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According to the development practice of transportation construction in my country and related literature records, the existing domestic and foreign research on the promotion of transportation construction on the social and economic development and the negative impact on the ecological environment is analyzed, and it is provided a valid reference to exploring the ideas of green transportation construction and establishing a green road evaluation index system.

To overcome the limitations of convolutional neural network in the process of facial expression recognition, a facial expression recognition model Capsule-LSTM based on video frame sequence is proposed. This model is composed of three networks includingcapsule encoders, capsule decoders and LSTM network. The capsule encoder extracts the spatial information of facial expressions in video frames. Capsule decoder reconstructs the images to optimize the network. LSTM extracts the temporal information between video frames and analyzes the differences in expression changes between frames. The experimental results from the MMI dataset show that the Capsule-LSTM model proposed in this paper can effectively improve the accuracy of video expression recognition., Comment: 14pages,4 figurews

Leveraging the side information associated with entities (i.e. users and items) to enhance the performance of recommendation systems has been widely recognized as an important modelling dimension. While many existing approaches focus on the integration scheme to incorporate entity side information -- by combining the recommendation loss function with an extra side information-aware loss -- in this paper, we propose instead a novel pre-training scheme for leveraging the side information. In particular, we first pre-train a representation model using the side information of the entities, and then fine-tune it using an existing general representation-based recommendation model. Specifically, we propose two pre-training models, named GCN-P and COM-P, by considering the entities and their relations constructed from side information as two different types of graphs respectively, to pre-train entity embeddings. For the GCN-P model, two single-relational graphs are constructed from all the users' and items' side information respectively, to pre-train entity representations by using the Graph Convolutional Networks. For the COM-P model, two multi-relational graphs are constructed to pre-train the entity representations by using the Composition-based Graph Convolutional Networks. An extensive evaluation of our pre-training models fine-tuned under four general representation-based recommender models, i.e. MF, NCF, NGCF and LightGCN, shows that effectively pre-training embeddings with both the user's and item's side information can significantly improve these original models in terms of both effectiveness and stability.

The flow-based generative model is a deep learning generative model, which obtains the ability to generate data by explicitly learning the data distribution. Theoretically its ability to restore data is stronger than other generative models. However, its implementation has many limitations, including limited model design, too many model parameters and tedious calculation. In this paper, a bi-parallel linear flow model for facial emotion generation from emotion set images is constructed, and a series of improvements have been made in terms of the expression ability of the model and the convergence speed in training. The model is mainly composed of several coupling layers superimposed to form a multi-scale structure, in which each coupling layer contains 1*1 reversible convolution and linear operation modules. Furthermore, this paper sorted out the current public data set of facial emotion images, made a new emotion data, and verified the model through this data set. The experimental results show that, under the traditional convolutional neural network, the 3-layer 3*3 convolution kernel is more conducive to extracte the features of the face images. The introduction of principal component decomposition can improve the convergence speed of the model., Comment: 20 pages, 10 figures

In China, the electricity industry is currently undergoing significant transform towards deregulation and competition. The long-term generation investment in electricity market has received more attention. A multi-level generation planning model in electricity market is proposed in this paper. The upper level problem of this model is the non-cooperative game equilibrium problem of generation investment decision, and the middle level problem is the profit maximization problem of multi-stage generation investment for individual generation company. Considering the fluctuation of system load, the lower level problem is a multi-scenario and multi-period market clearing problem. In view of the complexity of the model, this paper proposes a nested genetic algorithm to solve it. First, considering the integer programming characteristics of generation planning, a two-level nested genetic algorithm is used to deal with the upper level game problem and the middle level investment profit maximization problem. Then, linear programming is used to solve the lower level multi-scenario multi-period market clearing problem. The case results of graver-6 and IEEE 118 bus system validate the proposed model and the algorithms.

The field of recommender systems has rapidly evolved over the last few years, with significant advances made due to the in-flux of deep learning techniques. However, as a result of this rapid progress, escalating barriers-to-entry for new researchers is emerging. In particular, state-of-the-art approaches have fragmented into a large number of code-bases, often requiring different input formats, pre-processing stages and evaluating with different metric packages. Hence, it is time-consuming for new researchers to reach the point of having both an effective baseline set and a sound comparative environment. As a step towards elevating this problem, we have developed BETA-Rec, an open source project for Building, Evaluating and Tuning Automated Recommender Systems. BETA-Rec aims to provide a practical data toolkit for building end-to-end recommendation systems in a standardized way. It provides means for dataset preparation and splitting using common strategies, a generalized model engine for implementing recommender models using Pytorch with 9 models available out-of-the-box, as well as a unified training, validation, tuning and testing pipeline. Furthermore, BETA-Rec is designed to be both modular and extensible, enabling new models to be quickly added to the framework. It is deployable in a wide range of environments via pre-built docker containers and supports distributed parameter tuning using Ray. In this demo, we will illustrate the deployment and use of BETA-Rec for researchers and practitioners on a number of standard recommendation datasets. The source code of the project is available at github: https://github.com/beta-team/beta-recsys.

When equipped with thermal energy storage (TES), concentrating solar power (CSP) plants can provide both energy benefit and flexibility benefit for power systems. It is capable of shifting generation to periods with insufficient solar irradiation, providing ancillary services, and supplying firm capacity at peak load periods. In this paper, we focus on the operation behavior of CSP when simultaneously participating in three types of electricity market—energy market, ancillary service (AS) market, and capacity market. The results of case study show the quantified flexibility benefit of CSP in electricity markets. The optimal configuration of CSP in terms of TES and solar multiple (SM) is also analyzed.