Proposed Mitigation Method Research Articles

Array of Active Shielding Coils for Magnetic Field Mitigation in Automotive Wireless Power Transfer Systems

This paper deals with the mitigation of magnetic field levels produced by a wireless power transfer (WPT) system to recharge the battery of an electric vehicle (EV). In this work, an array of active coils surrounding the WPT coils is proposed as a mitigation technique. The theory and new methodological aspects are the focus of the paper. Magnetic field levels in the environment are calculated numerically without and with the presence of an array of active coils in a stationary WPT system for automotive applications. By the proposed mitigation method, the field levels beside the vehicle are significantly reduced and comply with the reference levels (RLs) of the ICNIRP 2010 guidelines for human exposure to electromagnetic fields and the magnetic flux density limits proposed by ISO 14117 for electromagnetic interference (EMI) in cardiac implantable electronic devices (CIEDs).

Towards 360 VR Sickness Mitigation: From Virtual Reality Eye-tracking to Visual Communication.

Most 360 virtual reality (VR) contents have been developed without considering that users could be affected by VR sickness. Accordingly, users' viewing safety has been steadily highlighted as a critical problem in the VR market. In this study, we investigate a novel VR sickness mitigation framework based on human visual characteristics for the rendered VR content. First, we build a large-scale 360 VR content database termed VRSP360 (VR Sickness and Presence 360) dedicated to the analysis of VR sickness and thoroughly conduct eye-tracking experiments to measure human perception. In the experiment, we observe that the users' gaze distribution is highly center-biased when they experience excessive VR sickness. From this observation, we design a foveated filtering framework that limits high-frequency textures in the peripheral view to mitigate VR sickness. Particularly, given the human visual system's (HVS) non-uniform resolution with respect to the fovea, we also adopt the foveation-based filtering method using the trade-off between sickness mitigation and presence conservation, which reduces any loss in perceptual quality despite the filtering. We further demonstrate that our framework can effectively compress visual information by applying foveated compression. In addition, we develop two metrics (visual texture index and perceptual information index) to measure the effective preservation of user-perceived information despite the filtration of peripheral vision textures by our proposed mitigation method. Through rigorous subjective evaluation on both original content and its VR-sickness-mitigated version, we demonstrate that the proposed framework successfully mitigates VR sickness with a reduction rate of ∼19% on the proposed dataset.

A Novel Method for Heat Haze-Induced Error Mitigation in Vision-Based Bridge Displacement Measurement.

Vision-based techniques have become widely applied in structural displacement monitoring. However, heat haze poses a great threat to the precision of vision systems by creating distortions in the images. This paper proposes a vision-based bridge displacement measurement technique with heat haze mitigation capability. The properties of heat haze-induced errors are illustrated. A dual-tree complex wavelet transform (DT-CWT) is used to mitigate the heat haze in images, and the speeded-up robust features (SURF) algorithm is employed to extract the displacement. The proposed method is validated through indoor experiments on a bridge model. The designed vision system achieves high measurement accuracy in a heat haze-free condition. The proposed mitigation method successfully corrects 61.05% of heat haze-induced errors in static experiments and 95.31% in dynamic experiments.

Active damage mitigation of the blade leading edge erosion for a wind turbine during rainfall events

Leading edge erosion (LEE) of wind turbine blades is a major concern for the wind turbine industry. LEE leads to increased blade surface roughness and incurs significant repair and maintenance costs. To address this issue, the present paper proposes an active LEE mitigation method by operating wind turbines in an erosion-safe mode with reduced rotor speed during precipitation. The proposed method involves first numerically modelling the NREL 5-MW reference wind turbine in openFAST. Then, during a regular operation in the presence of rainfall, the erosion-safe mode is designed, implemented and initiated with a modified blade pitch controller in Matlab-Simulink. To evaluate the effectiveness of this mode, the long-term rainfall distribution of the De Kooy site in the Netherlands is examined. The analysis considers a comparison of power output, erosion lifetime, and levelized cost of energy (LCOE) with and without the proposed safe modes. The study shows that the erosion-safe mode can significantly extend the erosion lifetime at the cost of reduced power generation. The controller settings affect the wind turbine performance, and a reduced rotor speed of 11 revolutions per minute is recommended considering the LCOE (308.64€/MWh). While the proposed mitigation method shows promise, it requires further refinement for real-life implementation in wind turbines.

Low-Complexity Wideband Interference Mitigation for UWB ToA Estimation.

