• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.19 no.6
• /
• pp.61-73
• /
• 2020

Real-time location-sensing technology using location information collected from IoT devices is being applied for safety management purposes in many industries, such as ports. On the other hand, positional error is always present owing to the characteristics of GPS. Therefore, accident-risk detection algorithms must consider positional error. This paper proposes an methodology of calibration for falling object accident-risk-zone approach detection algorithm considering GPS errors. A probability density function was estimated, with positional error data collected from IoT devices as a probability variable. As a result of the verification, the algorithm showed a detection accuracy of 93% and 77%. Overall, the analysis results derived according to the GPS error level will be an important criterion for upgrading algorithms and real-time risk managements in the future.

• Journal of the Korean Recycled Construction Resources Institute
• /
• v.8 no.4
• /
• pp.444-449
• /
• 2020

In this study, On-Board Truck Scale was installed on the construction waste collection / transportation vehicles to monitor the weight of the waste at all stages from generation to final treatment. It was performed as a case study of a construction waste control technology that can efficiently manage the total generating and recycling amount using real-time weight/location information obtained by the On-Board Truck Scale device. As a result of the study, it was confirmed that the total amount of construction waste can be monitored in real time, and a plan for efficient logistics transportation can be derived through the analysis of operation patterns by managing the real-time transport volume, transport distance, and transport time of the construction waste collection / transportation vehicles. It was confirmed that overloading can be prevented in advance by controlling the loading also.

• Journal of Information Technology Services
• /
• v.21 no.4
• /
• pp.91-103
• /
• 2022

This study proposes a model for predicting contracted power using electric power data collected in real time from convenience stores nationwide. By optimizing the prediction model using machine learning, it will be possible to predict the contracted power required to renew the contract of the existing convenience store. Contracted power is predicted through the XGBoost regression model. For the learning of XGBoost model, the electric power data collected for 16 months through a real-time monitoring system for convenience stores nationwide were used. The hyperparameters of the XGBoost model were tuned using the GridesearchCV, and the main features of the prediction model were identified using the xgb.importance function. In addition, it was also confirmed whether the preprocessing method of missing values and outliers affects the prediction of reduced power. As a result of hyperparameter tuning, an optimal model with improved predictive performance was obtained. It was found that the features of power.2020.09, power.2021.02, area, and operating time had an effect on the prediction of contracted power. As a result of the analysis, it was found that the preprocessing policy of missing values and outliers did not affect the prediction result. The proposed XGBoost regression model showed high predictive performance for contract power. Even if the preprocessing method for missing values and outliers was changed, there was no significant difference in the prediction results through hyperparameters tuning.

• Journal of Navigation and Port Research
• /
• v.46 no.6
• /
• pp.547-553
• /
• 2022

This study analyzed the changes in the operational productivity of fourteen Korean container lines from 2019 to 2021 using MP I(Malmquist Productivity Index). The results indicated that the operational productivity of the shipping companies has increased by 38.4% annually, representing the TCI (Technical Change Index) increasing by 58.3% and the TECI (Technical Efficiency Change Index) decreasing by 12.6%. The increase in the operational productivity of the container shipping lines was mainly attributed to the high rise in ocean freight rates rather than an increase in fleet size or ship technical efficiency. However, the deep-sea shipping lines (i.e. HMM and SM lines) experienced increases in both the TCI and TECI, which was not the case for other shipping lines(i.e. Intra-Asian short-sea shipping lines). The intra-Asian short-sea shipping lines enhance their productivity due to the TCI but failed to appreciate the cost savings of the increased fleet effects due to the low SECI(Scale Efficiency Change Index) values.

• Journal of Advanced Navigation Technology
• /
• v.27 no.1
• /
• pp.16-22
• /
• 2023

Terrorists have been attacking in the vulnerable points of aviation sector with the diverse methods of attacks. Recently, Vulnerability is increasing because the Modus Operandi of Terrorism is carried out by exploitation of people in the form of employee working in aviation sector whose role provides them with privileged access to secured locations, secured items or security sensitive information. Furthermore, cases of insider threat are rising across the world with the phenomenon of personal radicalization through internet and social network service. The government of ROK must respond to insider threat could exploit to acts of unlawful interference and the security regulations should be established to prevent from insider threat in advance refer to the acts of unlawful interference carried out in foreign countries and the recommendations by USA, UK and ICAO.

• Smart Media Journal
• /
• v.12 no.2
• /
• pp.42-55
• /
• 2023

The combination of blockchain (BC), artificial Intelligence (AI), quantum computing (QC), and the Internet of Things (IoT) can potentially transform various industries and domains, including healthcare, logistics, and finance. In this paper, we look at the trends and developments in integrating these emerging technologies and the potential benefits and challenges that come with them. We present a conceptual framework for integrating BC, AI, QC, and IoT and discuss the framework's key characteristics and challenges. We also look at the most recent cutting-edge research and developments in integrating these technologies, as well as the key challenges and opportunities that come with them. Our analysis highlights the potential benefits of integrating the technologies and looks to increased security, privacy, and efficiency to provide insights into the future of these technologies.

