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Lecture Notes in Electrical engineering 2020年 554卷 v-vi页

作者： Zelinka, Ivan Brandstetter, Pavel Dao, Tran Trong Duy, Vo Hoang Kim, Sang Bong Department of Computer Science Faculty of Electrical Engineering VŠB-TUO Poruba Ostrava Czech Republic Department of Electronics Faculty of Electrical Engineering VŠB-TUO Ostrava Czech Republic International Cooperation Research and Training Institute Ton Duc Thang University Ho Chi Minh Viet Nam Department of Automatic Control Ton Duc Thang University Ho Chi Minh City Viet Nam Department of Mechanical Design Engineering Pukyong National University Busan Korea Republic of

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Machine learning for optimal parameter prediction in quantum key distribution

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Physical Review A 2019年第6期100卷 062334-062334页

作者： Wenyuan Wang Hoi-Kwong Lo Centre for Quantum Information and Quantum Control Department of Electrical and Computer Engineering Department of Physics University of Toronto Toronto M5S3G4 ON Canada

For a practical quantum key distribution (QKD) system, parameter optimization, the choice of intensities and the probabilities of sending them, is a crucial step in gaining optimal performance, especially when one realistically considers a finite communication time. With the increasing interest in the field to implement QKD over free space on moving platforms, such as drones, handheld systems, and even satellites, one needs to perform parameter optimization with low latency and with very limited computing power. Moreover, with the advent of the internet of things, a highly attractive direction of QKD could be a quantum network with multiple devices and numerous connections, which provides a huge computational challenge for the controller that optimizes parameters for a large-scale network. Traditionally, such an optimization relies on brute-force search or local search algorithms, which are computationally intensive, and will be slow on low-power platforms (which increases latency in the system) or infeasible for even moderately large networks. In this work we present a method that uses a neural network to directly predict the optimal parameters for QKD systems. We test our machine learning algorithm on hardware devices including a Raspberry Pi 3 single-board computer (similar devices are commonly used on drones) and a mobile phone, both of which have a power consumption of less than 5 W, and we find a speedup of up to two to four orders of magnitude when compared to standard local search algorithms. The predicted parameters are highly accurate and can preserve, e.g., over 95%–99% of the optimal secure key rate for a given protocol. Moreover, our approach is highly general and can be applied effectively to various kinds of common QKD protocols.

关键词： Machine learning Quantum communication Quantum cryptography Quantum networks

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Efficient Pyramid Channel Attention Network for Pathological Myopia Recognition

arXiv

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arXiv 2023年

作者： Zhang, Xiaoqing Zhao, Jilu Li, Yan Wu, Hao Zhou, Xiangtian Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China National Clinical Research Center for Ocular Diseases Eye Hospital Wenzhou Medical University Wenzhou325027 China State Key Laboratory of Ophthalmology Optometry and Visual Science Eye Hospital Wenzhou Medical University Wenzhou325027 China Research Unit of Myopia Basic Research and Clinical Prevention and Control Chinese Academy of Medical Sciences Wenzhou325027 China Singapore Eye Research Institute 169856 Singapore

Pathological myopia (PM) is the leading ocular disease for impaired vision worldwide. Clinically, the characteristic of pathology distribution in PM is global-local on the fundus image, which plays a significant role in assisting clinicians in diagnosing PM. However, most existing deep neural networks focused on designing complex architectures but rarely explored the pathology distribution prior of PM. To tackle this issue, we propose an efficient pyramid channel attention (EPCA) module, which fully leverages the potential of the clinical pathology prior of PM with pyramid pooling and multi-scale context fusion. Then, we construct EPCA-Net for automatic PM recognition based on fundus images by stacking a sequence of EPCA modules. Moreover, motivated by the recent pretraining-and-finetuning paradigm, we attempt to adapt pre-trained natural image models for PM recognition by freezing them and treating the EPCA and other attention modules as adapters. In addition, we construct a PM recognition benchmark termed PM-fundus by collecting fundus images of PM from publicly available datasets. The comprehensive experiments demonstrate the superiority of EPCA-Net over state-of-the-art methods in the PM recognition task. For example, EPCA-Net achieves 97.56% accuracy and outperforms ViT by 2.85% accuracy on the PM-fundus dataset. The results also show that our method based on the pretraining-and-finetuning paradigm achieves competitive performance through comparisons to part of previous methods based on traditional fine-tuning paradigm with fewer tunable parameters, which has the potential to leverage more natural image foundation models to address the PM recognition task in limited medical data regime. Copyright © 2023, The Authors. All rights reserved.

