• Journal of Appropriate Technology
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• v.6 no.2
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• pp.136-145
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• 2020

Agriculture is the backbone of the economy of most developing countries. In these countries, agriculture or farming is mostly done manually with little integration of machinery, intelligent systems and data monitoring. Irrigation is an essential process that directly influences crop production. The fluctuating amount of rainfall per year has led to the adoption of irrigation systems in most farms. The absence of smart sensors, monitoring methods and control, has led to low harvests and draining water sources. In this research paper, we introduce a 433 MHz Radio Frequency and 2G based Smart Irrigation Meter System and a water prepayment system for rural areas of Tanzania with no reliable internet coverage. Specifically, Ngurudoto area in Arusha region where it will be used as a case study for data collection. The proposed system is hybrid, comprising of both weather data (evapotranspiration) and soil moisture data. The architecture of the system has on-site weather measurement controllers, soil moisture sensors buried on the ground, water flow sensors, a solenoid valve, and a prepayment system. To achieve high precision in linear and nonlinear regression and to improve classification and prediction, this work cascades a Dynamic Regression Algorithm and Naïve Bayes algorithm.

• Journal of the Korean Geographical Society
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• v.45 no.1
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• pp.49-74
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• 2010

Cities are major sources of greenhouse gas emissions but also suitable places for implementing proactive climate mitigation and adaptation strategies. Based on the interdisciplinary review of literature, we categorize the current discussion about urban climate mitigation and adaptation planning, policy and practices into four perspectives - sustainability science, global change science, multilevel governance, and structural engineering. While these four schools of thought have distinct perspectives rooted in different disciplinary lenses, our synthesis of the literature identifies several universal themes that are common to all of the perspectives in the context of combating threats posed by climate change. The Portland case study illustrates that a city can make changes to reduce greenhouse gas emissions and increase adaptive capacity to climate change impacts by implementing smart growth, devising local climate action plans that target emission reductions in various sectors, recognizing the interactions and influences of multiple scales of governance, and supporting the installation of various green infrastructures that contribute to green economy. Furthermore, a university can serve as a hub in this climate mitigation and adaptation arena by connecting various levels of community organizations in both public and private sectors, creating innovative research centers and spatially explicit green infrastructure, designing impact assessments and campus carbon inventories, and engaging students and the larger community through service learning.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.1
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• pp.92-111
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• 2021

As fine dust negatively affects disease, industry and economy, the people are sensitive to fine dust. Therefore, if the occurrence of fine dust can be predicted, countermeasures can be prepared in advance, which can be helpful for life and economy. Fine dust is affected by the weather and the degree of concentration of fine dust emission sources. The industrial sector has the largest amount of fine dust emissions, and in industrial complexes, factories emit a lot of fine dust as fine dust emission sources. This study targets regions with old industrial complexes in local cities. The purpose of this study is to explore the factors that cause fine dust and develop a predictive model that can predict the occurrence of fine dust. weather data and fine dust data were used, and variables that influence the generation of fine dust were extracted through multiple regression analysis. Based on the results of multiple regression analysis, a model with high predictive power was extracted by learning with a machine learning regression learner model. The performance of the model was confirmed using test data. As a result, the models with high predictive power were linear regression model, Gaussian process regression model, and support vector machine. The proportion of training data and predictive power were not proportional. In addition, the average value of the difference between the predicted value and the measured value was not large, but when the measured value was high, the predictive power was decreased. The results of this study can be developed as a more systematic and precise fine dust prediction service by combining meteorological data and urban big data through local government data hubs. Lastly, it will be an opportunity to promote the development of smart industrial complexes.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.149-170
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• 2023

This study analyzed the factors influencing users' intention to use the Metaverse by applying the extended technology acceptance model. In other words, the factors affecting users' intention to use the Metaverse were defined as technical characteristics (telepresence, interoperability, seamlessness, concurrence, and economy flow) and personal characteristics (social influence and perceived enjoyment) from the perspective of the Extended Technology Acceptance Model. For this purpose, a survey was conducted among men and women of various ages ranging from teenagers to 60s, and the data collected from 327 participants were analyzed using SPSS 22.0 and Smart PLS 4.0. The results showed that perceived usefulness and perceived ease of use, which are antecedents of the Extended Technology Acceptance Model, influence the intention to use Metaverse, and perceived ease of use influences perceived usefulness. Telepresence, interoperability and economy flow were found to have a positive effect on perceived usefulness, and interoperability, seamlessness and concurrence were found to have a positive effect on perceived ease of use. In addition, social influence and perceived enjoyment had a positive effect on intention to use the Metaverse. This study is significant in that it empirically analyzed the factors of users' acceptance of the Metaverse, which is attracting attention as a new platform that will bring significant changes to our daily lives and platform consumption environments.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.7
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• pp.1455-1463
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• 2017

Global economy has recorded low growth, low consumption, high unemployment rate, high risk, short boom and long recession. As a result, maritime economy declines and the reduction of maintenance costs is inevitable. Thus, Studies such as green ship, eco ship, and smart ship are being actively conducted to save energy of ship. Power management system that use batteries in green ship is an important research area. In this paper, we analyze the heavy load control of a power management system of a general ship using only a generator, and study a heavy load control algorithm for a battery connected power management system. To study this, a structure of battery connected power management system is proposed and a battery connected power simulator was constructed based on the proposed system. Through the simulator, the operation of the battery according to the heavy load control is defined and confirmed in the battery connected power management system.

