A two-sided matching model for complex equipment production of military–civilian merging platform with reference effects

Huang, Xin; Chen, Hongzhuan; Ma, Peng; Wang, Weiming; Cai, Xiang; Nafis, Malik

doi:10.1007/s00500-021-05731-3

Cited by 8 publications

(3 citation statements)

References 74 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the future, we will explore in at least two directions based on proposed method. The first one is to explore the clear image without reference for training the dehazing network [45,46,51,52]. The second is to explore video dehazing [47][48][49][50].…”

Section: Discussionmentioning

confidence: 99%

GGADN: Guided generative adversarial dehazing network

2021

View full text Add to dashboard Cite

Image dehazing has always been a challenging topic in image processing. The development of deep learning methods, especially the Generative Adversarial Networks(GAN), provides a new way for image dehazing. In recent years, many deep learning methods based on GAN have been applied to image dehazing. However, GAN has two problems in image dehazing. Firstly, For haze image, haze not only reduces the quality of the image, but also blurs the details of the image. For Gan network, it is difficult for the generator to restore the details of the whole image while removing the haze. Secondly, GAN model is defined as a minimax problem, which weakens the loss function. It is difficult to distinguish whether GAN is making progress in the training process. Therefore, we propose a Guided Generative Adversarial Dehazing Network(GGADN). Different from other generation adversarial networks, GGADN adds a guided module on the generator. The guided module verifies the network of each layer of the generator. At the same time, the details of the map generated by each layer are strengthened. Network training is based on the pre-trained VGG feature model and L1-regularized gradient prior which is developed by new loss function parameters. From the dehazing results of synthetic images and real images, proposed method is better than the state-of-the-art dehazing methods.

show abstract

Section: Discussionmentioning

confidence: 99%

GGADN: Guided generative adversarial dehazing network

2021

View full text Add to dashboard Cite

show abstract

“…What we discuss in this paper is a narrow sense of development process which is an important phase in the broad sense of complex equipment collaborative development process. For complex equipment collaborative development, research contents include quality management from supply chain about cooperation and competition between suppliers and manufacturer [2] and internal quality promotion [4,5], internal product scheduling [17] and life cycle management [18], and many other related researches. Even though large body of research has been done around complex equipment collaborative development, one point that is neglected in most researches is the comprehensive evaluation analysis for the selected influencing factors that affect complex equipment collaborative development process.…”

Section: Complex Equipment Collaborative Developmentmentioning

confidence: 99%

“…The internal development mode for complex equipment is manufacturer as leader and supplier as follower [1,2]. Furthermore, the development process of complex equipment is more complex, requiring more investment [3], bearing higher risk [4,5], and more intensive knowledge [6] when compared with general equipment.…”

Section: Introductionmentioning

confidence: 99%

Selecting Suitable Key Supplier for Core Components During Smart Complex Equipment Central-Private Enterprises Collaborative Development Process: From Two Different Forms of Evaluation Information and Matching Perspective

Huang,

Qi,

Chen

et al. 2023

J. of Syst. Eng. Electron.

Self Cite

View full text Add to dashboard Cite

With the development of central-private enterprises integration, selecting suitable key suppliers are able to provide core components for smart complex equipment. We consider selecting suitable key suppliers from matching perspective, for it not only satisfies natural development of smart complex equipment, it is also a good implementation of equipment project in central-private enterprises integration context. In in this paper, we carry out two parts of research, one is evaluation attributes based on comprehensive analysis, and the other is matching process between key suppliers and core components based on the matching attribute. In practical analysis process, we employ comprehensive evaluated analysis methods to acquire relevant attributes for the matching process that follows. In the analysis process, we adopt entropy-maximum deviation method (MDM)decision-making trial and evaluation laboratory (DEMATEL)-technique for order preference by similarity to an ideal solution (TOP-SIS) to obtain a comprehensive analysis. The entropy-MDM is applied to get weight value, DEMATEL is utilized to obtain internal relations, and TOPSIS is adopted to get ideal evaluated solution. We consider aggregating two types of evaluation information according to similarities of smart complex equipment based on the combination between geometric mean and arithmetic mean. Moreover, based on the aforementioned attributes and generalized power Heronian mean operator, we aggregate preference information to acquire relevant satisfaction degree, then combine the constructed matching model to get suitable key supplier. Through comprehensive analysis of selecting suitable suppliers, we know that two-sided matching and information aggregation can provide more research perspectives for smart complex equipment. Through analysis for relevant factors, we find that leading role and service level are also significant for the smart complex equipment development process.

show abstract