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Vol. 6 No. 1 - (March 2021)

DOI

Paper Title

10.21058/gjecs.2021.61001

Performance Prediction of SiOC on Insulator based PICs

Author Name

Volume No., Issue No., Year, & Page No.

Ameet Kumar, Abi Waqas, Faisal Memon, Umair Ahmed Korai

Vol. 6, No. 1, March 2021, pp. 1-10

Abstract:

In this emerging world of Photonics Silicon oxycarbide (SiOC) is introduced as a platform that has a wide range of tunable refractive indexes that possess very low absorption coefficients. Its physical properties likewise (Optical) and chemical properties can be altered over a large scale in different applications through its composition. In this manuscript, the results obtained using waveguides and directional couplers by multiple simulations that relied on the SiOC technology. In this paper, most simplified design of the coupling coefficient in a certain defined range of width, gap as well as coupling length is proposed. The directional coupler and the waveguide building block’s mathematical models are parameterized. In our defined model, the passive devices will be exploited in available circuit simulators used commercially for the stochastic and circuit simulations scheme for SiOC based photonic circuits.

Keywords:

Photonics, SiOC, Circuit Simulation, Building Blocks, Stochastic Analysis

Full Text:

References:

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  18. D. Melati, A. Waqas, and A. Melloni, ‘Stocastic photonics: Tools and approaches for the analysis and optimization of integrated circuits’, in 2017 Opto-Electronics and Communications Conference (OECC) and Photonics Global Conference (PGC), 2017, pp. 1–3.
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  22. A. Waqas, D. Melati, P. Manfredi, and A. Melloni, ‘Stochastic process design kits for photonic circuits based on polynomial chaos augmented macro-modelling’, Opt. Express, vol. 26, no. 5, p. 5894, Mar. 2018.

DOI

Paper Title

10.21058/gjecs.2021.61002

Rail Surface Faults Identification from Low Quality Image Data Using Machine Learning Algorithms

Author Name

Volume No., Issue No., Year, & Page No.

Asfar Arain, Tanweer Hussain, Sanaullah Mehran Ujjan, Bhawani Shankar Chowdhry, Tariq Rafique Memon

Vol. 6, No. 1, March 2021, pp. 11-21

Abstract:

Rail surface faults or deformities that form on railhead of the track, owe their existence to various operational and environmental factors. To ensure comfortable and safe operation of railway vehicles, on-time detection of these surface faults is necessary. It is also of paramount importance that fault types are identified because it can lead to the identification of causes. This eventually leads to development of better maintenance strategies. Automation of the rail inspection is highly desirable because it results in accurate, robust, and cost-effective condition monitoring of the railway track. Automated systems of track monitoring currently in use are highly sophisticated instrumentation systems, with high-speed cameras and equipped with state-of-the-art level hardware. In this research, a preliminary work towards developing a low-cost rail condition monitoring system is presented. A suitable action camera EKEN-H9R is used to acquire videos of track surface. This data is preprocessed and later used to train data-driven models for fault identification. A comparative analysis of multiple data-driven classification algorithms is conducted on the acquired data and research is concluded with support vector machine algorithm which was able to achieve about 96% accuracy on the fault classification task.

Keywords:

Rail Surface faults, Fault identification, Condition monitoring, Machine learning, Data-driven models, Classification, Noisy data.

Full Text:

References:

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  17. J. Gan, J. Wang, H. Yu, Q. Li, and Z. Shi, “Online Rail Surface Inspection Utilizing Spatial Consistency and Continuity,” IEEE Trans. Syst. Man, Cybern. Syst., vol. 50, no. 7, pp. 2741–2751, 2020.
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DOI

Paper Title

10.21058/gjecs.2021.62001

Railway Track Fault Analysis using Dynamic ROI Detection in Cluttered Environment using Deep Learning

Author Name

Volume No., Issue No., Year, & Page No.

Ghulam Hyder Palli, Khuhed Memon, Bhawani Shankar Chowdhry, Azam Rafique Memon, Tanweer Hussain

Vol. 6, No. 2, September 2021, pp. 1-15

Abstract:

The safety of the railway track can be accomplished by the regular inspection of the track by detecting the faults which occur due to several parameters. The fault analysis is very necessary, if they are not timely monitored then the train can face severe accidents which may result in the loss of several lives. The condition monitoring of the track is taken manually these days and the world is moving towards automation to avoid human error and acquire high accuracy. The inspection of faults can be carried out by either using sensors or image processing techniques in which Deep Learning can play a vital role to have efficient systems. This research is carried out using the Deep Learning techniques, the rail surface faults such as squats and corrugation are monitored by the Deep Learning techniques with an addition of IOT (Internet of Things) feature for real time fault alerts with GPS locations on Desktop Systems using the Web Portal.

Keywords:

Railway Fault Analysis, Dynamic ROI, Cluttered Environment, Deep Learning, IOT.

Full Text:

References:

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DOI

Paper Title

10.21058/gjecs.2021.62002

INDUCTION MOTOR FAULT DETECTION AND CLASSIFICATION USING THERMAL IMAGES AND DEEP LEARNING

Author Name

Volume No., Issue No., Year, & Page No.

Saira Parveen, Tanweer Hussain, Dileep Kumar, and Bhawani Shankar Chowdhry

Vol. 6, No. 2, September 2021, pp. 16-31

Abstract:

Electro-mechanical systems such as induction motors (IMs) are widely used in industry as driving forces for multiple small and large-scale systems. The advantages of IMs like high reliability, high efficiency, and robust operation have resulted in their widespread applications. The operation of the motors under various electrical and mechanical variations degrades their performance and reduces their life span. For the safe and sustained operation of IMs, existing condition monitoring methods use different sensors for diagnosis. Still, these methods pose some difficulties, such as they require proper sensor installation and are prone to noise. Therefore, more effective diagnosis methods are needed for effective condition monitoring of machines. Infrared thermography (IRT) as a non-invasive technique can efficiently be used to address sensor-related problems. Recently, IRT as a noncontact technique has been widely adopted for condition monitoring of rotating machines. In this work, IRT-based three-phase motor fault detection and identification is proposed. The thermal images acquired under different motor operating conditions are fed to the deep learning (DL) models for fault identification. The supervised DL methods effectively classified the motor conditions owing to their high generalization capabilities. The obtained results demonstrate that the RESNET-50 model achieved the best classification results with an accuracy of 97% without any manual feature extraction method.

Keywords:

Induction motor; Thermal imaging; Deep learning; Condition monitoring; Fault diagnosis.

Full Text:

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