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In the collaborative filtering (CF) recommendation applications, the sparsity of user rating data, the effectiveness of cold start, the strategy of item information neglection, and user profiles construction are critical to both the efficiency and effectiveness of the recommendation algorithm. In order to solve the above problems, a personalized recommendation approach combining semisupervised support vector machine and active learning (AL) is proposed in this paper, which combines the benefits of both TSVM (Transductive Support Vector Machine) and AL. Firstly, a "maximum-minimum segmentation" of version space-based AL strategy is developed to choose the most informative unlabeled samples for human annotation; it aims to choose the least data which is enough to train a high-quality model. And then, an AL-based semisupervised TSVM algorithm is proposed to make full use of the distribution characteristics of unlabeled samples by adding a manifold regularization into objective function, which is helpful to make the proposed algorithm to overcome the traditional drawbacks of TSVM. Furthermore, during the procedure of recommendation model construction, not only user behavior information and item information, but also demographic information is utilized. Due to the benefits of the above design, the quality of unlabeled sample annotation can be improved; meanwhile, both the data sparsity and cold start problems are alleviated. Finally, the effectiveness of the proposed algorithm is verified based on UCI datasets, and then it is applied to personalized recommendation. The experimental results show the superiority of the proposed method in both effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

In view of existing Visual SLAM (VSLAM) algorithms when constructing semantic map of indoor environment, there are problems with low accuracy and low label classification accuracy when feature points are sparse. This paper proposed a 3D semantic VSLAM algorithm called BMASK-RCNN based on Mask Scoring RCNN. Firstly, feature points of images are extracted by Binary Robust Invariant Scalable Keypoints (BRISK) algorithm. Secondly, map points of reference key frame are projected to current frame for feature matching and pose estimation, and an inverse depth filter is used to estimate scene depth of created key frame to obtain camera pose changes. In order to achieve object detection and semantic segmentation for both static objects and dynamic objects in indoor environments and then construct dense 3D semantic map with VSLAM algorithm, a Mask Scoring RCNN is used to adjust its structure partially, where a TUM RGB-D SLAM dataset for transfer learning is employed. Semantic information of independent targets in scenes provides semantic information including categories, which not only provides high accuracy of localization but also realizes the probability update of semantic estimation by marking movable objects, thereby reducing the impact of moving objects on real-time mapping. Through simulation and actual experimental comparison with other three algorithms, results show the proposed algorithm has better robustness, and semantic information used in 3D semantic mapping can be accurately obtained. [ABSTRACT FROM AUTHOR]

Standard scheduled flight block time (SBT) setting is of great concern for Civil Aviation Administration of China (CAAC) and airlines in China. However, the standard scheduled flight block times are set in the form of on-site meetings in practice and current literature has not provided any efficient mathematical models to calculate the flight block times fairly among the airlines. The objective of this paper is to develop and solve a mathematical model for standard SBT setting with consideration of both fairness and reliability. We use whale optimization algorithm (WOA) and an improved version of the whale optimization algorithm (IWOA) to solve the SBT setting problem. A novel nonlinear update equation of convergence factor for random iterations is used in place of the original linear one in the proposed IWOA algorithm. Experimental results show that the suggested approach is effective, and IWOA performs better than WOA in the concerned problem, whose solutions are better compared to the flight block times released by CAAC. In particular, it is interesting to find that MSE, RMSE, MAE, MAPE and Theil of the reliability in 60%–70% range are always the smallest and the average fairness of airlines is better than that of 60%–75% range. The model and solving approach presented in this article have great potential to be applied by CAAC to determine the standard SBTs strategically. [ABSTRACT FROM AUTHOR]

