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Research Progress and Future Development of Nonferrous Biomedical Materials

Guan Shaokang, Zhu Shijie, ZhengYufeng, Wang Yunbing, Zhang Xingdong

Strategic Study of Chinese Academy of Engineering doi: 10.15302/J-SSCAE-2023.01.008

Abstract:

Nonferrous biomedical materials have developed rapidly in recent years. A variety of new nonferrous biomedical materials and devices that adapt to different in vivo environments and tissues have been developed. It is of both theoretical and practical values to make research plans to improve the clinical application level of new nonferrous biomedical materials and devices. This study clarifies the key performance requirements of the nonferrous biomedical materials, regarding corrosion resistance, wear resistance, fatigue strength and toughness, and biocompatibility. The research progress, development trend, and scientific issues of nonferrous medical materials for permanent implants, biodegradable nonferrous medical materials, porous nonferrous medical materials, and surface modification of nonferrous medical materials are reviewed. After summarizing the future research directions of various nonferrous biomedical materials, this study proposes the following development suggestions: (1) strengthening basic research and the development of key core technologies, (2) establishing a collaborative innovation community that integrates industry, education, research, medicine, and supervision, (3) formulating relevant standards and evaluation norms, and (4) developing a highly skilled professional training system, thereby providing a guiding reference for developing the new material industry and relevant cutting-edge technologies.

Keywords: nonferrous biomedical materials     nonferrous materials for permanent implants     biodegradable nonferrous medical materials     porous nonferrous medical materials     surface modification of nonferrous medical materials    

Sodium Nitrate Passivation as a Novel Insulation Technology for Soft Magnetic Composites Article

Mi Yan, Qiming Chen, Dong Liu, Chen Wu, Jian Wang

Engineering 2023, Volume 20, Issue 1,   Pages 134-142 doi: 10.1016/j.eng.2022.01.016

Abstract:

 Sodium nitrate passivation has been developed as a new insulation technology for the production of FeSiAl soft magnetic composites (SMCs). In this work, the evolution of coating layers grown at different pH values is investigated involving analyses on their composition and microstructure. An insulation coating obtained using an acidic NaNO3 solution is found to contain Fe2O3, SiO2, Al2O3, and AlO(OH). The Fe2O3 transforms into Fe3O4 with weakened oxidizability of the NO3 at an elevated pH, whereas an alkaline NaNO3 solution leads to the production of Al2O3, AlO(OH), and SiO2. Such growth is explained from both thermodynamic and kinetic perspectives and is correlated to the soft magnetic properties of the FeSiAl SMCs. Under tuned passivation conditions, optimal performance with an effective permeability of 97.2 and a core loss of 296.4 mW∙cm−3 is achieved at 50 kHz and 100 mT.

Keywords: Soft magnetic composites     Surface passivation     Insulation technology     Growth mechanism     Magnetic performance    

A Thermo-Tunable Metamaterial as an Actively Controlled Broadband Absorber Article

Xiao-Chang Xing, Yang Cao, Xiao-Yong Tian, Lingling Wu

Engineering 2023, Volume 20, Issue 1,   Pages 143-152 doi: 10.1016/j.eng.2022.04.028

Abstract:

Metamaterials have attracted increasing attention in recent years due to their powerful abilities in manipulating electromagnetic (EM) waves. However, most previously reported metamaterials are unable to actively control full-band EM waves. In this paper, we propose a thermo-tunable broadband metamaterial (T-TBM) using paraffin-based composites (PD-Cs) with different phase transition temperatures. Active control of the T-TBM reflection loss peaks from low to high frequency is realized by manipulating the solid–liquid state of the PD-Cs at different phase transition temperatures. The absorption peak bandwidth (where the reflection loss value is less than −30 dB) can be changed, while the broad bandwidth absorption (where the reflection loss value is less than −10 dB) is satisfied by adjusting the temperature of the T-TBM. It is shown that the stagnation of the phase transition temperature of the PD-Cs in the T-TBM provides a time window for actively controlling the EM wave absorption response under different thermal conditions. The device has a broad application prospect in the fields of EM absorption, intelligent metamaterials, multifunctional structural devices, and more.

