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Selecting suppliers and allocating orders scientifically and rationally not only have important theoretical value but also have extremely important practical significance to enterprises. This study proposes an improved multicycle integration method that can effectively determine the optimal number of suppliers under the premise of considering the risk of supply interruption and transaction costs that may be caused by various uncertain factors. The supply interruption risk is divided into two categories: "common risk event" and "individual risk event." On the basis of balancing the transaction cost of the company's choice of suppliers and the financial loss of supply interruption, quantitative analysis methods are used to determine the optimal number of suppliers for the company. The problem is analyzed, the corresponding model is given, and the influence of each parameter on the optimal number of suppliers is analyzed. From the perspective of stochastic uncertainty in both customer demand and supplier supply capacity, this study analyzes the optimization problem of enterprise purchase quantity allocation under the volume discount environment. The relaxation factor is somewhat adaptive because the particle swarm is continuously updated. Aiming at ordering cost, transaction cost, transportation cost, order delay quantity, and product quality, considering random demand constraints, supplier supply capacity constraints, and multisupplier multiproduct situations, we establish supplier selection in an environment of uncertain demand and supply capacity and dynamic relaxation approximation algorithm for order allocation. The research results show that the dynamic relaxation approximation algorithm can not only determine the optimal supplier but also determine the optimal procurement allocation for each supplier. [ABSTRACT FROM AUTHOR]

In order to improve the collaborative optimization effect of tourism resources and highway network, this paper combines deep learning algorithm to construct a collaborative optimization model of tourism resources and highway network, and adopts an algorithm based on continuous convex approximation. By iterating the optimal solution obtained each time, a high-quality approximate beamforming matrix and artificial noise covariance matrix can finally be obtained, which eliminates the problem that the traditional algorithm cannot solve the noise. Moreover, this paper introduces artificial noise to prove that the rank relaxation is compact by considering the corresponding minimization power problem. The simulation results show that the proposed scheme and approximation algorithm can obtain better system security and speed than the existing literature, and there are certain improvements compared with the traditional method, so the effectiveness of the method in this paper is verified by simulation experiments. [ABSTRACT FROM AUTHOR]

With the continuous development of Internet technology, e-commerce has gradually become a popular field. As an important part of China's tertiary industry, the rapid development of e-commerce has played a huge role in promoting the development of the domestic economy. At the same time, e-commerce has also greatly contributed to the development of the logistics industry. Hence, with the rapid growth of the economy and the rapid development of e-commerce, the construction of logistics and distribution systems plays an essential role in the process of urban development. A perfect logistics and distribution system can promote the overall economic efficiency of the city and facilitate people's daily lives. In addition, the development of logistics systems can meet the escalating needs of consumers and the increasingly competitive market environment, thus promoting the transformation of e-commerce retail enterprises to high-quality development. The simulated annealing algorithm is an effective approximation algorithm for solving optimization problems, and the application of this algorithm to path optimization problems can be of practical value in solving problems in urban road traffic and logistics distribution. Therefore, exploring logistics distribution vehicle route optimization is of great practical significance to optimize the allocation of resources in cities, improve the economic efficiency of enterprises, and solve the problems of urban traffic congestion and can provide reference for relevant departments in their decision-making. This paper focuses on how to optimize vehicle paths in the logistics distribution process and constructs a path optimization model that satisfies the multiobjective premise. The path problem is solved using the simulated annealing algorithm, and the results are analyzed. [ABSTRACT FROM AUTHOR]

Heterogeneous multicore processor systems, as one of the highlights of multicore processor systems, are widely loved by people for their high efficiency and low cost, and they have also become the most commonly used processor systems in embedded systems. In the process of research on heterogeneous multicore processor systems, system task scheduling is particularly important. A good task scheduling algorithm can give full play to system performance. In this paper, the intelligent approximation algorithm is applied to the task scheduling problem of heterogeneous multicore processor system, and the heterogeneous multicore processor system is obtained as a highlight in the multicore processor system. Relying on the characteristics of high efficiency and low cost, it is widely loved by people, and at the same time, it has become the most commonly used processor system in embedded systems. In the process of research on heterogeneous multicore processor systems, the system task scheduling problem is particularly important. A good task scheduling algorithm can give full play to system performance. Some commonly used heuristic task scheduling algorithms are insufficient in solving such problems. This paper combines the granularity-based wavefront parallel decoding algorithm and the fast fusion loop filter algorithm to apply pipeline parallel technology between pixels. Decoding reconstruction module and fast loop filter module realize the fusion of multilevel parallel decoding. Based on the multicore platform, a dynamic multiparallel scheduling algorithm is designed to realize two-way video real-time parallel high-speed decoding, which improves the core resource utilization rate and decoding execution efficiency of the multicore processing platform. This paper also designs an indicator collector, a read-write hit collector, and a cache block priority determiner to implement a dynamic generation strategy with low hardware overhead. Multimodal teaching has certain feasibility and effectiveness and can have a positive impact on the English reading motivation and English reading comprehension ability of university students. Multimodal teaching improves students' English reading comprehension ability, deepens students' understanding and memory of words, and broadens the scope of knowledge, which has a significant promoting effect. The research results show that multimodality can be applied to college English teaching, and it can achieve better results than traditional teaching methods. By comparing the test results before and after the test, there are obvious differences between the experimental class and the control class. Whether it is the paired sample T-test of the experimental class or the independent sample T-test of two teaching methods in two classes, it proves that the scores under the multimodal English teaching mode are higher than the traditional teaching mode. Multimodal classrooms provide students with many opportunities to participate in classroom activities and form a competitive learning atmosphere. This competitive learning atmosphere has become a driving force to promote student learning. Multimodal teaching methods help to cultivate students' independent and cooperative learning. In a multimodal classroom, many activities require cooperation and discussion among students. When they encounter difficulties, they can help each other, discuss with each other, and cooperate to complete tasks. Therefore, the multimodal teaching method will stimulate students' interest in learning, give full play to students' initiative, and improve students' English ability. [ABSTRACT FROM AUTHOR]

