• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.2
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• pp.30-47
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• 2022

This study analyzed the characteristics of cold air flow according to spatial types in urban areas of Changwon-si, Gyeongsangnam-do. The spatial types were classified by cluster analysis considering the land use map, building information, and topographic characteristics produced on the Changwon biotope map. The amount of cold air and wind speed were derived by KLAM_21 modeling. As a result, spatial types were classified into a total of 14 types considering the density and height of buildings, land use types, and topographic characteristics. Cold air flow was found to generate cold air in the valley of the forest area outside urban area, move through roads and open spaces, and accumulate in the low-lying national industrial complex, and then spread cold air throughout the urban areas. There was a lot of cold air flow in the tall building area, and the cold air accumulation was less in the slope and ridge areas. The results of this study were able to understand the characteristics of cold air flow according to building density, land use type, and topography, which will be usefully used as basic data for urban wind road construction to mitigate climate and improve air quality in urban areas.

• International journal of advanced smart convergence
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• v.11 no.1
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• pp.19-27
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• 2022

Across the world, 'housing' comprises a significant portion of wealth and assets. For this reason, fluctuations in real estate prices are highly sensitive issues to individual households. In Korea, housing prices have steadily increased over the years, and thus many Koreans view the real estate market as an effective channel for their investments. However, if one purchases a real estate property for the purpose of investing, then there are several risks involved when prices begin to fluctuate. The purpose of this study is to design a real estate price 'return rate' prediction model to help mitigate the risks involved with real estate investments and promote reasonable real estate purchases. Various approaches are explored to develop a model capable of predicting real estate prices based on an understanding of the immovability of the real estate market. This study employs the LSTM method, which is based on artificial intelligence and deep learning, to predict real estate prices and validate the model. LSTM networks are based on recurrent neural networks (RNN) but add cell states (which act as a type of conveyer belt) to the hidden states. LSTM networks are able to obtain cell states and hidden states in a recursive manner. Data on the actual trading prices of apartments in autonomous districts between January 2006 and December 2019 are collected from the Actual Trading Price Disclosure System of the Ministry of Land, Infrastructure and Transport (MOLIT). Additionally, basic data on apartments and commercial buildings are collected from the Public Data Portal and Seoul Metropolitan Government's data portal. The collected actual trading price data are scaled to monthly average trading amounts, and each data entry is pre-processed according to address to produce 168 data entries. An LSTM model for return rate prediction is prepared based on a time series dataset where the training period is set as April 2015~August 2017 (29 months), the validation period is set as September 2017~September 2018 (13 months), and the test period is set as December 2018~December 2019 (13 months). The results of the return rate prediction study are as follows. First, the model achieved a prediction similarity level of almost 76%. After collecting time series data and preparing the final prediction model, it was confirmed that 76% of models could be achieved. All in all, the results demonstrate the reliability of the LSTM-based model for return rate prediction.

• Journal of Software Assessment and Valuation
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• v.16 no.1
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• pp.65-79
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• 2020

As Programmable Logic Controllers (PLCs), popular components in industrial control systems (ICS), are incorporated with the technologies such as micro-controllers, real-time operating systems, and communication capabilities. As the latest PLCs have been connected to the Internet, they are becoming a main target of cyber threats. This paper proposes two sanitizers that improve the security of uC/OS-II based firmware for a PLC. That is, we devise BU sanitizer for detecting out-of-bounds accesses to buffers and UaF sanitizer for fixing use-after-free bugs in the firmware. They can sanitize the binary firmware image generated in a desktop PC before downloading it to the PLC. The BU sanitizer can also detect the violation of control flow integrity using both call graph and symbols of functions in the firmware image. We have implemented the proposed two sanitizers as a prototype system on a PLC running uC/OS-II and demonstrated the effectiveness of them by performing experiments as well as comparing them with the existing sanitizers. These findings can be used to detect and mitigate unintended vulnerabilities during the firmware development phase.

