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Bolstering integrity in environmental data science and machine learning requires understanding socioecological inequity

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Frontiers of Environmental Science & Engineering 2024年第5期18卷 171-179页

作者： Joe F.Bozeman III school of Civil and Environmental Engineering Georgia Institute of TechnologyAtlantaGA30332USA school of Public Policy Georgia Institute of TechnologyAtlantaGA30332USA

Socioecological inequity in environmental data science—such as inequities deriving from data-driven approaches and machine learning(ML)—are current issues subject to debate and *** is growing consensus around embedding equity throughout all research and design domains—from inception to administration,while also addressing procedural,distributive,and recognitional ***,practically doing so may seem onerous or daunting to *** current perspective helps to alleviate these types of concerns by providing substantiation for the connection between environmental data science and socioecological inequity,using the Systemic Equity Framework,and provides the foundation for a paradigmatic shift toward normalizing the use of equity-centered approaches in environmental data science and ML *** the integrity of environmental data science and ML is just beginning from an equity-centered tool development and rigorous application *** this end,this perspective also provides relevant future directions and challenges by overviewing some meaningful tools and strategies—such as applying the Wells-Du Bois Protocol,employing fairness metrics,and systematically addressing irreproducibility;emerging needs and proposals—such as addressing data-proxy bias and supporting convergence research;and establishes a ten-step path ***,the work that environmental scientists and engineers do ultimately affect the well-being of us all.

关键词： Equity Bias Machine Learning Data Science Justice Systemic Equity

Electrothermal Model Based Remaining Charging Time Prediction of Lithium-Ion Batteries against Wide Temperature Range

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Chinese Journal of Mechanical Engineering 2024年第2期37卷 330-339页

作者： Rui Xiong Zian Zhao Cheng Chen Xinggang Li Weixiang Shen school of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China school of Science Computing and Engineering TechnologiesSwinburne University of TechnologyHawthornVIC 3122Australia

Battery remaining charging time(RCT)prediction can facilitate charging management and alleviate mileage anxiety for electric vehicles(EVs).Also,it is of great significance to improve EV users’***,the RCT for a lithiumion battery pack in EVs changes with temperature and other battery *** study proposes an electrothermal model-based method to accurately predict battery ***,a characteristic battery cell is adopted to represent the battery pack,thus an equivalent circuit model(ECM)of the characteristic battery cell is established to describe the electrical behaviors of a battery ***,an equivalent thermal model(ETM)of the battery pack is developed by considering the influence of ambient temperature,thermal management,and battery connectors in the battery pack to calculate the temperature which is then fed back to the ECM to realize electrothermal ***,the RCT prediction method is proposed based on the electrothermal model and validated in the wide temperature range from-20℃to 45℃.The experimental results show that the prediction error of the RCT in the whole temperature range is less than 1.5%.

关键词： Electric vehicles Lithium-ion batteries Remaining charging time Electrothermal model

Proposing a machine learning approach to analyze and predict basic high-temperature properties of iron ore fines and its factors

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Journal of Iron and Steel Research International 2024年第5期31卷 1082-1094页

作者： Qing-ke Sun Yao-zu Wang Jian-liang Zhang Zheng-jian Liu Le-le Niu Chang-dong Shan Yun-fei Ma Institute of Artificial Intelligence University of Science and Technology BeijingBeijing100083China school of Intelligence Science and technology University of Science and Technology BeijingBeijing100083China school of Metallurgical and Ecological Engineering University of Science and Technology BeijingBeijing100083China

The basic high-temperature properties of iron ore play a crucial role in optimizing sintering and ore blending,but the testing process for these properties is complex and has significant lag time,which cannot meet the actual needs of ore blending.A prediction model for the basic high-temperature properties of iron ore fines was thus proposed based on a combination of machine learning algorithms and genetic ***,the prediction accuracy of different machine learning models for the basic high-temperature properties of iron ore fines was ***,a random forest model optimized by genetic algorithms was built,further improving the prediction accuracy of the *** test results show that the random forest model optimized by genetic algorithms has the highest prediction accuracy for the lowest assimilation temperature and liquid phase fluidity of iron ore,with a determination coefficient of 0.903 for the lowest assimilation temperature and 0.927 for the liquid phase fluidity after *** trained model meets the fluctuation requirements of on-site testing and has been successfully applied to actual production on site.

