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Inspired by the idea of game learning, this paper studies the Cournot game model of the multi-oligopoly market and studies the realization of the Nash equilibrium by using adaptive differential evolution algorithm based on relaxation strategies. The realization path of Nash equilibrium is attained through an adaptive differential evolution based on a relaxation strategy procedure applied to an objective function with best response dynamics. Among them, the objective function is derived from the sum of the payoff changes of each oligopoly under best response decisions. And theoretically proved that the Nash equilibrium is the optimal solution of the objective function. [ABSTRACT FROM AUTHOR]

In this paper, we propose a sparse least squares (SLS) optimization model for solving multilinear equations, in which the sparsity constraint on the solutions can effectively reduce storage and computation costs. By employing variational properties of the sparsity set, along with differentiation properties of the objective function in the SLS model, the first-order optimality conditions are analysed in terms of the stationary points. Based on the equivalent characterization of the stationary points, we propose the Newton Hard-Threshold Pursuit (NHTP) algorithm and establish its locally quadratic convergence under some regularity conditions. Numerical experiments conducted on simulated datasets including cases of Completely Positive(CP)-tensors and symmetric strong M-tensors illustrate the efficiency of our proposed NHTP method. [ABSTRACT FROM AUTHOR]

In a chiller plant, primary or critical sensors are used to control the system operation while secondary sensors are installed to monitor the performance/health of individual equipment. Current sensor fault detection and diagnosis (SFDD) approaches are not applicable to secondary sensors which usually are not involved in the system control. Consequently, a hybrid multiple sensor fault detection, diagnosis and reconstruction (HMSFDDR) algorithm for chiller plants was developed. Machine learning and pattern recognition were used to predict the primary sensor faults through the comparison of the weekly performance curves. With the primary sensor signals reconstructed, the secondary sensor faults were estimated based on mass and energy balance. By applying the algorithm with various logged plant data and comparison with site checking results, a maximum of 75% effectiveness could be achieved. The merits of the present approach were further justified through off-site sensor testing which reinforced the usefulness of proposed HMSFDDR algorithm. [ABSTRACT FROM AUTHOR]

Electromagnetic tomography is a process detection technology based upon the principles of electromagnetic induction. The forward problem model and sensitivity distribution matrix of electromagnetic tomography are introduced as the basis of the inverse problem. The search direction and iterative parameters of the conjugate gradient algorithm are modified to improve the quality and convergence of image reconstruction. A new spectral parameter conjugate gradient algorithm is described to modify the search direction, which is used to control the angle between the old and new search directions. The search direction is determined according to the iteration of each step in order to find the optimal solution. Combining the advantages of the Fletcher-Reeves and Polak-Ribiere-Polyak algorithms in the nonlinear conjugate gradient algorithm, they are mixed in a specific proportion to obtain a new hybrid conjugate gradient algorithm. In order to verify the effectiveness of the modified conjugate gradient algorithm, three physical models of electromagnetic tomography system are constructed, and the modified conjugate gradient algorithm is compared with the traditional algorithm. The experimental results show that the reconstructed image quality of the modified spectral conjugate gradient algorithm is higher and has better numerical performance. The hybrid conjugate gradient algorithm highlights the advantages of the Fletcher-Reeves and Polak-Ribiere-Polyaks algorithms. The convergence speed is faster than the Polak-Ribiere-Polyak method, and the imaging quality is higher than the other algorithms. [ABSTRACT FROM AUTHOR]

In this paper, inertial splitting algorithms for nonlinear operators of pseudocontractive and accretive types are proposed. Weak and strong convergence theorems are established in uniformly convex and q-uniformly smooth Banach spaces. Numerical examples are given to illustrate the effectiveness of our proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes a constitutive model of fiber reinforced concrete (FRC) composites under uniaxial tensile load considering fiber bridging effect, and the corresponding numerical algorithm is given. Due to the linear simplification on plain concrete's constitutive model, and adoption of the explicit modified Euler integral algorithm, the solving process of this numerical model is greatly simplified. In addition, the numerical implementation algorithm of the model in ABAQUS through user-defined subroutine (UMAT) is given. The finite element calculation results and the experimental results under uniaxial tensile load are in good agreement, which proves that the model has good accuracy in predicting the peak stress of FRC under different fiber content. The simplification method proposed and the solution algorithm induced in this paper can ensure the stability and accuracy of the results, which provide a feasible development direction for FRC calculation in large-scale structures. [ABSTRACT FROM AUTHOR]

This paper proposes an improved zero-mean normalization sum of squared differences (ZNSSD) algorithm to solve the problem of the inability of traditional structural measurement to extract high-frequency vibration signals. In the proposed technique, the high-speed image sequence of target vibration is captured by a high-speed camera. Then, the ZNSSD template matching algorithm with subpixel accuracy is introduced to process the captured images in the computer. Additionally, a modified search algorithm, the ZNSSD template matching algorithm based on image pyramid (ZNSSD-P), is proposed to significantly reduce the computation time and increase efficiency. Then, a jumping ZNSSD template matching algorithm based on image pyramid (J-ZNSSD-P) is proposed to further improve the efficiency of the ZNSSD-P algorithm. Vibration signals were extracted with Grating Ruler Motion Platform and sound barriers. Results show that the vibration signal extraction method has high precision and efficiency. [ABSTRACT FROM AUTHOR]

