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Structural damage identification (SDI) plays a major role in structural health monitoring (SHM), which has been demanded by researchers to better face the challenges in the aging civil engineering, such as bridge structure and building structure. Many methods have been developed for the application to the real structures, but there are still some difficulties which result in inaccurate, even false damage identification. As a variant of particle swarm optimization (PSO), bare bones particle swarm optimization (BBPSO) is a simple but very powerful optimization tool. However, it is easy to be trapped in the local optimal state like other PSO algorithms, especially in SDI problems. In order to improve its performance in SDI problems, this paper aims to propose a novel optimization algorithm which is named as bare bones particle swarm optimization with double jump (BBPSODJ) for finding a new solution to the SDI problem in SHM field. To begin with, after the introduction of sparse recovery theory, the mathematical model for SDI is established where an objective function based on l1 regularization is constructed. Secondly, according to the basic theory of the BBPSODJ, a double jump strategy based on the BBPSO is designed to enhance the dynamic of particles, and it is able to make a large change in particle searching scopes, which can improve the search behaviour of BBPSO and prevent the algorithm from being trapped into local minimum state. Thirdly, three optimization test functions and a numerical example are utilized to validate the optimization performance of BBPSO, traditional PSO, and genetic algorithm (GA) comparatively; it is obvious that the proposed BBPSODJ shows great self-adapting property and good performance in the optimization process by introducing the novel double jump strategy. Finally, in the laboratory, an experimental example of steel frame with 4 damage cases is implemented to further assess the damage identification capability of the BBPSODJ with l1 regularization. From the damage identification results, it can be seen that the proposed BBPSODJ algorithm, which is efficient and robust, has great potential in the field of SHM.

Undisturbed loess is affected by external environmental disturbances, such as wetting and freeze-thaw cycles, which cause microstructural changes that have an important impact on the structural strength of the loess. These changes in turn affect the stability of structures such as embankments, slopes, and guards. This article takes the Q3 undisturbed loess in Lintong District, Xi’an, as an example. The effects of wetting and freeze-thaw cycles on the loess expansion ratio and pore structure were studied by wetting tests, freeze-thaw cycle tests, and scanning electron microscopy (SEM). The changes in the compression index and compression modulus were studied by a confined compression test. The loess e-lgp compression curve was obtained according to the confined compression test, and the newly defined concepts of the loess structural strength, residual structural strength, and structural strength damage variable, in addition to the e-lgp compression curve, were combined with the experimental data to calculate the damage value generated by the disturbance during the sampling and preparation of loess. The deterioration of the structural strength and damage variable of loess was analyzed. Based on the microscopic statistical damage theory and Weibull distribution, the model used the volume expansion ratio as a variable to establish a statistical damage model under wetting and freeze-thaw cycles. Finally, on the basis of the test, the model parameters were determined. The models were verified by taking loess from a foundation pit in the northern suburbs of Xi’an and were in good agreement with the results of the test. Ultimately, the models have good practicability and can provide guidance for engineering design and construction.

This paper aims to further increase the prediction accuracy of the grey model based on the existing discrete grey model, DGM(1,1). Herein, we begin by studying the connection between forecasts and the first entry of the original series. The results comprehensively show that the forecasts are independent of the first entry in the original series. On this basis, an effective method of inserting an arbitrary number in front of the first item of the original series to extract messages is applied to produce a novel grey model, which is abbreviated as FDGM(1,1) for simplicity. Incidentally, the proposed model can even forecast future data using only three historical data. To demonstrate the effectiveness of the proposed model, two classical examples of the tensile strength and life of the product are employed in this paper. The numerical results indicate that FDGM(1,1) has a better prediction performance than most commonly used grey models.

The control of body-fixed hovering over noncooperative target, as one of the key problems of relative motion control between spacecrafts, is studied in the paper. The position of the chaser in the noncooperative target’s body coordinate system is required to remain unchanged, and the attitude of the chaser and the target must be synchronized at the same time. Initially, a six-degrees-of-freedom-coupled dynamic model of a chaser relative to a target is established, and relative attitude dynamics is described through using modified Rodrigues parameters (MRP). Considering the model uncertainty and external disturbances of the noncooperative target system, an adaptive nonsingular terminal sliding mode (NTSM) controller is designed. Adaptive tuning method is used to overcome the effects of the model uncertainty and external disturbances. The upper bounds of the model uncertainty and external disturbances are not required to be known in advance. The actual control law is continuous and chatter-free, which is obtained by integrating the discontinuous derivative control signal. Finally, these theoretical results are verified by numerical simulation.

When the autonomous underwater vehicle (AUV) enters the water at a small angle, the head of the AUV will be subjected to a torque that causes it to rise, which may cause the AUV to ricochet. The occurrence of ricochet will have an important impact on the trajectory stability of the AUV. In this paper, the finite element method-smoothed particle hydrodynamics (FEM-SPH) coupling algorithm, which absorbs the high efficiency of FEM and the advantages of SPH in dealing with large deformation and meshes distortion, is used to study the small-angle water entry problem of the AUV numerically. In the coupled FEM-SPH algorithm, discrete particles were used to model the zone of water, while the part of the AUV was modeled with finite elements. A contact algorithm couples the finite elements and the particles. Particular attention was paid to the influence of different head hemisphere angles and different initial conditions on the ricochet trajectory of the AUV. The critical conditions and influencing factors of the AUV ricochet phenomenon were given.

The periodic nonuniform sampling plays an important role in digital signal processing and other engineering fields. In this paper, we introduce the Gaussian regularization method to accelerate the convergence rate of periodic nonuniform sampling series. We prove that the truncation error of the Gaussian regularized periodic nonuniform sampling series decays exponentially. Numerical experiments are presented to demonstrate our result.

