369 research outputs found
Comparison Of Fibers Properties Of Azadirachta Indica And Acacia Arabica Plant For Lightweight Composite Application
No full textIn this paper, the bark of Azadirachta Indica and Acacia Arabica plant is analysed for investigating and comparing. The aim is to investigate the potential use of these fibres as reinforcements in polymeric materials. The physicochemical properties of Azadirachta Indica fibres (AIFs) and Acacia Arabica fibres (AAFs) are examined by chemical constitutions, X-ray diffraction, thermogravimetry analysis, Fourier transform infrared spectroscopy analysis, and surface morphological analysis. AIF has a cellulose content of 68.42 wt.%, density of 740 kgm–3, crystallinity index of 65.04%; AAF has a cellulose content up to 68.1 wt.%, density equal to 1028 kgm–3 and crystallinity index of 51.72% respectively. The maximum peak temperature obtained in differential thermogravimetry (DTG) curve is 321.2°C for AIF, and 345.1°C for AAF. The physicochemical results confirm the structural application of AIF and AAF for several industrial fields
Design of Attack-Resilient System for Wide-Area Monitoring, Protection, and Control in Smart Grid
No full textCan you imagine that a simple cyber-attack can turn your lights off? Do you know more than a dozen U.S. utilities have been constantly targeted through cyber-attacks within the past year? The solution - an attack-resilient grid infrastructure that can quickly detect stealthy cyber-attacks and provide an intelligent incident response to restore the normal grid condition.This is a manuscript of an article published as Singh, Vivek Kumar, and Manimaran Govindarasu. "Design of Attack-Resilient System for Wide-Area Monitoring, Protection, and Control in Smart Grid." IEEE Smart Grid (2020). Posted with permission.</p
New Lignocellulosic Aristida adscensionis Fibers as Novel Reinforcement for Composite Materials: Extraction, Characterization and Weibull Distribution Analysis
No full textIn this research, the Aristida adscensionis fibers (AAFs) were taken out from the plants and its fundamental properties anlayzed for the first time. The AAFs were characterized and compared with other natural fibers by the use of physico-chemical analysis and various characterization techniques such as FT-IR, XRD, NMR, TGA, SEM and AFM. Chemical analysis showed that A. adscensionis fibers have a high cellulose content of 70.78% whereas the contents of lignin and wax are equal to 8.91% and 2.26%, respectively. The FT-IR, XRD and NMR analysis confirmed that AAFs are rich in cellulose content with CI and CS equal to 58.9% and 11.5 nm, respectively. Pycnometer analysis allowed to estimate a density of 790 kg/m3. The TGA revealed that these fibers are thermally stable up to 250 °C while SEM and AFM analysis evidenced that the fiber surface was rough. The fiber diameter and tensile properties was analysed by Weibull distribution. The characterization results and Weibull distribution analysis for the A. adscensionis fibers are an agreement with other natural fibers reported in literature. So this new lignocellulosic material is suitable as reinforcing phase in composites for potential engineering semi-structural applications like roofing sheets, bricks, door panels, furniture panels, interior paneling, storage tanks, pipelines, etc
An in vitro study to evaluate the difference in shade between commercially available shade guides and glazed porcelain
No full textIntroduction: Smile is one of the most important interactive communication skills of a person. A smile is the key factor for an aesthetic appearance. Hence aesthetics is one of the motivating factor for the patients to seek dental care. Correction of unaesthetic appearance gives a positive effect to the self esteem of the patient. Aim: The aim of this study was to compare the difference in the shade between the commercially available shade guides namely Vita Classical And Ivoclar Chromascop and the fired porcelain samples fabricated using Vita Zahnfabrik VMK 95 and Ivoclar Classic Materials respectively. Objectives: The objective of this study was to obtain a matching brand of material that has a particular shade tab among the brands used. Conclusion: To conclude, Ivoclar material matched the chromascop shade guide better than the vita material matched the vita classic shade guide
Event prediction algorithm using neural networks for the power management system of electric vehicles