In order to realize energy saving and high-efficiency production in the CNC turning process of parts, a novel hybrid approach for cutting parameters' optimization considering processing energy and efficiency is proposed. Through experimental fitting and orthogonal experiments, the no-load power and cutting power functions of the CNC lathe were obtained, and a multi-objective optimization model of energy saving and high efficiency was established. The model takes the minimum energy consumption and the fastest processing time as the goal, and comprehensively considers the actual constraints under the processing conditions of the lathe. An improved multi-objective teaching-learning-based optimization (TLBO) algorithm is designed to solve the multi-objective model, a self-study method is added in the learning stage to improve the global search ability of the algorithm, and a minimum partial optimization strategy is added in the teaching stage to ensure the balance of multi-objective optimization results. Finally, the proposed model and algorithm are verified via a typical case, the cutting parameter decision model is established by using analytic hierarchical process (AHP), and the Pareto front solution of cutting parameter optimization is obtained, which objectively selects a better combination of turning parameters. The results show that this method can guide the operator to select cutting parameters from qualitative random to quantitative, and provides theoretical guidance for enterprises implementing energy saving and high-efficiency production. [ABSTRACT FROM AUTHOR]

This work focuses on the scheduling problem of deadlock and failure-prone automated manufacturing systems, and presents a new scheduling method by combining a robust supervisory control policy and hybrid heuristic search. It aims to minimise makespan, i.e. the completion time of the last part. Based on the extended reach ability graph of the system, it establishes a new heuristic function and two dispatching rules to guide the search process for a schedule. By embedding a robust supervisory control policy into the search process, it develops a polynomial robust dynamic window search algorithm. Failure and repair events of unreliable resources may occur during the execution of a schedule obtained by the proposed algorithm and may make the schedule infeasible. To reduce the influence caused by them and ensure all parts to be finished, this work proposes two event-driven strategies. The first one suspends the execution of the parts requiring failed resources and those to be started until all failed resources are repaired and permits only those parts that have already been processed on working machines to be completed. The second one invokes the proposed algorithm to obtain a new schedule at the vertex generated after a resource failure or repair event and executes the new schedule. Both strategies are effective while the latter performs better at the expense of more computation. [ABSTRACT FROM PUBLISHER]

Dispatching and Control Cloud (DCC) is a cloud platform constructed by State Grid Corporation with the technology of cloud computing. DCC has improved the overall operation and monitoring capabilities, the smart level and many other advantages of power grid. However, the development of DCC has been hampered by security and privacy issues. Secure unified identity authentication, access control and authorisation management are significant topics in DCC. To find out a solution for the topics above, we have employed some encryption schemes using the attribute-based encryption(ABE), as ABE can preserve users' privacy and achieve access control with fine grain over the encrypted information. SM9 is a kind of Chinese official standard in the field of cryptography, which contains the SM9 encryption algorithm (SM9-IBE). In this paper, an ABE scheme based on SM9-IBE is proposed, making SM9 support fine-grained access control which would be better applied in DCC. Our proposed scheme (SM9-ABE) has been proven to be of great security in the selective CPA model under DBDH assumption. Furthermore, we implement SM9-ABE and evaluate its practical performance. The implementation indicates our scheme performs well in the matter of security and functionality, at an additional time cost which is acceptable. [ABSTRACT FROM AUTHOR]

Mud pulse transmission technology is widely used in logging while drilling (LWD), while its low data transmission efficiency has restricted the development of LWD technology for a long time. In order to improve the equivalent data transmission efficiency of LWD system, a data compression method, namely, Frame Prediction Huffman Coding (FPHC) with a group of frames, is proposed in this paper. Several factors including the data accuracy, the need for real-time transmission, and the existence of transmission errors are comprehensively considered in the proposed method. Through the probability statistical analysis of LWD data, it is proved that the data have a relatively stable probability distribution after predictive coding. In addition, the Huffman code table generated from predictive coding is universally applicable to other LWD data. Through data compression experiments on logging data, it is verified that the proposed method has higher compression efficiency compared with Huffman coding, Lempel–Ziv–Welch algorithm, adaptive Huffman coding and adaptive arithmetic coding. This lossless compression method will have a good application prospect because of its ability to prevent error diffusion, simple implementation and easy to be embedded in down-hole equipment. [ABSTRACT FROM AUTHOR]

In this study, a new low-speed control strategy based on active disturbance rejection control (ADRC) + fractional-order proportion integration differentiation (FOPID) is proposed to realize fast and accurate control of permanent magnet synchronous motor (PMSM) at low speed. First, a new nonlinear function with enhanced smoothness and continuity around the origin is designed. On the basis of the above nonlinear function, an improved extended state observer (ESO) is developed. Then, the parameters of ESO are adjusted online by the designed fuzzy rules. Considering the improved dynamic performance of the FOPID controller, the nonlinear state error feedback control law module of ADRC is replaced by FOPID to calculate the proportion, differential, and integral of the error signal. Finally, the simulation analysis of the new low-speed control system based on ADRC + FOPID is carried out. The results show that the proposed algorithm has faster response speed and better anti-interference ability in the low-speed control of PMSM than the traditional ADRC. [ABSTRACT FROM AUTHOR]

