• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.252-260
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• 2021

For frailty management, the importance of resistance exercise has been emphasized, and various devices have been developed. Recently, digital weight control technology that converts electromagnetic resistance to a digital weight is attracting attention, but there are no reports confirming the effectiveness and safety of the device for seniors in Korea. This study conducted a biomechanic-based ergonomic analysis of an elderly-friendly exercise device utilized in digital load control technology to suggest a direction for development. Twenty seniors (age: 62.40 ± 2.09 years) were included. The load of the device was classified into three levels, and the muscle activity and heart rate were assessed during three experimental motions. A questionnaire based on the International Organization for Standardization 9241-11 was adopted to evaluate the stability, operationality, efficiency, and satisfaction with the software and device. The program could be divided into three exercise intensities that can be utilized in the field depending on whether the exercise load, muscle activity, and heart rate were consistent. The monitor size needed to be enlarged to make the menu Korean, reduce the device size, and minimize noise. Considering these findings, the development of an advanced age-friendly exercise device by improving the size, display, and noise is suggested.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.4
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• pp.133-142
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• 2021

With the recent advancements of deep learning, companies such as smart home, healthcare, and intelligent transportation systems are utilizing its functionality to provide high-quality services for vehicle detection, emergency situation detection, and controlling energy consumption. To provide reliable services in such sensitive systems, deep learning models are required to have high accuracy. In order to develop a deep learning model for analyzing previously mentioned services, developers should utilize the state of the art deep learning models that have already been verified for higher accuracy. The developers can verify the accuracy of the referenced model by validating the model on the dataset. For this validation, the developer needs structural information to document and apply deep learning models, including metadata such as learning dataset, network architecture, and development environments. In this paper, we propose a description language that represents the network architecture of the deep learning model along with its metadata that are necessary to develop a deep learning model. Through the proposed description language, developers can easily verify the accuracy of the referenced deep learning model. Our experiments demonstrate the application scenario of a deep learning description document that focuses on the license plate recognition for the detection of illegally parked vehicles.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.318-331
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• 2022

With the recent development of hardware computing devices and software based frameworks, machine tasks using deep learning networks are expected to be utilized in various industrial fields and personal IoT devices. However, in order to overcome the limitations of high cost device for utilizing the deep learning network and that the user may not receive the results requested when only the machine task results are transmitted from the server, Collaborative Intelligence (CI) proposed the transmission of feature maps as a solution. In this paper, an efficient compression method for feature maps with vast data sizes to support the CI paradigm was analyzed and presented through experiments. This method increases redundancy by applying feature map reordering to improve compression efficiency in traditional video codecs, and proposes a feature map method that improves compression efficiency and maintains the performance of machine tasks by simultaneously utilizing image compression format and video compression format. As a result of the experiment, the proposed method shows 14.29% gain in BD-rate of BPP and mAP compared to the feature compression anchor of MPEG-VCM.

• The Korean Journal of Archival Studies
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• no.72
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• pp.165-203
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• 2022

Many people create records in digital space, and the amount of digital records left after individual dies has increased. The digital record left by the deceased is different from the record heritage that has physical substances. In many cases, the records of the deceased not just belong to the deceased, and many deceased did not explicitly disclose their online accounts and method of dispose of digital records during their lifetime, so this problem may lead to problems of inheritance to the bereaved family. In addition, digital records may be neglected or deleted after a person's death due to software problems, specific platform's terms of use, account deletion by bereaved family, etc. This leads to the problem that daily records, which are important clues to the social aspects at the time, are easily lost. Several studies have revealed that individuals are interested in preserving their digital records, but do not know how to do it, so they are benign neglect. For this reason, it is necessary to pay attention to personal digital records and personal digital legacy, and to prepare related policies and plans. Accordingly, this study analyzes problems related to the management of digital records after an individual's death, related to laws and systems, the status and policies of platforms and industries, the status of personal record management, etc. Various solutions were suggested, such as a need for enactment for digital personal record management act, platform's explicit policy for individual's post-mortem records, digital records management plan for archival institutions, individual's a preemptive management plan for his/her own records, and a method for writing a will related to digital account information.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.4
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• pp.817-839
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• 2021

To manage software safely, the military acquires and manages products in accordance with the RMF A&A. RMF A&A is standard for acquiring IT products used in the military. And it covers the requirements, acquisition through evaluation and maintenance of products. According to the RMF A&A, product development activities should reflect the risks of the military. In other words, developers have mitigated the risks through security by design and supply chain security. And they submit evidence proving that they have properly comply with RMF A&A's security requirements, and the military will evaluate the evidence to determine whether to acquire IT product. Previously, case study of RMF A&A have been already conducted. But it is difficult to apply in real-world, because it only address part of RMF A&A and detailed information is confidential. In this paper, we propose the evidence fulfilling method that can satisfy the requirements of the RMF A&A. Furthermore, we apply the proposed method to real-world drone system for verifying our method meets the RMF A&A.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.135-144
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• 2021