• Journal of Mushroom
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• v.20 no.3
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• pp.178-182
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• 2022

Despite the long history of mushroom use, studies examining the genetic function of mushrooms and the development of new varieties via bio-molecular methods are significantly lacking compared to those examining other organisms. However, owing to recent developments, attempts have been made to use a novel gene-editing technique involving CRISPR/Cas9 technology and genetic scissors in mushroom studies. In particular, research is actively being conducted to utilize ribonucleoprotein particles (RNPs) that can be genetically edited with high efficiency without foreign gene insertion for ease of selection. However, RNPs are too large for Cas9 protein to pass through the cell membrane of the protoplasmic reticulum. Furthermore, guide RNA is unstable and can be easily decomposed, which remarkably affects gene editing efficiency. In this study, nanoparticles were used to mitigate the shortcomings of RNP-based gene editing techniques and to obtain transformants stably. We used Lentinula edodes (shiitake mushroom) Sanjo705-13 monokaryon strain, which has been successfully used in previous genome editing experiments. To identify a suitable osmotic buffer for the isolation of protoplast, 0.6 M and 1.2 M sucrose, mannitol, sorbitol, and KCl were treated, respectively. In addition, with various nanoparticle-forming materials, experiments were conducted to confirm genome editing efficiency via the formation of nanoparticles with calcium phosphate (CaP), which can be bound to Cas9 protein without any additional amino acid modification. RNPs/NP complex was successfully formed and protected nuclease activity with nucleotide sequence specificity.

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.17-27
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• 2022

In this paper, ED-H algorithm, an optimal real-time scheduling algorithm dealing with the characteristics of the integrated energy harvester system with a capacitor, is extended to satisfy the time constraint under the blackout state which is a deliberate power-off state by an intelligent power management unit adopted in the system. If the power supply system does not have enough energy, it temporarily shuts off the power supply to protect the circuit and capacitor and resumes the supply again when the capacitor is fully charged, which may delay the task execution during these blackout states by calculating the time according to the occurrence of the events. To mitigate the problem, even if task execution is delayed by the original ED-H algorithm, the remaining time of the subsequent time units no longer can afford to delay the execution of the task is predicted in the extended algorithm and the task is forced to be scheduled to meet the time deadline. According to the simulation results, it is confirmed that the algorithm proposed in this paper has a high scheduling performance increase of 0.4% to 7.7% depending on the characteristics of the set of tasks compared to the ED-H.

• The Journal of Engineering Geology
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• v.32 no.4
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• pp.437-448
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• 2022

This work aims to assess the threat to human health of hazardous materials in groundwater that is used domestically and for drinking. Two distinct sub-assessments are considered: cancer and non-cancer risk. The studied groundwater is in an agricultural area of Gyeongnam Province, Korea, and is contaminated by arsenic at a mean level of 16.27 ㎍/L, far greater than the WHO guideline (10 ㎍/L for drinking water). We collected groundwater data from the National Groundwater Information Center (gims.go.kr) and assessed the risk to human health following the methodology of the United States Environmental Protection Agency. We considered three exposure scenarios: domestic use (scenario 1) and drinking use with different doses (scenarios 2 and 3). Scenario 1 had a median hazard quotient (HQ) of 0.77 and a cancer risk (CR) of 0.013. Scenario 2 had a median HQ of 0.08 and a CR of 3.69 × 10^-5, and the values for scenario 3 were 0.11 and 4.82 × 10^-5, respectively. Scenario 1 is likely the most hazardous to human health. Further study of the origin of arsenic in groundwater in the study area is required, as are remedial measures to mitigate its health effects.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.211-227
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• 2022