The experience-based learning (EBL) algorithm is a new global optimization algorithm that is free from any algorithm-specific control parameters and has been applied to solve structural damage identification problems. However, similar to other metaheuristic algorithms, the EBL algorithm has its disadvantages. To obtain better searching performance, a modified EBL algorithm is proposed for solving the nonlinear hysteretic parameter identification problem with a Bouc–Wen type model. A new updating equation is introduced to improve the global optimization ability of the algorithm. Numerical studies on a single-degree-of-freedom system with or without degradation and pinching are conducted to investigate the efficiency and robustness of the proposed algorithm. A laboratory test of four precast concrete infill walls is presented and their hysteretic parameters are also successfully identified. Both numerical and laboratory results are compared with those obtained from the original EBL algorithm, a cloud model based fruit fly optimization algorithm, the Jaya algorithm, a particle swarm optimization algorithm and a squirrel search algorithm, demonstrating the superiority of the proposed method for nonlinear hysteretic parameter identification. [ABSTRACT FROM AUTHOR]

Land-use maps are instrumental to inform urban planning and environmental research. Street view images (SVIs) have shown great potential for automated land-use classification for land-use mapping. However, previous studies overlooked SVI-derived location contextual information that may help improve land-use classification. This study proposes a novel land-use classification method that synthesizes location semantics from SVIs to account for contextual information from SVIs, land parcels and roads around the SVIs. The proposed method first generates land-use scene images (LUSIs) by using an SVI-derived straightforward algorithm. The LUSIs are then relocated to land parcels by using a displacement strategy and classified into land-use types by using a deep learning network. This study determines the land-use types of land parcels with classified LUSIs. Two case studies, consisting of LUSIs for five land-use types, show that introducing location semantics of SVIs can remarkably improve the classification accuracy of land-use types. [ABSTRACT FROM AUTHOR]

Partial absence of face information challenges the robustness of face recognition algorithms. In order to reduce the effect of partial information loss on face recognition, a Face Recognition Method based on partitioning Collaborative Representation (FRAPCR) is proposed in this paper. Firstly, the face image is divided into several sub-blocks. Secondly, the Collaborative Representation (CR) is used to calculate the minimum sparse representation coefficient of each sub-block and the residual between the sub-block and the corresponding samples of each class, taking the class corresponding to the minimum residual as the class to which the sub-block belongs. Thirdly, a voting mechanism is introduced to count the categories of all sub-blocks of each face image, and the category with the largest number of votes is the category to which the whole face image belongs. Through the experiments on face databases ORL, Extend Yale B, and AR by the proposed method (FRAPCR), the best partitioning way of the face image is obtained. When there is partial information missing (pixel information missing, corrosion block and occlusion) in the face image, the images in each face database is divided in its corresponding optimal partitioning way. And comparative experiments between the FRAPCR and traditional CR face recognition methods are performed. The results show that FRAPCR has high recognition rate and stable recognition effect when there is partial information missing in face images. [ABSTRACT FROM AUTHOR]

Sparse arrays play an important role in the widely used acoustic and electromagnetic applications such as sonar, radar, and satellite. In this paper, a recently proposed swarm intelligence algorithm, salp swarm algorithm (SSA), is introduced for design of both linear and planar sparse antenna arrays with optimum sidelobe level (SLL). The design of sparse antenna arrays can be regarded as a nonlinear optimization problem, which the main optimization goal is finding optimum excitations and positions for antenna elements to achieve antenna array beam pattern with desired SLL. The SSA is a new kind of swarm intelligence algorithm which inspired by the swarming behavior of salps. Compared to other evolutionary algorithms, the SSA requires few algorithm-specified tuning parameters, which makes it simple and easy to use for engineering. Illustrative examples of both linear and planar arrays are given to verify the validity of the SSA for SLL reduction of sparse arrays. [ABSTRACT FROM AUTHOR]

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This article presents a Bayesian approach for simultaneously calibrating models and soil parameters using field data on wall deflections and ground surface settlement from braced excavations. This approach is developed based on the Metropolis–Hastings algorithm, the Gibbs algorithm, and hybrid Markov chain Monte Carlo simulation. The Formosa excavation case history is adopted to demonstrate this approach. The predictions are made by using both calibrated models and soil parameters, and the predicted excavation-induced responses from the models are shown to have greater accuracy and less variability than the responses obtained when updating the soil parameters only. This Bayesian approach is effective given the availability of reasonably estimated prior distributions and field observation data with good quality. [ABSTRACT FROM AUTHOR]

