检索结果-南通市图书馆

同方期刊数据库

Combinatorial auction algorithm for project portfolio selection and scheduling to maximize the net present value

在线全文

学校读者我要写书评

暂无评论

Journal of Zhejiang University-Science C(Computers and Electronics) 2010年第7期11卷 562-574页

作者： yong-yi SHOU yi-lun HUANG Department of Management Science & Engineering Zhejiang University Hangzhou 310058 China Department of Instrument Science & Engineering Zhejiang University Hangzhou 310027 China

Scheduling projects at the activity level increases the complexity of decision making of project portfolio selection but also expands the search space to include better project portfolios. An integer programming model is formulated for the project portfolio selection and scheduling problem. An iterative multi-unit combinatorial auction algorithm is proposed to select and schedule project portfolios through a distributed bidding mechanism. Two price update schemes are designed to adopt either a standard or an adaptive Walrasian tatonnement process. Computational tests show that the proposed auction algorithm with the adaptive price update scheme selects and schedules project portfolios effectively and maximizes the total net present value. The price profile generated by the algorithm also provides managerial insights for project managers and helps to manage the scarce resources efficiently.

关键词： Project management Portfolio selection Combinatorial auction Project scheduling

Improvement of the Bayesian neural network to study the photoneutron yield cross sections

在线全文

学校读者我要写书评

暂无评论

Nuclear Science and Techniques 2022年第11期33卷 1-9页

作者： yong-yi Li Fan Zhang Jun Su Department of Computing Changzhi UniversityChangzhi 046011China Department of Physics Changzhi UniversityChangzhi 046011China Sino-French Institute of Nuclear Engineering and Technology Sun Yat-sen UniversityZhuhai 519082China

This work is an attempt to improve the Bayesian neural network (BNN) for studying photoneutron yield cross sections as a function of the charge number Z, mass number A, and incident energy ε. The BNN was improved in terms of three aspects:numerical parameters, input layer, and network structure. First, by minimizing the deviations between the predictions and data, the numerical parameters, including the hidden layer number, hidden node number, and activation function, were selected. It was found that the BNN with three hidden layers, 10 hidden nodes, and sigmoid activation function provided the smallest deviations. Second, based on known knowledge,such as the isospin dependence and shape effect, the optimal ground-state properties were selected as input neurons. Third, the Lorentzian function was applied to map the hidden nodes to the output cross sections, and the empirical formula of the Lorentzian parameters was applied to link some of the input nodes to the output cross sections. It was found that the last two aspects improved the predictions and avoided overfitting, especially for the axially deformed nucleus.

关键词： Bayesian neural network Photoneutron cross sections Giant dipole resonance

Normalized Solution for p-Kirchhoff Equation with a L2-supercritical Growth

在线全文

学校读者我要写书评

暂无评论

Acta Mathematicae Applicatae Sinica 2024年第2期40卷 414-429页

作者： Zhi-min REN yong-yi LAN School of Sciences Jimei UniversityXiamen 361005China

In this paper,we investigate the following p-Kirchhoff equation{∫R^(N)|u|^(2)dx=ρ,(a+b)∫RN(|Δu}^(p)+|u|^(p))dx)(-Δpu+|u|^(p-2u)=|u|^(s-2)u+μu,x∈R^(N),where a>0,b≥0,p>0 are constants,constants,p*=N-P/Np is the ... 详细信息

关键词： p-Kirchhoff equation normalized solution mountain-pass lemma

Modeling and simulation of chemically reacting flows in gas-solid catalytic and non-catalytic processes

在线全文

学校读者我要写书评

暂无评论

Particuology 2010年第6期8卷 525-530页

作者： Changning Wu Binhang Yan yong Jin yi Cheng Department of Chemical Engineering Tsinghua University Beijing 100084 China

