• Journal of Korea Society of Digital Industry and Information Management
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• v.12 no.1
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• pp.119-129
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• 2016

Rapid changes to the paradigm of smart media have created a communication environment through merging with various media such as IoT technology, which is internet of things. Among them, user' need for a start home, which is one of people living conditions, has been growing and values of the communication environment in a living space using a smart mirror have been growing. However, studies on figuring out behavior and analyzing needs of family members who actually use the living space are insufficient. This study is to draw the service system of the home smart mirror by analysis of behavior and needs of users of the living space. For a research method for analysis of behavior of family members, I wrote two kinds of user's experience maps, which are frequency of use of a quantitative space of a living space and space's important value scales by persona study and depth interview. Through this, applied spaces of the home smart mirror (living room, bathroom, powder room, dress room, porch, kitchen, room) and the types of user needs (type of providing information, entertainment type, control type, service type) have been drawn and statistical analysis methodology has been utilized for a research of user preferences in regard to correlation between living spaces and types of user needs based on a survey. As a result of analysis of ages and gender, types of user needs by space have been drawn and the service system of the home smart mirror has been drawn. It would be utilized as a basic material for various contents development and design using the smart mirror in the future.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.395-395
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• 2000

Optimization of existing processes becomes more important than the past as environmental problems and concerns about energy savings stand out. When we can model a process mathematically, we can easily optimize it by using the model as constraints. However, modeling is very difficult for most chemical processes as they include numerous units together with their correlation and we can hardly obtain parameters. Therefore, optimization that is based on the process models is, in turn, hard to perform. Especially, f3r unknown processes, such as bioprocess or microelectronics materials process, optimization using mathematical model (first principle model) is nearly impossible, as we cannot understand the inside mechanism. Consequently, we propose a few optimization method using empirical model evolutionarily instead of mathematical model. In this method, firstly, designing experiments is executed fur removing unecessary experiments. D-optimal DOE is the most developed one among DOEs. It calculates design points so as to minimize the parameters variances of empirical model. Experiments must be performed in order to see the causation between input variables and output variables as only correlation structure can be detected in historical data. And then, using data generated by experiments, empirical model, i.e. response surface is built by PLS or MLR. Now, as process model is constructed, it is used as objective function for optimization. As the optimum point is a local one. above procedures are repeated while moving to a new experiment region fur finding the global optimum point. As a result of application to the pulp digester benchmark model, kappa number that is an indication fur impurity contents decreased to very low value, 3.0394 from 29.7091. From the result, we can see that the proposed methodology has sufficient good performance fur optimization, and is also applicable to real processes.

• Journal of the Korea institute for structural maintenance and inspection
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• v.15 no.5
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• pp.113-123
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• 2011

LCC analysis is a method that coordinates with function evaluation for value improvement, rather than a separate one for cost evaluation. Although its accuracy is rising, materials and structural types developed or applied relatively recently have yet to obtain a sufficient maintenance profile DB, inducing reliability to reduce from difficulties in estimating maintenance records. Based on the above mentioned background, this paper presents the LCC methodology of coordinating functional intensification matters with cost for analysis on alternatives with difficulties in setting maintenance profile. Recently, steel plate bridge deck pavements are faced with problems such as plastic deformation due to the increase in heavy vehicles and traffic, promoting the development of a new compound pavement. This paper execute LCC analysis by mentioning case studies of SMA, Guss and PSMA pavements to include performance scale compared between alternatives as relative evaluation coefficients into the maintenance profile.

