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{ "item_title" : "Deep Learning Applications with Practical Measured Results in Electronics Industries", "item_author" : [" Mong-Fong Horng", "Hsu-Yang Kung", "Chi-Hua Chen "], "item_description" : "This book collects 14 articles from the Special Issue entitled Deep Learning Applications with Practical Measured Results in Electronics Industries of Electronics. Topics covered in this Issue include four main parts: (1) environmental information analyses and predictions, (2) unmanned aerial vehicle (UAV) and object tracking applications, (3) measurement and denoising techniques, and (4) recommendation systems and education systems. These authors used and improved deep learning techniques (e.g., ResNet (deep residual network), Faster-RCNN (faster regions with convolutional neural network), LSTM (long short term memory), ConvLSTM (convolutional LSTM), GAN (generative adversarial network), etc.) to analyze and denoise measured data in a variety of applications and services (e.g., wind speed prediction, air quality prediction, underground mine applications, neural audio caption, etc.). Several practical experiments were conducted, and the results indicate that the performance of the presented deep learning methods is improved compared with the performance of conventional machine learning methods.", "item_img_path" : "https://covers1.booksamillion.com/covers/bam/3/03/928/863/3039288636_b.jpg", "price_data" : { "retail_price" : "72.50", "online_price" : "72.50", "our_price" : "72.50", "club_price" : "72.50", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Deep Learning Applications with Practical Measured Results in Electronics Industries|Mong-Fong Horng

Deep Learning Applications with Practical Measured Results in Electronics Industries

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Overview

This book collects 14 articles from the Special Issue entitled "Deep Learning Applications with Practical Measured Results in Electronics Industries" of Electronics. Topics covered in this Issue include four main parts: (1) environmental information analyses and predictions, (2) unmanned aerial vehicle (UAV) and object tracking applications, (3) measurement and denoising techniques, and (4) recommendation systems and education systems. These authors used and improved deep learning techniques (e.g., ResNet (deep residual network), Faster-RCNN (faster regions with convolutional neural network), LSTM (long short term memory), ConvLSTM (convolutional LSTM), GAN (generative adversarial network), etc.) to analyze and denoise measured data in a variety of applications and services (e.g., wind speed prediction, air quality prediction, underground mine applications, neural audio caption, etc.). Several practical experiments were conducted, and the results indicate that the performance of the presented deep learning methods is improved compared with the performance of conventional machine learning methods.

Details

  • ISBN-13: 9783039288632
  • ISBN-10: 3039288636
  • Publisher: Mdpi AG
  • Publish Date: May 2020
  • Dimensions: 9.61 x 6.69 x 0.88 inches
  • Shipping Weight: 1.73 pounds
  • Page Count: 272

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