250 research outputs found
Fiber design for high-power low-cost Yb:Al-doped fiber laser operating at 980nm
Get PDFWe investigated an Yb:Al-doped depressed-clad hollow optical fiber (DCHOF) for cladding-pumped 980nm laser operation. With a careful design, the nonzero fundamental-mode cutoff characteristics of a DCHOF allows the competing 1030-1060nm emission to be filtered out, despite being quite close to 980nm. The laser yielded over 3W of output power in a diffraction limited beam (M -parameter degrades to 2.7, as a result of increased cladding-mode lasing, as the cladding thickness is reduced
E-Journal of Chemistry from 2007 to 2012: A Bibliometric Study
No full textBibliometric research is a complex area to comprehend and to undertake in which one might expect information about the intricacies of identifying data for a bibliometric study, discussion about what that data means when it has been extracted, as well as details about the assumptions and limitations researchers face when working in the field, will need to consult an alternative source (Haddow, 2010). Now-a-days, bibliometric studies are conducted for a given field of knowledge on specific literature, research output of a prolific author, research productivity of an organization or of an individual journal for a specific range of time. The present study attempts to unfold the publication characteristics of E-Journal of Chemistry which is published by World Wide Web Publications (P) India from the USA
Design of Real-Time Simulation Testbed for Advanced Metering Infrastructure (Ami) Network
Get PDFConventional power grids are being superseded by smart grids, which have smart meters as
one of the key components. Currently, for the smart metering communication, wireless technologies
have predominantly replaced the traditional Power Line Communication (PLC). Different
vendors manufacture smart meters using different wireless communication technologies. For example,
some vendors use WiMAX, others prefer Low-Power Wireless Personal Area Networks
(Lo-WPAN) for the Media Access Control (MAC) and physical layer of the smart meter network,
also known as Advanced Metering Infrastructure (AMI) network. Different communication techniques
are used in various components of an AMI network. Thus, it is essential to create a testbed
to evaluate the performance of a new wireless technology or a novel protocol to the network. It
is risky to study cyber-security threats in an operational network. Hence, a real-time simulation
testbed is considered as a substitute to capture communication among cyber-physical subsystems.
To design the communication part of our testbed, we explored a Cellular Internet of Things (CIoT)
: Co-operative Ultra NarrowBand (C-UNB) technology for the physical and the MAC layer of
the Neighborhood Area Network (NAN) of the AMI. After successful evaluation of its performance
in a Simpy python simulator, we integrated a module into Network Simulator-3 (NS-3). As NS-3
provides a platform to incorporate real-time traffic to the AMI network, we can inject traffic from
power simulators like Real Time Digital Simulator (RTDS). Our testbed was used to make a comparative
study of different wireless technologies such as IEEE 802.11ah, WiMAX, and Long Term
Evolution (LTE). For the traffic, we used HTTP and Constrained Application Protocol (CoAP),
a widely used protocol in IoT. Additionally, we integrated the NS-3 module of Device Language
Message Specification - Companion Specification for Energy Metering (DLMS-COSEM), that
follows the IEC 62056 standards for electricity metering data exchange. This module which comprises
of application and transport layers works in addition with the physical and MAC layer of the
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C-UNB module.
Since wireless communication is prone to eavesdropping and information leakages, it is crucial
to conduct security studies on these networks. Hence, we performed some cyber-attacks such
as Denial of Service (DoS), Address Resolution Protocol (ARP) spoofing and Man-in-the-Middle
(MiTM) attacks in the testbed, to analyze their impact on normal operation of AMI network. Encryption
techniques can alleviate the issue of data hijacking, but makes the network traffic invisible,
which prevents conventional Intrusion Detection Systems (IDS) from undertaking packet-level inspection.
Thus, we developed a Bayesian-based IDS for ARP spoof detection to prevent rogue
smart meters from modifying genuine data or injecting false data.
The proposed real time simulation testbed is successfully utilized to perform delay and throughput
analysis for the existing wireless technologies alongwith the evaluation of the novel features of
C-UNB module in NS-3. This module can be used to evaluate a broad range of traffic. Using the
testbed we also validated our IDS for ARP spoofing attack. This work can be further utilized by
security researchers to study different cyber attacks in the AMI network and propose new attack
prevention and detection solution. Moreover, it can also allow wireless communication researchers
to improve our C-UNB module for NS-3
Addressing Uncertainty in Cyber-Physical Power Systems - Modeling to Integration in a Cyber-Physical Energy Management System
Get PDFEnergy infrastructures are mission critical cyber-physical systems that are targets of persistent cyber attacks. While introducing new computing technologies and networks in power systems adds new capabilities for monitoring and control, dealing with the vast quantity of diverse devices with unknown trustworthiness and origin that can connect to the network and whose impact on operational reliability is also unknown is a frightening prospect. The threat landscape today is ex-tensive and constantly changing. Hence, to design resilient power systems it is inherent to develop cyber-physical models that can provide a platform to study the impact of cyber threats in a large scale interconnected grid as well as to propose a defense mechanism to operate them resiliently.
