• JSTS:Journal of Semiconductor Technology and Science
• /
• v.15 no.6
• /
• pp.594-600
• /
• 2015

A 1.25 GHz multi-phase phase-rotating PLL is proposed for oversampling CDR applications and implemented with a low power and small area. Eight equidistant clock phases are simultaneously adjusted by the phase interpolator inside the PLL. The phase interpolator uses only two complementary clocks from a VCO, but it can cover the whole range of phase from $0^{\circ}$ to $360^{\circ}$ with the help of a PFD timing controller. The output clock phases are digitally adjusted with the resolution of 25 ps and both INL and DNL are less than 0.44 LSB. The proposed PLL was implemented using a 110 nm CMOS technology. It consumes 3.36 mW from 1.2 V supply and occupies $0.047mm^2$. The $jitter_{rms}$ and $jitter_{pk-pk}$ of the output clock are 1.91 ps and 18 ps, respectively.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.18 no.4
• /
• pp.790-796
• /
• 2014

Identification of communication channels is a problem of important current theoretical and practical concerns. Recently proposed solutions for this problem exploit the diversity induced by antenna array or time oversampling. The method resorts to an adaptive filter with a linear constraint. In this paper, an approach is proposed that is based on decomposition. Indeed, the eigenvector corresponding to the minimum eigenvalue of the covariance matrix of the received signals contains the channel impulse response. And we present an adaptive algorithm to solve this problem. Proposed technique shows the better performance than one of existing algorithms.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.25 no.10A
• /
• pp.1473-1479
• /
• 2000

This paper presents the performance evaluation of an adaptive QAM scheme under flat and frequency selective fading channels for indoor wireless communication systems. The QAM modulation is combined with differential encoding and the demodulation process is carried out noncoherently. The adaptation is performed by varying the modulation level of QAM, depending upon received signal strength. The adaptation mechanism allows a 2- or 3-bit increase or decrease at a time, if the channel condition is considered to be significantly good or bad. Simulation results show that the average number of bits per symbol (ABPS) for each symbol block transmitted over a flat fading channel is higher than 5.0 and the BER performance is better than 10^-4 for a SNR value higher than 30 dB. For frequency selective fading channels, an oversampling technique in the receiver was employed. The BER performance obtained for frequency selective fading channels is better than 10^-4 with a SNR value of 40 dB and ABPS is found to be approximately 5.5. Therefore, this scheme is very useful in that it provides both very high bandwidth efficiency and acceptable performance with moderate SNR values over flat and frequency selective fading channels. In addition, this scheme provides reduced receiver complexity by way of noncoherent detection.

• Proceedings of the Earthquake Engineering Society of Korea Conference
• /
• 2003.03a
• /
• pp.50-58
• /
• 2003

We have developed a locally variable time-step scheme matching with discontinuous grids in the flute-difference method for the efficient simulation of seismic wave propagation. The first-order velocity-stress formulations are used to obtain the spatial derivatives using finite-difference operators on a staggered grid. A three-times coarser grid in the high-velocity region compared with the grid in the low-velocity region is used to avoid spatial oversampling. Temporal steps corresponding to the spatial sampling ratio between both regions are determined based on proper stability criteria. The wavefield in the margin of the region with smaller time-step are linearly interpolated in time using the values calculated in the region with larger one. The accuracy of the proposed scheme is tested through comparisons with analytic solutions and conventional finite-difference scheme with constant grid spacing and time step. The use of the locally variable time-step scheme with discontinuous grids results in remarkable saving of the computation time and memory requirement with dependency of the efficiency on the simulation model. This implies that ground motion for a realistic velocity structures including near-surface sediments can be modeled to high frequency (several Hz) without requiring severe computer memory

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.20 no.12
• /
• pp.85-91
• /
• 2021

In our daily life, artificial intelligence performs simple and complicated tasks like us, including operating mobile phones and working at homes and workplaces. Artificial intelligence is used in industrial technology for diagnosing various types of equipment using the machine learning technology. This study presents a fault mode effect analysis (FMEA) of start motors using machine learning and big data. Through multiple data collection, we observed that the primary failure of the start motor was caused by the melting of the magnetic switch inside the start motor causing it to fail. Long-short-term memory (LSTM) was used to diagnose the condition of the magnetic locations, and synthetic data were generated using the synthetic minority oversampling technique (SMOTE). This technique has the advantage of increasing the data accuracy. LSTM can also predict a start motor failure.

