8 research outputs found
ApplianceNet: a neural network based framework to recognize daily life activities and behavior in smart home using smart plugs
A smart plug can transform the typical electrical appliance into a smart multi-functional device, which can communicate over the Internet. It has the ability to report the energy consumption pattern of the attached appliance which offer the further analysis. Inside the home, smart plugs can be utilized to recognize daily life activities and behavior. These are the key elements to provide human-centered applications including healthcare services, power consumption footprints, and household appliance identification. In this research, we propose a novel framework ApplianceNet that is based on energy consumption patterns of home appliances attached to smart plugs. Our framework can process the collected univariate time-series data intelligently and classifies them using a multi-layer, feed-forward neural network. The performance of this approach is evaluated on publicly available real homes collected dataset. The experimental results have shown the ApplianceNet as an effective and practical solution for recognizing daily life activities and behavior. We measure the performance in terms of precision, recall, and F1-score, and the obtained score is 87%, 88%, 88%, respectively, which is 11% higher than the existing method in terms of F1-score. Furthermore, our scheme is simple and easy to adopt in the existing home infrastructure
A Novel Deep Reinforcement Learning-based Approach for Task-offloading in Vehicular Networks
Next-generation vehicular networks will impose unprecedented computation demand due to the wide adoption of compute-intensive services with stringent latency requirements. Computational capacity of vehicular networks can be enhanced by integration of vehicular edge or fog computing; however, the growing popularity and massive adoption of novel services make edge resources insufficient. This challenge can be addressed by utilizing the onboard computation resources of neighboring vehicles that are not resource-constrained along with the edge computing resources. To fill the gaps, in this paper, we propose to solve the problem of task offloading by jointly considering the communication and computation resources in a mobile vehicular network. We formulate a non-linear problem to minimize the energy consumption subject to the network resources. Further-more, we consider a practical vehicular environment by taking into account the dynamics of mobile vehicular networks. The formulated problem is solved via a deep reinforcement learning (DRL) based approach. Finally, numerical evaluations are performed that demonstrates the effectiveness of our proposed scheme
A Comparative Analysis of Distributed Ledger Technologies for Smart Contract Development
Adversarial Reconstruction Loss for Domain Generalization
The biggest fear when deploying machine learning models to the real world is their ability to handle the new data. This problem is significant especially in medicine, where models trained on rich high-quality data extracted from large hospitals do not scale to small regional hospitals. One of the clinical challenges addressed in this work is magnetic resonance image generalization for improved visualization and diagnosis of hip abnormalities such as femoroacetabular impingement and dysplasia. Domain Generalization (DG) is a field in machine learning that tries to solve the model's dependency on the training data by leveraging many related but different data sources. We present a new method for DG that is both efficient and fast, unlike the most current state of art methods, which add a substantial computational burden making it hard to fine-tune. Our model trains an autoencoder setting on top of the classifier, but the encoder is trained on the adversarial reconstruction loss forcing it to forget style information while extracting features useful for classification. Our approach aims to force the encoder to generate domain-invariant representations that are still category informative by pushing it in both directions. Our method has proven universal and was validated on four different benchmarks for domain generalization, outperforming state of the art on RMNIST, VLCS and IXMAS with a 0.70% increase in accuracy and providing comparable results on PACS with a 0.02% difference. Our method was also evaluated for unsupervised domain adaptation and has shown to be quite an effective method against over-fitting