No abstract

No abstract

No abstract

The potential impacts of data integrity attacks on multi-settlement electricity markets have been recently investigated and have sent a strong message to power grids independent system operators (ISOs) that adversaries could launch profitable cyber attacks by casting an incorrect image of transmission lines congestion pattern. However, these cautionary messages may be underestimated due to the adversaries unrealistic requirements (e.g. having access to real-time measurements) to launch a successful stealthy and profitable attack. This study examines the potential of the aforementioned risk by demonstrating how a malicious power market participant could disturb the electricity market operation, using a pre-designed false data injection attack along with bogus electricity trades in both day-ahead and real-time markets. The proposed attack design is robust against market uncertainties and the adversary can guarantee the success of the attack in advance. Hence, the existence of such cyber attacks against electricity markets can make the adversaries more aggressive. The numerical results on the IEEE 14-bus test system confirm the vulnerability of multi-settlement electricity markets to such financial cyber attacks. The results obtained from investigating such an attack design can be employed by ISOs in order to provide appropriate countermeasures.

The use of deep neural networks (DNNs) in plant phenotyping has recently received considerable attention. By using DNNs, valuable insights into plant traits can be readily achieved. While these networks have made considerable advances in plant phenotyping, the results are processed too slowly to allow for real-time decision-making. Therefore, being able to perform plant phenotyping computations in real-time has become a critical part of precision agriculture and agricultural informatics. In this work, we utilize state-of-the-art object detection networks to accurately detect, count, and localize plant leaves in real-time. Our work includes the creation of an annotated dataset of Arabidopsis plants captured using Cannon Rebel XS camera. These images and annotations have been complied and made publicly available. This dataset is then fed into a Tiny-YOLOv3 network for training. The Tiny-YOLOv3 network is then able to converge and accurately perform real-time localization and counting of the leaves. We also create a simple robotics platform based on an Android phone and iRobot create2 to demonstrate the real-time capabilities of the network in the greenhouse. Additionally, a performance comparison is conducted between Tiny-YOLOv3 and Faster R-CNN. Unlike Tiny-YOLOv3, which is a single network that does localization and identification in a single pass, the Faster R-CNN network requires two steps to do localization and identification. While with Tiny-YOLOv3, inference time, F1 Score, and false positive rate (FPR) are improved compared to Faster R-CNN, other measures such as difference in count (DiC) and AP are worsened. Specifically, for our implementation of Tiny-YOLOv3, the inference time is under 0.01 s, the F1 Score is over 0.94, and the FPR is around 24%. Last, transfer learning using Tiny-YOLOv3 to detect larger leaves on a model trained only on smaller leaves is implemented. The main contributions of the paper are in creating dataset (shared with the research community), as well as the trained Tiny-YOLOv3 network for leaf localization and counting.