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The development of novel nitride-based phosphors is always a significant work, especially for AlON system because of their rigid crystal structure, which is similar to the promising host material of β-SiAlON. In this work, Mg2+ ion was introduced into AlON-related phase Al 5 O 3 N 3 along with co-doped Si4+ ion used to keep charge neutrality, and thus a novel Mg-based nitroaluminosilicate MgAl 3 SiO 3 N 3 (MASON) was successfully synthesized under a mild preparation condition due to the high reactivity of MgO. Benefiting from its special crystal structure optimized by the introduction of Mg2+ ion, Eu2+-activated MASON performs significant photoluminescence (PL) and cathodoluminescence (CL) properties. In contrast to most Eu2+-activated AlON related phosphors that commonly emit high-energy blue light, the as-prepared sample emits a high-bright green light along with good thermal stability. Moreover, its broad emission band with the full width at half maximum (FWHM) of 134 nm well compensates the "cyan cavity", which leads to a full spectrum light-emitting diode. The structure-property relation of Eu2+ in MASON was established in this work, which confirms the significant role of the introduced Mg2+ ion in the structure optimization of AlON system. The result may pave an efficient way to explore more promising AlON-based phosphors. High-brightness green light emission was achieved in Eu2+-activated nitroaluminosilicate MgAl 3 SiO 3 N 3. [Display omitted] [ABSTRACT FROM AUTHOR]

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Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology 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.)

A ground-wall insulation structure optimization method based on a composite material for a stator bar is proposed in this paper. The trend regularity of the nonuniform coefficient of the electric field for a composite dielectric fillet electrode structure is established with the finite element method, and a composite ground-wall insulation structure optimization model is established in combination with the Nelder-Mead optimization algorithm. A composite ground-wall insulation optimization structure is acquired based on this model and material parameter, reducing the maximum electric field inside the ground-wall insulation and increasing the overall electrical performance of the stator bars with composite ground-wall insulation. The measured results are consistent with the predicted trend, which verifies the validity of the proposed method. [ABSTRACT FROM AUTHOR]

The hydrodynamics in a novel integral multi-jet spout-fluidized bed was simulated using both Eulerian-Eulerian approach and the kinetic theory of granular flows. The effect of side jet number and side jet width on gas-solid two phase flow in the integral multi-jet spout-fluidized bed (IMJSFB) was investigated. The CFD results show that when the number of side jet is greater than 1 ( N  = 2–5), a wave-like spout and stable spout structure is formed in the IMJSFB. Compared with simulation results in the single nozzle spouted bed, the profiles of particles' velocity and concentration along the radial direction in the IMJSFB become relatively flatter. The integral uniformity of particle velocity distribution in spouted bed can be enhanced by increasing δ/D i . The particle flow factor (η) decreases first and then increases with the increase of δ/D i , and η reaches its minimum value when δ/D i  = 0.208. On the whole, there is a set of optimal design parameters of side jet ( N  = 4, δ/D i  = 0.208), at which, the fluidization quality particle phase in spout-fluidized bed reaches the optimum state while the bypass fluidizing air supply auxiliary equipment is omitted. The fluidization quality of particles in annulus can be significantly enhanced in the IMJSFB while the bypass fluidizing air supply auxiliary equipment is omitted. [ABSTRACT FROM AUTHOR]

In this paper, a new type of permanent magnet type 3-DOF hybrid driven motor is proposed in view of the poor precision of existing deflection movement control and low speed of multi-degree-of-freedom motor rotation. The motor contains two part named lateral rotation module and internal deflection modules. The output torque of the motor, the rotation speed and control precision are improved by the coordinated control of rotation movement and deflection movement. The magnetic field shielding and optimization of two modules inside and outside the motor are realized by using Halbach permanent magnet array, and the magnetic field characteristics of the motor are improved. First, using analytical method and finite element method, the magnetic field characteristics and distribution of the permanent magnet of the motor are analyzed, and the electromagnetic characteristics of load and no load condition and output torque of the motor is analyzed by finite element method. Then, the loss of this motor is analyzed and calculated, the motor response surface equation is built by using taguchi method and central composite design method. Finally, the optimal structure parameters of motor is proposed by the numerical model based on maximizing the motor output torque of the target, and it is compared with finite element and experiment to verify the accuracy of the structural parameters. It provides a new theoretical basis for the follow-up optimization of the motor. [ABSTRACT FROM AUTHOR]

