Jin Zhou
Duke University
6 Papers
3 Citations
Jin Zhou is an academic researcher from Duke University. The author has contributed to research in topics: Computer science & Trap (plumbing). The author has an hindex of 3, co-authored 4 publications.
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Papers
Sensor-Array Optimization Based on Time-Series Data Analytics for Sanitation-Related Malodor Detection
TL;DR: The approach to an electronic nose for sanitation-related malodor is based on the use of electrochemical gas sensors, and machine-learning techniques for sensor selection and odor classification.
The Impact of Different Levels of Autonomy and Training on Operators’ Drone Control Strategies
Jin Zhou,Haibei Zhu,Minwoo Kim,Mary L. Cummings +3 more
- 15 Nov 2019
TL;DR: Investigating the impact of operators’ drone control strategies as a function of differing levels of autonomy found that people with both supervisory and enhanced teleoperation control training were not able to determine the right control action at the right time to the same degree.
18
Stool Image Analysis for Digital Health Monitoring By Smart Toilets
Jin Zhou,Jackson McNabb,Nick DeCapite,Jose R. Ruiz,Deborah A. Fisher,Sonia Grego,Krishnendu Chakrabarty +6 more
TL;DR: In this paper , an edge-cloud approach was used to achieve an optimal balance between accuracy and latency and for the classification of stool form, achieved a balanced accuracy of 84.4% and 84.2% reduction in latency compared to a cloud model only.
5
Sensor-based evaluation of a Urine Trap toilet in a shared bathroom
Prateek Kachoria,Sarani Sasidaran,Claire M. Welling,Praveen Rosario,Jin Zhou,Krishnendu Chakrabarty,Harald Gründl,Lotte Kristoferitsch,Sonia Grego +8 more
TL;DR: In this paper , the Urine Trap, a passive no-mix toilet design based on the teapot effect, where the urine stream inlet is invisible to the user and therefore it does not impact the user experience for increased adoption.
4
•Proceedings Article
Parallel Droplet Control in MEDA Biochips using Multi-Agent Reinforcement Learning
Tung-Che Liang,Jin Zhou,Yun-Sheng Chan,Tsung-Yi Ho,Krishnendu Chakrabarty,Cy Lee +5 more
- 18 Jul 2021
TL;DR: In this paper, a multi-agent reinforcement learning (MARL) droplet-routing solution was proposed for various sizes of MEDA biochips with integrated sensors, and the reliable execution of a serial-dilution bioassay with the MARL droplet router on a fabricated MEDA Biochip was demonstrated.