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In this paper, we consider multi-party coordination in a supply chain (SC) that consists of a set of independent producers and a set of resource managers. A decentralised decision-making approach is proposed for a coal SC, with three independent parties – multiple mines, a rail operator and a terminal. The rail operator and the terminal act as common resource managers and connects the independent mines via a rail network. The objective of this SC is to efficiently use an independent rail operator to transport coal from different mines to meet the shipping demand at the terminal. The underlying coordination problem can be seen as a multi-resource constrained scheduling problem. A major part of this paper addresses the key challenges in a decentralised approach based on column generation (CG), which are to compute the value of a column, better upper bounds and to update the multipliers using decentralised methods. We have also discussed the mathematical models for different decision units, the CG algorithm and different strengthening methods. A comprehensive computational experiment based on randomly generated instances highlights the effect of decentralisation and the value of information-sharing. The proposed solution approaches can be extended to a multi-party case with any number of common resources. [ABSTRACT FROM AUTHOR]

Path planning is crucial in the scheduling and motion planning of multiple robots. However, solving multi-robot path-planning problems efficiently and quickly is challenging due to their high complexity and long computational time, especially when dealing with many robots. This paper presents a unified mathematical model and algorithm for the path planning of multiple robots moving from one formation to another in an area with obstacles. The problem was initially simplified by constructing a cost matrix, and then the route planning was achieved by integrating an elite enhanced multi-population genetic algorithm and an ant colony algorithm. The performance of the proposed planning method was verified through numerical simulations in various scenarios. The findings indicate that this method exhibits high computational efficiency and yields a minimal overall path distance when addressing the path-planning problem of a multi-robot formation reconstruction. As a result, it holds promising potential for the path-planning problem of a multi-robot formation reconstruction. [ABSTRACT FROM AUTHOR]

This paper focuses on downlink power allocation for a cognitive radio-based non-orthogonal multiple access (CR-NOMA) system in a femtocell environment involving device-to-device (D2D) communication. The proposed power allocation scheme employs the greedy asynchronous distributed interference avoidance (GADIA) algorithm. This research aims to optimize the power allocation in the downlink transmission, considering the unique characteristics of the CR-NOMA-based femtocell D2D system. The GADIA algorithm is utilized to mitigate interference and effectively optimize power allocation across the network. This research uses a fairness index to present a novel fairness-constrained power allocation algorithm for a downlink non-orthogonal multiple access (NOMA) system. Through extensive simulations, the maximum rate under fairness (MRF) algorithm is shown to optimize system performance while maintaining fairness among users effectively. The fairness index is demonstrated to be adaptable to various user counts, offering a specified range with excellent responsiveness. The implementation of the GADIA algorithm exhibits promising results for sub-optimal frequency band distribution within the network. Mathematical models evaluated in MATLAB further confirm the superiority of CR-NOMA over optimum power allocation NOMA (OPA) and fixed power allocation NOMA (FPA) techniques. [ABSTRACT FROM AUTHOR]

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we research the dynamic car sequencing problem with car body buffer (DCSPwB) in automotive mixed-flow assembly. The objective is to reorder the sequence of cars in the paint shop using the post-painted body buffers to minimize the violation of constraint rules and the time cost of sequencing in the general assembly shop. We establish a mathematical model of DCSPwB and propose a decomposition-based algorithm based on the dynamic genetic algorithm (DGA) and greedy algorithm for delayed car release (PGDA). Experiments are conducted based on production orders from actual companies, and the results are compared with the solution results of the underlying genetic algorithm (GA) and greedy algorithm (GDA) to verify the effectiveness of the algorithm. In addition, the effect of buffer capacity on DCSPwB is investigated. [ABSTRACT FROM AUTHOR]

As an important technology in 3D vision, point-cloud registration has broad development prospects in the fields of space-based remote sensing, photogrammetry, robotics, and so on. Of the available algorithms, the Iterative Closest Point (ICP) algorithm has been used as the classic algorithm for solving point cloud registration. However, with the point cloud data being under the influence of noise, outliers, overlapping values, and other issues, the performance of the ICP algorithm will be affected to varying degrees. This paper proposes a global structure and adaptive weight aware ICP algorithm (GSAW-ICP) for image registration. Specifically, we first proposed a global structure mathematical model based on the reconstruction of local surfaces using both the rotation of normal vectors and the change in curvature, so as to better describe the deformation of the object. The model was optimized for the convergence strategy, so that it had a wider convergence domain and a better convergence effect than either of the original point-to-point or point-to-point constrained models. Secondly, for outliers and overlapping values, the GSAW-ICP algorithm was able to assign appropriate weights, so as to optimize both the noise and outlier interference of the overall system. Our proposed algorithm was extensively tested on noisy, anomalous, and real datasets, and the proposed method was proven to have a better performance than other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Convolutions are one of the most important operations in signal processing. They often involve large arrays and require significant computing time. Moreover, in practice, the signal data to be processed by convolution may be corrupted by noise. In this paper, we introduce a new method for computing the convolutions in the quantized tensor train (QTT) format and removing noise from data using the QTT decomposition. We demonstrate the performance of our method using a common mathematical model for synthetic aperture radar (SAR) processing that involves a sinc kernel and present the entire cost of decomposing the original data array, computing the convolutions, and then reformatting the data back into full arrays. [ABSTRACT FROM AUTHOR]

