Ethics,society, and technology: A Confucian role ethics perspective

This essay provides a very brief overview of Confucian ethics that focuses on the role-based morality. It compares Confucian ethics with two dominant schools of thought in Western ethics deontology and consequentialism. This essay then discusses how Confucian ethics provides a unique way to think about social and political issues such as how the political leaders are selected and evaluated, the ethical justification of political legitimacy, the prioritization of economic, social, and cultural rights over other rights, and moral egalitarianism. Finally, this essay explores the ways in which Confucian ethics serves as a useful intellectual resource for examining the issues arising from the ethical assessment of (emerging) technologies and the professional formation of engineers. The Appendix of this essay includes some teaching resources that instructors can employ to teach students about ethics, society, and technology from a Confucian role ethics perspective.     

Interval analysis of mode shapes to identify damage in beam structures

Various damage detection methods have been proposed by several researchers in the past few decades. Amongst them, the efficiency of mode shapes in detecting damage has been demonstrated by many researchers when further processed. In most cases, the processing involves expansion or reduction of the mode shape data. However, vital information that are damage-prints are often lost during processing of the mode shape data. In addition, most of these processes involve long and complex computation, thus, leading to inaccurate damage identification. In this study, a simple and fast damage identification technique is proposed to identify damage in beam structures. Interval analysis is applied to the mode shapes of a beam structure in the damaged and undamaged states. The interval situations of each of the beam's segment via mode shape are derived to obtain the upper and lower bounds and the derived bounds are compared. To establish a relationship for identify the damaged point, a possibility of damage existence is defined for each segment of the beam structure. The mode shape increment is defined as the increase in the mode shape value. Furthermore, a damage measure index that provide enhance damage information is obtained as the product of the possibility of damage existence and mode shape increment. A numerical model of a simply supported steel beam is applied to demonstrate this method by imposing damage through thickness reduction of elements in segments. In addition, a parametric analysis is carried out to evaluate noise effect by considering varying damage severities and different noise levels. The results showed that this method is simple and provides considerable accurate results.     

Qualitative analysis of thermal explosion of sodium droplet with variable thermophysical properties and thermal radiations

There are many coolants frequently used in the industry for controlling not only heat transfer, but also temperature distribution in a confined domain. However, little is known on the thermal properties of sodium droplets. The qualitative analysis of differential equations that model the thermal explosion, nonlinear dynamic of sodium droplet with variable thermophysical properties when thermal radiations are considered as suggested by Cogley et al, Sohrab et al, and P‐1 approximation Sazhin et al is deliberated upon in this study. The governing equations, first‐order nonlinear ordinary differential equations, are nondimensionalized using the appropriate similarity variables. The existence and uniqueness of the solutions, concavity, and convexity of the temperature distribution, and positivity nature of the solutions of the dimensionless governing equations are established. It is concluded that there exists a solution for a certain range of the admissible parameters and when the reduced activation energy is negative and temperature distribution fits concavity. More so, the major criteria to obtain a positive solution are outlined in this study.     

Reduction of waste biosolids by RAS-ozonation: Model validation and sensitivity analysis for biosolids reduction and nitrification

Biosolids reduction model by return activated sludge ozonation was validated by simulating nitrification data compiled from our pilot-scale and the literature studies. Then, a global sensitivity analysis (GSA) was performed to identify influential and non-influential parameters for biosolids reduction efficiency, change in specific nitrification activity (SNA), and alteration to expected nitrification stability. In general, the model outputs were sensitive to operational and ozone reaction parameters, but not to biochemical parameters. For operational parameters, mainly temperature and initial solids retention time (SRT) influenced all model outputs. For biosolids reduction, increase in the degradability of the influent COD decreased the reduction efficiency. For SNA, the changes were highly dependent on the influent TKN/COD ratio. Our findings also imply that the stability of the nitrification process in ozonated systems should be enhanced at constant MLVSS for warm temperatures, but could be reduced at temperatures below 12 °C and aerated SRTs below 10 days.     

Improved average consensus algorithm based distributed cost optimization for loading shedding of autonomous microgrids

This paper addresses a new distributed cost optimization (DCO) method for load shedding (LS) of an islanded microgrid considering cost. A two-layer improved average consensus algorithm (IACA) of multi-agent system (MAS) is proposed, and the consensus characteristic of which is analyzed in detail. With the global information discovered in the first layer of the IACA, the DCO of LS can be solved by using the synchronization processing of the IACA in the second layer. PSCAD/EMTDC-based simulation models are built to study the value settings of consensus constants and the performances of the proposed DCO method. Simulation results verified the convergence improvement of the IACA and the effectiveness of the proposed DCO.     

