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As an effective tool to deal with uncertain decision-making, three-way decisions (TWD) have gained wide attention in many applications. Decision-theoretic rough sets (DTRSs) as a classic model of TWD contain two key elements, i.e., conditional probability and loss functions. In this paper, we study the determination of these two elements in depth via the information interaction and modification strategy, and further propose a novel model of TWD. First, fuzzy c-means (FCM) is used to cluster the condition attribute information and loss function information, respectively. Considering the interaction of two types of information, we design the corresponding fusion tactic of these clustering results to get equivalent classes and develop a new calculation method of conditional probability. Then, we use probabilistic hesitant fuzzy sets (P-HFSs) to aggregate the loss functions of different members of the same equivalence class and get the probabilistic hesitant fuzzy elements (P-HFEs) loss functions. In this case, P-HFSs not only reflect the hesitant situation of decision-makers, but also depict the proportion of different opinions. Regarding P-HFEs loss functions, we also investigate the modification methods of its outliers in detail. Moreover, based on the P-HFEs loss functions, we propose TWD with probabilistic hesitant fuzzy decision-theoretic rough sets (P-HFDTRSs). Finally, in order to verify the effectiveness of our proposed method, we develop a series of comparative experiments and discuss the decision results on six UCI datasets.

The 0-1 knapsack problem (KP) is a classic NP-hard problem and could be handled by swarm intelligence algorithms. However, most of these algorithms might be trapped in the local optima as the scale increases. Hybrid rice optimization (HRO) is a novel swarm intelligence algorithm inspired by the breeding process of Chinese three-line hybrid rice, its population is classified into three types such as the maintainer, restorer and sterile line and several stages including hybridization, selfing and renewal are implemented. In this paper, a modified HRO algorithm is proposed for the complicated large-scale 0-1 KP. A dynamic step is introduced in the renewal stage to balance the exploration and exploitation phases. Moreover, HRO is combined with binary ant colony optimization (BACO) algorithm to compose the parallel model and serial model for enhancing the convergence speed and search efficiency. In the parallel model, HRO and BACO are independently implemented on two subpopulations and communicate during each iteration. In the serial model, BACO is embedded in HRO as an operator to update the maintainer line. The experimental results on 0-1 KPs of different scales and correlations demonstrate the outperformance of the parallel model and serial model.

In the crowdsourcing task allocation scheme, there is an emerging and realistic co-opetition phenomenon. To availably solve the crowdsourcing task allocation problem with co-opetition, this paper designs a two-stage co-opetition model by constructing novel three-way decision (TWD), including a competition-optimization model and a negotiation-cooperation model. Unlike the other studies, the two-stage co-opetition model with TWD can not only protect the profits of the task candidates, but also optimize the overall benefits. Specifically speaking, in the competition-optimization model, we construct an optimization model based on the data envelopment analysis (DEA) method in advance, which maximizes the personal benefit. By integrating information system and the loss function matrix, we consider the linkage of evaluation information and risk information and then improve the original TWD to make an initial allocation. In the negotiation-cooperation model, considering that the relationship among the candidates may influence the task performance, the fuzzy measure is introduced to describe a broader partnership. Meanwhile, we also design two different schemes to coordinate and optimize the best task allocations based on the initial allocation. In order to choose the best scheme, the selection strategy between schemes is further investigated under the guidance of the utility and the loss. Finally, we give an example of a medical supply chain crowdsourcing problem to illustrate and verify our proposed approach.

Hyperspectral Image (HSI) has become one of the important remote sensing sources for object interpretation by its abundant band information. Among them, band selection is considered as the main theme in HSI classification to reduce the data dimension, and it is a combinatorial optimization problem and difficult to be completely solved by previous techniques. Whale Optimization Algorithm (WOA) is a newly proposed swarm intelligence algorithm that imitates the predatory strategy of humpback whales, and membrane computing is able to decompose the band information into a series of elementary membranes that decreases the coding length. In addition, Support Vector Machine (SVM) combined with wavelet kernel is adapted to HSI datasets with high dimension and small samples, ensemble learning is an effective tool that synthesizes multiple sub-classifiers to solve the same problem and obtains accurate category label for each sample. In the paper, a band selection approach based on wavelet SVM (WSVM) ensemble model and membrane WOA (MWOA) is proposed, experimental results indicate that the proposed HSI classification technique is superior to other corresponding and newly proposed methods, achieves the optimal band subset with a fast convergence speed, and the overall classification accuracy has reached 93% for HSIs.

Waveform decomposition is widely used for the separation of echoes from full-waveform LiDAR (FWL) signal, and some previous studies employed Gaussian function for laser pulse modeling and waveform decomposition. However, it was difficult to guarantee the waveform parameters on the neighbor of optimal solution, because of the limited amplitude range. In addition, waveform parameters were usually set by the amplitude and location of inflection points, which may enlarge the difference between decomposed and original waveforms. Hence, a novel waveform decomposition technique based on wavelet function and differential cuckoo search algorithm is proposed, where wavelet function has a high-order vanishing moment, cuckoo search algorithm has a strong optimization ability, and differential operator avoids trapping into the local optima. The proposed technique is tested on airborne FWL point cloud and compared with other corresponding approaches, experimental results demonstrate that the decomposed waveforms are obtained with a reasonable convergence rate and feature characterization, as the rRMSE is lower than 7% for all of waveforms, the whole process of waveform decomposition only takes 0.3s, and waveform parameters are used as the features to recognize different objects from point cloud.

