• Composites Research
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• v.34 no.1
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• pp.35-46
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• 2021

This paper aims to reduce weight by replacing the reinforcements of the B-pillar used in vehicles with CFRP(Carbon Fiber Reinforced Plastics) and GFRP(Glass Fiber Reinforced Plastics) from the existing steel materials. For this, it is necessary to secure structural stability that can replace the existing B-pillar while reducing the weight. Existing B-pillar are composed of steel reinforcements of various shapes, including a steel outer. Among these steel reinforcements, two steel reinforcements are to be replaced with composite materials. Each steel reinforcement is manufactured separately and bonded to the B-pillar outer by welding. However, the composite reinforcements presented in this paper are manufactured at once through compression and injection processes using patch-type CFRP and rib-structured GFRP. CFRP is attached to the high-strength part of the B-pillar to resist side loads, and the GFRP ribs are designed to resist torsion and side loads through a topology optimization technique. Through structural analysis, the designed composite B-pillar was compared with the existing B-pillar, and the weight reduction ratio was calculated.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.127-134
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• 2022

Steganography is a hidden technique in which secret messages are hidden in various multimedia files, and it is widely exploited for cyber crime and attacks because it is very difficult for third parties other than senders and receivers to identify the presence of hidden information in communication messages. Botnet typically consists of botmasters, bots, and C&C (Command & Control) servers, and is a botmasters-controlled network with various structures such as centralized, distributed (P2P), and hybrid. Recently, in order to enhance the concealment of botnets, research on Stego Botnet, which uses SNS platforms instead of C&C servers and performs C&C communication by applying steganography techniques, has been actively conducted, but image or video media-oriented stego botnet techniques have been studied. On the other hand, audio files such as various sound sources and recording files are also actively shared on SNS, so research on stego botnet based on audio steganography is needed. Therefore, in this study, we present the results of comparative analysis on hidden capacity by file type and tool through experiments, using a stego botnet that performs C&C hidden communication using audio files as a cover medium in Telegram Messenger.

• Smart Media Journal
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• v.13 no.6
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• pp.9-15
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• 2024

In recent trends, there has been an increase in 'Qshing' attacks, a hybrid form of phishing that exploits fake QR (Quick Response) codes impersonating government agencies to steal personal and financial information. Particularly, this attack method is characterized by its stealthiness, as victims can be redirected to phishing pages or led to download malicious software simply by scanning a QR code, making it difficult for them to realize they have been targeted. In this paper, we have developed a classification technique utilizing machine learning algorithms to identify the maliciousness of URLs embedded in QR codes, and we have explored ways to integrate this with existing QR code readers. To this end, we constructed a dataset from 128,587 malicious URLs and 428,102 benign URLs, extracting 35 different features such as protocol and parameters, and used AutoML to identify the optimal algorithm and hyperparameters, achieving an accuracy of approximately 87.37%. Following this, we designed the integration of the trained classification model with existing QR code readers to implement a service capable of countering Qshing attacks. In conclusion, our findings confirm that deriving an optimized algorithm for classifying malicious URLs in QR codes and integrating it with existing QR code readers presents a viable solution to combat Qshing attacks.

• International Journal of Computer Science & Network Security
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• v.24 no.9
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• pp.30-40
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• 2024

Building an accurate 3-D spatial road network model has become an active area of research now-a-days that profess to be a new paradigm in developing Smart roads and intelligent transportation system (ITS) which will help the public and private road impresario for better road mobility and eco-routing so that better road traffic, less carbon emission and road safety may be ensured. Dealing with such a large scale 3-D road network data poses challenges in getting accurate elevation information of a road network to better estimate the CO₂ emission and accurate routing for the vehicles in Internet of Vehicle (IoV) scenario. Clustering and regression techniques are found suitable in discovering the missing elevation information in 3-D spatial road network dataset for some points in the road network which is envisaged of helping the public a better eco-routing experience. Further, recently Explainable Artificial Intelligence (xAI) draws attention of the researchers to better interprete, transparent and comprehensible, thus enabling to design efficient choice based models choices depending upon users requirements. The 3-D road network dataset, comprising of spatial attributes (longitude, latitude, altitude) of North Jutland, Denmark, collected from publicly available UCI repositories is preprocessed through feature engineering and scaling to ensure optimal accuracy for clustering and regression tasks. K-Means clustering and regression using Support Vector Machine (SVM) with radial basis function (RBF) kernel are employed for 3-D road network analysis. Silhouette scores and number of clusters are chosen for measuring cluster quality whereas error metric such as MAE ( Mean Absolute Error) and RMSE (Root Mean Square Error) are considered for evaluating the regression method. To have better interpretability of the Clustering and regression models, SHAP (Shapley Additive Explanations), a powerful xAI technique is employed in this research. From extensive experiments , it is observed that SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions with an accuracy of 97.22% and strong performance metrics across all classes having MAE of 0.0346, and MSE of 0.0018. On the other hand, the ten-cluster setup, while faster in SHAP analysis, presented challenges in interpretability due to increased clustering complexity. Hence, K-Means clustering with K=4 and SVM hybrid models demonstrated superior performance and interpretability, highlighting the importance of careful cluster selection to balance model complexity and predictive accuracy.