• Journal of Korea Multimedia Society
• /
• v.22 no.5
• /
• pp.588-600
• /
• 2019

Each year Malaria affects over 200 million people worldwide. Particularly, African continent is highly hit by this disease. According to many researches, this continent is ideal for Anopheles mosquitoes which transmit Malaria parasites to thrive. Rainfall volume is one of the major factor favoring the development of these Anopheles in the tropical Sub-Sahara Africa (SSA). However, the surveillance, monitoring and reporting of this epidemic is still poor and bureaucratic only. In our paper, we proposed a method to fast monitor and report Malaria instances by using Social Network Systems (SNS) and precipitation volume in Nigeria. We used Twitter search Application Programming Interface (API) to live-stream Twitter messages mentioning Malaria, preprocessed those Tweets and classified them into Malaria cases in Nigeria by using Support Vector Machine (SVM) classification algorithm and compared those Malaria cases with average precipitation volume. The comparison yielded a correlation of 0.75 between Malaria cases recorded by using Twitter and average precipitations in Nigeria. To ensure the certainty of our classification algorithm, we used an oversampling technique and eliminated the imbalance in our training Tweets.

• Journal of Korean Academy of Nursing
• /
• v.52 no.2
• /
• pp.144-156
• /
• 2022

Purpose: This study aimed to investigate the influence of social capital on the depression of older adults living in rural areas. Methods: Data sets were obtained from the 2019 Korea Community Health Survey. The participants were 39,390 older adults over 65 years old living in rural areas. Indicators of social capital included trust, reciprocity, network, and social participation. Depression-the dependent variable-was measured using the Patient Health Questionnaire-9 (PHQ-9). Hierarchical ordinal logistic regression was conducted to identify factors associated with depression after adjusting the data numbers to 102,601 by applying the Synthetic Minority Oversampling Technique (SMOTE). Results: The independent variables-indicators of social capital-exhibited significant association with the depression of older adults. The odds ratios of depression were higher in groups without social capital variables. Conclusion: To reduce depression, we recommend increasing social capital. Factors identified in this study need to be considered in older adult depression intervention programs and policies.

• International Journal of Computer Science & Network Security
• /
• v.23 no.2
• /
• pp.13-30
• /
• 2023

Since the COVID-19 vaccine became available, people have been sharing their opinions on social media about getting vaccinated, causing discussions of the vaccine to trend on Twitter alongside certain events, making the website a rich data source. This paper explores people's perceptions regarding the COVID-19 vaccine during certain events and how these events influenced public opinion about the vaccine. The data consisted of tweets sent during seven important events that were gathered within 14 days of the first announcement of each event. These data represent people's reactions to these events without including irrelevant tweets. The study targeted tweets sent in Arabic from users located in Saudi Arabia. The data were classified as positive, negative, or neutral in tone. Four classifiers were used-support vector machine (SVM), naïve Bayes (NB), logistic regression (LOGR), and random forest (RF)-in addition to a deep learning model using BiLSTM. The results showed that the SVM achieved the highest accuracy, at 91%. Overall perceptions about the COVID-19 vaccine were 54% negative, 36% neutral, and 10% positive.

• International journal of advanced smart convergence
• /
• v.12 no.1
• /
• pp.53-58
• /
• 2023

With the fast development of artificial intelligence day by day, users are demanding explanations about the results of algorithms and want to know what parameters influence the results. In this paper, we propose a model for bankruptcy prediction with interpretability using the SHAP framework. SHAP (SHAPley Additive exPlanations) is framework that gives a visualized result that can be used for explanation and interpretation of machine learning models. As a result, we can describe which features are important for the result of our deep learning model. SHAP framework Force plot result gives us top features which are mainly reflecting overall model score. Even though Fully Connected Neural Networks are a "black box" model, Shapley values help us to alleviate the "black box" problem. FCNNs perform well with complex dataset with more than 60 financial ratios. Combined with SHAP framework, we create an effective model with understandable interpretation. Bankruptcy is a rare event, then we avoid imbalanced dataset problem with the help of SMOTE. SMOTE is one of the oversampling technique that resulting synthetic samples are generated for the minority class. It uses K-nearest neighbors algorithm for line connecting method in order to producing examples. We expect our model results assist financial analysts who are interested in forecasting bankruptcy prediction of companies in detail.

• Smart Media Journal
• /
• v.13 no.2
• /
• pp.9-15
• /
• 2024

In this paper, a Diffusion Multi-step Classifier (DMC) is proposed to address the imbalance issue in credit prediction. DMC utilizes a Diffusion Model to generate continuous numerical data from credit prediction data and creates categorical data through a Multi-step Classifier. Compared to other algorithms generating synthetic data, DMC produces data with a distribution more similar to real data. Using DMC, data that closely resemble actual data can be generated, outperforming other algorithms for data generation. When experiments were conducted using the generated data, the probability of predicting delinquencies increased by over 20%, and overall predictive accuracy improved by approximately 4%. These research findings are anticipated to significantly contribute to reducing delinquency rates and increasing profits when applied in actual financial institutions.