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2023 Emerging Topics in Artificial Intelligence, ETAI 2023

作者： Alvarez, Michael Arzuaga, Emmanuel Sierra, Heidy Laboratory for Applied Remote Sensing Imaging and Photonics United States Department of Computer science & Engineering United States Department of Electrical and Computer Engineering United States University of Puerto Rico Mayaguez Puerto Rico

ISBN: (纸本)9781510665248

Measuring Hemoglobin (Hb) levels is required for the assessment of different health conditions, such as anemia, a condition where there are insufficient healthy red blood cells to carry enough oxygen to the body’s tissues. Measuring Hb levels requires the extraction of a blood sample, which is then sent to a laboratory for analysis. This is an invasive procedure that may add challenges to the continuous monitoring of Hb levels. Noninvasive techniques, including imaging and photoplethysmography (PPG) signals combined with machine learning techniques, are being investigated for continuous measurements of Hb. However, the availability of real data to train the algorithms is limited to establishing a generalization and implementation of such techniques in healthcare settings. In this work, we present a computational model based on Monte Carlo simulations that can generate multispectral PPG signals that cover a broad range of Hb levels. These signals are then used to train a Deep Learning (DL) model to estimate hemoglobin levels. Through this approach, valuable insights about the relationships between PPG signals, oxygen saturation, and Hb levels are learned by the DL model. The signals were generated by propagating a source in a volume that contains the skin tissue properties and the target physiological parameters. The source consisted of plane waves using the 660nm and 890 nm wavelengths. A range of 6 g/dL to 18dL Hb values was used to generate 468 PPGs to train a Convolutional Neural Network (CNN). The initial results show high accuracy in detecting low levels of Hb. To the best of our knowledge, the complexity of biological interactions involved in measuring hemoglobin levels has yet to be fully modeled. The presented model offers an alternative approach to studying the effects of changes in Hb levels on the PPGs signal morphology and its interaction with other physiological parameters that are present in the optical path of the measured signals. © 2023 SPIE.

关键词： Hemoglobin

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A fully-customized dataflow engine for 3D earthquake simulation with a complex topography

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science China(Information sciences) 2022年第5期65卷 65-80页

作者： Bingwei CHEN Haohuan FU Wayne LUK Guangwen YANG Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Tsinghua University National Supercomputer Center in Wuxi Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System ScienceTsinghua University Department of Computing Imperial College London

With HPC(high performance computing) evolving into the exascale era, improvements in computing performance and power efficiency have become increasingly more important. Based on our previous work on enabling earthquake simulations on a large scale on Sunway Taihu Light, we further explore other possibilities to improve the application through a fully-customized hardware design on reconfigurable FPGA(field programmable gate array) devices. We investigate the feasibility and the potential benefits of a complete fixed-point design. We first perform a coarse-resolution-based simulation to analyze the representation range and precision needed to capture both the total energy and the energy distribution of variables over space and time. We then derive a complete fixed-point design that identifies the suitable bitwidth for major categories of variables and dynamically represents the range through a dynamic scaling scheme. Finally, we use the optimized fixed-point design to run a case of the Wenchuan earthquake to demonstrate the potential of supporting large-scale scientific simulations on FPGA devices. The results demonstrate that an 18-bit fixed-point design already provides an almost identical description of the seismic events in the Wenchuan scenario down to a single-precision floating-point version and provides sustainable performance equivalent to 13.1 Intel Xeon Gold 6154 18-core CPUs or 2.10 Sunway 260-core processors, with performance per watt(power efficiency) improved by 15.3 and 3.72 times compared with the Intel Xeon Gold 6154 18-core CPUs and the Sunway 260-core processors, respectively.

