• Progress in Superconductivity and Cryogenics
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• v.25 no.4
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• pp.70-74
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• 2023

The Small Punch Test (SPT) was developed to evaluate the softening and embrittlement of materials such as power plants and nuclear fusion reactors by taking samples in the field. Specimens used in the SPT are very thin and small disk-shaped compared to specimens for general tensile test, and thus have economic advantages in terms of miniaturization and repeatability of the test. The cryogenic SPT can also be miniaturized and has a significantly lower heat capacity than conventional universal test machines. This leads to reduced cooling and warm-up times. In this study, the cryogenic SPT was developed by modifying the existing room temperature SPT to be cooled by liquid nitrogen using a super bellows and a thermal insulation structure. Since the cryogenic SPT was first developed, basic experiments were conducted to verify the effectiveness of it. For the validation, aluminum alloy 6061- T6 specimens were tested for mechanical properties at room and cryogenic temperature. The results of the corrected tensile properties from the SPT experiment results were compared with known room temperature and cryogenic properties. Based on the correction results, the effectiveness of the cryogenic SPT test was confirmed, and the surface fracture characteristics of the material were analyzed using a 3d image scanner. In the future, we plan to conduct property evaluation according to the development of various alloy materials.

• Journal of KIISE:Computing Practices and Letters
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• v.13 no.5
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• pp.293-299
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• 2007

Intelligent pipeline inspection gauges (PIGs) are inspection vehicles that move along within a (gas or oil) pipeline and acquire signals (also called sensor data) from their surrounding rings of sensors. By analyzing the signals captured in intelligent PIGs, we can detect pipeline defects, such as holes and curvatures and other potential causes of gas explosions. There are two major data access patterns apparent when an analyzer accesses the pipeline signal data. The first is a sequential pattern where an analyst reads the sensor data one time only in a sequential fashion. The second is the repetitive pattern where an analyzer repeatedly reads the signal data within a fixed range; this is the dominant pattern in analyzing the signal data. The existing PIG software reads signal data directly from the server at every user#s request, requiring network transfer and disk access cost. It works well only for the sequential pattern, but not for the more dominant repetitive pattern. This problem becomes very serious in a client/server environment where several analysts analyze the signal data concurrently. To tackle this problem, we devise a fast in-memory cache manager, called T-Cache, by considering pipeline sensor data as multiple time-series data and by efficiently caching the time-series data at T-Cache. To the best of the authors# knowledge, this is the first research on caching pipeline signals on the client-side. We propose a new concept of the signal cache line as a caching unit, which is a set of time-series signal data for a fixed distance. We also provide the various data structures including smart cursors and algorithms used in T-Cache. Experimental results show that T-Cache performs much better for the repetitive pattern in terms of disk I/Os and the elapsed time. Even with the sequential pattern, T-Cache shows almost the same performance as a system that does not use any caching, indicating the caching overhead in T-Cache is negligible.

• KIPS Transactions on Computer and Communication Systems
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• v.7 no.2
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• pp.49-58
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• 2018

A certificate is a means to certify users by conducting the identification of the users, the prevention of forgery and alteration, and non-repudiation. Most people use an accredited certificate when they perform a task using online banking, and it is often used for the purpose of proving one's identity in issuing various certificates and making electronic payments in addition to online banking. At this time, the issued certificate exists in a file form on the disk, and it is possible to use the certificate issued in an existing device in a new device only if one copies it from the existing device. However, most certificate duplication methods are a method of duplication, entering an 8-16 digit verification code. This is inconvenient because one should enter the verification code and has a weakness that it is vulnerable to security issues. To solve this weakness, this study proposes a method for enhancing security certificate duplication in a multi-channel using TCP and BLE. The proposed method: 1) shares data can be mutually authenticated, using BLE Advertising data; and 2) encrypts the certificate with a symmetric key algorithm and delivers it after the certification of the device through an ECC-based electronic signature algorithm. As a result of the implementation of the proposed method in a mobile environment, it could defend against sniffing attacks, the area of security vulnerabilities in the existing methods and it was proven that it could increase security strength about $10^{41}$ times in an attempt of decoding through the method of substitution of brute force attack existing method.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.11
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• pp.127-133
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• 2014

In order to protect information asset from various malicious code, Honeypot system is implemented. Honeypot system is designed to elicit attacks so that internal system is not attacked or it is designed to collect malicious code information. However, existing honeypot system is designed for the purpose of collecting information, so it is designed to induce inflows of attackers positively by establishing disguised server or disguised client server and by providing disguised contents. In case of establishing disguised server, it should reinstall hardware in a cycle of one year because of frequent disk input and output. In case of establishing disguised client server, it has operating problem such as procuring professional labor force because it has a limit to automize the analysis of acquired information. To solve and supplement operating problem and previous problem of honeypot's hardware, this thesis suggested hybrid honeypot. Suggested hybrid honeypot has honeywall, analyzed server and combined console and it processes by categorizing attacking types into two types. It is designed that disguise (inducement) and false response (emulation) are connected to common switch area to operate high level interaction server, which is type 1 and low level interaction server, which is type 2. This hybrid honeypot operates low level honeypot and high level honeypot. Analysis server converts hacking types into hash value and separates it into correlation analysis algorithm and sends it to honeywall. Integrated monitoring console implements continuous monitoring, so it is expected that not only analyzing information about recent hacking method and attacking tool but also it provides effects of anticipative security response.

• Spatial Information Research
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• v.20 no.5
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• pp.99-109
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• 2012

Recently, due to the growth of social media and spread of smart-phone, the amount of data has considerably increased by full use of SNS (Social Network Service). According to it, the Big Data concept is come up and many researchers are seeking solutions to make the best use of big data. To maximize the creative value of the big data held by many companies, it is required to combine them with existing data. The physical and theoretical storage structures of data sources are so different that a system which can integrate and manage them is needed. In order to process big data, MapReduce is developed as a system which has advantages over processing data fast by distributed processing. However, it is difficult to construct and store a system for all key words. Due to the process of storage and search, it is to some extent difficult to do real-time processing. And it makes extra expenses to process complex event without structure of processing different data. In order to solve this problem, the existing Complex Event Processing System is supposed to be used. When it comes to complex event processing system, it gets data from different sources and combines them with each other to make it possible to do complex event processing that is useful for real-time processing specially in stream data. Nevertheless, unstructured data based on text of SNS and internet articles is managed as text type and there is a need to compare strings every time the query processing should be done. And it results in poor performance. Therefore, we try to make it possible to manage unstructured data and do query process fast in complex event processing system. And we extend the data complex function for giving theoretical schema of string. It is completed by changing the string key word into integer type with filtering which uses keyword set. In addition, by using the Complex Event Processing System and processing stream data at real-time of in-memory, we try to reduce the time of reading the query processing after it is stored in the disk.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.