An Experimental and Analytical Study of a Radiative Cooling System with Flat Plate Collectors

AbstractA nocturnal radiative cooling system with flat plate solar collectors in Beijing, is assessed both experimentally and numerically. A cooling loop, including a radiator, a storage tank, pump, radiant floor and connecting pipes has been studied experimentally. The heat loss of an uncovered night-sky radiator was analysed according to radiation and convection theory. The water is circulated through the flat-plate radiator having 2 m2 of collector area at night to be cooled by convection and radiation to sky. The results indicate that the minimum temperature of the floor surface is 19.5°C. Vertical temperature field is uniform. Design temperature can increase 1°C compared with conventional heat convection. The average net cooling reached 26W/m2, as condensation does not occur. It is possible to increase the total cooling capacity while maintaining a low pressure drop. It demonstrates the feasibility of cooling using fluid medium through nocturnal radiation

Reuse: A knowledge-based approach

This paper describes our research in automating the reuse process through the use of application domain models. Application domain models are explicit formal representations of the application knowledge necessary to understand, specify, and generate application programs. Furthermore, they provide a unified repository for the operational structure, rules, policies, and constraints of a specific application area. In our approach, domain models are expressed in terms of a transaction-based meta-modeling language. This paper has described in detail the creation and maintenance of hierarchical structures. These structures are created through a process that includes reverse engineering of data models with supplementary enhancement from application experts. Source code is also reverse engineered but is not a major source of domain model instantiation at this time. In the second phase of the software synthesis process, program specifications are interactively synthesized from an instantiated domain model. These specifications are currently integrated into a manual programming process but will eventually be used to derive executable code with mechanically assisted transformations. This research is performed within the context of programming-in-the-large types of systems. Although our goals are ambitious, we are implementing the synthesis system in an incremental manner through which we can realize tangible results. The client/server architecture is capable of supporting 16 simultaneous X/Motif users and tens of thousands of attributes and classes. Domain models have been partially synthesized from five different application areas. As additional domain models are synthesized and additional knowledge is gathered, we will inevitably add to and modify our representation. However, our current experience indicates that it will scale and expand to meet our modeling needs

Domain and Specification Models for Software Engineering

This paper discusses our approach to representing application domain knowledge for specific software engineering tasks. Application domain knowledge is embodied in a domain model. Domain models are used to assist in the creation of specification models. Although many different specification models can be created from any particular domain model, each specification model is consistent and correct with respect to the domain model. One aspect of the system-hierarchical organization is described in detail

FINT: Field-aware INTeraction Neural Network For CTR Prediction

As a critical component for online advertising and marking, click-through rate (CTR) prediction has draw lots of attentions from both industry and academia field. Recently, the deep learning has become the mainstream methodological choice for CTR. Despite of sustainable efforts have been made, existing approaches still pose several challenges. On the one hand, high-order interaction between the features is under-explored. On the other hand, high-order interactions may neglect the semantic information from the low-order fields. In this paper, we proposed a novel prediction method, named FINT, that employs the Field-aware INTeraction layer which captures high-order feature interactions while retaining the low-order field information. To empirically investigate the effectiveness and robustness of the FINT, we perform extensive experiments on the three realistic databases: KDD2012, Criteo and Avazu. The obtained results demonstrate that the FINT can significantly improve the performance compared to the existing methods, without increasing the amount of computation required. Moreover, the proposed method brought about 2.72\% increase to the advertising revenue of a big online video app through A/B testing. To better promote the research in CTR field, we will release our code as well as reference implementation of those baseline models in the final version.Comment: 5 pages, Submitted to CIKM 202

A BIPV/T System Design Based on Simulation and its Application in Integrated Heating System

AbstractIn order to better master the internal airflow distribution characteristics of photovoltaic thermal (PV/T) collector on air source heat pump (ASHP) system heating performance, CFD software was applied in studying the building-integrated photovoltaic thermal (BIPV/T) sys-tem. A mathematical model of BIPV/T and ASHP integrated heating system was established. Numerical simulation of the system was conducted based on the typical meteorological data in Shenyang area. The influence of the inlet and outlet velocity, internal flow field distribution and temperature field distribution of the BIPV/T system were analyzed on the system thermal efficiency. The relationship between optimal COP and the inlet and outlet velocity of ASHP system was studied. The optimal inlet velocity of the BIPV/T – ASHP integrated system was determined to be 4 m/s, and the COP reached 4.6