• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.480-502
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• 2022

This work assesses the degree of satisfaction tourists receive as final recipients in a tourism destination based on the fact that satisfied tourists can make a significant contribution to the growth and continuous improvement of a tourism business. The work considers Pokhara, the tourism capital of Nepal as a prefecture of study. A stratified sampling methodology with open-ended survey questions is used as a primary source of data for a sample size of 1019 for both international and domestic tourists. The data collected through a survey is processed using a data mining tool to perform multi-dimensional analysis to discover information patterns and visualize clusters. Further, supervised machine learning algorithms, kNN, Decision tree, Support vector machine, Random forest, Neural network, Naive Bayes, and Gradient boost are used to develop models for training and prediction purposes for the survey data. To find the best model for prediction purposes, different performance matrices are used to evaluate a model for performance, accuracy, and robustness. The best model is used in constructing a learning-enabled model for predicting tourists as satisfied, neutral, and unsatisfied visitors. This work is very important for tourism business personnel, government agencies, and tourism stakeholders to find information on tourist satisfaction and factors that influence it. Though this work was carried out for Pokhara city of Nepal, the study is equally relevant to any other tourism destination of similar nature.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.46-52
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• 2023

With billions of IoT (Internet of Things) devices populating various emerging applications across the world, detecting anomalies on these devices has become incredibly important. Advanced Intrusion Detection Systems (IDS) are trained to detect abnormal network traffic, and Machine Learning (ML) algorithms are used to create detection models. In this paper, the NSL-KDD dataset was adopted to comparatively study the performance and efficiency of IoT anomaly detection models. The dataset was developed for various research purposes and is especially useful for anomaly detection. This data was used with typical machine learning algorithms including eXtreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Deep Convolutional Neural Networks (DCNN) to identify and classify any anomalies present within the IoT applications. Our research results show that the XGBoost algorithm outperformed both the SVM and DCNN algorithms achieving the highest accuracy. In our research, each algorithm was assessed based on accuracy, precision, recall, and F1 score. Furthermore, we obtained interesting results on the execution time taken for each algorithm when running the anomaly detection. Precisely, the XGBoost algorithm was 425.53% faster when compared to the SVM algorithm and 2,075.49% faster than the DCNN algorithm. According to our experimental testing, XGBoost is the most accurate and efficient method.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.2
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• pp.176-182
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• 2023

With about 80% of the global economy expected to shift to the global market by 2030, exports of reverse direct purchase products, in which foreign consumers purchase products from online shopping malls in Korea, are growing 55% annually. As of 2021, sales of reverse direct purchases in South Korea increased 50.6% from the previous year, surpassing 40 million. In order for domestic SMEs(Small and medium sized enterprises) to enter overseas markets, it is important to come up with export strategies based on various market analysis information, but for domestic small and medium-sized sellers, entry barriers are high, such as lack of information on overseas markets and difficulty in selecting local preferred products and determining competitive sales prices. This study develops an AI-based product recommendation and sales price estimation model to collect and analyze global shopping malls and product trends to provide marketing information that presents promising and appropriate product sales prices to small and medium-sized sellers who have difficulty collecting global market information. The product recommendation model is based on the LTR (Learning To Rank) methodology. As a result of comparing performance with nDCG, the Pair-wise-based XGBoost-LambdaMART Model was measured to be excellent. The sales price estimation model uses a regression algorithm. According to the R-Squared value, the Light Gradient Boosting Machine performs best in this model.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.1
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• pp.23-31
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• 2023

Research and interest in sustainable printing are increasing in the packaging printing industry. Currently, predicting the amount of ink required for each work is based on the experience and intuition of field workers. Suppose the amount of ink produced is more than necessary. In this case, the rest of the ink cannot be reused and is discarded, adversely affecting the company's productivity and environment. Nowadays, machine learning models can be used to figure out this problem. This study compares the ink usage prediction machine learning models. A simple linear regression model, Multiple Regression Analysis, cannot reflect the nonlinear relationship between the variables required for packaging printing, so there is a limit to accurately predicting the amount of ink needed. This study has established various prediction models which are based on CART (Classification and Regression Tree), such as Decision Tree, Random Forest, Gradient Boosting Machine, and XGBoost. The accuracy of the models is determined by the K-fold cross-validation. Error metrics such as root mean squared error, mean absolute error, and R-squared are employed to evaluate estimation models' correctness. Among these models, XGBoost model has the highest prediction accuracy and can reduce 2134 (g) of wasted ink for each work. Thus, this study motivates machine learning's potential to help advance productivity and protect the environment.

