In the field of marine detection and warning, predicting the heights of ocean wave is a very important project. In order to predict the ocean wave heights accurately and quickly, our methodology ...utilizes a hybrid Mind Evolutionary Algorithm-BP neural network strategy (MEA-BP). This paper investigates how the BP neural network (BPnn) evolution with MEA improves the generalization ability and predictability of BPnn. The MEA-BP model combines the local searching ability of the BPnn and the global searching ability of the MEA which can avoid premature convergence and poor prediction effect. In order to search individuals which contain optimal weights and thresholds, the MEA searches all the initial weights and thresholds intelligently by similartaxis and dissimilation operation, finally assign them to the initial BPnn. The study is conducted using data collected from 12 observation points across two geographically distinct regions, Bohai Sea, Yellow Sea, for the period from Jan 1, 2016 to Dec 31, 2016. The data is chosen such that the study covers a wide range of geographical locations and different weather. We compare the prediction performance and generalization capabilities of MEA-BP with the Genetic Algorithm-BP neural network model (GA-BP) which also developed with the BPnn. The performance study results demonstrate that MEA-BP performs better than the GA-BP and Standard BP neural network model (St-BP) with faster running time and higher prediction accuracy.
•MEA can remember more than one generation of evolutionary information.•Similartaxis and dissimilation operations towards a correct direction, never become worse.•Similartaxis and dissimilation operations are easier to find the optimal individual, saving the number of iterations.•Crossover and mutation operations in GA can produce good genes, or destroy the original genes.•The study data covers a wide range of geographical locations and different weather to eliminate the effects of geography and weather.
This paper proposes an innovative global solution which is a pioneering work applying automated machine learning algorithms to remarkable precision sparse underwater direction-of-arrival (DOA) ...estimation that views the subaquatic sparse-sampling DOA estimation problem as a classification prediction task. The proposed solution, termed automated multi-layer perceptron discriminative neural network (AutoMPDNN), is built upon a Bayesian optimization framework. AutoMPDNN transforms sparsely sampled time-domain signals into the complex domain, preserving essential components in a one-source single-snapshot scenario. Leveraging Bayesian optimization principles, the algorithm embeds necessary hyperparameters into the loss function, effectively defining it as a maximum likelihood problem using the upper confidence bound function and incorporating sparse signal features. We also explore the model space architecture and introduce variants of AutoMPDNN, denoted as AutoMPDNNs_ln (n = 2,3,4). Through a series of plane wave simulation experiments, it is demonstrated that AutoMPDNN achieves the highest prediction performance for one-source single-snapshot scenarios compared to classical DOA estimation algorithms that incorporate sparse representation approaches, as well as contemporary deep learning DOA methods under varying conditions.
Due to the lack of a specific design for scenarios such as scale change, illumination difference, and occlusion, current person re-identification methods are difficult to put into practice. A ...Multi-Branch Feature Fusion Network (MFFNet) is proposed, and Shallow Feature Extraction (SFF) and Multi-scale Feature Fusion (MFF) are utilized to obtain robust global feature representations while leveraging the Hybrid Attention Module (HAM) and Anti-erasure Federated Block Network (AFBN) to solve the problems of scale change, illumination difference and occlusion in scenes. Finally, multiple loss functions are used to efficiently converge the model parameters and enhance the information interaction between the branches. The experimental results show that our method achieves significant improvements over Market-1501, DukeMTMC-reID, and MSMT17. Especially on the MSMT17 dataset, which is close to real-world scenarios, MFFNet improves by 1.3 and 1.8% on Rank-1 and mAP, respectively.
As the most widely used storage device today, hard disks are efficient and convenient, but the damage incurred in the event of a failure can be very significant. Therefore, early warnings before hard ...disk failure, allowing the stored content to be backed up and transferred in advance, can reduce many losses. In recent years, an endless stream of research on the prediction of hard disk failure prediction has emerged. The detection accuracy of various methods, from basic machine learning models, such as decision trees and random forests, to deep learning methods, such as BP neural networks and recurrent neural networks, has also been improving. In this paper, based on the idea of blending ensemble learning, a novel failure prediction method combining machine learning algorithms and neural networks is proposed on the publicly available BackBlaze hard disk datasets. The failure prediction experiment is conducted only with S.M.A.R.T., that is, the learned characteristics collected by self-monitoring analysis and reporting technology, which are internally counted during the operation of the hard disk. The experimental results show that this ensemble learning model is able to outperform other independent models in terms of evaluation criterion based on the Matthews correlation coefficient. Additionally, through the experimental results on multiple types of hard disks, an ensemble learning model with high performance on most types of hard disks is found, which solves the problem of the low robustness and generalization of traditional machine learning methods and proves the effectiveness and high universality of this method.
Early risk prediction of diabetes could help doctors and patients to pay attention to the disease and intervene as soon as possible, which can effectively reduce the risk of complications. In this ...paper, a GA-stacking ensemble learning model is proposed to improve the accuracy of diabetes risk prediction. Firstly, genetic algorithms (GA) based on Decision Tree (DT) is used to select individuals with high adaptability, that is, a subset of attributes suitable for diabetes risk prediction. Secondly, the optimized convolutional neural network (CNN) and support vector machine (SVM) are used as the primary learners of stacking to learn attribute subsets, respectively. Then, the output of CNN and SVM is used as the input of the mate learner, the fully connected layer, for classification. Qingdao desensitization physical examination data from 1 January 2017 to 31 December 2019 is used, which includes body temperature, BMI, waist circumference, and other indicators that may be related to early diabetes. We compared the performance of GA-stacking with K-nearest neighbor (KNN), SVM, logistic regression (LR), Naive Bayes (NB), and CNN before and after adding GA through the average prediction time, accuracy, precision, sensitivity, specificity, and F1-score. Results show that prediction efficiency can be improved by adding GA. GA-stacking has higher prediction accuracy. Moreover, the strong generalization ability and high prediction efficiency of GA-stacking have also been verified on the early-stage diabetes risk prediction dataset published by UCI.
