scholarly journals Deep Neural Network for Slip Detection on Ice Surface

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6883
Author(s):  
Kent Wu ◽  
Suzy He ◽  
Geoff Fernie ◽  
Atena Roshan Fekr
Keyword(s):  
Neural Network ◽  
Human Error ◽  
Cross Validation ◽  
Occupational Injuries ◽  
Three Dimensional ◽  
Economic Loss ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Slip Resistance ◽  
Slip Detection

Slip-induced falls are among the most common causes of major occupational injuries and economic loss in Canada. Identifying the risk factors associated with slip events is key to developing preventive solutions to reduce falls. One factor is the slip-resistance quality of footwear, which is fundamental to reducing the number of falls. Measuring footwear slip resistance with the recently developed Maximum Achievable Angle (MAA) test requires a trained researcher to identify slip events in a simulated winter environment. The human capacity for information processing is limited and human error is natural, especially in a cold environment. Therefore, to remove conflicts associated with human errors, in this paper a deep three-dimensional convolutional neural network is proposed to detect the slips in real-time. The model has been trained by a new dataset that includes data from 18 different participants with various clothing, footwear, walking directions, inclined angles, and surface types. The model was evaluated on three types of slips: Maxi-slip, midi-slip, and mini-slip. This classification is based on the slip perception and recovery of the participants. The model was evaluated based on both 5-fold and Leave-One-Subject-Out (LOSO) cross validation. The best accuracy of 97% was achieved when identifying the maxi-slips. The minimum accuracy of 77% was achieved when classifying the no-slip and mini-slip trials. The overall slip detection accuracy was 86% with sensitivity and specificity of 81% and 91%, respectively. The overall accuracy dropped by about 2% in LOSO cross validation. The proposed slip detection algorithm is not only beneficial for footwear manufactures to improve their footwear slip resistance quality, but it also has other potential applications, such as improving the slip resistance properties of flooring in healthcare facilities, commercial kitchens, and oil drilling platforms.

The Optimal Detection and Analysis for High-Degree Camouflaged Incursion Features Based on Improved BP Neural Network

2013 ◽  
Vol 846-847 ◽  
pp. 1538-1541
Author(s):  
Hong Li Lu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Optimal Detection ◽  
Distributed Components ◽  
High Degree

In order to effectively increase the incursion features detection accuracy with high-degree camouflage in the network, this paper proposes a high-degree camouflaged incursion features detection algorithm based on BP neural network. This paper utilizes distributed components to collect and analyze the incursion data features with high-degree camouflage, and designs an incursion features detection proxy module based on BP neural network. This paper takes the protocol platform developed by some company in Zhengzhou city as an example to detect the incursion of malicious information and presents the detailed diagnose methods and procedures in which the accuracy can reach up to 93.7%.

Research on High-Density Network Intrusion Features Detection Algorithm

2014 ◽  
Vol 644-650 ◽  
pp. 1054-1057
Author(s):  
Tai Fu Lv
Keyword(s):  
Neural Network ◽  
Expert System ◽  
Expert Systems ◽  
Feature Detection ◽  
Detection System ◽  
Detection Algorithm ◽  
High Density ◽  
Detection Accuracy ◽  
Network Intrusion ◽  
The Neural Network

Research on high-density network intrusion features problems, which improves the detection accuracy. For high-density network, an intrusion feature detection system based on intelligent expert systems and neural networks in is proposed. First, use expert systems for known high-density network intrusion detection. Use the neural network expert system to detect those which cannot find the unknown high-density network intrusion. Finally test results using neural network expert system rule library to be updated. Experimental results show that this method can effectively detect high-density network intrusion features, which ensures the security of the network and achieves satisfactory results.

scholarly journals CXR-RefineDet: Single-Shot Refinement Neural Network for Chest X-Ray Radiograph Based on Multiple Lesions Detection

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Cong Lin ◽  
Yongbin Zheng ◽  
Xiuchun Xiao ◽  
Jialun Lin
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Disease Diagnosis ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Single Shot ◽  
Detection Model ◽  
Network Output ◽  
Artificial Intelligence Technology ◽  
Chest X Ray

