This work delves into the significant impact of Machine Learning (ML) on the advancement and improvement of Energy Management systems (EMS), focusing on the incorporation of renewable energy sources, smart grids, and ...
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ISBN:
(纸本)9798350361261;9798350361278
This work delves into the significant impact of Machine Learning (ML) on the advancement and improvement of Energy Management systems (EMS), focusing on the incorporation of renewable energy sources, smart grids, and the general enhancement of energy efficiency, reliability, and sustainability. The main aim of this work is to offer a detailed summary of Machine Learning technologies that can be used in modern energy systems. It explains how these technologies can improve certain tasks like load forecasting, energy optimization, predictive maintenance, fault detection and diagnosis, and incorporating renewable energy systems supported by relevant approaches and application areas. Moreover, the work examines the benefits and opportunities presented by machine learning in boosting efficiency, enhancing system stability and resilience, and contributing to environmental sustainability. In addition, it identifies challenges and outlines future research needed to facilitate the adoption of ML in energy systems. In conclusion, the study underlines the critical role of machine learning in the evolution of energy systems and underscores the importance of collaborative efforts to overcome existing challenges and fully leverage machine learning's potential in the smart energy management systems domain.
The main goal of this study is to compare the closed loop response of a DC-DC Buck Converter using different optimization strategies such as Model Predictive control (MPC) and Linear Quadratic Regulator (LQR). For a c...
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Split Learning (SL) is a promising Distributed Learning approach in electromyography (EMG) based prosthetic control, due to its applicability within resource-constrained environments. Other learning approaches, such a...
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ISBN:
(纸本)9798350386806;9798350386790
Split Learning (SL) is a promising Distributed Learning approach in electromyography (EMG) based prosthetic control, due to its applicability within resource-constrained environments. Other learning approaches, such as Deep Learning and Federated Learning (FL), provide suboptimal solutions, since prosthetic devices are extremely limited in terms of processing power and battery life. The viability of implementing SL in such scenarios is caused by its inherent model partitioning, with clients executing the smaller model segment. However, selecting an inadequate cut layer hinders the training process in SL systems. This paper presents an algorithm for optimal cut layer selection in terms of maximizing the convergence rate of the model. The performance evaluation demonstrates that the proposed algorithm substantially accelerates the convergence in an EMG pattern recognition task for improving prosthetic device control.
The intelligentcontrol of walking of humanoid robots is one of the key research directions in the field of robotics research, but the traditional motion model's constraint on the center of mass makes it difficult...
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ISBN:
(纸本)9798331530372;9798331530365
The intelligentcontrol of walking of humanoid robots is one of the key research directions in the field of robotics research, but the traditional motion model's constraint on the center of mass makes it difficult for robots to maintain walking stability and simulate human gait at the same time for its excessive attention on stability. Compared with the traditional deep reinforcement learning algorithm, the DDPG algorithm has the ability to handle continuous action space and high-dimensional state space, and has a wide application prospect in many practical problems. We proposed a bipedal robot control method based on reward function optimization to enable the robot to achieve both stable high-speed movement and human gait imitation. This paper combines the physical characteristics of the bipedal robot to establish a control system based on the DDPG algorithm. At the same time, the reward function is designed to guide the robot to learn the correct walking strategy. Through the comparative test, the weight limit ratio of each reward function for the bipedal robot to stably increase the speed is given. The simulation results show that the method proposed in this paper has good practicality and effectiveness.
This research work introduced an innovative iOS application to enhance Inventory Management through Augmented Reality (AR) technology. Leveraging VisionOS as the foundational platform, complemented by VisionPro for au...
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作者：
Krishna, Siram ChaitanyaReddy, Painti NagiKirubanantham, P.School of Computing
SRM Institute of Science and Technology Kattankulathur Department of Computing Technologies Chennai India School of Computing
College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Faculty of Engineering and Technology Department of Computing Technologies Chennai India
Today, the day-to-day generation of such a large amount of content on social media and other websites makes it impossible to maintain each image manually, so the need for automatically identifying sensitive images and...
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A complete intelligent voice-controlled home system should include voice capture, voice recognition, authentication, voice result processing, and other processes. Based on the intelligent voice interaction model of Ra...
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This study focuses on behavior patterns, which are characteristic actions common to groups or communities, to promote intercultural understanding. Understanding greeting gestures is crucial for comprehending social re...
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Unlike most human-engineered systems, biological systems are emergent from low-level interactions, allowing much broader diversity and superior adaptation to complex environments. Inspired by the process of morphogene...
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ISBN:
(纸本)9781665491907
Unlike most human-engineered systems, biological systems are emergent from low-level interactions, allowing much broader diversity and superior adaptation to complex environments. Inspired by the process of morphogenesis in nature, a bottom-up design approach for robot morphology is proposed to treat a robot's body as an emergent response to underlying processes rather than a predefined shape. This paper presents Loopy, a "Swarm-of-One" polymorphic robot testbed that can be viewed simultaneously as a robotic swarm and a single robot. Loopy's shape is determined jointly by self-organization and morphological computing using physically linked homogeneous cells. Experimental results show that Loopy can form symmetric shapes consisting of lobes. Using the same set of parameters, even small amounts of initial noise can change the number of lobes formed. However, once in a stable configuration, Loopy has an "inertia" to transfiguring in response to dynamic parameters. By making the connections among self-organization, morphological computing, and robot design, this work lays the foundation for more adaptable robot designs in the future.
Automatic access control system based on Raspberry Pi and Arduino UNO development board uses the CSI camera of Raspberry Pi to capture the face information, compare it with the face database, judge whether it meets th...
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