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Gabriel Avelino Sampedro,Samson Japay,Angela Cacatian,Jeravin Dumlao,Jae Min Lee(이재민),Dong-Seong Kim(김동성) 한국통신학회 2021 한국통신학회 학술대회논문집 Vol.2021 No.2
Hearing-aids are the most common devices for hearing impairment. It has been proven to be effective in helping patients hear better, especially in quiet and controlled environments. While hearing aids help amplify sounds, it does not take into account noise, thus it can worsen the hearing impairment in the long run. To address this problem, a system that would be able to filter sounds in real-time using a Redundant Convolutional Encoder-Decoder (R-CED) is developed with solutions that address multiple constraints brought about by various engineering standards. The chosen three system designs were mobile application, web application, and desktop application that all can handle deep learning algorithms. The mobile application design was chosen as the best design based on the trade-off analysis. In every testing conducted, the application is proven to reduce background noise, scoring 58 over 60 iterations or 96
Design of an Enhanced Object Detection Algorithm through Image Scaling
Gabriel Avelino Sampedro,Yohana Jayanti Aruan,Jae-Min Lee,Dong-Seong Kim 한국통신학회 2021 한국통신학회 학술대회논문집 Vol.2021 No.6
This paper proposes the implementation of an image scaling algorithm with the use of an object detection algorithm. You Only Look Once (YOLO) algorithm is a quick object detection algorithm that only needs to scan an image once, before detecting objects of interest; however, there are some inaccuracies in the detection of small objects, given the limitation of the clustering boxes generated by the algorithm. Through the implementation of a scaling algorithm, the researchers investigate the increase in detection rates and the design is deemed ready for implementation.
Design of a Fault-Detection System for FDM-type 3D Printer using Temporal Convolutional Network
Danielle Jaye S. Agron,Gabriel Avelino R. Sampedro,Gabriel Amaizu,Jae-Min Lee,Dong-Seong Kim 한국통신학회 2021 한국통신학회 학술대회논문집 Vol.2021 No.6
In the process of additive manufacturing, the devices used to print usually encounter errors and problems that are not easily detected by the device operator. Undetected errors can cause serious damage to the 3D printer and leads to the output being counted as reject, thus leading to both loss in time and resources. The research focuses on the development of a device to monitor the process of 3D printing. The design applies temporal convolutional networks (TCN) to train the device to identify whether certain measurements of the 3D printer will lead to errors in output. The prototype serves as an attachment to the 3D printer and displays measurements and if they are within the safe values.