Three-way group decisions provide an efficient method to settle complex and high risk decision-making problems. To obtain reasonable decision results that satisfy different backgrounds and knowledge of decision makers, it is necessary to design a proper consensus reaching process (CRP) for loss functions of decision-theoretic rough sets (DTRSs). Unlike existing researches, this paper not only extends the group relationship among decision makers to the social network, but also considers the externality of social trust network in group decision making. In light of this idea, we design a new CRP with the externality of social network for three-way group decisions. In the CRP, the adjustment of a decision maker who is persuaded by the moderator can influence other decision makers to accordingly adjust evaluations. Thus, by using the linkage externality influence among decision makers, we establish a two-stage mixed 0–1 linear optimization consensus model for the determination of loss functions of DTRSs. Then, based on Bayesian decision procedure, we construct a complete decision procedure for three-way group decisions with social network. Finally, we apply our proposed method to assess desert locust invasion areas and verify its validity.

Joint sparse representation (JSR) is a commonly used classifier that recognizes different objects with core features extracted from images. However, the generalization ability is weak for the traditional linear kernel, and the objects with similar feature values associated with different categories are not sufficiently distinguished especially for a hyperspectral image (HSI). In this article, an HSI classification technique based on the weight wavelet kernel JSR ensemble model and the $\beta$ -whale optimization algorithm is proposed to conduct pixel-level classification, where the wavelet function is acted as the kernel of JSR. Moreover, ensemble learning is used to determine the category label of each sample by comprehensive decision of some subclassifiers, and the $\beta$ function is utilized to enhance the exploration phase of the whale optimization algorithm and obtain the optimal weight of subclassifiers. Experimental results indicate that the performance of the proposed HSI classification method is better than that of other newly proposed and corresponding approaches, the misclassification and classified noise are eliminated to some extent, and the overall classification accuracy reaches 95% for all HSIs.

Radio frequency energy harvesting (EH) technology can harvest the electromagnetic energy in the surrounding environment, and reduce the dependence of the communication devices on battery or power grid as power source, which is a very promising means of energy substitution and acquisition scheme. The random characteristics of wireless channel fading and possible co-channel interference (CCI) have great influence on EH efficiency and wireless communication performance with more complicated theoretical analysis. In this paper, the exact closed-form expressions of effective throughput of "harvest-store-use" and "harvest-use" multi-slot EH schemes with Rayleigh channel fading and CCI are derived theoretically. The simulation results show that in Rayleigh fading channels with CCI, CCI is beneficial to energy harvesting of EH device, but it will deteriorate the reception of required signals by information receiving device. Although there are obvious differences in working mechanism, working conditions and communication performance between the "harvest-store-use" scheme and the "harvest-use" scheme, the optimal time slot should be selected to balance the transmission probability and delay, minimize the interference of CCI to the desired signal, and improve the energy conversion efficiency of the energy harvester.

Multi-beam echo sounders (MBESs) are characterized by the high resolution and high density of the sounding data. The processing of MBES bathymetry data is of special interest currently in marine surveying. The Combined Uncertainty and Bathymetry Estimator (CUBE) and surface filtering are the main MBES-processing algorithms for outliers. These algorithms involve five adjustable parameters; however, few studies have looked at parameter optimization. In this paper, a Parameter Group Optimization (PGO) method that determines the optimal parameters of CUBE and surface filtering based on the seafloor topographic characteristics of the survey area is presented. The method includes typical area selection, optimal grid resolution analysis, parameter group testing and batch processing, sounding and grid analysis. Raw MBES datasets from shallow- and deep-water survey areas (between 10 and 11000 m deep) are used to validate the proposed method. The results show that when the optimized parameters are used in the CUBE and filtering algorithm, the outliers are automatically eliminated; the processed bathymetry data is in good agreement with the bathymetry derived by a traditional manual processing method, while the processing efficiency can be improved by more than 8 times.

In this paper, we propose a probabilistic linguistic belief thermodynamic multiple attribute decision making method for evaluating mobile health Apps. To extract the evaluation information for mobile health Apps, the quantification transformation of linguistic evaluation of decision makers is critical. However, decision makers may have different perceived values for linguistic terms in their minds. Thus, we deeply investigate the quantification process of linguistic terms based on the psychological perception by introducing prospect theory which can describe the psychological influences of decision makers. Meanwhile, we extend the operational laws of probabilistic linguistic term set (PLTS). In order to efficiently use probabilistic linguistic evaluation information, we further introduce the thermodynamic method to measure the quantity and quality of evaluation information. To process the qualitative rankings for attributes, we propose a non-linear programming model for the determination of attribute weights by improving Borda score which considers the different preferences of decision makers for the rankings. Then, to address the uncertainty situation of probabilistic linguistic evaluation information during the aggregation process, we use the Dempsters��� combination rule of the evidence theory. Considering inconsistent of the ranking results of alternatives based on the exergy indicator and the entropy indicator, we construct a comprehensive score by combining the two indicators. Finally, we apply our proposed method to evaluate existing mobile health Apps and validate it by comparison analysis and sensitivity analysis.

