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As one of the world's most productive ecosystems, ecological land plays an important role in regional and global environments. Utilizing advanced optical and synthetic aperture radar (SAR) data for land cover/land use research becomes increasingly popular. This research aims to investigate the complementarity of fully polarimetric SAR and optical imaging for ecological land classification in the eastern coastal area of China.

Four polarimetric decomposition methods, namely, H/Alpha, Yamaguchi3, VanZyl3 and Krogager, were applied to Advanced Land Observing Satellite (ALOS) SAR image for scattering parameter extraction. These parameters were merged with ALOS optical parameters for subsequent classification using the object-based quick, unbiased, efficient statistical tree decision tree method.

The experimental results indicate that an improved classification performance was obtained in the decision level when merging the two data sources. In fact, unlike classification using only optical images, the proposed approach allowed to distinguish ecological land with similar spectrum but different scattering. Moreover, unlike classification using only polarimetric information, the integration of polarimetric and optical data allows to accurately distinguish reed from artemisia and sand from salt field and therefore achieve a detailed classification of the coastal area characteristics.

This research proposed an integrated classification method for coastal ecological land with polarimetric SAR and optical data. The object-based and decision-level fusion enables effective ecological land classification in coastal area was verified.

The purpose of this paper is to study various ship detection methodologies. The accuracy of ship detection using satellite images still suffers from disturbances due to cluttered scenes and varying ship sizes. The suitability of the techniques for various applications is explained in this survey.

A list of data on the subject was gathered and processed into tables. The test outcomes were then discussed to determine the most effective ship detection technique under various complex environments.

In this work, the advantages and disadvantages of different classification techniques of ship detection are highlighted. The suitability of the techniques for various applications is also explained in this survey. Several hybrid approaches can be developed in order to increase the accuracy of ship detection system. This survey also aids in highlighting the significant contributions of satellite images to effective ship detection system.

In this paper, studying various ship detection methodologies is given specific attention. A survey on ship detection and recognition is clarified with the detailed comparative analysis of various classifier techniques.

This paper aims to effectively explore the application effect of big data techniques based on an α-support vector machine-stochastic gradient descent (SVMSGD) algorithm in third-party logistics, obtain the valuable information hidden in the logistics big data and promote the logistics enterprises to make more reasonable planning schemes.

In this paper, the forgetting factor is introduced without changing the algorithm's complexity and proposed an algorithm based on the forgetting factor called the α-SVMSGD algorithm. The algorithm selectively deletes or retains the historical data, which improves the adaptability of the classifier to the real-time new logistics data. The simulation results verify the application effect of the algorithm.

With the increase of training times, the test error percentages of gradient descent (GD) algorithm, gradient descent support (SGD) algorithm and the α-SVMSGD algorithm decrease gradually; in the process of logistics big data processing, the α-SVMSGD algorithm has the efficiency of SGD algorithm while ensuring that the GD direction approaches the optimal solution direction and can use a small amount of data to obtain more accurate results and enhance the convergence accuracy.

The threshold setting of the forgetting factor still needs to be improved. Setting thresholds for different data types in self-learning has become a research direction. The number of forgotten data can be effectively controlled through big data processing technology to improve data support for the normal operation of third-party logistics.

It can effectively reduce the time-consuming of data mining, realize the rapid and accurate convergence of sample data without increasing the complexity of samples, improve the efficiency of logistics big data mining, reduce the redundancy of historical data, and has a certain reference value in promoting the development of logistics industry.

The classification algorithm proposed in this paper has feasibility and high convergence in third-party logistics big data mining. The α-SVMSGD algorithm proposed in this paper has a certain application value in real-time logistics data mining, but the design of the forgetting factor threshold needs to be improved. In the future, the authors will continue to study how to set different data type thresholds in self-learning.

Music sentiment analysis helps to promote the diversification of music information retrieval methods. Traditional music emotion classification tasks suffer from high manual workload and low classification accuracy caused by difficulty in feature extraction and inaccurate manual determination of hyperparameter. In this paper, the authors propose an optimized convolution neural network-random forest (CNN-RF) model for music sentiment classification which is capable of optimizing the manually selected hyperparameters to improve the accuracy of music sentiment classification and reduce labor costs and human classification errors.

