This paper focus on the granule description based on formal concept analysis. First, we introduce the notion of attribute logic formula in a formal context, and then prove a general granule ...description theorem by using attribute logic formulas. Second, we prove some basic granule description theorems based on concept, property-oriented concept and object-oriented concept respectively. Third, we propose some methods that use attribute logic formulas to describe concept, property-oriented concept and object-oriented concept respectively, and prove that a property-oriented concept lattice and an object-oriented concept lattice are anti-isomorphic. Finally, we apply granule description methods to build concept lattice, property-oriented concept lattice and object-oriented concept lattice, propose some algorithms for building concept lattice, property-oriented concept lattice and object-oriented concept lattice, and give some examples and experiments to show the utility of algorithms.
Taking MNIST data-set as research object, the HMM is introduced into the Handwritten Number Recognition for the first time. After the implementation of the classical HMM training algorithm, some ...optimization methods are proposed for the problems existing in the training. The general random initialization parameters lead to long training time and unstable data. The training of initialization parameters based on observations can speed up the training and avoid data overflow. The number of iterations in the training process is not positively related to the output probability. In order to obtain an optimal model, after the algorithm converges when the cross entropy loss function of the output probability of two adjacent iterations is the minimum, the training ends. Finally, a comparative experiment between the classical method and the optimization method is carried out in the two stages of training and recognition. In the training stage, compared with the classical method, the optimization method has the advantages of short average training time, high average output probability, fast iteration speed and high accuracy. In the test stage, the accuracy of the optimization method is higher than that of the classical method. The experimental results show that the HMM can be effectively applied in the field of Handwritten Number Recognition. And through the optimization method, to a certain extent, the recognition accuracy is improved. In a word, the method in this paper is an effective and feasible method.
•The NDVITs constructed by spectral and textural information performed well in wheat LNC monitoring and GPC estimation.•Textural information can assist spectral information to monitor wheat LNC and ...GPC effectively.•ANN model combining spectra, texture and ecological factor performed well in wheat GPC estimation.•Effective ecological factors are good predictors to improve the prediction accuracy of wheat GPC.
Nitrogen is an essential element of wheat growth and grain quality. Leaf nitrogen content (LNC), a critical monitoring indicator of crop nitrogen status, plays a reference role for later estimations of grain protein content (GPC). Developments in unmanned aerial vehicle (UAV) platforms and multispectral sensors have provided new approaches for LNC monitoring and GPC estimation, with great convenience for assessing the nutritional status of plants and grains without traditional destructive sampling. The objective of this study was to evaluate the feasibility of wheat LNC monitoring and GPC estimation based on UAV multispectral imagery. Wheat experiments were carried out in Xinghua, Kunshan and Suining of Jiangsu Province during 2018−2019 and in Rugao of Jiangsu Province during 2020−2021 with different varieties and nitrogen application rates. Remote sensing images were obtained by a multi-rotor UAV carrying a multispectral camera. The destructive sampling method was used to collect LNC, GPC and other field data. Wheat LNC monitoring and GPC estimation models were established after selection of the optimal indicators. Different modelling methods were used for the comparative analysis, including unitary linear regression, multiple linear regression and artificial neural network (ANN) methods. Three techniques were adopted to improve the GPC prediction accuracy: (1) multiple factors were substituted for single factor for the prediction; (2) texture information was added through further imagery mining; and (3) ecological factors were considered to improve the prediction mechanism. The results showed that the use of UAV-based Airphen multispectral imagery had a good effect on wheat LNC monitoring and GPC estimation. The vegetation indices constructed by red-edge and near-infrared bands had good performances in LNC monitoring and GPC estimation. The addition of texture information and ecological factors further improved the modelling accuracy. In this study, the optimal wheat GPC estimation model was established by NDVI (675, 730) at the jointing stage, NDVIT (730mea., 850) at the booting stage, NDVIT (730mea., 850) at the flowering stage and NDVI (730, 850) at the early filling stage. The modelling R2, validation R2 and relative root mean square error (RRMSE) reached 0.662, 0.7445 and 0.0635, respectively. The results provide a reference for crop LNC monitoring and GPC estimation based on UAV multispectral imagery.
