Search

In the paper industry, numerous studies have explored means of optimizing order allocation and cutting trim loss. However, enterprises may not adopt the resulting solutions because some widths of the inventory exceed or are less than those required for acceptable scheduling. To ensure that the results better suit the actual requirements, we present a new decision model based on the adjustment of scheduling and limitation of inventory quantity to differentiate trim loss and inventory distribution data. Differential analysis is used to reduce data filtering and the information is valuable for decision making. A numerical example is presented to illustrate the applicability of the proposed method. The results show that our proposed method outperforms the manual method regarding scheduling quantity and trim loss.

Splicing the shredded paper means the technology that, according the paper, which has been shredded to design a particular algorithm to splice and recover the original paper. This paper introduced the algorithm of splicing the shredded paper based on the matching to texture feature. By means of this algorithm, we modeled for the problem of splicing the shredded paper and solved it. And, we used the algorithm to splice both pieces of English shredded paper and Chinese shredded paper. The recovered paper had proved the accuracy of the algorithm.

This paper presents a new method for calculating the insulation health index (HI) of oil-paper transformers rated under 110 kV to provide a snapshot of health condition using binary logistic regression. Oil breakdown voltage (BDV), total acidity of oil, 2-Furfuraldehyde content, and dissolved gas analysis (DGA) are singled out in this method as the input data for determining HI. A sample of transformers is used to test the proposed method. The results are compared with the results calculated for the same set of transformers using fuzzy logic. The comparison results show that the proposed method is reliable and effective in evaluating transformer health condition.

We consider a one-dimensional cutting stock problem (CSP) in which the stock widths are not used to fulfill the order but kept for use in the future for the industrial-use paper production. We present a new model based on the flexible stock allocation and trim loss control to determine the production quantity. We evaluate our approach using a real data and show that we are able to solve industrial-size problems, while also addressing common cutting considerations such as aggregation of orders, multiple stock widths, and cutting different patterns on the same machine. In addition, we compare our model with others, including trim loss minimization problem (TLMP) and cutting stock problem (CSP). The results show that the proposed model outperforms the other two models regarding total flexibility and trim loss ratio.

We give a counter example to the comparison principle for the multipoint BVPs (by Xuxin Yang, Zhimin He, and Jianhua Shen, in Mathematical Problems in Engineering, Volume 2009, Article ID 258090, doi:10.1155/2009/258090). Then we suggest and prove a corrected version of the comparison principle.

We show that the sufficient condition of the above mentioned paper is also necessary for the boundedness of Bergman type projections on a class of regulated domains.

We improve a result in Silverman's paper (1999) and answer a question he posed. We also consider a similar problem and obtain sufficient conditions for starlikeness.

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.

A technique used by S. Haber to prove an elementary inequality is applied here to obtain a more general inequality for convex sequences.

In recent years, modeling and recognizing driver behavior have become crucial to understanding intelligence transport systems, human-vehicle systems, and intelligent vehicle systems. A wide range of both mathematical identification methods and modeling methods of driver behavior are presented from the control point of view in this paper based on the driving data, such as the brake/throttle pedal position and the steering wheel angle, among others. Subsequently, the driver’s characteristics derived from the driver model are embedded into the advanced driver assistance systems, and the evaluation and verification of vehicle systems based on the driver model are described.

On the basis of basic law in AASHTO2007 model, the forecasting mathematical model of drying shrinkage of recycled aggregate concrete (RAC) is established by regression analysis and experimental study. The research results show that (1) with the replacement rate of RCA increases, the drying shrinkage value of RAC increases; this trend is even more obvious in the early drying time. (2) The addition of fly ash can inhibit the drying shrinkage of RAC, but the effect is not very obvious. Specifically, the addition of fly ash will increase the shrinkage to some extent when the mixing amount is 20%. (3) The addition of expansive agent can obviously inhibit the shrinkage of RAC; the inhibition affection is better than that of fly ash. (4) The forecasting mathematical models of drying shrinkage of RAC established in this paper have high accuracy and rationality according to experiment validation and error analysis.

