• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.770-773
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• 2002

Two sinusoidal voltage-controlled oscillators using linear operational transconductance amplifiers are presented in this paper: One is based on the positive-feedback bandpass oscillator model and the other on the negative-feedback Colpitts model. The bandpass VCO consists of a noninverting amplifier and a current-controlled LC-tuned circuit which is realized by two linear OTA's and two grounded capacitors, while the Colpitts VCO consists of an inverting amplifier and a current-controlled LC-tuned circuit realized by three linear OTA's and three grounded capacitors. Prototype circuits have been built with discrete components. The experimental results have shown that the Colpitts VCO has a linearity error of less than 5 percent, a temperature coefficient of less than rm 100 ppm/$^{circ}C$, and a $pm1.5 Hz $frequency drift over an oscillation frequency range from 712Hz to 6.3kHz. A total harmonic distortion of 0.3 percent has been measured for a 3.3kHz oscillation and the corresponding peak-to-peak amplitude was 1V. The experimental results for bandpass VCO are also presented.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.6
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• pp.259-266
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• 2023

It is very difficult to measure the performance of the machine learning model in the business service stage. Therefore, managing the performance of the model through the operational department is not done effectively. Academically, various studies have been conducted on the concept drift detection method to determine whether the model status is appropriate. The operational department wants to know quantitatively the performance of the operating model, but concept drift can only detect the state of the model in relation to the data, it cannot estimate the quantitative performance of the model. In this study, we propose a performance prediction model (PPM) that quantitatively estimates precision through the statistics of concept drift. The proposed model induces artificial drift in the sampling data extracted from the training data, measures the precision of the sampling data, creates a dataset of drift and precision, and learns it. Then, the difference between the actual precision and the predicted precision is compared through the test data to correct the error of the performance prediction model. The proposed PPM was applied to two models, a loan underwriting model and a credit card fraud detection model that can be used in real business. It was confirmed that the precision was effectively predicted.

• International Journal of Computer Science & Network Security
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• v.24 no.9
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• pp.169-185
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• 2024

Due to levitate and rapid growth of E-Commerce, most of the organizations are moving towards cashless transaction Unfortunately, the cashless transactions are not only used by legitimate users but also it is used by illegitimate users and which results in trouncing of billions of dollars each year worldwide. Fraud prevention and Fraud Detection are two methods used by the financial institutions to protect against these frauds. Fraud prevention systems (FPSs) are not sufficient enough to provide fully security to the E-Commerce systems. However, with the combined effect of Fraud Detection Systems (FDS) and FPS might protect the frauds. However, there still exist so many issues and challenges that degrade the performances of FDSs, such as overlapping of data, noisy data, misclassification of data, etc. This paper presents a comprehensive survey on financial fraud detection system using such data mining techniques. Over seventy research papers have been reviewed, mainly within the period 2002-2015, were analyzed in this study. The data mining approaches employed in this research includes Neural Network, Logistic Regression, Bayesian Belief Network, Support Vector Machine (SVM), Self Organizing Map(SOM), K-Nearest Neighbor(K-NN), Random Forest and Genetic Algorithm. The algorithms that have achieved high success rate in detecting credit card fraud are Logistic Regression (99.2%), SVM (99.6%) and Random Forests (99.6%). But, the most suitable approach is SOM because it has achieved perfect accuracy of 100%. But the algorithms implemented for financial statement fraud have shown a large difference in accuracy from CDA at 71.4% to a probabilistic neural network with 98.1%. In this paper, we have identified the research gap and specified the performance achieved by different algorithms based on parameters like, accuracy, sensitivity and specificity. Some of the key issues and challenges associated with the FDS have also been identified.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.35 no.1
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• pp.77-85
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• 2002

In the southern sea of Korea and the areas of Tsushima warm currents the relationship between the distributions of eggs and larvae of anchovy (Engraulis japonica) and oceanic conditions was examined on July and August 1997, The south Korean coastal waters, the water temperature of below $20\~23^{\circ}$ and the salinity of above 33.0 (PSU), the mixed waters between the south Korean coastal waters and the Tsushima warm currents, $21\~25^{\circ}$ and $32.0\~32.5$ and the Tsushima warm currents, above $26^{\circ}$ and below 31.5 were distributed at the surface layer. The Tsushima warm currents were distributed at the northeast of Jeju Is. and off the southern sea of Korea. As an appearance of warm streamer, the mixed waters were intruded into the coastal areas of Komun Is.$\~$Sori Is. and Sori Is.$\~$Yokji Is.. Approximate paths of surface water by the drift card experiments were similar with the intrusions of the warm water identified from the water temperature and salinity distributions. The distributions of chlorophyll concentration were consistent with the distributions of water temperature and salinity, Anchovy eggs and larvae were mostly distributed at Komun Is., Yokji Is, and the southwest of Koie Is. where chlorophyll concentrations were high and cyclonic circulations by the warm water intrusions (warm streamers) were formed.