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14th International Conference on Machine Learning and Computing, ICMLC 2022

作者： Liu, Zhen Wang, Ruoyu Yang, Jin Zhang, Wenbin School of Information Science and Technology Guangdong University of Foreign Studies China Information and Network Engineering Research Center South China University of Technology China School of Medical Information Engineering Guangdong Pharmaceutical University China Information Systems University of Maryland United States

ISBN: (纸本)9781450395700

Convolutional neural networks have been used for cancer type prediction with gene expression data. However, its success is impeded by the lack of large labeled datasets in gene expression data. The class imbalance problem leads to that the model ignores the performance of the minority class. To handle the small sample size problem, fine-tuning CNN is used to transfer the knowledge of pre-trained model for cancer type predicting. The dataset with one cancer is used for training a model. The pre-model is fine-tuned with the training set of a new cancer type, and the fine-tuned model could be used for identifying the new cancer type. And the SMOTE resampling method is used for handling the class imbalance problem. We carried out experiments on The TCGA datasets with 1D-CNN and 2D-CNN models. The fine-tuned 1D-CNN obtains 97.5% accuracy, 98.6% Fscore of cancer type and 78.1% Fscore of normal type on average, and fine-tuned 2D-CNN obtains 97.4% accuracy, 98.5% Fscore of cancer type and 77.4% of normal type on average. Using fine-tuned CNN with SMOTE, the accuracy, Fscore of cancer type and the one of normal type are respectively increased about 1.5%, 0.5% and 21.5% on average. © 2022 ACM.

关键词： Convolutional neural networks

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A chattering free consensus controller for multiple Lur'e systems with a non-autonomous leader and directed switching topology

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Science China(Technological Sciences) 2023年第4期66卷 1110-1118页

作者： WANG PeiJun YU WenWu WEN GuangHui YU XingHuo HUANG TingWen School of Mathematics and Statistics Anhui Normal UniversityWuhu 241000China Anhui Province Engineering Laboratory of Intelligent Robot's Information Fusion and Control Anhui Normal UniversityWuhu 241000China School of Mathematics Frontiers Science Center for Mobile Information Communication and SecuritySoutheast UniversityNanjing 210096China Purple Mountain Laboratories Nanjing 21111lChina Department of Electrical Engineering Nantong UniversityNantong 226019China Department of Systems Science School of MathematicsSoutheast UniversityNanjing 210096China School of Engineering RMIT UniversityMelbourne VIC 3001Australia Science Program Texas A&M University at QatarDoha 23874Qatar

Achieving asymptotical cooperative goal for multi-agent systems(MASs)with non-autonomous leaders(i.e.,leaders with nonzero inputs)is a critical but challenging *** approach is to use discontinuous controllers which may cause chattering phenomenon in practical *** to achieve the asymptotical goal via a chattering free cooperative controller remains to be open so *** this paper,an adaptive continuous controller is designed to achieve zero error consensus tracking in multiple Lur’e systems with a non-autonomous leader under directed switching ***,an unknown input observer(UIO)based on relative outputs is given to estimate the relative full *** an adaptive continuous controller is designed by introducing a decay function which remains positive into the term that plays the role of eliminating the impacts of leader’s nonzero ***,by using multiple Lyapunov functions(MLFs)technique,it is proven that zero error consensus tracking can be achieved if the average dwell time(ADT)is greater than a positive ***,theoretical result is verified by performing simulations on Chua’s *** with existing work,the proposed controller can not only achieve asymptotical consensus,but also is chattering free.