Accurate prostate segmentation in ultrasound images is crucial for the clinical diagnosis of prostate cancer and for performing image-guided prostate surgery. However, it is challenging to accurately segment the prostate in ultrasound images due to their low signal-to-noise ratio, the low contrast between the prostate and neighboring tissues, and the diffuse or invisible boundaries of the prostate. In this paper, we develop a novel hybrid method for segmentation of the prostate in ultrasound images that generates accurate contours of the prostate from a range of datasets. Our method involves three key steps: (1) application of a principal curve-based method to obtain a data sequence comprising data coordinates and their corresponding projection index; (2) use of the projection index as training input for a fractional-order-based neural network that increases the accuracy of results; and (3) generation of a smooth mathematical map (expressed via the parameters of the neural network) that affords a smooth prostate boundary, which represents the output of the neural network (i.e., optimized vertices) and matches the ground truth contour. Experimental evaluation of our method and several other state-of-the-art segmentation methods on datasets of prostate ultrasound images generated at multiple institutions demonstrated that our method exhibited the best capability. Furthermore, our method is robust as it can be applied to segment prostate ultrasound images obtained at multiple institutions based on various evaluation metrics. [ABSTRACT FROM AUTHOR]

This paper reports an analysis of the software (SW) safety testing techniques, as well as the models and methods for identifying vulnerabilities. An issue has been revealed related to the reasoned selection of modeling approaches at different stages of the software safety testing process and the identification of its vulnerabilities, which reduces the accuracy of the modeling results obtained. Two steps in the process of identifying software vulnerabilities have been identified. A mathematical model has been built for the process of preparing security testing, which differs from the known ones by a theoretically sound choice of the moment-generating functions when describing transitions from state to state. In addition, the mathematical model takes into consideration the capabilities and risks of the source code verification phase for cryptographic and other ways to protect data. These features generally improve the accuracy of modeling results and reduce input uncertainty in the second phase of software safety testing. An advanced security compliance algorithm has been developed, with a distinctive feature of the selection of laws and distribution parameters that describe individual state-to-state transitions for individual branches of Graphical Evaluation and Review Technique networks (GERT-networks). A GERT-network has been developed to prepare for security testing. A GERT-network for the process of checking the source code for cryptographic and other data protection methods has been developed. A graphic-analytical GERT model for the first phase of software safety testing has been developed. The expressions reported in this paper could be used to devise preliminary recommendations and possible ways to improve the effectiveness of software safety testing algorithms [ABSTRACT FROM AUTHOR]

The main semantic symbol systems for people to express their emotions include natural language and music. The analysis and establishment of semantic association between language and music is helpful to provide more accurate retrieval and recommendation services for text and music. Existing researches mainly focus on the surface symbolic features and association of natural language and music, which limits the performance and interpretability of applications based on semantic association of natural language and music. Emotion is the main meaning of music expression, and the semantic range of text expression includes emotion. In this paper, the semantic features of music are extracted from audio features, and the semantic matching model of audio emotion analysis is constructed to analyze ethnic music audio emotion through feature extraction ability of deep structure. The model is based on the framework of emotional semantic matching technology and realizes the emotional semantic matching of music fragments and words through semantic emotional recognition algorithm. Multiple experiments show that when W = 0.65 , the recognition rate of multichannel fusion model is 88.42%, and the model can reasonably realize audio emotion analysis. When the spatial dimension of music data changes, the classification accuracy reaches the highest when the spatial dimension is 25. Analysing the semantic association of audio promotes the application of folk music in occupational therapy. [ABSTRACT FROM AUTHOR]

This paper adopts the method of psychological data analysis to conduct in-depth research and analysis on the correlation between teachers' classroom teaching behaviors and students' knowledge acceptance. Firstly, this paper proposes a health factor prediction model, which is specifically divided into clustering and then classification model and a clustering and classification synthesis model. The classroom learning process is coded, sampled, and quantified to obtain data on students' learning behaviors, and a visualization system based on classroom students' learning behaviors is designed and developed to record and analyze students' behaviors in the classroom learning process and grasp students' classroom learning. These two models use algorithms to fine-grained divide the dataset from the perspective of subject users and mental health factors, respectively, and then use decision tree algorithms to classify and predict the mental health factor information by the subject user base information. Second, based on the collected datasets, we designed comparison experiments to validate the clustering-then-classification model and the integrated clustering-classification model and selected the optimal model for comparison. Teachers should increase effective praise and encouragement behaviors; teachers should increase meaningful teacher-student interaction behaviors; teachers should be proficient in teaching media technology to reduce unnecessary time wastage. Strategies to enhance teachers' TPACK include enriching teachers' knowledge base of CK, TK, and PK; developing teachers' integration thinking; and enriching teachers' types of activities for integrating technology. [ABSTRACT FROM AUTHOR]

Ribonucleic acid (RNA) structure alignment is an important problem in computational biology to identify structural similarity of RNAs. Obtaining an efficient method for this problem is challenging due to the high computational time for the optimal solution and the low accuracy of a heuristic solution. In this paper, an efficient algorithm is proposed based on a mathematical model called longest arc-preserving common subsequence. The proposed algorithm uses a heuristic technique and high-performance computing to optimize the solution of RNA structure alignment, both in terms of the running time and the accuracy of the output. Extensive experimental studies on a multicore system are conducted to show the effectiveness of the proposed algorithm on two types of data. The first is simulated data that consists of 450 comparisons of RNA structures, while the second is real biological data that consists of 357 comparisons of RNA structures. The results show that the proposed algorithm outperforms the best-known heuristic algorithm in terms of execution time, with a percentage improvement of 71% and increasing the length of the output, i.e., accuracy, by approximately 45% in all studied cases. Finally, future approaches are discussed. [ABSTRACT FROM AUTHOR]

The article presents comments on the management science paper "On Sequencing n Jobs on One Machine to Minimize the Number of Late Jobs," by William L. Maxwell. The paper focused on an integer programming formulation of a one-machine job shop problem. The author contends that Maxwell made in error in proving the validity of an optimal algorithm by applying cutting plane constraints to the problem. The author explains that the problem cannot be solved through traditional cutting plane procedures. The author provides a mathematical model in an attempt to correct the proof.

