172 research outputs found
Dual-modulus 3D printing technology for magnetorheological Metamaterials-Part II: Negative regulation theory and application
No full textMetamaterials are artificially structured periodic materials that have remarkable property of wave attenuation in bandgaps. However, metamaterials with adjustable and low-frequency bandgap are still challenge in traditional method. In this work, a novel magnetorheological metamaterial (MRM) with negative regulation and low -frequency bandgaps was fabricated by dual-modulus 3D printing technology. The bandgaps of negative regulation MRM were analyzed theoretically by using the mass-spring model. As a result, the starting frequency of bandgap reduced by 37.6% and ending frequency increased by 47.8% under external magnetic field. Moreover, the propagation characteristics of longitudinal wave in negative regulation MRM were also studied and the results indicated that the stiffnesses were magnetic-related, and the bandgap can be tuned substantially under external magnetic field. This work presented a negative regulation MRM that the bandgap was broadened and moved to lower frequency under the external magnetic field, showing a great potential in the field of vibration isolation
Probing the roles of surface characteristic of suspended nanoparticle in shear thickening suspensions
No full textThe surface characteristic of suspended nanoparticle is an extremely important property for the non-Newtonian behavior of shear thickening suspensions. Easy access to distinct surface characteristics and a deep understanding of the role of friction and adhesion forces remains challenging yet critical to study the suspension performance. In this work, by synthesizing mesoporous silica with different surface roughness and groups, the influence of surface characteristics on the non-Newtonian behavior of suspension is comprehensively explored. The results show that the suspensions containing particles with rough surface and hydroxyl groups on the surface have higher shear thickening power, lower critical shear rate, lower discontinuous shear thickening transition fraction and lower jamming fraction. Furthermore, the test of the first normal stress difference N1 shows that the interparticle friction and adhesion both reduce the critical volume fraction of the transition from fluid lubrication dominance to frictional contact dominance in the suspension. This finding deepens the understanding of phase transformation mechanism, and open an approach to accelerate the advanced shear thickening suspensions design for next-generation intelligent materials
Autonomous greenhouse climate control with Q-learning using ENMPC as a function approximator
No full textGreenhouses allow production of crops that would otherwise be impossible. Permitting more local, fresher and nutrient richer crop production. Eorts are taken to minimize societal harm due to energy and resource consumption by greenhouse production systems. One way to control such systems is by using model predictive control. Optimal crop yield and resource eciency can, in theory, be achieved by model predictive control. Unfortunately, one major drawback of model predictive control is that it is not well equipped to deal with parametric uncertainty. Significant prediction errors can occur when a mismatch between the model and the real system exists, resulting in deteriorated performance of the system. Strategies exist, such as robust MPC, that are designed to handle uncertainty, but those often result in conservative control policies. This thesis proposes to use model predictive control as a function approximator for RL in order to learn values for model and MPC parameters that can deliver optimal performance in the case of model mismatch.In this thesis, data-driven economic nonlinear model predictive control using Q-learning is proposed as a method to alter the model parameters. The performance of the system af- ter learning is compared to approaches using robust and nominal model predictive control. Three dierent goals are determined: maximizing economic profit, minimizing the constraint violations and maximizing the economic performance while minimizing constraint violations.In this work, an ENMPC scheme is used as a function approximator in a Q-learning envi- ronment. The optimization solution from the ENMPC scheme is used as the input to the system, while the Q-learning agent optimizes the parameter values of the ENMPC scheme and model for the environment. The performance of the system after learning is compared to approaches using robust and nominal model predictive control. The simulation results show that the data-driven ENMPC using reinforcement learning is able to decrease constraint vi- olations by up to 94%, but unable to increase economic performance compared to nominal MPC, compared to robust MPC the EPI is increased by almost 10% while keeping constraint violations at a similar level.Mechanical Engineering | Systems and Contro
Understanding the evolution of open government data research: towards open data sustainability and smartness
