Xinheng Wang’s research while affiliated with Xi’an Jiaotong-Liverpool University and other places

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Publications (174)


Viewpoint planning optimization for structure from motion-based 3D reconstruction of industrial products with sim-to-real proximal policy optimization
  • Article

May 2025

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16 Reads

Expert Systems with Applications

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Ruxin Xiao

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Xinheng Wang

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Junqing Zhang

Viewpoint planning determines the accuracy, processing speed, and lightweight of structure from motion. Despite the importance of viewpoint planning optimization to industrial digital services, existing methods show evident shortages in balancing between the reconstruction accuracy and the viewpoint number. Hence, this paper defines a new next-best-view problem for structure from motion, which aims to improve the accuracy, reduce the viewpoint number, and strike a balance between the two, simultaneously. Besides, to resolve the problem, this paper presents a novel viewpoint planning optimization method based on Proximal Policy Optimization. This method incorporates double models, action mask, and sim-to-real training to improve the training efficiency. Additionally, this method applies transfer-learning and fine-tuning to improve the versatility of the optimized viewpoint plan. A case study and experiments with multiple house models illustrate the method. In the experiment, the optimized viewpoint plan achieved 12.42%, 14.87%, 16.39%, 15.58%, and 32.35% reduction in Chamfer Distance, Earth Mover’s Distance, the viewpoint number, the file size, and reconstruction processing time compared to the naïve baseline, respectively. Also, compared to existing methods, the proposed method showed advantages from different perspectives, particularly in the balance between the reconstruction accuracy and the viewpoint number.


Submillimeter Acoustic Vibration Measurement and Monitoring Using a Single Smartphone

January 2025

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3 Reads

IEEE Transactions on Instrumentation and Measurement

Liu Yang

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Xiaofei Li

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Wenwu Wang

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[...]

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Zhi Wang

Accurate vibration measurement is crucial for monitoring and diagnosing industrial equipment. Existing solutions require either installing contact sensors on the equipment or using non-contact sensors such as laser. Both approaches involve complex deployment, stringent environmental conditions, and high cost. As a better alternative, we propose a sub-millimeter acoustic vibration measurement system using a single smartphone, called Mobile-Vib. Firstly, we develop a novel acoustic ranging method that builds on traditional acoustic ranging techniques, incorporating the reflection principle of acoustic signals from vibrating objects. This approach addresses the challenge of acoustic signal refresh rate in vibration measurement by employing advanced signal design and processing techniques. Secondly, we design a noise removal algorithm utilizing the dual-channel technology of smartphones to minimize multipath signals and noise interference, enabling accurate phase estimation. To mitigate the impact of unrelated human motions in real-world measurements, we implement an optimisation-based method to correct distortions and reduce errors. Finally, by clarifying the relationship between phase changes and actual displacement, we enable tracking of vibration displacement in industrial environments. We have implemented Mobile-Vib, and the extensive experimental results demonstrate an average error of approximately 0.629 mm in displacement estimation and 5.6 Hz in frequency estimation at a 1-meter distance from the vibrating object in real industrial vibration monitoring scenarios.


Reinforcement Learning Based Edge-End Collaboration for Multi-Task Scheduling in 6G Enabled Intelligent Autonomous Transport Systems

January 2025

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3 Reads

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4 Citations

IEEE Transactions on Intelligent Transportation Systems

As communication and computing technologies advance, vehicular edge computing emerges as a promising paradigm for delivering a wide array of intelligent services in 6G enabled Intelligent Autonomous Transport Systems. These service requests, are safety-oriented and typically require the fusion of processing results from multiple independent computation tasks generated by various onboard sensors, in which the computation tasks are delay-sensitive and computation-intensive. Consequently, the allocation of multiple tasks within a single service request while efficiently reducing request completion time and energy consumption presents a substantial challenge. In order to address the problem of multi-task simultaneous scheduling, this paper proposed to employ deep reinforcement learning and edge computing architecture to make task scheduling decisions for vehicles. Firstly, the Vehicle-Infrastructure Network (VINET) is designed, in which the vehicles can assign multiple tasks to the edge servers and other idle vehicles, thus extending the task processing capabilities for vehicles. Secondly, Fully-decentralized Multi-agent Proximal Policy Optimization (FMPPO) algorithm is proposed to make task scheduling decisions for autonomous driving, the large model trained via FMPPO is adaptable to different scenarios with various numbers of vehicles. Thirdly, by taking into account task characteristic, environmental status, and vehicle mobility, the proposed method can make task scheduling decisions in real-time and then dynamically distributes tasks based on the decisions. Finally, experimental results demonstrate that the designed method outperforms benchmark methods in terms of both completion time and energy consumption of computation tasks.