Reliable time of arrival (ToA) estimation in dense multipath (DM) environments is a difficult task, especially when strong interference is present. The increasing number of multiple services in a shared spectrum comes with the demand for interference mitigation techniques. Multiple receiver elements, even in low-energy devices, allow for interference mitigation by processing coherent signals, but computational complexity has to be kept at a minimum. We propose a low-complexity, linearly constrained minimum variance (LCMV) interference mitigation approach in combination with a detection-based ToA estimator. The performance of the method within a realistic multipath and interference environment is evaluated based on measurements and simulations. A statistical analysis of the ToA estimation error is provided in terms of the mean absolute error (MAE), and the results are compared to those of a band-stop filter-based interference blocking approach. While the focus is on receivers with only two elements, an extension to multiple elements is discussed as well. Results show that the influence of strong interference can be drastically reduced, even when the interference bandwidth exceeds 60% of the signal bandwidth. Moreover, the algorithm is robust to uncertainties in the angle of arrival (AoA) of the desired signal. Based on these results, the proposed mitigation method is well suited when the interference bandwidth is large and when computational power is a critical resource.

Exposing Reliability Degradation and Mitigation in Approximate DNNs Under Permanent Faults

Approximate computing is known for enhancing deep neural network accelerators' energy efficiency by introducing inexactness with a tolerable accuracy loss. However, small accuracy variations may increase the sensitivity of these accelerators towards undesired subtle disturbances, such as permanent faults. The impact of permanent faults in accurate deep neural network (AccDNN) accelerators has been thoroughly investigated in the literature. Conversely, the impact of permanent faults and their mitigation in approximate DNN (AxDNN) accelerators is vastly under-explored. Towards this, we first present an extensive fault resilience analysis of approximate multi-layer perceptrons (MLPs) and convolutional neural networks (CNNs) using the state-of-the-art Evoapprox8b multipliers in GPU and TPU accelerators. Then, we propose a novel fault mitigation method, i.e., fault-aware retuning of weights (Fal-reTune). Fal-reTune retunes the weights using a weight mapping function in the presence of faults for improved classification accuracy. To evaluate the fault resilience and the effectiveness of our proposed mitigation method, we used the most widely used MNIST, Fashion-MNIST, and CIFAR10 datasets. Our results demonstrate that the permanent faults exacerbate the accuracy loss in AxDNNs compared to the AccDNN accelerators. For instance, a permanent fault in AxDNNs can lead to 56\% accuracy loss, whereas the same faulty bit can lead to only 4\% accuracy loss in AccDNN accelerators. We empirically show that our proposed Fal-reTune mitigation method improves the performance of AxDNNs up to 98%, even with fault rates of up to 50%. Furthermore, we observe that the fault resilience in AxDNNs is orthogonal to their energy efficiency.

Analyzing and Mitigating the Impacts of Integrating Fast-Charging Stations on the Power Quality in Electric Power Distribution Systems

The research presented in this paper focuses on the impacts of fast-charging stations on three power quality phenomena, namely voltage magnitude variations, voltage unbalance, and voltage fluctuation. The Markov Chain Monte Carlo is proposed to estimate the energy requirements of each fast-charging station when it is utilized to charge electric vehicles, considering their sto-chastic parameters such as battery capacities, state-of-charge levels at time of arrivals, and time of arrivals at charging stations. Two charging methods are implemented in this work: charging with an estimated output power and charging with an actual output power. The results reveal that the impact of fast-charging stations on voltage fluctuation by either of these charging methods can lead to light flickers. When the estimated output power is utilized, the light flicker is higher compared to when the actual output power is utilized; the proposed mitigation method is to integrate distribution static compensators, which can effectively eliminate the light flickers, whether the output power of the fast-charging station is estimated or actual. Not only will the system’s power quality be improved by installing distribution static compensators, but also the electricity consumers will not be annoyed by the light flicker, even when the rated power of the fast-charging stations is increased; this can positively lead to a reduction in charging time and an increase in customer satisfaction with electric vehicles, lending them to be more widely adopted.