In real life, there are many complex multiple attribute group decision making (MAGDM) problems with high decision risk and uncertainty. The decision-making process of the complex MAGDM can encounter the following three problems: (1) Since different experts have different knowledge structures and interests, they master different individual attribute information of alternatives. (2) Experts may have different consensus degrees for alternatives under different attributes. (3) For some alternatives, the experts can not make an immediate decision in the actual decision-making process. The experts need much more information to decide on these alternatives in the subsequent decision step. To solve the problems as mentioned above, we propose sequential three-way multiple attribute group decision making (STWMAGDM) with individual attributes by introducing sequential three-way decisions. Meantime, we construct a multilevel granular structure based on the consensus degree of attributes. Further, at each granularity level , the experts need to reach consensus before deducing decision results. For improving the consensus reaching process, we take into account the social influence among experts with the aid of opinion dynamics. In this case, we construct social networks based on the similarity of experts and the amount of attribute information mastered by experts to describe the social influence. Meanwhile, we modify the model of opinion dynamics by introducing the interaction willingness of experts and establish the corresponding adjustment rules of interaction willingness. Finally, we use two diagnosis examples of breast cancer and heart disease to explain our model in detail. In order to verify the effectiveness of our method, we also perform the corresponding comparative experiments and sensitivity analyses.

Hyperspectral image (HSI) has become one of the most important remote sensing techniques for object interpretation by its abundant band information. As the data dimension increases, band se...

In our real life, the decision maker is always of bounded rationality under risk and uncertainty. The decision result also can be impacted by the risk appetite of the decision maker. However, the existing research works of three-way decisions rarely involve the risk appetite of the decision maker. To address this drawback and enrich the application, we introduce the risk appetite into three-way decisions. More specifically, under the dual hesitant fuzzy environment, we utilize TODIM (an acronym in Portuguese for Interactive Multi-Criteria Decision Making) as a valuable tool to handle the risk appetite character to construct risk appetite dual hesitant fuzzy three-way decisions. This study takes into account the risk appetite of decision maker and designs a series of decision analysis methodologies for three-way decisions. It can preferably satisfy the practical situation and vastly extend the range of applications. Firstly, based on the dual hesitant fuzzy loss functions, we mainly study dual hesitant fuzzy entropy and cross-entropy measures for determining the conditional probability. In this scenario, the conditional probability information can be objectively learned from the loss function matrix with the aid of the power average (PA) aggregated operator. Then, considering the different comparison methods of dual hesitant fuzzy elements (DHFEs), we further design two types of strategies to deduce risk appetite dual hesitant fuzzy three-way decisions in the framework of TODIM, i.e., Strategies 1 and 2, which mainly rely on the pairwise comparison of loss functions with DHFEs. Strategy 1 is designed based on the score function of DHFEs. Strategy 2 is the likelihood of DHFEs. Meanwhile, some properties are carefully investigated. Finally, the project investment evaluation of online peer-to-peer (P2P) is applied to illustrate and validate these proposed strategies.

Tool wear prediction is essential to ensure part quality and machining efficiency. Tool wear is affected by factors such as the material, structure, process, and processing time of the parts. Tool wear under the variable working conditions and the above factors show a complex coupling and timing correlation, which makes it challenging to predict tool wear under variable working conditions. This article aims to resolve this issue. First, we establish a unified representation of working condition factors. The stacked autoencoder (SAE) model adaptively extracts tool wear features from the machining signal. The extracted wear features and respective working conditions then combine into a working condition feature sequence for predicting tool wear. Finally, the advantages of the long short-term memory (LSTM) model to solve memory accumulation effects learn the regular wear pattern of the working condition feature sequence to realize the prediction of the tool wear. An experiment illustrates the effectiveness of the proposed method.

Clouds and snow in remote sensing imageries cover underlying surface information, reducing image availability. Moreover, they interact with each other, decreasing the cloud and snow detection accuracy. In this study, we propose a convolutional neural network for cloud and snow detection, named the cloud and snow detection network (CSD-Net). It incorporates the multi-scale feature fusion module (MFF) and the controllably deep supervision and feature fusion structure (CDSFF). MFF can capture and aggregate features at various scales, ensuring that the extracted high-level semantic features of clouds and snow are more distinctive. CDSFF can provide a deeply supervised mechanism with hinge loss and combine information from adjacent layers to gain more representative features. It ensures the gradient flow is more oriented and error-less, while retaining more effective information. Additionally, a high-resolution cloud and snow dataset based on WorldView2 (CSWV) was created and released. This dataset meets the training requirements of deep learning methods for clouds and snow in high-resolution remote sensing images. Based on the datasets with varied resolutions, CSD-Net is compared to eight state-of-the-art deep learning methods. The experiment results indicate that CSD-Net has an excellent detection accuracy and efficiency. Specifically, the mean intersection over the union (MIoU) of CSD-Net is the highest in the corresponding experiment. Furthermore, the number of parameters in our proposed network is just 7.61 million, which is the lowest of the tested methods. It only has 88.06 GFLOPs of floating point operations, which is less than the U-Net, DeepLabV3+, PSPNet, SegNet-Modified, MSCFF, and GeoInfoNet. Meanwhile, CSWV has a higher annotation quality since the same method can obtain a greater accuracy on it.

