• Korean Journal of Materials Research
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• v.28 no.1
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• pp.32-37
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• 2018

We report on the efficient detection of NO gas by an all-oxide semiconductor p-n heterojunction diode structure comprised of n-type zinc oxide (ZnO) nanorods embedded in p-type copper oxide (CuO) thin film. The CuO thin film/ZnO nanorod heterostructure was fabricated by directly sputtering CuO thin film onto a vertically aligned ZnO nanorod array synthesized via a hydrothemal method. The transport behavior and NO gas sensing properties of the fabricated CuO thin film/ZnO nanorod heterostructure were charcterized and revealed that the oxide semiconductor heterojunction exhibited a definite rectifying diode-like behavior at various temperatures ranging from room temperature to $250^{\circ}C$. The NO gas sensing experiment indicated that the CuO thin film/ZnO nanorod heterostructure had a good sensing performance for the efficient detection of NO gas in the range of 2-14 ppm under the conditions of an applied bias of 2 V and a comparatively low operating temperature of $150^{\circ}C$. The NO gas sensing process in the CuO/ZnO p-n heterostructure is discussed in terms of the electronic band structure.

• The KIPS Transactions:PartD
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• v.15D no.6
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• pp.825-840
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• 2008

Refactoring is a kind of software modification process that improves system qualities internally but maintains system functions externally. What should be improved on the existing source codes should take precedence over the others in such a modification process using this refactoring. Martin Fowler and Kent Beck proposed a method that identifies code smells for this purpose. Also, some studies on determining what refactoring will be applied to which targets through detecting code smells in codes were presented. However, these studies have a lot of disadvantages that show a lack of precise description for such code smells and detect limited code smells only. In addition, these studies showed other disadvantages that generate ambiguity in behavior preservation due to the fact that a description method of pre-conditions for the behavior preservation is included in a refactoring process or unformalized. Thus, our study represents a precise specification of code smells using OCL and proposes a framework that performs a refactoring process through the automatic detection of code smells using an OCL interpreter. Furthermore, we perform the automatic detection in which the code smells are be specified by using OCL to the java program and verify its applicability and effectivity through applying a refactoring process.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.5
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• pp.839-850
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• 2014

As the social and financial damages caused by APT attack such as 3.20 cyber terror are increased, the technical solution against APT attack is required. It is, however, difficult to protect APT attack with existing security equipments because the attack use a zero-day malware persistingly. In this paper, we propose a host based anomaly detection method to overcome the limitation of the conventional signature-based intrusion detection system. First, we defined 39 features to identify between normal and abnormal behavior, and then collected 8.7 million feature data set that are occurred during running both malware and normal executable file. Further, each process is represented as 83-dimensional vector that profiles the frequency of appearance of features. the vector also includes the frequency of features generated in the child processes of each process. Therefore, it is possible to represent the whole behavior information of the process while the process is running. In the experimental results which is applying C4.5 decision tree algorithm, we have confirmed 2.0% and 5.8% for the false positive and the false negative, respectively.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.1
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• pp.105-115
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• 2019

Recently, intelligent Android malicious codes have become difficult to detect malicious behavior by static analysis alone. Malicious code with SO file, dynamic loading, and string obfuscation are difficult to extract information about original code even with various tools for static analysis. There are many dynamic analysis methods to solve this problem, but dynamic analysis requires rooting or emulator environment. However, in the case of dynamic analysis, malicious code performs the rooting and the emulator detection to bypass the analysis environment. To solve this problem, this paper investigates a variety of root detection schemes and builds an environment for bypassing the rooting detection in real devices. In addition, SDK code hooking module for Android malicious code analysis is designed using Xposed, and intent tracking for code flow, dynamic loading file information, and various API information extraction are implemented. This work will contribute to the analysis of obfuscated information and behavior of Android Malware.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.491-500
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• 2021

In this study, we propose a method for detecting fall behavior using MS Kinect v2 RGBD Camera-based Human-Skeleton Keypoints and a 2-Stacked Bi-LSTM model. In previous studies, skeletal information was extracted from RGB images using a deep learning model such as OpenPose, and then recognition was performed using a recurrent neural network model such as LSTM and GRU. The proposed method receives skeletal information directly from the camera, extracts 2 time-series features of acceleration and distance, and then recognizes the fall behavior using the 2-Stacked Bi-LSTM model. The central joint was obtained for the major skeletons such as the shoulder, spine, and pelvis, and the movement acceleration and distance from the floor were proposed as features of the central joint. The extracted features were compared with models such as Stacked LSTM and Bi-LSTM, and improved detection performance compared to existing studies such as GRU and LSTM was demonstrated through experiments.