• Journal of Technology Innovation
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• v.26 no.3
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• pp.37-68
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• 2018

Since the 20th century, automobiles, which are the most common means of transportation, have been evolving as the use of electronic control devices and automotive semiconductors increases dramatically. Automotive semiconductors are a key component in automotive electronic control devices and are used to provide stability, efficiency of fuel use, and stability of operation to consumers. For example, automotive semiconductors include engines control, technologies for managing electric motors, transmission control units, hybrid vehicle control, start/stop systems, electronic motor control, automotive radar and LIDAR, smart head lamps, head-up displays, lane keeping systems. As such, semiconductors are being applied to almost all electronic control devices that make up an automobile, and they are creating more effects than simply combining mechanical devices. Since automotive semiconductors have a high data rate basically, a microprocessor unit is being used instead of a micro control unit. For example, semiconductors based on ARM processors are being used in telematics, audio/video multi-medias and navigation. Automotive semiconductors require characteristics such as high reliability, durability and long-term supply, considering the period of use of the automobile for more than 10 years. The reliability of automotive semiconductors is directly linked to the safety of automobiles. The semiconductor industry uses JEDEC and AEC standards to evaluate the reliability of automotive semiconductors. In addition, the life expectancy of the product is estimated at the early stage of development and at the early stage of mass production by using the reliability test method and results that are presented as standard in the automobile industry. However, there are limitations in predicting the failure rate caused by various parameters such as customer's various conditions of use and usage time. To overcome these limitations, much research has been done in academia and industry. Among them, researches using data mining techniques have been carried out in many semiconductor fields, but application and research on automotive semiconductors have not yet been studied. In this regard, this study investigates the relationship between data generated during semiconductor assembly and package test process by using data mining technique, and uses data mining technique suitable for predicting potential failure rate using customer bad data.

• Korean Security Journal
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• no.19
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• pp.225-244
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• 2009

Electronic security system is composed mainly of electronic-information-communication device, so system technology, configuration and management of the electronic security system could be affected by the change of information-communication environment. This study is to propose the future prospect on the development of technique for electronic security system through the analysis of the trend and the actual condition on the development of technique. This study is based on literature study and interview with user and provider of electronic security system, also survey was carried out by system provider and members of security integration company to come up with more practical result. Hybrid DVR technology that has multi-function such as motion detection, target tracking and image identification is expected to be developed. And 'Embedded IP camera' technology that internet server and image identification software are built in. Those technologies could change the configuration and management of CCTV system. Fingerprint identification technology and face identification technology are continually developed to get more reliability, but continual development of surveillance and three-dimension identification technology for more efficient face identification system is needed. As radio identification and tracking function of RFID is appreciated as very useful for access control system, hardware and software of RFID technology is expected to be developed, but government's support for market revitalization is necessary. Behavior pattern identification sensor technology is expected to be developed and could replace passive infrared sensor that cause system error, giving security guard firm confidence for response. The principle of behavior pattern identification is similar to image identification, so those two technology could be integrated with tracking technology and radio identification technology of RFID for total monitoring system. For more efficient electronic security system, middle-ware's role is very important to integrate the technology of electronic security system, this could make possible of installing the integrated security system.