The backtracking search optimization algorithm (BSA) is a recently proposed evolutionary algorithm with simple structure and well global exploration capability, which has been widely used to solve optimization problems. However, the exploitation capability of the BSA is poor. This paper proposes a deep-mining backtracking search optimization algorithm guided by collective wisdom (MBSAgC) to improve its performance. The proposed algorithm develops two learning mechanisms, i.e., a novel topological opposition-based learning operator and a linear combination strategy, by deeply mining the winner-tendency of collective wisdom. The topological opposition-based learning operator guides MBSAgC to search the vertices in a hypercube about the best individual. The linear combination strategy contains a difference vector guiding individuals learning from the best individual. In addition, in order to balance the overall performance, MBSAgC simulates the clusterity-tendency strategy of collective wisdom to develop another difference vector in the above linear combination strategy. The vector guides individuals to learn from the mean value of the current generation. The performance of MBSAgC is tested on CEC2005 benchmark functions (including 10-dimension and 30-dimension), CEC2014 benchmark functions, and a test suite composed of five engineering design problems. The experimental results of MBSAgC are very competitive compared with those of the original BSA and state-of-the-art algorithms.

In the terminal guidance section of large caliber naval gun-guided projectile while striking nearshore maneuvering target, an integrated guidance and control (IGC) method based on an adaptive fuzzy and block dynamic surface sliding mode (AFCBDSM) was proposed with multiple constraints, including the impact angle, control limitations, and limited measurement of the line of sight (LOS) angle rate. The strict feedback cascade model of rolling-guided projectile IGC in space was constructed, and the extended state observer (ESO) was used to estimate the LOS angle rate and uncertain disturbances inside and outside the system, such as target maneuvering, model errors, and wind. A nonsingular terminal sliding mode (NTSM) was designed to zero the LOS angle tracking errors and LOS angle rate in finite time, with the adaptive exponential reaching law. The cascade system was effectively stabilized by the block dynamic surface sliding mode, which prevented differential explosions. To compensate for the saturated nonlinearity of canard control constraints, an adaptive Nussbaum gain function was adopted. The switching chatter of the block dynamic surface sliding mode was reduced through adaptive fuzzy control. Proven by Lyapunov theory, the LOS angle tracking error and LOS angle rate were convergent in finite time, the closed-loop system was uniformly ultimately bounded (UUB), and the system states could be made arbitrarily small at the steady state. Hardware-in-the-loop simulation (HILS) experiments showed that the AFCBDSM provided the guided projectile with good guidance performance while striking targets with different maneuvering forms.

Climate prediction is given a high priority by many countries due to its importance in mitigation of extreme weather conditions. However, the prediction is not an easy task as the climatic parameters not only show spatial variations but also temporal variations. In addition, the climatic parameters are interrelated. To overcome these difficulties, soft computing techniques are widely used in prediction of climate variables with respect to the other variables. On the other hand, Colombo, Sri Lanka, is experiencing adverse or extreme weather conditions over the last few years. However, a climate prediction study is yet to be carried out in this tropical climatic zone. Therefore, this paper presents a study, identifying relationships between the two most impacted climate parameters (atmospheric temperature and rainfall) and other climatic parameters. Artificial neural network (ANN) models are developed to define the relationships and then to predict the atmospheric temperature as a function of other parameters including monthly rainfall, minimum and maximum relative humidity, and average wind speed. Same analysis is carried out to define the prediction model to the monthly rainfall. The best algorithm out of several other ANN algorithms is chosen for the analyses. Results revealed that the atmospheric temperature in Colombo can be presented with respect to the other climatic variables. However, the rainfall does not show a greater relationship with the other climatic parameters.

More and more high-end brand companies are exploring new markets by extending low-end product lines. When selecting agents in new markets, a principal often faces the problem of identifying the promotional ability of agents. This paper aims to study the dynamic incentive of supply chain under information asymmetry for the information screening in long-term supply chain cooperation. Also, it pioneers the rhythm of information disclosure in the extant literature. By establishing three two-stage supply chain models, this paper analyzes the impact of agents’ information disclosure speed on the brand. We find that it is better to separate agents earlier out of the brand perspective; the semiseparated contract is beneficial for the brand to control information disclosure speed more flexibly and get more profits from the retailer. But, from the perspective of the supply chain, it is also possible to prefer the mixed contract. Under certain conditions, the pooling contract could also increase the profits of supply chain and improve the allocation efficiency of supply chain.

The instability of dams will bring immeasurable personal and property losses to the downstream, so it has always been a trendy topic worthy of investigation. Currently, the rigid body limit equilibrium method is the most commonly used method for the dynamic stability analysis of dams. However, under the action of earthquakes, the instability of the integral dam-foundation system threatens the safety of the dams and is of great concern. In this paper, a stability analysis method that can reflect the complex geological structural forms of dam foundations is proposed in this paper. The advantages are that this method deals with the difficulty in assuming sliding surfaces and the lack of quantitative criteria for the dynamic instability analysis of dams with complex geological structural forms of dam foundations. In addition, through the method, the sliding channels that may appear in the dam foundations can be automatically searched under random earthquake action, and the safety factors of the dynamic instability of dams be quantitatively obtained. Taking a high RCC gravity dam under construction in China as an example, the proposed method is applied to the three-dimensional finite element model of the dam-foundation system of this dam, and then the dynamic stability calculation is carried out. Through this method, the formation process of the dam foundation’s plastic zone and the failure of sliding channels with different strength reduction coefficients are studied on and analyzed detailedly, and the quantitative acquisition of the safety factors is realized. The results show that the method is reasonable and feasible, and helps provide a new idea and method for the dynamic stability analysis of dams.

The pose estimation of the aircraft in the airport plays an important role in preventing collisions and constructing the real-time scene of the airport. However, current airport target surveillance methods regard the aircraft as a point, neglecting the importance of pose estimation. Inspired by human pose estimation, this paper presents an aircraft pose estimation method based on a convolutional neural network through reconstructing the two-dimensional skeleton of an aircraft. Firstly, the key points of an aircraft and the matching relationship are defined to design a 2D skeleton of an aircraft. Secondly, a convolutional neural network is designed to predict all key points and components of the aircraft kept in the confidence maps and the Correlation Fields, respectively. Thirdly, all key points are coarsely matched based on the matching relationship and then refined through the Correlation Fields. Finally, the 2D skeleton of an aircraft is reconstructed. To overcome the lack of benchmark dataset, the airport surveillance video and Autodesk 3ds Max are utilized to build two datasets. Experiment results show that the proposed method get better performance in terms of accuracy and efficiency compared with other related methods.

