• Journal of the Korea Society of Computer and Information
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• v.17 no.12
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• pp.137-148
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• 2012

In this paper, we proposed the location-aware coordinate routing algorithm for improving the performance of routing algorithm by using ZigBee in Smart Grid environment. A distributed address allocation scheme used an existing algorithm that has wasted in address space. The x, y and z coordinate axes from divided address space of 16 bit to solve this problems. However, coordinate routing does not take account of wireless link condition. If wiress link condition is not considered, when the routing table is updated, the nodes with bad link conditions are updated in the routing table and can be chosen as the next hop. This brings out the retransmissions because of received packet's errors. Also, because of these retransmissions, additional power is consumed. In this paper, we propose the location-aware coordinate routing algorithm considering wiress link condition, where reliable data transmission is made and the consumed enegy is minimize. and we compared and connected region and transition region of ZigBee location based routing in the aspect of average number of multi hops, subordinate packet delivery ratio, delay time, and energy consumption of proposed algorithm. It turned out that there were improvements in performances of each items.

• Journal of Climate Change Research
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• v.9 no.4
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• pp.453-460
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• 2018

The purpose of this study is to analyze the potential quantity of Korean Offset Credits (KOC) resulting from Certified Emission Reductions (CER) in 98 domestic Clean Development Mechanism (CDM) projects that were registered with the United Nations Framework Convention on Climate Change (UNFCCC) as of the end of 2016. Our results show that the total amount of potential KOC is 62,774 kt CO2eq. The potential KOC is only 23.4% of the total CER Issuance. During the first phase, this will be 3.2% of the allocated volume. This is because many projects are related to Renewable Portfolio Standard (RPS), HFC-23, and adipic acid N2O. There is a strong bias in some sectors and projects which could act as market distortion factors. Therefore, it is necessary to expand the target CDM project and activate non CDM offset projects. RPS projects bring fundamental changes to the energy sector, and it is worth reconsidering their acceptability. A wide variety of policy incentives are needed to address strong biases toward certain sectors and projects. The offset scheme has the advantage of allowing entities to reduce their GHG emissions cost effectively through a market mechanism as well as enabling more entities to participate in GHG reduction efforts both directly and indirectly. In contrast, having an inadequate offset scheme range and size might decrease the effort on GHG reduction or concentrate available resources on specific projects. As such, it is of paramount importance to design and operate the offset scheme in such a way that it reflects the situation of the country.

• Journal of Climate Change Research
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• v.9 no.3
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• pp.263-271
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• 2018

Considering the high needs of recipient countries and the regional biases of Korean Official Development Assistance (ODA) policy, increases in the total amount of ODA and allocation to the forest sector are needed for Central Asia. In Tajikistan, illegal harvesting and grazing cause a gradual decrease in forest area. The Tajikistan government conserves forests by requesting international cooperation and establishing a policy for sustainable management of forest resources and prevention of further damages. To suggest suitable strategies and plans, the current statuses of forests and forestry were investigated, and ODA projects that can illustrate successful Korean experiences of forestation were conceived based on various geographical features and local conditions of Tajikistan in this study. Forest resource creation type projects (fuelwood and Tugai forest restoration) could result in energy independence and enhancement of ecosystem service such as a climate regulation and an increase in biodiversity. Community development type projects (introduction of ecotourism, agroforestry, and planting of fruit trees) can contribute to job creation and encourage participation of local residents, thus improving their incomes. In addition, establishment of a project foundation, including education, training, and a national forest inventory, would help those projects be sustainable in the long-term.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.6
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• pp.33-41
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• 2018

The idea of the Internet of Things as a platform on the Smart Campus has become increasingly popular. It requires an infrastructure consisting of communication networks, sensor nodes and gateways to connect to the Internet. Each sensor node is responsible for gathering data from the environment. This document outlines a network of wireless sensors on the Internet for the application of Smart Campus monitoring. Wireless sensor network Monitoring have become a complete solution to using a low power implementation and integrated systems. The numerous restrictions however result from the low communication range, the limited computing power, the lack of availability of the network protocol, the lack of programming security and the security failures in the areas of confidentiality, integrity and availability. A new security technique and its functionality for WSNM nodes developed. Development in the research of a secure network and suggestions for avoiding denial of service (DOS) and complexity attacks. These systems if properly implemented can provide an energy efficiency mechanism through pre-allocation and a new key from key management models with a secure routine algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.10
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• pp.1279-1286
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• 2021

The electrocardiogram (ECG) signal is a good indicator for early diagnosis of heart abnormalities. The ECG signal has a different reference normal signal for each person. And it requires lots of data to diagnosis. In this paper, we propose an adaptive OpenCL-based FPGA-GPU hybrid-layer platform to efficiently accelerate ECG signal diagnosis. As a result of diagnosing 19870 number of ECG signals of MIT-BIH arrhythmia database on the platform, the FPGA accelerator takes 1.15s, that the execution time was reduced by 89.94% and the power consumption was reduced by 84.0% compared to the software execution. The GPU accelerator takes 1.87s, that the execution time was reduced by 83.56% and the power consumption was reduced by 62.3% compared to the software execution. Although the proposed FPGA-GPU hybrid platform has a slower diagnostic speed than the FPGA accelerator, it can operate a flexible algorithm according to the situation by using the GPU.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.199-201
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• 2021

