• Smart Media Journal
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• v.11 no.3
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• pp.18-25
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• 2022

Neuromorphic computing generally shows significantly better power, area, and speed performance than neural network computation using CPU and GPU. These characteristics are suitable for resource-constrained IoT environments where energy consumption is important. However, there is a problem in that it is necessary to modify the source code for environment setting and application operation according to heterogeneous IoT devices that support neuromorphic computing. To solve these problems, NAAL was proposed and implemented in this paper. NAAL provides functions necessary for IoT device control and neuromorphic architecture abstraction and inference model operation in various heterogeneous IoT device environments based on common APIs of NAAL. NAAL has the advantage of enabling additional support for new heterogeneous IoT devices and neuromorphic architectures and computing devices in the future.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.433-449
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• 2021

In this study, a newly enhanced Fluid-Structure Interaction (FSI) model which incorporates mooring lines was used to simulate a floating structure. The model has two parts: a Computational Fluid Dynamics (CFD) model and a mooring model. The open-source CFD OpenFOAM® v1712 toolbox was used in the present study, and the convergence criteria and relaxation method were added to the computational procedure used for the OpenFOAM multiphase flow solver, interDyMFoam. A newly enhanced, tightly coupled solver, CoupledinterDyMFoam, was used to decrease the artificial added mass effect, and the results were validated through a series of benchmark cases. The mooring model, based on the finite element method, was established in MATLAB® and was validated against a benchmark analytical elastic catenary solution and numerical results. Finally, a model which simulates a floating structure with mooring lines was successfully constructed by connecting the mooring model to CoupledinterDyMFoam.

• Current Optics and Photonics
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• v.6 no.5
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• pp.479-488
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• 2022

To account for the internal thermal effects of solid-state lasers, a method using a back propagation (BP) neural network integrated with a particle swarm optimization (PSO) algorithm is developed, which is a new wavefront distortion correction technique. In particular, by using a slab laser model, a series of fiber pumped sources are employed to form a controlled array to pump the gain medium, allowing the internal temperature field of the gain medium to be designed by altering the power of each pump source. Furthermore, the BP artificial neural network is employed to construct a nonlinear mapping relationship between the power matrix of the pump array and the thermally induced wavefront aberration. Lastly, the suppression of thermally induced wavefront distortion can be achieved by changing the power matrix of the pump array and obtaining the optimal pump light intensity distribution combined using the PSO algorithm. The minimal beam quality β can be obtained by optimally distributing the pumping light. Compared with the method of designing uniform pumping light into the gain medium, the theoretically computed single pass beam quality β value is optimized from 5.34 to 1.28. In this numerical analysis, experiments are conducted to validate the relationship between the thermally generated wavefront and certain pumping light distributions.

• Asia-Pacific Journal of Business
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• v.12 no.4
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• pp.13-40
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• 2021

Purpose - The purpose of this study is to predict corporate corruption in emerging markets such as Brazil, Russia, India, and China (BRIC) using different machine learning techniques. Since corruption is a significant problem that can affect corporate performance, particularly in emerging markets, it is important to correctly identify whether a company engages in corrupt practices. Design/methodology/approach - In order to address the research question, we employ predictive analytic techniques (machine learning methods). Using the World Bank Enterprise Survey Data, this study evaluates various predictive models generated by seven supervised learning algorithms: k-Nearest Neighbour (k-NN), Naïve Bayes (NB), Decision Tree (DT), Decision Rules (DR), Logistic Regression (LR), Support Vector Machines (SVM), and Artificial Neural Network (ANN). Findings - We find that DT, DR, SVM and ANN create highly accurate models (over 90% of accuracy). Among various factors, firm age is the most significant, while several other determinants such as source of working capital, top manager experience, and the number of permanent full-time employees also contribute to company corruption. Research implications or Originality - This research successfully demonstrates how machine learning can be applied to predict corporate corruption and also identifies the major causes of corporate corruption.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.77-86
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• 2022

The destructive impact of contagious diseases outbreaks on all life facets necessitates developing effective solutions to control these diseases outbreaks. This research proposes an end-to-end, data-driven platform which consists of multiple modules that are working in harmony to achieve a concrete goal: early detection of contagious diseases outbreaks (i.e., epidemic diseases detection). Achieving that goal enables decision makers and people in power to act promptly, resulting in robust prevention management of contagious diseases. It must be clear that the goal of this proposed platform is not to predict or forecast the spread of contagious diseases, rather, its goal is to promptly detect contagious diseases outbreaks as they happen. The front end of the proposed platform is a web-based dashboard that visualizes diseases outbreaks in real-time on a real map. These outbreaks are detected via another component of the platform which utilizes data mining techniques and algorithms on gathered datasets. Those gathered datasets are managed by yet another component. Specifically, a mobile application will be the main source of data to the platform. Being a vital component of the platform, the datasets are managed by a DBMS that is specifically tailored for this platform. Preliminary results are presented to showcase the performance of a prototype of the proposed platform.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.143-143
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• 2022

