• Advanced Industrial SCIence
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• v.3 no.2
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• pp.17-22
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• 2024

Recently, as the ICT field has been used in various environments, it has become possible to analyze pests by crops, use robots when harvesting crops, and predict by big data by utilizing ICT technologies in a sustainable agricultural environment. However, in a sustainable agricultural environment, efforts to solve resource depletion, agricultural population decline, poverty increase, and environmental destruction are constantly being demanded. This paper proposes an artificial intelligence-based big data processing analysis method to reduce the production cost and increase the efficiency of crops based on a sustainable agricultural environment. The proposed technique strengthens the security and reliability of data by processing big data of crops combined with AI, and enables better decision-making and business value extraction. It can lead to innovative changes in various industries and fields and promote the development of data-oriented business models. During the experiment, the proposed technique gave an accurate answer to only a small amount of data, and at a farm site where it is difficult to tag the correct answer one by one, the performance similar to that of learning with a large amount of correct answer data (with an error rate within 0.05) was found.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.177-188
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• 2024

As technology evolves, Internet usage continues to grow, resulting in a geometric increase in network traffic and communication volumes. The network path selection process, which is one of the core elements of the Internet, is becoming more complex and advanced as a result, and it is important to effectively manage and analyze it, and there is a need for a representation and visualization method that can be intuitively understood. To this end, this study designs a framework that analyzes network data using BGP, a network path selection method, and applies it to the cyber common operating picture for situational awareness. After that, we analyze the visualization elements required to visualize the information and conduct an experiment to implement a simple visualization. Based on the data collected and preprocessed in the experiment, the visualization screens implemented help commanders or security personnel to effectively understand the network situation and take command and control.

• Smart Media Journal
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• v.12 no.11
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• pp.36-47
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• 2023

Industrial Control System(ICS), which controls facilities at major industrial sites, is increasingly connected to other systems through networks. With this integration and the development of intelligent attacks that can lead to a single external intrusion as a whole system paralysis, the risk and impact of security on industrial control systems are increasing. As a result, research on how to protect and detect cyber attacks is actively underway, and deep learning models in the form of unsupervised learning have achieved a lot, and many abnormal detection technologies based on deep learning are being introduced. In this study, we emphasize the application of preprocessing methodologies to enhance the anomaly detection performance of deep learning models on time series data. The results demonstrate the effectiveness of a Wavelet Transform (WT)-based noise reduction methodology as a preprocessing technique for deep learning-based anomaly detection. Particularly, by incorporating sensor characteristics through clustering, the differential application of the Dual-Tree Complex Wavelet Transform proves to be the most effective approach in improving the detection performance of cyber attacks.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.4
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• pp.35-42
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• 2024

In this study, we propose a method for real-time recognition and analysis of dog behavior using a motion sensor and deep learning techonology. The existing home CCTV (Closed-Circuit Television) that recognizes dog behavior has privacy and security issues, so there is a need for new technologies to overcome them. In this paper, we propose a system that can analyze and care for a dog's behavior based on the data measured by the motion sensor. The study compares the MLP (Multi-Layer Perceptron) and CNN (Convolutional Neural Network) models to find the optimal model for dog behavior analysis, and the final model, which has an accuracy of about 82.19%, is selected. The model is lightened to confirm its potential for use in embedded environments.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.3
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• pp.278-287
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• 2013

Modern information technologies continue to provide industries with new and improved methods. With the rapid development of Machine to Machine (M2M) communication, a smart container supply chain management is formed based on high performance sensors, computer vision, Global Positioning System (GPS) satellites, and Globle System for Mobile (GSM) communication. Existing supply chain management has limitation to real time container tracking. This paper focuses on the studies and implementation of real time container chain management with the development of the container identification system and automatic alert system for interrupts and for normal periodical alerts. The concept and methods of smart container modeling are introduced together with the structure explained prior to the implementation of smart container tracking alert system. Firstly, the paper introduces the container code identification and recognition algorithm implemented in visual studio 2010 with Opencv (computer vision library) and Tesseract (OCR engine) for real time operation. Secondly it discusses the current automatic alert system provided for real time container tracking and the limitations of those systems. Finally the paper summarizes the challenges and the possibilities for the future work for real time container tracking solutions with the ubiquitous mobile and satellite network together with the high performance sensors and computer vision. All of those components combine to provide an excellent delivery of supply chain management with outstanding operation and security.

