• IE interfaces
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• v.13 no.1
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• pp.29-36
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• 2000

Many organizations have applied the traditional Information Strategy Planning methodologies to planning knowledge management. This approach, however, cannot sufficiently realize the strategic value of various kinds of knowledge. To overcome above problems, this research classifies knowledge according to its features and identifies characteristics of each knowledge category. And then, structured Knowledge Strategy Planning methodology, $P^2-KSP$, is proposed. With $P^2-KSP$, an organization can identify organizational knowledge and develop KM architecture from both perspectives of the performance measurement system and business processes.

• International Journal of Aeronautical and Space Sciences
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• v.11 no.2
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• pp.136-144
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• 2010

This paper presents an algorithm for planning waypoints for the operation of a surveillance mission using cooperative unmanned aerial vehicles (UAVs) in a given map. This algorithm is rather simple and intuitive; therefore, this algorithm is easily applied to actual scenarios as well as easily handled by operators. It is assumed that UAVs do not possess complete information about targets; therefore, kinematics, intelligence, and so forth of the targets are not considered when the algorithm is in operation. This assumption is reasonable since the algorithm is solely focused on a surveillance mission. Various parameters are introduced to make the algorithm flexible and adjustable. They are related to various cost functions, which is the main idea of this algorithm. These cost functions consist of certainty of map, waypoints of co-worker UAVs, their own current positions, and a level of interest. Each cost function is formed by simple and intuitive equations, and features are handled using the aforementioned parameters.

• The Journal of Korea Robotics Society
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• v.1 no.2
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• pp.107-116
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• 2006

We propose a planning algorithm to automatically generate a robust behavior plan (RBP) with which mobile robots can achieve their task goal from any initial states under dynamically changing environments. For this, task description space (TDS) is formulated, where a redundant task configuration space and simulation model of physical space are employed. Successful task episodes are collected, where $A^*$ algorithm is employed. Interesting TDS state vectors are extracted, where occurrence frequency is used. Clusters of TDS state vectors are found by using state transition tuples and features of state transition tuples. From these operations, characteristics of successfully performed tasks by a simulator are abstracted and generalized. Then, a robust behavior plan is constructed as an ordered tree structure, where nodes of the tree are represented by attentive TDS state vector of each cluster. The validity of our method is tested by real robot's experimentation for a box-pushing-into-a-goal task.

• Proceedings of the Korean Institute of Landscape Architecture Conference
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• 2007.10b
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• pp.19-29
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• 2007

The making of mountains and waters are recognized as essential to traditional Chinese landscapes and it is this concept that guided the Beijing Olympic Landscape, "Axis to Nature". The Olympic landscape extends the central axis of Beijing north until it is punctuated by the Forest Park Hill and dissipated by Forest Park Lake. Traditional landscape gardens, paintings, and poetry were researched and the plan assessed by eminent scholars and experts to conceive and construct an ecological park that features unique design and traditional Chinese landscape art.

• The Journal of Korea Robotics Society
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• v.19 no.2
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• pp.196-202
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• 2024

The ability to understand given environments and plan a sequence of actions leading to goal state is crucial for personal service robots. With recent advancements in deep learning, numerous studies have proposed methods for state representation in planning. However, previous works lack explicit information about relationships between objects when the state observation is converted to a single visual embedding containing all state information. In this paper, we introduce graph-based state representation that incorporates both object and relationship features. To leverage these advantages in addressing the task planning problem, we propose a Graph Neural Network (GNN)-based subgoal prediction model. This model can extract rich information about object and their interconnected relationships from given state graph. Moreover, a search-based algorithm is integrated with pre-trained subgoal prediction model and state transition module to explore diverse states and find proper sequence of subgoals. The proposed method is trained with synthetic task dataset collected in simulation environment, demonstrating a higher success rate with fewer additional searches compared to baseline methods.

• Proceedings of the Korea Society of Environmental Toocicology Conference
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• 2001.11a
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• pp.87-87
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• 2001

• Research in Mathematical Education
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• v.5 no.2
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• pp.119-126
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• 2001

In this paper we deal with two questions: 1) How have prospective teachers reflected mathematics curriculum reform in their planning of mathematics lessons\ulcorner 2) To what extent were the pre-service teachers able to be reflective about their planning of mathematics lessons\ulcorner Form analyses of videotapes, field notes, discussions among the college students, we found four features in the prospective teacher\\`s lesson plans and their teaching.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1991.04a
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• pp.309-315
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• 1991

This paper presents a method for automatic part feature recognition from the database of AutoCAD system for automatic process planning input. The parts considered in this study are primastic parts composed of faces perpendicular to the X, Y, Z axes and the types of features considered are through steps, blind steps, through slots, blind slots, and pockets. Features are recognized by using the concept of convex points and concave points. The software program is coded by using Turbo Pascal on the IBM PC/AT.

• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.2246-2255
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• 2023

Evacuation time estimation (ETE) is crucial for the effective implementation of resident protection measures as well as planning, owing to its applicability to nuclear emergencies. However, as confirmed in the Fukushima case, the ETE performed by nuclear operators does not reflect behavioral features, exposing thus, gaps that are likely to appear in real-world situations. Existing research methods including surveys and interviews have limitations in extracting highly feasible behavioral features. To overcome these limitations, we propose a VR-based immersive experiment system. The VR system realistically simulates nuclear emergencies by structuring existing disasters and human decision processes in response to the disasters. Evacuation behavioral features were quantitatively extracted through the proposed experiment system, and this system was systematically verified by statistical analysis and a comparative study of experimental results based on previous research. In addition, as part of future work, an application method that can simulate multi-level evacuation dynamics was proposed. The proposed experiment system is significant in presenting an innovative methodology for quantitatively extracting human behavioral features that have not been comprehensively studied in evacuation. It is expected that more realistic evacuation behavioral features can be collected through additional experiments and studies of various evacuation factors in the future.

• Architectural research
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• v.16 no.3
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• pp.109-120
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• 2014

South and North Korea, the divided nations on the Korean peninsula, began with the same modern city planning system, but following the adoption of different national ideologies-capitalism and socialism-, they established their own city planning system and developed their cities accordingly. However, the process of planning and developing a joint city of Kaesong Industrial Complex (KIC) led to the recognition about the need to bridge the differences and prepare for future joint development. This study examines North Korea's city planning system by comparing the two Koreas' systems, and identify the characteristics of North Korea's city planning system through a case analysis of the KIC. By comparing two cases, we found two distinct characteristics of North Korea's. The one is a tool for realizing ideologies and maintaining national system. The other one is that it is ideology-oriented system for the supreme leader and party while South Korea has control-oriented system for the public. We analyze the KIC case based on four pillars: planning system, development system and procedure, master plan, and use behaviors. Result of analysis shows that in KIC there are many socialistic features as we mentioned above in terms of planning system, urban design & structure, and use behavior even though a private enterprise-Hyundai Asan, in South Korea led the whole process. This study is meaningful in that it identifies the specific characteristics of North Korea's city planning focusing on the KIC and is expected to serve as a baseline material to set the future direction for North Korea's development.