• KIPS Transactions on Software and Data Engineering
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• v.12 no.3
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• pp.133-140
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• 2023

The performance of lithium ion batteries depends on the usage environment and the combination ratio of cathode materials. In order to develop a high-performance lithium-ion battery, it is necessary to manufacture the battery and measure its performance while varying the cathode material ratio. However, it takes a lot of time and money to directly develop batteries and measure their performance for all combinations of variables. Therefore, research to predict the performance of a battery using an artificial intelligence model has been actively conducted. However, since measurement experiments were conducted with the same battery in the existing published battery data, the cathode material combination ratio was fixed and was not included as a data attribute. In this paper, we define a training data model required to develop an artificial intelligence model that can predict battery performance according to the combination ratio of cathode materials. We analyzed the factors that can affect the performance of lithium-ion batteries and defined the mass of each cathode material and battery usage environment (cycle, current, temperature, time) as input data and the battery power and capacity as target data. In the battery data in different experimental environments, each battery data maintained a unique pattern, and the battery classification model showed that each battery was classified with an error of about 2%.

• International Journal of Korean Welding Society
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• v.1 no.1
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• pp.17-22
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• 2001

Because the quantitative relationships between welding parameters and welding result are not yet blown, optimal values of welding parameters for $CO_2$ robotic arc welding is a difficult task. Using the various artificial data processing methods may solve this difficulty. This research aims to develop an expert system for $CO_2$ robotic arc welding to recommend the optimal values of welding parameters. This system has three main functions. First is the recommendation of reasonable values of welding parameters. For such work, the relationships in between the welding parameters are investigated by the use of regression analysis and fuzzy system. The second is the estimation of bead shape by a neural network system. In this study the welding current voltage, speed, weaving width, and root gap are considered as the main parameters influencing a bead shape. The neural network system uses the 3-layer back-propagation model and a generalized delta rule as teaming algorithm. The last is the optimization of the parameters for the correction of undesirable weld bead. The causalities of undesirable weld bead are represented in the form of rules. The inference engine derives conclusions from these rules. The conclusions give the corrected values of the welding parameters. This expert system was developed as a PC-based system of which can be used for the automatic or semi-automatic $CO_2$ fillet welding with 1.2, 1.4, and 1.6mm diameter the solid wires or flux-cored wires.

• The Journal of Bigdata
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• v.6 no.2
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• pp.71-84
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• 2021

Since small and medium-sized enterprises fell short of the securement of technological competitiveness in the field of big data and artificial intelligence (AI) field-core technologies of the Fourth Industrial Revolution, it is important to strengthen the competitiveness of the overall industry through technology commercialization. In this study, we aimed to propose a priority related to technology transfer and commercialization for practical use of public research results. We utilized public research performance information, improving missing values of 6T classification by deep learning model with an ensemble method. Then, we conducted topic modeling to derive the converging fields of big data and AI. We classified the technology fields into four different segments in the technology portfolio based on technology activity and technology efficiency, estimating the potential of technology commercialization for those fields. We proposed a priority of technology commercialization for 10 detailed technology fields that require long-term investment. Through systematic analysis, active utilization of technology, and efficient technology transfer and commercialization can be promoted.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.135-147
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• 2023

Internet of things has revolutionaries every field of life due to the use of artificial intelligence within Machine Learning. It is successfully being used for the study of Radiation monitoring, prediction of Ultraviolet and Electromagnetic rays. However, there is no particular system available that can monitor and detect waves. Therefore, the present study designed in which IOT enables intelligence system based on machine learning was developed for the prediction of the radiation and their effects of human beings. Moreover, a sensor based system was installed in order to detect harmful radiation present in the environment and this system has the ability to alert the humans within the range of danger zone with a buzz, so that humans can move to a safer place. Along with this automatic sensor system; a self-created dataset was also created in which sensor values were recorded. Furthermore, in order to study the outcomes of the effect of these rays researchers used Support Vector Machine, Gaussian Naïve Bayes, Decision Trees, Extra Trees, Bagging Classifier, Random Forests, Logistic Regression and Adaptive Boosting Classifier were used. To sum up the whole discussion it is stated the results give high accuracy and prove that the proposed system is reliable and accurate for the detection and monitoring of waves. Furthermore, for the prediction of outcome, Adaptive Boosting Classifier has shown the best accuracy of 81.77% as compared with other classifiers.

• Korean Journal of Remote Sensing
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• v.36 no.3
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• pp.449-460
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• 2020

Super-resolution is a technique used to reconstruct an image with low-resolution into that of high-resolution. Recently, deep-learning based super resolution has become the mainstream, and applications of these methods are widely used in the remote sensing field. In this paper, we propose a super-resolution method based on the deep back-projection network model to improve the satellite image resolution by the factor of four. In the process, we customized the loss function with the edge loss to result in a more detailed feature of the boundary of each object and to improve the stability of the model training using generative adversarial network based on Wasserstein distance loss. Also, we have applied the detail preserving image down-scaling method to enhance the naturalness of the training output. Finally, by including the modified-residual learning with a panchromatic feature in the final step of the training process. Our proposed method is able to reconstruct fine features and high frequency information. Comparing the results of our method with that of the others, we propose that the super-resolution method improves the sharpness and the clarity of WorldView-3 and KOMPSAT-2 images.

