Short-time Fourier Transform Research Articles

Utilizing Deep Learning Algorithms for Signal Processing in Electrochemical Biosensors: From Data Augmentation to Detection and Quantification of Chemicals of Interest.

Nanomaterial-based aptasensors serve as useful instruments for detecting small biological entities. This work utilizes data gathered from three electrochemical aptamer-based sensors varying in receptors, analytes of interest, and lengths of signals. Our ultimate objective was the automatic detection and quantification of target analytes from a segment of the signal recorded by these sensors. Initially, we proposed a data augmentation method using conditional variational autoencoders to address data scarcity. Secondly, we employed recurrent-based networks for signal extrapolation, ensuring uniform signal lengths. In the third step, we developed seven deep learning classification models (GRU, unidirectional LSTM (ULSTM), bidirectional LSTM (BLSTM), ConvGRU, ConvULSTM, ConvBLSTM, and CNN) to identify and quantify specific analyte concentrations for six distinct classes, ranging from the absence of analyte to 10 μM. Finally, the second classification model was created to distinguish between abnormal and normal data segments, detect the presence or absence of analytes in the sample, and, if detected, identify the specific analyte and quantify its concentration. Evaluating the time series forecasting showed that the GRU-based network outperformed two other ULSTM and BLSTM networks. Regarding classification models, it turned out signal extrapolation was not effective in improving the classification performance. Comparing the role of the network architectures in classification performance, the result showed that hybrid networks, including both convolutional and recurrent layers and CNN networks, achieved 82% to 99% accuracy across all three datasets. Utilizing short-term Fourier transform (STFT) as the preprocessing technique improved the performance of all datasets with accuracies from 84% to 99%. These findings underscore the effectiveness of suitable data preprocessing methods in enhancing neural network performance, enabling automatic analyte identification and quantification from electrochemical aptasensor signals.

Multi-view domain-adaptive representation learning for EEG-based emotion recognition

Current research suggests that there exist certain limitations in EEG emotion recognition, including redundant and meaningless time-frames and channels, as well as inter- and intra-individual differences in EEG signals from different subjects. To deal with these limitations, a Cross-attention-based Dilated Causal Convolutional Neural Network with Domain Discriminator (CADD-DCCNN) for multi-view EEG-based emotion recognition is proposed to minimize individual differences and automatically learn more discriminative emotion-related features. First, differential entropy (DE) features are obtained from the raw EEG signals using short-time Fourier transform (STFT). Second, each channel of the DE features is regarded as a view, and the attention mechanisms are utilized at different views to aggregate the discriminative affective information at the level of the time-frame of EEG. Then, a dilated causal convolutional neural network is employed to distill nonlinear relationships among different time frames. Next, a feature-level fusion is used to fuse features from multiple channels, aiming to explore the potential complementary information among different views and enhance the representational ability of the feature. Finally, to minimize individual differences, a domain discriminator is employed to generate domain-invariant features, which projects data from both the different domains into the same data representation space. We evaluated our proposed method on two public datasets, SEED and DEAP. The experimental results illustrate that our CADD-DCCNN method outperforms the SOTA methods.

A reduced-order machine-learning-based method for fault recognition in tool condition monitoring

The application of Machine Learning methodologies has been particularly noteworthy and abundant in pattern and symptom recognition across various research areas. However, Tool Condition Monitoring remains a challenging subject due to the gradual wearing out of cutting tools during the machining process. Such failure leads to reduced accuracy and quality of the machined surface of the workpiece, resulting in increased costs. This research proposes an innovative ML-based method to clarify failure symptoms of cutting tools in the frequency and time–frequency domains. The study involves five cutting tools as experimental case studies during a 200-minute machining operation. The results are validated using the Fast Fourier Transform, Short-time Fourier Transform, Empirical Mode Decomposition, and Variational Mode Decomposition methods, to demonstrate that the suggested methodology better identifies failure symptoms compared to other mentioned methods. One advantage of the proposed method is that considering a lower order of the system results in clearer frequency and time–frequency domain diagrams without sacrificing accuracy.