Precipitation plays an essential role in water resources management and disaster prevention. Therefore, the understanding related to spatiotemporal characteristics of rainfall is necessary. Nowadays, highly accurate precipitation is mainly obtained from gauge observation systems. However, the density of gauge stations is a sparse and uneven distribution in mountainous areas. With the proliferation of technology, satellite-based precipitation sources are becoming increasingly common and can provide rainfall information in regions with complex topography. Nevertheless, satellite-based data is that it still remains uncertain. To overcome the above limitation, this study aims to take the strengthens of machine learning to generate a new reanalysis of precipitation data by fusion of multiple satellite precipitation products (SPPs) with gauge observation data. Several machine learning algorithms (i.e., Random Forest, Support Vector Regression, and Artificial Neural Network) have been adopted. To investigate the robustness of the new reanalysis product, observed data were collected to evaluate the accuracy of the products through Kling-Gupta efficiency (KGE), probability of detection (POD), false alarm rate (FAR), and critical success index (CSI). As a result, the new precipitation generated through the machine learning model showed higher accuracy than original satellite rainfall products, and its spatiotemporal variability was better reflected than others. Thus, reanalysis of satellite precipitation product based on machine learning can be useful source input data for hydrological simulations in ungauged river basins.

• Asia pacific journal of information systems
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• v.33 no.3
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• pp.677-701
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• 2023

With the rapid advancement of social media analytics and artificial intelligence, many studies have used online customer reviews as an important source to measure service quality in many industries, including the hotel industry. However, these studies have failed to identify the relative importance of different dimensions of service quality and their role in customer satisfaction. To fill this research gap, this study aims to identify the effects of service quality on hotel customer satisfaction from the multidimensional perspectives using sentiment analysis with self-training on online reviews. Additionally, the moderating role of the brand of origin for each service quality dimension is also investigated. Drawing on the SERVQUAL model and brand of origin concept, this study develops 12 hypotheses and empirically tests them using 30,070 online customer hotel reviews collected from TripAdvisor.com. The results indicated that overall service quality and each dimension of SERVQUAL significantly influenced customer satisfaction of hotels. The results also confirmed the moderating effects of brand of origin on overall service quality. However, the moderating effects of brand of origin for the tangible, reliability, and empathy dimensions of service quality were significant, whereas the effects for responsiveness and assurance were not. This study sheds new light on service quality measurement by analyzing the multidimensional features of service quality and the role of brand of origin in the hotel service context.

• East Asian Journal of Business Economics (EAJBE)
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• v.12 no.2
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• pp.35-42
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• 2024

Purpose: With the growth of the hospitality industry, it is imperative to identify how smart tourism characteristics may be used in hotel management. Current and emerging technologies such as analytic tools, automation, and Artificial Intelligence (AI) help to create value for the guests while also contributing to waste reduction, resource optimization, and increased profitability in the industry. Research design, data and methodology: The literature review was conducted to examine a broad scope of research in analyzing smart tourism characteristics for the improved management of hotels and establish the necessary background for this issue. The analysis was employed to specify the systematic approach of selecting, scrutinizing, and integrating the source of information. Results: According to the systematic literature analysis, four smart tourism characteristics have been established, which can improve various aspects of hotel management. They are as follows: (1) Smart Guest Experience Management, (2) Smart Operations and Resource Management, (3) Smart Customer Relationship Management, and (4) Smart Destination Management. Conclusions: The findings expose the radical approach that smart tourism characteristics take towards the management of hotels. The developments in IT and science-oriented solutions have opened greater opportunities as the hotel industry can enhance clients' satisfaction, productivity, and participation in environmental conservation initiatives for tourism.

• Journal of Aquaculture
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• v.21 no.4
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• pp.272-277
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• 2008

We investigated the growth and survival in seedling production, and growth performance was compared with the different rearing tanks and protein source of formulated feed for juvenile Pacific bluefin tuna Thunnus orientalis (PBT). The survival rate at the end of nursery culture at 30 days after hatching was $0.69{\pm}0.40%$, and total length and mean body weight were $49.83{\pm}2.52\;mm$ and $1.03{\pm}0.09\;g$, respectively. Growth performance has no significant difference in fish reared by different tanks forms for 10 days. In order to develop an artificial diet, we evaluated the dietary utility of enzyme treated fish meal (Bio-CP, BIO) for juvenile PBT. Only diet BIO sustained similar growth and higher feed efficiency, and final carcass lipid content as compared to those of Sand lance (SL) These results revealed that BIO-CP a suitable dietary protein source, could sustain growth of PBT.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.1
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• pp.27-33
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• 2006

Evaluating comfort of illumination environment of building interior is recognizing the degree of glare causing discomfort. Currently, to use the experimental formula for discomfort glare studied abroad it would be not appropriate because each races feel about the degree of glare differently. Therefore, this study aim to make up prediction formula for evaluating discomfort glare reasonably from Koreans' vision and it proceeded with 4 stages as follows: First, after reviewing the existing discomfort glare evaluation formula, I selected experimental variables. Second, I made a mock-up that I can control experimental variables and conditions according to the purpose of this study. Third, 1 conducted discomfort glare evaluation experiment. Finally, compared with UGR evaluation method suggested for Westerner in prior studies. In conclusion, 1) it's proved that discomfort glare is influenced highly by a light source luminance, background luminance and location of testee and the line of vision. 2) In interior discomfort glare experiment whether the glare light source is placed within range of vision or not has more significant influence than the distance between the light source and testee. 3) I compared and analyzed with UGR, the most representative discomfort glare evaluation system and I found there is a little difference in the results. This shows discomfort glare of Koreans and Westerners are different.