• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1812-1824
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• 2016

The neural dynamics underlying the causal network during motor planning or imagery in the human brain are not well understood. The lack of signal processing tools suitable for the analysis of nonlinear and nonstationary electroencephalographic (EEG) hinders such analyses. In this study, noise-assisted multivariate empirical mode decomposition (NA-MEMD) is used to estimate the causal inference in the frequency domain, i.e., partial directed coherence (PDC). Natural and intrinsic oscillations corresponding to the motor imagery tasks can be extracted due to the data-driven approach of NA-MEMD, which does not employ predefined basis functions. Simulations based on synthetic data with a time delay between two signals demonstrated that NA-MEMD was the optimal method for estimating the delay between two signals. Furthermore, classification analysis of the motor imagery responses of 29 subjects revealed that NA-MEMD is a prerequisite process for estimating the causal network across multichannel EEG data during mental tasks.

• Proceedings of the KIPE Conference
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• 2004.07a
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• pp.38-41
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• 2004

Artificial intelligence control that use Fuzzy, Neural network, genetic algorithm etc. in the speed control of induction motor recently is studied much. Also, sensors such as Encoder and Resolver are used to receive the speed of induction motor and information of position. However, this control method or sensor use receives much effects in surroundings environment change and react sensitively to parameter change of electric motor and control Performance drops. Presume the speed and position of induction motor by ANN in this treatise, and because using FMRLC that is consisted of two Fuzzy Logic, can correct Fuzzy Rule Base through teaming and save good response special quality in change of condition such as change of parameter.

• Journal of the Korean Society for Precision Engineering
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• v.10 no.4
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• pp.142-151
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• 1993

An intelligent mulitiple monitoring system to monitor tool/machining states synthetically was proposed and developed. It consists of 2 fundamental subsystems : the multiple sensor detection unit and the intellignet integrated diagnosis unit. Three signals, that is, spindle motor current, Z-axis motor current, and machining sound were adopted to detect tool/machining states more reliably. Based on the multiple sensor information, the diagnosis unit judges either tool breakage or degree of tool wear state using fuzzy reasoning. Tool breakage is diagnosed by the level of spindle/z-axis motor current. Tool wear is diagnosed by both the result of fuzzy pattern recognition for motor currents and the result of pattern matching for machining sound. Fuzzy c-means algorithm was used for fuzzy pattern recognition. Experiments carried out for drill operation in the machining center have shown that the developed system monitors abnormal drill/states drilling very reliably.

• Biomolecules & Therapeutics
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• v.29 no.5
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• pp.483-491
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• 2021

Parkinson's disease (PD) is a neurodegenerative disorder that involves the loss of dopaminergic neurons in the substantia nigra (SN). Matrix metalloproteinases-8 (MMP-8), neutrophil collagenase, is a functional player in the progressive pathology of various inflammatory disorders. In this study, we administered an MMP-8 inhibitor (MMP-8i) in Leucine-rich repeat kinase 2 (LRRK2) G2019S transgenic mice, to determine the effects of MMP-8i on PD pathology. We observed a significant increase of ionized calcium-binding adapter molecule 1 (Iba1)-positive activated microglia in the striatum of LRRK2 G2019S mice compared to normal control mice, indicating enhanced neuro-inflammatory responses. The increased number of Iba1-positive activated microglia in LRRK2 G2019S PD mice was down-regulated by systemic administration of MMP-8i. Interestingly, this LRRK2 G2019S PD mice showed significantly reduced size of cell body area of tyrosine hydroxylase (TH) positive neurons in SN region and MMP-8i significantly recovered cellular atrophy shown in PD model indicating distinct neuro-protective effects of MMP-8i. Furthermore, MMP-8i administration markedly improved behavioral abnormalities of motor balancing coordination in rota-rod test in LRRK2 G2019S mice. These data suggest that MMP-8i attenuates the pathological symptoms of PD through anti-inflammatory processes.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1478-1481
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• 2005

As the model of most practical system cannot be obtained, the practice of typical control method is limited. Accordingly, numerous artificial intelligence control methods have been used widely. Fuzzy control and neural network control have been an important point in the developing process of the field. This paper is proposed adaptive fuzzy-neural network based on the vector controlled interior permanent magnet synchronous motor drive system. The fuzzy-neural network is first utilized for the speed control. A model reference adaptive scheme is then proposed in which the adaptation mechanism is executed using fuzzy-neural network. Also, this paper is proposed estimation of speed of interior permanent magnet synchronous motor using artificial neural network controller. The back-propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back-propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the analysis results to verify the effectiveness of the new method.

• Journal of Power Electronics
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• v.8 no.2
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• pp.181-191
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• 2008

The task performed by induction motors grows increasingly complex in modern industry and hence improvements are sought in the field of fault diagnosis. It is essential to diagnose faults at their very inception, as unscheduled machine down time can upset critical dead lines and cause heavy financial losses. Artificial intelligence (AI) techniques have proved their ability in detection of incipient faults in electrical machines. This paper presents an application of AI techniques for the detection of inter-turn insulation and bearing wear faults in single-phase induction motors. The single-phase induction motor is considered a proto type model to create inter-turn insulation and bearing wear faults. The experimental data for motor intake current, rotor speed, stator winding temperature, bearing temperature and noise of the motor under running condition was generated in the laboratory. The different types of fault detectors were developed based upon three different AI techniques. The input parameters for these detectors were varied from two to five sequentially. The comparisons were made and the best fault detector was determined.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.26 no.1
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• pp.22-29
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• 2015