"Vehicle damage insurance" in China, "Protection Plus Mobile Elite" launched by Samsung, "iPhone Apple Care +" offered by Apple, and so on are special warranty services aiming at external shocks. These warranty terms, named accident insurance, exist widely in the marketplace. Considering the effect of external shocks on degradation, a two-dimensional preventive maintenance and replacement strategy for products, sold with a nonrenewing and two-dimensional rectangle warranty service, is proposed in this paper. Under this strategy, preventive maintenance actions are scheduled based on units of age or usage, which occurs first. There is a reduction in the intensity function after a preventive maintenance action. Each shock before the Nth shock causes a failure of the product or an increase in product failure rate. The product is replaced by a new one on the Nth shock if it survives the N − 1 shocks. From the view of manufacturers, the mean warranty servicing cost over the whole warranty region is obtained by using the renewal theory. Based on direct numerical Riemann–Stieltjes integration, an approximation algorithm of the cost is also given. The mean cost is minimized by the optimization of preventive maintenance interval and the number of shocks N. A numerical example is given to illustrate the feasibility of the proposed strategy. The effects of maintenance cost, the arrival rate of the shocks, and other model parameters on the optimal strategy are also investigated numerically. [ABSTRACT FROM AUTHOR]

Dynamic programming is difficult to apply to large-scale Bayesian network structure learning. In view of this, this article proposes a BN structure learning algorithm based on dynamic programming, which integrates improved MMPC (maximum-minimum parents and children) and MWST (maximum weight spanning tree). First, we use the maximum weight spanning tree to obtain the maximum number of parent nodes of the network node. Second, the MMPC algorithm is improved by the symmetric relationship to reduce false-positive nodes and obtain the set of candidate parent-child nodes. Finally, with the maximum number of parent nodes and the set of candidate parent nodes as constraints, we prune the parent graph of dynamic programming to reduce the number of scoring calculations and the complexity of the algorithm. Experiments have proved that when an appropriate significance level α is selected, the MMPCDP algorithm can greatly reduce the number of scoring calculations and running time while ensuring its accuracy. [ABSTRACT FROM AUTHOR]

Achieving a high-resolution approximation and hitting the Pareto optimal set with some if not all members of the population is the goal for multi- and many-objective optimization problems, and more so in real-world applications where there is also the desire to extract knowledge about the problem from this set. The task requires not only to reach the Pareto optimal set but also to be able to continue discovering new solutions, even if the population is filled with them. Particularly in many-objective problems where the population may not be able to accommodate the full Pareto optimal set. In this work, our goal is to investigate some tools to understand the behavior of algorithms once they converge and how their population size and particularities of their selection mechanism aid or hinder their ability to keep finding optimal solutions. Through the use of features that look into the population composition during the search process, we will look into the algorithm's behavior and dynamics and extract some insights. Features are defined in terms of dominance status, membership to the Pareto optimal set, recentness of discovery, and replacement of optimal solutions. Complementing the study with features, we also look at the approximation through the accumulated number of Pareto optimal solutions found and its relationship to a common metric, the hypervolume. To generate the data for analysis, the chosen problem is MNK-landscapes with settings that make it easy to converge, enumerable for instances with 3 to 6 objectives. Studied algorithms were selected from representative multi- and many-objective optimization approaches such as Pareto dominance, relaxation of Pareto dominance, indicator-based, and decomposition. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are extensively utilized in various circumstances. For applications, the construction of the virtual backbones (VBs) of WSNs has attracted considerable attention in this field. Generally, a homogeneous WSN is formulated as a unit disk graph (UDG), and the VB of the corresponding WSN is modeled as a connected dominating set (CDS) in the UDG. In certain applications, communication between sensors in a network may fail for various reasons, such as sensor movement, signal interference, and the appearance of obstacles. Consequently, a CDS in a UDG should possess fault tolerance on the edges. In this paper, we introduce a new concept called the 2 edge-connected 2 edge-dominating set ( 2 , 2 -ECDS); then, we design an approximation algorithm for computing 2 , 2 -ECDSs in UDGs, the performance ratio of which is 30.51. By means of simulations, we compare our algorithm and existing algorithms in terms of the CDS size, running time, success rate, and network lifetime. The simulation results indicate that our algorithm exhibits better performance and is more suitable for constructing a VB with edge fault tolerance in a WSN. [ABSTRACT FROM AUTHOR]

In this paper, a route-planning approach is proposed based on the region-segmentation Dynamic Programming (DP) algorithm for Automated Guided Vehicles (AGVs) in large Smart Road Network Systems (SRNSs) to deal with the problem of low route computation efficiency of the classical DP algorithm. We introduced an improved Markov Decision Process (MDP) to describe SRNSs, in which the SRNSs are divided into several regions according to the AGVs' start nodes and their goal nodes to improve the route-planning efficiency. Moreover, the route with the minimum number of turns is selected to reduce the system running time and energy cost in the following way: first, all the equidistant shortest routes are acquired from the AGVs' start nodes to their goal nodes using the improved DP algorithm; then, the routes are screened by calculating the angular deviation between all feasible routes and AGVs' initial directions, and the route with the fewest number of turns is taken as the shortest-time route. The simulation results verified that the proposed method can effectively solve the route-planning problem of AGVs in current SRNSs. [ABSTRACT FROM AUTHOR]