Keywords: Metamaterials     Active control     Thermally tunable     Broadband absorption    

Development Strategy for Recovery Resilience of Urban Underground Space

Lu Dechun, Liao Yingze, Zeng Jiao, Jiang Yuan, Wang Guosheng, Qin Boyu, Du Xiuli

Strategic Study of Chinese Academy of Engineering doi: 10.15302/J-SSCAE-2023.01.013

Abstract:

The concept of disaster prevention and mitigation is vital for the safe development of urban underground space. Considering the coexistence of natural and engineering safety risks, the concept needs to be improved for further development of the urban underground space, and it is imperative to follow a disaster-adaptation-oriented concept that emphasizes post-disaster recovery resilience of the urban underground space. This study analyzes the implications and influencing factors of recovery resilience and summarizes the research on recovery resilience from the engineering and non-engineering perspectives. Moreover, the development status of recovery resilience research is analyzed from the aspects of structural system, evaluation method, management mechanism, space planning, and emergency plan. On this basis, a strategy that consists of three stages and an evaluation system is proposed; it categorizes post-disaster recovery into three stages—emergency rescue, recovery, and plan adaptation—and proposes recovery goals for each stage, providing a basis for the establishment of a recovery resilience evaluation system for the urban underground space.Furthermore, we suggest that the management regulations and emergency plans of the urban underground space should be optimized, the resilience planning improved, and intelligent management promoted, thereby realizing the orderliness and high efficiency of urban underground space recovery.

Keywords: urban underground space     disaster prevention and mitigation     disaster adaptation     recovery resilience    

Technology trends in large-scale high-efficiency network computing Review

Jinshu SU, Baokang ZHAO, Yi DAI, Jijun CAO, Ziling WEI, Na ZHAO, Congxi SONG, Yujing LIU, Yusheng XIA

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 12,   Pages 1733-1746 doi: 10.1631/FITEE.2200217

Abstract: is the basis for large-scale high-efficiency network computing, such as , , big data processing, and artificial intelligence computing. The network technologies of network computing systems in different fields not only learn from each other but also have targeted design and optimization. Considering it comprehensively, three , i.e., integration, differentiation, and optimization, are summarized in this paper for network technologies in different fields. Integration reflects that there are no clear boundaries for network technologies in different fields, differentiation reflects that there are some unique solutions in different application fields or innovative solutions under new application requirements, and optimization reflects that there are some optimizations for specific scenarios. This paper can help academic researchers consider what should be done in the future and industry personnel consider how to build efficient practical network systems.

Keywords: Supercomputing     Cloud computing     Network technology     Development trends    

FinBrain 2.0: when finance meets trustworthy AI Review

Jun ZHOU, Chaochao CHEN, Longfei LI, Zhiqiang ZHANG, Xiaolin ZHENG

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 12,   Pages 1747-1764 doi: 10.1631/FITEE.2200039

Abstract: Artificial intelligence (AI) has accelerated the advancement of financial services by identifying hidden patterns from data to improve the quality of financial decisions. However, in addition to commonly desired attributes, such as model accuracy, financial services demand trustworthy AI with properties that have not been adequately realized. These properties of trustworthy AI are interpretability, fairness and inclusiveness, robustness and security, and privacy protection. Here, we review the recent progress and limitations of applying AI to various areas of financial services, including , , , personalized services, and regulatory technology. Based on these progress and limitations, we introduce FinBrain 2.0, a research framework toward trustworthy AI. We argue that we are still a long way from having a truly trustworthy AI in financial services and call for the communities of AI and financial industry to join in this effort.

