• Journal of Internet Computing and Services
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• v.7 no.2
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• pp.81-92
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• 2006

Recently, the number of mobile phone and PDA users has been rapidly increased. Such monitoring and control applications as geographical and traffic information systems are being used widely with wireless devices. In this paper, we introduce the mobile M/VC application framework that supports the rapid constructions of mobile monitoring and control (M/VC) applications. The mobile M/VC application framework uses the mobile Observer/Observable pattern that extends the Java's Observer/Observable for automatic interactions of server and client objects in wireless environments. It also provides the Multiplexer and Demultiplexer classes that supports the assembly feature of Observer and Observable objects. To construct an application using the framework, developers just need to create necessary objects from the Observable and MobileObserver classes and inter-connect them structurally(like the plug-and-play style) through the Multiplexer and Demultiplexer objects. Then, the state change of Observable objects is notified to the connected Observer objects and user's input with Observer objects is propagated to Observable objects. These mechanism is the main process for monitoring and control applications. Therefore, the mobile M/VC application framework can improve the productivity of mobile applications and enhance the reusability of such components as Observer and Observable objects in wireless environments.

• Journal of Internet Computing and Services
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• v.10 no.2
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• pp.1-13
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• 2009

Intrusion detection is a process that identifies the attacks and responds to the malicious intrusion actions for the protection of the computer and the network resources. Due to the fast development of the Internet, the types of intrusions become more complex recently and need immediate and correct responses because the frequent occurrences of a new intrusion type rise rapidly. Therefore, to solve these problems of the intrusion detection systems, we propose a sequential pattern miner for analysis of the alert data in order to support intelligent and automatic detection of the intrusion. Sequential pattern mining is one of the methods to find the patterns among the extracted items that are frequent in the fixed sequences. We apply the prefixSpan algorithm to find out the alert sequences. This method can be used to predict the actions of the sequential patterns and to create the rules of the intrusions. In this paper, we propose an extended prefixSpan algorithm which is designed to consider the specific characteristics of the alert data. The extended sequential pattern miner will be used as a part of alert data analyzer of intrusion detection systems. By using the created rules from the sequential pattern miner, the HA(high-level alert analyzer) of PEP(policy enforcement point), usually called IDS, performs the prediction of the sequence behaviors and changing patterns that were not visibly checked.

• Journal of KIISE:Computing Practices and Letters
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• v.5 no.6
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• pp.727-737
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• 1999

프로세스 중심 소프트웨어 개발 환경(PSEE : Process-centered Software Engineering Environment)은 소프트웨어 개발자를 위한 여러가지 정보의 제공과 타스크의 수행, 소프트웨어 개발 도구의 수행 및 제어, 필수적인 규칙이나 업무의 수행등과 같은 다양한 행위를 제공하는 프로세스 모형의 수행을 통하여 소프트웨어 개발 행위를 지원한다. SEED(Software Engineering Environment for Development)는 효율적인 소프트웨어 개발과 프로세스 모형의 수행을 제어하기 위해 ETRI에서 개발된 PSEE이다.본 논문에서는 SEED에서 프로세스 모형을 설계하기 위해 사용되는 SimFlex 프로세스 프로그래밍 언어와, 수행지원시스템인 SEED Engine의 구현에 대하여 기술한다. SimFlex는 간단한 언어 구조를 가진 프로세스 프로그래밍 언어이며, 적절한 적합화를 통하여 다른 PSEE에서 사용될 수 있다. SimFlex 컴파일러는 SimFlex에 의해 기술된 프로세스 모형을 분석하고, 모형의 오류를 검사하며, SEED Engine에 의해 참조되는 중간 프로세스 모형을 생성한다. 중간 프로세스 모형을 사용하여 SEED Engine은 외부 모니터링 도구와 연관하여 사용자를 위한 유용한 정보뿐만 아니라 SimFlex에 의해 기술된 프로세스 모형의 자동적인 수행을 제공한다. SimFlex 언어와 수행지원 시스템의 지원을 통하여 소프트웨어 프로세스를 모형화하는데 드는 비용과 시간을 줄일 수 있으며, 편리하게 프로젝트를 관리하여 양질의 소프트웨어 생산물을 도출할 수 있다. Abstract Process-centered Software Engineering Environments(PSEEs) support software development activities through the enaction of process models, providing a variety of activities such as supply of various information for software developers, automation of routine tasks, invocation and control of software development tools, and enforcement of mandatory rules and practices. The SEED(Software Engineering Environment for Development) system is a PSEE which was developed for effective software process development and controlling the enactment of process models by ETRI.In this paper, we describe the implementation of the SimFlex process programming language used to design process models in SEED, and its runtime support system called by SEED Engine. SimFlex is a software process programming language to describe process models with simple language constructs, and it could be embedded into other PSEEs through appropriate customization. The SimFlex compiler analyzes process models described by SimFlex, check errors in the models, and produce intermediate process models referenced by the SEED Engine. Using the intermediate process models, the SEED Engine provides automatic enactment of the process models described by SimFlex as well as useful information for agents linked to the external monitoring tool. With the help of the SimFlex language and its runtime support system, we can reduce cost and time in modeling software processes and perform convenient project management, producing well-qualified software products.

