• Journal of Korea Society of Industrial Information Systems
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• v.8 no.2
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• pp.30-37
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• 2003

To effectively control a vast amount of postal packages, we need the automatic system for extracting the address region from CCD scanner images. In this paper, we propose a address region extraction algorithm in the standard postal label. We used geometric characteristics of the underlying address regions and defined several criteria for fast detection of address regions. As a result, we accomplished a successful detection and classification of the postal package labels in real time.

• The Journal of Bigdata
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• v.7 no.2
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• pp.1-9
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• 2022

In the global era, the importance of imported food safety management is increasing. Address information of overseas food companies is key information for imported food safety management, and must be verified for prompt response and follow-up management in the event of a food risk. However, because each country's address system is different, one verification system cannot verify the addresses of all countries. Also, the purpose of address verification may be different depending on the field used. In this paper, we deal with the problem of classifying a given overseas food business address into the administrative district level of the country. This is because, in the event of harm to imported food, it is necessary to find the administrative district level from the address of the relevant company, and based on this trace the food distribution route or take measures to ban imports. However, in some countries the administrative district level name is omitted from the address, and the same place name is used repeatedly in several administrative district levels, so it is not easy to accurately classify the administrative district level from the address. In this study we propose a deep learning-based administrative district level classification model suitable for this case, and verify the actual address data of overseas food companies. Specifically, a method of training using a label powerset in a multi-label classification model is used. To verify the proposed method, the accuracy was verified for the addresses of overseas manufacturing companies in Ecuador and Vietnam registered with the Ministry of Food and Drug Safety, and the accuracy was improved by 28.1% and 13%, respectively, compared to the existing classification model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2168-2187
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• 2021

The smart grid replaces the traditional power structure with information inventiveness that contributes to a new physical structure. In such a field, malicious information injection can potentially lead to extreme results. Incorrect, FDI attacks will never be identified by typical residual techniques for false data identification. Most of the work on the detection of FDI attacks is based on the linearized power system model DC and does not detect attacks from the AC model. Also, the overwhelming majority of current FDIA recognition approaches focus on FDIA, whilst significant injection location data cannot be achieved. Building on the continuous developments in deep learning, we propose a Deep Learning based Locational Detection technique to continuously recognize the specific areas of FDIA. In the development area solver gap happiness is a False Data Detector (FDD) that incorporates a Convolutional Neural Network (CNN). The FDD is established enough to catch the fake information. As a multi-label classifier, the following CNN is utilized to evaluate the irregularity and cooccurrence dependency of power flow calculations due to the possible attacks. There are no earlier statistical assumptions in the architecture proposed, as they are "model-free." It is also "cost-accommodating" since it does not alter the current FDD framework and it is only several microseconds on a household computer during the identification procedure. We have shown that ANN-MLP, SVM-RBF, and CNN can conduct locational detection under different noise and attack circumstances through broad experience in IEEE 14, 30, 57, and 118 bus systems. Moreover, the multi-name classification method used successfully improves the precision of the present identification.

• Proceedings of the KIEE Conference
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• 2004.07c
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• pp.2128-2130
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• 2004

High throughput analysis using a DNA chip microarray is powerful tool in the post genome era. Less labor-intensive and lower cost-performance is required. Thus, this paper aims to develop the multi-channel type label-free DNA chip and detect SNP (Single nucleotide polymorphisms). At first, we fabricated a high integrated type DNA chip array by lithography technology. Various probe DNAs were immobilized on the microelectrode array. We succeeded to discriminate of DNA hybridization between target DNA and mismatched DNA on microarray after immobilization of a various probe DNA and hybridization of label-free target DNA on the electrodes simultaneously. This method is based on redox of an electrochemical ligand.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04a
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• pp.436-438
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• 2004

인터넷 서비스에 QoS(Quality of Service)를 제공하면서 IP(Internet Protocol)의 유연성과 확장성을 제공하기 위해 IETF에서는 MPLS(Multiple Protocol Label Switching)기술을 표준화하였다. 또한, MPLS기술의 기본이 되는 시그널링 프로토콜로 LDP(Label Distribution Protocol), CR-LDP(Constrained based LDP). RSVP-TE(Resource Reservation Protocol-Traffic Engineering)의 표준화를 진행해 왔다. 따라서 본 논문에서는 MPLS 기술을 적용하여 현재의 IP망에서 보다 안정적이고 차별화된 서비스를 제공하기 위해 리눅스 상에서 구연한 라우터기반 MPLS 시스템을 제시한다. 본 논문에서 제시하는 라우터 기반 MPLS 시스템은 기존 IP망과의 연동을 위해 MPLS 도메인상의 OSPF데몬을 수정하며, MPLS망에서 명시적 경로를 제어하기 위해 MPLS데몬을 수정한다. 그리고 MPLS데몬과 OSPF데몬은 수정된 Zebra데몬에 의해 스트림 형태로 정보를 교환한다.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2002.04a
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• pp.274-283
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• 2002

This paper presents an efficient models for reinforeced concrete structures using CART-ANFIS(classification and regression tree-adaptive neuro fuzzy inference system). a fuzzy decision tree parttitions the input space of a data set into mutually exclusive regions, each of which is assigned a label, a value, or an action to characterize its data points. Fuzzy decision trees used for classification problems are often called fuzzy classification trees, and each terminal node contains a label that indicates the predicted class of a given feature vector. In the same vein, decision trees used for regression problems are often called fuzzy regression trees, and the terminal node labels may be constants or equations that specify the Predicted output value of a given input vector. Note that CART can select relevant inputs and do tree partitioning of the input space, while ANFIS refines the regression and makes it everywhere continuous and smooth. Thus it can be seen that CART and ANFIS are complementary and their combination constitutes a solid approach to fuzzy modeling.

