Motoaki Mouri

Analysis of environmental electromagnetic signal using nonnegative Matrix Factorization minimizing quasi-L1 norm

Anomalous environmental electromagnetic (EM) radiation waves have been reported as the portents of earthquakes. We have been measuring the Extremely Low Frequency (ELF) range all over Japan. Our goal is to predict earthquakes using EM radiation waves. The recorded data often contain signals unrelated to earthquakes. These signals, as noise, confound earthquake prediction efforts. It is necessary to eliminate noises from observed signals in a preprocessing step. In previous researches, we used ISRA, an algorithm of the Nonnegative Matrix Factorization (NMF), to estimate source signal. However, ISRA is not robust for outliers because ISRAs cost function is based on square distance. In order to improve robustness, we should use lower order cost function. In this paper, we propose a nonnegative matrix factorization method using quasi-L1 norm in cost function (quasi-L1 NMF). In the experiment using ELF observed signals that include outliers, the proposed method extracts source signals more accurately than ISRA.

Effectiveness of global signal elimination from environmental electromagnetic signals for earthquake prediction

Anomalous environmental electromagnetic (EM) radiation waves have been reported as the portents of earthquakes. We have been measuring the extremely low frequency (ELF) range all over Japan. Our goal is to predict earthquakes using EM radiation waves. Previously, we proposed a method of detecting anomalous signals by focusing on linear prediction errors. However, this method also sensitively responds to earthquake-unrelated anomaly. For accurate earthquake-prediction, we should eliminate earthquake-unrelated signals. In this paper, we try to reduce false detection rate by global signal elimination using non-negative matrix factorization (NMF) and evaluate the effectiveness of this method.

Global Signal Elimination and Local Signals Enhancement from EM Radiation Waves Using Independent Component Analysis

Anomalous environmental electromagnetic (EM) radiation waves have been reported as the portents of earthquakes. Our studys goal is predicting earthquakes using EM radiation waves by detecting some anomalies. We have been measuring the Extremely Low Frequency (ELF) range EM radiation waves all over Japan. However, the recorded data contain signals unrelated to earthquakes. These signals, as noise, confound earthquake prediction efforts. In this paper, we propose an efficient method of global signal elimination and enhancement local signals using Independent Component Analysis (ICA). We evaluated the effectiveness of this method.

Global signal elimination from ELF band electromagnetic signals by Independent Component Analysis

Anomalous radiations of environmental electromagnetic (EM) waves have been reported as the portents of earthquakes. Our studys goal is predicting earthquakes using EM signals. We have been measuring the Extremely Low Frequency (ELF) range all over Japan. However, the recorded data contain signals or noises unrelated to earthquakes. These signals and noises work to fail in earthquake-prediction. It is necessary to eliminate noises from observed signals in a preprocessing step. In this paper, we propose a method of global signal elimination by Independent Component Analysis (ICA) and evaluate the effectiveness of this method.

A study of using nonnegative matrix factorization to detect solder-voids from radiographic images of solder

Accurate detection of voids in solder bumps on ball grid arrays (BGAs) is important for improving device quality. Radiographic imaging is commonly used to inspect BGA packages incorporate into LSI circuits. In the case of conventional method, imaging is normally done four times, and the images obtained are averaged to reduce noises. We have developed a nonnegative matrix factorization method for detecting solder-voids using only three radiographic images. Computer simulation demonstrated that it has the same level of accuracy as the conventional method.

Improvement of Earthquake Prediction by using Global Signal Elimination from Environmental Electromagnetic Signals

Anomalous environmental electromagnetic (EM) radiation waves have been reported as the portents of earthquakes. We have been measuring the Extremely Low Frequency (ELF) range all over Japan. Our goal is to predict earthquakes using EM radiation waves. Previously, we proposed a method of detecting anomalous signals by focusing on linear prediction errors. However, this method also sensitively responds to earthquake-unrelated anomaly. For accurate earthquake-prediction, we should eliminate earthquake-unrelated signals. In this paper, we try to reduce false detection rate by global signal elimination using Non-negative Matrix Factorization (NMF) and evaluate the effectiveness of this method.

Analysis on EEG signals in visually and auditorily guided saccade task by FICAR

Recently an independent component analysis (ICA) becomes powerful tools to processing bio-signals. In our studies, the ICA is applied to processing on saccade-related EEG signals in order to predict saccadic eye movements because an ensemble averaging, which is a conventional processing method of EEG signals, is not suitable for real-time processing. We have already detected saccade-related independent components (ICs) by ICA. However, features of saccade-related EEG signals and saccade-related ICs were not compared. In this paper, saccade-related EEG signals and saccade-related ICs in visually and auditorily guided saccade task are compared in the point of the latency between starting time of a saccade and time when a saccade-related EEG signal or an IC has maximum value and in the point of the peak scale where a saccade-related EEG signal or an IC has maximum value.

Implementation of valid and stable algorithm of QL1-NMF for analyzing environmental elf magnetic signals

Previously, we developed two NMF algorithms named QL1-NMF using quasi-L1 norm for analyzing environmental ELF magnetic field measurements. When the data includes many outliers, the QL1-NMF algorithms returned better results than other BSS algorithms using L1 norm. However, the derivative of cost function in our first algorithm was not based on a monotonically increasing function. This problem decreased the validity of algorithm. Our second algorithm had problems about stability though it was based on a monotonically increasing derivative. The method therefore required improvement of validity and stability. In the work described in this paper, we newly introduced the update functions that were based on a monotonically increasing derivative. Computer simulation results and real datas results confirm the new algorithm works more stable than the previous one.

Incorporation of blind source separation in X-ray energy subtraction for extracting solder bumps

Accurate extraction of solder bumps in radiographs is a key factor in improving device quality. The conventional method, X-ray-energy subtraction, needs detailed research to determine parameters by numerous experiments and rich human experience. The parameters, however, are not published and are thus not considered general-purpose. Two automated methods were implemented with blind-source separation. The experimentation showed our proposed method using an algorithm called QL1-NMF automatically extracts solder bumps from radiographs well.

Revising algorithm for nonnegative matrix factorization based on minimizing quasi-L1 norm

Previously, we developed a nonnegative matrix factorization (NMF) algorithm named QL1-NMF that is based on minimizing the quasi-L1 norm of an error matrix. When the data includes many outliers, the QL1-NMF algorithm returns better results than ISRA, which is one of the basic NMF algorithms. However, the update functions in the QL1-NMF algorithm are based on a differential function with distortion. Moreover, the solutions it provides sometimes diverge to infinity. The method therefore required improvement to enable it to produce more accurate analysis. In the work described in this paper, we replaced its update functions with others that were based on a simple differential function without distortion. We also contrived ways to implement adjustment factors into the update functions. Computer simulation results confirm the revised algorithm works better than the previous one.