• The Journal of Korean Academy of Prosthodontics
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• v.46 no.2
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• pp.105-115
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• 2008

Statement of problem: Proper finishing and polishing enhance both the esthetics and the longevity of restored teeth. Blade finishing technique would be suited for smoothing and finishing. Evaluation of this technique are necessary. Purpose: The purpose of this study was to evaluate the blade finishing and polishing procedures on the surface profile and roughness of ormocer-based composite resin and nanohybrid composite resin. Material and methods: The material included a ormocer-based composite resin ($Admira^{(R)}$ & $Admira^{(R)}$ Flow); a nanohybrid composite resin ($Grandio^{(R)}$ & $Grandio^{(R)}$ Flow). One hundred forty specimens of each group were prepared using a mylar strip and randomly divied into blade finishing and rubber polishing groups (n=10). The average surface roughness (Ra) in micrometers was measured and the surface profile was examined by scanning electron microscopy (SEM) (Magnification ${\times}$ 200). The data were analyzed by Mann-Whitney Test at 0.05 significance level. Conclusion: The results of this study indicated that the mylar strip produced the smoothest surface on all materials and among the finishing-polishing methods was not significanct difference (P>0.05). Ormocer-based flowable composite resin performedthe lowest variability in initial surface roughness among the tested materials.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.23-46
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• 2021

Collaborative filtering, which is often used in personalization recommendations, is recognized as a very useful technique to find similar customers and recommend products to them based on their purchase history. However, the traditional collaborative filtering technique has raised the question of having difficulty calculating the similarity for new customers or products due to the method of calculating similaritiesbased on direct connections and common features among customers. For this reason, a hybrid technique was designed to use content-based filtering techniques together. On the one hand, efforts have been made to solve these problems by applying the structural characteristics of social networks. This applies a method of indirectly calculating similarities through their similar customers placed between them. This means creating a customer's network based on purchasing data and calculating the similarity between the two based on the features of the network that indirectly connects the two customers within this network. Such similarity can be used as a measure to predict whether the target customer accepts recommendations. The centrality metrics of networks can be utilized for the calculation of these similarities. Different centrality metrics have important implications in that they may have different effects on recommended performance. In this study, furthermore, the effect of these centrality metrics on the performance of recommendation may vary depending on recommender algorithms. In addition, recommendation techniques using network analysis can be expected to contribute to increasing recommendation performance even if they apply not only to new customers or products but also to entire customers or products. By considering a customer's purchase of an item as a link generated between the customer and the item on the network, the prediction of user acceptance of recommendation is solved as a prediction of whether a new link will be created between them. As the classification models fit the purpose of solving the binary problem of whether the link is engaged or not, decision tree, k-nearest neighbors (KNN), logistic regression, artificial neural network, and support vector machine (SVM) are selected in the research. The data for performance evaluation used order data collected from an online shopping mall over four years and two months. Among them, the previous three years and eight months constitute social networks composed of and the experiment was conducted by organizing the data collected into the social network. The next four months' records were used to train and evaluate recommender models. Experiments with the centrality metrics applied to each model show that the recommendation acceptance rates of the centrality metrics are different for each algorithm at a meaningful level. In this work, we analyzed only four commonly used centrality metrics: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Eigenvector centrality records the lowest performance in all models except support vector machines. Closeness centrality and betweenness centrality show similar performance across all models. Degree centrality ranking moderate across overall models while betweenness centrality always ranking higher than degree centrality. Finally, closeness centrality is characterized by distinct differences in performance according to the model. It ranks first in logistic regression, artificial neural network, and decision tree withnumerically high performance. However, it only records very low rankings in support vector machine and K-neighborhood with low-performance levels. As the experiment results reveal, in a classification model, network centrality metrics over a subnetwork that connects the two nodes can effectively predict the connectivity between two nodes in a social network. Furthermore, each metric has a different performance depending on the classification model type. This result implies that choosing appropriate metrics for each algorithm can lead to achieving higher recommendation performance. In general, betweenness centrality can guarantee a high level of performance in any model. It would be possible to consider the introduction of proximity centrality to obtain higher performance for certain models.