Recently, data-driven decision-making technology has become a key technology leading the data industry, and machine learning technology for this requires high-quality training datasets. However, real-world data contains missing values for various reasons, which degrades the performance of prediction models learned from the poor training data. Therefore, in order to build a high-performance model from real-world datasets, many studies on automatically imputing missing values in initial training data have been actively conducted. Many of conventional machine learning-based imputation techniques for handling missing data involve very time-consuming and cumbersome work because they are applied only to numeric type of columns or create individual predictive models for each columns. Therefore, this paper proposes a new data imputation technique called 'Denoising Self-Attention Network (DSAN)', which can be applied to mixed-type dataset containing both numerical and categorical columns. DSAN can learn robust feature expression vectors by combining self-attention and denoising techniques, and can automatically interpolate multiple missing variables in parallel through multi-task learning. To verify the validity of the proposed technique, data imputation experiments has been performed after arbitrarily generating missing values for several mixed-type training data. Then we show the validity of the proposed technique by comparing the performance of the binary classification models trained on imputed data together with the errors between the original and imputed values.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.35 no.4
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• pp.227-233
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• 2022

The 3D-printing technique is used for manufacturing objects by adding multiple layers, and it is relatively easy to manufacture objects with complex shapes. The 3D concrete printing technique, which incorporates 3D printing into the construction industry, does not use a formwork when placing concrete, and it requires less workload and labor, so economical construction is possible. However, 3D-printed concrete is expected to have a lower strength than that of molded concrete. In this study, the properties of 3D-printed concrete were analyzed. To fabricate the 3D-printed concrete samples, the extrusion path and shape of the samples were designed with Ultimaker Cura. Based on this, G-codes were generated to control the 3D printer. The optimal concrete mixing proportion was selected considering such factors as extrudability and buildability. Molded samples with the same dimensions were also fabricated for comparative analysis. The properties of each sample were measured through a three-point bending test and uniaxial compression test, and a comparative analysis was performed.

• Advanced Industrial SCIence
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• v.1 no.1
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• pp.7-15
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• 2022

To adopt the development in the medical scenario IoT developed towards the advancement with the processing of a large amount of medical data defined as an Internet of Medical Things (IoMT). The vast range of collected medical data is stored in the cloud in the structured manner to process the collected healthcare data. However, it is difficult to handle the huge volume of the healthcare data so it is necessary to develop an appropriate scheme for the healthcare structured data. In this paper, a machine learning mode for processing the structured heath care data collected from the IoMT is suggested. To process the vast range of healthcare data, this paper proposed an MTGPLSTM model for the processing of the medical data. The proposed model integrates the linear regression model for the processing of healthcare information. With the developed model outlier model is implemented based on the FinTech model for the evaluation and prediction of the COVID-19 healthcare dataset collected from the IoMT. The proposed MTGPLSTM model comprises of the regression model to predict and evaluate the planning scheme for the prevention of the infection spreading. The developed model performance is evaluated based on the consideration of the different classifiers such as LR, SVR, RFR, LSTM and the proposed MTGPLSTM model and the different size of data as 1GB, 2GB and 3GB is mainly concerned. The comparative analysis expressed that the proposed MTGPLSTM model achieves ~4% reduced MAPE and RMSE value for the worldwide data; in case of china minimal MAPE value of 0.97 is achieved which is ~ 6% minimal than the existing classifier leads.

• Journal of Navigation and Port Research
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• v.47 no.1
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• pp.10-17
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• 2023

A series of studies on the development of autonomous surface ships have been promoted in domestic and foreign countries. One of the main technologies for the development of autonomous ships is path-following control, which is closely related to securing the safety of ships at sea. In this regard, the path-following performance of an autonomous ship should be first evaluated at the design stage. The main aim of this study was to develop a visual and quantitative evaluation method for the path-following control performance of an autonomous ship at the design stage. This evaluation technique was developed using a computational fluid dynamics (CFD)-based path-following control model together with a line-of-sight (LOS) guidance algorithm. CFD software was utilized to visualize waves around the ship, performing path-following control for visual evaluation. In addition, a quantitative evaluation was carried out using the difference between the desired and estimated yaw angles, as well as the distance difference between the planned and estimated trajectories. The results demonstrated that the ship experienced large deviations from the planned path near the waypoints while changing its course. It was also found that the fluid phenomena around the ship could be easily identified by visualizing the flow generated by the ship. It is expected that the evaluation method proposed in this study will contribute to the visual and quantitative evaluation of the path-following performance of autonomous ships at the design stage.

• Journal of the Korea Society for Simulation
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• v.32 no.1
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• pp.1-11
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• 2023

Smart factory environments and digital twin environments are established, and today's factories accumulate vast amounts of production data and are managed in real time as visualized results suitable for user convenience. Production simulation techniques are in the spotlight as a way to prevent delays in delivery and predict factory volatility in situations where production schedule planning becomes difficult due to the diversification of production products. With the development of the digital twin environment, new packages are developed and functions of existing packages are updated, making it difficult for users to make decisions on which packages to use to develop simulations. Therefore, in this study, the concept of Discrete Event Simulation (DES) performed based on discrete events is defined, and the characteristics of various simulation packages were compared and analyzed. To this end, studies that solved real problems using discrete event simulation software for 10 years were analyzed, and three types of software used by the majority were identified. In addition, each package was classified by simulation technique, type of industry, subject of simulation, country of use, etc., and analysis results on the characteristics and usage of DES software were provided. The results of this study provide a basis for selection to companies and users who have difficulty in selecting discrete event simulation package in the future, and it is judged that they will be used as basic data.