• Journal of the Computational Structural Engineering Institute of Korea
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• v.24 no.1
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• pp.79-85
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• 2011

Bridge Weigh-in-Motion(BWIM) system calculates a travelling vehicle's weight without interruption of traffic flow by analyzing the signals that are acquired from various sensors installed in the bridge. BWIM system or data accumulated from the BWIM system can be utilized to development of updated live load model for highway bridge design, fatigue load model for estimation of remaining life of bridges, etc. Field test with moving trucks including various load cases should be performed to guarantee successful development of precise BWIM system. In this paper, a numerical simulation technique is adopted as an alternative or supplement to the vehicle traveling test that is indispensible but expensive in time and budget. The constructed numerical model is validated by comparison experimentally measured signal with numerically generated signal. Also vehicles with various dynamic characteristics and travelling conditions are considered in numerical simulation to investigate the variation of bridge responses. Considered parameters in the numerical study are vehicle velocity, natural frequency of the vehicle, height of entry bump, and lateral position of the vehicle. By analyzing the results, it is revealed that the lateral position and natural frequency of the vehicle should be considered to increase precision of developing BWIM system. Since generation of vehicle travelling signal by the numerical simulation technique costs much less than field test, a large number of test parameters can effectively be considered to validate the developed BWIM algorithm. Also, when artificial neural network technique is applied, voluminous data set required for training and testing of the neural network can be prepared by numerical generation. Consequently, proposed numerical simulation technique may contribute to improve precision and performance of BWIM systems.

• The Transactions of the Korea Information Processing Society
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• v.4 no.1
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• pp.237-245
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• 1997

In this paper, a flow control mechanism is proposed which is based on the priority control between communication path of a node. In this scheme, demanding length of a data queue for any pre-defined, then each node in that path is forced to maintains buffer size under the limit by controlling priority level of the path. The communication path which requires higher bandwidth sets its demanding queue length smaller. By providing relationship between the priority of a path and length of its queue, the high bandwidth requesting path has a better chance to get high bandwidth by defining the smaller demanding queue size. And also, by forcing a path which has high flow rate to maintain small queue size in the path of the communication, the scheme keep the transmission delay of the path small. The size of the demanding queue of a path is regularly adjusted to meet the applications requirement, and the load status of the network during the life time of the communication. The priority control based on the demanding queue size is also provided in the intermediate nodes as well as the end nodes. By that the flow control can provide a quicker result than end to-end flow control, it provides better performance advantage especially for the high speed network.

• Journal of Korea Water Resources Association
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• v.51 no.8
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• pp.703-711
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• 2018

Water distribution system (WDS) pipe bursts are caused from excessive pressure, pipe aging, and ground shift from temperature change and earthquake. Prompt detection of and response to the failure event help prevent large-scale service interruption and catastrophic sinkhole generation. To that end, this study proposes a improved Western Electric Company (WECO) method to improve the detection effectiveness and efficiency of the original WECO method. The original WECO method is an univariate Statistical Process Control (SPC) technique used for identifying any non-random patterns in system output data. The improved WECO method multiples a threshold modifier (w) to each threshold of WECO sub-rules in order to control the sensitivity of anomaly detection in a water distribution network of interest. The Austin network was used to demonstrated the proposed method in which normal random and abnormal pipe flow data were generated. The best w value was identified from a sensitivity analysis, and the impact of measurement frequency (dt = 5, 10, 15 min etc.) was also investigated. The proposed method was compared to the original WECO method with respect to detection probability, false alarm rate, and averaged detection time. Finally, this study provides a set of guidelines on the use of the WECO method for real-life WDS pipe burst detection.

