• Proceedings of KOSOMES biannual meeting
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• 2017.11a
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• pp.34-34
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• 2017

• Korean Journal of Materials Research
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• v.33 no.4
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• pp.135-141
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• 2023

The volatilization of alkali ions in (K,Na)NbO₃ (KNN) ceramics was inhibited by doping them with alkaline earth metal ions. In addition, the grain growth behavior changed significantly as the sintering duration (t_s) increased. At 1,100 ℃, the volatilization of alkali ions in KNN ceramics was more suppressed when doped with alkaline earth metal ions with smaller ionic size. A Ca²⁺-doped KNN specimen with the least alkali ion volatilization exhibited a microstructure in which grain growth was completely suppressed, even under long-term sintering for t_s = 30 h. The grain growth in Sr²⁺-doped and Ba²⁺-doped KNN specimens was suppressed until t_s = 10 h. However, at t_s = 30 h, a heterogeneous microstructure with abnormal grains and small-sized matrix grains was observed. The size and number of abnormal grains and size distribution of matrix grains were considerably different between the Sr²⁺-doped and Ba²⁺-doped specimens. This microstructural diversity in KNN ceramics could be explained in terms of the crystal growth driving force required for two-dimensional nucleation, which was directly related to the number of vacancies in the material.

• Journal of Sensor Science and Technology
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• v.30 no.6
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• pp.408-414
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• 2021

This study analyzes the phase transition behavior and electrical properties of lead-free (1-x)K_0.5Na_0.5NbO₃-xBaZrO₃ (KNN-100xBZ) piezoelectric ceramics. The stabilized crystal structures in BaZrO₃-modified KNN ceramics is clarified to be pseudocubic. The polymorphic phase transition from the orthorhombic to pseudocubic phases can be observed with KNN-6BZ ceramics considering the optimized piezoelectric constant (d₃₃). Electromechanical strain behaviors are discussed. Accordingly, the enhancement of strain value at x = 0.08 (composition) may originate from the coexistence of ferroelectric domains and polar nanoregions. A schematic of domains for KNN, KNN-8BZ, and KNN-15BZ ceramics has been proposed to describe the relationship between the stabilized relaxor and changes in electrical properties.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.6
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• pp.105-111
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• 2017

In this paper, the analysis results are shown about several weights of Weighted K-Nearest Neighbor method, Recently, it is employed for the indoor positioning technologies using WiFi fingerprint which has been actively studied. In spite of the simplest feature, the W-KNN method shows comparable performance to another methods using WiFi fingerprint technology. So W-KNN method has employed in the existing indoor positioning system. It shows positioning error performance according to data preprocessing and weight factor, and the analysis on the weight is very important. In this paper, based on the real measured WiFi fingerprint data, the estimation error is analyzed and the performances are compared, for the case of data processing methods, of the weight of average, variance, and distance, and of the averaging several position of number K. These results could be practically useful to construct the real indoor positioning system.

• Advances in materials Research
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• v.5 no.2
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• pp.93-105
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• 2016

KNN-based ceramics modified with small amounts of $Bi_4Zr_3O_{12}$ (BiZ) has been synthesized using high-throughput experimentation (HTE). The results from X-ray diffraction show that for samples with base composition $(K_{0.5}Na_{0.5})NbO_3$ (KNN), the phase present changes from orthorhombic to pseudo-cubic with more than 0.2 mol% BiZ addition; for samples with base composition $(K_{0.48}Na_{0.48}Li_{0.04})(Nb_{0.9}Ta_{0.1})O_3$ (KNNLT), the phase present changes from a mixture of orthorhombic and tetragonal symmetry to pseudo-cubic with more than 0.4 mol % while for samples with base composition $(K_{0.48}Na_{0.48}Li_{0.04})(Nb_{0.86}Ta_{0.1}Sb_{0.04})O_3$ (KNNLST), the phase present is tetragonal with <0.3 mol% BiZ addition and transforms to pseudo-cubic with more dopant addition. The microstructures of the samples show that addition of BiZ decreases the average grain size and increases the volume of pores at the grain boundaries. The values of dielectric constant for KNN and KNNLT compositions increase slightly with BiZ addition while that for KNNLST decreases gradually with BiZ addition. The dielectric loss values are between 0.02 and 0.04 for KNNLT and KNNLST compositions while they are ~ 0.05 for KNN samples. The resistivity values increases with BiZ addition and values in the range of $10^{10}{\Omega}cm$ and $10^{12}{\Omega}cm$ are obtained. The piezoelectric charge coefficient ($d{^*}_{33}$) is highest for KNNLST samples and decreases gradually from ~400 pm/V to ~100 pm/V with BiZ addition.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.202-209
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• 2023

Android malware is now on the rise, because of the rising interest in the Android operating system. Machine learning models may be used to classify unknown Android malware utilizing characteristics gathered from the dynamic and static analysis of an Android applications. Anti-virus software simply searches for the signs of the virus instance in a specific programme to detect it while scanning. Anti-virus software that competes with it keeps these in large databases and examines each file for all existing virus and malware signatures. The proposed model aims to provide a machine learning method that depend on the malware detection method for Android inability to detect malware apps and improve phone users' security and privacy. This system tracks numerous permission-based characteristics and events collected from Android apps and analyses them using a classifier model to determine whether the program is good ware or malware. This method used the machine learning techniques KNN-SVM, DBN, and GRU in which help to find the accuracy which gives the different values like KNN gives 87.20 percents accuracy, SVM gives 91.40 accuracy, Naive Bayes gives 85.10 and DBN-GRU Gives 97.90. Furthermore, in this paper, we simply employ standard machine learning techniques; but, in future work, we will attempt to improve those machine learning algorithms in order to develop a better detection algorithm.

• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1372-1384
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• 2024

The discrimination of the source for xenon gases' release can provide an important clue for detecting the nuclear activities in the neighboring countries. In this paper, three machine learning techniques, which are logistic regression, support vector machine (SVM), and k-nearest neighbors (KNN), were applied to develop the predictive models for discriminating the source for xenon gases' release based on the xenon isotopic activity ratio data which were generated using the depletion codes, i.e., ORIGEN in SCALE 6.2 and Serpent, for the probable sources. The considered sources for the neighboring countries of South Korea include PWRs, CANDUs, IRT-2000, Yongbyun 5 MWe reactor, and nuclear tests with plutonium and uranium. The results of the analysis showed that the overall prediction accuracies of models with SVM and KNN using six inputs, all exceeded 90%. Particularly, the models based on SVM and KNN that used six or three xenon isotope activity ratios with three classification categories, namely reactor, plutonium bomb, and uranium bomb, had accuracy levels greater than 88%. The prediction performances demonstrate the applicability of machine learning algorithms to predict nuclear threat using ratios of xenon isotopic activity.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.186-192
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• 2023

Malwares are becoming a major problem nowadays all around the world in android operating systems. The malware is a piece of software developed for harming or exploiting certain other hardware as well as software. The term Malware is also known as malicious software which is utilized to define Trojans, viruses, as well as other kinds of spyware. There have been developed many kinds of techniques for protecting the android operating systems from malware during the last decade. However, the existing techniques have numerous drawbacks such as accuracy to detect the type of malware in real-time in a quick manner for protecting the android operating systems. In this article, the authors developed a hybrid model for android malware detection using a decision tree and KNN (k-nearest neighbours) technique. First, Dalvik opcode, as well as real opcode, was pulled out by using the reverse procedure of the android software. Secondly, eigenvectors of sampling were produced by utilizing the n-gram model. Our suggested hybrid model efficiently combines KNN along with the decision tree for effective detection of the android malware in real-time. The outcome of the proposed scheme illustrates that the proposed hybrid model is better in terms of the accurate detection of any kind of malware from the Android operating system in a fast and accurate manner. In this experiment, 815 sample size was selected for the normal samples and the 3268-sample size was selected for the malicious samples. Our proposed hybrid model provides pragmatic values of the parameters namely precision, ACC along with the Recall, and F1 such as 0.93, 0.98, 0.96, and 0.99 along with 0.94, 0.99, 0.93, and 0.99 respectively. In the future, there are vital possibilities to carry out more research in this field to develop new methods for Android malware detection.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2005.11a
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• pp.210-211
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• 2005

[ $(K_{0.5}Na_{0.5})NbO_3$ ](KNN) 세라믹스의 소결 특성과 압전 특성을 높이기 위해 B-site에 Sb를 치환하여 Sb함량에 따른 특성을 측정 하였다. Sb 의 함량을 0mol $\sim$ 0.1mol 까지 첨가한 결과 소결 밀도는 Sb의 첨가량이 많아질수록 증가하다 Sb-0.08mol에서 4.40g/$cm^3$으로 가장 높은 밀도를 가졌으며, 여기서의 전기기계 결합 계수가(Kp) 0.45로 높은 값을 나타내었다. 상전 이 온도는 375$^{\circ}C$로 순수한 KNN 의 420$^{\circ}C$ 보다 약 45$^{\circ}C$정도 떨어졌으나 orthorhombic에 서 tetragonal 로 바뀌는 전이 온도는 KNN이 220$^{\circ}C$, KNNS 가 225$^{\circ}C$로 크게 변하지 않았다.

• Journal of the Semiconductor & Display Technology
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• v.21 no.4
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• pp.65-70
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• 2022

In manufacturing and semiconductor industries, transfer robots increase productivity through accurate and continuous work. Due to the nature of the semiconductor process, there are environments where humans cannot intervene to maintain internal temperature and humidity in a clean room. So, transport robots take responsibility over humans. In such an environment where the manpower of the process is cutting down, the lack of maintenance and management technology of the machine may adversely affect the production, and that's why it is necessary to develop a technology for the machine failure diagnosis system. Therefore, this paper tries to identify various causes of failure of transport robots that are widely used in semiconductor automation, and the Prognostics and Health Management (PHM) method is considered for determining and predicting the process of failures. The robot mainly fails in the driving unit due to long-term repetitive motion, and the core components of the driving unit are motors and gear reducer. A simulation drive unit was manufactured and tested around this component and then applied to 6-axis vertical multi-joint robots used in actual industrial sites. Vibration data was collected for each cause of failure of the robot, and then the collected data was processed through signal processing and frequency analysis. The processed data can determine the fault of the robot by utilizing machine learning algorithms such as SVM (Support Vector Machine) and KNN (K-Nearest Neighbor). As a result, the PHM environment was built based on machine learning algorithms using SVM and KNN, confirming that failure prediction was partially possible.