• Journal of the Korean Institute of Intelligent Systems
• /
• v.11 no.3
• /
• pp.223-230
• /
• 2001

Since self-organizing map (SOM) preserves the topology of ordering in input spaces and trains itself by unsupervised algorithm, it is Llsed in many areas. However, SOM has a shortcoming: structure cannot be easily detcrmined without many trials-and-errors. Structure-adaptive self-orgnizing map (SASOM) which can adapt its structure as well as its weights overcome the shortcoming of self-organizing map: SASOM makes use of structure adaptation capability to place the nodes of prototype vectors into the pattern space accurately so as to make the decision boundmies as close to the class boundaries as possible. In this scheme, the initialization of weights of newly adapted nodes is important. This paper proposes a method which optimizes SASOM with genetic algorithm (GA) to determines the weight vector of newly split node. The leanling algorithm is a hybrid of unsupervised learning method and supervised learning method using LVQ algorithm. This proposed method not only shows higher performance than SASOM in terms of recognition rate and variation, but also preserves the topological order of input patterns well. Experiments with 2D pattern space data and handwritten digit database show that the proposed method is promising.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.20 no.2
• /
• pp.421-426
• /
• 2016

Recent developments in internet technologies has enabled widespread growth of embedded systems like Arduino, Raspberry Pi and other smart home systems. A research in the industrial sector on the utilization of the board has been made. The development needs of the embedded board in a reagent bottle case system has been highlighted. Current reagent Management System has to hold and manage the reagent itself is mostly to save the program using handwritten or machine. In addition, there is a risk to the system during the vulnerable zone administrator to manage the situation of the reagent bottle case can lead to a massive fire. In this paper, reagent bottle case RFID readers and data in real-time is monitored by attaching a sensor management through the database and sends a warning message to the mobile device of the administrator in real time during hazardous situations in the reagent bottle case. This is improve the reliability and efficiency of reagent bottle case.

• Journal of the Korea Institute of Building Construction
• /
• v.16 no.2
• /
• pp.177-183
• /
• 2016

The daily report includes the most detailed information and various daily planning and performance data recorded at a site, and includes work types, the number of workers, and the equipment and materials used. However, in the daily reporting process, some information can be omitted or distorted or even made redundant due to there being several steps of manual input. For this reason, the daily report is utilized for a simple report rather than for an appropriate purpose. Thus, to resolve the issues of the existing daily report system mentioned above, a daily report system using a digital pen was developed and then applied to an actual construction site to verify its applicability. As a result, it was found that 96.610% recognition accuracy of handwritten letters could be achieved. In addition, it was no longer necessary to enter the same information multiple times and no additional training or education for using a mobile device was needed. The digital pen-based daily report program developed in this study is expected to contribute to an improvement of information management efficiency and site document management work by addressing the problems of the existing report system.

• Journal of KIISE:Software and Applications
• /
• v.34 no.11
• /
• pp.1001-1010
• /
• 2007

HMM tends to output high probability for not only the proper class data but confusable class data, since the modeling power increases as the number of parameters increases. Thus it may not be helpful for discrimination to simply increase the number of parameters of HMM. We proposed two methods in this paper. One is a CMC(Confusion Likelihood Model Selection Criterion) using confusion class data probability, the other is a new recognition method, RCM(Recognition Using Confusion Models). In the proposed recognition method, confusion models are constructed using confusable class data, then confusion models are used to depress misrecognition by confusion likelihood is subtracted from the corresponding standard model probability. We found that CMC showed better results using fewer number of parameters compared with ML, ALC2, and BIC. RCM recorded 93.08% recognition rate, which is 1.5% higher result by reducing 17.4% of errors than using standard model only.

• The Transactions of the Korea Information Processing Society
• /
• v.3 no.3
• /
• pp.449-459
• /
• 1996

The main goal of this paper is to introduce a novel definition for fuzzy mathematical morphology and a neural network implementation. The generalized- mean operator plays the key role for the definition. Such definition is well suited for neural network implementation. The first stage of the shared-weight neural network has adequate architecture to perform morphological operation. The shared- weight network performs classification based on the features extracted with the fuzzy morphological operation defined in this paper. Therefore, the parameters for the fuzzy definition can be optimized using neural network learning paradigm. Learning rules for the structuring elements, degree of membership, and weighting factors are precisely described. In application to handwritten digit recognition problem, the fuzzy morphological shared-weight neural network produced the results which are comparable to the state-of art for this problem.