关键词： multi-agent system consensus chattering free non-autonomous leader directed switching topology

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Call for Paper: The 6th International Conference on Fuzzy information and engineering (ICFIE' 2012): University of Mazandaran on 25-26 October 2012, Iran

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Fuzzy information and engineering 2024年第1期4卷 117-119页

作者： Cao Chang Hadi Nasseri Fuzzy Information & Engineering Branch of Operation Research Society of China International Society of Fuzzy Information and Engineering (preparation) China Iranian Fuzzy Systems Society Iranian Operation Research Society Research Institute of Fuzzy Systems and Research Center of Algebraic Hyperstructures & Fuzzy Mathematics in University of Mazandaran Iran Iran

The International Conference on Fuzzy information and engineering (ICFIE) is an important international fuzzy academic continuously-held meeting. The 6th ICFIE is hosted by Fuzzy information and engineering Branch of Operation Research Society of China, and the International Society of Fuzzy information and engineering (preparation), it will be supported by Iranian Fuzzy systems Society, Iranian Operation Research Society, sponsored by the Research Institute of Fuzzy systems, and Research center of Algebraic Hyperstructures & Fuzzy Mathematics, University of Mazandaran and some other big universities in Iran, cooperated by Guangzhou University. The 6th ICFIE in University of Mazandaran on 25-26 October 2012, in the city of Babolsar, Iran.

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Electrification of Utility Tractors at Maritime Container Ports

Electrification of Utility Tractors at Maritime Container Po...

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IEEE Symposium on systems and information engineering Design, SIEDS

作者： Bethany K. Bazemore Matthew W. Cha Zachary J. Goss Henry B. Haywood Brodie K. Dye Megan E. Gunn Davis C. Loose Thomas J. Polmateer Daniel C. Hendrickson James H. Lambert Department of Systems & Information Engineering University of Virginia Charlottesville VA USA Commonwealth Center for Advanced Logistics Systems VA USA Port of Virginia Norfolk VA USA

ISBN: (数字)9798350385144

ISBN: (纸本)9798350385151

Maritime container ports use various technologies to achieve decarbonization including investing in electrification of vehicles and facilities. There is particular urgency to ensure that capital investments are consistent with future charging facilities and vehicles. Mathematical simulation has been used to predict and avoid disruption and surprises, including evolving requirements, organizations, contract negotiations, anomalous demands, supply chains, grid outages, workforce behaviors, commodity and service markets, obsolescence, regulation, and environmental protection. This study develops a simulation to explore the integration of electric vehicles into freight operations of a maritime container port. The simulation enables the comparison of alternative configurations and capacities of chargers on several time horizons. The effort optimizes performance indices for managers, users, and customers, including emissions, resource utilization, costs, and energy. The simulation addresses four performance metrics, thirty utility tractor rigs, three to fifteen chargers, ten to twenty drivers, five container stacks, and five rail sidings. The simulation describes daily, weekly, and annual schedules and use cases. The results guide $3 billion in investment and suggest how particular business decisions are sensitive to the trajectory of investment in advanced technologies and their configurations.

关键词： System performance Supply chains Low-carbon economy Seaports Containers Agricultural machinery Mathematical models

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Deep Learning Based Modulation Classification in Radio Access Network

Deep Learning Based Modulation Classification in Radio Acces...

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IEEE Globecom Workshops

作者： Hongqing Guo Shengliang Peng Tongyun Li Michael Crisp Richard Penty College of Information Science and Engineering Huaqiao University Xiamen China Department of Electrical Engineering Division of Signals and Systems Uppsala University Uppsala Sweden Department of Engineering Centre for Photonic Systems Electrical Division University of Cambridge Cambridge UK

The growing demand for traffic shaping and manipulation for efficient last-mile coverage has driven extensive research using emerging artificial intelligence to overcome the capacity hurdle in next-generation wireless systems. In the radio access network (RAN), modulation format can be critical information to determine the channel condition and implement resource allocation and data compression so as to improve the quality of service and spectral efficiency of data transmission. This paper proposes a novel deep learning-based automatic modulation classification algorithm that trains neural networks as classifiers to determine the modulation format of signals transmitted in the RAN. To conquer the problem of limited training data with the received dataset, the proposed algorithm exploits a signal generation method that generates massive simulated data for training instead of relying on the received signal stream. To eliminate the issue of inconsistent data distribution between the training and inferring stages, data augmentation is applied to increase the diversity of training data and enrich the training dataset. The experiment results demonstrate that the proposed algorithm accurately identifies the modulation formats of signals received from the RAN.

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OpenACC Unified Programming Environment for Multi-hybrid Acceleration with GPU and FPGA 38th

OpenACC Unified Programming Environment for Multi-hybrid Ac...

