Anti-Noise Quantum Network Coding Protocol Based on Bell States and Butterfly Network Model

How to establish a secure and efficient quantum network coding algorithm isone of important research topics of quantum secure communications. Based on thebutterfly network model and the characteristics of easy preparation of Bell states, a novelanti-noise quantum network coding protocol is proposed in this paper. The new protocolencodes and transmits classical information by virtue of Bell states. It can guarantee thetransparency of the intermediate nodes during information, so that the eavesdropper Evedisables to get any information even if he intercepts the transmitted quantum states. Inview of the inevitability of quantum noise in quantum channel used, this paper analyzesthe influence of four kinds of noises on the new protocol in detail further, and verifies theefficiency of the protocol under different noise by mathematical calculation and analysis.In addition, based on the detailed mathematical analysis, the protocol has functioned wellnot only on improving the efficiency of information transmission, throughput and linkutilization in the quantum network, but also on enhancing reliability and antieavesdroppingattacks.

Decarbonizing in Maritime Transportation: Challenges and Opportunities

As global warming caused by greenhouse gases grows (GHGs) into a global environmental threat, carbon dioxide emissions are drawing increasing attention in these years. Among all emission sources, transportation is a major contributor to climate change because of its high dependence on fossil fuels. The International Maritime Organization (IMO) has therefore been promoting the reduction of fuel usage and carbon emissions for container ships by such measures as improving shipping route selection, shipping speed optimization, and constructing clean energy propulsion systems. In this paper, a review of the impact of carbon dioxide emissions on climate change is presented;the current situations of carbon dioxide emissions, decarbonizing methods, IMO regulations, and possible future directions of decarbonizing in the maritime transportation industry are also discussed. Based on the result, it is found that in the case that non intelligent ships still occupy the vast majority of operating ships, the use of new energy as the main propulsion fuel has the defects of high renewal cost and long effective period. It is more likely to achieve energy conservation and emission reduction in the shipping industry in a short period of time by using intelligent means and artificial intelligence to assist ship operation. .

Ship Fuel and Carbon Emission Estimation Utilizing Artificial Neural Network and Data Fusion Techniques

Ship energy consumption and emission prediction are the main concern of the shipping industry for ship energy efficiency management and pollution gas emission control. And they are attracting more global attention and research interests because of the increase in global shipping trade volume. As the core of maritime transportation, a large volume of data is collected around ships such as voyage data. Due to the rapid development of computational power and the widely equipped AIS device on ships, the use of maritime big data for improving and monitoring ship’s energy efficiency is becoming possible. In this paper, a fuel consumption and carbon emission model using the artificial neural network (ANN) framework is proposed by using AIS, ship machinery, and weather data. The proposed work is a complete framework including data collection, data cleaning, data clustering and model-building methodology. To obtain the suitable parameters of the model, the number of neurons, data inputs and activate functions were tested on both AIS-based data and MRV-based data for comparison. The results show that the proposed method can provide a solid prediction of ship’s fuel consumption and carbon emissions under varying weather conditions.