• Korean Journal of Remote Sensing
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• v.14 no.4
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• pp.376-391
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• 1998

Images from satellites will have 1 to 3 meter ground resolution and will be very useful for analyzing current status of earth surface. An image processing system named GeoWatch with more intelligent image processing algorithms has been designed and implemented to support the detailed analysis of the land surface using high-resolution satellite imagery. The GeoWatch is a valuable tool for satellite image processing such as digitizing, geometric correction using ground control points, interactive enhancement, various transforms, arithmetic operations, calculating vegetation indices. It can be used for investigating various facts such as the change detection, land cover classification, capacity estimation of the industrial complex, urban information extraction, etc. using more intelligent analysis method with a variety of visual techniques. The strong points of this system are flexible algorithm-save-method for efficient handling of large size images (e.g. full scenes), automatic menu generation and powerful visual programming environment. Most of the existing image processing systems use general graphic user interfaces. In this paper we adopted visual program language for remotely sensed image processing for its powerful programmability and ease of use. This system is an integrated raster/vector analysis system and equipped with many useful functions such as vector overlay, flight simulation, 3D display, and object modeling techniques, etc. In addition to the modules for image and digital signal processing, the system provides many other utilities such as a toolbox and an interactive image editor. This paper also presents several cases of image analysis methods with AI (Artificial Intelligent) technique and design concept for visual programming environment.

• Journal of the Korean Society of Marine Environment & Safety
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• v.23 no.6
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• pp.639-645
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• 2017

Application Specific Messages (ASM) have been introduced by a number of international bodies, such as the International Maritime Organization (IMO), the International Telecommunication Union (ITU) and the International Association of Lighthouse Authorities (IALA), for the purpose of resolving AIS overloading issues caused by an increasing number of ships using AIS systems. ASM communication will transmit a large amount of safety-related information at sea, including meteorological information, accident reporting, and navigational warnings. Specifically, this message transaction system is expected to be actively used for communication among ships and for ship-to-shore (4S), where VHF communication through voice was standard. In order to design a user-oriented service through standardised AIS-ASM messaging in the future, the need for analysis of this seems to be quite critical. In order to reflect users' AIS-ASM communication needs, therefore, frequently-occurring marine communication messages were analysed through a questionnaire survey conducted on 57 marine officers and 50 VTS operators. Based on the survey results, a list of key standardised messages was suggested as a reference for future AIS message development.

• 재활복지
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• v.20 no.3
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• pp.141-161
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• 2016

This research is to anticipate problems of Ray's disabled experiment welfare vehicles for 17 drivers with disability, improving them. In addition it is to provide the criteria for ergonomic design based on perspective of drivers with disability by investigating 18 items of usability test. The results are as follows. First, satisfaction degree of Ray, disabled experiment welfare vehicle, was shown to be 3.88 which is higher than normal vehicles whose degree is 3.20. This showed that the disabled experiment welfare vehicle is the one with much improvement. Second, so as to develop a welfare vehicle it needs to take into account some factors including rear slope, wheelchair locker, seat belt, safety grip, and high roof. Third, in case of rear slope, high roof and width of manual or automatic wheel chairs should be considered and motor-operated device should also be taken into account for safety lockers, which make a wheelchair firmly fixed. Moreover, motor-operated seat and rear slope can be chosen for either of the disabled and the elderly.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.161-170
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• 2019

Multi-product mass production small and medium enterprise factories have a wide variety of products and a large number of products, wasting manpower and expenses for inventory management. In addition, there is no way to check the status of inventory in real time, and it is suffering economic damage due to excess inventory and shortage of stock. There are many ways to build a real-time data collection environment, but most of them are difficult to afford for small and medium-sized companies. Therefore, smart factories of small and medium enterprises are faced with difficult reality and it is hard to find appropriate countermeasures. In this paper, we implemented the contents of extension of existing inventory management method through character extraction on label with barcode and QR code, which are widely adopted as current product management technology, and evaluated the effect. Technically, through preprocessing using OpenCV for automatic recognition and classification of stock labels and barcodes, which is a method for managing input and output of existing products through computer image processing, and OCR (Optical Character Recognition) function of Google vision API. And it is designed to recognize the barcode through Zbar. We propose a method to manage inventory by real-time image recognition through Raspberry Pi without using expensive equipment.

