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Streamflow estimation is important in hydrology, especially in drought and flood-prone areas. Accurate estimation of streamflow values is crucial for the sustainable management of water resources, the development of early warning systems for disasters, and for various applications such as irrigation, hydropower production, dam sizing, and siltation management. This study developed the ANN algorithm by optimizing with an artificial bee colony (ABC). Then, the ABC-ANN hybrid model, which was established, was combined with different signal decomposition techniques to evaluate its performance in streamflow estimation in the East Black Sea Region, Türkiye. For this purpose, the lagged streamflow values were divided into subcomponents using the local mean decomposition (LMD) with the empirical envelope and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) signal decomposition techniques presented to the ABC-ANN algorithm. Thus, the success of the novel hybrid LMD-ABC-ANN and CEEMDAN-ABC-ANN approaches in streamflow prediction was evaluated. The outputs are reliable strategies and resources for water resource planners and policymakers., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Objective. Attention deficit hyperactivity disorder (ADHD) is considered one of the most common psychiatric disorders in childhood. The incidence of this disease in the community draws an increasing graph from the past to the present. While the ADHD diagnosis is basically made with the psychiatric tests, there is no active clinically used objective diagnostic tool. However, some studies in the literature has reported development of an objective diagnostic tool that facilitates the diagnosis of ADHD. Approach. In this study, it was aimed to develop an objective diagnostic tool for ADHD using electroencephalography (EEG) signals. In the proposed method, EEG signals were decomposed into subbands by robust local mode decomposition and variational mode decomposition techniques. These subbands and the EEG signals were fed as input data to the deep learning algorithm designed in the study. Main results. As a result, an algorithm has been put forward that distinguishes over 95% of ADHD and healthy individuals through using a 19-channel EEG signal. In addition, a classification accuracy of over 87% was obtained by the proposed approach of EEG signal decomposition followed by data processing in the designed deep learning algorithm. Significance. The findings of the current research enrich the literature based on originality and proposed method can be used as a clinical diagnostic tool in the near future., (© 2023 IOP Publishing Ltd.)

In this paper, the structure and working principle of four-mass vibration MEMS gyroscope (FMVMG) are introduced, and the working modes of FMVMG are simulated and analyzed. On the basis of this, an improved noise reduction method based on interval local mean decomposition (ILMD) and parabolic tracking time-frequency peak filtering (PTTFPF) is proposed. PTTFPF can resample the signal along a parabolic path and select the optimal filtering trajectory, but there is still a contradiction, choosing a short window length may lead to good signal amplitude retention, but the random noise reduction effect is not good, while choosing a long window length may lead to serious amplitude attenuation, but the random noise reduction effect is better. In order to achieve a better balance between effective signal amplitude preservation and random noise reduction, the ILMD method was used to improve PTTFPF. First, the original signal was decomposed into product functions (PFs) by local mean decomposition (LMD) method, and the sample entropy (SE) of each PF was calculated. The PFs are divided into three different components. Then, short window PTTFPF is used for useful PF and long window PTTFPF is used for mixed PF, noise PF is directly removed. Then the final signal is reconstructed. Finally, the denoised useful PF and mixed PF are reconstructed to obtain the final signal. The proposed ILMD-PTTFPF algorithm was verified by temperature experiments. The results show that the denoising performance of the ILMD-PTTFPF algorithm is better than that of traditional wavelet threshold denoising and Kalman filtering.

As the wind turbine operates in harsh conditions, numerous of its components are critical and present an important downtime for maintenance. In this paper, we propose a fault diagnosis algorithm to detect and locate the defects affecting the generator rotor and the pinion of the gearbox lay shaft in a real 25 kW wind turbine drivetrain. The induction generator was used as a fault sensor for gear teeth damage. Through the use of the wavelet packet transform, and the local mean decomposition combined with the Fast Fourier Transform, the detection of gear meshing frequency in the stator current reflects teeth faults. Hence, the principal component analysis of the stator current gives a suitable classification for the gearbox states under different working stages. The obtained results have been significant, despite the use of a short duration and a low sampling frequency of the experimental data., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 ISA. Published by Elsevier Ltd. All rights reserved.)

The classification of electroencephalography (EEG) induced by the same joint is one of the major challenges for brain-computer interface (BCI) systems. In this paper, we propose a new framework, which includes two parts, feature extraction and classification. Based on local mean decomposition (LMD), cloud model, and common spatial pattern (CSP), a feature extraction method called LMD-CSP is proposed to extract distinguishable features. In order to improve the classification results multi-objective grey wolf optimization twin support vector machine (MOGWO-TWSVM) is applied to discriminate the extracted features. We evaluated the performance of the proposed framework on our laboratory data sets with three motor imagery (MI) tasks of the same joint (shoulder abduction, extension, and flexion), and the average classification accuracy was 91.27%. Further comparison with several widely used methods showed that the proposed method had better performance in feature extraction and pattern classification. Overall, this study can be used for developing high-performance BCI systems, enabling individuals to control external devices intuitively and naturally., Competing Interests: The authors declare there are no competing interests., (©2021 Guan et al.)

