Prediction of Wave Power Generation Using a Convolutional Neural Network with Multiple Inputs

Successful development of a marine wave energy converter (WEC) relies strongly on the development of the power generation device, which needs to be efficient and cost-effective. An innovative multi-input approach based on the Convolutional Neural Network (CNN) is investigated to predict the power generation of a WEC system using a double-buoy oscillating body device (OBD). The results from the experimental data show that the proposed multi-input CNN performs much better at predicting results compared with the conventional artificial network and regression models. Through the power generation analysis of this double-buoy OBD, it shows that the power output has a positive correlation with the wave height when it is higher than 0.2 m, which becomes even stronger if the wave height is higher than 0.6 m. Furthermore, the proposed approach associated with the CNN algorithm in this study can potentially detect the changes that could be due to presence of anomalies and therefore be used for condition monitoring and fault diagnosis of marine energy converters. The results are also able to facilitate controlling of the electricity balance among energy conversion, wave power produced and storage.

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Detection of Strawberry Diseases Using a Convolutional Neural Network

Plants ◽

10.3390/plants10010031 ◽

2020 ◽

Vol 10 (1) ◽

pp. 31

Author(s):

Jia-Rong Xiao ◽

Pei-Che Chung ◽

Hung-Yi Wu ◽

Quoc-Hung Phan ◽

Jer-Liang Andrew Yeh ◽

...

Keyword(s):

Neural Network ◽

Powdery Mildew ◽

Convolutional Neural Network ◽

Image Recognition ◽

Cost Effective ◽

Gray Mold ◽

Leaf Blight ◽

Crown Rot ◽

Accuracy Rate ◽

Fruit Disease

The strawberry (Fragaria × ananassa Duch.) is a high-value crop with an annual cultivated area of ~500 ha in Taiwan. Over 90% of strawberry cultivation is in Miaoli County. Unfortunately, various diseases significantly decrease strawberry production. The leaf and fruit disease became an epidemic in 1986. From 2010 to 2016, anthracnose crown rot caused the loss of 30–40% of seedlings and ~20% of plants after transplanting. The automation of agriculture and image recognition techniques are indispensable for detecting strawberry diseases. We developed an image recognition technique for the detection of strawberry diseases using a convolutional neural network (CNN) model. CNN is a powerful deep learning approach that has been used to enhance image recognition. In the proposed technique, two different datasets containing the original and feature images are used for detecting the following strawberry diseases—leaf blight, gray mold, and powdery mildew. Specifically, leaf blight may affect the crown, leaf, and fruit and show different symptoms. By using the ResNet50 model with a training period of 20 epochs for 1306 feature images, the proposed CNN model achieves a classification accuracy rate of 100% for leaf blight cases affecting the crown, leaf, and fruit; 98% for gray mold cases, and 98% for powdery mildew cases. In 20 epochs, the accuracy rate of 99.60% obtained from the feature image dataset was higher than that of 1.53% obtained from the original one. This proposed model provides a simple, reliable, and cost-effective technique for detecting strawberry diseases.

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Rapid identification of wood species using XRF and neural network machine learning

Scientific Reports ◽

10.1038/s41598-021-96850-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Aaron N. Shugar ◽

B. Lee Drake ◽

Greg Kelley

Keyword(s):

Neural Network ◽

Machine Learning ◽

Convolutional Neural Network ◽

Wood Species ◽

Cost Effective ◽

Rapid Identification ◽

Invasive Technique ◽

X Ray ◽

Non Invasive ◽

Cost Effective Alternative

AbstractAn innovative approach for the rapid identification of wood species is presented. By combining X-ray fluorescence spectrometry with convolutional neural network machine learning, 48 different wood specimens were clearly differentiated and identified with a 99% accuracy. Wood species identification is imperative to assess illegally logged and transported lumber. Alternative options for identification can be time consuming and require some level of sampling. This non-invasive technique offers a viable, cost-effective alternative to rapidly and accurately identify timber in efforts to support environmental protection laws and regulations.

