Probabilistic prediction of cyanobacteria abundance in a Korean reservoir using a Bayesian Poisson model

There have been increasing reports of harmful algal blooms (HABs) worldwide. However, the factors that influence cyanobacteria dominance and HAB formation can be site‐specific and idiosyncratic, making prediction challenging. The drivers of cyanobacteria blooms in Lake Paldang, South Korea, the summer climate of which is strongly affected by the East Asian monsoon, may differ from those in well‐studied North American lakes. Using the observational data sampled during the growing season in 2007–2011, a Bayesian hurdle Poisson model was developed to predict cyanobacteria abundance in the lake. The model allowed cyanobacteria absence (zero count) and nonzero cyanobacteria counts to be modeled as functions of different environmental factors. The model predictions demonstrated that the principal factor that determines the success of cyanobacteria was temperature. Combined with high temperature, increased residence time indicated by low outflow rates appeared to increase the probability of cyanobacteria occurrence. A stable water column, represented by low suspended solids, and high temperature were the requirements for high abundance of cyanobacteria. Our model results had management implications; the model can be used to forecast cyanobacteria watch or alert levels probabilistically and develop mitigation strategies of cyanobacteria blooms. Key Points A Bayesian hurdle Poisson model predicted cyanobacteria abundance Temperature, flushing rate, and water column stability were key factors The model forecasted cyanobacteria watch and alert levels probabilisticallyPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106958/1/wrcr20820.pd

An Indoor Multi-Environment Sensor System Based on Intelligent Edge Computing

Monitoring and predicting the environment in an indoor space plays an important role in securing big data and detecting abnormal conditions in the industrial environment and living space. This study proposes an indoor multi-environment sensor system based on intelligent edge computing that collects and predicts environmental data. The system collects data using 14 types of environmental sensors and object detection technology models and implements a model that predicts indoor air quality based on the bi-directional LSTM network. The trained model shows high performance in predicting indoor air quality (IAQ) factors, such as CO2, PM2.5, and total volatile organic compounds (TVOC). The indoor multi-environment sensor system based on intelligent edge computing is available for data collection and environmental prediction in various spaces without restrictions on specific locations. This study proposes an integrated approach with various functions by applying edge computing to indoor environment monitoring. We verify the proposed system through various experiments

Path Planning for Multi-Arm Manipulators Using Soft Actor-Critic Algorithm with Position Prediction of Moving Obstacles via LSTM

This paper presents a deep reinforcement learning-based path planning algorithm for the multi-arm robot manipulator when there are both fixed and moving obstacles in the workspace. Considering the problem properties such as high dimensionality and continuous action, the proposed algorithm employs the SAC (soft actor-critic). Moreover, in order to predict explicitly the future position of the moving obstacle, LSTM (long short-term memory) is used. The SAC-based path planning algorithm is developed using the LSTM. In order to show the performance of the proposed algorithm, simulation results using GAZEBO and experimental results using real manipulators are presented. The simulation and experiment results show that the success ratio of path generation for arbitrary starting and goal points converges to 100%. It is also confirmed that the LSTM successfully predicts the future position of the obstacle

Adaptive Discount Factor for Deep Reinforcement Learning in Continuing Tasks with Uncertainty

Reinforcement learning (RL) trains an agent by maximizing the sum of a discounted reward. Since the discount factor has a critical effect on the learning performance of the RL agent, it is important to choose the discount factor properly. When uncertainties are involved in the training, the learning performance with a constant discount factor can be limited. For the purpose of obtaining acceptable learning performance consistently, this paper proposes an adaptive rule for the discount factor based on the advantage function. Additionally, how to use the advantage function in both on-policy and off-policy algorithms is presented. To demonstrate the performance of the proposed adaptive rule, it is applied to PPO (Proximal Policy Optimization) for Tetris in order to validate the on-policy case, and to SAC (Soft Actor-Critic) for the motion planning of a robot manipulator to validate the off-policy case. In both cases, the proposed method results in a better or similar performance compared with cases using the best constant discount factors found by exhaustive search. Hence, the proposed adaptive discount factor automatically finds a discount factor that leads to comparable training performance, and that can be applied to representative deep reinforcement learning problems