Defensive Distillation-Based Adversarial Attack Mitigation Method for Channel Estimation Using Deep Learning Models in Next-Generation Wireless Networks

Future wireless networks (5G and beyond), also known as Next Generation or NextG, are the vision of forthcoming cellular systems, connecting billions of devices and people together. In the last decades, cellular networks have dramatically grown with advanced telecommunication technologies for high-speed data transmission, high cell capacity, and low latency. The main goal of those technologies is to support a wide range of new applications, such as virtual reality, metaverse, telehealth, online education, autonomous and flying vehicles, smart cities, smart grids, advanced manufacturing, and many more. The key motivation of NextG networks is to meet the high demand for those applications by improving and optimizing network functions. Artificial Intelligence (AI) has a high potential to achieve these requirements by being integrated into applications throughout all network layers. However, the security concerns on network functions of NextG using AI-based models, i.e., model poising, have not been investigated deeply. It is crucial to protect the next-generation cellular networks against cybersecurity threats, especially adversarial attacks. Therefore, it needs to design efficient mitigation techniques and secure solutions for NextG networks using AI-based methods. This paper proposes a comprehensive vulnerability analysis of deep learning (DL)-based channel estimation models trained with the dataset obtained from MATLAB’s 5G toolbox for adversarial attacks and defensive distillation-based mitigation methods. The adversarial attacks produce faulty results by manipulating trained DL-based models for channel estimation in NextG networks while making models more robust against attacks through mitigation methods. This paper also presents the performance of the proposed defensive distillation mitigation method for each adversarial attack. The results indicate that the proposed mitigation method can defend the DL-based channel estimation models against adversarial attacks in NextG networks.

Setting the net lower: A potential low‐cost mitigation method to reduce cetacean bycatch in drift gillnet fisheries

Abstract Bycatch is the most significant threat to marine megafauna (sea turtles, marine mammals, elasmobranchs, seabirds) worldwide, and the leading cause of the decline of several cetacean species. The bycatch issue in the Indian Ocean is poorly understood, but high bycatch levels in gillnet fisheries have been documented for the past two decades, in both small‐scale and semi‐industrial fisheries. Unfortunately, methods to reduce bycatch are often unavailable, financially non‐viable or socially unacceptable to fishermen. Using a network of trained boat captains in the tuna drift gillnet fishery in the Arabian Sea, targeted catch and bycatch data were collected from 2013 to 2017 off the coast of Pakistan (northern Indian Ocean). Two fishing methods using multifilament gillnets were used: surface deployment and subsurface deployment (i.e. headline of net set below 2 m depth). Predicted catch rates for targeted species did not differ significantly between the two fishing practices, although a drop in tuna (6.2%) and tuna‐like (10.9%) species captures was recorded in subsurface sets. The probability of cetacean bycatch, however, was 78.5% lower in subsurface than in surface sets. Cetacean bycatch in tuna drift gillnet fisheries has the potential to be significantly reduced at a relatively low cost for fishers. However, further research with an appropriate sampling design and a large sample size is required to confirm the efficacy of the proposed mitigation method. The acceptability and adoption of subsurface setting by fishers also needs to be further investigated. Despite some limitations, this preliminary study also highlights the importance of crew‐based observer data as an alternative source of data when observers cannot be deployed on fishing vessels.

An Inertia Phase Locked Loop for Suppressing Sub-Synchronous Resonance of Renewable Energy Generation System Under Weak Grid

Due to impedance interaction, the sub-synchronous resonance (SSR) issue of the renewable energy generation (REG) system connected to weak grid has recently become prominent. Firstly, the mechanism and characteristics of the SSR occurring in the REG system under weak grid are analyzed based on the impedance method. With the decline of grid stiffness, the REG system can easily cause SSR owing to the impedance interaction between the inductive grid impedance and the capacitive REG system impedance in the low frequency range. To suppress this SSR, an inertia phase locked loop (IPLL) is proposed, which can optimize the impedance of the REG system in the low frequency range. According to the impedance based SSR analysis, the REG system with the proposed IPLL can hardly cause SSR under weak grid. Moreover, the mechanism, characteristics and design method of the proposed IPLL is introduced in detail. By using the proposed IPLL, the SSR issue of the REG system under weak grid can be effectively solved. Finally, the theoretical analysis and the proposed mitigation method of SSR are validated with the simulation and experimental study.

Issues and challenges impacting the successful management of agile-hybrid projects: A grounded theory approach

The demand for success in agile projects requires serious attention in addressing its issues and challenges, besides merely implementing suitable agile methods. A comprehensive number of factors have been discussed in the literature, but it does not reflect the crucial challenges confronted by the Information Technology industry. This study utilises a Grounded Theory approach, to capture 38 unique issues and challenges faced by 42 participating industry practitioners, through several rounds of interviews. The issues and challenges, simplified as factors, were grouped into Organisation, People, Process and Technical categories. The proposed mitigation methods were also captured, and mapped into a framework, to be used as a guide to manage agile-hybrid projects. Most factors were related to the process and people categories. Key issues were in the areas of executive support, misalignment of agile methods with the business objectives, deficient understanding of agile principles, lack of teamwork and inadequate skillset of the project stakeholders.

