• Journal of Korea Society of Industrial Information Systems
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• v.12 no.5
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• pp.39-46
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• 2007

Research on human identification in ubiquitous environment has recently attracted a lot of attention. As one of those research, gait recognition is an efficient method of human identification using physical features of a walking person at a distance. In this paper, we present a human shape extraction and tracking for gait recognition using geodesic active contour models(GACMs) combined with mean shift algorithm The active contour models (ACMs) are very effective to deal with the non-rigid object because of its elastic property. However, they have the limitation that their performance is mainly dependent on the initial curve. To overcome this problem, we combine the mean shift algorithm with the traditional GACMs. The main idea is very simple. Before evolving using level set method, the initial curve in each frame is re-localized near the human region and is resized enough to include the targe region. This mechanism allows for reducing the number of iterations and for handling the large object motion. The proposed system is composed of human region detection and human shape tracking modules. In the human region detection module, the silhouette of a walking person is extracted by background subtraction and morphologic operation. Then human shape are correctly obtained by the GACMs with mean shift algorithm. In experimental results, the proposed method show that it is extracted and tracked efficiently accurate shape for gait recognition.

• Korean Journal of Construction Engineering and Management
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• v.10 no.6
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• pp.135-145
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• 2009

The main goal of construction projects from the past has been enhancing efficiency by reducing cost and time. but, seeing the current condition of safety management of many construction companies nowadays, it is true that not much attention has been paid to safety management for a long time. However, there are paradigm shift from the cost and term of works to safety management in the construction industry, from this circumstance the safety management is evaluated more importantly. Though less accident happens compared to past, the accidents are getting greater because construction projects nowadays are bigger and more complex and monetary loss from the accidents are increasing. Also, the severity is getting greater and even fatal. For this reason, more improved safety management is very necessary. Therefore, we are to propose more efficient system for safety management in this thesis. Technical parts for developing system include many technique such as Real Time Locating system, and other techniques like Monitoring module based on BIM, Data Mart, Alarm are also applied together. Through this system, in the construction site, safety management is performed more effectively and widely because the system can manage the human resource and fluid situation. Also, safety manager can conduct more systematically and advanced safety management through human resource dominated safety management.

• Journal of Korean Physical Therapy Science
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• v.9 no.4
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• pp.83-94
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• 2002

Within physical therapy education, there has been increased attention to curricula and course that emphasize problem solving, clinical reasoning, and synthesis of information across traditional discipline-specific boundaries. This article describes the development implementation, and outcomes of a problem-based learning course in Physical therapy. The course was designed to help students to integrate the various elements of a physical therapy curriculum and to enhance their abilities to respond to an ever-changing health care environment. An evaluation of the course by the first 50 students who completed it revealed both strengths and weaknesses. Students responded that the course enhanced their professional behavior, including interpersonal communication skills, team work, and follow-through with professional responsibilities. The learning package was developed by the authors and implemented to a college students during three weeks of the first semester of 2001. Most studies which conducted PBL module development were short period or temporary PBL package application and evaluation rather than a whole semester's. While, this study carried on partial integrated PBL curriculum development and application with recomposing content of the two subjects to one subject Physical therapy which includes four PBL packages. This package was developed from a simple concept to complex and partial integrated PBL curriculum application systematically variable learning methods such as discussion, practice, lecture, video. There are 2 classes, each class has 25 students, in the college. Each class has 5 small groups consisting 5 students. Two tutors proceeded discussion charging each class also, they used multiple methods and materials like tutorials, self-directed learning, lecture, and video. The package is 5 grades and 5 hours per week and the rate of discussion, lecture is 4, 1 respectively. One of the most change is the increase of interaction between students and tutors. Whenever students need information and suggestion, they can visit tutors who provide reading materials and guide for the direction of self learning. Therefore, this study describes the PBL package development process and application during one semester recomposing contents of two subjects to Physical therapy concepts. Besides, it will contribute to active application of existing each subject to tutors who intend to convert as PBL methods. The study has significant meaning to show potentiality of partially integrated PBL application, using systematic PBL package development from two subjects contents. However, when students' need of yearning is over the extent of Introduction of Physical therapy and Rehabilitation medicine, tutors should set learning extent. So, there is limitation to attain completely integrated PBL education within one subject, therefore, it is high lighted to proceed development of integrated curriculum to maximize learning effects of PBL. It is exected that partial integrated PBL package development and application will distribute to prosper excellent physiotherapist in practice.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.1
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• pp.115-119
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• 2010

