• Transactions of the KSME C: Technology and Education
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• v.3 no.4
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• pp.273-283
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• 2015

In this study, we suggested the methodology and the results of planning of promising technologies in mechanical engineering by focusing on the case of the Korea Institute of Machinery and Materials (KIMM). For dedicated commitment to planning of promising technologies, KIMM newly introduced task-force called as 'specialist unit'. In addition, KIMM combined the investigation of external environments with the analysis of internal capabilities of KIMM and utilized the bibliographic coupling analysis in the process of the exploring sub themes. Finally, we provided 8 promising fields and their sub themes in the mechanical engineering. Our study contributed to the strategic development of the main research programs of KIMM. Our findings can be also utilized as the best practice of planning of promising technologies in the field of mechanical engineering.

• Journal of Information Technology Services
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• v.17 no.3
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• pp.117-138
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• 2018

The purpose of this study is to empirically analyze the relationships between the Boundary Spanning Activities (BSA) of project team and system performance in ERP system development projects. We could theoretically classify the BSA in the ERP development projects into five categories on the basis of existing studies on the BSA in the research fields of organization theory, new product development, and information systems development. These five categories are 'Ambassador' activities, 'Task-coordinator' activities, 'Scout' activities, 'Sentry' activities and 'Guard' activities. And the relationship between the implementation level of activities included in each category and the project performance (system usage and users' satisfaction) was hypothesized with respect to five BSA categories. In order to test the hypotheses, we conducted on/offline survey of the participants who were involved in the ERP system development projects, and received 345 valid responses. The unit of analysis was the project team, and the total number of teams that survey participants belonged to were 103 ones. The Structural Equation Model Analysis using the SMART PLS 3.0 was applied to statistically testing the hypotheses. The results showed that 10 hypotheses among 12 hypotheses could be supported. The theoretical implications of this study can be summarized as following; first, redefining and categorizing the BSA (Boundary Spanning Activities) in the ERP system development projects, secondly, deriving measurement indicators of the implementation level for each BSA category and statistically proving the validity and the reliability of them, and finally, suggesting the theoretical background of expanding the management area of ERP systems development projects. Furthermore, the practical implication of this study is that concrete BSA items which are empirically derived can be utilized as effective guidelines for successfully implementing the BSA in the process of managing the ERP system development projects.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.12
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• pp.1113-1119
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• 2000

To operate a process plant safely and economically, process monitoring is very important. Process monitoring is the task to identify the state of the system from sensor data. Process monitoring includes data acquisition, regulatory control, data reconciliation, fault detection, etc. This research focuses on the data recon-ciliation using scale-space filtering and fault detection using functional-link associative neural networks. Scale-space filtering is a multi-resolution signal analysis method. Scale-space filtering can extract highest frequency factors(noise) effectively. But scale-space filtering has too large calculation costs and end effect problems. This research reduces the calculation cost of scale-space filtering by applying the minimum limit to the gaussian kernel. And the end-effect that occurs at the end of the signal of the scale-space filtering is overcome by using extrapolation related with the clustering change detection method. Nonlinear principal component analysis methods using neural network have been reviewed and the separately expanded functional-link associative neural network is proposed for chemical process monitoring. The separately expanded functional-link associative neural network has better learning capabilities, generalization abilities and short learning time than the exiting-neural networks. Separately expanded functional-link associative neural network can express a statistical model similar to real process by expanding the input data separately. Combining the proposed methods-modified scale-space filtering and fault detection method using the separately expanded functional-link associative neural network-a process monitoring system is proposed in this research. the usefulness of the proposed method is proven by its application a boiler water supply unit.

• Korean Journal of Cognitive Science
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• v.14 no.3
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• pp.1-11
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• 2003

Two experiments were conducted to investigate effects of target types and retinal eccentricity on the search of a target while both target and background stimuli were static or moving. A visual search task was used in both experiments. The retinal eccentricity was determined by five concentric circles increasing by the unit of 1.6 and the target was different from the background stimuli in either orientation(orientation target) or a distinctive feature(feature target). In Experiment 1 where both the target and background stimuli were presented statically, an interaction between retinal eccentricity arid target type was found. While search time of the orientation target was not affected by the retinal eccentricity, that of the feature target increased as the retinal eccentricity increased. In Experiment 2 where all stimuli were moving, the interaction effect was also found. But the reason was not the same as that in Experiment 1. In the moving condition, while the search time of the orientation target decreased consistently as the retinal eccentricity increased, that of the feature target was not affected by the retinal eccentricity. The implications and limitations of the present results were discussed with respects to the real world situations such as driving cars or flying airplanes.

• Journal of KIISE
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• v.42 no.3
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• pp.320-327
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• 2015

Although service robots in various fields are being commercialized, most of them have problems that depend on explicit commands by users and have difficulty to generate robust reactions of the robot in the unstable condition using insufficient sensor data. To solve these problems, we modeled mirror neuron and theory of mind systems, and applied them to a robot agent to show the usefulness. In order to implement quick and intuitive response of the mirror neuron, the proposed intention-response model utilized behavior selection networks considering external stimuli and a goal, and in order to perform reactions based on the long-term action plan of theory of mind system, we planned behaviors of the sub-goal unit using a hierarchical task network planning, and controled behavior selection network modules. Experiments with various scenarios revealed that appropriate reactions were generated according to external stimuli.

