• Korean Journal of Culture and Social Issue
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• v.24 no.1
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• pp.29-47
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• 2018

Do social situations have an impact on an individual's information processing and emotional experiences? Two studies were conducted to investigate relationships between self-reference effects, emotional experiences and social information processing. Study 1 examined whether biases favoring self-related stimuli could occur automatically. Participants had to judge whether sequential geometric shape-label pairs matched or mismatched. The results showed that self-related stimuli are more rapidly processed than friends/others-related stimuli. In Study 2, the participants had to recall items which were presented with different instructions (either chosen by a friend or by the computer). Here we explored whether the self-reference effect is reduced in a social learning condition. When comparing the social learning condition (seated in pairs) with the nonsocial learning condition (seated alone), the participants recalled more self-related words in the nonsocial learning condition than in the social learning condition. Importantly, the automatic self-reference effect disappeared in the social learning condition. More friends-related words were recalled in the social condition than self-related words. In addition, while tasting chocolates, the participants judged them to be more likeable in the social condition than in the nonsocial condition. These results implicated that social processing can be useful for reducing the automatic self-reference effects and shared experiences are perceived more intensely than unshared experiences.

• Journal of Practical Engineering Education
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• v.15 no.2
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• pp.381-388
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• 2023

Through artificial neural network education using spreadsheets, non-major undergraduate students can understand the operation principle of artificial neural networks and develop their own artificial neural network software. Here, training of the operation principle of artificial neural networks starts with the generation of training data and the assignment of correct answer labels. Then, the output value calculated from the firing and activation function of the artificial neuron, the parameters of the input layer, hidden layer, and output layer is learned. Finally, learning the process of calculating the error between the correct label of each initially defined training data and the output value calculated by the artificial neural network, and learning the process of calculating the parameters of the input layer, hidden layer, and output layer that minimize the total sum of squared errors. Training on the operation principles of artificial neural networks using a spreadsheet was conducted for undergraduate non-major students. And image training data and basic artificial neural network development results were collected. In this paper, we analyzed the results of collecting two types of training data and the corresponding artificial neural network SW with small 12-pixel images, and presented methods and execution results of using the collected training data for Orange machine learning model learning and analysis tools.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.123-132
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• 2023

Git's commit messages manage the history of source changes during project progress or operation. By utilizing this historical data, project risks and project status can be identified, thereby reducing costs and improving time efficiency. A lot of research related to this is in progress, and among these research areas, there is research that classifies commit messages as a type of software maintenance. Among published studies, the maximum classification accuracy is reported to be 95%. In this paper, we began research with the purpose of utilizing solutions using the commit classification model, and conducted research to remove the limitation that the model with the highest accuracy among existing studies can only be applied to programs written in the JAVA language. To this end, we designed and implemented an additional step to standardize source change data into natural language using GPT. This text explains the process of extracting commit messages and source change data from Git, standardizing the source change data with GPT, and the learning process using the DistilBERT model. As a result of verification, an accuracy of 91% was measured. The proposed model was implemented and verified to ensure accuracy and to be able to classify without being dependent on a specific program. In the future, we plan to study a classification model using Bard and a management tool model helpful to the project using the proposed classification model.

• Journal of Food Hygiene and Safety
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• v.38 no.6
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• pp.464-475
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• 2023

In this study, based on an analysis of two DNA barcode markers (cytochrome c oxidase subunit I and cytochrome b genes), we performed species identification and monitored labeling compliance for 50 commercial pufferfish products sold in on-line markets in Korea. Using these barcode sequences as a query for species identification and phylogenetic analysis, we screened the GenBank database. A total of seven pufferfish species (Takifugu chinensis, T. pseudommus, T. xanthopterus, T. alboplumbeus, T. porphyreus, T. vermicularis, and Lagocephalus cheesemanii) were identified and we detected 35 products (70%) that were non-compliant with the corresponding label information. Moreover, the labels on 12 commercial products contained only the general common name (i.e., pufferfish), although not the scientific or Korean names for the 21 edible pufferfish species. Furthermore, the proportion of mislabeled highly processed products (n = 9, 81.8%) was higher than that of simply processed products (n = 26, 66.7%). With respect to the country of origin, the percentage of mislabeled Chinese products (n = 8, 80%) was higher than that of Korean products (n = 26, 66.7%). In addition, the market and dialect names of different pufferfish species were labeled only as Jolbok or Milbok, whereas two non-edible pufferfish species (T. vermicularis and T. pseudommus) were used in six commercial pufferfish products described as JolboK and Gumbok on their labels, which could be attributable to the complex classification system used for pufferfish. These monitoring results highlight the necessity to develop genetic methods that can be used to identify the 21 edible pufferfish species, as well as the need for regulatory monitoring of commercial pufferfish products.