No full textThe past decade has witnessed a rapid development of open government data practices and academic research. However, there is no systematic survey of existing research to understand the evolution of open government data. Such research can facilitate knowledge transfer within and across domains, and foster learning for countries in the early stages of open government data development. This study quantitively extracted the evolution trajectory of open government data based on the main path analysis method and then analysed the underlying motivations. The results show that open government data research went through four main phases and that the open government data movement has spread towards developing countries and smart cities. Different challenges and issues faced by the researchers in each phase drove the evolution of open government data research. Finally, we discuss future directions of open government data research based on our findings and recent development. There is a tendency to create sustainable open government data and smartness by employing artificial intelligence and creating data marketplaces. Points for practitioners: Open government data efforts have evolved over the years into a global phenomenon. Countries have learned from each other and more and more efforts are focused on innovating with open government data by stimulating co-creation and using other incentives. The way that data are opened should focus on achieving goals like innovation, participation, transparency and accountability. There is a tendency to create sustainable open government data and smartness by employing artificial intelligence and creating data marketplaces.</p
Reviving the rock-salt phases in Ni-rich layered cathodes by mechano-electrochemistry in all-solid-state batteries
No full textThe rock-salt phase (RSP) formed on the surface of Ni-rich layered cathodes in liquid-electrolyte lithium-ion batteries is conceived to be electrochemically "dead". Here we show massive RSP forms in the interior of LiNixMnyCo(1−x-y)O2 (NMC) crystals in sulfide based all solid state batteries (ASSBs), but the RSP remains electrochemically active even after long cycles. The RSP and the layered structure constitute a two-phase mixture, a material architecture that is distinctly different from the RSP in liquid electrolytes. The tensioned layered phase affords an effective percolation channel into which lithium is squeezed out of the RSPs by compressive stress, rendering the RSPs electrochemically active. Consequently, the ASSBs with predominant RSP in the NMC cathode deliver remarkable long cycle life of 4000 cycles at high areal capacity of 4.3 mAh/cm2. Our study unveils distinct mechano-electrochemistry of RSPs in ASSBs that can be harnessed to enable high energy density and durable ASSBs.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.RST/Storage of Electrochemical Energ
Chinese Foreign Language Online Course Design to Improve English Monolingual Teachers' Awareness of ELLs
No full textTo improve English monolingual teachers' awareness of obstacles that English language learners (ELL) may encounter at school, in 2012 the author conducted a study to explore preservice teachers' perceptions of learning a foreign language online. No participant had Chinese learning experience and their interest varied. This study suggested that preservice teachers perceived their initial experiences as online language learners increased their linguistic, cultural and technological awareness, which would further benefit them when working with ELLs. However, it was unclear if teachers perceived they could transfer their awareness into teaching practice. Thus, this follow-up study explores in-service teachers' perceptions of linguistic, cultural, and technological awareness transfer in teaching ELLs by asking them to engage and reflect on their experiences with a Chinese language online course. This chapter proposes a model for language teacher linguistic, cultural, and technological awareness development and transfer, as well as discussing issues related to language teacher awareness transfer.</jats:p
Adaptively weighted decision fusion in 30 m land-cover mapping with Landsat and MODIS data
No full text© 2015 Taylor & Francis. Although the combined use of Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) time-series data in land-cover classification has been widely adopted, the majority of such use of Landsat and MODIS data is done at the pixel level or feature input level in land-cover classification. We propose in this research a new method to make integrated use of different satellite data by adaptively weighted decision-level fusion. Training and validation samples were collected independently. Training samples were obtained from 329 regions and validation samples from 439 randomly distributed single-point positions. A Support Vector Machine (SVM) classifier was applied to the Landsat 8 data for classification and probability estimation. A Random Forests (RF) classifier was applied to the MODIS time-series data for probability estimation. Weight values