• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.6
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• pp.241-251
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• 2002

The effect of sensor faults in direct torque control(DTC) based induction motor drives is analyzed and a new Instrument fault detection isolation scheme(IFDIS) is proposed. The proposed IFDIS, which operated in real-time, detects and isolates the incipient fault(s) of speed sensor and current sensors that provide the feedback information. The scheme consists of an adaptive gain scheduling observer as a residual generator and a special sequential test logic unit. The observer provides not only the estimate of stator flux, a key variable in DTC system, but also the estimates of stator current and rotor speed that are useful for fault detection. With the test logic, the IFDIS has the functionality of fault isolation that only multiple estimator based IFDIS schemes can have. Simulation results for various type of sensor faults show the detection and isolation performance of the IFDIS and the applicability of this scheme to fault tolerant control system design.

• Review of KIISC
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• v.5 no.2
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• pp.32-48
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• 1995

Our paper describes an Intrusion Detection Parallel System(IDPS) which detects an anomaly activity corresponding to the actions that interaction between near detection events. IDES uses parallel inductive approaches regarding the problem of real-time anomaly behavior detection on rule-based system. This approach uses sequential rule that describes user's behavior and characteristics dependent on time. and that audits user's activities by using rule base as data base to store user's behavior pattern. When user's activity deviates significantly from expected behavior described in rule base. anomaly behaviors are recorded. Observed behavior is flagged as a potential intrusion if it deviates significantly from the expected behavior or if it triggers a rule in the parallel inductive system.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.109-126
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• 2017

In the various kinds of intelligent services, many studies for detecting emotion are in progress. Particularly, studies on emotion recognition at the particular time have been conducted in order to provide personalized experiences to the audience in the field of exhibition though facial expressions change as time passes. So, the aim of this paper is to build a model to predict the audience's emotion from the changes of facial expressions while watching an exhibit. The proposed model is based on both sequential neural network and the Valence-Arousal model. To validate the usefulness of the proposed model, we performed an experiment to compare the proposed model with the standard neural-network-based model to compare their performance. The results confirmed that the proposed model considering time sequence had better prediction accuracy.

• Animal Bioscience
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• v.34 no.12
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• pp.1995-2002
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• 2021

Objective: Detection of adulteration in processed meats is an important issue for some countries due to substitution of beef with a cheaper source of protein like poultry. In this study, the presence of chicken meat was investigated using real-time polymerase chain reaction (real-time PCR) and enzyme-linked immunosorbent assay (ELISA) techniques to verify adulteration of fermented sausage samples. Methods: A total of 60 commercial samples were collected from 20 establishments in three replicates including 10 fermented sausage manufacturers and 10 butchers to investigate the presence of chicken meat with the sequential use of real-time PCR and ELISA techniques. In addition, pH, moisture content, water activity and color values of the samples were determined. Results: Both real-time PCR and ELISA showed agreement on the presence or absence of chicken meat in 55 out of 60 fermented sausage samples and chicken meat was identified with both methods in 16 samples. Five samples produced inconsistent results for the presence of chicken meat in the first run. Nevertheless, the presence of chicken meat was verified with both methods when these samples were analyzed for the second time. In addition, the average physico-chemical values of the fermented sausage samples tested positive for chicken meat were not significantly different from some of those fermented sausage samples tested negative for the chicken meat. Conclusion: The sequential use of real-time PCR and ELISA techniques in fermented sausages could be beneficial for the government testing programs to eliminate false negatives for detection of adulteration with chicken meat. Furthermore, consumers should not rely on some of the quality cues including color to predict the adulteration of fermented sausages with chicken meat since there were no statistical differences among some of the samples tested positive and negative for chicken meat.

