• Journal of the Korean BIBLIA Society for library and Information Science
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• v.20 no.4
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• pp.85-100
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• 2009

This study suggested the expansion strategies of school library catalog considering educational context which should be used teaching and learning process. To achieve the purpose of research, this study derived educational context categories by comparing and analyzing teaching and learning related factors, information resource related factors. Also, this study analysed case system considering educational context. Based on the results, this study designed the catalog data elements as an element to be added to an existing school libraries system(DLS). The derived data element is end user(teacher, students), instructional situations (teaching method, instructional object, curriculum, evaluation type), resource type(feature, discipline, format), reading situation(contextual reading, literature topic), related materials(teacher representation, student representation).

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.88-88
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• 2022

Detection of stress responses in crops is important to diagnose crop growth and evaluate yield. Also, the multi-spectral sensor is effectively known to evaluate stress caused by nutrient and moisture in crops or biological agents such as weeds or diseases. Therefore, in this experiment, multispectral images were taken by an unmanned aerial vehicle(UAV) under field condition. The experiment was conducted in the long-term fertilizer field in the National Institute of Crop Science, and experiment area was divided into different status of NPK(Control, N-deficiency, P-deficiency, K-deficiency, Non-fertilizer). Total 11 vegetation indices were created with RGB and NIR reflectance values using python. Variations in nutrient content in plants affect the amount of light reflected or absorbed for each wavelength band. Therefore, the objective of this experiment was to evaluate vegetation indices derived from multispectral reflectance data as input into machine learning algorithm for the classification of nutritional deficiency in rice. RandomForest model was used as a representative ensemble model, and parameters were adjusted through hyperparameter tuning such as RandomSearchCV. As a result, training accuracy was 0.95 and test accuracy was 0.80, and IPCA, NDRE, and EVI were included in the top three indices for feature importance. Also, precision, recall, and f1-score, which are indicators for evaluating the performance of the classification model, showed a distribution of 0.7-0.9 for each class.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.6
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• pp.45-52
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• 2023

Recently, as the importance of walking in daily life has been emphasized, projects to guarantee walking rights and create a pedestrian environment are being promoted throughout the region. In previous studies, a pedestrian environment assessment was conducted using Jeonju-si road images, and an image comparison pair data set was constructed. However, data sets expressed in numbers have difficulty in generalizing the judgment criteria of pedestrian environment assessors or visually identifying the pedestrian environment preferred by pedestrians. Therefore, this study proposes a method to interpret the results of the pedestrian environment assessment through data visualization by building a web application. According to the semantic segmentation result of analyzing the walking environment components that affect pedestrian environment assessors, it was confirmed that pedestrians did not prefer environments with a lot of "earth" and "grass," and preferred environments with "signboards" and "sidewalks." The proposed study is expected to identify and analyze the results randomly selected by participants in the future pedestrian environment evaluation, and believed that more improved accuracy can be obtained by pre-processing the data purification process.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2627-2642
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• 2023

Parkinson's disease (PD) is a typical, chronic neurodegenerative disease involving the concentration of dopamine, which can disrupt motor activity and cause different degrees of gait disturbance relevant to PD severity in patients. As current clinical PD diagnosis is a complex, time-consuming, and challenging task that relays on physicians' subjective evaluation of visual observations, gait disturbance has been extensively explored to make automatic detection of PD diagnosis and severity rating and provides auxiliary information for physicians' decisions using gait data from various acquisition devices. Among them, wearable sensors have the advantage of flexibility since they do not limit the wearers' activity sphere in this application scenario. In this paper, an attention-based temporal network (ATN) is designed for the time series structure of gait data (vertical ground reaction force signals) from foot sensor systems, to learn the discriminative differences related to PD severity levels hidden in sequential data. The structure of the proposed method is illuminated by Transformer Network for its success in excavating temporal information, containing three modules: a preprocessing module to map intra-moment features, a feature extractor computing complicated gait characteristic of the whole signal sequence in the temporal dimension, and a classifier for the final decision-making about PD severity assessment. The experiment is conducted on the public dataset PDgait of VGRF signals to verify the proposed model's validity and show promising classification performance compared with several existing methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3330-3344
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• 2023

