• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.13-20
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• 2022

IEEE 802.11ay Wi-Fi is the next generation wireless technology and operates in mmWave band. It supports the MU-MIMO (Multiple User Multiple Input Multiple Output) transmission in which an AP (Access Point) can transmit multiple data streams simultaneously to multiple STAs (Stations). To this end, the AP should perform MU-MIMO beamforming training with the STAs. For efficient MU-MIMO beamforming training, it is important for the AP to estimate signal strength measured at each STA at which multiple beams are used simultaneously. Therefore, in the paper, we propose a deep learning-based link quality estimation scheme. Our proposed scheme estimates the signal strength with high accuracy by utilizing a deep learning model pre-trained for a certain indoor or outdoor propagation scenario. Specifically, to estimate the signal strength of the multiple concurrent beams, our scheme uses the signal strengths of the respective single beams, which can be obtained without additional signaling overhead, as the input of the deep learning model. For performance evaluation, we utilized a Q-D (Quasi-Deterministic) Channel Realization open source software and extensive channel measurement campaigns were conducted with NIST (National Institute of Standards and Technology) to implement the millimeter wave (mmWave) channel. Our simulation results demonstrate that our proposed scheme outperforms comparison schemes in terms of the accuracy of the signal strength estimation.

• Journal of the Korea Society for Simulation
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• v.31 no.3
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• pp.1-10
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• 2022

Defense fields widely operate unmanned systems to lower vulnerability and enhance combat effectiveness. In the navy, swarm unmanned surface vehicles(USVs) form a cluster within communication range, share situational awareness information among the USVs, and cooperate with them to conduct military missions. This paper proposes an interface system, i.e., Interface Adapter System(IAS), to achieve inter-USV and intra-USV interoperability. We focus on the mission planning subsystem(MPS) for interoperability, which is the core subsystem of the USV to decide courses of action such as automatic path generation and weapon assignments. The central role of the proposed system is to exchange interface data between MPSs and other subsystems in real-time. To this end, we analyzed the operational requirements of the MPS and identified interface messages. Then we developed the IAS using the distributed real-time middleware. As experiments, we conducted several integration tests at swarm USVs simulation environment and measured delay time and loss ratio of interface messages. We expect that the proposed IAS successfully provides bridge roles between the mission planning system and other subsystems.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.5
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• pp.133-138
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• 2022

Recently, as GPU performance has improved in HPC and artificial intelligence, its use is becoming more common, but GPU programming is still a big obstacle in terms of productivity. In particular, due to the difficulty of managing host memory and GPU memory separately, research is being actively conducted in terms of convenience and performance, and various CPU-GPU memory transfer programming methods are suggested. Meanwhile, recently many SoC (System on a Chip) products such as Apple M1 and NVIDIA Tegra that bundle CPU, GPU, and integrated memory into one large silicon package are emerging. In this study, data between CPU and GPU devices are used in such an integrated memory device and performance-related research is conducted during transmission. It shows different characteristics from the existing environment in which the host memory and GPU memory in the CPU are separated. Here, we want to compare performance by CPU-GPU data transmission method in NVIDIA SoC chips, which are integrated memory devices, and NVIDIA SMX-based V100 GPU devices. For the experimental workload for performance comparison, a two-dimensional matrix transposition example frequently used in HPC applications was used. We analyzed the following performance factors: the difference in GPU kernel performance according to the CPU-GPU memory transfer method for each GPU device, the transfer performance difference between page-locked memory and pageable memory, overall performance comparison, and performance comparison by workload size. Through this experiment, it was confirmed that the NVIDIA Xavier can maximize the benefits of integrated memory in the SoC chip by supporting I/O cache consistency.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.32 no.1
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• pp.21-30
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• 2022

Objectives: The purpose of this study is to suggest an optimal method by comparing the analysis methods of work environment measurement datasets including left-censored data where one or more measurements are below the limit of detection (LOD). Methods: A computer program was used to generate left-censored datasets for various combinations of censoring rate (1% to 90%) and sample size (30 to 300). For the analysis of the censored data, the simple substitution method (LOD/2), β-substitution method, maximum likelihood estimation (MLE) method, Bayesian method, and regression on order statistics (ROS)were all compared. Each method was used to estimate four parameters of the log-normal distribution: (1) geometric mean (GM), (2) geometric standard deviation (GSD), (3) 95th percentile (X95), and (4) arithmetic mean (AM) for the censored dataset. The performance of each method was evaluated using relative bias and relative root mean squared error (rMSE). Results: In the case of the largest sample size (n=300), when the censoring rate was less than 40%, the relative bias and rMSE were small for all five methods. When the censoring rate was large (70%, 90%), the simple substitution method was inappropriate because the relative bias was the largest, regardless of the sample size. When the sample size was small and the censoring rate was large, the Bayesian method, the β-substitution method, and the MLE method showed the smallest relative bias. Conclusions: The accuracy and precision of all methods tended to increase as the sample size was larger and the censoring rate was smaller. The simple substitution method was inappropriate when the censoring rate was high, and the β-substitution method, MLE method, and Bayesian method can be widely applied.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.10
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• pp.345-352
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• 2022

Recent LiDAR(Light Detection And Ranging) sensor is used for scanning object around in real-time. This sensor can detect movement of the object and how it has changed. As the production cost of the sensors has been decreased, LiDAR begins to be used for various industries such as facility guard, smart city and self-driving car. However, LiDAR has a large input data size due to its real-time scanning process. So another way for processing a large amount of data are needed in LiDAR system because it can cause a bottleneck. This paper proposes edge computing to compress massive point cloud for processing quickly. Since laser's reflection range of LiDAR sensor is limited, multiple LiDAR should be used to scan a large area. In this reason multiple LiDAR sensor's data should be processed at once to detect or recognize object in real-time. Edge computer compress point cloud efficiently to accelerate data processing and decompress every data in the main cloud in real-time. In this way user can control LiDAR sensor in the main system without any bottleneck. The system we suggest solves the bottleneck which was problem on the cloud based method by applying edge computing service.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.32 no.3
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• pp.231-241
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• 2022

