• 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 Computer and Communication Systems
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• v.1 no.3
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• pp.171-180
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• 2012

In the development of MDMS(Meter Data Management System) based on CIM(Common Information Model), which is international standard in information model and data exchange on power system, the two focused issues are the effective management of data collected in a shorter time period and the way to integrate services supporting legacy system to use the AMI(AMI, Advanced Metering Infrastructure) data. In this paper, we propose MDMS implementation methods and functions in AMI environment which are differ from existing AMR system environments in that the methods support bi-directional service infrastructure. The proposed MDMS in this paper has two unique features, one is the secure of interoperability by utilizing the CIM and ESB, the other is the improvement of field application by implementing system module based on components. On an implementation of smart grid, the result of proposed methods is expected to contribute to the efficient development and operation of CIM-based power system.

• Journal of Korean Academy of Nursing
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• v.46 no.5
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• pp.697-709
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• 2016

Purpose: This study was conducted to develop and test a structural model of evidence-based practice (EBP) implementation among clinical nurses. The model was based on Melnyk and Fineout-Overholt's Advancing Research and Clinical Practice through Close Collaboration model and Rogers' Diffusion of Innovations theory. Methods: Participants were 410 nurses recruited from ten different tertiary hospitals in Korea. A structured self-report questionnaire was used to assess EBP knowledge/skills, EBP beliefs, EBP attitudes, organizational culture & readiness for EBP, dimensions of a learning organization and organizational innovativeness. Collected data were analyzed using SPSS/WINdows 20.0 and AMOS 20.0 program. Results: The modified research model provided a reasonable fit to the data. Clinical nurses' EBP knowledge/skills, EBP beliefs, and the organizational culture & readiness for EBP had statistically significant positive effects on the implementation of EBP. The impact of EBP attitudes was not significant. The dimensions of the learning organization and organizational innovativeness showed statistically significant negative effects on EBP implementation. These variables explained 32.8% of the variance of EBP implementation among clinical nurses. Conclusion: The findings suggest that not only individual nurses' knowledge/skills of and beliefs about EBP but organizational EBP culture should be strengthened to promote clinical nurses' EBP implementation.

• Journal of the Korea Institute of Military Science and Technology
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• v.23 no.4
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• pp.389-398
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• 2020

Test cases in the existing weapon system software were created manually by the tester analyzing the test items defined in the software integration test procedure. However, existing test case generation method has two limitations. First, the quality of test cases can vary depending on the tester's ability to analyze the test items. Second, excessive time and cost may be incurred in writing test cases. This paper proposes a method to automatically generate test cases based on the requirements model and specifications to overcome the limitations of the existing weapon system software test case generation. Generate test sequences and test data based on the use case event model, a model representing the requirements of the weapon system software, and the use case specification specifying the requirements. The proposed method was applied to 8 target models constituting the avionics control system, producing 30 test sequences and 8 test data.

• Current Optics and Photonics
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• v.6 no.1
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• pp.92-103
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• 2022

The development of midinfrared (mid-IR) quantum cascade lasers (QCLs) has enabled rapid high-contrast measurement of the mid-IR spectra of biological tissues. Several studies have compared the differences between the mid-IR spectra of colon cancer and noncancerous colon tissues. Most mid-IR spectrum classification studies have been proposed as machine-learning-based algorithms, but this results in deviations depending on the initial data and threshold values. We aim to develop a process for classifying colon cancer and noncancerous colon tissues through a deep-learning-based convolutional-neural-network (CNN) model. First, we image the midinfrared spectrum for the CNN model, an image-based deep-learning (DL) algorithm. Then, it is trained with the CNN algorithm and the classification ratio is evaluated using the test data. When the tissue microarray (TMA) and routine pathological slide are tested, the ML-based support-vector-machine (SVM) model produces biased results, whereas we confirm that the CNN model classifies colon cancer and noncancerous colon tissues. These results demonstrate that the CNN model using midinfrared-spectrum images is effective at classifying colon cancer tissue and noncancerous colon tissue, and not only submillimeter-sized TMA but also routine colon cancer tissue samples a few tens of millimeters in size.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2178-2198
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• 2024