An alternative approach to classical correspondence analysis was developed in [3] and involves decomposing the matrix of Pearson contingencies of a contingency table using orthogonal polynomials rather than via singular value decomposition. It is especially useful in analysing contingency tables which are of an ordinal nature. This short paper demonstrates that the confidence circles of Lebart, Morineau and Warwick (1984) for the classical approach can be applied to ordinal correspondence analysis. The advantage of the circles in analysing a contingency table is that the researcher can graphically identify the row and column categories that contribute or not to the hypothesis of independence.

There are some papers, such as [1], [2] and [3], in which some properties on isomorphism of closed subspace lattices of Hilbert spaces were studied. In this short paper we will point out that the hyper-reflexivity of closed subspace lattice is invariant under isomorphism ofξ(H1)onξ(H2). We also proved that ifTis inL(H)such that0∈¯π(T)andℱis a hyper-reflexive subspace lattice, thenϕT(ℱ)∪{H}is hyper-reflexive whereϕTis a homomorphism induced byT.

A sludge composite modifier (SCM) which comprises a mixture of three cementitious components was proposed for sludge drying and stabilization. Effect of SCM components on sludge moisture content was analyzed using uniform design and the optimum composition of SCM was determined by computer-aided modeling and optimization. To compare the drying effect of SCM, quicklime, and Portland cement, the effects of material content and curing time on moisture content of sludge were also studied. The results showed that the optimum ratio of modifier component was slag/cement clinker/dihydrate gypsum = 0.64/0.292/0.068 and the moisture content of SCM-stabilized sludge decreased with the increasing material content and extending curing time. Besides, the experimental results showed that optimized SCM behaved better than quicklime and Portland cement in sludge semi-drying and XRD analysis revealed that the main hydrated product of stabilization was ettringite, which played an important role in the effective drying process. Sewage sludge stabilized using SCM could be used as an effective landfill cover.

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.

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.

With the rapid development of communication technology and the popularization of network, information security has been highly valued by all walks of life. Random numbers are used in many cryptographic protocols, key management, identity authentication, image encryption, and so on. True random numbers (TRNs) have better randomness and unpredictability in encryption and key than pseudorandom numbers (PRNs). Chaos has good features of sensitive dependence on initial conditions, randomness, periodicity, and reproduction. These demands coincide with the rise of TRNs generating approaches in chaos field. This survey paper intends to provide a systematic review of true random number generators (TRNGs) based on chaos. Firstly, the two kinds of popular chaotic systems for generating TRNs based on chaos, including continuous time chaotic system and discrete time chaotic system are introduced. The main approaches and challenges are exposed to help researchers decide which are the ones that best suit their needs and goals. Then, existing methods are reviewed, highlighting their contributions and their significance in the field. We also devote a part of the paper to review TRNGs based on current-mode chaos for this problem. Finally, quantitative results are given for the described methods in which they were evaluated, following up with a discussion of the results. At last, we point out a set of promising future works and draw our own conclusions about the state of the art of TRNGs based on chaos.

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 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.

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.

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.

Thresholding is an efficient step to extract quantitative information since the potential artefacts are often introduced by the point-spread effect of tomographic imaging. The thresholding value was previously selected only relying on engineering experience or histogram of tomographic image, which often presents a great challenge to determine an accurate thresholding value for various applications. As the tomographic image features often do not provide sufficient information to choose the best thresholding value, the information implicit in the measured boundary data is introduced into the thresholding process in this paper. A projection error, the relative difference between the computed boundary data of current segmentation and the measured boundary data, is proposed as a quantitative measure of such image segmentation quality. Then, a new multistep image segmentation process, called size projection algorithm (SPA), is proposed to automatically determine an optimal thresholding value by minimising the projection error. Results of simulation and experiment demonstrate the improved performance of the SPA-based tomographic image segmentation. An application of size projection algorithm for gas-water two-phase flow visualisation is also reported in this paper.

This paper builds a coupling coordinated development model for urban-rural logistics based on coupling theory and synergy theory, designs an index system for the coordinated development of urban-rural logistics based on supply chain management, and determines the weight of the index system using the entropy method. Supported by official statistics, this paper measures the level and analyzes the changing trend of the coupling coordinated development of urban-rural logistics from 2001 to 2015 in China. The results show that, in the study period, the orderly development level of both urban and rural logistics was improved, and the coordinated development level of urban-rural logistics generally maintained a slow upward trend. However, the interactive coupling level between urban and rural logistics was stagnant, and the orderly development differences were gradually enlarged. This study suggests that setting up urban-rural logistics integrated systems, including material flow, financing flow, information flow, and commerce flow, has become an effective way to break through the division between urban and rural logistics and realize the coordinated development of urban-rural logistics.

