• Economic and Environmental Geology
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• v.55 no.3
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• pp.309-316
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• 2022

In order to minimize the human and time consumption required for rock classification, research on rock classification using artificial intelligence (AI) has recently developed. In this study, basic volcanic rocks were subdivided by using polarizing microscope thin section images. A convolutional neural network (CNN) model based on Tensorflow and Keras libraries was self-producted for rock classification. A total of 720 images of olivine basalt, basaltic andesite, olivine tholeiite, trachytic olivine basalt reference specimens were mounted with open nicol, cross nicol, and adding gypsum plates, and trained at the training : test = 7 : 3 ratio. As a result of machine learning, the classification accuracy was over 80-90%. When we confirmed the classification accuracy of each AI model, it is expected that the rock classification method of this model will not be much different from the rock classification process of a geologist. Furthermore, if not only this model but also models that subdivide more diverse rock types are produced and integrated, the AI model that satisfies both the speed of data classification and the accessibility of non-experts can be developed, thereby providing a new framework for basic petrology research.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.11
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• pp.1022-1028
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• 2013

It is well knowns that based on the CSP (Common Spatial Pattern) algorithm, the linear projection of an EEG (Electroencephalography) signal can be made to spaces that optimize the discriminant between two patterns. Sharing disadvantages from linear time invariant systems, CSP suffers from the non-stationary nature of EEGs causing the performance of the classification in a BCI (Brain-Computer Interface) system to drop significantly when comparing the training data and test data. The author has suggested a simple idea based on the parallel model of CSP filters to improve the performance of BCI systems. The model was tested with a simple CSP algorithm (without any elaborate regularizing methods) and a perceptron learning algorithm as a classifier to determine the improvement of the system. The simulation showed that the parallel model could improve classification performance by over 10% compared to conventional CSP methods.

• Economic and Environmental Geology
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• v.55 no.3
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• pp.231-239
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• 2022

The irregular shape of the disturbances is a fundamental issue for mining engineers at the Sidi Chennane phosphate deposit in Morocco. A precise classification of disturbed areas is therefore necessary to understand their part in the overall volume of phosphate. In this paper, we investigate the theoretical and practical aspects of studying and measuring multifractal spectrums as a defining and representative parameter for distinguishing between the phosphate deposit of a low rate of disturbances and the deposit of a high rate. An empirical multifractal approach was used by analyzing the disturbed areas through the geoelectric images of an area located in the Sidi Chennane phosphate deposit. The Generalized fractal dimension, D(q), the Singularities of strength, α(q), the local dimension, f(α) and their conjugate parameter the mass exponent, τ(q) as well as f(α)-α spectrum were the common multifractal parameters used. The results reported show wide variations of the analyzed images, indicating that the multifractal analysis is an indicator for evaluate and characterize the disturbed areas within the phosphates deposits through the studied geoelectric images. This could be the starting point for future work aimed at improving phosphate exploration planning.

• Proceedings of the KOSOMBE Conference
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• v.1995 no.11
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• pp.189-192
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• 1995

In Visually Evoked Potentials(VEP) or Auditory Evoked Potentials(AEP), the components by the stimulation and the components which are irrelevant to the stimulation(noise or nonstationary spontaneous EEG) are mixed together. So one should average hundreds of EP waves to extract the components by the stimulation only. In this study, we have classified EP's, which are the responses of the different stimulations and different states of subjects. To classify the EP waves, the cross-correlation coefficients and neural network method(error back propagation) are used and compared.

• ETRI Journal
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• v.28 no.1
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• pp.111-114
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• 2006

An alternative human interface enabling people with severe motor disabilities to control an assistive system is presented. Since this interface relies on the biosignals originating from the contraction of muscles on the face during particular movements, even individuals with a paralyzed limb can use it with ease. For real-world application, a dedicated hardware module employing a general-purpose digital signal processor was implemented and its validity tested on an electrically powered wheelchair. Furthermore, an additional attempt to reduce error rates to a minimum for stable operation was also made based on the entropy information inherent in the signals during the classification phase. In the experiments, most of the five participating subjects could control the target system at their own will, and thus it is found that the proposed interface can be considered a potential alternative for the interaction of the severely disabled with electronic systems.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.935-939
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• 2003

The emotion recognition is one of the most significant interface technologies which make the high level of human-machine communication possible. The central nervous system stimulated by emotional stimuli affects the autonomous nervous system like a heart, blood vessel, endocrine organs, and so on. Therefore bio-signals like HRV, ECG and EEG can reflect one' emotional state. This study investigates the correlation between emotional states and bio-signals to realize the emotion recognition. This study also covers classification of human emotional states, selection of the effective bio-signal and signal processing. The experimental results presented in this paper show possibility of the emotion recognition.

