• The Journal of the Acoustical Society of Korea
• /
• v.39 no.5
• /
• pp.414-423
• /
• 2020

In this paper, we propose a Dilated Convolution Gate Linear Unit (DCGLU) to mitigate the lack of sparsity and small receptive field problems caused by the segmentation map extraction process in sound event detection with weak labels. In the advent of deep learning framework, segmentation map extraction approaches have shown improved performance in noisy environments. However, these methods are forced to maintain the size of the feature map to extract the segmentation map as the model would be constructed without a pooling operation. As a result, the performance of these methods is deteriorated with a lack of sparsity and a small receptive field. To mitigate these problems, we utilize GLU to control the flow of information and Dilated Convolutional Neural Networks (DCNNs) to increase the receptive field without additional learning parameters. For the performance evaluation, we employ a URBAN-SED and self-organized bird sound dataset. The relevant experiments show that our proposed DCGLU model outperforms over other baselines. In particular, our method is shown to exhibit robustness against nature sound noises with three Signal to Noise Ratio (SNR) levels (20 dB, 10 dB and 0 dB).

• The KIPS Transactions:PartB
• /
• v.9B no.5
• /
• pp.663-672
• /
• 2002

In this paper, Using an automatic tracking system, behavior of an aquatic insect, Chironomus sp. (Chironomidae), was observed in semi-natural conditions in response to sub-lethal treament of a carbamate insecticide, carbofuran. The fourth instar larvae were placed in an observation cage $(6cm\times{7cm}\times{2.5cm)}$ at temperature of $18^\circ{C}$ and the light condition of 10 time (light) : 14 time (dark). The tracking system was devised to detect the instant, partial movement of the insect body. Individual movement was traced after the treatment of carbofuran (0.1ppm) for four days 2days : before treatment, 2 days : after treatment). Along with the other irregular behaviors, "ventilation activity", appearing as a shape of "compressed zig-zag", was more frequently observed after the treatment of the insecticide. The activity of the test individuals was also generally depressed after the chemical treatment. In order to detect behavioral changes of the treated specimens, wavelet analysis was implemented to characterize different movement patterns. The extracted parameters based on Discrete Wavelet Transforms (DWT) were subsequently provided to artificial neural networks to be trained to represent different patterns of the movement tracks before and after treatments of the insecticide. This combined model of wavelets and artificial neural networks was able to point out the occurrence of characteristic movement patterns, and could be an alternative tool for automatically detecting presences of toxic chemicals for water quality monitoring. quality monitoring.

• The Journal of the Acoustical Society of Korea
• /
• v.20 no.4
• /
• pp.35-41
• /
• 2001

This paper describes Linear Discriminant Analysis and common vector extraction for speech recognition. Voice signal contains psychological and physiological properties of the speaker as well as dialect differences, acoustical environment effects, and phase differences. For these reasons, the same word spelled out by different speakers can be very different heard. This property of speech signal make it very difficult to extract common properties in the same speech class (word or phoneme). Linear algebra method like BT (Karhunen-Loeve Transformation) is generally used for common properties extraction In the speech signals, but common vector extraction which is suggested by M. Bilginer et at. is used in this paper. The method of M. Bilginer et al. extracts the optimized common vector from the speech signals used for training. And it has 100％ recognition accuracy in the trained data which is used for common vector extraction. In spite of these characteristics, the method has some drawback-we cannot use numbers of speech signal for training and the discriminant information among common vectors is not defined. This paper suggests advanced method which can reduce error rate by maximizing the discriminant information among common vectors. And novel method to normalize the size of common vector also added. The result shows improved performance of algorithm and better recognition accuracy of 2％ than conventional method.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.44 no.5
• /
• pp.45-54
• /
• 2007

