• Journal of KIISE
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• v.43 no.12
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• pp.1365-1375
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• 2016

One of the most important capabilities for autonomous service robots working in living environments is to recognize and understand the correct context in dynamically changing environment. To generate high-level context knowledge for decision-making from multiple sensory data streams, many technical problems such as multi-modal sensory data fusion, uncertainty handling, symbolic knowledge grounding, time dependency, dynamics, and time-constrained spatio-temporal reasoning should be solved. Considering these problems, this paper proposes an effective dynamic context management and spatio-temporal reasoning method for intelligent service robots. In order to guarantee efficient context management and reasoning, our algorithm was designed to generate low-level context knowledge reactively for every input sensory or perception data, while postponing high-level context knowledge generation until it was demanded by the decision-making module. When high-level context knowledge is demanded, it is derived through backward spatio-temporal reasoning. In experiments with Turtlebot using Kinect visual sensor, the dynamic context management and spatio-temporal reasoning system based on the proposed method showed high performance.

• Journal of KIISE
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• v.43 no.9
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• pp.963-973
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• 2016

Providing a semantic knowledge system using media ontologies requires not only conventional axiom reasoning but also knowledge extension based on various types of reasoning. In particular, spatio-temporal information can be used in a variety of artificial intelligence applications and the importance of spatio-temporal reasoning and expression is continuously increasing. In this paper, we append the LOD data related to the public address system to large-scale media ontologies in order to utilize spatial inference in reasoning. We propose an RDFS/Spatial inference system by utilizing distributed memory-based framework for reasoning about large-scale ontologies annotated with spatial information. In addition, we describe a distributed spatio-temporal SPARQL parallel query processing method designed for large scale ontology data annotated with spatio-temporal information. In order to evaluate the performance of our system, we conducted experiments using LUBM and BSBM data sets for ontology reasoning and query processing benchmark.

• Spatial Information Research
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• v.22 no.5
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• pp.9-18
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• 2014

According to the recent development of the ubiquitous computing environment, the use of spatio-temporal data from sensors with GPS is increasing, and studies on the Semantic Sensor Web using spatio-temporal data for providing different kinds of services are being actively conducted. Especially, the W3C developed the SSNO(Semantic Sensor Network Ontology) which uses sensor-related standards such as the SWE(Sensor Web Enablement) of OGC and defines classes and properties for expressing sensor data. Since these studies are available for the query processing about non-spatio-temporal sensor data, it is hard to apply them to spatio-temporal sensor data processing which uses spatio-temporal data types and operators. Therefore, in this paper, we developed the SWE based on SSNO which supports the spatio-temporal sensor data types and operators expanding spatial data types and operators in "OpenGIS Simple Feature Specification for SQL" by OGC. The system receives SensorML(Sensor Model Language) and O&M (Observations and Measurements) Schema and converts the data into SSNO. It also performs the efficient query processing which supports spatio-temporal operators and reasoning rules. In addition, we have proved that this system can be utilized for the web service by applying it to a virtual scenario.

• Journal of Korean Society of Transportation
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• v.32 no.5
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• pp.522-530
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• 2014

Spatio-temporal congestion evolution pattern can be reproduced using the VDS(Vehicle Detection System) historic speed dataset in the TMC(Traffic Management Center)s. Such dataset provides a pool of spatio-temporally experienced traffic conditions. Traffic flow pattern is known as spatio-temporally recurred, and even non-recurrent congestion caused by incidents has patterns according to the incident conditions. These imply that the information should be useful for traffic prediction and traffic management. Traffic flow predictions are generally performed using black-box approaches such as neural network, genetic algorithm, and etc. Black-box approaches are not designed to provide an explanation of their modeling and reasoning process and not to estimate the benefits and the risks of the implementation of such a solution. TMCs are reluctant to employ the black-box approaches even though there are numerous valuable articles. This research proposes a more readily understandable and intuitively appealing data-driven approach and developes an algorithm for identifying congestion patterns for recurrent and non-recurrent congestion management and information provision.

• Journal of the Korean Magnetic Resonance Society
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• v.24 no.1
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• pp.23-29
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• 2020

Mozart's music has been suggested to affect spatio-temporal reasoning of listeners, which has been called "Mozart effect". However, the effects of Mazart's music on human metabolism have not been known. We dissected Mozart's music into its compositional elements and studied their effects on metabolism of experimental animals. Mozart music significantly reduced cortisol level induced by stress. NMR metabolomic study revealed different urine metabolic profile according to the listening to Mozart's music. In addition, each element of music exhibited different metabolic profile. Functional MRI study also showed enhanced brain activity upon listening to Mozart's music. Taken together, Mozart's music seems to be related with brain activity, stress hormone and whole body metabolism.

• Journal of Korea Society of Industrial Information Systems
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• v.12 no.1
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• pp.54-60
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• 2007

The Digital Multimedia Broadcasting(DMB) system is developed to offer high quality multimedia content to the mobile environment. The system adopts the MPEG-4 standard for the main video, audio and other media format. For providing interactive contents, it also adopts the MPEG-4 scene description that refers to the spatio-temporal specifications and behaviors of individual objects. With more interactive contents, the scene description also needs higher bitrate. However, the bandwidth for allocating meta data, such as scene description is restrictive in the mobile environment. On one hand, the DMB terminal renders each media stream according to the scene description. Thus the binary format for scene(BIFS) stream corresponding to the scene description should be decoded and parsed in advance when presenting media data. With this reasoning, the transmission delay of the BIFS stream would cause the delay in transmitting whole audio-visual scene presentations, although the audio or video streams are encoded in very low bitrate. This paper presents the effective optimization technique in adapting the BIFS stream into the expected bitrate without any waste in bandwidth and avoiding transmission delays inthe initial scene description for interactive DMB content.

• Physical Therapy Rehabilitation Science
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• v.7 no.3
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• pp.114-118
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• 2018

Objective: The left and right sides of the brain has different roles. This study investigated the differences in cognitive driving ability between stroke survivors with damage to the left brain and right brain. Therefore, the purpose of this study was to compare the driving cognitive ability of left and right hemispheric drivers following stroke. Design: Cross-sectional study. Methods: The Stroke Drivers' Screening Assessment (SDSA) from the UK was translated to the Korean Stroke Drivers' Screening Assessment (K-SDSA) to meet the specific traffic environments of Korea. The SDSA is composed of 4 tasks :1) a dot cancellation task that measures concentration and visuospatial abilities necessary for driving, 2) a directional matrix task to measure spatio-temporal executive function required for driving, 3) a compass matrix task to measure accurate direction determination ability required for driving, and 4) recognition of traffic signs and reasoning ability to understanding traffic situation. The SDSA assessment time is about 30 minutes. The K-SDSA was used to compare the cognitive driving abilities between 15 stroke survivors with left and 15 stroke survivors with right brain damage. Results: There were significant differences between the persons with stroke patients with left brain lesions (right hemiplegia) compared to the persons with stroke with right brain lesions (left hemiplegia) (p<0.05). It was found that the cognitive driving ability of those with right brain damage was lower than that of the group of left brain damage. Conclusions: This research investigated the driving cognitive ability of persons with stroke. The therapists can use this information as basis for the driving test and training purposes. It could also be used as a basis to understanding if the cognitive ability of not only stroke survivors but also those with brain damage is adequate to actually drive.