Design and Analysis for Early Warning of Rotor UAV Based on Data-Driven DBN

Wu, Chunxue; Wu, Yan; Xiong, Naixue; Ren, Han; Ju, Bobo; Zhang, Sheng

doi:10.3390/electronics8111350

Cited by 19 publications

(13 citation statements)

References 31 publications

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“…BP, which was developed in 1986, by scientists led by Rumelhart and McClelland, is a multi-layer feed-forward neural network that is trained by error reverse propagation algorithms and is the most widely used neural network. DBN consists of several restricted Boltzmann machine (RBM) layers and a BP layer, and its unsupervised pre-training can extract high-level abstract representations from the input data [58][59][60]. RBF is often used to approximate multi-dimensional surfaces by a linear combination of radial symmetric functions, based on Euclidean distance.…”

Section: Resultsmentioning

confidence: 99%

Improved Random Forest Algorithm Based on Decision Paths for Fault Diagnosis of Chemical Process with Incomplete Data

Zhang

Luo

et al. 2021

Sensors

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Fault detection and diagnosis (FDD) has received considerable attention with the advent of big data. Many data-driven FDD procedures have been proposed, but most of them may not be accurate when data missing occurs. Therefore, this paper proposes an improved random forest (RF) based on decision paths, named DPRF, utilizing correction coefficients to compensate for the influence of incomplete data. In this DPRF model, intact training samples are firstly used to grow all the decision trees in the RF. Then, for each test sample that possibly contains missing values, the decision paths and the corresponding nodes importance scores are obtained, so that for each tree in the RF, the reliability score for the sample can be inferred. Thus, the prediction results of each decision tree for the sample will be assigned to certain reliability scores. The final prediction result is obtained according to the majority voting law, combining both the predicting results and the corresponding reliability scores. To prove the feasibility and effectiveness of the proposed method, the Tennessee Eastman (TE) process is tested. Compared with other FDD methods, the proposed DPRF model shows better performance on incomplete data.

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Section: Resultsmentioning

confidence: 99%

Improved Random Forest Algorithm Based on Decision Paths for Fault Diagnosis of Chemical Process with Incomplete Data

Zhang

Luo

et al. 2021

Sensors

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“…The structure of DBN [57] Journal of Artificial Intelligence and Systems Autoencoder: An autoencoder (AE) is an unsupervised DL approach originally proposed by Lecun et al, in 1987 [58]. It involves dimensional reduction of the input data and reconstruction of the input in the output layer [41] (see Figure 3).…”

Section: Figurementioning

confidence: 99%

Deep Learning Algorithms based Fingerprint Authentication: Systematic Literature Review

Chiroma¹

2021

AIS

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Deep Learning algorithms (DL) have been applied in different domains such as computer vision, image detection, robotics and speech processing, in most cases, DL demonstrated better performance than the conventional machine learning algorithms (shallow algorithms). The artificial intelligence research community has leveraged the robustness of the DL because of their ability to process large data size and handle variations in biometric data such as aging or expression problem. Particularly, DL research in automatic fingerprint recognition system (AFRS) is gaining momentum starting from the last decade in the area of fingerprint pre-processing, fingerprints quality enhancement, fingerprint feature extraction, security of fingerprint and performance improvement of AFRS. However, there are limited studies that address the application of DL to model fingerprint biometric for different tasks in the fingerprint recognition process. To bridge this gap, this paper presents a systematic literature review and an insightful meta-data analysis of a decade applications of DL in AFRS. Discussion on proposed model’s tasks, state of the art study, dataset, and training architecture are presented. The Convolutional Neural Networks models were the most saturated models in developing fingerprint biometrics authentication. The study revealed different roles of the DL in training architecture of the models: feature extractor, classifier and end-to-end learning. The review highlights open research challenges and present new perspective for solving the challenges in the future. The author believed that this paper will guide researchers in propose novel fingerprint authentication scheme.

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“…where T r (k) is the expected value and λ is a small normal number to avoid an infinite variable of control if β 0 (k) = 0. α 1 (k), α 2 (k) and β 0 (k) are calculated by (12).…”

Section: A the Feedforward Maintaining And Tracking Control Lawmentioning

confidence: 99%

“…Because communications and control are highly coupled in UAV systems and UAV attitude variation affects the beam direction of data communications, attitude control plays an important role in UAV communications [9]- [11], and a stable quadrotor attitude controller is the basis of these applications. A quadrotor is an underactuated, strongly coupled and nonlinear complex system [12], so there are many challenges in designing the attitude controller. Currently, the design of the attitude controller is an academic research focus [13].…”

Section: Introductionmentioning

confidence: 99%

Quadrotor Attitude Control via Feedforward All-Coefficient Adaptive Theory

et al. 2020

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The use of quadrotors is proliferating across many civil applications, including those in the areas of the Internet of Things (IoT) and communications. Attitude control plays an important role in these applications, and designing a quadrotor attitude controller is a challenging task. Recently, several design methods for the attitude controller were presented with an accurate model. However, during the flying state, the quadrotor may be affected by several factors that cause unpredictable disturbances to the model. As a result, an accurate model of disturbances is difficult to establish. To address this problem, in this paper, a novel linear feedforward all-coefficient adaptive control (FACAC) strategy based on characteristic modeling is proposed. The contributions of our work are as follows. First, a second-order characteristic model of the quadrotor attitude control is established based on the dynamic equations. Second, an all-coefficient adaptive control (ACAC) controller that consists of the golden-section adaptive control law, the logic integral, and logic differential control laws is designed. The feedforward maintaining and tracking control law is integrated into the ACAC controller to shorten the settling time. Finally, extensive simulation results demonstrate that the performance of the proposed FACAC controller outperforms the state-of-the arts.

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Design and Analysis for Early Warning of Rotor UAV Based on Data-Driven DBN

Cited by 19 publications

References 31 publications

Improved Random Forest Algorithm Based on Decision Paths for Fault Diagnosis of Chemical Process with Incomplete Data

Improved Random Forest Algorithm Based on Decision Paths for Fault Diagnosis of Chemical Process with Incomplete Data

Deep Learning Algorithms based Fingerprint Authentication: Systematic Literature Review

Quadrotor Attitude Control via Feedforward All-Coefficient Adaptive Theory

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