This paper describes a valuable linear yaw-roll tractor-semitrailer (TST) model with five-degree-of-freedom (DOFs) for control algorithm development when steering and braking. The key parameters, roll stiffness, axle cornering stiffness, and fifth-wheel stiffness, are identified by the genetic algorithm (GA) and multistage genetic algorithm (MGA) based on TruckSim outputs to increase the accuracy of the model. Thus, the key parameters of the simplified model can be modified according to the real-time vehicle states by online lookup table and interpolation. The TruckSim vehicle model is built referring to the real tractor (JAC-HFC4251P1K7E33ZTF6×2) and semitrailer (Luyue LHX9406) used in the field test later. The validation of the linear yaw-roll model of a tractor-semitrailer using field test data is presented in this paper. The field test in the performance testing ground is detailed, and the test data of roll angle, roll rate, and yaw rate are compared with the outputs of the model with maps of the key parameters. The results indicate that the error of the tractor’s roll angle and semitrailer’s roll angle between model data and test data is 1.13% and 1.24%, respectively. The roll rate and yaw rate of the tractor and semitrailer are also in good agreement.

The traditional predictive method cannot fully reflect the complex nonlinear characteristics and regularities of automobile and parts sales data, so the prediction precision is not high. The purpose of this paper is to propose the gray GM(1,1) nonlinear periodic predictive model by introducing the seasonal variation index to improve predictive accuracy of the single GM(1,1) model. Firstly, the paper analyzes concept of GM(1,1) and then proposes the gray GM(1,1) nonlinear periodic predictive model to forecast automobile parts sales. The model algorithm used gray theory and accumulated technology to generate new data and set up unified differential equations to find the fitting curve of automobile parts sales prediction by the seasonal variation index to remove random elements. Lastly, the gray GM(1,1) nonlinear periodic predictive model is used for empirical analysis; the result of example shows that the model proposed in the paper is feasible. The superiority of the proposed predictive model compared with the single gray GM(1,1) model is demonstrated. The reliability of this model is experienced by the accuracy test, which provides a theoretical guidance for the prediction of automobile part sales. And the average relative error is reduced by 8.52% compared with the single GM(1,1) model.

The dual-channel supply chain is widely adopted by main manufacturers, potentially incurring channel conflicts between the traditional retail channel which is owned by the independent retailer and the online channel which is directly managed by the manufacturer. The purpose of this paper is to deal with the scenario where channel conflicts may arise under production capacity uncertainty, when the manufacturer tends to privilege the direct selling channel over the retail selling channel. To achieve the goal, this paper establishes a Stackelberg game model consisting of a manufacturer and a retailer, studies the scenario where the manufacturer satisfies the direct selling channel first in the presence of capacity uncertainty, employs the decision optimization and the backward induction method to find the optimal inventory decision in the direct selling channel and the optimal order quantity decision making in the retail selling channel, and designs a compensation mechanism aiming to coordinate the channel conflict in the decentralized decision-making process. Results show that the optimal decisions aiming to maximize the expected profit of each supply chain member are not able to maximize the expected profit of entire dual-channel supply chain. However, when the manufacturer compensates the retailer’s profit loss based on the unsatisfied order and, in the meantime, adjusts the wholesale price to prevent the retailer which obtains the compensation from increasing order significantly, the compensation mechanism can coordinate the decision of each supply chain member, mitigate the channel conflict, maximize the expected profit of entire dual-channel supply chain, and achieve the Pareto improvement of supply chain members’ expected profit in the decentralized decision-making process.