关键词： Pathology

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Achieving data-driven actionability by combining learning and planning

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计算机科学前沿 2018年第5期12卷 939-949页

作者： Qiang LV Yixin CHEN Zhaorong LI Zhicheng CUI Ling CHEN Xing ZHANG Haihua SHEN College of Information Engineering Yangzhou University Yangzhou 225127 China Department of Computer Science and Engineering Washington University in St.Louis St.Louis MO 63130 USA School of Management Fudan University Shanghai 200433 China School of Computer and Control Engineering University of Chinese Academy of Science Beijing 100049 China

A main focus of machine learning research has been improving the generalization accuracy and efficiency of prediction ***,what emerges as missing in many applications is actionability,i.e.,the ability to turn prediction results into *** effort in deriving such actionable knowledge is few and limited to simple action models while in many real applications those models are often more complex and harder to extract an optimal *** this paper,we propose a novel approach that achieves actionability by combining learning with planning,two core areas of *** particular,we propose a framework to extract actionable knowledge from random forest,one of the most widely used and best off-the-shelf *** formulate the actionability problem to a sub-optimal action planning (SOAP) problem,which is to find a plan to alter certain features of a given input so that the random forest would yield a desirable output,while minimizing the total costs of ***,the SOAP problem is formulated in the SAS+ planning formalism,and solved using a Max-SAT based *** experimental results demonstrate the effectiveness and efficiency of the proposed approach on a personal credit dataset and other *** work represents a new application of automated planning on an emerging and challenging machine learning paradigm.

关键词： actionable knowledge extraction machine learning planning random forest

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Retraction Note to: An improved approach for automatic spine canal segmentation using probabilistic boosting tree (PBT) with fuzzy support vector machine

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Journal of Ambient Intelligence and Humanized Computing 2022年第1期14卷 303-303页

作者： Viji, C. Rajkumar, N. Suganthi, S. T. Venkatachalam, K. Rajesh kumar, T. Pandiyan, Sanjeevi Department of CSE Akshaya College of Engineering and Technology Coimbatore India Department of Computer Engineering Lebanese French University Erbil Iraq School of CSE VIT University Bhopal Bhopal India Department of Information Technology Sri Krishna College of Technology Kovaipudur India Key Laboratory of Advanced Process Control for Light Industry Ministry of Education Jiangnan University Wuxi China

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Semantic Web Annotation using Deep Learning with Arabic Morphology

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Procedia computer Science 2019年 151卷 385-392页

作者： Saeed Albukhitan Ahmed Alnazer Tarek Helmy Information and Computer Science Department College of Computer Science & Engineering King Fahd University of Petroleum & Minerals Dhahran 31216 Mail Box 413 Saudi Arabia On leave from College of Engineering Department of Computers & Automatic Control Tanta University Egypt

In order to realize the vision of Semantic Web, which is a Web of things instead of Web of documents, there is a need to convert existing Web of documents into Semantic content that could be processed by machines. Semantic annotation tool could be used to perform this task through using common and public ontologies. Due to exponential growth and the huge size of Web sources, there is a need to have a fast and automatic Semantic annotation of Web documents. The aim of this paper is to investigate the use of word embeddings from deep learning algorithms to semantically annotate the Arabic Web documents. To enhance the performance of the Semantic annotation, we utilized the complex morphological structure of Arabic words. Moreover, evaluating the performance of the proposed framework requires selecting a set of domain ontologies with relevant and annotated related documents. The proposed framework produces Semantic annotations for these documents by using different standard output formats. The initial results show a promising performance that will support the research in the Semantic Web with respect to Arabic language.

关键词： Deep Learning Semantic Annotation Arabic Language Ontology

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A Learner Model Integrating Cognitive and Metacognitive And Its Application on Scratch Programming Projects

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IFAC-PapersOnLine 2020年第5期53卷 644-649页

作者： Sifeng Jing Ying Tang Xiwei Liu Xiaoyan Gong The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao 266044 China Department of Electrical and Computer Engineering Rowan University Glassboro NJ 08028 USA

learner’s cognitive and metacognitive are key personal profile for individualized teaching. To evaluate learner’s comprehensive characteristics, existing learner model were reviewed. Two challenges of constructing an accurate and comprehensive learner model integrating cognitive and metacognitive were summarized. A plan of constructing a comprehensive learner model was made based on analysis of existing massive online learning environment, sensor information technology and educational data-mining. As a case study, a method of how to map learning data onto learners’ cognitive and metacognitive was proposed based on an analysis of a number of pupils’ Scratch projects. Three mapping table were established. Pupil’s cognitive skill could be evaluated from technology shown from Scratch project, namely, data structure, algorithm, computational practices and overall evaluation. Content shown from Scratch project were used to infer pupil’s cognitive style. Meta-cognitive ability can be measured from computational practices and behavior in programming process.

关键词： learner model cognitive state metacognitive ability individualized teaching

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Saliency Detection using the Combination of Boundary Aware Color-map and Seam-map

Saliency Detection using the Combination of Boundary Aware C...