• The KIPS Transactions:PartB
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• v.9B no.6
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• pp.853-862
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• 2002

Face Detection can be defined as follows : Given a digitalized arbitrary or image sequence, the goal of face detection is to determine whether or not there is any human face in the image, and if present, return its location, direction, size, and so on. This technique is based on many applications such face recognition facial expression, head gesture and so on, and is one of important qualify factors. But face in an given image is considerably difficult because facial expression, pose, facial size, light conditions and so on change the overall appearance of faces, thereby making it difficult to detect them rapidly and exactly. Therefore, this paper proposes fast and exact face detection which overcomes some restrictions by using neural network. The proposed system can be face detection irrelevant to facial expression, background and pose rapidily. For this. face detection is performed by neural network and detection response time is shortened by reducing search region and decreasing calculation time of neural network. Reduced search region is accomplished by using skin color segment and frame difference. And neural network calculation time is decreased by reducing input vector sire of neural network. Principle Component Analysis (PCA) can reduce the dimension of data. Also, pose estimates in extracted facial image and eye region is located. This result enables to us more informations about face. The experiment measured success rate and process time using the Squared Mahalanobis distance. Both of still images and sequence images was experimented and in case of skin color segment, the result shows different success rate whether or not camera setting. Pose estimation experiments was carried out under same conditions and existence or nonexistence glasses shows different result in eye region detection. The experiment results show satisfactory detection rate and process time for real time system.

• Journal of KIISE:Software and Applications
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• v.31 no.9
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• pp.1233-1245
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• 2004

Though biometrics to authenticate a person is a good tool in terms of security and convenience, typical authentication algorithms using biometrics may not be executed on resource-constrained devices such as smart cards. Thus, to execute biometric processing on resource-constrained devices, it is desirable to develop lightweight authentication algorithm that requires only small amount of memory and computation. Also, among biological features, face is one of the most acceptable biometrics, because humans use it in their visual interactions and acquiring face images is non-intrusive. We present a new face authentication algorithm in this paper. Our achievement is two-fold. One is to present a face authentication algorithm with low memory requirement, which uses support vector machines (SVM) with the feature set extracted by genetic algorithms (GA). The other contribution is to suggest a method to reduce further, if needed, the amount of memory required in the authentication at the expense of verification rate by changing a controllable system parameter for a feature set size. Given a pre-defined amount of memory, this capability is quite effective to mount our algorithm on memory-constrained devices. The experimental results on various databases show that our face authentication algorithm with SVM whose input vectors consist of discriminating features extracted by GA has much better performance than the algorithm without feature selection process by GA has, in terms of accuracy and memory requirement. Experiment also shows that the number of the feature ttl be selected is controllable by a system parameter.

• Proceedings of the KAIS Fall Conference
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• 2009.12a
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• pp.474-478
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• 2009