• Journal of Internet Computing and Services
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• v.24 no.5
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• pp.17-27
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• 2023

Today, as AI (Artificial Intelligence) technology develops and its practicality increases, it is widely used in various application fields in real life. At this time, the AI model is basically learned based on various statistical properties of the learning data and then distributed to the system, but unexpected changes in the data in a rapidly changing data situation cause a decrease in the model's performance. In particular, as it becomes important to find drift signals of deployed models in order to respond to new and unknown attacks that are constantly created in the security field, the need for lifecycle management of the entire model is gradually emerging. In general, it can be detected through performance changes in the model's accuracy and error rate (loss), but there are limitations in the usage environment in that an actual label for the model prediction result is required, and the detection of the point where the actual drift occurs is uncertain. there is. This is because the model's error rate is greatly influenced by various external environmental factors, model selection and parameter settings, and new input data, so it is necessary to precisely determine when actual drift in the data occurs based only on the corresponding value. There are limits to this. Therefore, this paper proposes a method to detect when actual drift occurs through an Anomaly analysis technique based on XAI (eXplainable Artificial Intelligence). As a result of testing a classification model that detects DGA (Domain Generation Algorithm), anomaly scores were extracted through the SHAP(Shapley Additive exPlanations) Value of the data after distribution, and as a result, it was confirmed that efficient drift point detection was possible.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.12
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• pp.505-518
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• 2023

3D point cloud semantic segmentation is a computer vision task that involves dividing the point cloud into different objects and regions by predicting the class label of each point. Existing 3D semantic segmentation models have some limitations in performing sufficient fusion of multi-modal features while ensuring both characteristics of 2D visual features extracted from RGB images and 3D geometric features extracted from point cloud. Therefore, in this paper, we propose MMCA-Net, a novel 3D semantic segmentation model using 2D-3D multi-modal features. The proposed model effectively fuses two heterogeneous 2D visual features and 3D geometric features by using an intermediate fusion strategy and a multi-modal cross attention-based fusion operation. Also, the proposed model extracts context-rich 3D geometric features from input point cloud consisting of irregularly distributed points by adopting PTv2 as 3D geometric encoder. In this paper, we conducted both quantitative and qualitative experiments with the benchmark dataset, ScanNetv2 in order to analyze the performance of the proposed model. In terms of the metric mIoU, the proposed model showed a 9.2% performance improvement over the PTv2 model using only 3D geometric features, and a 12.12% performance improvement over the MVPNet model using 2D-3D multi-modal features. As a result, we proved the effectiveness and usefulness of the proposed model.

• Advanced Industrial SCIence
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• v.3 no.2
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• pp.8-16
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• 2024

This paper shows the system of drug classification, the goal of this is to foretell the apt drug for the patients based on their demographic and physiological traits. The dataset consists of various attributes like Age, Sex, BP (Blood Pressure), Cholesterol Level, and Na_to_K (Sodium to Potassium ratio), with the objective to determine the kind of drug being given. The models used in this paper are K-Nearest Neighbors (KNN), Logistic Regression and Random Forest. Further to fine-tune hyper parameters using 5-fold cross-validation, GridSearchCV was used and each model was trained and tested on the dataset. To assess the performance of each model both with and without hyper parameter tuning evaluation metrics like accuracy, confusion matrices, and classification reports were used and the accuracy of the models without GridSearchCV was 0.7, 0.875, 0.975 and with GridSearchCV was 0.75, 1.0, 0.975. According to GridSearchCV Logistic Regression is the most suitable model for drug classification among the three-model used followed by the K-Nearest Neighbors. Also, Na_to_K is an essential feature in predicting the outcome.

• Journal of Life Science
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• v.34 no.5
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• pp.320-332
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• 2024

The aims of this study were to compare the differences in hemodynamics between cigarette smoking and using heated tobacco products (HTPs) and to determine the acute effects of using HTPs on cardiac autonomic regulation. Another goal was to examine the acute cardiac autonomic responses when using different tobacco sticks in HTPs. Sixteen healthy male smokers completed an open-label, randomized, crossover trial consisting of non-smoking (NS), cigarette smoking, and the use of two different HTPs (IQOS with HEETS; lil SOLID with Fiit). Sub-trials, which included NS, lil SOLID with Fiit, and lil SOLID with HEET, were performed on eight smokers among the total subjects. Hemodynamic variables, such as systolic blood pressure (SBP) and diastolic blood pressure (DBP), and heart rate variability were measured before, during, and 30 minutes after using each tobacco product. Using HTPs resulted in a significant increase in both SBP and DBP, comparable to smoking cigarettes. Cardiac sympathetic activity significantly increased, and cardiac vagal tone (CVT) significantly decreased after acute exposure to HTP aerosol, similar to the effects of cigarette smoke exposure. Furthermore, differences in the withdrawal of CVT were observed when using different tobacco sticks in the same HTPs. The findings of this study indicate that acute exposure to HTP aerosol increases the hemodynamic burden and disrupts cardiac autonomic balance, similar to exposure to cigarette smoke. Moreover, depending on the type of tobacco stick inserted into the HTP device, acute withdrawal of CVT may have been enhanced.