were computed based on decision credibility, and reliability values were computed based on data quality. Three decision fusion procedures were performed. In the first procedure, decisions obtained from a Landsat 8 pixel and its corresponding MODIS pixel were fused for improvements (FUSION1). In the second, decisions obtained from the spatial neighbours of the Landsat 8 pixel were added to FUSION1 (FUSION2). In the third, decision fusion only among the Landsat 8 pixel and its spatial neighbours was performed (FUSION3) for comparison. Overall accuracies for the results with Landsat data only, FUSION1, FUSION2, and FUSION3 are 74.0%, 79.3%, 80.6%, and 75.6%, respectively. As a comparison, we also experimented on the use of Landsat and MODIS data by concatenating their features directly. Two classifiers, SVM and RF, were trained and validated on the concatenated features. The overall accuracies were 72.9% and 75.4%, respectively. Results show that the proposed method can utilize information selectively, so that considerable improvements can be obtained and fewer errors introduced. Moreover, it can be easily extended to handle more than two types of data source.link_to_subscribed_fulltex
Dong Qing Fang’s Musical Depiction of Lin Chong: An Analysis of Chinese Traditional Music Elements in His Cello Composition of 2009
No full textDong Qing Fang (b. 1981) is a prolific composer of cello music. His contribution includes twenty compositions, many of which have been performed around the world.[1] While Fang is the most famous composer in China as well as a leading figure of recent music, his compositions have not been recognized world-wide. This project focuses on his representative cello composition Lin Chong (2009). Lin Chong is a significant character in Chinese literature, known for his role in the classic novel, Water Margin (1524). Fang\u27s musical portrayal of Lin Chong\u27s life incorporates elements of Chinese traditional music.
The methodology for this research comprises interviews with Dong Qing Fang, related literature reviews, analysis of Lin Chong, and a review of published articles about Lin Chong by two authors.[2] The interview with Fang provides valuable insights into his composition process, inspiration, and goals. Reviewing literature related to Lin Chong will assist in recognizing the concepts Fang has employed. This research contains a detailed analysis of Fang\u27s composition, including the background, musical interpretation, compositional techniques, and Chinese traditional music elements. Lastly, the two published articles about Lin Chong by Di and Guan provide additional insights into his work and may help identify new exploration, thereby offering different perspectives on this project.
The research aims to introduce Lin Chongto a broader cello community. This valuable addition to a performers’ repertoire collection provides an opportunity to gain exposure to Chinese literature. Furthermore, the importance of promoting works from underrepresented composers cannot be overstated. By recognizing and performing Fang\u27s compositions, the author hopes to inspire others to explore lesser-known works and composers from various backgrounds. This project aspires to contribute to a more diverse music community that celebrates and promotes all musicians.
[1]Dong, Qingfang. “Chinese composer and photographer – Dong, Qingfang.” Baidu Baike. Accessed May 26, 2023. https://baike.baidu.com/link?url=Pyron6lo9G8nBoYaX4n8LkYXGf9SiddrxCh60RmukFCar5pZ 5y8sfHBg-Y18FdExwzK4o44mvz436VAHaXEMn1Z5xismDlrqCVRzO2gn-A-qmE-k-dG9jWB10uBQXii O.
[2] Di, “The Analysis of Music and Performance of Fang Dongqing’s cello Lin Chong.” 5-15. Ibid., “Cong Fang Dongqing datiqin zuopin tan zhongguo dangdai datiqin datiqin zuopin De fazhan,” [The Development of Chinese Contemporary Cello Works from the Works of Fang Ddongqing], 183-185
Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery
Full text linkAlthough a large number of new image classification algorithms have been developed, they are rarely tested with the same classification task. In this research, with the same Landsat Thematic Mapper (TM) data set and the same classification scheme over Guangzhou City, China, we tested two unsupervised and 13 supervised classification algorithms, including a number of machine learning algorithms that became popular in remote sensing during the past 20 years. Our analysis focused primarily on the spectral information provided by the TM data. We assessed all algorithms in a per-pixel classification decision experiment and all supervised algorithms in a segment-based experiment. We found that when sufficiently representative training samples were used, most algorithms performed reasonably well. Lack of training samples led to greater classification accuracy discrepancies than classification algorithms themselves. Some algorithms were more tolerable to insufficient (less representative) training samples than others. Many algorithms improved the overall accuracy marginally with per-segment decision making
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