• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.21 no.10
• /
• pp.8-15
• /
• 2020

A forecasting method using deep learning does not have consistent results due to the differences in the characteristics of the dataset, even though they have the same forecasting models and parameters. For example, the forecasting model X optimized with dataset A would not produce the optimized result with another dataset B. The forecasting model with the characteristics of the dataset needs to be optimized to increase the accuracy of the forecasting model. Therefore, this paper proposes novel optimization steps for outlier removal, dataset classification, and a CNN-LSTM-based hyperparameter tuning process to forecast the daily power usage of a university campus based on the hourly interval. The proposing model produces high forecasting accuracy with a 2% of MAPE with a single power input variable. The proposing model can be used in EMS to suggest improved strategies to users and consequently to improve the power efficiency.

• Journal of Wetlands Research
• /
• v.22 no.4
• /
• pp.245-256
• /
• 2020

The purpose of this study is to evaluate the technological competitiveness of the environmental industry with developed countries in order to establish an international market expansion strategy of the Korean environmental industry and technology. In order to evaluate the competitiveness of the environmental industry and technology, core technologies were classified by the environmental industry sectors based on the classification system of the domestic and international environmental industry and technology. After developing the evaluation index data, the Delphi analysis, journal and patent analysis, as well as the export and import analysis were carried out and the standardization analysis was performed on the index data. Moreover, the weights of each evaluation index were calculated using the AHP(Analytic Hierarchy Process) method and the evaluation results of competitiveness of the environmental industry and technology in Korea, the United States, the United Kingdom, Germany, and France were derived. As a result of the evaluation, the United States was rated with the highest technological competitiveness in all the environmental industry sectors, while Korea got the lowest technological competitiveness rating compared to the 4 developed countries. In particular, Korea got the lowest level of technological competitiveness in the sector of multi-media environmental management and development for a sustainable social system. Therefore, in order for the Korean environmental industry and technology to enter the global advanced market, it is necessary to strengthen the competitiveness through the development of the fourth environmental industry based on IoT(Internet of Things), cloud, big data, mobile, and AI(Artificial Intelligence), which are currently the country's domestic strengths.

• The Journal of Bigdata
• /
• v.6 no.2
• /
• pp.17-28
• /
• 2021

Current development of technologies related to 4^th industrial revolution and the pandemic of COVID-19 lead the rapid expansion of e-marketplace. The level of competition among several companies gets increased by introducing different strategies. To cope with the current change in the market and satisfy the customers who request the better delivery service, the new concept, fulfillment, has been introduced. It makes the leadtime of process from order picking to delivery reduced and the efficiency improved. Still, the efficiency of operation in fulfillment centers constrains the service level of the entire delivery process. In order to solve this problem, several different approaches for demand forecasting and coordinating supplies using Bigdata, IoT and AI, which there exists the trivial limitations. Because it requires the most lead time for operation and leads the inefficiency the process from picking to packing the ordered items, the logistics service providers should try to automate this procedure. In this research, it has been proposed to develop the efficient plans to automate the process to move the ordered items from the location where it stores to stage for packing using AGV and AMR. The efficiency of automated devices depends on the number of items and total number of devices based on the demand. Therefore, the result of simulation based on several different scenarios has been analyzed. From the result of simulation, it is possible to identify the several factors which should be concerned for introducing the automated devices in the fulfillment centers. Also, it can be referred to make the optimal decisions based on the efficiency metrics.

• Convergence Security Journal
• /
• v.23 no.3
• /
• pp.13-20
• /
• 2023

This study conducted a digital forensic analysis of Signal and Telegram, two secure messengers widely used in the Android environment. As mobile messengers currently play an important role in daily life, data management and security within these apps have become very important issues. Signal and Telegram, among others, are secure messengers that are highly reliable among users, and they safely protect users' personal information based on encryption technology. However, much research is still needed on how to analyze these encrypted data. In order to solve these problems, in this study, an in-depth analysis was conducted on the message encryption of Signal and Telegram and the database structure and encryption method in Android devices. In the case of Signal, we were able to successfully decrypt encrypted messages that are difficult to access from the outside due to complex algorithms and confirm the contents. In addition, the database structure of the two messenger apps was analyzed in detail and the information was organized into a folder structure and file format that could be used at any time. It is expected that more accurate and detailed digital forensic analysis will be possible in the future by applying more advanced technology and methodology based on the analyzed information. It is expected that this research will help increase understanding of secure messengers such as Signal and Telegram, which will open up possibilities for use in various aspects such as personal information protection and crime prevention.

• Management & Information Systems Review
• /
• v.36 no.3
• /
• pp.167-184
• /
• 2017

In this study, we investigated the effect of offline banking trust on smart banking trust. As influencing factors of smart banking trust, this study compared offline banking trust, smart banking's system quality, and information quality. For the empirical study, 186 questionnaire data were collected from smart banking users and the data were analyzed using Smart-PLS 2.0. As results, it was verified that there is trust transfer in FinTech service, by the significant effect of offline banking trust on smart banking trust. And it was proved that the effect of offline banking trust on smart banking trust is lower than that of smart banking itself. The contribution of this study can be seen in both academic and industrial aspects. First, it is the contribution of the academic aspect. Previous studies on banking were focused on either offline banking or smart banking. But this study, focus on the relationship between offline banking and online banking, proved that offline banking trust affects smart banking trust. Next, it is the industrial contribution. This study showed that offline banking characteristics of traditional commercial banks affect the trust of emerging smart banking service. This means that the emerging FinTech companies are not advantageous in the competition of trust building compared to traditional commercial banks. Unlike traditional commercial banks, the emerging FinTech is innovating the convenience of customers by arming them with new technologies such as mobile Internet, social network, cloud technology, and big data. However, these FinTech strengths alone can not guarantee sufficient trust needed for financial transactions, because banking customers do not change a habit or an inertia that they already have during using traditional banks. Therefore, emerging FinTech companies should strive to create destructive value that reflects the connection with various Internet services and the strength of online interaction such as social services, which have an advantage over customer contacts. And emerging FinTech companies should strive to build service trust, focused on young people with low resistance to new services.