• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.154.1-154
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• 2001

The paper describes a new type of robot called "artificial liferobot" which is able to learn, make decisions, and behave by itself based on a brain-type computing technique called "artificial brain". The artificial liferobot has self-learning ability from the environment by the interactions between human being and it. The artificial brain makes the artificial liferobot to behave by itself with its intensions like living things as human being. We briefly introduce one attempt of our researches for developing cognitive and behavioral intelligent artificial liferobot in out laboratory. One of our purposes is the development of the artificial liferobot, which plays an Important role in taking care of elderly and infirm people in a rapidly aging society.

• The Journal of Korea Robotics Society
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• v.1 no.2
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• pp.163-171
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• 2006

This paper introduces the research progress on the artificial brain in the Telerobotics and Control Laboratory at KAIST. This series of studies is based on the assumption that it will be possible to develop an artificial intelligence by copying the mechanisms of the animal brain. Two important brain mechanisms are considered: spike-timing dependent plasticity and dopaminergic plasticity. Each mechanism is implemented in two coding paradigms: spike-codes and rate-codes. Spike-timing dependent plasticity is essential for self-organization in the brain. Dopamine neurons deliver reward signals and modify the synaptic efficacies in order to maximize the predicted reward. This paper addresses how artificial intelligence can emerge by the synergy between self-organization and reinforcement learning. For implementation issues, the rate codes of the brain mechanisms are developed to calculate the neuron dynamics efficiently.

• Electronics and Telecommunications Trends
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• v.33 no.1
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• pp.1-10
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• 2018

Artificial intelligence, which is based on deep learning, is emerging as a fundamental technology that will bring about future social changes. Artificial intelligence technology in IT is an essential intelligent system, and will overcome the performance limit of computing systems, and is expected to be the foundation for the development of computing environment destructively. The development of artificial intelligence technology in developed countries is a direction toward convergence with brain science. In this article, we will look at the prospect of artificial intelligence as the manifestation of imagination, as well as the technology and policy trends of artificial intelligence both at home and abroad, and discuss the direction of future technology development in terms of fusion with brain science.

• Journal of Biomedical Engineering Research
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• v.26 no.6
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• pp.393-398
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• 2005

An electromechanical type is the most useful mechanism in the various pumping mechanisms. It, however, requires a movement converting system including a ball screw, a helical cam, or a solenoid-beam spring, which makes the device complex and may lessen reliability. Thus, the authors have hypothesized that an electromagnetic actuator mechanism can eliminate the movement converting system and that thereby enhance the mechanical reliability and operative simplicity of an electro­pneumatic pump. The purpose of this study was to show a novel application of electromagnetic actuator mechanism in pulsatile pump and to provide preliminary data for further evaluations. The electromagnetic actuator consists of stators with a single winding excitation coil and movers with a high energy density neodymium-iron-boron permanent magnet. A 0.5mm diameter wire was used for the excitation coil, and 1000 turns were wound onto the stators core with parallel. A prototype of extracorporeal electro-pneumatic pump was constructed, and the pump performance tests were performed using a mock system to evaluate the efficiency of the electromagnetic actuator mechanism. When forward and backward electric currents were supplied to the excitation coil, the mover effectively moved back and forth. The nominal stroke length of the actuator was 10mm. The actuator dimension was 120mm in diameter and 65mm in height with a mass of 1.4kg. The prototype pump unit was 150mm in diameter, 150mm in thickness and 4.5kg in weight. The maximum force output was 70N at input current of 4.5A and the maximum pump rate was 150 beats per minute. The maximum output was 2.0 L/minute at a rate of 80bpm when the afterload was 100mmHg. The electromagnetic actuator mechanism was successfully applied to construct the prototype of extracorporeal electro­pneumatic pump. The authors provide the above results as a preliminary data for further studies.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.181-184
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• 1998

CAM-Brain is a model to develop neural networks based in cellular automata by evolution, and finally aims at a model as and artificial brain,. In order to show the feasibility of evolutionary engineering to develop an artificial brain we have attempted to evolve a module of CAM-Brain for the problem to control a mobile robot, In this paper, we present some recent results obtained by analyzing the behaviors of the evolved neural module. Several experiments reveal a couple of problems that should be solved when CAM-Brain evolves to control a mobile robot. so that some modification of the original model is proposed to solve them. The modified CAM-Brain has evolved to behave well in a simulated environment, and a thorough analysis proves the power of evolution.

