• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.919-922
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• 1998

A content-base search method is required for video which has an unformatted and huge size of data. The index techniue is necessary for the content-based search of the video data. The first step of the video indexing is a cut detection. We propose a dynamic threshold method which change a threshold value during the cut detection process. We demonstrate that the proposed method is more efficient than the existing methods.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.11
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• pp.968-973
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• 2000

Fault diagnosis plays an important role in the performance and safe operation of many modern engineering plants. This paper investigates the problem of fault detection using neural networks in dynamic systems. A general framework for constructing a nonlinear fault detection scheme for nonlinear dynamic systems containing modeling uncertaintly is proposed. The main idea behind the proposed approach is to monitor the physical system with an off -line learning neural network and then to approximate the upper and lower thresholds of acceleration of the nominal system with the model-based threshold(ThMB) method, The performance of the proposed fault detection scheme is investigated through simulations of a pendulum with uncertainty.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.5
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• pp.361-367
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• 2004

This paper describes design of an algorithm for analyzing human activity using body-fixed 3-axis accelerometer in the small of the back. In the first step, we distinguish static and dynamic activity period using AC signal analysis. Then five postures were classified by applying the threshold in DC signal corresponding to the static activity period. Also, after comparison of average power and taking negative peak signal in the dynamic activity period, the four dynamic activities were classified by adaptive threshold method. To evaluate the performance of the proposed algorithm, the measured signals obtained from six subjects were applied to the proposed algorithm and the results were compared with the simultaneously measured video data. As a result, the activity classification rate of 95.7％ on average was obtained. Overall results show that the proposed classification algorithm has a possibility to be used to analyze the static and dynamic physical activity.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.2
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• pp.279-287
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• 1993

In this paper, double laxity threshold MLT(Minimum Laxity Threshold) algorithm and double queue threshold QLT(Queue Length Threshold) algorithm are proposed as DPS(Dynamic Priority Scheduling) techniques for advanced processing of multiple class traffics. Also, the performance of the proposed algorithms is analyzed by a computer simulation. According to the simulation results, it can be shown that the proposed double laxity threshold ML T algorithm advances the processing performance versus ML T algorithm for 2 or more classes delay sensitive traffics, and that double queue length threshold QL T algorithm provides more efficient performance than QL T for 2 or more classes of non real time traffics.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.7 s.184
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• pp.152-158
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• 2006

The objective of this work is to develop the knee joint model for representing various pendulum motions and quantifying the spasticity. Knee joint model included the extension and flexion muscles. The joint moment consists of both the active moment from the stretch reflex and the passive moment from the viscoelastic joint properties. The stretch reflex was modeled as nonlinear feedback of muscle length and the muscle lengthening velocity, which is Physiologically-feasible. Moreover, we modeled the spastic reflex as having dynamic threshold to account far the various pendulum trajectories of spastic patients. We determined the model parameters of three patients who showed different pendulum trajectories through minimization of error between experimental and simulated trajectories. The simulated joint trajectories closely matched with the experimental ones, which show the proposed model can predict pendulum motions of patients with different spastic severities. The predicted muscle force from spastic reflex appeared more frequently in the severe spastic patient, which indicates the dynamic threshold relaxes slowly in this patient as is manifested by the variation coefficient of dynamic threshold. The proposed method provides prediction of muscle force and intuitive and objective evaluation of spasticity and it is expected to be useful in quantitative assessment of spasticity.

• Proceedings of the KIEE Conference
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• 2004.05a
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• pp.126-128
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• 2004

As e-mail is becoming increasingly important in every day life activity, mail users spend more and more time organizing and classifying the e-mails they receive into folder. Many existing recommendation systems or text classification are mostly focused on recommending the products for the commercial purposes or web documents. So this study aims to apply these application to e-mail more necessary to users. This paper suggests a dynamic recommendation agent system based on Rule Filtering Component recommending the relevant category to enable users directly to manage the optimum classification when a new e-mail is received as the effective method for E-Mail Management. Moreover we try to improve the accuracy as eliminating the limits of misclassification that can be key in classifying e-mails by category. While the existing Bayesian Learning Algorithm mostly uses the fixed threshold, we prove to improve the satisfaction of users as increasing the accuracy by changing the fixed threshold to the dynamic threshold. We designed main modules by rule filtering component for enhanced scalability and reusability of our system.

• Journal of the Korea Computer Industry Society
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• v.2 no.3
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• pp.389-396
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• 2001

In detection of a scene change of the moving pictures which has massive information capacity, the temporal sampling method has a faster searching speed and lower missing scene change detection than the sequential searching method for the whole moving pictures, yet employed searching algorithm and detection interval greatly affect missing frame and searching precision. In this study, the whole moving pictures were primarily retrieved threshold by the temporal difference of histogram scene change detection method. We suggested a dynamic threshold algorithm using cut detection interval and derived an equation formula to determine optimal primary retrieval threshold which can cut detection interval computation. Experimental results show that the proposed dynamic threshold algorithm using cut detection interval method works up about 30 percent in precision of performance than the sequential searching method.

• The KIPS Transactions:PartC
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• v.9C no.6
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• pp.841-846
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• 2002

We applies a seasonal ARIMA model to the timely forecasting in a line utilization and its confidence interval on the base of the past data of the line utilization that QoS of the network is greatly influenced by. And this paper proposes the learning algorithm of dynamic threshold in line utilization using the SARIMA model. We can find the proper dynamic threshold in timely line utilization on the various network environments and provide the confidence based on probability. Also, we have evaluated the validity of the proposed model and estimated the value of a proper threshold on real network. Network manager can overcome a shortcoming of original threshold method and maximize the performance of this algorithm.

• 한국정보통신설비학회:학술대회논문집
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• 2008.08a
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• pp.197-200
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• 2008

In most sensor networks, nodes can be easily compromised by adversaries due to hostile environments. Adversaries may use compromised nodes to inject false reports into the sensor networks. Such false report attacks will cause false alarms that can waste real-world response effort, and draining the finite amount of energy resource in the battery-powered network. A dynamic enroute scheme proposed by Yu and Guan can detect and drop such false reports during the forwarding phase. In this scheme, choosing a threshold value is very important, as it trades off between security power and energy consumption. In this paper, we propose a threshold determining method which uses the fuzzy rule-based system. The base station periodically determines a threshold value though the fuzzy rule-based system. The number of cluster nodes, the value of the key dissemination limit, and the remaining energy of nodes are used to determine the threshold value.

• Smart Media Journal
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• v.13 no.4
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• pp.48-56
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• 2024

In the livestock industry, detecting environmental outliers and predicting data are crucial tasks. Outliers in livestock environment data, typically gathered through time-series methods, can signal rapid changes in the environment and potential unexpected epidemics. Prompt detection and response to these outliers are essential to minimize stress in livestock and reduce economic losses for farmers by early detection of epidemic conditions. This study employs two methods to experiment and compare performances in setting thresholds that define outliers in livestock environment data outlier detection. The first method is an outlier detection using Mean Squared Error (MSE), and the second is an outlier detection using a Dynamic Threshold, which analyzes variability against the average value of previous data to identify outliers. The MSE-based method demonstrated a 94.98% accuracy rate, while the Dynamic Threshold method, which uses standard deviation, showed superior performance with 99.66% accuracy.