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IEEE International Conference on systems, Man and Cybernetics

作者： Wing W. Y. Ng Jing Xu Yinhao Liang Ting Wang Jianjun Zhang Hui Zhou Dan Liang Guangming Li Xinhua Wei School of Computer Science and Engineering South China University of Technology Guangzhou China Department of Radiology Guangzhou First People's Hospital School of Medicine South China University of Technology Guangzhou China College of Mathematics and Informatics South China Agricultural University Guangzhou China Department of Radiology The Sixth Affiliated Hospital Guangzhou Medical University Qingyuan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

When analyzing CT images of patients with suspected appendicitis, radiologists need to observe and examine consecutive 2D CT slices. computer-assisted detection of the appendix in 2D CT slices significantly improve the diagnostic efficiency of radiologists. However, existing 2D medical image object detection methods primarily focus on spatial features within a single CT slice, which overlook spatial relationships between consecutive slices. We propose an Appendix Object Correction Network (AOCN) to refine predictions of universal object detectors. Although AOCN is a 2D network, it effectively leverages spatial relationships across consecutive CT slices. AOCN requires only a few training epochs to improve the accuracy of bounding boxes significantly, which offers advantages such as high scalability, low cost, and reduced training time. It consists of a global case feature learning module for extracting global feature map from the CT case and an object feature relation module for modeling the relationships between objects across slices. Experimental results demonstrate the effectiveness and efficiency of AOCN in correcting the output bounding boxes of several mainstream object detection networks, with a 6% to 14% improvement in Recall while requiring only a few training epochs.

关键词： Training Representation learning Shape Computed tomography Scalability Refining Object detection Feature extraction Search problems Medical diagnostic imaging

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UPDExplainer: an Interpretable Transformer-based Framework for Urban Physical Disorder Detection Using Street View Imagery

arXiv

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arXiv 2023年

作者： Hu, Chuanbo Jia, Shan Zhang, Fan Xiao, Changjiang Ruan, Mindi Thrasher, Jacob Li, Xin Lane Department of Computer Science and Electrical Engineering West Virginia University MorgantownWV United States University at Buffalo State University of New York BuffaloNY United States Department of Civil and Environmental Engineering The Hong Kong University of Science and Technology Hong Kong College of Surveying and Geo-Informatics Tongji University Shanghai China

Urban Physical Disorder (UPD), such as old or abandoned buildings, broken sidewalks, litter, and graffiti, has a negative impact on residents’ quality of life. They can also increase crime rates, cause social disorder, and pose a public health risk. Currently, there is a lack of efficient and reliable methods for detecting and understanding UPD. To bridge this gap, we propose UPDExplainer, an interpretable transformer-based framework for UPD detection. We first develop a UPD detection model based on the Swin Transformer architecture, which leverages readily accessible street view images to learn discriminative representations. In order to provide clear and comprehensible evidence and analysis, we subsequently introduce a UPD factor identification and ranking module that combines visual explanation maps with semantic segmentation maps. This novel integrated approach enables us to identify the exact objects within street view images that are responsible for physical disorders and gain insights into the underlying causes. Experimental results on the re-annotated Place Pulse 2.0 dataset demonstrate promising detection performance of the proposed method, with an accuracy of 79.9%. For a comprehensive evaluation of the method’s ranking performance, we report the mean Average Precision (mAP), R-Precision (RPrec), and Normalized Discounted Cumulative Gain (NDCG), with success rates of 75.51%, 80.61%, and 82.58%, respectively. We also present a case study of detecting and ranking physical disorders in the southern region of downtown Los Angeles, California, to demonstrate the practicality and effectiveness of our framework. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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An Investigation of Smart Detection for Small Lung Tumor with Tumor Pattern Recognition Algorithm

An Investigation of Smart Detection for Small Lung Tumor wit...

