• The Journal of Information Systems
• /
• v.33 no.2
• /
• pp.125-142
• /
• 2024

Purpose The purpose of this study is to identify and analyze the key factors influencing congestion in the in-out transportation at port container terminals, and to design of a predictive model for in-out congestion based on these analysis. This study focused on architecting a deep learning-based predictive model. Design/methodology/approach This study was conducted through the following methodology. First, hypotheses were established and data were analyzed to examine the impact of vessel schedules and external truck schedules on in-out transportation. Next, explored time series forecasting models to a design the architecture for deep learning-based predictive model. Findings According to the empirical analysis results, this study confirmed that vessel schedules significantly affect in-out transportation. Specifically, the volume of transportation increases as the vessel arrival/departure time and the cargo cutoff time approach. Additionally, significant congestion patterns in transportation volume depending on the day of the week and the time of day were observed.

• Journal of Engineering Education Research
• /
• v.9 no.2
• /
• pp.52-70
• /
• 2006

Systems Engineering(SE), as a special discipline evolved from multidisciplinary and interdisciplinary design knowledges and practical lessons learned from development practices, is required to develop today's ever-growing large complex systems. As computer speed and analytic sophistication accelerate their applications, modern society's needs have become increasingly varied and complex. Rapid advances in Systems Engineering and its education programs among the developed countries demonstrate their needs as an alternative to what is lacking from traditional disciplinary engineering to meet new challenges of our changing social environments. Systems Engineering is an interdisciplinary approach that includes both management and technical processes. Its processes, methods, and tools are used to evolve, define, and verify an integrated, life-cycle balanced set of system solution that satisfy customer needs and requirements. The process methodology offers a top-down comprehensive, iterative and recursive problem solving process which includes the stating the problems, investigating the alternatives, architecting and modeling the system, integrating and operating the system, assessing and re-evaluating the system performances. The purpose of this paper is to research the cases of SE educational programs for both domestic and other developed countries and to propose recommendations for the domestic SE educational programs in the future.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.15 no.4
• /
• pp.19-24
• /
• 2015

Security has been a major concern in software development. Security tactics are reusable building blocks providing a general solution for recurring security concerns at the architectural level. They are often used together with architectural patterns which provide a general solution for architecting software systems. However, use of security tactics in architectural patterns has been understood only conceptually without concrete understanding of how their involved elements should be structurally and behaviorally co-designed. In this paper, we present model-driven analyses of security tactics in the context of Broker architectural patterns and provide evidences of the analyses in real world case studies.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.3
• /
• pp.1014-1025
• /
• 2020

Cloud computing services changed the way the data are managed across the healthcare system that can improve patient care. Currently, most healthcare organizations are using cloud-based applications and related services to deliver better healthcare facilities. But architecting a cloud-based healthcare system needs deep knowledge about the working nature of these services and the requirements of the healthcare environment. The success is based on the usage of appropriate cloud services in the architecture to manage the data flow across the healthcare system.Cloud service providers offer a wide variety of services to ingest, store and process healthcare data securely. The top three public cloud providers- Amazon, Google, and Microsoft offers advanced cloud services for the solution that the healthcare industry is looking for. This article proposes a framework that can effectively utilize cloud services to handle the data flow among the various stages of the healthcare infrastructure. The useful cloud services for ingesting, storing and analyzing the healthcare data for the proposed framework, from the top three cloud providers are listed in this work. Finally, a cloud-based healthcare architecture using Amazon Cloud Services is constructed for reference.

• ETRI Journal
• /
• v.40 no.6
• /
• pp.759-773
• /
• 2018

Uncore components such as on-chip memory systems and on-chip interconnects consume a large amount of energy in emerging embedded applications. Few studies have focused on next-generation analytical models for future chip-multiprocessors (CMPs) that simultaneously consider the impacts of the power consumption of core and uncore components. In this paper, we propose a convex-optimization approach to design heterogeneous uncore architectures for embedded CMPs. Our convex approach optimizes the number and placement of memory banks with different technologies on the memory layer. In parallel with hybrid memory architecting, optimizing the number and placement of through silicon vias as a viable solution in building three-dimensional (3D) CMPs is another important target of the proposed approach. Experimental results show that the proposed method outperforms 3D CMP designs with hybrid and traditional memory architectures in terms of both energy delay products (EDPs) and performance parameters. The proposed method improves the EDPs by an average of about 43% compared with SRAM design. In addition, it improves the throughput by about 7% compared with dynamic RAM (DRAM) design.