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IEEE Workshop on Evolving and Adaptive Intelligent systems (EAIS)

作者： Vitor O. Barth Henrique O. Caetano Carlos D. Maciel Marco Aiello Dept. of Electrical and Computer Engineering São Carlos School of Engineering University of São Paulo Department of Service Computing Institute for Architecture of Application Systems University of Stuttgart

ISBN: (数字)9798350366235

ISBN: (纸本)9798350366242

The key goals in learning Bayesian networks (BNs) from data are to identify significant statistical relationships between variables and to build a Directed Acyclic Graph (DAG) that represents these relationships through Joint Probability Distributions. Most research relies on score-based or conditional test methods for model selection. However, when using real-world data, it can be challenging to identify whether the learned DAG represents the underlying relations inherent in the limited datasets, particularly when evaluating data obtained from multiple independent sources. This study presents a methodology to assess the credible interval for both the existence and direction of each edge within Bayesian networks derived from data. Furthermore, it explores the fusion of models acquired from distinct and independent datasets. This approach enables the Bayesian learning of Bayesian Networks (BNs) from data by treating the uncertainty associated with the existence and orientation of each edge as a random variable. By evaluating the probability of the orientation of each edge, it is possible to suggest the existence of a potential latent variable within the dataset. If an edge exhibits equiprobable directions and is verified to exist, it becomes a plausible hypothesis for a latent variable. The Fast Causal Algorithm, originally introduced by [1], is the foundation of this approach. Finally, by employing a maximum a posteriori estimation, the most prominent edges and their respective orientations are identified and employed to create the leading DAG. We present our findings in simulated datasets with different length sizes. By comparing the structure of the learned DAGs with existing structures and evaluating the inference capabilities of the final BN, we establish that our approach achieves results comparable to the most recent studies in the field, while offering insights into the model’s reliability and improving the use of the data.

关键词： Maximum a posteriori estimation Uncertainty Scalability Data models Probability distribution Bayes methods Random variables

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Calibrating Panoramic dept. Estimation for Practical Localization and Mapping

Calibrating Panoramic Depth Estimation for Practical Localiz...

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International Conference on computer Vision (ICCV)

作者： Junho Kim Eun Sun Lee Young Min Kim Dept. of Electrical and Computer Engineering Seoul National University Interdisciplinary Program in Artificial Intelligence and INMC Seoul National University

The absolute dept. values of surrounding environments provide crucial cues for various assistive technologies, such as localization, navigation, and 3D structure estimation. We propose that accurate dept. estimated from panoramic images can serve as a powerful and light-weight input for a wide range of downstream tasks requiring 3D information. While panoramic images can easily capture the surrounding context from commodity devices, the estimated dept. shares the limitations of conventional image-based dept. estimation; the performance deteriorates under large domain shifts and the absolute values are still ambiguous to infer from 2D observations. By taking advantage of the holistic view, we mitigate such effects in a self-supervised way and fine-tune the network with geometric consistency during the test phase. Specifically, we construct a 3D point cloud from the current dept. prediction and project the point cloud at various viewpoints or apply stretches on the current input image to generate synthetic panoramas. Then we minimize the discrepancy of the 3D structure estimated from synthetic images without collecting additional data. We empirically evaluate our method in robot navigation and map-free localization where our method shows large performance enhancements. Our calibration method can therefore widen the applicability under various external conditions, serving as a key component for practical panorama-based machine vision systems.

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Corrected: On Confident Policy Evaluation for Factored Markov Decision Processes with Node Dropouts

arXiv

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arXiv 2023年

作者： Fiscko, Carmel Kar, Soummya Sinopoli, Bruno The Dept. of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA United States The Dept. of Electrical and Systems Engineering Washington University St. LouisMO United States

In this work we investigate an importance sampling approach for evaluating policies for a structurally time-varying factored Markov decision process (MDP), i.e. the policy’s value is estimated with a high-probability confidence interval. In particular, we begin with a multi-agent MDP controlled by a known policy but with unknown transition dynamics. One agent is then removed from the system - i.e. the system experiences node dropout - forming a new MDP of the remaining agents, with a new state space, action space, and new transition dynamics. We assume that the effect of removing an agent corresponds to the marginalization of its factor in the transition dynamics. The reward function may likewise be marginalized, or it may be entirely redefined for the new system. Robust policy importance sampling is then used to evaluate candidate policies for the new system, and estimated values are presented with probabilistic confidence bounds. This computation is completed with no observations of the new system, meaning that a safe policy may be found before dropout occurs. The utility of this approach is demonstrated in simulation and compared to Monte Carlo simulation of the new system. © 2023, CC BY.

