MOQEA/D: Multi-Objective QEA With Decomposition Mechanism and Excellent Global Search and Its Application

With the rise in traffic congestion in urban centers, predicting accidents has become paramount for city planning and public safety. This work comprehensively studied the efficacy of modern deep learning (DL) methods in forecasting traffic accidents and enhancing Level-4 and Level-5 (L-4 and L-5) driving assistants with actionable visual and language cues. Using a rich dataset detailing accident occurrences, we juxtaposed the Transformer model against traditional time series models like ARIMA and the more recent Prophet model. Additionally, through detailed analysis, we delved deep into feature importance using principal component analysis (PCA) loadings, uncovering key factors contributing to accidents. We introduce the idea of using real-time interventions with large language models (LLMs) in autonomous driving with the use of lightweight compact LLMs like LLaMA-2 and Zephyr-7b-α. Our exploration extends to the realm of multimodality, through the use of Large Language-and-Vision Assistant (LLaVA)—a bridge between visual and linguistic cues by means of a Visual Language Model (VLM)—in conjunction with deep probabilistic reasoning, enhancing the real-time responsiveness of autonomous driving systems. In this study, we elucidate the advantages of employing large multimodal models within DL and deep probabilistic programming for enhancing the performance and usability of time series forecasting and feature weight importance, particularly in a self-driving scenario. This work paves the way for safer, smarter cities, underpinned by data-driven decision making.

LLM Multimodal Traffic Accident Forecasting

Early detection of blade defects is crucial for turbines in Internet of Things (IoT) as it can prevent failures, minimize downtime, and enhance system reliability. Deep-learning-based methods have improved the accuracy of defect recognition and classification. However, there are challenges in balancing recognition efficiency, accuracy, and the ability to detect small targets. Therefore, a lightweight defect detection model of turbine blades using single-shot multibox detection based on ShuffleNetv2 and coordinate attention (SN-CA-SSD) in IoT is developed in this article. First, within the single-shot multibox detector framework, ShuffleNetv2 replaces the VGG-16 network as the fundamental feature extraction network. This substitution ensures detection accuracy while reducing model complexity. Second, the coordinate attention mechanism is introduced after the feature map, weighting important features to enhance the model’s ability to perceive key information. Finally, the loss function is redesigned using Efficient Intersection over Union (EIoU) loss to improve detected targets’ accuracy and localization precision. The proposed method is applied to defect detection images of turbine blades, and the results demonstrate that the algorithm significantly improves accuracy, detection efficiency, and the ability to detect small targets, striking a balance between precision and efficiency. By combining the model with an interpretable algorithm, the algorithm’s decision-making process is analyzed from the perspective of representation visualization, enhancing the algorithm’s interpretability.

Defect Detection Using Shuffle Net-CA-SSD Lightweight Network for Turbine Blades in IoT

For the poor model generalization and low diagnostic efficiency of fault diagnosis under imbalanced distributions, a novel fault diagnosis method using variational autoencoder generation adversarial network and improved convolutional neural network, named VGAIC-FDM, is proposed in this paper. First, to capture local features of vibration signals, continuous wavelet transform is employed to convert the original one-dimensional fault signals into wavelet time–frequency images. Second, for the data dimensionality reduction and model simplification, the time–frequency wavelet images are processed in grayscale to generate single-channel grayscale time–frequency images. Then, sample augmentation is performed on grayscale time–frequency images to balance the dataset by using a variational autoencoder generation adversarial network. Finally, the generated images and the original images are fused and trained by using a focus-loss-optimized CNN classifier to achieve fault diagnosis under unbalanced conditions. The experimental results show that the VGAIC-FDM effectively captures the potential spatial distribution of real samples and alleviates the impact caused by the inconsistent difficulty of sample classification. As a result, it enhances the fault diagnosis performance of the model when dealing with unbalanced datasets, leading to higher accuracy and F1-score values. 

Fault diagnosis using variational autoencoder GAN and focal loss CNN under unbalanced data

Federated learning (FL) represents a distributed machine learning approach that leverages a centralized server to train models while keeping the data on edge devices isolated. FL has the benefits of preserving data privacy and improving model accuracy. However, the occurrence of unexpected device exits during model training can severely impact the performance of the models. To address the communication overhead issue and accelerate model convergence, a novel adaptive FL with a negative inner product aggregation approach, namely, NIPAFed is proposed in this article. The NIPAFed leverages a congestion control algorithm inspired by TCP, known as additive multiplication subtraction strategy, to adaptively predict the workload of devices based on historical workload. So NIPAFed effectively mitigates the impact of stragglers on the training process. Additionally, to reduce communication overhead and latency, a negative inner product aggregation strategy is employed to accelerate model convergence and minimize the number of communication rounds required. The convergence of the model is also analyzed theoretically. The validity of NIPAFed is tested on federated public data sets and the NIPAFed is compared with some algorithms. The experimental results clearly demonstrate the superiority of the NIPAFed in terms of performance. By reducing device dropouts and minimizing the communication rounds, the NIPAFed effectively controls the communication overhead while the convergence is ensured.

