• Journal of the Korea Society of Computer and Information
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• v.15 no.10
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• pp.147-156
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• 2010

There are some of the studies on sensor middleware. However the standard middleware has not yet been defined. Especially, this paper focuses on the processing an intelligent service of the main functions of middleware. Several applications in the sensor network environment support not only monitoring services, but also sensor-based context-awareness and intelligent services based on sensors. However, the previous studies about USN middleware only mentioned the need for intelligent service and did not discuss the architecture and method for supporting the intelligent service in detail. Therefore this paper designs a USN middleware for providing intelligent services and proposes the method for processing the services. For this purpose, this paper proposes the Sensor-Service ontology to define the concept of services and sensors for USN applications and the relationship between them. The Sensor-Service ontology is used to infer high-level information from low-level information. To apply a variety of environmental context to intelligent services, the paper uses the rule-based reasoning. This paper implements the proposed intelligent service middleware as a prototype and then shows that the middleware can be used for a variety of USN applications through the performance evaluation.

• Electronics and Telecommunications Trends
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• v.34 no.5
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• pp.14-25
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• 2019

As the Internet of Things, artificial intelligence and big data have received a lot of attention as key growth engines in the era of the fourth industrial revolution, data acquisition and utilization in mobile, automotive, robotics, manufacturing, agriculture, health care and national defense are becoming more important. Due to numerous data-based industrial changes, demand for sensor technologies is exploding, especially for intelligent sensor technologies that combine control, judgement, storage and communication functions with the sensors's own functions. Intelligent sensor technology can be defined as a convergence component technology that combines intelligent sensor units, intelligent algorithms, modules with signal processing circuits, and integrated plaform technologies. Intelligent sensor technology, which can be applied to variety of smart IT convergence services such as smart devices, smart homes, smart cars, smart factory, smart cities, and others, is evolving towards intelligent and convergence technologies that produce new high-value information through recognition, reasoning, and judgement based on artificial intelligence. As a result, development of intelligent sensor units is accelerating with strategies for miniaturization, low-power consumption and convergence, new form factor such as flexible and stretchable form, and integration of high-resolution sensor arrays. In the future, these intelligent sensor technologies will lead explosive sensor industries in the era of data-based artificial intelligence and will greatly contribute to enhancing nation's competitiveness in the global sensor market. In this report, we analyze and summarize the recent trends in intelligent sensor technologies, especially those for four core technologies.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.12
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• pp.705-712
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• 2005

This paper deals with speed-sensorless control of an AC servo motor using Fuzzy-Tuning High Gain Observer(FTHGO). Resolver or encoder can be used to measure a rotor speed, but it has a limit to detect motor speed precisely. To solve this problem, it is studied to measure a speed of an AC servo motor without sensor. In this paper, the gain of an observer to estimate motor speed is properly set up and designed using the fuzzy control theory. It calculates the differentiation of the rotor current of the AC motor and estimates the rotor speed using it. Proposed speed sensorless control is performed using the estimated speed as the control variable. Designed FTHGO is applied to AC servo motor to verify the feasibility of the proposed observer. Feasibility of the FTHGO proposed in this paper is proven comparing the experimental results with/without the speed sensor.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.6
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• pp.19-29
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• 2007

This paper introduces a robot knowledge framework which is represented with multiple classes, levels and layers to implement robot intelligence at real environment for mobile robot. Our root knowledge framework consists of four classes of knowledge (KClass), axioms, rules, a hierarchy of three knowledge levels (KLevel) and three ontology layers (OLayer). Four KClasses including perception, model, activity and context class. One type of rules are used in a way of unidirectional reasoning. And, the other types of rules are used in a way of bi-directional reasoning. The robot knowledge framework enable a robot to integrate robot knowledge from levels of its own sensor data and primitive behaviors to levels of symbolic data and contextual information regardless of class of knowledge. With the integrated knowledge, a robot can have any queries not only through unidirectional reasoning between two adjacent layers but also through bidirectional reasoning among several layers even with uncertain and partial information. To verify our robot knowledge framework, several experiments are successfully performed for object recognition and navigation.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.44 no.3 s.357
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• pp.12-19
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• 2007

