Using a Machine Learning Algorithm Integrated with Data De-Noising Techniques to Optimize the Multipoint Sensor Network

In this paper, for an intensity wavelength division multiplexing (IWDM)-based multipoint fiber Bragg grating (FBG) sensor network, an effective strain sensing signal measurement method, called a long short-term memory (LSTM) machine learning algorithm, integrated with data de-noising techniques is proposed. These are considered extremely accurate for the prediction of very complex problems. Four ports of an optical coupler with distinct output power ratios of 70%, 60%, 40%, and 30% have been used in the proposed distributed IWDM-based FBG sensor network to connect a number of FBG sensors for strain sensing. In an IWDM-based FBG sensor network, distinct power ratios of coupler ports can contain distinct powers or intensities. However, unstable output power in the sensor system due to random noise, harsh environments, aging of the equipment, or other environmental factors can introduce fluctuations and noise to the spectra of the FBGs, which makes it hard to distinguish the sensing signals of FBGs from the noise signals. As a result, noise reduction and signal processing methods play a significant role in enhancing the capability of strain sensing. Thus, to reduce the noise, to improve the signal-to-noise ratio, and to accurately measure the sensing signal of FBGs, we proposed a long short-term memory (LSTM) deep learning algorithm integrated with discrete waveform transform (DWT) data smoother (de-noising) techniques. The DWT data de-noising methods are important techniques for analyzing and de-noising the sensor signals, and it further improves the strain sensing signal measurement accuracy of the LSTM model. Thus, after de-noising the sensor data, these data are fed into the LSTM model to measure the sensing signal of each FBG. The experimental results prove that the integration of LSTM with the DWT data de-noising technique achieved better sensing signal measurement accuracy, even in noisy data or environments. Therefore, the proposed IWDM-based FBG sensor network can accurately sense the signal of strain, even in bad or noisy environments; can increase the number of FBG sensors multiplexed in the sensor system; and can enhance the capacity of the sensor system

An Extremely Close Vibration Frequency Signal Recognition Using Deep Neural Networks

This study proposes the utilization of an optical fiber vibration sensor for detecting the superposition of extremely close frequencies in vibration signals. Integration of deep neural networks (DNN) proves to be meaningful and efficient, eliminating the need for signal analysis methods involving complex mathematical calculations and longer computation times. Simulation results of the proposed model demonstrate the remarkable capability to accurately distinguish frequencies below 1 Hz. This underscores the effectiveness of the proposed image-based vibration signal recognition system embedded in DNN as a streamlined yet highly accurate method for vibration signal detection, applicable across various vibration sensors. Both simulation and experimental evaluations substantiate the practical applicability of this integrated approach, thereby enhancing electric motor vibration monitoring techniques

Utilizing a Tunable Delay Line Interferometer to Improve the Sensing Accuracy of an FBG Sensor System

This paper proposes a novel sensing system based on a tunable delay line interferometer. The tunable delay line interferometer has been used to interpret strain, bringing us high accuracy as well as tunability. The shifted wavelength of the fiber Bragg grating (FBG) sensor caused by the applied strain can be visualized by an optical power meter (OPM) instead of an optical spectrum analyzer (OSA) by converting it to a power change using a tunable delay line interferometer (TDI). Different free spectral ranges (FSRs) are assigned to the TDI to investigate the accuracy and operation range of the proposed system. Thus, we achieve high accuracy and sensitivity by adjusting the FSR to 0.47 nm. Experimental results show that the maximum output power variation corresponding to a strain of 10 με is about 0.9 dB when the FSR is set to 0.47 nm. The proposed system is also cost-effective regarding the equipment utilized for interrogation: a tunable delay line interferometer and an optical power meter

Utilizing a Tunable Delay Line Interferometer to Improve the Sensing Accuracy of an FBG Sensor System

This paper proposes a novel sensing system based on a tunable delay line interferometer. The tunable delay line interferometer has been used to interpret strain, bringing us high accuracy as well as tunability. The shifted wavelength of the fiber Bragg grating (FBG) sensor caused by the applied strain can be visualized by an optical power meter (OPM) instead of an optical spectrum analyzer (OSA) by converting it to a power change using a tunable delay line interferometer (TDI). Different free spectral ranges (FSRs) are assigned to the TDI to investigate the accuracy and operation range of the proposed system. Thus, we achieve high accuracy and sensitivity by adjusting the FSR to 0.47 nm. Experimental results show that the maximum output power variation corresponding to a strain of 10 με is about 0.9 dB when the FSR is set to 0.47 nm. The proposed system is also cost-effective regarding the equipment utilized for interrogation: a tunable delay line interferometer and an optical power meter

