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The constant expansion of higher education has led to the continuous increase of the teaching staff and the complexity of the work in the modern era of education, forcing the traditional colleges and universities (it is abbreviated as CU in this article) to participate in trade union activities to open up the road of innovation. What kind of method can effectively and quickly optimize trade union work (TUW) and improve satisfaction has become one of the research topics that has attracted much attention. Aiming at this problem, it has great research significance for the field of CU to participate in trade union activities. With the in-depth research on the work of trade unions in CU, the research on multivariate optimal design in the work of trade unions in CU is gradually carried out, and its advantages are of great significance for solving problems in the work of trade unions in CU. The purpose of this paper is to study the innovation of TUW mode in CU based on multivariate optimization. Through the analysis and research of the multivariate optimization idea, it can be applied to the innovation of the construction of the labor union work model in CU, and the multiobjective linear weighting method is used to solve the objective function, so as to solve the problem of improving the evaluation of labor union work satisfaction in CU. This paper analyzed the TUW in CU (it is abbreviated as TUW in this article) and the multivariate optimization design, conducted an experimental analysis on the improved TUW mode in CU, and used relevant theoretical formulas to explain. The results have shown that the overall evaluation score of the college TUW method proposed in this paper is higher than the traditional college TUW method, and the difference between the two is 18.7%. It can be seen that the university TUW based on mathematical modeling and multivariable optimal design can meet the innovation needs of university TUW, and the satisfaction and work efficiency are greatly improved. [ABSTRACT FROM AUTHOR]

In order to solve the problem of maximizing the utilization of resources through reasonable deployment under limited resources, this paper studies from two aspects: one is to establish the mathematical model of maximum coverage of space detection, and the other is to improve the harmony algorithm. The exploration performance and convergence performance of the harmony search algorithm are analyzed theoretically, and the more general formulas of exploration performance and convergence performance are proved. Based on theoretical analysis, the algorithm called opposition-based learning parameter adjusting harmony search is proposed. By using the algorithm to test the functions of different properties, the value range of key control parameters of the algorithm are given. The proposed algorithm is applied to optimize the problem of radar deployment. This paper takes a certain area of the Shandong Peninsula as the deployment scope. The simulation results show that the proposed algorithm is effective and practical. Although there is a large amount of calculation, it provides ideas and ways for other problems, such as the site selection of new observation and communication post, the deployment of maneuvering radar stations, and the track planning of fleet. [ABSTRACT FROM AUTHOR]

In order to solve the problem of resource ecological compensation, this paper proposed a model of resource ecological compensation mechanism based on a rural leisure sports environment. The model is carried out in many places in China. The survey data involves 31 provinces, 10 years, and 43 indicators, with a total of 43 × 10 × 31 = 13,330 data. The preliminary basis of mechanism construction is summarized from four aspects. Finally, make full use of modern information technology to improve the network platform of the compensation mechanism, promote the efficient allocation and comprehensive utilization of ecotourism resources, and lay a solid foundation for establishing a reasonable and perfect resource ecological compensation mechanism and ensuring the long-term and stable operation of the mechanism. Through the experiment, it is found that the timely and effective publication of information can eliminate the inner estrangement between the two sides, so as to make the behavior of both sides more rational. A special information feedback department is established to deal with the opinions put forward by all compensation parties in tourism development, extract effective information, summarize and publish reasonable guidance information, and guide the compensation of both sides to an ideal balance through the feedback of this information. The effectiveness of the experiment is verified. [ABSTRACT FROM AUTHOR]

Sparrow search algorithm has the problem of redundancy of convergence speed due to its fast convergence speed, and it is easy to fall into local optimum in multimodal environment. To solve the above problem, this paper presents a personalized multipopulation sparrow search algorithm (MPSSA). By introducing multiple population mechanisms to reduce the probability of falling into the local optimum due to single-population search, by using a personalized subpopulation strategy to improve the personalized differences of subpopulations and balance the exploratory ability of algorithm development, then by using weighted center-of-gravity communication strategy to improve the quality of communication between populations, and finally by using dimension by dimension dynamic reverse learning to improve the accuracy of search. The superiority of MPSSA is validated by comparing the benchmark function and CEC2017. Finally, the algorithm solves the problem of poor quality due to the dimension increase of the UAV cooperative track. MPSSA helps the UAV to quickly plan a better and stable track group to ensure the UAV to complete the cooperative task safely and stably. [ABSTRACT FROM AUTHOR]