The digging resistance in a normal state is the key to excavator design and automated excavation. It is difficult to accurately predict, simulate, or directly measure the digging resistance in a normal state due to uncertainties in the soil properties and excavation parameters. In this paper, a research idea is proposed that uses the working device as the entry point to indirectly calculate the digging resistance in a normal state by measuring the motion parameters and the cylinder pressure intensity. Based on the rule of combination for spatial force systems, a method for combining and projecting the system of the digging resistance is proposed in which the system is projected as six parts, and the tangential force, normal force, and bending moment in the plane of symmetry of the working device are the objects of the solution to avoid redundant equations. Based on kinematics and dynamics models of the excavator and the force and moment equilibrium conditions of the working device, equations for the active-side calculation of the incomplete digging resistance are derived. Based on these equations, the motion parameters of the working device and data on the cylinder pressure intensity obtained by measurement are used to calculate the incomplete digging resistance. The validation scheme and process proposed use the incomplete digging resistance as the external load to obtain the simulated stress of the working device through transient analysis. The simulated stress and the measured stress corresponding to the position of the measurement point are extracted and compared. The results show that there is a difference in the size of the numerical value between the simulated and measured stress, but the variation law is highly consistent, which validates the calculation method. In this paper, an active-side calculation method is provided for the incomplete digging resistance in a normal state without considering the soil-tool interaction relationships, which lays a theoretical foundation for the study of the digging resistance characteristics in a normal state, as well as excavator design and automated excavation.

The paper studies the initial-boundary value problem of a porous medium equation with exponent variable. How to deal with nonlinear term with the exponent variable is the main dedication of this paper. The existence of the weak solution is proved by the monotone convergent method. Moreover, according to the different boundary value conditions, the stability of weak solutions is studied. In some special cases, the stability of weak solutions can be proved without any boundary value condition.

In this paper, we prove that the solvability of dynamic equations of second order is sufficient for the validity of some Hardy and Opial type inequalities with two different weights on time scales. In particular, the results give new characterizations of two different weights in inequalities containing Hardy and Opial operators. The main contribution in this paper is the characterizations of weights in discrete inequalities that will be formulated from our results as special cases.

This paper examines the stability analysis of discrete-time control systems particularly during the event of actuator saturation and time-varying state delay. With the help of Wirtinger inequality along with Lyapunov-Krasovskii functional gain of state feedback controller is determined for stabilization of above system. The saturation nonlinearity is represented in the terms of convex hull. A new linear matrix inequality (LMI) criterion is settled with reciprocally convex combination based inequality which is dependent on delay. The proposed criterion is less conservative in concern to increase the delay bound and a controller is also simulated for real time problem of missile control system in this paper. It is also attained that projected stability criterion is less conservative compared to other outcomes. Furthermore, an optimization procedure together with LMI constraints has been proposed to maximize the attraction of domain.

Vehicle states estimation (e.g., vehicle sideslip angle and tire force) is a key factor for vehicle stability control. However, the accurate values of these parameters could not be obtained directly. In this paper, an interacting multiple model-cubature Kalman filter (IMM-CKF) is used to estimate the vehicle state parameters. And improvements about estimation method are achieved in this paper. Firstly, the accuracy of the reference model is improved by building two different models: one is 7-degree-of-freedom (7 DOF) vehicle model with linear tire model, and the other is 7 DOF vehicle model with nonlinear Dugoff tire model. Secondly, the different models are switched by IMM-CKF to match different driving condition. Thirdly, the lateral acceleration correction for sideslip angle estimation is considered, because the sensor of lateral acceleration is easy to be influenced by the gravity on banked road. Then, to compare cubature Kalman filter (CKF) estimation method and IMM-CKF estimation method Hardware-In-Loop (HIL) tests are carried out in the paper. And simulation results show that IMM-CKF methodology can provide accurate estimation values of vehicle states parameters.

Selecting the most appropriate new product(s) is regarded as a critical decision which greatly influences the development of manufacturing enterprises. In order to improve the accuracy of selection, more experts are required to be invited to predict key indicators for new products selection. Due to limited knowledge, experts use fuzzy numbers more confidently than using numerical values in the prediction. Therefore, new product selection is a multiattribute group decision-making process under epistemic uncertainty. The purpose of this paper is to introduce a new hybrid decision-making approach based on Analytic Hierarchy Process (AHP) and Dempster-Shafer Theory (DST) to evaluate and select a new product. AHP and DST are used in weight determination to improve the accuracy and objectivity. In addition, this paper proposes that DST is a proper mathematical framework to deal with the epistemic uncertainty on the indicators of new product scheme selection. In particular, the initial assessments from experts are disassembled and then combined into the evidence information. By setting confidence degree, reliability function and likelihood function are used to evaluate and rank new products. A case study in a home appliance manufacturer is provided to illustrate the proposed hybrid approach and demonstrate its applicability.