Objectives: The purpose of this study is to evaluate the input status of exposure-related information in the working environment monitoring database (WEMD) and special health examination database (SHED) for the construction of a national exposure surveillance system. Methods: The industrial and process code input status of WEMD and SHED for 21 carcinogens from 2014 to 2016 was compared. Data from workers who performed both work environment monitoring and special health examinations in 2019 and 2020 were extracted and the actual status of input of industrial and process codes was analyzed. We also investigated the cause of input errors through a focus group interview with 12 data input specialists. Results: As a result of analyzing WMED and SHED for 21 carcinogens, the five-digit industrial code matching rate was low at 53.5% and the process code matching rate was 19% or less. Among the data that simultaneously conducted work environment monitoring and special health examination in 2019 and 2020, the process code matching rate was very low at 18.1% and 5.2%, respectively. The main causes of exposure-related data input errors were the difference between the WEMD and SHED process code input systems from 2020, the number of standard process and job codes being too large, and the inefficiency of the standard code search system. Conclusions: In order to use WEMD and SHED as a national surveillance system, it is necessary to simplify the number of standard code input codes and improve the search system efficiency.

• Journal of Convergence for Information Technology
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• v.11 no.5
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• pp.38-49
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• 2021

This study is single-group pretest-posttest design study to examine the effects of web-based cognitive training program using tablet on cognition and depression in the elderly with high risk of dementia or mild dementia living in a rural area, enrolled in dementia center. Intervention was provided to the 18 participants once a week for 10 weeks within 1 hour. Data was analyzed with SPSS 24.0 and interview data was categorized. The study result proves that after intervention, the participants' cognitive score increased significantly(Z=-3.35, p=.001) and the depression scores were significantly decreased(Z=-3.13, p=.002). Also, interview shows positive effect of the intervention on cognition and depression. It is necessary to improve access environment for smart devices so as not to be restricted by time and place, and to develop and apply various types of web-based programs for each cognitive level. Then, the intervention could be used as a cognitive training program incorporating information and communication technology for the prevention and management of dementia in rural areas.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.1
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• pp.57-69
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• 2023

In a situation where digital transformation using artificial intelligence is active around the world, the growth of domestic AI companies or AI industrial ecosystems is slow. Where a large amount of government funds related to AI are being invested to overcome the difficult economic situation, systematic research on the effect is insufficient. So, this study aimed to examine the policy effectiveness of the government artificial intelligence solution voucher support project for small and medium-sized enterprises (SMEs) using Propensity Score Matching (PSM) and Difference-in-Differences (DID) on the financial performance of beneficiary companies. For empirical analysis, PSM-DID analysis was performed using sales performance since 2019 for 461 companies with a history of voucher support among the AI SMEs data released by the National IT Industry Promotion Agency. As a result of the analysis, the beneficiary companies' asset growth, salary, and R&D expenses increased overall after government support, and no significant contribution could be confirmed in terms of profits. This study suggests that the voucher policy business directly contributed to the company's growth in the short term, but it requires a certain period of time to generate profits.

• Journal of Broadcast Engineering
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• v.27 no.6
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• pp.923-935
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• 2022

This paper deals with detection and classification of leaf diseases for phenomics systems. As the smart farm systems of plants are increased, It is important to determine quickly the abnormal growth of plants without supervisors. This paper considers the color distribution and shape information of leaf diseases, and designs two deep leaning networks in training the leaf diseases. In the first step, color distribution of input image is analyzed for possible diseases. In the second step, the image is first partitioned into small segments using mean shift clustering, and the color information of each segment is inspected by the proposed Color Network. When a segment is determined as disease, the shape parameters of the segment are extracted and inspected by proposed Shape Network to classify the leaf disease types in the third step. According to the experiments with two types of diseases (frogeye/rust and tipburn) for apple leaves and iceberg, the leaf diseases are detected with 92.3% recall for a segment and with 99.3% recall for an input image where there are usually more than two disease segments. The proposed method is useful for detecting leaf diseases quickly in the smart farm environment, and is extendible to various types of new plants and leaf diseases without additional learning.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.11
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• pp.403-410
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• 2022

Non-Fungible Tokens (NFTs) created on the basis of blockchain have their own unique value, so they cannot be forged or exchanged with other tokens or coins. Using these characteristics, NFTs can be issued to digital assets such as images, videos, artworks, game characters, and items to claim ownership of digital assets among many users and objects in cyberspace, as well as proving the original. However, interest in NFTs exploded from the beginning of 2020, causing a lot of load on the blockchain network, and as a result, users are experiencing problems such as delays in computational processing or very large fees in the mining process. Additionally, all actions of users are stored in the blockchain, and digital assets are stored in a blockchain-based distributed file storage system, which may unnecessarily expose the personal information of users who do not want to identify themselves on the Internet. In this paper, we propose an NFT platform using cloud computing, access gate, conversion table, and cloud ID to improve usability and privacy problems that occur in existing system. For performance comparison between local and cloud systems, we measured the gas used for smart contract deployment and NFT-issued transaction. As a result, even though the cloud system used the same experimental environment and parameters, it saved about 3.75% of gas for smart contract deployment and about 4.6% for NFT-generated transaction, confirming that the cloud system can handle computations more efficiently than the local system.