• Safety and Health at Work
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• v.4 no.3
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• pp.149-155
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• 2013

Background: Stochastic resonance whole-body vibration training (SR-WBV) was tested to reduce work-related musculoskeletal complaints. Methods: Participants were 54 white-collar employees of a Swiss organization. The controlled crossover design comprised two groups each given 4 weeks of exercise and no training during a second 4-week period. Outcome was daily musculoskeletal well-being, musculoskeletal pain, and surefootedness. In addition, participants performed a behavioral test on body balance prior to when SR-WBV started and after 4 weeks of SR-WBV. Results: Across the 4-week training period, musculoskeletal well-being and surefootedness were significantly increased (p < 0.05), whereas musculoskeletal pain was significantly reduced only in those who reported low back pain during the last 4 weeks prior to the study (p < 0.05). Body balance was significantly increased by SR-WBV (p < 0.05). Conclusion: SR-WBV seems to be an efficient option in primary prevention of musculoskeletal complaints and falls at work.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.2
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• pp.1348-1354
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• 2015

In this paper, chaotic circuit of the voltage controlled Lorentz system for engineering applications has been designed and implemented in an electronic circuit. The proposed circuit consists of MOS variable resistor, multipliers, capacitors, fixed resistors and operational amplifiers. The circuit was analysed by PSPICE program. PSPICE simulation results show that chaotic dynamics of the circuit can be controlled by the MOS variable resistor through time series analysis, frequency analysis and phase diagrams. Also, we implemented the proposed circuit in an electronic hardware system with discrete elements. Measured results of the circuit showed controllability of the circuit using the MOS resistor.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.4
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• pp.499-505
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• 2015

Trend Impact Analysis(: TIA) is an advanced forecasting tool used in futures studies for identifying, understanding and analyzing the consequences of unprecedented events on future trends. An adaptive neuro-fuzzy inference system is a kind of artificial neural network that integrates both neural networks and fuzzy logic principles, It is considered to be a universal estimator. In this paper, we propose an advanced mechanism to generate more justifiable estimates to the probability of occurrence of an unprecedented event as a function of time with different degrees of severity using Adaptive Neuro-Fuzzy Inference System(: ANFIS). The key idea of the paper is to enhance the generic process of reasoning with fuzzy logic and neural network by adding the additional step of attributes simulation, as unprecedented events do not occur all of a sudden but rather their occurrence is affected by change in the values of a set of attributes. An ANFIS approach is used to identify the occurrence and severity of an event, depending on the values of its trigger attributes. The trigger attributes can be calculated by a stochastic dynamic model; then different scenarios are generated using Monte-Carlo simulation. To compare the proposed method, a simple simulation is provided concerning the impact of river basin drought on the annual flow of water into a lake.