Society is being reshaped by large volumes of data generated from various operations, and research in handling dynamic issues related to data-driven systems has greatly increased in the past decades, where most rely upon discrete optimization models for handling dynamic features contained in operations management (OM) activities. With the rapid growth in computational technologies, including data mining technologies, discrete event simulation techniques, and intelligence algorithms, OM relies more and more on optimal solutions (or their approximates) based on high-performance models and algorithms. This special issue collects 32 original research contributions that present recent advances in models and algorithms concerning discrete optimizations on dynamic OM systems relevant to the data-driven society. In particular, the papers address the topics related to discrete optimization methodologies for stochastic OM problems, dynamic programming based exact methods for stochastic OM problems, system dynamics in behavior OM for E-commerce, data-driven risk analysis and modeling for OM decisions in dynamic contexts, data-driven models for dynamic supply chain management, etc. It is hoped that this special issue will help the integration of the latest research achievements in the relevant field. The paper by S. Li et al. entitled “Probability Mechanism Based Particle Swarm Optimization Algorithm and Its Application in Resource-Constrained Project Scheduling Problems” proposes a new probability mechanism based particle swarm optimization (PMPSO) algorithm to solve combinatorial optimization problems by introducing new particles based on the optimal particles in the population and the historical optimal particles in the individual changes. This method is applied to solve resource-constrained project scheduling problems and the experimental results are quite encouraging.

In order to realize the multithreshold segmentation of images, an improved segmentation algorithm based on graph cut theory using artificial bee colony is proposed. A new weight function based on gray level and the location of pixels is constructed in this paper to calculate the probability that each pixel belongs to the same region. On this basis, a new cost function is reconstructed that can use both square and nonsquare images. Then the optimal threshold of the image is obtained through searching for the minimum value of the cost function using artificial bee colony algorithm. In this paper, public dataset for segmentation and widely used images were measured separately. Experimental results show that the algorithm proposed in this paper can achieve larger Information Entropy (IE), higher Peak Signal to Noise Ratio (PSNR), higher Structural Similarity Index (SSIM), smaller Root Mean Squared Error (RMSE), and shorter time than other image segmentation algorithms.

The existence and persistence of financial statement fraud (FSF) are detrimental to the financial health of global capital markets. A number of detective and predictive methods have been used to prevent, detect, and correct FSF, but their practicability has always been a big challenge for researchers and auditors, as they do not address real-world problems. In this paper, both supervised and unsupervised approaches are employed for analysing the financial data obtained from China’s stock market in detecting FSF. The variables used in this paper are 18 financial datasets, representing a fraud triangle. Additionally, this study examined the properties of five widely used supervised approaches, namely, multi-layer feed forward neural network (MFFNN), probabilistic neural network (PNN), support vector machine (SVM), multinomial log-linear model (MLM), and discriminant analysis (DA), applied in different real-life situations. The empirical results show that MFFNN yields the best classification results in detection of fraudulent data presented in financial statement. The outcomes of this study can be applied to different types of financial statement datasets, as they present a practical way for constructing predictive models using a combination of supervised and unsupervised approaches.

As the computation delays between the reference torques and the load torques, the speed and position synchronous errors of the multimotor drive system employed traditional electronic line shafting (ELS) control would become significant during the process of large load disturbances. Therefore, an improved ELS control strategy is proposed in this paper. In this strategy, the load torques observed by the sliding model observers are fed back to the virtual motor directly, so as to shorten the adjusting time and improve the antidisturbance performance of entire control system. Meanwhile, to reduce the chattering of the sliding mode observer, a novel exponential reaching law is designed in this paper. The experimental results show that the improved ELS control strategy could reduce the speed and position synchronous errors effectively.

In order to solve the problem that the model-based State of Charge (SOC) estimation method is too dependent on the model parameters in the SOC estimation of electric vehicles, an improved genetic algorithm is proposed in this paper. The method has the advantages of being able to quickly determine the search range, reducing the probability of falling into local optimum, and having high recognition accuracy. Then we can realize online dynamic identification of power battery model parameters and improve the accuracy of model parameter identification. In addition, considering the complex application environment and operating conditions of electric vehicles, an SOC estimation method based on improved genetic algorithm and unscented particle filter (improved GA-UPF) is proposed. And we compare the improved GA-UPF algorithm with the least square unscented particle filter (LS-UPF) and improved GA unscented Kalman filter (improved GA-UKF) algorithm. The comparison results show that the improved GA-UPF algorithm proposed in this paper has higher estimation accuracy and better stability. It also reflects the practicability and accuracy of the improved GA parameter identification algorithm proposed in this paper.

