• Polymer(Korea)
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• v.32 no.1
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• pp.63-69
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• 2008

The mechanical properties and morphologies of cellulose blends with two different additives were compared. Poly (vinyl alcohol) (PVA) of ethylene glycol (EG) were used as additives in the formation of cellulose blends through the solution blending. The properties of blends were varied with the additive content in the polymer matrix. The ultimate tensile strength and initial modulus of the cellulose blends were highest for a blend PVA content of 30 wt% and for a blend EG content of 10 wt%, respectively. Ternary blended systems of composition of cellulose/PVA (70/30=w/w)/EG were also prepared by the solution blending method with different EG contents. The mechanical properties of these systems were found to be optimal for EG contents of up to 40 wt%. The mechanical properties of the cellulose ternary blend films were superior to those of the cellulose binary blend films. The oxygen permeability transmission rate ($O_2TR$) monotonically decreased with increasing EG content in the ternary blend films. Overall, the mechanical properties of the cellulose blend films were found to be better than those of pure cellulose films.

• Journal of the Korean Society of Safety
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• v.25 no.6
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• pp.169-179
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• 2010

The aim of this study was to investigate occupational and individual risk factors and working conditions in relation to musculoskeletal symptoms in street cleaners. Investigation was conducted through a survey of 395 male street cleaners employed by the government office in Seoul, Gyeonggi and Chung-Nam from July to August of 2009. The control group was comprised of 143 male drivers and security guards. Risk factors for musculoskeletal symptoms in street cleaners were investigated by multiple logistic regression analysis and also evaluated ergonomic risk factors by assessing working conditions of 4 street cleaners. As a result of symptom questionnaires, all of the prevalent rates of musculoskeletal symptoms in street cleaners had significantly higher results than those of the control group(p<0.05). On binary logistic regression analysis of musculoskeletal symptoms, street cleaners showed significant higher odds ratio as 18.84(95%CI: 6.56-54.12) in the arm/elbow, 10.49(95%CI: 4.29-25.65) in the hand/wrist compared to the control group. Both absence of rest breaks and exposure to ergonomic risk factors showed to be important internal risk factors of musculoskeletal symptoms among street cleaners. The exposure levels of QEC(Quick exposures checklist) in street cleaners were revealed to be higher on the shoulder/arm, wrist/hand, and neck than back, or from stress. The findings appear to show that street cleaners were high-risk group of work-related musculoskeletal disorders. Therefore street cleaners require a holistic interventional strategy, including adequate arrangement of rest breaks, improvement of working tools and control of individual risk factors such as obesity and smoking.

• Korean Chemical Engineering Research
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• v.50 no.2
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• pp.292-299
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• 2012

We made 8 wt% silica dispersion system with fumed silica and photo curable acrylic monomer by beads mill process. These dispersions could be applied in organic/inorganic hybrid coating systems. These dispersions could be applied in organic/inorganic hybrid coating systems. The 4 species of photo curable acrylic monomer which was presence of hydroxyl group, different solubility parameter, and different molecular size were used in the silica dispersions. Stability of polar solvent, isopropyl alcohol, in silica dispersions was investigated. We investigated the stability of silica dispersions by using steady-state and dynamic rheology. As the monomer has hydroxyl group increased in mono and binary monomer silica dispersions, they showed non flocculated stable sol (loss modulus (G")> storage modulus (G')). When polar solvent IPA was added into slightly flocculated silica dispersions, they changed to non flocculated stable sol.

• 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.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.4
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• pp.115-124
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• 2022

Unlike the semi-public system of city buses, Seoul's townbus are operated on a private operating system, which is poor condition to the changes in the environment. Sales decreased due to a decrease in the number of passengers due to COVID-19 and a demand for conversion due to the advent of competitive transportation methods, and the financial support of Seoul Metropolitan Government is continuously increasing. In this study, to analyze the characteristics of townbus operated by a private operating system, the townbus sales and surplus companies were analyzed by what factors were affected. For the analysis data, townbus financial statements of Seoul in 2018 were used, and townbus sales and surplus companies were applied as dependent variables, and townbus operation system, satisfaction survey, humanities and social variables, and subway and public bicycle characteristics were applied as independent variables. As a result of the analysis, the sales is affected by operating hours per vehicle, in-vehicle safety, the number of households, the number of elderly people, and public bicycle variables, and surplus companies are affected by in-vehicle safety, reliability, and public bicycle variables. In particular, public bicycles, a competitive means of transportation, had an impact on industry sales, and the townbus business environment is expected to become more difficult as time goes by. The industry is seeking self-rescue measures, and Seoul is required to strengthen financial support so that townbus can operate stably.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.25-41
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• 2011

