• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.1022-1025
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• 2009

USN(Ubiquitous Sensor Network) is a core infrastructure that makes come true the u-life in the ubiquitous society through various services of area such as u-city and u-Health. Therefore, we need to reseach about the domestic standards to establish the core technique of USN. Currently, the status of USN standards is most of technical standard and reseach that are technology for sensor node implementation and a protocol for energy-efficient communication and interlock with existing network. But, Standard and reseach for sensor network, integration management of heterogeneous sensor networks for USN application, sensing data management and USN database structure definition such as application and middleware are weak level. In this paper, we researched for standard development of the domestic sensor network service and relevant standard analysis to configure SWE(Sensor Web Enablement) of OGC(Open Geospatial Consortium) for standarded plattform technoloy in part of the middleware. Also we researched that it's a connection between domestic TTA (Telecommunications Technology Association) standards and SWE Standard. Finally, we researched for standard service plattform architecture on sensor network through analysis on the possibility of applying OGC-based services platform.

• Journal of Software Engineering Society
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• v.24 no.1
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• pp.9-17
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• 2011

Software reuse the concept of developing software by using existing software assets, rather than developing it from scratch. Developers may face difficulties of reusing existing software assets because existing assets are normally developed by other developers for different purposes. Developers tend to seek appropriate knowledge about effectively reusing software assets from the developers who have faced and solved similar problems in reusing software assets previously. In other words, the reuse-related knowledge of domain experts or other developers usually provides important clues to solve reuse-related problems. Such reuse-relalted knowledge can help developers to reduce the time and effort to identify and solve the difficulties and problems that may arise in reusing software assets and in minimizing the risks of reusing them by allowing them to reuse reliable software assets in an appropriate way and by recognizing similar requirements or constraints of resuing the assets. In this paper, we describe a model to represent reuse-related knowledge in a formal way, and explain the architecture and a prototype implementation of Software Reuse Wiki (SRW) that enables collaborative organization and sharing of software reuse-related knowledge. We have conducted an experiment pertaining to problem solving in reusing assets based on reuse-related knowledge. We also discuss about our evaluation plan for showing the benefits and contributions of reuse knowledge representation model and management methods in SRW. We expect that SRW can contribute to facilitate users' participations and make efficient sharing and growing of reuse-related knowledge. In addition, the representation model of reuse-related knowledge and management methods can make developers acquire more reliable and useful reuse-related knowledge in a straightforward manner without spending additional efforts to find solutions to solve reuse-related problems.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.6
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• pp.43-52
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• 2018

Livestock production is the second largest economic activity of Pakistan's rural population, more specifically; sixty-seven percent of Pakistan's total population that live in rural areas sources their income from livestock activities. As this subsector of agriculture within rural Pakistan is so critical to Pakistan's economy it is especially important to further develop the sector through the introduction of cost effective, efficient, and practical technologies. In an effort to improve such an important sector within the agriculture sector in Pakistan research has been carried out to better understand the capabilities and feasibility of leveraging Internet of Things based technologies, such as, microprocessors and microcontrollers within Pakistan's livestock production and management. The internet of Things can potentially allow for the scaling of small-scale rural livestock production to larger operations through cost effective and efficient livestock management through the application of IoT technologies. This paper discusses the architecture models of IoT based smart animal farms and delves into the pitfalls and advantages of applying IoT technologies in this sector. In this work we will explore the cheap sensors to monitor the internal activities of cattle farm with the aim of using these sensors as part of system to detect the important operations that need on the time response. This system should provide the feed and water as required, and control the temperature in sheds to protect the cattle being ill and on heat, and humidity level .internet connection used to connect these devices with smartphones or computers. In this paper we proposed the architecture model of IoT based smart animal farm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.12
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• pp.1577-1588
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• 2018

Recently, many researches are progressing to support the operation and maintenance works of vessels using analyzed result based on various information of equipment. The interfaces of communication equipment are standardized very well, but the interfaces of machinery and other parts are not standardized yet. For that reason, there has limitations for data exchange and management. To solve the problem, the ISO is establishing new standards which are ISO 19847 for shipboard data servers th share field data at sea and ISO 19848 for standard data for shipboard machinery and equipment. In this paper, we developed a data management platform for shipboard machinery equipment, and tested the field data exchanging using the developed platform based on the standards. To do this, we analyzed the requirements of the standards and related researches, and designed an architecture of shipboard data platform that satisfied the requirements. Also, we developed components of the designed platform architecture and verified the effectiveness for it.

• Journal of the Korea Society for Simulation
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• v.31 no.1
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• pp.11-18
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• 2022

Recently, research to classify human activity using imagery has been actively conducted for the purpose of crime prevention and facility safety in public places and facilities. In order to improve the performance of human activity classification, most studies have applied deep learning based-transfer learning. However, despite the increase in the number of backbone network models that are the basis of deep learning as well as the diversification of architectures, research on finding a backbone network model suitable for the purpose of operation is insufficient due to the atmosphere of using a certain model. Thus, this study applies the transfer learning into recently developed deep learning backborn network models to build an intelligent system that classifies human activity using imagery. For this, 12 types of active and high-contact human activities based on sports, not basic human behaviors, were determined and 7,200 images were collected. After 20 epochs of transfer learning were equally applied to five backbone network models, we quantitatively analyzed them to find the best backbone network model for human activity classification in terms of learning process and resultant performance. As a result, XceptionNet model demonstrated 0.99 and 0.91 in training and validation accuracy, 0.96 and 0.91 in Top 2 accuracy and average precision, 1,566 sec in train process time and 260.4MB in model memory size. It was confirmed that the performance of XceptionNet was higher than that of other models.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.793-810
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• 2022

