• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.43-52
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• 2024

Medical imaginings assume a important part in the analysis of tumors and cerebrospinal fluid (CSF) leak. Magnetic resonance imaging (MRI) is an image segmentation technology, which shows an angular sectional perspective of the body which provides convenience to medical specialists to examine the patients. The images generated by MRI are detailed, which enable medical specialists to identify affected areas to help them diagnose disease. MRI imaging is usually a basic part of diagnostic and treatment. In this research, we propose new techniques using the 4D-MRI image segmentation process to detect the brain tumor in the skull. We identify the issues related to the quality of cerebrum disease images or CSF leakage (discover fluid inside the brain). The aim of this research is to construct a framework that can identify cancer-damaged areas to be isolated from non-tumor. We use 4D image light field segmentation, which is followed by MATLAB modeling techniques, and measure the size of brain-damaged cells deep inside CSF. Data is usually collected from the support vector machine (SVM) tool using MATLAB's included K-Nearest Neighbor (KNN) algorithm. We propose a 4D light field tool (LFT) modulation method that can be used for the light editing field application. Depending on the input of the user, an objective evaluation of each ray is evaluated using the KNN to maintain the 4D frequency (redundancy). These light fields' approaches can help increase the efficiency of device segmentation and light field composite pipeline editing, as they minimize boundary artefacts.

• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.892-895
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• 1991

In this study, it has been refering that disposal of rapidly international information society and artificial intelligence neural networks of the vanguard software technology. This paper is human brain cell structure modeling in order to neural networks realization for order language and computer embodiment of parallel processing. And it is shown that the usage extreme of time saving and correct judgement for business services, Overviews some of the currently popular neural networks architectures, and describes the current state of the neural networks technology.

• Geomechanics and Engineering
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• v.1 no.1
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• pp.75-84
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• 2009

Artificial neural networks (ANNs) are a new type of information processing system based on modeling the neural system of human brain. The prediction of swell pressures from easily determined soil properties, namely, initial dry density, initial water content, and plasticity index, have been investigated by using artificial neural networks. The results of the constant volume swell tests in oedometers, performed on statically compacted specimens of Bentonite-Kaolinite clay mixtures with varying soil properties, were trained in an ANNs program and the results were compared with the experimental values. It is observed that the experimental results coincided with ANNs results.

• Journal of Biomedical Engineering Research
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• v.27 no.2
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• pp.64-72
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• 2006

The present study introduces a new cortical patch-based source model for EEG/MEG cortical source imaging to consider anatomical constraints more precisely. Conventional source models for EEG/MEG cortical source imaging have used coarse cortical surface mesh or sampled small number of vertices from fine surface mesh, and thus they failed to utilize full anatomical information which nowadays we can get with sub-millimeter modeling accuracy. Conventional ones placed a single dipolar source on each cortical patch and estimated its intensity by means of various inverse algorithms; whereas the suggested cortical patch-based model integrates whole cortical area to construct lead field matrix and estimates current density that is assumed to be constant in each cortical patch. We applied the proposed and conventional models to realistic EEG data and compared the results quantitatively. The quantitative comparisons showed that the proposed model can provide more precise spatial descriptions of neuronal source distribution.

• Korean Journal of Computational Design and Engineering
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• v.5 no.4
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• pp.359-372
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• 2000

Current CAD systems are good enough to be used as a tool to manipulate three-dimensional shapes. This is a very important capability to be owned by a design tool because a major portion of designers'activities is spent on the shape manipulation in the design detailing process. However, the whole design process involves a lot more than the, shape manipulation. Currently, these remaining tasks, mostly logical reasoning process for the function realization together with structure decomposition in the top-down manner, are processed in the designer's brain. To support the top-down functional design process of a mechanical product, a system integrating the functional, structural and geometrical aspects of a product design in a unified environment is presented. Using this system, a designer can perform function decomposition, structure decomposition, and geometry detailing, and function verification activities in parallel and the whole design process it modeled resultantly. Once the whole design process is modeled, any redesign task can be automatically performed with the verification of the desired functions.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.2
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• pp.69-78
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• 2016