This paper gives an overview of the recent development of modeling and simulation of chemically react- ing flows in gas-solid catalytic and non-catalytic processes. General methodology has been focused on the Eulerian-Lagrangian description of particulate flows, where the particles behave as the catalysts or the reactant materials. For the strong interaction between the transport phenomena （i.e., momentum, heat and mass transfer） and the chemical reactions at the particle scale, a cross-scale modeling approach, i.e., CFD-DEM or CFD-DPM, is established for describing a wide variety of complex reacting flows in multiphase reactors, Representative processes, including fluid catalytic cracking （FCC）, catalytic conversion of syngas to methane, and coal pyrolysis to acetylene in thermal plasma, are chosen as case studies to demonstrate the unique advantages of the theoretical scheme based on the integrated particle-scale information with clear physical meanings, This type of modeling approach provides a solid basis for understanding the multiphase reacting flow problems in general.

关键词： Gas-solid chemically reacting flow Cross-scale modeling and simulation Eulerian-Lagrangian scheme Computational fluid dynamics (CFD) Discrete element method (DEM) Discrete phase model (DPM)

Multiconditional machining process quality prediction using deep transfer learning network

在线全文

学校读者我要写书评

暂无评论

Advances in Manufacturing 2023年第2期11卷 329-341页

作者： Bo-Hao Li Li-Ping Zhao yi-yong Yao State Key Laboratory for Manufacturing Systems Engineering Xi'an Jiaotong UniversityXi'an710049People's Republic of China School of Mechanical Engineering Xi'an Jiaotong UniversityXi'an710049People's Republic of China

The quality prediction of machining processes is essential for maintaining process stability and improving component quality. The prediction accuracy of conventional methods relies on a significant amount of process signals under the same operating conditions. However, obtaining sufficient data during the machining process is difficult under most operating conditions, and conventional prediction methods require a certain amount of training data. Herein, a new multiconditional machining quality prediction model based on a deep transfer learning network is proposed. A process quality prediction model is built under multiple operating conditions. A deep convolutional neural network (CNN) is used to investigate the connections between multidimensional process signals and quality under source operating conditions. Three strategies, namely structure transfer, parameter transfer, and weight transfer, are used to transfer the trained CNN network to the target operating conditions. The machining quality prediction model predicts the machining quality of the target operating conditions using limited data. A multiconditional forging process is designed to validate the effectiveness of the proposed method. Compared with other data-driven methods, the proposed deep transfer learning network offers enhanced performance in terms of prediction accuracy under different conditions.

关键词： Multiconditional machining process Intelligent manufacturing Deep transfer learning Quality prediction Process stability

A novel predict-prevention quality control method of multi-stage manufacturing process towards zero defect manufacturing

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Advances in Manufacturing 2023年第2期11卷 280-294页

作者： Li-Ping Zhao Bo-Hao Li yi-yong Yao State Key Laboratory for Manufacturing Systems Engineering Xi'an Jiaotong UniversityXi'an710049People's Republic of China School of Mechanical Engineering Xi'an Jiaotong UniversityXi'an710049People's Republic of China

Zero defection manufacturing (ZDM) is the pursuit of the manufacturing industry. However, there is a lack of the implementation method of ZDM in the multi-stage manufacturing process (MMP). Implementing ZDM and controlling product quality in MMP remains an urgent problem in intelligent manufacturing. A novel predict-prevention quality control method in MMP towards ZDM is proposed, including quality characteristics monitoring, key quality characteristics prediction, and assembly quality optimization. The stability of the quality characteristics is detected by analyzing the distribution of quality characteristics. By considering the correlations between different quality characteristics, a deep supervised long-short term memory (SLSTM) prediction network is built for time series prediction of quality characteristics. A long-short term memory-genetic algorithm (LSTM-GA) network is proposed to optimize the assembly quality. By utilizing the proposed quality control method in MMP, unqualified products can be avoided, and ZDM of MMP is implemented. Extensive empirical evaluations on the MMP of compressors validate the applicability and practicability of the proposed method.