Product evaluation practices spread throughout from initial creative stages to final products before communicating them to the market. They emphasize on assessing design alternatives against specified criteria, which can help promote design process, ensure design quality, and diminish design risk before making a decision. However, how to identify and improve the reliability of product evaluation opinions toward design alternatives has an important role in adding additional insurance and reducing uncertainty to successful product design. Aiming at this issue, this study employs a consensus model by integrating network analysis and analytic hierarchy process (AHP) method. The consensus model is constructed to measure the consistency of evaluators’ preferences and determine the rounds of evaluation. Complex network theory is integrated into the product evaluation process for network analysis of evaluators’ opinions, which can help determine evaluators’ weights in each evaluation round dynamically by changing the network topology. To obtain the weight of product evaluation indices, the AHP method is applied to avoid subjectivity given by evaluators. The process of the proposed method is presented, and the details are illustrated using a product evaluation example. The case study demonstrates that the proposed method is promising for improving the consensus level of evaluators’ opinions, reducing the influence of subjectivity, and finally improving the quality of design decision-making.

This study mainly establishes a novel intelligent assessment model for peak shear strength of rock joints based on the relevance vector machine (RVM). RVM is a state-of-the-art soft computing technique that has been rarely utilized in joint shear strength assessment. To establish the hybrid intelligent model, three-dimensional scanning tests and direct shear tests on 36 granite joint specimens were conducted. The peak shear strength ratio (τp/σn) is perceived as an explanation of four types of influencing factors, including joint surface roughness, strength of rock material, basic friction angle, and normal stress. In particular, the compressive strength and tensile strength of rock material are first considered together. A total of 36 experimental data were used in this study to train the RVM model to predict the peak shear strength of rock joints. The performance of the RVM model was assessed using the direct shear test data of rock joints collected from previous researches. Four different kinds of kernel functions were adopted to obtain the optimal model. Results show that the proposed model is significantly efficient in predicting the peak shear strength of rock joints. The proposed model is also a promising tool for peak shear strength of rock joints and provides a new research approach to research the mechanical properties of rock joints.

Coalbed water injection is the most basic and effective dust-proof technology in the coal mining face. To understand the influence of coalbed water injection process parameters and coalbed characteristic parameters on coal wetting radius, this paper uses Fluent computational fluid dynamics software to systematically study the seepage process of coalbed water injection under different process parameters and coalbed characteristic parameters, calculation results of which are applied to engineering practice. The results show that the numerical simulation can help to predict the wetness range of coalbed water injection, and the results can provide guidance for the onsite design of coalbed water injection process parameters. The effect of dust reduction applied to onsite coalbed water injection is significant, with the average dust reduction rates during coal cutting and support moving being 67.85% and 46.07%, respectively, which effectively reduces the dust concentration on the working face and improves the working environment.

This paper proposes a brush-type tire model with a new mathematical representation. The presented model can be seen as a generic model that describes the distributed viscoelastic force and Coulomb-like friction force, which are balancing each other at each point, in the contact patch. The model is described as a partial differential algebraic inclusion (PDAI), which involves the set-valuedness to represent the static friction. A numerical integration algorithm for this PDAI is derived through the implicit Euler discretization along both space and time. Some numerical comparisons with Magic Formula and a LuGre-based tire model are presented. The results show that, with appropriate choice of parameters, the proposed model is capable of producing steady-state characteristics similar to those of Magic Formula. It is also shown that the proposed model realizes a proper static friction state, which is not realized with a LuGre-based tire model.

The aim of this study was to explore the evolution and release rule of internal energy storage in the process of coal and gas outburst and to further reveal the mechanism of coal and gas outburst from the perspective of energy. In this paper, the experiment of gas expansion energy release of coal samples under different adsorption pressures and with different moisture contents was carried out with the self-developed experimental device for release of gas-bearing coal expansion energy under load, and the energy of the whole outburst process was divided into three parts: the total expansion energy of gas, the energy consumed by destroying and throwing out coal body and the energy released inefficiently. On the basis of reasonable assumption, the energy evolution calculation model of each part was constructed with mathematical method. By analyzing the changes and distribution rules of three parts of energy under different experimental conditions, this paper explored the controlling effects of gas pressure, water content, and other variables on the energy evolution rules in the process of coal and gas outburst. Experimental and theoretical studies showed that in the gas-dominated coal and gas outburst process, the destruction of coal body was in the form of stratification; under each experimental condition, there existed a critical gas pressure value for the occurrence of coal and gas outburst, and there was a sudden change of energy evolution near this value; the existence of water made the critical pressure and the minimum energy consumption of coal and gas outburst increase obviously; under the experimental conditions, there was a linear relationship between the critical gas pressure and water content and a positive exponential relationship between the minimum energy consumption and water content.

Flange bearing frogs are designed to provide continuous rolling surfaces for trains traveling on the through line, but the interaction between wheel and rail in a diverging line is more complex than that for a common crossing, especially including flange bearing mode and multipoint contact during the transition. The wheel load will be transited from tread to flange and back to tread, which will intensify the wheel-rail interaction. In this paper, a numerical procedure is presented for the analysis of wheel-rail rolling contact behavior and damage prediction for the flange bearing frog. The three-dimensional explicit finite element (FE) model of a wheel passing the flange bearing frog is established to obtain the dynamic wheel-rail interaction in both the facing and the trailing move. The evolution of contact forces, the distribution of adhesion-slip regions, and shear surface stress and microslip at the contact patch are revealed. Then, the competition relationship between RCF (rolling contact fatigue) and wear of a flange bearing frog is analyzed. The results of numerical simulations can contribute to an understanding of the mechanism of the transient rolling contact behavior and provide guidance in design optimization for flange bearing frogs.

