Aims In the case of uneven vegetation coverage, it is still facing many problems that using UAV-remote sensing to assess the canopy nutrient status. The research objective is to determine the optimal ground feature cl...
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Aims In the case of uneven vegetation coverage, it is still facing many problems that using UAV-remote sensing to assess the canopy nutrient status. The research objective is to determine the optimal ground feature classification and establish the leaf nitrogen concentration (LNC) inversion model. Methods UAV-Phantom 4 multispectral platform was used to acquire apple orchard images. The ground features of remote sensing image were classified with the minimum distance, maximum likelihood, and object-oriented feature extraction classifications (MDC, MLC, OFEC), while spectral vegetation indices were used to perform LNC inversion using the backpropagation neural network (BP) and extreme learning machine (ELM). Further, genetic algorithm (GA) and particle swarm optimization (PSO) were used to optimize two inversion models. Results Compared with the MDC, the overall accuracy of MLC increased 20.30% and 26.69% in 2021 and 2022, while the OFEC increased 30.48% and 31.04%, respectively. The LNC inversion model of BP and ELM produced an acceptable performance (R-c (2)> R(2)p > 0.60, RRMSE < 15%). The use of GA and PSO algorithms did not improve the prediction performance of the BP inversion model. Compared with ELM, the RRMSE of GA_ELM and PSO_ELM inversion models decreased 2.63% and 20.39%. Furthermore, the PSO_ELM inversion model decreased the RRMSE 10.95% compared to the BP model. ConclusionThe combination of reverse thinking iterative approach with the PSO algorithm significantly enhances the predictive performance of the ELM inversion model, which can present a rapid assessment method for leaf nitrogen nutrition diagnosis based on the relationship between LNC and fruit yield.
Stable aerial flight capability is crucial for amphibious vehicles to perform cross-domain motion, yet it is often influenced by unpredictable cross-domain environments and model uncertainties. A fractional-order acti...
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Stable aerial flight capability is crucial for amphibious vehicles to perform cross-domain motion, yet it is often influenced by unpredictable cross-domain environments and model uncertainties. A fractional-order active disturbance rejection controller based on finite-time convergent extended state observer is proposed for the anti-interference control problem in the attitude trajectory tracking of bullet-shaped trans-domain amphibious vehicle in complex environments. Firstly, a complete dynamic model of the airborne flight system of the bullet-shaped trans-domain amphibious vehicle is established. A hierarchical control scheme based on the proposed controller is designed assuming that all states of the system are available, in order to improve the control reliability and robustness of the flight system in multi-source interference environments. Then, based on a novel hybrid particle swarm search technique called the Particle Swarm optimization with Levy Flight and Wavelet Mutation algorithm, which combines Levy flight and wavelet mutation, a new parameter identification and tuning algorithm is proposed for the proposed controller to obtain optimal control parameters for attitude and position. To provide a comprehensive comparison, the stability of the optimization algorithm and the robustness performance of the controller were evaluated. Finally, all controllers were tested under three different disturbance scenarios. The results show that the proposed controller can achieve better stability in flight attitude and trajectory tracking tasks compared to Proportion Integration Differentiation controller, Fractional-order Proportion Integration Differentiation controller, and Active Disturbance Rejection controller in all scenarios. & COPY;2023 Elsevier Inc. All rights reserved.
As an important basic industry of national economy, the iron and steel industry has provided an important raw material guarantee for a long time. However there are a large number of hazard sources which are prone to s...
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As an important basic industry of national economy, the iron and steel industry has provided an important raw material guarantee for a long time. However there are a large number of hazard sources which are prone to safety accidents in the production process. Then safety evaluation in the production system is highly needed to effectively prevent the occurrence of accidents in iron and steel enterprises. Hence we introduce a method based on deep learning model to evaluate safety of the enterprises. Firstly, the risk factors and casualties in production process are investigated, and a set of safety evaluation index system is ***, since deep neural network model has the characteristics of strong feature extraction ability and simple model structure, we design a safety evaluation model based on deep neural network. The 25-dimensional evaluation index value is the input of the network, and the network output corresponds to five risk levels. On this basis, the optimization algorithm of deep neural network model is designed to explore the mapping relationship between risk characteristics and safety level. Tensorflow deep learning framework is used to build the evaluation model, classification loss function and network optimization method are designed to train the model. Finally, through experiments, the optimal model structure is determined by comparing the influence of different parameter optimization strategies, different hidden layer structures, and different activation functions on the safety evaluation performance. A three hidden layer structure with the Adam back propagation algorithm and LeakyRelu activation function is adopted to obtain higher accuracy and faster convergence rate. The experiments show that our evaluation model provides an operational method for evaluating the safety management status of iron and steel enterprises.