Image processing technology is to use computer, camera, and other technologies to calculate and process images and make the image clearer and convenient for quick extraction of information. Image processing technology has entered an all-round development stage. It also plays a great role in the components of the intelligent service model for the aged. Now many countries in the world have entered the aging stage, but old-age equipment is relatively backward and personnel management is not standardized. Based on these problems, this paper studies the intelligent model of old-age care in small- and medium-sized cities by using the image recognition method. Based on the analysis of the present situation of intelligent old-age care, an intelligent system is proposed, which solves the problems of defects in old-age care facilities and insufficient comprehensive management of medical staff in some small- and medium-sized cities. This system has RTID positioning system and APP client, which can ensure the privacy of the elderly. Through real-time identification of images in the elderly service, the rationality and layout optimization of existing old-age facilities are analyzed. The mathematical model is used to detect the regularity of participants' daily activities. The image experiment results show that the prediction accuracy is over 90%, and the optimal prediction effect is obtained. In addition, a questionnaire survey was conducted among many elderly people over 50 years old to investigate their willingness to use smart old-age products. [ABSTRACT FROM AUTHOR]

Summary: Mathematical model parameter estimation of solar photovoltaic (PV) cell, module and array represent a prodigious challenge in recent researches, where analytical, metaheuristic and hybrid techniques play a vital role for parameter extraction by using manufacturer's datasheet and experimental data, although the precise and highly reliable solution identification is still a complex problem to be solved. In this paper, a new Emperor Penguin Optimisation‐ (EPO) based parameter estimation approach for a single‐diode Rp solar PV model is presented that is cohesively analysed under different sets of temperature and irradiance (G). Validation of the proposed technique is perceived on the analysis of performance characteristics, that is, current‐voltage (I‐V) and power‐voltage (P‐V) of KC200GT, PWP201, and STP6 120‐36 PV modules under various simulation conditions. Moreover, estimated results are compared with the experimental data and several established estimation techniques in the literature for validation and demonstrate the proposed technique with highly precise outcomes having reduced computational cost for the PV model parameter extraction problem. [ABSTRACT FROM AUTHOR]

Soft open point (SOP) is a key power electronic device to improve the flexibility and stability of the distribution network and is becoming a research hotspot. The traditional double closed-loop control of SOP has a complex structure, difficult process of parameter design, and poor output power quality. To solve these problems, finite control set model predictive control (FCS-MPC) has been adopted. Since FCS-MPC involves a large amount of calculation and experiences delay in current tracking, an improved FCS-MPC with delay compensation is proposed for three-port SOP in this paper to replace the inner loop current control strategy. Improved model predictive control combines the two-step prediction method based on the voltage vector and vector angle compensation method. The vector method is used to construct a mathematical model and a prediction model of three-port SOP. Based on FCS-MPC, the two-step prediction method that takes voltage as the target is used to compensate for the current delay problem, and the amount of calculation is reduced by converting the control target. Meanwhile, the vector angle compensation method is used to compensate the future reference value. Finally, a simulation model is built in MATLAB/Simulink. The simulation results under steady state and dynamic conditions show that the proposed strategy can effectively improve the current delay and reduce the amount of calculation. Furthermore, it has better current tracking accuracy and faster dynamic response speed. [ABSTRACT FROM AUTHOR]

This paper focuses on the two-echelon location routing problem with time constraints in city logistics system. The aim is to define the structure of a system which can optimize the location and the number of two different kinds of logistics facilities as well as the related routes on each echelon. A mathematic model considering the problem characteristics has been set up. Based on probability selection principle, this paper first puts forward a metaheuristic algorithm with comprehensive consideration of time and space accessibility to solve it. Then, random instances of different sizes are generated to verify the effectiveness of our method. [ABSTRACT FROM AUTHOR]