Asphalt Pavement Transverse Cracking Detection Based on Vehicle Dynamic Response

Transverse cracking is thought of as the typical distress of asphalt pavements. A faster detection technique can provide pavement performance information for maintenance administrations. This paper proposes a novel vehicle-vibration-based method for transverse cracking detection. A theoretical model of a vehicle-cracked pavement vibration system was constructed using the d’Alembert principle. A testing system installed with a vibration sensor was put in and applied to a testing road. Then, parameter optimization of the Short-time Fourier transform (STFT) was conducted. Transverse cracking and normal sections were processed by the optimized STFT algorithm, generating two ideal indicators. The maximum power spectral density and the relative power spectral density, which were extracted from 3D time–frequency maps, performed well. It was found that the power spectral density caused by transverse cracks was above 100 dB/Hz. The power spectral density at normal sections was below 80 dB/Hz. The distribution of the power spectral density for the cracked sections is more discrete than for normal sections. The classification model based on the above two indicators had an accuracy, true positive rate, and false positive rate of 94.96%, 92.86%, and 4.80%, respectively. The proposed vehicle-vibration-based method is capable of accurately detecting pavement transverse cracking.

Improved STFT analysis using time-frequency masking for chatter detection in the milling process

In the field of machining, the purposeful increase in cutting depth and rotation speed can potentially cause chatter vibration. Chatter lowers surface quality, tool and spindle life, and produces excessive noise, which limits the productivity of the machining process. In this paper, an improved Short-Time Fourier Transform (STFT) involving time–frequency masking and adaptive thresholding is proposed. Initially, hammering tests were undertaken to ascertain the inherent natural frequency of the cutting system. Subsequently, milling tests were conducted across varied conditions utilizing an accelerometer sensor and a microphone (capturing sound signals) to acquire the requisite experimental data. A 3-level Variational Mode Decomposition (VMD) was used for pre-processing due to its inherentcapabilities for noise reduction. The chatter threshold is established by averaging the total intrinsic mode function (IMF) energy levels that have been selected. To demonstrate the efficacy of the proposed method, root mean square (RMS) and skewness were calculated as chatter indicators. The improved STFT time–frequency representation (TFR) was compared to the conventional TFR. Notably, both indicators demonstrate effectiveness and chatter sensitivity, while the improved STFT offers greater time–frequency resolution than its conventional counterpart and can be successfully used for monitoring the milling process.

Classifying marine mammals signal using cubic splines interpolation combining with triple loss variational auto-encoder

In practical applications of passive sonar principles for extracting characteristic frequencies of acoustic signals, scientists typically employ traditional time-frequency domain transformation methods such as Mel-frequency, Short time Fourier transform (STFT), and Wavelet transform (WT). However, these solutions still face limitations in resolution and information loss when transforming data collected over extended periods. In this paper, we present a study using a two-stage approach that combines pre-processing by Cubic-splines interpolation (CSI) with a probability distribution in the hidden space with Siamese triple loss network model for classifying marine mammal (MM) communication signals. The Cubic-splines interpolation technique is tested with the STFT transformation to generate STFT-CSI spectrograms, which enforce stronger relationships between characteristic frequencies, enhancing the connectivity of spectrograms and highlighting frequency-based features. Additionally, stacking spectrograms generated by three consecutive methods, Mel, STFT-CSI, and Wavelet, into a feature spectrogram optimizes the advantages of each method across different frequency bands, resulting in a more effective classification process. The proposed solution using an Siamese Neural Network-Variational Auto Encoder (SNN-VAE) model also overcomes the drawbacks of the Auto-Encoder (AE) structure, including loss of discontinuity and loss of completeness during decoding. The classification accuracy of marine mammal signals using the SNN-VAE model increases by 11% and 20% compared to using the AE model (2013), and by 6% compared to using the Resnet model (2022) on the same actual dataset NOAA from the National Oceanic and Atmospheric Administration - United State of America.

