Deveci, MuhammetOner, Sultan CerenCiftci, Muharrem EnisOzcan, Ender...
14页
查看更多>>摘要：Choosing the most appropriate aircraft type for a given route is one of the crucial issues that the decision makers at airline companies have to address under uncertainty based on various commercial, marketing and operational criteria. A novel multi-criteria decision making approach integrating Entropy-based Weighted Aggregated Sum Product Assessment (WASPAS) method and interval type-2 hesitant fuzzy sets (IT2HFS) is introduced for tackling this problem and tested using a particular case study obtained from a full service carrier in Turkey. This study contributes to representing and handling degrees of uncertainty in the decision making process of aircraft type selection based on the IT2HFS. The results showed that Airbus 32C is the suitable alternative for a given route in between Kuwait and Istanbul airports. The experts evaluated the results and confirmed that the proposed approach is the most suitable one when compared to four other IT2HFS based approaches. (C) 2021 Elsevier B.V. All rights reserved.
查看更多>>摘要：Y The rapid advance of online social networks and the tremendous growth in the number of participants and attention have led to information overload and increased the difficulty of making accurate recommendations of new friends. Existing recommendation methods based on semantic similarity, social graphs, or collaborative filtering are unsuitable for very large social networks because of their high computational cost or low effectiveness. We present an approach entitled Hybrid Recommendation Through Community Detection (HRTCD) for friend prediction with linear runtime complexity that makes full use of the characteristics of social media based on hybrid information fusion. It extracts the content topics of microblog for each participant along with the appraisal of domain-dependent user impact, builds a small-size heterogeneous network for each target user by fusing the interest similarity and social interaction between individuals, discovers all of the implicit clusters of target user via a community detection algorithm, and establishes the recommendation set consisting of a fixed number of potential friends. Experimental results on both the synthetic and real-world social networks demonstrate that our scheme provides a higher prediction rating and significantly improves the recommendation accuracy and offers much faster performance. (C) 2021 Elsevier B.V. All rights reserved.
查看更多>>摘要：The data-driven deep probabilistic latent variable model (DPLVM) has attracted much attention for industrial process soft sensing in recent years. The DPLVM has well handled the nonlinear characteristics of the processes with powerful feature extracting capability. However, the multimode process property and the dynamic data features seldom be considered in those applications. To tackle the two issues, the article starts from the basic DPLVM, i.e., the Variational Autoencoder (VAE), to build a deep dynamic latent variable regression model (i.e., the Gated Recurrent Unit-based VAE regression, GVAER), where GRU cells are utilized to capture dynamic features from the process time sequence data. With the GVAER, a Gaussian Mixture GVAER (GM-GVAER) model is proposed. The Gaussian Mixture priors are used in the latent space to characterize the multimode process data features. In particular, a semi-supervised learning scheme is also proposed for the model to deal with the unequal scale of input and output data. A numerical example and a real chemical process case are provided to verify the feasibility and effectiveness of the proposed soft sensor model. (C) 2021 Elsevier B.V. All rights reserved.
Ribas, Lucas C.de Mesquita Sa Junior, Jarbas JoaciManzanera, AntoineBruno, Odemir M....
14页
查看更多>>摘要：Dynamic textures (DTs) are pseudo periodic data on a space x time support, that can represent many natural phenomena captured from video footages. Their modeling and recognition are useful in many applications of computer vision. This paper presents an approach for DT analysis combining a graph-based description from the Complex Network framework, and a learned representation from the Randomized Neural Network (RNN) model. First, a directed space x time graph modeling with only one parameter (radius) is used to represent both the motion and the appearance of the DT. Then, instead of using classical graph measures as features, the DT descriptor is learned using a RNN, that is trained to predict the gray level of pixels from local topological measures of the graph. The weight vector of the output layer of the RNN forms the descriptor. Several structures are experimented for the RNNs, resulting in networks with final characteristics of a single hidden layer of 4, 24, or 29 neurons, and input layers of sizes 4 or 10, meaning 6 different RNNs. Experimental results on DT recognition conducted on Dyntex++ and UCLA datasets show a high discriminatory power of our descriptor, providing an accuracy of 99.92%, 98.19%, 98.94% and 95.03% on the UCLA-50, UCLA-9, UCLA-8 and Dyntex++ databases, respectively. These results outperform various literature approaches, particularly for UCLA-50. More significantly, our method is competitive in terms of computational efficiency and descriptor size. It is therefore a good option for real-time dynamic texture segmentation, as illustrated by experiments conducted on videos acquired from a moving boat. (C) 2021 Published by Elsevier B.V.