Resource-efficient technologies for cutting sheet materials allow for the contours of cut-off details to be overlapped. This includes the Intermittent Cutting Problem and the Endpoint Cutting Problem. This paper reviews mathematical models of such resource-efficient cutting processes and suggests algorithms for defining the cutter route under technological constraints. As soon as a cutting plan is given, optimisation with shortening the total length of idle passes. No information of the detail shape is required to define the sequence of detail cutting. This is why all curves without self-intersections and contiguities that are a constituent part of detail boundaries may be interpreted as edges of the plane graph G, and all points of intersection and contiguity may be interpreted as vertices of the graph G. Up to homeomorphism, plane graph G can be represented by a list of edges with incident vertices and faces . This allows the restrictions of the planed cutter trajectory to be formalised in terms of graph theory. A series of algorithms for constructing the permitted route in the plane graph G as an image of the cutting plan is suggested. The constructed route for the graph G can be interpreted as a tool trajectory for the cutting plan which is the inverse image of graph G. [ABSTRACT FROM AUTHOR]

Because quantum computing can break encryption systems based on mathematical models, this paper proposes a new color quantum image encryption algorithm based on hyperchaotic system and quantum revolving gate. First, the classic image is diffused, the matrix generated by the four-wing chaotic system is serialized, and XORed with the key and plaintext to obtain the semi-ciphertext image. Then, the quantum rotation method is used to perform scrambling operations on images quantized using the improved novelty enhanced quantum representation (NEQR) model. The angle of rotation is determined by the sequence generated by the piecewise chaotic map. Finally, the experimental results and performance analysis prove that the algorithm has high security performance. [ABSTRACT FROM AUTHOR]

A fast flash calculation of the Rachford-Rice equation is very much needed in many engineering applications, especially in multi-component mixtures. This paper suggests a direct and hands-on calculation by a pocket calculator or a Chinese abacus. The calculation is based on an ancient Chinese mathematics called as He Chengtian average. An example is given to show the simplicity and effectiveness of the ancient Chinese algorithm. [ABSTRACT FROM AUTHOR]

The famous 80-20 rule states that for many events, 80% of influence is caused by 20% of those involved. This principle also applies in network science: Only a few nodes have a significant influence and impact on the whole network. We employ a Relation Distance Model and an Authority- Popularity Evaluation Model to measure the 20% and analyze its influence. For Requirements 1 and 2, we construct the undirected co-author network based on the 511th-order relationship matrix. We propose a Relation Distance Model based on the SNA (social network analysis) technique. It combines three centrality indexes in a vector to calculate the "distance" from the most influential node. Another measure (eigenvector centrality), which takes both degree and the influence of co-authors into consideration, outputs a different rank. Validation of the model is discussed by comparing the two rankings of the top 15 authors in the Erdos1 network: We find ALON, NOGA M., is the most influential person in the network. We show that the degree distribution of the Erdos1 network approximates a power-law distribution, which indicates that it is a scale-free network. For Requirement3,weestablishan Authority-PopularityEvaluationModel to analyze the depth and the width of the influence of nodes. We calculate an Authority Index by our Modified PageRank Algorithm to measure the depth of impact. We define a Popularity Index as citations per year, to reflect the width of influence. We implement the algorithm on a citation-directed network of 24 papers with weighted nodes. The ranking of the papers is obtained by combining the Authority and Popularity Indexes. For Requirement 4, we construct for 15 actors a bidirectional network with movie co-stars as links. For Requirement 5, we discuss two characteristics of a scale-free network: growth and preferential attachment. The philosophy of the dynamics of a scale-free network is revealed to be the "Matthew effect." We propose a method to boost influence: Find the shortest links to the most influential author, cooperate step-by-step with the 80% with low closeness-centrality, and finally co-author with the key figure in the field. We conduct a sensitivity analysis to study the robustness of our algorithm, and the results show a good stability. We further discuss strengths and weaknesses of our models. [ABSTRACT FROM AUTHOR]

An efficient and more accurate millimeter-wave imaging algorithm, applied to a close-range monostatic personnel screening system, with consideration of dual path propagation loss, is presented in this paper. The algorithm is developed in accordance with a more rigorous physical model for the monostatic system. The physical model treats incident waves and scattered waves as spherical waves with a more rigorous amplitude term as per electromagnetic theory. As a result, the proposed method can achieve a better focusing effect for multiple targets in different range planes. Since the mathematical methods in classical algorithms, such as spherical wave decomposition and Weyl identity, cannot handle the corresponding mathematical model, the proposed algorithm is derived through the method of stationary phase (MSP). The algorithm has been validated by numerical simulations and laboratory experiments. Good performance in terms of computational efficiency and accuracy has been observed. The synthetic reconstruction results show that the proposed algorithm has significant advantages compared with the classical algorithms, and the reconstruction by using full-wave data generated by FEKO further verifies the validity of the proposed algorithm. Finally, the proposed algorithm performs as expected over real data acquired by our laboratory prototype. [ABSTRACT FROM AUTHOR]