With the rapid development of intelligent transportation, intelligent algorithms and path planning have become effective methods to relieve traffic pressure. Intelligent algorithm can realize the priority selection mode in realizing traffic optimization efficiency. However, there is local optimization in intelligence and it is difficult to realize global optimization. In this paper, the antilearning model is used to solve the problem that the gray wolf algorithm falls into local optimization. The positions of different wolves are updated. When falling into local optimization, the current position is optimized to realize global optimization. Extreme Learning Machine (ELM) algorithm model is introduced to accelerate Improved Gray Wolf Optimization (IGWO) optimization and improve convergence speed. Finally, the experiment proves that IGWO-ELM algorithm is compared in path planning, and the algorithm has an ideal effect and high efficiency. [ABSTRACT FROM AUTHOR]

In this paper, an improved two-dimensional logistic-sine coupling map (N2D-LSCM) and an improved Henon map (NHenon) are proposed. Furthermore, by combining N2D-LSCM and NHenon map, an image encryption algorithm is proposed based on these two chaotic systems and DNA coding. The chaotic sequences generated by N2D-LSCM are used as the parameters of NHenon. In the scrambling stage, DNA encoding is carried out for pixels after scrambling by two chaotic sequences generated by N2D-LSCM; in the stage of diffusion, DNA random coding acts on random matrix obtained by two chaotic sequences generated by NHenon, and DNA XOR operation is carried out with the image obtained in the scrambling stage to diffuse. Compared with other 2D map for image encryption algorithm, this algorithm exhibits good security and holds high efficiency. [ABSTRACT FROM AUTHOR]

This paper investigates a scheme of adaptive neural network control for a stochastic switched system with input saturation. The unknown smooth nonlinear functions are approximated directly by neural networks. A modified approach is proposed to deal with unknown functions with nonstrict feedback form in the design process. Furthermore, by combining the auxiliary design signal and the adaptive backstepping design, a valid adaptive neural tracking controller design algorithm is presented such that all the signals of the switched closed-loop system are in probability semiglobally, uniformly, and ultimately bounded, and the tracking error eventually converges to a small neighborhood of the origin in probability. In the end, the effectiveness of the proposed method is verified by a simulation example. [ABSTRACT FROM AUTHOR]

Sustainable growth plays an important role in the development of small- and medium-sized construction enterprises and has increasingly become a hot topic in the political and academic arenas. Growth is an important indicator for measuring the operational status and development potential of listed enterprises and is of great significance for studying sustainable growth. Considering the development of small- and medium-sized construction enterprises, a growth evaluation index system that includes growth resources, growth ability, and growth innovation was established, and a growth evaluation model was constructed using the improved entropy-VIKOR algorithm. Evaluation and analysis of the sustainable growth of small- and medium-sized construction enterprises listed before the end of 2012 were conducted. The parameter sensitivity, weight sensitivity, and effectiveness of the evaluation model were analysed. The empirical results showed that compared with the TOPSIS method and the traditional VIKOR method, the multicriteria evaluation model for the sustainable growth of small- and medium-sized construction enterprises proposed in this paper had better stability and effectiveness. Finally, the key influencing factors, growth ranking, and dynamic growth types of small- and medium-sized construction enterprises were analysed, and targeted suggestions were proposed. The research results can provide a reference for managers, investors, and regulators. [ABSTRACT FROM AUTHOR]

In this paper, we mainly study an exponential spline function space, construct a basis with local supports, and present the relationship between the function value and the first and the second derivative at the nodes. Using these relations, we construct an exponential spline-based difference scheme for solving a class of boundary value problems of second-order ordinary differential equations (ODEs) and analyze the error and the convergence of this method. The results show that the algorithm is high accurate and conditionally convergent, and an accuracy of 1 / 240 h 6 was achieved with smooth functions. [ABSTRACT FROM AUTHOR]

In this paper, we extend the variational method of M. Agueh to a large class of parabolic equations involving q(x)-Laplacian parabolic equation ∂ ρ t , x / ∂ t = div x ρ t , x ∇ x G ′ ρ + V q x − 2 ∇ x G ′ ρ + V . The potential V is not necessarily smooth but belongs to a Sobolev space W 1 , ∞ Ω . Given the initial datum ρ 0 as a probability density on Ω , we use a descent algorithm in the probability space to discretize the q(x)-Laplacian parabolic equation in time. Then, we use compact embedding W 1 , q. Ω ↪↪ L q. Ω established by Fan and Zhao to study the convergence of our algorithm to a weak solution of the q(x)-Laplacian parabolic equation. Finally, we establish the convergence of solutions of the q(x)-Laplacian parabolic equation to equilibrium in the p(.)-variable exponent Wasserstein space. [ABSTRACT FROM AUTHOR]