• Korean Journal of Clinical Laboratory Science
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• v.55 no.4
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• pp.261-268
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• 2023

[¹⁸F]Fluorocholine is a radiopharmaceutical used non-invasively in positron emission tomography to diagnose parathyroid adenoma, prostate cancer, and hepatocellular carcinoma by evaluating the choline metabolism. In this study, a radiolabeling method for [¹⁸F]fluorocholine was optimized using a solid phase extraction (SPE) cartridge. [¹⁸F]Fluorocholine was labeled in two steps using an automated synthesizer. In the first step, dibromomethane was reacted with [¹⁸F]KF/K2.2.2/K₂CO₃ to obtain the intermediate [¹⁸F]fluorobromomethane. In the second step, [¹⁸F]fluorobromomethane was passed through a Sep-Pak^Ⓡ Silica SPE cartridge to remove the impurities and then reacted with N,N-dimethylaminoethanol (DMAE) in a Sep-Pak^Ⓡ C18 SPE cartridge to label [¹⁸F]fluorocholine. The reaction conditions of [¹⁸F]fluorocholine were optimized. The synthesis yield was confirmed according to the number of silica cartridges and DMAE concentration. No statistically significant difference in the synthesis yield of [¹⁸F]fluorocholine was observed when using four or three silica cartridges (P>0.05). The labeling yield was 11.5±0.5% (N=4) when DMAE was used as its original solution. On the other hand, when diluted to 10% with dimethyl sulfoxide, the radiochemical yield increased significantly to 30.1±5.2% (N=20). In conclusion, [¹⁸F]Fluorocholine for clinical use can be synthesized stably in high yield by applying an optimized synthesis method.

• Korean Journal of Radiology
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• v.23 no.9
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• pp.911-920
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• 2022

Objective: ⁶⁸Ga-NGUL is a novel prostate-specific membrane antigen (PSMA)-targeting tracer based on Glu-Urea-Lys derivatives conjugated to a 1,4,7-triazacyclononane-N,N',N''-triacetic acid (NOTA) chelator via a thiourea-type short linker. This phase I clinical trial of ⁶⁸Ga-NGUL was conducted to evaluate the safety and radiation dosimetry of ⁶⁸Ga-NGUL in healthy volunteers and the lesion detection rate of ⁶⁸Ga-NGUL in patients with prostate cancer. Materials and Methods: We designed a prospective, open-label, single-arm clinical trial with two cohorts comprising six healthy adult men and six patients with metastatic prostate cancer. Safety and blood test-based toxicities were monitored throughout the study. PET/CT scans were acquired at multiple time points after administering ⁶⁸Ga-NGUL (2 MBq/kg; 96-165 MBq). In healthy adults, absorbed organ doses and effective doses were calculated using the OLINDA/EXM software. In patients with prostate cancer, the rates of detecting suspicious lesions by ⁶⁸Ga-NGUL PET/CT and conventional imaging (CT and bone scintigraphy) during the screening period, within one month after recruitment, were compared. Results: All 12 participants (six healthy adults aged 31-32 years and six prostate cancer patients aged 57-81 years) completed the clinical trial. No drug-related adverse events were observed. In the healthy adult group, ⁶⁸Ga-NGUL was rapidly distributed, with the highest uptake in the kidneys. The median effective dose coefficient was calculated as 0.025 mSv/MBq, and cumulative activity in the bladder had the highest contribution. In patients with metastatic prostate cancer, 229 suspicious lesions were detected using either ⁶⁸Ga-NGUL PET/CT or conventional imaging. Among them, ⁶⁸Ga-NGUL PET/CT detected 199 (86.9%) lesions and CT or bone scintigraphy detected 114 (49.8%) lesions. Conclusion: ⁶⁸Ga-NGUL can be safely applied clinically and has shown a higher detection rate for the localization of metastatic lesions in prostate cancer than conventional imaging. Therefore, ⁶⁸Ga-NGUL is a valuable option for prostate cancer imaging.