2023, Vol.28, No.3

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

pISSN : 1598-849X / eISSN : 2383-9945
https://journal.kci.go.kr/jksci

In this paper, we identify the factors that hinder the application of quantum computing technology to solve meaningful real-world problems, and suggest related research trends and directions. To this end, we summarize the basic knowledge of quantum mechanics from the perspective of computer science, which is necessary to understand the difficulties in applying quantum computing technology, and analyze the currently commercialized quantum computers and quantum programming layers from the literature. Through an analysis of the current status and utilization of cloud-based commercial quantum computing services, we identify four factors that currently hinder the practical application of quantum computing: high barriers to entry for quantum computer programming, constraints on noisy intermediate-scale quantum computers, a still-growing open source ecosystem, and difficulties in simulating realistic problem sizes, and suggest trends and directions for related research. In doing so, it is expected to contribute to laying the groundwork for practical applications of quantum computing technology.

As techniques for indoor positioning, fingerprinting, indoor positioning method using trilateration, and utilizing information obtained from equipments by Wi-Fi/Bluetooth, etc are common and representative methods to specify the user's indoor position. However, in these methods, an indoor space should be provided with enough space to install a large number of equipment (AP, Beacon). In this paper, we propose a technique that can express the user's location within a building by simultaneously using the GPS signal and the signal transmitted from the beacon in a building structure where the conventional method cannot be applied, such as a narrow building. A shortest path search system was designed and implemented by applying the Dijkstra Algorithm, one of the most representative and efficient shortest path search algorithms for shortest path search. The proposed technique can be considered as one of the methods for measuring the user's indoor location considering the structural characteristics of a building in the future.

Based on the problem of 'Tatanic – Machine Learning from Disaster', a representative competition of Kaggle that presents challenges related to data science and solves them, we want to see how data preprocessing and model construction affect prediction accuracy and score. We compare and analyze the features by selecting seven top-ranked solutions with high scores, except when using redundant models or ensemble techniques. It was confirmed that most of the pretreatment has unique and differentiated characteristics, and although the pretreatment process was almost the same, there were differences in scores depending on the type of model. The comparative analysis study in this paper is expected to help participants in the kaggle competition and data science beginners by understanding the characteristics and analysis flow of the preprocessing methods of the top score participants.