查看更多>>摘要：The prevention of Unsafe Behaviors (UBs) requires a deep understanding of their root causes and motivations. Therefore, the present study aimed at modeling the factors affecting UBs using the bestworst (FBWM) and Fuzzy Cognitive Map (FCM) methods. The present study consisted of three parts: identifying the factors affecting UBs using content analysis, weighing the factors using the FBWM, and examining the relationships between the factors using the FCM. The first part of the study included semi-structured interviews with 40 workers and safety and health professionals, and the second and third parts were completed with an expert panel. Based on the results, the factors affecting UBs were classified into three categories, namely organizational, individual, and socio-economic factors. The results of weighing the factors showed that among the organizational factors (with a mean weight of 0.36), organizational safety culture was the most important factor. In addition, community safety culture among the socio-economic factors and personality traits among the individual factors were considered significant by the experts. The results of the FCM also showed that management's attitude towards safety, internal monitoring, training, and organization's tendency to UBs were the most centralized factors in the map. Overall, management's attitude towards safety was one of the important factors affecting UBs. The centrality of this factor was high, as well. Hence, by changing the management's attitude towards safety, the underlying factors of UBs could be controlled and guided for controlling such behaviors. (C) 2021 Elsevier B.V. All rights reserved.
查看更多>>摘要：Y Accurate disease prediction is an effective way to reduce medical costs. Due to the difference of eating habits and physical fitness of patients, the traditional disease prediction methods are facing an enormous challenge. How to find a reliable disease prediction method in the uncertain environment and improve the accuracy of prediction will be a valuable scientific problem. To obtain accurate prediction and help patients reduce medical costs, this paper introduces neighborhood rough set into multivariate variational mode decomposition, and proposes a new multi-attribute prediction approach. Firstly, to avoid the interference of redundant attributes, a multi-attribute reduction method based on neighborhood rough set is established. Then, to reduce the volatility and complexity of multi attribute data in hybrid information system, a neighborhood rough set-based multivariable variational mode decomposition method is constructed. Subsequently, a predictor of extreme learning machine with kernel function, clearly defining the mapping relationship, is developed. Furthermore, Diebold-Mariano (DM) test and probability density distribution are used to evaluate the prediction results. Finally, 2041 random physical examination samples of potential gout patients are utilized to verify the effectiveness and practicability of the proposed approach. Experimental results show that the neighborhood rough set-based multi-attribute prediction approach has high accuracy and stability. Meanwhile, a new quantitative theory and method for chronic disease management decision-making can be provided in medical decision-making. (C) 2021 Elsevier B.V. All rights reserved.
查看更多>>摘要：The classical optimal power flow problem is usually formulated with only thermal generators, in which the fuel used to generate power is limited and emissions from the network system are often ignored. Due to several promising features like renewability, richness, and cleanness, renewable energy sources have been drew growing attention. As a result, more and more renewable energy sources are penetrated into the electrical grid. In this paper, the standard IEEE-30 bus system is modified by integrating renewable energy sources as the case study, where the traditional thermal generators on buses 5 and 11 are replaced by wind generators, and bus 13 is replaced by solar generators. In addition, to address the intermittence and uncertainty of renewable sources, the Weibull probability density function is used to calculate the available wind power. Meanwhile, the lognormal probability density function is employed to calculate the available solar power. The optimal power flow with stochastic wind and solar energy is formulated as a multi-objective optimization problem. A multi objective evolutionary algorithm based on non-dominated sorting with constraint handling technique are presented to solve it. In addition, another larger test system i.e., IEEE-57 bus system is selected to further verify the performance of the proposed approach in handling large dimensional problem. Simulation results indicate that proposed approach can obtain competitive compromise solution on different optimization objectives. (C) 2021 Elsevier B.V. All rights reserved.