On-line defect recognition of MIG lap welding for stainless steel sheet based on weld image and CMT voltage: Feature fusion and attention weights visualization

At present, sensor recognition technology has become a research hotspot in welding process monitoring. However, most of the existing defect recognition methods are based on single-sensor information, which hinders the further improvement of recognition accuracy and weakens the anti-interference ability under complex conditions. To overcome the shortcomings of single-sensor recognition technology, multi-source information fusion technology can exploit the advantages of different types of sensors to achieve more comprehensive and accurate monitoring of the welding process. However, given the process of fusion of different information, the feature mining mechanism of the deep learning model is still under-explained, and the direct complementarity of the different information remains unclear. In this paper, we propose a hybrid model that uses the fusion of molten pool image and voltage signals to quickly and accurately identify three types of defects in lap MIG welding: burn-through, weld bias, and uneven width. Firstly, a synchronized trigger device is used to acquire molten pool images and weld voltage information, and the voltage data is sliced and matched with the molten pool image data. Secondly, the Short-Time Fourier Transform (STFT) is applied to explore the frequency variation components in the voltage data, and the resulting time-frequency matrix is transformed into a time spectrogram. Finally, the molten pool image and the time spectrogram are simultaneously fed into the hybrid CNN model for online monitoring of welding defect states. The test results show that the average accuracy of the hybrid CNN model in detecting five different welding states reaches 98.87 %, which is significantly higher than three other models based on single sensor information. The visualization of the confusion matrix and Grad-CAM shows that the hybrid CNN model based on multi-source heterogeneous information has a higher accuracy for detecting the transition states that are difficult to detect in welding, and has a more reasonable mechanism for allocating attention, so it can achieve a more accurate and balanced recognition effect in the whole welding manufacturing process. These studies can provide guidance for quality monitoring of welding processes based on multi-source sensor technology, which can be extended to the field of arc additive monitoring.

A Few-Shot Learning Based Fault Diagnosis Model Using Sensors Data from Industrial Machineries

Efficient maintenance in the face of complex and interconnected industrial equipment is crucial for corporate competitiveness. Traditional reactive approaches often prove inadequate, necessitating a shift towards proactive strategies. This study addresses the challenges of data scarcity and timely defect identification by providing practical guidance for selecting optimal solutions for various equipment malfunction scenarios. Utilizing three datasets—Machine Sound to Machine Condition Monitoring and Intelligent Information (MIMII), Case Western Reserve University (CWRU), and Machinery Failure Prevention Technology (MFPT)—the study employs the Short-Time Fourier Transform (STFT) as a preprocessing method to enhance feature extraction. To determine the best preprocessing technique, Gammatone Transformation, and raw data are also considered. The research optimizes performance and training efficiency by adjusting hyperparameters, minimizing overfitting, and using the KERAS Early Halting API within resource constraints. To address data scarcity, which is one of the major obstacles to detecting faults in the industrial environment, Few-shot learning (FSL) is employed. Various architectures, including ConvNeXt Base, Large MobileNetV3, ResNet-18, and ResNet-50, are incorporated within a prototypical network-based few-shot learning model. MobileNet’s lower parameter count, high accuracy, efficiency, and portability make it the ideal choice for this application. By combining few-shot learning, MobileNet architecture, and STFT preprocessing, this study proposes a practical and data-efficient fault diagnosis method. The model demonstrates adaptability across datasets, offering valuable insights for enhancing industrial fault detection and preventive maintenance procedures.