• Bulletin of the Korean Chemical Society
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• v.17 no.11
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• pp.1018-1022
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• 1996

Adenosine deaminase (ADA) has been utilized as the label in devising a potentiometric homogeneous assay for riboflavin and riboflavin binding protein (RBP). The proposed homogeneous assay method employs an ADA-biotin conjugate as the signal generator and an avidin-riboflavin conjugate as the signal modulator in the solution phase. The catalytic activity of the ADA-biotin conjugate is inhibited in the presence of an excess amount of the avidin-riboflavin conjugate, and the observed inhibition is reversed in an amount proportional to the concentration of RBP added. When the analyte riboflavin is added to this mixture of ADA-biotin, avidin-riboflavin and RBP, the activity of the enzyme conjugate is re-inhibited in an amount proportional to the concentration of riboflavin. Since the enzyme label used in this system is ADA, an ammonia-producing enzyme, a potentiometric rather than photometric detection scheme is used to monitor the enzymatic activity in the assay.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.5
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• pp.343-350
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• 2019

Map labels are the most recognizable map elements using the human visual system because they are essentially a natural language. In this study, an experiment was conducted using an eye-tracker to objectively record and analyze the response of subjects regarding visual attention to map labels. A primary building object was identified by analyzing visit counts, average visit duration, fixation counts, and the average fixation duration of a subject's gaze for an area of interest acquired using the eye-tracker. The unmarked rate of map labels in Google map, Naver map, and Daum map was calculated. As a result, this rate exceeded fifty-one percent, with the lowest rate recorded for Google map. It is expected that the results of this study will contribute to an increase in the diversity of research in terms of the spatial cognition approach for map labels, which is more helpful to users than the existing body of work on methods of expression for labels.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.365-368
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• 2021

In this study, a plastic bottle recycling bin system that utilizes an infrared sensor was implemented. The proposed system consists of a recognition unit, a control unit, an alarm unit, and a driving unit. The recognition unit detects the plastic bottle, measures the distance between the plastic bottle and the infrared sensor, extracts the value of the bottle, compares the extracted value with a standard range, and then transmits the control value to the control unit if the extracted value of the bottle is outside the standard range. In this case, the result of the presence or absence of a brand label or bottle cap is transmitted to the controller. The control unit opens the entrance of the recycling bin or alerts the alarm unit according to the result value transmitted from the sensor unit. In order to implement the proposed system, the recognition unit was implemented with an infrared sensor, and the control unit was made with an Arduino IDE controller, based on the C programming language. Additionally, the recognition unit and the control unit are able to communicate using analog signals. The proposed system accurately judges the presence or absence of a brand label and bottle cap of plastic bottles according to a predetermined algorithm. It then blocks the entrance of the recycling bin when a brand label or bottle cap is still attached. As the amount of waste discharged per person is relatively high and the majority of such waste is incinerated rather than recycled, the system proposed in this study is expected to increase the recycling rate of plastic bottles.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.6
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• pp.2895-2921
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• 2018

Security has become one of the major concerns in mobile adhoc networks (MANETs). Data and voice communication amongst roaming battlefield entities (such as platoon of soldiers, inter-battlefield tanks and military aircrafts) served by MANETs throw several challenges. It requires complex securing strategy to address threats such as unauthorized network access, man in the middle attacks, denial of service etc., to provide highly reliable communication amongst the nodes. Intrusion Detection and Prevention System (IDPS) undoubtedly is a crucial ingredient to address these threats. IDPS in MANET is managed by Command Control Communication and Intelligence (C3I) system. It consists of networked computers in the tactical battle area that facilitates comprehensive situation awareness by the commanders for timely and optimum decision-making. Key issue in such IDPS mechanism is lack of Smart Learning Engine. We propose a novel behavioral based "Smart Multi-Instance Multi-Label Intrusion Detection and Prevention System (MIML-IDPS)" that follows a distributed and centralized architecture to support a Robust C3I System. This protocol is deployed in a virtually clustered non-uniform network topology with dynamic election of several virtual head nodes acting as a client Intrusion Detection agent connected to a centralized server IDPS located at Command and Control Center. Distributed virtual client nodes serve as the intelligent decision processing unit and centralized IDPS server act as a Smart MIML decision making unit. Simulation and experimental analysis shows the proposed protocol exhibits computational intelligence with counter attacks, efficient memory utilization, classification accuracy and decision convergence in securing C3I System in a Tactical Battlefield environment.