Miscorrection Mitigation for Generalized Integrated Interleaved BCH Codes

The generalized integrated interleaved (GII) codes nest BCH sub-codewords to form codewords of more powerful BCH codes. They can achieve hyper-throughput decoding with excellent error-correcting performance and are among the most suitable codes for next-generation memories. However, the new storage class memories require high code rate and short codeword length. In this case, the sub-codewords have small correction capability. The miscorrections of the sub-codewords lead to much more severe degradation on the GII error-correcting performance compared to the miscorrections of classic BCH codes. This letter investigates the miscorrections in GII decoding. Three low-complexity methods are developed to identify and mitigate the miscorrections by utilizing the nested syndromes, adding one single parity to each sub-codeword, and keeping track of the error locator polynomial degree. Besides, formulas for estimating the miscorrection rates are given. Using the proposed mitigation methods, the actual GII decoding performance becomes very close to that of the case without any miscorrections.

Implementation of Resilient Self-Healing Microgrids with IEC 61850-Based Communications

Due to the high penetration of distributed energy resources (DER) and emerging DER interconnection and interoperability requirements, fast and standardized information exchange is essential for stable, resilient, and reliable operations in microgrids. This paper proposes fast fault detection, isolation, and restoration (F-FDIR) for microgrid application with the IEC 61850 Generic Object Oriented Substation Event (GOOSE) communication considering the communication/system failure. GOOSE provides a mechanism for lightweight low latency peer-to-peer data exchange between devices, which reduces the restoration time compared to conventional client-server communication paradigm. The proposed mitigation method for the communication/system failure can find an available restoration scenario and reduce the overall process time. Hardware-in-the-loop (HIL) testbed is designed and implemented with real time digital simulator, microgrid control system, and protection and control intelligent electric devices (IEDs) for the validation. The experimental results show that the proposed F-FDIR and IEC 61850 models can enhance the reliability and interoperability of the microgrid operation and enable self-healing microgrids.

Assessing and Mitigating Impacts of Electric Vehicle Harmonic Currents on Distribution Systems

Harmonic currents of electric vehicle (EV) chargers could jeopardize the power quality of distribution systems and add to the transformer’s losses, thus degrading its lifetime. This paper assesses and mitigates the impacts of different EV chargers on distribution transformers and the voltage quality of distribution systems. The effect of state-of-charge (SOC) of the EV battery is considered through applying weighted arithmetic mean to accurately assess the impacts of EV harmonic currents on aging and losses of the EV interfacing transformer. The voltage quality of the IEEE 33-bus distribution system, supplying several EV parking lots, is also assessed at different charging levels using a fast-decoupled harmonic power flow. A new optimal harmonic power flow algorithm—that incorporates photovoltaic-based distribution generation units (DGs)—is developed to enhance the voltage quality of distribution systems, and elongate the lifetime of the substation transformer. The effectiveness of the proposed mitigation method is confirmed using the IEEE 33-bus distribution system, hosting several EV charging stations and photovoltaic-based DGs.

Site-specific real-time GPS multipath mitigation based on coordinate time series window matching

Multipath effect is one of the main errors in precise global navigation satellite system (GNSS) positioning. Although there are many GNSS multipath mitigation models available, each model has some limitations. We discuss the problem of mitigating low-frequency multipath in a specific site from the coordinate domain. At present, sidereal filtering is one of the most commonly used methods, in which the repetition period of multipath has to be estimated before multipath mitigation. However, in such a method, the maneuver of the satellite orbit and the long ephemeris update interval will cause inaccurate estimation of the repetition period. We propose a variation of the sidereal filtering method based on window matching. In that method, the window of coordinate time series formed by the current epoch and several previous epochs is used as the query window, and the matched window is searched in the time series of the previous day. The degree of similarity between windows is evaluated by similarity measures. Based on the coordinate point pairs in the matching window, multipath mitigation of the current epoch is performed by an affine transformation. The effects of different similarity measures in window matching are explored, and an early and late matching algorithm is proposed to solve the ambiguous problem of multiple matching sequences in integer-valued similarity metrics. Two sets of short baseline data were collected by different types of receivers in harsh environments to validate the method. The results show the feasibility and effectiveness of the proposed method for site-specific real-time multipath mitigation.

