• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.250-255
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• 2005

For navigation of a service robot, mapping and localization are very important. To estimate the robot pose, the map of the environment is required and it can be built by exploration or SLAM. Exploration is the fundamental task of guiding a robot autonomously during mapping such that it covers the entire environment with its sensors. In this paper, an efficient exploration scheme based on the position probability of the end nodes of a topological map is proposed. In this scheme, a topological map is constructed in real time using the thinning-based approach. The robot then updates the position probability of each end node maintaining its position at the current location based on the Bayesian update rule using the range data. From this probability, the robot can determine whether or not it needs to visit the specific end node to examine the environment around this node. Various experiments show that the proposed exploration scheme can perform exploration more efficiently than other schemes in that, in most cases, exploration for the entire environment can be completed without directly visiting everywhere in the environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.4
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• pp.1555-1570
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• 2016

For monitoring forest fires, a real-time system to prevent fires in wider areas should be supported consistently. However, there has still been a lack of the support for real-time system related to forest fire monitoring. In addition, the 'real-time' processing in a forest fire detection system can lead to excessive consumption of energy. To solve these problems, the intelligent data acquisition of sensing nodes is required, and the maximum energy savings as well as rapid and accurate detection by flame sensors need to be done. In this regard, this paper proposes a node built-in filter algorithm for intelligent data collection of sensing nodes for the rapid detection of forest fires with focus on reducing the power consumption of the remote sensing nodes and providing efficient wireless sensor network-based forest environment monitoring in terms of data transmission, network stability and data acquisition. The experimental result showed that battery life can be extended through the intelligent sampling of remote sensing nodes, and the average accuracy of the measurement of flame detection based on the distance is 44%.

• International Journal of Contents
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• v.15 no.3
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• pp.32-38
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• 2019

We usually suffer from difficulties in treating or managing Big Data generated from various digital media and/or sensors using traditional mining techniques. Additionally, there are many problems relative to the lack of memory and the burden of the learning curve, etc. in an increasing capacity of large volumes of text when new data are continuously accumulated because we ineffectively analyze total data including data previously analyzed and collected. In this paper, we propose a general-purpose classifier and its structure to solve these problems. We depart from the current feature-reduction methods and introduce a new scheme that only adopts changed elements when new features are partially accumulated in this free-style learning environment. The incremental learning module built from a gradually progressive formation learns only changed parts of data without any re-processing of current accumulations while traditional methods re-learn total data for every adding or changing of data. Additionally, users can freely merge new data with previous data throughout the resource management procedure whenever re-learning is needed. At the end of this paper, we confirm a good performance of this method in data processing based on the Big Data environment throughout an analysis because of its learning efficiency. Also, comparing this algorithm with those of NB and SVM, we can achieve an accuracy of approximately 95% in all three models. We expect that our method will be a viable substitute for high performance and accuracy relative to large computing systems for Big Data analysis using a PC cluster environment.

• Journal of Sensor Science and Technology
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• v.30 no.6
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• pp.429-435
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• 2021

In this study, the creation of emotional dialogue was investigated within the process of developing a robot's natural language understanding and emotional dialogue processing. Unlike an English-based dataset, which is the mainstay of natural language processing, the Korean-based dataset has several shortcomings. Therefore, in a situation where the Korean language base is insufficient, the Korean dataset should be dealt with in detail, and in particular, the unique characteristics of the language should be considered. Hence, the first step is to base this study on a specific Korean dataset consisting of conversations on emotional topics. Subsequently, a model was built that learns to extract the continuous dialogue features from a pre-trained language model to generate sentences while maintaining the context of the dialogue. To validate the model, a chatbot system was implemented and meaningful results were obtained by collecting the external subjects and conducting experiments. As a result, the proposed model was influenced by the dataset in which the conversation topic was consultation, to facilitate free and emotional communication with users as if they were consulting with a chatbot. The results were analyzed to identify and explain the advantages and disadvantages of the current model. Finally, as a necessary element to reach the aforementioned ultimate research goal, a discussion is presented on the areas for future studies.

• International journal of advanced smart convergence
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• v.12 no.2
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• pp.47-55
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• 2023

The automotive industry is undergoing a paradigm shift due to the convergence of IT and rapid digital transformation. Various components, including embedded structures and systems with complex architectures that incorporate IC semiconductors, are being integrated and modularized. As a result, there has been a significant increase in vehicle defects, raising expectations for the quality of automotive parts. As more and more data is being accumulated, there is an active effort to go beyond traditional reliability analysis methods and apply machine learning models based on the accumulated big data. However, there are still not many cases where machine learning is used in product development to identify factors of defects in performance and durability of products and incorporate feedback into the design to improve product quality. In this paper, we applied a prediction algorithm to the defects of automotive door devices equipped with automatic responsive sensors, which are commonly installed in recent electric and hydrogen vehicles. To do so, we selected test items, built a measurement emulation system for data acquisition, and conducted comparative evaluations by applying different machine learning algorithms to the measured data. The results in terms of R² score were as follows: Ordinary multiple regression 0.96, Ridge regression 0.95, Lasso regression 0.89, Elastic regression 0.91.

