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Remote sensing image change detection, as one of the important branches of remote sensing technology, has been greatly developed and improved in recent years. The effectiveness of change detection is unstable, as deep learning components exhibit a high degree of sensitivity to hyperparameters, necessitating substantial fine-tuning to achieve optimal outcomes. Additionally, large and intricate deep learning models are not appropriate for smaller-scale datasets. In this paper, a remote sensing image change detection method based on general deep forest module (GDFM) is proposed. In the proposed GDFM, the common remote sensing image change detection method is firstly used for preliminary detection to obtain the detection result to be optimized, and the optimized change detection result is then obtained by using the classification function of the deep forest. This work aims to achieve a general enhancement of the performance of existing change detection methods by utilizing the characteristics of multi-grained scanning in deep forest and the data classification capabilities of cascaded forests. At the same time, this model alleviates the problems of parameter tuning complexity and inapplicability to small datasets in additional deep learning modules. It is worth noting that this general method is not a direct splicing process, but combines the advantages of the two parts and uses the characteristics of the deep forest to achieve the goal. Experiments are conducted on multiple common remote sensing change detection models. The results show that the proposed GDFM has significant improvements in metrics, which include varying degrees of improvement in F1 score by around 1% -14%.

Ensuring flight safety for unmanned aerial vehicles (UAVs) is a critical concern, necessitating effective mathematical modeling for safety forecasting in both academic and industrial contexts. This study addresses this need by combining the capabilities of deep neural networks and grey analysis to create a comprehensive mathematical modeling approach focused on spatial information service (SIS). The paper introduces a novel spatial information-aware flight security forecasting model for UAVs, emphasizing the transformative impact of the new methodology. Traditionally, factors influencing flight safety are identified and formulated based on SIS chain technology, SIS management rules, SIS business processes, and spatial SIS chain verification. To address the challenges posed by significant data volatility and missing data in the sample dateset, a non-equally spaced GM (1,1) model with an approximate non-simultaneous exponential law series is developed for prediction. Subsequently, multiple influencing factors are encoded and input into a specific BP neural network structure. The paper concludes with simulation experiments to evaluate the proposed model. The results of the simulation analysis demonstrate that the integration of deep learning and grey analysis in the proposed model effectively recognizes flight security risks with high efficiency. This underscores the transformative potential of the new approach in enhancing UAV flight safety forecasting.

Palm vein recognition plays a crucial role in identity verification, requiring highly discriminative features. However, for touchless palm vein datasets, selecting a suitable and fixed Region of Interest (ROI) is challenging due to variations in capture scales and anisotropy. Moreover, manually annotating ROIs for each palm vein image is a time-consuming and labor-intensive task. To address these challenges, the method based on competitive mechanism is currently a popular approach for palm vein recognition. However, traditional competitive mechanisms only focus on selecting winners from different channels without considering the spatial information of features. In this paper, we reformulate the traditional competition mechanism and propose a Coordinate Encoding Competitive Neural Network (CECNet). Our method takes into account the spatial competition relationship between features, which means we pay attention to features of different directions and scales. We also perform spatial encoding on the competitive features to extract a more comprehensive set of competitive features. To extract the textures, the CECNet employs three parallel Adaptive Gabor Filter Encoders (AGFEs) to learn features of different directions and scales, effectively capturing the variations present in palm vein images. To enhance feature discrimination, the Soft Large Margin Centralized Cosine Loss (SLMCCL) function is utilized, taking into account with inter-class separation and introducing centralized cosine similarity to achieve better intra-class similarity. By optimizing this loss function, the network learns to prioritize and rank features based on their importance. Experimental results on public palm vein datasets demonstrate the effectiveness of the proposed approach.

Cooperative co-evolution (CC) is an effective framework for solving large-scale global optimization (LSGO) problems by using the “divide-and-conquer” method. However, the decomposition stage faces the challenges of either insufficient decomposition accuracy or extremely high computational cost to achieve correct decomposition. The significant amount of resources consumed during the decomposition stage greatly affects optimization. A decomposition method called Recursive Differential Grouping (RDG) has shown impressive results in solving large-scale continuous optimization problems. To improve the performance of RDG and reduce the resource consumption during decomposition, this paper proposes the Three Stages Recursive Differential Grouping (TSRDG) method. The first stage is the determination of whether a function is fully separable or not. In the second stage, separable variables are divided into one group and non-separable variables are divided into another group. In the third stage, this study identifies the interacting decision variables that are not in a separable group and reuses the effective information that was gained in the first two stages. Compared with some state-of-the-art methods, TSRDG has an effective strategy for decomposing functions. Moreover, it avoids the resource consumption of identifying the interaction between separable and non-separable variables in recursions. Effective historical information is fully exploited throughout the process of variable decomposition. Simulation experiments on the benchmark functions of CEC’2010 and CEC’2013 demonstrate that TSRDG achieves higher decomposition accuracy and lower computational cost than state-of-the-art decomposition methods. The experiments show that TSRDG is a promising algorithm in LSGO.

