The consumption of energy in buildings holds considerable importance within the realm of overall energy usage. This underscores the critical nature of employing efficient strategies for managing energy. Accurate assessments of energy consumption in buildings serve as a central factor in improving energy efficiency and providing guidance for energy management choices in the context of residential buildings. Therefore, predicting and optimizing building energy utilization has become a popular area of research because it has the potential to greatly improve how efficiently energy is used in buildings. In this study, in the first step, four different models, including three artificial neural network frameworks and a regression model are expanded to predict cooling and heating loads. After selecting the best network, using four optimization techniques, the hyperparameters of the selected network are tuned and the best hybrid model is obtained. Furthermore, the multi-objective optimization process is extended to define the optimal conditions using Particle Swarm Optimization, and Biogeography-Based Optimization optimizers and LINMAP, TOPSIS decision-making approaches. The findings of this investigation underscore the enhanced efficiency conferred by the BBO algorithm on the Extreme Learning Machine. Specifically, an increase in the correlation coefficient from 0.9959 to 0.9969 for cooling load estimation and from 0.9973 to 0.9993 for heating load estimation reflects the improved alignment of results from the ELM-BBO model with actual experimental data. These values surpass those of all other models, indicating that the ELM-BBO model demonstrates the best performance among the hybrid models. Importantly, the results obtained underscore the overall effectiveness of the selected optimizers in delivering accurate outcomes. 

Building energy consumption prediction and optimization using different neural network-assisted models; comparison of different networks and optimization algorithms

Model-based robustness testing for avionics-embedded software

Avionics system integration is a system technology through which can improve the effectiveness of system task, the efficiency of system function and the availability of resources operating. Therefore, the most important core technology of integrated avionics system is to implement optimized system organizing for system efficiency, effectiveness and validity by adopting the uniform methods of system synthesis, fusion and integration the base of system application requirements, system capacity and resource operating. The aim of integrating avionics system is to minimize resource allocation and maximize operating effectiveness, to optimize function executing and maximize system efficiency. In this paper, based on the requirements, organization and targets, an avionics system integration model - Tiger model is presented, which organizing architecture model including system application, ability and operation. The paper includes the following three contributions: (1) The mechanism of task synthesis for system application organizing, function fusion for system capability organizing, and resource integrating for system resource organizing is analyzed; (2) The generating process and organizing process for system task, function and resource integration are described; (3) The implementing technology of integrated avionics system is detailed based on the presented integrating theory and mechanism.

http://ieeexplore.ieee.org/iel7/6950783/6979393/06979604.pdf

Research on integrated technology and model in avionics system

The application of data-driven building energy load prediction technologies remains a time-consuming effort, since it highly relies on human expertise to train data-driven building energy load prediction models. To address this issue, this study proposes an automated machine learning-based method which can develop accurate data-driven building energy load prediction models with little human assistance. The potential of six representative automated machine learning frameworks (AutoWeka, H2O, TPOT, AutoGluon, FLAML and AutoKeras) are investigated through utilizing them to forecast 1-h ahead heating, cooling and electrical loads of three real-world buildings. The results show that the proposed method outperforms manual modeling. The accuracy of the proposed method increases by 1.10%–18.66% compared to manual modeling. It is also demonstrated that AutoGluon and FLAML can obtain high prediction accuracy using short training times. Moreover, AutoWeka, H2O and TPOT usually need longer training times than AutoGluon and FLAML to develop high-accuracy prediction models. As for AutoKeras, it doesn't show high accuracy on building energy load prediction. This study provides guidelines for making data-driven building energy load prediction technologies easier to use in practical applications. 

Automated machine learning-based building energy load prediction method

为安全批评的嵌入的软件测试的坚韧性仍然是在它的初生的阶段的挑战。在这份报纸，我们建议实际方法论并且由采用为嵌入的软件系统测试的基于模型的坚韧性实现环境。它是嵌入的软件的差错行为被测试环境的可执行的模型驱动的 hardware-in-loop (HIL ) 的支持在基于模型的差错注射的帮助下在触发的一条系统级的黑盒子的测试途径。基于建议途径测试环境的坚韧性的原型实现被工业案例研究试验性地基于几个嵌入电子设备的软件系统讨论并且说明。我们测试方法和环境的建议并且实现的坚韧性是有效的发现更多的臭虫，并且减少测试的负担的结果表演设计提高严峻的任务的效率，特别为测试复杂嵌入的系统。

Application of the hybrid neural network model for energy consumption prediction of office buildings

A Modeling Language Based on UML for Modeling Simulation Testing System of Avionic Software

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Application of the hybrid neural network model for energy consumption prediction of office buildings

A Modeling Language Based on UML for Modeling Simulation Testing System of Avionic Software