Recently, support vector machines (SVMs) are being recognized as competitive tools as compared with other data mining techniques for solving pattern recognition or classification decision problems. Furthermore, many researches, in particular, have proved them more powerful than traditional artificial neural networks (ANNs) (Amendolia et al., 2003; Huang et al., 2004, Huang et al., 2005; Tay and Cao, 2001; Min and Lee, 2005; Shin et al., 2005; Kim, 2003).The classification decision, such as a binary or multi-class decision problem, used by any classifier, i.e. data mining techniques is so cost-sensitive particularly in financial classification problems such as the credit ratings that if the credit ratings are misclassified, a terrible economic loss for investors or financial decision makers may happen. Therefore, it is necessary to convert the outputs of the classifier into wellcalibrated posterior probabilities-based multiclass credit ratings according to the bankruptcy probabilities. However, SVMs basically do not provide such probabilities. So it required to use any method to create the probabilities (Platt, 1999; Drish, 2001). This paper applied AdaBoost algorithm-based support vector machines (SVMs) into a bankruptcy prediction as a binary classification problem for the IT companies in Korea and then performed the multi-class credit ratings of the companies by making a normal distribution shape of posterior bankruptcy probabilities from the loss functions extracted from the SVMs. Our proposed approach also showed that their methods can minimize the misclassification problems by adjusting the credit grade interval ranges on condition that each credit grade for credit loan borrowers has its own credit risk, i.e. bankruptcy probability.

• Korean Chemical Engineering Research
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• v.40 no.6
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• pp.703-708
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• 2002

High pressure phase behavior data for poly(propyl acrylate) and poly(propyl methacrylate) with supercritical $CO_2$, ethylene, propane, butane, propylene, 1-butene, dimethyl ether, and $CHClF_2$ were measured in the temperature range from $23^{\circ}C$ to $186^{\circ}C$ and at pressures up to 2,400 bar. The cloud point were obtained at dissolved pressure below 2,070, 1,400, 1,880, 450, 2,200, 250, and 150 bar for poly(propyl acrylate) in supercritical $CO_2$, ethylene, propane, propylene, butane, 1-buthen, and dimethyl ether, respectively. The temperature range is $23-175^{\circ}C$. The poly(propyl methacrylate) does not dissolve in $CO_2$ at temperature of $240^{\circ}C$ and pressure 2,900 bar. The poly(propyl methacrylate)-propane, poly(propyl methacrylate)-butane, poly(propyl methacrylate)-propylene, poly(propyl methacrylate)-1-butene, and poly(propyl methacrylate)-$CHClF_2$ systems were dissolved at the pressures less than 2,390 bar, below 2,100 bar, below 570 bar, below 310 bar, below 300 bar, and below 170 bar, respectively. The temperature range shows from 40 to $186^{\circ}C$. The phase behavior of between binary poly(propyl acrylate)-$CO_2$ and poly(propyl acrylate)-dimethyl ether system were measured from upper critical solution temperature region to lower critical solution temperature region with added dimethyl ether concentrations of 5, 15 and 50 wt%.

• KIPS Transactions on Computer and Communication Systems
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• v.7 no.6
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• pp.155-164
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• 2018

Many malicious programs have been compressed or encrypted using various commercial packers to prevent reverse engineering, So malicious code analysts must decompress or decrypt them first. The OEP (Original Entry Point) is the address of the first instruction executed after returning the encrypted or compressed executable file back to the original binary state. Several unpackers, including PinDemonium, execute the packed file and keep tracks of the addresses until the OEP appears and find the OEP among the addresses. However, instead of finding exact one OEP, unpackers provide a relatively large set of OEP candidates and sometimes OEP is missing among candidates. In other words, existing unpackers have difficulty in finding the correct OEP. We have developed new tool which provides fewer OEP candidate sets by adding two methods based on the property of the OEP. In this paper, we propose two methods to provide fewer OEP candidate sets by using the property that the function call sequence and parameters are same between packed program and original program. First way is based on a function call. Programs written in the C/C++ language are compiled to translate languages into binary code. Compiler-specific system functions are added to the compiled program. After examining these functions, we have added a method that we suggest to PinDemonium to detect the unpacking work by matching the patterns of system functions that are called in packed programs and unpacked programs. Second way is based on parameters. The parameters include not only the user-entered inputs, but also the system inputs. We have added a method that we suggest to PinDemonium to find the OEP using the system parameters of a particular function in stack memory. OEP detection experiments were performed on sample programs packed by 16 commercial packers. We can reduce the OEP candidate by more than 40% on average compared to PinDemonium except 2 commercial packers which are can not be executed due to the anti-debugging technique.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.175-197
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• 2021

