• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.311-326
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• 2024

The rapid development of neural network technology promotes the neural network model driven by big data to overcome the texture effect of complex objects. Due to the limitations in complex scenes, it is necessary to establish custom template matching and apply it to the research of many fields of computational vision technology. The dependence on high-quality small label sample database data is not very strong, and the machine learning system of deep feature connection to complete the task of texture effect inference and speculation is relatively poor. The style transfer algorithm based on neural network collects and preserves the data of patterns, extracts and modernizes their features. Through the algorithm model, it is easier to present the texture color of patterns and display them digitally. In this paper, according to the texture effect reasoning of custom template matching, the 3D visualization of the target is transformed into a 3D model. The high similarity between the scene to be inferred and the user-defined template is calculated by the user-defined template of the multi-dimensional external feature label. The convolutional neural network is adopted to optimize the external area of the object to improve the sampling quality and computational performance of the sample pyramid structure. The results indicate that the proposed algorithm can accurately capture the significant target, achieve more ablation noise, and improve the visualization results. The proposed deep convolutional neural network optimization algorithm has good rapidity, data accuracy and robustness. The proposed algorithm can adapt to the calculation of more task scenes, display the redundant vision-related information of image conversion, enhance the powerful computing power, and further improve the computational efficiency and accuracy of convolutional networks, which has a high research significance for the study of image information conversion.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.75-81
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• 2024

In this paper, we proposes a method for real-time processing of inter-floor noise problems by embedding TinyML, which includes a deep learning model, into ultra-low-power systems. The reason this method is feasible is because of lightweight deep learning model technology, which allows even systems with small computing resources to perform inference autonomously. The conventional method proposed to solve inter-floor noise problems was to send data collected from sensors to a server for analysis and processing. However, this centralized processing method has issues with high costs, complexity, and difficulty in real-time processing. In this paper, we address these limitations by employing On-Sensor AI using TinyML. The method presented in this paper is simple to install, cost-effective, and capable of processing problems in real-time.

• Geomechanics and Engineering
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• v.37 no.4
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• pp.307-321
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• 2024

This paper delves into the critical assessment of predicting sidewall displacement in underground caverns through the application of nine distinct machine learning techniques. The accurate prediction of sidewall displacement is essential for ensuring the structural safety and stability of underground caverns, which are prone to various geological challenges. The dataset utilized in this study comprises a total of 310 data points, each containing 13 relevant parameters extracted from 10 underground cavern projects located in Iran and other regions. To facilitate a comprehensive evaluation, the dataset is evenly divided into training and testing subset. The study employs a diverse array of machine learning models, including recurrent neural network, back-propagation neural network, K-nearest neighbors, normalized and ordinary radial basis function, support vector machine, weight estimation, feed-forward stepwise regression, and fuzzy inference system. These models are leveraged to develop predictive models that can accurately forecast sidewall displacement in underground caverns. The training phase involves utilizing 80% of the dataset (248 data points) to train the models, while the remaining 20% (62 data points) are used for testing and validation purposes. The findings of the study highlight the back-propagation neural network (BPNN) model as the most effective in providing accurate predictions. The BPNN model demonstrates a remarkably high correlation coefficient (R2 = 0.99) and a low error rate (RMSE = 4.27E-05), indicating its superior performance in predicting sidewall displacement in underground caverns. This research contributes valuable insights into the application of machine learning techniques for enhancing the safety and stability of underground structures.

• The Transactions of the Korea Information Processing Society
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• v.4 no.9
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• pp.2280-2288
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• 1997

An agent is a software element that, by making use of knowledge and inference, performs tasks on behalf of the user. In general, an agent has the properties of autonomy, social ability, reactivity, and durability. Many researches on agents are more and more aiming at the multiagent systems since it is not sufficient to let a single agent do the whole things, especially in a real world where tasks require many diverse activities. However, the multiagent frameworks still have some limitations in the processing of user queries that are often ambiguous and goal-oriented. Also, a series of procedures or plans could not be generated from a single query directly. In order to give more intelligence to the multiagent framework, we propose a method of extending the framework with a plan generation module. The open agent architecture (OAA), which is a multiagent framework that we developed, is integrated with UCPOP, which is a AI planner. A travel schedule management agent (TSMA) system is implemented to explore the effects of the method. The extended system enables the user to only specify goal-oriented queries, and the plans and procedures to satisfy these goals are generated automatically. Also, this system provides a cooperative and knowledge-sharing environment that integrates several knowledge-based systems and planning systems that are distributed and used independently.

• Journal of Korea Multimedia Society
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• v.6 no.1
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• pp.148-160
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• 2003

TMA(Tissue Mineral Analysis) is very popular method in hair mineral analysis for health care professionals in over 48 countries medical center. Assesment of nutritional minerals and toxic elements in the hair is very important not only for determining adequacy, deficiencies and unbalance, but also for assessing their relative relationships in a body. In Korea, there are some problems in TMA method. Because of not haying a medical information database which is suitable for korean to do analyze, the requested TMA has to send to TEI-USA. However, as the TMA results from TEI-USA is composed of English documents and graphic files prohibited to open, its usability is very low and a lot of dollars has to be payed. Also, it can make some problems in the reliability of the TMA results, since the TMA results are based on the database of western health and mineral standards, To solve these problems, I developed the first Medical Information System of TMA in Korea here. The system can analyze the complex tissue mineral data with multiple stage decision tree classifier. It is also constructed with multiple fuzzy database and hence analyze the TMA data by fuzzy inference methods. The effectiveness test of this systems can be shown the increased business efficiency and satisfaction rate 86% and 92% respectively.