We give a Bézier variant of Baskakov-Durrmeyer-type hybrid operators in the present article. First, we obtain the rate of convergence by using Ditzian-Totik modulus of smoothness and also for a class of Lipschitz function. Then, weighted modulus of continuity is investigated too. We study the rate of point-wise convergence for the functions having a derivative of bounded variation. Furthermore, we establish the quantitative Voronovskaja-type formula in terms of Ditzian-Totik modulus of smoothness at the end. [ABSTRACT FROM AUTHOR]

The low-power wide-area network (LPWAN) technologies, such as LoRa, Sigfox, and NB-IoT, bring new renovation to the wireless communication between end devices in the Internet of things (IoT), which can provide larger coverage and support a large number of IoT devices to connect to the Internet with few gateways. Based on these technologies, we can directly deploy IoT devices on the candidate locations to cover targets or the detection area without considering multihop data transmission to the base station like the traditional wireless sensor networks. In this paper, we investigate the problems of the minimum energy consumption of IoT devices for target coverage through placement and scheduling (MTPS) and minimum energy consumption of IoT devices for area coverage through placement and scheduling (MAPS). In the problems, we consider both the placement and scheduling of IoT devices to monitor all targets (or the whole detection area) such that all targets (or the whole area) are (or is) continuously observed for a certain period of time. The objectives of the problems are to minimize the total energy consumption of the IoT devices. We first, respectively, propose the mathematical models for the MTPS and MAPS problems and prove that they are NP-hard. Then, we study two subproblems of the MTPS problem, minimum location coverage (MLC), and minimum energy consumption scheduling deployment (MESD) and propose an approximation algorithm for each of them. Based on these two subproblems, we propose an approximation algorithm for the MTPS problem. After that, we investigate the minimum location area coverage (MLAC) problem and propose an algorithm for it. Based on the MLAC and MESD problems, we propose an approximation algorithm to solve the MAPS problem. Finally, extensive simulation results are given to further verify the performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this research, an approximation symbolic algorithm is suggested to obtain an approximate solution of multipantograph system of type delay differential equations (DDEs) using a combination of Laplace transform and variational iteration algorithm (VIA). The corresponding convergence results are acquired, and an efficient algorithm for choosing a feasible Lagrange multiplier is designed in the solving process. The application of the Laplace variational iteration algorithm (LVIA) for the problems is clarified. With graphics and tables, LVIA approximates to a high degree of accuracy with a few numbers of iterates. Also, computational results of the considered examples imply that LVIA is accurate, simple, and appropriate for solving a system of multipantograph delay differential equations (SMPDDEs). [ABSTRACT FROM AUTHOR]

With the development of the Internet age, the gradual deepening of reforms, and the continuous improvement of marketization, the financial ecological environment is directly related to the risk of financial institutions' loans and the enthusiasm to support local economic development. Capital is accompanied by the physical development and evolution of the production and circulation of goods. After thousands of years of historical development and evolution, it has formulated its own development law and internal logic. In view of the unclear environmental and social evaluation indicators in supply chain research and the model's failure to accurately describe the real supply chain network, an adaptive dynamic relaxation approximation algorithm model is proposed. This model introduces upstream and downstream transit stations as distributors and as a hub between customers, the transportation mode is improved to horizontal and vertical coordinated transportation, and multiple transportation methods are used for transportation. In this paper, the experimental group adopts a new model based on the approximate algorithm of dynamic relaxation factors, and the control group adopts the original old model. The two groups conduct comprehensive data analysis on social indicators, self-indicators, initial indicators, economic indicators, and environmental indicators in each year. Starting from the dimension, comprehensively consider the selection of indicators to measure the level of enterprise performance. The experiment proves that the transportation loss in the green supply chain is between the new model and the old model. When the number of partners is small, the degree of the optimal solution between the new and old models is not obvious, and the degree of optimization of the new model relative to the old model is increasing. It has a significant statistical difference (P < 0.05). This shows that efforts to build a good financial ecological environment and an honest and reliable and healthy economic ecological environment are of great importance for promoting the continuous and rapid growth of the social economy. [ABSTRACT FROM AUTHOR]

The arrival of cloud computing age makes data outsourcing an important and convenient application. More and more individuals and organizations outsource large amounts of graph data to the cloud computing platform (CCP) for the sake of saving cost. As the server on CCP is not completely honest and trustworthy, the outsourcing graph data are usually encrypted before they are sent to CCP. The optimal route finding on graph data is a popular operation which is frequently used in many fields. The optimal route finding with support for semantic search has stronger query capabilities, and a consumer can use similar words of graph vertices as query terms to implement optimal route finding. Due to encrypting the outsourcing graph data before they are sent to CCP, it is not easy for data customers to manipulate and further use the encrypted graph data. In this paper, we present a solution to execute privacy-guarding optimal route finding with support for semantic search on the encrypted graph in the cloud computing scenario (PORF). We designed a scheme by building secure query index to implement optimal route finding with support for semantic search based on searchable encryption idea and stemmer mechanism. We give formal security analysis for our scheme. We also analyze the efficiency of our scheme through the experimental evaluation. [ABSTRACT FROM AUTHOR]