Intensity and Wavelength Division Multiplexing FBG Sensor System Using a Raman Amplifier and Extreme Learning Machine

A fiber Bragg grating (FBG) sensor is a favorable sensor in measuring strain, pressure, vibration, and temperature in different applications, such as in smart structures, wind turbines, aerospace, industry, military, medical centers, and civil engineering. FBG sensors have the following advantages: immune to electromagnetic interference, light weight, small size, flexible, stretchable, highly accurate, longer stability, and capable in measuring ultra-high-speed events. In this paper, we propose and demonstrate an intensity and wavelength division multiplexing (IWDM) FBG sensor system using a Raman amplifier and extreme learning machine (ELM). We use an IWDM technique to increase the number of FBG sensors. As the number of FBG sensors increases and the spectra of two or more FBGs are overlapped, a conventional peak detection (CPD) method is unappropriate to detect the central Bragg wavelength of each FBG sensor. To solve this problem, we use ELM techniques. An ELM is used to accurately detect the central Bragg wavelength of each FBG sensor even when the spectra of FBGs are partially or fully overlapped. Moreover, a Raman amplifier is added to a fiber span to generate a gain medium within the transmission fiber, which amplifies the signal and compensates for the signal losses. The transmission distance and the sensing signal quality increase when the Raman pump power increases. The experimental results revealed that a Raman amplifier compensates for the signal losses and provides a stable sensing output even beyond a 45 km transmission distance. We achieve a remote sensing of strain measurement using a 45 km single-mode fiber (SMF). Furthermore, the well-trained ELM wavelength detection methods accurately detect the central Bragg wavelengths of FBG sensors when the two FBG spectra are fully overlapped

Self-Start Multi-Wavelength Laser Source with Tunable Delay-Line Interferometer and Optical Fiber Reflector for Wireless Communication System

The radio-over-fiber (RoF) technique has gained a lot of interest recently, as the millimeter-wave signals can be generated and delivered in the optical domain with the advantages of low attenuation, high capacity, and being free from electromagnetic noise interference (EMI). In this paper, we propose and experimentally prove a self-start multi-wavelength laser source based on a distributed feedback laser diode (DFB-LD) for the RoF transport system. The self-start multi-wavelength laser source generates stable laser power with less than 0.18 dB power fluctuation and exhibits good stability. In order to estimate the transmission performance, data is externally modulated onto the multi-wavelength by a reflective semiconductor optical amplifier (RSOA) and transmitted through single-mode fiber (SMF). The experimental result proves that the proposed RoF transport system achieves error-free transmission and clear eye diagrams

Enhancing Smart City Safety and Utilizing AI Expert Systems for Violence Detection

Violent attacks have been one of the hot issues in recent years. In the presence of closed-circuit televisions (CCTVs) in smart cities, there is an emerging challenge in apprehending criminals, leading to a need for innovative solutions. In this paper, the propose a model aimed at enhancing real-time emergency response capabilities and swiftly identifying criminals. This initiative aims to foster a safer environment and better manage criminal activity within smart cities. The proposed architecture combines an image-to-image stable diffusion model with violence detection and pose estimation approaches. The diffusion model generates synthetic data while the object detection approach uses YOLO v7 to identify violent objects like baseball bats, knives, and pistols, complemented by MediaPipe for action detection. Further, a long short-term memory (LSTM) network classifies the action attacks involving violent objects. Subsequently, an ensemble consisting of an edge device and the entire proposed model is deployed onto the edge device for real-time data testing using a dash camera. Thus, this study can handle violent attacks and send alerts in emergencies. As a result, our proposed YOLO model achieves a mean average precision (MAP) of 89.5% for violent attack detection, and the LSTM classifier model achieves an accuracy of 88.33% for violent action classification. The results highlight the model’s enhanced capability to accurately detect violent objects, particularly in effectively identifying violence through the implemented artificial intelligence system