• Proceedings of the Zoological Society Korea Conference
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• 2000.10a
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• pp.288.1-288
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• 2000

No Abstract, See Full Text

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1872-1879
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• 2016

The techniques to recycle and reuse plastics attract public attention. These public attraction and needs result in improving the recycling technique. However, the identification technique for black plastic wastes still have big problem that the spectrum extracted from near infrared radiation spectroscopy is not clear and is contaminated by noise. To overcome this problem, we apply Raman spectroscopy to extract a clear spectrum of plastic material. In addition, to improve the classification ability of fuzzy Radial Basis Function Neural Networks, we apply supervised learning based clustering method instead of unsupervised clustering method. The conditional fuzzy C-Means clustering method, which is a kind of supervised learning based clustering algorithms, is used to determine the location of radial basis functions. The conditional fuzzy C-Means clustering analyzes the data distribution over input space under the supervision of auxiliary information. The auxiliary information is defined by using k Nearest Neighbor approach.

• 한국생물공학회:학술대회논문집
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• 2000.04a
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• pp.125-128
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• 2000

A recycling reactor system operated under sequential anoxic and oxic conditions for the swine wastewater has been developed, in which piggery slurry is fermentatively and aerobically treated and then part of the effluent recycled to the pigsty. This system significantly removes offensive smells (at both pigsty and treatment plant), BOD and other loads, and appears to be costeffective for the small-scale farms. The most dominant heterotrophs were Alcaligenes faecalis, Brevundimonas diminuta and Streptococcus sp. in order while lactic acid bacteria were dominantly observed in the anoxic tank. We propose a novel monitoring system for a recycling piggery slurry treatment system through neural networks. Here we tried to model treatment process for each tank(influent, fermentation, aeration, first sedimentation and fourth sedimentation tanks) in the system based on population densities of heterotrophic and lactic acid bacteria. Principle component analysis(PCA) was first applied to identify a relation between input(microbial densities and parameters for the treatment such as population densities of heterotrophic and lactic acid bacteria, suspended solids (SS), COD, $NH_3-N$, ortho-P, and total-P) and output, and then multilayer neural networks were employed to model the treatment process for each tank. PCA filtration of input data as microbial densities was found to facilitate the modeling procedure for the system monitoring even with a relatively lower number of input. Neural networks independently trained for each treatment tank and their subsequent combinatorial data analysis allowed a successful prediction of the treatment system for at least two days.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.65-70
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• 2021

Waste accumulation is becoming a significant challenge in most urban areas and if it continues unchecked, is poised to have severe repercussions on our environment and health. The massive industrialisation in our cities has been followed by a commensurate waste creation that has become a bottleneck for even waste management systems. While recycling is a viable solution for waste management, it can be daunting to classify waste material for recycling accurately. In this study, transfer learning models were proposed to automatically classify wastes based on six materials (cardboard, glass, metal, paper, plastic, and trash). The tested pre-trained models were ResNet50, VGG16, InceptionV3, and Xception. Data augmentation was done using a Generative Adversarial Network (GAN) with various image generation percentages. It was found that models based on Xception and VGG16 were more robust. In contrast, models based on ResNet50 and InceptionV3 were sensitive to the added machine-generated images as the accuracy degrades significantly compared to training with no artificial data.

• Resources Recycling
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• v.26 no.2
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• pp.46-55
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• 2017

In this study, the design methodology of Radial Basis Function Neural Networks is developed with the aid of Laser Induced Breakdown Spectroscopy and also applied to the practical plastics sorting system. To identify black plastics such as ABS, PP, and PS, RBFNNs classifier as a kind of intelligent algorithms is designed. The dimensionality of the obtained input variables are reduced by using PCA and divided into several groups by using K-means clustering which is a kind of clustering techniques. The entire data is split into training data and test data according to the ratio of 4:1. The 5-fold cross validation method is used to evaluate the performance as well as reliability of the proposed classifier. In case of input variables and clusters equal to 5 respectively, the classification performance of the proposed classifier is obtained as 96.78%. Also, the proposed classifier showed superiority in the viewpoint of classification performance where compared to other classifiers.

• 한국정보통신설비학회:학술대회논문집
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• 2008.08a
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• pp.513-515
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• 2008

Due to the rapid development of telecommunication industry, most telecom operators are investing to upgrade their access networks by using optical cables instead of copper cables. Building new telecom ducts for optical cable installation requires so huge capital expenses that most telecom operators need to remove unused copper cables in order to secure enough space to in-stall optical cables. In this paper, we will present a alternative method to extract copper from removed copper cables.

