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arXiv 2023年

作者： Ünlü, Atabey Çevrim, Elif Sarıgün, Ahmet Yiğit, Melih Gökay Çelikbilek, Hayriye Bayram, Osman Güvenilir, Heval Ataş Koyaş, Altay Kahraman, Deniz Cansen Olğaç, Abdurrahman Rifaioğlu, Ahmet Banoğlu, Erden Doğan, Tunca Biological Data Science Lab Dept. of Computer Engineering Hacettepe University Turkey Dept. of Bioinformatics Graduate School of Health Sciences Hacettepe University Turkey Dept. of Chemistry Middle East Technical University Turkey Dept. of Physics Middle East Technical University Turkey Dept. of Computer Engineering Middle East Technical University Turkey Dept. Of Artificial Intelligence Engineering Bahcesehir University Turkey Cancer Systems Biology Lab Graduate School of Informatics Middle East Technical University Turkey Dept. of Pharmaceutical Chemistry Faculty of Pharmacy Gazi University Turkey Laboratory of Molecular Modeling Evias Pharmaceutical R&D Ltd Turkey Dept. of Electrical and Electronics Engineering Iskenderun Technical University Turkey Institute for Computational Biomedicine Heidelberg University Germany

Discovering novel drug candidate molecules is one of the most fundamental and critical steps in drug development. Generative deep learning models, which create synthetic data given a probability distribution, offer a high potential for designing de novo molecules. However, for them to be useful in real-life drug development pipelines, these models should be able to design drug-like and target-centric molecules. In this study, we propose an end-to-end generative system, DrugGEN, for the de novo design of drug candidate molecules that interact with intended target proteins. The proposed method represents molecules as graphs and processes them via a generative adversarial network comprising graph transformer layers. The system is trained using a large dataset of drug-like compounds and target-specific bioactive molecules to design effective inhibitory molecules against the AKT1 protein, which is critically important in developing treatments for various types of cancer. We conducted molecular docking and dynamics to assess the target-centric generation performance of the model, as well as attention score visualisation to examine model interpretability. Results indicate that our de novo molecules have a high potential for interacting with the AKT1 protein at the level of its native ligands. Using the open-access DrugGEN codebase, it is possible to easily train models for other druggable proteins, given a dataset of experimentally known bioactive molecules. © 2023, CC BY-NC-SA.

关键词： Generative adversarial networks

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Towards Industrial Private AI: A two-tier framework for data and model security

arXiv

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arXiv 2021年

作者： Khowaja, Sunder Ali Dev, Kapal Qureshi, Nawab Muhammad Faseeh Khuwaja, Parus Foschini, Luca Faculty of Engineering and Technology University of Sindh Jamshoro Pakistan Department of Mechatronics Engineering Korea Polytechnic University Korea Republic of The Department of Computer Science Munster Technological University Ireland Institute of Intelligent Systems University of Johannesburg South Africa The Department of Computer Education Sungkyunkwan University Seoul Korea Republic of University of Sindh Jamshoro Pakistan University of Bologna Italy

With the advances in 5G and IoT devices, the industries are vastly adopting artificial intelligence (AI) techniques for improving classification and prediction-based services. However, the use of AI also raises concerns regarding data privacy and security that can be misused or leaked. Private AI was recently coined to address the data security issue by combining AI with encryption techniques, but existing studies have shown that model inversion attacks can be used to reverse engineer the images from model parameters. In this regard, we propose a Federated Learning and Encryption-based Private (FLEP) AI framework that provides two-tier security for data and model parameters in an IIoT environment. We propose a three-layer encryption method for data security and provided a hypothetical method to secure the model parameters. Experimental results show that the proposed method achieves better encryption quality at the expense of slightly increased execution time. We also highlight several open issues and challenges regarding the FLEP AI framework's realization. © 2021, CC BY-NC-ND.