• Applied Biological Chemistry
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• v.30 no.4
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• pp.364-370
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• 1987

The $O_2^-$ level of the extract from young rice leaves, which was cold treated for 2 days and then placed at room temperature for a period of time significantly higher than that from tissues untreated. $O_2^-$ level in leaves was practically unchanged during cold treatment for 48 hours. But it started to increase to arrive at maximum in 8 hours, once the plants were placed under room temperature. The abnormal production of $O_2^-$ in mitochondria during postchilling process was interpreted as a biochemical consequence of accumulation of glycolysis product(s) in cytosol and/or NADH in mitochondrial matrix due to disruption of catabolic balance at low temperature. Mitochondria isolated from the chilling injured tissue was found to have lost considerably their respiratory activity. This fact may imply the involvement of intramitochondrial accumulation of $O_2^-$ in the inactivation of electron transport chain system. The observation that mitochondria in the presence of the $O_2^--producing$ enzymatic system (Xanthine/Xanthine oxidase) lost their respiratory activity supports this inference. It was also found in this work that Superoxide dismutase (SOD) is a substrate inducible enzyme, and that SOD is a possible protective agent in plant cell against chilling injury.

• Korean Journal of Cognitive Science
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• v.16 no.4
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• pp.369-386
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• 2005

Although Intelligent Tutoring System(ITS) offers individualized learning environment that overcome limited function of existent CAI, and consider many learners' variable, there is little development to be using at the sites of schools because of inefficiency of investment and absence of pedagogical content knowledge representation techniques. To solve these problem, we should study a method, which represents knowledge for ITS, and which reuses knowledge base. On the pedagogical content knowledge, the knowledge in education differs from knowledge in a general sense. In this paper, we shall primarily address the multi-complex structure of knowledge and explanation of learning vein using multi-complex structure. Multi-Complex, which is organized into nodes, clusters and uses by knowledge base. In addition, it grows a adaptive knowledge base by self-learning. Therefore, in this paper, we propose the 'Extended Neural Logic Network(X-Neuronet)', which is based on Neural Logic Network with logical inference and topological inflexibility in cognition structure, and includes pedagogical content knowledge and object-oriented conception, verify validity. X-Neuronet defines that a knowledge is directive combination with inertia and weights, and offers basic conceptions for expression, logic operator for operation and processing, node value and connection weight, propagation rule, learning algorithm.

• Journal of the Korean School Mathematics Society
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• v.10 no.1
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• pp.27-43
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• 2007

The aim of this paper is to develop a web-based learning system in order to motivate college students in the area of science and engineering to study college calculus. We designed and developed web-based contents, named MathBooster, using Mathematica, webMathematica and phpMath taking advantages of rapid computation and symbolic computation. The features of MathBooster consists of four parts: graphical representation of calculus concepts, textual illustrations of conceptual understanding, example-based step-by-step learning with phpMath, and quizzes with diagnostic feedback. After the MathBooster was practiced with engineering students, the formative evaluation was conducted with survey items composed in four categories: user responses, screen layout, practicing examples and diagnostic feedback in solving quizzes. The overall level of user satisfaction was statistically measured using SPSS. Those results indicate which parts of MathBooster are needed for future enhancement.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.1
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• pp.56-63
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• 2016

In this study, we introduce a design of Fuzzy RBFNNs-based digit recognition system using the incremental-PCA in order to recognize the handwritten digits. The Principal Component Analysis (PCA) is a widely-adopted dimensional reduction algorithm, but it needs high computing overhead for feature extraction in case of using high dimensional images or a large amount of training data. To alleviate such problem, the incremental-PCA is proposed for the computationally efficient processing as well as the incremental learning of high dimensional data in the feature extraction stage. The architecture of Fuzzy Radial Basis Function Neural Networks (RBFNN) consists of three functional modules such as condition, conclusion, and inference part. In the condition part, the input space is partitioned with the use of fuzzy clustering realized by means of the Fuzzy C-Means (FCM) algorithm. Also, it is used instead of gaussian function to consider the characteristic of input data. In the conclusion part, connection weights are used as the extended diverse types in polynomial expression such as constant, linear, quadratic and modified quadratic. Experimental results conducted on the benchmarking MNIST handwritten digit database demonstrate the effectiveness and efficiency of the proposed digit recognition system when compared with other studies.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.5
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• pp.495-503
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• 2014

In recent days, a study of the intrusion detection system collecting and analyzing network data, packet or logs, has been actively performed to response the network threats in computer security fields. In particular, Bayesian network has advantage of the inference functionality which can infer with only some of provided data, so studies of the intrusion system based on Bayesian network have been conducted in the prior. However, there were some limitations to calculate high detection performance because it didn't consider the problems as like complexity of the relation among network packets or continuos input data processing. Therefore, in this paper we proposed two methodologies based on K-menas clustering to improve detection rate by reforming the problems of prior models. At first, it can be improved by sophisticatedly setting interval range of nodes based on K-means clustering. And for the second, it can be improved by calculating robust CPT through applying weighted-leaning based on K-means clustering, too. We conducted the experiments to prove performance of our proposed methodologies by comparing K_WTAN_EM applied to proposed two methodologies with prior models. As the results of experiment, the detection rate of proposed model is higher about 7.78% than existing NBN(Naive Bayesian Network) IDS model, and is higher about 5.24% than TAN(Tree Augmented Bayesian Network) IDS mode and then we could prove excellence our proposing ideas.