Time/frequency analysis has been extensively used in biomedical signal processing. By extracting some essential features from the electro-physiological signals, these methods are able to determine the clinical pathology mechanisms of some diseases. However, this method assumes that the signal should be stationary, which limits its application in non-stationary system. In this paper, we develop a new signal processing method using Hilbert-Huang Transform to perform analysis of the nonlinear and non-stationary ventricular fibrillation(VF). Hilbert-Huang Transform combines two major analytical theories: Empirical Mode Decomposition(EMD) and the Hilbert Transform. Hilbert-Huang Transform can be used to decompose natural data into independent Intrinsic Mode Functions using the theories of EMD. Furthermore, Hilbert-Huang Transform employs Hilbert Transform to determine instantaneous frequency and amplitude, and therefore can be used to accurately describe the local behavior of signals. This paper studied for Return Of Spontaneous Circulation(ROSC) and non-ROSC prediction performance by Support Vector Machine and three parameters(EMD-IF, EMD-FFT) extracted from ventricular fibrillation ECG waveform using Hilbert-Huang transform. On the average results of sensitivity and specificity were 87.35% and 76.88% respectively. Hilbert-Huang Transform shows that it enables us to predict the ROSC of VF more precisely.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.24 no.2
• /
• pp.173-178
• /
• 2014

In this study, we introduce ASM-based face recognition classifier and its design methodology with the aid of 2-dimensional 2-directional hybird preprocessing algorithm. Since the image of face recognition is easily affected by external environments, ASM(active shape model) as image preprocessing algorithm is used to resolve such problem. In particular, ASM is used widely for the purpose of feature extraction for human face. After extracting face image area by using ASM, the dimensionality of the extracted face image data is reduced by using $(2D)^2$hybrid preprocessing algorithm based on LDA and PCA. Face image data through preprocessing algorithm is used as input data for the design of the proposed polynomials based radial basis function neural network. Unlike as the case in existing neural networks, the proposed pattern classifier has the characteristics of a robust neural network and it is also superior from the view point of predictive ability as well as ability to resolve the problem of multi-dimensionality. The essential design parameters (the number of row eigenvectors, column eigenvectors, and clusters, and fuzzification coefficient) of the classifier are optimized by means of ABC(artificial bee colony) algorithm. The performance of the proposed classifier is quantified through yale and AT&T dataset widely used in the face recognition.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.13 no.6
• /
• pp.2441-2451
• /
• 2012

This paper presents the design of a control system for improving performance of a washer fluid heating system (WFHS) which is capable of removing frost, ice, snow and/or other debris from windshield. First, for the WFHS, a modeling process is described, and the extraction of characteristic parameters of the model are made by experimental studies. Design variables that affect on performance of the WFHS are also presented. Secondly, a control system is proposed for improving heating performance of the WFHS, and its performance is verified through experiments. The key feature of the proposed control system is to regulate the current of a booster converter input to the WFHS up to the target value that is set to guarantee heating performance. Target current is calculated by using initial temperature value and employing the mathematical model derived in the paper. Computer simulation and experimental results show that the proposed control system can perform heating operations in a way to satisfy per-determined target performance of the WFHS.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2006.05a
• /
• pp.697-700
• /
• 2006

A sensor and measuring system were implemented to measure two signals that uroflow and urophonography during urination for diagnose the lower urinary tract symptom. The implemented system was composed of the uroflow sensor using the load-cell, the urophonography measurement sensor so as stethoscope type, pre-processing part for sensors signal detection, amplifier and filter, system control parts and PC measurement program. A simulator of the lower urinary system that is experimental equipment implemented for evaluate the implemented system. The two signals were measured using implemented system and analyzed these signal by means of time domain and frequency domain for extraction of the characteristic parameter the most influence of effect according to occlusion of the lower urinary system. Furthermore two signals were measured and analyzed with the subject of 5 healthy adult for clinical application possibility of the implemented system. As a result, the most influence bandwidth of effect according to occlusion of the lower urinary system is $253\sim282Hz$ of the urophonography signal.

