Calculation of energy transaction allocation factors

The ability to allocate the MW loading on transmission lines and groups of lines is the basis of NERCs “flow based” transmission allocation system. In such a system, the MW flows must be allocated to each line or group of lines in proportion to the MWs being transmitted by each transaction. This is accomplished through the use of the linear power transfer distribution factors. Unfortunately, no linear allocation models exist for other transmission phenomena such as MW losses, MVA flows, etc. This paper presents a methodology to calculate energy transaction allocation factors (ETA Factors, or η factors), using the well-known process of integration of a first derivative function. The factors give a highly accurate allocation of any nonlinear transmission system quantity to the active transactions placed on a transmission system     

Deep learning based neural network application for automatic ultrasonic computed tomographic bone image segmentation

Deep-learning techniques have led to technological progress in the area of medical imaging segmentation especially in the ultrasound domain. In this paper, the main goal of this study is to optimize a deep-learning-based neural network architecture for automatic segmentation in Ultrasonic Computed Tomography (USCT) bone images in a short time process. The proposed method is based on an end to end neural network architecture. First, the novelty is shown by the improvement of Variable Structure Model of Neuron (VSMN), which is trained for both USCT noise removal and dataset augmentation. Second, a VGG-SegNet neural network architecture is trained and tested on new USCT images not seen before for automatic bone segmentation. Therefore, we offer a free USCT dataset. In addition, the proposed model is implemented on both the CPU and the GPU, hence overcoming previous works by a value of 97.38% and 96% for training and validation and achieving high segmentation accuracy for testing with a small error of 0.006, in a short time process. The suggested method demonstrates its ability to augment USCT data and then to automatically segment USCT bone structures achieving excellent accuracy outperforming the state of the art.

     

Real-time video security system using chaos- improved advanced encryption standard (IAES)

Real-time multimedia applications are increasingly achieving success in the everyday world. Thereby, multimedia information relies on security to protect private life. The Advanced Encryption Standard (AES) has been designed to secure different applications. Yet, some limitations are given, making it inappropriate for secure video storation and transmission. The limitations are the time complexity, the multiple iterations, and the predefined substitution box. Thus, any user can use it to break the encryption. Moreover, the multiple iterations augment the need for CPU usage, and so the overall run time. Hence, it is necessary to modify the AES algorithm to make it more appropriate for securing video frames transmission over insecure channel. In this paper, an Improved AES (IAES) is put forward, which improves both diffusion and confusion in ciphered video. Our work consists in the following two main points: First, we propose to eliminate both shift-row and sub-byte transformations and replace them with a mix-row operation. This task reduces the run time, which presents a significant factor for real-time video transmission. Equally important, we propose to use the henon chaotic map in the key generation procedure, which provides more randomness. The Hash Algorithm SHA-3 is used to generate the initial conditions of the chaotic attractor. The video encryption procedure is verified with success, and the experimental results confirm that the novel algorithm combining chaos and IAES augments the entropy of the ciphered video by 15% and reduces the complexity time for both encryption and decryption compared to the standard one. Security analysis is successfully performed, and the results prove that our suggested technique provides the basics of cryptography with more correctness. The PRNG is tested by NIST 800–22 test suit, which indicates that it is suitable for secure image encryption. It provides a large key space of 2¹²⁸ which resists the brute-force attack. All in all, the findings confirm that the novel security approach eliminates the limitation of the existing AES and provides a trade-off between speed and safety levels to secure video transmission.