In this paper, the cooling efficiency of the parallel air-cooled battery thermal management system (BTMS) with U-type flow is improved through optimizing the structure of the system. The flow resistance network model is developed to calculate the airflow rates of the cooling channels in the system. Based on the developed flow resistance network model, the angles of the plenums and the widths of the inlet and the outlet are optimized using the nested looped procedure and the numeration method. The numerical results of typical cases show that the temperature and the temperature difference among the battery cells cannot be effective reduced through arranging the angles of the plenums of the system, while optimizing the widths of the inlet and the outlet can remarkably improve the cooling efficiency of the BTMS. For the process with 5C discharge rate process, the temperature difference among the battery cells is reduced by 70% after optimization, with the power consumption reduced by 32%. Moreover, compared to the optimized BTMS in the previous study, the temperature difference among the battery cell for the present optimized BTMS is 43% lower, with power consumption reduced by 50%. The similar improvement can be achieved for various inlet airflow rates. [ABSTRACT FROM AUTHOR]

The belief-rule-based system (BRBS) is one of the most visible and fastest growing branches of decision support systems. As the knowledge base in the BRBS, the belief-rule-base (BRB) is required to be equipped with the optimal parameters and structure, which means the optimal value and number of parameters, respectively. Several optimization methods were therefore proposed in the past decade. However, these methods presented different limitations, such as the use of the incomplete parameter optimization model, lack of structure optimization, and so on. Moreover, it is impracticable to determine the optimal parameters and structure of a BRB using the training error because of over-fitting. The present work is focused on the joint optimization on parameter and structure for the BRB. Firstly, a simple example is utilized to illustrate and analyze the generalization capability of the BRBS under different numbers of rules, which unveils the underlying information that the BRBS with a small training error may not have superior approximation performances. Furthermore, by using the Hoeffding inequality theorem in probability theory, it is a constructive proof that the generalization error could be a better choice of criterion and measurement to determine the optimal parameters and structure of a BRB. Based on the above results, a heuristic strategy to optimize the structure of the BRB is proposed, which is followed by a parameter optimization method using the differential evolution (DE) algorithm. Finally, a joint optimization method is introduced to optimize the parameters and structure of the BRB simultaneously. In order to verify the generality and effectiveness of the proposed method, two practical case studies, namely oil pipeline leak detection and bridge risk assessment, are examined to demonstrate how the proposed method can be implemented in the BRB under disjunctive and conjunctive assumptions along with their performance comparative analysis. [ABSTRACT FROM AUTHOR]

We investigate a possible route for optimization of organic P3HT:ICBA photovoltaic cells. In order to ensure a more efficient charge separation and collection at the electrodes, two- and three-layer structures are produced, where additional P3HT and ICBA single layers are placed adjacent to the mixed layer. The J – V characteristics are modeled using Monte-Carlo simulations in a flexible computational framework, reproducing the typical morphologies of the active layers. We discuss the implications of the structural modifications, in particular the enhancement of the open circuit voltage. Qualitative features of the theoretical simulations are validated by experiment. The proposed fabrication technique of using solvents with different boiling points for successive deposition of the individual layers may constitute an accessible route for producing optimized solar cell structures. [ABSTRACT FROM AUTHOR]

Many social spreading phenomena can be modeled as epidemic spreading models over networks, and the studies of these phenomena are important to avoid epidemic outbreaks. Epidemic threshold of the network, which fundamentally depends on the network structure itself, is a critical measure to judge whether the epidemic dies out or results in an epidemic breakout. In this study, epidemic threshold is regarded as the objective function to control the spreading process. In addition, an efficient structure optimization strategy based on memetic algorithm is proposed to adjust the spreading threshold without changing the degree of each node. Lowering the threshold can promote the spreading process whereas heightening the threshold can prevent the spreading process. In the proposed algorithm, genetic algorithm is adopted as the global search strategy and a modified simulated annealing algorithm combined with the properties of networks is proposed as the local search strategy. Experiments on computer-generated and real-world networks demonstrate that the proposed algorithm has superior performances for both the threshold minimization and maximization problems. [ABSTRACT FROM AUTHOR]