A novel position estimation method for wayside pass-by noise sources based on Doppler effect correction

The localization of moving noise sources has garnered substantial attention in recent years, prompting researchers to develop a range of methodologies for positioning sound sources across diverse scenarios. Despite this progress, the intricate interplay of complex spatial sound fields and the Doppler effect during motion presents a formidable challenge in achieving accurate sound source localization. To address these challenges, this paper proposes a novel method for estimating positions of wayside pass-by noise sources based on Doppler Effect Correction (Do-bPE). The proposed method capitalizes on a spatial microphone array to capture signals emanating from moving sound sources. Each channel’s sound signal is transformed into the time–frequency domain using Short Time Fourier Transform (STFT). The Approximate Greatest Common Divisor (AGCD) algorithm is then applied to extract the instantaneous frequency of the moving source from the time–frequency spectrogram. By leveraging nonlinear least squares fitting and the acoustic Doppler model, this instantaneous frequency facilitates the kinematic parameters estimation of Doppler effect, which provides the distances between each microphone within the array and the trajectory of moving sound source. Then, by employing the weighted least squares method to solve the relationship between the sound source and the array microphones, an accurate spatial position for the sound source can be obtained. The method successfully applies the Doppler effect to estimate the source distances, subsequently refining position estimation accuracy through microphone array processing. Simulation and experimental results show that the method’s positioning accuracy and robustness are significantly improved compared to existing approaches.

EnViTSA: Ensemble of Vision Transformer with SpecAugment for Acoustic Event Classification.

Recent successes in deep learning have inspired researchers to apply deep neural networks to Acoustic Event Classification (AEC). While deep learning methods can train effective AEC models, they are susceptible to overfitting due to the models' high complexity. In this paper, we introduce EnViTSA, an innovative approach that tackles key challenges in AEC. EnViTSA combines an ensemble of Vision Transformers with SpecAugment, a novel data augmentation technique, to significantly enhance AEC performance. Raw acoustic signals are transformed into Log Mel-spectrograms using Short-Time Fourier Transform, resulting in a fixed-size spectrogram representation. To address data scarcity and overfitting issues, we employ SpecAugment to generate additional training samples through time masking and frequency masking. The core of EnViTSA resides in its ensemble of pre-trained Vision Transformers, harnessing the unique strengths of the Vision Transformer architecture. This ensemble approach not only reduces inductive biases but also effectively mitigates overfitting. In this study, we evaluate the EnViTSA method on three benchmark datasets: ESC-10, ESC-50, and UrbanSound8K. The experimental results underscore the efficacy of our approach, achieving impressive accuracy scores of 93.50%, 85.85%, and 83.20% on ESC-10, ESC-50, and UrbanSound8K, respectively. EnViTSA represents a substantial advancement in AEC, demonstrating the potential of Vision Transformers and SpecAugment in the acoustic domain.

Time-Frequency Analysis and Neural Networks for Detecting Short-Circuited Turns in Transformers in Both Transient and Steady-State Regimes Using Vibration Signals

Transformers are vital elements in electrical networks, but they are prone to various faults throughout their service life. Among these, a winding short-circuit fault is of particular concern to researchers, as it is a crucial and vulnerable component of the transformers. Therefore, if this fault is not addressed at an early stage, it can increase costs for users and affect industrial processes as well as other electrical machines. In recent years, the analysis of vibration signals has emerged as one of the most promising solutions for detecting faults in transformers. Nonetheless, it is not a straightforward process because of the nonstationary properties of the vibration signals and their high-level noise, as well as their different features when the transformer operates under different conditions. Based on the previously mentioned points, the motivation of this work is to contribute a methodology that can detect different severities of short-circuited turns (SCTs) in transformers in both transient and steady-state operating regimes using vibration signals. The proposed approach consists of a wavelet-based denoising stage, a short-time Fourier transform (STFT)-based analysis stage for the transient state, a Fourier transform (FT)-based analysis stage for the steady-state, the application of two fault indicators, i.e., the energy index and the total harmonic distortion index, and two neural networks for automatic diagnosis. To evaluate the effectiveness of the proposed methodology, a modified transformer is used to experimentally reproduce different levels of SCTs, i.e., 0-healthy, 5, 10, 15, 20, 25, and 30 SCTs, in a controlled way. The obtained results show that the proposed approach can detect the fault condition, starting from an initial stage for consolidation and a severe stage to accurately assess the fault severity, achieving accuracy values of 90%.

