Autonomous vehicles decision-making enhancement using self-determination theory and mixed-precision neural networks

Ali, Mohammed Hasan; Jaber, Mustafa Musa; Daniel, Joseph; Vignesh, C. Chandru; Iyapparaja, M.; Kumar, B. Santhosh; Punitha, P.

doi:10.1007/s11042-023-14375-4

Cited by 15 publications

(4 citation statements)

References 59 publications

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“…Already today, machine learning is the key to the steady operation of mining equipment, which is especially important for surface mining robots, where equipment with high specific productivity is used. With the development of deep machine learning, equipment diagnostics are moving under the control of unmanned self-learning systems that receive information from smart sensors and use neural networks for analysis and decision making [111]. This forms a system of machine vision; however, machine knowledge technologies are already being formed today, which have been accumulated and improved without human participation, thus increasing the adequacy of decisions made by machines [112].…”

Section: Machine Scene Analysis and Scene Understandingmentioning

confidence: 99%

Development of Surface Mining 4.0 in Terms of Technological Shock in Energy Transition: A Review

Zhironkin

Taran

2023

Energies

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The expansion of end-to-end Industry 4.0 technologies in various industries has caused a technological shock in the mineral resource sector, wherein itsdigital maturity is lower than in the manufacturing sector. As a result of the shock, the productivity and profitability of raw materials extraction has begun to lag behind the industries of its deep processing, which, in the conditions of volatile raw materials markets, can provoke sectoral crises. The diffusion of Industry 4.0 technologies in the mining sector (Mining 4.0) can prevent a technological shock if they are implemented in all segments, including quarrying (Surface Mining 4.0). The Surface Mining 4.0 technological platform would connect the advanced achievements of the Fourth Industrial Revolution (end-to-end digital artificial intelligence technologies, cyber-physical systems and unmanned production with traditional geotechnology) without canceling them, but instead bringing them to a new level of productivity, resource consumption, and environmental friendliness. In the future, the development of Surface Mining 4.0 will provide a response to the technological shock associated with the acceleration of the digital modernization of the mining sector and the increase in labor productivity, which are reducing the operating costs of raw materials extraction. In this regard, the given review is an attempt to analyze the surface mining digital transformation over the course of the diffusion of Industry 4.0 technologies covered in scientific publications. The authors tried to show the core and frontiers of Surface Mining 4.0 development to determine the production, economic, and social effect of replacing humans with digital and cyber-physical systems in the processes of mineral extraction. Particular attention was paid to the review of research on the role of Surface Mining 4.0 in achieving sustainable development goals.

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Section: Machine Scene Analysis and Scene Understandingmentioning

confidence: 99%

Development of Surface Mining 4.0 in Terms of Technological Shock in Energy Transition: A Review

Zhironkin

Taran

2023

Energies

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“…Moreover, by combining the adaptive algorithms with the sliding mode controllers, fast convergence of the FTC in finite time has been achieved (Sadigh et al., 2023; Xia et al., 2019), and the performances of the closed‐loop FTC were correspondingly enhanced. In addition to the above methods, fault estimation observers such as augmented observer (Qian et al., 2020), learning observer (Cao et al., 2022), and extended state observer (B. Li et al., 2017), have combined with the adaptive finite time controllers to implement the adaptive finite time composite FTC (Ali et al., 2023). These finite time control methods provide excellent FTC performance for ACSs of satellites but still need to be improved.…”

Section: Introductionmentioning

confidence: 99%

“…In addition to the above methods, fault estimation observers such as augmented observer (Qian et al, 2020), learning observer (Cao et al, 2022), and extended state observer (B. Li et al, 2017), have combined with the adaptive finite time controllers to implement the adaptive finite time composite FTC (Ali et al, 2023). These finite time control methods provide excellent FTC performance for ACSs of satellites but still need to be improved.…”

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confidence: 99%

Neuro‐Adaptive Finite Time Composite Fault Tolerant Control for Attitude Control Systems of Satellites

Wang,

Liang,

Yang

et al. 2023

Radio Science

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An adaptive finite time composite fault tolerant control strategy based on an optimized neural network for Attitude control systems (ACSs) of satellites is proposed considering the state time‐varying delays, concurrent actuator and sensor faults, system uncertainties, modelable external disturbance and operating noise. An uncertain time‐varying state space model for ACSs of satellites is established, and sensor faults are equivalent to actuator‐like faults. A disturbance observer is designed for estimating the modelable external disturbance, and an improved dwarf mongoose optimization (DMO) algorithm based on the Levy flight distribution is utilized to optimize the basis function of hyperbasis function neural networks to better estimate the augmented actuator faults that include the actuator fault and the actuator‐like fault. Furthermore, an adaptive finite time composite fault‐tolerant controller is proposed, which includes the delay‐dependent feedback control law, disturbance estimation based‐disturbance compensation law and the adaptive fault compensation law based on the augmented fault estimation using the improved DMO‐hyper basis function neural network. The finite time boundness of the close‐loop dynamics to the uncertainties, operating noise, and augmented actuator faults and the robustness of the measurement to the uncertainties, operating noise and augmented actuator faults are analyzed, and the observer and controller design is formulated as the linear matrix inequalities. Simulation examples for ACSs in different working conditions are considered to exhibit the proposed method's effectiveness.

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“…A public cloud is made available to the general public on a pay-as-you-go basis, while a private cloud refers to a company's or organization's internal data centers that are not accessible to the general public. A cloud permits workloads to be easily installed and scaled owing to the fast provisioning of a virtual or physical machine [6,7]. In a cloud computing environment, multiple virtual machines (VMs) can share physical resources (CPU, memory, and bandwidth) on a single physical host, and multiple VMs can share a data center's bandwidth using network virtualization.…”

mentioning

confidence: 99%

Task Scheduling in Cloud Computing: A Priority-Based Heuristic Approach

2024

IJSREM

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With the rapid development of cloud computing, more and more enterprises would like to upload and store their data in the public cloud. When the parts of the business of an enterprise are purchased by another enterprise, the corresponding data will be transferred to the acquiring enterprise. For the usual case, how to outsource the computation cost of data transfer to the cloud? How to ensure the remote purchased data integrity? Thus, it is important to study provable data possession with outsourced data transfer (DT-PDP). For the first time, we propose the novel concept: DT-PDP. By taking use of DT- PDP, the following three security requirements can be satisfied: (1) the other un-purchased data security of acquired enterprise can be ensured; (2) the purchased data integrity and privacy can be ensured; (3) the data transferability’s computation can be outsourced to the public cloud servers. For the security concept of DT-PDP, we give its motivation, system model and security model. Then, we design a concrete DT-PDP scheme based on the bilinear pairings. At last, we analyze the security, efficiency and flexibility of the concrete DT- PDP scheme. It shows that our scheme is provably secure and efficient.

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Autonomous vehicles decision-making enhancement using self-determination theory and mixed-precision neural networks

Cited by 15 publications

References 59 publications

Development of Surface Mining 4.0 in Terms of Technological Shock in Energy Transition: A Review

Development of Surface Mining 4.0 in Terms of Technological Shock in Energy Transition: A Review

Neuro‐Adaptive Finite Time Composite Fault Tolerant Control for Attitude Control Systems of Satellites

Task Scheduling in Cloud Computing: A Priority-Based Heuristic Approach

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