• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.4
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• pp.27-36
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• 1993

This paper proposes a shortest path allocation algorithm over the processors on the hypercube system based on the message passing techniques with the optimized module allocation. On multiprocessor systems, how to divide one task into multiple tasks efficiently is an important issue due to the hardness of the life cycle estimation of each process. To solve the life cycle discrepancies, the appropriate task assignment to each processor and the flexible communications among the processors are indispensible. With the concurrent program execution on hypercube systems, each process communicaties to others with the method of message passing. And, each processor has its own memory. The proposed algorithm generates a callable tree out of the module, assigns the weight factors, constructs the allocation graph, finds the shortest path allocation tree, and maps them with hypercube.

• Nuclear Engineering and Technology
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• v.53 no.11
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• pp.3764-3771
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• 2021

Based on the convolutional neural network (CNN), a novel technique is investigated for lost gamma source detection in a room. The CNN is trained with the result of a GEANT4 simulation containing a gamma source inside a meshed room. The dataset for the training process is the deposited energy in the meshes of different n-step paths. The neural network is optimized with parameters such as the number of input data and path length. Based on the proposed method, the place of the gamma source can be recognized with reasonable accuracy without human intervention. The results show that only by 5 measurements of the energy deposited in a 5-step path, (5 sequential points 50 cm apart within 1600 meshes), the gamma source location can be estimated with 94% accuracy. Also, the method is tested for the room geometry containing the interior walls. The results show 90% accuracy with the energy deposition measurement in the meshes of a 5-step path.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.8
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• pp.1483-1490
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• 2009

This parer is presented multiple path-planning of unmanned autonomous forklift using modified genetic algorithm and fuzzy inference system. There are a task-level feedback method and a method that path is dynamically replaned in realtime while the autonomous vehicles are moving by means of an optimal algorithm for existing multiple path-planning. However, such methods cause malfunctions and inefficiency in the sense of time and energy, and path-planning should be dynamically replanned in realtime. To solve these problems, we propose multiple path-planning using modified genetic algorithm and fuzzy inference system and show the performance with autonomous vehicles. For experiment, we designed and built two autonomous mobile vehicles that equipped with the same driving control part used in actual autonomous forklift, and test the proposed multiple path-planning algorithm. Experimental result that actual autonomous mobile vehicle, we verified that fast optimized path-planning and efficient collision avoidance are possible.

• Bulletin of the Korean Chemical Society
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• v.23 no.2
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• pp.295-300
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• 2002

The construction of the T-shaped post-column flow cell has been changed to enhance the practicability as a UV-visible absorbance detector for capillary electrophoresis. In this new design, a rectangular cube-shaped inner structure is employed, which completely fits the outer rectangular tubing. This arrangement has greatly facilitated the fabrication of the T-cells. In addition, the volume for the auxiliary flow has been dramatically reduced down to 300 ${\mu}L$, and its volume flow rate is optimized at 4.2 ${\mu}L$/min. The short optical path length in the sheath flows (500 ${\mu}m$ on each side) minimizes background absorption, and thus enhances its performance in low-UV wavelengths. We have optimized the auxiliary flow rate at 50 ${\mu}m$/s, so that migration times are insensitive to the flow rate. This optimization has improved repeatabilities in migration times and peak heights. A double-beam detection scheme using a pair of photodiodes is employed to increase the signal-to-noise ratio.

• Bulletin of the Korean Chemical Society
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• v.30 no.12
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• pp.2973-2978
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• 2009

The decomposition reaction mechanism of $CH_3CF_2O$ radical formed from hydroflurocarbon, $CH_3CHF_2$ (HFC-152a) in the atmosphere has been investigated using ab-initio quantum mechanical methods. The geometries of the reactant, products and transition states involved in the decomposition pathways have been optimized and characterized at DFT-B3LYP and MP2 levels of theories using 6-311++G(d,p) basis set. Calculations have been carried out to observe the effect of basis sets on the optimized geometries of species involved. Single point energy calculations have been performed at QCISD(T) and CCSD(T) level of theories. Out of the two prominent decomposition channels considered viz., C-C bond scission and F-elimination, C-C bond scission is found to be the dominant path involving a barrier height of 12.3 kcal/mol whereas the F-elimination path involves that of a 28.0 kcal/mol. Using transition-state theory, rate constant for the most dominant decomposition pathway viz., C-C bond scission is calculated at 298 K and found to be 1.3 ${\times}$ 10$^4s{-1}$. Transition states are searched on the potential energy surfaces involving both decomposition channels and each of the transition states are characterized. The existence of transition states on the corresponding potential energy surface are ascertained by performing Intrinsic Reaction Coordinate (IRC) calculation.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.4
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• pp.539-547
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• 2011

