• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1998.11a
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• pp.189-194
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• 1998

Design factors are very important and deterministic in determining the first impression of products and environment. The final 30 number of channel button were chosen as a design factors at the Audio Unit. Then, we made the 8 types of prototype. with combining the design factors for experiment. Subjects rated the SD(Semantic Differential) evaluation sheets which have the 30 adjectives after watching each prototype. With the evaluated values, we simulated to identify the relation between the design factors and the adjectives using Neural Network. As a results, we could abstract the affective adjectives on each 8 types. Therefore, this research suggested the possibilities that we can infer the optimal design factors and types using Neural Network, if adjectives were given.

• Journal of Korea Multimedia Society
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• v.24 no.10
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• pp.1358-1368
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• 2021

A research on agricultural automation is a main issues to overcome the shortage of labor in Korea. A sweet persimmon farmers need much time and labors for classifying profitable sweet persimmon and ill profitable products. In this paper, we propose a mixed two-step synthetic neural network model for efficiently classifying sweet persimmon images. In this model, we suggested a surface direction classification model and a quality screening model which constructed from image data sets. Also we studied Class Activation Mapping(CAM) for visualization to easily inspect the quality of the classified products. The proposed mixed two-step model showed high performance compared to the simple binary classification model and the multi-class classification model, and it was possible to easily identify the weak parts of the classification in a dataset.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.44 no.10
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• pp.7-16
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• 2007

In this paper, we introduce a new design environment for efficient multiprocessor system-on-chip design space exploration. The design environment takes a process network model as input system specification. The process network model has been widely used for modeling signal processing applications because of its excellent modeling power. However, it has limitation in predictability, which could cause severe problem for real time systems. This paper proposes a new approach that enables static analysis of a process network model by converting it to a hierarchical synchronous dataflow model. For efficient design space exploration in the early design step, mapping application to target architectures has been a crucial part for finding better solution. In this paper, we propose an efficient mapping algorithm. Our mapping algorithm supports both single bus architecture and multiple bus architecture. In the experiments, we show that the automatic conversion approach of the process network model for static analysis is performed successfully for several signal processing applications, and show the effectiveness of our mapping algorithm by comparing it with previous approaches.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.373-380
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• 2022

The field of land surveying is changing dramatically due to the way data is processed, analyzed and presented. Also, there is a growing demand for digital spatial information, coming primarily from the GIS (Geographical Information System) user community. Such a demand has created a strong development potential for a new land surveying software. An overview of the development and capabilities of a land surveying software platform based on the Windows system, SurveyingMap, is presented. Among its many features, SurveyingMap provides a lot of adaptability for networks adjustment, geodetic and plane coordinates transformation, contouring, sectioning, DTM (Digital Terrain Model) generation, and large scale mapping applications. The system output is compatible with well known computer aided drafting (CAD) /GIS packages to expand its scope of applications. SurveyingMap is also suitable for non-technical users due to the user-friendly graphic user interface. The system could be used in engineering, architecture, GIS, and academic teaching and research, among other fields. Two applications of SurveyingMap, extension of field control and large scale mapping, for the case study area are established. The results demonstrate that the system is adaptable and reasonably priced for use by college and university students.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2002.11a
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• pp.350-362
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• 2002

A new architecture called the Storage Area Network(SAN) was developed in response to the requirements of high availability of data, scalable growth and system performance In order to use SAN more efficiently, must SAN operating systems support storage virtualization concepts that allow users to view physical storage devices attached to SAN as a large volume virtually. A logical volume manager Days a key role in storage virtualization it realizes the storage virtualization by mapping logical addresses to physical addresses. In this paper, we design and implement an efficient and flexible mapping method for logical volume manager. The mapping method in this paper supports a snapshot that preserves a volume image at certain time and on-line reorganization to allow users to add or remove storage devices to SAN even while the system is running.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.1125-1128
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• 1989

In this paper, the inverse model of a servo system is realized in a PDP-type neural network. The neural network learns the mapping between the input and output of the servo system. Some simulation results show the effectiveness of this inverse model obtained here.

