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Understanding the dynamics between the East Asian summer (EASM) and winter monsoon (EAWM) is needed to predict their variability under future global warming scenarios. Here, we investigate the relationship between EASM and EAWM as well as the mechanisms driving their variability during the last 10,000 years by stacking marine and terrestrial (non-speleothem) proxy records from the East Asian realm. This provides a regional and proxy independent signal for both monsoonal systems. The respective signal was subsequently analysed using a linear regression model. We find that the phase relationship between EASM and EAWM is not time-constant and significantly depends on orbital configuration changes. In addition, changes in the Atlantic Meridional Overturning circulation, Arctic sea-ice coverage, El Niño-Southern Oscillation and Sun Spot numbers contributed to millennial scale changes in the EASM and EAWM during the Holocene. We also argue that the bulk signal of monsoonal activity captured by the stacked non-speleothem proxy records supports the previously argued bias of speleothem climatic archives to moisture source changes and/or seasonality.

The tail-flapping propulsion of a robotic fish forms a hydrodynamic pressure field that depends primarily on the flapping frequency and amplitude. In a two-robot aligned group, the tail of the front robot generates an oscillating pressure that is detectable by its follower. This paper proposes a position estimator for the follower to locate the position of the leading robotic fish. The position estimator uses the hydrodynamic pressure measured on a sensor array installed on the forefront of the following vehicle body. We derive a potential flow model to describe the pressure field of the leader in the presence of the follower. Using this pressure field model, we further derive an observability measure which is used to determine the relative positions of the leader and follower for which the position estimator will produce a reliable estimate. The position estimator employs the Levenberg–Marquardt algorithm, due to the nonlinearity of the pressure model. Results from the observability analysis show that a satisfactory estimation of the leader position is achieved when the leader is located directly ahead, on the starboard-bow, or the port-bow of the follower, similar to the formation pattern generally found in a school of fish. The observability analysis also shows that poor estimation is obtained when the leader is abeam of the follower. Tank experiments confirm the observability analysis and also demonstrate the use of the position estimator for feedback control by the follower.

This work develops a method for controlling a robotic fish, as it swims in a school, by following periodic stimuli generated by a neighboring robot. To track the motion of a periodically oscillating neighboring source, the hydrodynamic pressure around the body of the robotic fish is measured by a PVDF sensor attached to the robot. The pressure induced by the source is obtained by subtracting the predicted pressure generated by the robotic fish from the measured pressure. The controlled fish tail mechanism is represented as an oscillator, and a moving oscillating sphere is considered to be an external source. A dipole model based on the potential flow theory is used to predict the hydrodynamic pressure close to the fish tail. The relative phase between the tail and the oscillating source is estimated from the pressure measurements of the PVDF sensor. The difference in phase angle between the tail and the oscillating source is used to determine the torque used to drive the tail mechanism. The proposed method is demonstrated by the tank experiments that involve a captured model of a robotic fish that swims close to an oscillating source.

This paper presents a method to control a robotic fish to swim along a straight wall by measuring nearby hydrodynamic pressure variations. This robotic fish is built for exhibiting in a museum, and is expected to swim back and forth along the boundary of an aquarium. Transparent glass walls of the aquarium are not easy to detect for the robotic fish using the optical camera. In nature, fish can detect pressure variations around their bodies to extract near-field information. This concept is adopted for a fish-like swimming mechanism. In this study, the tail of the robot is modeled as an oscillating dipole in a 2-D potential flow, and the wall effect is described by an image dipole on the opposite side of the wall. Based on this model, the distance and angle of the robot with respect to the wall are derived from the magnitudes and ratios of pressure variations measured by pressure sensors distributed on the head and the body of the robot. The pressure information is then fed back to control the orientation of the robot. Experimental results show that the robotic fish is able to swim with a distance-to-wall/tail-span ratio of 1-1.33 beside a wall. The wall effect decreases as the ratio increases. The proposed technique can be used to navigate a robotic fish with a close distance along a wall.

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This work controlled a robotic fish swimming alongside a wall. Research shows that fish swimming alongside a wall use lateral lines to obtain nearfield information. This concept is also applicable in man-made underwater vehicles such as robotic fish, which mimic the swimming action of a fish. Pressure sensors on the surface of a robotic fish measure hydrodynamic pressure variation. According to two-dimensional potential flow theory and the image method, the pressure variation caused by a robotic fish swimming near a wall is related to the relative angle and distance between the robot and the wall. Thus, the pressure signals can be used as feedback and inputs for controlling the direction in which the robot swims. The experimental results of this study show that a robotic fish 90 cm long can swim alongside a straight wall approximately 30–40 cm away (0.33–0.44 body length, 1–1.33 tail fin height). This method can also be combined with a well-designed strategy for using a robotic fish to swim next to an underwater structure to inspect details of structure parts.

The work describes a compliance control scheme for the caudal joint motion of a biomimetic autonomous underwater vehicle (BAUV). The purpose of the control method is to use the motor power more effectively for propulsion. A symmetric foil executing large-amplitude sway and yaw motions in a flow imitates a flapping tail fin which is used as the propulsive device of BAUV. Motions of the oscillating foil are then actuated by motors through springs. A control method was derived for the determination of the spring compliance for better use of the motors’ driving power. It is verified that the compliance control method can reduce the amount of energy for the foil propulsion and is beneficial for the energy saving design for future BAUVs.