AI-Based Energy Transportation Safety: Pipeline Radial Threat Estimation Using Intelligent Sensing System

• URL: http://arxiv.org/abs/2312.11583v2
• Date: Tue, 26 Dec 2023 03:01:19 GMT
• Title: AI-Based Energy Transportation Safety: Pipeline Radial Threat Estimation Using Intelligent Sensing System
• Authors: Chengyuan Zhu, Yiyuan Yang, Kaixiang Yang, Haifeng Zhang, Qinmin Yang, C. L. Philip Chen
• Abstract summary: This paper proposes a radial threat estimation method for energy pipelines based on distributed optical fiber sensing technology. We introduce a continuous multi-view and multi-domain feature fusion methodology to extract comprehensive signal features. We incorporate the concept of transfer learning through a pre-trained model, enhancing both recognition accuracy and training efficiency.
• Score: 52.93806509364342
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The application of artificial intelligence technology has greatly enhanced and fortified the safety of energy pipelines, particularly in safeguarding against external threats. The predominant methods involve the integration of intelligent sensors to detect external vibration, enabling the identification of event types and locations, thereby replacing manual detection methods. However, practical implementation has exposed a limitation in current methods - their constrained ability to accurately discern the spatial dimensions of external signals, which complicates the authentication of threat events. Our research endeavors to overcome the above issues by harnessing deep learning techniques to achieve a more fine-grained recognition and localization process. This refinement is crucial in effectively identifying genuine threats to pipelines, thus enhancing the safety of energy transportation. This paper proposes a radial threat estimation method for energy pipelines based on distributed optical fiber sensing technology. Specifically, we introduce a continuous multi-view and multi-domain feature fusion methodology to extract comprehensive signal features and construct a threat estimation and recognition network. The utilization of collected acoustic signal data is optimized, and the underlying principle is elucidated. Moreover, we incorporate the concept of transfer learning through a pre-trained model, enhancing both recognition accuracy and training efficiency. Empirical evidence gathered from real-world scenarios underscores the efficacy of our method, notably in its substantial reduction of false alarms and remarkable gains in recognition accuracy. More generally, our method exhibits versatility and can be extrapolated to a broader spectrum of recognition tasks and scenarios.

Related papers

• An Attention-Based Deep Generative Model for Anomaly Detection in Industrial Control Systems [3.303448701376485]
  Anomaly detection is critical for the secure and reliable operation of industrial control systems. This paper presents a novel deep generative model to meet this need.
  arXiv  Detail & Related papers  (2024-05-03T23:58:27Z)
• Verification of Neural Reachable Tubes via Scenario Optimization and Conformal Prediction [10.40899456282141]
  Hamilton-Jacobi reachability analysis is a popular formal verification tool for providing such guarantees. DeepReach has been used to synthesize reachable tubes and safety controllers for high-dimensional systems. We propose two verification methods, based on robust scenario optimization and conformal prediction, to provide probabilistic safety guarantees.
  arXiv  Detail & Related papers  (2023-12-14T02:03:36Z)
• NPVForensics: Jointing Non-critical Phonemes and Visemes for Deepfake Detection [50.33525966541906]
  Existing multimodal detection methods capture audio-visual inconsistencies to expose Deepfake videos. We propose a novel Deepfake detection method to mine the correlation between Non-critical Phonemes and Visemes, termed NPVForensics. Our model can be easily adapted to the downstream Deepfake datasets with fine-tuning.
  arXiv  Detail & Related papers  (2023-06-12T06:06:05Z)
• Unsupervised Self-Driving Attention Prediction via Uncertainty Mining and Knowledge Embedding [51.8579160500354]
  We propose an unsupervised way to predict self-driving attention by uncertainty modeling and driving knowledge integration. Results show equivalent or even more impressive performance compared to fully-supervised state-of-the-art approaches.
  arXiv  Detail & Related papers  (2023-03-17T00:28:33Z)
• Increasing the Confidence of Deep Neural Networks by Coverage Analysis [71.57324258813674]
  This paper presents a lightweight monitoring architecture based on coverage paradigms to enhance the model against different unsafe inputs. Experimental results show that the proposed approach is effective in detecting both powerful adversarial examples and out-of-distribution inputs.
  arXiv  Detail & Related papers  (2021-01-28T16:38:26Z)
• Efficient falsification approach for autonomous vehicle validation using a parameter optimisation technique based on reinforcement learning [6.198523595657983]
  The widescale deployment of Autonomous Vehicles (AV) appears to be imminent despite many safety challenges that are yet to be resolved. The uncertainties in the behaviour of the traffic participants and the dynamic world cause reactions in advanced autonomous systems. This paper presents an efficient falsification method to evaluate the System Under Test.
  arXiv  Detail & Related papers  (2020-11-16T02:56:13Z)
• Smart Anomaly Detection in Sensor Systems: A Multi-Perspective Review [0.0]
  Anomaly detection is concerned with identifying data patterns that deviate remarkably from the expected behaviour. This is an important research problem, due to its broad set of application domains, from data analysis to e-health, cybersecurity, predictive maintenance, fault prevention, and industrial automation. We review state-of-the-art methods that may be employed to detect anomalies in the specific area of sensor systems.
  arXiv  Detail & Related papers  (2020-10-27T09:56:16Z)
• Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems [34.945482759378734]
  We employ a probabilistic approach to safety evaluation in simulation, where we are concerned with computing the probability of dangerous events. We develop a novel rare-event simulation method that combines exploration, exploitation, and optimization techniques to find failure modes and estimate their rate of occurrence.
  arXiv  Detail & Related papers  (2020-08-24T17:46:27Z)
• Robust Reinforcement Learning with Wasserstein Constraint [49.86490922809473]
  We show the existence of optimal robust policies, provide a sensitivity analysis for the perturbations, and then design a novel robust learning algorithm. The effectiveness of the proposed algorithm is verified in the Cart-Pole environment.
  arXiv  Detail & Related papers  (2020-06-01T13:48:59Z)
• Guided Uncertainty-Aware Policy Optimization: Combining Learning and Model-Based Strategies for Sample-Efficient Policy Learning [75.56839075060819]
  Traditional robotic approaches rely on an accurate model of the environment, a detailed description of how to perform the task, and a robust perception system to keep track of the current state. reinforcement learning approaches can operate directly from raw sensory inputs with only a reward signal to describe the task, but are extremely sample-inefficient and brittle. In this work, we combine the strengths of model-based methods with the flexibility of learning-based methods to obtain a general method that is able to overcome inaccuracies in the robotics perception/actuation pipeline.
  arXiv  Detail & Related papers  (2020-05-21T19:47:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.