Abstract: An information handling system may include at least one processor and a memory. The information handling system may be configured to: receive telemetry information regarding a target workload; receive configuration data regarding a computing cluster that is to execute a simulation of the target workload; train a workload artificial intelligence (AI) model based on the telemetry information and the configuration data to create the simulation of the target workload; generate a benchmarking configuration file based on the workload AI model; and deploy the benchmarking configuration file to the computing cluster for execution.

Abstract: An information handling system may include at least one processor and a memory. The information handling system may be configured to: receive a request to create a plurality of virtual computing nodes; build at least one parent virtual computing node based on an operating system image; create a plurality of clone virtual computing nodes by copying the at least one parent virtual computing node; for each of the plurality of clone virtual computing nodes, reconfigure at least one setting of the clone virtual computing node, such that none of the reconfigured clone virtual computing nodes are identical to one another; and run the plurality of reconfigured clone virtual computing nodes.

Abstract: Disclosed systems and methods align virtual machines (VMs) with hyperconverged infrastructure (HCl)-based hardware based on a determination of the VM's characteristic type. Each cluster node may be provisioned with a specific combination of hardware. In some embodiments, disclosed methods include determining, for each of the plurality of nodes, a target workload type based at least in part on the combination of information handling resources provisioned on each node. Disclosed methods may manage deployment of the VMs among the cluster nodes based on one or more factors including a workload compatibility factor determined in accordance with a workload type of each VM and a target workload type of each node, where the target workload type may reflect a hardware characteristic of the node.

Abstract: An information handling system may include at least one processor and a memory. The information handling system may be configured to: receive configuration data and evaluation data regarding a target information handling system; train an artificial intelligence (AI) model based on the configuration data and evaluation data; receive information regarding a desired workload for the target information handling system; and predict, based on the AI model, whether or not the target information handling system will be able to satisfy the desired workload.

Abstract: An intelligent ball passing system is provided, including: a ball passing robot configured for carrying out a ball passing action according to human body postures, including: a video capture device configured for acquiring human body posture data, a movement mechanism configured for driving the ball passing robot to move according to a ball passing instruction, and a central processing module connected to the movement mechanism and the video capture device respectively; and a software system configured for controlling the ball passing robot to carry out the ball passing action, the central processing module recognizing and classifying the human body postures by using the software system that uses a BP neural network.

Abstract: Disclosed is a method for analyzing dynamic characteristics of EEG functional connectivity related to driving fatigue including: using independent component analysis and wavelet packet transformation to preprocess EEG data; constructing the preprocessed EEG data into a temporal brain network with dynamic characteristics based on a sliding window method; measuring a spatiotemporal topology of the temporal brain network based on a temporal efficiency analysis framework; and performing statistical analysis on the spatiotemporal topology of the temporal brain network to obtain a correlation between behaviors related to driving fatigue and dynamic characteristics of the temporal brain network.

Abstract: Disclosed is a method for identifying driving fatigue based on a CNN-LSTM deep learning model including: collecting electroencephalograph signals of a subject during simulated driving; randomly issuing an operating command during simulated driving, and dividing the electroencephalograph signals into fatigue data and non-fatigue data according to a reaction time for the subject to complete the operating command; performing band-pass filtering and mean removal preprocessing on the electroencephalograph signals, and respectively extracting N minutes of fatigue electroencephalograph signal data and N minutes of non-fatigue electroencephalograph signal data to be detected; performing independent component analysis on the electroencephalograph signal data to remove interference signals; establishing a CNN-LSTM model and setting network parameters of the CNN-LSTM model; transmitting the electroencephalograph signal data with interference signals removed to a CNN network for feature extraction; and reshaping data of

Abstract: An intelligent ball passing system is provided, including: a ball passing robot configured for carrying out a ball passing action according to human body postures, including: a video capture device (1) configured for acquiring human body posture data, a movement mechanism (2) configured for driving the ball passing robot to move according to a ball passing instruction, and a central processing module (3) connected to the movement mechanism (2) and the video capture device (1) respectively; and a software system configured for controlling the ball passing robot to carry out the ball passing action, the central processing module recognizing and classifying the human body postures by using the software system that uses a BP neural network.