• Journal of Digital Convergence
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• v.10 no.4
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• pp.119-128
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• 2012

The changing paradigm due to the emergence of smart phone and the rapid spread of internet-based social media usage is considerably impacting the politics, economy, society, culture and many other fields. As the new communication tools, social media is receiving spotlight for its advantage of low cost but high efficiency. With the accelerating of the fulfillment of knowledge-based economy, country image is more and more recognized as the core competitiveness of a country. Therefore, it is very important to grasp the factors that affect country image. In this study, a literature review on the factors provided was conducted and affecting factors on country image were analyzed. In addition, on the assumption that social media, which are widely used on current days, have meaningful effects on country image and purchasing intention, features of social media are added as variables and analyzed. The results of analysis show that social media has meaningful effects on the country image and purchasing intention.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.156-159
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• 2013

The ICT technology by using smartphone is leading the world. Apple opened the smart age with its smartphone on the first place in the world. In 2013, Samsung of Korea is spotlighted in the world, but China will run after Samsung with medium- and low-priced smartphones equipped with functionality and low and medium prices after 2014. That is, the life cycle of ICT technology gets shorter, and the volume of investment is increased. There is increasing uncertainty of enterprises and nations because the expanded volume of investment. Therefore, it is very important to predict emerging ICT technology, and investment development. Korea based on the creative economy is at the point of strengthening ICT. Therefore, this study aims to analyze intellectual property rights (patent) and the ICT market environment for the emerging ICT technology. The result of analysis will contribute to studying the intellectual property rights (patent) and the R&D matrix module in the ICT market environment for discovering and predicting national emerging ICT technology.

• Journal of the Korea Convergence Society
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• v.10 no.11
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• pp.327-333
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• 2019

The Fuel-cut driving is started when the acceleration pedal released with transmission gear engaged. Fuel economy of the vehicle improves by active fuel-cut driving. A deep-learning technique is proposed to predict fuel-cut driving with vehicle speed, acceleration and road gradient data in the study. It's 3~10 of hidden layers and 10~20 of variables and is applied to the 9600 data obtained in the test driving of a vehicle in the road of 12km. Its accuracy is about 84.5% with 10 variables, 7 hidden layers and Relu as activation function. Its error is regarded from the fact that the change rate of input data is higher than the rate of fuel consumption data. Therefore the accuracy can be better by the normalizing process of input data. It's unnecessary to get the signal of vehicle injector or OBD, and a deep-learning technique applied to the data to be got easily, like GPS. It can contribute to eco-drive for the computing time small.

• Journal of Digital Convergence
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• v.19 no.3
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• pp.161-166
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• 2021

The macroeconomic concept represents the movement of a country's economy, and it affects the overall economic activities of business, government, and households. In the macroeconomy, by looking at changes in national income, inflation, unemployment, currency, interest rates, and raw materials, it is possible to understand the effects of economic actors' actions and interactions on the prices of products and services. The US Federal Reserve System (FED) is leading the world economy by offering various stimulus measures to overcome the corona economic recession. Although the stock price continued to decline on March 20, 2020 due to the current economic recession caused by the corona, the US S&P 500 index began rebounding after March 23 and to 3,694.62 as of December 15 due to quantitative easing, a powerful stimulus for the FED. Therefore, the FED's economic stimulus measures based on macroeconomic indicators are more influencing, rather than judging the stock price forecast from the corporate financial statements. Therefore, this study was conducted to reduce losses in stock investment and establish sound investment by analyzing the FED's economic stimulus measures and its effect on stock prices.

• Journal of the Korea Convergence Society
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• v.12 no.7
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• pp.31-36
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• 2021

As the global economy stagnated due to the Corona 19 virus from Wuhan, China, most countries, including the US Federal Reserve System, introduced policies to boost the economy by increasing the amount of money. Most of the stock investors tend to invest only by listening to the recommendations of famous YouTubers or acquaintances without analyzing the financial statements of the company, so there is a high possibility of the loss of stock investments. Therefore, in this research, I have used artificial intelligence deep learning techniques developed under the existing automatic trading conditions to analyze and predict macro-indicators that affect stock prices, giving weights on individual stock price predictions through correlations that affect stock prices. In addition, since stock prices react sensitively to real-time stock market news, a more accurate stock price prediction is made by reflecting the weight to the stock price predicted by artificial intelligence through stock market news text mining, providing stock investors with the basis for deciding to make a proper stock investment.