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In this paper, we propose a control framework for intelligent surgical robots under the Remote Center of Motion (RCM). The goal of a surgical robot is to assist surgeons in performing complex surgeries. RCM constraint implies that the surgical tip attached to the end-effector of the surgical robot does not slide away from the point of the incision while performing surgery. Implementation of a control algorithm to comply with RCM constraints is a complicated task because of the nonlinear model of the surgical robots and stringent conditions of accuracy imposed by the patient's safety. This paper proposes an optimization-driven approach to perform the surgical maneuver under RCM constraints. We then applied a bio-inspired optimization algorithm to solve the problem efficiently. For testing the performance of ZNNBAS, we used MATLAB to simulate a surgical procedure. A 7-DOF surgical robot (KUKA LBR IIWA 7) was used as a test bench for running the simulations. The simulation results show that the ZNNBAS is comparable with BAS, PSO, and GA and efficiently and robustly performed the task commanded maneuvers while enforcing the RCM constraints. [ABSTRACT FROM AUTHOR]

• An analysis of lexicographic optimization for feature selection and data classification. • Cooperative co-evolution for feature selection while the classifier hyperparameters are also being optimized. • Optimization of multiple objectives using a simple evolutionary algorithm. • A new lexicographic ranking methodology to compare the results of different feature selection methods. • Quite good results for real high-dimensional datasets. This paper starts with two hypotheses. The first one is that the simultaneous optimization of the hyperparameters regulating the classifier within a wrapper method, while the best subset of features is being determined, should improve the results with respect to those obtained with a pre-parameterized classifier. The second one is that solving these two problems can be formulated as a lexicographic optimization problem, allowing the use of a simple single-objective evolutionary algorithm to solve this multi-objective problem. The fitness function is of key importance for such wrapper methods. It is responsible for guiding the search towards potentially good solutions and it also consumes most of the runtime. Having these issues in mind, this paper also proposes a new lexicographic fitness function, designed to minimize the runtime of the algorithm and also to avoid over-fitting. Furthermore, the execution time and the quality of the results obtained by the wrapper procedure also depend on some algorithmic hyperparameters: the similarity thresholds used when comparing two different solutions lexicographically and the percentage of data samples used for validation during the training process. Thus, an experimental analysis has been carried out to find adequate values for these hyperparameters. Finally, the lexicographic cooperative co-evolutionary wrapper approach, using the new fitness function proposed in this paper, has been tested with several datasets belonging to the University of California, Irvine (UCI) repository and also with some real high-dimensional datasets, obtaining quite good results, compared to other state-of-the-art wrapper methods. The comparison has also been made lexicographically, with a new methodology proposed in this paper. [ABSTRACT FROM AUTHOR]

Person re-identification is to recognize a pedestrian who has been observed at different places in monitoring system, which is done by computer automatically. Important practical significance can be found in this paper on improving the intelligence of monitoring system. There are two challenging problems needed to be solved for person re-identification, which is feature representation and matching. Designing a suitable feature representation method is both challenging and crucial. Ideally, the extracted features should be robust enough to cope with the various situations of the monitoring environment, such as posture difference, illumination changing, and shooting angle difference, etc. There are a lot of challenges for feature matching too, such as small sample size, inter-class confusion, and intra-class variation, etc. In order to meet these challenges, a new person re-identification scheme was put forward in this paper. The following three aspects would be used to describe it. First, an improved BOF feature extraction algorithm based on SURF was put forwarded, SURF algorithm extracts the preliminary feature descriptor and generates visual dictionary, so the influence factors such as illumination and scale invariants can be deal with. And then, covariance descriptor was adopted by the algorithm. It has high robustness, and matching accuracy can be improved too for the case of small sample. Finally, an effective classifier was designed by LIBSVM based on improved BOF algorithm, so the efficiency of the person re-identification algorithm can be improved. The proposed method was compared with the current mainstream algorithm through experiment, and it can be found through the experimental results that it is effective to solve the difficult problems for person re-identification. [ABSTRACT FROM AUTHOR]

In this paper, a distributed nonsmooth nonconvex optimization (DNNO) problem with affine inequality and nonsmooth convex inequality constraints is studied. A continuous-time distributed neurodynamic algorithm is proposed to solve this problem. Under the assumed conditions, for any initial state, the solution of distributed neurodynamic algorithm is bounded and globally exists, and will converge to the critical point set of distributed problems in a finite time. Compared with other DNNO algorithms, distributed neurodynamic algorithm has a lower dimension and does not need to satisfy the assumption that the feasible region is bounded. Finally, a series of numerical examples are given to verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