It is known that it is critical for train rescheduling problem to address some uncertain disturbances to keep the normal condition of railway traffic. This paper is keen on a mathematical model to reschedule high-speed trains controlled by the quasi-moving blocking signalling system impacted by multidisturbances (i.e., primary delay, speed limitation, and siding line blockage). To be specific, a mixed-integer linear programming is formulated based on an improved alternative graph theory, by the means of rerouting, reordering, retiming, and train control. In order to adjust the train speed and find the best routes for trains, the set of alternative arcs and alternative arrival/departure paths are considered in the constraints, respectively. Due to this complex NP-hard problem, a two-step algorithm with three scheduling rules based on a commercial optimizer is applied to solve the problem efficiently in a real-word case, and the efficiency, validity, and feasibility of this method are demonstrated by a series of experimental tests. Finally, the graphical timetables rescheduled are analysed in terms of free conflicts of the solution. Consequently, the proposed mathematical model enriches the existing theory about train rescheduling, and it can also assist train dispatchers to figure out disturbances efficiently. [ABSTRACT FROM AUTHOR]

In phrase-based machine translation (PBMT) systems, the reordering table and phrase table are very large and redundant. Unlike most previous works which aim to filter phrase table, this paper proposes a novel deep neural network model to prune reordering table. We cast the task as a deep learning problem where we jointly train two models: a generative model to implement rule embedding and a discriminative model to classify rules. The main contribution of this paper is that we optimize the reordering model in PBMT by filtering reordering table using a recursive autoencoder model. To evaluate the performance of the proposed model, we performed it on public corpus to measure its reordering ability. The experimental results show that our approach obtains high improvement in BLEU score with less scale of reordering table on two language pairs: English-Chinese (+0.28) and Uyghur-Chinese (+0.33) MT. [ABSTRACT FROM AUTHOR]

Ever since it was proposed, grey system theory has attracted the attention of scientific researchers and scholars. And it also has been widely used in many fields and solved a large number of practical problems in production, life, and scientific research. With the development and popularization of computer science and network technology, this traditional mathematical model can be applied more simply and efficiently to solve practical problems. Firstly, this paper, to implement steps of grey relational analysis, has made the exclusive analysis and has made the simple introduction to grey relational analysis characteristics. Then, based on grey relational theory and ASP.NET technology, the crop evaluation system is developed. Lastly, by using Excel and the crop evaluation system, the paper carries out a comprehensive evaluation about eight features of Fuji apple, which is from nine different producing areas, respectively. The experiment results show that the crop evaluation system is effective and could greatly improve the work efficiency of the researcher and expand the application scope. [ABSTRACT FROM AUTHOR]

This paper presents an improved ant colony algorithm for the path planning of the omnidirectional mobile vehicle. The purpose of the improved ant colony algorithm is to design an appropriate route to connect the starting point and ending point of the environment with obstacles. Ant colony algorithm, which is used to solve the path planning problem, is improved according to the characteristics of the omnidirectional mobile vehicle. And in the improved algorithm, the nonuniform distribution of the initial pheromone and the selection strategy with direction play a very positive role in the path search. The coverage and updating strategy of pheromone is introduced to avoid repeated search reducing the effect of the number of ants on the performance of the algorithm. In addition, the pheromone evaporation coefficient is segmented and adjusted, which can effectively balance the convergence speed and search ability. Finally, this paper provides a theoretical basis for the improved ant colony algorithm by strict mathematical derivation, and some numerical simulations are also given to illustrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

Nowadays, a wide class of problems can be solved by using the classical newsboy model. However, in problems where uncertainty of events and randomness are omnipresent, there is a necessity to adapt the existing solutions and/or find new extensions that will properly answer all requirements. This paper considers a multistage assembly system where interrelated assembly operations with independent stochastic operation times should be planned in an optimal way. Delivery of items in a requested time implies that either delay costs or holding costs appear. The goal is to find optimal safety times. We propose an approximate technique based on successive application of the solution of simpler one-stage problem. The generalized mathematical model suggested is built up on the relaxed hypothesis and can be used in multistage assembly networks. The existence and uniqueness of the solution are proven. The preliminary tests are performed and our approximate technique is compared to exact results. [ABSTRACT FROM AUTHOR]