• Steel and Composite Structures
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• v.49 no.5
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• pp.487-502
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• 2023

In the realm of nanotechnology, the nonlocal strain gradient theory takes center stage as it scrutinizes the behavior of spinning cantilever nanobeams and nanotubes, pivotal components supporting various mechanical movements in sport structures. The dynamics of these structures have sparked debates within the scientific community, with some contending that nonlocal cantilever models fail to predict dynamic softening, while others propose that they can indeed exhibit stiffness softening characteristics. To address these disparities, this paper investigates the dynamic response of a nonlocal cantilever cylindrical beam under the influence of external discontinuous dynamic loads. The study employs four distinct models: the Euler-Bernoulli beam model, Timoshenko beam model, higher-order beam model, and a novel higher-order tube model. These models account for the effects of functionally graded materials (FGMs) in the radial tube direction, giving rise to nanotubes with varying properties. The Hamilton principle is employed to formulate the governing differential equations and precise boundary conditions. These equations are subsequently solved using the generalized differential quadrature element technique (GDQEM). This research not only advances our understanding of the dynamic behavior of nanotubes but also reveals the intriguing phenomena of both hardening and softening in the nonlocal parameter within cantilever nanostructures. Moreover, the findings hold promise for practical applications, including drug delivery, where the controlled vibrations of nanotubes can enhance the precision and efficiency of medication transport within the human body. By exploring the multifaceted characteristics of nanotubes, this study not only contributes to the design and manufacturing of rotating nanostructures but also offers insights into their potential role in revolutionizing drug delivery systems.

• Advances in nano research
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• v.16 no.2
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• pp.111-125
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• 2024

Recently, a lot of research has been done on the analysis of axial vibrations of homogeneous and FG nanotubes (nanorods) with various aspects of vibrations that have been fully mentioned in history. However, there is a lack of investigation of the dynamic internal resonances of FG nanotubes (nanorods) between them. This is one of the essential or substantial characteristics of nonlinear vibration systems that have many applications in various fields of engineering (making actuators, sensors, etc.) and medicine (improving the course of diseases such as cancers, etc.). For this reason, in this study, for the first time, the dynamic internal resonances of FG nanorods in the simultaneous presence of large-amplitude size dependent behaviour, inertial and shear effects are investigated for general state in detail. Such theoretical patterns permit as to carry out various numerical experiments, which is the key point in the expansion of advanced nano-devices in different sciences. This research presents an AFG novel nano resonator model based on the axial vibration of the elastic nanorod system in terms of derivation from large-amplitude size dependent internal modals interactions. The Hamilton's Principle is applied to achieve the basic equations in movement and boundary conditions, and a harmonic deferential quadrature method, and a multiple scale solution technique are employed to determine a semi-analytical solution. The interest of the current solution is seen in its specific procedure that useful for deriving general relationships of internal resonances of FG nanorods. The numerical results predicted by the presented formulation are compared with results already published in the literature to indicate the precision and efficiency of the used theory and method. The influences of gradient index, aspect ratio of FG nanorod, mode number, nonlinear effects, and nonlocal effects variations on the mechanical behavior of FG nanorods are examined and discussed in detail. Also, the inertial and shear traces on the formations of internal resonances of FG nanorods are studied, simultaneously. The obtained valid results of this research can be useful and practical as input data of experimental works and construction of devices related to axial vibrations of FG nanorods.

• Investigative Magnetic Resonance Imaging
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• v.1 no.1
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• pp.119-124
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• 1997

Purpose: To assess the utility of magnetic resonance(MR) cerebral blood volume (CBV) map in the evaluation of brain tumors. Materials and Methods: We performed perfusion MR imaing preoperatively in the consecutive IS patients with intracranial masses(3 meningiomas, 2 glioblastoma multiformes, 3 low grade gliomas, 1 lymphoma, 1 germinoma, 1 neurocytoma, 1 metastasis, 2 abscesses, 1 radionecrosis). The average age of the patients was 42 years (22yr -68yr), composed of 10 males and S females. All MR images were obtained at l.ST imager(Signa, CE Medical Systems, Milwaukee, Wisconsin). The regional CBV map was obtained on the theoretical basis of susceptibility difference induced by first pass circulation of contrast media. (contrast media: IScc of gadopentate dimeglumine, about 2ml/sec by hand, starting at 10 second after first baseline scan). For each patient, a total of 480 images (6 slices, 80 images/slice in 160 sec) were obtained by using gradient echo(CE) single shot echo-planar image(EPI) sequence (TR 2000ms, TE SOms, flip angle $90^{\circ}$, FOV $240{\times}240mm,{\;}matrix{\;}128{\times}128$, slice-thick/gap S/2.S). After data collection, the raw data were transferred to CE workstation and rCBV maps were generated from the numerical integration of ${\Delta}R2^{*} on a voxel by voxel basis, with home made software (${\Delta}R2^{*}=-ln (S/SO)/TE). For easy visual interpretation, relative RCB color coding with reference to the normal white matter was applied and color rCBV maps were obtained. The findings of perfusion MR image were retrospectively correlated with Cd-enhanced images with focus on the degree and extent of perfusion and contrast enhancement. Results: Two cases of glioblastoma multiforme with rim enhancement on Cd-enhanced Tl weighted image showed increased perfusion in the peripheral rim and decreased perfusion in the central necrosis portion. The low grade gliomas appeared as a low perfusion area with poorly defined margin. In 2 cases of brain abscess, the degree of perfusion was similar to that of the normal white matter in the peripheral enhancing rim and was low in the central portion. All meningiomas showed diffuse homogeneous increased perfusion of moderate or high degree. One each of lymphoma and germinoma showed homogenously decreased perfusion with well defined margin. The central neurocytoma showed multifocal increased perfusion areas of moderate or high degree. A few nodules of the multiple metastasis showed increased perfusion of moderate degree. One radionecrosis revealed multiple foci of increased perfusion within the area of decreased perfusion. Conclusion: The rCBV map appears to correlate well with the perfusion state of brain tumor, and may be helpful in discrimination between low grade and high grade gliomas. The further study is needed to clarify the role of perfusion MR image in the evaluation of brain tumor.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.35 no.3
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• pp.309-319
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• 2011

