• East Asian Economic Review
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• 제18권1호
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• pp.55-81
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• 2014

This study aims to answer two questions using input-output decomposition analysis: 1) Have emerging Asian economies decoupled? 2) What are the sources of structural changes in gross outputs and value-added of emerging Asian economies related to the first question? The main findings of the study are as follows: First, since 1990, there has been a trend of increasing dependence on exports to extra-regions such as G3 and the ROW, indicating no sign of "decoupling", but rather an increasing integration of emerging Asian countries into global trade. Second, there is a contrasting feature in the sources of structural changes between non-China emerging Asia and China. Dependence of non-China emerging Asia on intra-regional trade has increased in line with strengthening economic integration in East Asia, whereas China has disintegrated from the region. Therefore, it can be said that China has contributed to no sign of decoupling of emerging Asia as a whole.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권1호
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• pp.213-231
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• 2014

Existing image search systems allow users to search images by keywords, or by example images through content-based image retrieval (CBIR). On the other hand, users might learn more relevant textual information about an image from its text captions or surrounding contexts within documents or Web pages. Without such contexts, it's difficult to extract semantic description directly from the image content. In this paper, we propose an annotation watermarking system for users to embed text descriptions, and retrieve more relevant textual information from similar images. First, tags associated with an image are converted by two-dimensional code and embedded into the image by discrete wavelet transform (DWT). Next, for images without annotations, similar images can be obtained by CBIR techniques and embedded annotations can be extracted. Specifically, we use global features such as color ratios and dominant sub-image colors for preliminary filtering. Then, local features such as Scale-Invariant Feature Transform (SIFT) descriptors are extracted for similarity matching. This design can achieve good effectiveness with reasonable processing time in practical systems. Our experimental results showed good accuracy in retrieving similar images and extracting relevant tags from similar images.

• 한국정보과학회논문지:소프트웨어및응용
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• 제29권7호
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• pp.425-435
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• 2002

본 논문에서는 SOFM기반 신경망 분류기를 이용한 매크로블록 기반 전송률제어 방식을 제안한다. 수학적 왜곡 비트율 모델과 귀환회기 방식을 기반으로 하는 기존의 전송률 제어 방법에 비하여, 제안된 방법은 전송비트 제어용 전역모델을 설정하고 이를 최적으로 제어할 수 있는 SOFM기반 신경망 분류기를 이용하여 영상특성 변화에 적극적인 대처를 할 수 있다. 제안된 전송률 제어 알고리즘은 기존의 MPEG-4 매크로블록 기반 전송률 제어 알고리즘에 비해 전체 연산 복잡도는 비슷하게 유지하면서 피크신호 대 잡음비의 비교에 있어서 0.2 dB ~ 0.6 dB 정도 성능이 우수함을 확인하였다.

• 대한원격탐사학회지
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• 제21권1호
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• pp.31-49
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• 2005

We first review some history of the Global Geodynamics Project (GGP), particularly in the progress of ground-satellite gravity comparisons. The GGP Satellite Project has involved the measurement of ground-based superconducting gravimeters (SGs) in Europe for several years and we make quantitative comparisons with the latest satellite GRACE data and hydrological models. The primary goal is to recover information about seasonal hydrology cycles, and we find a good correlation at the microgal level between the data and modeling. One interesting feature of the data is low soil moisture resulting from the European heat wave in 2003. An issue with the ground-based stations is the possibility of mass variations in the soil above a station, and particularly for underground stations these have to be modeled precisely. Based on this work with a regional array, we estimate the effectiveness of future SG arrays to measure co-seismic deformation and silent-slip events. Finally we consider gravity surveys in volcanic areas, and predict the accuracy in modeling subsurface density variations over time periods from months to years.

• 한국측량학회지
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• 제37권5호
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• pp.397-402
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• 2019

Recently, with the emergence of autonomous vehicles and the increasing interest in safety, a variety of research has been being actively conducted to precisely estimate the position of a vehicle by fusing sensors. Previously, researches were conducted to determine the location of moving objects using GNSS (Global Navigation Satellite Systems) and/or IMU (Inertial Measurement Unit). However, precise positioning of a moving vehicle has lately been performed by fusing data obtained from various sensors, such as LiDAR (Light Detection and Ranging), on-board vehicle sensors, and cameras. This study is designed to enhance kinematic vehicle positioning performance by using feature-based recognition. Therefore, an analysis of the required precision of the observations obtained from the images has carried out in this study. Velocity and attitude observations, which are assumed to be obtained from images, were generated by simulation. Various magnitudes of errors were added to the generated velocities and attitudes. By applying these observations to the positioning algorithm, the effects of the additional velocity and attitude information on positioning accuracy in GNSS signal blockages were analyzed based on Kalman filter. The results have shown that yaw information with a precision smaller than 0.5 degrees should be used to improve existing positioning algorithms by more than 10%.

