scholarly journals Command Recognition Using Binarized Convolutional Neural Network with Voice and Radar Sensors for Human-Vehicle Interaction

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3906
Author(s):  
Seunghyun Oh ◽  
Chanhee Bae ◽  
Jaechan Cho ◽  
Seongjoo Lee ◽  
Yunho Jung
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Continuous Wave ◽  
Low Complexity ◽  
Cluttered Environments ◽  
Radar Sensors ◽  
Wave Radar ◽  
Single Voice ◽  
Neural Network Algorithm ◽  
Single Sensor

Recently, as technology has advanced, the use of in-vehicle infotainment systems has increased, providing many functions. However, if the driver’s attention is diverted to control these systems, it can cause a fatal accident, and thus human–vehicle interaction is becoming more important. Therefore, in this paper, we propose a human–vehicle interaction system to reduce driver distraction during driving. We used voice and continuous-wave radar sensors that require low complexity for application to vehicle environments as resource-constrained platforms. The proposed system applies sensor fusion techniques to improve the limit of single-sensor monitoring. In addition, we used a binarized convolutional neural network algorithm, which significantly reduces the computational workload of the convolutional neural network in command classification. As a result of performance evaluation in noisy and cluttered environments, the proposed system showed a recognition accuracy of 96.4%, an improvement of 7.6% compared to a single voice sensor-based system, and 9.0% compared to a single radar sensor-based system.

The Prediction of the Error Rate in the Data Transmission Based on Improved Chaotic Optimization Neural Network

2013 ◽  
Vol 380-384 ◽  
pp. 1829-1833
Author(s):  
Xin Ping Liu ◽  
Jun Peng Xu ◽  
Hui Liu ◽  
Xiao Ling Wu
Keyword(s):  
Neural Network ◽  
Error Rate ◽  
Bp Neural Network ◽  
Data Transmission ◽  
Local Minimum ◽  
Continuous Wave ◽  
Transmission Error ◽  
Small Samples ◽  
Chaos Optimization ◽  
Neural Network Algorithm

As the slurry continuous wave changes according to the measurement of drilling (MWD) date, the precision of error rate prediction is low and the process of transferring data will be affected by signals. Based on the BP neural networks extensive mapping ability and chaos optimization algorithms global convergent ability, we structure a kind of improved chaos optimization of BP neural network algorithm. This algorithm can avoid several problems, such as the convergent speed of BP neural network is slow and the BP neural network is easy to sink into local minimum. With the powerful ability of generalization and prediction, this kind of algorithm can also be used to predict the data transmission error rate in slurry continuous wave. Under the condition of small samples, we create a model of data transmission in slurry continuous wave, which is based on improved chaos optimization of BP neural network. Simulate experiment has tested this algorithms feasibility and effectiveness

Classification of Batik Authenticity Using Convolutional Neural Network Algorithm with Transfer Learning Method

2021 ◽  
Author(s):  
Farrel Athaillah Putra ◽  
Dwi Anggun Cahyati Jamil ◽  
Briliantino Abhista Prabandanu ◽  
Suhaili Faruq ◽  
Firsta Adi Pradana ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Learning Method ◽  
Network Algorithm ◽  
Neural Network Algorithm

A 3-D Lightweight Convolutional Neural Network for Detecting Docking Structures in Cluttered Environments

2021 ◽  
Vol 55 (4) ◽  
pp. 88-98
Author(s):  
Maria Inês Pereira ◽  
Pedro Nuno Leite ◽  
Andry Maykol Pinto
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Research Work ◽  
Point Clouds ◽  
Measurement Unit ◽  
Maritime Industry ◽  
Worst Case ◽  
Cluttered Environments ◽  
Autonomous Surface Vehicles ◽  
Average Accuracy

Abstract The maritime industry has been following the paradigm shift toward the automation of typically intelligent procedures, with research regarding autonomous surface vehicles (ASVs) having seen an upward trend in recent years. However, this type of vehicle cannot be employed on a full scale until a few challenges are solved. For example, the docking process of an ASV is still a demanding task that currently requires human intervention. This research work proposes a volumetric convolutional neural network (vCNN) for the detection of docking structures from 3-D data, developed according to a balance between precision and speed. Another contribution of this article is a set of synthetically generated data regarding the context of docking structures. The dataset is composed of LiDAR point clouds, stereo images, GPS, and Inertial Measurement Unit (IMU) information. Several robustness tests carried out with different levels of Gaussian noise demonstrated an average accuracy of 93.34% and a deviation of 5.46% for the worst case. Furthermore, the system was fine-tuned and evaluated in a real commercial harbor, achieving an accuracy of over 96%. The developed classifier is able to detect different types of structures and works faster than other state-of-the-art methods that establish their performance in real environments.

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