Optical Label-Based Cost-Effective DWDM Optical Network Performance Monitoring Using Low-Bandwidth PD with Novel SRS Mitigation DSP

Large-scale dense wavelength division multiplexing(DWDM)multi-channel performance monitoring is one of the indispensable technologies for the flexible optical networks.The existing Labelbased monitoring scheme requires expensive optical demultiplexing components/equipment to avoid the influence of stimulated Raman scattering(SRS),which is not only costly and bulky,but also could not monitor the wavelength channels simultaneously.In this paper,a low-cost,high-accuracy monitoring scheme based on Optical Label Method is proposed for DWDM networks,where the optical channel power and node identification(ID),as the main monitoring targets that both can indicate or evaluate the channel connection status,could be efficiently monitored.In the scheme,a novel digital signal processing(DSP)method of SRS mitigation is proposed and demonstrated,and an asynchronous code-division multiple access(A-CDMA)based digital label encoding and decoding method is adopted to distinguish the node ID so that channel initial added node can be accurately verified,thereby wavelength connection status can be reliably monitored by combining the channel power and node ID information.The simulation results show that each wavelength channel power and node ID can be accurately monitored only by low bandwidth photoelectric detector(PD)under the condition of 80 wavelengths and 10 spans at C-band.

Optimal Machine Learning Enabled Performance Monitoring for Learning Management Systems

Learning Management System(LMS)is an application software that is used in automation,delivery,administration,tracking,and reporting of courses and programs in educational sector.The LMS which exploits machine learning(ML)has the ability of accessing user data and exploit it for improving the learning experience.The recently developed artificial intelligence(AI)and ML models helps to accomplish effective performance monitoring for LMS.Among the different processes involved in ML based LMS,feature selection and classification processesfind beneficial.In this motivation,this study introduces Glowworm-based Feature Selection with Machine Learning Enabled Performance Monitoring(GSO-MFWELM)technique for LMS.The key objective of the proposed GSO-MFWELM technique is to effectually monitor the performance in LMS.The pro-posed GSO-MFWELM technique involves GSO-based feature selection techni-que to select the optimal features.Besides,Weighted Extreme Learning Machine(WELM)model is applied for classification process whereas the parameters involved in WELM model are optimallyfine-tuned with the help of May-fly Optimization(MFO)algorithm.The design of GSO and MFO techniques result in reduced computation complexity and improved classification performance.The presented GSO-MFWELM technique was validated for its performance against benchmark dataset and the results were inspected under several aspects.The simulation results established the supremacy of GSO-MFWELM technique over recent approaches with the maximum classification accuracy of 0.9589.

Performance Monitoring and Diagnosis of Multivariable Model Predictive Control Using Statistical Analysis

A statistic-based benchmark was proposed for performance assessment and monitoring of model predic- tive control; the benchmark was straightforward and achievable by recording a set of output data only when the control performance was good according to the user’s selection. Principal component model was built and an auto- regressive moving average filter was identified to monitor the performance; an improved T2 statistic was selected as the performance monitor index. When performance changes were detected, diagnosis was done by model validation using recursive analysis and generalized likelihood ratio (GLR) method. This distinguished the fact that the per- formance change was due to plant model mismatch or due to disturbance term. Simulation was done about a heavy oil fractionator system and good results were obtained. The diagnosis result was helpful for the operator to improve the system performance.

Multiple-impairment monitoring for optical duobinary system based on delay-tap asynchronous sampling

A technique using artificial neural networks trained with parameters derived from delay tap plots for optical performance monitoring in 40 Gbit/s duobinary system is demonstrated. Firstly, the optical signal is delay tap sampled to obtain two-dimensional histogram, known as delay tap plots. Secondly, the features of delay tap plots are extracted to train the feed forward, three-layer preceptor structure artificial neural networks. Finally, the outputs of trained neural network are used to monitor optical duobinary signal impairments. Simulation of optical signal noise ratio （ OSNR）, chromatic dispersion （CD）, and differential group delay （DGD） monitoring in 40 Gbit/s optical duo- binary system is presented. The proposed monitoring scheme can accurately identify simultaneous impairments without requiring synchronous sampling or data clock recovery. The proposed technique is simple, cost-effective and suitable for in-service distributed OPM.

Monitoring actual performance of ground source heat pump system using GPRS-based data transmission: A case analysis in Tangshan

The energy efficiency monitoring is an essential precondition for ground source heat pump system's controlling and energy saving operation. Based on the data monitoring applied in the school building, this work is focused on the parameters acquisition and operation analysis of the GSHP system in Tangshan. Results show the average COPs(coefficient of performance) are2.85 and 2.70 in summer and winter, respectively, and heat(cold) unbalance underground existed after whole year operation. The analysis of data also indicates that the direct borehole air-conditioning saved some power consumption obviously in the early stage of summer and energy saving of the GSHP system depended remarkably on its operation and management level. Besides the observation points of ground temperature are laid for a large-scale GSHP system, and the hydraulic balance of the pipes group needs to be concerned specially in safeguarding better reliability.

