• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.23-28
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• 2021

The article discusses the features of adaptation of the parameters of the physical layer of data transmission in self-organizing networks based on unmanned aerial vehicles operating in the conditions of "smart cities". The concept of cities of this type is defined, the historical path of formation, the current state and prospects for further development in the aspect of transition to "smart cities" of the third generation are shown. Cities of this type are aimed at providing more comfortable and safe living conditions for citizens and autonomous automated work of all components of the urban economy. The perspective of the development of urban mobile automated technical means of infocommunications is shown, one of the leading directions of which is the creation and active use of wireless self-organizing networks based on unmanned aerial vehicles. The advantages of using small-sized unmanned aerial vehicles for organizing networks of this type are considered, as well as the range of tasks to be solved in the conditions of modern "smart cities". It is shown that for the transition to self-organizing networks in the conditions of "smart cities" of the third generation, it is necessary to ensure the adaptation of various levels of OSI network models to dynamically changing operating conditions, which is especially important for the physical layer. To maintain an acceptable level of the value of the bit error probability when transmitting command and telemetry data, it is proposed to adaptively change the coding rate depending on the signal-to-noise ratio at the receiver input (or on the number of channel decoder errors), and when transmitting payload data, it is also proposed to adaptively change the coding rate together with the choice of modulation methods that differ in energy and spectral efficiency. As options for the practical implementation of these solutions, it is proposed to use an approach based on the principles of neuro-fuzzy control, for which examples of determining the boundaries of theoretically achievable efficiency are given.

• The Journal of Korean Physical Therapy
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• v.32 no.6
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• pp.372-377
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• 2020

Purpose: The purpose of this study was to verify the effect of applying clam exercise on improving trunk control and balance ability in stroke patients. Based on this, we tried to provide clinical information. Methods: In this study, 18 patients with chronic stroke were recruited from a rehabilitation hospital. The patients were divided into two groups: a clam exercise group (9 patients) and a control group (9 patients). After 30 minutes of neuro-development therapy, they performed clam exercise or bridge exercise for 3 weeks, 5 times a week for 30 minutes. A trunk impairment scale (TIS) and a postural assessment scale for stroke patients-trunk control (PASS-TC) were performed to evaluate the subjects' ability to control trunk before and after intervention. Balance ability was measured by Balancia before and after intervention. Results: After the training periods, area 95% COP and weight distribution of the affected side were significantly different from the clam exercise group compared to the control group (p<0.05). Conclusion: Based on the results of this study, in can be seen that the clam exercise is effective in improving the balance ability compared to the bridge exercise. Maintaining the standing posture requires muscle strength of the hip abduction and extension, which is the result of the clam exercise selectively strengthening these muscles. Therefore, if you want to provide intervention to improve the balance of stroke patients, it is recommended to perform a clam exercise.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.249-254
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• 2001

Abstract In this paper, we propose a method of cooperative control based on artifical intelligent system in distributed autonomous robotic system. In general, multi-agent behavior algorithm is simple and effective for small number of robots. And multi-robot behavior control is a simple reactive navigation strategy by combining repulsion from obstacles with attraction to a goal. However when the number of robot goes on increasing, this becomes difficult to be realized because multi-robot behavior algorithm provide on multiple constraints and goals in mobile robot navigation problems. As the solution of above problem, we propose an architecture of fuzzy system for each multi-robot speed control and fuzzy-neural network for obstacle avoidance. Here, we propose an architecture of fuzzy system for each multi-robot speed control and fuzzy-neural network for their direction to avoid obstacle. Our focus is on system of cooperative autonomous robots in environment with obstacle. For simulation, we divide experiment into two method. One method is motor schema-based formation control in previous and the other method is proposed by this paper. Simulation results are given in an obstacle environment and in an dynamic environment.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.6 s.171
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• pp.207-212
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• 2005

The purpose of this work is to develop the FES controller that can cope with the muscle fatigue which is one of the most important problems of current FES (Functional Electrical Stimulation). The feasibility of the proposed FES controller was evaluated by simulation. We used a fitness function to describe the effect of muscle fatigue and recovery process. The FES control system was developed based on the biological neuronal system. Specifically, we used PD (Proportional and Derivative) and GC (Gravity Compensation) control, which was described by the neuronal feedback structure. It was possible to control of multiple joints and muscles by using the phase-based PD and GC control method and the static optimization. As a result, the proposed FES control system could maintain the cycling motion in spite of the muscle fatigue. It is expected that the proposed FES controller will play an important role in the rehabilitation of SCI patient.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.6 no.4
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• pp.89-95
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• 1997

