results.

A 4096 channel event-based multielectrode array with asynchronous outputs compatible with neuromorphic processors

Bio-signal sensing is pivotal in medical bioelectronics. Traditional methods focus on high sampling rates, leading to large amounts of irrelevant data and high energy consumption. We introduce a self-clocked microelectrode array (MEA) that digitizes bio-signals at the pixel level by encoding changes as asynchronous digital address-events only when they exceed a threshold, significantly reducing off-chip data transmission. This novel MEA comprises a 64 × 64 electrode array, an asynchronous 2D-arbiter, and an Address-Event Representation (AER) communication block. Each pixel operates autonomously, monitoring voltage fluctuations from cellular activity and producing digital pulses for significant changes. Positive and negative signal changes are encoded as “up” and “down” events and are routed off-chip via the asynchronous arbiter. We present results from chip characterization and experimental measurements using electrogenic cells. Additionally, we interface the MEA to a mixed-signal neuromorphic processor, demonstrating a prototype for end-to-end event-based bio-signal sensing and processing.

Statistical blade tip timing measurement, Part II: High-order cumulant architecture

Blade tip timing (BTT) is a potential non-contact vibration measurement technique for rotating blades owing to its high efficiency and long service life. However, probe layout design and vibration parameter identification are critical challenges in BTT due to its inherent undersampling. To address this issue, we have proposed the concept of statistical BTT measurement which promotes efficient layouts and parameter identification methods by shifting the recovery target from the signal to itself to its statistics. In the first paper of series papers, we have developed covariance architecture for BTT measurement and signal post-processing. In this second part of series papers, we set out to develop BTT measurement from a higher-order statistic perspective. Specifically, we find that the 2qth-order cumulant retains the vibration information (frequency and amplitude) of the signal itself and it can be recovered at lower sampling rates than what is prescribed by the covariance architecture. On this basis, we propose a higher-order cumulant architecture for BTT measurement, which contains two aspects: higher-order difference layout (HODL) and higher-order cumulant-based parameter identifications. HODL is a special family of layouts that physically guarantee the availability of consecutive higher-order cumulant estimations and thus leads to a satisfactory parameter identification. To facilitate application, a series of concrete HODLs are provided for users, including a four-level nested layout and its enhanced version, and optimal fourth-order difference layout. The quantitative comparison in the simulations and experiments shows the effectiveness of the proposed higher-order cumulant architecture. Compared with the architectures developed from covariance and signal itself perspective, the proposed higher-order cumulant architecture is not only more robust to noise but also further reduces the number of required probes.

An efficient low-delay polyphase implementation method for active noise control systems

When implementing active noise control systems on signal processing hardware, the time delay introduced by electronic components (especially components requiring additional lowpass filters or introducing fixed-sample-size delays) may adversely affect the noise control performance. One common approach to reducing this delay is to use a high sampling rate, but this increases the computation significantly when implementing the ANC filters in real time. In the current work, a polyphase-structure-based filter design method is developed for active noise control systems that can reduce the computation load for real-time filter implementation but do not introduce an additional time delay. Although the computation reduction capability of a polyphase filter structure is well known for multi-rate systems, the traditional use of such multi-rate systems requires additional anti-aliasing and reconstruction filters which introduces an additional time delay. Thus, in delay-sensitive applications, such as active noise control, this method was previously applied only on the filter adaption phase, instead of directly on the real-time filtering process. In this article, a filter decomposition method using the minimum-phase technique is proposed to decompose an ANC filter into two multiplicative causal filters both of which have lowpass frequency response shapes at high frequencies such that the polyphase structure can be applied directly to the two multiplicative causal control filters without introducing additional anti-aliasing and reconstruction filters. Results show that, compared with various traditional low sampling rate implementations, the proposed method can significantly improve the noise control performance. Compared with the non-polyphase high-sampling rate method, the real-time computations that increase with the sampling rate are improved from quadratically to linearly.

General heterogeneous real-time baseband backend system scheme applied to QTT based on RFSoC+GPU

This paper proposes a heterogeneous real-time baseband astronomical backend solution centered on RFSoC and combined GPU. The solution implemented the integration of the high-performance, completely configurable RF signal link to digitize, process, and transmit increasingly massive radio astronomy signals and provided a highly flexible and scalable scheme for the QTT backend. The RFSoC-based integration RF pre-processing front-end directly digitizes dual-polarization signals at the sampling rate of 2048 MHz and converts them to dual-16-channel data streams for parallel mixing and polyphase decimation filtering. The generated baseband data with 256 MHz center frequency and 100 MHz bandwidth is packaged and output to the GPU server through the 100GbE Interface. Complete processes of direct RF sampling, pre-processing, and high-speed transmission are implemented at the receiver. The GPU server executes real-time post-processing at the remote end. The dynamic configuration of two observation modes can be achieved with frequency resolutions of 3.051 and 0.763 kHz. The H2CO absorption line observations with the Nanshan 26-m radio telescope verified the system’s performance and the design’s rationality. This paper explores and confirms the RFSoC’s applicability on the backend. The flexible and efficient QTT backend scheme was planned based on the proposed real-time baseband pattern by expanding multi-RFSoC and GPU computing nodes.

