• The Journal of Asian Finance, Economics and Business
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• v.8 no.1
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• pp.247-255
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• 2021

The study aims to determine the influence of intellectual capital and education on public financial services in increasing financial inclusion. This study applied a survey technique by selecting respondents according to the purposive sampling method. The number of respondents in this study was 500 people and came from various regions in Indonesia. Data analysis was performed using Structural Equation Modeling (SEM) techniques. The results indicate that intellectual capital has a positive effect on financial inclusion. Conversely, education about public financial services has no influence in increasing financial inclusion. The results of this study indicate that the success of financial inclusion programs in Indonesia is caused more by the role of public intellectual capital. Studies show that the public does not have the courage to be involved in banking institutions because the image of this institution tends to be inflexible in its operations. The results of this study are expected to make a real contribution, especially for the government and banks, that in order to achieve the success of the financial inclusion program in Indonesia; the related parties should educate the public directly to citizens who have not been reached by banking services continuously and with a more down to earth approach.

• Korean Chemical Engineering Research
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• v.59 no.3
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• pp.316-325
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• 2021

Human-made plastics takes more than 500 years to break down and have been introduced into the ecosystem, threatening terrestrial and aquatic organisms. By 2025, there will be more than 250 million tons of plastics in the ocean. Although studies regarding microplastics have been exponentially increasing since 2015, international standards for defining the size classification for microplastics, as well as methods for qualitative/quantitative analysis have not been concluded yet. Thus, in this study, the latest trends in analytical methodologies have been reviewed. Further, the study suggests directions for future research approaches can be taken to analyze aquatic microplastics, which could be as useful information for establishing effective microplastic management policy via standardization in microplastic analysis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2328-2344
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• 2022

2D human pose estimation still faces difficulty in low-resolution images. Most existing top-down approaches scale up the target human bonding box images to the large size and insert the scaled image into the network. Due to up-sampling, artifacts occur in the low-resolution target images, and the degraded images adversely affect the accurate estimation of the joint positions. To address this issue, we propose a multi-resolution input feature fusion network for human pose estimation. Specifically, the bounding box image of the target human is rescaled to multiple input images of various sizes, and the features extracted from the multiple images are fused in the network. Moreover, we introduce a guiding channel which induces the multi-resolution input features to alternatively affect the network according to the resolution of the target image. We conduct experiments on MS COCO dataset which is a representative dataset for 2D human pose estimation, where our method achieves superior performance compared to the strong baseline HRNet and the previous state-of-the-art methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3868-3888
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• 2022

A widely used social networking service like Twitter has the ability to disseminate information to large groups of people even during a pandemic. At the same time, it is a convenient medium to share irrelevant and unverified information online and poses a potential threat to society. In this research, conventional machine learning algorithms are analyzed to classify the data as either non-rumor data or rumor data. Machine learning techniques have limited tuning capability and make decisions based on their learning. To tackle this problem the authors propose a deep learning-based Rumor Detection Neural Network model to predict the rumor tweet in real-world events. This model comprises three layers, AttCNN layer is used to extract local and position invariant features from the data, AttBi-LSTM layer to extract important semantic or contextual information and HPOOL to combine the down sampling patches of the input feature maps from the average and maximum pooling layers. A dataset from Kaggle and ground dataset #gaja are used to train the proposed Rumor Detection Neural Network to determine the veracity of the rumor. The experimental results of the RDNN Classifier demonstrate an accuracy of 93.24% and 95.41% in identifying rumor tweets in real-time events.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.35 no.6
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• pp.568-575
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• 2022

This paper is a study on frequency analysis and electronic noise reduction of energy storage system (ESS). We acquired 4 necessary data for about 2 minutes and 4 seconds using a sampling frequency of 10,000 Hz in ESS. Fast Fourier transform (FFT) was used for electronic noise analysis from the acquired data. As a result, it was confirmed that DC component, fundamental wave, second and higher harmonic component exist. For the attenuation of harmonics, low-pass filter (LPF) was applied. We confirmed that an attenuation of approximately 59.3% appears from the second harmonic. The presence of many harmonic components in the data of the ESS was expected to occur due to the insufficiency of optimization among the modules inside the ESS. Therefore, we propose that a national certification system for ESS should be introduced to settle down the issue properly.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2458-2482
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• 2023

Medical image segmentation techniques based on convolution neural networks indulge in feature extraction triggering redundancy of parameters and unsatisfactory target localization, which outcomes in less accurate segmentation results to assist doctors in diagnosis. In this paper, we propose a multi-level semantic-rich encoding-decoding network, which consists of a Pooling-Conv-Former (PCFormer) module and a Cbam-Dilated-Transformer (CDT) module. In the PCFormer module, it is used to tackle the issue of parameter explosion in the conservative transformer and to compensate for the feature loss in the down-sampling process. In the CDT module, the Cbam attention module is adopted to highlight the feature regions by blending the intersection of attention mechanisms implicitly, and the Dilated convolution-Concat (DCC) module is designed as a parallel concatenation of multiple atrous convolution blocks to display the expanded perceptual field explicitly. In addition, MultiHead Attention-DwConv-Transformer (MDTransformer) module is utilized to evidently distinguish the target region from the background region. Extensive experiments on medical image segmentation from Glas, SIIM-ACR, ISIC and LGG demonstrated that our proposed network outperforms existing advanced methods in terms of both objective evaluation and subjective visual performance.