In this paper, a branching tree evolution is established, in which the birth rate and the death rate are both dependent on node's age. The extinction probability and the t-pre-extinction (extinct before time t) probability are studied, by which the distribution of the extinction moment can be given. The analytical formula and the approximation algorithm for the distribution of extinction moment are given; furthermore, the analytical formula and the approximation algorithm of extinction probability are given, and a necessary and sufficient condition of extinction with probability 1 is given. It is the first time to study the distribution of extinction time for the branching process with birth rate and the death rate both depending on node's age, and the results will do great help in the theory of branching process. It is expected to be applied in the fields of biology, genetics, medicine, epidemiology, demography, nuclear physics, actuarial mathematics, algorithm, and data structures, etc. [ABSTRACT FROM AUTHOR]

We consider a scheduling problem where a set of jobs are first processed on a machine with an unavailability interval and, then, delivered to the customer directly. We focus on an integrated schedule of production and distribution such that the sum of the maximum delivery time and total delivery cost is optimized. We study two classes of processing machines in the production part. In the first class, the serial-batch machine, the processing time of a batch is the sum of the processing times of its jobs. In the second class, the parallel-batch machine, the processing time of a batch is the maximum processing time of the jobs contained in the batch. The machine has a fixed capacity, and the jobs are processed in batches under the condition that the total size of the jobs in a batch cannot exceed the machine capacity. Two patterns of job's processing, i.e., resumable and non-resumable, are considered if it is interrupted by the unavailability interval on the machine. In the distribution part, there are sufficient vehicles with a fixed capacity to deliver the completed jobs. The total size of the completed jobs in one delivery cannot exceed the vehicle capacity. We show that these four problems are NP-hard in the strong sense in which the jobs have the same processing times and arbitrary sizes, and we propose an approximation algorithm for solving these four problems. Moreover, we show that the performance ratio of the algorithm is 2 for the serial-batch machine setting, and the error bound is 71/99 for the parallel-batch machine setting. We also evaluate the performance of the approximation algorithm by the computational results. [ABSTRACT FROM AUTHOR]

We introduce a new two-side approximation method for the channel scheduling problem, which controls the accuracy of approximation in two sides by a pair of parameters f , g . We present a series of simple and practical-for-implementation greedy algorithms which give constant factor approximation in both sides. First, we propose four approximation algorithms for the weighted channel allocation problem: 1. a greedy algorithm for the multichannel with fixed interference radius scheduling problem is proposed and an one side O 1 -IS-approximation is obtained; 2. a greedy O 1 , O 1 -approximation algorithm for single channel with fixed interference radius scheduling problem is presented; 3. we improve the existing algorithm for the multichannel scheduling and show an E O d / ε time 1 − ϵ -approximation algorithm; 4. we speed up the polynomial time approximation scheme for single-channel scheduling through merging two algorithms and show a 1 − ϵ , O 1 -approximation algorithm. Next, we study two polynomial time constant factor greedy approximation algorithms for the unweighted channel allocation with variate interference radius. A greedy O 1 -approximation algorithm for the multichannel scheduling problem and an O 1 , O 1 -approximation algorithm for single-channel scheduling problem are developed. At last, we do some experiments to verify the effectiveness of our proposed methods. [ABSTRACT FROM AUTHOR]

This paper investigates access link control and resource allocation for the device-to-device (D2D) communication in the fifth generation (5G) cellular networks. The optimization objective of this problem is to maximize the number of admitted D2D links and minimize the total power consumption of D2D links under the condition of meeting the minimum transmission rate requirements of D2D links and common cellular links. This problem is a two-stage nondeterministic polynomial (NP) problem, the solving process of which is very complex. So, we transform it into a one-stage optimization problem. According to the monotonicity of objective function and constraint conditions, a monotone optimization problem is established, which is solved by reverse polyblock approximation algorithm. In order to reduce the complexity of this algorithm, a solution algorithm based on iterative convex optimization is proposed. Simulation results show that both algorithms can maximize the number of admitted D2D links and minimize the total power consumption of D2D links. The proposed two algorithms are better than the energy efficiency optimization algorithm. [ABSTRACT FROM AUTHOR]

This paper studies a flexible flowshop scheduling problem with step-deteriorating jobs and sequence-dependent setup times (FFSP-SDJ&SDST) where there are multiple unrelated parallel machines at each stage. The actual processing time of each job is modeled as a step function of its starting time. An integer programming model is first formulated with the objective of minimizing the total weighted completion time. Since this problem is NP-complete, it becomes an interesting and challenging topic to develop effective approximation algorithms for solving it. The artificial bee colony (ABC) algorithm has been successfully applied to solve both continuous and combinatorial optimization problems with the advantages of fewer control parameters and ease of implementation. So, an improved discrete artificial bee colony algorithm is proposed. In this algorithm, a dynamic generation mechanism of initial solutions is designed based on job permutation encoding. A genetic algorithm and a modified variable neighborhood search are introduced, respectively, to obtain new solutions for the employed and onlooker bees. A greedy heuristic is proposed to generate the solutions of the scout bees. Finally, to verify the performance of the proposed algorithm, an orthogonal test is performed to optimize the parameter settings. Simulation results on different scale problems demonstrate that the proposed algorithm is more effective compared against several presented algorithms from the existing literatures. [ABSTRACT FROM AUTHOR]