Uncertainty and complexity can cause an increasingly risk impact on our decision-making processes. Different from two-way decisions, three-way decisions can effectively reduce the decision risk by adopting a new delayed decision. However, when different decision makers face the decision risks, they may have different behaviors and attitudes in the actual decision-making process. Considering this phenomenon, we introduce prospect theory into three-way decisions and propose behavioral three-way decisions under interval type-2 fuzzy environment. In this scenario, we utilize prospect theory to describe decision makers’ risk attitude. In this paper, we firstly construct the relative loss function and relative benefit function that learn from single interval type-2 fuzzy attribute information with attribute aspiration in advance and then establish behavioral three-way decisions based on Bayesian decision procedure. In this case, we discuss five cases of the attribute aspirations, respectively. Subsequently, we apply behavioral three-way decisions model into multi-attribute decision-making (MADM) problem. In order to deal with interactions between attributes under interval type-2 fuzzy environment, a nonadditivity fusion of heterogeneous multi-attribute behavioral three-way decisions is constructed with 2-additive Choquet integral (TAC). The nonadditivity fusion of heterogeneous multi-attribute behavioral three-way decisions can process the decision information more comprehensively and take into account the decision makers’ attitudes towards risks. Finally, a decision-making problem of distinctive street identification of Chengdu is used to prove the validity of our method.

Feature selection is one of the most important issues in hyperspectral image (HSI) classification to achieve high correlation between the adjacent bands. The main concern is selecting fewer bands with the highest accuracy as possible. Generally, it is a combinatorial optimization problem and cannot be fully solved by swarm intelligence algorithms. Ant lion optimizer (ALO) is a newly proposed swarm intelligence algorithm that mimics the swarming behaviour of antlions. In addition, wavelet support vector machine (WSVM) is able to enhance the stability of the classification result, and Levy flight helps swarm intelligence algorithms jump out of the local optimum. Therefore, in this paper, a novel feature selection method based on a modified ALO (MALO) and WSVM is proposed to reduce the dimensionality of HSIs. The proposed method is compared with some state-of-the-art algorithms on some HSI datasets. Moreover, a new evaluating criteria is formulated to estimate the performance of feature selection, and the classification accuracy and selected number of bands are balanced as much as possible. Experimental results demonstrate that the proposed method outperforms other approaches, finds the optimal solution with a reasonable convergence orientation, and its classification accuracy is satisfied with fewer bands, it is robust, adaptive and might be applied for practical work of feature selection.

Texture classification is an important issue for a number of applications in machine vision, which could be addressed through learning texture features by using a convolutional mask. However, the traditional convolutional mask has a single orientation, and the learning ability is limited. In addition, the optimization process often falls into the local optima, and the discrimination capacity of the learned mask is unsatisfactory. Thus, a novel moving convolutional mask is presented to enhance the discrimination of specific texture features in the proposed approach. Furthermore, how to achieve the satisfactory convolutional mask is considered as a combinatorial optimization problem and acquired by maximizing the texture energy by using ant lion optimizer (ALO). The proposed approach was tested on some public images, and the results were compared with those of the state-of-the-art approaches. The experimental results showed that ALO has strong optimization ability, and the proposed method is robust, adaptive and superior to the improved grey level co-occurrence matrix (GLCM), directional statistical Gabor filter and Tuned convolutional mask in terms of the fitness value and classification accuracy which has reached 27 and 91%, respectively, for all images.

As buildings constitute the main component of urban areas, which can provide several kinds of information. In this paper, a building extraction method based on the high resolution remote sensing image via Gabor filter and multi-orientation π local binary pattern (LBP) operator is proposed to aim at the application requirements of rapid, accurate urban planning and visual management. At first, the multi-dimensional texture features are extracted by using Gabor filter for the original image. Further, some training samples are obtained by multi-orientation π LBP operator at different orientations. Finally, the pixel-level discrimination is conducted for texture features, and achieves the location and shape of buildings. Experimental results demonstrate that the overall extraction accuracy has reached 94%, and the extracted results coincide with the distribution of each building, the proposed method is accurate to complete the task of building extraction, and has an excellent applicability for land management.

The angular velocity and rotor position of the motor are computed by PI observer. Clark and Park transformations is utilized to establish a dynamics model of the motor. Space vector pulse width modulation algorithm is used for voltage modulation. The main control chip of the hardware board selects TI's TMS320F28035, and the power device selects Mitsubishi's IPM(PS22A79). The hardware board is designed to test and verify the PI observer. The test results are analyzed and indicated that the PI observer is a good candidate for sensorless field oriented control of permanent magnet synchronous motor.

Consensus boost and opinion guidance are two important problems during the opinion management process. Considering that opinion interaction with opinion dynamics, this paper formalizes the two problems as markov decision process. To solve the two problems with minimum cost, we proposes consensus boost algorithm and opinion guidance algorithm based on reinforcement learning. Meantime, we construct opinion management framework by combining consensus boost algorithm and opinion guidance algorithm which is beneficial to the opinion management of managers. Finally, through experimental analysis, we verify the effectiveness and properties of the proposed framework.

The key issue of the model-based state of charge estimation approach is the accuracy of the battery model. In this paper, a fractional order model is built to simulate the electrochemistry dynamics of lithium-ion battery, whose model parameters are identified by adaptive genetic algorithm. Based on the computation simplification of central difference algorithm, an adaptive central difference Kalman filter by fractional order model is designed to estimate the state of charge. The designed approach is modelled by simulink and translated into C code, and then embedded in the battery management system for the validation by two dynamic cycles. Comparing experiments adopt two approaches, i.e. the central difference Kalman filter by fractional order model, the adaptive central difference Kalman filter by Thevenin model. Experimental results indicate that the designed approach has the better accuracy and robustness, and also show that fractional order model is more accurate than Thevenin model. With respect ot the ability to deal with noise, the robustness of the designed approach is verified by adding artificial noise. Experimental results show that the proposed approach has the best robustness to noise. Therefore, the proposed approach is a good candidate for the state of charge estimation in engineering practice.