Consider the problem of minimizing the sum of two convex functions, one being smooth and the other non-smooth in Banach space. In this paper, we introduce a non-traditional forward-backward splitting method for solving such minimization problem. We establish different convergence estimates under different stepsize assumptions. [ABSTRACT FROM AUTHOR]

Modal-free optimization algorithms do not require specific mathematical models, and they, along with their other benefits, have great application potential in adaptive optics. In this study, two different algorithms, the single-dimensional perturbation descent algorithm (SDPD) and the second-order stochastic parallel gradient descent algorithm (2SPGD), are proposed for wavefront sensorless adaptive optics, and a theoretical analysis of the algorithms' convergence rates is presented. The results demonstrate that the single-dimensional perturbation descent algorithm outperforms the stochastic parallel gradient descent (SPGD) and 2SPGD algorithms in terms of convergence speed. Then, a 32-unit deformable mirror is constructed as the wavefront corrector, and the SPGD, single-dimensional perturbation descent, and 2SPSA algorithms are used in an adaptive optics numerical simulation model of the wavefront controller. Similarly, a 39-unit deformable mirror is constructed as the wavefront controller, and the SPGD and single-dimensional perturbation descent algorithms are used in an adaptive optics experimental verification device of the wavefront controller. The outcomes demonstrate that the convergence speed of the algorithm developed in this paper is more than twice as fast as that of the SPGD and 2SPGD algorithms, and the convergence accuracy of the algorithm is 4% better than that of the SPGD algorithm. [ABSTRACT FROM AUTHOR]

Objective: Bioinformatics analyses have been becoming important research tools in the field of life science since 2000. New tools have been developed for the detection of allergens in food sources. This study investigated potential allergen proteins similar to the endochitinase class 1(Pers a 1) protein found in avocado using bioinformatics analysis tools. Materials and Methods: The potential allergens were examined with four different in silico tools: Clustal W for multiple sequence comparison, Swiss-MODEL for homology modeling, ProtParam for comparing protein parameters, and AlgPred for allergen prediction. Results: In silico analyses revealed high rates of similarities in protein sequences among the studied species. The highest percent identity was found in Cinnamomum micranthum f. kanehirae as 97.55 while the lowest percent identity was found in Diospyros kaki as 70.23. It is very important to note that 9 of the 10 potential allergen species investigated are plats consumed in daily life and these species were identified as allergens. According to the algorithm of BLAST search on allergen representative peptides (ARPs), ARPs were found in eight potential allergen proteins except for Prunus avium and Citrus unshiu. The ARP sequence related to Persea americana is LQLTWNYNYGAAGNSIGFNGLSNP. Conclusion: People who are allergic to avocado (Persea americana) may be advised to be careful while consuming foods such as Phoenix dactylifera, Allium sativum, Citrus unshiu, Diospyros kaki, and Spinacia oleracea. The present paper can be considered as a model study for understanding allergens in foods by using in silico tools. [ABSTRACT FROM AUTHOR]

The theory of variational inequalities is an important tool in physics, engineering, finance, and optimization theory. The projection algorithm and its variants are useful tools for determining the approximate solution to the variational inequality problem. This paper introduces three distinct extragradient algorithms for dealing with variational inequality problems involving quasi-monotone and semistrictly quasi-monotone operators in infinite-dimensional real Hilbert spaces. This problem is a general mathematical model that incorporates a set of applied mathematical models as an example, such as equilibrium models, optimization problems, fixed point problems, saddle point problems, and Nash equilibrium point problems. The proposed algorithms employ both fixed and variable stepsize rules that are iteratively transformed based on previous iterations. These algorithms are based on the fact that no prior knowledge of the Lipschitz constant or any line-search framework is required. To demonstrate the convergence of the proposed algorithms, some simple conditions are used. Numerous experiments have been conducted to highlight the numerical capabilities of algorithms. [ABSTRACT FROM AUTHOR]

Frequency response analysis (FRA) is considered as a reliable method for the diagnosis of the power transformer winding. Despite this popularity, interpretation of FRA signatures has challenges steel. This paper proposes an elegant and a robust decentralized computation methodology based on logistic chaotic JAYA algorithm (LCJAYA) to construct a high frequency mutually coupled equivalent circuit (HF-MCEC) of the transformer winding for diagnosis purposes. The decentralized HF-MCEC is firstly built from three frequency sub-bands (low, medium, and high frequency) separately. Afterwards, the synthesized LCJAYA algorithm with high efficiency and the mathematical model for HF-MCEC are elaborated. As well, the optimisation methods have difficulties in identifying the nearly unique equivalent circuit parameter in a large search space area. The proposed methodology uses two main strategies. At first, a logistic chaotic to enhance the population diversity and to improve the exploration search areas. Then, a chaotic mutation strategy to improve the exploration and the exploitation at different search process. The success of the proposed study can be imputed to a careful physical correlation between the inductances, capacitances, resistances parameters and their natural frequencies. FRA measurements has been conducted on transformer winding models. The performance of the proposed method is evaluated and compared under different cases. [ABSTRACT FROM AUTHOR]

