• International Journal of Control, Automation, and Systems
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• v.4 no.2
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• pp.204-216
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• 2006

Latest advances in hardware technology and state of the art of mobile robot and artificial intelligence research can be employed to develop autonomous and distributed monitoring systems. And mobile service robot requires the perception of its present position to coexist with humans and support humans effectively in populated environments. To realize these abilities, robot needs to keep track of relevant changes in the environment. This paper proposes a localization of mobile robot using the images by distributed intelligent networked devices (DINDs) in intelligent space (ISpace) is used in order to achieve these goals. This scheme combines data from the observed position using dead-reckoning sensors and the estimated position using images of moving object, such as those of a walking human, used to determine the moving location of a mobile robot. The moving object is assumed to be a point-object and projected onto an image plane to form a geometrical constraint equation that provides position data of the object based on the kinematics of the intelligent space. Using the a priori known path of a moving object and a perspective camera model, the geometric constraint equations that represent the relation between image frame coordinates of a moving object and the estimated position of the robot are derived. The proposed method utilizes the error between the observed and estimated image coordinates to localize the mobile robot, and the Kalman filtering scheme is used to estimate the location of moving robot. The proposed approach is applied for a mobile robot in ISpace to show the reduction of uncertainty in the determining of the location of the mobile robot. Its performance is verified by computer simulation and experiment.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.2
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• pp.37-44
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• 2020

In this paper, we proposed that a fully automatic multi-class whole heart segmentation algorithm using deep learning. The proposed method is based on U-Net architecture which consist of recurrent convolutional block, residual multi-dilated convolutional block. The evaluation was accomplished by comparing automated analysis results of the test dataset to the manual assessment. We obtained the average DSC of 96.88%, precision of 95.60%, and recall of 97.00% with CT images. We were able to observe and analyze after visualizing segmented images using three-dimensional volume rendering method. Our experiment results show that proposed method effectively performed to segment in various heart structures. We expected that our method can help doctors and radiologist to make image reading and clinical decision.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.7
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• pp.157-164
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• 2020

As the consumption of fishery products continues to increase, aquaculture industry has emerged instead of fishing industry facing limitations of fish stock resources. Recently, smart fish farming industry has rapidly developed through convergence with 4th Industrial Revolution technology. Accordingly, it is important to derive a future model of smart fish farming platforms in order to secure the superiority of the aquaculture industry and the technology standard hegemony. In this study, the future direction of smart fish farm platform was derived through the analysis of environment related to politics, economy, society, and technology related to smart fish farming by applying PEST methodology of macro-environment analysis. It is expected that it will help the public and industrial circles in planning and implementing related projects by including the entire process of value chain of aquaculture industry of breeding, production, management and distribution, and by presenting advanced models based on artificial intelligence and digital twin.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.477-482
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• 1994

As the need for switchover to FA and for rationalization increases in the industrial world, educational courses in schools are more and more taking up the subjects of electronic machines, mechatronics and systems, etc., subjects which are a fusion of the previous subjects of electricity, electronics and machines. At our junior college, a control engineering course was inaugurated in 1974 prior to any other schools that offered such courses. As automation progressed, the use of industrial robots spread rapidly. The year of 1980 is regarded as the first year that the use of industrial robots become widespread. Responding to the current requests, a one-year research course was added to the control engineering course in 1983. Moreover, a robot engineering course was newly established in 1984, in which mechatronics and industrial robotics were instructed intensively in high efficiency. As a teaching aid, an air robot system which was based particularly on the FMS model and possessed pattern recognition capabilities was completed in 1982. This system has been used since then as the nucleus for hands-on training with robots and systems. As more and more intelligent machines and artificial intelligence become widespread in industry, these subjects are taking on greater importance and greater sophistication in the education offered by this department. Educational institutions are seeking to provide facilities and curricula which will meet the technological needs of this age. Our college is not an institution at the graduate school level, but rather a school which is at the more practical junior college level. An outline of the facilities introduced at our school is presented and the results of utilizing it in industrial robot engineering education is reported.

• Computers and Concrete
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• v.21 no.4
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• pp.407-417
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• 2018

Concrete which is a composite material is one of the most important construction materials. Compressive strength is a commonly used parameter for the assessment of concrete quality. Accurate prediction of concrete compressive strength is an important issue. In this study, we utilized an experimental procedure for the assessment of concrete quality. Firstly, the concrete mix was prepared according to C 20 type concrete, and slump of fresh concrete was about 20 cm. After the placement of fresh concrete to formworks, compaction was achieved using a vibrating screed. After 28 day period, a total of 100 core samples having 75 mm diameter were extracted. On the core samples pulse velocity determination tests and compressive strength tests were performed. Besides, Windsor probe penetration tests and Schmidt hammer tests were also performed. After setting up the data set, twelve artificial intelligence (AI) models compared for predicting the concrete compressive strength. These models can be divided into three categories (i) Functions (i.e., Linear Regression, Simple Linear Regression, Multilayer Perceptron, Support Vector Regression), (ii) Lazy-Learning Algorithms (i.e., IBk Linear NN Search, KStar, Locally Weighted Learning) (iii) Tree-Based Learning Algorithms (i.e., Decision Stump, Model Trees Regression, Random Forest, Random Tree, Reduced Error Pruning Tree). Four evaluation processes, four validation implements (i.e., 10-fold cross validation, 5-fold cross validation, 10% split sample validation & 20% split sample validation) are used to examine the performance of predictive models. This study shows that machine learning regression techniques are promising tools for predicting compressive strength of concrete.