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38th International Conference on High Performance Computing, ISC High Performance 2023

作者： Boku, Taisuke Tsunashima, Ryuta Kobayashi, Ryohei Fujita, Norihisa Lee, Seyong Vetter, Jeffrey S. Murai, Hitoshi Nakao, Masahiro Tsuji, Miwako Sato, Mitsuhisa Center for Computational Sciences University of Tsukuba Tsukuba Japan Degree Programs in Systems and Information Engineering University of Tsukuba Tsukuba Japan Oak Ridge National Laboratory Oak Ridge United States RIKEN Center for Computational Science Kobe Japan

ISBN: (纸本)9783031408427

Accelerated computing in HPC such as with GPU, plays a central role in HPC nowadays. However, in some complicated applications with partially different performance behavior is hard to solve with a single type of accelerator where GPU is not the perfect solution in these cases. We are developing a framework and transpiler allowing the users to program the codes with a single notation of OpenACC to be compiled for multi-hybrid accelerators, named MHOAT (Multi-Hybrid OpenACC Translator) for HPC applications. MHOAT parses the original code with directives to identify the target accelerating devices, currently supporting NVIDIA GPU and Intel FPGA, dispatching these specific partial codes to background compilers such as NVIDIA HPC SDK for GPU and OpenARC research compiler for FPGA, then assembles binaries for the final object with FPGA bitstream file. In this paper, we present the concept, design, implementation, and performance evaluation of a practical astrophysics simulation code where we successfully enhanced the performance up to 10 times faster than the GPU-only solution. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Field programmable gate arrays (FPGA)

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Scattering Analysis of Polarimetric Inverse SAR Image using Scale Model under Vegetation Canopy 8

Scattering Analysis of Polarimetric Inverse SAR Image using ...

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8th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2023

作者： Ibrahim, Muhammad Hamka Wang, Jing-Yuan Pramono, Subuh Kashihara, Hisato Izumi, Yuta Cai, YuFan Munandar, Muhammad Arif Sumantyo, Josaphat Tetuko Sri Electrical Engineering Department Universitas Sebelas Maret Surakarta Indonesia College of Information and Systems Muroran Institute of Technology Muroran Japan Faculty of Geography Universitas Gadjah Yogyakarta Indonesia Center for Environmental Remote Sensing Chiba University Chiba Japan Department of Electrical Engineering Faculty of Engineering Universitas Sebelas Maret Indonesia

ISBN: (纸本)9798350393590

Radar scattering of the hidden object under the canopy of vegetation or soil is difficult to verify without priori information of the target. A scale model of the manmade structure is deployed and measured to verify the phenomenon of the scatterer and the effect of the vegetation canopy. Full polarization inverse synthetic aperture radar (ISAR) imaging is captured using C-band for each model. A two-dimensional polarimetric image is decomposed and its independent scattering is analyzed. It is shown that the C-band wavelength can penetrate vegetation canopy. While it is difficult to distinguish using visual images, it is shown that vegetation canopy contributes to reducing total received power by 2.7 dB. The experiment result of ISAR on scale model shows that it is coherent with the phenomenon of scattering and is a potential method to validate real SAR targets. ©2023 IEEE.

关键词： Inverse synthetic aperture radar

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Basic Study on Cybernic Interface for Amyotrophic Lateral Sclerosis Patients to Perform Daily Living Tasks by Transiting Seamlessly Between Cyberspace and Physical Space

Basic Study on Cybernic Interface for Amyotrophic Lateral Sc...

