• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.11a
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• pp.537-543
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• 2003

Condition monitoring and fault diagnosis of machines are gaining importance in the industry because of the need to increase reliability and to decrease possible loss of production due to machine breakdown. By comparing the vibration signals of a machine running in normal and faulty conditions, detection of faults like mass unbalance, shaft misalignment and bearing defects is possible. This paper presents a novel approach for applying the fault diagnosis of rotating machinery. To detect multiple faults in rotating machinery, a feature selection method and support vector machine (SVM) based multi-class classifier are constructed and used in the faults diagnosis. The results in experiments prove that fault types can be diagnosed by the above method.

• Proceedings of the Korean Society of Sericultural Science Conference
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• 1997.06a
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• pp.103-117
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• 1997

Decrease in the cocoon production of Japan is drastic because of low price of cocoon, scarcity of successors and so on. To tide over the difficulty, the automation system in the sericulture was discussed and some trials have been conducted by the Ministry of Agriculture, Forestry and Fisheries of Japan. The attempts are based on a low cost artificial diet which does not rely on mulberry leaves. Automatic machines developed until now are a rearing machine constituted with repeated belt conveyor, an reformation type of former rearing machine "Bombyx" and a mounting machine. Running parallel with them, utilization of 20-hydroxyecdysone extracted from a plant to the mounting was also studied to use their machines efficiently in the fields. In conclusion, 10 tons of law cocoon will come to be produced by two persons labor. At present, an automatic rearing system on low cost artificial diet has been developing for the future sericulture.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.997-1000
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• 2005

Dynamic job shop environment requires not only more flexible capabilities of a CAPP system but higher utility of the generated process plans. In order to meet the requirements, this paper develops an algorithm that can select machines for the machining operations to be performed by predicting the machine loads. The developed algorithm is based on the multiple objective genetic algorithm that gives rise to a set of optimal solutions (in general, known as Pareto-optimal solutions). The objective shows a combination of the minimization of part movement and the maximization of machine utility balance. The algorithm is characterized by a new and efficient method for nondominated sorting, which can speed up the running time, as well as a method of two stages for genetic operations, which can maintain a diverse set of solutions. The performance of the algorithm is evaluated by comparing with another multiple objective genetic algorithm, called NSGA-II.

• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.33 no.3
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• pp.142-155
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• 2022

Diagnosis using voice is non-invasive and can be implemented through various voice recording devices; therefore, it can be used as a screening or diagnostic assistant tool for laryngeal voice disease to help clinicians. The development of artificial intelligence algorithms, such as machine learning, led by the latest deep learning technology, began with a binary classification that distinguishes normal and pathological voices; consequently, it has contributed in improving the accuracy of multi-classification to classify various types of pathological voices. However, no conclusions that can be applied in the clinical field have yet been achieved. Most studies on pathological speech classification using speech have used the continuous short vowel /ah/, which is relatively easier than using continuous or running speech. However, continuous speech has the potential to derive more accurate results as additional information can be obtained from the change in the voice signal over time. In this review, explanations of terms related to artificial intelligence research, and the latest trends in machine learning and deep learning algorithms are reviewed; furthermore, the latest research results and limitations are introduced to provide future directions for researchers.

• International Journal of Advanced Culture Technology
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• v.12 no.1
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• pp.242-248
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• 2024

Contemporary societal and environmental transformations coincide with the emergence of novel mental health challenges. anxiety disorder, a chronic and highly debilitating illness, presents with diverse clinical manifestations. Epidemiological investigations indicate a global prevalence of 5%, with an additional 10% exhibiting subclinical symptoms. Notably, 9% of adolescents demonstrate clinical features. Untreated, anxiety disorder exerts profound detrimental effects on individuals, families, and the broader community. Therefore, it is very meaningful to predict anxiety disorder through machine learning algorithm analysis model. The main research content of this paper is the analysis of the prediction model of anxiety disorder by machine learning algorithms. The research purpose of machine learning algorithms is to use computers to simulate human learning activities. It is a method to locate existing knowledge, acquire new knowledge, continuously improve performance, and achieve self-improvement by learning computers. This article analyzes the relevant theories and characteristics of machine learning algorithms and integrates them into anxiety disorder prediction analysis. The final results of the study show that the AUC of the artificial neural network model is the largest, reaching 0.8255, indicating that it is better than the other two models in prediction accuracy. In terms of running time, the time of the three models is less than 1 second, which is within the acceptable range.