The workload of radiologists has dramatically increased in the context of the COVID-19 pandemic, causing misdiagnosis and missed diagnosis of diseases. The use of artificial intelligence technology can assist doctors in locating and identifying lesions in medical images. In order to improve the accuracy of disease diagnosis in medical imaging, we propose a lung disease detection neural network that is superior to the current mainstream object detection model in this paper. By combining the advantages of RepVGG block and Resblock in information fusion and information extraction, we design a backbone RRNet with few parameters and strong feature extraction capabilities. After that, we propose a structure called Information Reuse, which can solve the problem of low utilization of the original network output features by connecting the normalized features back to the network. Combining the network of RRNet and the improved RefineDet, we propose the overall network which was called CXR-RefineDet. Through a large number of experiments on the largest public lung chest radiograph detection dataset VinDr-CXR, it is found that the detection accuracy and inference speed of CXR-RefineDet have reached 0.1686 mAP and 6.8 fps, respectively, which is better than the two-stage object detection algorithm using a strong backbone like ResNet-50 and ResNet-101. In addition, the fast reasoning speed of CXR-RefineDet also provides the possibility for the actual implementation of the computer-aided diagnosis system.

scholarly journals Modeling of 3D Isotropic Distribution of Hydraulic Conductivity using Neural Network

2017 ◽  
Author(s):  
Hafidz Mabruri ◽  
Tedy Agung Cahyadi ◽  
Lilik Eko Widodo ◽  
Irwan Iskandar
Keyword(s):  
Neural Network ◽  
Hydraulic Conductivity ◽  
Joint Distribution ◽  
Cross Validation ◽  
Sedimentary Rocks ◽  
Rock Joint ◽  
Three Dimensional ◽  
Flow Modeling ◽  
Conductivity Distribution ◽  
Isotropic Distribution

In most natural condition, hydraulic conductivity distribution is heterogeneous and anisotropic that is affected by local lithological condition, such as rock porosity and rock joint distribution. Therefore, the more porous of lithology the more hydraulic conductivity number it gets. In the previous study, spatial hydraulic conductivity distribution is modeled using Kriging with the aid of SeGMS software. Three dimensional (3D) hydraulic conductivity distributions in sedimentary rocks, which are isotropic and heterogeneous, can be used for groundwater flow modeling. This paper discusses the modeling 3D hydraulic conductivity distribution using Neural Network (NN). The hydraulic conductivity as a target value is trained segmentally from its position in x, y, z coordinate using NN. Numbers of nodes and hidden layers will be affected by complexity of the data. Geological validation and cross validation show that NN can be applied for modeling the spatial hydraulic conductivity distribution

The Research of Network Intrusion Detection Technology Based on Genetic Algorithm and BP Neural Network

2014 ◽  
Vol 599-601 ◽  
pp. 726-730 ◽  
Author(s):  
Gang Ke ◽  
Ying Han Hong
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Intrusion Detection ◽  
Bp Neural Network ◽  
Detection System ◽  
Training Sample ◽  
Detection Algorithm ◽  
Network Intrusion Detection ◽  
Detection Accuracy ◽  
Network Intrusion

The traditional BP neural network algorithm is applied to intrusion detection system, detection speed slow and low detection accuracy. In order to solve the above problems, this paper proposes a network intrusion detection algorithm using genetic algorithms to optimize neural network weights. which find the most suitable weights of BP neural network by the genetic algorithm, and uses the optimized BP neural network to learn and detect the network intrusion detection data. Matlab simulation results show that the training sample time of the algorithm is shorter, has good intrusion recognition and detection effect, compared with the traditional network intrusion detection algorithm.

scholarly journals Ship Target Detection Algorithm Based on Improved Faster R-CNN

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 959 ◽  
Author(s):  
Qi ◽  
Li ◽  
Chen ◽  
Wang ◽  
Dong ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Target Detection ◽  
Texture Features ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Marine Transportation ◽  
Computational Speed ◽  
Neural Network Algorithm ◽  
Detection Search

Ship target detection has urgent needs and broad application prospects in military and marine transportation. In order to improve the accuracy and efficiency of the ship target detection, an improved Faster R-CNN (Faster Region-based Convolutional Neural Network) algorithm of ship target detection is proposed. In the proposed method, the image downscaling method is used to enhance the useful information of the ship image. The scene narrowing technique is used to construct the target regional positioning network and the Faster R-CNN convolutional neural network into a hierarchical narrowing network, aiming at reducing the target detection search scale and improving the computational speed of Faster R-CNN. Furthermore, deep cooperation between main network and subnet is realized to optimize network parameters after researching Faster R-CNN with subject narrowing function and selecting texture features and spatial difference features as narrowed sub-networks. The experimental results show that the proposed method can significantly shorten the detection time of the algorithm while improving the detection accuracy of Faster R-CNN algorithm.