Nonlinear microwave device modeling is an important part of computer-aided design (CAD) and many papers have been published in the literature. This paper presents a review of recent neural network approaches to the modeling of nonlinear microwave device including the dynamic Neuro-SM approach and the Wiener-type dynamic neural network approach and its applications. DC, small-signal and large-signal harmonic data are used as training data. The neural network based methods can fast and accurately build accurate models for nonlinear microwave devices. Compared with conventional equivalent circuit models, the models generated by these neural network based methods are more efficient to represent the behavior of the device.

Current automatic shadow compensation methods often suffer because their contrast improvement processes are not self-adaptive and, consequently, the results they produce do not adequately represent the real objects. The study presented in this paper designed a new automatic shadow compensation framework based on improvements to the Wallis principle, which included an intensity coefficient and a stretching coefficient to enhance contrast and brightness more efficiently. An automatic parameter calculation strategy also is a part of this framework, which is based on searching for and matching similar feature points around shadow boundaries. Finally, a final compensation combination strategy combines the regional compensation with the local window compensation of the pixels in each shadow to improve the shaded information in a balanced way. All these strategies in our method work together to provide a better measurement for customizing suitable compensation depending on the condition of each region and pixel. The intensity component I also is automatically strengthened through the customized compensation model. Color correction is executed in a way that avoids the color bias caused by over-compensated component values, thereby better reflecting shaded information. Images with clouds shadows and ground objects shadows were utilized to test our method and six other state-of-the-art methods. The comparison results indicate that our method compensated for shaded information more effectively, accurately, and evenly than the other methods for customizing suitable models for each shadow and pixel with reasonable time-cost. Its brightness, contrast, and object color in shaded areas were approximately equalized with non-shaded regions to present a shadow-free image.

The operation mode of a single strip provides incomplete side-scan sonar image in a specific environment and range, resulting in the overlapping area between adjacent strips often with imperfect detection information or inaccurate target contour. In this paper, a sum-modified Laplacian energy filtering (SMLF) and improved dual-channel pulse coupled neural network (IDPCNN) are proposed for image fusion of side-scan sonar in the domain of nonsubsampled contourlet transform (NSCT). Among them, SMLF energy is applied to extract the fusion coefficients of the low frequency sub-band, which combines the characteristics of energy information, human visual contrast, and guided filtering to eliminate the pseudo contour effect of block flow. In addition, the IDPCNN model, which utilizes the average gradient, soft limit function, and novel sum-modified Laplacian (NSML) to adaptively represent the corresponding excitation parameters, is applied to improve the depth and activity of pulse ignition, so as to quickly and accurately select the image coefficients of the high frequency sub-band. The experimental results show that the proposed method displays fine geomorphic information and clear target contour in the overlapping area of adjacent strips. The objective index values are generally optimal, which reflect the information of image edge, clarity, and overall similarity.

Building extraction from RGB VHR images is an important and popular topic for mapping, disaster emergency responding, and city management. The automation of most methodologies cannot meet the need for applications. In this paper, based on classification and optimization, we propose a novel methodology using shadows to automatically extract building samples and verify buildings accurately to improve automation and accuracy. On one hand, in order to acquire various and reliable building samples automatically for classification, detected shadows first are shifted opposite to the direction of illumination to extract building shadows. Furthermore, each building shadow will be shifted again in the same way. Then according to the distribution of classes in these customized shifted regions, building samples can be filtered out by removing those recognized objects. On the other hand, besides the common measures to optimize the initial building during post-processing; a new, original, and an efficient shadow-based index for building verification is also designed. Shadow rate on the intersect boundary between the expanding edge of candidate regions and their shifted regions following the illumination direction can efficiently recognize buildings. When the proposed method is compared to other sample acquisition methods based on shadow, experimental results show that the approach for building samples acquisition is helpful to get accurate initial building results. Moreover, in comparison with other building extraction methods, the proposed building verification method can distinguish buildings from non-buildings. This significantly improves the accuracy of the final results. Numerical assessments performed on a series of test images indicate that our proposed approach for building extraction is efficient and feasible, especially in suburban areas.

The extraction of building information with terrestrial laser scanning (TLS) has a number of important applications. As the density of projected points (DoPP) of facades is commonly greater than for other types of objects, building points can be extracted based on projection features. However, such methods usually suffer from density variation and parameter setting, as illustrated in previous studies. In this paper, we present a building extraction method for single-scan TLS data, mainly focusing on those problems. To adapt to the large density variation in TLS data, a filter using DoPP is applied on a polar grid, instead of a commonly used rectangular grid, to detect facade points. In DoPP filtering, the threshold to distinguish facades from other objects is generated adaptively for each cell by calculating the point number when placing the lowest building in it. Then, the DoPP filtering result is further refined by an object-oriented decision tree mainly based on grid features, such as compactness and horizontal hollow ratio. Finally, roof points are extracted by region growing on the non-facade points, using the highest point in each facade cell as a seed point. The experiments are conducted on two datasets with more than 1.7 billion points and with point density varying from millimeter to decimeter levels. The completeness and correctness of the first dataset containing more than 50 million points are 91.8% and 99.8%, with a running time of approximately 970 s. The second dataset is Semantic3D, of which the point number, completeness and correctness are about 1.65 billion, 90.2% and 94.5%, with a running time of about 14,464 s. The test shows that the proposed method achieves a better performance than previous grid-based methods and a similar level of accuracy to the point-based classification method and with much higher efficiency.