• Korean journal of applied entomology
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• v.61 no.1
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• pp.1-14
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• 2022

Moths have a well-developed sex pheromone communication system. Male moths exhibit an extremely sensitive and selective sex pheromone detection system so that they can detect the sex pheromone produced by conspecific females and locate them for successful mating. Using the pheromone detection system, male moths display characteristic stereotypic behavioral responses, flying upwind to follow intermittent filamentous pheromone strands in pheromone plume. The chemical composition of female sex pheromone in moths, typically comprised of multiple compounds, is species-specific. Male moths contain specialized pheromone receptor neurons on the antennae to detect conspecific sex pheromone accurately, and distinguish it from the pheromones produced by other species. The signals from pheromone receptor neurons are integrated and induce relevant behavior from the male moths. Male moths also contain olfactory sensory neurons in pheromone sensilla, specialized for pheromone-related behavioral antagonist compounds, which can enhance discrimination between conspecific and heterospecific pheromones. Here we review reports on the sex pheromone detection system in male moths and their related responses, and suggest future research direction.

• Convergence Security Journal
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• v.23 no.5
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• pp.117-123
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• 2023

Recently, as the demand for Generative Artificial Intelligence (AI) and artificial intelligence has increased, the seriousness of misuse and abuse has emerged. However, intelligent CCTV, which maximizes detection of abnormal behavior, is of great help to prevent crime in the military and police. AI performs learning as taught by humans and then proceeds with self-learning. Since AI makes judgments according to the learned results, it is necessary to clearly understand the characteristics of learning. However, it is often difficult to visually judge strange and abnormal behaviors that are ambiguous even for humans to judge. It is very difficult to learn this with the eyes of artificial intelligence, and the result of learning is very many False Positive, False Negative, and True Negative. In response, this paper presented standards and methods for clarifying the learning of AI's strange and abnormal behaviors, and presented learning measures to maximize the judgment ability of intelligent CCTV's False Positive, False Negative, and True Negative. Through this paper, it is expected that the artificial intelligence engine performance of intelligent CCTV currently in use can be maximized, and the ratio of False Positive and False Negative can be minimized..

• Journal of the Korean Data and Information Science Society
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• v.20 no.2
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• pp.387-399
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• 2009

Mobile wireless networks continue to be plagued by theft of identify and intrusion. Both problems can be addressed in two different ways, either by misuse detection or anomaly-based detection. In this paper, we propose a dissimilarity-based anomaly detection method which can effectively identify abnormal behavior such as mobility patterns of mobile wireless networks. In the proposed algorithm, a normal profile is constructed from normal mobility patterns of mobile nodes in mobile wireless networks. From the constructed normal profile, a dissimilarity is computed by a weighted dissimilarity measure. If the value of the weighted dissimilarity measure is greater than the dissimilarity threshold that is a system parameter, an alert message is occurred. The performance of the proposed method is evaluated through a simulation. From the result of the simulation, we know that the proposed method is superior to the performance of other anomaly detection methods using dissimilarity measures.

• Computers and Concrete
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• v.14 no.1
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• pp.73-90
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• 2014

The traditional destructive tests in damage detection require high cost, long consuming time, repairing of damaged members, etc. In addition to these, powerful equipments with advanced technology have motivated development of global vibration based damage detection methods. These methods base on observation of the changes in the structural dynamic properties and updating finite element models. The existence, location, severity and effect on the structural behavior of the damages can be identified by using these methods. The main idea in these methods is to minimize the differences between analytical and experimental natural frequencies. In this study, an application of damage detection using model updating method was presented on a one storey reinforced concrete (RC) building model. The model was designed to be 1/2 scale of a real building. The measurements on the model were performed by using ten uni-axial seismic accelerometers which were placed to the floor level. The presented damage identification procedure mainly consists of five steps: initial finite element modeling, testing of the undamaged model, finite element model calibration, testing of the damaged model, and damage detection with model updating. The elasticity modulus was selected as variable parameter for model calibration, while the inertia moment of section was selected for model updating. The first three modes were taken into consideration. The possible damaged members were estimated by considering the change ratio in the inertia moment. It was concluded that the finite element model calibration was required for structures to later evaluations such as damage, fatigue, etc. The presented model updating based procedure was very effective and useful for RC structures in the damage identification.

• Journal of the Korea institute for structural maintenance and inspection
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• v.18 no.3
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• pp.101-107
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• 2014

Damage location and extent of structure could be detected by the inverse analysis on dynamic response properties such as frequencies and mode shapes. The dynamic response of building structures has many noise and affected by nonstructural members and, above all, the behavior of building structure is more complex than civil structure and this makes the damage detection difficult. In recent researches the damage is detected by the indirect index such as sensitivity or assumed values. However, for the more reasonable damage detection, it needs to use the damage index directly induced from dynamic equation. The purpose of this study is to provide the damage detection method on shear building structures by the damage index directly induced from dynamic equation. The provided damage index could be estimated from measured mode shape of undamaged structure and frequency difference between undamaged and damaged structure. The damage detection method is applied to numerical analysis model such as MATLAB and MIDAS GENw for the verification. The damage index at damaged story represents (-) sign and 15 times than other undamaged sories.