Direct visual odometry (DVO) is an important vision task which aims to obtain the camera motion via minimizing the photometric error across the different correlated images. However, the previous research on DVO rarely considered the motion bias and only calculated using single direction, therefore potentially ignoring useful information compared with leveraging diverse directions. We assume that jointly considering forward and backward calculation can improve the accuracy of pose estimation. To verify our assumption and solid this contribution, in this paper, we test various combination of direct dense methods, including different error metrics, e.g., (intensity, gradient magnitude), alignment strategies (Forward-Compositional, Inverse-Compositional), and calculation directions (forward, backward, and bi-direction). We further study the issue of motion bias in RGB-D visual odometry and propose four strategy options to improve pose estimation accuracy, e.g., joint bi-direction estimation; two stage bi-direction estimation; transform average with weights; and transform fusion with covariance. We demonstrate the effectiveness and efficiency of our proposed algorithms across a range of popular datasets, e.g., TUM RGB-D and ICL-NUIM, in which we achieve an impressive performance through comparing with state of the art methods and provide benefits for existing RGB-D visual odometry and visual SLAM systems. [ABSTRACT FROM AUTHOR]

This paper presents a new robust predictive controller for a special class of continuous-time non-linear systems with uncertainty. These systems have bounded disturbances with unknown upper bound as well as constraints on input states. The controller is designed in the form of an optimization problem of the 'worst-case' objective function over an infinite moving horizon. Through this objective function, constraints and uncertainties can be applied explicitly on the controller design, which guarantees the system stability. Next, LMI tool is used to improve the calculation time and complexity. To do this, in order to find the optimum gain for state-feedback, the optimization problem is solved using LMI method in each time step. Finally, to show the efficiency and effectiveness of the proposed algorithm, a surge phenomenon avoidance problem in centrifugal compressors is solved. [ABSTRACT FROM AUTHOR]

This paper presents a two-stage approach, named Cooperatively Coevolving Competition Swarm Optimiser (C3SO), for large-scale optimisation. C3SO first detects interactions among the original variables by a differential grouping algorithm, therein decomposing a large-scale problem into several sub-components. In the next stage, a new approach is developed using the competition mechanism to independently optimise each sub-component obtained in the first stage. Hence, C3SO takes advantages of both divide-and-conquer and competition strategies, achieving the ability to address large-scale and complex optimisation problems. The proposed method is evaluated by comparing with several state-of-the-art algorithms on different benchmark functions, and the comparative results demonstrated its effectiveness. [ABSTRACT FROM AUTHOR]

A simple yet powerful optimization algorithm, named the experience-based learning (EBL) algorithm, is proposed in this article for structural damage identification based on vibration data. This algorithm is free from any algorithm-specific control parameters and requires only common control parameters. The natural frequencies and/or mode shapes are utilized in establishing an objective function. The efficiency and robustness of the proposed method are demonstrated by two numerical examples, namely a television tower and a functionally graded material beam. A set of experimental work on a cantilever beam is studied for further verification. Both numerical and experimental results confirm the superiority of the proposed EBL algorithm in terms of convergence and accuracy for structural damage identification, in comparison with particle swarm optimization, the cloud model-based fruit fly optimization algorithm, squirrel search algorithm and teaching–learning-based optimization. [ABSTRACT FROM AUTHOR]

On the premise of satisfying the process priority relationship, there are many kinds of feasible sequencing schemes. How to obtain the optimal process sequencing meeting the process priority relationship has always been a popular research area in the field of CAPP (Computer-Aided Process Planning). Currently, some achievements have been made in the field of small and medium-size problems. For large-size problems, due to the explosion of solution space, the existing bionic algorithms are easy to fall into local optimum or even non-convergence. In this paper, a hybrid algorithm combining genetic algorithm and variable neighborhood search is proposed to solve the above problems. The basic idea is to decompose the complex and huge solution space into relatively simple multi-neighborhood spaces, and then search in each neighborhood space by genetic algorithm in turn. The global optimal solution is obtained when a solution is the best solution through all neighborhood spaces. Based on this idea, the hybrid algorithm framework and neighborhood construction rules are developed, and the implementation steps of the hybrid algorithm are detailed. Taking a real-world case as the case study, the feasibility and superiority of the proposed hybrid algorithm are demonstrated by algorithm comparison tests. [ABSTRACT FROM AUTHOR]

Conventional feature detection algorithms are largely based on clustered two-dimensional (2D) blocks of information. However, corners located at the centre of gradually greying blocks of information cannot be extracted using these algorithms. The edge feature points described by the algorithms are often affected by background changes, leading to significant differences in the descriptors for the same feature. These issues are detrimental to subsequent matching processes. Therefore, we propose a new feature detection method that will provide more useful corner information for subsequent tracking and detection processes, particularly for edge features. The edge information of corners is used to search for points that satisfy the requirements for inner greyscale consistency. The points are then used to construct point-symmetric structures. The zeroth-order inner greyscale data, first-order gradient orientation differences, and angular directions of the point-symmetric connections of the structures are considered structural attributes, which help search for feature points. Similar feature points are then clustered using a hierarchical clustering algorithm, followed by extracting the feature points from point pair features of the same type. It was experimentally demonstrated that the proposed point-symmetric structural features would help increase the number of valid feature points that can be extracted from an image. [ABSTRACT FROM AUTHOR]