The DYCAST (Dynamic Crash Analysis of Structures) experiments that started at NASA Langley Research Center during the late 1970s have greatly influenced the methodology and thinking of aircraft crashworthiness and survivability studies, and was continued and refined at other aerospace establishments. Nevertheless, so far most of the existing work has emphasized the impact damage to the aircraft section. Issues related to potential passenger injuries have not been properly addressed in the literature, to the best of our knowledge. Here, we study the DYCAST problem integrally by treating and combining impact damage and passenger injuries altogether. We develop the biomechanics by way of modal analysis of passenger dummy motions coupled with the vibration of aircraft structures in order to understand their basic interactions. Two types of mechanical dummies are used in this study. Such a modal analysis can help identify basic injury types, but is valid only in the constructed models, linear regime. However, we are able to extend the linear elastic model to a nonlinear elastoplastic computational model by using the versatile software LS-DYNA as the platform. Computer simulations are carried out on the supercomputer clusters and the numerical results are rendered into video animations for visualization and analysis. One can see, for example, how the passenger-dummy interactive motions with the fuselage and fixtures and the potential injuries caused in the event of general aircraft crashes on a fractal domain.

Support vector machine (SVM) is one of the most successful classifiers for remote sensing image classification. However, the performance of SVM is mainly dependent on its parameters; in addition, for remote sensing images with high-dimensional features, feature redundancy will have a major influence on the classification efficiency and accuracy. Feature selection and parameter optimization are two important factors for improving the performance of SVM and are traditionally solved separately. In fact, these two issues are affected by each other, so to obtain the better classification performance, selection of the optimal feature subset and tuning of SVM parameters should be considered simultaneously, as they both belong to the combinatorial optimization problem and could be handled with evolutionary algorithms or swarm intelligence algorithms. In this paper, a remote sensing image classification technique based on the optimal SVM is proposed, in which the parameters of SVM and feature selection are handled integrally by a modified coded ant colony optimization algorithm combined with genetic algorithm. The results are compared with other evolutionary algorithms and swarm intelligence algorithms, such as genetic algorithm (GA), binary-coded particle swarm optimization (BPSO) algorithm, binary-coded ant colony optimization (BACO) algorithm, binary-coded differential evolution (BDE) algorithm, and binary-coded cuckoo search (BCS) algorithm. It is demonstrated that the proposed method is robust, adaptive and exhibits the better performance than the other methods involved in the paper in terms of fitness values, so could be suitable for some practical applications.

Graphical abstractDisplay Omitted HighlightsWe propose a novel binary coded ant colony algorithm by blending of GA and BACO.The proposed algorithm is adopted to handle with the problem of feature selection.Results show the method outperforms GA, BPSO, BACO, ABACO, BDE in feature selection. Feature selection is a significant task for data mining and pattern recognition. It aims to select the optimal feature subset with the minimum redundancy and the maximum discriminating ability. In the paper, a feature selection approach based on a modified binary coded ant colony optimization algorithm (MBACO) combined with genetic algorithm (GA) is proposed. The method comprises two models, which are the visibility density model (VMBACO) and the pheromone density model (PMBACO). In VMBACO, the solution obtained by GA is used as visibility information; on the other hand, in PMBACO, the solution obtained by GA is used as initial pheromone information. In the method, each feature is treated as a binary bit and each bit has two orientations, one is for selecting the feature and another is for deselecting. The proposed method is also compared with that of GA, binary coded ant colony optimization (BACO), advanced BACO (ABACO), binary coded particle swarm optimization (BPSO), binary coded differential evolution (BDE) and a hybrid GA-ACO algorithm on some well-known UCI datasets; furthermore, it is also compared with some other existing techniques such as minimum Redundancy Maximum Relevance (mRMR), Relief algorithm for a comprehensive comparison. Experimental results display that the proposed method is robust, adaptive and exhibits the better performance than other methods involved in the paper.

A new morphological attribute building index (MABI) and shadow index (MASI) are proposed here for automatically extracting building features from very high-resolution (VHR) remote sensing satellite images. By investigating the associated attributes in morphological attribute filters (AFs), the proposed method establishes a relationship between AFs and the characteristics of buildings/shadows in VHR images (e.g., high local contrast, internal homogeneity, shape, and size). In the pre-processing step of the proposed work, attribute filtering was conducted on the original VHR spectral reflectance data to obtain the input, which has a high homogeneity, and to suppress elongated objects (potential non-buildings). Then, the MABI and MASI were calculated by taking the obtained input as a base image. The dark buildings were considered separately in the MABI to reduce the omission of the dark roofs. To better detect buildings from the MABI feature image, an object-oriented analysis and building-shadow concurrence relationships were utilized to further filter out non-building land covers, such as roads and bare ground, that are confused for buildings. Three VHR datasets from two satellite sensors, i.e., Worldview-2 and QuickBird, were tested to determine the detection performance. In view of both the visual inspection and quantitative assessment, the results of the proposed work are superior to recent automatic building index and supervised binary classification approach results.