• Korean Chemical Engineering Research
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• v.59 no.2
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• pp.209-218
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• 2021

First principle-based modeling studies have been performed to improve the heat exchange efficiency of ORV and optimize operation, but the heat transfer coefficient of ORV is an irregular system according to time and location, and it undergoes a complex modeling process. In this study, FNN, LSTM, and AutoML-based modeling were performed to confirm the effectiveness of data-based modeling for complex systems. The prediction accuracy indicated high performance in the order of LSTM > AutoML > FNN in MSE. The performance of AutoML, an automatic design method for machine learning models, was superior to developed FNN, and the total time required for model development was 1/15 compared to LSTM, showing the possibility of using AutoML. The prediction of NG and seawater discharged temperatures using LSTM and AutoML showed an error of less than 0.5K. Using the predictive model, real-time optimization of the amount of LNG vaporized that can be processed using ORV in winter is performed, confirming that up to 23.5% of LNG can be additionally processed, and an ORV optimal operation guideline based on the developed dynamic prediction model was presented.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.2
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• pp.93-101
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• 2021

Recently, with the urban redevelopment and the spread of the planned cities, there is increasing interest in the wind environment, which is related not only to design of buildings and landscaping but also to the comfortability of pedestrians. Numerical analysis for wind environment prediction is underway in many fields, such as dense areas of high-rise building or composition of the apartment complexes, a precisive 3D building model is essentially required in this process. Many studies conducted for wind environment analysis have typically used the method of creating a 3D model by utilizing the building layer included in the GIS (Geographic Information System) data. These data can easily and quickly observe the flow of atmosphere in a wide urban environment, but cannot be suitable for observing precisive flow of atmosphere, and in particular, the effect of a complicated structure of a single building on the flow of atmosphere cannot be calculated. Recently, drone photogrammetry has shown the advantage of being able to automatically perform building modeling based on a large number of images. In this study, we applied photogrammetry technology using a drone to evaluate the flow of atmosphere around two buildings located close to each other. Two 3D models were made into an automatic modeling technique and manual modeling technique. Auto-modeling technique is using an automatically generates a point cloud through photogrammetry and generating models through interpolation, and manual-modeling technique is a manually operated technique that individually generates 3D models based on point clouds. And then the flow of atmosphere for the two models was compared and analyzed. As a result, the wind environment of the two models showed a clear difference, and the model created by auto-modeling showed faster flow of atmosphere than the model created by manual modeling. Also in the case of the 3D mesh generated by auto-modeling showed the limitation of not proceeding an accurate analysis because the precise 3D shape was not reproduced in the closed area such as the porch of the building or the bridge between buildings.

• Journal of Animal Science and Technology
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• v.63 no.2
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• pp.367-379
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• 2021

The objectives of this study were to evaluate convolutional neural network models and computer vision techniques for the classification of swine posture with high accuracy and to use the derived result in the investigation of the effect of dietary fiber level on the behavioral characteristics of the pregnant sow under low and high ambient temperatures during the last stage of gestation. A total of 27 crossbred sows (Yorkshire × Landrace; average body weight, 192.2 ± 4.8 kg) were assigned to three treatments in a randomized complete block design during the last stage of gestation (days 90 to 114). The sows in group 1 were fed a 3% fiber diet under neutral ambient temperature; the sows in group 2 were fed a diet with 3% fiber under high ambient temperature (HT); the sows in group 3 were fed a 6% fiber diet under HT. Eight popular deep learning-based feature extraction frameworks (DenseNet121, DenseNet201, InceptionResNetV2, InceptionV3, MobileNet, VGG16, VGG19, and Xception) used for automatic swine posture classification were selected and compared using the swine posture image dataset that was constructed under real swine farm conditions. The neural network models showed excellent performance on previously unseen data (ability to generalize). The DenseNet121 feature extractor achieved the best performance with 99.83% accuracy, and both DenseNet201 and MobileNet showed an accuracy of 99.77% for the classification of the image dataset. The behavior of sows classified by the DenseNet121 feature extractor showed that the HT in our study reduced (p < 0.05) the standing behavior of sows and also has a tendency to increase (p = 0.082) lying behavior. High dietary fiber treatment tended to increase (p = 0.064) lying and decrease (p < 0.05) the standing behavior of sows, but there was no change in sitting under HT conditions.