• IE interfaces
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• v.6 no.2
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• pp.3-17
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• 1993

Traditionally, programs for NC machine tools have been created using either a maunual method or a computer-assisted method. However, both methods are known to be complex, time-consuming and error-prone. This paper presents an intelligent system, called "INPPC" which is interfaced with a CAD system to generate APT NC part program automatically. The INPPC is developed by using VP-Expert rule-based expert system development tool, and obtains the information about the part shape by searching the CAD database, about the process by asking the related questions to the user, and about the machine tooling by searching the tool database. The INPPC is implemented on an IBM compatible PC/AT under MS-DOS, and its performance is demonstrated by consulting a simple example problem.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.4204-4222
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• 2015

Cloud is the latest buzz word in the internet community among developers, consumers and security researchers. There have been many attacks on the cloud in the recent past where the services got interrupted and consumer privacy has been compromised. Denial of Service (DoS) attacks effect the service availability to the genuine user. Customers are paying to use the cloud, so enhancing the availability of services is a paramount task for the service provider. In the presence of DoS attacks, the availability is reduced drastically. Such attacks must be detected and prevented as early as possible and the power of computational approaches can be used to do so. In the literature, machine learning techniques have been used to detect the presence of attacks. In this paper, a novel approach is proposed, where intelligent rule based feature selection and classification are performed for DoS attack detection in the cloud. The performance of the proposed system has been evaluated on an experimental cloud set up with real time DoS tools. It was observed that the proposed system achieved an accuracy of 98.46% on the experimental data for 10,000 instances with 10 fold cross-validation. By using this methodology, the service providers will be able to provide a more secure cloud environment to the customers.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.286-293
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• 2021

Recently, the quick development rate of apps in the Android platform has led to an accelerated increment in creating malware applications by cyber attackers. Numerous Android malware detection tools have utilized conventional signature-based approaches to detect malware apps. However, these conventional strategies can't identify the latest apps on whether applications are malware or not. Many new malware apps are periodically discovered but not all malware Apps can be accurately detected. Hence, there is a need to propose intelligent approaches that are able to detect the newly developed Android malware applications. In this study, Radial Basis Function (RBF) networks are trained using known Android applications and then used to detect the latest and new Android malware applications. Initially, the optimal permission features of Android apps are selected using Information Gain Ratio (IGR). Appropriately, the features selected by IGR are utilized to train the RBF networks in order to detect effectively the new Android malware apps. The empirical results showed that RBF achieved the best detection accuracy (97.20%) among other common machine learning techniques. Furthermore, RBF accomplished the best detection results in most of the other measures.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.2
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• pp.22-28
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• 2019

In this study, we describe the mechanical design of the cylindrical workpiece inspection system which that can inspect the workpiece machined in the CNC lathe. The workpiece automatic measuring device is composed of a workpiece aligning mechanism, a workpiece diameter measuring mechanism, and a workpiece height measuring mechanism. If the workpiece machined on the CNC lathe is placed on the pedestal of the cylindrical workpiece inspection system, the workpiece aligning mechanism moves the workpiece to the diameter-measuring position and the height- measuring positions, and the diameter-measuring mechanism and the height- measuring mechanisms sequentially measure the diameter and the height of the workpiece. The cylindrical workpiece inspection system was designed and manufactured. The characteristic experiment was conducted to confirm the operation of the machine tool of the cylindrical workpiece inspection system. As a The result of the characteristic test shows that, the workpiece automatic measuring device operated safely.

• Korean Journal of Artificial Intelligence
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• v.9 no.1
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• pp.15-20
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• 2021

This paper was conducted to prevent and respond to crimes by predicting crimes based on artificial intelligence. While the quality of life is improving with the recent development of science and technology, various problems such as poverty, unemployment, and crime occur. Among them, in the case of crime problems, the importance of crime prediction increases as they become more intelligent, advanced, and diversified. For all crimes, it is more critical to predict and prevent crimes in advance than to deal with them well after they occur. Therefore, in this paper, we predicted crime types and crime tools using the Multiclass Logistic Regression algorithm and Multiclass Neural Network algorithm of machine learning. Multiclass Logistic Regression algorithm showed higher accuracy, precision, and recall for analysis and prediction than Multiclass Neural Network algorithm. Through these analysis results, it is expected to contribute to a more pleasant and safe life by implementing a crime prediction system that predicts and prevents various crimes. Through further research, this researcher plans to create a model that predicts the probability of a criminal committing a crime again according to the type of offense and deploy it to a web service.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.04a
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• pp.354-358
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• 2004

There was not the evident analysis about the cutting process of CNC machining, and wouldn't be difficult to estimate the result of machining for the various cutting conditions. Therefore they were not founded the systemic technology about the optimum cutting conditions and selection of cutting tools. So this study have investigated the common facts for needs through the end-mill cutting machining by Machining-Centers or High-speed cutting machines, and developed the user-centered intelligent decision system to selection of the methodology about cutting conditions to improve the machining efficiency of end-mill cutting process.

• Journal of the Korea Safety Management & Science
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• v.10 no.3
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• pp.81-87
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• 2008

The reliability prediction and evaluation for general electronic components are required to guarantee in quality and in efficiency. Although many methodologies for predicting the reliability of electronic components have been developed, their reliability might be subjective according to a particular set of circumstances, and therefore it is not easy to quantify their reliability. In this study reliability prediction of electronic components, that is the interface card, which is used in the CNC(Computerized Numerical Controller) of machine tools, was carried out using PRISM reliability prediction specification. Reliability performances such as MTBF(Mean Time Between Failure), failure rate and reliability were obtained, and the variation of failure rate for electronic components according to temperature change was predicted. The results obtained from this study are useful information to consider a counter plan for weak components before they are used.

• Asia pacific journal of information systems
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• v.13 no.2
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• pp.23-46
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• 2003

This paper proposes an analysis methodology that enables the establishment of improved customer satisfaction via decision support system using customer satisfaction index data and customer database of a company. The proposed methodology establishes rational future goal of a company by applying DEA, finds potential customers which correspond to demographic features of the previous target group, and improve quality factors which distinguish the quality-satisfaction-group from the quality-dissatisfaction-group through the use of machine learning tools, SOM and C4.5. Finally, we illustrate the effectiveness of our research methodology using actual data of a camera company.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.43-46
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• 1997

Reliability engineering is regarded as the major and important roll for all industry. And advanced manufacturing systems with high sped and intelligent have been developing for betterment of machining ability. In this study, we have systemized evaluation of reliability for machinery system. We proposed the reliability assessment and designed and manufactured reliability test-bed to evaluate reliability. In addition we acquired reliability data using test-bed system and made database to handle reliability data. And also we not only use reliability data by analyzing reliability, but also apply design review method using analyzing critical units of machinery system. Form this study, we will expect to guide and increase the reliability engineering in developing and processing phase of high quality product.