• Journal of the Korean Institute of Intelligent Systems
• /
• v.18 no.1
• /
• pp.32-40
• /
• 2008

Fuzzy rules are very useful and efficient to describe classification rules especially when the attribute values are continuous and fuzzy in nature. However, it is generally difficult to determine membership functions for generating efficient fuzzy classification rules. In this paper, we propose a method of automatic generation of efficient fuzzy classification rules using evolutionary algorithm. In our method we generate a set of initial membership functions for evolutionary algorithm by supervised clustering the training data set and we evolve the set of initial membership functions in order to generate fuzzy classification rules taking into consideration both classification accuracy and rule comprehensibility. To reduce time to evaluate an individual we also propose an evolutionary algorithm with data partition evaluation in which the training data set is partitioned into a number of subsets and individuals are evaluated using a randomly selected subset of data at a time instead of the whole training data set. We experimented our algorithm with the UCI learning data sets, the experiment results showed that our method was more efficient at average compared with the existing algorithms. For the evolutionary algorithm with data partition evaluation, we experimented with our method over the intrusion detection data of KDD'99 Cup, and confirmed that evaluation time was reduced by about 70%. Compared with the KDD'99 Cup winner, the accuracy was increased by 1.54% while the cost was reduced by 20.8%.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.42 no.3 s.303
• /
• pp.17-28
• /
• 2005

Recently intelligent characters in computer games have been an important element more and more because they continually stimulate gamers' interests. As a typical method for implementing such intelligent characters, neural networks have been used for training action patterns of opponent's characters and game rules. However, some of the game rules can be abruptly changed and action properties of garners in on-line game environments are quite different according to gamers. In this paper, we address how a neural network adapts to those environmental changes. Our adaptation solution includes two components: an individual adaptation mechanism and a group adaptation mechanism. With the individual adaptation algorithm, an intelligent character steadily checks its game score, assesses the environmental change with taking into consideration of the lastly earned scores, and initiates a new learning process when a change is detected. In multi-user games, including massively multiple on-line games, intelligent characters confront diverse opponents that have various action patterns and strategies depending on the gamers controlling the opponents. The group adaptation algorithm controls the birth of intelligent characters to conserve an equilibrium state of a game world by using a genetic algorithm. To show the performance of the proposed schemes, we implement a simple fighting action game and experiment on it with changing game rules and opponent characters' action patterns. The experimental results show that the proposed algorithms are able to make intelligent characters adapt themselves to the change.

• The Journal of Bigdata
• /
• v.6 no.1
• /
• pp.51-62
• /
• 2021

In the field of medical artificial intelligence, there have been a lot of researches on disease prediction and classification algorithms that can help doctors judge, but relatively less interested in artificial intelligence that can help medical consumers acquire and judge information. The fact that more than 150,000 questions have been asked about which hospital to go over the past year in NAVER portal will be a testament to the need to provide medical information suitable for medical consumers. Therefore, in this study, we wanted to establish a classification model that classifies 8 medical subjects for symptom text directly described by patients which was collected from NAVER portal to help consumers choose appropriate medical subjects for their symptoms. In order to ensure the validity of the data involving patients' subject matter, we conducted similarity measurements between objective symptom text (typical symptoms by medical subjects organized by the Seoul Emergency Medical Information Center) and subjective symptoms (NAVER data). Similarity measurements demonstrated that if the two texts were symptoms of the same medical subject, they had relatively higher similarity than symptomatic texts from different medical subjects. Following the above procedure, the classification model was constructed using a ridge regression model for subjective symptom text that obtained validity, resulting in an accuracy of 0.73.

• The Journal of Bigdata
• /
• v.6 no.1
• /
• pp.91-113
• /
• 2021

Despite the influence of real estate in the Korean asset market, it is not easy to predict market trends, and among them, apartments are not easy to predict because they are both residential spaces and contain investment properties. Factors affecting apartment prices vary and regional characteristics should also be considered. This study was conducted to compare the factors and characteristics that affect apartment prices in Seoul as a whole, 3 Gangnam districts, Nowon, Dobong, Gangbuk, Geumcheon, Gwanak and Guro districts and to understand the possibility of price prediction based on this. The analysis used machine learning algorithms such as neural networks, CHAID, linear regression, and random forests. The most important factor affecting the average selling price of all apartments in Seoul was the government's policy element, and easing policies such as easing transaction regulations and easing financial regulations were highly influential. In the case of the three Gangnam districts, the policy influence was low, and in the case of Gangnam-gu District, housing supply was the most important factor. On the other hand, 6 mid-lower-level districts saw government policies act as important variables and were commonly influenced by financial regulatory policies.

• The Journal of Engineering Geology
• /
• v.31 no.3
• /
• pp.395-432
• /
• 2021

Groundwater recharge rates vary widely by location and with time. They are difficult to measure directly and are thus often estimated using simulations. This study employed frequency and regression analysis and a classification and regression tree (CART) algorithm in a machine learning method to estimate groundwater recharge. CART algorithms are considered for the distribution of precipitation by subbasin (PCP), geomorphological data, indices of the relationship between vegetation and landuse, and soil type. The considered geomorphological data were digital elevaion model (DEM), surface slope (SLOP), surface aspect (ASPT), and indices were the perpendicular vegetation index (PVI), normalized difference vegetation index (NDVI), normalized difference tillage index (NDTI), normalized difference residue index (NDRI). The spatio-temperal distribution of groundwater recharge in the SWAT-MOD-FLOW program, was classified as group 4, run in R, sampled for random and a model trained its groundwater recharge was predicted by CART condidering modified PVI, NDVI, NDTI, NDRI, PCP, and geomorphological data. To assess inter-rater reliability for group 4 groundwater recharge, the Kappa coefficient and overall accuracy and confusion matrix using K-fold cross-validation were calculated. The model obtained a Kappa coefficient of 0.3-0.6 and an overall accuracy of 0.5-0.7, indicating that the proposed model for estimating groundwater recharge with respect to soil type and vegetation cover is quite reliable.