Researchers can examine numerous ailments and forecast improved treatments using a huge number of medical databases. The Michael Fox PPMI data sets provide a baseline evaluation of the disease using the Unified Parkin...
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Researchers can examine numerous ailments and forecast improved treatments using a huge number of medical databases. The Michael Fox PPMI data sets provide a baseline evaluation of the disease using the Unified Parkinson's Disease Rating Scales, the most prevalent sequential scales for identifying Parkinson's conditions. Existing research uses a Gaussian mixture model to predict PD disease using min-max normalization and dimensionality reduction based on principal component analysis. It significantly improved the findings, but it required more data to make predictions. This may be developed by decreasing the numbers of features utilized for prediction utilizing optimization algorithms. As a result, the ant-colony optimizations approach is suggested in this paper to enhance the classifier with few features. The ant colony approach uses this information to select the lowest features to use in training the regression neural network for disease prediction. When compared to various dimensionality reductions methods including Fast-ICA, PCAs, Kernel-PCA, as well as NMF, the findings suggest that the suggested optimization approaches performing-well. The neural regression network also reveals that the suggested strategy outperforms the Gaussian mixture model with the least patient information on the UPDRSs dataset.
Smart sensor systems have gained increasing importance in various fields, including healthcare, environmental monitoring, industrial automation, and security. Photoacoustic gas sensors are an emerging type of optical ...
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Smart sensor systems have gained increasing importance in various fields, including healthcare, environmental monitoring, industrial automation, and security. Photoacoustic gas sensors are an emerging type of optical sensor used in various applications due to its enhanced performance characteristics. However, the accuracy and reliability of gas concentration measurements from photoacoustic gas sensors may be impacted by several known limitations, including drift of the gas cell resonant frequency over extended periods of time. Researchers have proposed various solutions, including optimization methods and signal processing algorithms, to address this and others issues. In this paper, we propose a novel solution using an extremum-seeking control algorithm to manage the laser modulation frequency of photoacoustic gas sensors. By tracking the changing resonant frequency of the gas cell, long-term stability can be achieved, making it suitable for environmental monitoring, petroleum exploration, and industrial process control. Our approach has the potential to improve the accuracy and reliability of long-term measurements obtained from photoacoustic gas sensors, providing a stable and reliable method for gas concentration estimation.
The complexity of machinery makes accurate identification of rolling bearing fault signals difficult. Convolutional neural networks (CNNs) have made some progress, but they rely on the expertise of the network designe...
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The complexity of machinery makes accurate identification of rolling bearing fault signals difficult. Convolutional neural networks (CNNs) have made some progress, but they rely on the expertise of the network designer and the iterative process of optimizing numerous parameters. Therefore, there is an urgent need to develop a method that reduces the threshold for designing CNNs for a given task. In this article, we propose a reinforcement neural architecture search CNN to address this problem. Firstly, we design a neural architecture search algorithm that can generate different types of sub-networks specifically for fault diagnosis tasks. Secondly, we execute a reinforcement learning-based search strategy to discover promising sub-networks. Furthermore, we enhance the performance of the sub-network by improving the optimizer and training parameters. We conduct extensive experiments using two different types of datasets and verify that the proposed method's fault classification capability is superior to existing methods.
Loan default risk prediction is necessary in credit risk assessment, as it helps financing institutions and investors make decisions. However, existing prediction models focus more on using individual classifiers to o...
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Loan default risk prediction is necessary in credit risk assessment, as it helps financing institutions and investors make decisions. However, existing prediction models focus more on using individual classifiers to obtain higher prediction accuracy, which is far from the core purpose of business (i.e., maximizing profit) and leaves opportunities to explore profit-oriented and interpretable weighting models. This study proposes a profit-oriented weighting model for loan default prediction. The model consists of three stages: constructing multiple profit-oriented sub-classifiers, determining profit-oriented weight coefficients, and providing interpretable analysis. Five lending datasets are examined based on accuracy and profit-based metrics. The empirical results demonstrate that the proposed weighting prediction system helps lenders achieve higher profits and provides concise and intuitive interpretability. Thus, it can help practitioners make better decisions and manage risk.