Multiagent Reinforcement Learning-Based Multimodel Running Latency Optimization in Vehicular Edge Computing Paradigm

December 2024

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3 Reads

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2 Citations

IEEE Systems Journal

With the advancement of edge computing, more and more intelligent applications are being deployed at the edge in proximity to end devices to provide in-vehicle services. However, the implementation of some complex services requires the collaboration of multiple AI models to handle and analyze various types of sensory data. In this context, the simultaneous scheduling and execution of multiple model inference tasks is an emerging scenario and faces many challenges. One of the major challenges is to reduce the completion time of time-sensitive services. In order to solve this problem, a multiagent reinforcement learning-based multimodel inference task scheduling method was proposed in this article, with a newly designed reward function to jointly optimize the overall running time and load imbalance. First, the multiagent proximal policy optimization algorithm is utilized for designing the task scheduling method. Second, the designed method can generate near-optimal task scheduling decisions and then dynamically allocate inference tasks to different edge applications based on their status and task characteristics. Third, one assessment index, quality of method, is defined and the proposed method is compared with the other five benchmark methods. Experimental results reveal that the proposed method can reduce the running time of multimodel inference by at least 25% or more, closing to the optimal solution.


A Novel NLOS Acoustic Signal Identification Method for Indoor Localization Based on Machine Learning (Revised and Resubmit)

November 2024

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4 Reads

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1 Citation

IEEE Transactions on Vehicular Technology

Near-ultrasound acoustic localization has emerged as a cost-effective and precise technique for indoor localization. Acoustic Non-Line-of-Sight (NLOS) identification is essential in indoor localization systems. Current NLOS identification approaches predominantly utilize Matched Filter (MF) characteristics or signal spectrum output. However, these methods often fail to deliver adequate identification performance in cross- scenario environments, including those that employ statistical analysis and Deep Learning (DL) techniques. In this paper, we propose a novel NLOS identification method specifically designed for near-ultrasound localization signals. Our approach utilizes an enhanced Frequency-Modulated Continuous Wave (FMCW) technique to obtain an improved intermediate frequency (IF) signal. Subsequently, a subset of eight distinct features is extracted and selected from the IF signal. The features are then employed to enhance an XGBoost classifier for the identification of NLOS conditions. Experimental results across three scenarios demonstrate that our method attains a classification accuracy of 99.9% within identical-room settings, and achieves accuracies of 93.73% and 86.20% in cross-scenario environments, respectively. Additionally, the extracted features demonstrate promising performance not only within the presented XGBoost classifier but also across various statistical machine-learning models.


FDME-RATE: Frequency Domain-Based Multipath Extraction for Robust Acoustic TOA Estimation (Revised and Resubmit)

October 2024

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3 Reads

IEEE Internet of Things Journal

High-accuracy Time of Arrival (TOA) estimation is essential for indoor acoustic localization, but traditional TOA estimation methods often fail to obtain accurate and robust TOA estimation performance due to severe multipath challenges such as peak extraction by General Cross Correlation (GCC). To address the problem, this paper presents a novel acoustic TOA estimation technique called FDME-RATE, a high-accuracy acoustic ranging system in acoustic localization. The proposed method utilizes GCC to determine the coarse signal arrival time, followed by an enhanced Frequency Modulated Continuous Wave (FMCW) technique to convert the various arrival paths into distinct frequencies within the spectrum. Ultimately, we employ an Adaptive Multiple Signal Classification (AMUSIC) algorithm to achieve high-resolution TOA estimation. Through simulations and experiments conducted in a reverberant corridor, the results indicate that our method achieves an average TOA ranging error of just 0.16 m and 0.19 m at the 95% confidence level across a 30-m span. Within a 20-m range, there is only a centimeter-level error. Additionally, in non-line-of-sight (NLOS) scenarios, our proposed method further enhances TOA-based ranging accuracy by an additional 0.3 m over the same distance. The experimental results also demonstrate that our method outperforms traditional methods and recent research in terms of both estimation accuracy and robustness.



Anchor Deployment Optimization for Range-Based Indoor Positioning Systems in Non-Line-of-Sight Environment

August 2024

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3 Reads

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1 Citation

IEEE Sensors Journal

Optimizing anchor deployment is critical to ensure the performance and positioning stability of indoor positioning systems in real-world applications. In this paper, a new anchor deployment optimization method is proposed to enhance the positioning performance of range-based positioning systems in the Non-Line-of-Sight (NLOS) environment without increasing the application cost. Firstly a new fitness function is proposed by simultaneously considering the mean geometric dilution of precision (GDOP) and the coverage of available positioning area in the indoor NLOS environment. Then, a search architecture based on a particle swarm optimization (PSO) algorithm is proposed to optimize anchor deployment. The initialization method of swarm’s position and velocity is given, and the calculation process of the search architecture is introduced in detail. The results obtained from numerical simulations and experimental investigations verified that, for range-based positioning systems in NLOS environment, the accuracy and stability can be significantly improved by optimizing the anchor deployment through our proposed method.



Citations (53)


... Hence, the state detection of PMs is a crucial determinant for allocation. This research employs a threshold-based technique [33] to determine the status of the PM. There are three states of power management: high utilization, medium usage, and low usage. ...

