• International Journal of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.204-211
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• 2020

The era of the 4th industrial revolution is a complex environment in which the cyber world and the physical world are integrated and interacted. In order to successfully implement and be sustainable the 4th industrial revolution of hyper-connectivity, hyper-convergence, and hyper-intelligence, not only the technological aspects that implemented digitalization but also the social aspects must be recognized and dealt with as important. There are socio-technical systems and socio-technical systems theory as concepts that describe systems involving complex interactions between the environmental aspects of human, mechanical and tissue systems. This study confirmed how the Socio-technical System was applied in the research literature for the last 10 years through machine learning-based analysis. Eight clusters were derived by performing co-occurrence keywords network analysis, and 13 research topics were derived and analyzed by performing a structural topic model. This study provides consensus and insight on the social and technological perspectives necessary for the sustainability of the 4th industrial revolution.

• Nuclear Engineering and Technology
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• v.39 no.6
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• pp.703-716
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• 2007

As digital and computer technologies have grown, human-machine interfaces (HMIs) have evolved. In safety-critical systems, especially in nuclear power plants (NPPs), HMIs are important for reducing operational costs, the number of necessary operators, and the probability of accident occurrence. Efforts have been made to improve main control room (MCR) interface design and to develop automated or decision support systems to ensure convenient operation and maintenance. In this paper, an integrated decision support system to aid operator cognitive processes is proposed for advanced MCRs of future NPPs. This work suggests the design concept of a decision support system which accounts for an operator's cognitive processes. The proposed system supports not only a particular task, but also the entire operation process based on a human cognitive process model. In this paper, the operator's operation processes are analyzed according to a human cognitive process model and appropriate support systems that support each cognitive process activity are suggested.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.75-78
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• 2004

In this paper, we suggest machine control method by the Recognition of Literal Commands. This method that we design is human friendly interface to be able to command easy We distinguish words that is related to command directly or not in the Literal Commands. And vague expressions to move machine directly make behaviors by intelligent recognition model. We suggest The Literal Commands control method that is able to obtain more realistic output equivalent to users' desire throgh the literary style commands. The proposed method is experimentally tested by a mobile car using bluetooth module and mobile phone in real time using Literal language commands.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.42.3-42
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• 2002

Fuzzy Neural Network (FNN) is a compulsory element in a voice-controlled machine due to its inherent capability of interpreting imprecise natural language commands. To control such a machine, user's perception of imprecise words is very important because the words' meaning is highly subjective. This paper presents a voice based controller centered on an adaptable FNN to capture the user's perception of imprecise words. Conversational interface of the machine facilitates the learning through interaction. The system consists of a dialog manager (DM), the conversational interface, a Knowledge base, which absorbs user's perception and acts as a replica of human understanding of imprecise words,...

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.6
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• pp.857-860
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• 2021

The 21st century society has entered the fourth industrial society of machine to machine from the information society of human to machine. Accordingly, countries around the world are always operating efficient crisis management systems that can quickly respond to disasters or crises. As cyber attacks such as cyber warfare are actually progressing, countries around the world are conducting defense training in response to cyber attacks, and reflecting the results of simulation attacks in improving or building security systems. In this paper, we would like to consider the future cyber training development guide by comparing and analyzing the trends of cyber training in domestic and foreign countries.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.521-524
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• 2004

Recently, acgis of authentication and crcator's copyright has become a matter of great concern by the diffusion of multimedia technique and the growth of the internet and the easily duplicated property of digital data. Consequently, many active researches have been made to protect copyright and to assure integrity by inserting watermark into the digital data. In this paper, watermark is repealed through the entire image and adapted to the content of the image. Achieved by an underlying process of transforming the digital image to the frequency domain by wavelet transform, which has three (vertical, horizontal, diagonal) directions and Multi-resolution features, and then choosing frequency area inferior to the human perceptibility and significant for invisible and robust watermark. We realize wavelet based image watermarking using Machine Vision of LabVIEW.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.1
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• pp.68-75
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• 1999

Machine recognition of faces from still and video images is emerging as an active research area spanning several disciplines such as image processing, pattern recognition, computer vision and neural networks. In addition, human face identification has numerous applications such as human interface based systems and real-time video systems of surveillance and security. In this paper, we propose an algorithm that can identify a particular individual face. We consider human face identification system in color space, which hasn't often considered in conventional in conventional methods. In order to make the algorithm insensitive to luminance, we convert the conventional RGB coordinates into normalized CIE coordinates. The normalized-CIE-based facial images are KL-transformed. The transformed data are used as the input of multi-layered neural network and the network are trained using error-backpropagation methods. Finally, we verify the system performance of the proposed algorithm by experiments.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.6 no.8
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• pp.55-61
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• 1983

In order to increase productivity, modern business which use the techniques of mass production should progress gradually from to use of human labor to total mechanization in the various steps of the production process. This will not only avoid the prospect of future labor shortages, but will also solve nancy of the problems involved in increasing productivity In this paper, we have approached the problem of determining the number of machine units that can be economically assigned to one operator by using Palm's model.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.9
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• pp.1402-1408
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• 2017

Recently, services for personal biometric data analysis based on real-time monitoring systems has been increasing and many of them have focused on recognition of emotions. In this paper, we propose a classification model to classify anxiety emotion using biometric data actually collected from people. We propose to deploy the support vector machine to build a classification model. In order to improve the classification accuracy, we propose two data pre-processing procedures, which are normalization and data deletion. The proposed algorithms are actually implemented based on Real-time Traffic Flow Measurement structure, which consists of data collection module, data preprocessing module, and creating classification model module. Our experiment results show that the proposed classification model can infers anxiety emotions of people with the accuracy of 65.18%. Moreover, the proposed model with the proposed pre-processing techniques shows the improved accuracy, which is 78.77%. Therefore, we can conclude that the proposed classification model based on the pre-processing process can improve the classification accuracy with lower computation complexity.

• Journal of Sensor Science and Technology
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• v.29 no.3
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• pp.194-200
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• 2020

Human detection is an important aspect in many video-based sensing and monitoring systems. Studies have been actively conducted for the automatic detection of humans in camera images, and various methods have been proposed. However, there are still problems in terms of performance and computational cost. In this paper, we describe a method for efficient human detection in the field of view of a camera, which may be static or moving, through multiple processing steps. A detection line is designated at the position where a human appears first in a sensing area, and only the one-dimensional gray pixel values of the line are monitored. If any noticeable change occurs in the detection line, corner detection and optical flow computation are performed in the vicinity of the detection line to confirm the change. When significant changes are observed in the corner numbers and optical flow vectors, the final determination of human presence in the monitoring area is performed using the Histograms of Oriented Gradients method and a Support Vector Machine. The proposed method requires processing only specific small areas of two consecutive gray images. Furthermore, this method enables operation not only in a static condition with a fixed camera, but also in a dynamic condition such as an operation using a camera attached to a moving vehicle.