본 연구는 2008년 맞춤형 방문건강관리사업에 사용되고 있는 관절통증을 중심으로 한 사례관리를 수정 보안하여 우리나라 실정에 맞는 관절통증 사례관리 프로그램을 개발, 제안하며, 맞춤형 방문건강 관리사업의 활성화와 완성도를 높이는데 있다. 연구방법으로는 2007년 전국 12주 관절통증 사례관리 결과자료 분석하고, 전국 253개 보건소의 맞춤형 방문건강관리사업 인력에 대한 자료 분석과 전국 보건소 전문가 자문회의와 토론 결과를 통해 설문지를 수정 보완하여 2008년도 충청남도 관절통증 12주 사례관리를 실시하였다. 자료분석은 SPSS 12.0 통계 프로그램을 이용하여, p-value가 0.05 미만과 0.01미만인 경우를 통계적으로 유의한 것으로 판정하였으며, 전국자료는 빈도분석, wilcoxon 부호순위 검정과 McNemar's 검정을 실시하였으며, 12주의 관절통증 사례관리의 연구기간동안 수집된 자료를 1주와 8주간, 1주와 12주간, 8주와 12주간을 paired t-test 검정과 McNemar's 검정을 실시하여 유의성 평가를 실시하였다. 연구결과는 다음과 같다. 12주 기간 동안 사전 사례관리 방문간호사의 교육을 통한 사례관리 서비스의 강도의 조절 및 매주로 서비스의 횟수를 조절하여 사례관리를 실시한 결과 총 109개 항목에서 1주와 8주간에 유의한 항목은 TG(mg/dl)를 비롯한 51개 항목, 1주와 12주간에는 콜레스테롤(mg/dl)을 비롯한 53개 항목, 8주와 12주간에는 지난 48시간동안 관절통증 점수를 비롯한 3개 항목으로 유의한 차이를 볼 수 있었으며, 1주와 8주간은 유의하나 1주와 12주간은 유의하지 않게 나타나는 항목은 TG(mg/dl)를 비롯한 3개 항목, 1주와 8주간은 유의하지 않다가 1주와 12주간은 유의하게 나타나는 항목은 콜레스테롤(mg/dl)를 비롯한 6개 항목, 1주, 8주, 12주간의 모든 기간에서 유의한 항목은 지난 48 시간동안 관절통증 점수를 비롯한 3개 항목으로 조사되었다. 결론적으로 현재 우리나라에서 추진되고 있는 맞춤형 방문건강관리 사업의 사업지침에 대한 보완을 위해 관절통증사례관리 프로그램에 있어 중재 서비스 또는 프로그램의 기간은 12주간에서 8주간으로 조정 되어야 하며, 추가가 필요한 항목으로는 교육, 자기역량 강화, 운동처방, 물리치료, 약물치료, 대체요법, 식이, 영양, 생활지도 등이며, 어골도 분석을 위한 기본 틀 및 주요 구성요소를 제시 및 기여 요인 및 결정요인을 위한 논리적 모형 제시가 필요하며, 개선목표를 위한 유지증진 및 관리능력, 지기 관리 수행도 개선과 대상자별 맞춤형 사례관리를 위한 표준화된 행동 체크리스트 제작 보급 및 사례별 운동, 물리치료 지도 방법 계획 수립에 대한 인력 충원이 필요하다.

• Journal of KIISE:Computer Systems and Theory
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• v.35 no.1
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• pp.1-17
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• 2008

Recently, the object based storage devices systems(OSDs) have been actively researched. They are different from existing block based storage systems(BSDs) in terms of the storage unit. The storage unit of the OSDs is an object that includes the access methods, the attributes of data, the security information, and so on. The object has no size limit and no influence on the internal storage structures. Therefore, the OSDs improve the I/O throughput and the scalability. But the backup systems for the OSDs still use the existing backup techniques for the BSDs. As a result, they need much backup time and do not utilize the characteristics of the OSDs. In this paper, we design and implement a new object based backup system that utilizes the features of the OSDs. Our backup system significantly improves the backup time over existing backup systems because the raw objects are directly transferred to the backup devices in our system. It also restores the backup data much faster than the existing systems when system failures occur. In addition, it supports various types of backup and restore requests.

• The Journal of the Korea Contents Association
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• v.18 no.9
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• pp.675-683
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• 2018

As interest in eco-friendly architecture and Korean traditional culture is increasing, interest in contemporary han-ok is steadily increasing. Recently, many people experienced the han-ok directly and indirectly with the attention of a commercial contemporary han-ok such as restaurants, coffee shops, and lodging facilities, and as a result, the house has a preference for the residence of the contemporary han-ok. Compared to modern residential houses, however, han-ok is lack the convenience of heating and cooling, energy management, security, and maintenance. For this reason, the increased interest and preference for han-ok does not lead to living in contemporary han-ok. This study was conducted in the following ways to improve inconvenience by applying smart home services to contemporary han-ok. Recent technology trends in smart home services and technologies developed and marketed to date have been identified in previous research cases and literature studies. Based on this, a list of smart home services and their application methods were derived that would relieve the inconvenience of contemporary han-ok for smart home services. We hope that this research will serve as a reference for subsequent researchers studying contemporary han-ok.