In recent years, O2O takeaway has a rapid development with increasingly fierce market competitions. The online reviews of these takeaway platforms give restaurants an opportunity to better understand their customers and find their own shortages. However, how to dig customer satisfaction information from online reviews of restaurants is a challenge. Therefore, we establish an O2O takeaway customer satisfaction evaluation system and evaluate the satisfaction degree for restaurants by analyzing online reviews. First, we use European customer satisfaction index and Service performance-aware service quality measurement method as a logical framework. Meantime, we adopt content analysis to construct the evaluation system of customer satisfaction. Taking probabilistic linguistic term sets and the frequency into consideration, we improve analytic hierarchy process and develop a combination way to calculate the index weights of the evaluation system. The fuzzy comprehensive evaluation method is further designed to calculate the customer satisfaction level. Finally, we have an empirical analysis on Meituan and Ele.me platforms to validate our method.

A multiple access chaos shift keying cooperative communication (MA-CSK-CC) system over Rayleigh fading channels and in the presence of Gaussian white noise is investigated in depth in this paper. The simplified half-duplex with a single relay decode-and-forward (DF) cooperative strategy is proposed to illustrate and analysis as an example. Exact and approximation bit-error-ratio (BER) performance expressions are derived. Simulation results demonstrate that the proposed system has a prominent advantage of high BER performance over the conventional multiple access CSK systems that have a single path to transmit signal at the same data rate over Rayleigh fading channels with additive Gaussian white noise. Moreover, based on the BER performance analysis, some useful results are also derived such as the relationships between BER versus spread spectrum factors, BER versus the number of users, etc.

Texture image classification is an important topic in many applications in machine vision and image analysis. Texture feature extracted from the original texture image by using “Tuned” mask is one of the simplest and most effective methods. However, hill climbing based training methods could not acquire the satisfying mask at a time; on the other hand, some commonly used evolutionary algorithms like genetic algorithm (GA) and particle swarm optimization (PSO) easily fall into the local optimum. A novel approach for texture image classification exemplified with recognition of residential area is detailed in the paper. In the proposed approach, “Tuned” mask is viewed as a constrained optimization problem and the optimal “Tuned” mask is acquired by maximizing the texture energy via a newly proposed gravitational search algorithm (GSA). The optimal “Tuned” mask is achieved through the convergence of GSA. The proposed approach has been, respectively, tested on some public texture and remote sensing images. The results are then compared with that of GA, PSO, honey-bee mating optimization (HBMO), and artificial immune algorithm (AIA). Moreover, feature extracted by Gabor wavelet is also utilized to make a further comparison. Experimental results show that the proposed method is robust and adaptive and exhibits better performance than other methods involved in the paper in terms of fitness value and classification accuracy.

Acoustic backscatter data are widely applied to study the distribution characteristics of seabed sediments. However, the ghosting and mosaic errors in backscatter images lead to interference information being introduced into the feature extraction process, which is conducted with a convolutional neural network or auto encoder. In addition, the performance of the existing classifiers is limited by such incorrect information, meaning it is difficult to achieve fine classification in survey areas. Therefore, we propose a sediment classification method based on the acoustic backscatter image by combining a stacked denoising auto encoder (SDAE) and a modified extreme learning machine (MELM). The SDAE is used to extract the deep-seated sediment features, so that the training network can automatically learn to remove the residual errors from the original image. The MELM model, which integrates weighted estimation, a Parzen window and particle swarm optimization, is applied to weaken the interference of mislabeled samples on the training network and to optimize the random expression of input layer parameters. The experimental results show that the SDAE-MELM method greatly reduces mutual interference between sediment types, while the sediment boundaries are clear and continuous. The reliability and robustness of the proposed method are better than with other approaches, as assessed by the overall classification effect and comprehensive indexes.

The life cycle of intracellular RNA mainly involves transcriptional production, splicing maturation and degradation processes. Their dynamic changes are termed as RNA life cycle dynamics (RLCD). It is still challenging for the accurate and robust identification of RLCD under unknow the functional form of RLCD. By using the pulse model, we developed an R package named pulseTD to identify RLCD by integrating 4sU-seq and RNA-seq data, and it provides flexible functions to capture continuous changes in RCLD rates. More importantly, it also can predict the trend of RNA transcription and expression changes in future time points. The pulseTD shows better accuracy and robustness than some other methods, and it is available on the GitHub repository (https://github.com/bioWzz/pulseTD_0.2.0).

Cooperative communication enables users to transmit information in a cooperative way, which is one of the key technologies used in wireless communication systems to improve spectral efficiency. A cascade Nakagami-m fading function is proposed to describe the fading characteristics of complex wireless channels, and a communication model of decoding-and-forward opportunistic relay selection (DF-ORS) cooperative communication under total power constraints is established. Theoretical analysis and simulation results show that DF-ORS cooperative communication can effectively reduce outage probability and improve spectrum efficiency under total power constraints and complex cascade Nakagami-m fading channels. The transmission power allocation of source node and relay node has an important impact on communication performance. Equal power allocation scheme cannot guarantee the optimal system performance, and the obtained conclusions have more general applicability.

This note aims to report and correct a mistake in the mentioned paper by Min Long et al. An error occurs in the derivation of the average bit error rate for binary signals with non-coherent and coherent detection in Rayleigh fading channels. The corrected derivation is substantiated by the results of Numerical and Monte Carlo simulations.