• Journal of Korean Neurosurgical Society
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• v.39 no.2
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• pp.114-119
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• 2006

Objective : The authors investigate appropriate evaluation and surgical methods in treatment of the cerebral paragonimiasis accompanying epilepsy. Methods : Thirteen patients with the cerebral paragonimiasis accompanying epilepsy were included for this study. Preoperative evaluation methods included history taking, skin and serologic tests for Paragonimus westermani, neurologic examinations, computerized tomography, magnetic resonance imaging, amytal test, PET or SPECT, and video-EEG monitoring with depth and subdural grid electrodes. Seizure outcome was evaluated according to Engel's classification. Results : Surgical methods were temporal lobectomy including lesions in six, lesionectomy in five, and temporal lobectomy plus lesionectomy in two. Postoperative neurological complications were not noticed, and seizure outcomes were class I in 12 patients [92%], class II in one [8%]. Conclusion : In patients with a cerebral paragonimiasis accompanying epilepsy, further evaluation methods must be done to define the epileptogenic zone, and complete resection of the epileptogenic zone with different surgical methods should be performed for seizure control.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.569-570
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• 2016

본 논문은 뇌파 기반 감정 분류 기술의 상용화를 위한 낮은 성능을 보완하기 위하여 Regularized Common Spatial Pattern 필터링을 통한 전처리 방식을 제안하고 있다. RCSP 필터는 뇌파 기반 행동 인식 시스템에서 높은 성능 향상을 보이는 것으로 알려져 있다. 본 연구에서는 장기적이고 복합적인 뇌파의 감성 인지 연구에도 RCSP 필터의 적용 방법을 설명하고, 제안하는 알고리즘이 뇌파를 통한 감정 인식에 성능 향상을 보여준다는 것을 설명하고 있다.

• Annals of Clinical Neurophysiology
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• v.19 no.1
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• pp.58-63
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• 2017

Studies of interictal epileptiform discharges are essential for improving the diagnosis, classification, and management of epilepsy. In this case series we sought to identify the clinical and neurophysiological significance of bifid spikes, whose pattern bears a strong resemblance to the cardiac M pattern. We hypothesize that, analogous to the cardiac M pattern, the cerebral M pattern is generated by a conduction defect associated with asynchronous spatiotemporal averaging of electrical signals in the cortex, resulting in the signals reaching the scalp with different latencies. Unlike the cardiac M pattern, the pathology underlying the cerebral M pattern is unknown, although congenital CNS anomalies may be a culprit.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.05a
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• pp.215-220
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• 2002

본 연구에서는 다중채널 뇌파에서 특징 파라미터로 선형 예측기 계수(Linear predictor coefficients)를 추출하고, 패턴인식기로는 신경회로망을 이용한 쾌적성 분류 알고리즘을 개발하여 다중 템플릿 방법으로 쾌적성 분류 실험을 하고자 하였다. 뇌파 데이터는 대학생 10명으로부터 쾌적한 환경과 불쾌적한 환경에서의 데이터를 수집하였으며, 전극 위치는 Fpl, Fp2, F3, F4, T3, T4, P3, P4, O1, O2를 사용하였다. 수집된 뇌파는 전처리를 거친 후 특징 파라미터를 추출하고 패턴 분류기로 사용된 신경회로망의 입력으로 사용하였다. 쾌적성 분류 방법은 다중템플릿 방법으로 여러 명의 피검자를 각각 학습시켜 이로부터 생성되는 신경회로망의 가중치들을 템플릿에 저장한다. 그리고 테스트를 할 때에는 먼저 처음의 안정 상태의 뇌파를 이용하여 템플릿 검색을 하고 가장 가까운 템플릿을 선택한다. 그리고 선택된 템플릿을 이용하여 다른 감정에 대한 쾌적성 분류 실험을 하게 된다. 쾌적성 분류 실험 결과 평균 인식률이 약 75%의 성능을 나타내었다.