Copyright of Przeglad Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The generation of mathematical models for the analysis of buildings with multiple thermal zones is a large and complex task. Furthermore, the order and complexity of the dynamical model are increased by the number of included thermal zones. To overcome this problem, this paper presents an algorithm to define the mathematical model automatically, using the geometric and physics parameters as inputs. Additionally, the spatial position of each thermal zone must be recorded in an arrangement called a contact matrix. The algorithm for modeling systems with multiple thermal zones is the main contribution of this work. This algorithm is presented in pseudocode format and as an annex, an implementation in MATLAB software. One of the advantages of this methodology is that it allows us to work with parallelepipeds and not necessarily cubic thermal zones. The algorithm allows us to generate mathematical models with symbolic variables, starting from the knowledge of how many thermal zones compose the system and its geometric organization. This information must be organized in a matrix arrangement called a contact matrix. Different arrays of thermal zones were constructed with wooden boxes to verify the functionality of the models generated with the algorithm. Each case provided information that allowed us to adjust the mathematical models and their simulations, obtaining a range of errors between experimental and simulated temperatures from 2.08 to 5.6 , depending on the number of thermal zones studied. [ABSTRACT FROM AUTHOR]

Stochastic configuration network (SCN) is a mathematical model of incremental generation under a supervision mechanism, which has universal approximation property and advantages in data modeling. However, the efficiency of SCN is affected by some network parameters. An optimized searching algorithm for the input weights and biases is proposed in this paper. An optimization model with constraints is first established based on the convergence theory and inequality supervision mechanism of SCN; Then, a hybrid bat-particle swarm optimization algorithm (G-BAPSO) based on gradient information is proposed under the framework of PSO algorithm, which mainly uses gradient information and local adaptive adjustment mechanism characterized by pulse emission frequency to improve the searching ability. The algorithm optimizes the input weights and biases to improve the convergence rate of the network. Simulation results over some datasets demonstrate the feasibility and validity of the proposed algorithm. The training RMSE of G-BAPSO-SCN increased by 5.57 × 10 − 5 and 3.2 × 10 − 3 compared with that of SCN in the two regression experiments, and the recognition accuracy of G-BAPSO-SCN increased by 0.07% on average in the classification experiments. [ABSTRACT FROM AUTHOR]

This paper examines the capacitated lot-sizing and scheduling problem (CLSP) with sequence-dependent setup times, time windows, machine eligibility and preference constraints. Such a problem frequently arises in the semiconductor manufacturing industry by which this paper is motivated. A mixed integer programming (MIP) model is constructed for the problem. Two MIP-based fix-and-optimise algorithms are proposed in which the binary decision variables associated with the assignment of machines are first fixed using the randomised least flexible machine (RLFM) rule and the rest of the decision variables are settled by an MIP solver. Extensive experiments show that the proposed algorithms outperform the state-of-the-art MIP-based fix-and-optimise algorithms in the literature, especially for instances with high machine flexibility and high demand variation. [ABSTRACT FROM PUBLISHER]

One of the main characteristics of a Vehicular Ad-hoc Networks (VANET) is high mobility. Therefore, the high overhead and end-to-end delay are natural results. The prediction of future movements for vehicles provides traffic observations to improve and enhance the network performance and to serve the VANET applications. The clustering approach is a popular technique used in most studies to deal with the network's dynamic topology. In most studies, the clustering algorithms are tested and evaluated according to the cluster head (CH) selection approach. Selecting a suitable CH will lead to the prolonging of the cluster lifetime. A long life time represents an indication for cluster stability, also known as cluster maintenance. Cluster maintenance has got less consideration by the previous VANET studies. The stability prediction represents a challenging task in the clustering approach. This paper highlights the stability prediction in the VANET's Highway scenario. It proposes a new approach to evaluate the vehicle's cluster stability prediction by utilizing the stochastic process. The modeling of joining and leaving takes place using the birth-death process as a stochastic process for evaluating the stability of the clustering algorithm. The main advantage of this approach is to predict and evaluate the vehicle cluster stability with time, using a new mathematical model. [ABSTRACT FROM AUTHOR]

The Unlimited Sensing Framework (USF) was recently introduced to overcome the sensor saturation bottleneck in conventional digital acquisition systems. At its core, the USF converts a continuous-time high-dynamic-range (HDR) signal into folded, low-dynamic-range, modulo samples and allows the recovery of the HDR signal via algorithmic unfolding. In hardware, however, implementing ideal modulo folding requires careful calibration, analog design and high precision. At the interface of theory and practice, this paper explores a computational sampling strategy that relaxes strict hardware requirements via a novel, mathematically guaranteed reconstruction. We start with a generalized model for USF with two new parameters modeling hysteresis and folding transients in addition to the modulo threshold. Hysteresis accounts for mismatches between the reset threshold and the amplitude displacement at the folding time and the folding transient is a continuous transition period in the implementation of a reset. Both these effects are motivated by our hardware experiments and also occur in previous, domain-specific applications. We show that hysteresis is beneficial for USF and leverage it to derive the first recovery guarantees in the context of our generalized USF model for a certain sampling rate regime. Additionally, we show how the sampling rate requirement can be greatly reduced via a direct generalization of the proposed recovery. Our theoretical work is corroborated by hardware experiments with a hysteresis enabled, modulo ADC testbed comprising off-the-shelf electronic components. Thus, by capitalizing on a collaboration between hardware and algorithms, our paper enables an end-to-end pipeline for HDR sampling allowing more flexible hardware implementations. [ABSTRACT FROM AUTHOR]

In this paper, we propose to extend the standard Convolutional Dictionary Learning problem to a tensor representation where the activations are constrained to be “low-rank” through a Canonical Polyadic decomposition. We show that this additional constraint increases the robustness of the CDL with respect to noise and improve the interpretability of the final results. In addition, we discuss in detail the advantages of this representation and introduce two algorithms, based on ADMM or FISTA, that efficiently solve this problem. We show that by exploiting the low rank property of activations, they achieve lower complexity than the main CDL algorithms. Finally, we evaluate our approach on a wide range of experiments, highlighting the modularity and the advantages of this tensorial low-rank formulation. [ABSTRACT FROM AUTHOR]

