• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.21 no.4
• /
• pp.575-581
• /
• 2012

The electrochemical oxygen demand (ECOD) is an additional sum parameter, which has not yet found the attention it deserves. It is defined as the oxygen equivalent of the charge consumed during an electrochemical oxidation of the solution. Only one company has yet developed an instrument to determine the ECOD. This instrument uses $PbO_2$-electrodes for the oxidation and has been successfully implemented in an automatic on-line monitor. A general problem of the ECOD determination is the high overpotential of electrochemical oxidations of most organic compounds at conventional electrodes. Here we present a new approach for the ECOD determination, which is based on the use of a solid composite electrodes with highly efficient electro-catalysts for the oxidation of a broad spectrum of different organic compounds. Lead dioxide as an anode material has found commercial application in processes such as the manufacture of sodium per chlorate and chromium regeneration where adsorbed hydroxyl radicals from the electro-oxidation of water are believed to serve as the oxidizing agent. The ECOD sensors based on the Au/$PbO_2$ electrode were operated at an optimized applied potential, +1.6 V vs. Ag/AgCl/sat. KCl, in 0.01 M $Na_2SO_4$ solution, and reduced the effect of interference ($Cl^-$ and $Fe^{2-}$) and an expended lifetime (more than 6 months). The ECOD sensors were installed in on-line auto-analyzers, and used to analyze real samples.

• IE interfaces
• /
• v.17 no.3
• /
• pp.330-337
• /
• 2004

Since XML (eXtensible Markup Language) was highlighted as an information interchange format, there is an increasing demand for incorporating XML with databases. Most of the approaches are focused on RDB (Relational Databases) because of legacy systems. But these approaches depend on the database system. Countless researches are being focused on DTD (Document Type Definition). However XML Schema is more comprehensive and efficient in many perspectives. We propose a meta-model for XML Schema that is independent of the database. There are three processes to build our meta-model: DOM (Document Object Model) tree analysis, object modeling and storing object into a fixed DB schema using model mapping approach. We propose four mapping rules for object modeling, which conform to the ODMG (Object Data Management Group) 3.0 standard. We expect that the model will be especially useful in building XML-based e-business applications.

• Journal of Information Processing Systems
• /
• v.16 no.5
• /
• pp.1113-1128
• /
• 2020

Ad hoc networks play an important role in mobile communications, and the performance of nodes has a significant impact on the choice of communication links. To ensure efficient and secure data forwarding and delivery, an intelligent routing protocol (IAODV) based on learning method is constructed. Five attributes of node energy, rate, credit value, computing power and transmission distance are taken as the basis of segmentation. By learning the selected samples and calculating the information gain of each attribute, the decision tree of routing node is constructed, and the rules of routing node selection are determined. IAODV algorithm realizes the adaptive evaluation and classification of network nodes, so as to determine the optimal transmission path from the source node to the destination node. The simulation results verify the feasibility, effectiveness and security of IAODV.

• ETRI Journal
• /
• v.41 no.6
• /
• pp.760-770
• /
• 2019

Based on the growing demand for neural network technologies, various neural network inference engines are being developed. However, each inference engine has its own neural network storage format. There is a growing demand for standardization to solve this problem. This study presents interworking techniques for ensuring the compatibility of neural networks and data among the various deep learning frameworks. The proposed technique standardizes the graphic expression grammar and learning data storage format using the Neural Network Exchange Format (NNEF) of Khronos. The proposed converter includes a lexical, syntax, and parser. This NNEF parser converts neural network information into a parsing tree and quantizes data. To validate the proposed system, we verified that MNIST is immediately executed by importing AlexNet's neural network and learned data. Therefore, this study contributes an efficient design technique for a converter that can execute a neural network and learned data in various frameworks regardless of the storage format of each framework.

• Journal of the Korean Society for Aviation and Aeronautics
• /
• v.29 no.4
• /
• pp.1-20
• /
• 2021

Due to the recent rapid increase in passenger and cargo air transport demand, the capacity of Jeju International Airport has been approaching its limit. Even though in COVID-19 crisis which has started from Nov 2019, Jeju International Airport still suffers from strong demand in terms of air passenger and cargo transportation. However, it is an undeniable fact that the delay has also increased in Jeju International Airport. In this study, we analyze the correlation between weather and delayed departure operation based on both datum collected from the historical airline operation information and aviation weather statistics of Jeju International Airport. Adopting machine learning techniques, we then analyze weather condition Jeju International Airport and construct a delay prediction model. The model presented in this study is expected to play a useful role to predict aircraft departure delay and contribute to enhance aircraft operation efficiency and punctuality in the Jeju International Airport.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.40 no.1
• /
• pp.57-64
• /
• 2017