• Korean Journal of Cognitive Science
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• v.27 no.4
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• pp.541-572
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• 2016

Causal reasoning is actively studied not only by psychologists but, in recent years, also by cognitive scientists taking the Bayesian approach. This paper seeks to provide an overview of the recent trends in causal reasoning research with a focus on the power probabilistic contrast theory of causality, a major psychological theory on causal inference. The power probabilistic contrast theory (PPCT) assumes that a cause is a power that initiates or inhibits the result. This power is purported be understood through statistical correlation under certain conditions. The paper examines the supporting empirical evidence in the development of PPCT. Also, introduced are the theoretical dispute between the PPCT and the model based on Bayesian approach, and the current developments and implications of research on causal invariance hypothesis, which states that cause operates identically regardless of the context. Recent studies have produced experimental results that cannot be readily explained by existing empirical approach. Therefore, these results call for serious examination of the power theory of causality by researchers in neighboring fields such as philosophy, statistics, and artificial intelligence.

• Journal of Korea Game Society
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• v.18 no.5
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• pp.23-30
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• 2018

With the development of ICT technology, the game industry is growing rapidly. However, due to the excessive play of the game, there are cases where normal life is difficult. It is necessary to study the diagnosis and countermeasures against such game addiction. This paper study a system for diagnosing game addiction based on game usage information of game users. In order to develop a game addiction diagnostic system, this study classifies game addiction and abuse users and collects and models log information in real game environment. In addition, the addiction and good use model of game users was used as a knowledge to diagnose the addiction of new game users. Through the experiment, we could confirm the meaningful results. Also, according to the result of the diagnosis, the service such as SMS and reporting was suggested.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.11
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• pp.411-418
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• 2018

With the proliferation of artificial intelligence technology, it is becoming important to obtain, store, and utilize scientific data in research and science sectors. A number of methods for extracting meaningful objects such as graphs and tables from research articles have been proposed to eventually obtain scientific data. Existing extraction methods using heuristic approaches are hardly applicable to electronic documents having heterogeneous manuscript formats because they are designed to work properly for some targeted manuscripts. This paper proposes a prototype of an object extraction system which exploits a recent deep-learning technology so as to overcome the inflexibility of the heuristic approaches. We implemented our trained model, based on the Faster R-CNN algorithm, using the Google TensorFlow Object Detection API and also composed an annotated data set from 100 research articles for training and evaluation. Finally, a performance evaluation shows that the proposed system outperforms a comparator adopting heuristic approaches by 5.2%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5701-5722
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• 2018

The continuous increase in the cost of energy production and concerns for environmental sustainability are leading research communities, governments and industries to amass efforts to reduce energy consumption and global $CO_2$ footprint. Players in the information and communication industry are keen on reducing the operational expenditures (OpEx) and maintaining the profitability of cellular networks. Meanwhile, network virtualization has been proposed in this regard as the main enabler for 5G mobile cellular networks. In this paper, we propose a generic framework of slice resource provisioning and customized physical resource allocation for energy-efficiency and quality of service optimization. In resource slicing, we consider user demand and population resources provisioning scheme aiming to satisfy quality of service (QoS). In customized physical resource allocation, we formulate this problem with an integer non-linear programming model, which is solved by a heuristic algorithm based on minimum vertex coverage. The proposed algorithm is compared with the existing approaches, without consideration of slice resource constraints via system-level simulations. From the perspective of infrastructure providers, traffic is scheduled over a limited number of active small-cell base stations (sc-BSs) that significantly reduce the system energy consumption and improve the system's spectral efficiency. From the perspective of virtual network operators and mobile users, the proposed approach can guarantee QoS for mobile users and improve user satisfaction.

• Journal of Internet Computing and Services
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• v.20 no.2
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• pp.93-100
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• 2019

Recently, in various business fields, companies are concentrating on providing chatbot services to various environments by adding artificial intelligence to existing messenger platforms. Organizations in the field of job placement also require chatbot services to improve the quality of employment counseling services and to solve the problem of agent management. A text-based general chatbot classifies input user sentences into learned sentences and provides appropriate answers to users. Recently, user sentences inputted to chatbots are inputted as short texts due to the activation of social network services. Therefore, performance improvement of short text classification can contribute to improvement of chatbot service performance. In this paper, we propose T-EBOW (Translation-Extended Bag Of Words), which is a method to add translation information as well as concept information of existing researches in order to strengthen the short text classification for employment chatbot. The performance evaluation results of the T-EBOW applied to the machine learning classification model are superior to those of the conventional method.