Recently, with the development of deep learning technology, research on unstructured data analysis is being actively conducted, and it is showing remarkable results in various fields such as classification, summary, and generation. Among various text analysis fields, text classification is the most widely used technology in academia and industry. Text classification includes binary class classification with one label among two classes, multi-class classification with one label among several classes, and multi-label classification with multiple labels among several classes. In particular, multi-label classification requires a different training method from binary class classification and multi-class classification because of the characteristic of having multiple labels. In addition, since the number of labels to be predicted increases as the number of labels and classes increases, there is a limitation in that performance improvement is difficult due to an increase in prediction difficulty. To overcome these limitations, (i) compressing the initially given high-dimensional label space into a low-dimensional latent label space, (ii) after performing training to predict the compressed label, (iii) restoring the predicted label to the high-dimensional original label space, research on label embedding is being actively conducted. Typical label embedding techniques include Principal Label Space Transformation (PLST), Multi-Label Classification via Boolean Matrix Decomposition (MLC-BMaD), and Bayesian Multi-Label Compressed Sensing (BML-CS). However, since these techniques consider only the linear relationship between labels or compress the labels by random transformation, it is difficult to understand the non-linear relationship between labels, so there is a limitation in that it is not possible to create a latent label space sufficiently containing the information of the original label. Recently, there have been increasing attempts to improve performance by applying deep learning technology to label embedding. Label embedding using an autoencoder, a deep learning model that is effective for data compression and restoration, is representative. However, the traditional autoencoder-based label embedding has a limitation in that a large amount of information loss occurs when compressing a high-dimensional label space having a myriad of classes into a low-dimensional latent label space. This can be found in the gradient loss problem that occurs in the backpropagation process of learning. To solve this problem, skip connection was devised, and by adding the input of the layer to the output to prevent gradient loss during backpropagation, efficient learning is possible even when the layer is deep. Skip connection is mainly used for image feature extraction in convolutional neural networks, but studies using skip connection in autoencoder or label embedding process are still lacking. Therefore, in this study, we propose an autoencoder-based label embedding methodology in which skip connections are added to each of the encoder and decoder to form a low-dimensional latent label space that reflects the information of the high-dimensional label space well. In addition, the proposed methodology was applied to actual paper keywords to derive the high-dimensional keyword label space and the low-dimensional latent label space. Using this, we conducted an experiment to predict the compressed keyword vector existing in the latent label space from the paper abstract and to evaluate the multi-label classification by restoring the predicted keyword vector back to the original label space. As a result, the accuracy, precision, recall, and F1 score used as performance indicators showed far superior performance in multi-label classification based on the proposed methodology compared to traditional multi-label classification methods. This can be seen that the low-dimensional latent label space derived through the proposed methodology well reflected the information of the high-dimensional label space, which ultimately led to the improvement of the performance of the multi-label classification itself. In addition, the utility of the proposed methodology was identified by comparing the performance of the proposed methodology according to the domain characteristics and the number of dimensions of the latent label space.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.3
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• pp.149-155
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• 2021

Early warning systems for weather risk management in the agricultural sector have been developed to predict potential wind damage to crops. These systems take into account the daily maximum wind speed to determine the critical wind speed that causes fruit drops and provide the weather risk information to farmers. In an effort to increase the accuracy of wind risk predictions, an artificial neural network for binary classification was implemented. In the present study, the daily wind speed and other weather data, which were measured at weather stations at sites of interest in Jeollabuk-do and Jeollanam-do as well as Gyeongsangbuk- do and part of Gyeongsangnam- do provinces in 2019, were used for training the neural network. These weather stations include 210 synoptic and automated weather stations operated by the Korean Meteorological Administration (KMA). The wind speed data collected at the same locations between January 1 and December 12, 2020 were used to validate the neural network model. The data collected from December 13, 2020 to February 18, 2021 were used to evaluate the wind risk prediction performance before and after the use of the artificial neural network. The critical wind speed of damage risk was determined to be 11 m/s, which is the wind speed reported to cause fruit drops and damages. Furthermore, the maximum wind speeds were expressed using Weibull distribution probability density function for warning of wind damage. It was found that the accuracy of wind damage risk prediction was improved from 65.36% to 93.62% after re-classification using the artificial neural network. Nevertheless, the error rate also increased from 13.46% to 37.64%, as well. It is likely that the machine learning approach used in the present study would benefit case studies where no prediction by risk warning systems becomes a relatively serious issue.