During Sept. 20~23, 2023, the 6th workshop, entitled 2023 International Joint Workshop on Intelligent IoT Key Technology, was held in Nanjing city, China. This is the first time members from the China, Japan and Korea could gather on-site for face-to-face communication. We have 5 keynotes and 10 oral presentation sessions. The topics ranges from wireless sensing and communication to IoT and smart home applications. From our team, we sent 13 attendees and two students, Wenhao Huang and Taiga Kume presented their work on machine learning driven sensing technology. In addition to the academic events, we also learned a lot about Nanjing city’s history and culture. A very great trip! See you next time, Nanjing.
ウェアラブルデバイスと地理的なチームコラボレーションを活用した新しいロゲイニングゲーム「SFC GO Around」です。ゲーミフィケーションアプローチを採用することで、キャンパス内のいくつかの仮想ロケーションを設定し、参加者を二つのチームに分けて互いに競い合わせます。このセットアップは、キャンパスの地理をより深く理解するだけでなく、ゲームを通じてクラスメイトとの親しさも育てます。このシステムは、個人が新しいエリアにすばやく慣れ、イベント中に学生や同僚との関係を築くのツールです。競争的なチームゲームを通じて参加者の体験を向上させ、プレイヤーに単なるコミュニケーションだけでなく、お互いを真に認識し理解することを促し、チームメンバー間のより深いつながりを育むことを奨励します。
発表概要：Forecasting household waste generation using conventional methods can present challenges due to the substantial variability and uncertainty in the process. Furthermore, previous studies focused on forecasting household waste generation at municipal or national levels may not be directly applicable to on-site waste collection processes. The objective of this research is to attain daily-level predictions of household waste generation and assess the advantages of using a leading-edge deep learning approach over conventional methods. We applied Multi-variable Long Short-Term Memory (LSTM) neural network utilizing a granular garbage disposal database for forecasting. This database is curated from a garbage disposal sensing platform currently operational in three cities within the Kanagawa prefecture, Japan, with plan for operation until 2025. Additionally, relevant web applications based on findings from this research will be developed for data visualization and routine optimization.
博士2年生 黄君が研究内容をIoT2023で口頭発表しました – ‘ Real-Time Image-Based Automotive Sensing: A Practice on Fine-Grained Garbage Disposal ’
発表概要：This research presents a real-time automotive sensing system for the data of urban garbage disposal. The proposed solution is implemented on an edge computing device mounted on garbage truck where a deep learning based image processing algorithm is implemented to automatically counted the number of collected garbage bags from garbage collection video. A MQTT-based data server was developed to enable data publication from sensing device to server and data accumulation and to facilitate application development. Our system has the functions of high concurrency and low transmission delay, offline reconnection, breakpoint transmission and client authentication. This work is to provide a real-time, low-cost, reliable and replicable system for the implementation of a widespread sensing network for automotive edge computing and smart city applications.