关键词： Iron ore Basic high-temperature property Machine learning Random forest Genetic algorithm

Optimizing the power conversion processes in diluted donor/acceptor heterojunctions towards 19.4%efficiency all-polymer solar cells

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Journal of Energy Chemistry 2024年第9期96卷 345-350页

作者： Liang Wang Chen Chen Zirui Gan Chenhao Liu Chuanhang Guo Weiyi Xia Wei Sun Jingchao Cheng Yuandong Sun Jing Zhou Zexin Chen Dan Liu Wei Li Tao Wang school of Materials Science and Engineering Wuhan University of TechnologyWuhan 430070HubeiChina school of Materials and Microelectronics Wuhan University of TechnologyWuhan 430070HubeiChina

All polymer solar cells(all-PSCs)promise mechanically-flexible and morphologically-stable organic photovoltaics and have aroused increased interests very ***,due to their disorderly conformation structures within the photoactive film,inefficient charge generation and carrier transport are observed which lead to inferior photovoltaic performance compared to smaller molecular acceptor-based ***,by diluting PM6 with a cutting-edge polymeric acceptor PY-IT and diluting PY-IT with PM6 or D18,donor-dominating or acceptor-dominating heterojunctions were *** X-ray and multiple spectrometer techniques reveal that the diluted heterojunctions receive increased structural order,translating to enhanced carrier mobility,improved exciton diffusion length,and suppressed non-radiative recombination loss during the power *** the results,the corresponding PM6+1%PY-IT/PY-IT+1%D18 and PM6+1%PY-IT/PY-IT+1%PM6 devices fabricated by layer-by-layer deposition received superior power conversion efficiency(PCE)of 19.4%and 18.8%respectively,along with enhanced operational lifetimes in air,outperforming the PCE of 17.5%in the PM6/PY-IT reference device.

关键词： All-polymer solar cells Power conversion efficiency Structural order Charge generation

Task-specific Part Discovery for Fine-grained Few-shot Classification

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Machine Intelligence Research 2024年第5期21卷 954-965页

作者： Yongxian Wei Xiu-Shen Wei school of Computer Science and technology Nanjing University of Science and TechnologyNanjing210094China

Localizing discriminative object parts(e.g.,bird head)is crucial for fine-grained classification tasks,especially for the more challenging fine-grained few-shot *** work always relies on the learned object parts in a unified manner,where they attend the same object parts(even with common attention weights)for different few-shot episodic *** this paper,we propose that it should adaptively capture the task-specific object parts that require attention for each few-shot task,since the parts that can distinguish different tasks are naturally *** for a few-shot task,after obtaining part-level deep features,we learn a task-specific part-based dictionary for both aligning and reweighting part features in an ***,part-level categorical prototypes are generated based on the part features of support data,which are later employed by calculating distances to classify query data for *** retain the discriminative ability of the part-level representations(i.e.,part features and part prototypes),we design an optimal transport solution that also utilizes query data in a transductive way to optimize the aforementioned distance calculation for the final *** experiments on five fine-grained benchmarks show the superiority of our method,especially for the 1-shot setting,gaining 0.12%,8.56%and 5.87%improvements over state-of-the-art methods on CUB,Stanford Dogs,and Stanford Cars,respectively.

关键词： Fine-grained image recognition few-shot learning transductive learning visual dictionary part feature discovery

A Modified CycleGAN for Multi-Organ Ultrasound Image Enhancement via Unpaired Pre-Training

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Journal of Beijing Institute of technology 2024年第3期33卷 194-203页

作者： Haonan Han Bingyu Yang Weihang Zhang Dongwei Li Huiqi Li school of Information and Electronics Beijing Institute of TechnologyBeijing 100081China school of Medical technology Beijing Institute of TechnologyBeijing 100081China

Handheld ultrasound devices are known for their portability and affordability,making them widely utilized in underdeveloped areas and community healthcare for rapid diagnosis and early ***,the image quality of handheld ultrasound devices is not always satisfactory due to the limited equipment size,which hinders accurate diagnoses by *** the same time,paired ultrasound images are difficult to obtain from the clinic because imaging process is ***,we propose a modified cycle generative adversarial network(cycleGAN) for ultrasound image enhancement from multiple organs via unpaired *** introduce an ultrasound image pre-training method that does not require paired images,alleviating the requirement for large-scale paired *** also propose an enhanced block with different structures in the pre-training and fine-tuning phases,which can help achieve the goals of different training *** improve the robustness of the model,we add Gaussian noise to the training images as data *** approach is effective in obtaining the best quantitative evaluation results using a small number of parameters and less training costs to improve the quality of handheld ultrasound devices.

