• Journal of Advanced Navigation Technology
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• v.23 no.5
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• pp.481-486
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• 2019

Smart Factory requires real-time monitoring and analysis of all process processes for optimal production environment. Monitoring system for data collection from various sensors is necessary to make all production processes automatic. By storing and analyzing the collected data, we can check whether there are any signs of abnormalities in any machine or equipment. Thus, in this paper, an integrated monitoring system for smart factory incorporating a working environment monitoring system and an automatic storage system of measurement values was implemented. By using the automatic storage system of measurement values, it is possible to carry out reliable inspection in any place without misentry. Also, through working environment monitoring system using LoRa, production environments such as temperature, humidity and atmospheric pressure can be monitored in real time.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1243-1248
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• 2018

Metal Powder Metallurgy is a manufacturing technology that makes unique model parts or a certain type of product by using a hardening phenomenon when a powder of metal or metal oxide is put it into a mold and compression-molded by a press and then heated and sintered at a high temperature. Powder metallurgical press equipment is mainly used to make the parts of automobile, electronic parts and so on, and most of them are manufactured using precise servo motor. The intelligent precision servo control powder molding press system which is designed and implemented in this paper has advantages of lowering the price and maintaining the precision by using the mechanical camshaft for the upper ram part and precisely controlling the lower ram part using the high precision servo system. In addition, OPC-based monitoring and process data collection systems are designed and implemented to provide scalability that can be applied to smart manufacturing management systems that utilize Big Data in the future.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.2
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• pp.533-543
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• 2021

This study aims to determine and analyze the effect of social shopping, adventure shopping, value shopping, relaxation shopping, and idea shopping in influencing impulsive online buying behavior moderated by scarcity and serendipity information. The research method used is the quantitative research paradigm using surveys as a medium to obtain primary data. The paper examines the theoretical research model and tested fifteen hypotheses. The questionnaire was developed based on indicators from previous research. A non-probability sampling framework is used in this study. The data collection method uses electronic and online questionnaires to collect primary data with a total sample of 330 taken with the criteria of having made transactions in e-commerce Shopee in the last three months. Data analysis tools using Structural Equation Modelling (SEM) approach. The results showed that 8 out of 15 hypotheses were accepted and supported. The results show that there is a relationship between the value of hedonic shopping, scarcity, and serendipity information on impulsive online buying behavior. Therefore, analyzing the needs of customers, optimizing customer satisfaction, service excellence, website quality, and the ease of use of e-shopping itself especially in the e-commerce industry should be taken seriously nowadays.

• Water for future
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• v.7 no.2
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• pp.11-22
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• 1974

Comparing with Seoul city's other administrative works, the work to arrange and collect monthly water rates with 470,000 faucets is tremendous in volume and simple repetition in quality. In order to cover the shortage of handling, it is urgent for us to replace the present manual system with EDP(Electronic Data Processing) system to mechanize a series of handling works of simple repeated calculation such as water consumption, rate calculation, statistics arrangement, bills and specification of water rate by computer. When this work is completely mechanized, inspectors of water meter just turn over their checking results to the Data Center and all data are processed through Input Media(OMR Card, Punched Card) and computer for programming final bills. Then, the delivery of the bills to citizens will be the only work to be carried out. such mechanization will bring about the following benefits: 1. Improvement of administrative work by efficiency and rationalization. 2. Improvement of administrative service with people. 3. Possibility of scientific with trustworthy multi-purpose policy-making data. 4. An effect to cover the personnel shortage of 252 persons (at all the water works offices) and save manpower of 166 persons (47,619 man-days). The application of the above mentioned mechanization will be started to only Chongro and Chung-ku water works offices as model cases out of all water works offices in Seoul. As the electronic calculating machines are inducted, this system will be gradually applied to other water works offices. The billing and collection works of water rates which are connected directly to the daily life of the citizenes, should be handled by the scientific EDP system as soon as possible in order to promote the convenience of consumers and effective operation. This study is to promote the sound and rational operation of this work.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2888-2890
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• 2005

In this paper, we introduce a new structure of fuzzy-neural networks Fuzzy Set-based Polynomial Neural Networks (FSPNN). The two underlying design mechanisms of such networks involve genetic optimization and information granulation. The resulting constructs are Fuzzy Polynomial Neural Networks (FPNN) with fuzzy set-based polynomial neurons (FSPNs) regarded as their generic processing elements. First, we introduce a comprehensive design methodology (viz. a genetic optimization using Genetic Algorithms) to determine the optimal structure of the FSPNNs. This methodology hinges on the extended Group Method of Data Handling (GMDH) and fuzzy set-based rules. It concerns FSPNN-related parameters such as the number of input variables, the order of the polynomial, the number of membership functions, and a collection of a specific subset of input variables realized through the mechanism of genetic optimization. Second, the fuzzy rules used in the networks exploit the notion of information granules defined over systems variables and formed through the process of information granulation. This granulation is realized with the aid of the hard C- Means clustering (HCM). The performance of the network is quantified through experimentation in which we use a number of modeling benchmarks already experimented with in the realm of fuzzy or neurofuzzy modeling.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1505-1509
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• 2005

