Minimum BER Criterion and Adaptive Moment Estimation Based Enhanced ICA for Wireless Communications

Luo, Zhi-Quan; Chen, Yan; Li, Chengjie; Xiong, Xingzhong; Zhu, Long

doi:10.1109/access.2020.3016989

Cited by 7 publications

(4 citation statements)

References 21 publications

(35 reference statements)

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“…As an optimizer, we utilized adaptive moment estimation (ADAM), which combines momentum and root mean square propagation (RMSprop). Momentum remembers the direction of past movements by adding a certain value to the calculated gradient, while RMSProp [34,35] uses an exponential moving average to give more weight to the most recent gradients rather than simply accumulating them. The total number of parameters in the entire model was 3,340,674.…”

Section: Behavior Recognition Learning Modelmentioning

confidence: 99%

GLBRF: Group-Based Lightweight Human Behavior Recognition Framework in Video Camera

Lee,

Kim

et al. 2024

Applied Sciences

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Behavioral recognition is an important technique for recognizing actions by analyzing human behavior. It is used in various fields, such as anomaly detection and health estimation. For this purpose, deep learning models are used to recognize and classify the features and patterns of each behavior. However, video-based behavior recognition models require a lot of computational power as they are trained using large datasets. Therefore, there is a need for a lightweight learning framework that can efficiently recognize various behaviors. In this paper, we propose a group-based lightweight human behavior recognition framework (GLBRF) that achieves both low computational burden and high accuracy in video-based behavior recognition. The GLBRF system utilizes a relatively small dataset to reduce computational cost using a 2D CNN model and improves behavior recognition accuracy by applying location-based grouping to recognize interaction behaviors between people. This enables efficient recognition of multiple behaviors in various services. With grouping, the accuracy was as high as 98%, while without grouping, the accuracy was relatively low at 68%.

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Section: Behavior Recognition Learning Modelmentioning

confidence: 99%

GLBRF: Group-Based Lightweight Human Behavior Recognition Framework in Video Camera

Lee,

Kim

et al. 2024

Applied Sciences

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“…According to the behavior of the convergence curves, a good adaptation of the networks to the data is observed as the training progresses, resulting in a profile of gradual loss to the validation set, therefore, with adequate learning rates, without presenting overfitting, with fast training that lasted 200 computational epochs (average of 200 ms/epoch). These validation factors were possible due to adaptive gradient optimization, which has advantages, such as a low memory requirement during gradient formation [34], high computational efficiency [34,37], and simple and straightforward implementation [37,38]. The main validation parameters of the proposed models are summarized in Table 1.…”

Section: Prediction Of Retention Times Through Deep Learning Modelsmentioning

confidence: 99%

A deep learning‐based simulator for comprehensive two‐dimensional GC applications

Minho

Cardeal

Menezes

2023

J of Separation Science

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Among the main approaches for predicting the spatial positions of eluates in comprehensive two-dimensional gas chromatography, the still under-explored computational models based on deep learning algorithms emerge as robust and reliable options due to their high adaptability to the structure and complexity of the data. In this work, an open-source program based on deep neural networks was developed to optimize chromatographic methods and simulate operating conditions outside the laboratory. The deep neural networks models were fit to convenient experimental predictors, resulting in scaled losses (mean squared error) equivalent to 0.006 (relative average deviation = 8.56%, R 2 = 0.9202) and 0.014 (relative average deviation = 1.67%, R 2 = 0.8009) in the prediction of the first-and second-dimension retention times, respectively. Good compliance was observed for the main chemical classes, such as environmental contaminants: volatile, semivolatile organic compounds, and pesticides; biochemistry molecules: amino acids and lipids; pharmaceutical industry and personal care products and residues: drugs and metabolites; among others. On the other hand, there is a need for continuous database updates to predict retention times of less common compounds accurately. Thus, forming a collaborative database is proposed, gathering voluntary findings from other users.

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“…When the loss function is smallest, the network optimal parameter update is achieved through the gradient information of the network backpropagation. Adaptive moment estimation (Adam) [27] is a parameter adaptive learning rate method based on first-order moment estimation and second-order moment estimation of the gradient to dynamically adjust the learning rate of each parameter, expressed by…”

Section: Layer Typementioning

confidence: 99%

Intelligent Life Prediction of Thermal Barrier Coating for Aero Engine Blades

et al. 2021

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The existing methods for thermal barrier coating (TBC) life prediction rely mainly on experience and formula derivation and are inefficient and inaccurate. By introducing deep learning into TBC life analyses, a convolutional neural network (CNN) is used to extract the TBC interface morphology and analyze its life information, which can achieve a high-efficiency accurate judgment of the TBC life. In this thesis, an Adap–Alex algorithm is proposed to overcome the problems related to the large training time, over-fitting, and low accuracy in the existing CNN training of TBC images with complex tissue morphologies. The method adjusts the receptive field size, stride length, and other parameter settings and combines training epochs with a sigmoid function to realize adaptive pooling. TBC data are obtained by thermal vibration experiments, a TBC dataset is constructed, and then the Adap–Alex algorithm is used to analyze the generated TBC dataset. The average training time of the Adap–Alex method is significantly smaller than those of VGG-Net and Alex-Net by 125 and 685 s, respectively. For a fixed number of thermal vibrations, the test accuracy of the Adap–Alex algorithm is higher than those of Alex-Net and VGG-Net, which facilitates the TBC identification. When the number of thermal vibrations is 300, the accuracy reaches 93%, and the performance is highest.

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Minimum BER Criterion and Adaptive Moment Estimation Based Enhanced ICA for Wireless Communications

Cited by 7 publications

References 21 publications

GLBRF: Group-Based Lightweight Human Behavior Recognition Framework in Video Camera

GLBRF: Group-Based Lightweight Human Behavior Recognition Framework in Video Camera

A deep learning‐based simulator for comprehensive two‐dimensional GC applications

Intelligent Life Prediction of Thermal Barrier Coating for Aero Engine Blades

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