275 research outputs found
Changes of benthic macroinvertebrates in Thi Vai River and Cai Mep Estuaries under polluted conditions with industrial wastewater
The pollution on the Thi Vai River has been spreading out rapidly over the two lasted decades caused by the wastewater from the industrial parks in the left bank of Thi Vai River and Cai Mep Estuaries. The evaluation of the benthic macroinvertebrate changes was very necessary to identify the consequences of the industrial wastewater on water quality and aquatic ecosystem of Thi Vai River and Cai Mep Estuaries.
In this study, the variables of benthic macroinvertebrates and water quality were investigated in Thi Vai River and Cai Mep Estuaries, Southern Vietnam. The monitoring data of benthic macroinvertebrates and water quality parameters covered the period from 1989 to 2015 at 6 sampling sites in Thi Vai River and Cai Mep Estuaries. The basic water quality parameters were also tested including pH, dissolved oxygen (DO), total nitrogen, and total phosphorus. The biodiversity indices of benthic macroinvertebrates were applied for water quality assessment.
The results showed that pH ranged from 6.4 – 7.6 during the monitoring. The DO concentrations were in between 0.20 – 6.70 mg/L. The concentrations of total nitrogen and total phosphorous ranged from 0.03 – 5.70 mg/L 0.024 – 1.380 mg/L respectively. Macroinvertebrate community in the study area consisted of 36 species of polychaeta, gastropoda, bivalvia, and crustacea, of which, species of polychaeta were dominant in species number. The benthic macroinvertebartes density ranged from 0 – 2.746 individuals/m2 with the main dominant species of Neanthes caudata, Prionospio malmgreni, Paraprionospio pinnata, Trichochaeta carica, Maldane sarsi, Capitella capitata, Terebellides stroemi, Euditylia polymorpha, Grandidierella lignorum, Apseudes vietnamensis. The biodiversity index values during the monitoring characterized for aquatic environmental conditions of mesotrophic to polytrophic. Besides, species richness positively correlated with DO, total nitrogen, and total phosphorus. The results confirmed the advantage of using benthic macroinvertebrates and their indices for water quality assessment
Digital Twins for Marine Operations: A Brief Review on Their Implementation
While the concept of a digital twin to support maritime operations is gaining
attention for predictive maintenance, real-time monitoring, control, and
overall process optimization, clarity on its implementation is missing in the
literature. Therefore, in this review we show how different authors implemented
their digital twins, discuss our findings, and finally give insights on future
research directions.Comment: Submitte
An NMPC-ECBF Framework for Dynamic Motion Planning and Execution in vision-based Human-Robot Collaboration
To enable safe and effective human-robot collaboration (HRC) in smart
manufacturing, seamless integration of sensing, cognition, and prediction into
the robot controller is critical for real-time awareness, response, and
communication inside a heterogeneous environment (robots, humans, and
equipment). The proposed approach takes advantage of the prediction
capabilities of nonlinear model predictive control (NMPC) to execute a safe
path planning based on feedback from a vision system. In order to satisfy the
requirement of real-time path planning, an embedded solver based on a penalty
method is applied. However, due to tight sampling times NMPC solutions are
approximate, and hence the safety of the system cannot be guaranteed. To
address this we formulate a novel safety-critical paradigm with an exponential
control barrier function (ECBF) used as a safety filter. We also design a
simple human-robot collaboration scenario using V-REP to evaluate the
performance of the proposed controller and investigate whether integrating
human pose prediction can help with safe and efficient collaboration. The robot
uses OptiTrack cameras for perception and dynamically generates collision-free
trajectories to the predicted target interactive position. Results for a number
of different configurations confirm the efficiency of the proposed motion
planning and execution framework. It yields a 19.8% reduction in execution time
for the HRC task considered
Research and Development of the Pupil Identification and Warning System using AI-IoT
Currently, pupils being left in the classroom, in the house or in the car is happening a lot, causing unintended incidents. The reason is that parents or caregivers of pupils go through busy and tiring working hours, so they accidentally leave pupils in the car, indoors, or forget to pick up students at school. In this paper, we developed an algorithm to recognize students who use neural networks and warn managers, testing on a model integrated Raspberry Pi 4 kit programmed on Python in combination with cameras, sim modules, and actuators to detect and alert abandoned pupils to the manager to take timely remedial measures and avoid unfortunate circumstances. With the ability to manage students, the system collects and processes images and data on student information for artificial intelligence (AI) systems to recognize when operating. The system of executive structures serves to warn when students are left in the car, in the classroom, or in the house to avoid unintended incidents or safety risks
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