Ischemic stroke is a disease that the brain tissues cannot function by reducing blood flow due to thrombosis or embolisms. Due to the nature of the disease, it is most important to identify the status of cerebral vessel and the medical images are necessarily used for its diagnosis. Among many indicators, brain MRI is most widely utilized because experts can effectively obtain the semantic information such as cerebral anatomy aiding the diagnosis with it. However, in case of emergency diseases like ischemic stroke, even though a intelligent system is required for supporting the prompt diagnosis and treatment, the current systems have some difficulties to provide the information of medical images intuitively. In other words, as the current systems have managed the medical images based on the basic meta-data such as image name, ID and so on, they cannot consider semantic information inherent in medical images. Therefore, in this paper, to provide core information like cerebral anatomy contained in brain MRI, we suggest a template-driven medical images mapping method. The key idea of the method is defining the mapping characteristics between anatomic feature and representative images by using template images that can be representative of the whole brain MRI image set and revealing the semantic relations that only medical experts can check between images. With our method, it will be possible to manage the medical images based on semantic.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.97-99
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• 2021

As the aging society continues in recent years, interest in dementia is increasing. Among them, Alzheimer's disease is a degenerative brain disease that accounts for the largest percentage of all dementia patients, with the medical community currently not offering clear prevention and treatment for Alzheimer's disease, and the importance of early treatment and early prevention is emphasized. In this paper, we intend to find the most efficient activation function by combining various activation functions centering on convolutional neural networks using MRI datasets of normal people and patients with Alzheimer's disease. In addition, it is intended to be used as a dementia classification modeling suitable for the medical field in the future through Alzheimer's dementia classification modeling.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1781-1785
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• 2004

In existing factory, robot has less necessity that consider person. However, person should be considered at design and use of service robot. To service robot can be used in everyday life along with this, more functions are required. Specially, medical service robot needs function that is intelligence function. Especially, to help patient brain disease patient (cerebral hemorrhage, cerebral infarction, imbecility), gait assistance Mobile robot consider ergonomic element necessarily. In order to develop the medical support service robot, the ergonomic design should be considered. This robot ergonomic design parameters are treated in ("evelopment of Medical Support Service Robot Using Ergonomic Design" 2003, ICASS) Fig2 show this Robot. In this study, navigation algorithm of walk assistance robot is analyzed in ergonomic view. Navigation algorithm of Mobile robot can divide by two patterns. Traditional derivative method has shortcoming in dynamic environment. Reactive method is result that react excellently in dynamic environment. However, number of behavior function is limited. So hybrid navigation algorithm was proposed by the alternative way. We consider enough user specificity at navigation algorithm application of gait assistance robot.

• BMB Reports
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• v.43 no.12
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• pp.836-841
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• 2010

In the era of personal genomics, predicting the individual response to drug-treatment is a challenge of biomedical research. The aim of this study was to validate whether interaction information between genetic and transcriptional signatures are promising features to predict a drug response. Because drug resistance/susceptibilities result from the complex associations of genetic and transcriptional activities, we predicted the inter-relationships between genetic and transcriptional signatures. With this concept, captured genetic polymorphisms and transcriptional profiles were prepared in cancer samples. By splitting ninety-nine samples into a trial set (n = 30) and a test set (n = 69), the outperformance of relationship-focused model (0.84 of area under the curve in trial set, P = $2.90{\times}10^{-4}$) was presented in the trial set and validated in the test set, respectively. The prediction results of modeling show that considering the relationships between genetic and transcriptional features is an effective approach to determine outcome predictions of drug-treatment.

• Biomolecules & Therapeutics
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• v.19 no.1
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• pp.70-76
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• 2011

Bone remodeling is a dynamic process involving a constant balance between osteoclast-induced bone resorption and osteoblast-induced bone formation. Osteoclasts play a crucial homeostatic role in skeletal modeling and remodeling, and destroy bone in many pathological conditions. Previously, we reported that the hexane soluble fraction of Ficus carica inhibited osteoclast differentiation. Poly unsaturated fatty acids, such as ethyl docosahexaenoate (E-DHA), docosahexaenoic acid (DHA), cis-11,14-eicosadienoic acid (EDA) and eicosapentaenoic acid (EPA), were identified from the hexane soluble fraction of Ficus carica. Among them, E-DHA most potently inhibited osteoclastogenesis in RAW264.7 cells. E-DHA reduced the activities of JNK and NF-$\kappa}B$. E-DHA suppressed the expression of c-Fos and nuclear factor of activated T cells c1 (NFATc1). Interestingly, DHA increased the activity of alkaline phosphatase and expression of bone morphogenetic protein 2 (BMP2) more than E-DHA in MC3T3-E1 cells, suggesting that DHA may induce osteoblast differentiation. The data suggests that a combination of E-DHA and DHA has potential use in the treatment of diseases involving abnormal bone lysis, such as osteoporosis, rheumatoid arthritis and periodontal bone erosion.