This paper presents the synchronized and integrated two-level lot sizing and scheduling problem (SITLSP). This problem is found in beverage production, foundry, glass industry, and electrofused grains, where the production processes have usually two interdependent levels with sequence-dependent setups in each level. For instance, in the first level of soft drink production, raw materials are stored in tanks flowing to production lines in the second level. The amount and the time the raw materials and products have to be stored and produced should be determined. A synchronization problem occurs because the production in lines and the storage in tanks have to be compatible with each other throughout the time horizon. The SITLSP and its mathematical model are described in detail by this paper. The lack of similar models in the literature has led us to also propose a set of instances for the SITLSP, based on data provided by a soft drink company. Thus, a set of benchmark results for these problem instances are established using an exact method available in an optimization package. Moreover, results for two relaxations proved that the modeling methodology could be useful in real-world applications. [ABSTRACT FROM AUTHOR]

The two-phase Stefan problem is widely used in industrial field. This paper focuses on solving the two-phase inverse Stefan problem when the interface moving is unknown, which is more realistic from the practical point of view. With the help of optimization method, the paper presents a hybrid method which combines the homotopy perturbation method with the improved Adomian decomposition method to solve this problem. Simulation experiment demonstrates the validity of this method. Optimization method plays a very important role in this paper, so we propose a modified spectral DY conjugate gradient method. And the convergence of this method is given. Simulation experiment illustrates the effectiveness of this modified spectral DY conjugate gradient method. [ABSTRACT FROM AUTHOR]

The problems of improving computational efficiency and extending representational capability are the two hottest topics in approaches of global manifold learning. In this paper, a new method called extensive landmark Isomap (EL-Isomap) is presented, addressing both topics simultaneously. On one hand, originated from landmark Isomap (L-Isomap), which is known for its high computational efficiency property, EL-Isomap also possesses high computational efficiency through utilizing a small set of landmarks to embed all data points. On the other hand, EL-Isomap significantly extends the representational capability of L-Isomap and other global manifold learning approaches by utilizing only an available subset from the whole landmark set instead of all to embed each point. Particularly, compared with other manifold learning approaches, the data manifolds with intrinsic low-dimensional concave topologies and essential loops can be unwrapped by the new method more successfully, which are shown by simulation results on a series of synthetic and real-world data sets. Moreover, the accuracy, robustness, and computational complexity of EL-Isomap are analyzed in this paper, and the relation between EL-Isomap and L-Isomap is also discussed theoretically. [ABSTRACT FROM AUTHOR]

Robust optimization is a powerful and relatively novel methodology to cope with optimization problems in the presence of uncertainty. The positive aspect of robust optimization approach is its computational tractability that attracts more and more attention. In this paper, we focus on an ambiguous P-model where probability distributions are partially known. We discuss robust counterpart (RC) of uncertain linear constraints under two refined uncertain sets by robust approach and further find the safe tractable approximations of chance constraints in the ambiguous P-model. Because of the probability constraints embedded in the ambiguous P-model, it is computationally intractable. The advantage of our approach lies in choosing an implicit way to treat stochastic uncertainty models instead of solving them directly. The process above can enable the transformation of proposed P-model to a tractable deterministic one under the refined uncertainty sets. A numerical example about portfolio selection demonstrates that the ambiguous P-model can help the decision maker to determine the optimal investment proportions of various stocks. Sensitivity analyses explore the trade-off between optimization and robustness by adjusting parameter values. Comparison study is conducted to validate the benefit of our ambiguous P-model. [ABSTRACT FROM AUTHOR]

Conditional nonlinear optimal perturbation (CNOP) has been widely applied to study the predictability of weather and climate. The classical method of solving CNOP is adjoint method, in which the gradient is obtained using the adjoint model. But some numerical models have no adjoint models implemented, and it is not realistic to develop from scratch because of the huge amount of work. The gradient can be obtained by the definition in mathematics; however, with the sharp growth of dimensions, its calculation efficiency will decrease dramatically. Therefore, the gradient is rarely obtained by the definition when solving CNOP. In this paper, an efficient approach based on the gradient definition is proposed to solve CNOP around the whole solution space and parallelized. Our approach is applied to solve CNOP in Zebiak-Cane (ZC) model, and, compared with adjoint method, which is the benchmark, our approach can obtain similar results in CNOP value and pattern aspects and higher efficiency in time consumption aspect, only 12.83 s, while adjoint method spends 15.04 s and consumes less time if more CPU cores are provided. All the experimental results show that it is feasible to solve CNOP with our approach based on the gradient definition around the whole solution space. [ABSTRACT FROM AUTHOR]

