Papers

Ade Pitra Hermawan

Title 1: Toward Deep Learning-based Low Latency Communication in Industrial IoT – KICS Summer 2019

Title 2: CNN-based Automatic Modulation Classification for 5G Communications - IEEE Communications Letters (Published)

Title 3a: Automatic Wireless Signal Classification using Combined CNN and LSTM Algorithm in Multimedia IoT (ETFA 2020 Regular)
Title 3b: Sensor Failure Recovery using Deep Learning Technique in Industrial Internet of Things (ETFA 2020 WiP) --> KICS 2020


Title 4: Real-time Data Recovery using Multi-directional LSTM in Wireless Sensor Networks - Winter Intensive (Submitted in Wireless Communications Letters)

Title 5: UAV-Based Sensor Nodes Localization Scheme utilizing Shallow Neural Network - Extension (Resubmit Elsewhere)

Title 6: Remaining Useful Life Estimation of Aircraft Engine using Deep Learning Technique (Summer Intensive Paper) --> ICTC 2020 (1st draft, extended to Journal)

Last Week's Progress

Title 4:
- Submitted.

Title 5:
- Rejected.

Title 6:
- Survey some papers
- Found the dataset of aircraft engine
- Finalize the system design
- Finalize the problems
- Finalize the goal that I want to achieve

This Week Todo's

Title 5:
- Submission decision from Prof. Kim and Prof. Lee.

Title 6:
- Simulated first draft of the paper.

Project Progress

Website management, Hanhwa System

Last Week's Project

Website management
- Create a manual guide of NSL Website (How to create a new menu, make a menu private, etc)
- Added newcomers profile
- Moved some members to alumni


Hanhwa System:
- Normalized the dataset
- Split the dataset into several batches.
- Simulated the prediction algorithm using LSTM.
- Find recent 7 SCI papers regarding RUL research works.
- Summarize the trends of those papers.

This Week's Project

Website management
- Create new section for summer intensive
- Explore each sub-menu of the database
- Create a manual guide of NSL Website (How to add new journal menu, etc)


Hanhwa System:
- Find the drawbacks and find a solution to the existing trends and issues.
- Simulate normal & abnormal devices classification based on the time-series data set (Matlab)

Monthly Goals

Able to use supporting tools to do a simulation (Python, Matlab, NS3, Tensorflow)
- Increase simulation skill every month (self-measurement)

Summer Intensive:
- July: Finish 80% of the simulation, finish the abstract, introduction, related works, all the references are fixed.
- August: Finish the 2nd simulation (enhance the accuracy, comparison, etc)
- September: Finish the 1st version of the paper (before revision)
- End of September: Finalisation & Submission

Annual Goals

2019
- Paper Accepted in the domestic conference (Done)
- Paper Accepted in the international conference
- Submit at least one journal (Done)

2020
- Paper Accepted in the domestic conference
- Paper Accepted in the international conference
- Paper Accepted in the International Journal (Done)