• The Journal of Asian Finance, Economics and Business
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• v.7 no.8
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• pp.69-76
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• 2020

The study examines lagged economic effects of research and development (R&D) investment on the market value of manufacturing firms listed on the Shanghai Stock Exchange or the Shenzhen Stock Exchange in China. This study applies panel data analysis methods to address the following issues: 1) There might be an adjustment lag in the impact of R&D investment on corporate market value, and 2) Unobserved firm effects must be taken into account. The balanced panel data includes a total of 1,462 observations with 34 cross-sections of manufacturing firms listed on Chinese stock markets and with 27 time-specific quarterly periods from 2007 to 2017. The results indicate that the R&D investment of Chinese manufacturing firms tends to yield favorable market value of the firm with some adjustments to time. The results show that R&D investment exhibits a strong positive impact on their market value of manufacturing firms in Chinese stock markets. Moreover, R&D investment has a positive time-lag effect on the market value of the firm. Interestingly, the R&D investment of Chinese manufacturing firms generate a relatively constant positive effect on their market value, supporting the notion that the corresponding returns of R&D investment for such firms yield lagged but added market values.

• Journal of IKEEE
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• v.25 no.3
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• pp.451-461
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• 2021

As various smart devices spread and the damage caused by malicious codes becomes more serious, malicious code detection technology using machine learning technology is attracting attention. However, if the training data of machine learning is constructed based on only the fragmentary characteristics of the code, it is still easy to create variants and new malicious codes that avoid it. To solve such a problem, a research using the function call relationship of malicious code as training data is attracting attention. In particular, it is expected that more advanced malware detection will be possible by measuring the similarity of graphs using GNN. This paper proposes an efficient method to generate a function call graph from binary code to utilize GNN for malware detection.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3121-3143
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• 2019

Segmentation plays an important role in the field of image processing and computer vision. Intuitionistic fuzzy C-means (IFCM) clustering algorithm emerged as an effective technique for image segmentation in recent years. However, standard fuzzy C-means (FCM) and IFCM algorithms are sensitive to noise and initial cluster centers, and they ignore the spatial relationship of pixels. In view of these shortcomings, an improved algorithm based on IFCM is proposed in this paper. Firstly, we propose a modified non-membership function to generate intuitionistic fuzzy set and a method of determining initial clustering centers based on grayscale features, they highlight the effect of uncertainty in intuitionistic fuzzy set and improve the robustness to noise. Secondly, an improved nonlinear kernel function is proposed to map data into kernel space to measure the distance between data and the cluster centers more accurately. Thirdly, the local spatial-gray information measure is introduced, which considers membership degree, gray features and spatial position information at the same time. Finally, we propose a new measure of intuitionistic fuzzy entropy, it takes into account fuzziness and intuition of intuitionistic fuzzy set. The experimental results show that compared with other IFCM based algorithms, the proposed algorithm has better segmentation and clustering performance.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.1
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• pp.49-59
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• 2019

Generative adversarial networks(GANs) have received great attention in the machine learning field for their capacity to model high-dimensional and complex data distribution implicitly and generate new data samples from the model distribution. This paper investigates the model training methodology, architecture, and various applications of generative adversarial networks. Experimental evaluation is also conducted for generating synthetic image dataset for defense using two types of GANs. The first one is for military image generation utilizing the deep convolutional generative adversarial networks(DCGAN). The other is for visible-to-infrared image translation utilizing the cycle-consistent generative adversarial networks(CycleGAN). Each model can yield a great diversity of high-fidelity synthetic images compared to training ones. This result opens up the possibility of using inexpensive synthetic images for training neural networks while avoiding the enormous expense of collecting large amounts of hand-annotated real dataset.

• Journal of Positioning, Navigation, and Timing
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• v.7 no.4
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• pp.267-275
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• 2018

The reference stations in a satellite-based augmentation system (SBAS) collect raw data from global navigation satellite system (GNSS) to generate correction and integrity information. The multipath signals degrade GNSS raw data quality and have adverse effects on the SBAS performance. The currently operating SBASs (WAAS and EGNOS, etc.) survey existing commercial equipment to perform multipath assessment around the antennas. For the multi-path assessment, signal power of GNSS and multipath at the MEDLL receiver of NovAtel were estimated and the results were replicated by a ratio of signal power estimated at NovAtel Multipath Assessment Tool (MAT). However, the same experiment environment used in existing systems cannot be configured in reference stations in Korean augmentation satellite system (KASS) due to the discontinued model of MAT and MEDLL receivers used in the existing systems. This paper proposes a test environment for multipath assessment around the antennas in KASS Multipath Assessment Tool (K-MAT) for multipath assessment. K-MAT estimates a multipath error contained in the code pseudorange using linear combination between the measurements and replicates the results through polar plot and histogram for multipath assessment using the estimated values.