This paper considers a single-machine due-window assignment scheduling problem with position-dependent weights, where the weights only depend on their position in a sequence. The objective is to minimise the total weighted penalty of earliness, tardiness, due-window starting time, and due-window size of all jobs. Optimal properties of the problem are given, and then, a polynomial-time algorithm is provided to solve the problem. An extension to the problem is offered by assuming general position-dependent processing time. [ABSTRACT FROM AUTHOR]

In order to solve the problem of the user demands to the quality of service (QoS) of network, this paper provides a novel QoS evaluation model based on the intelligent water droplets (IWD) algorithm. The model firstly gives the evaluation indexes of network QoS and establishes a multiobjective optimization function for solution in combination with the improved IWD. Then, mathematical simulation is used to conduct the in-depth study of the key factors influencing the evaluation model. Compared with the other methods, the algorithm has shown better adaptability in the simulation results. [ABSTRACT FROM AUTHOR]

The similarity graphs of most spectral clustering algorithms carry lots of wrong community information. In this paper, we propose a probability matrix and a novel improved spectral clustering algorithm based on the probability matrix for community detection. First, the Markov chain is used to calculate the transition probability between nodes, and the probability matrix is constructed by the transition probability. Then, the similarity graph is constructed with the mean probability matrix. Finally, community detection is achieved by optimizing the NCut objective function. The proposed algorithm is compared with SC, WT, FG, FluidC, and SCRW on artificial networks and real networks. Experimental results show that the proposed algorithm can detect communities more accurately and has better clustering performance. [ABSTRACT FROM AUTHOR]

In order to reduce the difficulty in the selection of the quantization factor and the proportion factor in the general fuzzy controller, a controller with the novel algorithm based on the Logistic chaotic variable is proposed in this paper. By introducing the chaotic variable factor into the global search, the suboptimal solution can be obtained and the optimization interval reduced. The global optimal solution is obtained in the vicinity of the suboptimal solution under an integrated performance index. Finally, the control scheme is applied to the control of a double inverted pendulum model. The simulated results show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Constructing network models of biological systems is important for effective understanding and control of the biological systems. For the construction of biological networks, a stochastic approach for link weights has been recently developed by using experimental data and belief propagation on a factor graph. The link weights were variable nodes of the factor graph and determined from their marginal probability mass functions which were approximated by using an iterative scheme. However, there is no convergence analysis of the iterative scheme. In this paper, at first, we present a detailed explanation of the complicated multistep process step by step with a network of small size and artificial experimental data, and then we show a sufficient condition for the convergence of the iterative scheme. Numerical examples are given to illustrate the whole process and to verify our result. [ABSTRACT FROM AUTHOR]

This paper focuses on the numerical study and precise forecast of Zika epidemic. An early warning index algorithm of Zika epidemic is given, and the applications of this algorithm are investigated. We pay attention to the mathematical approaches to estimate the tendency of Zika epidemic in detail. A numerical experiment is provided to illustrate the effectiveness of the proposed method by the numerical calculation of early warning index of Brazil. [ABSTRACT FROM AUTHOR]

Aiming at route optimization problem of hazardous materials transportation in uncertain environment, this paper presents a multiobjective robust optimization model by taking robust control parameters into consideration. The objective of the model is to minimize not only transportation risk but also transportation time, and a robust counterpart of the model is introduced through applying the Bertsimas-Sim robust optimization theory. Moreover, a fuzzy C-means clustering-particle swarm optimization (FCMC-PSO) algorithm is designed, and the FCMC algorithm is used to cluster the demand points. In addition the PSO algorithm with the adaptive archives grid is used to calculate the robust optimization route of hazmat transportation. Finally, the computational results show the multiobjective route robust optimization model with 3 centers and 20 demand points’ sample studied and FCMC-PSO algorithm for hazmat transportation can obtain different robustness Pareto solution sets. As a result, this study will provide basic theory support for hazmat transportation safeguarding. [ABSTRACT FROM AUTHOR]

