• Korean journal of food and cookery science
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• v.23 no.1 s.97
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• pp.90-98
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• 2007

The purpose of this study was to apply the activity-based costing (ABC) model to restaurant menu costing. The overhead cast of six entr${\acute{e}}$es in XYZ restaurant was calculated for all levels of activity. When comparing activity-based costing with traditional costing applied to BBQ pork rib and an assorted seafood platter, the total difference of costs between two items was 2,191 won in activity-based costing and 600 won in traditional costing. The average food cast percentage of the six entr${\acute{e}}$es was 27% using traditional costing, while the average activity-based cost percentage was 40%. Therefore, there was a 13% difference between the actual margin volume and the expected margin volume. The application of activity-based costing to the restaurant industry would be a milestone from a cost point of view as well as from a process point of view. In particular, the limitation that traditional costing only accounts for food costs could be overcome b considering the overhead cost as an important part of the cast structure. Furthermore, activity-based costing would not only help to reduce the costs associated with the process of analyzing the activities but it would also provide more accurate cost information for menu pricing.

• Science of Emotion and Sensibility
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• v.25 no.4
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• pp.77-94
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• 2022

Social interactions often involve encountering inconsistent information about social others. We conducted a functional magnetic resonance imaging (fMRI) study to comprehensively investigate voxel-wise temporal dynamics showing how impressions are anchored and/or adjusted in response to inconsistent social information. The participants performed a social impression task inside an fMRI scanner in which they were shown a male face, together with a series of four adjectives that described the depicted person's personality traits, successively presented beneath the image of the face. Participants were asked to rate their impressions of the person at the end of each trial on a scale of 1 to 8 (where 1 is most negative and 8 is most positive). We established two hypothetical models that represented two temporal patterns of voxel activity: Model 1 featured decreasing patterns of activity towards the end of each trial, anchoring impressions to initially presented information, and Model 2 showed increasing patterns of activity toward the end of each trial, where impressions were being adjusted using new and inconsistent information. Our data-driven model fitting analyses showed that the temporal activity patterns of voxels within the ventral anterior cingulate cortex, medial orbitofrontal cortex, posterior cingulate cortex, amygdala, and fusiform gyrus fit Model 1 (i.e., they were more involved in anchoring first impressions) better than they did Model 2 (i.e., showing impression adjustment). Conversely, voxel-wise neural activity within dorsal ACC and lateral OFC fit Model 2 better than it did Model 1, as it was more likely to be involved in processing new, inconsistent information and adjusting impressions in response. Our novel approach to model fitting analysis replicated previous impression-related neuroscientific findings, furthering the understanding of neural and temporal dynamics of impression processing, particularly with reference to functionally segmenting each region of interest based on relative involvement in impression anchoring as opposed to adjustment.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.512-517
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• 2008

It is show that increasing of aged and handicapped people requires development of Ubiquitous computing technique to offer the specialized service for handicapped-people. For this, we need a development of Context Awareness and Service Reasoning Technique that the technique is supplied interaction between user and U-environment instead of the old unilateral relation. The old research of context awareness needed probabilistic presentation model like a Bayesian Network based on expert Systems for recognize given circumstance by a domain of uncertain real world. In this article, we define a domain of disorder activity assistant service application and context model based on ontology in diversified environment and minimized intervention of user and developer. By use this context model, we apply the structure learning of Bayesian Network and decide the service and activity to development of application service for handicapped people. Finally, we define the proper Conditional Probability Table of the structured Bayesian Network and if random situation is given to user, then present state variable of Activity and Service by given Causal relation of Bayesian Network based on Conditional Probability Table and it can be result of context awareness.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.2
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• pp.87-92
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• 2014

In this paper, we present a two-phase activity recognition system using smartphone's accelerometers. To consider the unique temporal pattern of accelerometer data for each activity, our system executes the decision-tree(DT) learning in the first phase, and then, in the second phase, executes the hidden Markov model(HMM) learning based on the sequences of classification results of the first phase classifier. Moreover, to build a robust recognizer for each activity, we trained our system using a large amount of data collected from different users, different positions and orientations of smartphone. Through experiments using 6720 examples collected for 6 different indoor activities, our system showed high performance based on its novel design.