We experimentally investigated lifted propane jet flames diluted with nitrogen to obtain flame-stability maps based on heat-loss-induced self-excitation. We found that heat-loss-induced self-excitations are caused by conductive heat loss from premixed flame branches to trailing diffusion flames as well as soot radiation. The conductive-heat-loss-induced self-excitation at frequencies less than 0.1 Hz is explained well by a suggested mechanism, whereas the oscillation of the soot region induces a self-excitation of lift-off height of the order of 0.1 Hz. The suggested mechanism is also verified from additive experiments in a room at constant temperature and humidity. The heat-loss-induced self-excitation is explained by the Strouhal numbers as a function of the relevant parameters.

• 한국신재생에너지학회:학술대회논문집
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• 2005.06a
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• pp.306-309
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• 2005

One of the most critical issues in sol id oxide fuel cell (SOFC)running on hydrocarbon fuels is the risk of carbon formation from the fuel gas. The simple method to reduce the risk of carbon formation from the reactions is to add steam to the fuel stream, leading to the carbon gasification react ion. However, the addition of steam to fuel is not appropriate for the auxiliary power unit (APU) and potable power generation (PPG) systems due to an increase of complexity and bulkiness. In this regard, many researchers have focused on so-called 'direct methane' operation of SOFC, which works with dry methane without coking. However, coking can be suppressed only by the operation with a high current density, which may be a drawback especially for the APU and PPG systems. The single chamber fuel cell (SC-SOFC) is a novel simplification of the conventional SOFC into which a premixed fuel/air mixture is introduced. It relies on the selectivity of the anode and cathode catalysts to generate a chemical potential gradient across the cell. Moreover it allows compact and seal-free stack design. In this study, we fabricated honeycomb type mixed-gas fuel cell (MGFC) which has advantages of stacking to the axial direction and increasing volume power density. Honeycomb-structured SOFC with four channels was prepared by dry pressing method. Two alternative channels were coated with electrolyte and cathode slurry in order to make cathodic reaction sites. We will discuss that the anode supported honeycomb type cell running on mixed gas condition.

• Korean Journal of Microbiology
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• v.28 no.2
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• pp.151-157
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• 1990

The maximum nitrogen fixation activity of the associative, microaerobic and acid tolerant bacteria, Azospirillum amazonense Kp1 was obtained with 0.2Kpa of $O_{2}$ and showed a reversible inhibition by the higher concentrations. Ammonium treatment caused a gradual inhibition of the activity up to 350mM. The nitrogenase systems were purified by gradient chromatography on DEAE-52 cellulose, heat treatment and preparative PAGE. The MoFe protein showed molecular weight of 210,000 including two nonidentical subunits with apparent molecular weights of 55,000 and 50,000 and an isoelectricpoint of 5.2 and contained 2, 24 and 28 atoms of Mo, Fe and acid labile S per molecule. The Fe protein revealed molecular weight of 66,000 including two types of subunits with molecular weights of 35,000 and 31,000 and an isoelectric point of 4.6, and contained 4 atoms of Fe and 6 atoms of S per molecule. The maximum specific nitrogenase activity attained 2,200 and 1,700nM $C_2H_4mg^{-1} min^{-1}$, respectively for MoFe and Fe proteins at pH7 and $35^{\circ}C$. The activity was lost after 10 and 30 days under the cold room ($4^{\circ}C$) condition for Fe and MoFe proteins, respectively.