The classical one-dimension chaotic maps are not adequately secure, and some improved maps usually have a high time complexity. To address these problems, this paper presents a new Piecewise-Logistic-Sine map (PLSM). The bifurcation diagrams, Lyapunov exponent, and running time of the proposed PLSM are analyzed, and the outcomes show that it has better chaotic behavior with low time complexity. Furthermore, diffusion by certain rules is vulnerable to attack, so a PLSM-based image encryption scheme with random exclusive OR diffusion is proposed. Using a 256-bit secret key, the initial value and parameters of PLSM are calculated by the key distribution method. Then four chaotic sequences are generated by PLSM, they are used in three rounds of random exclusive OR diffusion. This scheme can spread a small change in the original image to all pixels, and the performance evaluation shows the security of this image encryption scheme.

The mathematical form of many optimization problems in engineering is constrained optimization problems. In this paper, an improved genetic algorithm based on two-direction crossover and grouped mutation is proposed to solve constrained optimization problems. In addition to making full use of the direction information of the parent individual, the two-direction crossover adds an additional search direction and finally searches in the better direction of the two directions, which improves the search efficiency. The grouped mutation divides the population into two groups and uses mutation operators with different properties for each group to give full play to the characteristics of these mutation operators and improve the search efficiency. In experiments on the IEEE CEC 2017 competition on constrained real-parameter optimization and ten real-world constrained optimization problems, the proposed algorithm outperforms other state-of-the-art algorithms. Finally, the proposed algorithm is used to optimize a single-stage cylindrical gear reducer.

Global warming has become an issue of increasing worldwide concern. At present, the most direct way to solve this problem is to control carbon emissions. In our paper, carbon tax policy is injected into a two-stage supply chain consisting of one manufacturer and two retailers. We establish six game models according to the relationship (competitive or cooperative) between the retailers and three power structures (manufacturer Stackelberg (MS), retailer Stackelberg (RS), and vertical Nash (VN)) between the two retailers and the manufacturer. We then adopt the inverse induction method to derive optimal pricing decisions for each model. Optimal profits for supply chain members are obtained. Finally, we analyse: i) the effects of competition and cooperation between the two retailers on equilibrium results; ii) the effects of the three possible power structures existing between the manufacturer and the two retailers on equilibrium results; and especially iii) the effect of the carbon tax rate on pricing decision and the profits of the supply chain. We find that the different power structure and the horizontal relationship between the two retailers have important influence on equilibrium results. Further, the change of carbon tax rate have different influence on pricing under the different power structure and the horizontal relationship between the two retailers. When the government adjusts the carbon tax rate, it should fully consider the different power structure and the horizontal relationship for two-stage supply chain consisting of one manufacturer and two retailers. Although the implementation of the carbon tax policy reduces the corporate profits, it reduces the total carbon emission. The carbon tax policy is helpful to build an environment-friendly and resource-conserving society.

Cities are centers for the integration of capital and incubators of inventions. Attracting venture capital (VC) is of great importance for cities to advance in innovative technology and business models towards a sustainable and prosperous future. Yet we still lack a quantitative understanding of the relationship between urban characteristics and VC activities. In this paper, we find a clear nonlinear scaling relationship between VC activities and the urban population of Chinese cities. In such nonlinear systems, the widely applied linear per capita indicators would be either biased to larger cities or smaller cities depends on whether it is superlinear or sublinear, while the residual of cities relative to the prediction of scaling law is a more objective and scale-invariant metric. Such a metric can distinguish the effects of local dynamics and scaled growth induced by the change of population size. The spatiotemporal evolution of such metrics on VC activities reveals three distinct groups of cities, two of which stand out with increasing and decreasing trends, respectively. The taxonomy results together with spatial analysis also signify different development modes between large urban agglomeration regions. Besides, we notice the evolution of scaling exponents on VC activities are of much larger fluctuations than on socioeconomic output of cities, and a conceptual model that focuses on the growth dynamics of different sized cities can well explain it, which we assume would be general to other scenarios.