• Steel and Composite Structures
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• v.35 no.4
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• pp.599-609
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• 2020

The tension-only braced frames (TOBFs) are widely used as a lateral force resisting system (LFRS) in low-rise steel buildings due to their simplicity and economic advantage. However, the system has poor seismic energy dissipation capacity and pinched hysteresis behavior caused by early buckling of slender bracing members. The main concern in utilizing the TOBF system is the determination of appropriate performance factors for seismic design. A formalized approach to quantify the seismic performance factor (SPF) based on determining an acceptable margin of safety against collapse is introduced by FEMA P695. The methodology is applied in this paper to assess the SPFs of the TOBF systems. For this purpose, a trial value of the R factor was first employed to design and model a set of TOBF archetype structures. Afterwards, the level of safety against collapse provided by the assumed R factor was investigated by using the non-linear analysis procedure of FEMA P695 comprising incremental dynamic analysis (IDA) under a set of prescribed ground motions. It was found that the R factor of 3.0 is appropriate for safe design of TOBFs. Also, the system overstrength factor (Ω₀) was estimated as 2.0 by performing non-linear static analyses.

• Food Science of Animal Resources
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• v.36 no.2
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• pp.194-197
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• 2016

The alkaline phosphatase (ALP) assay is a rapid and convenient method for verifying milk pasteurization. Since colorimetric ALP assays rely on subjective visual assessments, their results are especially unreliable near the detection limits. In this study, we attempted to establish quantitative criteria for residual ALP in milk by using a more objective method based on spectrophotometric measurements. Raw milk was heat-treated for 0, 10, 20, 30, and 40 min and then subjected to ALP assays. The quantitative criteria for residual ALP in the milk was determined as 2 μg phenol/mL of milk, which is just above the ALP value of milk samples heat-treated for 30 min. These newly proposed methodology and criteria could facilitate the microbiological quality control of milk.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.225-231
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• 2002

POSCO $\#2$ Stainless steel making plant produces more than 600 thousand ton per year with a variety of products consisting of austenite and ferrite stainless steel to meet custrmers' needs since 1996. The plant has four different major processes, that are, EAF-AOD-VOD-CC to finally produce semi-product called as slab. In this study, we importantly took AOD process into consideration due to its roles such as to check and verify the final qualities through sampling inspection. But the lead-time from sampling to its verification takes five to ten minutes causing produrtivity loss as muck as the lead-time as a result. Of all indices for quality and process control the plant has, carbon ingredient in liquid type of steel is the most important since it affects in a great way to the characteristics of steel, if any problem. customers not satisfied with quality could issue a claim; therefore there is no way hut to guarantee it before delivery. in this study, to reasonably reduce lead-time ran save a cycle time and finally improve our productivity from a state-or-art alternative just such as applying statistical model based on multi-regression analysis into the A.O.D line by analyzing the statistical and technical relationship between carbon and the relevant some vital independent variables. In consequence, the model with R-square $87\%$ allowed the plant to predict, abbreviating the process in relations to sampling to verification. approximately the value of [C] so that operators could run the process line with reliability on data automatically calculated instead of actual inspection. In the future, we are going to do the best to share this type of methodology with other processes, if possible, to apply into them.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.16 no.4
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• pp.431-437
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• 2003

In this paper, an approximate optimization program based on GUI(graphic user interface) environment is developed. This program is coded by using Fortran and Visual basic. Fortran is used to Progress approximate optimization process. Visual basic is used to make user environment for user to use conveniently. Inside of this program, it uses two independent programs. One is commercial program, ANSYS, and the other is optimization program, PLBA(Pshenichny-Lim-Belegundu Arora). The former is used to obtain approximate equation of stress and displacement of a structure. The latter is used to solve approximate optimization. This algorithm uses second-order information of a function and active set strategy. This program is connecting ANSYS and PLBA. And it progress the process repeatedly until it obtain optimum value. As a method of approximate optimization, sequential design domain(SDD) is introduced. SDD starts with a certain range which is offseted from midpoint of an initial design domain and then SDD of the next step is determined by optimal point of a prior step.

