• Journal of Information Processing Systems
• /
• v.19 no.3
• /
• pp.377-384
• /
• 2023

The existence of abnormal breakpoint data leads to poor channel balance in wireless sensor networks (WSN). To enhance the communication quality of WSNs, a method for positioning abnormal breakpoint data in WSNs on the basis of signal reconstruction is studied. The WSN signal is collected using compressed sensing theory; the common part of the associated data set is mined by exchanging common information among the cluster head nodes, and the independent parts are updated within each cluster head node. To solve the non-convergence problem in the distributed computing, the approximate term is introduced into the optimization objective function to make the sub-optimization problem strictly convex. And the decompressed sensing signal reconstruction problem is addressed by the alternating direction multiplier method to realize the distributed signal reconstruction of WSNs. Based on the reconstructed WSN signal, the abnormal breakpoint data is located according to the characteristic information of the cross-power spectrum. The proposed method can accurately acquire and reconstruct the signal, reduce the bit error rate during signal transmission, and enhance the communication quality of the experimental object.

• The Journal of the Acoustical Society of Korea
• /
• v.40 no.4
• /
• pp.337-346
• /
• 2021

Detection of abnormal signal generally can be done by using features of normal signals as main information because of data imbalance. This paper propose an efficient method for abnormal signal detection using parallel AutoEncoder (AE) which can use features of abnormal signals as well. The proposed Parallel AE (PAE) is composed of a normal and an abnormal reconstructors having identical AE structure and train features of normal and abnormal signals, respectively. The PAE can effectively solve the imbalanced data problem by sequentially training normal and abnormal data. For further detection performance improvement, additional binary classifier can be added to the PAE. Through experiments using public acoustic data, we obtain that the proposed PAE shows Area Under Curve (AUC) improvement of minimum 22 % at the expenses of training time increased by 1.31 ~ 1.61 times to the single AE. Furthermore, the PAE shows 93 % AUC improvement in detecting abnormal underwater acoustic signal when pre-trained PAE is transferred to train open underwater acoustic data.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.18 no.7
• /
• pp.1531-1539
• /
• 2014

Premature ventricular contraction(PVC) is the most common disease among arrhythmia and it may cause serious situations such as ventricular fibrillation and ventricular tachycardia. Nevertheless personalized difference of ECG signal exist, performance degradation occurs because of carrying out diagnosis by general classification rule. In other words, the design of algorithm that exactly detects abnormal signal and classifies PVC by analyzing the persons's physical condition and/or environment and variable QRS pattern is needed. Thus, PVC classification by personalized abnormal signal detection and QRS pattern variability is presented in this paper. For this purpose, we detected R wave through the preprocessing method and subtractive operation method and selected abnormal signal sets. Also, we classified PVC in realtime through QS interval and R wave amplitude. The performance of abnormal beat detection and PVC classification is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate the average of 98.33% in abnormal beat classification error and 94.46% in PVC classification.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.26 no.4
• /
• pp.469-474
• /
• 2016

Abnormal sound was heard from a heat exchanger of condensate water system in a nuclear power plant, which was identified as impact sound of a loose part later. Nuclear power plants are normally equipped with loose part monitoring system for primary water system, but not for secondary water system. The abnormal sound was analyzed by using the impact signal-processing methodology based on the Hertz theory. The predicted results for impact location and size of the loose part showed good agreement with those of the actual loose part found during the overhaul period in the plant. So, this analysis methodology for the impact signal will be widely utilized for the primary and secondary side of the nuclear power plant.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.7 no.3
• /
• pp.671-677
• /
• 2012

A black-box program was implemented in order to monitor abnormal symptoms of human body irregularly occurring during sleep. The system consists of sensor probing body signals, auxiliary devices such as the alarm, lamp, network camera, and signal monitoring computer. Various types of sensors, PPG, ECG, EEG, temperature, respiration sensor, G-sensor, and microphone were used to more exactly identify the causes of abnormal symptoms. If a symptom occurs, the system records the patient's condition to provide information being utilized in the treatment. The sensors are attached on some locations of body being proper to check a specific type of abnormal reaction. Based on the normal range and type of measurement data, criteria of signal levels were set to distinguish abnormal reaction. An abnormal signal being probed, the program starts to operate the lamp, alarm, and network camera at the same time and stores the signal and video data.