With the intensification of market competition, the choice of partners in the private sector plays a vital role in the success or failure of the government’s public sector in the bidding process. Based on FRS (factor risk scoring system), SAW (simple additive weighted), and the integrated subjective or objective fuzzy group decision-making methods, the private sector partner selection is carried out under the PPP (public-private partnership) model in this paper. First of all, a decision committee is established to select attributes and identify potential partners. Secondly, we determine the important decision of the consistency of the manufacturer, introduce linguistic variables, calculate and collect the fuzzy weighted personal attributes, evaluate the importance of the attributes, and get the comprehensive fuzzy evaluation. Thirdly, we establish fuzzy value matrix based on fuzzy evaluation and get the total fuzzy vector by multiplying respective weighted vectors and fuzzy evaluation matrix, then calculate the defuzzification value of each total score, and choose the alternative with the maximum score. Finally, an example is given to demonstrate the rationality of the algorithm. [ABSTRACT FROM AUTHOR]

Many real-world optimization problems are actually of dynamic nature. These problems change over time in terms of the objective function, decision variables, constraints, and so forth. Therefore, it is very important to study the performance of a metaheuristic algorithm in dynamic environments to assess the robustness of the algorithm to deal with real-word problems. In addition, it is important to adapt the existing metaheuristic algorithms to perform well in dynamic environments. This paper investigates a recently proposed version of Bees Algorithm, which is called Patch-Levy-based Bees Algorithm (PLBA), on solving dynamic problems, and adapts it to deal with such problems. The performance of the PLBA is compared with other BA versions and other state-of-the-art algorithms on a set of dynamic multimodal benchmark problems of different degrees of difficulties. The results of the experiments show that PLBA achieves better results than the other BA variants. The obtained results also indicate that PLBA significantly outperforms some of the other state-of-the-art algorithms and is competitive with others. [ABSTRACT FROM AUTHOR]

Reliability and transportation cost are two important indicators to measure the performance of logistics network. As a combination of reliability and transportation cost, the performance index R(d,c) of logistics network is defined as the probability that at least d units of flow demand can be successfully transmitted from the source to the destination with the total transportation cost less than or equal to c. In this paper, an algorithm is developed to calculate R(d,c) in terms of (d,c)-minimal paths ((d,c)-MPs for short). The proposed algorithm employs a decomposition technique to divide the search space of (d,c)-MPs such that the search space of (d,c)-MPs can be dramatically reduced, and thereby (d,c)-MPs can be efficiently obtained. An example is provided to illustrate the proposed algorithm. Finally, computational experiments conducted on one benchmark network indicate that the proposed method has an advantage over the existing methods. [ABSTRACT FROM AUTHOR]

This paper develops an extended ellipsoidal outer-bounding set-membership estimation (EEOB-SME) algorithm with high accuracy and efficiency for nonlinear discrete-time systems under unknown-but-bounded (UBB) disturbances. The EEOB-SME linearizes the first-order terms about the current state estimations and bounds the linearization errors by ellipsoids using interval analysis for nonlinear equations of process and measurement equations, respectively. It has been demonstrated that the EEOB-SME algorithm is stable and the estimation errors of the EEOB-SME are bounded when the nonlinear system is observable. The EEOB-SME decreases the computation load and the feasible sets of EEOB-SME contain more true states. The efficiency of the EEOB-SME algorithm has been shown by a numerical simulation under UBB disturbances. [ABSTRACT FROM AUTHOR]