Efficient multi-criteria optimization on noisy machine learning problems

Recent research revealed that model-assisted parameter tuning can improve the quality of supervised machine learning (ML) models. The tuned models were especially found to generalize better and to be more robust compared to other optimization approaches. However, the advantages of the tuning often came along with high computation times, meaning a real burden for employing tuning algorithms. While the training with a reduced number of patterns can be a solution to this, it is often connected with decreasing model accuracies and increasing instabilities and noise. Hence, we propose a novel approach defined by a two criteria optimization task, where both the runtime and the quality of ML models are optimized. Because the budgets for this optimization task are usually very restricted in ML, the surrogate-assisted Efficient Global Optimization (EGO) algorithm is adapted. In order to cope with noisy experiments, we apply two hypervolume indicator based EGO algorithms with smoothing and re-interpolation of the surrogate models. The techniques do not need replicates. We find that these EGO techniques can outperform traditional approaches such as latin hypercube sampling (LHS), as well as EGO variants with replicates.     

A directional mutation operator for differential evolution algorithms

Differential evolution (DE) is widely studied in the past decade. In its mutation operator, the random variations are derived from the difference of two randomly selected different individuals. Difference vector plays an important role in evolution. It is observed that the best fitness found so far by DE cannot be improved in every generation. In this article, a directional mutation operator is proposed. It attempts to recognize good variation directions and increase the number of generations having fitness improvement. The idea is to construct a pool of difference vectors calculated when fitness is improved at a generation. The difference vector pool will guide the mutation search in the next generation once only. The directional mutation operator can be applied into any DE mutation strategy. The purpose is to speed up the convergence of DE and improve its performance. The proposed method is evaluated experimentally on CEC 2005 test set with dimension 30 and on CEC 2008 test set with dimensions 100 and 1000. It is demonstrated that the proposed method can result in a larger number of generations having fitness improvement than classic DE. It is combined with eleven DE algorithms as examples of how to combine with other algorithms. After its incorporation, the performance of most of these DE algorithms is significantly improved. Moreover, simulation results show that the directional mutation operator is helpful for balancing the exploration and exploitation capacity of the tested DE algorithms. Furthermore, the directional mutation operator modifications can save computational time compared to the original algorithms. The proposed approach is compared with the proximity based mutation operator as both are claimed to be applicable to any DE mutation strategy. The directional mutation operator is shown to be better than the proximity based mutation operator on the five variants in the DE family. Finally, the applications of two real world engineering optimization problems verify the usefulness of the proposed method.     

A multi-start central force optimization for global optimization

Central force optimization (CFO) is an efficient and powerful population-based intelligence algorithm for optimization problems. CFO is deterministic in nature, unlike the most widely used metaheuristics. CFO, however, is not completely free from the problems of premature convergence. One way to overcome local optimality is to utilize the multi-start strategy. By combining the respective advantages of CFO and the multi-start strategy, a multi-start central force optimization (MCFO) algorithm is proposed in this paper. The performance of the MCFO approach is evaluated on a comprehensive set of benchmark functions. The experimental results demonstrate that MCFO not only saves the computational cost, but also performs better than some state-of-the-art CFO algorithms. MCFO is also compared with representative evolutionary algorithms. The results show that MCFO is highly competitive, achieving promising performance.     

Polishing pad for reducing edge roll-off while maintaining good global flatness of silicon wafer

In the double-sided polishing process of silicon wafers, there is a strong demand to reduce amount of edge roll-off (ERO) while improving global flatness of a wafer. In the present study, we clarified the negative effects of uneven wear of the polishing pads on the global flatness of a wafer can be suppressed when the deformation of the polishing pads is large. As for the ERO, we found small deformation of the polishing pad near the top surface was effective in reducing the amount of ERO. In addition, we revealed small distance from the surface of the polishing pad at the area under the wafer to that at the area around the wafer was also effective in reducing the amount of ERO. On the basis of the findings, we developed a three-layered polishing pad which was expected to reduce the amount of ERO while achieving the good global flatness.