다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

Modeling of solar energy systems using artificial neural network: A comprehensive review

Hydrogen technologies can play an important role in decarbonising our energy system in a variety of ways across the energy value chain. It is therefore critical to identify the strategic roles as well as the conditions under which hydrogen energy systems become attractive for the energy transition. In this paper, the authors present a techno-economic review of hydrogen energy systems including power-to-power, power-to-gas, hydrogen refuelling and stationary fuel cells. We focus on their optimal operation as flexible assets and we identify three actions that can foster their uptake beyond technological progress. First, we recommend optimal electricity supply with dedicated control strategies considering that electricity dominates the levelised cost of hydrogen production via electrolysis. Secondly, hydrogen can enable the further integration of traditionally independent sectors, namely electricity, heat and transport while contributing to decarbonise all. This position can also be advantageous for investors who sell heat and fuels as energy efficient products. Lastly, we examine a whole range of revenues from different products and applications which can be combined (i.e. benefit stacking) to match capital and operational expenditures. We discuss these roles in depth and we conclude that policy makers together with technology developers should elaborate smart strategies to reduce cost by scaling production, stimulate standardisation (e.g., similar to the PV industry) as well as develop new market structures and regulatory frameworks which allow hydrogen technologies to deliver multiple low carbon applications and products.

A review on the role, cost and value of hydrogen energy systems for deep decarbonisation

Demand response programs offer efficient solutions for many power system problems, such as high generation cost, high demand’s peak to average ratio, high emissions, reliability issues and congestion in generation, transmission and distribution systems. Their main function is to assist power systems during peak demand hours and also during contingencies. They are a subcategory of the family of demand side management (DSM) strategies. DR programs are classified into two broad categories; price-based DR programs and incentive-based DR programs. In order to exploit their full potential, DR programs must be implemented optimally. Such a problem, which here is referred to as “DR optimisation problem”, is a hot research topic and has been frequently researched in the literature. This paper aims to review different research works on DR optimisation problems. Based on the conducted review, some directions for future research are proposed.

Optimisation of demand response in electric power systems, a review

Optimal dispatch for a microgrid incorporating renewables and demand response

In this paper, we perform a comparative evaluation of seven most commonly used first-order stochastic gradient-based optimization techniques in a simple Convolutional Neural Network (ConvNet) architectural setup. The investigated techniques are the Stochastic Gradient Descent (SGD), with vanilla (vSGD), with momentum (SGDm), with momentum and nesterov (SGDm+n)), Root Mean Square Propagation (RMSProp), Adaptive Moment Estimation (Adam), Adaptive Gradient (AdaGrad), Adaptive Delta (AdaDelta), Adaptive moment estimation Extension based on infinity norm (Adamax) and Nesterov-accelerated Adaptive Moment Estimation (Nadam). We trained the model and evaluated the optimization techniques in terms of convergence speed, accuracy and loss function using three randomly selected publicly available image classification datasets. The overall experimental results obtained show Nadam achieved better performance across the three datasets in comparison to the other optimization techniques, while AdaDelta performed the worst.

A Comparative Analysis of Gradient Descent-Based Optimization Algorithms on Convolutional Neural Networks

Multi-objective dynamic economic emission dispatch of electric power generation integrated with game theory based demand response programs

Reverse osmosis (RO) desalination has become a prominent desalination method for fresh-water production from either saline seawater or brackish water to meet the ever-growing demand for water, especially in water-scarce regions. Its integration with renewable energy sources (RES) reduces the environmental impact of carbon emission by conventional fossil fuel energy sources. The optimisation of the RES-RO desalination system is intended mainly to minimise total system cost and energy requirements and to guarantee system reliability. In this study, an extensive review of the optimisation of the RES-RO desalination system is presented based on optimal system sizing, optimal system operation and optimal thermodynamic analysis. RES such as geothermal, ocean, wind and solar energy and their hybrids were considered alongside desalination methods. Important findings of the review were discussed, and recommendations made for future work. Key recommendations of this study include the suggestion that extensive optimisation and analysis of a RES-RO system should utilise optimisation approaches that combine the sizing, operation and thermodynamic effect of the system. Future work should furthermore incorporate both economic and reliability indices in the formation of objective functions. Finally, demand response programmes can be introduced to the RES-RO system for demand side management. This has the potential of minimising system cost while maximising fresh-water production.

Optimisation of renewable energy powered reverse osmosis desalination systems: A state-of-the-art review

This study proposes a multi-objective mathematical optimisation model which includes minimisation of the investment costs of new offshore wind farm units, investment costs of new transmission lines, fuel costs, emissions of the fossil fuel generators as well as the maximisation of incentives for new generating units. The mathematical model can be used to determine the optimal expansion planning for generation and transmission facilities with consideration of the impact of location and feed-in tariffs for offshore wind power on the overall system capacity and efficiency. The proposed model is solved using CPLEX 12.6.3 solver in advanced interactive multidimensional modelling system and tested on the modified Garver 6-bus test system and the IEEE 24-bus reliability test system. Simulation results indicate that improving the incentive support scheme and minimisation of the total cost will improve the effective utilisation of offshore power.

Nnamdi Nwulu

Papers

Optimal dispatch for a microgrid incorporating renewables and demand response

A Comparative Analysis of Gradient Descent-Based Optimization Algorithms on Convolutional Neural Networks

Multi-objective dynamic economic emission dispatch of electric power generation integrated with game theory based demand response programs

Optimisation of renewable energy powered reverse osmosis desalination systems: A state-of-the-art review

Multi-objective optimisation for composite generation and transmission expansion planning considering offshore wind power and feed-in tariffs