Smart card datasets in the public transit network provide opportunities to analyse the behaviour of passengers as individuals or as groups. Studying passenger behaviour in both spatial and temporal space is important because it helps to find the pattern of mobility in the network. Also, clustering passengers based on their trips regarding both spatial and temporal similarity measures can improve group-based transit services such as Demand-Responsive Transit (DRT). Clustering passengers based on their trips can be carried out by different methods, which are investigated in this paper. This paper sheds light on differences between sequential and combined spatial and temporal clustering alternatives in the public transit network. Firstly, the spatial and temporal similarity measures between passengers are defined. Secondly, the passengers are clustered using a hierarchical agglomerative algorithm by three different methods including sequential two-step spatial-temporal (S-T), sequential two-step temporal-spatial (T-S), and combined one-step spatiotemporal (ST) clustering. Thirdly, the characteristics of the resultant clusters are described and compared using maps, numerical and statistical values, cross correlation techniques, and temporal density plots. Furthermore, some passengers are selected to show how differently the three methods put the passengers in groups. Four days of smart card data comprising 80,000 passengers in Brisbane, Australia, are selected to compare these methods. The analyses show that while the sequential methods (S-T and T-S) discover more diverse spatial and temporal patterns in the network, the ST method entails more robust groups (higher spatial and temporal similarity values inside the groups).

The avoidance behavior of pedestrians was characterized in the present paper by simulating the movement of crowds in both unidirectional and bidirectional pedestrian flow. A phase change of alternative lane formation observed in real bidirectional pedestrian flows has been studied, where pedestrians tended to evade individuals in counterflow and simultaneously keep a certain distance from each other in the uniform pedestrian flow when the counterflow disappeared. What is more, the comparison between the effect of evading and pushing behavior on evacuation has been investigated in the room egress scenario. Additionally, the evading and overtaking behavior of fast pedestrians have also been simulated in heterogeneous crowds. The performance of the proposed model was compared to the experimental data and the results obtained using other evacuation models. Numerical results showed that both the phase change of alternative lane formation in bidirectional pedestrian flow and the effective evading behavior in unidirectional pedestrian flow were conductive to reduce the evacuation time of pedestrian crowds. Even though pushing behavior of fast pedestrians seemed to improve the flow through the wide exit, it might lead to the panic and other negative effect on the crowds, such as crowds trample. The proposed model in this paper could provide a theoretical basis for the pedestrian crowd management during emergency evacuation.

This paper presents stress influence functions for uniformly distributed, time-harmonic rectangular loads within a three-dimensional, viscoelastic, isotropic full-space. The coupled differential equations relating displacements and stresses in the full-space are solved through double Fourier integral transforms in the wave number domain, in which they can be solved algebraically. The final stress fields are expressed in terms of double indefinite integrals arising from the Fourier transforms. The paper presents numerical schemes with which to integrate these functions accurately. The article presents numerical validation of the synthesized stress kernels and their behavior for high frequencies and large distances from the excitation source. The influence of damping ratio on the dynamic results is also investigated. This article is complementary to previous results of the authors in which the corresponding displacement solutions were derived. Stress influence functions, together with their displacement counterparts, are a fundamental part of many numerical methods of discretization such the boundary element method.

The Coarse-Time Positioning method is important for the quick positioning of the BeiDou Navigation Satellite System (BDS) navigation receiver in the weak signal environment. The coarse position estimation is the key technology of the Coarse-Time Positioning (CTP) for the BDS navigation receiver without coarse position assistance. In this paper, a Coarse-Position-Free Coarse-Time Positioning method based on mixed-type code phase ambiguity resolution is proposed. In this CTP method, in order to estimate the approximate position, the coarse position estimation method based on the mixed-type code phase ambiguity search is used. This method does not require any additional external auxiliary position information to complete the coarse position estimation. Based on the real observation data and dynamic simulation observation data, the test experiment is designed. According to the error of coarse position estimation, the positioning accuracy, and the success rate of the coarse-time solution, the Coarse-Position-Free Coarse-Time Positioning (CPFCTP) method of the BDS receiver based on the mixed-type code phase ambiguity resolution is evaluated. The experimental results show that, when the coarse-time deviation is within 30 s, the CPFCTP method has a success rate of over 97%. When the coarse-time deviation is within 5 s, the success rate of positioning is 100%. At the same time, the experimental results also show that, for the situation that the navigation signals of some BeiDou satellites are completely occluded, the CPFCTP method proposed in this paper can obtain the correct code phase ambiguity and still maintain a high success rate of CTP at the same time.