Broadcasting is a fundamental function for disseminating messages in multihop wireless networks. Minimum-Transmission Broadcasting (MTB) problem aims to find a broadcast schedule with minimum number of transmissions. Previous works on MTB in duty-cycled networks exploit a rigid assumption that nodes have only active time slot per working cycle. In this paper, we investigated the MTB problem in duty-cycled networks where nodes are allowed arbitrary active time slots per working cycle (MTBDCA problem). Firstly, it is proved to be NP-hard and o(ln⁡Δ)-inapproximable, where Δ is the maximum degree in the network. Secondly, an auxiliary graph is proposed to integrate nodes' active time slots into the network and a novel covering problem is proposed to exploit nodes' multiple active time slots for scheduling. Then, a ln⁡(Δ+1)-approximation algorithm is proposed for MTBDCA and a (ln⁡(Δ+1)+Δ)-approximation algorithm is proposed for all-to-all MTBDCA. Finally, extensive experimental results demonstrate the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a versatile solver of a nonconformal volume integral equation based on the Schaubert-Wilton-Glisson (SWG) basis function is presented. Instead of using a piecewise constant function, the robust conventional SWG basis function is chosen and used directly for discontinuous boundaries. A new map method technique is proposed for constructing SWG pairs, which reduces the complexity from ON2 to ONlogN compared with a brute-force method. The integral equation is solved by the method of moments (MoM) and further accelerated by the multilevel fast multipole algorithm (MLFMA). What's more, the hybrid scheme of MLFMA and adaptive cross approximation (ACA) is developed to resolve the low-frequency (LF) breakdown when dealing with over-dense mesh objects. Numerical results show that when in analysis of radiation or scattering problems from inhomogeneous dielectric objects or in LF conditions, the proposed solver shows high efficiency without loss of accuracy, which demonstrates the versatile performance of the proposed method. [ABSTRACT FROM AUTHOR]

The min-sum algorithm (MSA) for decoding Low-Density Parity-Check (LDPC) code is an approximation algorithm that can greatly reduce the computational complexity of the belief propagation algorithm (BPA). To reduce the error between MSA and BPA, an improved MSA such as normalized min-sum algorithm (NMSA) that uses the normalization factor when updating the check node is used in many LDPC decoders. When obtaining an optimal normalization factor, density evolution (DE) is usually used. However, not only does the DE method require a large number of calculations, it may not be optimal for obtaining a normalization factor due to the theoretical assumptions that need to be satisfied. This paper proposes a new method obtaining a normalization factor for NMSA. We first examine the relationship between the minimum value of variable node messages’ magnitudes and the magnitudes of check node outputs of BPA using the check node message distribution (CMD) chart. And then, we find a normalization factor that minimizes the error between the magnitudes of check node output of NMSA and BPA. We use the least square method (LSM) to minimize the error. Simulation on ATSC 3.0 LDPC codes demonstrates that the normalization factor obtained by this proposed method shows better decoding performance than the normalization factor obtained by DE. [ABSTRACT FROM AUTHOR]

In this paper, an effective method based on Transform Invariant Low-rank Textures (TILT) and HOG is proposed to identify woven fabric pattern. Firstly, the method based on TILT is used to solve the deflection phenomenon in the process of woven fabric image acquisition. Secondly, the yarn floats in the fabric image is localized by the yarns segmentation method based on the 2D spatial-domain gray projection, which is used to segment the weft and warp yarns. Thirdly, HOG is applied to extract distinctive invariant features in the process of feature extraction. According to the HOG feature, the texture features of the woven fabric are acquired. Finally, the yarn floats are classified by Fuzzy C-Means (FCM) clustering to recognize the weft and warp cross. Experimental results demonstrate that the proposed method can achieve the recognition of the three woven fabrics, plain, twill, and satin, and obtain accurate classification results. [ABSTRACT FROM AUTHOR]

Background modeling plays an important role in the application of intelligent video surveillance. Researchers have presented diverse approaches to support the development of dynamic background modeling. However, in the case of pumping unit surveillance, traditional background modeling methods often mistakenly detect the periodic rotational pumping unit as the foreground object. To address this problem here, we propose a novel background modeling method for foreground segmentation, particularly in dynamic scenes that include a rotational pumping unit. In the proposed method, the ViBe method is employed to extract possible foreground pixels from the sequence frames and then segment the video image into dynamic and static regions. Subsequently, the kernel density estimation (KDE) method is used to build a background model with dynamic samples of each pixel. The bandwidth and threshold of the KDE model are calculated according to the sample distribution and extremum of each dynamic pixel. In addition, the strategy of sample adjustment combines regular and real-time updates. The performance of the proposed method is evaluated against several state-of-the-art methods applied to complex dynamic scenes consisting of a rotational pumping unit. Experimental results show that the proposed method is available for periodic object motion scenario monitoring applications. [ABSTRACT FROM AUTHOR]

This paper studies the problem of adaptive stabilization for a class of stochastic high-order nonholonomic systems. Under the weaker assumptions, by constructing the appropriate Lyapunov function and combining sign function technique, an adaptive state feedback controller is designed to guarantee global asymptotic stability in probability of the closed-loop system. The effectiveness of the controller is demonstrated by a mechanical system. [ABSTRACT FROM AUTHOR]

An IMM-GLR approach based on interacting multiple model (IMM) and generalized likelihood ratio (GLR) estimation was developed to detect, isolate, and estimate gas turbine gas path fault (including abrupt fault and multiple faults) in the underdetermine estimation conditions. In this approach, a model set representing gas turbine health condition and different fault condition was established, and a corresponding bank of filters was designed. An IMM-based FDI algorithm based on these filters is applied to detect and isolate fault, and a GLR estimation algorithm is used to estimate the fault severity. Then a model set update strategy based on the diagnosed fault was proposed to enable the diagnosis of multiple faults. Several simulation case studies on a marine gas turbine were conducted, and the results show that the IMM-GLR approach not only accurately diagnoses the abrupt gas path fault and multiple gas path faults but also accurately estimates the severity of the detected fault in the underdetermine estimation conditions. [ABSTRACT FROM AUTHOR]