• Journal of Sensor Science and Technology
• /
• v.21 no.1
• /
• pp.39-45
• /
• 2012

Recently the convergence of healthcare technology is used for daily life healthcare monitoring. Cardiac arrhythmia is presented by the state of the heart irregularity. Abnormal heart's electrical signal pathway or heart's tissue disorder could be the cause of cardiac arrhythmia. Fatal arrhythmia could put patient's life at risk. Therefore arrhythmia detection is very important. Previous studies on the detection of arrhythmia in various ECG analysis and classification methods had been carried out. In this paper, an ECG signal processing techniques to detect abnormal ECG based on DTW minimum accumulation distance through the template matching for normalized data and variable threshold method for ECG R-peak detection. Signal processing techniques able to determine the occurrence of normal ECG and abnormal ECG. Abnormal ECG detection algorithm using DTW minimum accumulation distance method is performed using MITBIH database for performance evaluation. Experiment result shows the average percentage accuracy of using the propose method for Rpeak detection is 99.63 % and abnormal detection is 99.60 %.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.8 no.3
• /
• pp.76-82
• /
• 1999

This study was carried out to verify finding performance of dental diamond bur and investigate the possibility of AE application in density field. Work pieces were made of acryl and bovine respectively for the experiments in this study. Grinding test was conducted to get the data of grinding resistance and specific finding energy of low different types of diamond bur by using tool dynamometer. AE signal was acquired to verify grinding process in the AE measuring system. AErms value was increased as the grinding velocity and depth were increasing, but it decreased as the feed rate was increasing. The case of the small value of AE signal is due to abnormal grinding in D type diamond bur. By analyzing AErms start and finish time of grinding working, abnormal grinding state can be confined. Abnormal state can be found through the behavior of AE signal in the finding working. As a result, it is expected that forecast of abnormal state is possible using AE equipments under real time process.

• 한국지구물리탐사학회:학술대회논문집
• /
• 2003.11a
• /
• pp.437-440
• /
• 2003

As we know, roof is composed of heterogeneous rock. When roof fractures, a large amount of energy would be released in the form of seismic wave. How to identify the abnormal signal of seismic wave is a much difficult problem, there are many methods used usually, such as Fourier Transformation, filter technique etc., but abnormal signal can't be recognized accurately. In this paper, multi-resolution wavelet technique is used to identify the first and second variation point, based on the Lipschitz $\alpha$. A living example analysis shows, multi-resolution wavelet technique can identify the abnormal signal of seismic wave effectively in different scale, and the omen of roof fall can be grasped in order to forecast the roof fall accurately. It provides a new idea for the predication of catastrophe on rock mechanics and engineering.

• The Journal of the Acoustical Society of Korea
• /
• v.42 no.6
• /
• pp.500-510
• /
• 2023

Passive sonar signals mainly contain both normal and abnormal signals. The abnormal signals mixed with normal signals are primarily detected using an AutoEncoder (AE) that learns only normal signals. However, existing AEs may perform inaccurate detection by reconstructing distorted normal signals from mixed signal. To address these limitations, we propose an abnormal signal detection model based on a Recurrent Neural Network (RNN) and vector quantization. The proposed model generates a codebook representing the learned latent vectors and detects abnormal signals more accurately through the proposed search process of code vectors. In experiments using publicly available underwater acoustic data, the AE and Variational AutoEncoder (VAE) using the proposed method showed at least a 2.4 % improvement in the detection performance and at least a 9.2 % improvement in the extraction performance for abnormal signals than the existing models.

• Journal of Biomedical Engineering Research
• /
• v.14 no.4
• /
• pp.387-396
• /
• 1993

In this paper, a laryngeal function identification system based-on the EGG signal is proposed as the decision basis whether the laryngeal function is normal or abnormal. The normal EGG signal is approved an autoregressive model which has the optimal order of 9. It can be analized by determining the transfer function. But it is not meaningful that the determi- nation is made using the transfer function of an autoregressive model on the abnormal EGG signal. The power spectral analysis was applied to discriminate the normal or abnormal cases. The SNR of the EGG signal was enhanced by the optimal position of electrodes.