• The Journal of Korean Society for Radiation Therapy
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• v.30 no.1_2
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• pp.49-63
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• 2018

Purpose : The purpose of this study is investigating the changes of treatment plan and comparing skin dose with or without the skin flash. To investigate optimal applications of the skin flash, the changes of skin dose of each plans by various thicknesses of skin flash were measured and analyzed also. Methods and Material : Anthropomorphic phantom was scanned by CT for this study. The 2 fields hybrid IMRT and the 6 fields static IMRT were generated from the Eclipse (ver. 13.7.16, Varian, USA) RTP system. Additional plans were generated from each IMRT plans by changing skin flash thickness to 0.5 cm, 1.0 cm, 1.5 cm, 2.0 cm and 2.5 cm. MU and maximum doses were measured also. The treatment equipment was 6MV of VitalBeam (Varian Medical System, USA). Measuring device was a metal oxide semiconductor field-effect transistor(MOSFET). Measuring points of skin doses are upper (1), middle (2) and lower (3) positions from center of the left breast of the phantom. Other points of skin doses, artificially moved to medial and lateral sides by 0.5 cm, were also measured. Results : The reference value of 2F-hIMRT was 206.7 cGy at 1, 186.7 cGy at 2, and 222 cGy at 3, and reference values of 6F-sIMRT were measured at 192 cGy at 1, 213 cGy at 2, and 215 cGy at 3. In comparison with these reference values, the first measurement point in 2F-hIMRT was 261.3 cGy with a skin flash 2.0 cm and 2.5 cm, and the highest dose difference was 26.1 %diff. and 5.6 %diff, respectively. The third measurement point was 245.3 cGy and 10.5 %diff at the skin flash 2.5 cm. In the 6F-sIMRT, the highest dose difference was observed at 216.3 cGy and 12.7 %diff. when applying the skin flash 2.0 cm for the first measurement point and the dose difference was the largest at the application point of 2.0 cm, not the skin flash 2.5 cm for each measurement point. In cases of medial 0.5 cm shift points of 2F-hIMRT and 6F-sIMRT without skin flash, the measured value was -75.2 %diff. and -70.1 %diff. at 2F, At -14.8, -12.5, and -21.0 %diff. at the 1st, 2nd and 3rd measurement points, respectively. Generally, both treatment plans showed an increase in total MU, maximum dose and %diff as skin flash thickness increased, except for some results. The difference of skin dose using 0.5 cm thickness of skin flash was lowest lesser than 20 % in every conditions. Conclusion : Minimizing the thickness of skin flash by 0.5 cm is considered most ideal because it makes it possible to keep down MUs and lowering maximum doses. In addition, It was found that MUs, maximum doses and differences of skin doses did not increase infinitely as skin flash thickness increase by. If the error margin caused by PTV or other factors is lesser than 1.0 cm, It is considered that there will be many advantages in with the skin flash technique comparing without it.

• Journal of Mushroom
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• v.10 no.3
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• pp.109-114
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• 2012

To develop new variety of oyster mushroom, 63 intra-specific hybrids between the strain Suhan and #Nongi201 were developed using hyphal anastomosis technique in 2004. The Po2008-275 hybrid between the dikaryon strain 04-154(Suhan x #Nongi201) and the monokaryon strain derived from ASI2487 were developed using hyphal anastomosis in 2008. The Po2008-275 was shown the best cultural characteristics, selected to be a new variety and named as 'Guseol'. The new commercial strain, 'Guseol' had dark grey pilei and grows well under spring and autumn conditions in Korea. The fruiting bodies of 'Guseol' were of an excellent quality in that not only the stipe was thick and long but also the pileus was small and hard. The optimum temperatures for mycelial growth and fruiting body development were $25{\sim}30^{\circ}C$ and $10{\sim}16^{\circ}C$, respectively. Time period required for the initiation of the first fruiting body was about 3 to 5 days depending on the temperatures. The shape of fruiting body was thin funnel shape. Fruiting body production per box($43{\times}43{\times}12cm$) was about $1545{\pm}400.9g$ which was almost 137% quantity compared to that of parental strain 04-154. Relatively low temperature incubation ($11^{\circ}C$) resulted in the development of better quality of 'Guseol' mushrooms. When two different media including potato dextrose medium and mushroom complete medium were compared, the mycelial growth of this mushroom were much faster in mushroom complete medium. Similar results were observed with other variety '#Chunchu2'. Analysis of the genetic characteristics of the new commercial strain 'Guseol' showed a major DNA profile as that of the parental 04-154 when primer URP 1, primer URP 2 and primer URP 5 were used, but different to '#Chunchu2' that was used as a control. This new variety of the dark grey oyster mushroom had smart and high quality image that corresponds well to "health food". We therefore expect that this new strain will satisfy the consumers demand for variety and excellent mushrooms.