During the operation of turbines, one of the common accidents is due to the structure failure of blades. The contact model with strong non-linearity and time variation makes it difficult to be analyzed. In this paper, firstly, the contact model is described by using fractal theory. Secondly, the new method for the optimization of turbine blade is proposed, which is a kind of structure with local nonlinearity and multi degree of freedom. The method reduces the number of degrees of freedom by forming a new super element, which makes the linear part of turbine blade without repeated calculation in the non-linear iteration process. Therefore, it can shorten the calculation time and reduce the demand for computing resources. Finally, an optimization of the turbine blade is carried out, and the maximum equivalent stress reduces by 13.19%, which proves the effectiveness of the new optimization method. [ABSTRACT FROM AUTHOR]

How to reconstruct 3D face model from wild photos is such a difficult issue that camera calibration is necessary and the images must be from video sequences. In this paper, a face reconstruction model with structure optimization is proposed to build 3D face surface with individual geometry and physical features reservation through wild face images directly and without camera calibration. Low rank and B-Spline are employed to estimate the aligned 2D structure, to calculate the depth information with SSIM, and to reconstruct the 3D face surface from control points and their space transformation. Furthermore, LFW and Bosphorus datasets, as well as Young-to-Aged samples, are introduced to verify the proposed approach and the experimental results demonstrate the feasibility and effectiveness even with different poses, expressions and age-variety. [ABSTRACT FROM AUTHOR]

Beam is an important component of CNC double column vertical lathe, its structural design and static and dynamic characteristics have direct influence on the machining accuracy and service life of the machine tool. The test object is a machine tool beam provided by the Heng-sheng machine tool plant, using test method of single-point excitation provided by hammer force and mobile acceleration sensor. The paper employs Poly-Max, the most widely used modal parameter identification method, to analyze the measured frequency response function data so as to derive the modal parameters, including intrinsic frequency, damping ratio and mode of vibration. Beams of the modal test of beam model is set up, collecting data and setting parameters. By analyzing the intrinsic characteristics of structure, beam structure optimization strategy can be developed. The paper provides a theoretical basis for the structural optimization design and manufacturing of similar components and parts. [ABSTRACT FROM AUTHOR]

In order to reduce computational cost during satellite bus structure optimization and satisfy the global convergence, the radial basis function is applied to dealing with structure optimization. As the truss structure optimization involves size, topology and geometry variables, continuous relaxation is used to treat discrete variables. Furthermore, the Lagrangian multiple method is applied to converting a constrained optimization problem to an equivalent unconstrained optimization problem with bound constraint. Then the radial basis function is used to approximate the Lagrangian function, and the optimum is obtained through using global optimization strategy for the approximated model. During the satellite bus structure optimization process, the parameter of Lagrange function and radial basis are updated until the process searches a feasible optimum. Finally, the proposed strategy is applied to a practical satellite bus truss structure, thus demonstrating the high efficiency. [ABSTRACT FROM AUTHOR]

In engineering, a broad range of environments exist for modeling and simulation with integrated parameter optimization. The established techniques only optimize model parameter values, the model structure is considered to be fixed. As system performance is optimized, one may have to redesign the model structure. The redesign is done manually by an analyst. The suboptimal combination of automatic parameter optimization and manual structural changes leads to an optimization task that is prone to error. This paper details an approach that provides optimization through automatic reconfiguration of both the model structure and model parameters. An optimization method that uses an evolutionary algorithm is supported by a model management method. This method is based on the system entity structure/model base framework. The admissible model structures and their associated model parameter sets are specified using the system entity structure ontology. Basic dynamic model components are organized in a model base. In addition to this, new algorithms are introduced. These map knowledge coded in the system entity structure to a set of numerical (structure) parameters, and also perform this mapping in reverse. In this manner a combined structure and parameter optimization problem is derived. Since both methods – evolutionary algorithm and model management – work together concurrently, different system configurations can be evaluated automatically. The objective is to provide an optimal solution; a model optimized for both parameter and structure. [ABSTRACT FROM AUTHOR]

Highlights: [•] Asphalt plug joints are damaged and failed due to different mechanisms. [•] Joint structure optimization reduces stress concentration. [•] Binder properties are related to joint performance. [•] Performance based material specifications can be developed. [Copyright &y& Elsevier]

