• Proceedings of the Korean Society of Computer Information Conference
• /
• 2012.01a
• /
• pp.41-43
• /
• 2012

Today many people are greatly interested in the environment. Especially increasing affluent of food causes a great amount of food waste. To handle this effectively, we now have a lot of problems of disposing garbage all over the world. In Korea, in order to reduce this garbage, we should use a standard plastic garbage bag in which we have to throw away our garbage. So it has an effect on significantly reducing the waste amount every year. Now, there are a lot of cases that residents again use a standard plastic garbage bag. The purpose of this study is to propose algorithm preventing the re-cycling of a standard plastic garbage bag.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2022.07a
• /
• pp.377-378
• /
• 2022

본 연구는 폐그물, 폐현수막, 비닐, 캔 등을 활용한 친환경 패션 소품을 제작하기 위한 선행연구로, 패션산업에서의 환경문제와 친환경 패션 브랜드에 대해 분석해보고자 하였다. 이를 통해 친환경 패션 소품 제작에 필요한 기초자료를 얻고자 하였다. 연구는 국내 포털사이트에서 '친환경 패션 브랜드', '패션산업의 환경오염' 등의 단어를 입력하여 검색되는 기사를 중심으로 진행되었다. 패션산업은 생산과 소비 과정에서 수질, 대기오염을 유발하며, 쓰레기 문제 등을 유발하였다. 제조과정 중 엄청난 양의 탄소를 발생시키며, 세탁과정에서는 미세플라스틱을 방출하여 수질을 오염시키는 것으로 나타났다. 친환경 패션 브랜드에 글로베 호프, 프라이탁, 스텔라 맥카트니, 파타고니아, 비건 타이거, 낫 아워스 등이 있다. 광고 현수막, 군복, 안전벨트, 공장작업복, 병원 이불, 보트의 돛, 자동차 방수포 등을 활용하여 다양한 가방, 파우치, 핸드백 등의 패션 아이템을 제작 판매하고 있다. 이 외에도 비건 소재를 사용하고 동물성 소재와 PVC 소재를 사용하지 않는 등 패션업계에서도 환경오염을 줄이기 위한 다양한 노력을 기울이고 있는 것으로 나타났다.

• The Journal of Korean Association of Computer Education
• /
• v.12 no.4
• /
• pp.65-75
• /
• 2009

The result verification is necessary to support a guarantee for the correctness of the task results be executed by any unspecified resources in desktop grid environments. Typically, voting-based and trust-based result verification schemes have been used in the environments. However, these suffer from two potential problems: waste of resources due to redundant replicas of each task and increase in turnaround time due to the inability to deal with a dynamic changeable execution environment. To overcome these problems, we propose a fuzzy inference-based replication scheme which can adaptively determine the number of replicas per task by using both trusty degree and result return probability of resources. Therefore our proposal can reduce waste of resources by determining the number of replicas meeting with a dynamic execution environment of desktop grids, not to mention an enhancement of turnaround time for entire asks. Simulation results show that our scheme is superior to other ones in terms of turnaround time, the waste of resources, and the number of re-replications per task.

• Journal of the Korea Society of Computer and Information
• /
• v.17 no.9
• /
• pp.85-90
• /
• 2012

Today many people are greatly interested in the environment. Especially increasing affluent of food causes a great amount of food waste. To handle this effectively, we now have a lot of problems of disposing garbage all over the world. In Korea, in order to reduce this garbage, we should use a standard plastic garbage bag in which we have to throw away our garbage. So it has an effect on significantly reducing the waste amount every year. Now, there are a lot of cases that residents use several times a standard plastic garbage bag. The purpose of this study is to develop the recognition device preventing the re-cycling of a standard plastic garbage bag. As a result, we obtain 1% error rate.