The satellite photograph scheduling problem has attracted increasing attention owing to its difficulty of solution and remarkable economic importance. The optimization problem involves scheduling the on-board cameras of the Earth observation satellite to gain more profit from the oversubscribed photograph requests. To tackle the real-world problem, a hybrid heuristic algorithm is developed. The algorithm skilfully integrates the common genetic operators and a well-designed two-phase repair operator into the evolutionary framework of an artificial bee colony. The performance of the proposed algorithm is compared with that of three state-of-the-art metaheuristics on an open test suite consisting of 20 benchmark instances and a new test set composed of 10 randomly generated large-scale problems. The experimental results indicate that the proposed algorithm achieves the best performance. [ABSTRACT FROM AUTHOR]

Concerning the drawbacks that particle swarm optimisation algorithm is easy to fall into the local optima, and has low solution precision, the simplified particle algorithm which based on the nonlinear decrease extreme disturbance and Cauchy mutation is proposed. The algorithm simplifies particle updating formula, and uses logistic chaotic sequence to initialise the particle position, which can improve the global search ability of population; nonlinear decrease extreme disturbance strategy enhanced the diversity of the population and avoid the particles trapping in local optimum; a novel Cauchy mutation is used for the optimal particle variation to generate more optimal guiding particle movement. The experimental simulation on seven typical test functions shows that the proposed algorithm can effectively avoid falling into local optimal solution, the search speed and optimisation accuracy have improved significantly. The algorithm is suitable to solve the function optimisation problem. [ABSTRACT FROM AUTHOR]

A single pixel in time of flight cameras receives multiple reflected light from different scene points, resulting in erroneous depth information. In this paper, based on the proposed sparse decomposition, a coarse and fine sparse decomposition based on compressed sensing is applied to multipath separation. The applied method uses a linear combination of multiple frequency signals to modulate the source. The measured vector obtained through finite random measurements is subjected to two sparse decompositions – rough separation and detailed positioning, and finally the minimum direct path depth is accurately recovered. Under the premise of the same number of measurements, calculation amount, and storage space, the accuracy of the coarse and fine sparse decomposition based on compressed sensing is improved by nearly an order of magnitude compared to the sparse decomposition without compressed sensing. Moreover, our method can basically achieve multi-path separation accuracy to the sub-millimeter level. [ABSTRACT FROM AUTHOR]

In a one-of-a-kind production (OKP) company, the operation routing and processing time of an order are usually different from the others due to high customisation. As a result, an OKP company needs to dynamically adjust the production resources to keep the production lines reconfigurable. Through a proper assignment of operators in different sections of a production line, bottlenecks and operator re-allocation during production can be reduced effectively. In this paper, a mathematical model is introduced for optimal operator allocation planning on a reconfigurable production line in OKP. The optimisation objectives are to minimise the total number of the operators, total job earliness and tardiness, and the average work-in-process storage. A branch-and-bound algorithm with efficient pruning strategies is developed to solve this problem. The proposed model and the algorithm are empirically validated by using the data of a windows and doors manufacturing company. A software system based on the proposed approach has been implemented in the company. [ABSTRACT FROM AUTHOR]

Some dominance properties are proposed for the NP-hard problems of scheduling N jobs on a single machine with due dates, and sequence-dependent setup times. The algorithms based on Ragatz's branch and bound scheme with the dominance properties are developed to minimize the maximum tardiness or the total tardiness. Computational experiments demonstrate the effectiveness of the dominance rules. [ABSTRACT FROM AUTHOR]

Over the last decade, there has been a rapid growth of the use of genetic algorithms in the various areas of production and operations management. This paper provides a review of genetic algorithms research published in twenty-one major production and operations management journals from 1990–2001. More specifically, it identifies research trends and publication outlets of genetic algorithms applications. Our findings show that there are only a handful of production and operations management areas to which genetic algorithms have been applied as the solution approach. Furthermore, we recognize and discuss potential research areas and outlets in which researchers may target their work as well as the need for top ranked POM journals to consider publishing genetic algorithms related papers. [ABSTRACT FROM AUTHOR]

A new fabric defect detection algorithm is presented in this paper. Instead of a Gabor filter bank, an optimal Elliptical Gabor filter (EGF) is used in the proposed algorithm. For a given non-defective template image, the parameters of the optimal EGF is determined by using the random drift particle swarm optimization (RDPSO) algorithm during the training procedure. During the detecting procedure, a sample image containing the same texture background with the template image is convolved with the optimal EGF. Then, defects discrimination can be carried out to determine whether the sample image contains defects or not. Finally, if the sample image is distinguished to have defects, an adaptive thresholding technique can be applied to locate the defects. Extensive experiments have been carried out to demonstrate the effective detecting performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Detail enhancement algorithms are important for raw infrared images to improve their overall contrast and highlight important information in them. To solve the problems that current algorithms like GF&DDE have, an improved adaptive detail enhancement algorithm for infrared images based on a guided image filter is proposed in this paper. It chooses the threshold for the base layer image adaptively according to the histogram statistical information and adjusts the mapping range of the histograms according to the dynamic range of the image. Besides, the detail layer is handled by a simpler adaptive gain control method to achieve the good detail enhancement effect. Finally, the base layer and the detail are merged according to the approximate proportion of the background and the details. Experimental results show that the proposed algorithm can adaptively and efficiently enhance different dynamic range images in different scenarios. Moreover, this algorithm has high real-time performance. [ABSTRACT FROM AUTHOR]

