DDoS Defense Method in Software-Defined Space-Air-Ground Network from Dynamic Bayesian Game Perspective

The centralized management of Software-Defined Network (SDN) brings convenience to Space-Air-Ground Integrated Networks (SAGIN), which also makes it vulnerable to Distributed Denial of Service (DDoS). At present, the popular detection methods are based on machine learning, but most of them are fixed detection strategies with high overhead and real-time control, so the efficiency is not high. This paper designs different defense methods for different DDoS attacks and constructs a multitype DDoS defense model based on a dynamic Bayesian game in the Software-Defined Space-Air-Ground Integrated Networks (SD-SAGIN). The proposed game model’s Nash equilibrium is solved based on the different costs and payoffs of each method. We simulated the attack and defense of DDoS in Ryu controller and Mininet. The results show that, under our model, the attacker and defender’s strategies are in a dynamic balance, and the controller can effectively reduce the defense cost while ensuring detection accuracy. Compared with the existing traditional Support Vector Machine (SVM) defense method, the performance of the proposed method is better, and it provides one of the references for DDoS defense in SD-SAGIN.

A deep learning technique-based data-driven model for accurate and rapid flood prediction

An accurate and rapid urban flood prediction model is essential to support decision-making on flood management, especially under increasing extreme precipitation conditions driven by climate change and urbanization. This study developed a deep learning technique-based data-driven flood prediction model based on an integration of LSTM network and Bayesian optimization. A case study in north China was applied to test the model performance and the results clearly showed that the model can accurately predict flood maps for various hyetograph inputs, meanwhile with substantial improvements in computation time. The model predicted flood maps 19,585 times faster than the physical-based hydrodynamic model and achieved a mean relative error of 9.5 %. For retrieving the spatial patterns of water depths, the degree of similarity of the flood maps was very high. In a best case, the difference between the ground truth and model prediction was only 0.76 % and the spatial distributions of inundated paths and areas were almost identical. The proposed model showed a robust generalizability and high computational efficiency, and can potentially replace and/or complement the conventional hydrodynamic model for urban flood assessment and management, particularly in applications of real time control, optimization and emergency design and plan.

sEMG-Based Motion Recognition of Upper Limb Rehabilitation Using the Improved Yolo-v4 Algorithm

The surface electromyography (sEMG) signal is widely used as a control source of the upper limb exoskeleton rehabilitation robot. However, the traditional way of controlling the exoskeleton robot by the sEMG signal requires one to specially extract and calculate for complex sEMG features. Moreover, due to the huge amount of calculation and individualized difference, the real-time control of the exoskeleton robot cannot be realized. Therefore, this paper proposes a novel method using an improved detection algorithm to recognize limb joint motion and detect joint angle based on sEMG images, aiming to obtain a high-security and fast-processing action recognition strategy. In this paper, MobileNetV2 combined the Ghost module as the feature extraction network to obtain the pretraining model. Then, the target detection network Yolo-V4 was used to estimate the six movement categories of the upper limb joints and to predict the joint movement angles. The experimental results showed that the proposed motion recognition methods were available. Every 100 pictures can be accurately identified in approximately 78 pictures, and the processing speed of every single picture on the PC side was 17.97 ms. For the train data, the [email protected] could reach 82.3%, and [email protected]–0.95 could reach 0.42; for the verification data, the average recognition accuracy could reach 80.7%.

Cloud-Based Dynamic Line Rating

Introduction of the Dynamic Line Rating (DLR) concept has an important role in implementing smart grids in the power utility's transmission network. DLR assumes real-time control of the overhead transmission line, based on the continuous evaluation of the actual thermal and other operating conditions, and further estimation of the maximum transmission line's load and other relevant parameters that determine operational limitations. This chapter presents cloud-based DLR systems in terms of architecture, cloud services, and cyber security issues. DLR systems are explored with regards to cloud computing in industry, applicable cloud services and infrastructures, and communication system's performance. Security and privacy of cloud-based DLR systems have been addressed in terms of public and private services. A secure hybrid cloud-based architecture to support DLR is proposed.

Direct Sliding Mode Control for Dynamic Instabilities in DC-Link Voltage of Standalone Photovoltaic Systems with a Small Capacitor

Large electrolytic capacitors used in grid-connected and stand-alone photovoltaic (PV) applications for power decoupling purposes are unreliable because of their short lifetime. Film capacitors can be used instead of electrolytic capacitors if the energy storage requirement of the power conditioning units (PCUs) is reduced, since they offer better reliability and have a longer lifetime. Film capacitors have a lower capacitance than electrolytic capacitors, causing enormous frequency ripples on the DC-link voltage and affecting the standalone photovoltaic system’s dynamic performance. This research provided novel direct sliding mode controllers (DSMCs) for minimizing DC-link capacitor, regulating various components of the PV/BES system that assists to manage the DC-link voltage with a small capacitor. DSMCs were combined with the perturb and observe (P&O) method for DC boost converters to increase the photovoltaic system’s dynamic performance, and regulate the battery’s bidirectional converter (BDC) to overcome the DC-link voltage instabilities caused via a lower DC-link capacitor. The system is intended to power both AC and DC loads in places without grid connection. The system’s functions are divided into four modes, dependent on energy supply and demand, and the battery’s state of charge. The findings illustrate the controllers’ durability and the system’s outstanding performance. The testing was carried out on the MT real-time control platform NI PXIE-1071 utilizing Hardware-In-The-Loop experiments and MATLAB/Simulink.

Turbidity Informed Real-Time Control of a Dry Extended Detention Basin: A Case Study

Dry extended detention basins are static stormwater infrastructure, unable to adapt to shifts in water quality caused by urbanization in their source watersheds or long-term changes in rainfall patterns. As...