关键词： ultrasound image enhancement handheld devices unpaired images pre-train and finetune cycleGAN

Carbon efficiency modeling and optimization of solar-powered cellular networks

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Science China(Information Sciences) 2024年第5期67卷 228-243页

作者： Yuxi ZHAO Xiaohu GE Wen YAN Tao HAN Yi ZHONG school of Electronic Information and Communications Huazhong University of Science and Technology Huawei technology Co. Ltd.

As wireless communication traffic experiences rapid growth, the carbon emissions caused by the communication industry are also on the rise. To achieve “carbon neutrality”, researchers are considering the use of renewable energy sources to power cellular networks, thereby reducing carbon emissions. However, a challenge arises when using renewable energy, specifically owing to the unpredictable nature of both the energy consumption of the cellular network and the power generation from renewable sources. This inconsistency results in low renewable energy utilization and reduced carbon efficiency. Herein, we construct a carbon efficiency model of solar-powered cellular networks using practical data from solar radiation. We propose a mechanism that alternately optimizes the performance of the renewable energy network and the cellular network. This approach is based on convex optimization theory and the Dinkelback algorithm, and it leads to the design of a carbon efficiency optimization algorithm. This algorithm aims to improve the carbon efficiency of cellular networks and reduce their carbon emissions. Simulation results demonstrate that our optimization scheme yields a maximum improvement of 2.56 × 108bps/g in the carbon efficiency of the cellular network as compared to conventional power allocation schemes such as the traditional water filling method and heuristic energy sharing and charge/discharge algorithms.

关键词： cellular networks renewable energy carbon efficiency convex optimization theory dinkelbach

Strong Limit Theorems for Weighted Sums under the Sub-linear Expectations

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Acta Mathematicae Applicatae Sinica 2024年第3期40卷 862-874页

作者： Feng-xiang FENG Ding-cheng WANG Qun-ying WU Hai-wu HUANG school of Mathematics and Statistics Guilin University of TechnologyGuilin 541004China school of Mathematical Science University of Electronic Science and Technology of ChinaChengdu 611731China school of Science Guilin University of Aerospace TechnologyGuilin 541004China

In this article,we study strong limit theorems for weighted sums of extended negatively dependent random variables under the sub-linear *** establish general strong law and complete convergence theorems for weighted sums of extended negatively dependent random variables under the sub-linear *** results of strong limit theorems are more general than some related results previously obtained by Thrum(1987),Li et al.(1995)and Wu(2010)in classical probability space.

关键词： sub-linear expectation complete convergence complete moment convergence the maximal weighted sums

A model reduction approach for discrete-time linear time-variant systems with delayed inputs

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Science China(Information Sciences) 2024年第4期67卷 197-217页

作者： Ai-Guo WU Guang-Ren DUAN Yu WANG Jie ZHANG school of Mechanical Engineering and Automation Harbin Institute of Technology(Shenzhen) HIT Wuhu Robot technology Research Institute Center for Control Science and technology Southern University of Science and Technology

A model reduction approach is presented for discrete-time linear time-variant input-delayed systems. According to this proposed approach, a dynamical variable is constructed by taking advantage of the current state and historical information of input. It is revealed that the behavior of this dynamical variable is governed by a discrete-time linear delay-free system. It is worth noting that the presented variable transformation does not require the system matrix to be invertible. Based on the reduced delay-free models,stabilizing control laws can be easily obtained for the original delayed system. For the case with a single input delay, the constructed variable is an exact prediction for the future state, and thus the stabilizing control law could be designed by replacing the future state with its prediction. Finally, three discrete-time periodic systems with delayed input are employed to illustrate how to utilize the presented model reduction approaches.

关键词： reduction approach predictor-based feedback time-varying systems delayed input periodic system