• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1283-1293
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• 2022

The purpose of this study is to predict residual chlorine in order to maintain stable residual chlorine concentration in sedimentation basin by using artificial intelligence algorithms in water treatment process employing pre-chlorination. Available water quantity and quality data are collected and analyzed statistically to apply into mathematical multiple regression and artificial intelligence models including multi-layer perceptron neural network, random forest, long short term memory (LSTM) algorithms. Water temperature, turbidity, pH, conductivity, flow rate, alkalinity and pre-chlorination dosage data are used as the input parameters to develop prediction models. As results, it is presented that the random forest algorithm shows the most moderate prediction result among four cases, which are long short term memory, multi-layer perceptron, multiple regression including random forest. Especially, it is result that the multiple regression model can not represent the residual chlorine with the input parameters which varies independently with seasonal change, numerical scale and dimension difference between quantity and quality. For this reason, random forest model is more appropriate for predict water qualities than other algorithms, which is classified into decision tree type algorithm. Also, it is expected that real time prediction by artificial intelligence models can play role of the stable operation of residual chlorine in water treatment plant including pre-chlorination process.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.3
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• pp.103-110
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• 2023

With the spread of the Internet, anonymous communities emerged along with the activation of communities for communication between people, and many users are doing harm to others, such as posting aggressive posts and leaving comments using anonymity. In the past, administrators directly checked posts and comments, then deleted and blocked them, but as the number of community users increased, they reached a level that managers could not continue to monitor. Initially, word filtering techniques were used to prevent malicious writing from being posted in a form that could not post or comment if a specific word was included, but they avoided filtering in a bypassed form, such as using similar words. As a way to solve this problem, deep learning was used to monitor posts posted by users in real-time, but recently, the community uses words that can only be understood by the community or from a human perspective, not from a general Korean word. There are various types and forms of characters, making it difficult to learn everything in the artificial intelligence model. Therefore, in this paper, we proposes a preprocessing technique in which each character of a sentence is imaged using a CNN model that learns the consonants, vowel and spacing images of Korean word and converts characters that can only be understood from a human perspective into characters predicted by the CNN model. As a result of the experiment, it was confirmed that the performance of the LSTM, BiLSTM and CNN-BiLSTM models increased by 3.2%, 3.3%, and 4.88%, respectively, through the proposed preprocessing technique.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.5
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• pp.323-330
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• 2023

The development of an analysis model that reflects the microstructure characteristics of polyvinyl alcohol (PVA) fiber-reinforced cementitious composites, which have a highly complex microstructure, enables synergy between efficient material design and real experiments. PVA fiber orientations are an important factor that influences the mechanical behavior of PVA fiber-reinforced cementitious composites. Owing to the difficulty in distinguishing the gray level value obtained from micro-CT images of PVA fibers from adjacent phases, fiber segmentation is time-consuming work. In this study, a micro-CT test with a voxel size of 0.65 ㎛³ was performed to investigate the three-dimensional distribution of fibers. To segment the fibers and generate training data, histogram, morphology, and gradient-based phase-segmentation methods were used. A U-net model was proposed to segment fibers from micro-CT images of PVA fiber-reinforced cementitious composites. Data augmentation was applied to increase the accuracy of the training, using a total of 1024 images as training data. The performance of the model was evaluated using accuracy, precision, recall, and F1 score. The trained model achieved a high fiber segmentation performance and efficiency, and the approach can be applied to other specimens as well.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1195-1210
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• 2023

Coastal debris has emerged as a salient issue due to its adverse effects on coastal aesthetics, ecological systems, and human health. In pursuit of effective countermeasures, the present study delineated the construction of a specialized image dataset for coastal debris detection and embarked on a comparative analysis between two paramount real-time object detection algorithms, YOLOv8 and RT-DETR. Rigorous assessments of robustness under multifarious conditions were instituted, subjecting the models to assorted distortion paradigms. YOLOv8 manifested a detection accuracy with a mean Average Precision (mAP) value ranging from 0.927 to 0.945 and an operational speed between 65 and 135 Frames Per Second (FPS). Conversely, RT-DETR yielded an mAP value bracket of 0.917 to 0.918 with a detection velocity spanning 40 to 53 FPS. While RT-DETR exhibited enhanced robustness against color distortions, YOLOv8 surpassed resilience under other evaluative criteria. The implications derived from this investigation are poised to furnish pivotal directives for algorithmic selection in the practical deployment of marine debris monitoring systems.

• 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.