• 한국전산유체공학회:학술대회논문집
• /
• 2002.10a
• /
• pp.133-138
• /
• 2002

A 3-D axisymmetric computer program is developed to predict the NO behavior in SNCR system for the stoker incinerator with the waste treatment capacity, 200ton/day. To this end a turbulent reacting flow field calculation is made using proper assumption and empiricism. The stoker bed is assumed to be a homogeneous waste-volatilized gaseous state. The initial composition or reactants are assumed based on the data of the ultimate analysis. Turbulent is resolved by k-e model and turbulent reaction is handled by eddy-breakup model harmonized with empirical chemistry data for gaseous combustion, NO and urea reaction. The liquid droplet is traced by Lagrangian method incorporated by aerodynamic drag, Coriolis and crntrifugal forces. Radiation is treated by sensible heat loss model. Calculation results are in good agreement with experimental data at the outlet of post combustion chamber in Daejon 4th industrial complex. The flue gas shows the temperature range of $900\sim1000^{\circ}C$, velocity of 5m/s and NO concentration of 140ppm at the exit while the measured temperature, flue gas velocity and NO concentration are $967^{\circ}C$, $3\sim4m/s$ and $100\sim200ppm$respectively. Using the developed computer program a parametric study has been made with the variation of heat content of waste, castable length and SNCR variables for the determination of proper injector location. In general, the calculated results are consistent and physically acceptable.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.8
• /
• pp.1231-1237
• /
• 2022

It is necessary to dynamic recognition system with real time loading height and pressure of the loading waste, the drying of wood, batteries, and plastic wastes, which are representative compositional wastes, and the carbonization changes on the surface. The dynamic context awareness service constituted a platform based on Universal Middleware system using BCN convergence communication service as a Ambient SDK model. A context awareness system should be constructed to determine the cause of the fire based on the analysis data of fermentation heat point with natural ignition from the load waste. Furthermore, a real-time dynamic service platform that could be apply to the configuration of scenarios for each type from early warning fire should be built using Universal Middleware. Thus, this issue for Internet of Things realize recognition platform for analyzing low temperature fired fire possibility data should be dynamically configured and presented.

• Proceedings of the IEEK Conference
• /
• 2001.06c
• /
• pp.125-128
• /
• 2001

Nowadays, Internet users are widespread internationally. Fallowing the users requirements, server has to transfer real-time multimedia data to many clients. Multicast can significantly reduce the system overhead and the network resource waste, so it supports efficient mechanism when server has to transfer same data to many clients. We can deliver multimedia data transfer in real-time using Patching Algorithm which has no delay for service request. And multicast algorithm can support different network bandwidth using Receiver driven Layered Multicast under heterogeneous environment network. The purpose of this paper is to suggest an algorithm that can create new multicast group adequately and transfer real-time multimedia data efficiently under heterogeneous environment.

• Proceedings of the IEEK Conference
• /
• 2008.06a
• /
• pp.637-638
• /
• 2008

In this paper, we describe a new guard lamp and traffic light controller using Zigbee one of near field communication. It can control guard lamp and traffic light system by wireless system. Also, it can definite in the sever system New guard lamp and traffic light using zibee system improved for waste power, an accident and management condition.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.05a
• /
• pp.639-641
• /
• 2023

이미지 기반 폐기물 처리시스템에서 품목별 상이한 수집 난이도로 인해 발생하는 데이터 불균형으로 분류 모델 학습에 어려움이 따른다. 따라서 본 논문에서는 폐기물 분류 모델의 성능 비교를 통해 적합한 이미지 생성 모델을 탐색한다. 데이터의 불균형을 해결할 수 있도록 VAE(Variational Auto-Encoder), GAN(Generative Adversarial Networks) 및 Diffusion Model을 이용하여 이미지를 생성한다. 이후 각각의 생성 방법에 따라 학습데이터와 병합하여 객체 분류를 진행하였다. 정확도는 VAE가 84.41%로 3.3%의 성능 향상을, F1-점수는 Diffusion Model이 91.94%로 6.14%의 성능 향상을 이루었다. 이를 통해, 데이터 수집에서 나타나는 데이터 불균형을 해결하여 실 사용환경에 알맞은 시스템을 구축이 가능함을 확인하였다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.05a
• /
• pp.667-669
• /
• 2023

데이터의 균형은 객체 인식 분야에서 영향을 미치는 요인 중 하나이다. 본 논문에서는 폐기물 데이터 균형을 위해 Chat-GPT와 Diffusion model 기반 데이터 생성 모델을 제안한다. Chat-GPT를 사용하여 폐기물의 속성에 해당하는 단어를 생성하도록 질문하고, 생성된 단어는 인코더를 통해 벡터화시킨다. 이 중 폐기물과 관련 없는 단어를 삭제 후, 남은 단어들을 결합하는 전처리 과정을 거친다. 결합한 벡터는 디코더를 통해 텍스트 데이터로 변환 후, Stable Diffusion model에 입력되어 텍스트와 상응하는 폐기물 데이터를 생성한다. 이 데이터는 AI Hub의 공공 데이터를 활용하며, 객체 인식 모델인 YOLOv5로 학습해 F1-score와 mAP로 평가한다.