Direction Finding Using Convolutional Neural Networks and Convolutional Recurrent Neural Networks

Uckun, Fehmi Ayberk; Ozer, Hakan; Nurbas, Ekin; Onat, Emrah

doi:10.1109/siu49456.2020.9302448

Cited by 25 publications

(10 citation statements)

References 6 publications

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“…The state layer can also be regarded as a hidden layer, but the difference from MLP is that the state layer of RNN will have one more output value of the previous layer as input. Therefore, RNN is a kind of neural network with short-term memory [11,16,18,19].…”

Section: Rnn (Recurrent Neural Network)mentioning

confidence: 99%

The Estimation Life Cycle of Lithium-Ion Battery Based on Deep Learning Network and Genetic Algorithm

et al. 2021

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This study uses deep learning to model the discharge characteristic curve of the lithium-ion battery. The battery measurement instrument was used to charge and discharge the battery to establish the discharge characteristic curve. The parameter method tries to find the discharge characteristic curve and was improved by MLP (multilayer perceptron), RNN (recurrent neural network), LSTM (long short-term memory), and GRU (gated recurrent unit). The results obtained by these methods were graphs. We used genetic algorithm (GA) to obtain the parameters of the discharge characteristic curve equation.

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Section: Rnn (Recurrent Neural Network)mentioning

confidence: 99%

The Estimation Life Cycle of Lithium-Ion Battery Based on Deep Learning Network and Genetic Algorithm

et al. 2021

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“…Online EEG categorization has thrived as a critical component of brain health services for remote monitoring and assessment of brain illnesses such as epilepsy [ 1 ] and depression (MDD) [ 2 ]. Accurate evaluation of the brain's health and early surveillance of its growth can help limit the risk of danger [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

“…The “data recorder” character, higher computing cost, proclivity for classifier, and experimental character of model construction are all drawbacks. They have shown success in detecting epilepsy [ 1 ] and Parkinson's illness [ 11 ]. Sufficient performance has been achived while maintaining a high level of noise immunity [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Classification of Bioinformatics EEG Data Signals to Identify Depressed Brain State Using CNN Model

Thakare¹,

Bhende²,

Deb

et al. 2022

BioMed Research International

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Patients suffering from severe depression may be precisely assessed using online EEG categorization and their progress tracked over time, minimizing the risk of danger and suicide. Online EEG categorization systems, on the other hand, suffer additional challenges in the absence of empirical oversight. A lack of effective decoupling between brain regions and neural networks occurs during brain disease attacks, resulting in EEG data with poor signal intensity, high noise, and nonstationary characteristics. CNN employs momentum SGD optimization. By using a tiny momentum decay factor, the literature’s starting strategy, and the same batch normalization, this work attempts to decrease model error. Before being utilized to form a training set, samples are shuffled, followed by validation and testing on the new samples in the set. An online EEG categorization system driven by a convolution neural network has been developed to do this. The approach is applied directly to the EEG input and is able to accurately and quickly identify depressed states without the need for preprocessing or feature extraction. The healthy control group and the depression control group had accuracy, sensitivity, and specificity of 99.08 percent, 98.77 percent, and 99.42 percent, respectively, in experiments on depression evaluation based on publicly accessible data. The machine learning technique based on feature extraction is often getting more and more complex, making it only suited for offline EEG categorization. While neural networks have become increasingly important in the study of artificial intelligence in recent years, they are still essentially black-box function approximations with limited interpretability. In addition, quantitative study of the neural network shows that depressed patients and healthy persons have remarkable dissimilarity between the right and left temporal lobe brain regions.

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“…Modern-day machine learning is a part of artificial intelligence and hence does not require any commands after the algorithm has been developed, and a training dataset has been provided to it. In recent times, the use of machine learning has increased manifold due to its ability to analyze complex data, which was otherwise impossible with the traditional methods with reliable accuracy [13]. Data processing is a crucial task of machine learning.…”

Section: Methodsmentioning

confidence: 99%

Research on the Identification and Application of Immovable Cultural Relics in the Historic City of Macau Based on Condition of Intelligent Remote Sensing Technology

Zhao

2021

Wireless Communications and Mobile Computing

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The archeological sites are a heritage that we have gained from our ancestors. These sites are crucial for understanding the past and the way of life of people during those times. The monuments and the immovable relics of ancient times are a getaway to the past. The critical cultural relics however actually over the years have faced the brunt of nature. The environmental conditions have deteriorated the condition of many important immovable relics over the years since these could not be just shifted away. People also move around the ancient cultural relics that may also deform these relics. The machine learning algorithms were used to identify the location of the relics. The data from the satellite images were used and implemented machine learning algorithm to maintain and monitor the relics. This research study dwells into the importance of the area from a research point of view and utilizes machine learning techniques called CaffeNet and deep convolutional neural network. The result showed that 96% accuracy of predicting the image, which can be used for tracking human activity, protects heritage sites in a unique way.

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Direction Finding Using Convolutional Neural Networks and Convolutional Recurrent Neural Networks

Cited by 25 publications

References 6 publications

The Estimation Life Cycle of Lithium-Ion Battery Based on Deep Learning Network and Genetic Algorithm

The Estimation Life Cycle of Lithium-Ion Battery Based on Deep Learning Network and Genetic Algorithm

[Retracted] Classification of Bioinformatics EEG Data Signals to Identify Depressed Brain State Using CNN Model

Research on the Identification and Application of Immovable Cultural Relics in the Historic City of Macau Based on Condition of Intelligent Remote Sensing Technology

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