Under raising pressure of global energy and environmental issues in recent years, wind power has been considered as one of the most promising energy sources owing to with its advantages of being renewable and pollution-free. The accurate and efficient wind speed forecasting (WSF) plays a key role in the generation, distribution, and management of wind power. This study proposes a meta learning based novel hybrid ensemble approach and model for short-term WSF. The ensemble prediction model consists of meta learning part and individual predictor part. The meta learning part is based on a multi-input and multi-output back propagation (BP) neural network (NN) with multiple hidden layers, whereas the individual predictor part is composed of three pre-trained individual predictors based on BP NN, long short-term memory (LSTM) recurrent neural network (RNN), and gated recurrent units (GRU) RNN, respectively. The wind speed value to be predicted can be obtained by weighted summation of two parts of the ensemble prediction model based on historical wind speed data. The innovation in the proposed ensemble WSF model is to build a BP NN and use environmental feature data as input data to generate weight coefficients for updating the individual predictor. In order to illustrate the forecasting performance of the proposed ensemble prediction approach for short-term WSF, the prediction results of the proposed ensemble prediction model are compared with those of several single prediction models and an average coefficient hybrid prediction model under the same conditions. The results illustrate that the meta learning based ensemble prediction model proposed in this study has better forecasting performance in both of prediction accuracy, prediction stability, and data correlation than other WSF models.

Electric power dispatch issue mainly consists of two optimization tasks: active and reactive power dispatches, each of which is a non-linear multi-objective optimization problem with a series of constraints. Traditional evolutionary algorithms are focused on single-task optimization for active or reactive power dispatch and they are not able to deal with several (single- or multi-objective) optimization tasks simultaneously. In this paper, to solve this problem, a multitasking electric power dispatch approach is proposed by introducing the multi-objective multifactorial optimization (MO-MFO) algorithm and integrating it with the characteristics of power system. The approach exhibits the great potential to be developed as a cloud-computing solver or platform for future large-scale smart grid applications involving different market entities because of its implicit parallel computation mechanism. The multitasking approach is thoroughly tested and benchmarked with IEEE-30-bus and IEEE-118-bus standard systems and exhibits generally better performances as compared to previously proposed Pareto heuristic approaches for electric power dispatch.

Despite the promising progress made in recent years, person re-identification (Re-ID) remains a challenging task due to the intra-class variations. Most of the current studies used the traditional Softmax loss for solutions, but its discriminative capability encounters a bottleneck. Therefore, how to improve person Re-ID performance is still a challenging task. To address this problem, we proposed a novel loss function, namely additive distance constraint with similar labels loss (ADCSLL). Specifically, we reformulated the Softmax loss by adding a distance constraint to the ground truth label, based on which similar labels were introduced to enhance the learned features to be much more stable and centralized. Experimental evaluations were conducted on two popular datasets (Market-1501 and DukeMTMC-reID) to examine the effectiveness of our proposed method. The results showed that our proposed ADCSLL was more discriminative than most of the other compared state-of-the-art methods. The rank-1 accuracy and the mAP on Market-1501 were 95.0% and 87.0%, respectively. The numbers were 88.6% and 77.2% on DukeMTMC-reID, respectively.

We present an asymmetric controlled bidirectional remote preparation scheme under the control of Charlie, where Alice transmits an arbitrary single-qubit equatorial state to Bob and Bob transmits an arbitrary three-qubit equatorial state to Alice. First, the quantum channel is constructed by using Hadamard ( ${H}$ ) and CNOT operations. Second, we consider our scheme in the noiseless environment and construct the appropriate measurement bases. In addition, the participants can recover the prepared states through implementing the corresponding recovery operations determinately. Third, we discuss our scheme in five types of noisy environments (amplitude-damping, phase-damping, bit-flip, phase-flip, and depolarizing noisy environment). The fidelities of the output states are calculated, which depend on the coefficients of the prepared states and the decoherence rates. Finally, some discussions and conclusions are given. Compared with previous schemes, our scheme does not need additional operations or auxiliary qubits. So it is slightly more efficient.

Quantum image steganography is one of the important research branches of quantum secure communications. In this paper, a large payload quantum image steganography protocol based on quantum image expansion and the Grover search algorithm is proposed. The new algorithm adopts quantum log-polar image (QUALPI) representation to prepare the quantum image before introducing a quantum expansion technique to form the superposition of multiple image copies with the same size angle difference as the carrier. Then, it embeds a secret message into one quantum image copy with a specific rotation angle encoded. In order to accurately extract the secret message embedded, the Grover search algorithm is used to locate the correct quantum image copy. Based on the quantum uncertainty and quantum non-cloning theorems, the new algorithm can not only achieve good imperceptibility and security but also large payload due to the algorithm's good coding scalability. The experimental results by performing MATLAB simulation prove the conclusions.

