• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.4
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• pp.69-74
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• 2009

Abandoned objects should be treated as possibly dangerous things for public areas until they turn out to be safe because explosive material or chemical substance is intentionally contained in them for public terrors. For large public areas such as airports or train stations, there are limits in man-power for security staffs to check all the monitors for covering the entire area under surveillance. This is the basic motivation of developing the automatic detection system for dangerous abandoned objects based on vision technology. In this research, well-known DBE is applied to stably extract background images and the HOG algorithm is adapted to discriminate between human and stuff for object classification. To show the effectiveness of the proposed system, experiments are carried out in detecting intrusion for a forbidden area and alarming for abandoned objects in a room under surveillance.

• Journal of Advanced Navigation Technology
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• v.17 no.1
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• pp.123-131
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• 2013

If Industrial state-of-the-art technology that made through IT convergence should be to build safely environment that can protect then IT technology and manufacturing industry become convergence and a growth engine become stable positioning. In each industry, there has been a steady effort for the industrial security. However, they introduced only managerial/technical/physical countermeasures. Therefore, it is difficult to find a reference point as industrial security necessity, protecting coverage and things and so on. It is to lack that academic research in industrial security for protecting industrial technology. In detail, a clear definition lack for industrial security. And target range classification lack for industrial security studies. In this study, we redefined the concept of industry security through previous studies. Academic classification designed industrial security studies through delphi method. we analyzed industry security trends based industrial security studies classification and presented domestic industry research orientations.

• Journal of the Korea Society of Computer and Information
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• v.18 no.11
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• pp.31-38
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• 2013

Feature normalization as a pre-processing step has been widely used in classification problems to reduce the effect of different scale in each feature dimension and error as a result. Most of the existing methods, however, assume some distribution function on feature distribution. Even worse, existing methods do not use the labels of data points and, as a result, do not guarantee the optimality of the normalization results in classification. In this paper, proposed is a supervised rank normalization which combines rank normalization and a supervised learning technique. The proposed method does not assume any feature distribution like rank normalization and uses class labels of nearest neighbors in classification to reduce error. SVM, in particular, tries to draw a decision boundary in the middle of class overlapping zone, the reduction of data density in that area helps SVM to find a decision boundary reducing generalized error. All the things mentioned above can be verified through experimental results.

• Korean Journal of Agricultural Science
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• v.46 no.2
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• pp.381-393
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• 2019

A smart farm is a system that combines information and communication technology (ICT), internet of things (IoT), and agricultural technology that enable a farm to operate with minimal labor and to automatically control of a greenhouse environment. Machine learning based on recently data-driven techniques has emerged with big data technologies and high-performance computing to create opportunities to quantify data intensive processes in agricultural operational environments. This paper presents research on the application of machine learning technology to diagnose the growth status of crops and predicting the harvest time of strawberries in a greenhouse according to image processing techniques. To classify the growth stages of the strawberries, we used object inference and detection with machine learning model based on deep learning neural networks and TensorFlow. The classification accuracy was compared based on the training data volume and training epoch. As a result, it was able to classify with an accuracy of over 90% with 200 training images and 8,000 training steps. The detection and classification of the strawberry maturities could be identified with an accuracy of over 90% at the mature and over mature stages of the strawberries. Concurrently, the experimental results are promising, and they show that this approach can be applied to develop a machine learning model for predicting the strawberry harvesting time and can be used to provide key decision support information to both farmers and policy makers about optimal harvest times and harvest planning.

• 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 Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2003.10a
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• pp.391-396
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• 2003

The digital map expresses natural topography and artificial things with 3D position coordinates in the computer such as the road, railway, building, river, mountain, paddy and dryland. Therefore, those should contribute to the information-oriented society by maintaining information and providing it to users quickly. However it is difficult to maintain the most recent topographic information all the time because of restricted budget and time. The purpose of this study is to investigate and analyze the updating area of the digital map using remotely sensed data, and to furnish the useful information reducing cost and time. To predict updating area of the digital map, we applied the urban changes analysis method to Landsat TM images from produced date of the digital map to up-to-date. Classification method for urban change analysis applied single band process algorithm. This study presents that updating area of the digital map is predicted by only the rate of 40% on total research area.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.674-678
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• 2008

Our research is for development of smart object in WBAN environment. Too many smart object in various computing device were developed, but users always attention to their lot of smart objects and any personal things now. So we need smart object that classification and management all of user's personal devices and any effects in invisible and seamless system. We discussed how develop do that object in WBAN(Wireless Body Area Network) system and how make components in this system to wireless network in this paper.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.1138-1140
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• 2017

센서 및 초근거리 통신 기술의 발전으로 다양한 사물인터넷 서비스가 등장하였다. 현재 사물인터넷 서비스는 단일화된 서비스만을 제공하고 있지만 서비스들이 융합된 새로운 서비스로 발전되고 있다. 서비스 융합시 발생할 수 있는 프로토콜의 다양성, 모듈의 중복성등의 문제를 해결하기 위하여 통합 서비스 플랫폼의 필요성이 대두되었다. 이에 본 연구에서는 보다 효율적인 통합 서비스 플랫폼을 제공하기 위한 기반 연구로 사물인터넷 서비스 분류 알고리즘을 제안한다. 제안하는 서비스 분류 알고리즘은 서비스 별 세부 동작을 기반으로 구성된다. 그리고 후속 연구로 실제 서비스에 제안한 서비스 분류알고리즘을 적용하여 서비스간 유사도 분석을 통한 서비스 그룹화에 관한 연구를 진행할 예정이다.

• Journal of Distribution Research
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• v.1 no.2
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• pp.199-220
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• 1996

Our courtry's distribrtion business is on the luring point by opening the market to foreign countries from this year. It can be said that industrial goods are intermediated goods for the production of other goods or service So it requires quality and specialty than any other things. But the lack of understanding, small scale and classification by non-productive industry by government bring about difficulty to the distribution itself and other fields. Industrial goods have a long distribution channel. This distribution stracture can be reduce the channel by establishment of distribution complex. Establishment of distribution complex will strengthen the role of quality management and go far toward quality improvement. This study examine the distribution status of industrial goods, problems and study the extablishment of complex and expected affect.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.6
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• pp.2333-2353
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• 2020

This paper proposes a novel data prioritization and transmission mechanism to minimize the number of packets transmitted and reduce network overload using the constrained application protocol (CoAP) in resource-constrained networks. The proposed scheme adopts four classification parameters to classify and prioritize data from a sensor. With the packet prioritization scheme, the sensed data having the lowest priority is only delivered using the proposed keep-alive message notification to decrease the number of packets transmitted. The performance evaluation demonstrates that the proposed scheme shows the improvement of resource utilization in energy consumption, and bandwidth usage compared with the existing CoAP methods. Furthermore, the proposed scheme supports quality-of-service (QoS) per packet by differentiating transmission delays regarding priorities.