My colleagues and I are guest editing a special issue related to the application of #machinelearning and #IoT in industry, which will be published continuously by Computers (Scopus SJR Q2).
Abstract
Maintaining product quality is essential for smart factories, hence detecting abnormal events in assembly line is important for timely decision-making. This study proposes an affordable fast early warning system based on edge computing to detect abnormal events during assembly line. The proposed model obtains environmental data from various sensors including gyroscopes, accelerometers, temperature, humidity, ambient light, and air quality. The fault model is installed close to the facilities, so abnormal events can be timely detected. Several performance evaluations are conducted to obtain the optimal scenario for utilizing edge devices to improve data processing and analysis speed, and the final proposed model provides the highest accuracy in terms of detecting abnormal events compared to other classification models. The proposed model was tested over four months of operation in a Korean automobile parts factory, and provided significant benefits from monitoring assembly line, as well as classifying abnormal events. The model helped improve decision-making by reducing or preventing unexpected losses due to abnormal events.
Published in: Applied Sciences
DOI: 10.3390/app9010084
Abstract
Current technology such as Bluetooth Low Energy (BLE) provides an efficient way for Real-Time Location System (RTLS). This study proposes a BLE-based Real-Time Location System that utilizes Smartphone and NoSQL database as gateway and data storage respectively. Firstly, we develop a smartphone-based tracking app to gather the location of employees. Secondly, the generated sensor data from gateway is then stored into NoSQL MongoDB. The proposed system was tested for monitoring the movement of employees in the workplace. The results showed that commercial versions of the BLE-based device and the proposed system are sufficiently efficient for RTLS. Furthermore, proposed system is capable of processing a massive input/output of sensor data efficiently when the number of BLE-based devices and users increases.
Published in: BDIOT 2018 Proceedings of the 2018 2nd International Conference on Big Data and Internet of Things, ACM New York, NY, USA ©2018
DOI: 10.1145/3289430.3289470