As the most important core process in the dyeing and finishing workshop of knitting companies, the dyeing process has the characteristics of multi-variety, small-batch, parallel machine processing of multiple types, and high cost in equipment cleaning, which render the dyeing scheduling problem a bottleneck in the production management of a dyeing and finishing workshop. In this paper, the dyeing process scheduling problem in dyeing and finishing workshops is described and abstracted, and an optimized mathematical model of dyeing scheduling is constructed with the goal of minimizing the delay cost and switching cost. Constraints such as multiple types of equipment, equipment capacity, weights of orders and equipment cleaning time are considered. For the sub-problem of equipment scheduling in the dyeing scheduling problem, a heuristic rule that considers equipment utilization and order delay is proposed. For the sub-problem of order sorting of the equipment in the dyeing scheduling problem, a hybrid genetic algorithm with a variable neighbourhood search strategy has been designed to optimize sorting. The algorithm proposed in this paper has been demonstrated via case simulation to be effective in solving the scheduling problem in dyeing and finishing workshops. [ABSTRACT FROM AUTHOR]

This paper presents a method for determination of heat transfer coefficients for finite-element method housing model of medium-voltage switchgear cell. Suggested method is based on the particle swarm optimization algorithm. For that purpose, a real test model of partition wall has been created as well as an equivalent numerical FE model. Since the preciseness of thermal model is dependent on the preciseness of thermal coefficients, the goal of the optimization procedure is to assure even better match between the measured and the FE model calculated temperature values. Because of the fact that the temperature field is a consequence of eddy currents that appear in metal parts of switchgear devices, this paper deals with a coupled problem. Nonlinear calculation of magnetic field is also considered. [ABSTRACT FROM AUTHOR]

In recent years, the interests of disassembly line have increased owing to economic reasons and the increase of environmental awareness. Effective line can provide many advantages in terms of economic aspect and it facilitates competition the companies with others. This study contributes to the relevant literature by a branch, bound and remember algorithm for disassembly line balancing problem with AND/OR precedence. The proposed exact solution method employs the memory-based dominance rule to eliminate the reduplicated sub-problems by storing all the searched sub-problems and to utilise cyclic best-first search strategy to obtain high-quality complete solutions fast. In this paper, minimising the number of stations is taken as the performance measure. The proposed methodology is tested on a set of 260 instances and compared with the mathematical model using CPLEX solver and five well-known metaheuristics. Computational results show that the proposed method is capable of obtaining the optimal solutions for all the tested instances with less than 0.1 seconds on average. Additionally, comparative study demonstrates that the proposed method is the state-of-the-art algorithm and outperforms the CPLEX solver and metaheuristics in terms of both solution quality and search speed aspects. [ABSTRACT FROM AUTHOR]

In this paper, the extended Resource Renting Problem (RRP/extended) is presented. The RRP/extended is a time-constrained project scheduling problem, in which the total project cost is minimised. In the RRP/extended, this total project cost is determined by a number of extra costs, which are defined in this paper. These costs are based on the costs that are used in the traditional Resource Renting Problem and the Total Adjustment Cost Problem. Therefore, the RRP/extended represents a union of these two problems. To solve the RRP/extended, a scatter search is developed. The building blocks of this scatter search are specifically designed for the RRP/extended. We introduce two crossovers and an improvement method. The efficiency of these building blocks will be shown in the paper. Furthermore, a sensitivity analysis is presented in which the five costs have diverse values. [ABSTRACT FROM AUTHOR]

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Electroencephalogram (EEG) analysis has been widely used in the diagnosis of stroke diseases for its low cost and noninvasive characteristics. In order to classify the EEG signals of stroke patients with cerebral infarction and cerebral hemorrhage, this paper proposes a novel EEG stroke signal classification method. This method has two highlights. The first is that a multi-feature fusion method is given by combining wavelet packet energy, fuzzy entropy and hierarchical theory. The second highlight is that a suitable classification model based on ensemble classifier is constructed for perfectly classification stroke signals. Entropy is an accessible way to measure information and uncertainty of time series. Many entropy-based methods have been developed these years. By comparing with the performances of permutation entropy, sample entropy, approximate entropy in measuring the characteristic of stroke patient's EEG signals, it can be found that fuzzy entropy has best performance in characterization stroke EEG signal. By combining hierarchical theory, wavelet packet energy and fuzzy entropy, a multi-feature fusion method is proposed. The method first calculates wavelet packet energy of EEG stroke signal, then extracts hierarchical fuzzy entropy feature by combining hierarchical theory and fuzzy entropy. The experimental results show that, compared with the fuzzy entropy feature, the classification accuracy based on the fusion feature of wavelet packet energy and hierarchical fuzzy entropy is much higher than benchmark methods. It means that the proposed multi-feature fusion method based on stroke EEG signal is an efficient measure in classifying ischemic and hemorrhagic stroke. Support vector machine (SVM), decision tree and random forest are further used as the stroke signal classification models for classifying ischemic stroke and hemorrhagic stroke. Experimental results show that, based on the proposed multi-feature fusion method, the ensemble method of random forest can get the best classification performance in accuracy among three models. [ABSTRACT FROM AUTHOR]

