• KIPS Transactions on Software and Data Engineering
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• v.10 no.10
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• pp.421-428
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• 2021

The High Efficiency Image File Format (HEIF) is an MPEG-developed image format that utilizes the video codec H.265 to store still screens in a single image format. The iPhone has been using HEIF since 2017, and Android devices such as the Galaxy S10 have also supported the format since 2019. The format can provide images with good compression rates, but it has a complex internal structure and lacks significant compatibility between devices and software, making it not popular to replace commonly used JPEG (or JPG) files. However, despite the fact that many devices are already using HEIF, digital forensics research regarding it is lacking. This means that we can be exposed to the risk of missing potential evidence due to insufficient understanding of the information contained inside the file during digital forensics investigations. Therefore, in this paper, we analyze the HEIF formatted photo file taken on the iPhone and the motion photo file taken on the Galaxy to find out the information and features contained inside the file. We also investigate whether or not the software we tested support HEIF and present the requirement of forensic tools to analyze HEIF.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.7
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• pp.279-286
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• 2021

Recently, We are carrying out a policy of physical distancing of at least 1m from each other to prevent the spreading of COVID-19 disease in public places. In this paper, we propose a method for measuring distances between people in real time and an automation system that recognizes objects that are within 1 meter of each other from stereo images acquired by drones or CCTVs according to the estimated distance. A problem with existing methods used to estimate distances between multiple objects is that they do not obtain three-dimensional information of objects using only one CCTV. his is because three-dimensional information is necessary to measure distances between people when they are right next to each other or overlap in two dimensional image. Furthermore, they use only the Bounding Box information to obtain the exact coordinates of human existence. Therefore, in this paper, to obtain the exact two-dimensional coordinate value in which a person exists, we extract a person's key point to detect the location, convert it to a three-dimensional coordinate value using Stereo Vision and Camera Calibration, and estimate the Euclidean distance between people. As a result of performing an experiment for estimating the accuracy of 3D coordinates and the distance between objects (persons), the average error within 0.098m was shown in the estimation of the distance between multiple people within 1m.

• KIPS Transactions on Computer and Communication Systems
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• v.7 no.12
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• pp.301-312
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• 2018

In industrial wireless sensor networks, the continuous object detection such as fire or toxic gas detection is one of major applications. A continuous object occurs at a specific point and then diffuses over a wide area. Therefore, many studies have focused on accurately detecting a continuous object and delivering data to a static sink with an energy-efficient way. Recently, some applications such as fire suppression require mobile sinks to provide real-time response. However, the sink mobility support in continuous object detection brings challenging issues. The existing approaches supporting sink mobility are designed for individual object detection, so they establish one-to-one communication between a source and a mobile sink for location update. But these approaches are not appropriate for a continuous object detection since a mobile sink should establish one-to-many communication with all sources. The one-to-many communication increases energy consumption and thus shortens the network lifetime. In this paper, we propose the origin-centric communication scheme to support sink mobility in a continuous object detection. Simulation results verify that the proposed scheme surpasses all the other work in terms of energy consumption.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.1
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• pp.45-50
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• 2019

With the gradual advancement of IT, passive work processes are automated and the overall quality of life has greatly improved. This is made possible by the formation of an organic topology between a wide variety of real-life smart devices. To serve these diverse smart devices, businesses or users are using the cloud. The services in the cloud are divided into Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). SaaS runs on PaaS, and PaaS runs on IaaS. Since IaaS is the basis of all services, an algorithm is required to operate virtualization resources efficiently. Among them, desktop resource virtualization is used for resource high availability of unused state time of existing desktop PC. Clustering of hierarchical structures is important for high availability of these resources. In addition, it is very important to select a suitable algorithm because many clustering algorithms are mainly used depending on the distribution ratio and environment of the desktop PC. If various attempts are made to find an algorithm suitable for desktop resource virtualization in an operating environment, a great deal of power, time, and manpower will be incurred. Therefore, this paper proposes a resource clustering simulator for cluster selection of desktop virtualization. This provides a clustering simulation to properly select clustering algorithms and apply elements in different environments of desktop PCs.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.1
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• pp.1-12
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• 2019

In this paper, it was found that most of the machinery facilities problems generated by clients could be prevented in advance by systematically managing the mechanical equipment production process of small and medium enterprises (SMEs) that produce machinery facilities. Major point of this process is to establish an operating system that corresponds to reality of facility manufacturers as it represents 63% of machinery facilities problems that occur in customers and is a task that needs to be solved most intensively. Technical issues account for 23% of machinery facilities problems occurring at the client's companies and should be approached from a long-term perspective as they are directly related to the technical capabilities of the manufacturers. Organizational problems account for 14% of machinery facilities problems occurring in customer companies, and can change depending on the relationship of members and the nature of the human being, such as morality and motivation. In addition, we propose the establishment of an Internet-based production process management platform for smooth and efficient transfer of information between customers and machinery facilities manufacturers.

