Optimal scheduling of industrial task-continuous load management for smart power utilization

In the context of climate change and energy crisis around the world, an increasing amount of attention has been paid to developing clean energy and improving energy efficiency. The penetration of distributed generation (DG) is increasing rapidly on the user’s side of an increasingly intelligent power system. This paper proposes an optimization method for industrial task-continuous load management in which distributed generation (including photovoltaic systems and wind generation) and energy storage devices are both considered. To begin with, a model of distributed generation and an energy storage device are built. Then, subject to various constraints, an operation optimization problem is formulated to maximize user profit, renewable energy efficiency, and the local consumption of distributed generation. Finally, the effectiveness of the method is verified by comparing user profit under different power modes

Learning the Relation between Similarity Loss and Clustering Loss in Self-Supervised Learning

Self-supervised learning enables networks to learn discriminative features from massive data itself. Most state-of-the-art methods maximize the similarity between two augmentations of one image based on contrastive learning. By utilizing the consistency of two augmentations, the burden of manual annotations can be freed. Contrastive learning exploits instance-level information to learn robust features. However, the learned information is probably confined to different views of the same instance. In this paper, we attempt to leverage the similarity between two distinct images to boost representation in self-supervised learning. In contrast to instance-level information, the similarity between two distinct images may provide more useful information. Besides, we analyze the relation between similarity loss and feature-level cross-entropy loss. These two losses are essential for most deep learning methods. However, the relation between these two losses is not clear. Similarity loss helps obtain instance-level representation, while feature-level cross-entropy loss helps mine the similarity between two distinct images. We provide theoretical analyses and experiments to show that a suitable combination of these two losses can get state-of-the-art results. Code is available at https://github.com/guijiejie/ICCL.Comment: This paper is accepted by IEEE Transactions on Image Processin

Convolutional neural network model by deep learning and teaching robot in keyboard musical instrument teaching.

Keyboard instruments play a significant role in the music teaching process, providing students with an enjoyable musical experience while enhancing their music literacy. This study aims to investigate the current state of keyboard instrument teaching in preschool education, identify existing challenges, and propose potential solutions using the literature review method. In response to identified shortcomings, this paper proposes integrating intelligent technology and subject teaching through the application of teaching robots in keyboard instrument education. Specifically, a Convolutional Neural Network model of Deep Learning is employed for system debugging, enabling the teaching robot to analyze students' images and movements during musical instrument play and deliver targeted teaching. Feedback from students who participated in keyboard instrument teaching with the robot indicates high satisfaction levels. This paper aims to diversify keyboard instruments' teaching mode, introduce the practical application of robots in classroom teaching, and facilitate personalized teaching catering to individual students' aptitudes

Mice Lacking the TNF 55 kDa Receptor Fail to Sleep More After TNFα Treatment

Tumor necrosis factor (TNF) is a well characterized sleep-regulatory substance. To study receptor mechanisms for the sleep-promoting effects of TNF, sleep patterns were determined in control and TNF 55 kDa receptor knock-out (TNFR-KO) mice with a B6 × 129 background after intraperitoneal injections of saline or murine TNFα. The TNFR-KO mice had significantly less baseline sleep than the controls. TNFα dose-dependently increased non-rapid eye movement sleep (NREMS) in the controls but did not influence sleep in TNFR-KO mice. Although TNFR-KO mice failed to respond to TNFα, they had an increase in NREMS and a decrease in rapid eye movement sleep after interleukin-1β treatment. These results indicate that TNFα affects sleep via the 55 kDa receptor and provide further evidence that TNFα is involved in physiological sleep regulation. Current results also extend the list of species to mice in which TNFα and interleukin-1β are somnogenic

Mice Lacking the TNF 55 kDa Receptor Fail to Sleep More After TNFα Treatment

Tumor necrosis factor (TNF) is a well characterized sleep-regulatory substance. To study receptor mechanisms for the sleep-promoting effects of TNF, sleep patterns were determined in control and TNF 55 kDa receptor knock-out (TNFR-KO) mice with a B6 × 129 background after intraperitoneal injections of saline or murine TNFα. The TNFR-KO mice had significantly less baseline sleep than the controls. TNFα dose-dependently increased non-rapid eye movement sleep (NREMS) in the controls but did not influence sleep in TNFR-KO mice. Although TNFR-KO mice failed to respond to TNFα, they had an increase in NREMS and a decrease in rapid eye movement sleep after interleukin-1β treatment. These results indicate that TNFα affects sleep via the 55 kDa receptor and provide further evidence that TNFα is involved in physiological sleep regulation. Current results also extend the list of species to mice in which TNFα and interleukin-1β are somnogenic

A Robust Optimization Strategy for Domestic Electric Water Heater Load Scheduling under Uncertainties

In this paper, a robust optimization strategy is developed to handle the uncertainties for domestic electric water heater load scheduling. At first, the uncertain parameters, including hot water demand and ambient temperature, are described as the intervals, and are further divided into different robust levels in order to control the degree of the conservatism. Based on this, traditional load scheduling problem is rebuilt by bringing the intervals and robust levels into the constraints, and are thus transformed into the equivalent deterministic optimization problem, which can be solved by existing tools. Simulation results demonstrate that the schedules obtained under different robust levels are of complete robustness. Furthermore, in order to offer users the most optimal robust level, the trade-off between the electricity bill and conservatism degree are also discussed