Automatic seizure detection technology not only reduces workloads of neurologists for epilepsy diagnosis but also is of great significance for treatments of epileptic patients. A novel seizure detection method based on the deep bidirectional long short-term memory (Bi-LSTM) network is proposed in this paper. To preserve the non-stationary nature of EEG signals while decreasing the computational burden, the local mean decomposition (LMD) and statistical feature extraction procedures are introduced. The deep architecture is then designed by combining two independent LSTM networks with the opposite propagation directions: one transmits information from the front to the back, and another from the back to the front. Thus the deep model can take advantage of the information both before and after the currently analyzing moment to jointly determine the output state. A mean sensitivity of 93.61% and a mean specificity of 91.85% were achieved on a long-term scalp EEG database. The comparisons with other published methods based on either traditional machine learning models or convolutional neural networks demonstrated the improved performance for seizure detection., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

The Performance of rolling element bearings has a significant influence on reliability and safety in the various engineering fields. While heading toward the condition evaluation of rolling element bearings, the higher dimensionality of feature space became a crucial concern. Thus, to have a check on the hurdles, this paper proposes a novel approach of bearing condition assessment utilizing Local mean decomposition (LMD) and spectral clustering (SC). Spectral Clustering (SC) is an influential tool, which heals the curse of dimensionality and facilitates the model by imparting a systematized structure. SC focuses more on connectivity rather than geometrical vicinity; moreover, the provision of a similarity matrix makes SC more reliable and increases its performance. To enrich the efficiency of SC, a novel decision criterion, PI (Parting Index) aiming the optimal number of clusters based on a new similarity indicator and disorder indicator coined as Synergic Association Index (SAI) and Separation Index (SI) respectively, has been proposed. The work aims to achieve better performance degradation assessment (PDA) by the application of the new SC-PI approach with the following steps; the first step is the decomposition of vibration signal into product functions (PFs) utilizing LMD; the second step involves the extraction of proper fault features; third, the extracted features are classified with the SC-PI method; finally the feature or attribute vectors are endowed to trained model and confidence value (CV) is calculated. The proposed method is validated on two datasets of different fault types. The results so obtained indicates that the SC-PI method easily differentiates the various stages when compared to different assessment techniques i.e. time-domain features, self-organizing map (SOM), k-medoids and Gaussian mixture model (GMM)., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 ISA. Published by Elsevier Ltd. All rights reserved.)

The effective fault diagnosis in the prognostic and health management of reciprocating compressors has been a research hotspot for a long time. The vibration signal of reciprocating compressors is nonlinear and non-stationary. However, the traditional methods applied to processing such signals have three issues, including separating the useful frequency bands from overlapped signals, extracting fault features with strong subjectivity, and processing the massive data with limited learning abilities. To address the above issues, this paper, which is based on the idea of deep learning, proposed an intelligent fault diagnosis method combining Local Mean Decomposition (LMD) and the Stack Denoising Autoencoder (SDAE). The vibration signal is firstly decomposed by LMD and reconstructed based on the cross-correlation criterion. The virtual noise channel is constructed to reduce the noise of the vibration signal. Then, the de-noised signal is input into the trained SDAE model to learn the fault features adaptively. Finally, the conditions of the reciprocating compressor valve are classified by the proposed method. The results show that classification accuracy is 92.72% under the condition of a low signal-noise ratio, which is 5 percentage points higher than that of the traditional methods. This shows the effectiveness and robustness of the proposed method.

Compared with the strong background noise, the energy entropy of early fault signals of bearings are weak under actual working conditions. Therefore, extracting the bearings' early fault features has always been a major difficulty in fault diagnosis of rotating machinery. Based on the above problems, the masking method is introduced into the Local Mean Decomposition (LMD) decomposition process, and a weak fault extraction method based on LMD and mask signal (MS) is proposed. Due to the mode mixing of the product function (PF) components decomposed by LMD in the noisy background, it is difficult to distinguish the authenticity of the fault frequency. Therefore, the MS method is introduced to deal with the PF components that are decomposed by the LMD and have strong correlation with the original signal, so as to suppress the modal aliasing phenomenon and extract the fault frequencies. In this paper, the actual fault signal of the rolling bearing is analyzed. By combining the MS method with the LMD method, the fault signal mixed with the noise is processed. The kurtosis value at the fault frequency is increased by eight-fold, and the signal-to-noise ratio (SNR) is increased by 19.1%. The fault signal is successfully extracted by the proposed composite method.

In order to extract fault features of large-scale power equipment from strong background noise, a hybrid fault diagnosis method based on the second generation wavelet de-noising (SGWD) and the local mean decomposition (LMD) is proposed in this paper. In this method, a de-noising algorithm of second generation wavelet transform (SGWT) using neighboring coefficients was employed as the pretreatment to remove noise in rotating machinery vibration signals by virtue of its good effect in enhancing the signal-noise ratio (SNR). Then, the LMD method is used to decompose the de-noised signals into several product functions (PFs). The PF corresponding to the faulty feature signal is selected according to the correlation coefficients criterion. Finally, the frequency spectrum is analyzed by applying the FFT to the selected PF. The proposed method is applied to analyze the vibration signals collected from an experimental gearbox and a real locomotive rolling bearing. The results demonstrate that the proposed method has better performances such as high SNR and fast convergence speed than the normal LMD method., (Crown Copyright © 2016. Published by Elsevier Ltd. All rights reserved.)