In order to reduce damages to major railroad components, which have the potential to cause interruptions to railroad services and safety accidents and to generate unnecessary maintenance costs, the development of rolling stock maintenance technology is switching from preventive maintenance based on the inspection period to predictive maintenance technology, led by advanced countries. Furthermore, to enhance trust in accordance with the speedup of system and reduce maintenances cost simultaneously, the demand for fault diagnosis and prognostic health management technology is increasing. The objective of this paper is to propose a highly reliable learning model using various machine learning algorithms that can be applied to critical rolling stock components. This paper presents a model for railway rolling stock component fault diagnosis and conducts a mechanical failure diagnosis of motor components by applying the machine learning technique in order to ensure efficient maintenance support along with a data preprocessing plan for component fault diagnosis. This paper first defines a failure diagnosis model for rolling stock components. Function-based algorithms ANFIS and SMO were used as machine learning techniques for generating the failure diagnosis model. Two tree-based algorithms, RadomForest and CART, were also employed. In order to evaluate the performance of the algorithms to be used for diagnosing failures in motors as a critical railroad component, an experiment was carried out on 2 data sets with different classes (includes 6 classes and 3 class levels). According to the results of the experiment, the random forest algorithm, a tree-based machine learning technique, showed the best performance.

• Journal of Internet Computing and Services
• /
• v.18 no.1
• /
• pp.43-50
• /
• 2017

In this paper, we are proposing improved vanishing points detection and segments labeling methods for inside wall frame detection from indoor image of a piece of having a colour RGB. A lot of research related to recognizing the frame of artificial structures from the image is being performed due to increase in demand for AR technology. But detect the inside wall frame in indoor images have many objects that caused the occlusion is still a difficult issue. Inner wall frame detection methods are usually segment labeling methods and detect vanishing point methods are used together. In order to improve the vanishing point detection method we proposed using inner wall orthogonality which forms the cube. Also we proposed labeling method using tree based learning and superpixel based segmentation method for labelingthe segments in indoor images. Finally, in experiments have shown improved results about inside wall frame detection according to our methods.

• ETRI Journal
• /
• v.27 no.1
• /
• pp.13-21
• /
• 2005

Many multicast applications, such as video-on-demand and video conferencing, desire quality of service (QoS) support from an underlying network. The differentiated services (DiffServ) approach will bring benefits for theses applications. However, difficulties arise while integrating native IP multicasting with DiffServ, such as multicast group states in the core routers and a heterogeneous QoS requirement within the same multicast group. In addition, a missing per-flow reservation in DiffServ and a dynamic join/leave in the group introduce heavier and uncontrollable traffic in a network. In this paper, we propose a distributed and stateless admission control in the edge routers. We also use a mobile agents-based approach for dynamic resource availability checking. In this approach, mobile agents act in a parallel and distributed fashion and cooperate with each other in order to construct the multicast tree satisfying the QoS requirements.

• Journal of Forest and Environmental Science
• /
• v.35 no.4
• /
• pp.272-280
• /
• 2019

Forest medicinal resources, which constitute one of the non-timber forest products, have been regarded as healthy and highly valued products. To meet the increasing demand of the medicinal resources, it is necessary to improve the propagation methods of medicinal plants. In vitro propagation not only allows an opportunity for propagating plants in large numbers but also allows for enhancing the quality and quantity of the desired functional component of a plant by altering the growth factors, such as medium, carbon source, and plant growth regulators influence plant. There have been several studies of in vitro propagation methods, such as axillary bud culture, shooting, and embryogenesis, on Kalopanax septemlobus, Eleutherococcus sessiliflorus, Hovenia dulcis, and Schisandra chinensis in Korea between from 2000 through 2010. Furthermore, there have been attempts to proliferate callus and plantlets for producing useful natural compounds by using bioreactors. Here, we provide an account of the in vitro propagation methods of medicinal trees in Korea based on a review of several micropropagation studies.

• ETRI Journal
• /
• v.33 no.5
• /
• pp.704-711
• /
• 2011

This paper proposes hybrid distributed stochastic addressing (HDSA), which combines the advantages of distributed addressing and stochastic addressing, to solve the problems encountered when constructing a network in a ZigBee-based wireless sensor network. HDSA can assign all the addresses for ZigBee beyond the limit of addresses assigned by the existing distributed address assignment mechanism. Thus, it can make the network scalable and can also utilize the advantages of tree routing. The simulation results reveal that HDSA has better addressing performance than distributed addressing and better routing performance than other on-demand routing methods.