We have developed a new discrete-time algorithm of stiffness extraction from muscle surface electromyography (sEMG) collected from human operator’s arms and have applied it for antidisturbance control in robot teleoperation. The variation of arm stiffness is estimated from sEMG signals and transferred to a telerobot under variable impedance control to imitate human motor control behaviours, particularly for disturbance attenuation. In comparison to the estimation of stiffness from sEMG, the proposed algorithm is able to reduce the nonlinear residual error effect and to enhance robustness and to simplify stiffness calibration. In order to extract a smoothing stiffness enveloping from sEMG signals, two enveloping methods are employed in this paper, namely, fast linear enveloping based on low pass filtering and moving average and amplitude monocomponent and frequency modulating (AM-FM) method. Both methods have been incorporated into the proposed stiffness variance estimation algorithm and are extensively tested. The test results show that stiffness variation extraction based on the two methods is sensitive and robust to attenuation disturbance. It could potentially be applied for teleoperation in the presence of hazardous surroundings or human robot physical cooperation scenarios. [ABSTRACT FROM AUTHOR]

This paper studies the pinning controllability of directed complex delayed dynamical networks by using periodic intermittent control scheme. The general and low-dimensional pinning synchronization criteria are derived to illustrate the design of periodic intermittent control scheme. According to our low-dimensional pinning criterion, especially, the constraint condition of coupling strength is obtained when the network structure and amounts of pinned nodes are fixed. An algorithm is presented to determine the amounts of periodically intermittent controllers and locate these intermittent controllers in a directed network, in which the significance of nodes out- (in-) degree in pinning control of complex network is also illustrated. Finally, a directed network consisting of 12 coupled delayed Chua oscillators is designed as numerical example to verify the effectiveness of the theoretical analysis. [ABSTRACT FROM AUTHOR]

In VANET (vehicular ad hoc network) environment, the successive vehicle position data actually are discrete, so the key to the moving vehicle modeling is to effectively reduce the updating frequency of the position data so as to alleviate the communication and database management load. This paper proposes vehicle position data updating strategy with packet repetition based on Kalman filter predicting. Firstly, we design a position data updating model based on Kalman filter difference predicting equations. Then, we design a packet repetition mode decision algorithm, which is applied to deliver vehicle position updating data. The model with packet repetition can not only generate position updating data according to preset threshold, but also decide packet repetition mode related to the distance of two adjacent vehicles in order to reduce data loss. Both simulated highway and realistic urban road experimental results show that vehicle position data updating frequency could be obviously reduced and the reliability of the communication is greatly improved through packet repetition mechanism by using this position updating strategy. [ABSTRACT FROM AUTHOR]

Intention recognition is significant in many applications. In this paper, we focus on team intention recognition, which identifies the intention of each team member and the team working mode. To model the team intention as well as the world state and observation, we propose a Logical Hierarchical Hidden Semi-Markov Model (LHHSMM), which has advantages of conducting statistical relational learning and can present a complex mission hierarchically. Additionally, the LHHSMM explicitly models the duration of team working mode, the intention termination, and relations between the world state and observation. A Logical Particle Filter (LPF) algorithm is also designed to infer team intentions modeled by the LHHSMM. In experiments, we simulate agents’ movements in a combat field and employ agents’ traces to evaluate performances of the LHHSMM and LPF. The results indicate that the team working mode and the target of each agent can be effectively recognized by our methods. When intentions are interrupted within a high probability, the LHHSMM outperforms a modified logical hierarchical hidden Markov model in terms of precision, recall, and F-measure. By comparing performances of LHHSMMs with different duration distributions, we prove that the explicit duration modeling of the working mode is effective in team intention recognition. [ABSTRACT FROM AUTHOR]

Melting-casting is the first process in copper alloy strip production. The schedule scheme on this process affects the subsequent processes greatly. In this paper, we build the multiobjective model of melting-casting scheduling problem, which considers minimizing the makespan and total weighted earliness and tardiness penalties comprehensively. A novel algorithm, which we called Multiobjective Artificial Bee Colony/Decomposition (MOABC/D) algorithm, is proposed to solve this model. The algorithm combines the framework of Multiobjective Evolutionary Algorithm/Decomposition (MOEA/D) and the neighborhood search strategy of Artificial Bee Colony algorithm. The results on instances show that the proposed MOABC/D algorithm outperforms the other two comparison algorithms both on the distributions of the Pareto front and the priority in the optimal selection results. [ABSTRACT FROM AUTHOR]