Welding technique is widely applied to general industry such as pressure vessel for chemical plant, pipe system, heavy industry, and automobile. There are some points which must be considered when robot system is used in welding automation process for productivity improvement. Welding quality is governed by heat input, and this quantity can be different according to shape, property, and thick of material . For desired heat input , weld input parameters such as welding voltage, current, and welding velocity must be determined with those consideration. Until now these parameters have been determined mainly by experience of operator. In this study, the size of welding zone was predicted by fuzzy rules were constructed from the relation between welding variables and weld pool size. Inverse model method which welding control input for welder is determined with optimum voltage and current by fuzzy controller is validatied by computer simulation.

• Korean Journal of Pharmacognosy
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• v.47 no.4
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• pp.319-326
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• 2016

We investigated the effect of Selaginella tamariscina extract on the learning and memory impairments in scopolamine-induced (5 mg/kg, i.p.) dementia rats. Rats treated with oral tacrin (20 mg/kg, p.o.) as positive control group and S. tamariscina extract 100, 200mg/kg, p. o. (SME 100, SME 200) as experimental group had significantly reduced scopolamine-induced memory deficits in the passive avoidance test. The acetylcholine content were paralleled the results of the behavior experiment. The acetylcholine contents of the experimental groups (SME 200 group) was higher than that of control group. We also evaluated expression of VAchT, vesicular acetylcholine transporter. SME was significantly increased VAchT expression on hippocampus of scopolamine-induced dementia rats. We suggest that S. tamariscina might exert a significantly neuro-protective effect on cognitive impairment.

• Agricultural and Biosystems Engineering
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• v.1 no.1
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• pp.22-29
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• 2000

The development of bio-response feedback control system known as the speaking plant approach has been a challenging task for plant production engineers and scientists. In order to achieve the aim of developing such a bio-response feedback control system, the primary concern should be to develop a practical non-invasive technique for monitoring plant growth. Those who are skilled in raising plants can sense whether their plants are under adequate water conditions or not, for example, by merely observing minor color and tone changes before the plants wilt. Consequently, using machine vision, it may be possible to recognize changes in indices that describe plant conditions based on the appearance of growing plants. The interpretation of image information of plants may be based on image features extracted from the original pictorial image. In this study, the performance of a finite element retina was optimized by a genetic algorithm. The optimized finite element retina was evaluated based on the performance of neural plant growth monitor that requires input data given by the finite element retina.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.65-69
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• 1992

In this paper, an implementation of neuro-controller with an application of artificial neural network for an adjustment and tuning process for the completed electronics devices is presented. Multi-layer neural network model is employed with the learning method of error back-propagation. For the intelligent control of adjustment and tuning process, the neural network emulator (NNE) and the neural network controller(NNC) are developed. Computer simulation reveals that the intelligent controllers designed can function very effectively as tools for computer aided adjustment system. The applications of the controllers to the real systems are also demonstrated.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1173-1177
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• 1990

Cerebellar Model Linear Associator Net(CMLAN), a kind of neuro-net based adaptive control function generator, was applied to the problem of direct inverse calibration of three and six d.o.f. POMA 560 robot. Since CMLAN autonomously maps and generalizes a desired system function via learning on the sampled input/output pair nodes, CMLAN allows no knowledge in system modeling and other error sources. The CMLAN based direct inverse calibration avoids the complex procedure of identifying various system parameters such as geometric(kinematic) or nongeometric(dynamic) ones and generates the corresponding desired compensated joint commands directly to each joint for given target commands in the world coordinate. The generated net outputs automatically handles the effect of unknown system parameters and dynamic error sources. On-line sequential learning on the prespecified sampled nodes requires only the measurement of the corresponding tool tip locations for three d.o.f. manipulator but location and orientation for six d.o.f. manipulator. The proposed calibration procedure can be applied to any robot.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.2
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• pp.161-164
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• 2004

The yo-yo system has been introduced as an interesting plant to demonstrate the effectiveness of intelligent controllers. Having nonlinear and asymmetric characteristics, the yo-yo plant requires a controller quite different from conventional controllers such as PID. In this paper is presented an adaptive method of controlling the yo-yo system. Fuzzy logic controller based on human expertise is referred at first. Then, an adaptive fuzzy controller which has adaptation features against the variation of plant parameters is proposed. Finally, experimental results are presented.