关键词： Importance sampling

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Dynamic Mesh Recovery from Partial Point Cloud Sequence

Dynamic Mesh Recovery from Partial Point Cloud Sequence

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International Conference on computer Vision (ICCV)

作者： Hojun Jang Minkwan Kim Jinseok Bae Young Min Kim Dept. of Electrical and Computer Engineering Seoul National University Interdisciplinary Program in Artificial Intelligence and INMC Seoul National University

The exact 3D dynamics of the human body provides crucial evidence to analyze the consequences of the physical interaction between the body and the environment, which can eventually assist everyday activities in a wide range of applications. However, optimizing for 3D configurations from image observation requires a significant amount of computation, whereas real-world 3D measurements often suffer from noisy observation or complex occlusion. We resolve the challenge by learning a latent distribution representing strong temporal priors. We use a conditional variational autoencoder (CVAE) architecture with a transformer to train the motion priors with a large-scale motion dataset. Then our feature follower effectively aligns the feature spaces of noisy, partial observation with the necessary input for pre-trained motion priors, and quickly recovers a complete mesh sequence of motion. We demonstrate that the transformer-based autoencoder can collect necessary spatio-temporal correlations robust to various adversaries, such as missing temporal frames, or noisy observation under severe occlusion. Our framework is general and can be applied to recover the full 3D dynamics of other subjects with parametric representations.

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An Artificial Neural Network Model to Forecast Grain Crops in North-Kazakstan Agricultural Experimental Station

An Artificial Neural Network Model to Forecast Grain Crops i...

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2023 IEEE International Conference on Communications, Information, Electronic and Energy systems, CIEES 2023

作者： Mimenbayeva, Aigul Omirtay, Maylen Niyazova, Rozamgul Bekmagambetova, Gulmira Suleimenova, Raya Tursumbayeva, Ainur Astana It University Dept. of Computational and Data Sciences Astana Kazakhstan Astana It University Specialty Smart Technologies Astana Kazakhstan L. Gumilyov Eurasian National University Dept. of Artificial Intelligence Technologies Astana Kazakhstan S. Seifullin Kazakh Agrotechnical Research University Dept. of Computer Science Astana Kazakhstan Eurasian Technological University School of Engineering and Information Technology Almaty Kazakhstan S. Seifullin Kazakh Agrotechnical Research University Dept. of Information Systems Astana Kazakhstan

ISBN: (纸本)9798350336917

The paper analyzes and predicts the yield of grain crops of LLP 'North-Kazakhstan Agricultural Experimental Station' for the last 30 years. The class of artificial neural networks-multilayer perceptron (MLP) was used as a research tool. The process of training of a model of a neural network consisting of 30 networks was performed on the basis of 70% training and 30% control samples. Based on the analysis of residuals histogram and scatter plot of the target and output function, the best neural network models MLP-1-7-1 (p=0.92), MLP-1-8-1 (p=0.97), MLP-1-5-1 (p=0.82) were selected. Next, using the best MLP 1-8-1 network, the model was tested the given data. Using time series projection, a graph was illustrated and a table of predicted grain yield values for the coming years was tabulated. The absolute error showed the high accuracy of the obtained best MLP 1-8-1 network for forecasting the amount of grain crop yield. The obtained model of artificial network can be applied in research and monitoring of agricultural production development. © 2023 IEEE.

关键词： Multilayer neural networks

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Optimizing Mobility IoT Device Networks with Dynamic RIS in mMIMO-Cooperative NOMA 6G systems

Optimizing Mobility IoT Device Networks with Dynamic RIS in ...

