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SSRN 2023年

作者： Li, Chuan Wang, Shiyi Wang, Yunsheng Zhang, Panpan Yang, Xi Wan, Xiaorong Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Computer Technologies Application Kunming650500 China Yunnan Aerospace Engineering Geophysical Detecting co.ltd Yunnan Kunming650500 China

Tunnel fissure water may induce geological disasters of mud bursting and collapse during the construction stage. Therefore, it is important to automatically extract and identify fissure water in front of the tunnel face for tunnel construction. The paper performs diffraction migration imaging of seismic data after P/S wave separation, and the imaging results reflect the three-dimensional distribution of undesirable geologic bodies. Second, based on the geologic bodies obtained above, ground-penetrating radar detection is added to identify alternative area of fissure water at the peak point of the A-scan. The boundary point of the alternative area of fissure water is located at the peak point where the phase of the reflected wave and the incident wave are reversed. Finally, the area with a larger amplitude and smaller frequency than the surrounding rock is screened as the target area among the alternative area, and the distribution of fissure water is labeled in the B-scan. The method is applied to the mileage section K38+440$\sim$+320 of Liangwangshan Tunnel in Yunnan, and the forecast results show that the rock bodies near the mileage section K38+440$\sim$+410, K38+410$\sim$+380, and K38+350$\sim$+320 is more fragmented, the fissure water is developed, and the target area is verified by excavation. © 2023, The Authors. All rights reserved.

关键词： Ground penetrating radar systems

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Identification of personalized driver genes for individuals using graph convolution network

Identification of personalized driver genes for individuals ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wei Peng Piaofang Yu Wei Dai Xiaodong Fu Li Liu Yi Pan Faculty of Information Engineering and Automation Kunming University of Science and Technology Computer Technology Application Key Lab of Yunnan Province Kunming University of Science and Technology Kunming China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

The correct identification of the driver genes that lead to cancer development is essential for understanding the mechanisms of cancer and developing drugs to treat it. Currently, most computational methods for identifying cancer driver genes are based on a cohort of patients. However, due to the heterogeneity of cancers, patients diagnosed with the same cancers may have different genomic characteristics and present varied clinical symptoms. It requires devising effective methods to identify personalized cancer driver genes in an individual. This work developed a novel method to predict personalized cancer driver genes of a single sample based on graph convolution networks, namely pDriverGCN. pDriverGCN constructed a mutant gene-sample heterogeneous network according to the known driver genes of samples. Then it employed two separate graph convolution network models to learn feature representations for genes and samples by gathering the features of themselves and their neighbors. Finally, pDriverGCN used the feature representations to reconstruct the association matrix between genes and samples through a linear correlation coefficient decoder. We apply our model to identify personalized driver genes of samples on the TCGA datasets. The experimental results show that our model outperforms state-of-the-art methods being evaluated at both population and individual levels.

关键词： Correlation coefficient Representation learning Drugs Convolution Sociology Genomics Heterogeneous networks

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HSR-Diff: Hyperspectral Image Super-Resolution via Conditional Diffusion Models

HSR-Diff: Hyperspectral Image Super-Resolution via Condition...

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International Conference on computer Vision (ICCV)

作者： Chanyue Wu Dong Wang Yunpeng Bai Hanyu Mao Ying Li Qiang Shen National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech & Image Information Processing School of Computer Science Northwestern Polytechnical University China Department of Computer Science Faculty of Business and Physical Sciences Aberystwyth University U.K.

Despite the proven significance of hyperspectral images (HSIs) in performing various computer vision tasks, its potential is adversely affected by the low-resolution (LR) property in the spatial domain, resulting from multiple physical factors. Inspired by recent advancements in deep generative models, we propose an HSI Super-resolution (SR) approach with Conditional Diffusion Models (HSR-Diff) that merges a high-resolution (HR) multispectral image (MSI) with the corresponding LR-HSI. HSR-Diff generates an HR-HSI via repeated refinement, in which the HR-HSI is initialized with pure Gaussian noise and iteratively refined. At each iteration, the noise is removed with a Conditional Denoising Transformer (CDFormer) that is trained on denoising at different noise levels, conditioned on the hierarchical feature maps of HR-MSI and LR-HSI. In addition, a progressive learning strategy is employed to exploit the global information of full-resolution images. Systematic experiments have been conducted on four public datasets, demonstrating that HSR-Diff outperforms state-of-the-art methods.

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Short-term Load Forecasting based on Kalman Filter and Nonlinear Autoregressive Neural Network

Short-term Load Forecasting based on Kalman Filter and Nonli...