• Transactions of the Korean hydrogen and new energy society
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• v.30 no.1
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• pp.76-82
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• 2019

This study was investigate the characteristics of GHGs ($CO_2$, $CH_4$, and $N_2O$) emissions of diesel medium duty trucks according to their various driving modes. GHGs emissions decreased as vehicle speed increased, and emissions increased after 64.7 km/h. The 4.5 ton trucks show higher values of $CO_2$, $CH_4$, $N_2O$, 35%, 25%, and 57%, respectively, comparing of the 2.5 ton trucks. Also, $CO_2$ emissions under WHVC mode were 20% lower than those under the NEDC mode. In the case of cold start condition, $CO_2$ emissions were 12-13% higher than those for hot start condition. In the future, the result of present study will provide basic data to set up GHGs emission standards for medium and heavy duty vehicles.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.8
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• pp.317-322
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• 2019

Programming judge systems for programming training support are typically built on the Web, where the examiners uploads a programming problem, which the student reads and submits an answer to the problem. The judge system executes the submitted answer of source code to provide feedback such as pass, failure, and error messages. Students who receive the feedback except for the pass continues debugging the source code until they are judged to pass. We developed an online judge system to support programming training and analyzed answers submitted by the students and found that many of the students who were not judged to pass that test did not know exactly where they were wrong but continued to solve the problem. The current judge system generally feeds runtime error messages back to students. However, with only runtime error message, it is difficult for student who train to find the wrong part of the answer. Therefore, in this paper, we propose a system that provides the feedback of programming failure by analyzing the control flow of the test cases used in the source code submitted by the student. The proposed system helps students find the wrong parts more quickly by feeding back the paths where faults in the control flow may exist. In addition, we show that this system is applicable to the answer source code that the actual student submitted.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.2
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• pp.61-66
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• 2019

Augmented reality is a technology that makes a virtual object appear as if it exists in reality by composing a virtual object in real time with the image captured by the camera. In order to augment the virtual object on the object existing in reality, the homography of images utilized to estimate the position and orientation of the object. The homography can be estimated by applying the RANSAC algorithm to the feature points of the images. But the homography estimation method using the RANSAC algorithm has a problem that accurate homography can not be estimated when there are many feature points in the background. In this paper, we propose a method to filter feature points of a background when the object is near and the background is relatively far away. First, we classified the depth image into relatively near region and a distant region using the Otsu's method and improve homography estimation performance by filtering feature points on the relatively distant area. As a result of experiment, processing time is shortened 71.7% compared to a conventional homography estimation method, and the number of iterations of the RANSAC algorithm was reduced 69.4%, and Inlier rate was increased 16.9%.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.2
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• pp.79-88
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• 2019

Machine translation (MT) system, especially Korean-Mongolian MT system, has recently attracted much attention due to its necessary for the globalization generation. Korean and Mongolian have the same sentence structure SOV and the arbitrarily changing of their words order does not change the meaning of sentences due to postpositional particles. The particles that are attached behind words to indicate their grammatical relationship to the clause or make them more specific in meaning. Hence, the particles play an important role in the translation between Korean and Mongolian. However, one Korean particle can be translated into several Mongolian particles. This is a major issue of the Korean-Mongolian MT systems. In this paper, to address this issue, we propose a method to use the combination of UTagger and a Korean-Mongolian particles table. UTagger is a system that can analyze morphologies, tag POS, and disambiguate homographs for Korean texts. The Korean-Mongolian particles table was manually constructed for matching Korean particles with those of Mongolian. The experiment on the test set extracted from the National Institute of Korean Language's Korean-Mongolian Learner's Dictionary shows that our method achieved the accuracy of 88.38% and it improved the result of using only UTagger by 41.48%.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.5
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• pp.213-222
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• 2019

Determining the meaning of a keyword in a speech dialogue system is an important technology for the future implementation of an intelligent speech dialogue interface. After extracting keywords to grasp intention from user's utterance, the intention of utterance is determined by using the semantic mark of keyword. One keyword can have several semantic marks, and we regard the task of attaching the correct semantic mark to the user's intentions on these keyword as a problem of word sense disambiguation. In this study, about 23% of all keywords in the corpus is manually tagged to build a semantic mark dictionary, a synonym dictionary, and a context vector dictionary, and then the remaining 77% of all keywords is automatically tagged. The semantic mark of a keyword is determined by calculating the context vector similarity from the context vector dictionary. For an unregistered keyword, the semantic mark of the most similar keyword is attached using a synonym dictionary. We compare the performance of the system with manually constructed training set and semi-automatically expanded training set by selecting 3 high-frequency keywords and 3 low-frequency keywords in the corpus. In experiments, we obtained accuracy of 54.4% with manually constructed training set and 50.0% with semi-automatically expanded training set.