A novel thermo-economic performance indicator for a waste heat power system, namely, MPC, is proposed in this study, which denotes the maximum net power output with the constraint ofEPC <= EPC0, whereEPCis the elec...
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A novel thermo-economic performance indicator for a waste heat power system, namely, MPC, is proposed in this study, which denotes the maximum net power output with the constraint ofEPC <= EPC0, whereEPCis the electricity production cost of the system and EPC(0)refers to theEPCof conventional fossil fuel power plants. The organic and steam Rankine cycle (ORC, SRC) systems driven by the flue gas are optimized to maximize the net power output with the constraint ofEPC <= EPC(0)by using the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The optimization process entails the design of the heat exchangers, the instantaneous calculation of the turbine efficiency, and the system cost estimation based on the Aspen Process Economic Analyzer. Six organic fluids, n-butane, R245fa, n-pentane, cyclo-pentane, MM (Hexamethyldisiloxane), and toluene, are considered for the ORC system. Results indicate that the MPC of the ORC system using cyclo-pentane is 39.7% higher than that of the SRC system under the waste heat source from a cement plant with an initial temperature of 360 degrees C and mass flow rate of 42.15 kg/s. The precondition of the application of the waste heat power system isEPC <= EPC0, and the minimum heat source temperatures to satisfy this condition for ORC and SRC systems are obtained. Finally, the selection map of ORC versus SRC based on their thermo-economic performance in terms of the heat source conditions is provided.
In multi-label learning, objects are essentially related to multiple semantic meanings, and the type of data is confronted with the impact of high feature dimensionality simultaneously, such as the bioinformatics and ...
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In multi-label learning, objects are essentially related to multiple semantic meanings, and the type of data is confronted with the impact of high feature dimensionality simultaneously, such as the bioinformatics and text mining applications. To tackle the learning problem, the key technology, i.e., feature selection, is developed to reduce dimensionality, whereas most of the previous methods for multi-label feature selection are either directly transformed from traditional single-label feature selection methods or half-baked in the label information exploitation, and thus causing the redundant or irrelevant features involved in the selected feature subset. Aimed to seek discriminative features across multiple class labels, we propose an embedded multi-label feature selection method with manifold regularization. To be specific, a low-dimensional embedding is constructed based on the original feature space to fit the label distribution for capturing the label correlations locally, which is also constrained using the label information in consideration of the co-occurrence relationships of label pairs. Following this principle, we design an optimization objective function involving l(2,1)-norm regularization to achieve multi-label feature selection, and the convergence is guaranteed. Empirical studies on various multi-label data sets reveal that the proposed method can obtain highly competitive performance against some state-of-the-art multi-label feature selection methods. (C) 2019 Elsevier Ltd. All rights reserved.
Purpose: The aim of this study was to investigate whether additional manual objectives are necessary for the RapidPlan (RP) with a single optimization. We conducted multi-institutional comparisons of plan quality for ...
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Purpose: The aim of this study was to investigate whether additional manual objectives are necessary for the RapidPlan (RP) with a single optimization. We conducted multi-institutional comparisons of plan quality for head and neck cancer (HNC) using the models created at each institute. Methods: The ability of RP to produce acceptable plans for dose requirements was evaluated in two types of oropharynx cancers at five institutes in Japan. Volumetric modulated arc therapy plans created without (RP plan) and with additional manual objectives (M-RP plan) were compared in terms of planning target volume (PTV), brainstem, spinal cord and parotid glands in dosimetric parameters. Results: There were no major dosimetric PTV differences between RP and M-RP plans. For the brainstem and spinal cord in the RP plans, only 40% and 30% of the plans achieved the dose requirements, while the M-RP plans with upper objective added to volume 0% at all institutes achieved them for 90% of the plans. For the L-parotid gland, there was no difference in the RP and M-RP plans (both were 40%) in achieving the acceptable criteria. For the R-parotid gland, 60% and 80% of the RP and M-RP plans achieved the constraint criteria, and in terms of the achievement rate, the RP plans were relatively high. Conclusions: M-RP plans did not require reoptimization;only an upper objective was needed for the brainstem and spinal cord, while the parotid gland dose was reduced in both RP plans with the auto generated line objectives alone.