Keywords: Artificial intelligence in finance     Trustworthy artificial intelligence     Risk management     Fraud detection     Wealth management    

Parallel cognition: hybrid intelligence for human-machine interaction and management Research Article

Peijun YE, Xiao WANG, Wenbo ZHENG, Qinglai WEI, Fei-Yue WANG

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 12,   Pages 1765-1779 doi: 10.1631/FITEE.2100335

Abstract: As an interdisciplinary research approach, traditional cognitive science adopts mainly the experiment, induction, modeling, and validation paradigm. Such models are sometimes not applicable in cyber-physical-social-systems (CPSSs), where the large number of human users involves severe heterogeneity and dynamics. To reduce the decision-making conflicts between people and machines in human-centered systems, we propose a new research paradigm called parallel cognition that uses the system of intelligent techniques to investigate cognitive activities and functionals in three stages: descriptive cognition based on artificial cognitive systems (ACSs), predictive cognition with computational deliberation experiments, and prescriptive cognition via parallel . To make iteration of these stages constantly on-line, a hybrid learning method based on both a psychological model and user behavioral data is further proposed to adaptively learn an individual's cognitive knowledge. Preliminary experiments on two representative scenarios, urban travel and cognitive visual reasoning, indicate that our parallel cognition learning is effective and feasible for human , and can thus facilitate human-machine cooperation in both complex engineering and social systems.

Keywords: Cognitive learning     Artificial intelligence     Behavioral prescription    

Dual collaboration for decentralized multi-source domain adaptation Research Article

Yikang WEI, Yahong HAN

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 12,   Pages 1780-1794 doi: 10.1631/FITEE.2200284

Abstract: The goal of decentralized is to conduct unsupervised in a scenario. The challenge of is that the source domains and target domain lack cross-domain collaboration during training. On the unlabeled target domain, the target model needs to transfer supervision knowledge with the collaboration of source models, while the domain gap will lead to limited adaptation performance from source models. On the labeled source domain, the source model tends to overfit its domain data in the scenario, which leads to the problem. For these challenges, we propose dual collaboration for decentralized by training and aggregating the local source models and local target model in collaboration with each other. On the target domain, we train the local target model by distilling supervision knowledge and fully using the unlabeled target domain data to alleviate the problem with the collaboration of local source models. On the source domain, we regularize the local source models in collaboration with the local target model to overcome the problem. This forms a dual collaboration between the decentralized source domains and target domain, which improves the domain adaptation performance under the scenario. Extensive experiments indicate that our method outperforms the state-of-the-art methods by a large margin on standard datasets.

Keywords: Multi-source domain adaptation     Data decentralization     Domain shift     Negative transfer    

Image-based traffic signal control via world models Research Article

Xingyuan DAI, Chen ZHAO, Xiao WANG, Yisheng LV, Yilun LIN, Fei-Yue WANG

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 12,   Pages 1795-1813 doi: 10.1631/FITEE.2200323

Abstract: is shifting from passive control to proactive control, which enables the controller to direct current traffic flow to reach its expected destinations. To this end, an effective prediction model is needed for signal controllers. What to predict, how to predict, and how to leverage the prediction for control policy optimization are critical problems for proactive . In this paper, we use an image that contains vehicle positions to describe intersection traffic states. Then, inspired by a model-based method, DreamerV2, we introduce a novel learning-based . The that describes traffic dynamics in image form is used as an abstract alternative to the traffic environment to generate multi-step planning data for control policy optimization. In the execution phase, the optimized traffic controller directly outputs actions in real time based on abstract representations of traffic states, and the world model can also predict the impact of different control behaviors on future traffic conditions. Experimental results indicate that the enables the optimized real-time control policy to outperform common baselines, and the model achieves accurate image-based prediction, showing promising applications in futuristic .

Keywords: Traffic signal control     Traffic prediction     Traffic world model     Reinforcement learning    

MSSSA: a multi-strategy enhanced sparrow search algorithm for global optimization Research Article

Kai MENG, Chen CHEN, Bin XIN

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 12,   Pages 1828-1847 doi: 10.1631/FITEE.2200237

Abstract: The (SSA) is a recent meta-heuristic optimization approach with the advantages of simplicity and flexibility. However, SSA still faces challenges of premature convergence and imbalance between exploration and exploitation, especially when tackling multimodal . Aiming to deal with the above problems, we propose an enhanced variant of SSA called the multi-strategy enhanced (MSSSA) in this paper. First, a chaotic map is introduced to obtain a high-quality initial population for SSA, and the opposition-based learning strategy is employed to increase the population diversity. Then, an is designed to accommodate an adequate balance between exploration and exploitation. Finally, a is embedded in the individual update stage to avoid falling into local optima. To validate the effectiveness of the proposed MSSSA, a large number of experiments are implemented, including 40 complex functions from the IEEE CEC2014 and IEEE CEC2019 test suites and 10 classical functions with different dimensions. Experimental results show that the MSSSA achieves competitive performance compared with several state-of-the-art optimization algorithms. The proposed MSSSA is also successfully applied to solve two engineering . The results demonstrate the superiority of the MSSSA in addressing practical problems.