关键词： Cryptography

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Investigating the effect of different type of separator on the multifunctional performance of structural lithium-ion composite battery

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AIP Conference Proceedings 2023年第1期2932卷

作者： Putu Suwarta Bagus Alifah Hasyim Daffa Satriyo Habib Saifuddin Fathoni Achmad Subhan Ika Dewi Wijayanti Suwarno Suwarno Sutikno Sutikno Fahmi Mubarok Yatim Lailun Nimah Mechanical Engineering Faculty of Industrial Technology and Systems Engineering (INDSYS) Institut Teknologi Sepuluh Nopember Surabaya 60111 Indonesia National Research and Innovation Agency (BRIN) Pusat Riset Fisika PUSPIPTEK Serpong Tangerang 15314 Indonesia Departement of Chemistry Faculty of Science and Data Analytical (SCIENTICS) Institut Teknologi Sepuluh Nopember Surabaya 60111 Indonesia

The multifunctional performance of a novel structural lithium-ion composite battery made of a mixture of solid polymer electrolyte (SPE), glass separator, and two commercial electrodes is investigated. Three types of glass separator known as WR200, WR50 dan Whatman where each of them has different geometry are being used in this research. The SPE is a mixture of ionic liquid (IL) and resin epoxy Bisphenol A compound. NMC532 and graphite layers are used as the positive and negative electrodes respectively. The structural battery is stacked into a pouch cell configuration to represent the commercial battery. To determine its multifunctionality, two types of testing were conducted: electrochemical and mechanical testing. Structural battery using each of the WR50 and WR200 separators produces slightly better charge-discharge capacity, Coulombic efficiency, and lower impedance compared to the Whatman separator. The Coulombic efficiency based on the Cyclic Voltammetry (CV) test is 11.9%, 11.3%, 4.3% for the WR50, WR200 and Whatman separators respectively. Based on the charge-discharge capacity test, the Coulombic efficiency is 40.38%, 3,34 %, 4.03% for the WR50, WR200 and Whatman separators respectively. The thin layer of WR 50 separator helps the Lithium ion to travel faster between the two electrodes thus resulting in higher Coulombic efficiency. The mechanical testing shows a degradation in the tensile properties of the structural battery for all types of separators after the charge-discharge cycle test due to graphite exfoliation and cracking. From the current research, a relationship between the stiffness of the structural battery and its discharge capacity has been made.

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Investigation of the Impact of Height and Bandwidth on Path Loss Exponent of 5G FR1 Wireless Networks at 800, 3500, and 5900 MHz

Investigation of the Impact of Height and Bandwidth on Path ...

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Multidisciplinary engineering and Applied science (ICMEAS), International Conference on

作者： Yusuf Olayinka Imam-Fulani Nasir Faruk Aliyu D. Usman Abubakar Abdulkarim Abdoulie M.S Tekanyi Abdulmalik S. Yaro Emmanuel Alozie Salisu Garba Kayode S. Adewole Abdulkarim A. Oloyede Olugbenga A. Sowande Bashir Abdullahi Baba Abdulwaheed Musa Agbotiname Lucky Imoize Yinusa A. Adediran Hawau I. Olagunju Lawan S. Taura Department of Telecommunication Science University of Ilorin Ilorin Nigeria Department of Electronics and Telecommunication Engineering Ahmadu Bello University Zaria Nigeria Department of Information Technology Sule Lamido University Kafin Hausa Nigeria Directorate of Information and Communication Technology Sule Lamido University Kafin Hausa Nigeria Department of Electrical Engineering Ahmadu Bello University Zaria Nigeria Department of Electrical and Telecommunication Engineering Kampala International University Kampala Uganda Department of Informatics and Qualitative Methods Faculty of Informatics and Management University of Hradec Králové Hradec Králové Czech Republic Department of Software Engineering Sule Lamido University Kafin Hausa Nigeria Department of Computer Science University of Ilorin Ilorin Nigeria Department of Electrical and Computer Engineering Kwara State University Malete Nigeria Institute for Intelligent Systems University of Johannesburg Johannesburg South Africa Department of Electrical and Electronics Engineering University of Lagos Lagos Nigeria Department of Electrical Engineering and Information Technology Institute of Digital Communication Ruhr University Bochum Germany Department of Electrical and Electronics Engineering University of Ilorin Ilorin Nigeria Department of Physics Sule Lamido University Kafin Hausa Nigeria

5G communication systems provide an end-to-end wireless connection to billions of users and devices across the globe. The quality of signals received during radio communication is notably influenced by the behavior of the radio propagation channel. A major parameter used in characterizing this channel is the Path Loss Exponent (PLE). Several works that have estimated and analyzed the PLE mostly considered the effect of distance and carrier frequency. However, the effect of base station antenna height and channel bandwidth on the PLE for 5G networks have not been adequately considered. To address this, the impact of antenna height of base station and channel bandwidth on the PLE within the 5G Frequency Range 1 (FR1) frequencies was investigated in this study, specifically at 800, 3500, and 5900 MHz. The licensed EDX Signal Pro software® with Cirrus high resolution global terrain and clutter data base was utilized to model, simulate and analyze the PLE for Kano City, Nigeria. Results showed that for the tested frequencies, an increase in either base station height or channel bandwidth leads to a significant reduction in the PLE. This study can be utilized by network planning engineers and the wireless research community to further improve network implementation and optimization toward understanding the behavior of signal propagation in 5G networks and beyond.

