Search Results (3)

Skin cancer represents a significant public health challenge, necessitating early detection and timely treatment for optimal management. Timely and accurate evaluation of skin lesions is crucial, as delays can lead to more severe outcomes. However, identifying skin lesions accurately can be challenging due to differences in color, shape, and the various types of imaging equipment used for diagnosis. While recent studies have demonstrated the potential of ensemble convolutional neural networks (CNNs) for early diagnosis of skin disorders, these models are often too large and inefficient for processing contextual information. Although lightweight networks like MobileNetV3 and EfficientNet have been developed to reduce parameters and enable deep neural networks on mobile devices, their performance is limited by inadequate feature representation depth. To mitigate these limitations, we propose a new hybrid attention dual-stream deep learning model for skin lesion detection. Our model uses one training process to preprocess the images and splits the task into two branches. Each branch extracts different features using multi-stage and multi-branch attention techniques, improving the model’s ability to detect skin lesions accurately. The first branch processes the original image using a convolutional layer integrated with three novel attention modules: Enhanced Separable Depthwise Convolution (SCAttn), stage attention, and branch attention. The second branch utilizes Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance the input image, improving local contrast and revealing finer details. The integration of CLAHE with SCAttn modules leverages enhanced local contrast to capture more nuanced features while maintaining computational efficiency. A classification module receives the concatenated hierarchical characteristics that were taken from both branches. Utilizing the PAD2020 and ISIC 2019 datasets, we assessed the proposed model and obtained an accuracy rate of 98.59% for PAD2020, surpassing the state-of-the-art performance by 2%, and stable performance accuracy for the ISIC 2019 dataset. This illustrates how well the model can integrate several attention mechanisms and feature enhancement methods, providing a reliable and effective means of detecting skin cancer. Full article

In this paper, we present an extended heat exchanger synthesis superstructure (HENS) formulation to consider streams with variable temperatures and flow capacities using mixed-integer linear programming (MILP). To keep the problem tractable and to leverage the potential of state-of-the-art MILP solvers, piecewise-linear models with logarithmic coding are used. Allowing for variable utility parameters within a feasible technical range, instead of a priori defined ones, removes limitations of the HENS. Increasing the utility’s degree of freedom offers advantages when sensible heat from, for example, flue gas, thermal oil, or water is used. Moreover, utilities are no longer limited to single-stage heat transfer without stream splits at the stream ends, generating opportunities for efficiency enhancement. We consider three representative case studies to evaluate the performance of the unchained HENS method. Our results show that representing utilities as streams in the HENS optimization problem leads to lower total annual costs (TAC). Significant cost savings arise due to more efficient utility placement, heat transfer, and smaller heat exchanger areas. The results indicate that this method can lead to cheaper and more resource-efficient HEN and thus positively contribute to the environment. Full article

We provide a new method to represent all potential flowsheet configurations for the superstructure-based simultaneous synthesis of work and heat exchanger networks (WHENS). The new representation is based on only two fundamental elements of abstract building blocks. The first design element is the block interior that is used to represent splitting, mixing, utility cooling, and utility heating of individual streams. The second design element is the shared boundaries between adjacent blocks that permit inter-stream heat and work transfer and integration. A semi-restricted boundary represents expansion/compression of streams connected to either common (integrated) or dedicated (utility) shafts. A completely restricted boundary with a temperature gradient across it represents inter-stream heat integration. The blocks interact with each other via mass and energy flows through the boundaries when assembled in a two-dimensional grid-like superstructure. Through observation and examples from literature, we illustrate that our building block-based WHENS superstructure contains numerous candidate flowsheet configurations for simultaneous heat and work integration. This approach does not require the specification of work and heat integration stages. Intensified designs, such as multi-stream heat exchangers with varying pressures, are also included. We formulate a mixed-integer non-linear (MINLP) optimization model for WHENS with minimum total annual cost and demonstrate the capability of the proposed synthesis approach through a case study on liquefied energy chain. The concept of building blocks is found to be general enough to be used in possible discovery of non-intuitive process flowsheets involving heat and work exchangers. Full article