• Korean Journal of Air-Conditioning and Refrigeration Engineering
• /
• v.25 no.11
• /
• pp.631-639
• /
• 2013

This study derives quality checking items in Building Mechanical Systems Design Criteria, and suggests quality criteria to review BIM models in the duct system of an air conditioning system for rule-based automatic checking. First, components for the duct system of an air conditioning system were reviewed, and the quality checking items were drawn from Building Mechanical Systems Design Criteria, through assessment according to object, attribute and relationship composing the BIM model. Second, quality checking types were derived, by analyzing the quality checking items and Rule set of the Solibri Model Checker. Finally, methods of algorithm functioning for checking the BIM models for mechanical systems in computers were drawn, and Elements to comprise the quality checking criteria (rule) were suggested. This study means that that checking items are derived from domestic criteria, and a way for the development process of determining quality checking criteria (rules) is suggested.

• International journal of advanced smart convergence
• /
• v.11 no.3
• /
• pp.64-71
• /
• 2022

Various types of attendance management systems are being introduced in a remote working environment and research on using face recognition is in progress. To ensure accurate worker's attendance, a face recognition-based attendance management system must analyze every frame of video, but face recognition is a heavy task, the number of the task should be minimized without affecting accuracy. In this paper, we proposed a search model using time interval variation to minimize the number of face recognition task of recorded videos for attendance management system. The proposed model performs face recognition by changing the interval of the frame identification time when there is no change in the attendance status for a certain period. When a change in the face recognition status occurs, it moves in the reverse direction and performs frame checks to more accurate attendance time checking. The implementation of proposed model performed at least 4.5 times faster than all frame identification and showed at least 97% accuracy.

• Journal of the Korea Convergence Society
• /
• v.10 no.11
• /
• pp.79-84
• /
• 2019

Video image based technology is being used in various fields with continuous development. The demand for vision system technology that analyzes and discriminates image objects acquired through cameras is rapidly increasing. Image processing is one of the core technologies of vision systems, and is used for defect inspection in the semiconductor manufacturing field, object recognition inspection such as the number of tire surfaces and symbols. In addition, research into license plate recognition is ongoing, and it is necessary to recognize objects quickly and accurately. In this paper, propose a recognition model through the rotational alignment of objects after checking the angle value of the tilt of the object in the input video image for the recognition of inclined objects such as numbers or symbols marked on the surface. The proposed model can perform object recognition of the rotationally sorted image after extracting the object region and calculating the angle of the object based on the contour algorithm. The proposed model extracts the object region based on the contour algorithm, calculates the angle of the object, and then performs object recognition on the rotationally aligned image. In future research, it is necessary to study template matching through machine learning.

• Journal of Digital Convergence
• /
• v.20 no.4
• /
• pp.397-404
• /
• 2022

Recently, in the industrial field, various studies are being conducted on converging IIoT devices that combine low-power processes and network cards into industrial sites to improve production efficiency and reduce costs. In this paper, we propose a processing model that can efficiently manage products produced by attaching IIoT sensor information to infrastructure built in industrial sites. The proposed model creates production data using IIoT data collection, preprocessing, characteristic generation, and labels to detect abnormally processed sensing information in real time by checking sensing information of products produced by IIoT at regular intervals. In particular, the proposed model can easily process IIoT data by performing tracking and monitoring so that product information produced in industrial sites can be processed in real time. In addition, since the proposed model is operated based on the existing production environment, the connection with the existing system is smooth.

• Journal of Digital Convergence
• /
• v.13 no.9
• /
• pp.297-302
• /
• 2015

The increase in applying IT to vehicles has given birth to smart cars or connected cars. As smarts cars become connected with external network systems, threats to communication security are on the rise. With simulation test results supporting such threats to Convergence security in vehicular communication, concerns are raised over relevant vulnerabilities, while an increasing number of studies on secure vehicular communication are published. Hacking attacks against vehicles are more dangerous than other types of hacking attempts because such attacks may threaten drivers' lives and cause social instability. This paper designed a Convergence security protocol for inter-vehicle and intra-vehicle communication using a hash function, nonce, public keys, time stamps and passwords. The proposed protocol was tested with a formal verification tool, Casper/FDR, and found secure and safe against external attacks.

