• Journal of Appropriate Technology
• /
• v.5 no.2
• /
• pp.106-113
• /
• 2019

In order to respond to the global environmental issues, developed countries have been helped the developing countries as the Official Development Assistance (ODA). It is important to understand technology needs of the developing countries to provide the optimum solutions. In this study, the information of the environmental R&D dealing with appropriate technology were comprehensively collected based on the conducted R&D projects from the ministry of environment in Korea. The technology needs by UNFCCC (United Nations Framework Convention on Climate Change) and Korean government were analyzed named as TNA and CPS according to the target developing countries. In South-East Asia and Africa region, there were technology needs on water, biota, air, solid wastes, infrastructures and resources. And they were related to the issues such as environmental pollution, construction, climate change, biodiversity, energy and water management. The technology needs by UNFCCC and Korean government were also compared. Furthermore, the environmental R&D on appropriate technology should be focused on localization and maintenance to provide sustainable solutions to the developing countries.

• Economic and Environmental Geology
• /
• v.53 no.5
• /
• pp.609-617
• /
• 2020

After introducing the industry 4.0 policy, Korean government announced 'Digital New Deal' and 'Green New Deal' as 'Korean New Deal' in 2020. We analyzed Korea Institute of Geoscience and Mineral Resources (KIGAM)'s research projects related to that policy and conducted markets analysis focused on Digital Twin and environmental monitoring technologies. Regarding 'Data Dam' policy, we suggested the digital geo-contents with Augmented Reality (AR) & Virtual Reality (VR) and the public geo-data collection & sharing system. It is necessary to expand and support the smart mining and digital oil fields research for '5th generation mobile communication (5G) and artificial intelligence (AI) convergence into all industries' policy. Korean government is suggesting downtown 3D maps for 'Digital Twin' policy. KIGAM can provide 3D geological maps and Internet of Things (IoT) systems for social overhead capital (SOC) management. 'Green New Deal' proposed developing technologies for green industries including resource circulation, Carbon Capture Utilization and Storage (CCUS), and electric & hydrogen vehicles. KIGAM has carried out related research projects and currently conducts research on domestic energy storage minerals. Oil and gas industries are presented as representative applications of digital twin. Many progress is made in mining automation and digital mapping and Digital Twin Earth (DTE) is a emerging research subject. The emerging research subjects are deeply related to data analysis, simulation, AI, and the IoT, therefore KIGAM should collaborate with sensors and computing software & system companies.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.9 no.1
• /
• pp.1-16
• /
• 2007

The use of information on natural resources is indispensable to most agricultural activities to avoid disasters, to improve input efficiency, and to increase lam income. Most information is prepared and managed at a spatial scale called the "Hydrologic Unit" (HU), which means watershed or small river basin, because virtually every environmental problem can be handled best within a single HU. South Korea consists of 840 such watersheds and, while other watershed-specific information is routinely managed by government organizations, there are none responsible for agricultural weather and climate. A joint research team of Kyung Hee University and the Agriculture, forestry and Fisheries Information Service has begun a 4-year project funded by the Ministry of Agriculture and forestry to establish a watershed-specific agricultural weather information service based on "high definition" digital climate maps (HD-DCMs) utilizing the state of the art geospatial climatological technology. For example, a daily minimum temperature model simulating the thermodynamic nature of cold air with the aid of raster GIS and microwave temperature profiling will quantify effects of cold air drainage on local temperature. By using these techniques and 30-year (1971-2000) synoptic observations, gridded climate data including temperature, solar irradiance, and precipitation will be prepared for each watershed at a 30m spacing. Together with the climatological normals, there will be 3-hourly near-real time meterological mapping using the Korea Meteorological Administration's digital forecasting products which are prepared at a 5 km by 5 km resolution. Resulting HD-DCM database and operational technology will be transferred to local governments, and they will be responsible for routine operations and applications in their region. This paper describes the project in detail and demonstrates some of the interim results.

• The KIPS Transactions:PartC
• /
• v.8C no.1
• /
• pp.97-104
• /
• 2001

In this paper, we present a new hierarchical model for performance analysis of channel allocation and packet service protocol in wireless n network. The proposed hierarchical model consists of two parts : upper and lower layer models. The upper layer model is the structure state model representing the state of the channel allocation and call service. The lower layer model, which captures the performance of the system within a given structure state, is the wireless packet retransmission protocol model. These models are developed using SRN which is an modeling tool. SRN, an extension of stochastic Petri net, provides compact modeling facilities for system analysis. To get the performance index, appropriate reward rates are assigned to its SRN. Fixed point iteration is used to determine the model parameters that are not available directly as input. That is, the call service time of the upper model can be obtained by packet delay in the lower model, and the packet generation rates of the lower model come from call generation rates of the upper model.

• Journal of the Korean Society for information Management
• /
• v.37 no.2
• /
• pp.171-195
• /
• 2020

This study analyzed the collaborative process in time series by dismantling the edit log big data of Wikipedia Korea, a representative online collaboration community, from early 2002 to 2019. Analysis elements were extracted from the document edit records, formatted in standardized XML, and analyzed using Python and R. The ways of editors' contribution, the characteristics of data contents, and the trend of document creation were explained by the analysis. An active contribution of a small set of editors and a loose participation of the majority were revealed. In addition, sociocultural characteristics that appear in online communities were also found in Wikipedia Korea. A new, diverse set of external resources is necessary to sustain the collective intelligence. An effort to settle new editors into the wikipedia community and an openness through circulation structure to avoid the exclusiveness of the management group are suggested.