Due to the wide contact area between the tool and the machining surface in flank milling, the engagement relationship between them cannot be well described by the local differential property of the surface. In terms of this problem, a new global space based approach is presented to realise the path planning for wide strip flank milling with barrel cutter. Firstly, the method for calculation of the global space in machining process is given through the analysis of the engagement relationship between the barrel cutter and the machined surface, and the error in the global space is analysed. Secondly, the maximum strip width-based optimisation of tool axis is established combining with the engagement relationship, and the corresponding tool orientation and feed direction are determined. Thirdly, by establishing the maximum strip width vector field in the parameter domain and combining the stream function to determine the initial toolpath, the path planning of the flank milling with the barrel cutter is achieved. Finally, the developed algorithm is validated by geometrical simulation and comparing with existing methods. [ABSTRACT FROM AUTHOR]

In this paper, we consider the varying stepsize gradient projection algorithm (GPA) for solving the split equality problem (SEP) in Hilbert spaces, and study its linear convergence. In particular, we introduce a notion of bounded linear regularity property for the SEP, and use it to establish the linear convergence property for the varying stepsize GPA. We provide some mild sufficient conditions to ensure the bounded linear regularity property, and then conclude the linear convergence rate of the varying stepsize GPA. To the best of our knowledge, this is the first work to study the linear convergence for the SEP. [ABSTRACT FROM AUTHOR]

In order to realize accurate control of underwater vehicles, this paper proposed a fractal prediction model of surge. Firstly, we introduce the current state-of-the-art of fractal prediction; secondly, we complete a fractal analysis with the R_S method, calculating the fractal dimension of surge using the short-time dimension method; thirdly, we correct the vertical scaling factor of the Iterated Function System (IFS) using the fractal dimension. Then the predictive value can be calculated through anti-iteration according to the statistical significantly IFS and proposing a fractal prediction model. Finally, we complete a simulation analysis based on the actual data and a comparison to another prediction algorithm. The result shows that the fractal prediction algorithm of this article has better comprehensive performance. [ABSTRACT FROM PUBLISHER]

In this letter, a novel alternating direction implicit (ADI) iterative algorithm for efficient 3-D weighted Laguerre polynomials finite-difference time-domain (WLP-FDTD) method is proposed. Using this approach, the Gauss-Seidel method can be applied in the whole iterative procedure, instead only the first step in the efficient WLP-FDTD. A correction item is added to speed up the convergence. The proposed method is based on the iterative algorithm for linear equations, which makes it more flexible. Numerical examples are given to demonstrate the computation accuracy and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

The detection of building changes plays an important role for land management and urban planning. This study proposes an automatic method that applies the morphology-based method for detection of building changes with multi-temporal airborne Light Detection and Ranging (LiDAR) data. In this method, three types of land use are classified as ground, building and vegetation by Triangulated Irregular Network (TIN) filter and Support Vector Machine (SVM) with the two sets of point clouds acquired at different times. Thereafter, the morphological algorithm is applied to distinguish the changed buildings from trees, which significantly reduces the identification of errors. The changed building objects are classified as three types: ‘newly built’, ‘taller’ and ‘demolished’, with the use of rule analysis. The case study area is located in Liujia village, Shenyang, China. The two sets of LiDAR test data were acquired by Leica ALS 60 (3 points/km2) and ALS 80 (15 points/km2) from September 2014 and February 2017, respectively. Field investigation proves that the accuracy of the identified changed buildings of ‘newly built’, ‘demolished’ and ‘taller’ are 95.4%, 92.9% and 100%. The proposed method has the advantages of high accuracy and reliability, which will provide a reference for the detection of building changes. [ABSTRACT FROM PUBLISHER]

Underground coalmine roof bolting operators exhibit a continued risk for overexposure to airborne levels of respirable coal and crystalline silica dust from the roof drilling operation. Inhaling these dusts can cause coal worker's pneumoconiosis and silicosis. This research explores the effect of drilling control parameters, specifically drilling bite depth, on the reduction of respirable dust generated during the drilling process. Laboratory drilling experiments were conducted and results demonstrated the feasibility of this dust control approach. Both the weight and size distribution of the dust particles collected from drilling tests with different bite depths were analyzed. The results showed that the amount of total inhalable and respirable dust was inversely proportional to the drilling bite depth. Therefore, control of the drilling process to achieve proper high-bite depth for the rock can be an important approach to reducing the generation of harmful dust. Different from conventional passive engineering controls, such as mist drilling and ventilation approaches, this approach is proactive and can cut down the generation of respirable dust from the source. These findings can be used to develop an integrated drilling control algorithm to achieve the best drilling efficiency as well as reducing respirable dust and noise. [ABSTRACT FROM PUBLISHER]

