Effective melanoma recognition using deep convolutional neural network with covariance discriminant loss.

Melanoma recognition is challenging due to data imbalance and high intra-class variations and large inter-class similarity. Aiming at the issues, we propose a melanoma recognition method using deep convolutional neural network with covariance discriminant loss in dermoscopy images. Deep convolutional neural network is trained under the joint supervision of cross entropy loss and covariance discriminant loss, rectifying the model outputs and the extracted features simultaneously. Specifically, we design an embedding loss, namely covariance discriminant loss, which takes the first and second distance into account simultaneously for providing more constraints. By constraining the distance between hard samples and minority class center, the deep features of melanoma and non-melanoma can be separated effectively. To mine the hard samples, we also design the corresponding algorithm. Further, we analyze the relationship between the proposed loss and other losses. On the International Symposium on Biomedical Imaging (ISBI) 2018 Skin Lesion Analysis dataset, the two schemes in the proposed method can yield a sensitivity of 0.942 and 0.917, respectively. The comprehensive results have demonstrated the efficacy of the designed embedding loss and the proposed methodology

Exemplar-supported representation for effective class-incremental learning

Catastrophic forgetting is a key challenge for class-incremental learning with deep neural networks, where the performance decreases considerably while dealing with long sequences of new classes. To tackle this issue, in this paper, we propose a new exemplar-supported representation for incremental learning (ESRIL) approach that consists of three components. First, we use memory aware synapses (MAS) pre-trained on the ImageNet to retain the ability of robust representation learning and classification for old classes from the perspective of the model. Second, exemplar-based subspace clustering (ESC) is utilized to construct the exemplar set, which can keep the performance from various views of the data. Third, the nearest class multiple centroids (NCMC) is used as the classifier to save the training cost of the fully connected layer of MAS when the criterion is met. Intensive experiments and analyses are presented to show the influence of various backbone structures and the effectiveness of different components in our model. Experiments on several general-purpose and fine-grained image recognition datasets have fully demonstrated the efficacy of the proposed methodology

Weakly supervised deep semantic segmentation using CNN and ELM with semantic candidate regions.

The task of semantic segmentation is to obtain strong pixel-level annotations for each pixel in the image. For fully supervised semantic segmentation, the task is achieved by a segmentation model trained using pixel-level annotations. However, the pixel-level annotation process is very expensive and time-consuming. To reduce the cost, the paper proposes a semantic candidate regions trained extreme learning machine (ELM) method with image-level labels to achieve pixel-level labels mapping. In this work, the paper casts the pixel mapping problem into a candidate region semantic inference problem. Specifically, after segmenting each image into a set of superpixels, superpixels are automatically combined to achieve segmentation of candidate region according to the number of image-level labels. Semantic inference of candidate regions is realized based on the relationship and neighborhood rough set associated with semantic labels. Finally, the paper trains the ELM using the candidate regions of the inferred labels to classify the test candidate regions. The experiment is verified on the MSRC dataset and PASCAL VOC 2012, which are popularly used in semantic segmentation. The experimental results show that the proposed method outperforms several state-of-the-art approaches for deep semantic segmentation

Content-sensitive superpixel generation with boundary adjustment.

Superpixel segmentation has become a crucial tool in many image processing and computer vision applications. In this paper, a novel content-sensitive superpixel generation algorithm with boundary adjustment is proposed. First, the image local entropy was used to measure the amount of information in the image, and the amount of information was evenly distributed to each seed. It placed more seeds to achieve the lower under-segmentation in content-dense regions, and placed the fewer seeds to increase computational efficiency in content-sparse regions. Second, the Prim algorithm was adopted to generate uniform superpixels efficiently. Third, a boundary adjustment strategy with the adaptive distance further optimized the superpixels to improve the performance of the superpixel. Experimental results on the Berkeley Segmentation Database show that our method outperforms competing methods under evaluation metrics

Urban PM2.5 concentration prediction via attention-based CNN–LSTM.

Urban particulate matter forecasting is regarded as an essential issue for early warning and control management of air pollution, especially fine particulate matter (PM2.5). However, existing methods for PM2.5 concentration prediction neglect the effects of featured states at different times in the past on future PM2.5 concentration, and most fail to effectively simulate the temporal and spatial dependencies of PM2.5 concentration at the same time. With this consideration, we propose a deep learning-based method, AC-LSTM, which comprises a one-dimensional convolutional neural network (CNN), long short-term memory (LSTM) network, and attention-based network, for urban PM2.5 concentration prediction. Instead of only using air pollutant concentrations, we also add meteorological data and the PM2.5 concentrations of adjacent air quality monitoring stations as the input to our AC-LSTM. Hence, the spatiotemporal correlation and interdependence of multivariate air quality-related time-series data are learned by the CNN-LSTM network in AC-LSTM. The attention mechanism is applied to capture the importance degrees of the effects of featured states at different times in the past on future PM2.5 concentration. The attention-based layer can automatically weigh the past feature states to improve prediction accuracy. In addition, we predict the PM2.5 concentrations over the next 24 h by using air quality data in Taiyuan city, China, and compare it with six baseline methods. To compare the overall performance of each method, the mean absolute error (MAE), root-mean-square error (RMSE), and coecient of determination (R2) are applied to the experiments in this paper. The experimental results indicate that our method is capable of dealing with PM2.5 concentration prediction with the highest performance

High Throughput Screening for CO2 Capture by Mof Pressure Swing Adsorption Based on Maximum Economic Benefit

The seriousness of the greenhouse effect has forced more and more national and international institutions to actively develop carbon dioxide separation technologies, capture and storage technologies to control CO2 emissions. Among the many CO2 separation technologies, adsorption separation is a common method used in mixed gas separation technology at present. Pressure swing adsorption (PSA) has less regeneration time and less energy consumption, so is used more widely than temperature swing adsorption (TSA) and vacuum swing adsorption (VSA), which are the other two types of adsorption separation technologies. However, the adsorption capacity and adsorption selectivity of the existing adsorbents are poor, resulting in high energy consumption. This study is based on the results of industrial process simulation of flue gas from power plants [Sai et al. 2022], when the economic benefits of CO2 / N2 mixed gas PSA separation process are optimal, the adsorption capacity of MOF material is within a certain range. More than 4000 computation-ready, experimental metal-organic frameworks (CoRE MOFs) were calculated using Grand Canonical Monte Carlo (GCMC) high-throughput simulation, the PSA process of 1bar and a temperature of 298K, obtained 142 MOFs with potential performance