• 한국산업융합학회 논문집
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• 제23권6_2호
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• pp.1025-1032
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• 2020

In this paper, we propose a method to classify road weather conditions into rain, fog, and sun using a SVM (Support Vector Machine) classifier after extracting weather features from images acquired in real time using an optical sensor installed on a roadside post. A multi-dimensional weather feature vector consisting of factors such as image sharpeness, image entropy, Michelson contrast, MSCN (Mean Subtraction and Contrast Normalization), dark channel prior, image colorfulness, and local binary pattern as global features of weather-related images was extracted from road images, and then a road weather classifier was created by performing machine learning on 700 sun images, 2,000 rain images, and 1,000 fog images. Finally, the classification performance was tested for 140 sun images, 510 rain images, and 240 fog images. Overall classification performance is assessed to be applicable in real road services and can be enhanced further with optimization along with year-round data collection and training.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권7호
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• pp.2356-2376
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• 2021

In this paper, we proposed a novel modulation recognition method based on quantum elephant herding algorithm (QEHA) evolving neural network under impulse noise environment. We use the adaptive weight myriad filter to preprocess the received digital modulation signals which passing through the impulsive noise channel, and then the instantaneous characteristics and high order cumulant features of digital modulation signals are extracted as classification feature set, finally, the BP neural network (BPNN) model as a classifier for automatic digital modulation recognition. Besides, based on the elephant herding optimization (EHO) algorithm and quantum computing mechanism, we design a quantum elephant herding algorithm (QEHA) to optimize the initial thresholds and weights of the BPNN, which solves the problem that traditional BPNN is easy into local minimum values and poor robustness. The experimental results prove that the adaptive weight myriad filter we used can remove the impulsive noise effectively, and the proposed QEHA-BPNN classifier has better recognition performance than other conventional pattern recognition classifiers. Compared with other global optimization algorithms, the QEHA designed in this paper has a faster convergence speed and higher convergence accuracy. Furthermore, the effect of symbol shape has been considered, which can satisfy the need for engineering.

• The Journal of Asian Finance, Economics and Business
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• 제8권8호
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• pp.117-126
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• 2021

The increasing number of retrenchments because of the current global pandemic, Covid-19, has led many to shift to the digital economy, especially among the low-income group (B40) in Malaysia. Crowdsourcing is the collection of information, opinions, or work from a group of people, usually sourced via the Internet. Fueled by the development of Internet-based platforms that provided its technological foundation, and the need for an agile and uniquely skilled workforce, crowdsourcing has grown from the grassroots, with a burgeoning body of research investigating its many aspects. However, very few studies examined crowd workers' motivation for continuous participation on online crowdsourcing platforms. Thus, this paper aims to explore the technological factors that facilitate B40's group motivation in Malaysia to continue to participate in online crowdsourcing platforms. This paper employed a qualitative approach, using a semi-structured interview. The thematic analysis method was used to decode the data extracted from the interview transcript. The finding of this study identified four main themes and seven sub-themes: (1) Technology efficacy, (2) Platform Management: client-worker management, safety net, payment mechanism, (3) Platform Design: UI design, rating feature and (4) Infrastructure: Internet connection, technology infrastructure. This study can provide a guideline for managing crowdsourcing practices in Malaysia, especially for the crowdsourcing platform developer.

• 우주기술과 응용
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• 제2권2호
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• pp.67-80
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• 2022

최근의 우주개발기술 선진국들은 우주의 평화적 이용이라는 보편적 가치를 넘어서 자국의 이익을 극대화하기 위한 발판으로 우주의 전략적 활용에 더욱 더 집중하고 있으며, 조기경보 위성과 같이 지상에서 발사된 화염 정보를 이용하여 우주로 발사된 발사체의 실시간 감시와 궤적 추적 기능 등을 담당하는 위성들을 지속적으로 개발해 오고 있다. 본 연구에서는 이러한 조기경보 위성에서 발사체의 궤적을 실시간으로 추정할 수 있는 알고리즘을 진화연산이라는 인공지능 기법을 적용하여 제안하고, 이러한 비행 궤적 추정 알고리즘을 비행 궤적 추정 시스템의 시뮬레이터를 통하여 임의의 발사체 비행 궤적에 적용함으로써 제안된 방법의 성능과 특징을 입체적으로 확인하고자 한다.

• 4차산업연구
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• 제1권1호
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• pp.31-40
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• 2021

Purpose - According to the importance of business simulation learning as a new type of business learning tool, this study reviews the dimensions of business education and a brief history of business education simulation. At the end Capsim strategic management simulation program is introduce with its feature. Research design, data, and methodology - This study has been analyzed in a way that reviews at previous literature on simulation learning and looks at examples and features of Capsim simulation, online business simulation tools which has been used in the global market. Result - Capsim simulations are designed to offer focused opportunities for deep practice. That's why they are often more effective than passive tools such as textbooks, videos, or lectures. By the way, 'deep practice' is very different from 'ordinary practice'. After commuters who drive to school or work can accumulate thousands of hours of driving, but that doesn't make them expert drivers. The key to deep practice is self-awareness. That is, paying attention to what you are doing well and not so well. This is so important to learn that scientists use a specific term for it: 'metacognition', or thinking about the way you think and learn. Conclusion - The use of business simulation learning, such as Capsim, which is a given case, can create similar local systems by potentially engaging a large number of users in the virtual market. It could also be used as an individual to complete business training for students and those who are active in the business field of business.