The science and technology of ultracapacitors are reviewed for a number of electrode materials, including carbon, mixed metal oxides, and conducting polymers. More work has been done using microporous carbons than with the other materials and most of the commercially available devices use carbon electrodes and an organic electrolytes. The energy density of these devices is 3¯5 Wh/kg with a power density of 300¯500 W/kg for high efficiency (90¯95%) charge/discharges. Projections of future developments using carbon indicate that energy densities of 10 Wh/kg or higher are likely with power densities of 1¯2 kW/kg. A key problem in the fabrication of these advanced devices is the bonding of the thin electrodes to a current collector such the contact resistance is less than 0.1 cm2. Special attention is given in the paper to comparing the power density characteristics of ultracapacitors and batteries. The comparisons should be made at the same charge/discharge efficiency.

Ultracapacitors: Why, How, and Where is the Technology

System Identification I

FusionGAN: A generative adversarial network for infrared and visible image fusion

Infrared and visible image fusion methods and applications: A survey

Carbon nanotubes (CNTs) promise to advance a number of real-world technologies. Of these applications, they are particularly attractive for uses in chemical sensors for environmental and health monitoring. However, chemical sensors based on CNTs are often lacking in selectivity, and the elucidation of their sensing mechanisms remains challenging. This review is a comprehensive description of the parameters that give rise to the sensing capabilities of CNT-based sensors and the application of CNT-based devices in chemical sensing. This review begins with the discussion of the sensing mechanisms in CNT-based devices, the chemical methods of CNT functionalization, architectures of sensors, performance parameters, and theoretical models used to describe CNT sensors. It then discusses the expansive applications of CNT-based sensors to multiple areas including environmental monitoring, food and agriculture applications, biological sensors, and national security. The discussion of each analyte focuses on the strategies used to impart selectivity and the molecular interactions between the selector and the analyte. Finally, the review concludes with a brief outlook over future developments in the field of chemical sensors and their prospects for commercialization.

Carbon Nanotube Chemical Sensors

First-principles insight into Ni-doped InN monolayer as a noxious gases scavenger

With the advent of the Internet of Things (IoT), the security of the network layer in the IoT is getting more and more attention. The traditional intrusion detection technologies cannot be well adapted in the complex Internet environment of IoT. For the deep learning algorithm of intrusion detection, a neural network structure may have fine detection accuracy for one kind of attack, but it may not have a good detection effect when facing other attacks. Therefore, it is urgent to design a self-adaptive model to change the network structure for different attack types. This paper presents an intrusion detection model based on improved genetic algorithm (GA) and deep belief network (DBN). Facing different types of attacks, through multiple iterations of the GA, the optimal number of hidden layers and number of neurons in each layer are generated adaptively, so that the intrusion detection model based on the DBN achieves a high detection rate with a compact structure. Finally, the NSL-KDD dataset was used to simulate and evaluate the model and algorithms. The experimental results show that the improved intrusion detection model combined with DBN can effectively improve the recognition rate of intrusion attacks and reduce the complexity of the neural network structure.

/pdf/intrusion-detection-for-iot-based-on-improved-genetic-2evnxeukex.pdf

Intrusion Detection for IoT Based on Improved Genetic Algorithm and Deep Belief Network

Dissolved gas analysis in transformer oil using Pd catalyst decorated MoSe2 monolayer: A first-principles theory

SF6 insulation devices are important components in the power system, wherein the SF6 acts as the insulating gas protecting the operation state of devices effectively. However, the inevitable decomposition of SF6 under partial discharge in a long-running device would deteriorate the insulation property of SF6 largely. In this paper, we investigate the application of a Ru-doped InN (Ru-InN) monolayer as a novel gas adsorbent scavenging SF6 decomposed species based on a first-principles theory. Results indicate that the Ru-InN monolayer possesses quite strong adsorption behaviors upon three SF6 decomposed gases: SO2, SOF2, and SO2F2, wherein chemisorption can be identified. This allows exploration of the Ru-InN monolayer-based adsorbent for removing pollutant gases from SF6 insulation devices. Meanwhile, giving the obvious changes in conductivity of the Ru-InN monolayer caused by gas adsorption, the exploration of gas sensor using the Ru-InN monolayer would also be a means to evaluate the operation status of SF6 insulation devices. Our investigation indicates that the Ru-InN monolayer can be a good candidate for sensing or adsorbing to prevent paralysis of a power system caused by SF6 decomposition.

Ru-InN Monolayer as a Gas Scavenger to Guard the Operation Status of SF 6 Insulation Devices: A First-Principles Theory

To retain the details of a visible image with a discernible target area, we propose a multi-scale decomposition image fusion method based on a local edge-preserving (LEP) filter and saliency detection. We first use a LEP filter to decompose the infrared and visible images. Then, a modified saliency detection method is utilized to detect the salient target areas of an infrared image, which determine the base layer's weights of fusion strategy. Finally, each layer is reconstructed to obtain a visually pleasing fused image. Comparison with 11 other state-of-the-art methods reveals the superiority of the proposed method in terms of quality and quantity results.

Ying Zhang

Papers

First-principles insight into Ni-doped InN monolayer as a noxious gases scavenger

Intrusion Detection for IoT Based on Improved Genetic Algorithm and Deep Belief Network

Dissolved gas analysis in transformer oil using Pd catalyst decorated MoSe2 monolayer: A first-principles theory

Ru-InN Monolayer as a Gas Scavenger to Guard the Operation Status of SF 6 Insulation Devices: A First-Principles Theory

Infrared and visible image fusion via saliency analysis and local edge-preserving multi-scale decomposition.