A Methodology of Condition Monitoring System Utilizing Supervised and Semi-Supervised Learning in Railway.

In this paper, research was conducted on anomaly detection of wheel flats. In the railway sector, conducting tests with actual railway vehicles is challenging due to safety concerns for passengers and maintenance issues as it is a public industry. Therefore, dynamics software was utilized. Next, STFT (short-time Fourier transform) was performed to create spectrogram images. In the case of railway vehicles, control, monitoring, and communication are performed through TCMS, but complex analysis and data processing are difficult because there are no devices such as GPUs. Furthermore, there are memory limitations. Therefore, in this paper, the relatively lightweight models LeNet-5, ResNet-20, and MobileNet-V3 were selected for deep learning experiments. At this time, the LeNet-5 and MobileNet-V3 models were modified from the basic architecture. Since railway vehicles are given preventive maintenance, it is difficult to obtain fault data. Therefore, semi-supervised learning was also performed. At this time, the Deep One Class Classification paper was referenced. The evaluation results indicated that the modified LeNet-5 and MobileNet-V3 models achieved approximately 97% and 96% accuracy, respectively. At this point, the LeNet-5 model showed a training time of 12 min faster than the MobileNet-V3 model. In addition, the semi-supervised learning results showed a significant outcome of approximately 94% accuracy when considering the railway maintenance environment. In conclusion, considering the railway vehicle maintenance environment and device specifications, it was inferred that the relatively simple and lightweight LeNet-5 model can be effectively utilized while using small images.

Vibration Analysis for Turning-Milling Process Condition Monitoring using Short-Time Fourier Transform Enhanced by Empirical Mode Decomposition

Turn-milling process is widely applied in industries that provides advantages for machining large-diameter mechanical parts with high speed, reducing cutting temperature, which in turn decreases tool wear. However, it needs to monitor the turn-milling process for preventing the onset of chatter vibration during operation and the chatter induces negative effects. Vibration analysis is one of the ways for monitoring it. However, acquired vibrations should be denoised from noises, wherein the conventional signal filter may have defiance to do that. This paper presents the utilization of the empirical mode decomposition (EMD) method as an efficient and adaptive noise filter. The Short-Time Fourier Transform (STFT) improvement using EMD is then used for monitoring turn-milling process conditions in the energy-time-frequency domain. The results showed that the reconstructed signal was quite impressive compared to the raw signal and the oscillation of the filtered signal was clearer than the raw signal. The improvement of the filtered signals was proved by the kurtosis index and spectral kurtosis. The improved STFT using EMD showed a significant spectrum with high resolution compared to conventional STFT. The energy density could be observed clearly in the machining characteristic frequencies with an improvement of about 10-100 times larger. The proposed method is therefore effectively applied to monitor the turn-milling condition.

Effects of Injector Configuration on the Detonation Characteristics and Propulsion Performance of Rotating Detonation Engine (RDE)

Fuel injection and mixing affect the characteristics of detonation initiation and propagation, as well as the propulsion performance of rotating detonation engine (RDE). A study on the injector is carried out in the present investigation. A rectangular-shaped hole-type fuel injector (RHFI) and slit-type fuel injector (SFI) were designed and compared experimentally at equivalent conditions. The investigation of the detonation propagation modes and the analysis of propulsion performance were carried out using fast Fourier transform (FFT), short-time Fourier transform (STFT), and unwrapped image post-processing. Under 50, 75, and 100 g/s flow rate conditions at an equivalence ratio of 1.0 ± 0.05, the RHFI has relatively stable detonation propagation characteristics, higher thrust, and specific impulse performance. Additionally, the results of the experiment indicate that the number of detonation waves affects performance.