No full textThe power management system for electronic vehicles selectively activates Electronic Control Units (ECUs) in the electronic control system according to time-series vehicle data and predefined operation states. However, at an operation state transition, the energy overheads used for the selective ECU activation could be higher than the energy saved by deactivating ECUs. To prevent these energy-inefficient state transitions, we apply two main ideas to our proposed algorithm: (A) unacceptable state transitions and (B) adaptive training speed. For the unacceptable transitions, our energy model evaluates the breakeven time where energy saving equals to energy overheads. Based on the breakeven time, our algorithm classifies training dataset as unacceptable and acceptable event sets. Especially when the algorithm trains neural networks for the two event sets, the adaptive training speed expedites its training speed based on a history of training errors. Consequently, without violating in-vehicle time constraints, the algorithm could provide real-time predictions and save energy overheads by avoiding unacceptable transitions. In the simulation results on real driving datasets, our algorithm improves the energy dissipation of the electronic control system by 5% to 7%.This is a manuscript of an article published as Koo, Ki-sung, Manimaran Govindarasu, and Jin Tian. "Event prediction algorithm using neural networks for the power management system of electric vehicles." Applied Soft Computing (2019): 105709. DOI: 10.1016/j.asoc.2019.105709. Posted with permission.</p
Anomaly Detection and Mitigation for Wide-Area Damping Control using Machine Learning
No full textIn an interconnected multi-area power system, wide-area measurement based damping controllers are used to damp out inter-area oscillations, which jeopardize grid stability and constrain the power flows below to their transmission capacity. The effect of wide-area damping control (WADC) significantly depends on both power and cyber systems. At the cyber system layer, an adversary can inflict the WADC process by compromising either measurement signals, control signals or both. Stealthy and coordinated cyber-attacks may bypass the conventional cybersecurity measures to disrupt the seamless operation of WADC. This paper proposes an anomaly detection (AD) algorithm using supervised Machine Learning and a model-based logic for mitigation. The proposed AD algorithm considers measurement signals (input of WADC) and control signals (output of WADC) as input to evaluate the type of activity such as normal, perturbation (small or large signal faults), attack and perturbation-and-attack. Upon anomaly detection, the mitigation module tunes the WADC signal and sets the control status mode as either wide-area mode or local mode. The proposed anomaly detection and mitigation (ADM) module works inline with the WADC at the control center for attack detection on both measurement and control signals and eliminates the need for ADMs at the geographically distributed actuators. We consider coordinated and primitive data-integrity attack vectors such as pulse, ramp, relay-trip and replay attacks. The performance of the proposed ADM algorithms was evaluated under these attack vector scenarios on a testbed environment for 2-area 4-machine power system. The ADM module shows effective performance with 96.5% accuracy to detect anomalies.This is a manuscript of an article published as Ravikumar, Gelli, and Manimaran Govindarasu. "Anomaly Detection and Mitigation for Wide-Area Damping Control using Machine Learning." IEEE Transactions on Smart Grid (2020). DOI: 10.1109/TSG.2020.2995313. Posted with permission.</p
Hardware-in-the-Loop CPS Security Architecture for DER Monitoring and Control Applications
No full textDeeper penetration of interoperable cyber-physical distributed energy resources (DER) and their utility-wide remote monitoring and control drastically increases cybersecurity attack surface. Utilities require to adopt the DER interconnection and communication standards to a range of autonomous, advanced and curve-based grid-support functions to securely monitor and control DER devices for ensuring power quality, voltage, and system frequency. In this paper, we present DER monitoring and control (DERMC) cyber-physical system (CPS) architecture including standard communication protocols such as IEEE 2030.5 [1] and discuss various stealthy cyber attack vectors that affect communications and operations of DER. We propose a hardware-in-the-loop (HIL) CPS security architecture and testbed design with industry-grade software and hardware systems and a real-time digital simulator for high-fidelity grid impact characteristic analysis against cyber attack vectors. We use the testbed to demonstrate impact characteristics for modified IEEE 13 bus system including 11 solar photovoltaic units. The experiments demonstrated significant results by 100% real-time performance and zero overruns.This is a manuscript of a proceeding published as Ravikumar, Gelli, Burhan Hyder, and Manimaran Govindarasu. "Hardware-in-the-Loop CPS Security Architecture for DER Monitoring and Control Applications." In 2020 IEEE Texas Power and Energy Conference (TPEC). (2020). DOI: 10.1109/TPEC48276.2020.9042578. Posted with permission.</p
