Application of Path Planning and Obstacle Avoidance for Riverbank Inspection.

Most coastal trash comes from land. To prevent and control ocean pollution, it should be handled using sources that can maintain a clean ocean and improve the marine ecological environment. The proposed system can be used to inspect riverbanks and identify garbage on riverbanks. This waste can then be cleaned up before flowing into the sea. In this study, we utilized an unmanned aerial vehicle (UAV) to inspect riverbanks and applied path planning and obstacle avoidance to enhance the efficiency of UAV performance and ensure good adaptability in a complicated environment. Since most rivers in the middle and upper sections of the study area are rough and meandering, path planning was first addressed so that the drone could use the shortest path and less energy to perform the inspection task. Branches frequently protrude from the riverbank on both sides. Therefore, an instant obstacle avoidance algorithm was added to avoid various obstacles. Path planning was based on an Improved Particle Swarm Optimization (IPSO). A fuzzy system was added to the IPSO to adjust the parameters that could shorten the planned path. The Artificial Potential Field (APF) was applied for real-time dynamic obstacle avoidance. The proposed UAV system could be used to perform riverbank inspection successfully.

Application of image identification to UAV control for cage culture.

The purpose of this study was to save manpower and reduce costs on water quality measurement in cage culture. An unmanned aerial vehicle system was applied to locate the target net cage and detect the water quality and temperature in the desired cage automatically. This paper presents the use of image recognition and deep learning to find a predefined target location of cage aquaculture. The whole drone control and image recognition process was based on an onboard computer and was successfully realized in an actual environment. When the drone approached the net cage, image recognition was utilized to fix the position of the unmanned aerial vehicle on the net cage and drop a sensor to check the water quality. The proposed system could improve conventional manned measurement methods and reduce the costs of cage culture.

Associations between Water Quality Measures and Chronic Kidney Disease Prevalence in Taiwan.

To determine the relationships between exposure to environmental contaminants in water and chronic kidney disease (CKD), we investigated the associations of 61 water attributes with the prevalence of CKD and End-Stage Renal Disease (ESRD) using data from 2005 to 2011 from all 22 counties and cities in the main island of Taiwan. We acquired patient information from the Taiwan Longitudinal Health Insurance Database to calculate the age-standardized CKD and ESRD prevalence rates and linked the patients' residences to the water quality monitoring data, which were sampled periodically for a total of over 45,000 observations obtained from the Taiwan Environmental Water Quality Information Database. The association analysis adjusting for gender, age, and annual effects showed that the zinc (Zn), ammonia, chemical oxygen demand (COD), and dissolved oxygen in rivers were weakly correlated with CKD (τ = 0.268/0.250/0.238/-0.267, p = 6.01×10-6/2.52×10-5/6.05×10-5/3.30×10-5, respectively), but none for ESRD. The importances of Zn and COD in rivers were also demonstrated in a CKD regression model. Moreover, an unusually high CKD prevalence was related to arsenic contamination in groundwater. A further prospective cohort study would improve our understanding of what level of environmental water with risky properties could affect the development of CKD.

A hybrid intelligent controller for a twin rotor MIMO system and its hardware implementation.

This paper presents a fuzzy PID control scheme with a real-valued genetic algorithm (RGA) to a setpoint control problem. The objective of this paper is to control a twin rotor MIMO system (TRMS) to move quickly and accurately to the desired attitudes, both the pitch angle and the azimuth angle in a cross-coupled condition. A fuzzy compensator is applied to the PID controller. The proposed control structure includes four PID controllers with independent inputs in 2-DOF. In order to reduce total error and control energy, all parameters of the controller are obtained by a RGA with the system performance index as a fitness function. The system performance index utilized the integral of time multiplied by the square error criterion (ITSE) to build a suitable fitness function in the RGA. A new method for RGA to solve more than 10 parameters in the control scheme is investigated. For real-time control, Xilinx Spartan II SP200 FPGA (Field Programmable Gate Array) is employed to construct a hardware-in-the-loop system through writing VHDL on this FPGA.