To realize three dimensional image resolution enhancement, focus tuning and beam angle tilting via use of electrowetting-on-dielectric (EWOD) liquid microlens has been reported in this paper. The ...lens comprises four control electrodes on a glass substrate, two spherical liquid lenses with different refractive indices, and an encapsulation polymer. Focus tuning can realized via lateral shift of the inner lens and change in refractive curvature of the liquid lens. As observed, focus tuning over distances in the range of 9.3-29.3 mm can be realized. Correspondingly, tuning range of tilt angle was observed to be of the order of ± 2.13°.
This study comprehensively investigates hydrogen production from green ammonia reforming, including synthesis of catalysts, reactor development, process integration, and techno-economic analysis. ...In-house developed Ru/La–Al2O3 pellet catalyst having perovskite structure showed high catalytic activity of 2827 h−1 at 450 °C and stability over 6700 h at 550 °C, exceeding the performance of the majority of powder catalysts reported in the literature. A scalable 12-faceted reactor adopting the as-produced catalyst was designed to enhance heat transfer, producing over 66 L min−1 of hydrogen with state-of-the-art ammonia reforming efficiency of 83.6 %. Near-zero CO2 emission of hydrogen extraction from green ammonia was demonstrated by-product gas recirculation as a combustion heat source. A techno-economic assessment was conducted for system scales from 10 kW to 10 MW, demonstrating the effect of reduced minimum hydrogen selling prices from 7.03 USD kg−1 at small modular scales to 3.98 USD kg−1 at larger industrial scales. Sensitivity analyses indicate that hydrogen selling prices may reduce even further (up to 50 %). The suggested hydrogen production route from green NH3 demonstrates superior CO2 reduction ranging from 78 % to 95 % in kg CO2 (kg H2)−1 compared to biomass gasification and steam methane reforming. These findings can be used as a basis for following economic and policy studies to further validate the effectiveness of the suggested system and process for H2 production from NH3.
•Experimental and techno-economic analyses of H2 from green NH3 are conducted.•As-developed Ru pellet catalysts showed high activity of 2827 h−1 at 450 °C.•Reforming efficiency of 83.6 % was achieved at 5 kWe scale with a COX free operation.•Minimum H2 selling price of 4 USD kg−1 is expected at industrial scales >10 MW.•CO2 emission can be reduced up to 95 %, compared to the conventional processes.
Whole-body center of gravity (CG) movements in relation to the center of pressure (COP) offer insights into the balance control strategies of the human body. Existing CG measurement methods using ...expensive measurement equipment fixed in a laboratory environment are not intended for continuous monitoring. The development of wireless sensing technology makes it possible to expand the measurement in daily life. The insole system is a wearable device that can evaluate human balance ability by measuring pressure distribution on the ground. In this study, a novel protocol (data preparation and model training) for estimating the 3-axis CG trajectory from vertical plantar pressures was proposed and its performance was evaluated. Input and target data were obtained through gait experiments conducted on 15 adult and 15 elderly males using a self-made insole prototype and optical motion capture system. One gait cycle was divided into four semantic phases. Features specified for each phase were extracted and the CG trajectory was predicted using a bi-directional long short-term memory (Bi-LSTM) network. The performance of the proposed CG prediction model was evaluated by a comparative study with four prediction models having no gait phase segmentation. The CG trajectory calculated with the optoelectronic system was used as a golden standard. The relative root mean square error of the proposed model on the 3-axis of anterior/posterior, medial/lateral, and proximal/distal showed the best prediction performance, with 2.12%, 12.97%, and 12.47%. Biomechanical analysis of two healthy male groups was conducted. A statistically significant difference between CG trajectories of the two groups was shown in the proposed model. Large CG sway of the medial/lateral axis trajectory and CG fall of the proximal/distal axis trajectory is shown in the old group. The protocol proposed in this study is a basic step to have gait analysis in daily life. It is expected to be utilized as a key element for clinical applications.