Hellinger distance based probability distribution approach to performance monitoring of nonlinear control systems

Control performance monitoring has attracted great attention in both academia and industry over the past two decades. However, most research efforts have been devoted to the performance monitoring of linear control systems, without considering the pervasive nonlinearities(e.g. valve stiction) present in most industrial control systems. In this work, a novel probability distribution distance based index is proposed to monitor the performance of non-linear control systems. The proposed method uses Hellinger distance to evaluate change of control system performance. Several simulation examples are given to illustrate the effectiveness of the proposed method.

Application of Kernel GDA to Performance Monitoring and Fault Diagnosis for Rotating Machinery

Faults in rotating machine are difficult to detect and identify,especially when the system is complex and nonlinear.In order to solve this problem,a novel performance monitoring and fault diagnosis method based on kernel generalized discriminant analysis(kernel GDA,KGDA)was proposed.Through KGDA,the data were mapped from the original space to the high-dimensional feature space.Then the statistic distance between normal data and test data was constructed to detect whether a fault was occurring.If a fault had occurred,similar analysis was used to identify the type of faults.The effectiveness of the proposed method was evaluated by simulation results of vibration signal fault dataset in the rotating machinery,which was scalable to different rotating machinery.

Control performance monitoring and advanced control towards energy conservation

High performance control of an interactive process such as iron and steel plant relies on ability to honor safety and operational constraints;reduce the standard deviations of variables that need to be controlled(e.g.product quantity,quality );de-bottlenecking the process;and,maximize profitability or lower cost(e.g.energy savings, improve hot metal content).These objectives may be prioritized in this order,but can vary and are very difficult to achieve optimally through conventional control.A multivariable predictive controller solution,along with its extensive inferential sensor and built-in optimizer,provides online closed loop control and optimization for many interactive metal and mining processes to lower the energy cost,increase throughput,and optimize product quality and yield. Control loop performance is also a key factor to improve iron and steel plant automation and operation result; Honeywell CPM offers vender-independent product which provides monitoring,tuning,modeling of control loop and sustainable loop performance analysis and maintenance solution towards operation stability and energy saving.

Lossy nodes inference based on end-to-end passive monitoring in wireless sensor networks

This paper presents a passive monitoring mechanism, loss）, nodes inference （LoNI）, to identify loss）, nodes in wireless sensor network using end-to-end application traffic. Given topology dynamics and bandwidth constraints, a space-efficient packet marking scheme is first introduced. The scheme uses a Bloom filter as a compression tool so that path information can bc piggybacked by data packets. Based on the path information, LoNI then adopts a fast algorithm to detect lossy nodes. The algorithm formulates the inference problem as a weighted set-cover problem and solves it using a greedy approach with low complexity. Simulations show that LoNI can locate about 80% of lossy nodes when lossy nodes are rare in the network. Furthermore, LoNI performs better for the lossy nodes near the sink or with higher loss rates.

A Web Service Framework for Environmental and Carbon Footprint Monitoring in Construction Supply Chains

With the growing environmental concerns, green supply chain management （GSCM） is gaining significant attention in the construction industry. Tracking and monitoring the environmental effects brought forth by the participating members along a supply chain is important to GSCM. The GreenSCOR model developed by the Supply Chain Council provides a generic framework for measuring the total carbon footprint and environmental footprint in a supply chain. The model is based on the Supply Chain Operations Reference （SCOR） model, which represents a supply chain network in a hierarchically structured manner. This paper describes the GreenSCOR framework and its potential application to the construction industry. This paper also presents a web services approach to incorporate the GreenSCOR model to the implementation of collaborative information systems. Each process element in the SCOR model is represented and delivered as individual web service units, which can be reused and integrated using standard web services technologies. The service units are combined and managed in a prototype web service collaborative framework, called SC Collaborator, which is designed and developed for supporting construction supply chain management. An illustrative example is presented to demonstrate the implementation of the GreenSCOR-based SC Collaborator framework.

Joint modulation format identification and OSNR monitoring based on Stokes vector distribution features for digital coherent optical receivers

For joint modulation format identification(MFI)and optical signal-to-noise ratio(OSNR)monitoring,a simple and intelligent optical communication performance monitoring method is proposed,and the feasibility is demonstrated by digital coherent optical communication experiments.The experiment results show that for all modulation formats,including 28 GBaud polarization division multiplexing(PDM)QPSK/8-QAM/16-QAM/64-QAM,100%MFI accuracies are achieved even at OSNR values lower than the corresponding theoretical 20%forward error correction limit,as well as the high accuracies for OSNR monitoring.Furthermore,the proposed scheme has a reasonable monitoring level when chromatic dispersion and fiber nonlinear effects are varied.

In-depth analysis of traffic congestion using computational fluid dynamics (CFD) modeling method

This paper introduces computational fluid used in aerospace engineering, to deal with surface physical and mathematical foundations of CFD, this traffic problems such as queue/platoon distribution, dynamics （CFD）, a numerical traffic flow related problems. approach widely and successfully After a brief introduction of the paper develops CFD implementation methodology for modeling shockwave propagation, and prediction of system performance. Some theoretical and practical applications are discussed in this paper to illustrate the implementation methodology. It is found that CFD approach can facilitate a superior insight into the formation and propagation of congestion, thereby supporting more effective methods to alleviate congestion. In addition, CFD approach is found capable of assessing freeway system performance using less ITS detectors, and enhancing the coverage and reliability of a traffic detection system.