• 모자간호학회지
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• v.4 no.1
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• pp.68-79
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• 1994

This study measured variables influencing the breast feeding patterns of lactating mothers over a 40 day period In 1993 in the Jeonla area. The Methodology used was a questionnaire covering 92 items based on statistical discriminant analysis. The results were as follows : The successful group was measured against the unsuccessful group over a 4month lactation period ; The successful group was measured over a 4month lactation period ; the unsuccessful less than 4month lactation period. Principal factor analysis was used to generate comparative data factors which were ; 1) nonunderstanding of mother's breast feeding, 2) physical and psychological stress, 3) insufficient milk supply, 4) mother's negative acceptance of baby, 5) lack of spousal support, 6) sore nipple and breast pain, 7) baby's negative acceptance, 8) lack of familial support, 9) baby's diarrhea and watery milk. Discriminant statistical analysis of sever factors included ; 1) insufficient milk supply 2) sore nipple and breast pain, 3) pre-natal planning of breast feeding method, 4) mother's occupation 5) breast feeding method of previous infant, 6) nipple type, and 7) infant birth order. This analysis predicted a 78.9% successful breast feeding. Criterion correlation analysis revealed ; D=-1.780+.165$\times$(Fac3)+.135$\times$(Fac6)+.927$\times$(prenatal planning of breast feeding method)+.900$\times$(mother's occupation)+.675$\times$ (breast feeding method of previous infant)+1.0l4$\times$(nipple type)+.378$\times$(infant birth order). We classified the unsuccessful group as more than .63937 and the successful group less than -.82742 of the D value obtained from the above criterion correlation in order to check the success or the non-success of breast feeding mothers. The rate of correct classification of the grouped cases employing a statistical discriminant analysis was significantly improved to 78.9% when these cases were compared with the actual grouped classification.

• The Journal of Information Systems
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• v.29 no.2
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• pp.131-147
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• 2020

Purpose The purpose of this study is to develop intelligent vehicle parking distribution system based on LoRa network at the circumstance of traffic congestion during cultural festival in a local city. This paper proposes a parking dispatch and distribution system using a Q-learning algorithm to rapidly disperse traffics that increases suddenly because of in-bound traffics from the outside of a city in the real-time base as well as to increase parking probability in a parking lot which is widely located in a city. Design/methodology/approach The system get information on realtime-base from the sensor network of IoT (LoRa network). It will contribute to solve the sudden increase in traffic and parking bottlenecks during local cultural festival. We applied the simulation system with Queuing model to the Yudeung Festival in Jinju, Korea. We proposed a Q-learning algorithm that could change the learning policy by setting the acceptability value of each parking lot as a threshold from the Jinju highway IC (Interchange) to the 7 parking lots. LoRa Network platform supports to browse parking resource information to each vehicle in realtime. The system updates Q-table periodically using Q-learning algorithm as soon as get information from parking lots. The Queuing Theory with Poisson arrival distribution is used to get probability distribution function. The Dijkstra algorithm is used to find the shortest distance. Findings This paper suggest a simulation test to verify the efficiency of Q-learning algorithm at the circumstance of high traffic jam in a city during local festival. As a result of the simulation, the proposed algorithm performed well even when each parking lot was somewhat saturated. When an intelligent learning system such as an O-learning algorithm is applied, it is possible to more effectively distribute the vehicle to a lot with a high parking probability when the vehicle inflow from the outside rapidly increases at a specific time, such as a local city cultural festival.

• Convergence Security Journal
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• v.12 no.2
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• pp.61-70
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• 2012

The objective of this study is to achieve the most optimal data mining for effective analysis of false alarm event on mechanical security system. To perform this, this study searches the cause of false alarm and suggests the data conversion and analysis methods to apply to several algorithm of WEKA, which is a data mining program, based on statistical data for the number of case on movement by false alarm, false alarm rate and cause of false alarm. Analysis methods are used to estimate false alarm and set more effective reaction for false alarm by applying several algorithm. To use the suitable data for effective analysis of false alarm event on mechanical security analysis this study uses Decision Tree, Naive Bayes, BayesNet Apriori and J48Tree algorithm, and applies the algorithm by deducting the highest value.