关键词： reconfigurable computing earthquake simulation FPGA hardware-software co-design architecture exploration

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Ultrafast magneto-optical dynamics in nickel(111)single crystal studied by the integration of ultrafast reflectivity and polarimetry probes

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Chinese Physics B 2024年第3期33卷 65-69页

作者：匡皓余军潇陈洁 H.E.Elsayed-Ali 李润泽 Peter M.Rentzepis School of Physical Science and Technology ShanghaiTech UniversityShanghai 201210China Center for Transformative Science ShanghaiTech UniversityShanghai 201210China Center for Ultrafast Science and Technology Key Laboratory for Laser Plasmas (Ministry of Education)Collaborative Innovation Center of IFSA (CICIFSA)School of Physics and AstronomyShanghai Jiao Tong UniversityShanghai 200240China Department of Electrical and Computer Engineering Old Dominion UniversityNorfolkVirginia 23529USA Department of Electrical and Computer Engineering Texas A&M UniversityCollege StationTexas 77843USA

With the integration of ultrafast reflectivity and polarimetry probes,we observed carrier relaxation and spin dynamics induced by ultrafast laser excitation of Ni(111)single *** carrier relaxation time within the linear excitation range reveals that electron-phonon coupling and dissipation of photon energy into the bulk of the crystal take tens of *** the other hand,the observed spin dynamics indicate a longer time of about 120 *** further understand how the lattice degree of freedom is coupled with these dynamics may require the integration of an ultrafast diffraction probe.

关键词： ultrafast spin dynamics non-equilibrium dynamics multi-probe

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Data Augmentation and Pseudo-sequence of fNIRS for Depression Recognition

Data Augmentation and Pseudo-sequence of fNIRS for Depressio...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Shao, Kai Hao, Yixue Hu, Long Zong, Xiaofen Chen, Min Huazhong University of Science and Technology School of Computer Science and Technology Wuhan China Wuhan University Renmin Hospital Department of Psychiatry Wuhan China South China University of Technology Pazhou Laboratory School of Computer Science and Engineering Guangzhou China

ISBN: (纸本)9798350337488

Depression is a mental disorder caused by factors such as genetics, life events and social influences, and has become a major public health problem worldwide. Previous studies have demonstrated the potential of functional near-infrared spectroscopy (fNIRS) in the diagnosis of depression. However, in the real medical scene, fNIRS data are difficult to obtain, limited in number and suffer from class imbalance. To overcome these problems, in this paper, we propose a novel model for depression identification based on data augmentation and pseudo-sequence of fNIRS. Specifically, the data augmentation using the time masking and warping method generates richer data. Then, a stimulation task-driven data pseudo-sequence method is designed to map the sequence data into pseudo-sequence activation images. Finally, a depression recognition model is established based on the class imbalance loss function. Experiments show that the precision of our depression recognition model reaches 0.94. This scheme transforms fNIRS data into image sequences, which provides a new solution idea for subsequent research. © 2023 IEEE.

关键词： Data Augmentation Depression Recognition fNIRS Pseudo-sequence

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A Secure and Cost-Effective Training Framework Atop Serverless Computing for Object Detection in Blasting

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computer Modeling in Engineering & sciences 2024年第5期139卷 2139-2154页

作者： Tianming Zhang Zebin Chen Haonan Guo Bojun Ren Quanmin Xie Mengke Tian Yong Wang Department of Computer and Science Shanghai Jiaotong UniversityShanghai200240China Aerospace System Engineering Shanghai Shanghai200240China State Key Laboratory of Precision Blasting Jianghan UniversityWuhan430056China Beijing Microelectronics Technology Institute Beijing100076China

The data analysis of blasting sites has always been the research goal of relevant *** rise of mobile blasting robots has aroused many researchers’interest in machine learning methods for target detection in the field of *** Computing can provide a variety of computing services for people without hardware foundations and rich software development experience,which has aroused people’s interest in how to use it in the field ofmachine *** this paper,we design a distributedmachine learning training application based on the AWS Lambda *** on data parallelism,the data aggregation and training synchronization in Function as a Service(FaaS)are effectively *** also encrypts the data set,effectively reducing the risk of data *** rent a cloud server and a Lambda,and then we conduct experiments to evaluate our *** results indicate the effectiveness,rapidity,and economy of distributed training on FaaS.

