• Journal of Information Processing Systems
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• v.19 no.5
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• pp.688-701
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• 2023

The conventional methods of network intrusion detection system (NIDS) cannot measure the trend of intrusiondetection targets effectively, which lead to low detection accuracy. In this study, a NIDS method which based on a deep neural network in a big-data environment is proposed. Firstly, the entire framework of the NIDS model is constructed in two stages. Feature reduction and anomaly probability output are used at the core of the two stages. Subsequently, a convolutional neural network, which encompasses a down sampling layer and a characteristic extractor consist of a convolution layer, the correlation of inputs is realized by introducing bidirectional long short-term memory. Finally, after the convolution layer, a pooling layer is added to sample the required features according to different sampling rules, which promotes the overall performance of the NIDS model. The proposed NIDS method and three other methods are compared, and it is broken down under the conditions of the two databases through simulation experiments. The results demonstrate that the proposed model is superior to the other three methods of NIDS in two databases, in terms of precision, accuracy, F1- score, and recall, which are 91.64%, 93.35%, 92.25%, and 91.87%, respectively. The proposed algorithm is significant for improving the accuracy of NIDS.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.7
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• pp.1817-1839
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• 2024

To solve the problem of insufficient recognition of key nodes in the existing software defect detection process, this paper proposes a key node recognition algorithm based on node expansion degree and improved K-shell position, shortened as SDD_KNR. Firstly, the calculation formula of node expansion degree is designed to improve the degree that can measure the local defect propagation capability of nodes in the software network. Secondly, the concept of improved K-shell position of node is proposed to obtain the improved K-shell position of each node. Finally, the measurement of node defect propagation capability is defined, and the key node recognition algorithm is designed to identify the key function nodes with large defect impact range in the process of software defect detection. Using real software systems such as Nano, Cflow and Tar to design three sets of experiments. The corresponding directed weighted software function invoke networks are built to simulate intentional attack and defect source infection. The proposed SDD_KNR algorithm is compared with the BC algorithm, K-shell algorithm, KNMWSG algorithm and NMNC algorithm. The changing trend of network efficiency and the strength of node propagation force are analyzed to verify the effectiveness of the proposed SDD_KNR algorithm.

• Journal of the Korean Society for information Management
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• v.22 no.1 s.55
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• pp.191-208
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• 2005

This study utilizes various approaches for new topic detection in the process of assigning and updating descriptors, which is a representation method of the knowledge structure. Particularly in the case of occurring changes on the knowledge structure due to the appearance and development of new topics in specific study areas, new topic detection can be applied to solving the impossibility or limitation of the existing index terms in representing subject concepts. This study confirms that the majority of newly developing topics in information science are closely associated with each other and are simultaneously in the phase of growth and development. Also, this study shows the possibility that the use of candidate descriptor lists generated by new topic detection methods can be an effective tool in assisting indexers. In particular. the provision of candidate descriptor lists to help assignment of appropriate descriptors will contribute to the improvement of the effectiveness and accuracy of indexing.

• Journal of Korean Society on Water Environment
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• v.33 no.1
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• pp.40-50
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• 2017

We were interested in the long-term temporal and spatial variability trends of water quality. Trend tests such as the Seasonal and Regional Kendall tests and LOWESS (LOcally WEighted Scatter plot Smoother) have been recommended as outstanding tools for trend detection. In this study, we conducted four types of nonparametric trend tests (Seasonal and Regional Kendall tests, LOWESS, and flow-adjusted Seasonal Kendall). We aimed to identify water quality trends using the monthly data for five variables (BOD, COD, TN, TP, and flow) collected from 24 sites in the Nakdong River from August 2004 to December 2013. According to the Regional Kendall test, BOD, COD, and TN increased but TP decreased trend. The Seasonal Kendall test showed that BOD, TN, and TP remained constant at 62.5-83.3% of the sites. COD remained constant at 58.3% of the sites. LOWESS showed that TP gradually increased between 2007 and 2008, then decreased slowly at the Gumi, Geumhogang6, Daeam-1 and Milyanggang3 sites. BOD increased slightly between 2008 and 2009, and then decreased slowly at the Namgang4-1 site. Lastly, a flow-adjusted Seasonal Kendall test was conducted. There were different results between Seasonal Kendall and flow-adjusted Seasonal Kendall tests at 11 of the 24 sites. According to the results from six of the eleven sites, BOD increased at one site, showed no trends at three sited, and decreased at two sites. Each of COD, TN increased at two, one site. but TP decreased at two sites.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.28 no.4
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• pp.393-405
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• 2018