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第33届中国控制与决策会议

作者： Zhang Liang Zheng Chengyuan Zhao Zhengang Zhang Dacheng Faculty of Information Engineering and Automations Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technology Application Kunming University of Science and Technology

Power load forecasting is significant to research for power supply and strategy departments of electric power *** affects the distribution and the demand for electrical energy in different *** load forecasting methods mainly include regression analysis,time series analysis,and other statistical methods combined with machine *** this paper,the authors analyzed the load data of a region in Yunnan Province,established a load calculation model by combining environmental factors,week types,special events,and daily load,and obtained the best load estimation data through the Kalman filtering ***,the authors analyzed the statistical characteristics of the load series and established the Nonlinear Autoregressive Neural Network(NARnn) based on the Kalman filtered *** following two weeks' load data was predicted by shifting the timeline keeping the total load data *** average daily mean absolute precentage error(MAPE) for the two weeks is 7.69%.The MAPE between two-week forecast data and actual load is 8.05%.

关键词： Short-term Load Forecasting Time Series Kalman Filter Nonlinear Autoregressive Neural Network

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Novel Deep Learning Network Architectures for Time Series Forecasting

Novel Deep Learning Network Architectures for Time Series Fo...

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Paradigm Shift in Information Technologies with Innovative applications in Global Scenario (ICPSITIAGS), IEEE International Conference on

作者： Jitendra Kumar Katariya Dhananjay Yadav Gayathri C Vikas K. Kolekar Umesh M. Daivagna V. Pushparajesh Department of Computer Science & Application Vivekananda Global University Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology UP India Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Engineering Vishwakarma Institute of Information Technology Pune INDIA Department of uGDX ATLAS SkillTech University Mumbai Maharastra India Department of Electrical and Electronics Engineering Faculty of Engineering and Technology Bangalore Karnataka

ISBN: (数字)9798350329773

ISBN: (纸本)9798350329780

This paper presents novel deep-learning network architectures for time series forecasting. First, a singular deep gaining knowledge of network architecture is proposed and tested for the usage of the Google tendencies dataset as a benchmark. The proposed structure combines 1-dimensional convolution neural networks, recurrent neural networks, and long-term cells to seize and study lengthy-term styles in the time series information. The structure is compared to current deep getting-to-know architectures and is shown to outperform them in phrases of accuracy. Moreover, the proposed structure is applied to a secondary time collection dataset, the COVID-19 confirmed infection dataset, to attain compelling effects. Eventually, an open-source implementation of the proposed architecture is made available for use in addition to research.

关键词： Knowledge engineering Deep learning Recurrent neural networks Time series analysis Network architecture Internet Forecasting

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Corn Processing by Pulsed Electric Fields with Respect to Microbial Inactivation and Improvement of Seed Vigour

SSRN

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SSRN 2023年

作者： Akdemir Evrendilek, Gulsun Atmaca, Bahar Uzuner, Sibel Boston University College of Engineering Department of Electric and Computer Engineering BostonMA United States Mardin Artuklu University Center Research Laboratory Application and Research Center Mardin Turkey Izmir Institute of Technology Faculty of Engineering Department of Food Engineering Izmir Turkey

Pulsed electric field (PEF) treatment of corn grains to improve seed vigour and inactivation of endogenous microflora by energies ranging from 1.20 to 28.8 J were applied to determine effectiveness of applied energies on germination rate (GR), normal seedling rate (NSR), electrical conductivity (EC), ability to germinate under salt (100- and 200 mM salt) and cold (at 10 C for 7 days and at 25C for 5 days) stresses in addition to inactivation of total aerobic mesophilic bacteria (TAMB), total mold and yeast (TMY), and inactivation rate (%) of Aspergillus parasiticus. Increased energy provided 11.10% increase in GR, 21.22% increase in NSR, 95.50% increase in germination at 10 °C for 7 days, 32.53% increase in germination at 24 °C for 5 days, 68.35% increase under 100 mM- and 76% increase under 200 mM NaCI salt stresses. Inactivation on the mean initial TAMB and TMY were approximately 9.25 and 7.93 log, respectively, with 63.33 ± 0.22% reduction in A. parasiticus culture. PEF treated corn seedlings had stronger and taller body formation with stronger roots. The most optimal processing parameters were detected as 300 Hz, 28.80 J, and 19.78 sec. PEF treatment carries a high potential to improve corn vigour with inactivation of surface microflora. © 2023, The Authors. All rights reserved.

关键词： Germination

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An Analysis of Mobile Security Protocols in Wireless Sensor Networks

An Analysis of Mobile Security Protocols in Wireless Sensor ...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Sheril. S Priyanka D. More Rahul Mishra Neeraj Das Ramachandran T Zahid Ahmed Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Mechanical Engineering Faculty of Engineering and Technology Bangalore Karnataka India Department of Computer Science & Application Vivekananda Global University Jaipur India

This examines examines the cutting-edge state of security protocols for wi-fi sensor networks within the mobile communications area. We examine the performance of present protection protocols for wi-fi sensor networks that specialize in their effectiveness, strength-efficiency, and scalability. We then endorse an advanced protection protocol which can higher cope with the security challenges of wi-fi sensor networks. Our new protection protocol incorporates cryptographic algorithms tailored for the unique necessities of wi-fi sensor networks, as well as key trade mechanisms that offer more desirable scalability and energy-performance. We evaluate our proposed security protocol, and examine its tradeoffs in evaluation with existing security protocols. Our consequences display that the proposed protocol is extra comfortable, gives higher scalability, and achieves more electricity-efficiency. We also check out the impact of different routing protocols on the security of wireless sensor networks in cellular communications and analyze how they may be tormented by exclusive security protocols. Our findings advise that a hybrid of two current routing protocols provides suitable security for wireless sensor networks.