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Motor System Integrated Magnetic Multiple Spur Gear and High Speed Motors for Electric Vehicle 12

Motor System Integrated Magnetic Multiple Spur Gear and High...

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12th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2020

作者： Aiso, Kohei Akatsu, Kan Aoyama, Yasuaki Waseda University Department of Electrical Engineering and Bio Science 3-4-1 Okubo Shinjuku-ku Tokyo Japan Yokohama National University Division of Intelligent Systems Engineering Department of Faculty of Engineering 79-5 Jobandai Hodogaya-ku Yokohama Japan Hitachi Ltd. Research Development Group Center for Technology Innovation - Materials 1-1 Omika-cho 7-chome Hitachi-shi Ibaraki319-1292 Japan

ISBN: (纸本)9781728158266

Magnetic gears realize the system with maintenance-free, low noise and low vibration characteristics by non-contact power transformation. However, conventional magnetic gears are not suitable for the high speed drive because of the low mechanical strength due to permanent magnets of the high speed rotor. In addition, the gear efficiency is much decreased by increasing the eddy current loss in the magnets and core loss due to the harmonic fluxes. To overcome these problems, in this research, a novel Magnetic Multiple Spur Gear (MMSG) is proposed to achieve the high efficiency and to make downsizing the system. In this paper, the performances of the torque density, the gear efficiency are evaluated by the simulation and the experiment. © 2020 IEEE.

关键词： Magnetism

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Passivity properties for regulation of DC networks with stochastic load demand ⁎

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IFAC-PapersOnLine 2020年第2期53卷 13113-13118页

作者： Amirreza Silani Michele Cucuzzella Jacquelien M.A. Scherpen Mohammad Javad Yazdanpanah Jan C. Wilems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Nijenborgh 4 9747 AG Groningen the Netherlands. Control & Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran

In this paper we present new (stochastic) passivity properties for Direct Current (DC) power networks, where the unknown and unpredictable load demand is modelled by a stochastic process. More precisely, the considered power network consists of distributed generation units supplying ZIP loads, i.e., nonlinear loads comprised of impedance (Z), current (I) and power (P) components. Differently from the majority of the results in the literature, where each of these components is assumed to be constant, we consider time-varying loads whose dynamics are described by a class of stochastic differential equations. Finally, we prove that an existing distributed control scheme achieving current sharing and (average) voltage regulation ensures the asymptotic stochastic stability of the controlled network.

关键词： DC power networks Nonlinear systems Stochastic modelling Passivity theory

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Insar Time-Series Deformation Forecasting Surrounding Salt Lake Using Deep Transformer Models

SSRN

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SSRN 2022年

作者： Wang, Jing Li, Chao Li, Lu Huang, Zhihua Wang, Chao Zhang, Hong Zhang, Zhengjia Research Institute of Intelligent Computing Zhejiang Laboratory. Hangzhou311121 China Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences No.9 Dengzhuang South Road Beijing100094 China International Research Center of Big Data for Sustainable Development Goals Beijing100094 China University of Chinese Academy of Sciences Beijing100049 China Faculty of Information Engineering China University of Geosciences. 388 Lumo Road Wuhan430074 China

The open source strategy of Sentinel-1 SAR images enables Radar interferometry (InSAR) to perform time series surface deformation monitoring over large *** deformation monitoring and prediction can investigate the freeze-thaw cycles of permafrost on the Qinghai-Tibet Plateau (QTP). However, the convolutional and recurrent neural networks cannot accurately model long-term and complex relations in multi-variate time series data, it is challenging to implement time series deformation prediction with high spatial resolution. We propose an InSAR deformation prediction integrated algorithm based on the transformer-based models to predict time series deformation more accurately in permafrost areas. This method takes advantage of the self-attention mechanism to study complicated dynamic deformation features of permafrost caused by temperature and other variables from InSAR time series ***, Sentinel-1 dataset acquired with 12-day repeat are collected to implement the Small Baseline Subset (SBAS) method and retrieve the seasonal and long-term deformation surrounding Salt Lake. Then, the InSAR time series deformation and meteorological data are combined to establish the deformation prediction model using the transformer neural network. These results demonstrated that the InSAR deformation prediction method achieves better prediction performance (the root mean square errors are 3.207mm and 2.27mm) in predicting permafrost deformation trends with a point scale compared with the prediction results of other models. Finally, the predicted time series deformation maps for the period 2021/04/05 to 2022/03/19 are mapped surrounding Salt Lake. Meanwhile the total area of Salt Lake increased by 57.32km 2 during the period 2015-2019 based on the predicted deformation and the water extraction *** the expansion rate of Salt Lake was slow, and the area decreased by 3.26 km 2 from 2019 to 2022. The time series deformation forecasting method provides early warn