This work proposes a synthetic communication network model for synthetic electric grid with a novel contribution of designing optimized firewall model that follows NERC-CIP-005 standards. The proposed cyber model for the electric grid is validated through creation of a cyber physical testbed RESLab which integrates multiple simulators, emulators and hardware devices to implement threat models targeting critical operations. A multi-sensor multi-domain fusion methodology is proposed to integrate sensor data from physical, cyber and security emulators. Given the un-certainty and untrustworthiness of these sensors under the compromised state, the real-time data generated in the testbed is treated with a theory of uncertainty called Dempster Shafer Theory of Evidence, to improve the inferencing of intrusion for better detection. This approach improved the performance in comparison to the conventional supervised and semi-supervised learning techniques. But a major limitation of this approach is that it does not easily support utilization of the existing domain knowledge. Hence, a Bayesian Approach is undertaken for inferencing and learning the structure of a novel cyber attack model, called Bayesian Attack Graph. The outcome of this approach is considered for risk assessment in Cyber Physical Dynamic Situational Awareness, necessary for state-estimation as well as partially observable control problem.
Controlling the grid operations under uncertainty is another big challenge which is currently addressed with various data-driven approaches from the machine learning fraternity. In the work, we have developed environment for making uncertain MDP models, called Partially Observable MDPs, for the power system cases and solve the control problem using Bayesian Reinforcement Learning that follows the principles of Bayesian Inferencing. A challenge for the control problem is selection of an appropriate metric to optimize, as resilience is time, situation, and state dependent. Hence, an adaptive resilience metric quantification mechanism using Inverse Reinforcement Learning is proposed that not only learns a resilience metric but improves the performance of learning an optimal policy for resilient control.
The proposed algorithms for communication network modeling, inferencing under uncertainty, and integration with the testbed are validated by developing software applications and incorporating them with the CYPRES Energy Management System
Inter-Domain Fusion for Enhanced Intrusion Detection in Power Systems: An Evidence Theoretic and Meta-Heuristic Approach
No full textFalse alerts due to misconfigured or compromised intrusion detection systems (IDS) in industrial control system (ICS) networks can lead to severe economic and operational damage. However, research using deep learning to reduce false alerts often requires the physical and cyber sensor data to be trustworthy. Implicit trust is a major problem for artificial intelligence or machine learning (AI/ML) in cyber-physical system (CPS) security, because when these solutions are most urgently needed is also when they are most at risk (e.g., during an attack). To address this, the Inter-Domain Evidence theoretic Approach for Inference (IDEA-I) is proposed that reframes the detection problem as how to make good decisions given uncertainty. Specifically, an evidence theoretic approach leveraging Dempster–Shafer (DS) combination rules and their variants is proposed for reducing false alerts. A multi-hypothesis mass function model is designed that leverages probability scores obtained from supervised-learning classifiers. Using this model, a location-cum-domain-based fusion framework is proposed to evaluate the detector’s performance using disjunctive, conjunctive, and cautious conjunctive rules. The approach is demonstrated in a cyber-physical power system testbed, and the classifiers are trained with datasets from Man-In-The-Middle attack emulation in a large-scale synthetic electric grid. For evaluating the performance, we consider plausibility, belief, pignistic, and general Bayesian theorem-based metrics as decision functions. To improve the performance, a multi-objective-based genetic algorithm is proposed for feature selection considering the decision metrics as the fitness function. Finally, we present a software application to evaluate the DS fusion approaches with different parameters and architectures
Liquid jet disintegration memory effect on downstream spray fluctuations in a coaxial twin-fluid injector
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Structural Learning Techniques for Bayesian Attack Graphs in Cyber Physical Power Systems
No full textImmune system dynamics in response to Pseudomonas aeruginosa biofilms
No full textAbstract Pseudomonas aeruginosa biofilms contribute to chronic infections by resisting immune attacks and antibiotics. This review explores how innate immunity, including neutrophils, macrophages, and dendritic cells, responds to biofilms and how adaptive mechanisms involving T cells, B cells, and immunoglobulins contribute to infection persistence. Additionally, it highlights immune evasion strategies and discusses emerging therapies such as immunotherapy, monoclonal antibodies, and vaccines, offering insights into enhancing biofilm clearance and improving treatment outcomes
Implementation of a C-UNB Module for NS-3 and Validation for DLMS-COSEM Application Layer Protocol
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