• KNOM Review
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• v.24 no.2
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• pp.24-34
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• 2021

Service providers using edge computing provide a high level of service. As a result, devices store important information in inner storage and have become a target of the latest cyberattacks, which are more difficult to detect. Although experts use a security system such as intrusion detection systems, the existing intrusion systems have low detection accuracy. Therefore, in this paper, we proposed a machine learning model for more accurate intrusion detections of devices in edge computing. The proposed model is a hybrid model that combines a stacked sparse autoencoder (SSAE) and a convolutional neural network (CNN) to extract important feature vectors from the input data using sparsity constraints. To find the optimal model, we compared and analyzed the performance as adjusting the sparsity coefficient of SSAE. As a result, the model showed the highest accuracy as a 96.9% using the sparsity constraints. Therefore, the model showed the highest performance when model trains only important features.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.125-130
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• 2024

With the rapid growth of mobility technology, the industrial sector is demanding storage devices that can reliably process data from various equipment and sensors in vehicles. NAND flash memory is being utilized as a storage device in mobility environments because it has the advantages of low power and fast data processing speed as well as strong external shock resistance. However, flash memory is characterized by data corruption due to long-term exposure to high temperatures. Therefore, a dedicated system for temperature management is required in mobility environments where high temperature exposure due to weather or external heat sources such as solar radiation is frequent. This paper designs a dedicated temperature management system for managing storage device temperature in a mobility environment. The designed temperature management system is a hybrid of traditional air cooling and water cooling technologies. The cooling method is designed to operate adaptively according to the temperature of the storage device, and it is designed not to operate when the temperature step is low to improve energy efficiency. Finally, experiments were conducted to analyze the temperature difference between each cooling method and different heat dissipation materials, proving that the temperature management policy is effective in maintaining performance.

• Journal of Applied Biological Chemistry
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• v.61 no.1
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• pp.83-91
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• 2018

Advances in bacterial and fungal genome mining uncover a plethora of cryptic secondary metabolite biosynthetic gene clusters. Guided by the genome information, targeted transcriptional derepression could be employed to determine the product of a cryptic gene cluster and to explore its biological role. Monascus spp. are food grade filamentous fungi popular in eastern Asia and several genome data belong to them are now available. We achieved transcription activation of a cryptic fungal polyketide synthase-nonribosomal peptide synthase gene Mpfus1 in Monascus purpureus ${\Delta}MpPKS5$ by inserting Aspergillus gpdA promoter at the upstream of Mpfus1 through double crossover gene replacement. The gene cluster with Mpfus1 show a high similarity to those for the biosynthesis of conjugated polyene derivatives with 2-pyrrolidone ring and the mycotoxin fusarin is the representative member of this group. The ${\Delta}MpPKS5$ is incapable of producing azaphilone pigment, providing an excellent background to identify chromogenic and UV-absorbing compounds. Activation of Mpfus1 resulted in a yellow hue on mycelia and its methanol extract exhibit a maximum absorption at 365 nm. HPLC analysis of the organic extracts indicated the presence of a variety of yellow compounds in the extract. This implies that the product of MpFus1 is metabolically or chemically unstable. LC-MS analysis guided us to predict the MpFus1 product and to propose that the Mpfus1-containing gene cluster encode the biosynthesis of a desmethyl analogue of fusarin. This study showcases the genome mining in Monascus and the possibility to unveil new biological activities embedded in it.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.4
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• pp.657-667
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• 2020

The Android framework allows apps to take full advantage of personal information through granting single permission, and does not determine whether the data being leaked is actual personal information. To solve these problems, we propose a tool with static/dynamic analysis. The tool analyzes the Source and Sink used by the target app, to provide users with information on what personal information it used. To achieve this, we extracted the Source and Sink through Control Flow Graph and make sure that it leaks the user's privacy when there is a Source-to-Sink flow. We also used the sensitive permission information provided by Google to obtain information from the sensitive API corresponding to Source and Sink. Finally, our dynamic analysis tool runs the app and hooks information from each sensitive API. In the hooked data, we got information about whether user's personal information is leaked through this app, and delivered to user. In this process, an automated Source/Sink classification model was applied to collect latest Source/Sink information, and the we categorized latest release version of Android(9.0) with 88.5% accuracy. We evaluated our tool on 2,802 APKs, and found 850 APKs that leak personal information.