The Internet of Things (IoT) is a technology that can organically connect people and things without time and space constraints by using communication network technology and sensors, and transmit and receive data in real time. The IoT used in all industrial fields has limitations in terms of storage allocation, such as device size, memory capacity, and data transmission performance, so it is important to manage power consumption to effectively utilize the limited battery capacity. In the prior research, there is a problem in that security is deteriorated instead of improving power efficiency by lightening the security algorithm of the encryption module. In this study, we proposes a low-power security architecture that can utilize high-performance security algorithms in the IoT environment. This can provide high security and power efficiency by using relatively complex security modules in low-power environments by executing security modules only when threat detection is required based on inspection results.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.1
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• pp.31-48
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• 2024

The domestic automotive industry must consider a strategic shift from traditional automotive component manufacturing to align with future trends such as connectivity, autonomous driving, sharing, and electrification. This research conducted topic modeling on R&D projects in the future automotive sector funded by the Ministry of Trade, Industry, and Energy from 2013 to 2021. We found that topics such as sensors, communication, driver assistance technology, and battery and power technology remained consistently prominent throughout the entire period. Conversely, topics like high-strength lightweight chassis were observed only in the first period, while topics like AI, big data, and hydrogen fuel cells gained increasing importance in the second and third periods. Furthermore, this research analyzed the areas of concentrated investment for each period based on topic-specific government investment amounts and investment growth rates.

• Smart Media Journal
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• v.13 no.6
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• pp.24-34
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• 2024

If the future can be predicted from network traffic data, which is a time series, it can achieve effects such as efficient resource allocation, prevention of malicious attacks, and energy saving. Many models based on statistical and deep learning techniques have been proposed, and most of these studies have focused on improving model structures and learning algorithms. Another approach to improving the prediction performance of the model is to obtain a good-quality data. With the aim of obtaining a good-quality data, this paper applies a dense sampling technique that augments time series data to the application of network traffic prediction and analyzes the performance improvement. As a dataset, UNSW-NB15, which is widely used for network traffic analysis, is used. Performance is analyzed using RMSE, MAE, and MAPE. To increase the objectivity of performance measurement, experiment is performed independently 10 times and the performance of existing sparse sampling and dense sampling is compared as a box plot. As a result of comparing the performance by changing the window size and the horizon factor, dense sampling consistently showed a better performance.

• Journal of Korea Foresty Energy
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• v.18 no.2
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• pp.92-99
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• 1999

This study was carried out to understand the primary production of biomass, vertical biomass distribution in the stand and the difference of biomass production for part of the trees by stand density for natural Pinus densiflora forest at Mt. Baekdoo located in northeastern China. The primary production of biomass was estimated by the layers of trees, shrubs, herbs for five density classes. For the biomass estimation of the Pinus densiflora trees in stern, stembark and the above-ground tree, the regression model of logW = a + blog(D$^2$H) + c(D$^2$H) was adapted for all of the density classes where W is dry weight, D$_1$ diameter at breast height, and H, tree height. For the biomass estimation in branch and needle, and the needle area, the regression model of logW=a+blogD+cD was adapted for all of the density classes. With increasing stand density the biomass of trees increased but that of shrubs and herbs decreased. Net primary production of biomass in parts of the tree also increased with increasing stand density. However the percentage of the needle biomass among the total biomass in the above-ground tree decreased with increasing stand density. Consequently, primary production rate of biomass in the above-ground tree increased. The primary production of biomass for each part of the trees in natural Pinus densiflora natural forests showed in descending order : stern, needle, branch, and stembark regardless of stand density.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.4
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• pp.181-196
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• 2019

Floods can be caused by a variety of factors, and the main cause of floods is the exceeding of urban drainage system or river capacity. In addition, rainfall frequently occurs that causes large watershed runoff. Since the existing methodology of preparing for flood risk map is based on hydraulic and hydrological modeling, it is difficult to analyse for a large area because it takes a long time due to the extensive data collection and complex analysis process. In order to overcome this problem, this study proposes a methodology of mapping for flood vulnerable area that considered the surface runoff mechanism. This makes it possible to reduce the time and effort required to estimate flood vulnerabilities and enable detailed analysis of large areas. The target area is Seoul, and it was confirmed that flood damage is likely to occur near selected vulnerable areas by verifying using 2×2 confusion matrix and ROC curve. By selecting and prioritizing flood vulnerable areas through the surface runoff mechanism proposed in this study, the establishment of systematic disaster prevention measures and efficient budget allocation will be possible.