• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.2
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• pp.76-86
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• 1989

Many intelligent robots that are equipped with special tools and sensors re currently used in assembly line. As automatic manufacturing systems including such robots become advanced and complicated, there are increasing needs for the development of the sophisticated programming systems which can control several robots and other manufacutring equipments in workcell at a time. In this paper a programming language, ARL (Assembly Robot Language), is proposed and developed, which can control the manufacturing devices as well as robots in workcell. It has not only all the common features of modern textual robot language but also debugging facilities. In this language system machine dependecy is minimized by using dedicated processes and a shared memory for communication between processes. Extensibility and adaptability of the programming system is increased by using such a technique against the changes of workcel environment.

• Journal of Intelligence and Information Systems
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• v.14 no.4
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• pp.179-200
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• 2008

We develop a feature selection method that can improve both the efficiency and the effectiveness of classification technique. In this research, we employ case-based reasoning as a classification technique. Basically, this research integrates the two existing feature selection methods, i.e., the univariate analysis and the LVF algorithm. First, we sift some predictive features from the whole set of features using the univariate analysis. Then, we generate all possible subsets of features from these predictive features and measure the inconsistency rate of each subset using the LVF algorithm. Finally, the subset having the lowest inconsistency rate is selected as the best subset of features. We measure the performances of our feature selection method using the data obtained from UCI Machine Learning Repository, and compare them with those of existing methods. The number of selected features and the accuracy of our feature selection method are so satisfactory that the improvements both in efficiency and effectiveness are achieved.

• Smart Media Journal
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• v.9 no.3
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• pp.41-45
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• 2020

Recently, artificial intelligence (AI) and the Internet of things (IoT), which are represented by machine learning and deep learning among IT technologies related to the Fourth Industrial Revolution, are applied to our real life in various fields through various researches. In this paper, IoT and AI using object recognition technology are applied to classify clothing. For this purpose, the image dataset was taken using webcam and raspberry pi, and GoogLeNet, a convolutional neural network artificial intelligence network, was applied to transfer the photographed image data. The clothing image dataset was classified into two categories (shirtwaist, trousers): 900 clean images, 900 loss images, and total 1800 images. The classification measurement results showed that the accuracy of the clean clothing image was about 97.78%. In conclusion, the study confirmed the applicability of other objects using artificial intelligence networks on the Internet of Things based platform through the measurement results and the supplementation of more image data in the future.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.5
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• pp.529-535
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• 2012

This paper deals with the determination of mechanical properties of various galvanized steel sheets that are used for fabricating automobile bodies; the instrumented indentation technique and finite element analysis were used for the determination. First, tensile tests were conducted to obtain the true stress-true strain curves of galvanized steel sheets with various thicknesses. Load-deformation curves were then obtained by using the instrumented indentation testing machine, and they were compared with load-deformation curves obtained by finite element analysis. Further, true stress-true strain curves were obtained at the optimal observation point by finite element analysis.

• Smart Structures and Systems
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• v.26 no.4
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• pp.403-418
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• 2020

In recent years, the use of Fiber Reinforced Polymers (FRPs) as one of the most common ways to increase the strength of concrete samples, has been introduced. Evaluation of the final strength of these specimens is performed with different experimental methods. In this research, due to the variety of models, the low accuracy and impact of different parameters, the use of new intelligence methods is considered. Therefore, using artificial intelligent-based models, a new solution for evaluating the bond strength of FRP is presented in this paper. 150 experimental samples were collected from previous studies, and then two new hybrid models of Imperialist Competitive Algorithm (ICA)-Artificial Neural Network (ANN) and Artificial Bee Colony (ABC)-ANN were developed. These models were evaluated using different performance indices and then, a comparison was made between the developed models. The results showed that the ICA-ANN model's ability to predict the bond strength of FRP is higher than the ABC-ANN model. Finally, to demonstrate the capabilities of this new model, a comparison was made between the five experimental models and the results were presented for all data. This comparison showed that the new model could offer better performance. It is concluded that the proposed hybrid models can be utilized in the field of this study as a suitable substitute for empirical models.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.1
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• pp.88-93
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• 2012

