KJCKorea
Journal Central

Achieving carbon neutrality and addressing power shortages in developing countries have emerged as key challenges in the future energy sector. Urban energy consumption and transportation-related carbon emissions are causing serious environmental problems, highlighting the urgent need for eco-friendly power infrastructure. This paper proposes an intelligent IoT-based, off-grid, eco-friendly EV charging platform that operates independently through photovoltaic (PV) generation and energy storage systems (ESS). The infrastructure includes three models—fixed, cartridge-based mobile, and mobile bus charging stations—to enhance energy and sharing efficiency in urban areas. An AI-based charging optimization scenario was developed and simulated, with algorithms to optimize charging schedules based on EV demand and PV generation forecasts. This system integrates intelligent charging and forecasting mechanisms while promoting a sustainable distributed energy system, contributing to carbon neutrality.

In 2014, the number of contests held in Korea was about 4,000, and it is expected to increase to about 10,000 by 2024. The number of contests is steadily increasing by sector. It's becoming increasingly important for contest organisers to select topics efficiently to maximise impact while reducing costs. This increases the need for data-driven analytical tools to help them do this. In this study, we propose a framework to analyse 7,587 (5,646 after deduplication) comic contest data from 2011 to 2024 using text mining techniques. Through an analysis methodology that combines TF-IDF and Word2Vec, we propose a methodology that reflects the frequency of keyword occurrence and semantic associations together, and comprehensively identifies trends over time to provide practical insights for contest planners. The framework creates a ranking of important keywords through a composite vector of frequency-based importance scores and semantics. Finally, it calculates a trend metric that spans the entire time period by averaging the percentage change from year to year.

Recent learning methods utilizing AI-based platforms have become a significant topic in the field of education, with active efforts to implement them in educational settings. This study verified the effects of applying AI-based adaptive learning education to 187 nursing students participating in adult nursing classes at B University in C region. After implementing an educational model based on Bloom's taxonomy of learning objectives, results showed statistically significant improvements in learning motivation, learning flow, academic achievement, and learning transfer. Students reported high satisfaction with the system's learning support, comprehensibility, learning effectiveness, convenience, and personalization aspects. This study suggests that AI-based personalized learning effectively enhances various learning elements for students, and provides foundational data for expanding its application to various subjects in the future.