Jonghyuk Kim

Store layout optimization using indoor positioning system

Indoor positioning systems have attracted considerable attention from practitioners and firms seeking to optimize the consumer shopping experience with the goal of attaining increased revenue and profitability. Acknowledging the importance of indoor positioning systems in store layout optimization, we conducted a field experiment for 11 months in order to develop algorithms for connecting indoor positioning data with customer transaction data. Using fingerprinting as a primary data collection technique, we compared positioning and transaction data before and after critical store layout optimization decisions in order to identify which customer movement patterns generated the highest sales. In contrast to previous works on indoor positioning systems, which focused solely on developing algorithms or techniques to increase accuracy rates, our algorithms in principle integrate computing and marketing perspectives. Our findings can be applied to store layout optimization and personalized marketing.

Sensor-Based Optimization Model for Air Quality Improvement in Home IoT

We introduce current home Internet of Things (IoT) technology and present research on its various forms and applications in real life. In addition, we describe IoT marketing strategies as well as specific modeling techniques for improving air quality, a key home IoT service. To this end, we summarize the latest research on sensor-based home IoT, studies on indoor air quality, and technical studies on random data generation. In addition, we develop an air quality improvement model that can be readily applied to the market by acquiring initial analytical data and building infrastructures using spectrum/density analysis and the natural cubic spline method. Accordingly, we generate related data based on user behavioral values. We integrate the logic into the existing home IoT system to enable users to easily access the system through the Web or mobile applications. We expect that the present introduction of a practical marketing application method will contribute to enhancing the expansion of the home IoT market.

Sensor-Based Real-Time Detection in Vulcanization Control Using Machine Learning and Pattern Clustering

Recent paradigm shifts in manufacturing have resulted from the need for a smart manufacturing environment. In this study, we developed a model to detect anomalous signs in advance and embedded it in an existing programmable logic controller system. For this, we investigated the innovation process for smart manufacturing in the domain of synthetic rubber and its vulcanization process, as well as a real-time sensing technology. The results indicate that only analysis of the pattern of input variables can lead to significant results without the generation of target variables through manual testing of chemical properties. We have also made a practical contribution to the realization of a smart manufacturing environment by building cloud-based infrastructure and models for the pre-detection of defects.

An empirical study on real-time data analytics for connected cars: Sensor-based applications for smart cars:

Connected cars, which are vehicles connected to wireless networks through the convergence of automotive and information technologies, have become an important topic of academic and industrial research on automobiles. In this research, we conducted a field experiment to understand vehicle maintenance mechanisms of a connected car platform. Specifically, we investigated the feasibility of prognostics and health management under different driving circumstances, with varying vehicle models, vehicle conditions, drivers’ propensity for speeding, and road conditions. We collected sensor data through a two-stage model of vehicle communication using an on-board diagnostics scanner and data transmission using wireless communication. We found that device defects can be predicted based on driving situations such as the driving mode, mechanical characteristics, and a driver’s speeding propensity.