关键词： Zero defection manufacturing(ZDM) Multi-stage manufacturing process(MMP) Moving window Deep supervised long-short term memory(SLSTM)network Assembly quality optimization

An Efficient Enantioselective Synthesis of Blastmycinone

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Chinese Journal of Chemistry 2005年第11期23卷 1519-1522页

作者： YANG, yong-Qing wu, yi-Kang State Key Laboratory of Bio-organic and Natural Products Chemistry Shanghai Institute of Organic Chemistry Chinese Academy of Sciences Shanghai 200032 China

Blastmycinone, a degradation product of the antibiotic antimycin A, was synthesized with a TiCl4-mediated asymmetric aldolization as the key step. Some unexpected yet interesting chemistry was also observed.

关键词： lactone asymmetric synthesis aldol acylation chiral auxiliary

P115 promotes growth of gastric cancer through interaction with macrophage migration inhibitory factor

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

World Journal of Gastroenterology 2013年第46期19卷 8619-8629页

作者： Xiu-Jun Li yi Luo yong-Fen yi Department of Pathology Chongqing Medical University Department of Obstetrics and Gynecology the First Affiliated Hospital of Chongqing Medical University

AIM:To investigate the role of P115 in the proliferation of gastric cancer cells and the mechanism ***:The RNA and protein level of P115 and macrophage migration inhibitory factor(MIF)in gastric cancer and normal gastric tissue/cells were measured and the effect of P115 on cell proliferation was *** role of P115 in cell cycle checkpoints was investigated and the related proteins and signaling pathways,such as cyclin D1,Mcm2,p53,PCNA as well as the MAPK signaling pathway were *** interaction between P115 and MIF and the effect of P115 on MIF secretion were *** data were analyzed via one-way ANOVA comparisons between groups and P<0.05 was considered ***:P115 and MIF were both specifically expressed in gastric cancer tissues compared with normal gastric mucosa(both P<0.01).The mRNA and protein levels of P115 and MIF in gastric cancer cell lines MKN-28 and BGC-823 were higher than in the human gastric epithelial cell line GES-1(both P<0.01).In MKN-28 and BGC-823 cell lines,P115 promoted cell proliferation and G0-G1to S phase *** addition,several cell cycle-related regulators,including cyclin D1,Mcm2,PCNA,pERK1/2 and p53 were up-regulated by ***,the interaction between P115 and MIF was confirmed by co-immunoprecipitation *** showed that P115 stimulated the secretion of MIF into the culture supernatant(P<0.01)and the compensative expression of MIF in cells was observed by Western ***:P115 promotes proliferation of gastric cancer cells through an interaction with MIF.

关键词： Gastric cancer P115 Migration inhibitory factor Proliferation Protein interaction

Computational Intelligence Prediction Model Integrating Empirical Mode Decomposition,Principal Component Analysis,and Weighted k-Nearest Neighbor

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Electronic Science and Technology 2020年第4期18卷 341-349页

作者： Li Tang He-Ping Pan yi-yong Yao School of Management and Economics University of Electronic Science and Technology of ChinaChengdu 611731 Tianfu College Southwestern University of Finance and EconomicsChengdu 610052 Intelligent Finance Research Center Chongqing Institute of FinanceChongqing 400067

On the basis of machine leaning,suitable algorithms can make advanced time series *** paper proposes a complex k-nearest neighbor(KNN)model for predicting financial time *** model uses a complex feature extraction process integrating a forward rolling empirical mode decomposition(EMD)for financial time series signal analysis and principal component analysis(PCA)for the dimension *** information-rich features are extracted then input to a weighted KNN classifier where the features are weighted with PCA ***,prediction is generated via regression on the selected nearest *** structure of the model as a whole is *** test results on real historical data sets confirm the effectiveness of the models for predicting the Chinese stock index,an individual stock,and the EUR/USD exchange rate.

关键词： Empirical mode decomposition(EMD) k-nearest neighbor(KNN) principal component analysis(PCA) time series