Reliability Management for Information System

An integrated intelligent management is presented to help organizations manage many heterogeneous resources in their information system. A general architecture of management for information system reliability is proposed, and the architecture from two aspects, process model and hierarchical model, described. Data mining techniques are used in data analysis. A data analysis system applicable to real-time data analysis is developed by improved data mining on the critical processes. The framework of the integrated management for information system reliability based on real-time data mining is illustrated, and the development of integrated and intelligent management of information system discussed.

Unified control and detection framework and its applications: a review, some new results, and future perspectives

Initiated three decades ago,integrated design of controllers and fault detectors has continuously attracted research attention.The recent development of the unified control and detection framework with an observer-based residual generator in its core gives a more general form of the previous works.Its applications to residual centred modelling of uncertain control systems,fault detection in feedback control systems with uncertainties,fault-tolerant control(FTC)as well as control performance degradation monitoring,detection and recovery are introduced.In conclusion,some future perspectives are proposed.

OWDP and Its Secure Implementation

Here we present one design based on OWDP for secure high-speed IP network performance monitor system. Based on the analysis of OWDP protocol and the high-speed IP network performance's real-time monitor infrastructure, the paper illustrates the potential security problems in OWDP and its possible weakness when applied in the monitor infrastructure. One secure improvement design based on Otway-Rees authentication protocol is put forward, which can improve the security of the implementation of OWDP and the monitor architecture. Having kept OWDP's simplicity and efficiency, the design satisfies the real-time demand of high-speed network performance monitor and will effectively safeguard the monitor procedure against intensive attacks.

Assessment of the Efficiency of a Mewis Duct Through Performance Analysis of Operational Data

Τhe efficiency of a Mewis propeller duct by the analysis of ship operational data is examined.The analysis employs data collected with high frequency for a three-year period for two siter vessels,one of them fitted with a Mewis type duct.Our approach to the problem of identifying improvements in the operational performance of the ship equipped with the duct is two-fold.Firstly,we proceed with the calculation of appropriate Key Performance Indicators to monitor vessels performance in time for different operational periods and loading conditions.An extensive pre-processing stage is necessary to prepare a dataset free from datapoints that could impair the analysis,such as outliers,as well as the appropriate preparations for a meaningful KPI calculation.The second approach concerns the development of multiple linear regression problem for the prediction of main engine fuel oil consumption based on operational and weather parameters,such as ship’s speed,mean draft,trim,rudder angle and the wind speed.The aim is to quantify reductions due to the Mewis duct for several scenarios.Key results of the studies reveal a contribution of the Mewis duct mainly in laden condition,for lower speed range and in the long-term period after dry-docking.

Toward Low-Cost Flexible Intelligent OAM in Optical Fiber Communication Networks

Low-cost,flexible and intelligent optical performance monitoring and management is a key enabling technology for network quality guarantee,especially in the era of explosive growth of communication capacity and network scale.However,to the best of our knowledge,it is extremely challenging to implement real-time performance monitoring and operations,administration and maintenance(OAM) in a highly complex dynamic network.In this paper,we propose an innovative optical identification(OID) scheme that can realize both performance monitoring and some advanced OAM sub-functions.The basic concepts,applications,challenges and evolution directions of this OID tool are also discussed.

Performance Monitoring in Transparent Reconfigurable WDM Networks

This paper classifies and surveys different approaches proposed for performance monitoring, in particular the optical signal-to-noise ratio (OSNR) monitoring, in transparent reconfigurable WDM networks. Some considerations for future monitoring schemes are discussed.

In-band OSNR monitoring based on low-bandwidth coherent receiver and tunable laser

An in-band optical signal-to-noise ratio （OSNR） monitoring technique with high resolution and large measurement range is demonstrated based on low- bandwidth coherent receiver and a tunable laser. The measurement range of OSNR is from 10 to 25 dB and the resolution can be controlled about ±1 dB.

Chromatic dispersion monitoring using semiconductor optical amplifier

An all-optical real-time chromatic dispersion （CD） monitoring technique is proposed and demonstrated for 40Gbit/s differential phase-shifts keying （DPSK） signal, utilizing the cross modulation effects of semicon- ductor optical amplifier （SOA）. The optical power of the output spectral components, which is from the probe＇s frequency up to the signal bandwidth, is used for CD monitoring. This technique provides a wide monitoring range with large variation scale. The impacts of the polarization mode dispersion （PMD） and the optical signal-to-noise ratio （OSNR） on the CD monitoring results are theoretically analyzed and then experimentally investigated, showing that they have slight influence on the monitoring results within a certain range. Furthermore, simulated results for quadrature phase shift keying （QPSK） signal at 80 Gbit/s are also demonstrated, indicating that this technique is suitable for advanced modulated format as well.