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User Acceptance of New HCI Technologies

User Acceptance of New HCI Technologies

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International IEEE Conference and Workshop: Óbuda on Electrical and Power Engineering (CANDO-EPE)

作者： Jozsef Katona Attila Kovari Kandó Kálmán Faculty of Electrical Engineering Institute of Electronics and Communication Systems Obuda University Budapest Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemét Hungary Faculty of Computer Science Institute of Digital Technology Eszterházy Károly Catholic University Eger Hungary Department of Natural Sciences Institute of Engineering University of Dunaujvaros Dunaujvaros Budapest Hungary

Human-computer interaction (HCI) is an evolving field of research that focuses on understanding and improving the communication and interaction between humans and computers. Over the past decades, we have seen many significant advances in this field, which have contributed to the widespread adoption and integration of technology into our daily lives. HCI research and development aims to design information technology systems to meet the needs and preferences of users. Usability, efficiency, accessibility and user satisfaction are important considerations in HCI design. This paper presents the results of a pilot study on user acceptance of HCIs. The results show that users are positive about and willing to use HCIs.

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RIS-Enabled Joint Near-Field 3D Localization and Synchronization in SISO Multipath Environments

arXiv

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arXiv 2024年

作者： Yan, Han Chen, Hua Liu, Wei Yang, Songjie Wang, Gang Yuen, Chau The Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China The Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo315211 China The School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom The National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China China The School of Electrical and Electronics Engineering Nanyang Technological University Singapore

Reconfigurable Intelligent Surfaces (RIS) show great promise in the realm of 6th generation (6G) wireless systems, particularly in the areas of localization and communication. Their cost-effectiveness and energy efficiency enable the integration of numerous passive and reflective elements, enabling near-field propagation. In this paper, we tackle the challenges of RIS-aided 3D localization and synchronization in multipath environments, focusing on the near-field of mmWave systems. Specifically, our approach involves formulating a maximum likelihood (ML) estimation problem for the channel parameters. To initiate this process, we leverage a combination of canonical polyadic decomposition (CPD) and orthogonal matching pursuit (OMP) to obtain coarse estimates of the time of arrival (ToA) and angle of departure (AoD) under the far-field approximation. Subsequently, distances are estimated using l1-regularization based on a near-field model. Additionally, we introduce a refinement phase employing the spatial alternating generalized expectation maximization (SAGE) algorithm. Finally, a weighted least squares approach is applied to convert channel parameters into position and clock offset estimates. To extend the estimation algorithm to ultra-large (UL) RIS-assisted localization scenarios, it is further enhanced to reduce errors associated with far-field approximations, especially in the presence of significant near-field effects, achieved by narrowing the RIS aperture. Moreover, the Cramér-Rao Bound (CRB) is derived and the RIS phase shifts are optimized to improve the positioning accuracy. Numerical results affirm the efficacy of the proposed estimation algorithm. © 2024, CC BY.

关键词： Cost effectiveness

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An Analysis of Encapsulating Security Payload in Wireless Network Security

An Analysis of Encapsulating Security Payload in Wireless Ne...

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Emerging Research in Computational Science (ICERCS), International Conference on

作者： Devika A. Verma Vaishali Singh Beemkumar. N Ritu Shree Deepak Minhas S. Yuvaraj Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Mechanical Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Department of Computer Science & Application Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai

The Encapsulating Safety Payload (ESP) is a necessary part of the IPsec suite of protocols, imparting a critical layer of security within a Wi-Fi community. This paper aims to evaluate ESP and its software in wireless community protection comprehensively. ESP capabilities as a secured payload encapsulating data via imparting integrity, authentication, and confidentiality. It commonly accomplishes this by using a mixture of cryptography and tunneling to shield the contents of the packets from alteration, eavesdropping, and unauthorized get entry. Moreover, this paper outlines the distinctive ESP modes and cipher suites, presenting how they may be used to defend data being despatched over a Wi-Fi community. ESP additionally affords numerous safety enhancements, such as protection from replay assaults, packet length control, and critical negotiation. Furthermore, this paper explores the challenges associated with implementing and deploying ESP in wireless networks. Subsequently, the paper will pay attention to the numerous overall performance issues for ESP utilization by analyzing the effect of latency, throughput, and power intake on ESP-enabled Wi-Fi networks.

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