Attention-guided deep neural network with a multichannel architecture for lung nodule classification.

Detecting and accurately identifying malignant lung nodules in chest CT scans in a timely manner is crucial for effective lung cancer treatment. This study introduces a deep learning model featuring a multi-channel attention mechanism, specifically designed for the precise diagnosis of malignant lung nodules. To start, we standardized the voxel size of CT images and generated three RGB images of varying scales for each lung nodule, viewed from three different angles. Subsequently, we applied three attention submodels to extract class-specific characteristics from these RGB images. Finally, the nodule features were consolidated in the model's final layer to make the ultimate predictions. Through the utilization of an attention mechanism, we could dynamically pinpoint the exact location of lung nodules in the images without the need for prior segmentation. This proposed approach enhances the accuracy and efficiency of lung nodule classification. We evaluated and tested our model using a dataset of 1018 CT scans sourced from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). The experimental results demonstrate that our model achieved a lung nodule classification accuracy of 90.11 %, with an area under the receiver operator curve (AUC) score of 95.66 %. Impressively, our method achieved this high level of performance while utilizing only 29.09 % of the time needed by the mainstream model.

Identifications and classifications of human locomotion using Rayleigh-enhanced distributed fiber acoustic sensors with deep neural networks.

This paper reports on the use of machine learning to delineate data harnessed by fiber-optic distributed acoustic sensors (DAS) using fiber with enhanced Rayleigh backscattering to recognize vibration events induced by human locomotion. The DAS used in this work is based on homodyne phase-sensitive optical time-domain reflectometry (φ-OTDR). The signal-to-noise ratio (SNR) of the DAS was enhanced using femtosecond laser-induced artificial Rayleigh scattering centers in single-mode fiber cores. Both supervised and unsupervised machine-learning algorithms were explored to identify people and specific events that produce acoustic signals. Using convolutional deep neural networks, the supervised machine learning scheme achieved over 76.25% accuracy in recognizing human identities. Conversely, the unsupervised machine learning scheme achieved over 77.65% accuracy in recognizing events and human identities through acoustic signals. Through integrated efforts on both sensor device innovation and machine learning data analytics, this paper shows that the DAS technique can be an effective security technology to detect and to identify highly similar acoustic events with high spatial resolution and high accuracies.

Distributed fiber sensor and machine learning data analytics for pipeline protection against extrinsic intrusions and intrinsic corrosions.

This paper presents an integrated technical framework to protect pipelines against both malicious intrusions and piping degradation using a distributed fiber sensing technology and artificial intelligence. A distributed acoustic sensing (DAS) system based on phase-sensitive optical time-domain reflectometry (φ-OTDR) was used to detect acoustic wave propagation and scattering along pipeline structures consisting of straight piping and sharp bend elbow. Signal to noise ratio of the DAS system was enhanced by femtosecond induced artificial Rayleigh scattering centers. Data harnessed by the DAS system were analyzed by neural network-based machine learning algorithms. The system identified with over 85% accuracy in various external impact events, and over 94% accuracy for defect identification through supervised learning and 71% accuracy through unsupervised learning.

Disturbed partial discharge detection system based on an improved Φ-OTDR assisted by a wFBG array.

Partial discharge (PD) detection is an effective method for evaluating the insulation status of power cables. In this paper, a disturbed acoustic sensing (DAS) system based on phase-sensitive optical time-domain reflectometry (Φ-OTDR) for online PD detecting is proposed and demonstrated. To detect the ultra-small fiber strain induced by PD, a weak fiber Bragg grating (wFBG) array is introduced into the sensing fiber to increase signal-to-noise ratio (SNR) of the system. Besides, the sensing fiber is packaged with aluminum tape to enhance the sensitivity. Consistent PD signals in a 10 kV cross linked polyethylene (XLPE) power cable with artificial defects have been observed, and characteristics of the signals are analyzed.

A time- and wavelength-division multiplexing sensor network with ultra-weak fiber Bragg gratings.

A time- and wavelength-division multiplexing sensor network based on ultra-weak fiber Bragg gratings (FBGs) was proposed. The low insertion loss and the high multiplexing capability of the proposed sensor network were investigated through both theoretical analysis and experimental study. The demodulation system, which consists of two semiconductor optical amplifiers and one high-speed charge-coupled device module, was constructed to interrogate 2000 serial ultra-weak FBGs with peak reflectivity ranging from -47 dB to -51 dB and a spatial resolution of 2 m along an optical fiber. The distinct advantages of the proposed sensor network make it an excellent candidate for the large-scale sensing network.