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Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF), International Conference on

作者： G. Hariharan P. Prasanth P. Arthi Devarani T. Sajana Indhumathi C Ashok Kumar Deportment of Artificial Intelligence and Machine Learning Malla Reddy University Hyderabad Telangana India Department of Information Technology Vel Tech Multi Tech Dr. Rangarajan Dr.Sakunthala Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering R. M K College of Engineering and Technology Thiruvallur Tamil Nadu India Department of Artificial Intelligence and Data Science KoneruLakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Computer Science and Business Systems R.M. K. Engineering College Kavaraipettai Tamil Nadu Department of Computer Science BanasthaliVidyapith Rajasthan India

The Small-cell lung tumor is the prime public concern, resulting in increased mortality. Various therapeutic approaches have made progress in the handling of small-cell lung tumor. It's considered the backbone of cytotoxic chemotherapy treatment. However, clinical research shows that because small-cell lung tumor is chemically approachable, high response rates among small-cell lung tumorinfecters are generally not translated into cure. Therefore, it is high time to make progress in small-cell lung tumor research because every success in clinical trials is fought, and every failure is a lesson. A recent study of small-cell lung tumor has improved the prognosis of patients. Some notable research areas have provided new options for patients by conducting multiple clinical trials. In this paper smart edge detection based small lung tumor identification method was proposed. This model operates with the pattern recognition algorithm model. This algorithm smartly finds the edges of the tumor cells and provides the edge results. This will helpful for the doctors to identify the tumor part exactly.

关键词： Chemotherapy Image edge detection Lung cancer Medical services Clinical trials Knowledge discovery Concurrent engineering

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Drought Prediction based Standardized Precipitation Index Using Multilayer Perceptron Model

Drought Prediction based Standardized Precipitation Index Us...

引用

Smart Computing, IoT and Machine Learning (SIML), International Conference on

作者： Muhammad Agung Prabowo Santoso Soekirno Naufal Ananda Devina Putri Asri David Yulizar Suko Prayitno Adi Martarizal Nazori Suhandi Departement of Physics Faculty of Mathematics and Natural Science University of Indonesia Depok Indonesia Departement of Instrumentation and Control Bandung Institute of Technology Bandung Indonesia Banten Climatology Station Indonesia Meteorology Climatology and Geophysics Agency South Tangerang Indonesia State College of Meteorology Climatology and Geophysics Tangerang Indonesia Department of Informatics Faculty of Computer Science Universitas Indo Global Mandiri Palembang Indonesia

ISBN: (数字)9798350364101

ISBN: (纸本)9798350364118

Drought events have an impact on various sectors such as the economic, agricultural, environmental and social sectors. The impact of drought needs to be minimized by designing a drought prediction system, this is an effort to disseminate early warning information based on climate and hydrological aspects. Drought prediction uses Multi-Layer Perceptron (MLP) algorithm model to predict drought based on Standardized Precipitation Index (SPI) for 1 and 3 months. SPI is one of the drought indices calculated through rainfall analysis. Predictions were made using rainfall data obtained from the FY-4A QPE satellite, then processed into monthly rainfall accumulations for the 2019-2020 period. FY-4A QPE data is corrected based on rainfall observation data at the observation rain gauge. The corrected FY-4A QPE data is used to predict drought using the SPI1 (1 Month) and SPI3 (3 Months) indices. SPI1 shows fluctuations and a very small Nash-Sutcliffe Efficiency (NSE) value of -0.11, while SPI3 shows a model that is able to follow the peak and valley fluctuations of the actual value as well as an increase in the NSE value of 0.65 and a decrease in the Root Mean Square Error (RMSE) value of 1.43 in SPI1 to 0.77 in SPI3. Drought modeling using MLP was successfully implemented and it showed that as the time span increases, the SPI prediction performance gets better.