查看更多>>摘要：This paper proposes Komodo Mlipir Algorithm (KMA) as a new metaheuristic optimizer. It is inspired by two phenomena: the behavior of Komodo dragons living in the East Nusa Tenggara, Indonesia, and the Javanese gait named mlipir. Adopted the foraging and reproduction of Komodo dragons, the population of a few Komodo individuals (candidate solutions) in KMA are split into three groups based on their qualities: big males, female, and small males. First, the high-quality big males do a novel movement called high-exploitation low-exploration to produce better solutions. Next, the middle-quality female generates a better solution by either mating the highest-quality big male (exploitation) or doing parthenogenesis (exploration). Finally, the low-quality small males diversify candidate solutions using a novel movement called mlipir (a Javanese term defined as a walk on the side of the road to reach a particular destination safely), which is implemented by following the big males in a part of their dimensions. A self-adaptation of the population is also proposed to control the exploitation-exploration balance. An examination using the well-documented twenty-three benchmark functions shows that KMA outperforms the recent metaheuristic algorithms. Besides, it provides high scalability to optimize thousand-dimensional functions. The source code of KMA is publicly available at: https://suyanto.staff.telkomuniversity.ac.id/komodo-mlipir-algorithm and https: //www.mathworks.com/matlabcentral/fileexchange/102514-komodo-mlipir-algorithm. (C) 2021 The Authors. Published by Elsevier B.V.
查看更多>>摘要：Although the optimization algorithms have been widely studied, the large-scale many-objective optimization problems (LSMaOPs) remain challenging. Due to the existence of a large number of decision variables, it is necessary to carry out decision variable analysis. However, it is often difficult to discriminate the diversity-related and convergence-related variables when the problem has complex characteristics. Meanwhile, as the number of decision variables and the number of objectives increase, many algorithms will suffer from the convergence challenge. To overcome these challenges, this paper proposes a Memetic Evolution System with Statistical Variable Classification (MES-SVC). A statistical variable classification method is proposed to discriminate the convergence-related and the diversity-related variables. A memetic evolution system, which includes a memetic exploitation and exploration module, and a memetic elite imitation module, is proposed to make information guidance during the evolution, thereby promote convergence. The performance of MES-SVC is compared with the state-of -the-art algorithms on 50 test instances with 3 to 10 objectives and 300 to 1000 decision variables. Experimental studies demonstrate the promising performance of the proposed MES-SVC in terms of both diversity and convergence of solutions. (C) 2021 Published by Elsevier B.V.
查看更多>>摘要：This paper presents an online rescue method based on offline learning of dynamics knowledge to solve the problem of the optimal rescue orbit and flight trajectory optimization (OROTO) of launch vehicles experiencing thrust-drop faults. Due to the unknown of the rescue orbit, solving the OROTO problem by the conventional aerospace orbit and trajectory optimization method is time-consuming. In this paper, benefiting from the decision-making of the optimal rescue orbit by the machine learning technology, the OROTO problem is decoupled into a decision-making of the optimal rescue orbit and a trajectory optimization problem with a known orbit. In the decision-making of the optimal rescue orbit, instead of the conventional iteration optimization process based on dynamics, the optimal rescue orbit is determined by the "fault-rescue" knowledge integration (FRKI) model which consists of probabilistic neural network (PNN) and radial basis function neural network (RBFNN) trained by "fault-rescue" knowledge. In the trajectory optimization part, the output of the FRKI model provides terminal constraints for the trajectory optimization problem to decrease the search scope for the optimal solution. Numerical simulation results show that the proposed method can solve the OROTO problem rapidly and accurately, and can potentially be implemented for online applications. (C) 2021 Elsevier B.V. All rights reserved.
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