Quadratic programming with interval coefficients developed to overcome cases in classic quadratic programming where the coefficient value is unknown and must be estimated. This paper discusses the extension of Wolfe method. The extended Wolfe method can be used to solve quadratic programming with interval coefficients. The extension process of Wolfe method involves the transformation of the quadratic programming with interval coefficients model into linear programming with interval coefficients model. The next step is transforming linear programming with interval coefficients model into two classic linear programming models with special characteristics, namely, the optimum best and the worst optimum problem. [ABSTRACT FROM AUTHOR]

Filter systems are widely used in petrochemical plants for removing solid impurities from hydrocarbon oils. The backwash is the cleaning process used to remove the impurities on the sieves of the filters without a need to interrupt the operation of the entire system. This paper presents a case study based on the actual project of a filter system in a petrochemical plant, to demonstrate the significant effect of vibration on the structural integrity of piping. The induced vibration had led to the structural fatigue failure of the pipes connecting the filter system. A preliminary assessment suggested that the vibrations are caused by the operation of backwashing of the filter system. A process for solving the vibration problem based on the modal analysis of the filter system using the commercial finite element software for simulation is therefore proposed. The computed natural frequencies of the system and the vibration data measured on site are assessed based on the resonance effect of the complete system including the piping connected to the filters. Several approaches are proposed to adjust the natural frequencies of the system in such a way that an optimal and a reasonable solution for solving the vibration problem is obtained. [ABSTRACT FROM AUTHOR]

This paper investigates the turning restriction design problem that optimizes the turning restriction locations so as to minimize the total system travel time under the assumption of asymmetric user equilibrium. We first transform a transportation network into a dual graph, where traffic turning movements are explicitly modeled as dual links. The dual transformation allows us to derive a link-based formulation for the turning restriction design problem. Asymmetric user equilibrium is incorporated in the model as a set of nonlinear constraints. A dual-based heuristic algorithm is employed to solve the problem, by sequentially solving a relaxed turning restriction design problem and a design updating problem. [ABSTRACT FROM AUTHOR]

The weapon-target assignment (WTA) problem, known as an NP-complete problem, aims at seeking a proper assignment of weapons to targets. The biobjective WTA (BOWTA) optimization model which maximizes the expected damage of the enemy and minimizes the cost of missiles is designed in this paper. A modified Pareto ant colony optimization (MPACO) algorithm is used to solve the BOWTA problem. In order to avoid defects in traditional optimization algorithms and obtain a set of Pareto solutions efficiently, MPACO algorithm based on new designed operators is proposed, including a dynamic heuristic information calculation approach, an improved movement probability rule, a dynamic evaporation rate strategy, a global updating rule of pheromone, and a boundary symmetric mutation strategy. In order to simulate real air combat, the pilot operation factor is introduced into the BOWTA model. Finally, we apply the MPACO algorithm and other algorithms to the model and compare the data. Simulation results show that the proposed algorithm is successfully applied in the field of WTA which improves the performance of the traditional P-ACO algorithm effectively and produces better solutions than the two well-known multiobjective optimization algorithms NSGA-II and SPEA-II. [ABSTRACT FROM AUTHOR]

Crane safety evaluation is significant for reducing the casualties and economic losses. Various evaluation methods have been applied to evaluate crane safety. However, when index standard with respect to every level is described in terms of intervals, existing crane safety evaluation models are not ideal. Although variable fuzzy sets (VFS) method has successfully solved similar problems, its evaluation processes are rather complex complicated and tedious. In this paper, we provided an improved VFS evaluation method with normal membership function for solving crane safety problem, in which the evaluation criteria are described in terms of intervals. To demonstrate our presented method, the improved variable fuzzy sets (IVFS) method was applied to two numerical cases including an application to crane safety assessment. It is shown that our proposed method possesses the merit to simplify assessment processes of traditional VFS and can be applied to assess crane safety when criteria values are defined as interval numbers. [ABSTRACT FROM AUTHOR]

