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In recent years, many image encryption schemes have adopted Hilbert curves for encryption. In this approach, the Hilbert curve is used to encrypt grayscale images by traversal scrambling. However, the correlation between pixels has not been fully considered and those algorithms are not safe enough. To solve this problem, a new image encryption algorithm based on a new chaotic system of 2D-LICM (Two-Dimensional Linear-Infinite-Collapse Chaotic Map) and an improved Hilbert curve is proposed in this paper. First, we propose a new 2D-chaotic system to address the shortcoming that the commonly used chaotic systems are too simple in scope and complexity. Then, a new image encryption algorithm is proposed using the newly designed 2D-LICM and the improved Hilbert curve. The proposed algorithm uses Hilbert curve to reduce the correlation between adjacent pixels of the image at the pixel and bit levels and increase the scrambling and diffusion effects. Simulation and security analysis results show that the proposed scheme has high security and is superior to several advanced image encryption algorithms. [ABSTRACT FROM AUTHOR]

At present, there are many semantic segmentation algorithms with excellent performance for intelligent driving vehicles, but most of them only work well on scenes with good illumination. In order to solve the problem of scene segmentation under low illumination, this paper proposes a novel semantic segmentation algorithm that combines visible and infrared images. In this algorithm, two parallel encoders are designed as the input of the images, and the decoder divides the fused images output from the encoder. The model is based on ResNet algorithm, and the residual attention module is used in each branch to mine and enhance the spatial features of multilevel channels to extract images information. Experiments are carried out on publicly available thermal infrared and visible datasets. The results show that the algorithm proposed in this paper is superior to the algorithm using only visible images in semantic segmentation of traffic environment. [ABSTRACT FROM AUTHOR]

This paper deals with fuzzy multi-depot bus scheduling (FMDBS) problem in which the objective function and constraints are defined with fuzzy attributes. Credibility relation is used to formulate the problem as an integer multicommodity flow problem. A novel combination of branch-and-price and heuristic algorithms, is proposed to efficiently solve FMDBS problem. In the proposed algorithm, the heuristic algorithm is applied to generate initial columns for the column generation method. Also, a heuristic algorithm is used to improve the generated solutions in each node of the branch-and-price tree. Two sets of benchmark examples are applied to demonstrate the efficiency of the proposed algorithm for large-scale instances. Also, the algorithm is applied to solve the classical multi-depot bus scheduling problem. The results show that the proposed algorithm decreases integrality gap and computational time in comparison with the state-of-the-art algorithms and normal branch-and-price algorithm. Finally, as a case study, the bus schedules in Tehran BRT network are generated. [ABSTRACT FROM AUTHOR]

As an infrastructure of biochemical laboratories, EP tube label plays a significant role in information extraction to meet the limitations of computing power in offline devices and solve the problem that the EP tube label cannot be accurately identified before identification because the label belongs to multi-angle random placement. This paper proposes a light-weight neural network YOLOv4-tiny-ECA to position tubes and a tilt correction method based on Hough transform. First, the EP tube rack is roughly positioned based on the diffuse filling algorithm combined with digital morphological corrosion, and then the EP tubes in the rack are precisely positioned using the light-weight YOLO target detection algorithm combined with the attention mechanism. Next, the baseline is added to the label as the basis for determining the tilt angle. For the valid target, the baseline is extracted using the Hough transform and the tilt angle is calculated by vector fork multiplication. Finally, baseline is removed using image processing algorithm for better recognition results. Our results show that the light-weight YOLO algorithm reduces the network parameters by 56% and computation by 55% while keeping the accuracy rate largely unchanged, the offline positioning tilt correction method can achieve 98.8% accuracy and 0.076 s processing speed for a single test tube on average, which meets the real-time requirement. [ABSTRACT FROM AUTHOR]

Due to the haze weather, the outdoor image quality is degraded, which reduces the image contrast, thereby reducing the efficiency of computer vision systems such as target recognition. There are two aspects of the traditional algorithm based on the principle of dark channel to be improved. First, the restored images obviously contain color distortion in the sky region. Second, the white regions in the scene easily affect the atmospheric light estimated. To solve the above problems, this paper proposes a single-image dehazing and image segmentation method via dark channel prior (DCP) and adaptive threshold. The sky region of hazing image is relatively bright, so sky region does not meet the DCP. The sky part is separated by the adaptive threshold, then the scenery and the sky area are dehazed, respectively. In order to avoid the interference caused by white objects to the estimation of atmospheric light, we estimate the value of atmospheric light using the separated area of the sky. The algorithm in this paper makes up for the shortcoming that the algorithm based on the DCP cannot effectively process the hazing image with sky region, avoiding the effect of white objects on estimating atmospheric light. Experimental results show the feasibility and effectiveness of the improved algorithm. [ABSTRACT FROM AUTHOR]