A Cyber-Physical Anomaly Detection for Wide-Area Protection using Machine Learning
No full textWide-area protection scheme (WAPS) provides system-wide protection by detecting and mitigating small and large-scale disturbances that are difficult to resolve using local protection schemes. As this protection scheme is evolving from a substation-based distributed remedial action scheme (DRAS) to the control center-based centralized RAS (CRAS), it presents severe challenges to their cybersecurity because of its heavy reliance on an insecure grid communication, and its compromise would lead to system failure. This paper presents an architecture and methodology for developing a cyber-physical anomaly detection system (CPADS) that utilizes synchrophasor measurements and properties of network packets to detect data integrity and communication failure attacks on measurement and control signals in CRAS. The proposed machine leaning-based methodology applies a rules-based approach to select relevant input features, utilizes variational mode decomposition (VMD) and decision tree (DT) algorithms to develop multiple classification models, and performs final event identification using a rules-based decision logic. We have evaluated the proposed methodology of CPADS using the IEEE 39 bus system for several performance measures (accuracy, recall, precision, and F-measure) in a cyber-physical testbed environment. Our experimental results reveal that the proposed algorithm (VMD-DT) of CPADS outperforms the existing machine learning classifiers during noisy and noise-free measurements while incurring an acceptable processing overhead.This is a manuscript of an article published as Singh, Vivek Kumar, and Manimaran Govindarasu. "A Cyber-Physical Anomaly Detection for Wide-Area Protection using Machine Learning." IEEE Transactions on Smart Grid (2021). DOI: 10.1109/TSG.2021.3066316. Posted with permission.</p
An Efficient Backup-Overloading for Fault-Tolerant Scheduling of Real-Time Tasks
No full textMany time-critical applications require dynamic scheduling with predictable performance. Tasks corresponding to these applications have deadlines to be met despite the presence of faults. In this paper, we propose a technique called dynamic grouping, to be used with backup overloading in a primary-backup based fault-tolerant dynamic scheduling algorithm in multiprocessor real-time systems. In dynamic grouping, the processors are dynamically grouped into logical groups in order to achieve efficient overloading of backups, there by improving the schedulability. We compare the performance of dynamic grouping with that of static grouping and no-grouping schemes through extensive simulation studies and show the effectiveness of dynamic grouping.This is a post-peer-review, pre-copyedit version of a proceeding published as Al-Omari R., Manimaran G., Somani A.K. (2000) An Efficient Backup-Overloading for Fault-Tolerant Scheduling of Real-Time Tasks. In: Rolim J. (eds) Parallel and Distributed Processing. IPDPS 2000. Lecture Notes in Computer Science, vol 1800. Springer, Berlin, Heidelberg. The final authenticated version is available online at DOI: 10.1007/3-540-45591-4_177. Posted with permission.</p
International Journal of Procurement Management: Volume 4, No.2, 2011.
No full textPagesTitle and authors121-138Successfully managing manufacturing supplier integration and procurement issues: case studies in recessionary environmentsAlan D. SmithDOI: 10.1504/IJPM.2011.038895139-155Integrative purchasing and inventory control at sawnwood retailer-case studyPhilip Hedenstierna, Per Hilletofth, Olli-Pekka HilmolaDOI: 10.1504/IJPM.2011.038896156-180Key drivers of buyer-supplier relationships in global sourcing strategiesJulio Sanchez Loppacher, Raffaella Cagliano, Gianluca SpinaDOI: 10.1504/IJPM.2011.038897181-202Supply chain success: key initiatives differentiating high- and low-performing firmsGregory M. Magnan, Amydee M. Fawcett, Stanley E. FawcettDOI: 10.1504/IJPM.2011.038898203-222A simple heuristics for optimisation of unbalanced multistage supply chain logistics associated with fixed chargesP. Manimaran, V. Selladurai, Rajesh RanganathanDOI: 10.1504/IJPM.2011.038899223-239Outsourcing in electricity distribution industryJyri P.P. VilkoDOI: 10.1504/IJPM.2011.038900237 hlm.; 24 cm
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