Traditional training algorithms in artificial neural networks (ANNs) show some inherent weaknesses, such as the possibility of falling into local optimum, slow learning speed, and the inability to determine the optimal neuronal structure. To remedy the deficiencies of traditional neural networks, this paper proposes a novel neural network classifier (NNC) using the beetle antennae search (BAS) algorithm, termed BASNNC. The BAS algorithm is explored to optimize the weights of the NNC. The network of the proposed BASNNC adopts three-layer structure, including an input layer, a hidden layer, and an output layer. Quite differing from the traditional training algorithm using a principle of gradient descent, the weights between the hidden and output layers are optimized by the BAS algorithm, which effectively improves the computational speed of the classifier. The numerical studies, applications to pattern classification and comparisons with an error back-propagation neural network model, show that the proposed BASNNC has faster computational speed and higher classification accuracy.

In this paper, we propose a scheme using a new kind of quantum walk to realize quantum communication well. In our scheme, obviously, there are three advantages. First, the initial states can be transferred from the source to the target by finite steps, which will make execution easier in practice. Second, due to fewer restrictions to the network models, our scheme can be adapted to more general network models, such as the butterfly network, the inverted crown network, the grail network, and the generalized butterfly network Gk . Finally, arbitrary single-qubit and two-qubit initial states can be transferred to different kinds of network models. At the same time, the fidelity and probability equal 1. Our work promotes more applications of combining quantum computation with quantum communication in the future.

As the volume of data available for analysis grows, feature selection is becoming a vital part of ensuring accurate classification results. In classification problems, selecting a small number of features reduces computational complexity, but selecting the right features is important to maintain a high level of accuracy. In this paper, we present a feature selection method based on hybrid improved quantum-behavior particle swarm optimization, called HI-BQPSO. The HI-BQPSO combines a filtering method with an improved quantum-behavior particle swarm optimization algorithm to greatly reduce the dimensionality of the data so as to overcome some of the shortcomings of BQPSO. Tests were conducted on nine gene expression datasets and 36 UCI datasets to evaluate and compare the classification accuracy of the HI-BQPSO's selected feature subsets against four other algorithms. The results, using a variety of different classifiers, show that the HI-BQPSO significantly reduces the number of features required for classification while maintaining higher levels of accuracy in many cases.

Integrated with the array technique, multi-channel processing can be applied to synthetic aperture radar of ground moving target imaging (SAR GMTIm), which is very powerful in remote sensing of smart city. To reduce the data sampling amounts, compressive sensing can be used by exploiting a sparse prior of moving targets. In this paper, the SAR GMTIm from data of compressive sampling is addressed by proposing a novel reweighted sparse algorithm. Here, we mainly focus on sparse imaging and clutter suppression for heterogeneous scene of urban areas. In the scheme, the phase of interferogram and the magnitude after displaced phase center antenna are incorporated to derive the weights on sparsity-constraint. Due to the joint usage of magnitude and phase, the proposed reweighted sparse algorithm can improve the performance of clutter suppression. Finally, experiments using the simulated and measured data are performed to confirm the effectiveness of the proposed algorithm.

Insufficient illumination makes driver face detection at night challenging. This paper proposes an adaptive attenuation quantification retinex (AAQR) method to enhance the details of nighttime images. There are three phases in this method: attenuation restriction, attenuation prediction, and adaptive quantification. The performance of the proposed method was evaluated by employing a robust face detection method via sparse representation. The collected driver face images at night were categorized into three groups (up-down, left-right, and mixed) according to the illumination distribution in each image. Results have shown that the detection rates of the images enhanced by the proposed AAQR method were 82%, 84%, and 91% for the up-down, left-right, and mixed illumination groups, respectively. In comparison to other image enhancement methods, the detection rate of the AAQR method was 2%-36% greater. Furthermore, the mean computing time for a single 640 × 480 nighttime image using AAQR was less than most of the compared advanced methods. Thus, the AAQR method is recommended for applications in driver face detection tasks at night.

Using the array technique, the multi-channel synthetic aperture radar (SAR) has the capability of high-resolution imaging, detection, and location of the ground moving targets, which is a powerful tool during the remote sensing of smart city. Recently, the theory of compressive sensing has been applied to radar imaging with data of compressive sampling, which can effectively reduce the burden of current radar system. In this paper, we focus on SAR moving targets imaging from sparse aperture (SA) data with accurate target motion compensation. The procedure of motion compensation is decomposed into two steps: the SPECAN processing in the range frequency and azimuth time domain is first applied and then followed by the residue component correction embedded into sparse imaging. To overcome the SA and target motion, a novel parametric sparse imaging approach is proposed by addressing the problems of Doppler ambiguity and phase errors. In the scheme, a parametric and dynamic dictionary is used to include these two important issues. Then, a modified orthogonal matching pursuit method is presented for high-quality imaging with Doppler ambiguity number estimation and phase error correction, which can deal with the case of multiple moving targets. Finally, experimental analysis is performed to confirm the effectiveness of the proposed algorithm.