As we all know, price is a very important factor that affects product sales. To a certain extent, price cuts will increase sales, and price increases within the acceptable range of users can increase manufacturers’ profits. Therefore, for manufacturing companies, scientific product pricing has always been a tricky issue. The pricing strategy model of traditional manufacturing companies is generally based on traditional estimates and conduct price reduction promotions. At present, there are not many pricing strategy models with better scalability for manufacturing companies, especially there are not many quantitative models that can effectively evaluate the impact of competing products on this product. For manufacturing companies, the easiest way to affect sales is to modify product prices. Therefore, based on the relatively novel ConvLSTM neural network model, this article constructs a pricing strategy model for manufacturing companies. To build a pricing strategy model based on cross-domain and cross-brand data, the traditional LSTM model cannot capture the complex relationships between different dimensions of data. Therefore, this article introduces the improved ConvLSTM neural network model of the LSTM model into the field of pricing strategy, and first passes the relevant data through the convolutional layer before ConvLSTM to fully explore the hidden high-dimensional logical associations between the cross-manufacturer and cross-domain data. Therefore, this chapter uses the ConvLSTM model to predict sales based on cross-domain and cross-brand data. At the same time, statistical methods are used to check the confidence interval of the prediction results to enhance the reliability of the model. Finally, use the predictive model to traverse the reasonable pricing interval to obtain the simulated highest sales and optimal product pricing. This chapter finally verifies the superiority of the ConvLSTM-based pricing strategy model proposed in this chapter through design comparison experiments.
The quality of the images collected by the coastal zone video surveillance equipment is seriously degraded due to the sea fog, which directly affects the analysis of the image. Therefore, the study of the costal image dehazing method is of great significance to the related research of the coastal zone. Costal image has the characteristics of large sky area and monotonous color. The traditional method based on atmospheric scattering physics model is not suitable for this kind of image for block effect and color distortion. In this paper, we introduce the generative adversarial mechanism into sea fog image defogging, and propose a coastal image dehazing network based on it. The proposed model includes a generative network and a discriminative model, and is trained by adversarial mechanism. The generative model is composed of multi-scale feature extraction module and residual connection module. The discriminative network consists of two subnetworks of receptive field of different sizes.
Sea-Land segmentation based on surveillance images is an important research content for real-time coast monitoring. However, the complex weather and environmental makes the segmentation of sea-land is a difficult task. Although previous deep learning methods based on convolutional neural networks have achieved excellent results in semantic segmentation, and there has been some work using deep convolutional neural networks for Sea-Land segmentation but we hope that the image segmentation model can achieve more accurate results in sea and land segmentation. In our method, we propose a novel sea-land segmentation framework called Multi Sea-Land U-net (MSLUnet), the framework base on a multi-scale. The proposed MSLUnet is mainly composed of a multi-scale layer and U-Net convolutional network. The multi-scale input layer constructs an image pyramid to accept multiple levels of image data in the network model. U-shaped convolutional networks are used as the back-bone network structure to learn rich hierarchical representations. Experimental results show that compared with other architectures, MSLUnet has achieved good performance.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.