• The Journal of Korean Institute of Communications and Information Sciences
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• v.37C no.10
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• pp.901-908
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• 2012

In an intelligent automated manufacturing environment, an administrator may use M2M (Machine-to-Machine) communication to recognize machine movement and the environment, as well as to respond to any potential dangers. However, commonly used wireless protocols for this purpose such WLAN (Wireless Local Area Network), ZigBee, and Bluetooth use the same ISM (Industrial Science Medical) band, and this may cause frequency interference among different devices. Moreover, an administrator is frequently exposed to dangerous conditions as a result of being surrounded by densely distributed moving machines. To address this issue, we propose in this paper to employ a location-based frequency interference management using fingerprinting scheme in industrial environments and its advanced localization schemes based on k-NN (Nearest Neighbor) algorithms. Simulation results indicate that the proposed schemes reduce distance error, frequency interference, and any potential danger may be responded immediately by continuous tracing of the locations.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.11
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• pp.479-488
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• 2022

In recent years, with the rapid development of machine learning technology, research utilizing machine learning has been actively conducted in fields such as cognition, reasoning and judgment, and action among various technologies constituting intelligent systems. In order to utilize this machine learning, it is indispensable to collect data for learning. However, the types of data generated vary according to the environment in which the data is generated, and the types and forms of data required are different depending on the learning model to be used for machine learning. Due to this, there is a problem that the existing data collection method cannot be reused in a new environment, and a specialized data collection module must be developed each time. In this paper, we propose a specification-based methology for data collection in artificial intelligence system to solve the above problems, ensure the reusability of the data collection method according to the data collection environment, and automate the implementation of the data collection function.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.449-451
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• 2021

Cyber-attacks targeting endpoints have developed sophisticatedly into targeted and intelligent attacks, Advanced Persistent Threat (APT) targeting the Industrial Internet of Things (IIoT) has increased accordingly. Machine learning-based Endpoint Detection and Response (EDR) solutions combine and complement rule-based conventional security tools to effectively defend against APT attacks are gaining attention. However, universal EDR solutions have a high false positive rate, and needs high-level analysts to monitor and analyze a tremendous amount of alerts. Therefore, the process of optimizing machine learning-based EDR solutions that consider the characteristics and vulnerabilities of IIoT environment is essential. In this study, we analyze the flow and impact of IIoT targeted APT cases and compare the method of machine learning-based APT detection EDR solutions.

• Journal of Korean Society of Transportation
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• v.19 no.3
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• pp.45-58
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• 2001

Subjective mental workload assessment technique becomes a standard human factors and human-machine interface evaluation tool for the evaluation of ITS(Intelligent Transport Systems)-oriented information systems as well as the drivers visual activity analysis, physiological indices(GSR, EEG, ECG, etc.), secondary task performance, reaction time. vehicle control parameters(speed, steering behavior, accelerator control) that are widely applied for transportation and vehicle systems to evaluate the safety, to decide the system or design alternatives, and to establish the design guidelines. This paper reviewed and compared the most globally employed four mental workload assessment techniques that have been designed for the use of various human-machine systems and ITS-oriented in-vehicle information systems. NASA-TLX(National Aeronautics and Space Administration-Task Load Index). SWAT(Subjective Workload Assessment Technique), MCH(Modified Cooper-Harper) scale, and recently developed RNASA-TLX(Revision of NASA-TH) were compared in terms of sensitivity and subjective evaluations to derive the human-machine interface evaluation guidelines for the application of ITS-oriented in-vehicle information systems. Then, experiment results supported that RNASA-TLX is the prospective tool for the mental workload assessment of ITS-oriented in-vehicle information systems.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.5
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• pp.416-422
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• 2016

Lots of plastics are widely used in a variety of industrial field. And the amount of plastic waste is massively produced. In the study of waste recycling, it is emerged as an important issue to prevent the waste of potentially useful resource materials as well as to reduce ecological damage. So, the recycling of plastic waste has been currently paid attention to from the view point of reuse. Existing automatic sorting system consist of near infrared ray (NIR) sensors to classify the types of plastics. But the classification of black plastics still remains a challenge. Black plastics which contains carbon black are not almost classified by NIR because of the characteristic of the light absorption of black plastics. This study is focused on handling how to identify black plastics instead of NIR. Raman spectroscopy is used to get qualitative as well as quantitative analysis of black plastics. In order to improve the performance of identification, Support Vector Machine(SVM) classifier and Principal Component Analysis(PCA) are exploited to more preferably classify some kinds of the black plastics, and to analyze the characteristic of each data.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.100-112
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• 2021