With the ability in addressing different types of information under uncertainty, the belief rule base (BRB) has been an efficient tool in modeling the nonlinearity of practical systems, as well as taking the experts k...
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ISBN:
(纸本)9781509007684
With the ability in addressing different types of information under uncertainty, the belief rule base (BRB) has been an efficient tool in modeling the nonlinearity of practical systems, as well as taking the experts knowledge and experience into the modeling process. However, the modeling accuracy has been the sole objective for BRB training and learning in the parameter optimization process, which does not take the modeling complexity into consideration. The exclusion of the modeling complexity can directly cause the infeasibility for constructing and further optimizing the BRB system, especially with human's involvement. In this study, the Akaike Information Criterion (AIC) is used the replace the conventional mean square error (MSE) as the modeling objective. Through a thorough deduction process, the AIC-based objective can represent both modeling accuracy and complexity. Furthermore, the BRB parameter model and the corresponding algorithm are proposed as well. The proposed BRB optimization with AIC-based objective is validated by a numeric case study.
Performance is main key of all modern day applications and connotation of performance differs with the target platform and its intended application. Every component of software stack including the compiler tools needs...
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ISBN:
(纸本)9781509052561
Performance is main key of all modern day applications and connotation of performance differs with the target platform and its intended application. Every component of software stack including the compiler tools needs to contribute towards achieving the better performance. The major hope and scope of further improvement through the highly matured and saturated domain of compiler optimization research is tuning compilations. Several researchers made significant contributions in this field over a period of time. In this paper the entire gamut of tuning compilation landscape is explored from various perspectives, like kind of problem being addressed, target platform, target objective and finally various tuning techniques been applied. This paper presents brief literature survey along with the trends in this important research area of compiler optimizations.
Performance is main key of all modern day applications and connotation of performance differs with the target platform and its intended application. Every component of software stack including the compiler tools needs...
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ISBN:
(纸本)9781509052578
Performance is main key of all modern day applications and connotation of performance differs with the target platform and its intended application. Every component of software stack including the compiler tools needs to contribute towards achieving the better performance. The major hope and scope of further improvement through the highly matured and saturated domain of compiler optimization research is tuning compilations. Several researchers made significant contributions in this field over a period of time. In this paper the entire gamut of tuning compilation landscape is explored from various perspectives, like kind of problem being addressed, target platform, target objective and finally various tuning techniques been applied. This paper presents brief literature survey along with the trends in this important research area of compiler optimizations.
The vibration suppression efficiency of so-called shunted piezoelectric systems is decisively influenced by the number, shape, dimensions and position of the piezoelectric ceramic elements integrated into the structur...
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The vibration suppression efficiency of so-called shunted piezoelectric systems is decisively influenced by the number, shape, dimensions and position of the piezoelectric ceramic elements integrated into the structure. This paper presents a procedure based on evolutionary algorithms for optimum placement of piezoelectric ceramic modules on highly constrained lightweight structures. The optimization loop includes the CAD software CATIA V5, the FE package ANSYS and DynOPS, a proprietary software tool able to connect the Evolving Object library with any simulation software that can be started in batch mode. A user-defined piezoelectric shell element is integrated into ANSYS 9.0. The generalized electromechanical coupling coefficient is used as the optimization objective. Position, dimensions, orientation, embedding location in the composite lay-up and wiring of customized patches are determined for optimum vibration suppression under consideration of operational and manufacturing constraints, such as added mass, maximum strain and requirements on the control circuit. A rear wing of a racing car is investigated as the test object for complex, highly constrained geometries.
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