关键词： Serverless computing object detection blasting

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Automatic ICD Coding Based on Segmented ClinicalBERT with Hierarchical Tree Structure Learning 28th

Automatic ICD Coding Based on Segmented ClinicalBERT with ...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Kang, Beichen Wang, Xiaosu Xiong, Yun Zhang, Yao Zhou, Chaofan Zhu, Yangyong Zhang, Jiawei Tang, Chunlei Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China IFM Lab Department of Computer Science University of California DavisCA United States Harvard Medical School BostonMA United States

ISBN: (纸本)9783031306778

Automatic ICD coding aims at assigning the international classification of disease (ICD) codes to clinical notes documented by clinicians, which is crucial for saving human resources and has attracted much research attention in recent years. However, facing the challenges brought by the complex long textual narratives in clinical notes and the long-tailed data distribution in ICD codes, existing studies are ineffectual in the struggle to extract key information from the clinical notes and handle large amounts of small-data learning problems on the tail codes, which makes it hard to achieve satisfactory performance. In this paper, we present a ClinicalBERT-based model for automatic ICD coding, which can effectively cope with complex long clinical narratives via a segmentation learning mechanism and take advantage of the tree-like structure of ICD codes to transmit information among code nodes. Specifically, a novel hierarchical tree structure learning module is proposed to enable each code to utilize information both from upper and lower nodes of the tree, so that better code classifiers are learned for both head and tail codes. Experiments on MIMIC-III dataset show that our model outperforms current state-of-the-art (SOTA) ICD coding methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Clinical research

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Learning Order Forest for Qualitative-Attribute Data Clustering 27

Learning Order Forest for Qualitative-Attribute Data Cluster...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Zhao, Mingjie Feng, Sen Zhang, Yiqun Li, Mengke Lu, Yang Cheung, Yiu-Ming School of Computer Science and Technology Guangdong University of Technology Guangzhou China Shenzhen China School of Computer Science and Software Engineering Shenzhen University Shenzhen China Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University China Department of Computer Science Hong Kong Baptist University Hong Kong

ISBN: (纸本)9781643685489

Clustering is a fundamental approach to understanding data patterns, wherein the intuitive Euclidean distance space is commonly adopted. However, this is not the case for implicit cluster distributions reflected by qualitative attribute values, e.g., the nominal values of attributes like symptoms, marital status, etc. This paper, therefore, discovered a tree-like distance structure to flexibly represent the local order relationship among intra-attribute qualitative values. That is, treating a value as the vertex of the tree allows to capture rich order relationships among the vertex value and the others. To obtain the trees in a clustering-friendly form, a joint learning mechanism is proposed to iteratively obtain more appropriate tree structures and clusters. It turns out that the latent distance space of the whole dataset can be well-represented by a forest consisting of the learned trees. Extensive experiments demonstrate that the joint learning adapts the forest to the clustering task to yield accurate results. Comparisons of 10 counterparts on 12 real benchmark datasets with significance tests verify the superiority of the proposed method. Source code of the proposed method is available at [39]. © 2024 The Authors.

关键词： Adversarial machine learning

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An Intelligent Optimization Method of Reinforcing Bar Cutting for Construction Site

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computer Modeling in Engineering & sciences 2023年第1期134卷 637-655页

作者： Zhaoxi Ma Qin Zhao Tianyou Cang Zongjian Li Yiyun Zhu Xinhong Hei Department of Civil Engineering and Architecture Xi’an University of TechnologyXi’an710048China State Key Laboratory of Rail Transit Engineering Informatization(FSDI) Xi’an710043China Department of Computer Science and Engineering Xi’an University of TechnologyXi’an710048China