关键词： Rain Fluctuations Satellites Droughts computer architecture Predictive models Prediction algorithms

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A hybrid FEM-PINN method for time-dependent partial differential equations

arXiv

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arXiv 2024年

作者： Feng, Xiaodong Shangguan, Haojiong Tang, Tao Wan, Xiaoliang Zhou, Tao Division of Science and Technology BNU-HKBU United International College Zhuhai519087 China School of Electrical and Computer Engineering Guangzhou Nanfang College Guangzhou510970 China Department of Mathematics Center for Computation and Technology Louisiana State University Baton Rouge70803 United States Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China

In this work, we present a hybrid numerical method for solving evolution partial differential equations (PDEs) by merging the time finite element method with deep neural networks. In contrast to the conventional deep learning-based formulation where the neural network is defined on a spatiotemporal domain, our methodology utilizes finite element basis functions in the time direction where the space-dependent coefficients are defined as the output of a neural network. We then apply the Galerkin or collocation projection in the time direction to obtain a system of PDEs for the space-dependent coefficients which is approximated in the framework of PINN. The advantages of such a hybrid formulation are twofold: statistical errors are avoided for the integral in the time direction, and the neural network’s output can be regarded as a set of reduced spatial basis functions. To further alleviate the difficulties from high dimensionality and low regularity, we have developed an adaptive sampling strategy that refines the training set. More specifically, we use an explicit density model to approximate the distribution induced by the PDE residual and then augment the training set with new time-dependent random samples given by the learned density model. The effectiveness and efficiency of our proposed method have been demonstrated through a series of numerical experiments. © 2024, CC BY.

关键词： Partial differential equations

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Analytical and Neural Network Analysis on Flux-Coated Aluminium Alloy by Activated TIG Welding with Synthesized Nanocomposites

引用

Journal of Nanomaterials 2023年第1期2023卷

作者： Raja, V.L. Kumar, A. M. Senthil Kumari, K. Shantha Bharanidharan, R. Ezhilarasi, P. Rajeshkannan, S. Nithya, T.M. Kumar, S. Venkatesh Department of Mechanical Engineering Loyola Institute of Technology Palanchur Tamil Nadu Chennai600123 India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai600127 India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Tamil Nadu Chennai603203 India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Tamil Nadu Coimbatore641021 India Department of Electronics and Communication Engineering St. Joseph's College of Engineering OMR Tamil Nadu Chennai600119 India Department of Computer Science and Engineering K. Ramakrishnan College of Engineering Tamil Nadu Trichy621112 India Department of Mechanical Engineering College of Engineering and Technology Mettu University P.O. Box: 318 Mettu Ethiopia

This research focused to synthesize the material by the tungsten inert gas (TIG) welding process with support of appropriate flux coating material. Therefore the required amount of flux coating material was utilized to enhance the mechanical properties of the specified localized welded regions. Hence, this study concentrated to select the nano-SiO2 flux particles that were employed for TIG process. This activated TIG welding composes the flux-coated welding on the base metal of AA5083-H111, as this material was highly reactive with SiO2 by the presence of magnesium precipitates and well synthesized after the welding. The post- and preheat treatment process was achieved before and after welding. The selection of activated TIG process parameters composed of strengthened weld specimens along with constant parameters like electrode tip angle and flow rate, respectively. Initially, the process parameters were designed by the statistical analysis of Box Behnken method with support of regression formulation to determine the optimal solution. The maximum tensile strength was attained at the welding process parameters of welding speed (100 mm/min), voltage (13 V), and current (125 amps). The higher hardness was achieved at the process parameters of welding speed (80 mm/min), voltage (12 V), and current (125 amps), respectively. Finally, the neural network approach was utilized to verify the predicted responses of tensile and microhardness properties. The interaction plots, mean plots, and 3D scatter plots were influenced to enhance the process parameters. In this research, mechanical properties were enhanced by the flux-coated SiO2 and the analytical method also advances the optimal parameters. © 2023 V. L. Raja et al.