The connected vertex cover (CVC) problem, which has many important applications, is a variant of the vertex cover problem, such as wireless network design, routing, and wavelength assignment problem. A good algorithm for the problem can help us improve engineering efficiency, cost savings, and resources consumption in industrial applications. In this work, we present an efficient algorithm GRASP-CVC (Greedy Randomized Adaptive Search Procedure for Connected Vertex Cover) for CVC in general graphs. The algorithm has two main phases, i.e., construction phase and local search phase. In the construction phase, to construct a high quality feasible initial solution, we design a greedy function and a restricted candidate list. In the local search phase, the configuration checking strategy is adopted to decrease the cycling problem. The experimental results demonstrate that GRASP-CVC is better than other comparison algorithms in terms of effectivity and efficiency. [ABSTRACT FROM AUTHOR]

It is important to eliminate systematic biases in the field of soil moisture data assimilation. One simple method for bias removal is to match cumulative distribution functions (CDFs) of modeled soil moisture data to satellite soil moisture data. Traditional methods approximate numerical CDFs using 12 or 20 uniformly spaced samples. In this paper, we applied the Douglas–Peucker curve approximation algorithm to approximate the CDFs and found that three nonuniformly spaced samples can achieve the same reduction in standard deviation. Meanwhile, the matching results are always closely related to the temporal and spatial availability of soil moisture observed by automatic soil moisture station (ASM). We also applied the new nonuniformly spaced sampling method to a shorter time series. Instead of processing a whole year of data at once, we divided it into 12 datasets and used three nonuniformly spaced samples to approximate the model data’s CDF for each month. The matching results demonstrate that NU-CDF3 reduced the SD, improved R, and reduced the RMSD in over 70% of the stations, when compared with U-CDF12. Additionally, the SD and RMSD have been reduced by over 4% with R improved by more than 9%. [ABSTRACT FROM AUTHOR]

Neurodegenerative diseases that affect serious gait abnormalities include Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), and Huntington disease (HD). These diseases lead to gait rhythm distortion that can be determined by stride time interval of footfall contact times. In this paper, we present a new method for gait classification of neurodegenerative diseases. In particular, we utilize a symbolic aggregate approximation algorithm to convert left-foot stride-stride interval into a sequence of symbols using a symbolic aggregate approximation. We then find string prototypes of each class using the newly proposed string grammar unsupervised possibilistic fuzzy C-medians. Then in the testing process the fuzzy k-nearest neighbor is used. We implement the system on three 2-class problems, i.e., the classification of ALS against healthy patients, that of HD against healthy patients , and that of PD against healthy patients. The system is also implemented on one 4-class problem (the classification of ALS, HD, PD, and healthy patients altogether) called NDDs versus healthy. We found that our system yields a very good detection result. The average correct classification for ALS versus healthy is 96.88%, and that for HD versus healthy is 97.22%, whereas that for PD versus healthy is 96.43%. When the system is implemented on 4-class problem, the average accuracy is approximately 98.44%. It can provide prototypes of gait signals that are more understandable to human. [ABSTRACT FROM AUTHOR]

Aiming at the difficulty of early fault vibration signal extraction of rolling bearing, a method of fault weak signal extraction based on variational mode decomposition (VMD) and quantum particle swarm optimization adaptive stochastic resonance (QPSO-SR) for denoising is proposed. Firstly, stochastic resonance parameters are optimized adaptively by using quantum particle swarm optimization algorithm according to the characteristics of the original fault vibration signal. The best stochastic resonance system parameters are output when the signal to noise ratio reaches the maximum value. Secondly, the original signal is processed by optimal stochastic resonance system for denoising. The influence of the noise interference and the impact component on the results is weakened. The amplitude of the fault signal is enhanced. Then the VMD method is used to decompose the denoised signal to realize the extraction of fault weak signals. The proposed method was applied in simulated fault signals and actual fault signals. The results show that the proposed method can reduce the effect of noise and improve the computational accuracy of VMD in noise background. It makes VMD more effective in the field of fault diagnosis. The proposed method is helpful to realize the accurate diagnosis of rolling bearing early fault. [ABSTRACT FROM AUTHOR]

Motivated by a number of recent investigations, we consider a new analogue of Bernstein-Durrmeyer operators based on certain variants. We derive some approximation properties of these operators. We also compute local approximation and Voronovskaja type asymptotic formula. We illustrate the convergence of aforementioned operators by making use of the software MATLAB which we stated in the paper. [ABSTRACT FROM AUTHOR]

Energy efficiency and fault tolerance are two of the major concerns in wireless sensor networks (WSNs) for the target coverage. Design of target coverage algorithms for a large scale WSNs should incorporate both the energy efficiency and fault tolerance. In this paper, we study the coverage problem where the main objective is to construct two disjoint cover sets in randomly deployed WSNs based on relay energy (Erelay). Further, we present an approximation algorithm called Energy Efficient Maximum Disjoint Coverage (EMDC) with provable approximation ratios. We analyze the performance of EMDC theoretically and also perform extensive simulations to demonstrate the effectiveness of EMDC in terms of fault tolerance and energy efficiency. [ABSTRACT FROM AUTHOR]