Functional Distributed Constraint Optimization Problems (F-DCOPs) are a constraint processing framework for continuous variables in the multi-agent system. Particle swarm optimization-based F-DCOP (PFD) is a population-based algorithm to solve F-DCOP collaboratively. Although it can significantly reduce the computational overhead and memory requirements, its solution depends on the decision of root agent in the Breadth First Search (BFS) pseudo-tree and it is easy to fall into local optimum. To solve the above problems, this paper designed an improved PFD algorithm with Local Decision named PFD-LD, which effectively reduces the dependence on root agent through local decision. In addition, a mutation operator is used to avoid falling into local optimum. It is proved that PFD-LD is an anytime algorithm and local decision can expand the search of the solution space. Finally, the extensive experiments based on four types of benchmark problems show that the proposed algorithm outperforms state-of-the-art F-DCOP solving algorithms. [ABSTRACT FROM AUTHOR]

To solve the problem of low accuracy in the traditional FunkSVD recommendation algorithm, an improved FunkSVD algorithm (RM-FS) is proposed. RM-FS is an improvement of the traditional FunkSVD algorithm, using RMSProp, a deep learning optimization algorithm. The RM-FS algorithm can not only solve the problem of reduced accuracy of the traditional FunkSVD algorithm because of iterative oscillations but also alleviate the impact of data sparseness on the accuracy of the algorithm, achieving the effect of improving the accuracy of the traditional algorithm. The experimental results show that the RM-FS algorithm proposed in this paper effectively improves the accuracy of the recommendation algorithm, which is better than the traditional FunkSVD recommendation algorithm and other improved FunkSVD algorithms. [ABSTRACT FROM AUTHOR]

Exploration algorithms based on the Boustrophedon path seldom consider the impacts of a robot turning at corners on the exploration time. This paper proposes the Forecast-Island and Bidding A*-Euclidean Selecting Boustrophedon Coordination (FIBA*ESBC) algorithm to calculate the turning time at corners in the overall exploration time and introduces a method to estimate the walking time in the Boustrophedon paths in order to determine the directions for path execution. Typically, in bidding-based exploration tasks, the cost is the Euclidean distance between the current position of the robot and the target point. When there is an obstacle between two points, the cost is set to infinity. Therefore, the selected target point is sometimes not optimal. The FIBA*ESBC algorithm is based on the exploration cost of a combination of the Euclidean distance and A* algorithm walking path, which can effectively solve this problem. Because the bidding is based on a greedy algorithm, the robot has a small unexplored island in the later exploration stage; therefore, full exploration is not possible or requires a long time with several repeated paths. The FIBA*ESBC algorithm prioritizes the exploration and estimation of hidden and existing unexplored islands. It can realize complete exploration and decrease the exploration time. Through simulation experiments conducted using Gazebo and RViz, the feasibility of the FIBA*ESBC algorithm is verified. Moreover, a simulation experiment is conducted in MATLAB for comparison with other algorithms. The analysis of the experimental data shows that the proposed algorithm has a relatively short exploration time. [ABSTRACT FROM AUTHOR]

Nowadays, dual-active-bridge (DAB) DC/DC converter is widely used for its high power density and flexible operation mode. However, complex control variables and high reflux power are the main disadvantages of the system, which have become its development bottleneck. In view of this, this paper presents an in-depth analysis of the impacts of three phase-shifting degrees of freedom on the transmission power and effective values of the inductive current based on the triple-phase-shift (TPS) control strategy by deducing the relationship equations between the three degrees of freedom and the other two variables in the TPS control system. Based on the equations, the method of using the multi-objective particle swarm optimization (MOPSO) algorithm to optimize three phase shift angles is proposed, which cannot only improve the convergence speed of the optimal phase shift angle but also be ideal for solving nonlinear problems. The experimental results show that this method can improve the transmission power and reduce the effective value of inductive current. [ABSTRACT FROM AUTHOR]

