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Edge AI based Hybrid Energy Management Systems
Henar Mike O. Canilang(헤나르),Danielle Jaye S. Agron(다니),Angela Caliwag(안젤라),Wansu Lim(임완수) 한국통신학회 2022 한국통신학회 학술대회논문집 Vol.2022 No.2
This paper proposes energy management system (EMS) convergence to edge AI based - applications owing to demands of intelligent applications. This in inline to modern embedded AI based deployment era. In terms of EMS deployment, the hybrid energy management system (HEMS) is an innovation to the conventional battery/capacitor (supercapacitor or ultracapacitor) based management system. The transition to renewable and clean energy resources to prevent global warming paves way to the demand of existing EMS and HEM. This paper presents an approach for energy management and AI convergence paving way for embedded machine learning applications in particular, edge AI based applications. The consideration for the proposed edge AI based HEMS includes IoT specifically, edge/cloud-based coprocessing capability enabling a plethora of hard and soft real time state-of-the-art applications.
Edge EEG: Edge AI Device-based EEG Signal Processing for Emotion Recognition
Henar Mike O. Canilang,Ej Miguel Francisco C. Caliwag,Judith Njoku Nyechinyere,Angela C. Caliwag,Wansu Lim 한국통신학회 2021 한국통신학회 학술대회논문집 Vol.2021 No.11
Edge AI devices paves way to portable and state-of-the-art deployment of AI applications using the edge computing paradigm. Owing to the current constraint of internet-of-thing (IoT), edge AI devices bridges the gap for computationally efficient AI deployment on resource constrained portable embedded device applications. Deep neural networks (DNN) enables the plethora of cutting edge AI device applications. DNN is deployed to edge AI devices for intelligent applications such as signal processing and local processing. For processing, an open-source and low-cost platform is utilized for the results verification. In this paper, an edge AI device-based Electroencephalogram (edge EEG) signal processing for compact real-time applications is proposed. The edge EEG is capable of raw EEG signal acquisition, processing data locally and wireless data transmission and receiving. An open-source brain computer-interface platform (OpenBCI) enables the data acquisition which is process and transmitted to the edge AI device. This edge EEG paves way to multitude of intelligent application using brain signals such as for medical, commercial, and industrial applications. Edge EEG is pivotal to the rapidly increasing EEG-based application demand.
Implementation of Emotion Recognition Using Edge-Cloud Computing
Ej Miguel Francisco Caliwag,Henar Mike O. Canilang,안젤라,임완수 한국통신학회 2022 韓國通信學會論文誌 Vol.47 No.3
Emotion recognition systems has been in demand due to its aid in several applications such as health monitoring, workload and drowsiness detection. For these applications, emotion recognition systems require to be deployed on an edge device. For deployment on an edge device, numerous limitations and bottlenecks such as implementation cost, deployment capabilities, and system efficiency affects the performance of the emotion recognition system. That is, emotion recognition on the edge suffers from either low accuracy or high inference time due to the hardware constraints. Hence, several previous studies focus on the deployment of emotion recognition on the edge. Despite that, low accuracy and high inference time still remains an issue. To resolve this, a platform with higher computation capacity must be employed. In this study, we implement an enhanced emotion recognition system by integrating cloud computing platform to the emotion recognition system process, whereby all emotion recognition tasks are performed on the cloud server, can overcome conventional edge device bottlenecks and provide cost-effectiveness, efficient power consumption, and enhanced computing process. Based on the results shown in this study, the proposed system is successful in predicting the emotion of the users in real-time.
Continuous Emotion Recognition on the Edge
Ej Miguel Francisco C. Caliwag(이제이),Henar Mike O. Canilang(헤나르),Angela C. Caliwag(안젤라),Wansu Lim(임완수) 한국통신학회 2021 한국통신학회 학술대회논문집 Vol.2021 No.6
Traditional Edge AI implementation methods requires high cost computing machines / device. Aside from these high cost devices, most implementation often requires complex AI models. In this study, a real-time implementation for Edge AI devices is implemented to address the implementation cost and complexity constraints. The Arduino Nano 33 BLE sense is used as an Edge AI Device for this implementation. An open source machine learning platform is used to develop an embedded machine learning implementation which addresses the high implementation and computational complexity of edge AI implementation.
Development of ultracapacitor management system controller for solar-powered streetlamp
Danielle Jaye S. Agron(다니),Henar Mike O. Canilang(헤나르),Angela Caliwag(안젤라),Wansu Lim(임완수) 한국통신학회 2022 한국통신학회 학술대회논문집 Vol.2022 No.2
In this paper, an ultra-capacitor management system (UCMS) controller for solar-powered streetlamp is developed. To increase the efficiency and deployment life of the ultra-capacitor, an active balancing scheme has been applied for the charging and discharging phase. Through the balancing approach, an active charge and discharge protection control capability is integrated for this application realizing a fault tolerant system. The development of hardware prototype and initial results are presented on this paper.