A critical prerequisite for controlling complex networks is to find a driver node set with a structural controllability guarantee. This paper introduces the control capacity for a driver node set and solves the problem of finding a complete minimum driver node set that not only guarantees network structural controllability but achieves the desired level of control capacity. A novel algorithmic framework is proposed which is based on the concept of equivalent set and approximate matching replacement technique. The proposed algorithmic framework is shown to outperform the state-of-the-art approaches in the literature. The validity of the proposed algorithm is analyzed and the performance is evaluated by experiments on artificial and real-world complex networks. [ABSTRACT FROM AUTHOR]

This paper presents a constrained adaptive dynamic programming (CADP) algorithm to solve general nonlinear nonaffine optimal control problems with known dynamics. Unlike previous ADP algorithms, it can directly deal with problems with state constraints. Firstly, a constrained generalized policy iteration (CGPI) framework is developed to handle state constraints by transforming the traditional policy improvement process into a constrained policy optimization problem. Next, we propose an actor-critic variant of CGPI, called CADP, in which both policy and value functions are approximated by multi-layer neural networks to directly map the system states to control inputs and value function, respectively. CADP linearizes the constrained optimization problem locally into a quadratically constrained linear programming problem, and then obtains the optimal update of the policy network by solving its dual problem. A trust region constraint is added to prevent excessive policy update, thus ensuring linearization accuracy. We determine the feasibility of the policy optimization problem by calculating the minimum trust region boundary and update the policy using two recovery rules when infeasible. The vehicle control problem in the path-tracking task is used to demonstrate the effectiveness of this proposed method. [ABSTRACT FROM AUTHOR]

Multinomial logistic regression (MLR) is a useful tool for solving multi-classification problems. The l q , 0 (q ⩾ 1) norm is an ideal regularization term for characterizing group sparsity in multinomial logistic regression and selecting important features in the high dimensional data. However, l q , 0 regularized multinomial logistic regression ( l q , 0 -MLR) is nonconvex, discontinuous, and NP-hard. Thus, most prior studies adopted a continuous approximation of the l q , 0 norm. In this paper, we present a novel l q , 0 -proximal Newton algorithm ( l q , 0 -PNA) to solve the l q , 0 -MLR. We first define a strong α -stationary point and prove that this point is a local minimizer of l q , 0 -MLR. We then convert such a point into a stationary equation and solve it by l q , 0 -PNA, which is a Newton-type method running on a group sparse subspace with a low computational cost. Furthermore, we establish a locally quadratic convergence of l q , 0 -PNA. Finally, numerical experiments on simulated and real data show the superiority of l q , 0 -PNA in terms of computational time and accuracy, when compared with six state-of-the-art solvers, especially for high dimensional data. [ABSTRACT FROM AUTHOR]

As one of the core technologies of the automatic control system for unmanned surface vehicles (USVs), autonomous collision avoidance algorithm is the key to ensure the safe navigation of USVs. In this paper, path planning and dynamic collision avoidance (PPDC) algorithm which obeys COLREGs is proposed for USVs. In order to avoid unnecessary collision avoidance actions, the risk assessment model is developed, which is used to determine the switching time of path planning and dynamic collision avoidance. In order to train the algorithm which complies with the COLREGs, the encounter situation is divided quantitatively, which is regarded as the input state of the system, so that the high-dimensional input is successfully avoided. The state space of the USV is defined by relative parameters to improve the generalization ability of the algorithm, meanwhile, a network structure based on DDPG is designed to achieve the continuous output of thrust and rudder angle. Combined with path planning, collision avoidance, compliance with COLREGs and smooth arrival task, four kinds of reward functions are designed. In order to solve the problem of low training efficiency of experience replay mechanism in DDPG, cumulative priority sampling mechanism is proposed. Through the simulation and verification in a variety of scenarios, it is proved that PPDC algorithm has the function of path planning and dynamic collision avoidance in compliance with COLREGs, which has good real-time performance and security. [ABSTRACT FROM AUTHOR]

This paper proposes a text removal model, Text Remove Network (TRNet), which achieves an unprecedented clearing effect of picture text. The network uses a jump-connected U-net structure to encode and decode the generator, so as to obtain clearer and sharper texture details of the original image. To solve the problem of color distortion, the generator removes the batch normalization layer and uses ELUs as the activation layer of all convolutional layers. Through the comprehensive loss, which include reconstruction, content, style, total variation, and structural similarity (SSIM) loss, we can clear the image text and preserve the image information of the background, which solves the problem of incomplete text removal and loss of background texture. The local discriminator is used to evaluate the local consistency of the text erasure area. In a text elimination experiment on a synthetic dataset and the ICDAR 2013 dataset, this method had a good effect on foreground text erasure and background authenticity restoration. Experiments on a comprehensive dataset of real documents also showed good results. To achieve targeted removal of sensitive text information on pictures, we collected datasets based on real and synthetic documents, and experimental results were satisfactory. Compared to current and classic algorithms, our text removal algorithm performs best in Image Quality Assessment (IQA). [ABSTRACT FROM AUTHOR]