关键词： Deformation

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Multilayer Clustering Based on Adaptive Resonance Theory for Noisy Environments

Multilayer Clustering Based on Adaptive Resonance Theory for...

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International Joint Conference on Neural Networks (IJCNN)

作者： Narito Amako Naoki Masuyama Chu Kiong Loo Yusuke Nojima Yiping Liu Hisao Ishibuchi Department of Computer Science and Intelligent Systems Osaka Prefecture University Osaka Japan Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Clustering based on Adaptive Resonance Theory (ART) has been actively studied. In previous studies, ART-based clustering algorithms with a topological structure have been proposed and showed their superior self-organizing ability. However, this method deteriorates the clustering performance at high noise ratios. In this paper, we propose a multilayer clustering algorithm based on a topological ART-based clustering for improving a noise reduction ability. Simulation experiments show that the proposed algorithm achieves excellent clustering performance on a 2D synthetic dataset in high noise environments.

关键词： Kernel Clustering algorithms Nonhomogeneous media Bandwidth Subspace constraints Computer science Topology

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Fuzzy Adaptive Decentralized Control for a Class of Large-Scale Fractional-Order systems

Fuzzy Adaptive Decentralized Control for a Class of Large-Sc...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Wenshan Bi Nan Wang Tong Wang Faculty of Science and Technology University of Macau Macau China School of Information Engineering Henan University of Science and Technology Luoyang China Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin China

ISBN: (数字)9781728176840

ISBN: (纸本)9781728176857

This paper proposes a fuzzy adaptive decentralized control scheme for a class of large-scale fractional-order nonlinear system with strick feedback form. In the design process, fuzzy logic systems is utilized to model the uncertain system. Based on the common Lyapunov function, a large-scale fractional-order control method is presented. It is testified that all the signals in the closed-loop system are bounded, and the outputs of systems have satisfactory tracking performance. Simulation results demonstrate the effectiveness and feasibility of the proposed control method.

关键词： Fuzzy logic Adaptation models Uncertain systems Decentralized control Nonlinear systems Thermal stability Lyapunov methods

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Ds-ui: dual-supervised mixture of gaussian mixture models for uncertainty inference

arXiv

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arXiv 2020年

作者： Xie, Jiyang Ma, Zhanyu Xue, Jing-Hao Zhang, Guoqiang Guo, Jun Pattern Recognition and Intelligent Systems Lab Beijing University of Posts and Telecommunications China Department of Statistical Science University College London United Kingdom School of Electrical and Data Engineering University of Technology Sydney Australia

This paper proposes a dual-supervised uncertainty inference (DS-UI) framework for improving Bayesian estimation-based uncertainty inference (UI) in deep neural network (DNN)-based image recognition. In the DS-UI, we combine the classifier of a DNN, i.e., the last fully-connected (FC) layer, with a mixture of Gaussian mixture models (MoGMM) to obtain an MoGMM-FC layer. Unlike existing UI methods for DNNs, which only calculate the means or modes of the DNN outputs’ distributions, the proposed MoGMM-FC layer acts as a probabilistic interpreter for the features that are inputs of the classifier to directly calculate the probability density of them for the DS-UI. In addition, we propose a dual-supervised stochastic gradient-based variational Bayes (DS-SGVB) algorithm for the MoGMM-FC layer optimization. Unlike conventional SGVB and optimization algorithms in other UI methods, the DS-SGVB not only models the samples in the specific class for each Gaussian mixture model (GMM) in the MoGMM, but also considers the negative samples from other classes for the GMM to reduce the intra-class distances and enlarge the inter-class margins simultaneously for enhancing the learning ability of the MoGMM-FC layer in the DS-UI. Experimental results show the DS-UI outperforms the state-of-the-art UI methods in misclassification detection. We further evaluate the DS-UI in open-set out-of-domain/distribution detection and find statistically significant improvements. Visualizations of the feature spaces demonstrate the superiority of the DS-UI. © 2020, CC-BY.

关键词： Gaussian distribution

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