With the rapid development of deep learning, researchers are actively exploring its applications in the field of industrial anomaly detection. Deep learning methods differ significantly from ...traditional mathematical modeling approaches, eliminating the need for intricate mathematical derivations and offering greater flexibility. Deep learning technologies have demonstrated outstanding performance in anomaly detection problems and gained widespread recognition. However, when dealing with multivariate data anomaly detection problems, deep learning faces challenges such as large-scale data annotation and handling relationships between complex data variables. To address these challenges, this study proposes an innovative and lightweight deep learning model—the Attention-Based Deep Convolutional Autoencoding Prediction Network (AT-DCAEP). The model consists of a characterization network based on convolutional autoencoders and a prediction network based on attention mechanisms. The AT-DCAEP exhibits excellent performance in multivariate time series data anomaly detection without the need for pre-labeling large-scale datasets, making it an efficient unsupervised anomaly detection method. We extensively tested the performance of AT-DCAEP on six publicly available datasets, and the results show that compared to current state-of-the-art methods, AT-DCAEP demonstrates superior performance, achieving the optimal balance between anomaly detection performance and computational cost.
A knowledge graph can structure heterogeneous knowledge in the field of power faults, construct the correlation between different pieces of knowledge, and solve the diversification, complexity, and ...island of fault data. There are many kinds of entities in power fault defect text, the relationship between entities is complex, and the data are often mixed with noise. It is necessary to research how to effectively mine the target data and separate the salient knowledge from the noise. Moreover, the traditional entity and relationship extraction methods used in the construction of a power fault knowledge graph cannot fully understand the text semantics, and the response accuracy is low. The Log system usually contains all kinds of information related to faults and a log analysis helps us collect fault information and perform association analysis. Therefore, a Bidirectional Sliced GRU with Gated Attention mechanism (BiSGRU-GA) model is proposed to detect the anomalous logs in the power system, this enriches the fault knowledge base and provides a good data resource for the construction of the knowledge graph. A new Bidirectional GRU with Gated Attention mechanism and Conditional Random Fields and a BERT input layer (BBiGRU-GA-CRF) model is proposed by introducing a BERT layer and Attention Mechanism into the Bidirectional GRU (BiGRU) model to more fully understand the context information of fault sentences and improve the accuracy of entity recognition of fault sentences. Aiming to solve the problems of large calculation cost and propagation error which occur in the traditional relationship extraction model, an improved Bidirectional Gated Recurrent Unit neural network with fewer parameters and the Gated Attention Mechanism (BiGRU-GA) model is proposed. This new model introduces an improved Gated Attention Mechanism to achieve better effects in relationship extraction. Compared with Bidirectional Long Short-Term Memory with Attention Mechanism (BiLSTM-Attention), the accuracy, recall, and F-measure of the model were improved by 1.79%, 13.83%, and 0.30% respectively, and the time cost is reduced by about 16%. The experimental results show that the BiGRU-GA model can capture local features, reduce the training time cost, and improve the model recognition effect.
To ensure secure and flexible data sharing in cloud storage, attribute-based encryption (ABE) is introduced to meet the requirements of fine-grained access control and secure one-to-many data ...sharing. However, the computational burden imposed by attribute encryption renders it unsuitable for resource-constrained environments such as the Internet of Things (IoT) and edge computing. Furthermore, the issue of accountability for illegal keys is crucial, as authorized users may actively disclose or sell authorization keys for personal gain, and keys may also passively leak due to management negligence or hacking incidents. Additionally, since all authorization keys are generated by the attribute authorization center, there is a potential risk of unauthorized key forgery. In response to these challenges, this paper proposes an efficient and accountable leakage-resistant scheme based on attribute encryption. The scheme adopts more secure online/offline encryption mechanisms and cloud server-assisted decryption to alleviate the computational burden on resource-constrained devices. For illegal keys, the scheme supports accountability for both users and the authorization center, allowing the revocation of decryption privileges for malicious users. In the case of passively leaked keys, timely key updates and revocation of decryption capabilities for leaked keys are implemented. Finally, the paper provides selective security and accountability proofs for the scheme under standard models. Efficiency analysis and experimental results demonstrate that the proposed scheme enhances encryption/decryption efficiency, and the storage overhead for accountability is also extremely low.
This paper presents an approach for improving the stability and adaptability of normal-mode theory, applied to marine environment sound field modeling and calculation. Firstly, based on the custom ...loss function, both Single Output Joint Neural Network (SOJNN) and Multi Output Neural Network (MONN) with physical constraints are constructed to obtain the prediction of wave numbers and eigenfunction. Secondly, we converted the complex wave numbers of normal-mode into a function to find the extreme value, as well as combining the boundary conditions set as the loss function. In this way, the problem space is constrained to the solution space and the dimension of the search space is reduced. Finally, the corresponding sound field is obtained according to the wave numbers and eigenfunction output by neural network. We set the sound velocity at 1500m/s with the density of water is 1g/cm 3 , place the sound source at 32 meters underwater. The upper boundary is absolutely soft and the lower boundary is absolutely hard. The effectiveness of our method is verified by simulation.
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