• Proceedings of the Korea Technical Association of the Pulp and Paper Industry Conference
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• 2007.05a
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• pp.83-125
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• 2007

Increasing energy costs have caused profitability problems for paper suppliers. Therefore unprofitable lines are being closed down. The actions aiming for improved profits are focused either on cost savings or on increasing the capacity of the remaining machines. The runnability of a paper machine and its total efficiency have a significant effect on energy consumption. Producing one ton of waste paper consumes at least as much energy as producing the same amount of sellable end product. New automation solutions enable significant cost-effective improvements to the total efficiency of a line without large investment projects. The measures focus on minimizing changes, interruptions, interruption recovery times and grade change times. Newest actuators, online quality measurements and wet end analysators create an improvement potential, which can be optimally implemented with the latest machine direction control solutions, based on model predictive control concepts. Equally, drying management is significant to the energy consumption. The newest control strategies optimize the use of various drying actuators for different situations; either by responding to changes as efficiently as possible or by using only the cheapest energy sources in stable situations. An even steam supply, which is vital for paper machines, is achieved with control for the power plant steam network. This makes possible to avoid the delays upon starting the paper machine and assure an even steam supply for the drying section and the actuators. This document describes means which have brought significant energy and raw material savings for paper machines. Metso Automation has provided efficiency improvement packages, which are usually based on optimized control of dry weight and drying in all running conditions. The solutions are based on performance analysis, on which the estimations for improvement potential and the necessary actions are based on. Typically benefits on an annual level have been from hundreds of thousands of euros to over one million euro. For example, variations in dry weight have been decreased more than 50%. The results are presented with a few examples. Additionally, the analysis models, adjustment solutions and the changes in running methods with which the results were achieved, are presented.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.4C
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• pp.581-593
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• 2004

The interest in wireless LAN security is on the increase owing to a role of high-speed wireless Internet infrastructure of wireless LAN. Wi-Fi has released WPA standard in order to overcome drawbacks of WEP algorithm that is security element of current IEEE 802.11-based wireless LAN system. Pairwise key management and group key management in a mixed-mode which supports both terminals running WPA and terminals running original WEP security are very complicate. In this paper, we analyze flaws in WPA authenticator key management state machine for key distribution and propose the countermeasures to overcome the analyzed problems. Additionally, WPA authenticator key management state machine to which the solutions are applied is described. The reconstructed WPA authenticator key management state machine helps the AP perform efficiently group key exchange and group key update in the mixed-mode.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.60-66
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• 2024

Host's data during transmission. Data tempering results in loss of host's sensitive information, which includes number of VM, storage availability, and other information. In the distributed cloud environment, each server (computing server (CS)) configured with Local Resource Monitors (LRMs) which runs independently and performs Virtual Machine (VM) migrations to nearby servers. Approaches like predictive VM migration [21] [22] by each server considering nearby server's CPU usage, roatative decision making capacity [21] among the servers in distributed cloud environment has been proposed. This approaches usage underlying server's computing power for predicting own server's future resource utilization and nearby server's resource usage computation. It results in running VM and its running application to remain in waiting state for computing power. In order to reduce this, a decentralized decision making hybrid model for VM migration need to be proposed where servers in decentralized cloud receives, future resource usage by analytical computing system and takes decision for migrating VM to its neighbor servers. Host's in the decentralized cloud shares, their detail with peer servers after fixed interval, this results in chance to tempering messages that would be exchanged in between HC and CH. At the same time, it reduces chance of over utilization of peer servers, caused due to compromised host. This paper discusses, an roatative decisive (RD) approach for VM migration among peer computing servers (CS) in decentralized cloud environment, preserving confidentiality and integrity of the host's data. Experimental result shows that, the proposed predictive VM migration approach reduces extra VM migration caused due over utilization of identified servers and reduces number of active servers in greater extent, and ensures confidentiality and integrity of peer host's data.

• Journal of Korea Multimedia Society
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• v.7 no.12
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• pp.1692-1699
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• 2004

Many researchers have developed various techniques for detection of human faces in ordinary still images. Face detection is the first imperative step of human face recognition systems. The two main problems of human face detection are how to cutoff the running time and how to reduce the number of false positives. In this paper, we present frontal and near-frontal face detection algorithm in still gray images using a thick edge image and neural network. We have devised a new filter that gets the thick edge image. Our overall scheme for face detection consists of two main phases. In the first phase we describe how to create the thick edge image using the filter and search for face candidates using a whole face detector. It is very helpful in removing plenty of windows with non-faces. The second phase verifies for detecting human faces using component-based eye detectors and the whole face detector. The experimental results show that our algorithm can reduce the running time and the number of false positives.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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• pp.900-905
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• 2004

Development of color printer, postal classification machine, ATM and so on requires higher moving performance of the flat belt. Skew of the flat belt running over misaligned roller has a bad effect on performances of media transport. The vibration of loose side of belt causes the escape of the belt from roller and the drop of velocity of driven roller after the start of driving roller revolution. The skew of flat belt is investigated by FEM and dynamic simulation. FEM results show parameters which affect the skew of belt and match with dynamic results qualitatively. The shape of loose side of belt can be found by dynamic simulation. Increase of the acceleration time and initial tension have diminished the unstable movement of the loose side of belt.