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Multi-Scale Convolutional Neural Network for Accurate Corneal Segmentation in Early Detection of Fungal Keratitis

Journal of Fungi ◽

10.3390/jof7100850 ◽

2021 ◽

Vol 7 (10) ◽

pp. 850

Author(s):

Veena Mayya ◽

Sowmya Kamath Shevgoor ◽

Uma Kulkarni ◽

Manali Hazarika ◽

Prabal Datta Barua ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Region Of Interest ◽

Cost Effective ◽

Fungal Keratitis ◽

Contact Lens Wear ◽

Multi Scale ◽

Effective Interventions ◽

Prolonged Contact ◽

Segmented Images

Microbial keratitis is an infection of the cornea of the eye that is commonly caused by prolonged contact lens wear, corneal trauma, pre-existing systemic disorders and other ocular surface disorders. It can result in severe visual impairment if improperly managed. According to the latest World Vision Report, at least 4.2 million people worldwide suffer from corneal opacities caused by infectious agents such as fungi, bacteria, protozoa and viruses. In patients with fungal keratitis (FK), often overt symptoms are not evident, until an advanced stage. Furthermore, it has been reported that clear discrimination between bacterial keratitis and FK is a challenging process even for trained corneal experts and is often misdiagnosed in more than 30% of the cases. However, if diagnosed early, vision impairment can be prevented through early cost-effective interventions. In this work, we propose a multi-scale convolutional neural network (MS-CNN) for accurate segmentation of the corneal region to enable early FK diagnosis. The proposed approach consists of a deep neural pipeline for corneal region segmentation followed by a ResNeXt model to differentiate between FK and non-FK classes. The model trained on the segmented images in the region of interest, achieved a diagnostic accuracy of 88.96%. The features learnt by the model emphasize that it can correctly identify dominant corneal lesions for detecting FK.

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Fuzzy Neural Network PID Control for Direct Drive Wave Power Generation System

10.23919/icems52562.2021.9634277 ◽

2021 ◽

Author(s):

Hongwei Fang ◽

Xiuna Wei ◽

Ziyan Li

Keyword(s):

Neural Network ◽

Power Generation ◽

Pid Control ◽

Fuzzy Neural Network ◽

Wave Power ◽

Direct Drive ◽

Generation System ◽

Fuzzy Neural ◽

Power Generation System ◽

Neural Network Pid

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A Cost-Effective Automatic Dial Meter Reader Using a Lightweight Convolutional Neural Network

2020 13th International Conference on Human System Interaction (HSI) ◽

10.1109/hsi49210.2020.9142669 ◽

2020 ◽

Author(s):

Cheng-Hung Lin ◽

Kuan-Yi Kuo

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Cost Effective

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Design of a Cost-Effective Deep Convolutional Neural Network–Based Scheme for Diagnosing Faults in Smart Grids

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) ◽

10.1109/icmla.2019.00232 ◽

2019 ◽

Author(s):

Hossein Hassani ◽

Maryam Farajzadeh-Zanjani ◽

Roozbeh Razavi-Far ◽

Mehrdad Saif ◽

Vasile Palade

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Smart Grids ◽

Cost Effective ◽

Deep Convolutional Neural Network

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Radar Emitter Signal Recognition Based on One-Dimensional Convolutional Neural Network with Attention Mechanism

Sensors ◽

10.3390/s20216350 ◽

2020 ◽

Vol 20 (21) ◽

pp. 6350

Author(s):

Bin Wu ◽

Shibo Yuan ◽

Peng Li ◽

Zehuan Jing ◽

Shao Huang ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Attention Mechanism ◽

Superior Performance ◽

Radar Signal ◽

Signal Recognition ◽

Electromagnetic Environment ◽

One Dimensional ◽

The Given ◽

And Storage

As the real electromagnetic environment grows complex and the quantity of radar signals turns massive, traditional methods, which require a large amount of prior knowledge, are time-consuming and ineffective for radar emitter signal recognition. In recent years, convolutional neural network (CNN) has shown its superiority in recognition so that experts have applied it in radar signal recognition. However, in the field of radar emitter signal recognition, the data are usually one-dimensional (1-D), which takes more time and storage space than by using the original two-dimensional CNN model directly. Moreover, the features extracted from convolutional layers are redundant so that the recognition accuracy is low. In order to solve these problems, this paper proposes a novel one-dimensional convolutional neural network with an attention mechanism (CNN-1D-AM) to extract more discriminative features and recognize the radar emitter signals. In this method, features of the given 1-D signal sequences are extracted directly by the 1-D convolutional layers and are weighted in accordance with their importance to recognition by the attention unit. The experiments based on seven different radar emitter signals indicate that the proposed CNN-1D-AM has the advantages of high accuracy and superior performance in radar emitter signal recognition.

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