• Convergence Security Journal
• /
• v.20 no.3
• /
• pp.39-46
• /
• 2020

In order to protect the business information of the enterprise, the company is engaged in various information security activities, such as establishing an information security management system, establishing and operating an information security system, checking vulnerabilities and security controls. It is an enterprise information security governance that organizes various information security activities for enterprise business, and it needs to be systematized to operate them effectively. In this study, to systematize the enterprise information security governance, we would like to explore the existing Enterprise Information Portal(EIP) model and propose an Enterprise Information Security Portal(EISP) model based on it. The Enterprise Information Security Portal(EISP) model provides an integrated environment for supporting the activities of the information security departments by systemizing the enterprise information security governance, which is a variety of information security activities of the enterprises, so that the information security activities of the enterprises can participate directly from CEO to executives and employees, not just from the information security departments.

• Journal of Intelligence and Information Systems
• /
• v.29 no.2
• /
• pp.285-301
• /
• 2023

Due to the increasing demand and importance of non-face-to-face education, open online learning platforms are getting interests both domestically and internationally. These platforms exhibit different characteristics from online courses by universities and other educational institutions. In particular, students engaged in these platforms can receive more learner autonomy, and the development of tools to assist learning is required. From the past, researchers have attempted to utilize process mining to understand realistic study behaviors and derive learning patterns. However, it has a deficiency to employ it to the open online learning platforms. Moreover, existing research has primarily focused on the process model perspective, including process model discovery, but lacks a method for the process pattern and instance perspectives. In this study, we propose a method to identify learning patterns within an open online learning platform using process mining techniques. To achieve this, we suggest three different viewpoints, e.g., model-level, variant-level, and instance-level, to comprehend the learning patterns, and various techniques are employed, such as process discovery, conformance checking, autoencoder-based clustering, and predictive approaches. To validate this method, we collected a learning log of machine learning-related courses on a domestic open education platform. The results unveiled a spaghetti-like process model that can be differentiated into a standard learning pattern and three abnormal patterns. Furthermore, as a result of deriving a pattern classification model, our model achieved a high accuracy of 0.86 when predicting the pattern of instances based on the initial 30% of the entire flow. This study contributes to systematically analyze learners' patterns using process mining.

• The Korean Journal of Applied Statistics
• /
• v.24 no.4
• /
• pp.677-693
• /
• 2011

In this paper, we consider Bayesian approaches to zero inflated Poisson model, one of the popular models to analyze zero inflated count data. To generate posterior samples, we deal with a Markov Chain Monte Carlo method using a Gibbs sampler and an exact sampling method using an Inverse Bayes Formula(IBF). Posterior sampling algorithms using two methods are compared, and a convergence checking for a Gibbs sampler is discussed, in particular using posterior samples from IBF sampling. Based on these sampling methods, a real data analysis is performed for Trajan data (Marin et al., 1993) and our results are compared with existing Trajan data analysis. We also discuss model selection issues for Trajan data between the Poisson model and zero inflated Poisson model using various criteria. In addition, we complement the previous work by Rodrigues (2003) via further data analysis using a hierarchical Bayesian model.

• Journal of Convergence for Information Technology
• /
• v.10 no.6
• /
• pp.140-146
• /
• 2020

Since the current crime prevention systems have a standard mechanism that victims request for help by themselves or ask for help from a third party nearby, it is difficult to obtain appropriate help in situations where a prompt response is not possible. In this study, we proposed and developed an automatic rescue request model and system using Deep Learning and OpenCV. This study is based on the prerequisite that immediate and precise threat detection is essential to ensure the user's safety. We validated and verified that the system identified by more than 99% of the object's accuracy to ensure the user's safety, and it took only three seconds to complete all necessary algorithms. We plan to collect various types of threats and a large amount of data to reinforce the system's capabilities so that the system can recognize and deal with all dangerous situations, including various threats and unpredictable cases.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.19 no.2
• /
• pp.86-97
• /
• 2016

This study aims to analyze the characteristics of the smart city governance through the multi-layer governance model, which includes administrative governance(AG), technological governance(TG), and global governance(GG). The results of the smart city governance are as follows. Multi-layered governance was modeled to enable cross-checking of each element of the propelling process and types of governance. AG has transitioned from a public partnership to a public-private people partnership(pppp) through a public-private partnership(ppp). TG has the characteristics of information communication technologies(ICTs) - eco technologies(EcoTs) - Spatial technology convergence including physical center, information software platforms such as the CCTV convergence center, and virtualization such as the cloud data center. GG aims at developing killer applications and ICTs-embedded space with intelligent buildings such as a smart city special zone to enable overseas exports. The smart city roadshow and forum have been developed as a platform for overseas exports with competition as well as cooperation.