A CNN-RF music sentiment classification model is designed based on quantum particle swarm optimization (QPSO). First, the audio data are transformed into a Mel spectrogram, and feature extraction is conducted by a CNN. Second, the music features extracted are processed by RF algorithm to complete a preliminary emotion classification. Finally, to select the suitable hyperparameters for a CNN, the QPSO algorithm is adopted to extract the best hyperparameters and obtain the final classification results.

The model has gone through experimental validations and achieved a classification accuracy of 97 per cent for different sentiment categories with shortened training time. The proposed method with QPSO achieved 1.2 and 1.6 per cent higher accuracy than that with particle swarm optimization and genetic algorithm, respectively. The proposed model had great potential for music sentiment classification.

The dual contribution of this work comprises the proposed model which integrated two deep learning models and the introduction of a QPSO into model optimization. With these two innovations, the efficiency and accuracy of music emotion recognition and classification have been significantly improved.

The density peak clustering algorithm (DP) is proposed to identify cluster centers by two parameters, i.e. ρ value (local density) and δ value (the distance between a point and another point with a higher ρ value). According to the center-identifying principle of the DP, the potential cluster centers should have a higher ρ value and a higher δ value than other points. However, this principle may limit the DP from identifying some categories with multi-centers or the centers in lower-density regions. In addition, the improper assignment strategy of the DP could cause a wrong assignment result for the non-center points. This paper aims to address the aforementioned issues and improve the clustering performance of the DP.

First, to identify as many potential cluster centers as possible, the authors construct a point-domain by introducing the pinhole imaging strategy to extend the searching range of the potential cluster centers. Second, they design different novel calculation methods for calculating the domain distance, point-domain density and domain similarity. Third, they adopt domain similarity to achieve the domain merging process and optimize the final clustering results.

The experimental results on analyzing 12 synthetic data sets and 12 real-world data sets show that two-stage density peak clustering based on multi-strategy optimization (TMsDP) outperforms the DP and other state-of-the-art algorithms.

The authors propose a novel DP-based clustering method, i.e. TMsDP, and transform the relationship between points into that between domains to ultimately further optimize the clustering performance of the DP.

The purpose of this paper is to develop an intelligent system for fashion style selection for non-standard female body shapes.

With the goal of creating natural aesthetic relationship between the body shape and the shape of clothing, garments designed for the upper and lower body are combined to fit different female body shapes, which are classified as V, A, H and O-shapes. The proposed intelligent system combines genetic algorithm (GA) with a neural network classifier, which is trained using the particle swarm optimization (PSO). The former, called genetic search, is used to find the optimal design parameters corresponding to a best fit for the desired target, while the task of the latter, called neural classification, is to evaluate fitness (goodness) of each evolved new fashion style.

The experimental results are fashion styling recommendations for the four female body shapes, drawn from 260 possible combinations, based on variations from 15 attributes. These results are considered to be a strong indication of the potential benefits of the application of intelligent systems to fashion styling.

The proposed intelligent system combines the effective searching capabilities of two approaches. The first approach uses the GA for identifying best fits to the target shape of the body in the solution space. The second is the PSO for finding optimal (with respect to training mean-squared error) weight and threshold parameters of the neural classifier, which is able to evaluate the fitness of successively evolved fashion styles.

To improve the speed and precise of online monitoring and diagnosis for satellite using satellite telemetry data.Design/methodology/approach – In monitoring system, a fuzzy range which gives the probability of alarm for telemetry channels using fuzzy reasoning is outlined. A failure confidence factor is presented to modify the traditional real‐time diagnosis algorithm based on multisignal model to describe the relative failure possibility for suspected components. According to the modified real‐time diagnosis algorithm based on multisignal model, it rapidly generates the states for all the components of the system such as good, bad, suspected and unknown. Then the failure probability for suspected components is obtained by Mamdani fuzzy reasoning algorithm.Findings – The experimental results reveal that the diagnosis system can not only improve diagnosis of speed but also can improve the diagnostic precision by giving failure probability for suspected fault components which may be potential failure components.Research limitations/implications – It requires the clear fault dependency relationship between components and tests.Practical implications – A very useful method for researchers and engineers who are engaged in satellite online monitoring and diagnosis.Originality/value – This paper presents a new method combining multisignal model and fuzzy theory to give the failure probability for suspected components which improves the speed and precision for fault diagnosis.