Abstract: With the development of computer applications in ship design, optimization, as a powerful approach, has been widely used in the design and analysis process. However, the running time, which often varies from several weeks to months in the current computing environment, has been a bottleneck problem for optimization applications, particularly in the structural design of ships. To speed up the optimization process and adjust the complex design environment, ship designers usually rely on their personal experience to assist the design work. However, traditional experience, which largely depends on the designer’s personal skills, often makes the design quality very sensitive to the experience and decreases the robustness of the final design. This paper proposes a new machine-learning-based ship design optimization approach, which uses machine learning as an effective tool to give direction to optimization and improves the adaptability of optimization to the dynamic design environment. The natural human learning process is introduced into the optimization procedure to improve the efficiency of the algorithm. Q-learning, as an approach of reinforcement learning, is utilized to realize the learning function in the optimization process. The multi-objective particle swarm optimization method, multi-agent system, and CAE software are used to build an integrated optimization system. A bulk carrier structural design optimization was performed as a case study to evaluate the suitability of this method for real-world application. [Copyright &y& Elsevier]

Abstract: Multi-objective optimization problems in practical engineering usually involve expensive black-box functions. How to reduce the number of function evaluations at a good approximation of Pareto frontier has been a crucial issue. To this aim, an efficient multi-objective optimization method based on a sequential approximate technique is suggested in this paper. In each iteration, according to the prediction of radial basis function with a micro multi-objective genetic algorithm, an extended trust region updating strategy is adopted to adjust the design region, a sample inheriting strategy is presented to reduce the number of new function evaluations, and then a local-densifying strategy is proposed to improve the accuracy of approximations in concerned regions. At the end of each iteration, the obtained actual Pareto optimal points are stored in an external archive and are updated as the iteration process. The effect of the present method is demonstrated by eight test functions. Finally, it is employed to perform the structure optimization of a vehicle door. [Copyright &y& Elsevier]

Abstract - Due to the operation mechanism of R&D input-output system that is imprecise, it is hard to predict the structure optimization of R&D funding investment. This paper confirms contribution degree of scientific and technological output from R&D funding by grey relational analysis, and establishes objective function. At last, we predict and optimize the R&D funding investment of Jiangsu Province in the eleventh five-year plan, and the best allocation program is given, which verifies the feasibility and effectiveness of the method. [ABSTRACT FROM AUTHOR]

This paper introduces a novel optimization algorithm, group search optimizer (GSO) algorithm. The implementation method of this algorithm is presented in detail. The GSO is used to investigate the truss structures with continuous variables and was tested by three truss structure optimization problems. The optimization results were compared with those of the particle swarm optimizer (PSO), the particle swarm optimizer with passive congregation (PSOPC) and the heuristic particle swarm optimizer (HPSO). The calculation results show that the GSO has preferable convergence rate and accuracy. Results from the three tested cases illustrate the competitive ability of the GSO to find the optimal results. It is desired for GSO to be used for structural optimal design problems. [ABSTRACT FROM AUTHOR]

A method for topological optimization of structures with discrete variables subjected to dynamic stress and displacement constraints is presented. By using the quasistatic method, the structure optimization problem under dynamic stress and displacement constraints is converted into one subjected to static stress and displacement constraints. The comprehensive algorithm for topological optimization of structures with discrete variables is used to find the optimum solution. [ABSTRACT FROM AUTHOR]

A method for topological optimization of structures with discrete variables subjected to dynamic stress and displacement constraints is presented. By using the quasi-static method, the structure optimization problem under dynamic stress and displacement constraints is converted into one subjected to static stress and displacement constraints. The comprehensive algorithm for topological optimization of structures with discrete variables is used to find the optimum solution. [ABSTRACT FROM AUTHOR]