The vertical distribution of soil properties is crucial in accurately representing various hydrological and ecological processes such as freeze-thaw cycles and diurnal variations. In this article, considering the comp...
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The vertical distribution of soil properties is crucial in accurately representing various hydrological and ecological processes such as freeze-thaw cycles and diurnal variations. In this article, considering the complexity of the multiparameter features of layered soil, we evaluate the potential to retrieve the vertical distribution of the moisture and temperature of soil using multichannel passive microwave observations. To enhance the inversion efficiency and accuracy, a novel physics-embedded artificial neural network (P-ANN) inversion algorithm combining multiangle (30 degrees-50 degrees), multifrequency (L-, C-, and X-bands), and multipolarization (horizontal and vertical polarization) passive observations is proposed. In this approach, the multichannel physical brightness temperature simulations corresponding to the predicted soil state parameters are integrated into the loss function of a standard fully connected feedforward neural network (NN), enabling efficient convergence with limited sampling data in the training process. Testing results exhibit that the inversion performance of P-ANN is superior to that of conventional NN approaches, which only adopts errors in soil states in the loss function to train the network. The test also shows that the proposed P-ANN approach outperforms traditional optimization algorithms in dealing with layered soil retrieval. In order to further improve the retrieval accuracy, an advanced local optimization scheme is also proposed, where the output from P-ANN is further treated as the initial value to a local optimization algorithm, achieving even closer results to the ground truths without excessive computational costs. In addition, to estimate the reliability of the model predictions, this article also establishes, in the testing process, a statistical relationship between the soil inversion error and the error of the corresponding brightness temperatures. When the trained NN is in operation, the error of brightness temper
Due to the variety of flight patterns in airport terminal airspace, as well as the high global similarity of different flight patterns entering and leaving from the same runway or corridor, it is difficult for current...
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Due to the variety of flight patterns in airport terminal airspace, as well as the high global similarity of different flight patterns entering and leaving from the same runway or corridor, it is difficult for current mainstream methods to achieve good clustering. To this end, this paper first constructs a truncated dynamic time warping (TDTW) trajectory similarity measurement to characterize different trajectory patterns with high global similarity and large local differences. Furthermore, a hierarchical flight pattern mining method is proposed, which is divided into four layers according to different characteristics. The first three layers of the method classify trajectories according to takeoff and landing types, runways, and corridors;whereas the fourth layer uses a K-medoid clustering method based on TDTW, thereby making the mining process more controllable and in line with actual operation. Compared to dynamic time warping, the experimental results show that the intraclass compactness and interclass separation of the cluster obtained by the proposed method have decreased and increased by 44.6 and 20.1%, respectively, and the overall performance has improved by 54.1%. More refined and reasonable flight patterns have been obtained.
Breast cancer is one of the most common reasons for the premature death of women worldwide. However, early detection and diagnosis of the same can save many lives. Hence, computer scientists across the world are also ...
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Breast cancer is one of the most common reasons for the premature death of women worldwide. However, early detection and diagnosis of the same can save many lives. Hence, computer scientists across the world are also striving to develop reliable models to deal with this disease. One of the reliable methods of detecting breast cancer is through thermal images or thermograms. In medical image analysis, the current research trend is the use of deep learning (DL) models. However, many state-of-the-art DL models generate a large number of features and for processing those features we need a significant amount of memory and computation time. To address this issue, we have proposed a lightweight model to detect signs of abnormality in breast thermograms using a combination of transfer learning-based DL model and feature selection approaches. At first, we employed a deep learning model, called SqueezeNet 1.1 (pre-trained on the ImageNet dataset), fine-tuned on the breast cancer thermal images for the feature extraction purpose. Then a hybrid of Genetic algorithm (GA) and Grey Wolf Optimizer (GWO) is used to reduce the dimension of the obtained feature vector. Before that, a chaotic map is used to create the initial population of GA. The proposed model performs very well in detecting and differentiating malignant and healthy breasts. We have evaluated our model on a publicly available dataset, namely Database For Mastology Research (DMR-IR) and achieved 100% accuracy on the test set using only 3% features extracted by the SqueezeNet 1.1 model.
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