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The Challenges and Opportunities of Human-Centered AI for Trustworthy Robots and Autonomous Systems
Then, the challenges in implementing trustworthy autonomous system are analytically reviewed, in respects of the five key properties, and the roles of AI technologies have been explored to ensure the trustiness of RAS with respects to safety, security, health and HMI, while reflecting the requirements of ethics in the design of RAS.
Share Price Prediction of Aerospace Relevant Companies with Recurrent Neural Networks based on PCA
To improve the prediction of share price for aerospace industry sector and well understand the impact of various indicators on stock prices, we provided a hybrid prediction model by the combination of Principal Component Analysis (PCA) and Recurrent Neural Networks.
Learnability and Robustness of Shallow Neural Networks Learned With a Performance-Driven BP and a Variant PSO For Edge Decision-Making
However, how fat is an SNN enough to solve a nonlinear decision-making problem in edge devices?
A Heuristically Self-Organised Linguistic Attribute Deep Learning in Edge Computing For IoT Intelligence
In this paper, we propose a heuristic approach to constructing an LAH, embedded with LDTs for decision making or classification by utilising the distance correlations between attributes and between attributes and the goal variable.
A Novel Navigation System for an Autonomous Mobile Robot in an Uncertain Environment
In this paper, we developed a new navigation system, which detects obstacles in a sliding window with an adaptive threshold clustering algorithm, classifies the detected obstacles with a decision tree, heuristically predicts potential collision and finds optimal path with a simplified Mophin algorithm.
Robotics I.2.9