Illegal gambling through online gambling sites has become a significant social problem. The development of Internet technology and the spread of smartphones have led to the proliferation of illegal gambling sites, so now illegal online gambling has become accessible to anyone. In order to mitigate its negative effect, the Korean government is trying to detect illegal gambling sites by using self-monitoring agents or reporting systems such as 'Nuricops.' However, it is difficult to detect all illegal sites due to limitations such as a lack of staffing. Accordingly, several scholars have proposed intelligent illegal gambling site detection techniques. Xu et al. (2019) found that fake or illegal websites generally have unique features in the HTML tag structure. It implies that the HTML tag structure can be important for detecting illegal sites. However, prior studies to improve the model's performance by utilizing the HTML tag structure in the illegal site detection model are rare. Against this background, our study aimed to improve the model's performance by utilizing the HTML tag structure and proposes Tag2Vec, a modified version of Doc2Vec, as a methodology to vectorize the HTML tag structure properly. To validate the proposed model, we perform the empirical analysis using a data set consisting of the list of harmful sites from 'The Cheat' and normal sites through Google search. As a result, it was confirmed that the Tag2Vec-based detection model proposed in this study showed better classification accuracy, recall, and F1_Score than the URL-based detection model-a comparative model. The proposed model of this study is expected to be effectively utilized to improve the health of our society through intelligent technology.

• Geophysics and Geophysical Exploration
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• v.25 no.2
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• pp.59-70
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• 2022

Recent studies demonstrate that machine learning has expanded in the field of seismic interpretation. Many convolutional neural networks have been developed for seismic sequence identification, which is important for seismic interpretation. However, expense and time limitations indicate that there is insufficient data available to provide a sufficient dataset to train supervised machine learning programs to identify seismic sequences. In this study, patch division and data augmentation are applied to mitigate this lack of data. Furthermore, to obtain spatial information that could be lost during patch division, an artificial channel is added to the original data to indicate depth. Seismic sequence identification is performed using a U-Net network and the Netherlands F3 block dataset from the dGB Open Seismic Repository, which offers datasets for machine learning, and the predicted results are evaluated. The results show that patch-based U-Net seismic sequence identification is improved by data augmentation and the addition of an artificial channel.

• Clean Technology
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• v.28 no.2
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• pp.163-173
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• 2022

Research and development of biofuels as one of the means to mitigate global warming and to avoid fossil fuel depletion has occurred for more than 30 years. However, there has only been limited distribution of a few first- and second-generation biofuels, and widespread supply and consumption of biofuels is still far from a reality. Although a relatively recently studied third-generation biofuel derived from seaweed biomass has been shown to have many advantages, it is yet to be deployed in commercial-scale seaweed biorefineries. This review paper examines the advantages and disadvantages of seaweed biorefineries for the entire value chain covering from seaweed and its cultivation to biofuel production based on an extensive literature search and the author's experience of conducting feasibility studies pertaining to seaweed biorefineries for over 10 years. For this purpose, the literature survey will cover the current status of seaweed production and its research and development worldwide, conversion technologies for biofuel production from seaweed based on bench-scale experiments, and large-scale techno-economic feasibility studies for seaweed conversion to biofuels and bioenergy. In addition, the main problems expected with the commercialization of seaweed-based biofuels will be identified. Finally, the current status of seaweed biorefinery technology and the author's views on its promising future will be summarized.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.4
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• pp.649-662
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• 2022

As information security(IS) is recognized as an organization's core value, organizations are making efforts to adopt strict IS policies and technologies. However, strict IS policies can cause negative behavior for employees of organizations who need to apply IS to their work. This study confirms that IS can express the role stress of employees, and suggests a way to mitigate the IS role stress. Specifically, we confirm that organization justice and individual social identity can reduce IS role stress, which reduces IS voice behavior. In the study, we surveyed workers of organizations that applied IS policies to their work and obtained 318 samples. Also, we tested the hypothesis by applying the structural equation model. As a result, IS organization justice increased IS voice behavior through social identity and partially reduced IS voice behavior by mitigating IS role stress. In addition, social identity moderated the relationship between IS role stress and IS voice behavior. This study suggests strategies for achieving internal IS goals by suggesting conditions for mitigating IS role stress from an organizational and individual perspective.