In order to solve the problem of blurred license plate image in road monitoring, the PSF estimation method based on spectrum is often used to restore the blurred license plate image. Due to the large error of motion blur information estimation, a blind license plate restoration algorithm based on image spectrum and cepstrum is proposed in this paper. First, Radon transform is used to detect the parallel line features in the spectrum of fuzzy license plate image to estimate the fuzzy angle. Then, the cepstrum data of the blurred image is used to measure the peak value and estimate the fuzzy length of the image. Finally, blind restoration of fuzzy license plate image is carried out by Wiener filter of autocorrelation function. The experimental results show that the PSF estimation result of this algorithm is more accurate, and it can achieve better restoration effect of fuzzy image for license plate images under different fuzzy conditions. It is verified that the blind restoration algorithm based on fusion of image spectrum and cepstrum has certain feasibility and adaptability. [ABSTRACT FROM AUTHOR]

This paper presents an end-to-end algorithm for solving circuit problems in secondary physics. A key challenge in solving circuit problems is to automatically understand circuit problems over the modals of both text and schematic. Existing methods have a limited capacity in problem understanding due to the they cannot deal with the numerous expressions of problems in natural language and the various circuit diagrams. In fact that this paper, a batch of methods is proposed to work against the challenge of solving circuit problems. The problem understanding is modeled as a problem of relation extraction and a scheme is proposed to extract relations from both text and schematic. A syntax–semantics model is adopted to extract explicit relations from text, whereas a unit-theorem-based method is proposed to extract implicit relations. And a mesh search method is proposed to extract relations from schematic. Based on the result of problem understanding, an algorithm is proposed to produce the solutions of circuit problems, in which the solutions are presented in a readable way. The experimental results demonstrate the effectiveness of the proposed algorithm in solving circuit problems. To the best of our knowledge, this paper is the first literature which reports the quantitative results in understanding and solving circuit problems. [ABSTRACT FROM AUTHOR]

In both practical applications and theoretical analysis, there are many fuzzy chance-constrained optimization problems. Currently, there is short of real-time algorithms for solving such problems. Therefore, in this paper, a continuous-time neurodynamic approach is proposed for solving a class of fuzzy chance-constrained optimization problems. Firstly, an equivalent deterministic problem with inequality constraint is discussed, and then a continuous-time neurodynamic approach is proposed. Secondly, a sufficient and necessary optimality condition of the considered optimization problem is obtained. Thirdly, the boundedness, global existence and Lyapunov stability of the state solution to the proposed approach are proved. Moreover, the convergence to the optimal solution of considered problem is studied. Finally, several experiments are provided to show the performance of proposed approach. [ABSTRACT FROM AUTHOR]

Distributed Constraint Optimization Problem (DCOP) is a powerful paradigm to model multi-agent systems through enabling multiple agents to coordinate with each other to solve a problem. These agents are often assumed to be cooperative, that is, they communicate with other agents in order to optimize a global objective. However, the communication times between all pairs of agents are assumed to be identical in the evaluation of most DCOP algorithms. This assumption is impractical in almost all real-world applications. In this paper, we study the impact of empirically evaluating a DCOP algorithm under the assumption that communication times between pairs of agents can vary. In addition, we evaluate a DCOP algorithm using ns-2, a discrete-event simulator that is widely used in the computer networking community, to simulate the communication times, as opposed to the standard DCOP simulators that are used to evaluate DCOP algorithms in the AI community. Furthermore, we propose heuristics that exploit the non-uniform communication times to speed up DCOP algorithms that operate on pseudo-trees. Our empirical results demonstrate that the proposed heuristics improve the runtime of those algorithms up to 20%. These heuristics are evaluated on different benchmarks such as scale-free graphs, random graphs, and an instance of the smart grid, Customer-Driven Microgrid (CDMG) application. [ABSTRACT FROM AUTHOR]

This paper presents an improved particle swarm optimization (PSO) technique for global optimization. Many variants of the technique have been proposed in literature. However, two major things characterize many of these variants namely, static search space and velocity limits, which bound their flexibilities in obtaining optimal solutions for many optimization problems. Furthermore, the problem of premature convergence persists in many variants despite the introduction of additional parameters such as inertia weight and extra computation ability. This paper proposes an improved PSO algorithm without inertia weight. The proposed algorithm dynamically adjusts the search space and velocity limits for the swarm in each iteration by picking the highest and lowest values among all the dimensions of the particles, calculates their absolute values and then uses the higher of the two values to define a new search range and velocity limits for next iteration. The efficiency and performance of the proposed algorithm was shown using popular benchmark global optimization problems with low and high dimensions. Results obtained demonstrate better convergence speed and precision, stability, robustness with better global search ability when compared with six recent variants of the original algorithm. [ABSTRACT FROM AUTHOR]