As the developments of new techniques, mobile social networks have been built wildly. To obtain and spread information over mobile social networks efficiently, the influence maximization problem is to find a seed nodes set with limited size such that it can influence as many nodes as possible. Previous works ignore the dynamic influence phenomenon of diffusing information on mobile social networks. In this article, we propose a new model to express the procedure of diffusing information under the existence of dynamic influence. Theoretical analysis shows that the influence maximization problem under new model is non-deterministic polynomial-time hard, and efficient approximation algorithm is proposed. Experimental studies on real data sets show that the new model can process dynamic influence well in the diffusing information procedure, and the proposed algorithms can solve the influence maximization problem on new model efficiently. [ABSTRACT FROM AUTHOR]

This study concerns the development of a straightforward numerical technique associated with Classical Newton’s Method for providing a more accurate approximate solution of scalar nonlinear equations. The proposed procedure is based on some practical geometric rules and requires the knowledge of the local slope of the curve representing the considered nonlinear function. Therefore, this new technique uses, only as input data, the first-order derivative of the nonlinear equation in question. The relevance of this numerical procedure is tested, evaluated, and discussed through some examples. [ABSTRACT FROM AUTHOR]

Measurements of delays of the signals radiated by transmitters of navigational satellites allow us to obtain the total electron content (TEC). In addition, measurements of TEC allow solving problems such as development of local, regional, and global models of TEC and correction of ionospheric delay for increasing accuracy of positioning. Now, it is possible to set the task of calculation of critical frequency foF2 with the use of experimental values of TEC in a global scale. For this purpose it is necessary to know an equivalent slab thickness of the ionosphere τ which is a coefficient of proportionality between TEC and a maximum density of the ionosphere. The present paper is devoted to the analysis of investigation and utilization of this parameter. It is shown that (1) existing models of τ are not empirical and not always can provide an adequate accuracy of foF2 calculation, (2) experimental median τ(med) provides much larger accuracy of foF2 calculation than the empirical model and variations from day to day and allows filling gaps in the ionosonde data, and (3) it is possible to use a hyperbolic approximation and coefficient K(τ) for development of a global model of τ. [ABSTRACT FROM AUTHOR]

A new method based on the second-order Taylor-series expansion is presented to identify the structural dynamic loads in the time domain. This algorithm expresses the response vectors as Taylor-series approximation and then a series of formulas are deduced. As a result, an explicit discrete equation which associates system response, system characteristic, and input excitation together is set up. In a multi-input-multi-output (MIMO) numerical simulation study, sinusoidal excitation and white noise excitation are applied on a cantilever beam, respectively, to illustrate the effectiveness of this algorithm. One also makes a comparison between the new method and conventional state space method. The results show that the proposed method can obtain a more accurate identified force time history whether the responses are polluted by noise or not. [ABSTRACT FROM AUTHOR]

A fundamental problem in wireless networks is the maximum link scheduling (MLS) problem. In this problem, interference is a key issue and past researchers have shown that determining reception using Signal-to-Interference plus Noise Ratio (SINR) is more realistic than graph-based interference models. Unfortunately, the MLS problem has been proven to be NP-hard for SINR interference models. To date, several approximation algorithms have been proposed to solve MLS under the SINR-based interference model. However, most of these works do not have either an approximation bound or a distributed version. To this end, we present a novel scheduling method with a constant approximation ratio which is much simpler and only 1/28 of it in past research. The improvement of constant ϕ also offers a better MLS set. In addition, based on our centralized method, we present a polynomial time, randomized, distributed algorithm, which only requires estimates of the number of links, and maximum and minimum link lengths. We prove its correctness and show that it can compute a MLS with time complexity of O(log2n), where n is an estimate of the number of links. [ABSTRACT FROM AUTHOR]

We present two collocation algorithms based on interpolating and approximating subdivision schemes for the solution of fourth order boundary value problems arising in the mathematical modeling of viscoelastic, and inelastic flows, deformation of beams, arches, and load bearing members like street lights and robotic arms in multipurpose engineering systems. Numerical examples are given to illustrate the algorithms. We conclude that approximating schemes based collocation algorithms give better solution than interpolating schemes based collocation algorithms. Main purpose of this paper is to explore and seek the applications of interpolating and approximating subdivision schemes in the field of boundary value problems along with intrinsic comparison of the results obtained by algorithms based on these schemes. A comparison with other approaches of this type of boundary value problems in order to see the advantages of the proposed methods is also given. [ABSTRACT FROM AUTHOR]

Spectral efficient transmission techniques are necessary and promising for future broadband wireless communications, where the quality of service (QoS) and/or max-min fair (MMF) of intended users are often considered simultaneously. In this paper, both the QoS problem and the MMF problem are investigated together for transmit beamforming in broadband multigroup multicast channels with frequency-selective fading characters. We first present a basic algorithm by directly using the results in frequency-flat multigroup multicast systems (Karipidis et al., 2008), namely, the approximation algorithms in this paper, for both problems, respectively. Due to high computational consumption nature of the approximation algorithms, two reduced-complexity algorithms for each of the two problems are proposed separately by introducing the time-frequency correlations. In addition, parameters in the new time-frequency formulations, such as the number of optimization matrix variables and the taps of the beamformer with finite impulse response (FIR) structure, can be used to make a reasonable tradeoff between computational burden and system performance. Insights into the relationship between the two problems and some analytical results of the computational complexity of the proposed algorithms are also studied carefully. Numerical simulations indicate the efficiency of the proposed algorithms. [ABSTRACT FROM AUTHOR]

The results generalizing some theorems on N, pnE, γ summability are shown. The same degrees of pointwise approximation as in earlier papers by weaker assumptions on considered functions and examined summability methods are obtained. From presented pointwise results, the estimation on norm approximation is derived. Some special cases as corollaries are also formulated. [ABSTRACT FROM AUTHOR]