• Nuclear Engineering and Technology
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• v.51 no.4
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• pp.1008-1016
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• 2019

In the SG (steam generator) of PWR (pressurized water reactor) for a nuclear plant, hundreds of U-shaped tubes are used for the heat exchanger system. They interact with primary pressurized cooling water flow, generating flow-induced vibration in the secondary flow region. A simplified U-tube model is proposed in this study to apply for experiment and its counterpart computation. Using the commercial code, ANSYS-CFX, we first verified the Moody chart, comparing the straight pipe theory with the results derived from CFD (computational fluid dynamics) analysis. Considering the virtual mass of fluid, we computed the major modes with the low natural frequencies through the comparison with impact hammer test, and then investigated the effect of pump flow in the frequency domain using FFT (fast Fourier transform) analysis of the experimental data. Using two-way fluid-structure interaction module in the CFD code, we studied the influence on mean flow rate to generate the displacement data. A feasible CFD method has been setup in this research that could be applied potentially in the field of nuclear thermal-hydraulics.

• Journal of Sensor Science and Technology
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• v.28 no.4
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• pp.240-245
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• 2019

Phase interpolation is widely adopted in frequency synthesizers and clock-and-data recovery systems to produce an intermediate phase from two existing phases. The intermediate phase is typically generated by combining two input phases with different weights. Unfortunately, this results in non-uniform phase steps. Alternatively, the intermediate phase can be generated by successive approximation, where the interpolated phase at each approximation stage is obtained using the same weight for the two intermediate phases. As a proof of concept, this study presents a 2-GHz 8-bit successive approximation digital-to-phase converter that is designed using 65-nm CMOS technology. The converter receives an 8-phase clock signal as input, and the most significant bit (MSB) section selects four phases to create two sinusoidal waveforms using a harmonic rejection filter. The remaining least significant bit (LSB) section applies the successive approximation to generate the required intermediate phase. Monte-Carlo simulations show that the proposed converter exhibits 0.46-LSB integral nonlinearity and 0.31-LSB differential nonlinearity with a power consumption of 3.12 mW from a 1.2-V supply voltage.

• Journal of Distribution Science
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• v.19 no.1
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• pp.5-15
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• 2021

Purpose: This study explores the types of relational benefits that generate loyalty to room-sharing services among Chinese customers based on the relationship marketing literature. The study also examines the moderating effect of alternative attractiveness on this relationship. Research design, data and methodology: Based on research hypotheses, questionnaires with items measuring the proposed constructs in three dimensions, including relational benefits, alternative attractiveness, and customer loyalty, were designed to test the hypotheses. Data were collected via an online questionnaire of 220 room-sharing service customers in China. Results: Results verify the effects of relational benefits on customers' loyalty to room-sharing services and the mediating effect of alternative attractiveness. More specifically, confidence, social, and safety benefits positively affect customer loyalty to room-sharing services, and alternative attractiveness moderates only the effect of social benefits. Conclusions: The results suggest that room-sharing service providers should concentrate on providing confidence, social, and safety benefits to maintain long-term relationships with customers. This study also provides practical implication for building relationships between channel members in service distribution channels. The study concludes that without customer relationships marketing for managing collaborative and social communication channels, the entire distribution channel might lose out eventually.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.237-249
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• 2021

Indonesia has successfully increased its ranking to 40th place in the 2019 Travel & Tourism Competitiveness Index. While tourism has become the country's second largest foreign exchange contributor, there is no existing competitive advantage model for Indonesian tourist destinations. The purpose and novelty of this study is to develop and formulate a competitive advantage model for Indonesia's tourism industry. The model will be based on the supply-side perception analysis of competitiveness indicators from Bali and five designated super-priority destinations in Indonesia. This model is expected to become a guideline for policymakers to design an effective and focused strategy. Data were obtained from in-depth interviews with, and questionnaires given to, 62 qualified industry players from the public and private sectors. This data-driven approach builds a relationship between competitiveness indicators and competitive advantages using a combination of importance-performance analysis and confirmatory factor analysis, thereby leveraging these advantages to generate a strategic model to compete in the international tourism industry. This would also be the first study to use this method in defining the competitive advantage of a destination. Using structural equation modeling, the study found that there are 54 indicators representing twelve dimensions of competitive advantages with good fit criteria.

• Annual Conference of KIPS
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• 2013.05a
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• pp.470-471
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• 2013

Accuracy of mappings is the key for achieving true interoperability among different healthcare systems. The initial step towards interoperable healthcare systems is compliancy with healthcare standards (HL7, openEHR, CEN 13606). Ontologies for these standards are developed that require ontology matching to generate generalized ontology mappings. Organizations conform to specific concepts of different standards based on their requirements. This step is called as conformance claims and is based on Personalized-Detailed Clinical Model. It invalidates some of the generalized mappings because of non-conformed concepts and leads to the necessity of the proposed technique of customized ontology mappings. These customized ontology mappings compliment the generalized ontology mapping to increase the level of accuracy of mappings and thus achieving data interoperability. The proposed system ensures quality of care to patients by timely delivery of healthcare information.