Serial lines and assembly systems are two of the most fundamental production system structures used in practical manufacturing environments. While serial lines have been studied extensively in production systems literature and practice, assembly systems have received much less attention. Among existing results on assembly systems, most are focused on steady state performance evaluation, system properties, and improvement. On the other hand, transient behavior of such systems remains largely unexplored. In the framework of assembly systems with Bernoulli machines and finite buffers, this paper develops a mathematical model for transient analysis and derives closed-form formulas to calculate the real-time production rate, consumption rates, work-in-process, and probabilities of machine starvation and blockage, during transients. Then, an improved computationally efficient algorithm for transient performance evaluation in Bernoulli serial lines is proposed. Based on the improved algorithm, an effective and efficient method for transient performance evaluation in Bernoulli assembly systems is derived. Finally, the method is extended to complex assembly system structures. [ABSTRACT FROM AUTHOR]

For the quantitative applications of the Suomi National Polar-orbiting Partnership (SNPP) Advanced Technology Microwave Sounder (ATMS), the geolocation accuracy of its sensor data records must be quantified during its on-orbit operation. In this paper, a refined coastline inflection point method is used to evaluate the on-orbit geolocation accuracy of SNPP ATMS. It is disclosed that for SNPP ATMS, the static error term with scan-angle-dependent feature is a dominant part among all the geolocation error sources. A mathematical model is then developed to convert the in-track and cross-track geolocation errors to the beam pointing Euler angles defined in the spacecraft coordinate system, which can be further used to construct the correction matrix for on-orbit geolocation process. By using the correction matrix built in this paper, the geolocation error is obviously reduced both at nadir and at the edge of the scan. The total geolocation error at nadir before/after correction is 3.8/0.8 km at K-band, 5.6/0.8 km at Ka-band, 3.3/0.4 km at V-band, and 1.5/0.1 km at W-band. The geolocation bias at the edge of the scan line before/after correction is 4.6/1.3 km at K-band, 9.4/1.8 km at Ka-band, 4.4/2.4 km at V-band, and 3.2/0.8 km at W-band. After correction, the scan-angle-dependent feature in geolocation error is also largely reduced. [ABSTRACT FROM AUTHOR]

This paper proposes a new hybrid optimization method for the phase-frequency characteristics of the double input power system stabilizer (PSS) based on the improved active set algorithm. This method takes the effect of the filtering section optimization on the parameter improvement into account, and the optimized model focuses on the minimum residual sum of squares between the actual and the target phase-frequency characteristics. The result shows that the improved parameters obtained from the proposed method provide much better phase-frequency characteristics than the widely used engineering parameters. The comparison between the proposed method and the typical commercial software indicates the universal superiority of the proposed method. And the studies on the impact of considering the filter section optimization on the phase-frequency improvement show that taking the filter section optimization into account will be beneficial for the phase-frequency improvement, though in application to the PSS2A model and the PSS2B model there are some differences. The achievements obtained in this paper provide a significant reference for the practical PSS parameter modification and improvement. [ABSTRACT FROM AUTHOR]

This paper presents a robust binary classification method, which is an extended version of the Modified Polyhedral Conic Functions (M-PCF) algorithm, earlier developed by Gasimov and Ozturk. The new version presented in this paper, has new features in comparison to the original algorithm. The mathematical model used in the new version, is relaxed by allowing some inaccuracies in an optimal way. By this way, it is aimed to reduce the overfitting and improve the generalization property. In the original version, the sublevel set of a separating function generated at every iteration, does not contain any element of the other set. This is changed in the new version, where the sublevel sets of separating functions generated by the new algorithm, are allowed to contain some elements from other set. On the other hand, the new algorithm uses a tolerance parameter which prevents generating "less productive separating functions". In the original version, the algorithm continues till all points of the "first" set are separated from the second one, where a separating function is generated if there still exist unseparated elements regardless the number of such elements. In the new version, the tolerance parameter is used to terminate iterations if there are only a few unseparated elements. By this way, it is aimed to improve the generalization property of the algorithm, and therefore the new version is called Parameterized Polyhedral Conic Functions (P-PCF) method. The performance and efficiency of the proposed algorithm is demonstrated on well-known datasets from the literature and on noisy data. [ABSTRACT FROM AUTHOR]

This paper proposes a new decentralized data-driven load restoration (DDLR) scheme for transmission and distribution (TD) systems with high penetration of wind power. Robust DDLR models are constructed in order to handle uncertainties and ensure the feasibility of decentralized schemes. The Wasserstein metric is used to describe the ambiguity sets of probability distributions in order to build the complete DDLR model and realize computationally tractable formulation. A data-driven model-nested analytical target cascading (DATC) algorithm is developed to obtain the final load restoration result by iteratively solving small-scale mathematical models. The proposed DDLR scheme provides load restoration results with adjustable robustness, and performance efficiency is independent from the amount of data. The DDLR scheme makes full use of the available data while respecting information privacy requirements of independent operated systems, and ensures the feasibility of a decentralized load restoration strategy even in the worst-case condition. The effectiveness of the proposed method is validated using a small-scale TD system and a large-scale system with the IEEE 118-bus TS and thirty IEEE-33 DSs, showing high computational efficiency and superior restoration performance. [ABSTRACT FROM AUTHOR]