One-slot link scheduling is important for enhancing the throughput capacity of wireless sensor networks. It includes two aspects: maximum links scheduling (MLS) and maximum weighted links scheduling (MWLS). In this paper we propose two heuristic algorithms for the two NP-hard problems with obvious power assignments under the SINR (signal-to-interference-plus-noise-ratio) model. For MLS, we propose an algorithm MTMA (maximum tolerance and minimum affectance), which improves the currently best approximation algorithm by 28%–62% on average. For MWLS, we give an effective heuristic algorithm MWMA (maximum weighted and minimum affectance), which performs better on improving the throughput and reducing the running time. The correctness and performance of our algorithms are confirmed through theoretical analysis and comprehensive simulations. [ABSTRACT FROM AUTHOR]

Population pharmacokinetic (PPK) models play a pivotal role in quantitative pharmacology study, which are classically analyzed by nonlinear mixed-effects models based on ordinary differential equations. This paper describes the implementation of SDEs in population pharmacokinetic models, where parameters are estimated by a novel approximation of likelihood function. This approximation is constructed by combining the MCMC method used in nonlinear mixed-effects modeling with the extended Kalman filter used in SDE models. The analysis and simulation results show that the performance of the approximation of likelihood function for mixed-effects SDEs model and analysis of population pharmacokinetic data is reliable. The results suggest that the proposed method is feasible for the analysis of population pharmacokinetic data. [ABSTRACT FROM AUTHOR]

The equilibrium network signal control problem is represented as a Stackelberg game. Due to the characteristics of a Stackelberg game, solving the upper-level problem and lower-level problem iteratively cannot be expected to converge to the solution. The reaction function of the lower-level problem is the key information to solve a Stackelberg game. Usually, the reaction function is approximated by the network sensitivity information. This paper firstly presents the general form of the second-order sensitivity formula for equilibrium network flows. The second-order sensitivity information can be applied to the second-order reaction function to solve the network signal control problem efficiently. Finally, this paper also demonstrates two numerical examples that show the computation of second-order sensitivity and the speed of convergence of the nonlinear approximation algorithm. [ABSTRACT FROM AUTHOR]

This paper considers the problem of centralized spectrum allocations in wireless sensor networks towards the following goals: (1) maximizing fairness, (2) reflecting the priority among sensor data, and (3) avoiding unnecessary spectrum handoff. We cast this problem into a multiobjective mixed integer nonconvex nonlinear programming that is definitely difficult to solve at least globally without any aid of conversion or approximation. To tackle this intractability, we first convexify the original problem using arithmetic-geometric mean approximation and logarithmic change of the decision variables and then deploy weighted Chebyshev norm-based scalarization method in order to collapse the multiobjective problem into a single objective one. Finally, we apply simple rounding method in order to obtain approximate integer solutions. The results obtained from the numerical experiments show that, by adjusting the weight on each objective function, the proposed algorithm allocates spectrum bands fairly with well observing each sensor's priority and reduced spectrum handoffs. [ABSTRACT FROM AUTHOR]

A Mahalanobis hyperellipsoidal learning machine class incremental learning algorithm is proposed. To each class sample, the hyperellipsoidal that encloses as many as possible and pushes the outlier samples away is trained in the feature space. In the process of incremental learning, only one subclassifier is trained with the new class samples. The old models of the classifier are not influenced and can be reused. In the process of classification, considering the information of sample's distribution in the feature space, the Mahalanobis distances from the sample mapping to the center of each hyperellipsoidal are used to decide the classified sample class. The experimental results show that the proposed method has higher classification precision and classification speed. [ABSTRACT FROM AUTHOR]

A numerical interpolating algorithm of collocation is formulated, based on 8-point binary interpolating subdivision schemes for the generation of curves, to solve the two-point third order boundary value problems. It is observed that the algorithm produces smooth continuous solutions of the problems. Numerical examples are given to illustrate the algorithm and its convergence. Moreover, the approximation properties of the collocation algorithm have also been discussed. [ABSTRACT FROM AUTHOR]

We prove that p = 1 and q = 2 are the best possible parameters in the interval (0, ∞ such that the double inequality (ep/(x + 1) - e-p/x)/2p < ψ'(x + 1) < (eq/(x + 1) - e-q/x)/2q holds for x > 0. As applications, some new approximation algorithms for the circumference ratio π and Catalan constant G = ∑n = 0∞((-1)n/(2n + 1)² are given. Here, ψ' is the trigamma function. [ABSTRACT FROM AUTHOR]

We obtain the global existence of weak solutions for the Cauchy problem of the nonhomogeneous, resonant system. First, by using the technique given in Tsuge (2006), we obtain the uniformly bounded L∞ estimates z(pδ,ε,uδ,ε) ≤ B(x) and w(pδ,ε,uδ,ε) ≤ β when a(x) is increasing (similarly, w(pδ,ε,uδ,ε) ≤ B(x) and z(pδ,ε,uδ,ε) ≤β when a(x) is decreasing) for the ε-viscosity and δ- flux approximation solutions of nonhomogeneous, resonant system without the restriction z0(x) ≤0 or w0(x) ≤0 as given in Klingenberg and Lu (1997), where z and w are Riemann invariants of nonhomogeneous, resonant system; B(x) >0 is a uniformly bounded function of x depending only on the function a(x) given in nonhomogeneous, resonant system, and β is the bound of B(x). Second, we use the compensated compactness theory, Murat (1978) and Tartar (1979), to prove the convergence of the approximation solutions. [ABSTRACT FROM AUTHOR]