This paper proposes the enhanced auto-focus algorithm detecting several objects and selecting the target object. Proposed algorithm first detects some objects distributed in the image using focus measure operator and multi-window and then selects the target object through fuzzy reasoning with three fuzzy membership functions. Implementation can be simple because it only needs image sensor instead of infrared or ultrasonic equipment. Experimental result shows that the proposed algorithm can improve the quality of image by focusing to the target object.

• Journal of the Korea Society of Computer and Information
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• v.24 no.3
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• pp.159-166
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• 2019

The collection and manipulation of Internet of Things (IoT) data is increasing at a fast pace and its importance is recognized in every sector of our society. For efficient utilization of IoT data, the vast and varied IoT data needs to be reliable and meaningful. In this paper, we propose an IoT framework to realize this need. The IoT framework is based on a four layer IoT architecture onto which context aware computing technology is applied. If the collected IoT data is unreliable it cannot be used for its intended purpose and the whole service using the data must be abandoned. In this paper, we include techniques to remove uncertainty in the early stage of IoT data capture and collection resulting in reliable data. Since the data coming out of the various IoT devices have different formats, it is important to convert them into a standard format before further processing, We propose the RDF format to be the standard format for all IoT data. In addition, it is not feasible to process all captured Iot data from the sensor devices. In order to decide which data to process and understand, we propose to use contexts and reasoning based on these contexts. For reasoning, we propose to use standard AI and statistical techniques. We also propose an experiment environment that can be used to develop an IoT application to realize the IoT framework.

• Journal of KIISE
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• v.42 no.3
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• pp.295-306
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• 2015

MOnCa2 is a framework for building intelligent smartphone applications based on smartphone sensors and ontology reasoning. In previous studies, MOnCa determined and inferred user situations based on sensor values represented by ontology instances. When this approach is applied, recognizing user space information or objects in user surroundings is possible, whereas determining the user's physical context (travel behavior, travel destination) is impossible. In this paper, MOnCa2 is used to build recognition models for travel behavior and routes using smartphone sensors to analyze the user's physical context, infer basic context regarding the user's travel behavior and routes by adapting these models, and generate high-level context by applying ontology reasoning to the basic context for creating intelligent applications. This paper is focused on approaches that are able to recognize the user's travel behavior using smartphone accelerometers, predict personal routes and destinations using GPS signals, and infer high-level context by applying realization.

• Fire Science and Engineering
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• v.11 no.2
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• pp.45-53
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• 1997

In this article, the Fuzzy Logic as the principle of the multcriteria fire detector is used to determine whether the fire takes out or not. The main contents of this method as follow; most of all, the degree of the fire is represented as the type of the Fuzzy, and then it is possible to examine whether the fire takes out or not by the principle of the Fuzzy Logic. The input fators of the Fuzzy fire detector are temperature sensor, smoke sensor, light sensor applied to digital type. On the result of this study, the first, the number of the case of the nonfire alarm which is represented in the existing fire detector is reduced, and the second, the applicability of the fuzzy detector is demonstrated by the test.

• 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 the Korean Institute of Telematics and Electronics T
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• v.36T no.1
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• pp.64-70
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• 1999

When manipulator moves the objects, the object position error can be occurred because of acceleration or negative acceleration according to the direction. So we make manipulator working path for establishing optimal gripper force control preventing occurrence of object position error. And we attached the tactile sensor on the gripper of manipulator which gives us very specific information between manipulator and object. Reasoning of continuous tactile image data, manipulator can sense rotation and slippage and change the grasping force that corrects calculated grasping force and compensation can be possible of the object position error. We use the FSR(Force Sensing Resistor)sensor which consists of 22 by 22 taxels and continuous taxel number is used for filtering and using the moment method for sensing algorithm in our experiment.