Yunshun Zhang 교수

Nonlinear energy harvesters are frequently considered in preference to linear devices because they can potentially overcome the narrow frequency bandwidth limitations inherent to linear variants; however, the possibility of variable harvesting efficiency is raised for the nonlinear case. This paper proposes a rotational energy harvester which may be fitted into an automobile tyre, with the advantage that it may broaden the rotating frequency bandwidth and simultaneously stabilise high-energy orb

The collection of clean power from ambient vibrations is considered a promising method for energy harvesting. For the case of wheel rotation, the present study investigates the effectiveness of a piezoelectric energy harvester, with the application of stochastic resonance to optimize the efficiency of energy harvesting. It is hypothesized that when the wheel rotates at variable speeds, the energy harvester is subjected to on-road noise as ambient excitations and a tangentially acting gravity for

The efficient harvesting of mechanical energy from ambient vibrations is an ongoing project. Recent research has shown that nonlinear energy harvesters can generally overcome many significant disadvantages of linear harvesters arising from their narrow bandwidth. This paper proposes an energy harvester within an automotive tire that boasts the advantages of nonlinear systems to increase the harvesting bandwidth by combining stochastic resonance with high-energy orbit oscillations. A major challe

This study reports a vibrational energy harvesting system applied for low-speed vehicle tires on the asphalt road. The systematic model was analyzed under the measured road noise, in which a cantilever beam pasted piezoelectric film and magnets with the same polarity are fabricated as a nonlinear bistable vibrating system, when vehicle travels on the asphalt road at the different speeds of 10-20 km/h. By the theoretical investigation and simulation study, in the case of combination of a periodic

Abstract Energy harvesting from rotating systems has been developed into an important topic as a promising solution for realizing the powering applications of tire monitoring systems. Because of relatively narrow bandwidth of the efficiently operating response, this paper proposes a principle for optimizing the centrifugal distance for tuning frequency matching between stochastic resonance and the external rotation environments. It can minimize the negative effect of a low energy orbit owing to

The economy and safety of passages through the urban road intersection environment is an important research topic in the field of intelligent transportation systems, but vehicle speed prediction as its subtopic is still under-researched, and its prediction accuracy is unsatisfactory. Therefore, a model for vehicle speed prediction based on the nonlinear autoregressive model with multisource exogenous inputs (NARXs) neural network is proposed. The model combines the human-vehicle-road model with

Abstract To tackle the issue of limited operating bandwidth encountered by energy harvesters in high-speed rotating contexts, this paper proposes a method for achieving rotational energy harvesting over a relatively high bandwidth through stabilizing high-energy orbit oscillations based on theoretically tailored centrifugal distance. The interaction between the cantilever beam tip permanent magnet and the fixed end magnet introduces nonlinear factors into the rotating piezoelectric energy harves

Energy harvesting from rotating system has been an important topic for realizing the applications of tire monitoring system. This paper proposes a self-tuning stochastic resonance for exploring its principle for further stabilizing the sustainable capability of energy harvesting. The principle of stochastic resonance of a nonlinear system in a rotating environment is studied which is used to increase the energy harvesting efficiency of the cantilever piezoelectric vibrator at low frequencies. Th

The interaction between vehicles and road equipment is an important section of intelligent transportation, which can help drivers know road condition in advance to maintain traffic efficiency and safety. Intersections especially unsignalized intersections are main regions occurring traffic accidents. Major part of reason is that drivers can not follow direction indicated by the traffic lights, that is to say, which drivers cannot know traffic lights in advance. And the interaction between tradit

To improve the efficiency of urban traffic systems and environments, a speed guidance model based on a predictive vehicle speed sequence is proposed in this study. The model introduces a multi-step model of vehicle speed prediction based on nonlinear autoregressive models with a multi-source exogenous inputs (NARXs) neural network, with fusion of multi-source exogenous inputs into the speed guidance model to assist the vehicles in efficiently passing the intersections. The implementation of traf