• ETRI Journal
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• v.36 no.3
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• pp.439-449
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• 2014

In this paper, a novel vision-based nighttime vehicle detection approach is presented, combining state machines and downhill simplex optimization. In the proposed approach, vehicle detection is modeled as a sequential state transition problem; that is, vehicle arrival, moving, and departure at a chosen detection area. More specifically, the number of bright pixels and their differences, in a chosen area of interest, are calculated and fed into the proposed state machine to detect vehicles. After a vehicle is detected, the location of the headlights is determined using the downhill simplex method. In the proposed optimization process, various headlights were evaluated for possible headlight positions on the detected vehicles; allowing for an optimal headlight position to be located. Simulation results were provided to show the robustness of the proposed approach for nighttime vehicle and headlight detection.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.10
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• pp.479-485
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• 2001

In this paper, we introduce a charge pump phase-locked loop (PLL) architecture which employs a precharge phase frequency detector (PFD) and a sequential PFD to achieve a high frequency operation and a fast acquisition. Operation frequency is increased by using the precharge PFD when the phase difference is within $-{\pi}{\sim}{\pi}$ and acquisition time is shortened by using the sequential PFD and the increased charge pump current when the phase difference is larger than ${\pm}{\pi}$. So error detection range of the proposed PLL structure is not limited to $-{\pi}{\sim}{\pi}$ and a high frequency operation and a higher speed lock-up time can be achieved. The proposed PLL was designed using 1.5 ${\mu}m$ CMOS technology with 5V supply voltage to verify the lock in process. The proposed PLL shows successful acquisition for 200 MHz input frequency. On the other hand, the conventional PLL with the sequential PFD cannot operate at up to 160MHz. Moreover, the lock-up time is drastically reduced from 7.0 ${\mu}s\;to\;2.0\;{\mu}s$ only if the loop bandwidth to input frequency ratio is regulated by the divide-by-4 counter during the acquisition process. By virtue of this dual PFDs, the proposed PLL structure can improve the trade-off between acquisition behavior and locked behavior.

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.699-706
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• 2016

Outlier detection methods without performing a test often do not succeed in detecting multiple outliers because they are structurally vulnerable to a masking effect or a swamping effect. This paper considers testing procedures supplemented to a clustering-based method of identifying the group with a minority of the observations as outliers. One of general steps is performing a variety of t-test on individual outlier-candidates. This paper proposes a sequential procedure for searching for outliers by changing cutoff values on a cluster tree and performing a test on a set of outlier-candidates. The proposed method is illustrated and compared to existing methods by an example and Monte Carlo studies.

• Journal of the Korea Society of Computer and Information
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• v.18 no.11
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• pp.125-136
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• 2013

In this paper, we proposed SSL VPN-based network access control technology which can verify user authentication and integrity of user terminal. Using this technology, user can carry out a safety test to check security services such as security patch and virus vaccine for user authentication and user terminal, during the VPN-based access to an internal network. Moreover, this system protects a system from external security threats, by detecting malicious codes, based on behavioral patterns from user terminal's window API information, and comparing the similarity of sequential patterns to improve the reliability of detection.

• Wind and Structures
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• v.24 no.4
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• pp.385-403
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• 2017

In recent years, the wind energy has played an increasingly important role in national energy sector of many countries. To harvest more electric power, the wind turbine (WT) tower structure becomes physically larger, which may cause more risks during long-term operation. Associated with the great development of WT projects, the number of accidents related to large-scaled WT has also been increased. Therefore, a structural health monitoring (SHM) system for WT structures is needed to ensure their safety and serviceability during operational time. The objective of this study is to develop a hybrid damage detection method for WT tower structures by measuring vibration and impedance responses. To achieve the objective, the following approaches are implemented. Firstly, a hybrid damage detection scheme which combines vibration-based and impedance-based methods is proposed as a sequential process in three stages. Secondly, a series of vibration and impedance tests are conducted on a lab-scaled model of the WT structure in which a set of bolt-loosening cases is simulated for the segmental joints. Finally, the feasibility of the proposed hybrid damage detection method is experimentally evaluated via its performance during the damage detection process in the tested model.

• 한국정보통신설비학회:학술대회논문집
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• 2009.08a
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• pp.382-386
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• 2009

Online or sequential learning is one of the most basic and powerful method to train neuron network, and it has been widely used in disease detection, weather prediction and other realistic classification problem. At present, there are many algorithms in this area, such as MRAN, GAP-RBFN, OS-ELM, SVM and SMC-RBF. Among them, SMC-RBF has the best performance; it has less number of hidden neurons, and best efficiency. However, all the existing algorithms use signal normal distribution as kernel function, which means the output of the kernel function is same at the different direction. In this paper, we use multi-variable normal distribution as kernel function, and derive EKF learning formulas for multi-variable normal distribution kernel function. From the result of the experience, we can deduct that the proposed method has better efficiency performance, and not sensitive to the data sequence.