Browsing by Author "Poornachari, P."

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    Advanced thermal vision techniques for enhanced fault diagnosis in electrical equipment: a review
    (Springer, 2025) Anbalagan, A.; Persiya, J.; Mohamed Mansoor Roomi, S.; Arumuga Perumal, D.A.; Poornachari, P.; Vijayalakshmi, M.; Ebenezer, L.
    Ensuring the reliability and safety of electrical equipment is essential for industrial and residential applications. Traditional fault diagnosis methods involving physical inspections are time-consuming and ineffective for early fault detection. Infrared (IR) thermography offers a non-invasive and efficient solution by identifying anomalies in temperature profiles. This review explores thermal vision-based fault diagnosis techniques, including region of interest (ROI) segmentation, image pre-processing, and fault diagnosis algorithms, with a focus on deep learning approaches. The study highlights the effectiveness of machine learning models in enhancing fault detection accuracy while identifying challenges such as environmental variations, data inconsistencies, and system integration issues. The review discusses the role of real-time applications, wireless technologies, and AI-based automation in improving fault detection. Research gaps are identified, and future directions are proposed to enhance efficiency, reliability, and industrial adoption. © The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2025.
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    Deep dual domain joint discriminant feature framework for emotion based music player
    (Springer, 2024) Anbalagan, A.; Challa, R.T.; Saketh, S.; Chakka, S.; Arumuga Perumal, D.; Poornachari, P.
    Emotion based music player is an interdisciplinary study of computer vision and psychology. As music enhances the positive vibes it plays a significant role in soothing people’s emotion. Emotions can be predicted through facial expression analysis using vision-based methods. However, challenges like environment and expression complexity have become hindrance to attain a good recognition rate. Therefore, we put forward a deep dual domain joint feature framework based on linear discriminant analysis for facial emotion recognition. First, we detect the human face and learn the emotion pattern using the popular complementary deep domain networks called EfficientNet and ResNet50. The learned deep dual domain space is projected onto linear discriminant space to achieve a joint discriminant feature space. The recognition rate of the proposed joint discriminant feature framework is analyzed using support vector machine. To prove the efficacy of the proposed framework, we validated it on two Benchmarks namely FER2013 and CK48+ datasets. The proposed framework achieved a good recognition rate of 99% and 98.6% on FER2013 and CK48+ respectively. Experimental analysis on our EmDe dataset showed an accuracy of 99% and proves that the deep dual domain joint discriminant framework as a promising pipeline for emotion-based music player system. © The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2024.