Adaptive Federated Learning With Negative Inner Product Aggregation

Recently, the rapid development of deep learning has greatly improved the performance of image classification. However, a central problem in hyperspectral image (HSI) classification is spectral uncertainty, where spectral features alone cannot accurately and robustly identify a pixel point in a hyperspectral image. This paper presents a novel HSI classification network called MS-RPNet, i.e., multiscale superpixelwise RPNet, which combines superpixel-based S3-PCA with two-dimensional singular spectrum analysis (2D-SSA) based on the Random Patches Network (RPNet). The proposed frame can not only take advantage of the data-driven method, but can also apply S3-PCA to efficiently consider more global and local spectral knowledge at the super-pixel level. Meanwhile, 2D-SSA is used for noise removal and spatial feature extraction. Then, the final features are obtained by random patch convolution and other steps according to the cascade structure of RPNet. The layered extraction superimposes the different sparial information into multi-scale spatial features, which complements the features of various land covers. Finally, the final fusion features are classified by SVM to obtain the final classification results. The experimental results in several HSI datasets demonstrate the effectiveness and efficiency of MS-RPNet, which outperforms several current state-of-the-art methods.

/pdf/hyperspectral-image-classification-based-on-fusing-s3-pca-2d-28d30ja9.pdf

Hyperspectral Image Classification Based on Fusing S3-PCA, 2D-SSA and Random Patch Network

With the rapid development of the air transportation industry, air traffic is facing a severe test. The accurate prediction of the estimated arrival time (EAT) plays an important role in rational and efficient scheduling of flights. Aircraft EAT predictions often rely on aircraft performance parameters or physical trajectories. This gives poor consideration to the flight environment and take-off and landing conditions. This paper applies a data-driven methodology for ETA prediction. A Cluster clustering-based modular integrated DNN (CC-MIDNN) is proposed. According to the information of flight mission and flight environment, it decomposes complex modeling tasks into multiple distinct subtasks using cluster-based clustering. Then Bayesian optimization is used to design corresponding parallel sub-networks to learn different sub-tasks in order to reduce the complexity of the subsequent sub-networks. Finally, a new integration scheme is proposed to solve the overfitting problem due to the reliance on the training accuracy of sub-modules. To verify the effectiveness of the method, CC-MIDNN performs EAT prediction on flight data of European Airlines LIS airport in 2017. The experimental results show that the CC-MIDNN improves the time prediction accuracy by 5.92 minutes and keeps the error within 13 minutes. Compared with other deep learning algorithms and integration algorithms, the CC-MIDNN has high accuracy and stability. 

A Flight Arrival Time Prediction Method Based on Cluster Clustering-Based Modular With Deep Neural Network

During the long and high-intensity railway use, all kinds of defects emerge, which often produce light to moderate damage on the surface, which adversely affects the stable operation of trains and even endangers the safety of travel. Currently, models for detecting rail surface defects are ineffective, and self-collected rail surface images have poor illumination and insufficient defect data. In light of the aforementioned problems, this article suggests an improved YOLOX and image enhancement method for detecting rail surface defects. First, a fusion image enhancement algorithm is used in the HSV space to process the surface image of the steel rail, highlighting defects and enhancing background contrast. Then, this paper uses a more efficient and faster BiFPN for feature fusion in the neck structure of YOLOX. In addition, it introduces the NAM attention mechanism to increase image feature expression capability. The experimental results show that the detection of rail surface defects using the algorithm improves the mAP of the YOLOX network by 2.42%. The computational volume of the improved network increases, but the detection speed can still reach 71.33 fps. In conclusion, the upgraded YOLOX model can detect rail surface flaws with accuracy and speed, fulfilling the demands of real-time detection. The lightweight deployment of rail surface defect detection terminals also has some benefits.

/pdf/rail-surface-defect-detection-based-on-image-enhancement-and-1tk8x1qq.pdf

Rail Surface Defect Detection Based on Image Enhancement and Improved YOLOX

This paper proposes a spectral clustering method using k-means and weighted Mahalanobis distance (Referred to as MDLSC) to enhance the degree of correlation between data points and improve the clustering accuracy of Laplacian matrix eigenvectors. First, we used the correlation coefficient as the weight of the Mahalanobis distance to calculate the weighted Mahalanobis distance between any two data points and constructed the weighted Mahalanobis distance matrix of the data set; then, based on the weighted Mahalanobis distance matrix, we used the K-nearest neighborhood (KNN) algorithm construct similarity matrix. Secondly, the regularized Laplacian matrix was calculated according to the similarity matrix, normalized and decomposed, and the feature space for clustering was obtained. This method fully considered the degree of linear correlation between data and special spatial structure and achieved accurate clustering. Finally, various spectral clustering algorithms were used to conduct multi-angle comparative experiments on artificial and UCI data sets. The experimental results show that MDLSC has certain advantages in each clustering index and the clustering quality is better. The distribution results of the eigenvectors also show that the similarity matrix calculated by MDLSC is more reasonable, and the calculation of the eigenvectors of the Laplacian matrix maximizes the retention of the distribution characteristics of the original data, thereby improving the accuracy of the clustering algorithm. 

Spectral Clustering Approach with K-Nearest Neighbor and Weighted Mahalanobis Distance for Data Mining

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