关键词： Silica

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EXPLANATION BY PROGRESSIVE EXAGGERATION 8

EXPLANATION BY PROGRESSIVE EXAGGERATION

引用

8th International Conference on Learning Representations, ICLR 2020

作者： Singla, Sumedha Pollack, Brian Chen, Junxiang Batmanghelich, Kayhan Department of Computer Science University of Pittsburgh United States Department of Biomedical Informatics University of Pittsburgh United States Intelligent Systems Program University of Pittsburgh United States

As machine learning methods see greater adoption and implementation in high stakes applications such as medical image diagnosis, the need for model interpretability and explanation has become more critical. Classical approaches that assess feature importance (e.g., saliency maps) do not explain how and why a particular region of an image is relevant to the prediction. We propose a method that explains the outcome of a classification black-box by gradually exaggerating the semantic effect of a given class. Given a query input to a classifier, our method produces a progressive set of plausible variations of that query, which gradually changes the posterior probability from its original class to its negation. These counter-factually generated samples preserve features unrelated to the classification decision, such that a user can employ our method as a "tuning knob" to traverse a data manifold while crossing the decision boundary. Our method is model agnostic and only requires the output value and gradient of the predictor with respect to its input. © 2020 8th International Conference on Learning Representations, ICLR 2020. All rights reserved.

关键词： Semantics

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Skin-like cryogel electronics from suppressed-freezing tuned polymer amorphization

引用

Nature Communications 2023年第1期14卷 1-10页

作者： Zhang, Xiansheng Yan, Hongwei Xu, Chongzhi Dong, Xia Wang, Yu Fu, Aiping Li, Hao Lee, Jin Yong Zhang, Sheng Ni, Jiahua Gao, Min Wang, Jing Yu, Jinpeng Ge, Shuzhi Sam Jin, Ming Liang Wang, Lili Xia, Yanzhi State Key Laboratory of Bio-Fibers and Eco-Textiles College of Textiles and Clothing Qingdao University Qingdao 266071 China Institute for Future Shandong Key Laboratory of Industrial Control Technology School of Automation Qingdao University Qingdao 266071 China College of Materials Science and Engineering Qingdao University Qingdao 266071 China CAS Key Laboratory of Engineering Plastics Institute of Chemistry Chinese Academy of Sciences Beijing 100190 China Department of Chemistry SungKyunKwan University Suwon 16419 South Korea State Key Laboratory of Fluid Power and Mechatronic Systems College of Mechanical Engineering Zhejiang University Hangzhou 310027 China College of Biological Science and Medical Engineering Donghua University Shanghai 201620 China Institute of Environmental Engineering ETH Zürich Zürich 8093 Switzerland Department of Electrical and Computer Engineering National University of Singapore Singapore 117576 Singapore

The sole situation of semi-crystalline structure induced single performance remarkably limits the green cryogels in the application of soft devices due to uncontrolled freezing field. Here, a facile strategy for achieving multifunctionality of cryogels is proposed using total amorphization of polymer. Through precisely lowering the freezing point of precursor solutions with an antifreezing salt, the suppressed growth of ice is achieved, creating an unusually weak and homogenous aggregation of polymer chains upon freezing, thereby realizing the tunable amorphization of polymer and the coexistence of free and hydrogen bonding hydroxyl groups. Such multi-scale microstructures trigger the integrated properties of tissue-like ultrasoftness (Young’s modulus <10 kPa) yet stretchability, high transparency (~92%), self-adhesion, and instantaneous self-healing (<0.3 s) for cryogels, along with superior ionic-conductivity, antifreezing (−58 °C) and water-retention abilities, pushing the development of skin-like cryogel electronics. These concepts open an attractive branch for cryogels that adopt regulated crystallization behavior for on-demand functionalities. © 2023, Springer Nature Limited.