As the subjects of education has been changed from the instructors to learners, a web-based self-directed learning which can accelerate the initiative of learners and can be free from the restriction of time and space has been received attention. In this paper, the web-based self-directed learning system was designed. For the design, to make the learners build their own lecture plan, the service was designed to provide three kinds of lectures of video clip, slide lecture, and e-text lecture that were focused on various lecture contents. In addition, a learner and an assistant was man to man matched to enable the on-line mentoring for mutual communication between learners and assistants. Implementation was carried out by three sets of module - Manager, Learner and Assistant - that were applied to the real educational activities. The survey on satisfaction for the education, efficiency of ability improvement, and educational intelligibility for the attendants on the education showed more than 67.2% of satisfaction in satisfaction for the education. Furthermore, more than 86.9% of attendants replied that their ability were improved after the education of this system. The educational system realized in this paper shows effectiveness for the self-directed learning.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.3
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• pp.95-105
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• 2019

In recent years, image processing techniques for detecting road surface damaged spot have been actively researched. Especially, it is mainly used to acquire images through a smart phone or a black box that can be mounted in a vehicle and recognize the road surface damaged region in the image using several algorithms. In addition, in conjunction with the GPS module, the exact damaged location can be obtained. The most important technology is image processing algorithm. Recently, algorithms based on artificial intelligence have been attracting attention as research topics. In this paper, we will also discuss artificial intelligence image processing algorithms. Among them, an object detection method based on an region-based convolution neural networks method is used. To improve the recognition performance of road surface damage objects, 600 road surface damaged images and 1500 general road driving images are added to the learning database. Also, supervised learning using background object recognition method is performed to reduce false alarm and missing rate in road surface damage detection. As a result, we introduce a new method that improves the recognition performance of the algorithm to 8.66% based on average value of mAP through the same test database.

• Journal of Wetlands Research
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• v.23 no.1
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• pp.74-84
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• 2021

Active stormwater management is essential to minimize the impact of urban development and improve the hydrological cycle system. In recent years, the Low Impact Development (LID) technique for urban stormwater management is attracting attention as a reasonable alternative. The Storm Water Management Model (SWMM) is actively used in urban hydrological cycle improvement projects as it provides simulation functions for various GI (Green Infra) facilities through its LID module. However, in order to simulate GI facilities using SWMM, there are many difficulties in setting up complex watersheds and deploying GI facilities. In this study, a model that can evaluate the performance of GI facilities is proposed while implementing the core hydrological process of GI facilities. Since the proposed model operates based on hydrological routing, it can not only reflect the infiltration, storage, and evapotranspiration of GI facilities, but also quantitatively evaluate the effect of improving urban hydrological cycle by GI facilities. The applicability of the proposed model was verified by comparing the results of the proposed model with the results of SWMM. In addition, a discussion of errors occurring in the SWMM's permeable pavement system simulation is included.

• Journal of Digital Convergence
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• v.19 no.8
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• pp.319-325
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• 2021

With the advancement of wireless technology and the rapid growth of the infrastructure of mobile communication technology, systems applying AI-based platforms are drawing attention from users. In particular, the system that understands users' tastes and interests and recommends preferred items is applied to advanced e-commerce customized services and smart homes. However, there is a problem that these recommendation systems are difficult to reflect in real time the preferences of various users for tastes and interests. In this research, we propose a Fuzzy-AHP-based movies recommendation system using the Gated Recurrent Unit (GRU) language model to address a problem. In this system, we apply Fuzzy-AHP to reflect users' tastes or interests in real time. We also apply GRU language model-based models to analyze the public interest and the content of the film to recommend movies similar to the user's preferred factors. To validate the performance of this recommendation system, we measured the suitability of the learning model using scraping data used in the learning module, and measured the rate of learning performance by comparing the Long Short-Term Memory (LSTM) language model with the learning time per epoch. The results show that the average cross-validation index of the learning model in this work is suitable at 94.8% and that the learning performance rate outperforms the LSTM language model.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.23-34
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• 2023

Wearable technology for military applications has received considerable attention as a means of personal status check and monitoring. Among many, an implementation to recognize specific motion states of a human is promising in that allows active management of troops by immediately collecting the operational status and movement status of individual soldiers. In this study, as an extension of military wearable application research, a new ankle wearable device is proposed that can glean the information of a soldier on the battlefield on which action he/she takes in which environment. Presuming a virtual situation, the soldier's upper limbs are easily exposed to uncertainties about circumstances. Therefore, a sensing module is attached to the ankle of the soldier that may always interact with the ground. The obtained data comprises 3-axis accelerations and 3-axis rotational velocities, which cannot be interpreted by hand-made algorithms. In this study, to discern the behavioral characteristics of a human using these dynamic data, a data-driven model is introduced; four features extracted from sliced data (minimum, maximum, mean, and standard deviation) are utilized as an input of the model to learn and classify eight primary military movements (Sitting, Standing, Walking, Running, Ascending, Descending, Low Crawl, and High Crawl). As a result, the proposed device could recognize a movement status of a solider with 95.16% accuracy in an arbitrary test situation. This research is meaningful since an effective way of motion recognition has been introduced that can be furtherly extended to various military applications by incorporating wearable technology and artificial intelligence.