In this paper, a flow shop scheduling problem with minimal and maximal machine idle time with the goal of minimizing makespan is considered. The mathematical model of the problem is presented. A generalization of the prefix sum, called the job shift scan, for computing required shifts for overlapping jobs is proposed. A work-efficient algorithm for computing the job shift scan in parallel for the PRAM model with n processors is proposed and its time complexity of O (log n) is proven. Then, an algorithm for computing the makespan in time O (m log n) in parallel using the prefix sum and job shift scan is proposed. Computer experiments on GPU were conducted using the CUDA platform. The results indicate multi-thread GPU vs. single-thread GPU speedups of up to 350 and 1000 for job shift scan and makespan calculation algorithms, respectively. Multi-thread GPU vs. single-thread CPU speedups up to 4.5 and 14.7, respectively, were observed as well. The experiments on the Taillard-based problem instances using a simulated annealing solving method and employing the parallel makespan calculation show that the method is able to perform many more iterations in the given time limit and obtain better results than the non-parallel version. [ABSTRACT FROM AUTHOR]

This paper deals with the objective of determining an optimal part sequence on a single-stage multifunctional machining system (SSMS) with a view to achieve the broad objectives of cost minimization and time minimization. SSMS has become a preferred alternative for manufacturers to use the resource efficiently, owing to the flexibility and process variety offered by it. This paper formulates a mathematical model that considers the minimization of both set-up cost and time simultaneously. The option of hiring an additional fixture has also been considered that enables the reduction in tool magazine replenishment and re-fixturing operations, which in turn offers economic advantage by way of reducing set-up cost. This study has proposed a new heuristic by modifying the simulated annealing concept to solve the underlying problem. The conventional simulated annealing search scheme is replaced by a chaotic search that takes into account the ergodic and stochastic properties of chaotic systems. In order to restrict the premature convergence and to diversify the search space, a modified perturbation scheme has been employed. The performance of the proposed algorithm was tested on a simulated case study adopted from the literature and the results obtained reveal the effectiveness and scalability of the proposed algorithm. The results establish that the proposed approach is effective and reactive to severe disturbances and must take place in the manufacturing environment. [ABSTRACT FROM AUTHOR]

This paper considers the material supply problem for aircraft moving assembly lines. Distinguished from general automobile assembly lines, multiple parallel jobs are assembled concurrently and durations of assembly jobs are quite long, thus amounts of materials are orderly stored at the line-side space at the same time. In addition, the line-side space should be reused in the time dimension. With these characteristics, decisions on line-side storage of materials were introduced on the basis of material batching and tow-trains scheduling problems. An integrated decision–making mathematical model with the objective of minimising the number of deliveries was established. A hybrid endocrine-immune algorithm (HEIA) was proposed to jointly make decisions on the delivery batch, delivery time and storage positions of each job's materials. Numerical experiments with the real-world data and randomly generated instances validate the effectiveness and efficiency of HEIA. [ABSTRACT FROM AUTHOR]

The wheel slide protection control system for rail vehicles plays a crucial role in ensuring a consistent braking performance in all operating environments, making it a vital factor in the safety and efficiency of rail transportation. In this paper, a hybrid approach to wheel slide protection control is presented, which combines the rule-based control strategy and the model-based control methods using adhesion force estimation. Model-based control usually relies on mathematical models to characterize the vehicle dynamics, requiring online estimators to be designed or extra sensors to be added for practical application. Rule-based control operates on predefined rules and thresholds and the available data from vehicles in service. A comparative test was conducted between the traditional rule-based control strategy and the proposed combined control strategy using a semi-physical simulation test bench. The performance differences of the control strategies were analyzed from two perspectives: adhesion utilization and air consumption. It was observed that among the traditional 2-phase, 3-phase, 4-phase and the optimized 4-phase combined control method, the combined control strategy has the best adhesion utilization and the traditional 4-phase control strategy has the least air consumption. [ABSTRACT FROM AUTHOR]

Mathematical modeling of many natural and physical phenomena in industry, engineering sciences and basic sciences lead to linear and non-linear devices. In many cases, the coefficients of these devices, taking into account qualitative or linguistic concepts, show their complexity in the form of Z-numbers. Since Z-number involves both fuzziness and reliability or probabilistic uncertainty, it is difficult to obtain the exact solution to the problems with Z-number. In this work, a method and an algorithm are proposed for the approximate solution of a Z-number linear system of equations as an important case of such problems. The paper is devoted to solving linear systems where the coefficients of the variables and right hand side values are Z-numbers. An algorithm is presented based on a ranking scheme and the neural network technique to solve the obtained system. Moreover, two examples are included to describe the procedure of the method and results. [ABSTRACT FROM AUTHOR]

This paper extends the notion of weak convergence in metric spaces to the case of S-metric spaces. Moreover, some results on the weak convergence of fixed point iterations of Banach’s, Kannan’s, Chatterjea’s, Reich’s, Hardy and Roger’s types of contractions on S-metric spaces are obtained. In addition, an example is presented to demonstrate our primary result. [ABSTRACT FROM AUTHOR]

Higher utilization of existing resources and facilities in order to increase efficiency and profitability is always one of the basic challenges for parallel processing systems and environments, and this challenge becomes more complicated when the system resources are heterogeneous. One way to achieve high efficiency and profitability of heterogeneous parallel systems is to schedule tasks optimally. In this paper, an extended tabu search-based scheduling algorithm (ESTS) is presented to improve the profitability of heterogeneous parallel systems, which can achieve suitable solutions in a short computational time. To evaluate the efficiency of the proposed solution, due to the lack of a suitable criterion to evaluate this problem, the obtained results are compared with both the results of an extended scheduling based on a genetic algorithm (ESGA) with a large number of chromosomes and a high number of generations, as well as an extended scheduling based on a simulated annealing algorithm (ESSA) with a linear temperature reduction. The benchmark files of different sizes were tested under the same conditions, and the comparison of results shows the superiority of the proposed solution in terms of profitability and computational time. [ABSTRACT FROM AUTHOR]