Lane detection methods using camera, which are part of the driver assistance system, have been developed due to the growth of the vehicle technologies. However, lane detection methods are often failed by varying road conditions such as rainy weather and degraded lanes. This paper proposes a method for lane detection which is robust in varying road condition. Lane candidates are extracted by intensity comparison and lane detection filter. Hough transform is applied to compute the lane pair using lane candidates which is straight line in image. Then, a curved lane is calculated by using B-Snake algorithm. Also, weighting value is computed using previous lane detection result to detect the lanes even in varying road conditions such as degraded/missed lanes. Experimental results proved that the proposed method can detect the lane even in challenging road conditions because of weighting process.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.203-210
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• 2017

Data clustering is one of the most difficult and challenging problems and can be formally considered as a particular kind of NP-hard grouping problems. The K-means algorithm is one of the most popular and widely used clustering method because it is easy to implement and very efficient. However, it has high possibility to trap in local optimum and high variation of solutions with different initials for the large data set. Therefore, we need study efficient computational intelligence method to find the global optimal solution in data clustering problem within limited computational time. The objective of this paper is to propose a combined artificial bee colony (CABC) with K-means for initialization and finalization to find optimal solution that is effective on data clustering optimization problem. The artificial bee colony (ABC) is an algorithm motivated by the intelligent behavior exhibited by honeybees when searching for food. The performance of ABC is better than or similar to other population-based algorithms with the added advantage of employing fewer control parameters. Our proposed CABC method is able to provide near optimal solution within reasonable time to balance the converged and diversified searches. In this paper, the experiment and analysis of clustering problems demonstrate that CABC is a competitive approach comparing to previous partitioning approaches in satisfactory results with respect to solution quality. We validate the performance of CABC using Iris, Wine, Glass, Vowel, and Cloud UCI machine learning repository datasets comparing to previous studies by experiment and analysis. Our proposed KABCK (K-means+ABC+K-means) is better than ABCK (ABC+K-means), KABC (K-means+ABC), ABC, and K-means in our simulations.

• Journal of Korean Society of Archives and Records Management
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• v.20 no.4
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• pp.1-17
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• 2020

Along with the development of information technology, the digitalization of archives has also been accelerating. However, digital archives have limitations in effectively searching, interlinking, and understanding records. In response to these issues, this study proposes a knowledge graph that represents comprehensive relationships among heterogeneous entities in digital archives. In this case, the knowledge graph organizes resources in the archives on the Korean financial crisis of 1997 by transforming them into named entities that can be discovered by machines. In particular, the study investigates and creates an overview of the characteristics of the archives on the Korean financial crisis as a digital archive. All resources on the archives are described as entities that have relationships with other entities using semantic vocabularies, such as Records in Contexts-Ontology (RiC-O). Moreover, the knowledge graph of the Korean Financial Crisis of 1997 is represented by resource description framework (RDF) vocabularies, a machine-readable format. Compared to conventional digital archives, the knowledge graph enables users to retrieve a specific entity with its semantic information and discover its relationships with other entities. As a result, the knowledge graph can be used for semantic search and various intelligent services.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.2
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• pp.211-218
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• 2015

This paper describes the implementation of the accelerated virtual switch using Intel DPDK(Data Plane Development Kit), and evaluates the virtual network functions of the virtual switch which is one of the most important components to build a virtual network for cloud computing. Nowadays, new information service platforms are appeared from the interconnection of intelligent IT systems like IoT(Internet of Things). And many companies want to use the new service platform for their new application service. The companies can apply there new service early which needs small investment and responses adaptively to the fast change of consumer environment. Using cloud computing technology, the new business service can be introduced as a commercial IT service for the time to market. In this study, an implementation and investigation were performed for the accelerated virtual switch, called Intel DPDK virtual switch, which is using multi processors in network interface card for virtual network functions. It can be useful for Internet-oriented companies to leverage the new cloud service and businesses for its creativeness.

• The Transactions of the Korean Institute of Power Electronics
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• v.10 no.2
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• pp.138-147
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• 2005

This paper presents a SPMSM(Surface-mounted Permanent Magnet Synchronous Motor) servo drive system using high performance TMS320F2812 DSP for the industrial application. The DSP(Digital Signal Processor) Controller enables an enhanced real time algorithm and cost-effective design intelligent for only exclusively motor drives which can be yield enhanced operation, fewer system components, lower control system cost, increased efficiency and high performance. The suggested system contain speed and current sensing circuits, SVPWM(Space Vector Pulse Width Modulation) and I/O interface circuit. The developed servo drive control system showns a good response characteristics results and high performance features in general purposed 400[w] machine. This system can achieve cost reduction and size minimization of controllers.