In this paper, an efficient mixed spectral conjugate gradient (EMSCG, for short) method is presented for solving unconstrained optimization problems. In this work, we construct a novel formula performed by using a conjugate gradient parameter which takes into account the advantages of Fletcher–Reeves (FR), Polak–Ribiere–Polyak (PRP), and a variant Polak-Ribiere-Polyak (VPRP), prove its stability and convergence, and apply it to the dynamic force identification of practical engineering structure. The analysis results show that the present method has higher efficiency, stronger robust convergence quality, and fewer iterations. In addition, the proposed method can provide more efficient and numerically stable approximation of the actual force, compared with the FR method, PRP method, and VPRP method. Therefore, we can make a clear conclusion that the proposed method in this paper can provide an effective optimization solution. Meanwhile, there is reason to believe that the proposed method can offer a reference for future research.

Noncircular signals are widely used in the area of radar, sonar, and wireless communication array systems, which can offer more accurate estimates and detect more sources. In this paper, the noncircular signals are employed to improve source localization accuracy and identifiability. Firstly, an extended real-valued covariance matrix is constructed to transform complex-valued computation into real-valued computation. Based on the property of noncircular signals and symmetric uniform linear array (SULA) which consist of dual-polarization sensors, the array steering vectors can be separated into the source position parameters and the nuisance parameter. Therefore, the rank reduction (RARE) estimators are adopted to estimate the source localization parameters in sequence. By utilizing polarization information of sources and real-valued computation, the maximum number of resolvable sources, estimation accuracy, and resolution can be improved. Numerical simulations demonstrate that the proposed method outperforms the existing methods in both resolution and estimation accuracy.

For the first time , the Holonic Particle Swarm Optimization (HPSO ) algorithm applies multiagent theory about the improvement in the PSO algorithm and achieved good results. In order to further improve the performance of the algorithm, this paper proposes an improved Adaptive Holonic Particle Swarm Optimization (AHPSO) algorithm. Firstly, a brief review of the HPSO algorithm is carried out, and the HPSO algorithm can be further studied in three aspects: grouping strategy, iteration number setting, and state switching discrimination. The HPSO algorithm uses an approximately uniform grouping strategy that is the simplest but does not consider the connections between particles. And if the particles with larger or smaller differences are grouped together in different search stages, the search efficiency will be improved. Therefore, this paper proposes a grouping strategy based on information entropy and system clustering and combines two grouping strategies with corresponding search methods. The performance of the HPSO algorithm depends on the setting of the number of iterations. If it is too small, it is difficult to search for the optimal and it wastes so many computing resources. Therefore, this paper constructs an adaptive termination condition that causes the particles to terminate spontaneously after convergence. The HPSO algorithm only performs a conversion from extensive search to exact search and still has the potential to fall into local optimum. This paper proposes a state switching condition to improve the probability that the algorithm jumps out of the local optimum. Finally, AHPSO and HPSO are compared by using 22 groups of standard test functions. AHPSO is faster in convergence than HPSO, and the number of iterations of AHPSO convergence is employed in HPSO. At this point, there exists a large gap between HPSO and the optimal solution, i.e., AHPSO can have better algorithm efficiency without setting the number of iterations.

A multiagent system (MAS) is a mechanism for creating goal-oriented autonomous agents in shared environments with communication and coordination facilities. Distributed data mining benefits from this goal-oriented mechanism by implementing various distributed clustering, classification, and prediction techniques. Hence, this study developed a novel multiagent model for distributed classification tasks in cancer detection with the collaboration of several hospitals worldwide using different classifier algorithms. A hospital agent requests help from other agents for instances that are difficult to classify locally. The agents communicate their beliefs (calculated classification), and others decide on the benefit of using such beliefs in classifying instances and adjusting their prior assumptions on each class of data. A MAS model state and behavior and communication are then developed to facilitate information sharing among agents. Regarding accuracy, implementing the proposed approach in comparison with typically different noncommunicated distributed classifications shows that sharable information considerably increases the classification task accuracy by 25.77%.

This paper intends to numerically study the steady-state free convection heat transfer in the presence of an exothermal chemical reaction governed by Arrhenius kinetics within a right-angled enclosure of triangular shape filled by porous media saturated with magnetized nanofluid. An approximation named as Darcy–Boussinesq approximation along with a nanofluid model mathematically propounded by Buongiorno has been implemented to model physical phenomenon representing fluid flow, heat transfer, and nanoparticle concentration. The mathematical equations in a dimensionless form describing the stream function for circulation of the fluid, the energy equation for heat, and nanoparticle volume fraction for concentration are solved using the finite difference method. The validity of the numerical procedure is established by comparing present results with the formerly available works in both statistical and graphical approaches. Streamlines, isotherms, and isoconcentrations are plotted and discussed for the various parametric regimes. The graphical description depicts that the average Nusselt and Sherwood numbers are the decreasing function of the Rayleigh number. The study revealed the accountable influence of model parameters such as thermophoresis and Brownian diffusion on the local Sherwood number, whereas a minimum impact on the local Nusselt number is observed.

Aluminum alloy spool valve body material is prone to severe wear on the wall under the condition of oil contamination. Aiming at this problem, combined with the theory of liquid-solid two-phase flow and erosion wear, the wear prediction model of aluminum alloy sliding valve wall is established based on computational fluid dynamics, and the effects of turbulence and wall materials, particle size distribution, and particle shape on particle motion are discussed. The calculation of the wear prediction model is done with Fluent software. This study predicts the wear of the wall under actual working conditions and calculates the influence of particle size, particle shape, and pressure difference on the wall wear of the aluminum alloy sliding valve. The research results have certain significance for the maintenance and upkeep of aluminum alloy sliding valve wall, improved design, and life prediction.