DDoS attack prevention using collaborative approach for cloud computing

Distributed denial of service (DDoS) attack is one of the prominent risk factors for the development of cloud service. It is a very hard task for novice cloud users to identify the real source of DDoS attack because of the attacker spoofs the internet protocol (IP) and media access control (MAC) addresses. To address this problem, we propose a third party auditor (TPA) based packet traceback approach. The method uses Weibull distribution for analyzing the source of the DDoS attack. The approach provides an efficient and fruitful solution because of its strong identification factor. The identification factor depends upon the weaknesses left by the intruder. We analyze the traffic pattern to generate attack alert for different cloud users. The advantage of this approach is that it reduces the overhead on the cloud user. With the help of Weibull distribution, we can easily obtain the availability, reliability and median life of DDoS defense in the cloud environment. To demonstrate our approach, we implement an application based on Hadoop and MapReduce framework. We tested this application based on various parameters. Our method has shown the tremendous improvement over the other state of the art methods. The experimental results are included to show the effectiveness of the proposed method for DDoS attack prevention and mitigation.

DDoS prevention using third party auditor in cloud computing

Distributed denial of service (DDoS) attack is one of the prominent risk factors for the development of cloud service. It is a very hard task for novice cloud users to identify the real source of DDoS attack because the attacker spoofs the Internet Protocol and Media Access Control addresses. To address this problem, we propose a third party auditor-based packet traceback approach. The method uses Weibull distribution for analyzing the source of the DDoS attack. The approach provides an efficient and fruitful solution because of its strong identification factor. The identification factor depends on the weaknesses left by the intruder. We analyze the traffic pattern to generate attack alert for different cloud users. The advantage of this approach is that it reduces the overhead on the cloud user. With the help of Weibull distribution, we can easily obtain the availability, reliability and median life of DDoS defense in the cloud environment. To demonstrate our approach, we implement an application based on Hadoop and MapReduce framework. We tested this application based on various parameters. Our method has shown the tremendous improvement over the other state of the art methods. The experimental results are included to show the effectiveness of the proposed method for DDoS attack prevention and mitigation.

Intelligent Electronic Devices With Collaborative Intrusion Detection Systems

This paper proposes the new concept of intelligent electronic devices (IEDs) with built-in distributed intrusion detection systems. The proposed IEDs have the capabilities to monitor and detect anomalies and abnormal behaviors of the host system of IED and IEC 61850-based messages, e.g., sampled values and generic object oriented substation event. The proposed IEDs collaborate with neighboring IEDs to make an accurate decision and find the origin of cyber attacks. The commodity embedded system has been used to verify the performance of the proposed intrusion detection system with power system protection functions of IEDs. The results show that the proposed mitigation methods work accurately and efficiently with protection functions, e.g., overcurrent and distance protections, on the embedded board.

Torsional Vibration Control in Oilwell Drilling

This paper studies wave propagation control in a one-dimensional (1D) wave guide using a single actuator at the boundary, which relates to an important problem of drilling torsional vibration mitigation in oilwell drilling. Using measurements of both time and spatial derivatives of the drillstring rotational angle at the surface, the torsional wave in the drillstring is decomposed into two independent wave components traveling in opposite directions based on the d’Alambert solution. A top drive speed control method is proposed to mitigate torsional vibration in drilling by reacting to each wave component differently. The method was validated by numerical simulation, which demonstrated its capability for preventing and eliminating stick-slip—a form of torsional vibration that is considered the most common and detrimental type of vibration for drilling. A laboratory experimental platform was also designed and constructed to replicate the torsional vibration time response of drilling system. Preliminary experimental results confirmed the effectiveness of proposed method for torsional vibration mitigation.

Distributed Storage Capacity Reservations in Microgrid for LV Network Operation

Abstract This paper introduces a novel method for Low Voltage (LV) network management with diverse distributed energy storage batteries in microgrid. These batteries are used for demand response, allowing distributed network operators (DNOs) to reserve part of storage capacity for relieving network congestion by releasing stored energy. The major difficulty in carrying out storage capacity reservations among diverse customers is the reserved capacity quantification for each type of customers based on the identified network pressure degree and duration. To overcome this difficulty, the algorithm of storage capacity reservation can be considered from both technical and financial sides. In the proposed approach, the amounts of reserved storage capacities are basically evaluated following two criteria: contribution of customers to network pressure and operational cost of storage battery. Besides, an optimal solution for allocating reservation amount is put forward through an enhanced mixed reservation algorithm. A case study is carried out in a practical network in UK to implement the proposed method for network pressure mitigation and investment deferral.