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Emerging Technologies for Dependable Internet of Things (ICETI), International Conference on

作者： Khalil Hassan Mohamed Hassan Khalid Hamid Elmuntaser Hassan Rania A. Mokhtar Mamoon M. Saeed Dept. of Comm. Systems Uni. of Science & Technology Omdurman Sudan Dept. of Electrical Eng Omdurman Islamic Uni. Omdurman Sudan Dept. of Computer Science Al-Neelain University Khartoum Sudan Department of Computer Engineering Taif University Taif KSA Department of Communications and Electronics Engineering Faculty of Engineering University of Modern Sciences (UMS) Yemen

ISBN: (数字)9798331533557

ISBN: (纸本)9798331533564

Advanced technologies like reconfigurable intelligent surfaces (RISs), massive multiple-input multiple-output (mMIMO), and non-orthogonal multiple access (NOMA) are crucial for the upcoming 6G communications landscape, particularly for IoT applications. This study uniquely explores the effects of RIS dynamics on the performance of mMIMO downlink (DL) cooperative NOMA systems in dense IoT environments. It introduces a novel methodology for modeling dynamic traffic distribution and integrating RIS, focusing on key performance metrics such as network capacity, spectral efficiency (SE), latency, and energy efficiency (EE). Various scenarios are evaluated, considering different distributions of IoT devices and signal-to-noise ratios (SNRs). Using simulation software, the research analyzes performance under unstable channel conditions with varying device distances, speeds, and power locations, employing 256-QAM modulation and successive interference cancellation (SIC). The results demonstrate that dynamic RIS significantly enhances capacity, spectrum efficiency, latency, and EE, paving the way for optimized IoT network architecture in future wireless systems.

关键词： 6G mobile communication Wireless communication NOMA Interference cancellation System performance Reconfigurable intelligent surfaces Network architecture Dynamic scheduling Internet of Things Resource management

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Comparative analysis of OFDM in AWGN & Local Acoustic Channel

Comparative analysis of OFDM in AWGN & Local Acoustic Channe...

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Humanitarian Technology Conference (KHI-HTC), IEEE Karachi Section

作者： Areeb Ahmed Aish Siddqui Rehan Khan Ghulam Nabi Murtaza Ali Dept. Electrical and Computer Systems Engineering Mohammad Ali Jinnah University Karachi Pakistan Centers of Excellence in Science and Applied Technologies (CESAT) Islamabad Pakistan

In this research study, a comparative analysis is presented that analyze the Orthogonal Frequency Division Multiplexing (OFDM) in two distinct communication systems Additive White Gaussian Noise (AWGN) and a Local Acoustic Channel. This study incorporates insights from the BELLHOP model to enhance understanding of sound propagation in dept. of water and guide the adaptation of OFDM for optimized communication. From simulated experiments, evaluate the performance of OFDM is assessed in the presence of local acoustic channel and AWGN, the important parameters like signal-to-noise ratio (SNR) and bit error rate (BER). The system of BELLHOP provides valuable context in the comparative analysis, highlighting the result of environmental factors on the performance of OFDM. The study not only contributes to the ongoing efforts in advancing underwater communication technique but also emphasizes the significance of tailored approach’s in different underwater scenarios, ultimately having the way for more robust and efficient communication systems beneath the waves. Through a comparative analysis between AWGN and a local acoustic channel, results show that the system achieves a BER of 10 −3 at SNR of 10 in local acoustic channel, while in the AWGN, a similar BER is achieved at SNR of 16.

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Multivariate Optimal Hybrid Deep Learning Model for Forecasting of Day-Ahead Solar Irradiance with Meteorological Constraints

Multivariate Optimal Hybrid Deep Learning Model for Forecast...