This paper introduces a new architecture of genetically optimized self-organizing fuzzy polynomial neural networks by means of information granulation. The conventional SOFPNNs developed so far are based on mechanisms of self-organization and evolutionary optimization. The augmented genetically optimized SOFPNN using Information Granulation (namely IG_gSOFPNN) results in a structurally and parametrically optimized model and comes with a higher level of flexibility in comparison to the one we encounter in the conventional FPNN. With the aid of the information granulation, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. The GA-based design procedure being applied at each layer of genetically optimized self-organizing fuzzy polynomial neural networks leads to the selection of preferred nodes with specific local characteristics (such as the number of input variables, the order of the polynomial, a collection of the specific subset of input variables, and the number of membership function) available within the network. To evaluate the performance of the IG_gSOFPNN, the model is experimented with using gas furnace process data. A comparative analysis shows that the proposed IG_gSOFPNN is model with higher accuracy as well as more superb predictive capability than intelligent models presented previously.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1595-1613
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• 2017

The IR camera and laser-based IR projector provide an effective solution for real-time collection of moving targets in RGB-D videos. Different from the traditional RGB videos, the captured depth videos are not affected by the illumination variation. In this paper, we propose a novel feature extraction framework to describe human activities based on the above optical video capturing method, namely spatial-temporal texture features for 3D human activity recognition. Spatial-temporal texture feature with depth information is insensitive to illumination and occlusions, and efficient for fine-motion description. The framework of our proposed algorithm begins with video acquisition based on laser projection, video preprocessing with visual background extraction and obtains spatial-temporal key images. Then, the texture features encoded from key images are used to generate discriminative features for human activity information. The experimental results based on the different databases and practical scenarios demonstrate the effectiveness of our proposed algorithm for the large-scale data sets.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.3
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• pp.245-252
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• 2016

The Service Platform for going to Kindergarten configured with Beacon Devices, Gateway attached to the BUS, Mobile Communication Network, and Application Server. In this paper, We understand the need for Commuting Kindergarten Services, And interface to Specification of Kindergartener's Beacon Identification, And so design to Gateway Resource Tree Functions. For the Service Interfacing, Commute alerts handled in Parental Cellphone through APP. The Service Platform can check the registered beacon data collection and operation management function.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.04a
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• pp.301-304
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• 2005

In this paper, we propose a new fuzzy set-based polynomial neuron (FSPN) involving the information granule, and new fuzzy-neural networks - Fuzzy Set based Polynomial Neural Networks (FSPNN). We have developed a design methodology (genetic optimization using Genetic Algorithms) to find the optimal structure for fuzzy-neural networks that expanded from Group Method of Data Handling (GMDH). It is the number of input variables, the order of the polynomial, the number of membership functions, and a collection of the specific subset of input variables that are the parameters of FSPNN fixed by aid of genetic optimization that has search capability to find the optimal solution on the solution space. We have been interested in the architecture of fuzzy rules that mimic the real world, namely sub-model (node) composing the fuzzy-neural networks. We adopt fuzzy set-based fuzzy rules as substitute for fuzzy relation-based fuzzy rules and apply the concept of Information Granulation to the proposed fuzzy set-based rules.

• Safety and Health at Work
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• v.14 no.1
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• pp.10-16
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• 2023

Whilst Ecological momentary assessment (EMA) can provide important insights over time and across contexts among rotation workers whose work periods alternate with leave at home, it can also be challenging to implement in the resources and construction sectors. This review aimed to provide a summary of the methodological characteristics of EMA studies assessing health outcomes and related behaviors in rotation workers. Systematic searches in PubMed, Medline, EMBASE, CINAHL, PsycINFO, and Scopus were done to include 23 studies using EMA methods in assessing health-related outcomes and behaviors. EMA designs included daily diary: assessments once per day typically fixed at the end of day (47.8%), within day fixed interval time-based design: assessments on multiple times per day at certain times of day (17.4%) and combination of both designs (34.8%). Studies employed paper and pencil diaries (73.9%) and one or more electronic methods (60.9%): wrist-worn actigraphy device (52.2%) and online-based diaries (26.1%) for data collection. Most of the studies (91.3%) did not report prompting -EMAs by schedule alerts or compliance. Daily diary and within day fixed interval dairies designs are common, with the increasing use of electronic EMA delivery techniques. It is unclear how well participants adhere to assessment schedules, as these are inadequately reported. Researchers should report compliance-related information.