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Abstract: Air-source heat pump (ASHP) has been widely used in domestic and commercial buildings because of its energy savings, high efficiency and environmental friendliness. However, the heating capacity of an ASHP system is greatly influenced by the frost accumulation on the surface of the outdoor exchanger. To solve this problem, a novel frost-free ASHP system, integrating with Phase Change Materials (PCMs) and dehumidification, has been developed. In this paper, the schematic design of this system is first presented. The effect of the match relationship between the desiccant materials and PCM thermal energy storage on the performance of the novel system is then studied using a dynamic mathematical model. The simulation results showed that the dehumidification efficiency was increased from 31.8 % to 34.7 % with the increase of the solid desiccant mass from 1.9 kg to 3.5 kg when the volume of the PCM was 1000 ml. The system COP was 2.87 when the desiccant was 2.2 kg and the volume of PCM was 1100 ml at a relative humidity of 80 % and ambient temperature of 0 °C. In addition, the water temperature was heated to 55 °C in one cycle which decreased the irreversible loss. Lastly, a correlation of the system COP with the amount of the solid desiccant and the PCM was obtained through a multivariate linear regression. The results obtained can facilitate optimal design and dynamic behavior investigation of this system. [ABSTRACT FROM AUTHOR]

Convolutional neural network (CNN)-based self-supervised visual representation learning (SSL) is a long-standing problem achieving significant successes with traditional handcrafted pretext tasks and contrastive learning. However, existing SSL methods typically suffer from high computational overhead and poor performance due to sluggish convergence speeds and poor detail extraction capabilities. In this work, to address these issues and improve the robustness, we provide a new self-supervised architecture for incorporating a single-branch backbone with hybrid tasks into the representation learning process. Specifically, our method takes advantage of features from both intra- and inter-images by using discrete montage images. Then a single backbone with a novel A daptive D ecouple C onfusion (ADC) module is proposed to improve the feature extraction capabilities and alleviate the confusion regions in montage images. Besides, both concatenated discrete vectors and patch-based global average pooled vectors in latent space are utilized to learn local detailed features and maintain semantic consistency simultaneously. Moreover, our method is optimized by hybrid tasks and enjoys faster convergence speed due to these improvements. Extensive experiments on several datasets demonstrate the effectiveness and robustness of our method. The proposed method has 2.0% improved in linear classification to the conventional single-branch methods. [Display omitted] • A new framework SSH that leverages concatenated discrete and PGAP vectors is proposed. • An A daptive D ecouple C onfusion (ADC) module is proposed to avoid degenerate performance in existing jigsaw-based methods. • We demonstrate the advantages of the above contributions through extensive experiments. [ABSTRACT FROM AUTHOR]

Self-supervised visual representation learning focuses on capturing comprehensive features via exploiting the unlabeled datasets. However, existing contrastive learning based SSL frameworks are subjected to higher computational consumption and unsatisfactory performance. To handle these issues, we present a novel single-branch SSL method that incorporates an adaptive feature selection and activation module and a progressive cascade dimensionality reduction module, called APNet. Specifically, our method first fully exploits the unlabeled datasets and extracts intra- and inter-image information via introducing montage image. In addition, a novel adaptive feature selection and activation module is designed to generate the most comprehensive features. Besides, a progressive cascade dimensionality reduction module is proposed to capture the most representative features from latent vectors through cascade dimensionality increasing–decreasing operations. Extensive experiments have demonstrated the robustness and effectiveness of APNet. Specifically, APNet exceeds MoCo-v3 by 3.1% on the ImageNet-100 dataset, and consumes only half of the calculation. Code is available at https://github.com/AI-TYQ/APNet. • We embed feature selection and dimensionality reduction modules into SSL framework. • We design an attention module that selects and activates representative features. • We introduce a dimensionality reduction module to retain discriminative features. • We demonstrate the advantages of the above contributions through experiments. [Display omitted] [ABSTRACT FROM AUTHOR]