This paper proposes a twice rapid transfer alignment algorithm based on dual models in order to solve the problems such as long convergence time, poor accuracy, and heavy computation burden resulting from the traditional nonlinear error models. The quaternion matching method based on quaternion error model along with the extended Kalman filter (EKF) is applied to deal with the large misalignment in the first phase. Then in the second transfer alignment phase, velocity plus attitude matching method as well as classical Kalman filter is adopted. The simulation and the results of vehicle tests demonstrate that this method combines the advantages of both nonlinear and linear error models with the guarantee of accuracy and fastness. [ABSTRACT FROM AUTHOR]

Parameter estimation of chaotic systems plays a key role for control and synchronization of chaotic systems. At first, the parameter estimation of chaotic systems is mathematically formulated as a global continuous optimization problem. Then through integrating two differential mutation strategies, an improved greedy selection mechanism and a population diversity balance scheme, an alternate iterative differential algorithm, called AIDE, is presented to solve the problem in this paper. Subsequently, experiments are tested on a set of cases of parameter estimation of chaotic systems and the results show that AIDE is better than or at least equal to DE/rand/1/bin, DE/best/1/bin, and other four well-known algorithms in all cases. [ABSTRACT FROM AUTHOR]

Bankruptcy prediction has been extensively investigated by data mining techniques since it is a critical issue in the accounting and finance field. In this paper, a new hybrid algorithm combining switching particle swarm optimization (SPSO) and support vector machine (SVM) is proposed to solve the bankruptcy prediction problem. In particular, a recently developed SPSO algorithm is exploited to search the optimal parameter values of radial basis function (RBF) kernel of the SVM. The new algorithm can largely improve the explanatory power and the stability of the SVM. The proposed algorithm is successfully applied in the bankruptcy prediction problem, where experiment data sets are originally from the UCI Machine Learning Repository. The simulation results show the superiority of proposed algorithm over the traditional SVM-based methods combined with genetic algorithm (GA) or the particle swarm optimization (PSO) algorithm alone. [ABSTRACT FROM AUTHOR]

In view of the SVM classification for the imbalanced sand-dust storm data sets, this paper proposes a hybrid self-adaptive sampling method named SRU-AIBSMOTE algorithm. This method can adaptively adjust neighboring selection strategy based on the internal distribution of sample sets. It produces virtual minority class instances through randomized interpolation in the spherical space which consists of minority class instances and their neighbors. The random undersampling is also applied to undersample the majority class instances for removal of redundant data in the sample sets. The comparative experimental results on the real data sets from Yanchi and Tongxin districts in Ningxia of China show that the SRU-AIBSMOTE method can obtain better classification performance than some traditional classification methods. [ABSTRACT FROM AUTHOR]

With the great risk exposed in IT outsourcing, how to assess IT outsourcing risk becomes a critical issue. However, most of approaches to date need to further adapt to the particular complexity of IT outsourcing risk for either falling short in subjective bias, inaccuracy, or efficiency. This paper proposes a dynamic algorithm of risk assessment. It initially forwards extended three layers (risk factors, risks, and risk consequences) of transferring mechanism based on transaction cost theory (TCT) as the framework of risk analysis, which bridges the interconnection of components in three layers with preset transferring probability and impact. Then, it establishes an equation group between risk factors and risk consequences, which assures the “attribution” more precisely to track the specific sources that lead to certain loss. Namely, in each phase of the outsourcing lifecycle, both the likelihood and the loss of each risk factor and those of each risk are acquired through solving equation group with real data of risk consequences collected. In this “reverse” way, risk assessment becomes a responsive and interactive process with real data instead of subjective estimation, which improves the accuracy and alleviates bias in risk assessment. The numerical case proves the effectiveness of the algorithm compared with the approach forwarded by other references. [ABSTRACT FROM AUTHOR]