• The Journal of the Korea Contents Association
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• v.6 no.2
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• pp.84-92
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• 2006

In this paper, we propose an efficient multicasting algorithm in multistage interconnection networks (MIN's) employing the region encoding scheme. The proposed algorithm uses the recursive scheme to recycle a multicast message at most two times through MIN, in order to send it to its desired destinations. It is composed of two recycling phases which are the copying phase and the routing phase of the multicast message. In the first phase, a source sends the message to a region that contains the largest number of destination regions, and destinations in these regions receive and store the message in this phase. The remaining destinations can finally receive the message in the second phase. This method of the algorithm can improve its performance by reducing the delay of message and the volume of traffic. Moreover, we evaluate the performance of our algorithm in terms of the average number of recycling and the number of internal links used per destination, comparing with the previously proposed algorithm.

• Journal of the Korea Safety Management & Science
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• v.17 no.3
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• pp.239-246
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• 2015

Enforced environmental regulations call for extending the domain of manufacturers' responsibility to the entire product life cycle. To comply with the environmental regulations, manufacturers have constructed reverse logistics networks to re-collect their leftover waste for recycling consumed resources. However, the operational activities associated with storage, loading and transportation processes within the networks inevitably impose environmental burdens. Particularly, the transportation process largely influences environmental performance due to perpetual uses of transportation vehicles. Therefore, there is a need to develop an environmentally-conscious transportation model that can efficiently manage the uses of transportation vehicles. Additionally, it is vital to analyze its significances of environmental performance to compare quantitatively it with existing models. This paper proposes a transportation model for improving environmental performance in a reverse logistics network. This paper also presents a case study to perform its comparative analysis using Life Cycle Assessment that evaluates potential environmental impacts of a product system.

• Computers and Concrete
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• v.26 no.4
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• pp.327-342
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• 2020

The objective of this study is to manufacture environmentally-friendly synthetic lightweight aggregates that may be used in the structural lightweight concrete production. The cold-bonding pelletization process has been used in the agglomeration of the pozzolanic materials to achieve these synthetic lightweight aggregates. In this context, it was aimed to recycle the waste fly ash by employing it in the manufacturing process as the major cementitious component. According to the well-known facts reported in the literature, it is stated that the main disadvantage of the synthetic lightweight aggregate produced by applying the cold-bonding pelletization technique to the pozzolanic materials is that it has a lower strength in comparison with the natural aggregate. Therefore, in this study, the metakaolin made of high purity kaolin and calcined kaolin obtained from impure kaolin have been employed at particular contents in the synthetic lightweight aggregate manufacturing as a cementitious material to enhance the particle crushing strength. Additionally, to propose a curing condition for practical attempts, different curing conditions were designated and their influences on the characteristics of the synthetic lightweight aggregates were investigated. Three substantial features of the aggregates, specific gravity, water absorption capacity, and particle crushing strength, were measured at the end of 28-day adopted curing conditions. Observed that the incorporation of thermally treated kaolin significantly influenced the crushing strength and water absorption of the aggregates. The statistical evaluation indicated that the investigated properties of the synthetic lightweight aggregate were affected by the thermally treated kaolin content more than the kaoline type and curing regime. Utilizing the thermally treated kaolin in the synthetic aggregate manufacturing lead to a more than 40% increase in the crushing strength of the pellets in all curing regimes. Moreover, two numerical formulations having high estimation capacity have been developed to predict the crushing strength of such types of aggregates by using soft-computing techniques: gene expression programming and artificial neural networks. The R-squared values, indicating the estimation performance of the models, of approximately 0.97 and 0.98 were achieved for the numerical formulations generated by using gene expression programming and artificial neural networks techniques, respectively.

• Journal of Environmental Science International
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• v.9 no.2
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• pp.177-183
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• 2000

This study is focused on the incustrial symbiosis based on industrial ecosystem theory. At first, the concept of industrial ecosystem was introduced. Industrial symbiosis is a good tool in order to make a harmony between industry and natural ecosystem. The good example of industrial symbiosis is the case of Kalundborg in Denmark, where 11 networks are working in four enterprises and one community nearby. It was proved that savings of natural resources and economic benefit are achieved by use of industrial symbiosis. Moreover, the control of pollutant emission was also done by use of advanced technology and investments. Based on this case. It was shown that industrial symbiosis through eco-industrial complex in Korea was confronted with many difficulties. First of all., loose emmision criteria, recycling system on wastes, and the absence of will for industrial symbiosis should be solved in Korea.