In order to accurately diagnose the faulty parts of the rolling bearing under different operating conditions, the KJADE (Kernel Function Joint Approximate Diagonalization of Eigenmatrices) algorithm is proposed to reduce the dimensionality of the high-dimensional feature data. Then, the VNWOA (Von Neumann Topology Whale Optimization Algorithm) is used to optimize the LSSVM (Least Squares Support Vector Machine) method to diagnose the fault type of the rolling bearing. The VNWOA algorithm is used to optimize the regularization parameters and kernel parameters of LSSVM. The low-dimensional nonlinear features contained in the multidomain feature set are extracted by KJADE and compared with the results of PCA, LDA, KPCA, and JADE methods. Finally, VNWOA-LSSVM is used to identify bearing faults and compare them with LSSVM, GA-LSSVM, PSO-LSSVM, and WOA-LSSVM. The results show that the recognition rate of fault diagnosis is up to 98.67% by using VNWOA-LSSVM. The method based on KJADE and VNWOA-LSSVM can diagnose and identify fault signals more effectively and has higher classification accuracy.

The method of single image-based dehazing is addressed in the last two decades due to its extreme variating properties in different environments. Different factors make the image dehazing process cumbersome like unbalanced airlight, contrast, and darkness in hazy images. Many estimating and learning-based techniques are used to dehaze the images to overcome the aforementioned problems that suffer from halo artifacts and weak edges. The proposed technique can preserve better edges and illumination and retain the original color of the image. Dark channel prior (DCP) and probability-weighted moments (PWMs) are applied on each channel of an image to suppress the hazy regions and enhance the true edges. PWM is very effective as it suppresses low variations present in images that are affected by the haze. We have proposed a method in this article that performs well as compared to state-of-the-art image dehazing techniques in various conditions which include illumination changes, contrast variation, and preserving edges without producing halo effects within the image. The qualitative and quantitative analysis carried on standard image databases proves its robustness in terms of the standard performance evaluation metrics.

Rule extraction is the core in rough set. Two procedures are contained in rule extraction: one is attribute reduction and another is attribute value reduction. It was proved through computational complexity perspective that obtaining all the reduction, minimum attribute reduction, and minimum attribute value reduction is an NP problem. So, generally, a heuristic reduction method is used to solve attribute reduction and attribute value reduction. However, for most heuristic methods, it is hard to put into practice and has high cost on computational complexity. Moreover, part of the methods extracted redundant rules. To approach a quick and effective model for rule extraction in decision systems, against the concept of distinguishable relation, relevant concepts and basic theorems of rule extraction are proposed. In order to get concise and accurate rules quickly, algorithms for finding conflict object set, finding duplicate object set, and finding redundant rules are given. After that, using decision dependency degree as attribute importance to determine the importance of each attribute in rule object, a new rule extraction model based on decision dependency degree is proposed in this paper. Compared with the previous models, this model does not generate matrix; instead, it finds conflict object set and duplicate object set by equivalence class, and consequently, improves the time performance tomaxOCU,OC2U/C, andOREDU/C/RED2. The theoretical analysis and experimental research show that the new model more accurately and effectively reduces the redundant data and extracts more concise decision rules from dataset.

Individuals’ decision-making depends on reference points in prospect theory. This research considers the bounded rationality of decision makers and constructs a dynamic hybrid multiple attribute decision-making (DHMADM) model. Unlike existing models, the DHMADM model focuses on dynamic reference point, which has been proven in prospect theory. This research presents the effects of reference point adaptation on decision-making through model calculation. The optimal choice of decision makers changed with the change of the reference point in the DHMADM model. By experiment, we found that the DHMADM model considering reference point adaptation can more accurately express the final choice of decision makers than models only considering the static reference point.

Recent frequent “thunderstorm incidents” of P2P lending industry have caused the panic of industry investors. To predict the investment risk of P2P lending, we should scientifically and rationally analyze the key influencing factors of P2P lending investment risk. Existing key influencing factors selection methods mainly involve traditional statistical approaches and artificial intelligence methods. The traditional statistical approaches cannot deal with the high-dimensional nonlinear problems, and it cannot find the exact key influencing factors of the P2P lending investment risk. The artificial intelligence methods cannot recognize and learn the application background, and the selected attributes without active thinking and personal perception may not be the key influencing factors of P2P lending investment risk. To address the above issues, a novel key influencing factors selection approach of P2P lending investment risk is proposed by combining the proposed fireworks coevolution binary glowworm swarm optimization (FCBGSO), multifractal dimension (MFD), probit regression, and artificial prior knowledge. First, multifractal dimension combined with the proposed FCBGSO is used to select the preliminary influencing factors of the investment risk; second, the nonsignificant relevant attributes in the preliminary influencing factors are removed using the probit regression, and we add the influencing factors extracted from the original dataset of P2P lending using the artificial prior knowledge into the retaining influencing factors after removing one by one. A small and reasonable number of influencing factor subsets are achieved. Finally, we evaluate each influencing factors subset using extreme learning machine (ELM), and the subset with the best classification accuracy is efficiently achieved, i.e., it is the key influencing factors of P2P lending investment risk. Experimental results on the real P2P lending dataset from the Renrendai platform demonstrate that the proposed approach performs better than other state-of-the-art methods and that it has validity and effectiveness. It provides a new research idea for the key influencing factors selection of P2P lending investment risk.

This paper analyzed the drying process of the household gas clothes dryer (GCD) based on the principle of heat and mass transfer. The drying models in three scenarios were established: natural gas burned in combustion chamber, ambient air mixed with exhausted gas in hot gas duct, and clothes were dried in the drying drum. The simulation of the drying process was performed on MATLAB software. And the effectiveness of the drying model was verified by comparison with experiment results. The effects of gas flow rate, dry mass of clothes, ambient temperature, ambient humidity, textile type, and moisture extraction rate (MER) on the performance of dryers were studied. This paper provided a theoretical basis for the design and optimization of GCDs. It was found that the drying time of the gas clothes dryer was mostly affected by the gas flow and dry mass of clothes, and the specific moisture extraction rate (SMER) was mostly affected by the ambient temperature and relative humidity. The nylon clothes have the fastest MER and the smallest SMER. Moreover, reducing operating time in falling-rate drying period can improve energy efficiency.

