scholarly journals A Deep Gaussian Process-Based Flight Trajectory Prediction Approach and Its Application on Conflict Detection

Algorithms ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 293
Author(s):  
Zhengmao Chen ◽  
Dongyue Guo ◽  
Yi Lin
Keyword(s):  
Gaussian Process ◽  
Gaussian Distribution ◽  
Network Architecture ◽  
Real Data ◽  
Detection Algorithm ◽  
Conflict Detection ◽  
Flight Trajectory ◽  
Trajectory Prediction ◽  
Temporal Correlations ◽  
Prediction Approach

In this work, a deep Gaussian process (DGP) based framework is proposed to improve the accuracy of predicting flight trajectory in air traffic research, which is further applied to implement a probabilistic conflict detection algorithm. The Gaussian distribution is applied to serve as the probabilistic representation for illustrating the transition patterns of the flight trajectory, based on which a stochastic process is generated to build the temporal correlations among flight positions, i.e., Gaussian process (GP). Furthermore, to deal with the flight maneuverability of performing controller’s instructions, a hierarchical neural network architecture is proposed to improve the modeling representation for nonlinear features. Thanks to the intrinsic mechanism of the GP regression, the DGP model has the ability of predicting both the deterministic nominal flight trajectory (NFT) and its confidence interval (CI), denoting by the mean and standard deviation of the prediction sequence, respectively. The CI subjects to a Gaussian distribution, which lays the data foundation of the probabilistic conflict detection. Experimental results on real data show that the proposed trajectory prediction approach achieves higher prediction accuracy compared to other baselines. Moreover, the conflict detection approach is also validated by a obtaining lower false alarm and more prewarning time.

scholarly journals UAV Path Prediction for CD&R to Manned Aircraft in a Confined Airspace for Cooperative Mission

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Chin E. Lin ◽  
Ya-Hsien Lai
Keyword(s):  
Unmanned Aircraft ◽  
Prediction Algorithm ◽  
Flight Trajectory ◽  
Fusion Model ◽  
Trajectory Prediction ◽  
System A ◽  
Front End ◽  
Path Prediction ◽  
Prediction Approach ◽  
Hybrid Fusion

In this paper, a new path prediction approach for unmanned aerial vehicles (UAVs) for conflict detection and resolution (CD&R) to manned aircraft in cooperative mission in a confined airspace is proposed. Path prediction algorithm is established to estimate UAV flight trajectory to predict conflict threat to manned aircraft in time advances (front-end process of CD&R system). A hybrid fusion model is formulated based on three different trajectory prediction conditions considering scenarios in geographical conditions to aid the generation of appropriate resolution advisory of conflict alert. It offers a more precise CD&R system for manned and unmanned aircraft in cooperative rescue missions.

scholarly journals A policy conflict detection mechanism for multi-controller software-defined networks

2019 ◽  
Vol 15 (5) ◽  
pp. 155014771984471 ◽  
Author(s):  
You Lu ◽  
Qiming Fu ◽  
Xuefeng Xi ◽  
Zhenping Chen ◽  
Encen Zou ◽  
...  
Keyword(s):  
Network Architecture ◽  
Detection Algorithm ◽  
Conflict Detection ◽  
Detection Methods ◽  
Software Defined Network ◽  
Software Defined Networks ◽  
Network Environment ◽  
Detection Mechanism ◽  
Policy Conflict ◽  
Detection Model

As the network environment expands and becomes more complex, the deficiencies of decision-making capabilities in the single-controller software-defined network architecture are increasingly exposed. Currently, software-defined networks have gradually adopted a multi-controller-based architecture. However, in this architecture, multiple controllers may cause conflicts in the flow policies, which may cause failures such as security and route conflicts. Most of the existing detection methods are only aimed at specific types of conflicts. Aiming at the above insufficiency, this article proposes a policy conflict detection mechanism for multi-controller software-defined network. First, it quantifies and classifies the software-defined policy conflict itself to provide the basis for detection mechanism; then, it proposes a conflict detection model and its deployment scheme for multi-controller software-defined networks; finally, based on the software-defined flow policy’s structure, a multi-branch tree-based policy conflict detection algorithm is proposed to accurately detect the universal types of conflicts. The experimental results under the campus network environment prove that our method can effectively detect the conflict of flow policies existing in the multi-controller software-defined network and has advantages over the existing methods in the integrity, accuracy, and efficiency of the detection.