• Journal of Bio-Environment Control
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• v.24 no.4
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• pp.317-325
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• 2015

This paper attempts to present a review about simulation of different greenhouse parameters and energy supplying techniques by using building energy simulation, to find out the optimal solution for keeping greenhouse microclimate favorable for the crop production. The objectives of conducting this study were, to describe the various energy systems and techniques used for the greenhouse energy management and efficiency analysis of these technologies by using building energy simulation. We describe different models to understand the behavior of the energy saving technologies with respect to the resources available and different outside climatic conditions. We identified main features of the building energy simulation software, that enable users, to simulate hybrid agricultural building projects by using user defined parameters. At the end of the paper we draw some important concluding remarks on the basis of reviewing all the investigators contributions for the developments of simulation model of agricultural greenhouse energy management, using a building energy simulation software specifically TRNSYS. In conclusion, this paper provides information that TRNSYS have great potential for agricultural buildings energy simulation along with the renewable energy resources and energy saving techniques. This review paper provides aid to greenhouse researcher and energy planner for the future studies of greenhouses energy planning.

• Journal of Wetlands Research
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• v.20 no.1
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• pp.43-53
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• 2018

Laguna Lake is the largest inland fresh water body in the Philippines. It primarily serves as a site for aquaculture, hydropower, transportation, and water supply industries. Due to Laguna Lake's diverse functionalities, competition among water users became prominent. Water quality began to deteriorate due to various pollutant contributions in this process, thereby affecting the soundness of the aquatic ecosystem. This study was conducted to evaluate the current water quality management policy from the viewpoint of ecological environment through the evaluation of the water quality of Laguna Lake. Concentrations of water pollutants such as ammonia ($NH_3$), biochemical oxygen demand (BOD), chloride ($Cl^-$), pH, and total suspended solids (TSS) exceeded the water quality standards of the Philippines' Department of Environment and Natural Resources (DENR). The water quality of the lake was also affected by the pollutant load due to agriculture and urban stormwater runoff in the watershed. The salinity and contaminated water from Pasig River also affected the water quality of Laguna Lake. Long-term water quality analysis showed that the water quality of Laguna Lake is also influenced by rainfall-related seasonal variations. The results of the water quality analysis of Laguna Lake indicated that the environmental management techniques of the Philippines should be changed from the conventional water management into an integrated watershed management scheme in the future. It is therefore necessary to study and introduce advanced watershed management measures in the Philippines based from the policies of other developed countries.

• Composites Research
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• v.31 no.6
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• pp.332-339
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• 2018

Fiber metal hybrid laminate (FML) can be used as an economic material with superior mechanical properties and light weight than conventional metal by bonding of metal and FRP. However, there are disadvantages that it is difficult to predict fracture behavior because of the large difference in properties depending on the type of fiber and lamination conditions. In this paper, we study the failure behavior of hybrid materials with laminated glass fiber reinforced plastics (GFRP, GEP118, woven type) in Al6061-T6 alloy. The Al alloys were coated with GFRP 1, 3, and 5 layers, and fracture behavior was analyzed by using a static test and a low cycle fatigue test. In the low cycle fatigue test, strain - life analysis and the total strain energy density method were used to analyze and predict the fatigue life. The Al alloy did not have tensile properties strengthening effect due to the GFRP coating. The fatigue hysteresis geometry followed the behavior of the Al alloy, the base material, regardless of the GFRP coating and number of coatings. As a result of the low cycle fatigue test, the fatigue strength was increased by the coating of GFRP, but it did not increase proportionally with the number of GFRP layers.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.4
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• pp.234-252
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• 2020

This paper is to verify the effectiveness of 'Hybrid Disaster Management Strategy' that integrates 'RTM(Real-time Monitoring) based On-line' and 'UAV based Off-line' system. For landslide prone area where sensors were installed, the conventional way of risk management so far has entirely relied on RTM data collected from the field through the instrumentation devices. But it's not enough due to the limitation of'Pin-point sensor'which tend to provide with only the localized information where sensors have stayed fixed. It lacks, therefore, the whole picture to be grasped. In this paper, utilizing 'Digital Photogrammetry Software Pix4D', the possibility of inference for the deformation of ungauged area has been reviewed. For this purpose, actual measurement data from RTM were compared with the estimated value from 3D point cloud outcome by UAV, and the consequent results has shown very accurate in terms of RMSE.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.21-33
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• 2022

Recently, the recommender system has been widely used in various fields such as movies, music, online shopping, and social media, and in the meantime, the recommender model has been developed from correlation analysis through the Apriori model, which can be said to be the first-generation model in the recommender system field. In 2005, many models have been proposed, including deep learning-based models, which are receiving a lot of attention within the recommender model. The recommender model can be classified into a collaborative filtering method, a content-based method, and a hybrid method that uses these two methods integrally. However, these basic methods are gradually losing their status as methodologies in the field as they fail to adapt to internal and external changing factors such as the rapidly changing user-item interaction and the development of big data. On the other hand, the importance of deep learning methodologies in recommender systems is increasing because of its advantages such as nonlinear transformation, representation learning, sequence modeling, and flexibility. In this paper, among deep learning methodologies, RNN, CNN, and GAN-based models suitable for sequential modeling that can accurately and flexibly analyze user-item interactions are classified, compared, and analyzed.