• Journal of Broadcast Engineering
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• v.23 no.6
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• pp.855-865
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• 2018

Sound event detection is one of the research areas to model human auditory cognitive characteristics by recognizing events in an environment with multiple acoustic events and determining the onset and offset time for each event. DCASE, a research group on acoustic scene classification and sound event detection, is proceeding challenges to encourage participation of researchers and to activate sound event detection research. However, the size of the dataset provided by the DCASE Challenge is relatively small compared to ImageNet, which is a representative dataset for visual object recognition, and there are not many open sources for the acoustic dataset. In this study, the sound events that can occur in indoor and outdoor are collected on a larger scale and annotated for dataset construction. Furthermore, to improve the performance of the sound event detection task, we developed a dual CNN structured sound event detection system by adding a supplementary neural network to a convolutional neural network to determine the presence of sound events. Finally, we conducted a comparative experiment with both baseline systems of the DCASE 2016 and 2017.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.12
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• pp.291-306
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• 2020

Nowadays, Data-Network-AI (DNA)-based intelligent services and applications have become a reality to provide a new dimension of services that improve the quality of life and productivity of businesses. Artificial intelligence (AI) can enhance the value of IoT data (data collected by IoT devices). The internet of things (IoT) promotes the learning and intelligence capability of AI. To extract insights from massive volume IoT data in real-time using deep learning, processing capability needs to happen in the IoT end devices where data is generated. However, deep learning requires a significant number of computational resources that may not be available at the IoT end devices. Such problems have been addressed by transporting bulks of data from the IoT end devices to the cloud datacenters for processing. But transferring IoT big data to the cloud incurs prohibitively high transmission delay and privacy issues which are a major concern. Edge computing, where distributed computing nodes are placed close to the IoT end devices, is a viable solution to meet the high computation and low-latency requirements and to preserve the privacy of users. This paper provides a comprehensive review of the current state of leveraging deep learning within edge computing to unleash the potential of IoT big data generated from IoT end devices. We believe that the revision will have a contribution to the development of DNA-based intelligent services and applications. It describes the different distributed training and inference architectures of deep learning models across multiple nodes of the edge computing platform. It also provides the different privacy-preserving approaches of deep learning on the edge computing environment and the various application domains where deep learning on the network edge can be useful. Finally, it discusses open issues and challenges leveraging deep learning within edge computing.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.259-267
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• 2022

With the rapid development and introduction of digital technologies, both everyday human life and technological processes of any production are changing, which stimulates the transformation of the economy and education. Digital technologies are not only a tool, but also a living environment of modern human, which opens up new opportunities: learning at any convenient time, continuing education, the ability to form individual educational learning trajectories and more. However, the digital environment requires teachers to take a modern approach to the organization of the educational process, the formation of new skills and abilities to work in the digital educational environment. As a result of the study, it was found that the system of vocational education should provide training for masters of industrial training who have a high level of digital competence. The purpose of the article is to single out, theoretically substantiate and determine the level of formation of structural components of digital competence of future masters of agricultural training. The structure of digital competence of agricultural master was analyzed on the basis of domestic and foreign scientists researches. Systematized research results indicate that digital competence consists of four components: motivational-value (combination of internal and external motives for the use of digital technologies in future professional activities), cognitive (a set of theoretical knowledge, skills and abilities of future master of industrial training to effectively build educational process with the use of digital technologies), activity-professional (expansion and deepening of knowledge, skills, necessary skills for effective implementation of digital technologies in the educational process) and evaluative-reflexive (ability to analyze and self-analyze own activities and its results taking into account professional characteristics, self-realization in professional activities through the use of digital technologies). These components are comparable with the indicators that describe the knowledge, skills and abilities needed by the future master of industrial training to organize the modern educational process. A questionnaire was conducted to determine the levels of this competence formation, which allows us to conclude that it is necessary to increase the level of formation of all components of digital competence of future masters of industrial training in agriculture. The results of the study can be used as a basis for the development of disciplines that form the special competencies of masters of industrial training in agriculture and programs of advanced training of teachers.