This paper proposes a real mathematical constraint satisfaction model which defines the timetabling problem in the Faculty of Chemical Sciences and Engineering (FCSE) at the Autonomous University of Morelos State, Mexico. A Constructive Approach Algorithm (CAA) is used to obtain solutions in the proposed model. A comparison is made between the CAA’s results and the schedule generated by the FCSE administration. Using the constraint satisfaction model, it is possible to improve the allocation of class hours in the FCSE so that classroom use is more efficient. [ABSTRACT FROM AUTHOR]

In order to solve the problem of access control among different security domains in cloud networks, this paper presents an access control model based on role and trust-degree. The model combines role-based access control and trust-based access control. The role assessment weights are defined based on the user’s role classes, and the trust-degree is calculated according to the role assessment weights and the role’s behavior. In order to increase the accuracy of access control, the model gives the concept and calculation methods of feedback trust-degree. To achieve fine-grained access control, the model introduces direct trust-degree, recommendation trust-degree, and feedback trust-degree, all of which participate in comprehensive trust-degree by adjusting their weights. A simulation experiment was conducted in the LAN environment, and a web system was used to construct an access control model with multisecurity domains in the experiment. The experimental results demonstrate that our model has higher security, expansibility, and flexibility. [ABSTRACT FROM AUTHOR]

The calculus of building energy consumption is a demanding task because multiple factors must be considered during experimentation. Additionally, the definition of the model and the experiments is complex because the problem is multidisciplinary. When we face complex models and experiments that require a considerable amount of computational resources, the application of solutions is imperative to reduce the amount of time needed to define the model and the experiments and to obtain the answers. In this paper, we first address the definition and the implementation of an environmental model that describes the behavior of a building from a sustainability point of view and enables the use of several simulations and calculus engines in a cosimulation scenario. Second, we define a distributed experimental framework that enables us to obtain results in an accurate amount of time. This methodology has been applied to the energy consumption calculation, but it can also be applied to other modeling problems that usually require a considerable amount of resources by reducing the amount of time needed to perform modeling, implementation, verification, and experimentation. [ABSTRACT FROM AUTHOR]

This paper proposes and analyses a mathematical model for the problem of distribution of a finite number of irradiation sources during radiotherapy in continuous environments to maximize the minimal cumulative effects. A new algorithm based on nondifferentiable optimization techniques has been developed to solve this problem. [ABSTRACT FROM AUTHOR]

The noncentralized model predictive control (NC-MPC) framework in this paper refers to any distributed, hierarchical, or decentralized model predictive controller (or a combination of them) the structure of which can change over time and the control actions of which are not obtained based on a centralized computation. Within this framework, we propose suitable online methods to decide which information is shared and how this information is used between the different local predictive controllers operating in a decentralized, distributed, and/or hierarchical way. Evaluating all the possible structures of the NC-MPC controller leads to a combinatorial optimization problem. Therefore, we also propose heuristic reduction methods, to keep the number of NC-MPC problems tractable to be solved. To show the benefits of the proposed framework, a case study of a set of coupled water tanks is presented. [ABSTRACT FROM AUTHOR]

Distant supervision (DS) automatically annotates free text with relation mentions from existing knowledge bases (KBs), providing a way to alleviate the problem of insufficient training data for relation extraction in natural language processing (NLP). However, the heuristic annotation process does not guarantee the correctness of the generated labels, promoting a hot research issue on how to efficiently make use of the noisy training data. In this paper, we model two types of biases to reduce noise: (1) bias-dist to model the relative distance between points (instances) and classes (relation centers); (2) bias-reward to model the possibility of each heuristically generated label being incorrect. Based on the biases, we propose three noise tolerant models: MIML-dist, MIML-dist-classify, and MIML-reward, building on top of a state-of-the-art distantly supervised learning algorithm. Experimental evaluations compared with three landmark methods on the KBP dataset validate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

It is extremely important to monitor the development of steam chambers in reservoirs in the process of SAGD (Steam Assisted Gravity Drainage). By analyzing the temperature data of monitoring wells, the extending velocity and direction of steam chamber can be obtained, which helps to regulate and improve production effectively. Based on Stefan’s inverse problem, a mathematical model is established in this paper for acquiring the extending velocity of steam chamber by using the temperature data of monitoring wells. Mathematical methods are utilized to solve the model such as Fourier transformation as well as an analytic solution using the moving velocity of steam chamber’s edges. Finally, sensitivity analyses concerning some factors such as the formation heat transfer coefficient, the temperature of steam chamber, and the rate of temperature rise of monitoring wells are made. Field application showed that the model can be used to calculate the moving velocity in real time and determine the boundary scope of the steam chambers easily. [ABSTRACT FROM AUTHOR]