scholarly journals Automatic Classification of the Ripeness Stage of Mango Fruit Using Machine a Learning Approach

2022 ◽  
Vol 4 (1) ◽  
pp. 32-47
Author(s):  
Denchai Worasawate ◽  
Panarit Sakunasinha ◽  
Surasak Chiangga
Keyword(s):  
Neural Network ◽  
Skin Color ◽  
Human Error ◽  
Cross Validation ◽  
The Other ◽  
Support Vector ◽  
Maturity Stage ◽  
Learning Approach ◽  
Mango Fruit

Most mango farms classify the maturity stage manually by trained workers using external indicators such as size, shape, and skin color, which can lead to human error or inconsistencies. We developed four common machine learning (ML) classifiers, the k-mean, naïve Bayes, support vector machine, and feed-forward artificial neural network (FANN), all of which were aimed at classifying the ripeness stage of mangoes at harvest. The ML classifiers were trained on biochemical data and then tested on physical and electrical data.The performance of the ML models was compared using fourfold cross validation. The FANN classifier performed the best, with a mean accuracy of 89.6% for unripe, ripe, and overripe classes, when compared to the other classifiers.

scholarly journals Exploring Underwater Target Detection Algorithm Based on Improved SSD

2020 ◽  
Vol 38 (4) ◽  
pp. 747-754
Author(s):  
Wei Qiang ◽  
Yuyao He ◽  
Yujin Guo ◽  
Baoqi Li ◽  
Lingjiao He
Keyword(s):  
Neural Network ◽  
Target Detection ◽  
Detection Algorithm ◽  
Convolution Neural Network ◽  
Detection Accuracy ◽  
Detection Model ◽  
Underwater Environment ◽  
Underwater Target Detection ◽  
Basic Network ◽  
Underwater Target

As the in-depth exploration of oceans continues, the accurate and rapid detection of fish, bionics and other intelligent bodies in an underwater environment is more and more important for improving an underwater defense system. Because of the low accuracy and poor real-time performance of target detection in the complex underwater environment, we propose a target detection algorithm based on the improved SSD. We use the ResNet convolution neural network instead of the VGG convolution neural network of the SSD as the basic network for target detection. In the basic network, the depthwise-separated deformable convolution module proposed in this paper is used to extract the features of an underwater target so as to improve the target detection accuracy and speed in the complex underwater environment. It mainly fuses the depthwise separable convolution when the deformable convolution acquires the offset of a convolution core, thus reducing the number of parameters and achieving the purposes of increasing the speed of the convolution neural network and enhancing its robustness through sparse representation. The experimental results show that, compared with the SSD detection model that uses the ResNet convolution neural network as the basic network, the improved SSD detection model that uses the depthwise-separated deformable convolution module improves the accuracy of underwater target detection by 11 percentage points and reduces the detection time by 3 ms, thus validating the effectiveness of the algorithm proposed in the paper.

Digitalization system of ancient architecture decoration art based on neural network and image features

2020 ◽  
pp. 1-12
Author(s):  
Wu Xin ◽  
Qiu Daping
Keyword(s):  
Neural Network ◽  
Construction Industry ◽  
Three Dimensional ◽  
Performance Testing ◽  
Image Features ◽  
Three Dimensional Model ◽  
Performance Effect ◽  
Data Process ◽  
And Performance ◽  
Construction Mode

The inheritance and innovation of ancient architecture decoration art is an important way for the development of the construction industry. The data process of traditional ancient architecture decoration art is relatively backward, which leads to the obvious distortion of the digitalization of ancient architecture decoration art. In order to improve the digital effect of ancient architecture decoration art, based on neural network, this paper combines the image features to construct a neural network-based ancient architecture decoration art data system model, and graphically expresses the static construction mode and dynamic construction process of the architecture group. Based on this, three-dimensional model reconstruction and scene simulation experiments of architecture groups are realized. In order to verify the performance effect of the system proposed in this paper, it is verified through simulation and performance testing, and data visualization is performed through statistical methods. The result of the study shows that the digitalization effect of the ancient architecture decoration art proposed in this paper is good.

Sign in / Sign up

Export Citation Format

Share Document