Features of the Organization and Interaction of Functional Network Components in Distributed Control Systems

Solving the problem of FNC interaction within a distributed control system requires an analysis of the nature of technological processes, analysis of the dependence of FNC algorithms on data or control, etc. This analysis makes it possible to identify the features of the FNC organization from the point of view of control and communication requirements for solving the task. The article analyzes the development trends of industrial network ecosystems, software and hardware tools for the implementation of network associations participants. In particular, the considered capabilities of the software platform based on the IEC 61499 standard for the development of FNC control programs meet the requirements for network components. Possible non-determinism of interaction (as noted in [9]) is proposed to be compensated by using FPGAs as part of FNC controllers when creating additional fast communication links for the implementation of deterministic network applications. The proposed additional FPGA-based computing platform together with the ARM controller forms a hybrid computer as part of the FNC, which allows you to control fast technological processes. A multi-level configuration of an industrial network with the unification of individual FNCs into clusters is proposed and substantiated. This configuration allows you to flexibly combine the boot and communication tasks of FNC, if necessary, performing the exchange of information between clusters. The developed model in the AnyLogic 8.6 software package showed the effectiveness of the proposed configuration. The results of the experiments led to the conclusion that an increase in the intensity of messages has a greater effect on work efficiency than an increase in the number of FNCs. The development of this topic is expected in the following areas: improving the characteristics of FNC in terms of computing and communication capabilities. Development of an algorithm for optimizing the message transmission path between clusters, with an analysis of the waiting time of busy DS-links.

HyCAD-OCT: A Hybrid Computer-Aided Diagnosis of Retinopathy by Optical Coherence Tomography Integrating Machine Learning and Feature Maps Localization

Optical Coherence Tomography (OCT) imaging has major advantages in effectively identifying the presence of various ocular pathologies and detecting a wide range of macular diseases. OCT examinations can aid in the detection of many retina disorders in early stages that could not be detected in traditional retina images. In this paper, a new hybrid computer-aided OCT diagnostic system (HyCAD) is proposed for classification of Diabetic Macular Edema (DME), Choroidal Neovascularization (CNV) and drusen disorders, while separating them from Normal OCT images. The proposed HyCAD hybrid learning system integrates the segmentation of Region of Interest (RoI), based on central serious chorioretinopathy (CSC) in Spectral Domain Optical Coherence Tomography (SD-OCT) images, with deep learning architectures for effective diagnosis of retinal disorders. The proposed system assimilates a range of techniques including RoI localization and feature extraction, followed by classification and diagnosis. An efficient feature fusion phase has been introduced for combining the OCT image features, extracted by Deep Convolutional Neural Network (CNN), with the features extracted from the RoI segmentation phase. This fused feature set is used to predict multiclass OCT retina disorders. The proposed segmentation phase of retinal RoI regions adds substantial contribution as it draws attention to the most significant areas that are candidate for diagnosis. A new modified deep learning architecture (Norm-VGG16) is introduced integrating a kernel regularizer. Norm-VGG16 is trained from scratch on a large benchmark dataset and used in RoI localization and segmentation. Various experiments have been carried out to illustrate the performance of the proposed system. Large Dataset of Labeled Optical Coherence Tomography (OCT) v3 benchmark is used to validate the efficiency of the model compared with others in literature. The experimental results show that the proposed model achieves relatively high-performance in terms of accuracy, sensitivity and specificity. An average accuracy, sensitivity and specificity of 98.8%, 99.4% and 98.2% is achieved, respectively. The remarkable performance achieved reflects that the fusion phase can effectively improve the identification ratio of the urgent patients’ diagnostic images and clinical data. In addition, an outstanding performance is achieved compared to others in literature.