To meet the requirements of specifications,intelligent optimization of steel bar blanking can improve resource utilization and promote the intelligent development of sustainable *** one of the most important building materials in construction engineering,reinforcing bars(rebar)account for more than 30%of the cost in civil engineering.A significant amount of cutting waste is generated during the construction *** cutting waste increases construction costs and generates a considerable amount of CO_(2)*** study aimed to develop an optimization algorithm for steel bar blanking that can be used in the intelligent optimization of steel bar engineering to realize sustainable *** the proposed algorithm,the integer linear programming algorithm was applied to solve the *** was combined with the statistical method,a greedy strategy was introduced,and a method for determining the dynamic critical threshold was developed to ensure the accuracy of large-scale data *** proposed algorithm was verified through a case study;the results confirmed that the rebar loss rate of the proposed method was reduced by 9.124%compared with that of traditional distributed processing of steel bars,reducing CO_(2)emissions and saving construction *** the scale of a project increases,the calculation quality of the optimization algorithmfor steel bar blanking proposed also increases,while maintaining high calculation *** the results of this study are applied in practice,they can be used as a sustainable foundation for building informatization and intelligent development.

关键词： Building construction rebar work cutting stock problem optimization algorithm integer linear programming

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Application of ANN and Equivalent Electrical Circuit for Understanding the Power Transformer Frequency Response Analysis

Application of ANN and Equivalent Electrical Circuit for Und...

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2024 International Conference on Green Energy, Computing and Sustainable Technology, GECOST 2024

作者： Alwady, Ahmed Allawy Al-Ameri, Salem Mgammal Alamal College for Specialized Medical Science Medical Laboratory Techniques Department Karbala56001 Iraq Curtin University Malaysia Department of Electrical and Computer Engineering Miri98000 Malaysia

ISBN: (纸本)9798350357905

Frequency Response Analysis (FRA) is a well-known method approved and increases interest in performing electric power utility tests. FRA has become a recognized technology for detecting winding and core deformations in power transformers. The weakness of the FRA method is that there is no approved standard or guidelines to understand the measured results, which depends on individual knowledge. For further understanding of the FRA measurement, there are the lumped circuit approaches for power transformer fault analysis. The presented circuit approaches elements of the transformer, including windings and core, can be measured, or calculated. The challenge of identifying parameters for a power transformer equivalent circuit can be solved using frequency response analysis (FRA) and artificial neural networks (ANN). In this paper, the two techniques proposed and employed to extract the appropriate values of transformer circuit parameters that can simulate the identical measured response. The proposed technique is examined through its application on a three-phase transformer with 33/11 kV 500kV A. The FRA measurement was conducted using the end-to-end open-circuit test. The results show that FRA and ANN techniques can be used to identify transformer-equivalent circuit parameters. Furthermore, the proposed methods can be extended and used to estimate the parameters of a three-phase power transformer of different ratings. © 2024 IEEE.

关键词： Power transformers

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Recognition of Fire Situation Using Graph Convolutional Network Model 15th

Recognition of Fire Situation Using Graph Convolutional Netw...

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15th International Conference on computer science and its Applications, CSA 2023 and 17th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2023

作者： Kim, Si Jin Park, Ji Su Kang, Jungho Shon, Jin Gon Advanced Convergence Technology Laboratory Korea National Open University Seoul Korea Republic of Department of Computer Science and Engineering Jeonju University Jeonju-Si Korea Republic of Department of Software Engineering Baewha Woman University Seoul Korea Republic of Department of Computer Science Korea National Open University Seoul Korea Republic of

ISBN: (纸本)9789819724468

Fires cause a lot of casualties and economic losses. In order to prevent fire accidents in advance, it is necessary to find out the cause of the fire. Existing fire alarm systems detected fires with temperature, smoke, and flame sensors, but could not distinguish the cause of the fire. In this paper, to solve this problem, the recognition of fire situation was tested using the GCN model. The fire dataset used in the experiment is a fire dataset composed of causes such as boxes, clothing, and electricity. The accuracy of 93.44% was confirmed as a result of the learning experiment with the GCN model, a graph-based deep learning model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Premixed flames

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