• Journal of Broadcast Engineering
• /
• v.26 no.2
• /
• pp.155-166
• /
• 2021

Most object detection algorithms are studied based on RGB images. Because the RGB cameras are capturing images based on light, however, the object detection performance is poor when the light condition is not good, e.g., at night or foggy days. On the other hand, high-quality infrared(IR) images regardless of weather condition and light can be acquired because IR images are captured by an IR sensor that makes images with heat information. In this paper, we performed the object detection algorithm based on the compression ratio in RGB and IR images to show the detection capabilities. We selected RGB and IR images that were taken at night from the Free FLIR Thermal dataset for the ADAS(Advanced Driver Assistance Systems) research. We used the pre-trained object detection network for RGB images and a fine-tuned network that is tuned based on night RGB and IR images. Experimental results show that higher object detection performance can be acquired using IR images than using RGB images in both networks.

• The Journal of the Convergence on Culture Technology
• /
• v.5 no.3
• /
• pp.197-202
• /
• 2019

In the era of the fourth revolution, interest in content production using proven engines in the broadcasting sector, such as Vizrt, is growing. The new visual effects required in the 5G era are critical to content production training. Vizrt has a good production time utility and affordability for broadcast and media content. In this paper, we are going to use this to present a practical case of the theorem and application of the basic training course in the production of virtual content, and to present the basic training direction. In the introduction, the graphic algorithm analyzed and studied the characteristics and environmental factors of the Vizrt engine. In this paper, the production process was studied separately, and the work carried out through engine implementation was presented. The VS Studio Foundation was provided as a practical production case at each stage. The Vizrt engine operator process is important in graphic approach and application, and through the results of the lecture, the method of understanding and implementing algorithms for virtual reality perspective suitable for basic learning was studied. Based on practice, the research method of main theory was to create Vizrt contents specialized in 5G contents work in each sector and to implement graphic production in new areas from contents image. Through this study, we came to the conclusion of the basic training method through virtual reality content work based on Vizrt by practicing content creation according to the subject. It also proposes the effect of creating Vizrt content and the direction of building Vizrt basic training courses.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.13 no.6
• /
• pp.461-469
• /
• 2020

In this paper, we propose a PCA-based feature feedback method for extracting important areas of handwritten numeric data sets and face data sets. A PCA-based feature feedback method is proposed by extending the previous LDA-based feature feedback method. In the proposed method, the data is reduced to important feature dimensions by applying the PCA technique, one of the dimension reduction machine learning algorithms. Through the weights derived during the dimensional reduction process, the important points of data in each reduced dimensional axis are identified. Each dimension axis has a different weight in the total data according to the size of the eigenvalue of the axis. Accordingly, a weight proportional to the size of the eigenvalues of each dimension axis is given, and an operation process is performed to add important points of data in each dimension axis. The critical area of the data is calculated by applying a threshold to the data obtained through the calculation process. After that, induces reverse mapping to the original data in the important area of the derived data, and selects the important area in the original data space. The results of the experiment on the MNIST dataset are checked, and the effectiveness and possibility of the pattern recognition method based on PCA-based feature feedback are verified by comparing the results with the existing LDA-based feature feedback method.

• Journal of Digital Convergence
• /
• v.19 no.6
• /
• pp.61-71
• /
• 2021

In a situation where the online learning is expanding due to COVID-19, the current maker education has limitations in applying it to classes. This study is to design the class of online maker education using artificial intelligence tools in elementary school. Also, it is to identify the responses to it and to confirm whether it helps improve the learner's computational thinking and creative problem solving ability. The class was designed by the literature review and redesign of the curriculum. Using interveiw, the responses of instructor and learners were identified. Pre- and post-test using corresponding sample t-test was conducted. As a result, the class consisted of ten steps including empathizing, defining making problems, identifying the characteristics of material and tool, designing algorithms and coding using remixes, etc. For computing thinking and creative problem solving ability, statistically significant difference was found. This study has the significance that practical maker activities using educational artificial intelligence tools in the context of elementary education can be practically applied even in the online environment.

• Journal of the Korea Convergence Society
• /
• v.13 no.4
• /
• pp.7-15
• /
• 2022

With the aim of an intelligent world in the age of individual customization through decentralization of information and technology, sharing/opening, and connection, we often see a tendency to cross expectations and concerns in the technological discourse and interest in artificial intelligence more than ever. Recently, it is easy to find claims by futurists that AI singularity will appear before and after 2045. Now, as part of preparations to create a paradigm of coexistence that coexists and prosper with AI in the coming age of artificial intelligence, a standard framework for setting up more correct AI ethics and regulations is required. This is because excluding the risk of omission of setting major guidelines and methods for evaluating reasonable and more reasonable guideline items and evaluation standards are increasingly becoming major research issues. In order to solve these research problems and at the same time to develop continuous experiences and learning effects on AI ethics and regulation setting, we collect guideline data on AI ethics and regulation of international organizations / countries / companies, and research and suggest ways to set up a standard framework (SF: Standard Framework) through a setting research model and text mining exploratory analysis. The results of this study can be contributed as basic prior research data for more advanced AI ethics and regulatory guidelines item setting and evaluation methods in the future.