Graph-based document models have good capabilities to reveal inter-dependencies among unstructured text data. Natural language processing (NLP) systems that use such models as an intermediate representation have shown good performance. This paper proposes a novel fuzzy graph-based document model and to demonstrate its effectiveness by applying fuzzy logic tools for text summarization. The proposed system accepts a text document as input and identifies some of its sentence level features, namely sentence position, sentence length, numerical data, thematic word, proper noun, title feature, upper case feature, and sentence similarity. The fuzzy membership value of each feature is computed from the sentences. We also propose a novel algorithm to construct the fuzzy graph as an intermediate representation of the input document. The Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metric is used to evaluate the model. The evaluation based on different quality metrics was also performed to verify the effectiveness of the model. The ANOVA test confirms the hypothesis that the proposed model improves the summarizer performance by 10% when compared with the state-of-the-art summarizers employing alternate intermediate representations for the input text.

• Design & Manufacturing
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• v.15 no.4
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• pp.24-31
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• 2021

In this study, an artificial neural network model was constructed to convert CAE analysis data into similar experimental data. In the analysis and experiment, the injection molding data for 50 conditions were acquired through the design of experiment and random selection method. The injection molding conditions and the weight, height, and diameter of the product derived from CAE results were used as the input parameters for learning of the convert model. Also the product qualities of experimental results were used as the output parameters for learning of the convert model. The accuracy of the convert model showed RMSE values of 0.06g, 0.03mm, and 0.03mm in weight, height, and diameter, respectively. As the next step, additional randomly selected conditions were created and CAE analysis was performed. Then, the additional CAE analysis data were converted to similar experimental data through the conversion model. An artificial neural network model was constructed to predict the quality of injection molded product by using converted similar experimental data and injection molding experiment data. The injection molding conditions were used as input parameters for learning of the predicted model and weight, height, and diameter of the product were used as output parameters for learning. As a result of evaluating the performance of the prediction model, the predicted weight, height, and diameter showed RMSE values of 0.11g, 0.03mm, and 0.05mm and in terms of quality criteria of the target product, all of them showed accurate results satisfying the criteria range.

• Journal of Digital Convergence
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• v.10 no.2
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• pp.287-292
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• 2012

In this paper, user personalized services through the emotion perception required to support location-based sensing data preprocessing techniques and emotion data preprocessing techniques is studied for user's emotion data building and preprocessing in V-A emotion model. For this purpose the granular context tree and string matching based emotion pattern matching techniques are used. In addition, context-aware and personalized recommendation services technique using probabilistic reasoning is studied for personalized services based on context awareness.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.377-382
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• 2005

This paper presents a new method for the attitude control of planar space robots. In order to control highly constrained non-linear system such as a 3D space robot, the analytical formulation for the system with complex dynamics and effective control methodology based on the formulation, are not always obtainable. In the proposed method, correspondingly, a non-analytical but effective self-organizing modeling method for controlling a highly constrained system is proposed based on a polynomial data mining algorithm. In order to control the attitude of a planar space robot, it is well known to require inputs characterized by a special pattern in time series with a non-deterministic length. In order to correspond to this type of control paradigm, we adopt the Model Predictive Control (MPC) scheme where the length of the non-deterministic horizon is determined based on implementation cost and control performance. The optimal solution to finding the size of the input pattern is found by a solving two-stage programming problem.

• Journal of the Korea Society of Computer and Information
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• v.21 no.12
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• pp.91-96
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• 2016

In this paper, we propose the CRM service model for analyzing and managing location based data collected by Wi-Fi and BLE. As mobile devices became personalized, enterprises became interested in individual location, and location based mobile marketing started to stand on spotlight. Location based proximity marketing is developing along with contactless data transmission technology, and payment system that uses NFC, Beacon that utilizes BLE, as well as advertisement via Wi-Fi are being serviced. We suggest the model that mobile devices can be detected and identified by MAC address with the need of being connected to Wi-FI or Bluetooth interface. MAC addresses are not associated with any specific user account or mobile phone number. The idea is to be able to measure the amount of people which are present in a certain point at a specific time, allowing the study of the evolution of data analysis and offers effective information for decision-makings.