In panel data analysis, predictors may impact response in substantially different manner. Some predictors are in homogenous effects across all individuals, while the others are in heterogenous way. How to effectively differentiate these two kinds of predictors is crucial, particularly in high-dimensional panel data, since the number of parameters should be greatly reduced and hence lead to better interpretability by homogenous assumption. In this article, based on a hierarchical Bayesian panel regression model, we propose a novel yet effective Markov chain Monte Carlo (MCMC) algorithm together with a simple maximum ratio criterion to detect the predictors in homogenous effects in high-dimensional panel data. Extensive Monte Carlo simulations show that this MCMC algorithm performs well. The usefulness of the proposed method is further demonstrated by a real example from China financial market. [ABSTRACT FROM AUTHOR]

Hyperspectral images are often unavoidably degraded by blur and noise in the acquisition process, which influences subsequent image processing and analysis. Under the maximum a posteriori framework, this article introduces a hyperspectral image deconvolution method based on spectral-spatial total variation prior and an explicit nonnegative constraint, which can preserve the edge in the spatial domain and maintain the discontinuity along the spectral dimension. Additionally, the proposed method fully exploits the spectral correlation between adjacent bands. The alternating direction method of multipliers (ADMM) is employed to efficiently optimize the proposed spectral-spatial total variation deconvolution model. The reason for this interest is that ADMM decomposes a difficult optimization problem into a sequence of much simpler and easily solved sub-problems. The regularization parameter is updated adaptively to make the proposed algorithm more stable. Numerical experiments are conducted in both simulated and real data to verify the performance of the proposed method. [ABSTRACT FROM AUTHOR]

In the aspect of numerical methods for incompressible flow problems, there are two different algorithms: semi-implicit method for pressure-linked equations (SIMPLE) series algorithms and the pressure Poisson algorithm. This paper introduced a new discretized pressure Poisson algorithm for the steady incompressible flow based on a nonstaggered grid. Compared with the SIMPLE series algorithms, this paper did not introduce three correction variables. So, there is no need to implement the guess-and-correct procedure for the calculation of pressure and velocity. Compared with the pressure Poisson algorithm, there is no need to calculate unsteady Navier–Stokes equations for steady problems in the new discretized pressure Poisson algorithm. Meanwhile, as the finite volume method and cell-centered grid are used, the governing equation for pressure is obtained from the continuity equation and the boundary conditions for pressure are easily obtained. This new discretized pressure Poisson algorithm was tested at the lid-driven cavity flow problem on a nonstaggered grid and the results are also reliable. [ABSTRACT FROM AUTHOR]

The result of feature selection for software birthmark has a direct bearing on software recognition rate. In this paper, we apply constrained clustering to analyze the software features (SF). The within-class (homogeneous software) and between-class (heterogeneous software) distances of features are measured based on mutual information. Information gain functions and penalty functions are constructed using homogeneous and heterogeneous SF, respectively; and redundancy is measured with correlation coefficients. Then the software birthmark features with high class distinction and minimum redundancy are selected. The example of extracting and detecting framework of birthmark feature is also given. The algorithm is analyzed and compared with the similar algorithms, and it is shown the algorithm provide an effective approach for software birthmark selection and optimization. [ABSTRACT FROM PUBLISHER]

A hyperspectral imaging system has been built for detecting external insect damage and acquiring reflectance images from jujubes in the near-infrared region of 900–1700 nm. Spectral information was extracted from each jujube, and six optimal wavelengths (987, 1028, 1160, 1231, 1285, and 1464 nm) were obtained using principal component analysis. The first principal component images (PC-1) using the selected six wavelengths were obtained for further image processing. The detection algorithm was then developed based on principal component analysis and two-band ratio (R1160/R1464) coupled with image subtraction algorithm (R1160-R1464). An identification accuracy of 93.1% for insect-infested jujubes and 100% classification rate for the intact ones were achieved. The results of this research demonstrated that it is feasible to discriminate insect-infested jujubes from intact jujubes using the near-infrared hyperspectral imaging technology. [ABSTRACT FROM PUBLISHER]

In this paper, we aim to solve the finite-horizon optimal control problem for a class of non-linear discrete-time switched systems using adaptive dynamic programming(ADP) algorithm. A new ε-optimal control scheme based on the iterative ADP algorithm is presented which makes the value function converge iteratively to the greatest lower bound of all value function indices within an error according to ε within finite time. Two neural networks are used as parametric structures to implement the iterative ADP algorithm with ε-error bound, which aim at approximating the value function and the control policy, respectively. And then, the optimal control policy is obtained. Finally, a simulation example is included to illustrate the applicability of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a novel theoretic formulation based on adaptive dynamic programming (ADP) is developed to solve online the optimal tracking problem of the continuous-time linear system with unknown dynamics. First, the original system dynamics and the reference trajectory dynamics are transformed into an augmented system. Then, under the same performance index with the original system dynamics, an augmented algebraic Riccati equation is derived. Furthermore, the solutions for the optimal control problem of the augmented system are proven to be equal to the standard solutions for the optimal tracking problem of the original system dynamics. Moreover, a new online algorithm based on the ADP technique is presented to solve the optimal tracking problem of the linear system with unknown system dynamics. Finally, simulation results are given to verify the effectiveness of the theoretic results. [ABSTRACT FROM PUBLISHER]