250 AI-Supported Sleep Staging from Activity and Heart Rate

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A101-A101
Author(s):  
Samadrita Chowdhury ◽  
TzuAn Song ◽  
Richa Saxena ◽  
Shaun Purcell ◽  
Joyita Dutta
Keyword(s):  
Heart Rate ◽  
Network Architecture ◽  
Class Imbalance ◽  
Nrem Sleep ◽  
Sleep Time ◽  
Deep Sleep ◽  
Sleep Staging ◽  
Temporal Correlations ◽  
Stage Classification ◽  
Small Form Factor

Abstract Introduction Polysomnography (PSG) is considered the gold standard for sleep staging but is labor-intensive and expensive. Wrist wearables are an alternative to PSG because of their small form factor and continuous monitoring capability. In this work, we present a scheme to perform such automated sleep staging via deep learning in the MESA cohort validated against PSG. This scheme makes use of actigraphic activity counts and two coarse heart rate measures (only mean and standard deviation for 30-s sleep epochs) to perform multi-class sleep staging. Our method outperforms existing techniques in three-stage classification (i.e., wake, NREM, and REM) and is feasible for four-stage classification (i.e., wake, light, deep, and REM). Methods Our technique uses a combined convolutional neural network coupled and sequence-to-sequence network architecture to appropriate the temporal correlations in sleep toward classification. Supervised training with PSG stage labels for each sleep epoch as the target was performed. We used data from MESA participants randomly assigned to non-overlapping training (N=608) and validation (N=200) cohorts. The under-representation of deep sleep in the data leads to class imbalance which diminishes deep sleep prediction accuracy. To specifically address the class imbalance, we use a novel loss function that is minimized in the network training phase. Results Our network leads to accuracies of 78.66% and 72.46% for three-class and four-class sleep staging respectively. Our three-stage classifier is especially accurate at measuring NREM sleep time (predicted: 4.98 ± 1.26 hrs. vs. actual: 5.08 ± 0.98 hrs. from PSG). Similarly, our four-stage classifier leads to highly accurate estimates of light sleep time (predicted: 4.33 ± 1.20 hrs. vs. actual: 4.46 ± 1.04 hrs. from PSG) and deep sleep time (predicted: 0.62 ± 0.65 hrs. vs. actual: 0.63 ± 0.59 hrs. from PSG). Lastly, we demonstrate the feasibility of our method for sleep staging from Apple Watch-derived measurements. Conclusion This work demonstrates the viability of high-accuracy, automated multi-class sleep staging from actigraphy and coarse heart rate measures that are device-agnostic and therefore well suited for extraction from smartwatches and other consumer wrist wearables. Support (if any) This work was supported in part by the NIH grant 1R21AG068890-01 and the American Association for University Women.

Patent Innovation Factors Evolution Based on P-SVM and GCCA

2010 ◽  
Vol 39 ◽  
pp. 247-252
Author(s):  
Sheng Xu ◽  
Zhi Juan Wang ◽  
Hui Fang Zhao
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machines ◽  
Correlation Coefficient ◽  
Network Architecture ◽  
Innovation Management ◽  
Model Development ◽  
Real Data ◽  
Support Vector ◽  
Vector Machines ◽  
Gray Correlation

A two-stage neural network architecture constructed by combining potential support vector machines (P-SVM) with genetic algorithm (GA) and gray correlation coefficient analysis (GCCA) is proposed for patent innovation factors evolution. The enterprises patent innovation is complex to conduct due to its nonlinearity of influenced factors. It is necessary to make a trade off among these factors when some of them conflict firstly. A novel way about nonlinear regression model with the potential support vector machines (P-SVM) is presented in this paper. In the model development, the genetic algorithm is employed to optimize P-SVM parameters selection. After the selected key factors by the PSVM with GA model, the main factors that affect patent innovation generation have been quantitatively studied using the method of gray correlation coefficient analysis. Using a set of real data in China, the results show that the methods developed in this paper can provide valuable information for patent innovation management and related municipal planning projects.

scholarly journals Autoencoders for Drug-Target Interaction Prediction

2020 ◽  
Author(s):  
Ming Chen ◽  
Xiuze Zhou
Keyword(s):  
Network Architecture ◽  
Drug Target ◽  
Real Data ◽  
Data Sets ◽  
Nonlinear Method ◽  
Target Interaction ◽  
Interaction Prediction ◽  
Biological Studies ◽  
Latent Features ◽  
Full Interaction

Abstract Background: Because it is so laborious and expensive to experimentally identify Drug-Target Interactions (DTIs), only a few DTIs have been verified. Computational methods are useful for identifying DTIs in biological studies of drug discovery and development. Results: For drug-target interaction prediction, we propose a novel neural network architecture, DAEi, extended from Denoising AutoEncoder (DAE). We assume that a set of verified DTIs is a corrupted version of the full interaction set. We use DAEi to learn latent features from corrupted DTIs to reconstruct the full input. Also, to better predict DTIs, we add some similarities to DAEi and adopt a new nonlinear method for calculation. Similarity information is very effective at improving the prediction of DTIs. Conclusion: Results of the extensive experiments we conducted on four real data sets show that our proposed methods are superior to other baseline approaches.Availability: All codes in this paper are open-sourced, and our projects are available at: https://github.com/XiuzeZhou/DAEi.

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