• KIPS Transactions on Software and Data Engineering
• /
• v.9 no.12
• /
• pp.431-438
• /
• 2020

In this paper, the method to extract lip contour by multiple threshold is proposed. Spyridonos et. el. proposed a method to extract lip contour. First step is get Q image from transform of RGB into YIQ. Second step is to find lip corner points by change point detection and split Q image into upper and lower part by corner points. The candidate lip contour can be obtained by apply threshold to Q image. From the candidate contour, feature variance is calculated and the contour with maximum variance is adopted as final contour. The feature variance 'D' is based on the absolute difference near the contour points. The conventional method has 3 problems. The first one is related to lip corner point. Calculation of variance depends on much skin pixels and therefore the accuracy decreases and have effect on the split for Q image. Second, there is no analysis for color systems except YIQ. YIQ is a good however, other color systems such as HVS, CIELUV, YCrCb would be considered. Final problem is related to selection of optimal contour. In selection process, they used maximum of average feature variance for the pixels near the contour points. The maximum of variance causes reduction of extracted contour compared to ground contours. To solve the first problem, the proposed method excludes some of skin pixels and got 30% performance increase. For the second problem, HSV, CIELUV, YCrCb coordinate systems are tested and found there is no relation between the conventional method and dependency to color systems. For the final problem, maximum of total sum for the feature variance is adopted rather than the maximum of average feature variance and got 46% performance increase. By combine all the solutions, the proposed method gives 2 times in accuracy and stability than conventional method.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_1
• /
• pp.1125-1139
• /
• 2022

In an unmanned aerial vehicles (UAVs) system, a physical offset can be existed between the global positioning system/inertial measurement unit (GPS/IMU) sensor and the observation sensor such as a hyperspectral sensor, and a lidar sensor. As a result of the physical offset, a misalignment between each image can be occurred along with a flight direction. In particular, in a case of multi-sensor system, an observation sensor has to be replaced regularly to equip another observation sensor, and then, a high cost should be paid to acquire a calibration parameter. In this study, we establish a precise sensor model equation to apply for a multiple sensor in common and propose an independent physical offset estimation method. The proposed method consists of 3 steps. Firstly, we define an appropriate rotation matrix for our system, and an initial sensor model equation for direct-georeferencing. Next, an observation equation for the physical offset estimation is established by extracting a corresponding point between a ground control point and the observed data from a sensor. Finally, the physical offset is estimated based on the observed data, and the precise sensor model equation is established by applying the estimated parameters to the initial sensor model equation. 4 region's datasets(Jeon-ju, Incheon, Alaska, Norway) with a different latitude, longitude were compared to analyze the effects of the calibration parameter. We confirmed that a misalignment between images were adjusted after applying for the physical offset in the sensor model equation. An absolute position accuracy was analyzed in the Incheon dataset, compared to a ground control point. For the hyperspectral image, root mean square error (RMSE) for X, Y direction was calculated for 0.12 m, and for the point cloud, RMSE was calculated for 0.03 m. Furthermore, a relative position accuracy for a specific point between the adjusted point cloud and the hyperspectral images were also analyzed for 0.07 m, so we confirmed that a precise data mapping is available for an observation without a ground control point through the proposed estimation method, and we also confirmed a possibility of multi-sensor fusion. From this study, we expect that a flexible multi-sensor platform system can be operated through the independent parameter estimation method with an economic cost saving.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.9 no.1
• /
• pp.1-12
• /
• 2006

Approaches to spatial analysis differ from the type of spatial objects to be treated. Especially, in here, the case where two spatial data sets coexist is considered. The goal of such case lies on detecting a subset of spatial objects out of a large set that affects the distribution of the other object. However, it is not easy to extract a subset from a large set by visualization just with the help of GIS since huge amount of data are provided nowadays. In this research, therefore, relationship between two different spatial data are analyzed by quantitative measure in the case study of marketing geography. A purchase history data of a small retail store and the location of its competitors are given as source data for the analysis. The goal of analysis from the aspect of this case study is to extract strong competitors of the store that affects the sales amount of the store among many competitors. With the result, therefore, it is expected that market area pattern and competitive power of stores under micro scale retail environment would be understood by quantitative measure.