• Membrane Journal
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• v.33 no.6
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• pp.352-361
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• 2023

Steam methane reforming is currently the most widely used technology for producing hydrogen, a clean fuel. Hydrogen produced by steam methane reforming contains impurities such as carbon monoxide, and it is essential to undergo an appropriate post-purification step for commercial usage, such as fuel cells. Recently, membrane separation technology has been gaining great attention as an effective purification method; in this study, we evaluated the feasibility of using commercial polysulfone membranes for biogas upgrading to separate and recover hydrogen from a hydrogen/carbon monoxide gas mixture. Initially, we examined the physicochemical properties of the commercial membrane used. We then conducted performance evaluations of the commercial membrane module under various conditions using mixed gas, considering factors such as stage-cut and operating pressure. Finally, based on the evaluation results, we carried out simulations for process design. The maximum H₂ permeability and H₂/CO separation factor for the commercial membrane process were recorded at 361 GPU and 20.6, respectively. Additionally, the CO removal efficiency reached up to 94%, and the produced hydrogen concentration achieved a maximum of 99.1%.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.79-96
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• 2012

As the pace of competition dramatically accelerates and the complexity of change grows, a variety of research have been conducted to improve firms' short-term performance and to enhance firms' long-term survival. In particular, researchers and practitioners have paid their attention to identify promising technologies that lead competitive advantage to a firm. Discovery of promising technology depends on how a firm evaluates the value of technologies, thus many evaluating methods have been proposed. Experts' opinion based approaches have been widely accepted to predict the value of technologies. Whereas this approach provides in-depth analysis and ensures validity of analysis results, it is usually cost-and time-ineffective and is limited to qualitative evaluation. Considerable studies attempt to forecast the value of technology by using patent information to overcome the limitation of experts' opinion based approach. Patent based technology evaluation has served as a valuable assessment approach of the technological forecasting because it contains a full and practical description of technology with uniform structure. Furthermore, it provides information that is not divulged in any other sources. Although patent information based approach has contributed to our understanding of prediction of promising technologies, it has some limitations because prediction has been made based on the past patent information, and the interpretations of patent analyses are not consistent. In order to fill this gap, this study proposes a technology forecasting methodology by integrating patent information approach and artificial intelligence method. The methodology consists of three modules : evaluation of technologies promising, implementation of technologies value prediction model, and recommendation of promising technologies. In the first module, technologies promising is evaluated from three different and complementary dimensions; impact, fusion, and diffusion perspectives. The impact of technologies refers to their influence on future technologies development and improvement, and is also clearly associated with their monetary value. The fusion of technologies denotes the extent to which a technology fuses different technologies, and represents the breadth of search underlying the technology. The fusion of technologies can be calculated based on technology or patent, thus this study measures two types of fusion index; fusion index per technology and fusion index per patent. Finally, the diffusion of technologies denotes their degree of applicability across scientific and technological fields. In the same vein, diffusion index per technology and diffusion index per patent are considered respectively. In the second module, technologies value prediction model is implemented using artificial intelligence method. This studies use the values of five indexes (i.e., impact index, fusion index per technology, fusion index per patent, diffusion index per technology and diffusion index per patent) at different time (e.g., t-n, t-n-1, t-n-2, ${\cdots}$) as input variables. The out variables are values of five indexes at time t, which is used for learning. The learning method adopted in this study is backpropagation algorithm. In the third module, this study recommends final promising technologies based on analytic hierarchy process. AHP provides relative importance of each index, leading to final promising index for technology. Applicability of the proposed methodology is tested by using U.S. patents in international patent class G06F (i.e., electronic digital data processing) from 2000 to 2008. The results show that mean absolute error value for prediction produced by the proposed methodology is lower than the value produced by multiple regression analysis in cases of fusion indexes. However, mean absolute error value of the proposed methodology is slightly higher than the value of multiple regression analysis. These unexpected results may be explained, in part, by small number of patents. Since this study only uses patent data in class G06F, number of sample patent data is relatively small, leading to incomplete learning to satisfy complex artificial intelligence structure. In addition, fusion index per technology and impact index are found to be important criteria to predict promising technology. This study attempts to extend the existing knowledge by proposing a new methodology for prediction technology value by integrating patent information analysis and artificial intelligence network. It helps managers who want to technology develop planning and policy maker who want to implement technology policy by providing quantitative prediction methodology. In addition, this study could help other researchers by proving a deeper understanding of the complex technological forecasting field.