At present, piezoelectric impedance technology has been used in the study of wood damage monitoring. However, little effort has been made in the research on the application of piezoelectric impedance system to monitor the change of wood moisture content (MC). The monitoring method of wood MC is used by piezoelectric impedance technique in this study.

One piezoceramic transducer is bonded to the surface of wood specimens. The MC of the wood specimens increases gradually from 0% to 60% with 10% increments; the mechanical impedance of the wood specimen will change, and the change in the mechanical impedance of the structure is reflected by monitoring the change in the electrical impedance of lead zirconate titanate. Therefore, this paper investigates the relationship between wood MC change and piezoelectric impedance change to verify the feasibility of the piezoelectric impedance method for monitoring wood MC change.

The experiment verified that the real part of impedance of the wood increased with the increase of wood MC. Besides, the damage index root mean square deviation is introduced to quantify the damage degree of wood under different MC. At the same time, the feasibility and validity of this experiment were verified from the side by finite element simulation. Finally, MC monitoring by piezoelectric impedance technique is feasible.

To the best of the authors’ knowledge, this work is the first to apply piezoelectric ceramics to the monitoring of wood MC, which provides a theoretical basis for the follow-up study of a wide range of wood components and even wood structure MC changes.

The issues of radiating sources in the existence of smooth convex matters by such objects are of huge significance in the modeling of antennas on structures. Conformal antenna arrays are necessary when an antenna has to match to certain platforms. A fundamental problem in the design is that the possible surfaces for a conformal antenna are infinite in number. Furthermore, if there is no symmetry, each element will see a different environment, and this complicates the mathematics. As a consequence, the element factor cannot be factored out from the array factor.

This paper intends to enhance the design of the conformal antenna. Here, the main objective of this task is to maximize the antenna gain and directivity from the first-side lobe and other side-lobes in the two way radiation pattern. Thus the adopted model is designed as a multiobjective concern. In order to attain this multiobjective function, both the element spacing and the radius of each antenna element should be optimized based on the probability of the Crow Search Algorithm (CSA). Thus the proposed method is named Probability Improved CSA (PI-CSA). Here, the First Null Beam Width (FNBW) and Side-Lobe Level (SLL) are minimized. Moreover, the adopted scheme is compared with conventional algorithms, and the results are attained.

From the analysis, the gain of the presented PI-CSA scheme in terms of best performance was 52.68% superior to ABC, 25.11% superior to PSO, 13.38% superior to FF and 3.21% superior to CS algorithms. Moreover, the mean performance of the adopted model was 62.94% better than ABC, 13.06% better than PSO, 24.34% better than FF and 10.05% better than CS algorithms. By maximizing the gain and directivity, FNBW and SLL were decreased. Thus, the optimal design of the conformal antenna has been attained by the proposed PI-CSA algorithm in an effective way.

This paper presents a technique for enhancing the design of the conformal antenna using the PI-CSA algorithm. This is the first work that utilizes PI-CSA-based optimization for improving the design of the conformal antenna.

This paper aims to propose a method for accurate radar echo simulation of wind turbines (WTs) array. It can solve the problem of passive interference from wind farms to neighboring radar stations.

First of all, the equivalent model of scattering centers of a single WT is obtained by using the spatial spectrum estimation method, and the accuracy of this model is verified by the scaled model experiment; then scattering centers model of WTs array was established by using the spatial coordinate transformation method. According to the position relationship between the model and the radar, and combined with the multipath scattering theory, the radar echo equation of WTs array was deduced. Finally, the simulation analysis is carried out with the four GoldWind 77/1500 WTs as an example and compared with the traditional methods.

This paper verifies the accuracy of the equivalent model of scattering centers through the WT scaled model experiment, and through simulation analysis, it is found that the result of this method is more consistent with the multipath scattering of radar echo between WTs array in practical engineering than the traditional method.

Based on the theory of high-frequency scattering, this paper introduces scattering centers into the solution of radar echo and considers the multipath scattering of radar echo, then a method for solving the radar echo of WTs array based on scattering centers is proposed.