Application Progress of Deep Learning in Imaging Examination of Breast Cancer

Breast cancer is the most common malignant tumor in women and its early detection is decisive. Breast imaging plays an important role in early detection of breast cancer as well as monitoring and evaluation during treatment, but manual detection of medical images is usually time-consuming and labor-intensive. Recently, deep learning algorithms have made significant progress in early breast cancer diagnosis. By combing the relevant literature in recent years, a systematic review of the application of deep learning techniques in breast cancer diagnosis with different imaging modalities is conducted, aiming to provide a reference for in-depth research on deep learning-based breast cancer diagnosis. Firstly, four breast cancer imaging modalities, namely mammography, ultrasonography, magnetic resonance imaging and positron emission tomography, are outlined and briefly compared, and the public datasets corresponding to multiple imaging modalities are listed. Focusing on the different tasks (lesion detection, segmentation and classification) of deep learning architectures based on the above four different imaging modalities, a systematic review of the algorithms is conducted, and the performance of each algorithm, improvement ideas, and their advantages and disadvantages are compared and analyzed. Finally, the problems of the existing techniques are analyzed and the future development direction is prospected with respect to the limitations of the current work

An integration fault detection method using stator voltage for marine current turbines

International audienceThe marine current turbine (MCT) is becoming more and more popular to produce eco-friendly electricity.However, its performance is negatively affected by MCT imbalance fault. In this paper, an integration faultdetection method using stator voltage for MCT is proposed. This method uses an integration way to detect theimbalance fault. The proposed method comprises three steps: First, the data conversion is based on Hilberttransform and the extreme value searching, and then the imbalance fault signature extraction based on thefrequency sequences subtraction (FSS). Last, to reduce the data dimension and to set the fault detection limit, adata vector selection method based on principal components analysis (PCA) (called preliminary-selection-basedPCA (PS-PCA)) is proposed, the adaptive fault detection is realized by calculating Hotelling T2 and SPE (squaredprediction error). Finally, a marine current prototype experimental platform was built to verify the proposedmethods. The experimental results show that the method in this paper has high detection accuracy in the faultdetection of MCT imbalance under the variable flow rate

Assessment of occupant-behavior-based indoor air quality and its impacts on human exposure risk: A case study based on the wildfires in Northern California.

The recent wildfires in California, U.S., have caused not only significant losses to human life and property, but also serious environmental and health issues. Ambient air pollution from combustion during the fires could increase indoor exposure risks to toxic gases and particles, further exacerbating respiratory conditions. This work aims at addressing existing knowledge gaps in understanding how indoor air quality is affected by outdoor air pollutants during wildfires-by taking into account occupant behaviors (e.g., movement, operation of windows and air-conditioning) which strongly influence building performance and occupant comfort. A novel modeling framework was developed to simulate the indoor exposure risks considering the impact of occupant behaviors by integrating building energy and occupant behaviour modeling with computational fluid dynamics simulation. Occupant behaviors were found to exert significant impacts on indoor air flow patterns and pollutant concentrations, based on which, certain behaviors are recommended during wildfires. Further, the actual respiratory injury level under such outdoor conditions was predicted. The modeling framework and the findings enable a deeper understanding of the actual health impacts of wildfires, as well as informing strategies for mitigating occupant health risk during wildfires

Imbalance Fault Classification Based on VMD Denoising and S-LDA for Variable-Speed Marine Current Turbine

International audienc

Research on the Optimization Design of Solar Energy-Gas-Fired Boiler Systems for Decentralized Heating

Solar energy-gas-fired boiler heating systems attract widespread attention due to their eco-friendly technologies and reasonable prices. In order to promote the application of a solar energy-gas-fired boiler system for decentralized heating, this study proposed a holistic method to optimize the combination of equipment specifications and control strategies of the system. A detailed mathematical model of the hybrid energy system was developed and validated by experiments to simulate various operating conditions and evaluate the optimal design results. A case study was conducted in Tianjin, China, and optimal schemes were obtained. The influence of different factors on the system’s annual comprehensive energy efficiency ratio (AEER) and annual cost (AC) were studied by sensitivity analysis; the results showed that the solar collector area was extremely valuable for the optimization of AEER and AC. The results of this study provide a reference for the optimization design of the solar energy-gas-fired boiler system, which is beneficial to the promotion of the utilization of solar energy