This paper proposes an antenna performance prediction model in the autonomous driving radar manufacturing process. Our research work is based upon a challenge dataset, Driving Radar Manufacturing Process Dataset, and a typical AutoML machine learning workflow engine, Pycaret open-source Python library. Note that the dataset contains the total 70 data-items, out of which 54 used as input features and 16 used as output features, and the dataset is properly built into resolving the multi-output regression problem. During the data regression analysis and preprocessing phase, we identified several input features having similar correlations and so detached some of those input features, which may become a serious cause of the multicollinearity problem that affect the overall model performance. In the training phase, we train each of output-feature regression models by using the AutoML approach. Next, we selected the top 5 models showing the higher performances in the AutoML result reports and applied the ensemble method so as for the selected models' performances to be improved. In performing the experimental performance evaluation of the regression prediction model, we particularly used two metrics, MAE and RMSE, and the results of which were 0.6928 and 1.2065, respectively. Additionally, we carried out a series of experiments to verify the proposed model's performance by comparing with other existing models' performances. In conclusion, we enhance accuracy for safer autonomous vehicles, reduces manufacturing costs through AutoML-Pycaret and machine learning ensembled model, and prevents the production of faulty radar systems, conserving resources. Ultimately, the proposed model holds significant promise not only for antenna performance but also for improving manufacturing quality and advancing radar systems in autonomous vehicles.

• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1165-1203
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• 2024

In the paper we validate theoretical models of the pulse against experimental data from the Jozef Stefan Institute TRIGA Mark II research reactor. Data from all pulse experiments since 1991 have been collected, analysed and are publicly available. This paper summarizes the validation study, which is focused on the comparison between experimental values, theoretical predictions (Fuchs-Hansen and Nordheim-Fuchs models) and calculation using computational program Improved Pulse Model. The results show that the theoretical models predicts higher maximum power but lower total released energy, full width at half maximum and the time when the maximum power is reached is shorter, compared to Improved Pulse Model. We evaluate the uncertainties in pulse physical parameters (maximum power, total released energy and full width at half maximum) due to uncertainties in reactor physical parameters (inserted reactivity, delayed neutron fraction, prompt neutron lifetime and effective temperature reactivity coefficient of fuel). It is found that taking into account overestimated correlation of reactor physical parameters does not significantly affect the estimated uncertainties of pulse physical parameters. The relative uncertainties of pulse physical parameters decrease with increasing inserted reactivity. If all reactor physical parameters feature an uncorrelated uncertainty of 10 % the estimated total uncertainty in peak pulse power at 3 $ inserted reactivity is 59 %, where significant contributions come from uncertainties in prompt neutron lifetime and effective temperature reactivity coefficient of fuel. In addition we analyse contribution of two physical mechanisms (Doppler broadening of resonances and neutron spectrum shift) that contribute to the temperature reactivity coefficient of fuel. The Doppler effect contributes around 30 %-15 % while the rest is due to the thermal spectrum hardening for a temperature range between 300 K and 800 K.

• Geomechanics and Engineering
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• v.37 no.1
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• pp.49-64
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• 2024

Rocks undergoing repeated loading and unloading over an extended period, such as due to earthquakes, human excavation, and blasting, may result in the gradual accumulation of stress and deformation within the rock mass, eventually reaching an unstable state. In this study, a CNN-CCM is proposed to address the mechanical behavior. The structure and hyperparameters of CNN-CCM include Conv2D layers × 5; Max pooling2D layers × 4; Dense layers × 4; learning rate=0.001; Epoch=50; Batch size=64; Dropout=0.5. Training and validation data for deep learning include 71 rock samples and 122,152 data points. The AI Rock Constitutive Model learned by CNN-CCM can predict strain values(ε₁) using Mass (M), Axial stress (σ₁), Density (ρ), Cyclic number (N), Confining pressure (σ₃), and Young's modulus (E). Five evaluation indicators R², MAPE, RMSE, MSE, and MAE yield respective values of 0.929, 16.44%, 0.954, 0.913, and 0.542, illustrating good predictive performance and generalization ability of model. Finally, interpreting the AI Rock Constitutive Model using the SHAP explaining method reveals that feature importance follows the order N > M > σ₁ > E > ρ > σ₃.Positive SHAP values indicate positive effects on predicting strain ε₁ for N, M, σ₁, and σ₃, while negative SHAP values have negative effects. For E, a positive value has a negative effect on predicting strain ε₁, consistent with the influence patterns of conventional physical rock constitutive equations. The present study offers a novel approach to the investigation of the mechanical constitutive model of rocks under cyclic loading and unloading conditions.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.3
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• pp.87-94
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• 2024

