• Journal of the Korean Operations Research and Management Science Society
• /
• v.17 no.2
• /
• pp.145-162
• /
• 1992

This paper proposes two design principles for expert systems to solve a stock market timing (SMART) problems : machine knowledge and fuzzy post-adjustment, Machine knowledge is derived from past SMART instances by using an inductive learning algorithm. A knowledge-based solution, which can be regarded as a prior SMART strategy, is then obtained on the basis of the machine knowledge. Fuzzy post-adjustment (FPA) refers to a Bayesian-like reasoning, allowing the prior SMART strategy to be revised by the fuzzy evaluation of environmental factors that might effect the SMART strategy. A prototype system, named K-SISS2 (Knowledge-based Stock Investment Support System 2), was implemented using the two design principles and tested for solving the SMART problem that is aimed at choosing the best time to buy or sell stocks. The prototype system worked very well in an actual stock investment situation, illustrating basic ideas and techniques underlying the suggested design principles.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1995.10b
• /
• pp.279-284
• /
• 1995

In CAI, it is very important to evaluate the grade of understanding which students reach about the scope of problem which students are studying. In this paper, to find out students' learning achievement, we make students reply to test which the system presents and then lead evaluation result using fuzzy number about answer result. Besides, we define the degree of prior knowledge of studentsd as conditioned fuzzy number and use existing fuzzy accuracy production function begore the stage of using fuzzy number, Next, we apply conditioned fuzzy number to accuracy degree of answer produces by this function. Through this, we come to the conclusion that evaluation result as to the same answer result is changed according to the degree of prior knowledge about the scope which students are studying.

• Journal of Information Technology Applications and Management
• /
• v.24 no.3
• /
• pp.51-61
• /
• 2017

The objective of the study is to examine the acceptance and usability of Augmented Reality (AR) visuals developed for industry analysis of Thai Banks and whether these visuals can outperform the table of numbers in representing financial accounting data. Convenient samples were used and the data were collected with self-assessed questionnaires from 109 users with minimum prior experiences with financial analyses. The results from descriptive statistics indicates that despite having over 80% of respondents with little prior experience in analyzing financial performance of banking industry, the majority of them were able to correctly make prediction (96.4%), identify trend (82.6%) and compare banks' performance (70.6%). Their attitudes and perception towards Bank-AR visuals were above average. Although the overall usability score is average (53%), the respondents rated the Bank-AR visuals to be highly useful and had high intention to use them in the future.

• ETRI Journal
• /
• v.41 no.3
• /
• pp.298-307
• /
• 2019

This paper investigates the use of the inverse-free sparse Bayesian learning (SBL) approach for peak-to-average power ratio (PAPR) reduction in orthogonal frequency-division multiplexing (OFDM)-based multiuser massive multiple-input multiple-output (MIMO) systems. The Bayesian inference method employs a truncated Gaussian mixture prior for the sought-after low-PAPR signal. To learn the prior signal, associated hyperparameters and underlying statistical parameters, we use the variational expectation-maximization (EM) iterative algorithm. The matrix inversion involved in the expectation step (E-step) is averted by invoking a relaxed evidence lower bound (relaxed-ELBO). The resulting inverse-free SBL algorithm has a much lower complexity than the standard SBL algorithm. Numerical experiments confirm the substantial improvement over existing methods in terms of PAPR reduction for different MIMO configurations.

• Journal of Korea Multimedia Society
• /
• v.23 no.8
• /
• pp.965-976
• /
• 2020

We present in this work a segmentation method of E. coli bacterial images generated via phase contrast microscopy using a deep learning based hybrid feature generation. Unlike conventional machine learning methods that use the hand-crafted features, we adopt the denoising autoencoder in order to generate a precise and accurate representation of the pixels. We first construct a hybrid vector that combines original image, difference of Gaussians and image gradients. The created hybrid features are then given to a deep autoencoder that learns the pixels' internal dependencies and the cells' shape and boundary information. The latent representations learned by the autoencoder are used as the inputs of a softmax classification layer and the direct outputs from the classifier represent the coarse segmentation mask. Finally, the classifier's outputs are used as prior information for a graph partitioning based fine segmentation. We demonstrate that the proposed hybrid vector representation manages to preserve the global shape and boundary information of the cells, allowing to retrieve the majority of the cellular patterns without the need of any post-processing.