AA127 at multi-task job shop scheduling problem, the improved dynamic scheduling method based on capability model of manufacturing unit was proposed. Through analyzing the characteristic of the multi-task scheduling in the actual working environment, the capacity model of the manufacturing unit was established. With the minimum completion time, delay time and total load of the machine as the target function, the genetic algorithm was used here to generate initial schedule as well as new ones. The double chains structure coding method based on ability selection chain and processing sequence chain was proposed to provide suitable simulation rules for the GA algorithm. The correctness was verified with experiments.

TA126 solve the job shop dynamic scheduling problem with disruptions, an integrated dynamic scheduling method based on shared mental models is proposed in this paper. Each manufacturing unit in the workshop is mapped into an intelligent agent with the individual mental model for the specific scheduling scenarios. After disturbance occurs, the Q-learning reinforcement learning method is adopted to achieve conflict resolution and consensus consultation among agents, and produce the globally optimized scheduling scheme.

Image segmentation is a significant step in image analysis and machine vision. Many approaches have been presented in this topic; among them, fuzzy C-means (FCM) clustering is one of the most widely used methods for its high efficiency and ambiguity of images. However, the success of FCM could not be guaranteed because it easily traps into local optimal solution. Cuckoo search (CS) is a novel evolutionary algorithm, which has been tested on some optimization problems and proved to be high-efficiency. Therefore, a new segmentation technique using FCM and blending of CS algorithm is put forward in the paper. Further, the proposed method has been measured on several images and compared with other existing FCM techniques such as genetic algorithm (GA) based FCM and particle swarm optimization (PSO) based FCM in terms of fitness value. Experimental results indicate that the proposed method is robust, adaptive and exhibits the better performance than other methods involved in the paper.

Image segmentation is a significant step in image analysis and computer vision. Many entropy based approaches have been presented in this topic; among them, Tsallis entropy is one of the best performing methods. However, 1D Tsallis entropy does not consider make use of the spatial correlation information within the neighborhood results might be ruined by noise. Therefore, 2D Tsallis entropy is proposed to solve the problem, and results are compared with 1D Fisher, 1D maximum entropy, 1D cross entropy, 1D Tsallis entropy, fuzzy entropy, 2D Fisher, 2D maximum entropy and 2D cross entropy. On the other hand, due to the existence of huge computational costs, meta-heuristics algorithms like genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization algorithm (ACO) and differential evolution algorithm (DE) are used to accelerate the 2D Tsallis entropy thresholding method. In this paper, considering 2D Tsallis entropy as a constrained optimization problem, the optimal thresholds are acquired by maximizing the objective function using a modified chaotic Bat algorithm (MCBA). The proposed algorithm has been tested on some actual and infrared images. The results are compared with that of PSO, GA, ACO and DE and demonstrate that the proposed method outperforms other approaches involved in the paper, which is a feasible and effective option for image segmentation.

In object-based image analysis of high-resolution images, the number of features can reach hundreds, so it is necessary to perform feature reduction prior to classification. In this paper, a feature selection method based on the combination of a genetic algorithm (GA) and tabu search (TS) is presented. The proposed GATS method aims to reduce the premature convergence of the GA by the use of TS. A prematurity index is first defined to judge the convergence situation during the search. When premature convergence does take place, an improved mutation operator is executed, in which TS is performed on individuals with higher fitness values. As for the other individuals with lower fitness values, mutation with a higher probability is carried out. Experiments using the proposed GATS feature selection method and three other methods, a standard GA, the multistart TS method, and ReliefF, were conducted on WorldView-2 and QuickBird images. The experimental results showed that the proposed method outperforms the other methods in terms of the final classification accuracy.

An electroencephalogram (EEG)-based brain–computer interface (BCI) system can be used to determine the intents for a paralyzed user by analyzing the EEG signals recorded from his scalp. The key technology of BCI systems is feature extraction and pattern recognition algorithm. Many of the BCI research show that nonlinear classification algorithms perform better than the linear, but they are usually far slower than required. Besides, many of these methods employ less significant features and could make the classification process less efficient. In this paper, a method is provided to identify different patterns of EEG signals. A fast nonlinear classification algorithm extreme learning machine (ELM) is provided to identify two different EEG signals. After feature extraction, we adopt a nonlinear feature selection algorithm partial mutual information-based feature selection (PMIS) to eliminate the less significant features. The experimental result shows that (1) ELM can discriminate the two patterns of EEG signals with a relatively satisfactory accuracy and (2) PMIS feature selection can find out the important features effectively and improve the classification accuracy. The final accuracy is 91.5 % against the best existed result of 88.7 % for the tested data set. The classification performance may be further improved by adding complementary features from EEG. This approach may eventually lead to a reliable EEG-based BCI studies.

Cognitive maps represent decision makers’ mental maps and their strategies, which are always uncertain, ambiguous and hard to be formalized. In order to make intelligent design decision-making, a Bayesian approach for constructing cognitive maps is proposed in this paper. The cognitive map is modeled compatible with a Bayesian Network. Then cause-effect mapping rules between design elements embedded in cognitive maps can be made explicit by means of network structure learning. A score-based greedy search algorithm is implemented for network structure learning, in which penalized mutual information is defined as the scoring metric and hill-climbing search algorithm is used to find the highest-scoring network. The eliminating loop operator is introduced into the algorithm according to the restriction of the edge directionality.