• International Journal of Control, Automation, and Systems
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• v.4 no.1
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• pp.10-16
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• 2006

In this paper, moving a fragile object from an initial point to a specific location in the minimum time without damage is studied. In order to achieve this goal, initially, the maximum acceleration and velocity ranges are specified. These ranges can be dynamically generate on the planned path by the manipulator. The path can be altered by considering the geometrical constraints. Later, considering the impulsive force constraint on the object, the range of maximum acceleration and velocity are obtained to preserve object safety while the manipulator is carrying it along the curved path. Finally, a time-optimal trajectory is planned within the maximum allowable range of acceleration and velocity. This time-optimal trajectory planning can be applied to real applications and is suitable for both continuous and discrete paths.

• Journal of Korea Multimedia Society
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• v.9 no.12
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• pp.1529-1541
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• 2006

Moving objects have been widely employed in traffic and logistic applications. Spatio-temporal aggregations mainly describe the moving object's behavior in the spatial data warehouse. The previous works usually express the object moving in some certain region, but ignore the object often moving along as the trajectory. Other researches focus on aggregation and comparison of trajectories. They divide the spatial region into units which records how many times the trajectories passed in the unit time. It not only makes the storage space quite ineffective, but also can not maintain spatial data property. In this paper, a spatio-temporal aggregation index structure for moving object trajectory in constrained network is proposed. An extended B-tree node contains the information of timestamp and the aggregation values of trajectories with two directions. The network is divided into segments and then the spatial index structure is constructed. There are the leaf node and the non leaf node. The leaf node contains the aggregation values of moving object's trajectory and the pointer to the extended B-tree. And the non leaf node contains the MBR(Minimum Bounding Rectangle), MSAV(Max Segment Aggregation Value) and its segment ID. The proposed technique overcomes previous problems efficiently and makes it practicable finding moving object trajectory in the time interval. It improves the shortcoming of R-tree, and makes some improvement to the spatio-temporal data in query processing and storage.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.467-470
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• 2003

When user wants to find objects which have the nearest position from him, we use the nearest neighbor (NN) query. The GIS applications, such as navigation system and traffic control system, require processing of NN query for moving objects (MOs). MOs have trajectory with changing their position over time. Therefore, we should be able to find NN object continuously changing over the whole query time when process NN query for MOs, as well as moving nearby on trajectory of query. However, none of previous works consider trajectory information between objects. Therefore, we propose a method of continuous NN query for trajectory of MOs. We call this CTNN (continuous trajectory NN) technique. It ran find constantly valid NN object on the whole query time by considering of trajectory information.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.5
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• pp.183-189
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• 2009

Recently, PCS, PDA and mobile devices, such as the proliferation of spread, GPS (Global Positioning System) the use of, the rapid development of wireless network and a regular user even images, audio, video, multimedia data, such as increased use is for. In particular, video data among multimedia data, unlike the moving object, text or image data that contains information about the movements and changes in the space of time, depending on the kinds of changes that have sigongganjeok attributes. Spatial location of objects on the flow of time, changing according to the moving object (Moving Object) of the continuous movement trajectory of the meeting is called, from the user from the database that contains a given query trajectory and data trajectory similar to the finding of similar trajectory Search (Similar Sub-trajectory Retrieval) is called. To search for the trajectory, and these variations, and given the similar trajectory of the user query (Tolerance) in the search for a similar trajectory to approximate data matching (Approximate Matching) should be available. In addition, a large multimedia data from the database that you only want to be able to find a faster time-effective ways to search different from the existing research is required. To this end, in this paper effectively divided into a grid to search for the trajectory to the trajectory of moving objects, similar to the effective support of the search trajectory offers a new grid-based search techniques.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.317-320
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• 1986

A co-operative position control is proposed for two robot manipulators with 5 degree of freedoms to transfer an object following a specified trajectory, where each manipulator is assumed to change the posture of its end effector without releasing the object.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.4
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• pp.577-582
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• 1987