• Nuclear Engineering and Technology
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• v.56 no.8
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• pp.3276-3285
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• 2024

This paper introduces the creation of a module for Uncertainty Quantification (UQ) specifically designed for VVER analysis through the implementation of the STREAM/RAST-V two-step approach. The aim was to expand the range of use by developing a UQ module tailored for analyzing VVER. This research presents two innovative computational functionalities: (1) development of a library for the pin-based pointwise energy slowing down method (PSM), and (2) extension of the analysis area to study hexagonal-geometry fuel assemblies. The proposed UQ scheme was evaluated through verification using UAM benchmark, and comparative analysis between codes using SCALE 6.2.2 for. STREAM provides an accuracy comparable to that of SCALE 6.2.2. Additionally, a PSM covariance library was utilized in the calculations, achieving 0.7941% and 0.7907% accuracies in the hot full power and hot zero power calculations, respectively. To assess the UQ sequences in the two-step method, the STREAM/RAST-V calculation scheme was verified using the STREAM lattice code. To conclude, this study furnishes comprehensive insights into the development of the UQ module within the two-step method for VVER analysis, and it validates its performance through utilization of the UAM benchmark.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.43 no.11 s.353
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• pp.37-47
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• 2006

This work proposes a 10b 25MS/s $0.8mm^2$ 4.8mW 0.13um CMOS A/D Converter (ADC) for high-performance wireless communication systems such as DVB, DAB and DMB simultaneously requiring low voltage, low power, and small area. A two-stage pipeline architecture minimizes the overall chip area and power dissipation of the proposed ADC at the target resolution and sampling rate while switched-bias power reduction techniques reduce the power consumption of analog amplifiers. A low-power sample-and-hold amplifier maintains 10b resolution for input frequencies up to 60MHz based on a single-stage amplifier and nominal CMOS sampling switches using low threshold-voltage transistors. A signal insensitive 3-D fully symmetric layout reduces the capacitor and device mismatch of a multiplying D/A converter while low-noise reference currents and voltages are implemented on chip with optional off-chip voltage references. The employed down-sampling clock signal selects the sampling rate of 25MS/s or 10MS/s with a reduced power depending on applications. The prototype ADC in a 0.13um 1P8M CMOS technology demonstrates the measured DNL and INL within 0.42LSB and 0.91LSB and shows a maximum SNDR and SFDR of 56dB and 65dB at all sampling frequencies up to 2SMS/s, respectively. The ADC with an active die area if $0.8mm^2$ consumes 4.8mW at 25MS/s and 2.4mW at 10MS/s at a 1.2V supply.

• The Journal of the Acoustical Society of Korea
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• v.21 no.6
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• pp.566-575
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• 2002

Low-titrate audio coding enables a number of Internet and mobile multimedia streaming service more efficiently. For the help of next-generation mobile telephone technologies and digital audio/video compression algorithm, we can enjoy the real-time multimedia contents on our mobile devices (cellular phone, PDA notebook, etc). But the limited available bandwidth of mobile communication network prohibits transmitting high-qualify AV contents. In addition, most bandwidth is assigned to transmit video contents. In this paper, we design a novel and simple method for reproducing high frequency components. The spectrum of high frequency components, which are lost by down-sampling, are modeled by the energy rate with low frequency band in Bark scale, and these values are multiplexed with conventional coded bitstream. At the decoder side, the high frequency components are reconstructed by duplicating with low frequency band spectrum at a rate of decoded energy rates. As a result of segmental SNR and MOS test, we convinced that our proposed method enhances the subjective sound quality only 10%∼20% additional bits. In addition, this proposed method can apply all kinds of frequency domain audio compression algorithms, such as MPEG-1/2, AAC, AC-3, and etc.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.38 no.2
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• pp.33-41
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• 2001

Currently, the compression is one of the most important technology in multimedia society. Audio files arc rapidly propagated throughout internet Among them, the most famous one is MP-3(MPEC-1 Laver3) which can obtain CD tone from 128Kbps, but tone quality is abruptly down below 64Kbps. MPEC-II AAC(Advanccd Audio Coding) is not compatible with MPEG 1, but it has high compression of 1.4 times than MP 3, has max. 7.1 and 96KHz sampling rate. In this paper, we propose an algorithm that decreased the capacity of AAC encoding computation but increased the processing speed by optimizing psychoacoustic model which has enormous amount of computation in MPEG 2 AAC encoder. The optimized psychoacoustic model algorithm was implemented by C++ language. The experiment shows that the psychoacoustic model carries out FFT(Fast Fourier Transform) computation of 3048 point with 44.1 KHz sampling rate for SMR(Signal to Masking Ratio), and each entropy value is inputted to the subband filters for the control of encoder block. The proposed psychoacoustic model is operated with high speed because of optimization of unpredictable value. Also, when we transform unpredictable value into a tonality index, the speed of operation process is increased by a tonality index optimized in high frequency range.