• Food Science and Industry
• /
• v.54 no.3
• /
• pp.184-195
• /
• 2021

Smart HACCP is a system that can check the monitoring of critical control point (CCP) in real time to implement improvement measures immediately after departure from limit criteria and prevent falsification of data by digitizing handwritten records. In this study, we developed the analysis model for the effectiveness ofsmart HACCP to compare and analyze with existing HACCP. By introducing of smart HACCP system, the evaluation index value of HACCP effectiveness for HACCP-certificated companies on a small scale increased by 9.25 points, corresponding to 11.52% of increase rate. General HACCP-certificated companies showed 4.52 point and 5.00% of increase rate by introducing of smart HACCP system. Thus, it was confirmed that the introduction of smart HACCP system contributes to the improvement of food safety management and especially it would be more effective for HACCP-certificated companies on a small scale than general HACCP-certificated companies.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.14 no.5
• /
• pp.969-976
• /
• 2019

A lightweight artificial intelligence hardware has made great strides in many application areas. In general, a lightweight artificial intelligence system consist of lightweight artificial intelligence engine and preprocessor including feature selection, generation, extraction, and normalization. In order to achieve optimal performance in broad range of applications, lightweight artificial intelligence system needs to choose a good preprocessing function and set their respective hyper-parameters. This paper proposes a unified framework for a lightweight artificial intelligence system and utilization method for finding models with optimal performance to use on a given dataset. The proposed unified framework can easily generate a model combined with preprocessing functions and lightweight artificial intelligence engine. In performance evaluation using handwritten image dataset and fall detection dataset measured with inertial sensor, the proposed unified framework showed building optimal artificial intelligence models with over 90% test accuracy.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.21 no.12
• /
• pp.619-628
• /
• 2020

This study examined the experiences of users regarding their use of electronic informed consent in hospital. A qualitative descriptive study was conducted using focus groups and in-depth interviews with 30 Korean nurses and 27 patients. Data were collected from one university hospital. The responses were analyzed by qualitative content analysis. Most participants perceived the use of electronic informed consent as convenient and straightforward, as well as saving space and money. On the other hand, the participants stated that the system was unsatisfactory in part because of the occasional unexpected machinery error or malfunction. Some patients wished for function improvements related to the e-signature, making it more comparable to a handwritten signature, and the adjustability of the font size. The nurses wanted a wider implementation of electronic informed consent because it was not being used for all informed consent cases, resulting in confusion and an additional workload. For the use of an electronic informed consent system, it is important to minimize the inconvenience and to maximize the satisfaction of the users, including nurses and patients.

• The Korean Journal of Applied Statistics
• /
• v.35 no.5
• /
• pp.579-591
• /
• 2022

Functional data is collected in various fields. It is often necessary to test whether there are differences among groups of functional data. In this case, it is not appropriate to explain using the point-wise ANOVA method, and we should present not the point-wise result but the integrated result. Various studies on functional data analysis of variance have been proposed, and recently implemented those methods in the package fdANOVA of R. In this paper, I first explain ANOVA and multivariate ANOVA, then I will introduce various methods of analysis of variance for univariate and multivariate functional data recently proposed. I also describe how to use the R package fdANOVA. This package is used to test equality of weekly temperatures in Seoul and Busan through univariate functional data ANOVA, and to test equality of multivariate functional data corresponding to handwritten images using multivariate function data ANOVA.

• Korean Journal of Construction Engineering and Management
• /
• v.24 no.5
• /
• pp.35-43
• /
• 2023

Along with the increase in Multi-unit housing defect disputes, the importance of defect management is also increased. However, previous studies have mostly focused on the Multi-unit housing's 'common part'. In addition, there is a lack of research on the system for the 'management office', which is a part of the subject of defect management. These resulted in the lack of defect management capability of the management office and the deterioration of management quality. Therefore, this paper proposes a machine learning-based defect data management system for management offices. The goal is to solve the inconvenience of management by using Optical Character Recognition (OCR) and Natural Language Processing (NLP) modules. This system converts handwritten defect information into online text via OCR. By using the language model, the defect information is regenerated along with the form specified by the user. Eventually, the generated text is stored in a database and statistical analysis is performed. Through this chain of system, management office is expected to improve its defect management capabilities and support decision-making.