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3-D radiotherapy is an effective treatment modality for breast cancer. In 3-D radiotherapy, delineation of the clinical target volume (CTV) is an essential step in the establishment of treatment plans. However, manual delineation is subjective and time consuming. In this study, we propose an automated segmentation model based on deep neural networks for the breast cancer CTV in planning computed tomography (CT). Our model is composed of three stages that work in a cascade manner, making it applicable to real-world scenarios. The first stage determines which slices contain CTVs, as not all CT slices include breast lesions. The second stage detects the region of the human body in an entire CT slice, eliminating boundary areas, which may have side effects for the segmentation of the CTV. The third stage delineates the CTV. To permit the network to focus on the breast mass in the slice, a novel dynamically strided convolution operation, which shows better performance than standard convolution, is proposed. To train and evaluate the model, a large dataset containing 455 cases and 50 425 CT slices is constructed. The proposed model achieves an average dice similarity coefficient (DSC) of 0.802 and 0.801 for right-0 and left-sided breast, respectively. Our method shows superior performance to that of previous state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

A rational leader selection strategy can enhance a swarm to manage the convergence and diversity during the entire search process. In this article, a novel adaptive multiobjective particle swarm optimization (MOPSO) is proposed on the basis of an evolutionary state estimation mechanism, which is used to detect the evolutionary environment whether in exploitation or exploration state. During the search process, different types of leaders, such as a convergence global best solution (c-gBest) and several diversity global best solutions (d-gBests), are to be selected from the external archive for particles under different evolutionary environments. The c-gBest is selected for improving the convergence when the swarm is in an exploitation state, while the d-gBests are chosen for enhancing the diversity in an exploration state. Furthermore, a modified archive maintenance strategy based on some predefined reference points is adopted to maximize the diversity of the Pareto solutions in the external archive. The experimental results demonstrate that the proposed algorithm performs significantly better than the several state-of-the-art multiobjective PSO algorithms and multiobjective evolutionary algorithms on 31 benchmark functions in terms of convergence and diversity of those obtained approximate Pareto fronts. [ABSTRACT FROM AUTHOR]

Residential customers account for an indispensable part in the demand response (DR) program for their capability to provide flexibility when the system required. However, their available DR capacity has not been fully comprehended by the aggregator, who needs the information to bid accurately on behalf of the residential customers in the market transaction. To this end, this article devised an optimal bidding strategy for the aggregator considering the bottom-up responsiveness of residential customers. First, we attempt to establish the customers’ responsiveness function in relation to different incentives, during which a home energy management system is introduced to implement load adjustment for electrical appliances. Second, the functional relation is applied to the aggregator's decision-making process to formulate the optimal bidding strategy in the day-ahead market and the optimal scheduling scheme for the energy storage system with the aim to maximize its own revenue. Finally, the validity of the proposed method is verified using the dataset from the Pecan Street experiment in Austin. The obtained outcome demonstrates the practical rationality of the proposed method. [ABSTRACT FROM AUTHOR]

Velocity of the acoustic signal generated by partial discharge (PD) in the transformer is considerably affected by temperature. Because of thermosiphon effect, the internal temperature of the transformer gradually decreases from the top to the bottom, which leads the acoustic signal to propagate faster in the upper part of the transformer. Besides, due to the complex internal structure of the transformer, it is difficult for the acoustic signal to propagate from the PD source to the sensor along a straight line. In order to further enhance the accuracy of PD localization results, this paper proposes a correction-iteration method with considering the influence of the insulation oil temperature rise and the internal structure of the transformer. Based on the coordinate of the PD source calculated in the last iteration, more accurate results of the next iteration can be estimated. Effectiveness of the proposed method is verified by simulations and experiments, and the obtained results indicate that the localization error is positively correlated with temperature rise. Compared with other present localization methods, such as the Newton's method, the genetic algorithm and the imperial competitive algorithm, the PD localization results estimated by the proposed correction-iteration method are more accurate. [ABSTRACT FROM AUTHOR]

The future renewable-based power system will have an increased need for balancing power. Prosumers, having both generation and consumption capabilities, are expected to provide balancing power to the grid, if their flexibility can be appropriately managed. Meanwhile, undesirable line congestions and voltage violations may arise in the distribution network, when flexible resources respond to external control or price signals on a large scale. Hence, the development of an effective framework to coordinate flexibility at the distribution system level is of utmost importance. Such a framework should allow for an optimal provision of prosumer balancing power services within the boundaries of local network security constraints. In this study, a balancing market participation framework is proposed, adopting the concept of network-constrained transactive energy, to facilitate the interactions between the transmission system operator and aggregators who manage prosumer energy profiles. This framework retains user privacy and complies with the current market setup, where flexible energy is traded on the spot and balancing markets; however, it is ensured that the resulting energy profiles do not cause problems in the distribution network. [ABSTRACT FROM AUTHOR]

Retinopathy of Prematurity (ROP) is a retinal vasproliferative disorder disease principally observed in infants born prematurely with low birth weight. ROP is an important cause of childhood blindness. Although automatic or semi-automatic diagnosis of ROP has been conducted, most previous studies have focused on “plus” disease, which is indicated by abnormalities of retinal vasculature. Few studies have reported methods for identifying the “stage” of the ROP disease. Deep neural networks have achieved impressive results in many computer vision and medical image analysis problems, raising expectations that it might be a promising tool in the automatic diagnosis of ROP. In this paper, convolutional neural networks with a novel architecture are proposed to recognize the existence and severity of ROP disease per-examination. The severity of ROP is divided into mild and severe cases according to the disease progression. The proposed architecture consists of two sub-networks connected by a feature aggregate operator. The first sub-network is designed to extract high-level features from images of the fundus. These features from different images in an examination are fused by the aggregate operator, then used as the input for the second sub-network to predict its class. A large data set imaged by RetCam 3 is used to train and evaluate the model. The high classification accuracy in the experiment demonstrates the effectiveness of the proposed architecture for recognizing the ROP disease. [ABSTRACT FROM AUTHOR]

Undersampling is a widely adopted method to deal with imbalance pattern classification problems. Current methods mainly depend on either random resampling on the majority class or resampling at the decision boundary. Random-based undersampling fails to take into consideration informative samples in the data while resampling at the decision boundary is sensitive to class overlapping. Both techniques ignore the distribution information of the training dataset. In this paper, we propose a diversified sensitivity-based undersampling method. Samples of the majority class are clustered to capture the distribution information and enhance the diversity of the resampling. A stochastic sensitivity measure is applied to select samples from both clusters of the majority class and the minority class. By iteratively clustering and sampling, a balanced set of samples yielding high classifier sensitivity is selected. The proposed method yields a good generalization capability for 14 UCI datasets. [ABSTRACT FROM PUBLISHER]