Objectives : Repetitive and stereotyped behaviors are core symptoms in children with autism spectrum disorders (ASD). The purpose of our study was to investigate the frequency of motor stereotypes in ASD children and their clinical features. Methods : Among 171 ASD children (age range, 3-15), the ASD group with motor stereotypes was defined according to two items in the Korean version of Autism Diagnostic Interview-Revised (K-ADI-R). We compared the clinical features, behavior problems and severity of other domains in the K-ADI-R and executive functions between the ASD group with motor stereotypes and the ASD group without motor stereotypes. Results : Ninety (52.6%) of 171 ASD children had motor stereotypes. The ASD group with motor stereotypes had a lower intelligence quotient score (62.23 vs. 84.94, p<.001) compared to the ASD group without motor stereotypes. The ASD group with motor stereotypes had more impairments in the social interaction domain [adjusted odds ratio (AOR) 1.11, p=.001] and communication domain (AOR 1.15, p=.008). Thought problems and lethargy were more frequent in the ASD group with motor stereotypes than the ASD group without motor stereotypes (AOR 2.059, p=.034 ; adjusted OR 1.045, p=.046). However, no significant differences in executive function were observed between the ASD group with motor stereotypes and the ASD group without motor stereotypes. Conclusion : The ASD group with motor stereotypes showed more impairment in social interaction and communication domains, which are core symptoms of autism. Motor stereotypes may indicate greater severity of ASD.

• Journal of Korean Neurosurgical Society
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• v.29 no.2
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• pp.196-202
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• 2000

Objective : This study was designed to evaluate the relationship between the initial neurosurgical or psychosocial factors and the psychosocial outcome. Patients and Methods : We analyzed 123 head-injured patients who were referred to the department of psychiatry for the evaluation of psychosocial function. We analyzed initial neurosurgical variables such as Glasgow Coma scale(GCS) score, skull fracture, CT finding, and psychosocial outcomes with regards to psychosis, personality change, depression, anxiety and IQ on Intelligence Scale. Results : Patients with mild head injury(GCS score 13-15, N＝94, 76.4%) had better recovery rate on Glasgow Outcome Scale(GOS), less personality change than those with moderate or severe head injury. However, depression, anxiety and intelligence were not significantly different between two groups. The skull fracture(N＝37, 30.1%) did not influence on the psychosocial outcome with reference to personality change, depression, anxiety and intelligence. The patients with abnormal CT findings(N＝64, 52%) had lower recovery rate on GOS, more frequent tendency in psychosis, personality change and severe depression, less frequent in anxiety and mild depression, than patients with normal CT finding. However, levels of intelligence were not different between two groups. The patients with industrial accidents(IA) had lower educational level, milder head injury, more delay for the psychiatric evaluation (longer treatment period) than those with motor vehicular accidents(MVA). The psychosocial outcome with reference to personality change, depression, anxiety, intelligence were not different between two groups. Conclusion : These findings indicate that the more severe initial trauma, the poorer psychosocial outcome. However, it was frequently observed that patients with mild head injury suffered from mild anxiety and depression. Therefore mild head injury appeared to be more complicated by psychosocial stressors. The patients with IA, despite the fact that initial head injury was mild, required longer treatment period than MVA.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.11 no.1
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• pp.74-81
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• 1998

It is not good joint that today's traffic control system that the course of traffic volume increase tendency is followed, in the traffic volume is approched into the time of my car. Accordingly when we analyzed the existing traffic signal control system, the traffic signal system is developed from the machine type that the motor was centered, to get up to date, to the intelligence electron signal control system. But yet, when we have a test and a A/S on the control circuit, the circuit that is designed to the center IC and ROM are complicated. Also, the time of pass lamp that the car line stream is going, can not extended automatically a time till the traffic volume is decreased to the same direction. This theme must be a real time intelligence control system that the time of pass lamp can extend aumatically. The circuit of sequence ladder diagram on the traffic signal control of a crossroads that is desinged, can be satisfied the complicated vehicle order. Therefore when the circuit is changed, the new developed system is economical with that dosen't needs any of components to require the circuit equipment, and the time is saved with needlessness of the circuit wiring again, and have a much trustworthy. The control method of pass signal lamp in the car line stream connecting among PLC and Relay and Temp Sensor, can be changed to hand operation and to semi-automation and to all-automation. New intelligence traffic signal system is composed with all-together system of T Sensor + Video Camera + IBM PC that is able to guiding the establishment of traffic order.

• Journal of the Korean Society of Systems Engineering
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• v.15 no.2
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• pp.72-78
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• 2019

Pathology is the motor that drives healthcare to understand diseases. The way pathologists diagnose diseases, which involves manual observation of images under a microscope has been used for the last 150 years, it's time to change. This paper is specifically based on tumor detection using deep learning techniques. Pathologist examine the specimen slides from the specific portion of body (e-g liver, breast, prostate region) and then examine it under the microscope to identify the effected cells among all the normal cells. This process is time consuming and not sufficiently accurate. So, there is a need of a system that can detect tumor automatically in less time. Solution to this problem is computational pathology: an approach to examine tissue data obtained through whole slide imaging using modern image analysis algorithms and to analyze clinically relevant information from these data. Artificial Intelligence models like machine learning and deep learning are used at the molecular levels to generate diagnostic inferences and predictions; and presents this clinically actionable knowledge to pathologist through dynamic and integrated reports. Which enables physicians, laboratory personnel, and other health care system to make the best possible medical decisions. I will discuss the techniques for the automated tumor detection system within the new discipline of computational pathology, which will be useful for the future practice of pathology and, more broadly, medical practice in general.