• Journal of Digital Policy
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• v.1 no.2
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• pp.61-69
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• 2022

The paper is a study to develop and verify a blood glucose level prediction model based on biosignals obtained from photoplethysmography (PPG) sensors, ICT technology and data. Blood glucose prediction used the MLP architecture of machine learning. The input layer of the machine learning model consists of 10 input nodes and 5 hidden layers: heart rate, heart rate variability, age, gender, VLF, LF, HF, SDNN, RMSSD, and PNN50. The results of the predictive model are MSE=0.0724, MAE=1.1022 and RMSE=1.0285, and the coefficient of determination (R²) is 0.9985. A blood glucose prediction model using bio-signal data collected from digital devices and machine learning was established and verified. If research to standardize and increase accuracy of machine learning datasets for various digital devices continues, it could be an alternative method for individual blood glucose management.

• Advanced Industrial SCIence
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• v.1 no.2
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• pp.24-29
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• 2022

Recently, smart farm research that applies IoT technology to various farms is being actively conducted to improve agricultural cooling power and minimize cost reduction. In particular, methods for automatically and remotely controlling environmental information data around smart farms through IoT devices are being studied. This paper proposes a processing model that can maintain an optimal growth environment by monitoring environmental information data collected from smart farms in real time based on machine learning. Since the proposed model uses machine learning technology, environmental information is grouped into multiple blockchains to enable continuous data collection through rich big data securing measures. In addition, the proposed model selectively (or binding) the collected environmental information data according to priority using weights and correlation indices. Finally, the proposed model allows us to extend the cost of processing environmental information to n-layer to a minimum so that we can process environmental information in real time.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.1
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• pp.1-8
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• 2023

It is impossible to overwrite on an already allocated page in SSDs, so whenever a write operation occurs a page replacement with a clean page is required. To resolve this problem, SSDs have an internal flash translation layer called FTL that maps logical pages managed by a file system of operating system to currently allocated physical pages. SSD pages discarded due to write operations must be recycled through initialization, but since the number of initialization times is limited the FTL provides a caching function to reduce the number of writes in addition to the page mapping function, which is a core function. In this study, we focus on the FTL cache methodologies reducing the number of page writes and analyze the related algorithms, and propose a write-only cache strategy. As a result of experimenting with the write-only cache using a simulator, it showed an improvement of up to 29%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3976-3990
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• 2022

Currently, cross-attention is widely used in Siamese trackers to replace traditional correlation operations for feature fusion between template and search region. The former can establish a similar relationship between the target and the search region better than the latter for robust visual object tracking. But existing trackers using cross-attention only focus on rich semantic information of high-level features, while ignoring the appearance information contained in low-level features, which makes trackers vulnerable to interference from similar objects. In this paper, we propose a Multi-level Cross-attention Siamese network(MCSiam) to aggregate the semantic information and appearance information at the same time. Specifically, a multi-level cross-attention module is designed to fuse the multi-layer features extracted from the backbone, which integrate different levels of the template and search region features, so that the rich appearance information and semantic information can be used to carry out the tracking task simultaneously. In addition, before cross-attention, a target-aware module is introduced to enhance the target feature and alleviate interference, which makes the multi-level cross-attention module more efficient to fuse the information of the target and the search region. We test the MCSiam on four tracking benchmarks and the result show that the proposed tracker achieves comparable performance to the state-of-the-art trackers.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1477-1485
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• 2021

Recently, super-resolution has been intensively studied only on upscaling models with integer magnification. However, the need to expand arbitrary magnification is emerging in representative application fields of actual super-resolution, such as object recognition and display image quality improvement. In this paper, we propose a model that can support arbitrary magnification by using the weights of the existing integer magnification model. This model converts super-resolution results into the DCT spectral domain to expand the space for arbitrary magnification. To reduce the loss of high-frequency information in the image caused by the expansion by the DCT spectral domain, we propose a high-frequency attention network for arbitrary magnification so that this model can properly restore high-frequency spectral information. To recover high-frequency information properly, the proposed network utilizes channel attention layers. This layer can learn correlations between RGB channels, and it can deepen the model through residual structures.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.9
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• pp.399-406
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• 2023