With the advent of big data, traffic prediction has been developed based on historical data analysis methods, but this method deteriorates prediction performance when a traffic incident that has not been observed occurs. This study proposes a method that can compensate for the reduction in traffic prediction accuracy in traffic incidents situations by hybrid approach of machine learning and traffic simulation. The blind spots of the data-driven method are revealed when data patterns that have not been observed in the past are recognized. In this study, we tried to solve the problem by reinforcing historical data using traffic simulation. The proposed method performs machine learning-based traffic prediction and periodically compares the prediction result with real time traffic data to determine whether an incident occurs. When an incident is recognized, prediction is performed using the synthetic traffic data generated through simulation. The method proposed in this study was tested on an actual road section, and as a result of the experiment, it was confirmed that the error in predicting traffic state in incident situations was significantly reduced. The proposed traffic prediction method is expected to become a cornerstone for the advancement of traffic prediction.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.121-132
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• 2018

Weather is an important factor affecting roadway transportation in many aspects such as traffic flow, driver 's driving patterns, and crashes. This study focuses on the relationship between weather and road surface condition and develops a model to estimate the road surface condition using machine learning. A road surface sensor was attached to the probe vehicle to collect road surface condition classified into three categories as 'dry', 'moist' and 'wet'. Road geometry information (curvature, gradient), traffic information (link speed), weather information (rainfall, humidity, temperature, wind speed) are utilized as variables to estimate the road surface condition. A variety of machine learning algorithms examined for predicting the road surface condition, and a two - stage classification model based on 'Random forest' which has the highest accuracy was constructed. 14 days of data were used to train the model and 2 days of data were used to test the accuracy of the model. As a result, a road surface state prediction model with 81.74% accuracy was constructed. The result of this study shows the possibility of estimating the road surface condition using the existing weather and traffic information without installing new equipment or sensors.

• Journal of the Korea Institute of Building Construction
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• v.10 no.4
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• pp.105-112
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• 2010

The demand for the development of automated construction equipments is gradually increasing to deal with the current problems of construction technology, such as a lack of experienced workers, the aging of engineers, safety issues, etc. In particular, earth work such as excavation is very machine-dependent, and there has been a great deal of research on the development of an intelligent excavator, which involves great safety concerns. Thus, the objective of this study is to develop the technology to enhance the safety of intelligent excavation systems by developing an omnidirectional object detection technology for the intelligent excavator and applying it to a user-friendly system. The existing literature was reviewed, and the function of various sensor technologies was investigated and analyzed. Then, the best laser sensor was selected for an experiment to determine its effectiveness. An omnidirectional object detection algorithm was developed for a user interface program, and this can be used as the fundamental technology for the development of a safety management system for an intelligent excavator.

• Journal of the Korea Society of Computer and Information
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• v.12 no.6
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• pp.21-30
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• 2007

Simulation means modeling structures or behaviors of the various objects, and experimenting them on the computer system. And the major approaches are DEVS(Discrete Event Systems Specification). Petri-net or Automata and so on. But, the simulation problems are getting more complex or complicated these days, so that an intelligent agent-based is being studied. In this paper, we are describing an intelligent agent-based simulation tool, which can supports the simulation experiment more efficiently. The significances of our system can be described as follows. First, the system can provide some AI algorithms through the system libraries. Second, the system supports simple method of designing the simulation model, since it's been built under the Finite State Machine (FSM) structure. And finally, the system acts as a simulation framework by supporting user not only the simulation engine, but also user-friendly tools, such as modeler scriptor and simulator. The system mainly consists of main simulation engine, utility tools, and some other assist tools, and it is tested and showed some efficient results in the three different problems.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.7
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• pp.895-901
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• 2004

This paper describes a two-phase intelligent method for filtering spam mail based on textual information and hyperlinks. Scince the body of spam mail has little text information, it provides insufficient hints to distinguish spam mails from legitimate mails. To resolve this problem, we follows hyperlinks contained in the email body, fetches contents of a remote webpage, and extracts hints (i.e., features) from original email body and fetched webpages. We divided hints into two kinds of information: definite information (sender`s information and definite spam keyword lists) and less definite textual information (words or phrases, and particular features of email). In filtering spam mails, definite information is used first, and then less definite textual information is applied. In our experiment, the method of fetching web pages achieved an improvement of F-measure by 9.4% over the method of using on original email header and body only.