In this paper, a method of generating path planning map is developed using digitalized geographic information such as FDB(Feature DataBase). FDB is widely used by the Army and needs to be applied to all weapon systems of newly developed. For the autonomous navigation of a robot, it is necessary to generate a path planning map by which a global path can be optimized. First, data included in FDB is analyzed in order to identify meaningful layers and attributes of which information can be used to generate the path planning map. Then for each of meaningful layers identified, a set of values of attributes in the layer is converted into the traverse cost using a matching table in which any combination of attribute values are matched into the corresponding traverse cost. For a certain region that is gridded, i.e., represented by a grid map, the traverse cost is extracted in a automatic manner for each gird of the region to generate the path planning map. Since multiple layers may be included in a single grid, an algorithm is developed to fusion several traverse costs. The proposed method is tested using a experimental program. Test results show that it can be a viable tool for generating the path planning map in real-time. The method can be used to generate other kinds of path planning maps using the digitalized geographic information as well.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.2
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• pp.268-275
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• 2011

This paper proposes two optimal path planning methods of a mobile robot using uDEAS (univariate Dynamic Encoding Algorithm for Searches). Before start of autonomous traveling, a self-controlled mobile robot must generate an optimal global path as soon as possible. To this end, numerical optimization method is applied to real time path generation of a mobile robot with an obstacle avoidance scheme and the basic path generation method based on the concept of knot and node points between start and goal points. The first improvement in the present work is to generate diagonal paths using three node points in the basic path. The second innovation is to make a smooth path plotted with the blending polynomial using uDEAS. Effectiveness of the proposed schemes are validated for several environments through simulation.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.981-988
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• 2023

As the usage of unmanned aerial vehicles expands, the development and the demand of related technologies are increasing. As the frequency of operation increases and the convenience of operation is emphasized, the importance of related autonomous flight technology is also highlighted. Establishing a path plan to reach the destination in autonomous flight of an unmanned aerial vehicle is important in guidance and control, and a technology for automatically generating path plan is required in order to maximize the effect of unmanned aerial vehicle. In this study, the optimization research of path planning using rapid-exploring random tree method was performed for increasing the effectiveness of autonomous operation. The path planning optimization method considering the characteristics of the unmanned aerial vehicle is proposed. In order to achieve indexes such as optimal distance, shortest time, and passage of mission points, the path planning was optimized in consideration of the mission goals and dynamic characteristics of the unmanned aerial vehicle. The proposed methods confirmed their applicability to the generation of path planning for unmanned aerial vehicles through performance verification for obstacle situations.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.42 no.2
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• pp.127-133
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• 2014

This paper proposes a determination method of waypoints to maximize the survivability of a UAV. Voronoi diagram which is used for the initial selection of waypoint candidates is the most widely used path planning technique to avoid the threat as far as possible when the location and strength of the threat are given. But if threat strength is different each other and flight path is constrained along with straight lines, Voronoi diagram has limitations in real applications. In this study, the initial waypoints obtained from Voronoi diagram are optimized considering the shape of each threat. Here, a waypoint is optimized while adjacent waypoints are fixed. By repeating this localized optimization until whole waypoints are converged, computation time for finding the best waypoints is greatly reduced.

• Journal of the Korean Society for Precision Engineering
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• v.27 no.1
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• pp.76-83
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• 2010

There have been many studies regarding development of autonomous excavation system which is helpful in construction sites where repetitive jobs are necessary. Unfortunately, bucket trajectory planning was excluded from the previous studies. Since, the best use of excavator is to dig efficiently; purpose of this research was set to determine an optimized bucket trajectory in order to get best digging performance. Among infinite ways of digging any given path, criterion for either optimal or efficient bucket moves is required to be established. One method is to adopt work know-how from experienced excavator operator; However the work pattern varies from every worker to worker and it is hard to be analyzed. Thus, other than the work pattern taken from experienced operator, we developed an efficiency model to solve this problem. This paper presents a method to derive a bucket trajectory from optimization theory with empirical CLUB soil model. Path is greatly influenced by physical constraints such as geometry, excavator dimension and excavator workspace. By minimizing a energy function under these constraints, an optimal bucket trajectory could be obtained.