CA : A Cancer Journal for Clinicians

This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h. The data used is 15-min observations of solar power from 21 PV systems located on rooftops in a small village in Denmark. The suggested method is a two-stage method where first a statistical normalization of the solar power is obtained using a clear sky model. The clear sky model is found using statistical smoothing techniques. Then forecasts of the normalized solar power are calculated using adaptive linear time series models. Both autoregressive (AR) and AR with exogenous input (ARX) models are evaluated, where the latter takes numerical weather predictions (NWPs) as input. The results indicate that for forecasts up to 2 h ahead the most important input is the available observations of solar power, while for longer horizons NWPs are the most important input. A root mean square error improvement of around 35% is achieved by the ARX model compared to a proposed reference model.

/pdf/online-short-term-solar-power-forecasting-1ljgpbshkb.pdf

Online Short-term Solar Power Forecasting

Screening for breast cancer.

Statistical methods in medical research , Statistical methods in medical research , کتابخانه دیجیتال جندی شاپور اهواز

Statistical methods in medical research

Whale Optimization Algorithm (WOA) is an optimization algorithm developed by Mirjalili and Lewis in 2016. An overview of WOA is described in this paper, rooted from the bubble-net hunting strategy, besides an overview of WOA applications that are used to solve optimization problems in various categories. The best solution has been determined to make something as functional and effective as possible through the optimization process by minimizing or maximizing the parameters involved in the problems. Research and engineering attention have been paid to Meta-heuristics for purposes of decision-making given the growing complexity of models and the needs for quick decision making in the engineering. An updated review of research of WOA is provided in this paper for hybridization, improved, and variants. The categories included in the reviews are Engineering, Clustering, Classification, Robot Path, Image Processing, Networks, Task Scheduling, and other engineering applications. According to the reviewed literature, WOA is mostly used in the engineering area to solve optimization problems. Providing an overview and summarizing the review of WOA applications are the aims of this paper.

A comprehensive survey: Whale Optimization Algorithm and its applications

Channel-wise Aggregation with Self-correction Mechanism for Multi-center Multi-Organ Nuclei Segmentation in Whole Slide Imaging.

Real-time implementation and robustness against illumination variation are two essential issues for traffic congestion classification systems, which are still challenging issues. This paper proposes an efficient automated system for traffic congestion classification based on compact image representation and deep residual networks. Specifically, the proposed system comprises three steps: video dynamics extraction, feature extraction, and classification. In the first step, we propose two approaches for modeling the dynamics of each video and produce a compact representation. In the first approach, we aggregate the optical flow in front direction, while in the second approach, we use a temporal pooling method to generate a dynamic image describing the input video. In the second step, we use a deep residual neural network to extract texture features from the compact representation of each video. In the third step, we build a classification model to discriminate between the classes of traffic congestion (low, medium, or high). We use the UCSD and NU1 traffic congestion datasets to assess the performance of the proposed method. The two datasets contain different illumination and shadow variations. The proposed method gives excellent results compared to state-of-the-art methods. It also can classify the input video in a short time (37 fps), and thus, we can use it with real-time applications.

Efficient and Fast Traffic Congestion Classification Based on Video Dynamics and Deep Residual Network

This Letter proposes a promising deep learning-based method for crack segmentation based on gated skip connection. The proposed gated skip connection enables the decoder layers to promote crack-aware feature representations from the encoder layers by applying high weights on the crack-relevant features that come from the encoder layers and lower weights for irrelevant features. Unlike the related methods, the authors do not apply any pre-processing or refinement steps to improve the crack segmentation results. The proposed method beats the state-of-the-art methods with an open benchmark database (IoU of 87.5).

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/el.2019.3919

Promising crack segmentation method based on gated skip connection

Millimeter wave (mmWave), reconfigurable intelligent surface (RIS), and unmanned aerial vehicles (UAVs) are considered vital technologies of future six-generation (6G) communication networks. In this paper, various UAV mounted RIS are distributed to support mmWave coverage over several hotspots where numerous users exist in harsh blockage environment. UAVs should be spread among the hotspots to maximize their average achievable data rates while minimizing their hovering and flying energy consumptions. To efficiently address this non-polynomial time (NP) problem, it will be formulated as a centralized budget constraint multi-player multi-armed bandit (BCMP-MAB) game. In this formulation, UAVs will act as the players, the hotspots as the arms, and the achievable sum rates of the hotspots as the profit of the MAB game. This formulated MAB problem is different from the traditional one due to the added constraints of the limited budget of UAVs batteries as well as collision avoidance among UAVs, i.e., a hotspot should be covered by only one UAV at a time. Numerical analysis of different scenarios confirm the superior performance of the proposed BCMP-MAB algorithm over other benchmark schemes in terms of average sum rate and energy efficiency with comparable computational complexity and convergence rate.

/pdf/distribution-of-multi-mmwave-uav-mounted-ris-using-budget-3k3onupx.pdf

Mohamed Abdel-Nasser

Papers

Channel-wise Aggregation with Self-correction Mechanism for Multi-center Multi-Organ Nuclei Segmentation in Whole Slide Imaging.

Efficient and Fast Traffic Congestion Classification Based on Video Dynamics and Deep Residual Network

Promising crack segmentation method based on gated skip connection

Distribution of Multi MmWave UAV Mounted RIS Using Budget Constraint Multi-Player MAB

Mass Detection in Mammograms Using a Robust Deep Learning Model.