10 posts tagged with "conference"

Abstract

Radio frequency identification (RFID) technology can be utilized to monitor tagged product movements and directions for the purpose of inventory management. It is important for RFID gate to identify the several RFID readings such as movement type and direction as well as the static tags (tags that accidentally read by the reader). In this study, random forest (RF) method is utilized to detect the movement type and direction of RFID passive tags. The input features are derived from received signal strength (RSS) and timestamp of tags. The result showed that machine learning models successfully distinguish direction and movement type of tag. In addition, proposed model based on random forest generated accuracy as much as 98.39% and was significantly superior to the other models considered.

Published in: IEEE Xplore
DOI: 10.1109/ICST47872.2019.9166196

Abstract

Predicting future blood glucose (BG) level for diabetic patients will help them to avoid critical conditions in the future. This study proposed Extreme Gradient Boosting (XGBoost), an ensemble learning model to predict the future blood glucose value of diabetic patients. The clinical dataset of Type 1 Diabetes (T1D) patients was utilized and the prediction models were generated to predict future BG of 30 and 60 minutes ahead of time. The prediction models have been tested tofive children who develop T1D and showed that BG prediction model based on XGBoost outperformed other models, with average of Root Mean Square Error (RMSE) are 23.219 mg/dL and 35.800 mg/dL for prediction horizon (PH) 30 and 60 minutes respectively. In addition, the result showed that by utilizing statistical-based features as additional attributes, most of the performance of predictions model were increased.

Published in: IOP Conference Series: Materials Science and Engineering
DOI: 10.1088/1757-899X/803/1/012012

Abstract

Understanding customer shopping behavior in retail store is important to improve the customers' relationship with the retailer, which can help to lift the revenue of the business. However, compared to online store, the customer browsing activities in the retail store is difficult to be analysed. Therefore, in this study the customer shopping behavior analysis (i.e., browsing activity) in retail store by utilizing radio frequency identification (RFID)-enabled shelf and machine learning model is proposed. First, the RFID technology is installed in the store shelf to monitor the movement tagged products. The dataset was gathered from receive signal strength (RSS) of the tags for different customer behavior scenario. The statistical features were extracted from RSS of tags. Finally, machine learning models were utilized to classify different customer shopping activities. The experiment result showed that the proposed model based on Multilayer Perceptron (MLP) outperformed other models by as much as 97.00%, 96.67%, 97.50%, and 96.57% for accuracy, precision, recall, and f-score, respectively. The proposed model can help the managers better understand what products customer interested in, so that can be utilized for product placement, promotion as well as relevant product recommendations to the customers.

Published in: IOP Conference Series: Materials Science and Engineering
DOI: 10.1088/1757-899X/803/1/012022

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

Abstract

Now days, customer’s health awareness is of extreme significance. Food can become contaminated at any point during production, distribution and preparation. Therefore, it is of key importance for the perishable food supply chain to monitor the food quality and safety. Traceability system offers complete food information and therefore, it guarantees food quality and safety. The current study postulates a low cost IoT-based traceability system that utilized RFID and smartphone-based sensors. The RFID handheld reader based on smartphone is utilized to track and trace product information. In addition the smartphone-based sensor is used to measure temperature, humidity, and location (based on GPS sensor) during storage and transportation. The proposed system was verified for kimchi supply chain in Korea, and revealed significant benefits to managers as well as customers by providing the real-time location as well as complete temperature and humidity history. The results displayed that compared to the traditional methods, the proposed system is capable of tracking products as well as processing an immense input of sensor data efficiently and effectively.

Abstract

To make manufacturers more competitive, there is a need to integrate advanced computing and cyber-physical systems to take advantage of the current technologies. With the advent of smart sensors such as IoT technologies (1), collecting data has become a simple task, but the question remains if these devices or data provide the right information for the right purpose at the right time. Data is not useful unless it is processed in a way that provides context and meaning that can be understood by the right personnel. Just connecting sensors to a machine or connecting a machine to another machine will not give users the insights needed to make better decisions. Thus, in this paper we proposed the real time monitoring system that utilized machine learning algorithm to predict the quality of product based on sensor data that was gathered by IoT device and showed the result in real time.

Published in: KSMTE Annual Autumn Conference 2017
Link: http://www.dbpia.co.kr/Journal/ArticleDetail/NODE07285510

Abstract

Smart factory is quickly becoming the new reality in the market, and every innovative manufacturer must embrace it to stay competitive.