Protection Scheme based on Artificial Neural Network for Fault Detection and Classification in Low Voltage PV-Based DC Microgrid

With the expansion of the DC distribution market, protection, and operational concerns for Direct Current (DC) Microgrids have increased. Different systems have been investigated for detecting, finding, and isolating defects utilising a variety of protective mechanisms. It might be difficult to locate high-resistance faults and shorted DC faults on low-voltage DC (LVDC) microgrids. Therefore, in this study, a Field Transform Technique like Short-Time Fourier Transform (STFT) is proposed for detecting the Fault Current (FC). This method detects the faults Pole-ground (PG), pole-pole (PP), and Arc fault are the major fault types in the DC network with PG fault as the most common and less severe. One of the difficulties the DC system faces in the incidence of a malfunction is the protection of essential converters. During this fault, the diodes, being the most vulnerable component of the system, may encounter a substantial surge in current, which can potentially cause damage if the current surpasses double their specified capacity for withstanding. After the Fault detection (FD), a Taguchi-based ANN is presented to classify the detected faults. This method effectively classifies PV-based faults. Then, to safeguard the FC, the Improved Self-Adaptive Solid State Circuit Breaker (I-SSCB) is introduced. It safeguards the FC in the low-voltage PV-based DC microgrid (DCMG) and restricts FC in the DCMG. The suggested approach is evaluated using the Matlab software and the proposed method produces 400A current and 100 KW power during the PV temperature of 25°C. The output current of the ANN is then 1A for a duration of 0.3 to 0.4 seconds. The fault voltage and FC produced in this proposed work are 1900V and 1950A. Therefore, the proposed work's current and voltage values are 21 KV and 0.35 I. Therefore, the proposed method produces more power and limits the FC in the LV-DCMG. In future studies, the improved or modified neural network or machine learning (ML)-based techniques can be utilized which may improve the protection scheme of the work.

Short-Time Fourier Transform Analysis of Current Charge/Discharge Response of Lithium-Sulfur Batteries

Measurements acquired on batteries in the form of time signals such as voltage-time and capacity-time to assess their cyclability performance can be supplemented by examining their frequency-domain response. This allows one to determine the global characteristics of the signals and the battery, but not the local behavior, which is very important for determining for example battery fading. In this study we examine the short-time Fourier transform for time-frequency deconstruction of galvanostatic charge/discharge signals of lithium-sulfur batteries, taken as an example. The results displayed in terms of spectrograms show how the frequency content of such signals (e.g. charge and voltage time series) evolve with the lifetime of the batteries allowing the detection of critical changes in the response that may lead to fading and eventually default.

Localizing False Data Injection Attacks in Smart Grid: A Spectrum-Based Neural Network Approach

Smart grid is confronted with cyberattacks due to the increasing dependence on cyberspace. False data injection attacks (FDIAs) represent a major type of cyberattacks that cannot be detected by the traditional bad data detection (BDD). The majority of existing researches focus on how to detect FDIAs, while little attention has been paid to obtaining the exact attack locations, which are crucial for deploying countermeasures in time. In this paper, we propose a neural network-based approach for online localizing FDIAs in AC power systems. The approach transforms the multi-dimensional time series measurements to their time-frequency and spectral graph representations, to jointly capture the temporal and spatial correlations in the spectral domain. The spectral decomposition could better characterize the latent properties of measurements, such as auto-correlations of a bus and key dependency between buses, which would be poorly represented in the time domain and vertex domain. Specifically, the proposed approach first adopts the short-time Fourier transform (STFT) and a two-channel convolutional neural network (2C-CNN) to model the spectral-temporal correlations. Then the spectral-spatial relationships are modeled with a graph convolutional network (GCN), which combines the nodal admittance matrix and physical property of power systems. The prior physical knowledge further improves our approach’s interpretability and localization performance. We evaluate the effectiveness of our proposed approach with comprehensive case studies on various standard test systems. Numerical results indicate the superiority of our approach over baselines.