Applying optical coherence tomography to inline quality monitoring of unidirectional glass-fiber-reinforced thermoplastic tapes

Originally developed for biomedical applications and diagnosis, optical coherence tomography (OCT) has recently been demonstrated to be a powerful non-destructive and non-invasive measurement method for detecting defects in glass-fiber reinforced polymer composites. While previous studies have focused mainly on the use of OCT in the analysis of thermoset composites, we were able to show in offline experiments that OCT can be used to quickly detect typical defects (e.g., dry fiber regions, gaps and fiber breakage) in thermoplastic unidirectional (UD) tapes at high resolution. To investigate the applicability of OCT to inline monitoring, we advanced our previously published approach in two major steps: First, we incorporated the OCT system into an industrial-scale UD-tape production line, and derived optimal settings for inline detection of dry region defects from a comprehensive design of experiments (DoE) to find an optimal balance between accuracy and data size for a stationary tape sample by varying A-scan sampling rate, A-scan averaging and OCT transverse travel velocity. Second, using these optimal settings, we went on to investigate moving tapes over a range of industrially relevant take-off speeds. Microscopy was used for validation in both cases. We developed a fast and robust statistical analysis of B-scans that visualizes the quality of full cross-sections in an interpretable manner for potential use in a real-time setting. Within an industrially relevant production speed range of up to 15 m/min, we are thus now able to investigate 120 mm wide (and potentially wider) UD tapes inline at a transverse resolution of 22 µm, producing only 21 MB of data per measurement.

The use of non-invasive tomographic imaging to monitor lung condition

The development of ultrasound tomography (UST) aimed to significantly enhance the precision and safety of non-invasive UST imaging, particularly for studying crystallization processes in biological tissues. The initiative sought to address the limitations of previous versions by incorporating advanced technological upgrades and providing a more user-friendly interface. The revised system comprises eight four-channel measurement cards interconnected via a high-speed FD CAN bus capable of 8 MBPS data transfer. Each card features a dedicated square wave generator, band-pass filters tailored to specific ultrasonic transducer frequencies, and a sophisticated signal envelope processing unit. The core processing unit is built around a 32-bit STM32G474RE microcontroller, ensuring robust data handling and image reconstruction capabilities. The UST device presented demonstrates improved image clarity and reduced noise interference. The measurement card redesign has achieved a sampling rate of 4 MBPS per channel and includes a two-stage amplification for dynamic range management. Upgraded power components, comprehensive shielding, and the integration of advanced analog switches have led to an enhanced signal-to-noise ratio, pivotal for high-resolution imaging. The advancements presented in the UST device mark a noteworthy progression in ultrasound imaging technology, extending beyond traditional applications. High-speed data collection, precise signal processing, and user-centered design have all come together to make a system that can image crystallization processes more accurately and accurately over and over again.

FluoAnalysis: An Open-Source MATLAB Toolbox for Analysis of Calcium Imaging Measurements of Oscillatory Astrocytic and Neuronal Networks.

Calcium imaging, especially two-photon imaging, has become essential in neuroscience for studying neuronal and astrocytic activity under in vivo and in vitro conditions. Current advances in the development of calcium sensors as well as imaging hardware enable high-frequency measurements of calcium signals in hundreds of cells simultaneously. The analysis of these large datasets requires special tools and usually a certain level of programming experience. Despite advancements in calcium imaging analysis software development, significant gaps remain, particularly for data acquired at a high sampling rate that would allow for the spectral analysis of calcium signals. The FluoAnalysis MATLAB toolbox addresses these gaps by offering a comprehensive solution for analyzing simultaneously measured calcium imaging and electrophysiological data. It features both GUI-based and command-line approaches, emphasizing frequency domain analysis to reveal network-level oscillatory signals linked to single-cell activity. In addition, the toolbox puts special emphasis on differentiating between astrocytes and neurons, revealing the interactions between the network activity of the two major cell types of the brain. It facilitates a streamlined workflow for data loading, ROI identification, cell classification, fluorescence intensity calculation, spectral analysis, and report generation, supporting both manual and automated high-throughput analysis. This versatile platform enables the comprehensive analysis of large imaging datasets. In conclusion, the FluoAnalysis MATLAB toolbox provides a robust and versatile platform for the integrated analysis of calcium imaging and electrophysiological data, supporting diverse neuroscience research applications.

Platform Design for Optical Screening and Conditioning for Injury Resilience.