Keywords: Swarm intelligence     Sparrow search algorithm     Adaptive parameter control strategy     Hybrid disturbance mechanism     Optimization problems    

Interpreting the vulnerability of power systems in cascading failures using multi-graph convolutional networks Research Article

Supaporn LONAPALAWONG, Changsheng CHEN, Can WANG, Wei CHEN

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 12,   Pages 1848-1861 doi: 10.1631/FITEE.2200035

Abstract: Analyzing the of in is generally regarded as a challenging problem. Although existing studies can extract some critical rules, they fail to capture the complex subtleties under different operational conditions. In recent years, several deep learning methods have been applied to address this issue. However, most of the existing deep learning methods consider only the grid topology of a power system in terms of topological connections, but do not encompass a power system's spatial information such as the electrical distance to increase the accuracy in the process of graph convolution. In this paper, we construct a novel power- that uses power system topology and spatial information to optimize the edge weight assignment of the line graph. Then we propose a multi-graph convolutional network (MGCN) based on a graph classification task, which preserves a power system's spatial correlations and captures the relationships among physical components. Our model can better handle the problem with that have parallel lines, where our method can maintain desirable accuracy in modeling systems with these extra topology features. To increase the interpretability of the model, we present the MGCN using layer-wise relevance propagation and quantify the contributing factors of model classification.

Keywords: Power systems     Vulnerability     Cascading failures     Multi-graph convolutional networks     Weighted line graph    

Observer-based control for fractional-order singular systems with order Research Article

Bingxin LI, Xiangfei ZHAO, Xuefeng ZHANG, Xin ZHAO

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 12,   Pages 1862-1870 doi: 10.1631/FITEE.2200294

Abstract: In this paper, for fractional-order with order (0<<1) and is studied. On the basis of the Smith predictor and approximation error, the system with is approximately equivalent to the system without . Furthermore, based on the (LMI) technique, the necessary and sufficient condition of is proposed. Since the condition is a nonstrict LMI, including the equality constraint, it will lead to some trouble when solving problems using toolbox. Thus, the strict LMI-based condition is improved in the paper. Finally, a numerical example and a direct current motor example are given to illustrate the effectiveness of the strict LMI-based condition.

Keywords: Observer-based control     Singular systems     Fractional order     Input delay     Linear matrix inequality    

Generalized labeled multi-Bernoulli filter with signal features of unknown emitters Research Article

Qiang GUO, Long TENG, Xinliang WU, Wenming SONG, Dayu HUANG

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 12,   Pages 1871-1880 doi: 10.1631/FITEE.2200286

Abstract:

A novel algorithm that combines the (GLMB) filter with signal features of the unknown emitter is proposed in this paper. In complex electromagnetic environments, emitter features (EFs) are often unknown and time-varying. Aiming at the unknown feature problem, we propose a method for identifying EFs based on of data fields. Because EFs are time-varying and the probability distribution is unknown, an improved algorithm is proposed to calculate the correlation coefficients between the target and measurements, to approximate the EF likelihood function. On this basis, the EF likelihood function is integrated into the recursive GLMB filter process to obtain the new prediction and update equations. Simulation results show that the proposed method can improve the tracking performance of multiple targets, especially in heavy clutter environments.