• Journal of Korea Multimedia Society
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• v.17 no.7
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• pp.858-865
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• 2014

In the case of system like MES, various sensors collect the data in real time and save it as a big data to monitor the process. However, if there is big data mining in distributed computing system, whole processing process can be improved. In this paper, system to analyze the cause of operation deviation was built using the big data which has been collected from deasphalting process at the two different plants. By applying multivariate statistical analysis to the big data which has been collected through MES(Manufacturing Execution System), main cause of operation deviation was analyzed. We present the example of analyzing the operation deviation of deasphalting process using the big data which collected from MES by using multivariate statistics analysis method. As a result of regression analysis of the forward stepwise method, regression equation has been found which can explain 52% increase of performance compare to existing model. Through this suggested method, the existing petrochemical process can be replaced which is manual analysis method and has the risk of being subjective according to the tester. The new method can provide the objective analysis method based on numbers and statistic.

• The Journal of Korea Robotics Society
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• v.5 no.3
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• pp.186-196
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• 2010

In this paper, we present a global localization and position error compensation method in a known indoor environment using magnet hall sensors. In previous our researches, it was possible to compensate the pose errors of $x_e$, $y_e$, ${\theta}_e$ correctly on the surface of indoor environment with magnets sets by regularly arrange the magnets sets of identical pattern. To improve the proposed method, new strategy that can realize the global localization by changing arrangement of magnet pole is presented in this paper. Total six patterns of the magnets set form the unique landmarks. Therefore, the virtual map can be built by using the six landmarks randomly. The robots search a pattern of magnets set by rotating, and obtain the current global pose information by comparing the measured neighboring patterns with the map information that is saved in advance. We provide experimental results to show the effectiveness of the proposed method for a differential drive wheeled mobile robot.

• Journal of Biosystems Engineering
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• v.19 no.2
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• pp.91-98
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• 1994

Planting, transplanting, and harvesting are important processes for the successful production of farm products in Korea because those require the high labor intensity during limitted period. Recently, many researches of using automatic control with a microcomputer are carried in the agricultural field, but are not much spread to the seeder development. Automatic sowing technology would be much attractive if there was a way to assure that each seed was count accurately in the seed metering device. Thus, an automatic seed metering device was designed and constructed to be controlled by microcomputer. This device could be improved in not only counting the number of seeds in but also sowing seeds between row spacings. Automatic seed metering device consisted of conveyor belt and temporary storage device. Performance of seed metering device depends on the apparatus including sensor, stepping motor and DC-solenoid. Research contents and results are summarized as follows. 1. The seed metering device involving seed hopper, sorter and temporary storage device was designed and constructed. 2. A seed counting system with six photo electric sensors, designed and built for this project, was adequate for tranferring and counting seeds accurately. 3. Operating algorithm for stepping motor and photo electric DC-solenoid was developed. The Seed metering device proved to be a smooth and accurate operating device using the algorithm. 4. The performance of second prototype metering device was examined with five kinds of seeds ; mung beans, red beans, white beans, black beans and corn to transfer and count the seeds. The error ratio of seed metering was less than 3.5%.

• Journal of International Academy of Physical Therapy Research
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• v.11 no.3
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• pp.2107-2112
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• 2020

Background: With the increasing elderly population, the need for gait analysis of these elderly individuals is also increasing. Most devices are costly and not portable; however, smartphones using built-in sensors capable of measuring motion and are easily available. Objectives: To examine the reliability and validity of knee joint angles of the elderly using smartphone measurements during walking. Design: Quasi-experimental research. Methods: Sixteen elderly people, aged 65+ and living in Daejeon and Chungbuk, South Korea, participated in the study. Electrogoniometers and smartphones were attached to the thigh and the side and front of the shank of each subject, respectively, using double-sided tape, an arm band, and an elastic band. Each subject completed two sets of at least seven gait cycles (14 steps). Results: Both the smartphones and electrogoniometers exhibited high agreement in terms of their primary and secondary measurements (ICC>.75). The agreement between the smartphones and electrogoniometers was also high in terms of both the primary and secondary measurements (ICC<.60). Conclusion: These results indicate that smartphones can be costly equipment cannot, even though they cannot completely replace existing clinical-grade devices. Their utility is emphasized herein for measuring knee joint angles of the elderly during walking.

• Smart Media Journal
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• v.8 no.4
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• pp.85-90
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• 2019

The Ultra-Wide Band sensor is widely used as a wireless indoor localization technology with frequency bands in the GHz range. Meanwhile, in museums, not only the real-time location of visitors but also information on visit route and duration time is required for patrons' behavior analysis. In this paper, the analysis system based Ultra-Wide Band radar for visitor's viewing behavior is introduced and experimented in the real environment. We built the system in National Museum of Korea, and its 22 Ultra-Wide Band radar sensors receive the real-time location of their visitors: this analyzes the visit route and visit time for patrons.