Predicting Network Controllability Robustness: A Convolutional Neural Network Approach

Network controllability measures how well a networked system can be controlled to a target state, and its robustness reflects how well the system can maintain the controllability against malicious attacks by means of node-removals or edge-removals. The measure of network controllability is quantified by the number of external control inputs needed to recover or to retain the controllability after the occurrence of an unexpected attack. The measure of the network controllability robustness, on the other hand, is quantified by a sequence of values that record the remaining controllability of the network after a sequence of attacks. Traditionally, the controllability robustness is determined by attack simulations, which is computationally time consuming. In this paper, a method to predict the controllability robustness based on machine learning using a convolutional neural network is proposed, motivated by the observations that 1) there is no clear correlation between the topological features and the controllability robustness of a general network, 2) the adjacency matrix of a network can be regarded as a gray-scale image, and 3) the convolutional neural network technique has proved successful in image processing without human intervention. Under the new framework, a fairly large number of training data generated by simulations are used to train a convolutional neural network for predicting the controllability robustness according to the input network-adjacency matrices, without performing conventional attack simulations. Extensive experimental studies were carried out, which demonstrate that the proposed framework for predicting controllability robustness of different network configurations is accurate and reliable with very low overheads.Comment: 11 pages, 7 figure

Photomolecular Effect: Visible Light Interaction with Air-Water Interface

Although water is almost transparent to visible light, we demonstrate that the air-water interface interacts strongly with visible light via what we hypothesize as the photomolecular effect. In this effect, transverse-magnetic polarized photons cleave off water clusters from the air-water interface. We use over 10 different experiments to demonstrate the existence of this effect and its dependence on the wavelength, incident angle and polarization of visible light. We further demonstrate that visible light heats up thin fogs, suggesting that this process can impact weather, climate, and the earth's water cycle. Our study suggests that the photomolecular effect should happen widely in nature, from clouds to fogs, ocean to soil surfaces, and plant transpiration, and can also lead to new applications in energy and clear water

Experimental Study of LiCl/LiBr-Zeolite Composite Adsorbent for Thermochemical Heat Storage

Adsorption-based thermochemical heat storage is a promising long-term energy storage technology that can be used for seasonal space heating, which has received significant amount of efforts on the research and development. In this paper, the heat storage capacity of composite adsorbents made by LiCl + LiBr salt and 3A zeolite was investigated. The basic characteristics of composite material groups were experimentally tested, and it was found that the adsorption composite with 15 wt% salt solution had excellent adsorption rate and adsorption capacity, which was considered as the optimal composite material. Furthermore, the heat storage density of the composite material could be as high as 585.3 J/g, which was 30.9% higher than that of pure zeolite. Using 3 kg of the composite material, the adsorption heat storage experiment was carried out using a lab-scale reactor. The effects of air velocity and relative humidity on the adsorption performance were investigated. It was found that a flow rate of 15 m3/h and a relative humidity of 70% led to the most released adsorption heat from the composite material, and 74.3% of energy discharge efficiency. Furthermore, an adsorption heat storage system and a residential model were built in the TRNSYS software to evaluate the building heating effect of such heat storage system. It is found that the ambient temperature will affect the heating effect of the adsorption heat storage system. The coefficient of performance (COP) of this model is as high as 6.67. Compared with the gas boiler heating system, the adsorption heat storage energy can replace part of the gas consumption to achieve energy savings

An Implementation of Multimodal Fusion System for Intelligent Digital Human Generation

With the rapid development of artificial intelligence (AI), digital humans have attracted more and more attention and are expected to achieve a wide range of applications in several industries. Then, most of the existing digital humans still rely on manual modeling by designers, which is a cumbersome process and has a long development cycle. Therefore, facing the rise of digital humans, there is an urgent need for a digital human generation system combined with AI to improve development efficiency. In this paper, an implementation scheme of an intelligent digital human generation system with multimodal fusion is proposed. Specifically, text, speech and image are taken as inputs, and interactive speech is synthesized using large language model (LLM), voiceprint extraction, and text-to-speech conversion techniques. Then the input image is age-transformed and a suitable image is selected as the driving image. Then, the modification and generation of digital human video content is realized by digital human driving, novel view synthesis, and intelligent dressing techniques. Finally, we enhance the user experience through style transfer, super-resolution, and quality evaluation. Experimental results show that the system can effectively realize digital human generation. The related code is released at https://github.com/zyj-2000/CUMT_2D_PhotoSpeaker

Knowledge-Based Prediction of Network Controllability Robustness

Network controllability robustness reflects how well a networked system can maintain its controllability against destructive attacks. Its measure is quantified by a sequence of values that record the remaining controllability of the network after a sequence of node-removal or edge-removal attacks. Traditionally, the controllability robustness is studied only for directed networks and is determined by attack simulations, which is computationally time consuming or even infeasible. In the present paper, an improved method for predicting the controllability robustness of undirected networks is developed based on machine learning using a group of convolutional neural networks (CNNs). In this scheme, a number of training data generated by simulations are used to train the group of CNNs for classification and prediction, respectively. Extensive experimental studies are carried out, which demonstrate that 1) the proposed method predicts more precisely than the classical single-CNN predictor; 2) the proposed CNN-based predictor provides a better predictive measure than the traditional spectral measures and network heterogeneity.Comment: 11 pages, 8 figures in Paper; 33 pages, 2 figures in Supplementary Informatio