Musculoskeletal injuries (MSKIs) among active duty soldiers result in more than 10 million limited duty days each year and account for more than 70% of the medically nondeployable population. Overuse injuries in lower limbs from running, foot marching long distances with heavy loads, and lifting heavy objects are the most common types of injuries in the military. Physical training and rehabilitation exercises for greater resiliency through aerobic, muscle strength, endurance, and agility conditioning programs can prevent or reduce the effects of MSKIs if Soldiers adhere to proper biomechanics and training techniques. We are introducing a three-dimensional (3D) camera-based platform for Optical Screening and Conditioning for Injury Resilience (OSCIR) that is designed to identify and correct high-risk movement patterns based on quantifiable biomechanical measurements in clinical or field settings. Our goal is to improve resilience to MSKI by offering greater access to quality of movement skills in warfighters through an autonomous device that can be used in Sports Medicine and Reconditioning Team (SMART) clinics and High-Intensity Tactical Training (HITT) sites. OSCIR fuses four pairs of Kinect Azure cameras into a concise footprint to achieve suitable sampling rates and an unobstructed field of view for accurate dynamic movement tracking using a custom point cloud solution. We designed a unique multistage 3D joint tracking algorithm architecture to methodically isolate the human body point cloud from the background, identify individual limb segments, and perform iterative joint optimization at the global and local joint levels. We evaluated the feasibility of our prototype system among N = 12 control participants (6 M/6 F; 21-37 years) in compliance with the Western Institutional Review Board (Tracking #20225920, approved on November 4, 2022). Five task-specific MSKI outcome metrics identified by end-user physical therapists and athletic trainers as indicators for movement quality were assessed across 7 lower-extremity exercises derived from standardized MSK assessment/conditioning batteries used in the military. Data were recorded concurrently by OSCIR and a reference standard Vicon motion capture system for validating system accuracy. Task-specific MSKI indicators for knee flexion and hip flexion range of motion achieved an average error of 4.05 ± 2.34°, while 3D position-based postural outcomes of left-right foot distance, left-right hand distance, and step length obtained mean absolute errors of 2.58 ± 2.30 cm. Results support the feasibility of our system in achieving outcomes that are comparable to currently accepted laboratory standards. Our study describes the integration process for a 3D camera-based clinical system for MSKI conditioning and rehabilitation. The impact of our system will enable key stakeholders in the military to manage MSKIs in warfighters by automating key assessment and rehabilitation test batteries; making tests more readily accessible, and interpretations more accurate by providing objective biomechanical measures. OSCIR is undergoing turn-key design features to serve as a screening tool for warfighters to readily assess susceptibility to MSKI or as a training platform to help guide exercise techniques to achieve resiliency against future injuries.

Simulation‐Training‐Based Deep Learning Approach to Microscopic Ghost Imaging

Herein, deep learning‐ghost imaging (DLGI) based on a digital micromirror device is realized to avoid the difficulties of a charge‐coupled device (CCD) scientific camera being unable to obtain the sample images in extremely weak illumination conditions and to solve the problem of the inverse relationship between imaging quality and imaging time in practical applications. Deep learning for computational ghost imaging typically requires the collection of a large set of labeled experimental data to train a neural network. Herein, we demonstrate that a practically usable neural network can be prepared based on the simulation results. The acquisition results of the CCD scientific camera and the simulation results with low sampling are used as the training set (1000 observations) and we can complete the data acquisition process within one hour. The results show that the proposed DLGI method can be used to significantly improve the quality of the reconstructed images when the sampling rate is 60%. This method also reduces the imaging time and the memory usage, while simultaneously improving the imaging quality. The imaging results of the proposed DLGI method have great significance for application in clinical diagnosis.

Map matching on low sampling rate trajectories through deep inverse reinforcement learning and multi-intention modeling

Analyzing freight vehicle movements using GPS trajectory data presents challenges due to environmental conditions and hardware limitations impacting data accuracy. Map matching, the process of aligning GPS signals with road networks, facilitates accurate route reconstruction. However, existing methods have limitations, particularly with low and ultra-low sampling rates. They often assume the shortest path between points, overlook historical data insights and neglect diverse driving behaviors, which may not align with real-world scenarios where shortest paths are not always optimal and different drivers exhibit varied behaviors. These limitations affect existing methods’ reliability, especially when we face low sampling rate trajectories. In this study, we propose multi-intention deep inverse reinforcement learning for map matching (MIDIRL) to address these challenges. MIDIRL integrates deep neural networks and multi-intention capturing mechanisms with inverse reinforcement learning to model complex driving preferences from historical trajectories, improving map matching accuracy, especially in ultra-low-frequency trajectories. Our experiments on real-world datasets demonstrate MIDIRL’s improved accuracy and efficiency of map matching compared to previous methods, even with limited training data.