Cloud computing has been evolved to support edge computing architecture that combines fog management layer with edge servers. The main reason why it is received much attention is low communication latency for real-time IoT applications. At the same time, various cloud task scheduling techniques based on artificial intelligence have been proposed. Artificial intelligence-based cloud task scheduling techniques show better performance in comparison to existing methods, but it has relatively high scheduling time. In this paper, we propose a deep learning-based dynamic scheduling with multi-agents supporting scalability in edge computing environments. The proposed method shows low scheduling time than previous artificial intelligence-based scheduling techniques. To show the effectiveness of the proposed method, we compare the performance between previous and proposed methods in a scalable experimental environment. The results show that our method supports real-time IoT applications with low scheduling time, and shows better performance in terms of the number of completed cloud tasks in a scalable experimental environment.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.928-934
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• 2009

Although the evolution and deployment of information technologies will undoubtedly play an important role in the current construction industry, many engineers are still unsure of the economic value of using these technologies. Especially for the planning of a construction project, a collaboration system to utilize the whole resources is a essential tool for the successful outcome. A detailed, authoritative, and readily accessible information model is needed to enable engineers to make cost-effective decisions among established and innovative plan alternatives. Most engineers rely on limited private experiences when they create solutions or design alternatives. Initial planning is crucial for the success of the construction project. Most construction projects are done through collaboration of engineers who have different specialized knowledge. Information technologies can dramatically enhance the performance of the collaboration. For the information delivery, we need a mediator between engineers. Object-based 3-D models are useful for the communication and decision assistance for the intelligent project design. In this paper, basic guidelines for the 3-D design according to different construction processes are suggested. Adequate interoperability of 3-D objects from any CAD system is essential for the collaboration. Basic architectures of geometry models and their information layer were established to enable interoperability for design checks, estimation and simulation. A typical international project for roadway was chosen for the pilot project. 3-D GIS model was created and bridge information models were created considering several requirements for planning and decision making of the project. From the pilot test, the integrated construction project planning using 3-D information models was discussed and several guidelines were suggested.

• Journal of the Korean Institute of Telematics and Electronics
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• v.23 no.1
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• pp.1-9
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• 1986

This is the third part of the four-part paper describing the development of a packet-switched computer communication network named the KORNET. In this paper we describe the design and implementation of the X.25 protocol connecting packet mode data terminal equipments(PDTE's) with data circuit terminating equipments(DCE's). In the KORNET, the X.25 protocol has been implemented on the line processing module-A(LPMA) of the network node processor(NNP). In the implementation of X.25, we have divided the software module according to the service function, and have determined the the rules that interact between the modules. Each layer protocol has been developed using the technique of the finite state machine. Before the actual coding of softwares, we hafve used formal software development tools based on the specification and description language (SDL) and program design languate (PDL) recommended by the CCITT. In addition, for the efficient operation of the X.25 protocol system we have analyzed the system performance and the service scheduling method of each module. The results will also be given.

• The Journal of the Acoustical Society of Korea
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• v.42 no.3
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• pp.262-269
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• 2023

In this paper, we propose a Linear Prediction (LP) analysis-based feature for detecting Snapping Shrimp (SS) Noise (SSN) in underwater acoustic data. SS is a species that creates high amplitude signals in shallow, warm waters, and its frequent and loud sound is a major source of noise. The proposed feature takes advantage of the characteristic of SSN, which is sudden and rapidly disappearing, by using LP analysis to detect the exact noise interval and reduce the effects of SSN. The error between the predicted and measured value is large and results in effective SSN detection. To further improve performance, a constant false alarm rate detector is incorporated into the proposed feature. Our evaluation shows that the proposed methods outperform the state-of-the-art MultiLayer-Wavelet Packet Decomposition (ML-WPD) in terms of receiver operating characteristic curve and Area Under the Curve (AUC), with the LP analysis-based feature achieving a higher AUC by 0.12 on average and lower computational complexity.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.194-198
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• 2021

LoRa is a physical layer technology that is designed to provide a reliable long-range communication with introducing CSS and with introducing a loco parentis tree network. Since a leaf can utilize multiple parents at the same time with a single transmission, PDR increases logarithmically as the number of gateways increases. Because of the ALOHA-like MAC of LoRa, however, the PDR degrades even under the loco parentis tree topology similarly to the single-gateway environment. Our proposed method is aimed to achieve SDMA approach to reuse the same frequency in different areas. For that purpose, it elaborately controls each TxPower of the senders for each message in concurrent transmission to survive the collision at each different gateway. The gain from this so-called capture effect increases the capacity of resource-hungry LPWAN. Compared to a typical collision-free controlled-access scheme, our method outperforms by 10-35% from the perspective of the total count of the consumed time slots. Also, due to the power control mechanism in our method, the energy consumption reduced by 20-40%.