• International Journal of Advanced Culture Technology
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• v.9 no.4
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• pp.288-294
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• 2021

There are various ways to be infected with malicious code due to the increase in Internet use, such as the web, affiliate programs, P2P, illegal software, DNS alteration of routers, word processor vulnerabilities, spam mail, and storage media. In addition, malicious codes are produced more easily than before through automatic generation programs due to evasion technology according to the advancement of production technology. In the past, the propagation speed of malicious code was slow, the infection route was limited, and the propagation technology had a simple structure, so there was enough time to study countermeasures. However, current malicious codes have become very intelligent by absorbing technologies such as concealment technology and self-transformation, causing problems such as distributed denial of service attacks (DDoS), spam sending and personal information theft. The existing malware detection technique, which is a signature detection technique, cannot respond when it encounters a malicious code whose attack pattern has been changed or a new type of malicious code. In addition, it is difficult to perform static analysis on malicious code to which code obfuscation, encryption, and packing techniques are applied to make malicious code analysis difficult. Therefore, in this paper, a method to detect malicious code through dynamic analysis and static analysis using Trojan-type Downloader/Dropper malicious code was showed, and suggested to malicious code detection and countermeasures.

• Applied Microscopy
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• v.47 no.1
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• pp.43-49
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• 2017

A new method was introduced to distinguish the ferrite, bainite and martensite in transformation induced plasticity (TRIP) steel by using electron backscatter diffraction (EBSD) and transmission electron microscopy (TEM). EBSD is a very powerful microstructure analysis technique at the length scales ranging from tens of nanometers to millimeters. However, iron BCC phases such as ferrite, bainite and martensite cannot be easily distinguished by EBSD due to their similar surface morphology and crystallographic structure. Among the various EBSD-based methodology, image quality (IQ) values, which present the perfection of a crystal lattice, was used to distinguish the iron BCC phases. IQ values are very useful tools to discern the iron BCC phases because of their different density of crystal defect and lattice distortion. However, there are still remaining problems that make the separation of bainite and martensite difficult. For instance, these phases have very similar IQ values in many cases, especially in deformed region; therefore, even though the IQ value was used, it has been difficult to distinguish the bainite and martensite. For more precise separation of bainite and martensite, IQ threshold values were determined by a correlative TEM analysis. By determining the threshold values, iron BCC phases were successfully separated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1690-1707
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• 2021

Precipitation prediction during flood season has been a key task of climate prediction for a long time. This type of prediction is linked with the national economy and people's livelihood, and is also one of the difficult problems in climatology. At present, there are some precipitation forecast models for the flood season, but there are also some deviations from these models, which makes it difficult to forecast accurately. In this paper, based on the measured precipitation data from the flood season from 1993 to 2019 and the precipitation return data of CWRF, ANN cycle modeling and a weighted integration method is used to correct the CWRF used in today's operational systems. The MAE and TCC of the precipitation forecast in the flood season are used to check the prediction performance of the proposed algorithm model. The results demonstrate a good correction effect for the proposed algorithm. In particular, the MAE error of the new algorithm is reduced by about 50%, while the time correlation TCC is improved by about 40%. Therefore, both the generalization of the correction results and the prediction performance are improved.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.35 no.1
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• pp.65-76
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• 1999

Our status of off-shore stow-net fishery is in face with many difficult problems; the lack of fisherman by evading the 3-D occupation, the safety accident by unskilled crew and old type fishing system. In order to solve those problems, it is necessary to save the man power and ensure the safety of fishing work by the effective utilization of power and automatization of fishing gear system. This is consists of the side drum driven by main engine, the net hauler, the bow and stern capstan, jib crane etc. Therefore, we suggest the design on unification of power device of fishing gear system as follows; (1) fishing system by uni-hydraulic power and (2) fishing system by electric motor and electro-hydraulic power.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1993.10a
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• pp.291-296
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• 1993

Taking a bledes axle of rotary tiller as a example, this paper discusses influences of four loading essential factors, which are strengthened amplitude, cycle times, loading sequence and loading frequency. in fatigue life. Determination principles of above four factors and monitoring methods of fatigue damage by local strain are dealt with. The actual field testing check of farm machinery is rapidly simulated by laboratory program fatigue test can shorten the period of development and improvement of a product. In the time of in-door simulation test, damage monitoring and four loading essential factors, which are strengthened amplitude , cycle times, loading sequence and loading frequency, have to be dealt with . If these problems are solved successfully, it is possible to accelerated test speed, reduce costs and manhours, and raise accuracy of test result. However strengthening method, loading pattern and influence of loading frequency on test result have not so far been discu sed systematically, damage monitoring is even more a difficult problem. Authors have studied above problems with the object of blades axle of rotary tiller.