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IEEE/SICE International Symposium on System Integration

作者： Akira Uehara Yoshiyuki Sankai Faculty of Engineering Information and systems Center for Cybernics Research R&D Center for Frontiers of MIRAI in Policy and Technology Ibraki Japan

Amyotrophic lateral sclerosis (ALS) gradually make it difficult for the patients to move, breath, and express their will as their symptoms progress associated with motor neuron. It is important for the patients to be able to not only communicate their daily needs to someone such as family member of caregivers but also perform their daily activities and participate in society and fulfill social roles without constant support by other people. To improve their ability of independence, the purpose of this study is to develop a cybernic interface that can perform daily living tasks by transiting seamlessly between cyberspace and physical space based on the patient's remaining voluntary motor functions, and confirm the basic performance of the developed interface. The system consists of bio-electrical signals measurement unit, display unit, processor unit, and IoT unit. The system controlled the flipping up and down the head-mounted display (HMD) by estimating the intention of transition between cyberspace and physical space based on the bio-electrical signals of the forearm. When the HMD is attached to the user's face, the user can move at the virtual environment, operate IoT device at the room of physical world via virtual environment, and select voice output for expressing own state by the gaze. To confirm the basic performance of the developed interface for performing daily living tasks by oneself, we conducted basic experiment with an able-bodied participant. As a result, the participant was able to flip up the HMD, move at virtual environment, and operate the TV and lock at the physical space without moving own body similar to ALS patients. In conclusion, we confirmed the basic performance of performing daily living tasks to improve the ability of independence for ALS patients. The cybernic interface that can connect central nervous system to cyberspace and physical space has a potential for removing constraints due to motor dysfunctions and spatial limitation.

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RDANet: Reject Domain Attention Network For Confused Facial Expression Recognition

RDANet: Reject Domain Attention Network For Confused Facial ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jintao Luo Juan Li Tonglin Cheng Faculty of Information Technology Beijing University of Technology Beijing Engineering Researeh Center for loT Software and Systems Beijing China

Due to the significant intra-class differences and inter-class similarities inherent in facial expressions, distinguishing certain highly similar facial expressions such as disgust, fear, and sadness has become one of the main challenges in facial expression recognition (FER).To address this challenge, we propose a novel Rejected Domain Attention Network (RDANet), specifically designed to distinguish these easily confusing facial expressions. We aggregate and embed the correlations of regional features and facial expressions generated by convolutional neural networks into a compact interval. Adaptive weight adjustment is performed on the features in the interval, and a strict penalty mechanism is added. We identify anti correlated regions by correlation scores and classify them as rejected domains and the rest as supported domains. We select important rejected feature elements via RDANet and give them higher weight than supported domain features. An optimized contrastive loss is used to achieve a dynamic balance between intra-class clustering and inter-class separation, combined with regularization constraints to make the model converge faster. Through experimental verification, our method has superior performance in recognizing confused facial expressions.

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Integrative analyses in omics data: Machine learning perspective 68

Integrative analyses in omics data: Machine learning perspec...

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68. Jahrestagung der Deutsche Gesellschaft fur Medizinische Informatik, Biometrie und Epidemiologie e.V., GMDS 2023 - 68th Annual Conference of the German Association for Medical Informatics, Biometry and Epidemiology, GMDS 2023

作者： Yazici, Miray Unlu Bakir-Gungor, Burcu Yousef, Malik Department of Bioengineering Faculty of Engineering Abdullah Gül University Kayseri Turkey Department of Computer Engineering Faculty of Engineering Abdullah Gül University Kayseri Turkey Department of Information Systems Zefat Academic College Zefat Israel Galilee Digital Health Research Center Zefat Academic College Zefat Israel

Developments in the high throughput technologies have enabled the production of an immense amount of knowledge at the multi-omics level. Considering complex diseases which are affected by multi-factors, single omics datasets might not be sufficient to unveil the molecular mechanisms of heterogeneous diseases. Providing a comprehensive and systematic overview to explain disease hallmarks in significant depth is critical. Utilizing multi-omics datasets has led to the development of a variety of tools and platforms. Machine learning models are utilized in a wide variety of tools to tackle the complexity of disorders and to identify new biomolecular signatures and potential markers. Underlying aspects of these approaches are based on training the models for making predictions and classification of the given data. In this review, we describe current machine learning-based approaches and available implementations. Challenges in the enlightenment of disease mechanisms of onset and progression and future development of the field of medicine will be discussed. The prominence of biological interpretation of model output with corresponding biological knowledge will be also covered in this review. © 2023 Unlu Yazici et al.

关键词： Machine learning

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