The balance of stability and maneuverability is the foundation of the trajectory correction projectile. For the terminal correction projectile without an attitude feedback loop, a larger control force is expected which may cause an instability. This paper proposes a novel method to derive instability boundaries for the control force magnitude. No additional coordinate system is needed in this method. By introducing the concept of angular compensation matrix, the exterior ballistic linearized equations considering control force are established. The necessary prerequisite for a stable flight under control is given by the Routh stability criterion. The instability boundaries for the control force magnitude are derived. The results of example flights are 13.5% more accurate compared with that in relevant research. Numerical simulations demonstrate that if the control force magnitude lies in the unstable scope derived in this paper, the projectile loses its stability. Furthermore, the effects of the projectile pitch, velocity, and roll rate on flight stability during correction are investigated using the proposed instability boundaries.

This paper presents a geometrical-information-assisted approach for matching local features. With the aid of Bayes’ theorem, it is found that the posterior confidence of matched features can be improved by introducing global geometrical information given by distances between feature points. Based on this result, we work out an approach to obtain the geometrical information and apply it to assist matching features. The pivotal techniques in this paper include (1) exploiting elliptic parameters of feature descriptors to estimate transformations that map feature points in images to points in an assumed plane; (2) projecting feature points to the assumed plane and finding a reliable referential point in it; (3) computing differences of the distances between the projected points and the referential point. Our new approach employs these differences to assist matching features, reaching better performance than the nearest neighbor-based approach in precision versus the number of matched features.

Multiattribute decision-making (MADM) problem is difficult to assess because of the large number of attribute indices and the diversity of data distribution. Based on the understanding of data dispersion degree, a new grey TOPSIS method for MADM is studied. The main idea of this paper is to redefine the grey relational analysis through the dispersion of data distribution and redesign the TOPSIS by using the improved grey relational analysis. As a classical multiattribute decision analysis method, traditional TOPSIS does not consider the data distribution of the degree of dispersion and aggregation when it is compared with the optimal and worst alternative solutions. In view of the limitations of traditional TOPSIS, this paper has made two major improvements to TOPSIS. Firstly, the new grey relational analysis is applied to evaluate the grey positive relational degree between each alternative and the optimal solution and compute the grey negative relational degree between each alternative and the worst solution. Secondly, the weights of every attribute index about the optimal and worst solutions are put forward based upon the distance standard deviation and the average distance. Finally, the comprehensive grey TOPSIS is utilized to analyze the ranking of weapon selection problem. The numerical results verify the feasibility of the improved grey relational analysis and also highlight the practicability of the grey comprehensive TOPSIS.

The paper is dedicated to the algebraic formulation of elastic frame equations. The obtained set of equations describe deformations of moderately thick frames made of both compressible and incompressible bars, grillages of rigid or pin-joined connections, and trusses. Plane as well as space structures are presented. The paper is an extension of the article of T. Lewiński written in 2001 related to thin bars. Algebraic equations with diagonal constitutive matrix are original and suitable for various engineering applications and for educational purposes.

This paper presents a backstepping controller using barrier Lyapunov function (BLF) for dynamic positioning (DP) system. For safety reasons, the position and heading of DP ship are to be maintained in certain range. Thus, in this paper, a control law based on BLF and backstepping technique is proposed to limit the position and heading. The closed-loop system is proved stable in the sense of Lyapunov stability theories. In addition, since the velocities of ship are not measurable and the wave frequency (WF) motion is unavailable, a passive observer is adopted to estimate the velocities and the effect of WF motion. The simulation results show that the proposed controller can limit the position and heading of the vessel in a predefined range and verify the performance of the proposed controller and the passive observer.