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Topic Review
Deep Learning in Controlled Environment Agriculture
Controlled environment agriculture (CEA) is an unconventional production system that is resource efficient, uses less space, and produces higher yields. Deep learning (DL) has been introduced in CEA for different applications including crop monitoring, detecting biotic and abiotic stresses, irrigation, microclimate prediction, energy efficient controls, and crop growth prediction.
  • 898
  • 07 Nov 2022
Topic Review
Deep Learning in SOC Estimation for Li-Ion Batteries
As one of the critical state parameters of the battery management system, the state of charge (SOC) of lithium batteries can provide an essential reference for battery safety management, charge/discharge control, and the energy management of electric vehicles (EVs). The SOC estimation of a Li-ion battery in the deep learning method uses deep learning theory of computer science to build a model that builds the approximate relationship between input data (voltage, current, temperature, power, capacity, etc.) and output data (SOC) by available data. According to different neural network structures, it can be classified as a single, hybrid, or trans structure. 
  • 976
  • 02 Nov 2022
Topic Review
Deep Learning Methods in a Moroccan Ophthalmic Center
Diabetic retinopathy (DR) remains one of the world’s frequent eye illnesses, leading to vision loss among working-aged individuals. Hemorrhages and exudates are examples of signs of DR. However, artificial intelligence (AI), particularly deep learning (DL), is poised to impact nearly every aspect of human life and gradually transform medical practice. Insight into the condition of the retina is becoming more accessible thanks to major advancements in diagnostic technology. AI approaches can be used to assess lots of morphological datasets derived from digital images in a rapid and noninvasive manner. Computer-aided diagnosis tools for automatic detection of DR early-stage signs will ease the pressure on clinicians.
  • 325
  • 22 May 2023
Topic Review
Deep Learning Models to Predict Prosthetic Ankle Torque
Inverse dynamics from motion capture is the most common technique for acquiring biomechanical kinetic data. However, this method is time-intensive, limited to a gait laboratory setting, and requires a large array of reflective markers to be attached to the body. A practical alternative must be developed to provide biomechanical information to high-bandwidth prosthesis control systems to enable predictive controllers.
  • 384
  • 25 Sep 2023
Topic Review
Deep Learning Network in Injection Molding
Based on Industry 4.0 smart manufacturing and for the prediction of injection molding quality of automobile bumpers, the deep learning network is proposed that combines artificial neural networks and recognizable performance evaluation methods to better achieve the prediction and control of product quality. A pressure sensor was used to monitor and collect real-time pressure data in the mold cavity of the bumper. The quality indicators reflecting the molding quality were selected, and the correlation between these indicators and the molding quality was evaluated using recognizable performance evaluation methods and Pearson’s correlation coefficient. 
  • 555
  • 27 Jun 2022
Topic Review
Deep Learning Stranded Neural Network Model
Maintenance processes are of high importance for industrial plants. They have to be performed regularly and uninterruptedly. To assist maintenance personnel, industrial sensors monitored by distributed control systems observe and collect several machinery parameters in the cloud. Then, machine learning algorithms try to match patterns and classify abnormal behaviors.
  • 191
  • 19 Oct 2023
Topic Review
Deep Learning towards Digital Additive Manufacturing
Machine learning is a type of deep learning. First in the machine learning (ML) process is the manual extraction of relevant image characteristics. These characteristics are also used to classify the image according to its particular characteristics. Researchers focused primarily on digital additive manufacturing, one of the most significant emerging topics in Industry 4.0.
  • 1.6K
  • 19 Dec 2022
Topic Review
Deep Learning-based Contactless PPG Methods
Physiological measurements are widely used to determine a person’s health condition. Photoplethysmography (PPG) is a physiological measurement method that is used to detect volumetric changes in blood in vessels beneath the skin. Medical devices based on PPG have been introduced to measure different physiological measurements including heart rate (HR), respiratory rate, heart rate variability (HRV), oxyhemoglobin saturation, and blood pressure. Due to its low cost and non-invasive nature, PPG is utilized in many devices such as finger pulse oximeters, sports bands, and wearable sensors. PPG-based physiological measurements can be categorized into two types: contact-based and contactless.
  • 1.7K
  • 08 Jun 2021
Topic Review
Deep Learning-based Fault Diagnosis of Electric Motors
Electric motors are used extensively in numerous industries, and their failure can result not only in machine damage but also a slew of other issues, such as financial loss, injuries, etc. As a result, there is a significant scope to use robust fault diagnosis technology. In recent years, interesting research results on fault diagnosis for electric motors have been documented. Deep learning in the fault detection of electric equipment has shown comparatively better results than traditional approaches because of its more powerful and sophisticated feature extraction capabilities.
  • 1.3K
  • 09 Dec 2021
Topic Review
Deep Neural Network Implementations
Deep neural networks have recently become increasingly used for a wide range of applications, (e.g., image and video processing). The demand for edge inference is growing, especially in the areas of relevance to the Internet-of-Things. Low-cost microcontrollers as edge devices are a promising solution for optimal application systems from several points of view such as: cost, power consumption, latency, or real-time execution. The implementation of these systems has become feasible due to the advanced development of hardware architectures and DSP capabilities, while the cost and power consumption have been maintained at a low level.
  • 423
  • 14 Sep 2022
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