The investigation of ecosystem respiration (RE) and its vital influential factors along with the timely and accurate detection of spatiotemporal variations in RE are essential for guiding ...agricultural production planning. RE observation in the plot region is primarily based on the laborious chamber method. However, upscaling the spatial-temporal estimates of RE at the canopy scale is still challenging. The present study conducted a field experiment to determine RE using the chamber method. A multi-rotor unmanned aerial vehicle (UAV) equipped with a multispectral camera was employed to acquire the canopy spectral data of wheat during each RE test experiment. Moreover, the agronomic indicators of aboveground plant biomass, leaf area index, leaf dry mass as well as agrometeorological and soil data were measured simultaneously. The study analyzed the potential of multi-information for estimating RE at the field scale and proposed two strategies for RE estimation. In addition, a semiempirical, yet Lloyd and Taylor-based, remote sensing model (LT1-NIRV) was developed for estimating RE observed across different growth stages with a small margin of error (coefficient of determination R2 = 0.60–0.64, root-mean-square error RMSE = 285.98–316.19 mg m−2 h−1). Further, five machine learning (ML) algorithms were utilized to independently estimate RE using two different datasets. The rigorous analyses, which included statistical comparison and cross-validation for estimating RE, confirmed that the XGBoost model, with the highest R2 and lowest RMSE (R2 = 0.88 and RMSE = 172.70 mg m−2 h−1), performed the best among the evaluated ML models. The LT1-NIRV model was less effective in estimating RE compared with the other ML models. Based on this comprehensive comparison analysis, the ML model can successfully estimate variations in wheat field RE using high-resolution UAV multispectral images and environmental factors from the wheat cropland system, thereby providing a valuable reference for monitoring and upscaling RE observations.
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•Abiotic and biotic traits greatly determine variations in ecosystem respiration (RE).•LT1–NIRV model was reliable in estimating the RE of winter wheat field.•XGBoost model exhibited the best performance in estimating CO2 fluxes.
At present, facial expression recognition technology is widely used in artificial intelligence, transportation, medical and other aspects, so it has important research value. Traditional facial ...expression recognition uses manual feature extraction method with low accuracy and weak generalization ability, which is difficult to be applied in real life. With the development of deep learning, convolution neural network appears in people's vision. Different from traditional manual feature extraction, convolution neural network can learn image features independently, and learn more features. In addition, it has the advantage of sharing the weight with the neural network. Although convolution neural network has multiple advantages, it also has some disadvantages, especially over fitting. In this paper, the model of convolution network is improved based on the classical VGGNet according to the working principle of convolution neural network. In this new model, the number of convolution kernels is reduced in parameters, and the global average pool layer is used to replace the full connection layer in the structure, so as to reduce the degree of over fitting and decrease the operation parameters. Finally, experiments show that the accuracy, generalization and consumption of resource are enhanced in the new model. It is proposed that the new method is better than the traditional convolution network VGGNet.
In this paper we propose a new decision analysis method combining quantitative logic and fuzzy soft set theory. Firstly, we transform a fuzzy information system into a fuzzy soft set, and then ...establish a formal language based on the fuzzy soft set, in which the parameters of fuzzy soft set are regarded as atomic formulas, some atomic formulas are connected by the logical connectives and then a logical formula is formed, and a implicative type of formula is interpreted as a soft decision rule (SDR). Secondly, various types of measures to evaluate the SDR are introduced and then the soft metric between two logical formulas is established. Thirdly, we apply the soft metric to the soft decision analysis, a SDR extraction algorithm for fuzzy decision information system and a corresponding recommendation algorithm are proposed. Finally, some attribute analysis examples, including the example as shown in rough sets and the practical credit card application example, are given to illustrate the newly proposed method and related concepts.