In order to balance the exploration and exploitation capabilities of the PSO algorithm to enhance its robustness, this paper presents a novel particle swarm optimization with improved learning strategies (ILSPSO). Firstly, the proposed ILSPSO algorithm uses a self-learning strategy, whereby each particle stochastically learns from any better particles in the current personal history best position (pbest), and the self-learning strategy is adjusted by an empirical formula which expresses the relation between the learning probability and evolution iteration number. The cognitive learning part is improved by the self-learning strategy, and the optimal individual is reserved to ensure the convergence speed. Meanwhile, based on the multilearning strategy, the global best position (gbest) of particles is replaced with randomly chosen from the top k of gbest and further improve the population diversity to prevent premature convergence. This strategy improves the social learning part and enhances the global exploration capability of the proposed ILSPSO algorithm. Then, the performance of the ILSPSO algorithm is compared with five representative PSO variants in the experiments. The test results on benchmark functions demonstrate that the proposed ILSPSO algorithm achieves significantly better overall performance and outperforms other tested PSO variants. Finally, the ILSPSO algorithm shows satisfactory performance in vehicle path planning and has a good result on the planned path.

In this paper, a novel hybrid optimization approach, namely, gravitational particle swarm optimization algorithm (GPSOA), is introduced based on particle swarm optimization (PSO) and gravitational search algorithm (GSA) to solve combined economic and emission dispatch (CEED) problem considering wind power availability for the wind-thermal power system. The proposed algorithm shows an interesting hybrid strategy and perfectly integrates the collective behaviors of PSO with the Newtonian gravitation laws of GSA. GPSOA updates particle’s velocity caused by the dependent random cooperation of GSA gravitational acceleration and PSO velocity. To describe the stochastic characteristics of wind speed and output power, Weibull-based probability density function (PDF) is utilized. The CEED model employed consists of the fuel cost objective and emission-level target produced by conventional thermal generators and the operational cost generated by wind turbines. The effectiveness of the suggested GPSOA is tested on the conventional thermal generator system and the modified wind-thermal power system. Results of GPSOA-based CEED problems by means of the optimal fuel cost, emission value, and best compromise solution are compared with the original PSO, GSA, and other state-of-the-art optimization approaches to reveal that the introduced GPSOA exhibits competitive performance improvements in finding lower fuel cost and emission cost and best compromise solution.

Inertial measurement unit (IMU) (an IMU usually contains three gyroscopes and accelerometers) is the key sensor to construct a self-contained inertial navigation system (INS). IMU manufactured through the Micromechanics Electronics Manufacturing System (MEMS) technology becomes more popular, due to its smaller column, lower cost, and gradually improved accuracy. However, limited by the manufacturing technology, the MEMS IMU raw measurement signals experience complicated noises, which cause the INS navigation solution errors diverge dramatically over time. For addressing this problem, an advanced Neural Architecture Search Recurrent Neural Network (NAS-RNN) was employed in the MEMS gyroscope noise suppressing. NAS-RNN was the recently invented artificial intelligence method for time series problems in data science community. Different from conventional method, NAS-RNN was able to search a more feasible architecture for selected application. In this paper, a popular MEMS IMU STIM300 was employed in the testing experiment, and the sampling frequency was 125 Hz. The experiment results showed that the NAS-RNN was effective for MEMS gyroscope denoising; the standard deviation values of denoised three-axis gyroscope measurements decreased by 44.0%, 34.1%, and 39.3%, respectively. Compared with the Long Short-Term Memory Recurrent Neural Network (LSTM-RNN), the NAS-RNN obtained further decreases by 28.6%, 3.7%, and 8.8% in standard deviation (STD) values of the signals. In addition, the attitude errors decreased by 26.5%, 20.8%, and 16.4% while substituting the LSTM-RNN with the NAS-RNN.

Herein, two-dimensional (2D) single-action die compaction process of copper (Cu) powder was simulated by the multiparticle finite element method (MPFEM) at particulate scale. The initial packing structure, generated by the discrete element method (DEM), was used as an input for the FEM model, where the mesh division of each particle was discretized. The evolution of macro- and microscopic properties, such as relative density, stress distribution, particle deformation, void filling behavior, and force transmission, during compaction and pressure release processes have been systematically studied. The results revealed that the force is mainly concentrated on largely deformed regions of the particles during compaction and formed a contact force network, which hindered the densification process. In the compact, the shorter side of the large void edges rendered higher stress than the longer side. On the other hand, the stress distribution of small void edges remained uniform. After pressure release, large residual stress was observed at the contact area of the adjacent particles and the maximum stress was observed at the particles’ edges. Moreover, the residual stress did not proceed to the interior of the particles. Meanwhile, the stress of large void edges has been completely released but exhibited a nonuniform distribution. The smaller fraction of void filling resulted in a larger reduction of the released stress after pressure removal. Also, the particles closer to the upper die exhibited higher average equivalent von Mises stress inside the particles during compaction and pressure release processes.

Wind tunnel tests on the rigid model of large-span shallow spherical shell roof structure were carried out. The variation rule and the calculation method for the average shape coefficient of the fluctuating wind pressure under six different typical wind directions were obtained. The wind pressure distribution of the node deflection and cross section stress was numerically investigated and analyzed. Meanwhile, the effect of mechanics-flow form of the typical spherical shell structure on the wind pressure distribution was analyzed quantitatively. In this study, it is found that the results of numerical simulation agree well with the wind tunnel test data. The study on the mechanical characteristics, as well as the wind vibration research, of the spherical shell structure in different working conditions provides a reliable theoretical basis for the mechanical index of the wind vibration.