• Journal of Advanced Navigation Technology
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• v.28 no.1
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• pp.149-154
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• 2024

Research on particulate matter is advancing in real-time, and various methods are being studied to improve the accuracy of prediction models. Furthermore, studies that take into account various factors to understand the precise causes and impacts of particulate matter are actively being pursued. This paper trains an LSTM model using seasonal data and another LSTM model using concentration-based data. It compares and analyzes the PM_2.5 prediction performance of the two models. To train the model, weather data and air pollutant data were collected. The collected data was then used to confirm the correlation with PM_2.5. Based on the results of the correlation analysis, the data was structured for training and evaluation. The seasonal prediction model and the concentration-specific prediction model were designed using the LSTM algorithm. The performance of the prediction model was evaluated using accuracy, RMSE, and MAPE. As a result of the performance evaluation, the prediction model learned by concentration had an accuracy of 91.02% in the "bad" range of AQI. And overall, it performed better than the prediction model trained by season.

• Journal of The Korean Association For Science Education
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• v.44 no.1
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• pp.39-55
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• 2024

This study aimed to examine research trends related to Natural Language Processing (NLP) in science education by analyzing 37 domestic and international documents that utilized NLP techniques in the field of science education from 2011 to September 2023. In particular, the study systematically analyzed the content, focusing on the main application areas of NLP techniques in science education, the role of teachers when utilizing NLP techniques, and a comparison of domestic and international perspectives. The analysis results are as follows: Firstly, it was confirmed that NLP techniques are significantly utilized in formative assessment, automatic scoring, literature review and classification, and pattern extraction in science education. Utilizing NLP in formative assessment allows for real-time analysis of students' learning processes and comprehension, reducing the burden on teachers' lessons and providing accurate, effective feedback to students. In automatic scoring, it contributes to the rapid and precise evaluation of students' responses. In literature review and classification using NLP, it helps to effectively analyze the topics and trends of research related to science education and student reports. It also helps to set future research directions. Utilizing NLP techniques in pattern extraction allows for effective analysis of commonalities or patterns in students' thoughts and responses. Secondly, the introduction of NLP techniques in science education has expanded the role of teachers from mere transmitters of knowledge to leaders who support and facilitate students' learning, requiring teachers to continuously develop their expertise. Thirdly, as domestic research on NLP is focused on literature review and classification, it is necessary to create an environment conducive to the easy collection of text data to diversify NLP research in Korea. Based on these analysis results, the study discussed ways to utilize NLP techniques in science education.

• The Journal of the Acoustical Society of Korea
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• v.43 no.3
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• pp.293-304
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• 2024

In this paper, efficient and reliable methodologies for conducting sea trials to evaluate the performance of hull-mounted sonar systems is discussed. These systems undergo performance verification during ship construction via sea trials. However, the evaluation procedures often lack detailed consideration of variabilities in detection performance due to seabed topography, seasonal factors. To resolve this issue, temperature and salinity structure data were collected from 1967 to 2022 using ARGO floats and ocean observers data. The paper proposes an efficient and reliable sea trial method incorporating Bellhop modeling. Furthermore, a machine learning model applying a Physics-Informed Neural Networks was developed using the acquired data. This model predicts the sound speed profile at specific points within the sea trial area, reflecting seasonal elements of performance evaluation. In this study, we predicted the seasonal variations in sound speed structure during sea trial operations at a specific location within the trial area. We then proposed a strategy to account for the variability in detection performance caused by seasonal factors, using results from Bellhop modeling.

• Journal of Advanced Navigation Technology
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• v.26 no.4
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• pp.211-218
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• 2022

Continuous wave (CW) radar has the advantage of reliability and accuracy compared to other sensors such as camera and lidar. In addition, binarized neural network (BNN) has a characteristic that dramatically reduces memory usage and complexity compared to other deep learning networks. Therefore, this paper proposes binarized neural network based human identification and motion classification system using CW radar. After receiving a signal from CW radar, a spectrogram is generated through a short-time Fourier transform (STFT). Based on this spectrogram, we propose an algorithm that detects whether a person approaches a radar. Also, we designed an optimized BNN model that can support the accuracy of 90.0% for human identification and 98.3% for motion classification. In order to accelerate BNN operation, we designed BNN hardware accelerator on field programmable gate array (FPGA). The accelerator was implemented with 1,030 logics, 836 registers, and 334.904 Kbit block memory, and it was confirmed that the real-time operation was possible with a total calculation time of 6 ms from inference to transferring result.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.274-286
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• 2022

Multi-object tracking has been studied for a long time under computer vision and plays a critical role in applications such as autonomous driving and driving assistance. Multi-object tracking techniques generally consist of a detector that detects objects and a tracker that tracks the detected objects. Various publicly available datasets allow us to train a detector model without much effort. However, there are relatively few publicly available datasets for training a tracker model, and configuring own tracker datasets takes a long time compared to configuring detector datasets. Hence, the detector is often developed separately with a tracker module. However, the separated tracker should be adjusted whenever the former detector model is changed. This study proposes a system that can train a model that performs detection and tracking simultaneously using only the detector training datasets. In particular, a Siam network with augmentation is used to compose the detector and tracker. Experiments are conducted on public datasets to verify that the proposed algorithm can formulate a real-time multi-object tracker comparable to the state-of-the-art tracker models.