In reinforcement learning (RL), the exploration/exploitation (E/E) dilemma is a very crucial issue, which can be described as searching between the exploration of the environment to find more profitable actions, and the exploitation of the best empirical actions for the current state. We focus on the single trajectory RL problem where an agent is interacting with a partially unknown MDP over single trajectories, and try to deal with the E/E in this setting. Given the reward function, we try to find a good E/E strategy to address the MDPs under some MDP distribution. This is achieved by selecting the best strategy in mean over a potential MDP distribution from a large set of candidate strategies, which is done by exploiting single trajectories drawn from plenty of MDPs. In this paper, we mainly make the following contributions: (1) We discuss the strategy-selector algorithm based on formula set and polynomial function. (2) We provide the theoretical and experimental regret analysis of the learned strategy under an given MDP distribution. (3) We compare these methods with the 'state-of-the-art' Bayesian RL method experimentally. [ABSTRACT FROM AUTHOR]

Several propositional satisfiability (SAT) based encodings have been proposed to deal with various data mining problems including itemset and sequence mining problems. This research issue allows to model data mining problems in a declarative way, while exploiting efficient SAT solving techniques. In this paper, we overview our contributions on the application of propositional satisfiability (SAT) to model and solve itemset mining tasks. We first present a SAT based encoding of frequent closed itemset mining task as a propositional formula whose models corresponds to the patterns to be mined. Secondly, we show that some data mining constraints can be avoided by reformulation. We illustrate this issue by reformulating the closeness constraint using the notion of minimal models. Finally, we also addressed the scalability issue, one of the most important challenge of these nice declarative framework. To this end, we proposed a complete partition based approach whose aim is to avoid encoding the whole database as a single formula. Using a partition on the set of items, our new approach leads to several propositional formulas of reasonable size. The experimental evaluation on several known datasets shows huge improvements in comparison to the direct approach without partitioning, while reducing significantly the performance gap with respect to specialized algorithms. [ABSTRACT FROM AUTHOR]

This paper considers the identification of problems which generate anomalies at firms through the observed symptoms on the basis of fuzzy relations and Zadeh's compositional rule of inference. A procedure for determining the fuzzy cause vector of an economic and financial diagnosis problem is proposed, which consists of the design of fuzzy relational matrix and the resolution of a system of fuzzy relational equations. An efficient algorithm for solving fuzzy relational equations in terms of the associated set covering problem is introduced. It utilizes a back-tracking method to generate each minimal covering, where no duplicate or non-minimal coverings exist. A numerical example of firms' insolvency causes diagnosis is also included. [ABSTRACT FROM AUTHOR]

In attempting to overcome the limitation of current methods to solve complicated constrained optimization problems, this paper proposes an adaptive hybrid particle swarm optimization multi-objective optimization (AHPSOMO) algorithm. In the early stage, this algorithm initializes the individuals in a population in an even manner using good point set (GPS) theory so that the diversity of the population can be guaranteed. In the process of local search, differential evolution (DE) algorithm is introduced for updating local optimal individuals. Particle swarm optimization method is further adopted to conduct global search as per the multi-objective approach. The results of simulation tests on 24 classic test functions and three engineering constrained optimization problems show that compared with other algorithms, our proposed algorithm is effective and feasible, which can offer highly accurate solutions with good robustness. [ABSTRACT FROM AUTHOR]

In order to overcome the drawbacks of the K-means (KM) for clustering problems such as excessively depending on the initial guess values and easily getting into local optimum, a clustering algorithm of invasive weed optimization (IWO) and KM based on the cloud model has been proposed in the paper. The so-called cloud model IWO (CMIWO) is adopted to direct the search of KM algorithm to ensure that the population has a definite evolution direction in the iterative process, thus improving the performance of CMIWO K-means (CMIWOKM) algorithm in terms of convergence speed, computing precision and algorithm robustness. The experimental results show that the proposed algorithm has such advantages as higher accuracy, faster constringency, and stronger stability. [ABSTRACT FROM AUTHOR]