A co-operative position control is proposed for two robots manipulation with five degrees of freedom to transfer an object following a specified trajectory, where each manifulator is assumed to change the postrue of its end effector without releasing the object.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.19 no.1
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• pp.25-29
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• 2009

The purpose of this study was to investigate the effect of two different lifting posture on the plantar foot pressure, force and COP(center of pressure) trajectory path during object lifting. Fourteen healthy adults who had no musculoskeletal disorders were instructed to lift with two postures(stoop and squat) and two object weights(empty box and 10 kg box). Plantar foot pressures, forces and COP trajectory path were recorded by the F-mat system(Tekscan, Boston, USA) during object lifting with barefoot. Plantar foot surface was defined as seven regions for pressure measurement; two toe regions, three forefoot regions, one midfoot region and one heel region. Paired t-test was used to compare the outcomes of peak pressure and maximum force with different two lifting postures and two object weights. Plantar peak pressure and maximum force under hallux was significantly greater in squat posture than stoop posture during the two different boxes lifting(p<.05). During the empty box lifting, maximum force under lessor toes was significantly less and plantar peak pressure under second metatarsal region was significantly greater in squat than stoop(p<.05). Maximum force under heel was significantly less in squat than stoop posture during 10kg box lifting(p<.05). Finally, COP trajectory path was significantly greater in squat than stoop(p<.05). These findings confirm that there are significantly change in the structure and function of the foot during the object lifting with different posture. Future studies should focus on the contribution of both structural and functional change to the development of common foot problems in adults.

• Journal of radiological science and technology
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• v.42 no.1
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• pp.39-46
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• 2019

This study conducted a comparative analysis of differences between cartesian trajectory in a linear rectangular coordinate system and MultiVane trajectory in a nonlinear rectangular coordinate system axial T1 and axial T2 images using an American College of Radiology(ACR) phantom. The phantom was placed at the center of the head coil and the top-to-bottom and left-to-right levels were adjusted by using a level. The experiment was performed according to the Phantom Test Guidance provided by the ACR, and sagittal localizer images were obtained. As shown in Figure 2, slices # 1 and # 11 were scanned after placing them at the center of a $45^{\circ}$ wedge shape, and a total of 11 slices were obtained. According to the evaluation results, the image intensity uniformity(IIU) was 93.34% for the cartesian trajectory, and 93.19% for the MultiVane trajectory, both of which fall under the normal range in the axial T1 image. The IIU for the cartesian trajectory was 0.15% higher than that for the MultiVane trajectory. In axial T2, the IIU was 96.44% for the cartesian trajectory, and 95.97% for the MultiVane trajectory, which fall under the normal range. The IIU for the cartesian trajectory was by 0.47% higher than that for the MultiVane trajectory. As a result, the cartesian technique was superior to the MultiVane technique in terms of the high-contrast spatial resolution, image intensity uniformity, and low-contrast object detectability.

• Spatial Information Research
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• v.19 no.2
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• pp.57-64
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• 2011

This paper describes how to storing and querying trajectory information on PostgreSQL/PostGIS. Recently as technology of mobile devices is advancing, many researches for location-based services and moving object's trajectory have been studied. Trajectory is the set of information of the location by the time, and is one of the most im portant information for location-based services. Traditional spatial database systems do not support trajectory data types and functions. In this paper, we propose a trajectory data type and query functions for moving objects on PostgreSQL/PostGIS.

• Journal of the Korea Convergence Society
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• v.10 no.4
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• pp.33-38
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• 2019

Recently, to support location-based services, there have been many researches which consider the spatial network. For this, there are many experimental data for data processing on the road network. However, the data to process the trajectory of moving objects are not suitable. Therefore, we propose index structure to process the trajectory data on the road network and the trajectory data generation algorithm. In addition, to prove efficiency of our index structure and algorithm, we show that edge-based trajectory data are generated through the proposed algorithm using the map data of San Francisco Bay.