The chicken industry, in which broiler chickens are bred, is the largest poultry industry in Taiwan. In a traditional poultry house, breeders must usually observe the health of the broilers in person on the basis of their breeding experience at regular times every day. When a breeder finds unhealthy broilers, they are removed manually from the poultry house to prevent viruses from spreading in the poultry house. Therefore, in this study, we designed and constructed a novel small removal system for dead chickens for Taiwanese poultry houses. In the mechanical design, this system mainly contains walking, removal, and storage parts. It comprises robotic arms with a fixed end and sweep-in devices for sweeping dead chickens, a conveyor belt for transporting chickens, a storage cache for storing chickens, and a tracked vehicle. The designed system has dimensions of approximately 1.038 × 0.36 × 0.5 m³, and two dead chickens can be removed in a single operation. The walking speed of the chicken removal system is 3.3 cm/s. In order to enhance the automation and artificial intelligence in the poultry industry, the identification system was used in a novel small removal system. The conditions of the chickens in a poultry house can be monitored remotely by using a camera, and dead chickens can be identified through deep learning based on the YOLO v4 algorithm. The precision of the designed system reached 95.24% in this study, and dead chickens were successfully moved to the storage cache. Finally, the designed system can reduce the contact between humans and poultry to effectively improve the overall biological safety.

Effective modeling and management are critical in wastewater treatment facilities since the aeration process accounts for 65–70% of the overall energy consumption. This study assesses control strategies specifically designed for different sizes of WWTP, analyzing their economic, environmental, and energy-related effects. Small WWTPs see advantages from the utilization of on/off and proportional–integral–derivative (PID) control methods, resulting in 10–25% energy savings and the reduction in dissolved oxygen (DO) levels by 5–30%. Cascade control and model predictive control (MPC) improve energy efficiency by 15–30% and stabilize DO levels by 15–35% in medium-sized WWTPs. Advanced WWTPs that utilize technologies such as MPC integrated with artificial intelligence (AI) and machine learning (ML) can decrease energy usage by 30–40% and enhance DO levels by 35–40%. Life cycle assessment (LCA) demonstrates substantial decreases in greenhouse gas (GHG) emissions: 5–20% for small, 10–25% for medium, and 30–35% for large WWTPs. These findings illustrate the feasibility and expandability of these tactics in both controlled laboratory environments and real-world situations, emphasizing the significance of customized methods for improving energy efficiency and sustainability in wastewater treatment. Subsequent investigations should prioritize integrating renewable energy sources and resolving obstacles in developing nations to enhance wastewater treatment plants’ energy efficiency and sustainability.

This study fabricated a two-dimensional pneumatic servomechanism-based mask sewing platform. The platform has a simple structure, operates quickly, and is energy-efficient. SOLIDWORKS was used to design the platform, which comprises two rod-less pneumatic cylinders, one auxiliary cylinder, and a fabric-clamping mechanism. These parts are integrated with a sewing machine. The mask sewing platform has two degrees of freedom, as indicated by a mobility analysis of its mechanism. To control the highly nonlinear and time variant pneumatic system, a mathematical model for the pneumatic servo control system was developed and then analyzed in analytical form. An intelligent parameter adjustment feature with a sliding-mode controller was formulated to control the mask sewing platform. An experimental mask sewing platform was then designed and constructed. Finally, a personal computer–based system was developed using MATLAB Simulink for real-time control of the platform. The hybrid and fifth-order paths were successfully implemented in the test rig, and this study’s platform was experimentally verified as being capable of automatically producing masks.

The study aims to integrate the image sensor for a three-axial pneumatic parallel manipulator which can pick and place objects automatically by the feature information of the image processed through the SURF algorithm. The SURF algorithm is adopted for defining and matching the features of a target object and an object database. In order to accurately mark the center of target and strengthen the feature matching results, the random sample and consensus method (RANSAC) is utilized. The ASUS Xtion Pro Live depth camera which can directly estimate the 3-D location of the target point is used in this study. A set of coordinate estimation calibrations is developed for enhancing the accuracy of target location estimation. This study also presents hand gesture recognition exploiting skin detection and noise elimination to determine the active finger count for input signals of the parallel manipulator. The end-effector of the parallel manipulator can be manipulated to the desired poses according to the measured finger count. Finally, the proposed methods are successfully to achieve the feature recognition and pick and place of the target object.

In recent years, the aging of the rural population worldwide has become a major concern, necessitating the development of agricultural automation. Pneumatic energy has emerged as a reliable and environmentally friendly option, aiding in the global effort to reduce carbon emissions. The purpose of this study is to reduce the amount of labor required for plug tray seeding by developing an automated seeder that employs a precision pneumatic servo system via the rod-less actuator with real-time trajectory tracking capabilities. The proposed seeder has a simple structure, is easy to maintain, and saves energy. It mainly consists of a rod-less pneumatic cylinder, a needle seeding mechanism, a soil drilling mechanism and a PC-based real-time controller. Mathematical models of the developed precision pneumatic plug tray seeder are analyzed and established, and an adaptive sliding mode controller is proposed. A PC-based real-time control system is developed using MATLAB/SIMULINK via an optical encoder with a sampling frequency of 1 kHz to enable the development of precise pneumatic plug tray seeder. An optical encoder is used to measure the displacement of the rod-less cylinder which represents real-time positions of the plug tray loading platform. Experiments are conducted using Brassicaceae seeds, and the rates of single seeding, multiple seeding, missed seeding and germination are carried out through manual measurement. The results indicate that the seeder exhibits satisfactory performance, with a root mean square error of less than 0.5 mm and a single-seeding rate of more than 97%. Overall, our findings provide new insights for nurseries and could contribute to the reduction in agricultural carbon emissions.