In view of the common existing problems in present video-to-video super-resolution reconstruction, this paper proposes a pioneering video-to-video super-resolution reconstruction algorithm based on segmentation and space-time regularisation to solve these problems. First, a video-to-video super-resolution reconstruction algorithm based on segmentation is proposed to eliminate reconstructed temporal ringing and to improve the times of reconstruction. Second, considering that image mosaic is involved in our proposed reconstruction algorithm, an improved fade-in and fade-out method is proposed to make the mosaic image looks more natural. At last, an improved space-time regularisation algorithm is put forward to remove noise and preserve image edge at the same time. Using several experiments, we prove that the proposed algorithm can achieve state-of-the -art reconstruction effect. [ABSTRACT FROM AUTHOR]

Micro-motion features are very useful for target recognition in inverse synthetic aperture radar (ISAR). In this paper, we propose an effective algorithm for micro-motion feature extraction in ISAR imaging with sparse aperture. The micro-Doppler effect induced by rotational micro-motion in ISAR with sparse aperture is analysed first, and then an improved four-parameter Hough transform and an Orthogonal matching pursuit decomposition method are proposed to extract the micro-motion parameters, such as rotation rate and radius. Simulations are given to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The present study examined genetic variability among Clonorchis sinensis isolates from four different geographical localities (Guangzhou, Nanning, Jiamusi and Daqing) and host species (cats, dogs, human and rabbits) in Mainland China by sequence analyses of two mitochondrial DNA (mtDNA) genes, namely NADH dehydrogenase subunits 2, 5 ( nad2 and nad5) and ribosomal internal transcribed spacer 1 (ITS1). A portion of the ITS1, nad2 (p nad2) and nad5 (p nad5) was amplified by polymerase chain reaction separately from adult C. sinensis individuals and the amplicons were subjected to sequencing from both directions. The length of the sequences of ITS1, p nad2 and p nad5 was 643, 666 and 771 bp, respectively. The intraspecific sequence variations within C. sinensis were 0-1.7% for ITS1, 0-1.4% for p nad2 and 0-0.9% for p nad5. The interspecific sequence variations within other zoonotic trematodes, which were published previously, were 4.5-84.9% for ITS1, 21.9-43.6% for p nad2 and 19.2-48.9% for p nad5. The A+T contents of the sequences were 45.26-45.88% (ITS1), 62.91-63.51% (p nad2) and 58.24-58.63% (p nad5). Phylogenetic analyses using ribosomal and mitochondrial sequence data set, with three different computational algorithms (Bayesian inference, maximum parsimony and maximum likelihood), all revealed distinct groups with high statistical support. These findings demonstrated the existence of low-level intraspecific variations in ribosomal DNA (rDNA) and mtDNA sequences among C. sinensis isolates from four different regions and hosts in China and elucidated that mtDNA sequences and rDNA sequences provided reliable genetic markers for phylogenetic studies of zoonotic trematodes. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) is an important parameter controlling many biological and physical processes associated with vegetation on the Earth's surface. In this study, an algorithm for estimating LAI from the ICESat (Ice, Cloud and land Elevation Satellite)/GLAS (Geoscience Laser Altimeter System) data was proposed and applied to a forest area in the Tibetan Plateau. First, Gaussian decomposition of the GLAS waveform was implemented to identify the ground peaks and calculate the ground and canopy return energy. Second, the ground-to-total energy ratio (Er) was computed as the ratio of the ground return energy to the total waveform return energy for each GLAS footprint. Third, a regression model between theErand the field-measured LAI was established based on the Beer–Lambert law. The coefficient of determination (R2) of the model was 0.81 and the root mean square error (RMSE) is 0.35 (n = 23,p< 0.001). Finally, the leave-one-out cross-validation procedure was used to assess the constructed regression model. The results indicate that the regression model is not overfitting the data and has a good generalization capability. We validated the accuracy of the GLAS-predicted LAIs using the other 15 field-measured LAIs (R2= 0.84), and the result shows that the accuracy of the GLAS-predicted LAI is high (RMSE = 0.31). [ABSTRACT FROM AUTHOR]

This paper integrates characteristics of the mould manufacturing industry with grid computing technologies and proposes the concept of mould manufacturing grid. In order to realise sharing and collaboration of manufacturing resources, a commonly unified resource model is needed to support expandability. Manufacturing service demands are classified into three different levels from a business point of view. Manufacturing services and grid nodes can then be divided correspondingly. Part and process features are used in describing service demands from the viewpoints of customers and production plans of manufacturers. The resulting unified meta-data model is based on the object-oriented concept. A standard and expandable resource model is established and a service match algorithm is developed based on service features. A simulation prototype of a mould manufacturing grid is demonstrated. [ABSTRACT FROM PUBLISHER]