• Korean Journal of Remote Sensing
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• v.33 no.4
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• pp.423-436
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• 2017

This paper presents a deep learning-based road segmentation framework from very high-resolution orthophotos. The proposed method uses Deep Convolutional Autoencoders for end-to-end mapping of orthophotos to road segmentations. In addition, a set of post-processing steps were applied to make the model outputs GIS-ready data that could be useful for various applications. The optimization of the model's parameters is explained which was conducted via grid search method. The model was trained and implemented in Keras, a high-level deep learning framework run on top of Tensorflow. The results show that the proposed model with the best-obtained hyperparameters could segment road objects from orthophotos at an average accuracy of 88.5%. The results of optimization revealed that the best optimization algorithm and activation function for the studied task are Stochastic Gradient Descent (SGD) and Exponential Linear Unit (ELU), respectively. In addition, the best numbers of convolutional filters were found to be 8 for the first and second layers and 128 for the third and fourth layers of the proposed network architecture. Moreover, the analysis on the time complexity of the model showed that the model could be trained in 4 hours and 50 minutes on 1024 high-resolution images of size $106{\times}106pixels$, and segment road objects from similar size and resolution images in around 14 minutes. The results show that the deep learning models such as Convolutional Autoencoders could be a best alternative to traditional machine learning models for road segmentation from aerial photographs.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.1
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• pp.128-136
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• 2003

In today's design trend of vehicle structure, crush zone is fiequently reinforced by adding a box-shaped sub-frame in order to avoid an excessive deformation against a high-speed offset barrier such as EU Directive 96/97 EC, IIHS offset test. That kind of vehicle structure design results in a relatively monotonic crash pulse for airbag ECU(Electronic Control Unit) located at non-crush zone. As for an angular crash event, the measured crash signal using a single-axis accelerometer in a longitudinal direction is usually weaker than that of frontal barrier crash. Therefore, it is not so easy task to achieve a satisfactory crash discrimination performance for offset and angular crash events. In this paper, we introduce a new crash discrimination algorithm using an electronic X-Y 2-axis accelerometer in order to improve crash discrimination performance especially for those crash events. The proposed method uses a crash signal in lateral direction(Y-axis) as well as in longitudinal direction(X-axis). A crash severity measure obtained from Y-axis acceleration is used to improve the discrimination between fire and no-fire events. The result obtained by the proposed measure is logically ORed with an existing algorithm block using X-axis crash signal. Simulation and pulse injection test have been conducted to verify the performance of proposed algorithm by using real crash data of a 2,000cc passenger vehicle.

• KIISE Transactions on Computing Practices
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• v.23 no.8
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• pp.498-503
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• 2017

SIMD (Single Instruction Multiple Data) is a representative parallelization architecture that processes multiple data loaded in a SIMD register with a single instruction. Quicksort is a sorting algorithm that picks an element as a pivot from the array and reorders the array such that all elements having the values less than the pivot value are located in the left side on the pivot as well as all elements having the value greater than the pivot value are located in the right side on the pivot and then the algorithm performs the same task on both sublist recursively. In this paper, we propose an efficient Quicksort algorithm applying the SIMD instructions which minimally invokes conditional branches to avoid the performance degradation incurred by branch misprediction in a pipeline architecture. In addition, we improve the performance of the Quicksort algorithm by fetching data into a SIMD register as a byte unit to apply VBP (Vertical Bit Parallel) and the early pruning technique.

• Journal of the Korean Chemical Society
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• v.61 no.3
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• pp.104-111
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• 2017

In this study, we analyzed verbal interactions in small group science learning using smart devices by the level of prior achievement. Four heterogeneous groups at a coed high school in Seoul participated. Verbal interactions during small group science learning were audio- and video-taped, transcribed, and analyzed. Verbal interactions were analyzed at the levels of a turn and an interaction unit. The results revealed that the frequencies of verbal interactions were high in task category, especially at information explanation, information question, and reflection on standards subcategories. Furthermore, the frequencies of high-level students at direction explanation, reflection on standards and progress subcategories were higher than those of low-level students, and the frequencies of low-level students at direction question and information explanation subcategories were higher than their counterpart. In the analyses of the interaction units, the frequencies in symmetric elaborated interaction were high, especially at cumulative and evaluative subcategories.

• Korean Journal of Occupational Health Nursing
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• v.17 no.2
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• pp.216-223
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• 2008

Purpose: This study was performed to evaluate the worker's job stress status in the workplace of a local area. Method: Data were collected from October to December, 2007. The subjects were 208 workers at 2 work sites in Busan Metropolitan area, who were examined using Job-Strain-Model Questionnaire. Data were analyzed by SPSS 12.0 Win Program to get the percentage, mean, standard deviation, t-test, ${\chi}^2$-test, ANOVA. Results: The results of this study were as follows: In the mean sub-factors job stress level, the mean of job demand was $28.7{\pm}4.4$(median 29.0), the mean of job discretion was $54.7{\pm}8.2$(median 54.0), the mean of social support was $21.8{\pm}2.9$(range:8-32). This study's subjects were appeared as active group with relatively higher score of job demand and job discretion than the average value of those. There was no statistically significant difference of general characteristics among the different job strain groups. There was statistically significant difference with of social supports among the different job strain groups. Conclusion: In conclusion, the subjects of this study's were active group. Thus, it is suggested that it is be necessary to repeated the education of the job task work for active group with high score of job demand and job discretion.