Accurate photovoltaic (PV) power forecasting is essential for the stable and reliable operation of PV power generation systems. Recently, various deep learning- (DL-) based forecasting models have ...been proposed for accurate forecasting, but newly built systems cannot benefit from them due to the absence of PV power data. Although zero-shot methods based on single site can be used for PV power forecasting, they suffer from performance degradation problems when the characteristics of the source data and target data are different. To address this issue, we propose a novel zero-shot PV power forecasting scheme that leverages historical data from multiple PV generation systems at different sites. The proposed scheme constructs an individual forecasting model using historical data from each PV generation system. Then, two correlation coefficients are calculated for each forecasting model: one based on the correlation between the input variables of the source data and target data and the other on the correlation between the input variables and output variables of the source data. Lastly, the final forecasting value is calculated as a weighted sum of the predicted values of the constructed forecasting models for the input variables of the target data. In the extensive experiments for diverse DL models for forecasting, correlation coefficient types for weights, and data time intervals, the combination of recurrent neural network, Pearson’s correlation coefficient, and solar-noon time yielded the best prediction performance, with an improvement of up to 34.47% in mean absolute error and up to 15.94% in root mean square error compared to the best single-site zero-shot prediction. In addition, in experiments on PV power data from 9 cities in Korea using this combination, the proposed scheme achieved the best predictive performance in almost all cases and the second-best performance with a very narrow margin only in a few cases.
Circuit design plays an essential role in all consumer electronics products. Printed circuit board (PCB) and very-large-scale integration (VLSI) circuit designing requires optimization of the ...electronic component's placement and wire routing to connect the components. Currently, circuit routing processes have been performed manually by experts, which greatly increases the cost of human resources and time. Such heuristic circuit designs are not optimized and may have errors, which is why automated circuit routing algorithms are important. However, it is difficult to obtain an optimal solution in circuit routing as it is an NP-hard problem. In addition, poor circuit routing increases the wire length of the circuit, which causes an increase in circuit cost and weight as well as performance degradation. In order to achieve routing optimization, many technologies have been proposed, in which some have applied artificial intelligence (AI) to improve the overall performance and reduce the designing time. Accordingly, in this paper, routing problems in PCB and VLSI are explained, and proposed technologies to solve these routing problems are introduced. Especially, a detailed investigation and analysis of AI technologies grafted into circuit routing algorithms are explained, and the considerations for AI-based routing algorithms are presented.
IoT (IoT) networks generate massive amounts of data while supporting various applications, where the security and protection of IoT data are very important. In particular, blockchain technology ...supporting IoT networks is considered as the most secure, expandable, and scalable database storage solution. However, existing blockchain systems have scalability problems due to low throughput and high resource consumption, and security problems due to malicious attacks. Several studies have proposed blockchain technologies that can improve the scalability or the security level, but there have been few studies that improve both at the same time. In addition, most existing studies do not consider malicious attack scenarios in the consensus process, which deteriorates the blockchain security level. In order to solve the scalability and security problems simultaneously, this paper proposes a Dueling Double Deep-Q-network with Prioritized experience replay (D3P) based secure trust-based delegated consensus blockchain (TDCB-D3P) scheme that optimizes the blockchain performance by applying deep reinforcement learning (DRL) technology. The TDCB-D3P scheme uses a trust system with a delegated consensus algorithm to ensure the security level and reduce computing costs. In addition, DRL is used to compute the optimum blockchain parameters under the dynamic network state and maximize the transactions per second (TPS) performance and security level. The simulation results show that the TDCB-D3P scheme can provide a superior TPS and resource consumption performance. Furthermore, in blockchain networks with malicious nodes, the simulation results show that the proposed scheme significantly improves the security level when compared to existing blockchain schemes by effectively reducing the influence of malicious nodes.
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•3D CFD analysis of PEM water electrolyser.•Developed model were validated with in-house experiment data.•Evaluate temperature, pressure, membrane thickness, PTL porosity and feed ...rate.•Key characteristics were quantified, and optimum conditions were suggested.