• Journal of Satellite, Information and Communications
• /
• v.11 no.2
• /
• pp.29-35
• /
• 2016

High-availability seamless redundancy (HSR) is a fault-tolerant protocol for Ethernet networks that provides two frame copies for each frame sent. Each copy is forwarded on a separate physical path. HSR is a potential candidate for several fault-tolerant Ethernet applications, including smart-grid communications. However, the major drawback of the HSR protocol is that it generates and circulates unnecessary frames within connected rings regardless of the presence of a destination node in the ring. This downside degrades network performance and can deplete network resources. Two simple but efficient approaches have previously been proposed to solve the above problem: quick removing (QR) and port locking (PL). In this paper, we will present a hybrid approach, QRPL, by combining QR with PL, resulting in further traffic reductions. Our analysis showed that network traffic is significantly reduced for a large-sized HSR connected ring network compared to the standard HSR protocol, QR, and PL.

• KIPS Transactions on Computer and Communication Systems
• /
• v.9 no.8
• /
• pp.181-188
• /
• 2020

The fisheries industry is rapidly shifting from a traditional fishery to aquaculture paradigm and it faces various problems such as depletion of fishery resources and aging of fishing villages. We need the establishment of a fisheries big data platform that includes both the data of the central and surrounding industries of the fisheries industry for enhancement of establishment of a fisheries, 6th industrialization of fishing villages, establishment of related technical standards, and discovery of the new industries to overcome this. Data center agencies should collect, link, and pre-processing, and the platform organizer should create a water industry data virtuous circle through the establishment, operation, and data market of big data platforms to help overcome the current crisis, secure smart fisheries hegemony, and use it as a key to value transfer. Through this study, I would like to propose a policy and technical big data platform construction plan to successfully promote it.

• Journal of KIISE:Computer Systems and Theory
• /
• v.30 no.5_6
• /
• pp.280-289
• /
• 2003

Deadlock recovery-based routing algorithms in wormhole networks have gained attraction due to low hardware complexity and high routing adaptability Progressive deadlock recovery techniques require a few dedicated resources to transmit deadlocked packets rather than killing them. Selection of deadlocked packets is primarily based on time-out value which should be carefully determined considering various traffic patterns or packet length. By its nature, current techniques using time-out accompany unignorable number of false deadlock detections especially in a heavy-loaded network or with long packet size. Moreover, when a deadlock occurs, more than one packet may be marked as deadlocked, which saturate the resources allocated for recovery. This paper proposes more accurate deadlock detection scheme which does not make use of time-out to declare deadlock. The proposed scheme reduces the probability to detect false deadlocks considerably. Furthermore, a single message is selected as deadlocked for each cycle of blocked messages, thereby eliminating recovery overheads.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.24 no.12
• /
• pp.1581-1587
• /
• 2020

With the emergence of convolutional neural network in the field of machine learning, the model for solving image processing problems has seen rapid development. However, the computing resources required are also rising, making it difficult to learn from a typical environment. Attention mechanism is originally proposed to prevent the gradient vanishing problem of the recurrent neural network, but this can also be used in a direction favorable to learning of the convolutional neural network. In this paper, attention mechanism is applied to convolutional neural network, and the excellence of the proposed method is demonstrated through the comparison of learning time and performance difference at this time. The proposed model showed that both learning time and performance were superior in object detection based on YOLO compared to models without attention mechanism, and experimentally demonstrated that learning time could be significantly reduced. In addition, this is expected to increase accessibility to machine learning by end users.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2021.06a
• /
• pp.86-86
• /
• 2021

온천천은 부산광역시 금정구, 동래구, 연제구를 흐르는 도심 하천으로 부산 시민들의 도심 속 산책길, 자전거 길 등으로 활용되는 도시하천이다. 그러나 온천천 양안의 동래 곡저 평야가 시가지화 되고 온천천 발원지인 금정산 주변에서 무허가 상수도를 사용하고 각종 쓰레기와 하수의 유입으로 인해 하천 전체가 하수관으로 변해왔다. 이에 따라 부산광역시는 온천천 정비 계획을 시행하여 하천 정비와 함께 자동측정망을 설치하여 하천의 DO (dissolved oxygen), 탁도, TDS농도 등 자료를 수집하고 있다. 그러나 자동측정망으로 쌓여가는 데이터를 활용하여 DO농도 예측은 거의 이뤄지지 않고 있다. DO는 하천의 수질 오염 정도를 판단하는 수질인자로 역사적으로 하천 연구의 주요 연구 대상이 되어 왔다. 본 연구에서는 일 자료 뿐만 아니라 시 자료를 기반으로 RNN-LSTM 알고리즘을 활용한 DO예측을 시도하였다. RNN-LSTM은 시계열 학습에 뛰어난 알고리즘으로 인공신경망의 발전된 형태인 순환신경망이다. 연구에 앞서 부산광역시 보건환경정보 공개시스템으로부터 받은 자료 중에서 교정, 보수 중, 비사용, 장비전원단절 등으로 인해 누락데이터를 2014년 1월 1일부터 2018년 12월 31일의 데이터 전수조사 후 이상데이터를 확인하여 선형 보간하여 데이터를 사용하였다. 연구에서는 Google에서 개발한 딥러닝 오픈소스 라이브러리인 텐서플로우를 활용하여 부산광역시 금정구 부곡동에 위치한 부곡교 관측소의 DO농도를 시간 또는 일 예측을 하였다. 일 예측 학습에는 2014년~ 2018년의 기상자료(기온, 상대습도, 풍속, 강수량), DO농도 자료를 사용하였고, 시 예측 학습에는 연속된 자료가 가장 많은 2015년 3월 ~ 12월까지의 데이터를 활용하여 연구를 진행하였다. 모형의 검증을 위해 결정계수(R square)를 이용하여 통계분석을 실시하였다.