This paper proposes a novel approach for intrusion detection system based on sampling with Least Square Support Vector Machine (LS-SVM). Decision making is performed in two stages. In the first stage, the whole dataset is divided into some predetermined arbitrary subgroups. The proposed algorithm selects representative samples from these subgroups such that the samples reflect the entire dataset. An optimum allocation scheme is developed based on the variability of the observations within the subgroups. In the second stage, least square support vector machine (LS-SVM) is applied to the extracted samples to detect intrusions. We call the proposed algorithm as optimum allocation-based least square support vector machine (OA-LS-SVM) for IDS. To demonstrate the effectiveness of the proposed method, the experiments are carried out on KDD 99 database which is considered a de facto benchmark for evaluating the performance of intrusions detection algorithm. All binary-classes and multiclass are tested and our proposed approach obtains a realistic performance in terms of accuracy and efficiency. Finally a way out is also shown the usability of the proposed algorithm for incremental datasets.

Abstract:The subject of this paper pertains to sliding mode control and its application in nonlinear electrical power systems as seen in wind energy conversion systems. Due to the robustness in dealing with unmodeled system dynamics, sliding mode control has been widely used in electrical power system applications. This paper presents first and high order sliding mode control schemes for permanent magnet synchronous generator-based wind energy conversion systems. The application of these methods for control using dynamic models of the d-axis and q-axis currents, as well as those of the high speed shaft rotational speed show a high level of efficiency in power extraction from a varying wind resource. Computer simulation results have shown the efficacy of the proposed sliding mode control approaches.

An Electronic Health Record (EHR) is designed to store diverse data accurately from a range of health care providers and to capture the status of a patient by a range of health care providers across time. Realising the numerous benefits of the system, EHR adoption is growing globally and many countries invest heavily in electronic health systems. In Australia, the Government invested $467 million to build key components of the Personally Controlled Electronic Health Record (PCEHR) system in July 2012. However, in the last three years, the uptake from individuals and health care providers has not been satisfactory. Unauthorised access of the PCEHR was one of the major barriers. We propose an improved access control model for the PCEHR system to resolve the unauthorised access issue. We discuss the unauthorised access issue with real examples and present a potential solution to overcome the issue to make the PCEHR system a success in Australia.

A new sliding-mode-control-based power conversion scheme is proposed for photovoltaic energy conversion systems. The perturbation and observation (P&O) maximum power-point tracking (MPPT) approach is adopted for optimizing the power generation capabilities from solar panels. Due to the inherent nonlinear dynamics of power converters, we need to adopt a nonlinear control approach to optimize the energy conversion efficiency and tolerate the fluctuations and changes of load and sunlight irradiance. In this manuscript, novel first-and higher-order sliding mode control approaches are proposed, aiming to provide a systematic approach for the robust and optimal control of solar energy conversion, which guarantees Lyapunov stability and consistent performance in the face of external perturbations and disturbances. Moreover, to eliminate the chattering phenomenon inherent in the first-order approach, super-twisting second-order sliding mode control is developed for the buck-boost converter. Furthermore, the output of DC–DC converter supplies a voltage-oriented-control (VOC)-based space-vector pulse-width-modulated inverter to generate three-phase AC power to the grid. To demonstrate the robustness and effectiveness of the proposed scheme, computer simulations and dSPACE hardware-in-the-loop platform have been carried on for examining the proposed sliding-mode-control-based solar energy conversion system.

Retinal vein occlusion (RVO) is one of the most common retinal vascular diseases leading to vision loss if not diagnosed and treated in time. RVO can be classified into two types: CRVO (blockage of the main retinal veins) and BRVO (blockage of one of the smaller branch veins). Automated diagnosis of RVO can improve clinical workflow and optimize treatment strategies. However, to the best of our knowledge, there are few reported methods for automated identification of different RVO types. In this study, we propose a new hypermixed convolutional neural network (CNN) model, namely, the VGG-CAM network, that can classify the two types of RVOs based on retinal fundus images and detect lesion areas using an unsupervised learning method. The image data used in this study is collected and labeled by three senior ophthalmologists in Shanxi Eye Hospital, China. The proposed network is validated to accurately classify RVO diseases and detect lesions. It can potentially assist in further investigating the association between RVO and brain vascular diseases and evaluating the optimal treatments for RVO.