• Journal of the Society of Disaster Information
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• v.8 no.1
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• pp.1-9
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• 2012

Informatization of society through the computer and the Internet, because large amounts of information production and exchange and new way of communicating is born. Passive way past the one-sided information flows actively interact to evolve in a manner of information producers and information consumers distinction and personal relationships that enhance the online Social Networking Service (SNS) has developed into the social structure of. Thus, the spread of information work closely with the social network structure spark social conflict may act as a factor, and systems and the environment, personal and cultural adaptation of speed to keep up with the rapid development of science and technology as the inability conflict and confusion should lead to even. This paper the characteristics of the information society, with a look at the evolution of social risk factors as the wavelength of information about this look at the role of private security sought to evaluate. Information Society in time and space by shrinking the area of human life that has brought the convenience and simplicity, whereas the non-performance due to the nature of anonymous raises many social side-effects are. This made the preparation of national regulatory measures, but for the protection of personal protection devices in the private sector has not yet been discussed. Way of life and property of the purchaser to protect an individual's private security will have to charge it.

• Journal of Life Science
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• v.19 no.8
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• pp.1159-1163
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• 2009

It has been postulated that endoplasmic (ER) stress is involved in the development of several diseases. However, the detailed molecular mechanisms have not been fully understood. Therefore, we characterized a genetic network of genes induced by ER stress using cDNA microarray and gene set expression coherence analysis (GSECA), and identified gene function as well as several transcription regulators associated with ER stress. We analyzed time-dependent gene expression profiles in thapsigargin-treated Sk-Hep1 using an oligonucleotide expression chip, and then selected functional gene sets with significantly high expression coherence which was processed into functional clusters according to the expression similarities. The functions related to sugar binding, lysosome, ribosomal protein, ER lumen, and ER to golgi transport increased, whereas the functions with mRNA processing, DNA replication, DNA repair, cell cycle, electron transport chain and helicase activity decreased. Furthermore, functional clusters were investigated for the enrichment of regulatory motifs using GSECA, and several transcriptional regulators associated with regulation of ER-induced gene expression were found.

• Journal of Family Resource Management and Policy Review
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• v.25 no.1
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• pp.35-45
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• 2021

Focusing on increasing of single-person households this study aims to develop a scale to measure the healthiness of lifestyle among single-person households. The concept of healthiness of lifestyle is based on the theories of family strength and family ecology. We draw 50 items that encompass basic needs, individual, familial, and social aspects of single-person life. Using a sample of 317 persons who live alone, this study examined a factor structure of the items and selected 44 items based on the results of factor analysis. Reliability and criterion- and construct validity were also examined. The final scale consists of four domains; basic needs (finance, housing, consumption, and future plan), work·life balance (time management, health, and stress), family relations, and social participation (social network, social interests, and community participation). This scale can be used as an assessment measure of the healthiness of lifestyle of single persons who participate in programs in Healthy and Multicultural Families Support Centers.

• Journal of Life Science
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• v.29 no.2
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• pp.181-190
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• 2019

The purpose of this study was to acquire the active compounds of Anisi stellati fructus (ASF) and to analyze the genes and diseases it targets, focusing on its antibacterial effects using a system pharmacological analysis approach. Active compounds of ASF were obtained through the Traditional Chinese Medicine Systems Pharmacology (TCMSP) Database and Analysis Platform. This contains the pharmacokinetic properties of active compounds and related drug-target-disease networks, which is a breakthrough in silico approach possible at the network level. Gene information of targets was gathered from the UnitProt Database, and gene ontology analysis was performed using the David 6.8 Gene Functional Classification Tool. A total of 201 target genes were collected, which corresponded to the nine screened active compounds, and 47 genes were found to act on biological processes related to antimicrobial activity. The representative active compounds involved in antibacterial action were luteolin, kaempferol, and quercetin. Among their targets, Chemokine ligand2, Interleukin-10, Interleukin-6, and Tumor Necrosis Factor were associated with more than three antimicrobial biological processes. This study has provided accurate evidence while saving time and effort to select future laboratory research materials. The data obtained has provided important data for infection prevention and treatment strategies.