关键词： Gels and hydrogels Polymers

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PARAMETERIZED PROXIMAL-GRADIENT ALGORITHMS FOR L1/L2 SPARSE SIGNAL RECOVERY

arXiv

引用

arXiv 2023年

作者： Zhang, Na Liu, Xinrui Li, Qia Department of Applied Mathematics College of Mathematics and Informatics South China Agricultural University Guangzhou510642 China School of Mathematics and Statistics Shandong Normal University Jinan250358 China Guangdong Province Key Laboratory of Computational Science School of Computer Science and Engineering Sun Yat-sen University Guangzhou510275 China

The ratio of 1 and 2 norms (1/2), serving as a sparse promoting function, receives considerable attentions recently due to its effectiveness for sparse signal recovery. In this paper, we propose an 1/2 based penalty model for recovering sparse signals from noiseless or noisy observations. It is proven that stationary points of the proposed problem tend to those of the elliptically constrained 1/2 minimization problem as the smoothing parameter goes to zero. Moreover, inspired by the parametric approach for the fractional programming, we design a parameterized proximal-gradient algorithm (PPGA) as well as its line search counterpart (PPGA_L) for solving the proposed model. The closed-form solution of the involved proximity operator is derived, which enable the efficiency of the proposed algorithms. We establish the global convergence of the entire sequences generated by PPGA and PPGA_L with monotone objective values by taking advantage of the fact that the objective of the proposed model is a KL function. Numerical experiments show the efficiency of the proposed algorithms over the state-of-the-art methods in both noiseless and noisy sparse signal recovery problems. Copyright © 2023, The Authors. All rights reserved.

关键词： Efficiency

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Reconfigurable intelligent surfaces for wireless communications:Overview of hardware designs,channel models,and estimation techniques

引用

Intelligent and Converged Networks 2022年第1期3卷 1-32页

作者： Mengnan Jian George C.Alexandropoulos Ertugrul Basar Chongwen Huang Ruiqi Liu Yuanwei Liu Chau Yuen the Wireless Research Institute ZTE CorporationBeijing 100029China the State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen 518055China Department of Informatics and Telecommunications National and Kapodistrian University of AthensAthens 15784Greece Communications Research and Innovation Laboratory(CoreLab) Department of Electrical and Electronics EngineeringKoçUniversityIstanbul 34450Turkey College of Information Science and Electronic Engineering Zhejiang UniversityHangzhou 310027China School of Electronic Engineering and Computer Science Queen Mary University of LondonLondonE14NSUK. the Engineering Product Development(EPD)Pillar Singapore University of Technology and DesignSingapore 487372Singapore

The demanding objectives for the future sixth generation(6G)of wireless communication networks have spurred recent research efforts on novel materials and radio-frequency front-end architectures for wireless connectivity,as well as revolutionary communication and computing *** the pioneering candidate technologies for 6G belong the reconfigurable intelligent surfaces(RISs),which are artificial planar structures with integrated electronic circuits that can be programmed to manipulate the incoming electromagnetic field in a wide variety of *** RISs in wireless networks have been recently advocated as a revolutionary means to transform any wireless signal propagation environment to a dynamically programmable one,intended for various networking objectives,such as coverage extension and capacity boosting,spatiotemporal focusing with benefits in energy efficiency and secrecy,and low electromagnetic field *** by the recent increasing interests in the field of RISs and the consequent pioneering concept of the RIS-enabled smart wireless environments,in this paper,we overview and taxonomize the latest advances in RIS hardware architectures as well as the most recent developments in the modeling of RIS unit elements and RIS-empowered wireless signal *** also present a thorough overview of the channel estimation approaches for RIS-empowered communications systems,which constitute a prerequisite step for the optimized incorporation of RISs in future wireless ***,we discuss the relevance of the RIS technology in the latest wireless communication standards,and highlight the current and future standardization activities for the RIS technology and the consequent RIS-empowered wireless networking approaches.

关键词： reconfigurable intelligent surfaces beyond 5G 6G channel modeling hardware architecture channel estimation smart wireless environment standardization

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