Clustering is an important research direction in data mining. However, there is no one clustering algorithm that can be applied efficiently in all situation. Clustering ensemble is the best way to solve the above-mentioned problems. It combines the results of multiple clustering algorithms, and the final result is significantly better than a single clustering algorithm. Although there is a lot of constraint information, the existing clustering ensemble algorithm does not utilize it. This paper uses constraint information in consensus function and proposes a Semi-supervised Selective Clustering Ensemble based on Chameleon (SSCEC) and Semi-supervised Selective Clustering Ensemble based on Ncut (SSCEN) to solve the above problem. SSCEC uses the chameleon algorithm as consensus function, and processes constraint information in subgraph partition and subgraph combining. SSCEN uses the Normalized cut algorithm as consensus function, and processes constraint information in the process of graph dichotomy. The experiment results show that our proposed two semi-supervised member selection clustering ensemble algorithms are better than other semi-supervised algorithms. [ABSTRACT FROM AUTHOR]

• Transform the max-bisection problem to a linearly constrained optimization problem. • Propose a deterministic annealing neural network algorithm by introducing a barrier function in square-root form. • Prove the existence of the integral minimum solution of the max-bisection problem in theory and the proof is simpler than other algorithms. • Experimental results show that the proposed method outperforms other methods. The max-bisection problem has extensive applications in network engineering, making it imperative to develop effective methods to solve this problem. However, the efficient computation on this problem remains challenging due to its NP-hard computational complexity. To address this question, in this paper, we transform the max-bisection problem to an artificial linearly constrained optimization problem and develop a modified deterministic annealing neural network algorithm by introducing a barrier function. The barrier parameter of this function acts as the temperature in the annealing process and decreases from a large positive number to zero. The initial value of the barrier parameter promises that the artificial problem is convex and can be easily solved at the beginning of the descending process. The solution to the original max-bisection problem is finally obtained through a descent algorithm, in which the bound limit of the solution is always satisfied as long as the iteration step length is less than one. We have proved the theoretical convergence of the proposed algorithm. Numerical results demonstrate that the proposed algorithm has several advantages over both the approximation algorithms and metaheuristic methods. [ABSTRACT FROM AUTHOR]

Multi-view clustering, which utilizes the ample information provided by multiple sources to obtain better performance, has attracted much attention. However, existing clustering algorithms either have no ability to offer confidence for each assignment or suffer from the disturbance of outliers. To address these problems, in this paper, we propose a novel multi-view fuzzy clustering method via transferring softmin to fuzzy models. To obtain fuzzy assignments, we utilize the softmin with temperature and further develop an efficient algorithm to solve the non-convex problem approximately. We also show another explanation for the algorithm from the aspect of the prior distribution of various views. Besides, we design a scalable robust loss function, which interpolates between ℓ 2 -norm and the squared ℓ 2 -norm, to enhance the robustness to outliers. Extensive experiments show the superiority of our model under different clustering metrics. [ABSTRACT FROM AUTHOR]

Today, artificial intelligence is everywhere in people's daily lives. Visual tracking, which is used to identify and continuously track specific targets, is an important research domain in the study of artificial intelligence. However, current visual tracking methods are not accurate enough for object tracking with background clutter, which can easily lead to tracking failures. Therefore, in this paper, in order to solve the problem of tracking failure in clutter background, we propose a template update mechanism to improve the accuracy of visual tracking. First, an original template is saved when the background clutter is detected. During background clutter, we use both the original template and the current template at the location estimated by the optical flow and choose better one. Next, the original template is reused after the background clutter is ended. Finally, the proposed mechanism is used both in the KCF and BACF algorithm to verify the effectiveness of the mechanism. With experiments on the OTB2015 dataset, results show that the proposed mechanism has improved accuracy and success rate of the two baseline algorithms. Meanwhile, in state-of-the-art algorithms, the algorithm using the proposed mechanism also has excellent tracking performance. In addition, this method also has strong tracking robustness and adaptation capability to sequential learning for video data. [ABSTRACT FROM AUTHOR]

The fitness landscape is a theory applied to the evolutionary dynamics of biological evolution to explain the behavior of evolutionary algorithms in the solution of optimization problems. With the continuous advancement of evolutionary algorithm optimization, a fitness landscape can present more abundant feature information, such as the local fitness, fitness distance correlation, and landscape roughness. These landscape features reflect the optimal solution distribution, quantity, and local unimodal topology of the optimization problem from various angles. This paper expresses the adaptability landscape features of typical optimization problems, engages in a quantitative analysis of the fitness distance correlation information, evaluates the difficulty of solving the problem within the search space, and obtains the correlation degree classification result. The search strategy adapts the mixed mutation and the fitness distance correlation for differential evolution. Empirical studies show that, the fitness distance correlation search strategy for the differential evolution algorithm can avoid falling into the local optimum, improve accuracy and convergence, and solve the single-objective optimization problem in a more comprehensive manner. [ABSTRACT FROM AUTHOR]