Objective: This study examines the types of hazardous factors in the working environment and the time-trend for their exposure levels over 10 years (2007 to 2016). Study Design and Method: The types of hazardous factors and exposure levels were drawn from the 19 measurement reports on the working environment over 10 years at a shock absorber manufacturing facility. Risk assessment of the types of factors and time-trend of exposure levels were evaluated using the factors and exposure levels. Results: A total of 34 hazardous factors were evaluated. The types were noise, 15 organic compounds, seven kinds of acid sand alkalis, eight kinds of heavy metals, and three other compounds. Special management materials used were nickel, hexavalent chrome, and sulfuric acid. Human carcinogens (1A) used were trichloroethylene, nickel, and sulfuric acid. There were six types of substances belonging to the IARC's 2B (body carcinogens) classification or higher, including, methyl isobutyl ketone, ethyl benzene, and trichloroethylene. No detection was found for 627 out of the 2065 total measurements in 19 exposure survey reports, representing 30.4%. Organic solvents, acid and alkali products, and heavy metals showed continuous low exposure concentrations. Noise, welding fumes, and the evaluation of mixed solvents show a gradual decrease in geometric mean and maximum over the time-trend of 10 years. Conclusions: In the case of a shock absorber manufacturing facility, the hazardous factors of noise and the evaluation of mixed solvents still indicate high concentrations exceeding the exposure limits and necessitate reduction studies. These two factors and welding fumes showed a continuous decrease in their ten-year tendency. Organic compounds, acids/alkalis, and heavy metals were managed smoothly in a work environment of continuous low concentrations.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.24 no.12
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• pp.977-984
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• 2014

Nanomaterial have grown from scientific interest to commercial products and the nanomaterial market has grown 19.1 % each year. As the nanomaterial market size increases, it is expected that nanomaterial production will increase and its contamination of outdoor environmental system will also increase in the form of industrial waste. Since most of nanomaterials are known as biologically non-degradable materials, nanomaterials will accumulate in the environment, and this will increase the potential threats to human health along the food chain. Recent studies have investigated the toxicity effect of nanomaterials due to their size, chemical composition and shape. For the development of nanomaterial while taking human health into consideration, a nanomaterial detecting sensor is required. In this paper, we have observed the trend of nanomaterial detecting sensor of mechanical, electrochemical, optical and kelvin probe force microscopy sensors and we believe that this trend will shed the light on the development of real-life nanomaterial detecting sensors.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.29 no.1
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• pp.21-26
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• 2019

Objective: The aim of this study is to analyze an excess trend for domestic permissible exposure limit of trichloroethylene based on previous literature review. Materials and Methods: The research object is a trichloroethylene among 13 chemical substances regulated with PEL(Permissible Exposure Limit) in Occupational Safety and Health Act. The information utilized from this study is the work environment measurement data from 2004 to 2013. The highest level among concentration data measured at various workplaces was selected as a representative value through data process. N.D. (Not Detected) data was considered as 1/2 of LOD(Limit Of Detection). Results: Among work environment measurement data between 2004 and 2013, the highest number of excess workplace and excess rate(24 sites & 1.15%) was observed in 2008's data when applying the PEL(50 ppm) of trichloroethylene. When they are compared with the ACGIH's TLV-TWA(10 ppm), 2008's data showed the highest number of excess workplace and excess rate(175 sites & 8.37%). The number of excess workplace and excess rate related to PEL of trichloroethylene showed increase trend in 2005 but tended to decrease after 2008. Conclusions: Based on the results obtained from this study, the exposure level of trichloroethylene in case of domestic workers is not considered as a safe phase regardless of the year of work environment measurement year. Thus, a strictly preventive management in workplace should be provided for reducing exposure level of trichloroethylene.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.203-208
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• 2024

The convergence of deep learning and smart factory is drawing a lot of attentions from not only industrial but also academic circles. The objective of this article is to quantitatively review on deep learning and smart factory from 2010 to 2023. This research analyzed the 138 articles, extracted from the Core Collection of Web of Science, in terms of four dimensions such as the main trend in article publications, the main trend in article citations, the distribution of article publications by research area, and the keywords representing the main contents of published articles. The quantitative review results reveal the following four points: First, the article publications drastically grew from 2019 to 2022 in its annual trend. Second, the article citations have rapidly grown since 2018. Third, Engineering, Computer Science, and Telecommunications are the top 3 research areas composing the 138 articles. Fourth, it is the top 10 keywords such as 'deep', 'learning', 'smart', 'detection', factory', 'data', 'system', 'manufacturing', 'neural', and 'network' that represent the main contents of the 138 articles published from 2010 to 2023 in deep learning and smart factory. These findings revealed by this quantitative review will be significantly useful for deepening and widening relevant future research on deep learning and smart factory.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.2
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• pp.105-114
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• 2013

Expansion and growth of body information monitoring service based on WBAN technology speeds up technological evolution in bio-signal detection and measurement, real time monitoring of vital sign and telemedicine control. It is essential for taking action against such technological evolution that newest technology trend and standardization issue should be included in designing and materializing body-information monitoring system strategically to secure preceding technology and to preoccupy market. This paper investigates and analyzes technological trend & issues, and suggests task to take action technologically.

• Journal of Genetic Medicine
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• v.5 no.1
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• pp.7-14
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• 2008

Twenty years have passed since a prenatal screening for Down syndrome and neural tube defect was applied to obstetric field. The Quad test (AFP, hCG, uE3, Inhibin-A) of the second trimester and the combination test (PAPP-A, hCG, NT) of the first trimester became popular now. The recent trend of prenatal screening is to combine these two screening tests together in order to increase a detection rate of Down syndrome. Three types of screening methods are introduced as follows; integrated test, sequential test and contingent test. In addition to combination of each test, an incorporation of characteristic ultrasound findings of Down syndrome is suggested for its risk calculation. The absence of fetal nasal bone would be a very useful marker especially in the first trimester screening test. According to a change of way calculating risk of Down syndrome, obstetrician's role will be more increased not by passive participation, but by active participation using ultrasound in risk calculation.