• Journal of Navigation and Port Research
• /
• v.48 no.1
• /
• pp.27-33
• /
• 2024

Following the COVID-19 pandemic, congestion within container terminals has led to a significant increase in waiting time and turnaround time for external trucks, resulting in a severe inefficiency in gate-in and gate-out operations. In response, port authorities have implemented a Vehicle Booking System (VBS) for external trucks. It is currently in a pilot operation. However, due to issues such as information sharing among stakeholders and lukewarm participation from container transport entities, its improvement effects are not pronounced. Therefore, this study proposed a deep learning-based predictive model for external trucks turnaround time as a foundational dataset for addressing problems of waiting time for external trucks' turnaround time. We experimented with the presented predictive model using actual operational data from a container terminal, verifying its predictive accuracy by comparing it with real data. Results confirmed that the proposed predictive model exhibited a high level of accuracy in its predictions.

• Journal of Korean Society of Transportation
• /
• v.18 no.1
• /
• pp.75-85
• /
• 2000

In existing models in optimization, the crisp data improve has been used in the objective or constraints to derive the optimal solution, Besides, the subjective environments are eliminated because the complex and uncertain circumstances were regarded as Probable ambiguity, In other words those optimal solutions in the existing models could be the complete satisfactory solutions to the objective functions in the Process of application for industrial engineering methods to minimize risks of decision-making. As a result of those, decision-makers in location Problems couldn't face appropriately with the variation of demand as well as other variables and couldn't Provide the chance of wide selection because of the insufficient information. So under the circumstance. it has been to develop the model for the location and size decision problems of logistics facility in the use of the fuzzy theory in the intention of making the most reasonable decision in the Point of subjective view under ambiguous circumstances, in the foundation of the existing decision-making problems which must satisfy the constraints to optimize the objective function in strictly given conditions in this study. Introducing the Process used in this study after the establishment of a general mixed integer Programming(MIP) model based upon the result of existing studies to decide the location and size simultaneously, a fuzzy mixed integer Programming(FMIP) model has been developed in the use of fuzzy theory. And the general linear Programming software, LINDO 6.01 has been used to simulate, to evaluate the developed model with the examples and to judge of the appropriateness and adaptability of the model(FMIP) in the real world.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.13 no.2
• /
• pp.115-123
• /
• 2018

This study is empirically intended to put forward the modeling and process improvement measures for the SCM capability in small venture firms. The findings are summarized as follows. There were strategic alliance, technological development and centralization in the modeling of strategic planning for supply chain, not the least of which is strategic alliance, followed by centralization and technological development. There were routing scheduling, network integration and third party logistics outsourcing in decision making, not the least of which was network integration. There were customer service management, productivity management and quality management in management control, not the least of which was quality management. And there were order management choice, pricing demand, shipment delivery and customer management in transaction support system, not the least of which was order management choice. As for the above-mentioned findings, to maximize the SCM capability and operate the optimized process in small venture firms, the existing strategic alliances can optimize the quality management and stabilize the transaction support system through the network sharing and integration from the perspective of relevant organizational members' capability and process improvement. And the strategic linkage between firms can maximize the integrated capability of information system beyond the simple exchange relation between electronic data, achieving a differentiated competitive advantage. Consequently, the systematization and centralization for the maximization of SCM capability, including the infrastructure construction based on the system compatibility and reliability for information integration, should be preceded before the modeling of the integrated capability for optimum supply chain and the best process management in the smart era.

• Journal of Korea Society of Industrial Information Systems
• /
• v.23 no.1
• /
• pp.13-21
• /
• 2018

AGVS (Automated Guided Vehicle System) is a core technology of logistics automation which automatically moves specific objects or goods within a certain work space. Conventional AGVS generally requires the in-door localization system and each AGV equips expensive sensors such as laser, magnetic, inertial sensors for the route recognition and automatic navigation. thus the high installation cost is inevitable and there are many restrictions on route(path) modification or expansion. To address this issue, in this paper, we propose a cost-effective and scalable AGV based on a light-weight pattern recognition technique. The proposed pattern recognition technology not only enables autonomous driving by recognizing the route(path), but also provides a technique for figuring out the loc ation of AGV itself by recognizing the simple patterns(bar-code like) installed on the route. This significantly reduces the cost of implementing AGVS as well as benefiting from route modification and expansion. In order to verify the effectiveness of the proposed technique, we first implement a pattern recognition algorithm on a light-weight MCU(Micro Control Unit), and then verify the results by implementing an MCU_controlled AGV prototype.