• Journal of Korean Society for Quality Management
• /
• v.9 no.2
• /
• pp.26-30
• /
• 1981

A typical industrial data - monitoring scheme often requires trend detection Trend detection can be accomplished in many ways. Common statistical methods are the sign test, the run test, and the trend test. Graphical methods include various smoothing schemes and the cusum. The cusum has established itself as an efficient method of detecting changes in the mean level of a process being monitored. The cusum requires a "target value" with which the raw data are compared. At production start - up it is often difficult to designate the target value. This paper offers a means of initiating the cusum technique without a target value.

• Proceedings of the KIEE Conference
• /
• 1995.11a
• /
• pp.456-458
• /
• 1995

This paper describes a diagnosis of power transformers by on-line detection of ultrasonic signals. Partial discharge in power transformer generates ultrasonic signals. The trend of the ultrasonic signal number can be measured when partial discharge is generated in HV Winding. The trend of the ultrasonic signal number could easily be distinguished by taking moving average. The insulation failure due to partial discharge in transformers can be predicted based in the trend analysis of the number of ultrasonic signals caused by partial discharge.

• Journal of Institute of Control, Robotics and Systems
• /
• v.18 no.11
• /
• pp.997-1004
• /
• 2012

In enterprise software projects, performance issues have become more critical during recent decades. While developing software products, many performance tests are executed in the earlier development phase against the newly added code pieces to detect possible performance regressions. In our previous research, we introduced the framework to enable automated performance anomaly detection and reduce the analysis overhead for identifying the root causes, and showed Statistical Process Control (SPC) can be successfully applied to anomaly detection. In this paper, we explain the special performance trend in which the existing anomaly detection system can hardly detect the noticeable performance change especially when a performance regression is introduced and recovered again a while later. Within the fixed number of sampling period, the fluctuation gets aggravated and the lower and upper control limit get relaxed so that sometimes the existing system hardly detect the noticeable performance change. To resolve the issue, we apply dynamically tuned sampling window size based on the performance trend, and Fuzzy theory to find an appropriate size of the moving window.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.8
• /
• pp.3862-3879
• /
• 2019

More and more problems for public security have occurred due to the limited solutions for drone detection especially for micro-drone in long range conditions. This paper aims at dealing with drones detection using a radar system. The radio frequency (RF) signals emitted by a controller can be acquired using the radar, which are usually too weak to extract. To detect the drone successfully, the static clutters and linear trend terms are suppressed based on the background estimation algorithm and linear trend suppression. The principal component analysis technique is used to classify the noises and effective RF signals. The automatic gain control technique is used to enhance the signal to noise ratios (SNR) of RF signals. Meanwhile, the empirical mode decomposition (EMD) based wavelet transform (WT) is developed to decrease the influences of the Gaussian white noises. Then, both the azimuth information between the drone and radar and the bandwidth of the RF signals are acquired based on the statistical analysis algorithm developed in this paper. Meanwhile, the proposed accumulation algorithm can also provide the bandwidth estimation, which can be used to make a decision accurately whether there are drones or not in the detection environments based on the probability theory. The detection performance is validated with several experiments conducted outdoors with strong interferences.

• Electronics and Telecommunications Trends
• /
• v.35 no.5
• /
• pp.30-42
• /
• 2020

Unlike in previous generations of mobile technology, machine learning (ML)-based self-healing research trend are currently attracting attention to provide high-quality, effective, and low-cost 5G services that need to operate in the HetNets scenario where various wireless transmission technologies are added. Self-healing plays a vital role in detecting and mitigating the faults, and confirming that there is still room for improvement. We analyzed the research trend in self-healing framework and ML-based fault detection, fault diagnosis, and fault compensation. We propose that to ensure that self-healing is a proactive instead of being reactive, we have to design an ML-based self-healing framework and select a suitable ML algorithm for fault detection, diagnosis, and outage compensation.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2019.10a
• /
• pp.145-148
• /
• 2019

Principal Component Analysis (PCA) is a powerful technique in data analysis and widely used to detect anomalies in Wireless Sensor Networks. However, the performance of conventional PCA is not high on time-series data collected by sensors. In this paper, we propose a Joint Exponential Smoothing and Trend-based Principal Component Analysis (JES-TBPCA) for Anomaly Detection which is based on conventional PCA. Experimental results on a real dataset show a remarkably higher performance of JES-TBPCA comparing to conventional PCA model in detection of stuck-at and offset anomalies.

• 한국환경농학회:학술대회논문집
• /
• 2009.07a
• /
• pp.157-168
• /
• 2009

Frequent outbreaks of foodborne illness have been increasing the awareness of agro-food safety. Conventional methods for pathogen detection and identification are labor.intensive and take days to complete. The increasing use of rapid food safety testing is receiving more and more attention. The major reason for this trend is that the food industry requires quick and accurate results. The rapid detection of contaminants in food is critical for ensuring the safety of consumers. Recent advances in technology make detection and identification faster, more sensitive and more specific than traditional method. In this paper, technology trends and recent developments in rapid methods for agro-food safety are discussed.

• Proceedings of the Korean Society of Propulsion Engineers Conference
• /
• 2007.11a
• /
• pp.345-349
• /
• 2007

In this study a fuzzy trend monitoring method for detecting the engine mechanical faults was proposed through analyzing performance trends of measurement data. The trend monitoring is an engine conditioning method which can find engine faults by monitoring important measuring parameters such as fuel flow, exhaust gas temperatures, rotational speeds, vibration. etc. Using engine condition data set as a input which generated by linear regression analysis of real engine instrument data, an application of fuzzy logic in diagnostics estimate a cause of fault in each components.

• Journal of Environmental Impact Assessment
• /
• v.4 no.2
• /
• pp.93-103
• /
• 1995

This study was carried out to suggest the best statistical test to analyze the trend in monthly water quality data. Traditional parametric tests such as t-test and regression analysis are based on the assumption that the underlying population has a normal distribution and regression analysis additionally assumes that residual errors are independent. Analyzing 9-years monthly COD data collected at Paldang in Han River, the underlying population was found to be neither normal nor independent. Therefore parametric tests are invalid for trend detection. Four Kinds of nonparametric statistical tests, such as Run Test, Daniel test, Mann-Kendall test, and Time Series Residual Analysis were applied to analyze the trend in the COD data, Daniel test and Mann-Kendall test indicated upward trend in COD data. The best nonparametric test was suggested to be Daniel test, which is simple in computation and easy to understand the intuitive meaning.

• Journal of IKEEE
• /
• v.13 no.2
• /
• pp.140-149
• /
• 2009

Traffic anomaly detection is one of important technology that should be considered in network security and administration. In this paper, we propose an abnormal traffic detection mechanism that includes traffic monitoring and traffic analysis. We develop analytical passive monitoring system called WISE-Mon which can inspect traffic behavior. We establish a criterion by analyzing the characteristics of a traffic training set. To detect abnormal traffic, we derive a hyperplane by using Fisher linear discriminant and chi-square distribution as well as the analyzed characteristics of traffic. Our mechanism can support reliable results for traffic anomaly detection and is compatible to real-time detection. In addition, since the trend of traffic can be changed as time passes, the hyperplane has to be updated periodically to reflect the changes. Accordingly, we consider the self-learning algorithm which reflects the trend of the traffic and so enables to increase the pliability of detection probability. Numerical results are presented to validate the accuracy of proposed mechanism. It shows that the proposed mechanism is reliable and relevant for traffic anomaly detection.