• International Journal of Precision Engineering and Manufacturing
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• v.9 no.3
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• pp.85-91
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• 2008

Robot technology is a remarkably interdisciplinary research area, one that can be employed in various industrial fields as well as higher value-added fields. The construction industry, on the other hand, has been known as one of the most difficult research fields to apply robotic schemes. Therefore, applying robot technologies in the construction industry is quite a challenging topic. This paper aims to introduce the progress of automated robotic systems in construction fields, namely with respect to construction robots. While construction robots have a very wide range of application depending on the huge market size of the construction industry, there still exist a lot of problems such as highly risky working environment and inefficiency due to the labor intensive characteristic. In order to solve these problems, a variety of construction robots have been developed and, in this paper, the current state of the robotic systems for construction works and the vision of future robot technology in the construction field are introduced.

• Journal of the Korea Institute of Military Science and Technology
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• v.3 no.2
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• pp.179-188
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• 2000

Multi-disciplinary optimization design concept can provide a solution to many engineering problems. In the field of structural analysis, much development of size or topology optimization has been achieved in the application of research. This paper demonstrates an optimum design of a multi-layer cylindrical tube which behaves thermoelastically. A multi-layer cylindrical tube that has several different material properties at each layer is optimized within allowable stress and temperature range when mechanical and thermal loads are applied simultaneously. When thermal loads are applied to a multi-layer tube, stress phenomena become complicated due to each layer's thermal expansion and the layer thicknesses. Factors like temperature; stress; and material thermal thicknesses of each tube layer are very difficult undertaking. To analyze these problems using an efficient and precise method, the optimization theories are adopted to perform thermoelastic finite element analysis.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.634-636
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• 2015

As the expansion of enterprise scale and the increase of staff, the amount of terminal is increasing as well. It is very difficult to the system manager of traditional data protection scheme to manage and maintenance for the large number of terminals. This problem can be solved by desktop virtualization, which use traditional security problems still exist and new security problems occur at the same time. Using desktop virtualization, it needs a method of automatic security protection. In this paper, the desktop virtualization security requirements are discussed.

• Smart Structures and Systems
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• v.31 no.5
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• pp.469-483
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• 2023

The dynamic characteristics of wind turbine blades are usually monitored by contact sensors with the disadvantages of high cost, difficult installation, easy damage to the structure, and difficult signal transmission. In view of the above problems, based on computer vision technology and the improved YOLOv5 (You Only Look Once v5) deep learning model, a non-contact dynamic characteristic monitoring method for wind turbine blade is proposed. First, the original YOLOv5l model of the CSP (Cross Stage Partial) structure is improved by introducing the CSP2_2 structure, which reduce the number of residual components to better the network training speed. On this basis, combined with the Deep sort algorithm, the accuracy of structural displacement monitoring is mended. Secondly, for the disadvantage that the deep learning sample dataset is difficult to collect, the blender software is used to model the wind turbine structure with conditions, illuminations and other practical engineering similar environments changed. In addition, incorporated with the image expansion technology, a modeling-based dataset augmentation method is proposed. Finally, the feasibility of the proposed algorithm is verified by experiments followed by the analytical procedure about the influence of YOLOv5 models, lighting conditions and angles on the recognition results. The results show that the improved YOLOv5 deep learning model not only perform well compared with many other YOLOv5 models, but also has high accuracy in vibration monitoring in different environments. The method can accurately identify the dynamic characteristics of wind turbine blades, and therefore can provide a reference for evaluating the condition of wind turbine blades.

• Journal of the Korea Society of Computer and Information
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• v.21 no.4
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• pp.73-80
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• 2016

Most fault prediction models have class imbalance problems because training data usually contains much more non-fault class modules than fault class ones. This imbalanced distribution makes it difficult for the models to learn the minor class module data. Data imbalance is much higher when severity-based fault prediction is used. This is because high severity fault modules is a smaller subset of the fault modules. In this paper, we propose severity-based models to solve these problems using the three sampling methods, Resample, SpreadSubSample and SMOTE. Empirical results show that Resample method has typical over-fit problems, and SpreadSubSample method cannot enhance the prediction performance of the models. Unlike two methods, SMOTE method shows good performance in terms of AUC and FNR values. Especially J48 decision tree model using SMOTE outperforms other prediction models.