• Electronics and Telecommunications Trends
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• v.36 no.3
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• pp.106-118
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• 2021

The field of brain science (or neuroscience in a broader sense) has inspired researchers in artificial intelligence (AI) for a long time. The outcomes of neuroscience such as Hebb's rule had profound effects on the early AI models, and the models have developed to become the current state-of-the-art artificial neural networks. However, the recent progress in AI led by deep learning architectures is mainly due to elaborate mathematical methods and the rapid growth of computing power rather than neuroscientific inspiration. Meanwhile, major limitations such as opacity, lack of common sense, narrowness, and brittleness have not been thoroughly resolved. To address those problems, many AI researchers turn their attention to neuroscience to get insights and inspirations again. Biologically plausible neural networks, spiking neural networks, and connectome-based networks exemplify such neuroscience-inspired approaches. In addition, the more recent field of brain network analysis is unveiling complex brain mechanisms by handling the brain as dynamic graph models. We argue that the progress toward the human-level AI, which is the goal of AI, can be accelerated by leveraging the novel findings of the human brain network.

• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1233-1241
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• 2022

Tumors of the brain are the deadliest, with a life expectancy of only a few years for those with the most advanced forms. Diagnosing a brain tumor is critical to developing a treatment plan to help patients with the disease live longer. A misdiagnosis of brain tumors will lead to incorrect medical treatment, decreasing a patient's chance of survival. Radiologists classify brain tumors via biopsy, which takes a long time. As a result, the doctor will need an automatic classification system to identify brain tumors. Image classification is one application of the deep learning method in computer vision. One of the deep learning's most powerful algorithms is the convolutional neural network (CNN). This paper will introduce a novel deep learning structure and image gradient to classify brain tumors. Meningioma, glioma, and pituitary tumors are the three most popular forms of brain cancer represented in the Figshare dataset, which contains 3,064 T1-weighted brain images from 233 patients. According to the numerical results, our method is more accurate than other approaches.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.201-206
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• 1996

This paper presents a new information processing machine which is called artificial brain(ABrain) and considers the structure of artificial neural networks constructed in a RICOH neurocomputer RN-2000 in the ABrain, in order to track given trajectories which are produced in a micro-computer or a moving light by hand in a recognition and tracking system.

• International Journal of Computer Science & Network Security
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• v.23 no.2
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• pp.47-54
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• 2023

This article reviews recent advances and perspectives in neuroscience related to consciousness, cognition, and neural networks in the brain. The neural mechanisms underlying cognitive processes, such as perception, attention, memory, and decision-making, are explored. The article also examines how these processes give rise to our experience of consciousness. The implications of these findings for our understanding of the brain and its functions are presented, as well as potential applications of this knowledge in fields such as medicine, psychology, and artificial intelligence. Additionally, the article explores the concept of a quantum viewpoint concerning consciousness, cognition, and creativity and how incorporating DNA as a key element could reconcile classical and quantum perspectives on human behaviour, consciousness, and cognition, as explained by genomic psychological theory. Furthermore, the article explains how the human brain processes external stimuli through the sensory nervous system and how it can be simulated using an artificial neural network (ANN) consisting of one input layer, multiple hidden layers, and an output layer. The law of learning is also discussed, explaining how ANNs work and how the modification of weight values affects the output and input values. The article concludes with a discussion of future research directions in this field, highlighting the potential for further discoveries and advancements in our understanding of the brain and its functions.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.73-88
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• 2023

Brain tumors can also be an abnormal collection or accumulation of cells in the brain that can be life-threatening due to their ability to invade and metastasize to nearby tissues. Accurate diagnosis is critical to the success of treatment planning, and resonant imaging is the primary diagnostic imaging method used to diagnose brain tumors and their extent. Deep learning methods for computer vision applications have shown significant improvements in recent years, primarily due to the undeniable fact that there is a large amount of data on the market to teach models. Therefore, improvements within the model architecture perform better approximations in the monitored configuration. Tumor classification using these deep learning techniques has made great strides by providing reliable, annotated open data sets. Reduce computational effort and learn specific spatial and temporal relationships. This white paper describes transfer models such as the MobileNet model, VGG19 model, InceptionResNetV2 model, Inception model, and DenseNet201 model. The model uses three different optimizers, Adam, SGD, and RMSprop. Finally, the pre-trained MobileNet with RMSprop optimizer is the best model in this paper, with 0.995 accuracies, 0.99 sensitivity, and 1.00 specificity, while at the same time having the lowest computational cost.