Motion Planning of Robot Manipulators for a Smoother Path Using a Twin Delayed Deep Deterministic Policy Gradient with Hindsight Experience Replay

In order to enhance performance of robot systems in the manufacturing industry, it is essential to develop motion and task planning algorithms. Especially, it is important for the motion plan to be generated automatically in order to deal with various working environments. Although PRM (Probabilistic Roadmap) provides feasible paths when the starting and goal positions of a robot manipulator are given, the path might not be smooth enough, which can lead to inefficient performance of the robot system. This paper proposes a motion planning algorithm for robot manipulators using a twin delayed deep deterministic policy gradient (TD3) which is a reinforcement learning algorithm tailored to MDP with continuous action. Besides, hindsight experience replay (HER) is employed in the TD3 to enhance sample efficiency. Since path planning for a robot manipulator is an MDP (Markov Decision Process) with sparse reward and HER can deal with such a problem, this paper proposes a motion planning algorithm using TD3 with HER. The proposed algorithm is applied to 2-DOF and 3-DOF manipulators and it is shown that the designed paths are smoother and shorter than those designed by PRM

Implementation of Hemispherical Resonator Gyroscope with 3 × 3 Optical Interferometers for Analysis of Resonator Asymmetry

A hemispherical resonator gyroscope (HRG) has been implemented by using a consumer wineglass as the resonator and 3 × 3 optical interferometers as the detectors. The poorness of the off-the-shelf wineglass as the resonator can be overcome by the high performance of the optical interferometer. The effects of asymmetries in stiffness and absorption of the resonator are analyzed theoretically and confirmed experimentally. We prove that the trace of the amplitude ratio of two n = 2 fundamental resonant modes of the resonator follows a straight line in a complex plane. By utilizing the straightness of the ratio and the high performance of the optical interferometer, we extract four real constant parameters characterizing the HRG system. Experimentally, by using a resonator having an average resonance frequency of 444 Hz and Q value of 1477.2, it was possible to measure the Coriolis force at the level of industrial grade. The bias stability was measured as small as 2.093°/h

Optical diagnosis of gastric tissue biopsies with Mueller microscopy and statistical analysis

We investigate a possibility of producing the quantitative optical metrics to characterize the evolution of gastric tissue from healthy conditions via inflammation to cancer by using Mueller microscopy of gastric biopsies, regression model and statistical analysis of the predicted images. For this purpose the unstained sections of human gastric tissue biopsies at different pathological conditions were measured with the custom-built Mueller microscope. Polynomial regression model was built using the maps of transmitted intensity, retardance, dichroism and depolarization to generate the predicted images. The statistical analysis of predicted images of gastric tissue sections with multi-curve fit suggests that Mueller microscopy combined with data regression and statistical analysis is an effective approach for quantitative assessment of the degree of inflammation in gastric tissue biopsies with a high potential in clinical applications

Spatiotemporal Protein Variations Based on VIIRS-Derived Regional Protein Algorithm in the Northern East China Sea

Over the past two decades, the environmental characteristics of the northern East China Sea (NECS) that make it a crucial spawning ground for commercially significant species have faced substantial impacts due to climate change. Protein (PRT) within phytoplankton, serving as a nitrogen-rich food for organisms of higher trophic levels, is a sensitive indicator to environmental shifts. This study aims to develop a regional PRT algorithm to characterize spatial and temporal variations in the NECS from 2012 to 2022. Employing switching chlorophyll-a and particulate organic nitrogen algorithms, the developed regional PRT algorithm demonstrates enhanced accuracy. Satellite-estimated PRT concentrations, utilizing data from the Visible Infrared Imaging Radiometer Suite (VIIRS), generally align with the 1:1 line when compared to in situ data. Seasonal patterns and spatial distributions of PRT in both the western and eastern parts of the NECS from 2012 to 2022 were discerned, revealing notable differences in the spatial distribution and major controlling factors between these two areas. In conclusion, the regional PRT algorithm significantly improves estimation precision, advancing our understanding of PRT dynamics in the NECS concerning PRT concentration and environmental changes. This research underscores the importance of tailored algorithms in elucidating the intricate relationships between environmental variables and PRT variations in the NECS