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Annual North-American Power Symposium

作者： Sohag Kumar Saha Satish M. Mahajan Dept. of Electrical & Computer Engineering (ECE) Tennessee Technological University Center for Energy Systems Research (CESR) Tennessee Technological University Cookeville TN USA

ISBN: (数字)9798331521035

ISBN: (纸本)9798331521042

With the growing integration of solar power gener-ation into smart grids, accurate solar irradiance forecasting is of paramount importance for efficient grid operation and renew-able energy management. In this paper, a data decomposition approach with two different methods of deep learning and grid search optimization was combined to develop a better hybrid model to forecast solar irradiance. The proposed model combines spatial and temporal information to improve forecasting accuracy by considering the impact of meteorological constraints such as global horizontal irradiance, temperature, relative humid-ity, wind speed, cloud type, direct normal irradiance, diffuse horizontal irradiance, and solar zenith angle. In addition to the model architecture, this work incorporates hyper-parameter optimization to fine-tune the parameters of the model for optimal performance. The design of the model ensures that the system adapts to the specific characteristics of the solar irradiance data and meteorological conditions under consideration. The proposed hybrid model was evaluated using real-world data to outperform traditional forecasting methods in terms of accuracy. The results of the proposed hybrid model indicate better prediction ability as measured by four parameters (lower RMSE and MAE, fewer epochs, and a higher R2 co-efficient).

关键词： Solar irradiance Deep learning Adaptation models Accuracy Predictive models Data models Smart grids Forecasting Optimization Load modeling

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Medical Knowledge-Guided Deep Learning for Mammographic Breast Density Classification

Medical Knowledge-Guided Deep Learning for Mammographic Brea...

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Medical Imaging 2023: computer-Aided Diagnosis

作者： Gao, Long Liu, Chang Arefan, Dooman Panigrahy, Ashok Zuley, Margarita L. Wu, Shandong Dept. of Radiology University of Pittsburgh PittsburghPA15213 United States Dept. of Bioengineering University of Pittsburgh PittsburghPA15213 United States Dept. of Biomedical Informatics University of Pittsburgh PittsburghPA15213 United States Intelligent Systems Program University of Pittsburgh PittsburghPA15213 United States College of Computer National University of Defense Technology Changsha410073 China Magee-Womens Hospital University of Pittsburgh Medical Center PittsburghPA15213 United States

ISBN: (纸本)9781510660359

Classification of Breast Imaging Reporting and Data System (BI-RADS) breast density categories generally reflects the amount of dense/fibroglandular tissue in the breast. Studies have consistently shown that breast with higher density has a higher risk of developing breast cancer compared to breast with lower density. In this paper, we propose a novel end-to-end method, namely, Medical Knowledge-guided Deep Learning (MKDL), for breast mammogram density classification. The principle behind MKDL lies in the fact that many breast image density classification tasks are partly or largely based on certain pre-known image features, such as image contrast and brightness. These pre-known features can be computationally represented and then leveraged as prior knowledge to facilitate more effective model learning and thus boost the classification performance. We designed specific knowledge-based transformations for breast density classification and showed that our model outperformed several state-of-the-art models. © 2023 SPIE.

关键词： Classification (of information)

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Angle of Arrival Estimation Using SRS in 5G NR Uplink Scenarios

arXiv

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arXiv 2024年

作者： Spanos, Thodoris Fabra, Fran López-Salcedo, José A. Seco-Granados, Gonzalo Kanistras, Nikos Lapin, Ivan Paliouras, Vassilis Dept. of Electrical and Computer Engineering University of Patras Greece Loctio Greece Spain Radio Navigation Systems and Techniques Section European Space Agency Netherlands

This paper presents a comprehensive exploration of Angle of Arrival (AoA) estimation techniques in 5G environments, using the Sounding Reference Signal (SRS) in Uplink scenarios both in simulations and with actual measurements. Leveraging 5G capabilities, we investigate AoA algorithms for single-base station positioning. The study includes simulations and practical tests on a developed dedicated testbed featuring a base station equipped with a three-element Uniform Linear Array (ULA), considering Line of Sight conditions in an open environment. The testbed, employing Ettus E312 as the transmitter and Ettus N310 as the receiver, details waveform structures and reception processes. Additionally, our study examines the performance of Angle of Arrival (AoA) estimation algorithms, such as Multiple Signal Classification (MUSIC), Estimation of Signal Parameters via Rotational Invariant Techniques (ESPRIT), and Joint Angle and Delay Estimation (JADE) ESPRIT. A MATLAB ray tracing propagation model of the environment where the measurements are conducted, has been constructed. Simulation results using this model are presented, along with the actual measurements. The obtained results affirm the effectiveness of our implementation. © 2024, CC BY-NC-ND.

关键词： Testbeds

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