Mineral extracting involves lots of uncertain factors which take on the typically nonlinear characteristics. To find out the accidents caused by main factors and make clear of disaster-causing factors and risk sources, a modified risk evaluation model-associated interaction effect analysis method was constructed from four aspects of man subsystem, machine subsystem, working environment subsystem, and operations management subsystem. With this model, the theory of brittleness relation was applied to analyze closely the connection between mine safety and various subsystems and their interaction effects on mine safety. Furthermore, brittleness factors were also proposed according to the characteristics of underground mining. Finally, based on data from working fields and investigation results, taking a typical mine as an example, the analysis was carried out. The results show that their entropies of brittle relation were equal to 0.006 ± 0.201 i, −0.012 ± 0.221 i, 0.142 ± 0.237 i, and 0.041 ± 0.202 i, respectively. More exactly speaking, the main brittleness influenced on its safety is the environment subsystem in the mine. Therefore, the key to controlling mine accidents is to introduce advanced equipment and improve mechanization level to reach the intrinsic safety of the mining enterprise. The research proves that the modified risk analysis model based on brittleness relation theory is an effective approach to making clear of the state of mine safety and keeping accidents away in time by taking corresponding measures.

During the last decade, Rapidly-exploring Random Tree star (RRT∗) algorithm based on sampling has been widely used in the field of unmanned aerial vehicle (UAV) path planning for its probabilistically complete and asymptotically optimal characteristics. However, the convergence rate of RRT∗ as well as B-RRT∗ and IB-RRT∗ is slow for these algorithms perform pure exploration. To overcome the weaknesses above, Biased Sampling Potentially Guided Intelligent Bidirectional RRT∗ (BPIB-RRT∗) algorithm is proposed in this paper, which combines the bidirectional artificial potential field method with the idea of bidirectional biased sampling. The proposed algorithm flexibly adjusts the sampling space, greatly reduces the invalid spatial sampling, and improves the convergence rate. Moreover, the deeply theoretical analysis of the proposed BPIB-RRT∗ algorithm is given regarding its probabilistic completeness, asymptotic optimality, and computational complexity. Finally, compared to the latest UAV path planning algorithms, simulation comparisons are demonstrated to show the superiority of our proposed BPIB-RRT∗ algorithm.

In this paper, we extend price wars to supply chain networks (SCNs), focusing on how price wars affect the performance of SCNs and how to contain a price war. We propose a computational model in which the price competition is modelled as a multistage evolutionary prisoner’s dilemma game between business-related neighbors in each stage of the SCN, and the temptation to defect of the prisoner’s dilemma game is modelled as a function of the quotation price, which couples the price competition and the dynamic of the SCN. It is found that the price defectors’ exposure rate is the key factor causing price war of the SCN, and only a large proportion of firms in a closely related industry join the price alliance, and the price war in the SCN can be contained effectively.

The angular variation of the joints may be large, and collision between workpieces and tools may occur in robotic grinding. Therefore, this paper proposes an optimal robotic grinding path search algorithm based on the recursive method. The algorithm is optimized by changing the position of the tool coordinate system on the belt wheel; thus, the pose of the robot during grinding is adjusted. First, the position adjustment formula of the tool coordinate system is proposed, and a coordinate plane is established to describe the grinding path of the robot based on the position adjustment formula. Second, the ordinate value of this coordinate plane is dispersed to obtain the search field of the optimal robotic grinding path search algorithm. Third, an optimal robotic grinding path search algorithm is proposed based on the recursive method and single-step search process. Finally, the algorithm is implemented on the V-REP platform. Robotic grinding paths for V-shaped workpieces and S-shaped workpieces are generated using this algorithm, and a grinding experiment is performed. The experimental results show that the robotic grinding paths generated by this algorithm can smoothly complete grinding operations and feature a smaller angular variation of the joint than other methods and no collision.

This paper puts forward a new viewpoint on optimization of boiler combustion, namely, reducing NOx emission while maintaining higher reheat steam temperature rather than reducing NOx emission while improving boiler efficiency like traditional practices. Firstly, a set of multioutputs nonlinear partial least squares (MO-NPLS) models are established as predictors to predict these two indicators. To guarantee better predictive performance, repeated double cross-validation (rdCV) strategy is proposed to identify the structure as well as parameters of the predictors. Afterward, some controllable process variables, taken as inputs of the predictors, are then optimized by minimizing NOx emission and maximizing reheat steam temperature via multiobjective artificial bee colony (MO-ABC). Results show that our rdCV-MO-NPLS model with MO-ABC optimization methods can reduce NOx emission synchronously and improve reheat steam temperature effectively compared with nondominated sorting genetic algorithm II (NSGA-II) and combustion adjustment experimental data on a real 1000 MW boiler.

The problems of the admissibility and state feedback stabilization for discrete-time singular systems with interval time-varying delay and norm-bounded uncertainty are studied. The system is equivalently transformed into a new comparison form by decomposition. By taking advantage of the Seuret summation inequality, the reciprocally convex inequality, and some relaxation techniques, a delay-dependent criterion that ensures the admissibility of the concerned systems is established. The result on robust stabilization is also obtained by fixing some parameters. It should be pointed out that the results are less dependent on the parameters so that some conservatism is reduced. A numerical example is included to illustrate the effectiveness and improvement of the proposed methods.

With the continuous urban scale expansion, traffic networks have become extremely complex. Finding an optimal route in the shortest time has become a difficult and important issue in traffic engineering study. In this study, a novel computing model, namely, probe machine, is used to solve this problem. Similar to previous studies, urban transport networks can be abstracted into maps, in which points representing places of origin, destinations, and other buildings constitute the data library and edges representing the road make up the probe library. The true solution can be obtained after one probe operation on the computing platform. And by comparing the solving process with Dijkstra’s and Floyd’s algorithms, the computing efficiency of the probe machine is clearly superior, although all three methods can solve the shortest path problem and obtain the same solution.

To efficiently transmit electric power to consumers, the power lines need to be inspected routinely for early fault detection. Thus, power line inspection robots are designed to replace the tedious and dangerous manual inspection using linemen or helicopters. However, most of the existing inspection robots are heavy, which make them slow and prone to external wind disturbance. This paper developed a lightweight dual-arm robot and investigates its robustness to wind disturbance on a lab-scale power line structure. The dynamic equations of the robot are derived using the Lagrangian equation for appropriate motor selection. Also, the components of the robot are designed to ensure low drag coefficient to wind flow, and the mechanism of the wind force on the robot-line coupled system is presented. To study the real-time impact of the wind, a wind speed of 4.5 m/s representing one of the windiest cities in China is considered as a case study. The experimental results for different wind directions, namely, 0°, 45°, and 90°, show that the maximum vibration is 8% higher than the normal vibration of the system in a controlled environment without wind. The results demonstrate that there is little influence of the wind on the system; hence, the robot has been successfully designed and can be applied for power line inspection.