Explainable artificial intelligence for searching frequency characteristics in Parkinson’s disease tremor

The distinction between Parkinson’s disease (PD) and essential tremor (ET) tremors is subtle, posing challenges in differentiation. To accurately classify the PD and ET, BiLSTM-based recurrent neural networks are employed to classify between normal patients (N), PD patients, and ET patients using accelerometry data on their lower arm (L), hand (H), and upper arm (U) as inputs. The trained recurrent neural network (RNN) has reached 80% accuracy. The neural network is analyzed using layer-wise relevance propagation (LRP) to understand the internal workings of the neural network. A novel explainable AI method, called LRP-based approximate linear weights (ALW), is introduced to identify the similarities in relevance when assigning the class scores in the neural network. The ALW functions as a 2D kernel that linearly transforms the input data directly into the class scores, which significantly reduces the complexity of analyzing the neural network. This new classification method reconstructs the neural network’s original function, achieving a 73% PD and ET tremor classification accuracy. By analyzing the ALWs, the correlation between each input and the class can also be determined. Then, the differentiating features can be subsequently identified. Since the input is preprocessed using short-time Fourier transform (STFT), the differences between the magnitude of tremor frequencies ranging from 3 to 30 Hz in the mean N, PD, and ET subjects are successfully identified. Aside from matching the current medical knowledge on frequency content in the tremors, the differentiating features also provide insights about frequency contents in the tremors in other frequency bands and body parts.

Hybrid Temporal Dynamics Feature Extraction in Recommendation Systems for Improved Ranking of Items

In today's retail landscape, shopping malls and e-commerce platforms employ various psychological tactics to influence customer behavior and increase profits. In line with these strategies, this paper introduces an innovative method for recognizing sentiment patterns, with a specific emphasis on the evolving temporal aspects of user interests within Recommendation Systems (RS). The projected method, called Temporal Dynamic Features based User Sentiment Pattern for Recommendation System (TDF-USPRS), aims to enhance the performance of RS by leveraging sentiment trends derived from a user's past preferences. TDF-USPRS utilizes a hybrid model combining Short Time Fourier Transform (STFT) and a layered architecture based on Bidirectional Long Short-Term Memory (BiLSTM) to retrieve temporal dynamics and discern a user's sentiment trend. Through an examination of a user's sequential history of item preferences, TDF-USPRS produces sentiment patterns to offer exceptionally pertinent recommendations, even in cases of sparse datasets. A variety of popular datasets, including as MovieLens, Amazon Rating Beauty, YOOCHOOSE, and CiaoDVD are utilised to assess the suggested technique. The TDF-USPRS model outperforms existing approaches, according to experimental data, resulting in recommendations with greater accuracy and relevance. Comparing the projected model to existing approaches, the projected model displays a 6.5% reduction in RMSE and a 4.5% gain in precision. Specifically, the model achieves an RMSE of 0.7623 and 0.996 on the MovieLens and CiaoDVD datasets, while attaining a precision score of 0.5963 and 0.165 on the YOOCHOOSE and Amazon datasets, respectively.

Vibration Event Recognition Using SST-Based Φ-OTDR System.

We propose a method based on Synchrosqueezing Transform (SST) for vibration event analysis and identification in Phase Sensitive Optical Time-Domain Reflectometry (Φ-OTDR) systems. SST has high time-frequency resolution and phase information, which can distinguish and enhance different vibration events. We use six tap events with different intensities and six other events as experimental data and test the effect of attenuation. We use Visual Geometry Group (VGG), Vision Transformer (ViT), and Residual Network (ResNet) as deep classifiers for the SST transformed data. The results show that our method outperforms the methods based on Continuous Wavelet Transform (CWT) and Short-Time Fourier Transform (STFT), while ResNet is the best classifier. Our method can achieve high recognition rate under different signal strengths, event types, and attenuation levels, which shows its value for Φ-OTDR system.