• Journal of the Ergonomics Society of Korea
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• v.29 no.6
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• pp.843-851
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• 2010

This study aims to systematically develop a usability evaluation model using the Structural Equation Model (SEM) from experiment of usability on using vehicle. Vehicle developers have been adding many functions for enhance the user satisfaction. But it will be made the trade-off problem of usability and design elements of vehicle interior from attempt to make best usability satisfaction in a restricted space. To solve the trade-off problem, we set a new solution criterion from usability evaluation model. The usability experiment is based on major activity pool from derived user's acts pattern in vehicle for make more accurate usability evaluation model. And this model was built with twenty-nine measurement variables for the evaluation of usability of vehicle user. As a result, the proposed SEM model showed statistical significance as well as a high level of R Square (0.7144). This model shows the relationships of detailed usability and design elements. According to the result, this study introduces the criterion to secure the best satisfaction of usability and design elements.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.2
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• pp.719-727
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• 2013

Four-step travel-demand modeling based on a trip-level has been widely used over many decades. However, there has been a wide variance between forecasted- and real-travel demands, which leads less reliable on the model implications. A primary reason is that person's real travel behavior is not properly captured throughout the model developments. An activity-based modeling (ABM) approach was proposed and developed toward increasing the accuracy and reality of person's travel behavior in the U.S. since 1990', and stands as a good alternative to replace the existing trip-based approach. The paper contributes to the understanding of how the ABM approaches are dissimilar to the trip-based modeling approach in terms of estimation units, estimation process, their pros and cons and etc. We examined three activity-based travel demand model systems (DaySim, CT-Ramp, and CEMDAP) that are most commonly applied by many MPOs (Metropolitan Planning Organization). We found that the ABM approach can effectively explain multi-dimensional travel decision-makings and be expected to increase the predictive accuracy. Overall, the ABM approach can be a good substitute for the existing travel-demand methods having unreliable forecasts.

• Journal of Intelligence and Information Systems
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• v.3 no.2
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• pp.33-55
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• 1997

A data model is a diagram that describes the properties of different categories of data and the associations among them within a business or information system. In spite of its importance and usefulness, data modeling activity requires not only a lot of time and effort but also extensive experience and expertise. The data models for similar business areas are analogous to one another. Therefore, it is reasonable to reuse the already-developed data models if the target business area is similar to what we have already analyzed before. In this research, we develop a case-based reasoning system for data model reuse, which we shall call CB-DM Reuser (Case-Based Data Model Reuser). CB-DM Reuse consists of four subsystems : the graphic user interface to interact with end user, the data model management system to build new data model, the case base to store the past data models, and the knowledge base to store data modeling and data model reusing knowledge. We present the functionality of CB-DM Reuser and show how it works on real-life a, pp.ication.

• Proceedings of the Korean Society for Environmental Edudation Conference
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• 2002.12a
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• pp.79-85
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• 2002

Inquiry activity contents presented at a chapter on natural environment and our lives in 6 science textbooks of the middle school were analyzed based on kinds of the inquiry Loaming classified by textbook. The number of inquiry activity subjects showed severe variation as mean value was 8.3${\times}$3.7 ranged from 4 to 15. Moreover, textbooks had little common point among each other as the percentage of subjects appeared together in the textbooks more than 3 kinds, 50% of total ones investigated, was just 26.3%. Data interpretation occupied significant position in the inquiry activity as 42% of total activity contents, whereas observation and experiment (or survey) did slight part of the activity as 10% and 16%, respectively even though they are main factors of science education. A model for field education based on the reasonable common subjects was prepared in the Gildong ecological park located In the eastern fringe of Seoul as a plan in order to solve the problems.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.119-128
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• 2023

This study was conducted to present an alternative instructional model through natural experience activities by developing a natural experience activity program that can learn and feel how to recognize and act on nature based on flow learning. In order to achieve the purpose of the study, a nature experience program, which consists of four stages of meeting nature, exploring nature, playing with nature and sharing emotions, was developed based on the main procedures of each stage of the ADDlE instructional design model. Through the research process, activities and precautions for each stage of the nature experience activity program were presented, and major educational implications were discussed based on the developed program. The nature experience program developed through the study can provide teachers with a basic direction for nature experience activities along with changing their perception of how to do nature experience activities, and infants are expected to become learners who freely feel, experience nature and make up their own knowledge through the nature experience program.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2767-2780
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• 2016

Video-based human-activity recognition has become increasingly popular due to the prominent corresponding applications in a variety of fields such as computer vision, image processing, smart-home healthcare, and human-computer interactions. The essential goals of a video-based activity-recognition system include the provision of behavior-based information to enable functionality that proactively assists a person with his/her tasks. The target of this work is the development of a novel approach for human-activity recognition, whereby human-body-joint features that are extracted from depth videos are used. From silhouette images taken at every depth, the direction and magnitude features are first obtained from each connected body-joint pair so that they can be augmented later with motion direction, as well as with the magnitude features of each joint in the next frame. A generalized discriminant analysis (GDA) is applied to make the spatiotemporal features more robust, followed by the feeding of the time-sequence features into a Hidden Markov Model (HMM) for the training of each activity. Lastly, all of the trained-activity HMMs are used for depth-video activity recognition.