This paper presents a robust controller with an extended state observer to solve the Synchronous Fly-Around problem of a chaser spacecraft approaching a tumbling target in the presence of unknown uncertainty and bounded external disturbance. The rotational motion and time-varying docking trajectory of tumbling target are given in advance and referred as the desired tracking objective. Based on dual quaternion framework, a six-degree-of-freedom coupled relative motion between two spacecrafts is modeled, in which the coupling effect, model uncertainties, and external disturbances are considered. More specially, a novel nonsingular terminal sliding mode is designed to ensure the convergence to the desired trajectory in finite time. Based on the second-order sliding mode, an extended state observer is employed to the controller to compensate the closed-loop system. By theoretical analysis, it is proved that the modified extended-state-observer-based controller guarantees the finite-time stabilization. Numerical simulations are taken to show the effectiveness and superiority of the proposed control scheme. Finally, Synchronous Fly-Around maneuvers can be accomplished with fast response and high accuracy.

In this paper, some fixed-point theorems are established for strongly subadditive maps on CΩ,ϒ (where CΩ,ϒ denotes the space of ϒ-valued continuous functions on a compact Hausdorff space Ω and ϒ is a unital Banach algebra). Finally, the result is applied to prove the existence and uniqueness of a solution for a system of nonlinear integrodifferential equations.

Based on the literature review on medical education and university performance evaluations and from the perspective of individual advantage identification, an evaluation method of medical universities’ performance in a hierarchical context was proposed. This study designs a method system, including individual learning benchmarking from the individual perspective, group learning benchmarking from the democratic perspective, and benchmarking of single and overall indicators. From the perspective of the most favorable to medical universities, this study applies the designed research methods to the performance evaluation of medical universities and takes 15 medical universities in China as samples to conduct an empirical research. The following conclusions were found. First, the method helps form various development modes of medical universities in the individual advantage characteristic context. Second, the method not only shows objectivity but also provides a basis for how medical universities find individual and integral benchmarking. Finally, the method demonstrates the democracy feature, which provides a basis for how relevant departments allocate resources and formulate policies.

In order to meet the user’s increasing demands for integrated quality of agricultural machinery products, a new KE-TRIZ approach based on Kansei engineering (KE) and theory of inventive problem solving (TRIZ) was proposed. Designers used semantic difference (SD) method and principal component analysis (PCA) method to quantify and reduce the dimensions of the extracted perceptual vocabulary, which aimed to select the comprehensive variables that represented the user’s needs. TRIZ theory was used in the product emotional intention to elaborate the invention principle of products’ appearance structure and functional technology, and improvement explanation was put forward based on further contradiction analysis. Then, the industrial design theory was combined with the innovation design of agricultural machinery products. In order to explain the reliability of innovative design, the analytic hierarchy process (AHP) was used in objective assessment. Finally, a walking rotary cultivator design was used as a case to check the above methods. The research can effectively adjust the contradiction between rationality and sensibility in products and apply it to the product equipment with function as the main part to design the products of comprehensive quality that conform to the trend of social aesthetics so as to form the brand effect of high added value. The results showed that these methods were effective and feasible for the innovative design of agricultural machinery products.

According to previous research studies, automated quayside cranes (AQCs) and automated guided vehicles (AGVs) in automated container terminals have a high potential synergy. In this paper, a collaborative scheduling model for AQCs and AGVs is established and the capacity limitation of the transfer platform on AQCs is considered in the model. The minimum total energy consumption of automated quayside cranes (AQCs) and Automatic Guided Vehicles (AGVs) is taken as the objective function. A two-stage taboo search algorithm is adopted to solve the problem of collaborative scheduling optimization. This algorithm integrates AQC scheduling and AGV scheduling. The optimal solution to the model is obtained by feedback from the two-stage taboo search process. Finally, the Qingdao Port is taken as an example of a data experiment. Ten small size test cases are solved to evaluate the performance of the proposed optimization methods. The results show the applicability of the two-stage taboo search algorithm since it can find near-optimal solutions, precisely and accurately.

Underground coal mining will cause large-scale surrounding rock movement, resulting in surface subsidence and irreversible deformation of surface morphology, which would lead to geological disasters and ecological environment problems. In this paper, FLAC3D numerical model is built based on the natural slope gradient, slope type, and included angle between the slope and working face, and their influences on the change of surface morphology and soil erosion caused by underground coal mining is studied. Research results show that the change of slope gradient caused by underground mining decreases with the increase of natural slope gradient, while slope length has opposite laws; different slope types have different changes of slope morphology. The order of slope types corresponding to gradient changes is mixed slope α

Through bringing nutrient-rich subsurface water to the surface, the artificial upwelling technology is applied to increase the primary marine productivity which could be assessed by Chlorophyll a concentration. Chlorophyll a concentration may vary with different water physical properties. Therefore, it is necessary to study the relationship between Chlorophyll a concentration and other water physical parameters. To ensure the accuracy of predicting the concentration of Chlorophyll a, we develop several models based on wavelet neural network (WNN). In this study, we build up a three-layer basic wavelet neural network followed by three improved wavelet neural networks, which are namely genetic algorithm-based wavelet neural network (GA-WNN), particle swarm optimization-based wavelet neural network (PSO-WNN), and genetic algorithm & particle swarm optimization-based wavelet neural network (GAPSO-WNN). The experimental data were collected from Qiandao Lake, China. The performances of the proposed models are compared based on four evaluation parameters, i.e., R-square, root mean square error (RMSE), mean of error (ME), and distance (D). The modeling results show that the wavelet neural network can achieve a certain extent of accuracy in modeling the relationships between Chlorophyll a concentration and the five input parameters (salinity, depth, temperature, pH, and dissolved oxygen).