Keywords: Multi-target tracking     Generalized labeled multi-Bernoulli     Signal features of emitter     Fuzzy C-means     Dynamic clustering    

Ensemble enhanced active learning mixture discriminant analysis model and its application for semi-supervised fault classification Research Article

Weijun WANG, Yun WANG, Jun WANG, Xinyun FANG, Yuchen HE

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 12,   Pages 1814-1827 doi: 10.1631/FITEE.2200053

Abstract: As an indispensable part of process monitoring, the performance of relies heavily on the sufficiency of process knowledge. However, data labels are always difficult to acquire because of the limited sampling condition or expensive laboratory analysis, which may lead to deterioration of classification performance. To handle this dilemma, a new strategy is performed in which enhanced is employed to evaluate the value of each unlabeled sample with respect to a specific labeled dataset. Unlabeled samples with large values will serve as supplementary information for the training dataset. In addition, we introduce several reasonable indexes and criteria, and thus human labeling interference is greatly reduced. Finally, the effectiveness of the proposed method is evaluated using a numerical example and the Tennessee Eastman process.

Keywords: Semi-supervised     Active learning     Ensemble learning     Mixture discriminant analysis     Fault classification    

FACTORS INFLUENCING FOOD-WASTE BEHAVIORS AT UNIVERSITY CANTEENS IN BEIJING, CHINA: AN INVESTIGATION BASED ON THE THEORY OF PLANNED BEHAVIOR

Frontiers of Agricultural Science and Engineering doi: 10.15302/J-FASE-2022472

Abstract:

● Investigate the actual situation of food waste at university canteens in Beijing, China.

Keywords: university students     food waste behavior     theory of planned behavior     university canteen    

Title Author Date Type Operation

Research Progress and Future Development of Nonferrous Biomedical Materials

Guan Shaokang, Zhu Shijie, ZhengYufeng, Wang Yunbing, Zhang Xingdong

Journal Article

Sodium Nitrate Passivation as a Novel Insulation Technology for Soft Magnetic Composites

Mi Yan, Qiming Chen, Dong Liu, Chen Wu, Jian Wang

Journal Article

A Thermo-Tunable Metamaterial as an Actively Controlled Broadband Absorber

Xiao-Chang Xing, Yang Cao, Xiao-Yong Tian, Lingling Wu

Journal Article

Development Strategy for Recovery Resilience of Urban Underground Space

Lu Dechun, Liao Yingze, Zeng Jiao, Jiang Yuan, Wang Guosheng, Qin Boyu, Du Xiuli

Journal Article

Technology trends in large-scale high-efficiency network computing

Jinshu SU, Baokang ZHAO, Yi DAI, Jijun CAO, Ziling WEI, Na ZHAO, Congxi SONG, Yujing LIU, Yusheng XIA

Journal Article

FinBrain 2.0: when finance meets trustworthy AI

Jun ZHOU, Chaochao CHEN, Longfei LI, Zhiqiang ZHANG, Xiaolin ZHENG

Journal Article

Parallel cognition: hybrid intelligence for human-machine interaction and management

Peijun YE, Xiao WANG, Wenbo ZHENG, Qinglai WEI, Fei-Yue WANG

Journal Article

Dual collaboration for decentralized multi-source domain adaptation

Yikang WEI, Yahong HAN

Journal Article

Image-based traffic signal control via world models

Xingyuan DAI, Chen ZHAO, Xiao WANG, Yisheng LV, Yilun LIN, Fei-Yue WANG

Journal Article

MSSSA: a multi-strategy enhanced sparrow search algorithm for global optimization

Kai MENG, Chen CHEN, Bin XIN

Journal Article

Interpreting the vulnerability of power systems in cascading failures using multi-graph convolutional networks

Supaporn LONAPALAWONG, Changsheng CHEN, Can WANG, Wei CHEN

Journal Article

Observer-based control for fractional-order singular systems with order

Bingxin LI, Xiangfei ZHAO, Xuefeng ZHANG, Xin ZHAO

Journal Article

Generalized labeled multi-Bernoulli filter with signal features of unknown emitters

Qiang GUO, Long TENG, Xinliang WU, Wenming SONG, Dayu HUANG

Journal Article

Ensemble enhanced active learning mixture discriminant analysis model and its application for semi-supervised fault classification

Weijun WANG, Yun WANG, Jun WANG, Xinyun FANG, Yuchen HE

Journal Article

FACTORS INFLUENCING FOOD-WASTE BEHAVIORS AT UNIVERSITY CANTEENS IN BEIJING, CHINA: AN INVESTIGATION BASED ON THE THEORY OF PLANNED BEHAVIOR

Journal Article