Due to the complex structure of a distributed energy resources system (DES) and a large amount of sensor data, local computers cannot provide enough computing resources to process the related data in a short time. Moreover, network integration causes a power system vulnerable to denial of service (DoS) attacks. DoS attacks result in the loss of partial sensor data, which affects the control performance of local computers on a power system. Therefore, this paper proposes a power system structure optimization strategy based on both sparse constraint optimization and cloud computing to solve the lack of computing power from local computers and prevent DoS attacks. Cloud computing is introduced to provide powerful computing resources for processing the related data in the proposed solution. The blocking probability of sensor data caused by DoS attacks is reduced by optimizing the sensor layout of a power system and reducing the transmission of sensor data. This paper also proposes a control strategy based on actor–critic reinforcement learning (RL) to maintain the stability of a power system during the structure optimization process. Three IEEE bus test systems are used to verify the effectiveness of the proposed structure optimization method and control strategy. The experimental results confirm that the proposed structure optimization method and control strategy can maintain the stability of a power system under DoS attacks. [ABSTRACT FROM AUTHOR]

On the basis of an IMPATT diode complex mathematical model and an optimization procedure, results are presented for a diode structure analysis and optimization suitable for a pulsed-mode 2 mm silicon diode. The energy characteristics of the semiconductor structures for high-power pulsed IMPATT diodes with a permanent and complex doping profile are optimized. © 1998 John Wiley & Sons, Inc. Microwave Opt Technol Lett 19: 4–9, 1998. [ABSTRACT FROM AUTHOR]

A truly bioinspired approach to design optimization should follow the energetically favorable natural paradigm of "minimum inventory with maximum diversity". This study was inspired by constructive regression of trabecular bone – a natural process of network connectivity optimization occurring early in skeletal development. During trabecular network optimization, the original excessively connected network undergoes incremental pruning of redundant elements, resulting in a functional and adaptable structure operating at lowest metabolic cost. We have recapitulated this biological network topology optimization algorithm by first designing in silico an excessively connected network in which elements are dimension-independent linear connections among nodes. Based on bioinspired regression principles, least-loaded connections were iteratively pruned upon simulated loading. Evolved networks were produced along this optimization trajectory when pre-set convergence criteria were met. These biomimetic networks were compared to each other, and to the reference network derived from mature trabecular bone. Our results replicated the natural network optimization algorithm in uniaxial compressive loading. However, following triaxial loading, the optimization algorithm resulted in lattice networks that were more stretch-dominated than the reference network, and more capable of uniform load distribution. As assessed by 3D printing and mechanical testing, our heuristic network optimization procedure opens new possibilities for parametric design. [Display omitted] • This is a bioinspired optimization algorithm to design 3D lattice structures. • The optimized network is a scale- and material-independent design blueprint. • Properties of a developing trabecular bone network are simulated and replicated. • Iterative network topology optimization can target different convergence criteria. • Mechanical behavior of optimized networks can be fine-tuned by parametric embodiment. [ABSTRACT FROM AUTHOR]

• A detailed mechanism of urea decomposition was used to analyze the formation process of urea deposition. • The structure of the mixer was optimized, and the pressure drop of mixer was reduced. • The uniformity indices of NH 3 concentration and velocity were improved. • Optimization scheme I produces urea solid, biuret, CYA, and ammelide that have the lowest qualities. A three-dimensional computational model of a diesel engine exhaust post-treatment system is built in the computational fluid dynamics software to simulate the deposit formation process in the mixer area of the diesel engine Urea-SCR (selective catalytic reduction) system. The SCR inlet uniformity, pressure drop in the mixer, urea solid, and three main urea by-products (biuret (biurea), CYA (cyanuric acid), and ammelide (cyanuric acid monoamide)) are investigated in the exhaust post-treatment system of the diesel engine. The results show that in the whole exhaust post-treatment system, the liquid film only appears in the mixer and nozzle areas. It is found that the production of biuret, CYA, and ammelide, which are by-products of urea decomposition, is mainly affected by the NCO− (isocyanate-group) concentration, and liquid film temperature and height. The thicker the liquid film and the smaller the rate of evaporation, the smaller the mass fraction of urea solid and the larger the mass fraction of urea decomposition by-products. According to the analysis results, the optimization scheme of the mixer is proposed, and the simulation analysis is performed. The analysis results show that the NH 3 concentration uniformity at the SCR inlet increases, and the pressure drop in the mixer decreases. The deposits, such as urea solids, biuret, CYA, and ammelide decrease. Consequently, the performance of the exhaust post-treatment system is improved. [ABSTRACT FROM AUTHOR]