Hydrogen produced by theelectrochemical water splitting is essential for expanding and utilizing renewable power sources and establishing a sustainable energy society. As renewable energy network widely established, the produced hydrogen can be utilized by connecting the energy demand and energy supply. The proton exchange membrane (PEM) water electrolyser technology is one of the ideal candidates for direct coupling with renewable energy sources. In recent years, bench-scale experiments, and computational fluid dynamics (CFD) simulation-based approaches are used to accelerate the advances in performance and cost of the technology. We studied the influence of the key performance parameter of a PEM water electrolyser, and a single channel-based three-dimensional CFD model was developed. The PEM water electrolyser CFD model is validated against in-house experiments, where the developed model successfully predicts the current–voltage polarization curve. The developed CFD model is then used to analyze the influence of temperature, cathode pressure, membrane thickness, porous transport layer porosity and water feed rate. The main observation from the numerical study was discussed to provide insight into the factors affecting the PEM water electrolyser performance.
•Developed a high-accuracy python-based software platform for developing neural networks.•Implemented both prediction and process parameter optimization through genetic algorithms.•Higher ...performances obtained when benchmarked against several published literature.•Graphical plots in 2D and 3D are also possible with the software package.•All validation case studies employed yielded low training time and neural network accuracies >99%.
Artificial neural networks are revolutionizing the field of engineering because of their ability to model complex non-linear problems without explicit programming. Their applications in different areas, such as manufacturing and healthcare, have provided a new path away from traditional modeling techniques. Nonetheless, users of this technology, especially those without prior knowledge of neural networks, spend a considerable amount of time gaining a basic understanding of the use of technology. Traditional trial-and-error approaches are often employed in training these neural networks, which further increases the time spent in developing them. Owing to the laborious nature of the trial-and-error method, the optimal or best hyperparameters of a particular neural network may not be determined, thereby affecting the accuracy of the developed model. Hence, in this study, a software platform is presented to aid in the training and development of neural networks by using genetic algorithms (GAs) for optimizing the model's hyperparameters, such as the number of neurons, learning rate, and activation function. As an essential aspect of chemical engineering processes, design or operating-parameter optimization is also included in this software package, wherein the best (optimized) weights and biases from the neural network are saved and employed in another GA to optimize key process variables, such as temperature, and velocity, as required by the user. This dual-purpose provides a complete application of neural networks that are primarily encountered in many engineering disciplines. The software platform can also plot 3D contours, heat maps (correlation plots), and other line graphs. For the validation and generalization of the software, it was benchmarked against five cases presented by different authors across various chemical engineering fields. The prediction results obtained using the software package were higher than those presented in the published literature, demonstrating the superior performance of the software package.
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•A techno-economic analysis is performed on the power generation system using ammonia.•The ammonia import price for the feasible operation is identified to be 421.3 $ t−1.•Fossil ...fuel-based ammonia import from ten exporters is stochastically optimized.•Indonesia emerges as top ammonia exporter across all scenarios.•Over 78% carbon–neutral ammonia is required for short-term low-carbon goal in Korea.
Ammonia is considered a promising energy source for achieving carbon neutrality because it is carbon-free. This study aims to evaluate the feasibility of ammonia-based power generation by conducting techno-economic and carbon footprint analyses of an integrated ammonia decomposition and phosphoric acid fuel cell system. Using a commercial process simulator, the power generation process is designed to reveal an energy efficiency of 46.7% and an upper limit for the ammonia price to compete with industrial electricity prices is identified as 421.3 $ tNH3–1 through economic analysis. Furthermore, the study establishes five different scenarios for ammonia import from the top ten exporting countries to the Republic of Korea (KOR), according to historical data, for optimization. Ammonia import is optimized in terms of exporting countries and quantities to satisfy the ammonia price while minimizing overall emissions using the Monte Carlo method for ammonia production costs and carbon dioxide emissions in each nation. The results show that carbon intensity falls within the range of 0.707–0.736 kgCO2-eq kWh−1, which exceeds the 20-year average value of carbon intensity in KOR if carbon-based ammonia is solely imported. However, the system can be competitive in terms of both economic and environmental aspects if the carbon–neutral ammonia ratio is over 78% (more than Scenario 4). In conclusion, this study statistically investigates the optimization results focusing on major ammonia export countries, identifies the trend of carbon intensity in the scenarios, and provides guidelines for reducing overall carbon intensity by considering the production, transportation, and utilization of ammonia.
