scholarly journals NEURAL NETWORK BASED MULTISENSOR FUSION IN A NOVEL PERMANENT MAGNET MULTI-DOF ACTUATOR ORIENTATION DETECTION SYSTEM

2012 ◽  
Vol 5 (4) ◽  
pp. 911-927
Author(s):  
Zheng LI
Keyword(s):  
Neural Network ◽  
Permanent Magnet ◽  
Detection System ◽  
Multisensor Fusion ◽  
Orientation Detection

Development of a Awaking Behavior Detection System Using a Neural Network

2008 ◽  
Vol 128 (11) ◽  
pp. 1649-1656 ◽  
Author(s):  
Hironobu Satoh ◽  
Fumiaki Takeda ◽  
Yuhki Shiraishi ◽  
Rie Ikeda
Keyword(s):  
Neural Network ◽  
Detection System ◽  
Behavior Detection

Optimizing Error Rate in Intrusion Detection System Using Artificial Neural Network Algorithm

2018 ◽  
Vol 6 (9) ◽  
pp. 152
Author(s):  
S. Vijaya Rani ◽  
G. N. K. Suresh Babu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Error Rate ◽  
Learning Process ◽  
Nearest Neighbor ◽  
Detection System ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Artificial Neural

The illegal hackers  penetrate the servers and networks of corporate and financial institutions to gain money and extract vital information. The hacking varies from one computing system to many system. They gain access by sending malicious packets in the network through virus, worms, Trojan horses etc. The hackers scan a network through various tools and collect information of network and host. Hence it is very much essential to detect the attacks as they enter into a network. The methods  available for intrusion detection are Naive Bayes, Decision tree, Support Vector Machine, K-Nearest Neighbor, Artificial Neural Networks. A neural network consists of processing units in complex manner and able to store information and make it functional for use. It acts like human brain and takes knowledge from the environment through training and learning process. Many algorithms are available for learning process This work carry out research on analysis of malicious packets and predicting the error rate in detection of injured packets through artificial neural network algorithms.

Real-time Detection of Aortic Valve in Echocardiography using Convolutional Neural Networks

2020 ◽  
Vol 16 (5) ◽  
pp. 584-591 ◽  
Author(s):  
Muhammad Hanif Ahmad Nizar ◽  
Chow Khuen Chan ◽  
Azira Khalil ◽  
Ahmad Khairuddin Mohamed Yusof ◽  
Khin Wee Lai
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Heart Disease ◽  
Aortic Valve ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Valvular Heart Disease ◽  
Detection System ◽  
Processing Unit ◽  
Real Time Detection

Background: Valvular heart disease is a serious disease leading to mortality and increasing medical care cost. The aortic valve is the most common valve affected by this disease. Doctors rely on echocardiogram for diagnosing and evaluating valvular heart disease. However, the images from echocardiogram are poor in comparison to Computerized Tomography and Magnetic Resonance Imaging scan. This study proposes the development of Convolutional Neural Networks (CNN) that can function optimally during a live echocardiographic examination for detection of the aortic valve. An automated detection system in an echocardiogram will improve the accuracy of medical diagnosis and can provide further medical analysis from the resulting detection. Methods: Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN) with various feature extractors were trained on echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography videos. Results: Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper- second (fps) during real-time detection but managed to perform better than the other neural network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all models. Conclusion: Our findings provide a foundation for implementing a convolutional detection system to echocardiography for medical purposes.

Deep convolutional neural networks for human movement detection using wireless signals

2021 ◽  
pp. 1-10
Author(s):  
Chien-Cheng Leea ◽  
Zhongjian Gao ◽  
Xiu-Chi Huanga
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Detection System ◽  
Deep Convolutional Neural Network ◽  
Human Detection ◽  
Two Dimensional ◽  
Dimensional Matrix ◽  
State Classification ◽  
Propagation Paths

This paper proposes a Wi-Fi-based indoor human detection system using a deep convolutional neural network. The system detects different human states in various situations, including different environments and propagation paths. The main improvements proposed by the system is that there is no cameras overhead and no sensors are mounted. This system captures useful amplitude information from the channel state information and converts this information into an image-like two-dimensional matrix. Next, the two-dimensional matrix is used as an input to a deep convolutional neural network (CNN) to distinguish human states. In this work, a deep residual network (ResNet) architecture is used to perform human state classification with hierarchical topological feature extraction. Several combinations of datasets for different environments and propagation paths are used in this study. ResNet’s powerful inference simplifies feature extraction and improves the accuracy of human state classification. The experimental results show that the fine-tuned ResNet-18 model has good performance in indoor human detection, including people not present, people still, and people moving. Compared with traditional machine learning using handcrafted features, this method is simple and effective.

scholarly journals Manipulation Planning for Object Re-Orientation Based on Semantic Segmentation Keypoint Detection

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2280
Author(s):  
Ching-Chang Wong ◽  
Li-Yu Yeh ◽  
Chih-Cheng Liu ◽  
Chi-Yi Tsai ◽  
Hisasuki Aoyama
Keyword(s):  
Neural Network ◽  
Robot Manipulator ◽  
Detection System ◽  
Semantic Segmentation ◽  
Target Object ◽  
Planning System ◽  
Normal Vector ◽  
Manipulation Planning ◽  
Keypoint Detection ◽  
3D Keypoint Detection

In this paper, a manipulation planning method for object re-orientation based on semantic segmentation keypoint detection is proposed for robot manipulator which is able to detect and re-orientate the randomly placed objects to a specified position and pose. There are two main parts: (1) 3D keypoint detection system; and (2) manipulation planning system for object re-orientation. In the 3D keypoint detection system, an RGB-D camera is used to obtain the information of the environment and can generate 3D keypoints of the target object as inputs to represent its corresponding position and pose. This process simplifies the 3D model representation so that the manipulation planning for object re-orientation can be executed in a category-level manner by adding various training data of the object in the training phase. In addition, 3D suction points in both the object’s current and expected poses are also generated as the inputs of the next operation stage. During the next stage, Mask Region-Convolutional Neural Network (Mask R-CNN) algorithm is used for preliminary object detection and object image. The highest confidence index image is selected as the input of the semantic segmentation system in order to classify each pixel in the picture for the corresponding pack unit of the object. In addition, after using a convolutional neural network for semantic segmentation, the Conditional Random Fields (CRFs) method is used to perform several iterations to obtain a more accurate result of object recognition. When the target object is segmented into the pack units of image process, the center position of each pack unit can be obtained. Then, a normal vector of each pack unit’s center points is generated by the depth image information and pose of the object, which can be obtained by connecting the center points of each pack unit. In the manipulation planning system for object re-orientation, the pose of the object and the normal vector of each pack unit are first converted into the working coordinate system of the robot manipulator. Then, according to the current and expected pose of the object, the spherical linear interpolation (Slerp) algorithm is used to generate a series of movements in the workspace for object re-orientation on the robot manipulator. In addition, the pose of the object is adjusted on the z-axis of the object’s geodetic coordinate system based on the image features on the surface of the object, so that the pose of the placed object can approach the desired pose. Finally, a robot manipulator and a vacuum suction cup made by the laboratory are used to verify that the proposed system can indeed complete the planned task of object re-orientation.

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