• Proceedings of the IEEK Conference
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• 2003.07b
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• pp.1165-1168
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• 2003

In this paper, a CLB-based CPLD low-power technology mapping algorithm is proposed. To perform low power technology mapping for CPLD, a given Boolean network have to be represented to DAG. The proposed algorithm are consist of three step. In the first step, TD(Transition Density) calculation have to be performed. In the second step, the feasible clusters are generated by considering the following conditions: the number of output, the number of input and the number of OR-terms for CLB(Common Logic Block) within a CPLD. The common node cluster merging method, the node separation method, and the node duplication method are used to produce the feasible clusters. In the final step, low power technology mapping based on the CLBs is packing the feasible clusters into the several proper CLBs. Therefore the proposed algorithm is proved an efficient algorithm for a low power CPLD technology mapping.

• The Journal of the Korea Contents Association
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• v.14 no.5
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• pp.107-116
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• 2014

The purpose of this study is to verify the applicability of concept mapping in the process of planning social welfare program based on community network. Concept mapping is a kind of decision-making method that structuralized complex ideas and presented visually. Already, concept mapping is widely utilized in counseling, nursing and public health area to plan and evaluation their program and service. For recent, effectiveness of concept mapping has been reported. Concept mapping is a effective decision-making method that they recognize outcome gap between service provider and client, reach the outcome's consensus in counseling and nursing, medical area. In this study, we conceptualized 3rd year outcomes of Community Impact Project that was supported from Busan Chest using concept mapping. This CI project intervenes children and youth who lives in Buk-gu, Busan. Concept mapping has six stages-preparation, collecting ideas, structuring statements, representing statement, interpreting the results of the analysis, applying the results. We followed these steps. The participants were working at social welfare organizations, total 11 persons. We obtained 60 statements and analyzed using multidimensional scaling. we collected 5 clusters, cluster 1 'awareness and attitude change of children and youth', cluster 2 'social system change of children and youth', cluster 3 'friendly community formation', cluster 4 'community people change', cluster 5 'service provider change'. As a result, among total 5 clusters formed, 'awareness and attitude change of children and youth' came to the strongest outcomes. When concept mapping was applied to the program planning, the consensus of the opinion came easily in the decision-making process, and the participants were empowered. In addition, clear conceptualization on each element of the program planning was made.

• Journal of Intelligence and Information Systems
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• v.14 no.4
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• pp.19-30
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• 2008

Semantic heterogeneity should be overcome to support automated information sharing process between information systems in ontology-based distributed environments. To do so, traditional approaches have been based on explicit mapping between ontologies from human experts of the domain. However, the manual tasks are very expensive, so that it is difficult to obtain ontology mappings between all possible pairs of information systems. Thereby, in this paper, we propose a system to make the existing mapping information sharable and exchangeable. It means that the proposed system can collect the existing mapping information and aggregate them. Consequently, we can estimate the ontology mappings in an indirect manner. In particular, this paper focuses on query propagation on the distributed networks. Once we have the indirect mapping between systems, the queries can be efficiently transformed to automatically exchange knowledge between heterogeneous information systems.

• Investigative Magnetic Resonance Imaging
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• v.25 no.4
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• pp.300-312
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• 2021

Compressed sensing (CS) has been investigated in magnetic resonance (MR) parametric mapping to reduce scan time. However, the relatively long reconstruction time restricts its widespread applications in the clinic. Recently, deep learning-based methods have shown great potential in accelerating reconstruction time and improving imaging quality in fast MR imaging, although their adaptation to parametric mapping is still in an early stage. In this paper, we proposed a novel deep learning-based framework DEMO for fast and robust MR parametric mapping. Different from current deep learning-based methods, DEMO trains the network in an unsupervised way, which is more practical given that it is difficult to acquire large fully sampled training data of parametric-weighted images. Specifically, a CS-based loss function is used in DEMO to avoid the necessity of using fully sampled k-space data as the label, thus making it an unsupervised learning approach. DEMO reconstructs parametric weighted images and generates a parametric map simultaneously by unrolling an interaction approach in conventional fast MR parametric mapping, which enables multi-tasking learning. Experimental results showed promising performance of the proposed DEMO framework in quantitative MR T1ρ mapping.