Synthetic Aperture Radar (SAR) is considered to be suitable for near real-time inundation monitoring. The distinctly different intensity between water and land makes it adequate for waterbody detection, but the intrinsic speckle noise and variable intensity of SAR images decrease the accuracy of waterbody detection. In this study, we suggest two modules, named 'morphology module' and 'edge-enhanced module', which are the combinations of pooling layers and convolutional layers, improving the accuracy of waterbody detection. The morphology module is composed of min-pooling layers and max-pooling layers, which shows the effect of morphological transformation. The edge-enhanced module is composed of convolution layers, which has the fixed weights of the traditional edge detection algorithm. After comparing the accuracy of various versions of each module for U-Net, we found that the optimal combination is the case that the morphology module of min-pooling and successive layers of min-pooling and max-pooling, and the edge-enhanced module of Scharr filter were the inputs of conv9. This morphologic and edge-enhanced U-Net improved the F1-score by 9.81% than the original U-Net. Qualitative inspection showed that our model has capability of detecting small-sized waterbody and detailed edge of water, which are the distinct advancement of the model presented in this research, compared to the original U-Net.

• Journal of Appropriate Technology
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• v.6 no.2
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• pp.136-145
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• 2020

Agriculture is the backbone of the economy of most developing countries. In these countries, agriculture or farming is mostly done manually with little integration of machinery, intelligent systems and data monitoring. Irrigation is an essential process that directly influences crop production. The fluctuating amount of rainfall per year has led to the adoption of irrigation systems in most farms. The absence of smart sensors, monitoring methods and control, has led to low harvests and draining water sources. In this research paper, we introduce a 433 MHz Radio Frequency and 2G based Smart Irrigation Meter System and a water prepayment system for rural areas of Tanzania with no reliable internet coverage. Specifically, Ngurudoto area in Arusha region where it will be used as a case study for data collection. The proposed system is hybrid, comprising of both weather data (evapotranspiration) and soil moisture data. The architecture of the system has on-site weather measurement controllers, soil moisture sensors buried on the ground, water flow sensors, a solenoid valve, and a prepayment system. To achieve high precision in linear and nonlinear regression and to improve classification and prediction, this work cascades a Dynamic Regression Algorithm and Naïve Bayes algorithm.

• Journal of Industrial Convergence
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• v.20 no.8
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• pp.77-84
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• 2022

This study aims to present a method for implementing a decision support system that can be used for selecting emerging technologies by applying a machine learning-based automatic classification technique. To conduct the research, the architecture of the entire system was built and detailed research steps were conducted. First, emerging technology candidate items were selected and trend data was automatically generated using a big data system. After defining the conceptual model and pattern classification structure of technological development, an efficient machine learning method was presented through an automatic classification experiment. Finally, the analysis results of the system were interpreted and methods for utilization were derived. In a DTW-kNN-based classification experiment that combines the Dynamic Time Warping(DTW) method and the k-Nearest Neighbors(kNN) classification model proposed in this study, the identification performance was up to 87.7%, and particularly in the 'eventual' section where the trend highly fluctuates, the maximum performance difference was 39.4% points compared to the Euclidean Distance(ED) algorithm. In addition, through the analysis results presented by the system, it was confirmed that this decision support system can be effectively utilized in the process of automatically classifying and filtering by type with a large amount of trend data.

• Journal of the Korea Society for Simulation
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• v.19 no.1
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• pp.63-71
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• 2010

In modern war, Intercept Performance of SAM(Surface to Air Missile) is gaining importance as range and precision of Missile and Guided Weapon on information warfare have been improved. An aerial defence system using Surface-to-air Radar and Guided Missile is needed to be built for prediction and defense from threatening aerial attack. When developing SAM, M&S is used to free from a time limit and a space restriction. M&S is widely applied to education, training, and design of newest Weapon System. This study was conducted to develop simulator for evaluation of Intercept Performance of SAM. In this study, architecture of Intercept Performance of SAM analysis simulator for estimation of Intercept Performance of various SAM was suggested and developed. The developed Intercept Performance of SAM analysis simulator was developed by C++ and Direct3D, and through 3D visualization using the Direct3D, it shows procedures of the simulation on a user animation window. Information about design and operation of Fighting model is entered through input window of the simulator, and simulation engine consisted of Object Manager, Operation Manager, and Integrated Manager conducts modeling and simulation automatically using the information, so the simulator gives user feedback in a short time.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.127-135
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• 2023

This research paper explores the application of FinBERT, a variational BERT-based model pre-trained on financial domain, for sentiment analysis in the financial domain while focusing on the process of identifying suitable training data and hyperparameters. Our goal is to offer a comprehensive guide on effectively utilizing the FinBERT model for accurate sentiment analysis by employing various datasets and fine-tuning hyperparameters. We outline the architecture and workflow of the proposed approach for fine-tuning the FinBERT model in this study, emphasizing the performance of various datasets and hyperparameters for sentiment analysis tasks. Additionally, we verify the reliability of GPT-3 as a suitable annotator by using it for sentiment labeling tasks. Our results show that the fine-tuned FinBERT model excels across a range of datasets and that the optimal combination is a learning rate of 5e-5 and a batch size of 64, which perform consistently well across all datasets. Furthermore, based on the significant performance improvement of the FinBERT model with our Twitter data in general domain compared to our news data in general domain, we also express uncertainty about the model being further pre-trained only on financial news data. We simplify the complex process of determining the optimal approach to the FinBERT model and provide guidelines for selecting additional training datasets and hyperparameters within the fine-tuning process of financial sentiment analysis models.