We investigated the feasibility of utilizing Large Language Models (LLMs) to perform text-based games without training on game data in advance. We adopted ChatGPT-3.5 and its state-of-the-art, ChatGPT-4, as the systems that implemented LLM. In addition, we added the persistent memory feature proposed in this paper to ChatGPT-4 to create three game player agents. We used Zork, one of the most famous text-based games, to see if the agents could navigate through complex locations, gather information, and solve puzzles. The results showed that the agent with persistent memory had the widest range of exploration and the best score among the three agents. However, all three agents were limited in solving puzzles, indicating that LLM is vulnerable to problems that require multi-level reasoning. Nevertheless, the proposed agent was still able to visit 37.3% of the total locations and collect all the items in the locations it visited, demonstrating the potential of LLM.

• Current Optics and Photonics
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• v.8 no.4
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• pp.406-415
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• 2024

The major error factors that degrade the efficiency of coherent beam combining (CBC) are numerically studied in a comprehensive manner, paying particular attention to phase, tip-tilt, polarization angle, and beam quality. The power in the bucket (PIB), normalized to the zero-error PIB, is used as a figure of merit to quantify the effect of each error factor. To maintain a normalized PIB greater than or equal to 95% in a 3-channel CBC configuration, the errors in phase, tip-tilt, and polarization angle should be less than 1.06 radians, 1.25 ㎛, and 1.06 radians respectively, when each of the three parameters is calculated independently with the other two set to zero. In a worst-case scenario of the composite errors within the parameter range for the independent-95%-normalized-PIB condition, the aggregate effect would reduce the normalized PIB to 83.8%. It is noteworthy that the PIB performances of a CBC system, depending on phase and polarization-angle errors, share the same characteristic feature. A statistical approach for each error factor is also introduced, to assess a CBC system with an extended number of channels. The impact of the laser's beam-quality factor M² on the combining efficiency is also analyzed, based on a super-Gaussian beam. When M² increases from 1 to 1.3, the normalized PIB is reduced by 2.6%, 11.8%, 12.8%, and 13.2% for a single-channel configuration and 3-, 7-, and 19-channel CBC configurations respectively. This comprehensive numerical study is expected to pave the way for advances in the evaluation and design of multichannel CBC systems and other related applications.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.7
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• pp.1986-2009
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• 2024

The integration of blockchain technology with the rapid growth of Internet of Things (IoT) devices has enabled secure and decentralised data exchange. However, security vulnerabilities and performance limitations remain significant challenges in IoT blockchain networks. This work proposes a novel approach that combines transaction representation and machine learning techniques to address these challenges. Various clustering techniques, including k-means, DBSCAN, Gaussian Mixture Models (GMM), and Hierarchical clustering, were employed to effectively group unlabelled transaction data based on their intrinsic characteristics. Anomaly transaction prediction models based on classifiers were then developed using the labelled data. Performance metrics such as accuracy, precision, recall, and F1-measure were used to identify the minority class representing specious transactions or security threats. The classifiers were also evaluated on their performance using balanced and unbalanced data. Compared to unbalanced data, balanced data resulted in an overall average improvement of approximately 15.85% in accuracy, 88.76% in precision, 60% in recall, and 74.36% in F1-score. This demonstrates the effectiveness of each classifier as a robust classifier with consistently better predictive performance across various evaluation metrics. Moreover, the k-means and GMM clustering techniques outperformed other techniques in identifying security threats, underscoring the importance of appropriate feature selection and clustering methods. The findings have practical implications for reinforcing security and efficiency in real-world IoT blockchain networks, paving the way for future investigations and advancements.