Reference:

Two-Stage Optimal Virtual Machine Load Balancing Algorithm for Cloud Computing
Reinforcement Learning Based Edge-End Collaboration for Multi-Task Scheduling in 6G Enabled Intelligent Autonomous Transport Systems
  • Citing Article
  • January 2025

IEEE Transactions on Intelligent Transportation Systems

... While the direct regulatory roles of FTO in T cells remain unclear, indirect effects have been reported in the context of tumors, where FTO influences T cell function by modulating tumor cells [29,30]. For example, FTO supports cancer (stem) cell growth and maintenance and the expression of immune checkpoint molecule PD-L1, thereby enhancing immune evasion [31][32][33]. Recently, FTOmediated m 6 A demethylation promotes glycolytic activity in tumor cells, which impedes CD8 + T cell infiltration and effector function [24,34]. ...

FTO activates PD-L1 promotes immunosuppression in breast cancer via the m6A/YTHDF3/PDK1 axis under hypoxic conditions
  • Citing Article
  • December 2024

Journal of Advanced Research

... Building on the strengths of XGBoost, Jia et al. [17] proposed a novel NLOS signal identification method that leverages the algorithm's powerful classification capabilities. This method integrates multiple input features into the XGBoost model, significantly enhancing the accuracy of NLOS signal identification. ...

A Novel NLOS Acoustic Signal Identification Method for Indoor Localization Based on Machine Learning (Revised and Resubmit)
  • Citing Article
  • November 2024

IEEE Transactions on Vehicular Technology

... To reduce or avoid positioning errors of the DUT or minimize the number of anchors in NLoS situations, various anchor deployment algorithms can be employed [9][10][11][12]. A particle swarm optimization algorithm was used to enhance positioning accuracy and stability by optimizing anchor placement based on coverage and geometric dilution of precision [9]. ...

Anchor Deployment Optimization for Range-Based Indoor Positioning Systems in Non-Line-of-Sight Environment
  • Citing Article
  • August 2024

IEEE Sensors Journal

... Moreover, our proposed SLE model processes the sensor data from an inertial measurement unit (IMU) mounted on a single foot rather than relying on smartphoneintegrated inertial sensors, reducing challenges related to smartphone carrying modes or placement positions. The proposed SLE model's performance was evaluated with ten pedestrian participants across two walking modes and compared with our previously developed traditional peak-valley detection-based SLE method [48] to demonstrate its superior performance. The key contributions of our present work on the SLE method are as follows. ...

Human Gait Modeling with Step Length Estimation based on Single Foot Mounted Inertial Sensors
  • Citing Preprint
  • May 2024

SSRN Electronic Journal

... Sensors collect real-time data, convert physical phenomena into digital signals, and transmit the information to the network layer for further processing [24]. Actuators, including LEDs, buzzers, and mechatronic devices, execute physical actions based on predictive models and control mechanisms [46]. This layer also integrates existing industrial digital systems, databases, and informatization systems, ensuring seamless connectivity between legacy and modern IIoT solutions [82]. ...

A Review of Hybrid Cyber Threats Modelling and Detection Using Artificial Intelligence in IIoT
  • Citing Article
  • Full-text available
  • May 2024

... For instance, refs. [33][34][35] combined DP with deep reinforcement learning to design control systems that protect user privacy while optimizing system performance. Similarly, ref. [36] introduced a differential privacy mechanism to deep learning-based system identification tasks, ensuring that the model remains robust even in the presence of noisy, perturbed gradients [37][38][39]. ...

MACNS: A generic graph neural network integrated deep reinforcement learning based multi-agent collaborative navigation system for dynamic trajectory planning
  • Citing Article
  • May 2024

Information Fusion

... In cloud environments, tasks such as refs. [28][29][30][31] leverage machine learning techniques to optimise computation offloading, enhance resource allocation, and improve task scheduling. Notably, for the first time, machine learning has been applied to critical systems within multi-core processors, marking a significant advancement in this domain. ...

TBDB: Token Bucket-Based Dynamic Batching for Resource Scheduling Supporting Neural Network Inference in Intelligent Consumer Electronics
  • Citing Article
  • January 2023

IEEE Transactions on Consumer Electronics

... Consequently, a hierarchical modeling method for DT models of mechanical products based on graph databases has emerged. This approach involves creating product subassemblies or component nodes, establishing node relationships in a predefined graph database, storing feature information in the nodes, and conducting postprocessing on the established DT models [91][92][93]. Additionally, a dynamic data modeling method based on timesequence databases is proposed. ...

Ontology of 3D virtual modeling in digital twin: a review, analysis and thinking

Journal of Intelligent Manufacturing

... These estimations are conducted through a range of methodologies, including the Inverted Pendulum Model [14,16,17], the method based on the peak values and the valley values of the acceleration in the center of gravity [18], and the method based on linear combination [19], among others. Recent studies have also been estimating the features by reading the sensor data collection with the help of smartphones [20,21]. Relatively fewer studies have used video recording to measure the gait features [6,22]. ...

IRT-SD-SLE: An Improved Real-time Step Detection and Step Length Estimation using Smartphone Accelerometer
  • Citing Article
  • December 2023

IEEE Sensors Journal