• Asia pacific journal of information systems
• /
• v.16 no.4
• /
• pp.179-202
• /
• 2006

The objective of this study is to identify the impacts of communication reinforcement on performance of learning in Web-PBL. Communication reinforcement is defined as the combination of information sharing and co-construction. As factors facilitating communication reinforcement, we propose learner's characteristics, task characteristics, and group characteristics. Learner's characteristics are collaboration-orientation, openness, holistic approach, and online community-orientation which reflects e-learning environment. Collaboration-oriented tasks as group projects were developed and given to groups with 5-6 members. The group characteristics are categorized into 'horizontal' and 'vertical', according to the patterns of communication between a group leader and members. To verify empirically the proposed research model, an experimental design was performed to learners who took on-line and off-line courses with group projects. We found important results as follows; First, field dependence has positive impacts on information sharing, and online community-orientation has positive impacts on co-construction. These results correspond with prior studies on relationship between field dependence and collaborative learning. Second, collaboration-oriented task directly impacts on information sharing, and indirectly affects co-construction, This result implicates that information sharing is pre-requisite of co-construction. Third, 'horizontal' was identified as a factor giving positive effects on information sharing and co-construction. This result implies that horizontal communication is very important to facilitate communication reinforcement.

• Journal of Advanced Marine Engineering and Technology
• /
• v.38 no.10
• /
• pp.1303-1309
• /
• 2014

In this paper, we present an iterative learning control (ILC) framework for tracking problems with predefined data points that are desired points at certain time instants. To design ILC systems for such problems, a new ILC scheme is proposed to produce output curves that pass close to the desired points. Unlike traditional ILC approaches, an algorithm will be developed in which the control signals are generated by solving an optimal ILC problem with respect to the desired sampling points. In another word, it is a direct approach for the multiple points tracking ILC control problem where we do not need to divide the tracking problem into two steps separately as trajectory planning and ILC controller.The strength of the proposed formulation is the methodology to obtain a control signal through learning law only considering the given data points and dynamic system, instead of following the direction of tracking a prior identified trajectory. The key advantage of the proposed approach is to significantly reduce the computational cost. Finally, simulation results will be introduced to confirm the effectiveness of proposed scheme.

• Journal of Korea Multimedia Society
• /
• v.21 no.9
• /
• pp.1027-1034
• /
• 2018

CCTV has been known to possess high level of objectivity and utility. Hence, the government has recently focused on replacing low quality CCTV with higher quality ones or even by adding high resolution CCTV. However, converting all existing low-quality CCTV to high quality can be extremely costly. Furthermore, low quality videos prior to CCTV replacement are likely to be of poor quality and thus not utilized correctly. In order to solve these problems, this paper proposes a method to improve videos quality of images using SRGAN(Super Resolution Generative Advisory Networks). Through experiments, we have proven that it is possible to improve low quality CCTV videos clearly. For this experiment, a total of 4 types of CCTV videos were used and 10,000 images were sampled from each type. Those images could then be used for machine learning. The fact that the pre-process for machine learning has been done manually and the long time that required for machine learning seems to be complementary.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.10
• /
• pp.4214-4230
• /
• 2020

This paper introduces a pilot allocation scheme for massive MIMO systems based on deep convolutional neural network (CNN) learning. This work is an extension of a prior work on the basic deep learning framework of the pilot assignment problem, the application of which to a high-user density nature is difficult owing to the factorial increase in both input features and output layers. To solve this problem, by adopting the advantages of CNN in learning image data, we design input features that represent users' locations in all the cells as image data with a two-dimensional fixed-size matrix. Furthermore, using a sorting mechanism for applying proper rule, we construct output layers with a linear space complexity according to the number of users. We also develop a theoretical framework for the network capacity model of the massive MIMO systems and apply it to the training process. Finally, we implement the proposed deep CNN-based pilot assignment scheme using a commercial vanilla CNN, which takes into account shift invariant characteristics. Through extensive simulation, we demonstrate that the proposed work realizes about a 98% theoretical upper-bound performance and an elapsed time of 0.842 ms with low complexity in the case of a high-user-density condition.

• Journal of The Korean Association For Science Education
• /
• v.24 no.1
• /
• pp.76-89
• /
• 2004

Recent studies of science and technology "in-the-making" revealed that the process of designing material artifacts is not a straightforward application of prior images or theories by one (or more) person(s) isolated from his or her (their) environment. Rather, designing is a process contingent on the social and material setting for both engineering designers and students. Over the past decade, designing technological artifacts has emerged as an important learning environment in science classrooms. Through the analyses of a large database concerning an innovative simple machines curriculum for sixth-and seventh-grade students, we accumulated valid evidence for the nature of the designing process and science learning through it. In this paper, we show that design actions intertwine with the transformation of the objectified raw materials and artifact, the designer collective, and the mediating tools enabling that transformation, which constitute the elements of an activity from the perspective of cultural-historical activity theory. We conceptualize the continuous change of relation between material artifacts, designers, and tools throughout the design activity as co-evolution. Two episodes were selected to exemplify synchronic and diachronic change of relations inherent in co-evolving activity system. Finally, we discuss the implications of co-evolution during design activity for science learning.