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Nguyen Viet Hung

East Asia University of Technology

Author-ID : 10385572

Computer Science & IT Electrical & Electronics Engineering

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Articles

RAC: a reusable adaptive convolution for CNN layer Nguyen Viet Hung; Phi Dinh Huynh; Pham Hong Thinh; Phuc Hau Nguyen; Trong-Minh Hoang
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 2: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i2.pp753-763

This paper proposes reusable adaptive convolution (RAC), an efficient alternative to standard 3×3 convolutions for convolutional neural networks (CNNs). The main advantage of RAC lies in its simplicity and parameter efficiency, achieved by sharing horizontal and vertical 1×k/k×1 filter banks across blocks within a stage and recombining them through a lightweight 1×1 mixing layer. By operating at the operator design level, RAC avoids post-training compression steps and preserves the conventional Conv–BN–activation structure, enabling seamless integration into existing CNN backbones. To evaluate the effectiveness of the proposed method, extensive experiments are conducted on CIFAR-10 using several architectures, including ResNet-18/50/101, DenseNet, WideResNet, and EfficientNet. Experimental results demonstrate that RAC significantly reduces parameters and memory usage while maintaining competitive accuracy. These results indicate that RAC offers a reasonable balance between accuracy and compression, and is suitable for deploying CNN networks on resource-constrained platforms.

Leveraging Self-Supervised Transformers for Heat Pump Consumption Classification Using the HEAPO Dataset Hoang Thi Thu; Phi Dinh Huynh; Ta Trung Kien; Le Mai Nam; Le Hong Duy; Phan Hong Thing; Nguyen Viet Hung
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 3: September 2026
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i3.7684

Heat pumps play a crucial role in reducing carbon emissions in residential heating; however, their electricity consumption varies significantly across households, making large-scale performance assessment challenging. Existing data-driven approaches often rely on fully supervised learning and require extensive labeled data, which are costly to obtain through on-site inspections. This paper investigates whether routinely collected smart meter and weather data can be leveraged to automatically classify household heat pump electricity consumption into three categories: low, normal, and rather high. We propose a self-monitoring transformer framework that combines self-supervised pretraining and supervised fine-tuning for time series classification. A transformer encoder is first pre-trained on multi-day electricity consumption windows from largely unlabeled households using a reconstruction-based objective. The learned representations are then fine-tuned with a lightweight fully connected classifier on a limited labeled subset of the HEAPO dataset. Experimental results demonstrate that the proposed approach consistently outperforms state-of-the-art baselines, including RNN-, CNN-, and transformer-based time series models, achieving approximately 93% classification accuracy, an F1 score above 0.9, and a recovery rate close to 91%. These results indicate that self-supervised representation learning on smart meter data provides an effective and scalable solution for monitoring heat pump electricity consumption and supporting inspection planning.

Leveraging Self-Supervised Transformers for Heat Pump Consumption Classification Using the HEAPO Dataset Hoang Thi Thu; Phi Dinh Huynh; Ta Trung Kien; Le Mai Nam; Le Hong Duy; Pham Hong Thinh; Nguyen Viet Hung
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol. 14 No. 3: September 2026
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i3.7684

Heat pumps play a crucial role in reducing carbon emissions in residential heating; however, their electricity consumption varies significantly across households, making large-scale performance assessment challenging. Existing data-driven approaches often rely on fully supervised learning and require extensive labeled data, which are costly to obtain through on-site inspections. This paper investigates whether routinely collected smart meter and weather data can be leveraged to automatically classify household heat pump electricity consumption into three categories: low, normal, and rather high. We propose a self-monitoring transformer framework that combines self-supervised pretraining and supervised fine-tuning for time series classification. A transformer encoder is first pre-trained on multi-day electricity consumption windows from largely unlabeled households using a reconstruction-based objective. The learned representations are then fine-tuned with a lightweight fully connected classifier on a limited labeled subset of the HEAPO dataset. Experimental results demonstrate that the proposed approach consistently outperforms state-of-the-art baselines, including RNN-, CNN-, and transformer-based time series models, achieving approximately 93% classification accuracy, an F1 score above 0.9, and a recovery rate close to 91%. These results indicate that self-supervised representation learning on smart meter data provides an effective and scalable solution for monitoring heat pump electricity consumption and supporting inspection planning.

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