As a common biometric recognition technology, face recognition is also an important research direction in the field of computer. Although compared with the initial research, the current research has ...made great progress, but there are still many difficulties in practical application. In this paper, by extracting HOG features, after introducing the detailed steps of PCA and LDA subspace feature extraction methods, dimensionality reduction feature extraction method combing PCA with LDA is applied to extract face features. This method first uses PCA to reduce the dimension of face features, and then uses LDA for linear discriminant analysis. Finally, the feature extraction methods based on PCA and LDA are tested and compared in FERET standard face database and CAS-PEAL database of Chinese Academy of Sciences
Increasing nitrogen (N) diagnosis efficiency and accuracy is crucial for optimizing wheat N management. We aimed to establish a spatially and temporally explicit model for the diagnosis of winter ...wheat N status on small scale farms using multivariate information. To determine the most accurate approach, seven field experiments involving different cultivars and N treatments were conducted in east China over five years. A fixed-wing unmanned aerial vehicle (UAV) mounted multispectral camera was used to acquire canopy spectral data of winter wheat at the jointing and booting stages, while agronomic indicators of plant dry matter (PDM), plant N accumulation (PNA) and N nutrition index (NNI), as well as agrometeorological (AM) and field management (FM) data, were measured synchronously. Direct and indirect strategies of NNI estimation were applied for N diagnosis at the jointing and booting stages. Four machine learning (ML) algorithms were used to characterize the relationships between agronomic variables and UAV remote sensing, AM and FM data. The results demonstrated the random forest (RF) model that integrated UAV remote sensing, AM and FM data achieved the higher accuracy for predicting NNI (R2 = 0.82–0.87, RMSE = 0.11–0.12 and RE = 12.94%−15.57%) amongst the four ML models based on the direct strategy at the jointing and booting stages. Similarly, the RF model performed most accurate estimation for PDM (R2 = 0.69–0.78, RMSE = 0.43–0.61 t ha−1 and RE = 12.74%−24.49%) and PNA (R2 = 0.83–0.84, RMSE = 13.00–17.53 kg ha−1 and RE = 17.03%−25.44%), then NNI (R2 = 0.54–0.55, RMSE = 0.09–0.13 and RE = 8.34–12.65%) was further calculated using the indirect diagnosis strategy. Based on the optimal NNI diagnosis interval derived from the relationships between relative yield (RY) and plant NNI at the jointing (0.92–1.04) and booting (0.97–1.15) stages, the two diagnosis strategies obtained similar diagnostic accuracies in the three study farms and performed more accurately at the booting (areal agreement = 0.70–0.90, Kappa = 0.49–0.82) than jointing (areal agreement = 0.54–0.71, Kappa = 0.36–0.53) stages. The combination of fixed-wing UAV remote sensing with AM and FM information using the RF algorithm can significantly increase the accuracy and efficiency of in-season wheat N diagnosis at the farm scale.
•Four machine learning algorithms were used to characterize the relationships between agronomic variables and multivariate data.•Direct and indirect strategies of NNI estimation were evaluated based on the optimal NNI diagnosis interval.•Mapping the wheat N status based on fixed-wing UAV imagery at the farm scale.
This article designs and develops an aquarium AI system based on the daily maintenance of the family aquarium, including three modules: applet, server and hardware.In the system the Raspberry Pi 4b ...is used as the development board of the hardware side, and the router is connected to achieve Internet access and remote control.The WeChat applet is used as the software front-end, and the Springboot framework is used for the server program.Remotely controlled of the aquarium are realized by the WeChat applet of the mobile phone.these functions are temperature monitoring, video viewing, automatic water change and feeding reminder. In this way, even if users go out, they can always pay attention to the condition of the fish in the aquarium, which makes fish farming more worry-free and labor-saving, truly realizes leisure fish farming and cultivates the taste of life.
Research on Text Emotion Analysis Based on LSTM Liu, Xia; Zhou, Jiaxin; Lu, Ruhua
Proceedings of the 2023 International Conference on Computer, Vision and Intelligent Technology,
08/2023
Conference Proceeding
Sentiment analysis of text based on deep learning has become an active research direction in the field of natural language processing, where the sentiment analysis of text is used to automatically ...identify the sentiment tendency embedded in the text, such as positive or negative. Sentiment analysis based on deep learning usually adopts neural network models, which are trained on a large amount of labelled data to capture the sentiment information in the text. The commonly used neural network models include Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and so on. In this study, Long Short-Term Memory (LSTM) is used to build a model to construct a text sentiment analyzer, which, after testing 23982 textual data, can predict whether the sentiment expressed is negative or positive according to the input statements, and the accuracy of its prediction results can reach 90.37%. The experimental results show that it is more flexible and generalizable than the traditional rule-based or feature engineering methods, and can further improve the performance of sentiment analysis.
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