• Korean Chemical Engineering Research
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• v.58 no.4
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• pp.565-572
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• 2020

This study developed a software platform using machine learning of artificial intelligence to optimize the distillation column system. The distillation column is representative and core process in the petrochemical industry. Process stabilization is difficult due to various operating conditions and continuous process characteristics, and differences in process efficiency occur depending on operator skill. The process control based on the theoretical simulation was used to overcome this problem, but it has a limitation which it can't apply to complex processes and real-time systems. This study aims to develop an empirical simulation model based on machine learning and to suggest an optimal process operation method. The development of empirical simulations involves collecting big data from the actual process, feature extraction through data mining, and representative algorithm for the chemical process. Finally, the platform for the distillation column was developed with verification through a developed model and field tests. Through the developed platform, it is possible to predict the operating parameters and provided optimal operating conditions to achieve efficient process control. This study is the basic study applying the artificial intelligence machine learning technique for the chemical process. After application on a wide variety of processes and it can be utilized to the cornerstone of the smart factory of the industry 4.0.

• Journal of the Korea Institute of Building Construction
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• v.11 no.3
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• pp.238-246
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• 2011

Choosing the best-performance materials is a crucial task for the successful completion of a project in the construction field. In general, the process of material selection is performed through the use of information by a highly experienced expert and the purchasing agent, without the assistance of logical decision-making techniques. For this reason, the construction field has considered various artificial intelligence (AI) techniques to support decision systems as their own selection method. This study proposes the application of a systematic and efficient support vector machine (SVM) model to select optimal exterior materials. The dataset of the study is 120 completed construction projects in South Korea. A total of 8 input determinants were identified and verified from the literature review and interviews with experts. Using data classification and normalization, these 120 sets were divided into 3 groups, and then 5 binary classification models were constructed in a one-against-all (OAA) multi classification method. The SVM model, based on the kernel radical basis function, yielded a prediction accuracy rate of 87.5%. This study indicates that the SVM model appears to be feasible as a decision support system for selecting an optimal construction method.

• Journal of The Korean Astronomical Society
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• v.53 no.6
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• pp.139-147
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• 2020

The sunspot area is a critical physical quantity for assessing the solar activity level; forecasts of the sunspot area are of great importance for studies of the solar activity and space weather. We developed an innovative hybrid model prediction method by integrating the complementary ensemble empirical mode decomposition (CEEMD) and extreme learning machine (ELM). The time series is first decomposed into intrinsic mode functions (IMFs) with different frequencies by CEEMD; these IMFs can be divided into three groups, a high-frequency group, a low-frequency group, and a trend group. The ELM forecasting models are established to forecast the three groups separately. The final forecast results are obtained by summing up the forecast values of each group. The proposed hybrid model is applied to the smoothed monthly mean sunspot area archived at NASA's Marshall Space Flight Center (MSFC). We find a mean absolute percentage error (MAPE) and a root mean square error (RMSE) of 1.80% and 9.75, respectively, which indicates that: (1) for the CEEMD-ELM model, the predicted sunspot area is in good agreement with the observed one; (2) the proposed model outperforms previous approaches in terms of prediction accuracy and operational efficiency.

• International Journal of High-Rise Buildings
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• v.9 no.4
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• pp.351-360
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• 2020

Monitoring civil structures periodically is necessary for ensuring the fitness of the structures. Cracks on inner and outer surfaces of the building plays a vital role in indicating the health of the building. Conventionally, human visual inspection techniques were carried up to human reachable altitudes. Monitoring of high rise infrastructures cannot be done using this primitive method. Also, there is a necessity for more accurate prediction of cracks on building surfaces for ensuring the health and safety of the building. The proposed research focused on developing an efficient crack classification model using Transfer Learning enabled EfficientNet (TL-EN) architecture. Though many other pre-trained models were available for crack classification, they rely on more number of training parameters for better accuracy. The TL-EN model attained an accuracy of 0.99 with less number of parameters on large dataset. A bench marked METU dataset with 40000 images were used to test and validate the proposed model. The surfaces of high rise buildings were investigated using vision enabled Unmanned Arial Vehicles (UAV). These UAV is fabricated with TL-EN model schema for capturing and analyzing the real time streaming video of building surfaces.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.737-745
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• 2023

This paper discusses a study on developing an artificial intelligence model to detect incidents of fires in distribution switchboard using thermal images. The objective of the research is to preprocess collected thermal images into suitable data for object detection models and design a model capable of determining the occurrence of fires within distribution panels. The study utilizes thermal image data from AI-HUB's industrial complex for training. Two CNN-based deep learning object detection algorithms, namely Faster R-CNN and RetinaNet, are employed to construct models. The paper compares and analyzes these two models, ultimately proposing the optimal model for the task.