Coupling map lattice is an efficient mathematical model for studying complex systems. This paper studies the topology identification of coupled map lattice (CML) under the sparsity condition. We convert the identification problem into the problem of solving the underdetermined linear equations. The l1 norm method is used to solve the underdetermined equations. The requirement of data characters and sampling times are discussed in detail. We find that the high entropy and small coupling coefficient data are suitable for the identification. When the measurement time is more than 2.86 times sparsity, the accuracy of identification can reach an acceptable level. And when the measurement time reaches 4 times sparsity, we can receive a fairly good accuracy. [ABSTRACT FROM AUTHOR]

The sparsity problems have attracted a great deal of attention in recent years, which aim to find the sparsest solution of a representation or an equation. In the paper, we mainly study the sparsity of underdetermined linear system via lp minimization for 00 such that the following conclusions hold when p<γ(A,b): (1) the problem (Pp) generates sparser solution as the value of p decreases; (2) the sparsest optimal solution to the problem (Pp) is unique under the sense of absolute value permutation; (3) let X1 and X2 be the sparsest optimal solution to the problems (Pp1) and (Pp2)  (p1

Despite other variants of the standard knapsack problem, very few solution approaches have been devised for the multiscenario max-min knapsack problem. The problem consists in finding the subset of items whose total profit is maximized under the worst possible scenario. In this paper, we describe an exact solution method based on column generation and branch-and-bound for this problem. Our approach relies on a reformulation of the standard compact integer programming model based on the Dantzig-Wolfe decomposition principle. The resulting model is potentially stronger than the original one since the corresponding pricing subproblem does not have the integrality property. The details of the reformulation are presented and analysed together with those concerning the column generation and branch-and-bound procedures. To evaluate the performance of our algorithm, we conducted extensive computational experiments on large scale benchmark instances, and we compared our results with other state-of-the-art approaches under similar circumstances. We focused in particular on different relevant aspects that allow an objective evaluation of the efficacy of our approach. From different standpoints, the branch-and-price algorithm proved to outperform the other state-of-the-art methods described so far in the literature. [ABSTRACT FROM AUTHOR]

This paper presents a new method on the problem of organically structured control based on state observation for a class of large-scale systems with expanding construction. This problem is to design a local state feedback controller and an observer for a new subsystem which is added to a large-scale system without changing the decentralized state feedback control laws of the original construction, so that both the new subsystem and the resulting expanded system are robustly connectively stable. Firstly, based on state observers, the mathematical model of a large-scale system with expanding construction is reestablished and analyzed. In addition, the sufficient condition for robust decentralized connective stabilization of the expanded construction of large-scale systems is deduced by taking an LMI approach, which is further relaxed by removing the square matrix condition on the output matrix. This problem is transformed into solving an LMI problem. The new design method of an organically structured controller and observer for the expanded construction is also given. Finally, the simulation examples show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper develops a hybrid demon algorithm for a two-dimensional orthogonal strip packing problem. This algorithm combines a placement procedure based on an improved heuristic, local search, and demon algorithm involved in setting one parameter. The hybrid algorithm is tested on a wide set of benchmark instances taken from the literature and compared with other well-known algorithms. The computation results validate the quality of the solutions and the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Evolutionary spatial game is a promising way to unravel the mystery of cooperation, and it has been well recognized that spatial structure could enable cooperation to persist. Schweitzer et al.’s lattice model provides an innovative method to solve the problem. In this paper, we conduct simulations using the same von Neumann neighborhood as in Schweitzer et al.’s study (2002) and observe the effect of initial population and lattice size on the evolution of cooperation. Then, we extend the model with a more complicated Moore neighborhood and self-playing rule for each central player. Simulation results not only provide new evidence for the persistence of cooperation in the evolution with spatial structures, but also investigate critical conditions for the spatial coexistence or the invasion of cooperators and defectors with the more complicated neighborhood. [ABSTRACT FROM AUTHOR]