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International Conference on computer, Communication, Chemical, Material and Electronic engineering (IC4ME2)

作者： Aminul Islam Sk. Md. Masudul Ahsan Joo Kooi Tan Department of Computer Science and Engineering Khulna University of Engineering & Technology Khulna Bangladesh Department of Control Engineering Kyushu Institute of Technology Kitakyushu-shi Fukuoka Japan

ISBN: (数字)9781728130606

ISBN: (纸本)9781728130613

Salient region of an image is usually detected by using contrast and boundary priors. Along with those cues the use of seam importance map has shown promising output previously. In this study, better result is found by further exploiting the seam-map using spatial distance and color information in combination with boundary prior. Color and seam maps are also down-weighted using average spatial distance to other regions. Moreover, passing the superpixelized version of the input image into seam and color map generation procedure has improved the output. Experimental results based on MSRA 1k dataset are presented with ten state of the art methods. F-beta measures are presented along with precision recall curves to better understand the outcome. The performance comparison with compared researches proofs superiority of the proposed method.

关键词： Image color analysis Saliency detection Visualization computer vision Image segmentation Object detection

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Monitoring the Moisture Content in Pharmaceutical Batch Fluidized Bed Dryers Using Observer-Based Soft Sensors

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IFAC-PapersOnLine 2020年第2期53卷 12056-12061页

作者： Marc-Olivier Roseberry Francis Gagnon André Desbiens Jocelyn Bouchard Pierre-Philippe Lapointe-Garant Department of Electrical and Computer Engineering LOOP Université Laval Québec City Québec G1V 6A6 Canada Department of Chemical Engineering LOOP Université Laval Québec City Québec G1V 6A6 Canada Process Monitoring Automation and Control Global Engineering Pfizer Montréal Québec H4R 1J6 Canada

Tablet manufacturing in the pharmaceutical industry involves batch fluidized bed drying for particle moisture removal. This paper introduces five approaches for moisture content monitoring, relying either on a complex phenomenological model or its simplified version. The first two soft sensors consist of open-loop estimators, i.e. they simply simulate the models fed by the manipulated variables. Three closed-loop moving horizon estimators based on the simplified model are also proposed for improved robustness. In the first one, the measurements of the inlet gas and particle temperatures feed back the soft sensor. The last two closed-loop observers additionally can take into account infrequent delayed moisture content measurements, such as at-line loss on drying analysis. A validation of the soft sensors is performed with experimental data collected on a pilot scale fluidized bed dryer. Results show that the closed-loop observer with the delayed moisture content measurements still has an accuracy that is equivalent (and sometimes better) than the complex phenomenological model.

关键词： state estimation batch fluidized bed dryer moving horizon estimator offline measurement measurement delay

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Zinc Oxide Quantum Dots as Photoanode for Dye-Sensitized Solar Cell

Zinc Oxide Quantum Dots as Photoanode for Dye-Sensitized Sol...

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IEEE International Conference on Semiconductor Electronics (ICSE)

作者： M. I. Idris Yasmin Zainol Abidin Huda Abdullah S. Shafie Siti Amaniah Mohd Chachuli M. Rashid K. J. Xian Micro and Nano Electronic (MiNE) Faculty of Electronic and Computer Engineering Melaka Malaysia Faculty of Electronic and Electronic Engineering (UTeM) Melaka Malaysia Department of Electrical Electronic and Systems Engineering Universiti Kebangsaan Malaysia (UKM) Selangor Malaysia Department of Electrical and Electronic Engineering Universiti Putra Malaysia (UPM) Selangor Malaysia Advanced Sensors and Embedded Control System (ASECs) Faculty of Electronic and Computer Engineering Melaka Malaysia School of Physics Universiti Sains Malaysia (USM) Penang Malaysia Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia (UKM) Selangor Malaysia

ISBN: (数字)9781728159683

ISBN: (纸本)9781728159690

Quantum dots dye-sensitized solar cells (QDSSC) has emerged as a highly promising photovoltaic technology for next-generation solar cells due to the distinct optoelectronics features of quantum dots (QDs) light-harvesting materials such as high absorption coefficient, multiple exciton generation possibility and easily tunable absorption range that can deliver both low production costs. TiO 2 is currently the conventional electron transport material (ETM) used in dye sensitized solar cell (DSSC) but incur high cost and high-temperature processing. Zinc oxide (ZnO) is identified as a low-cost material with higher electron mobility than TiO2 for efficient electron extraction. Furthermore, solution-process able ZnO quantum dots (QDs) allow manipulation of quantum confinement, light scattering and energy band alignment to improve charge extraction. ZnO QDs were synthesised using a self-assembly method of zinc acetate dihydrate solution with potassium hydroxide (KOH) solution and deposited on the Indium Tin Oxide (ITO) glass substrate by using spin coater (200-1500 rpm) for 20 seconds. Afterward, annealed for 5 hours in the furnace at temperatures of 450 ° C. In the characterization the solar cell used X-Ray Diffraction (XRD), UV-Visible spectroscopy, and Scanning Electron Microscope (SEM).

关键词： Zinc oxide II-VI semiconductor materials Photovoltaic cells Indium tin oxide Quantum dots Glass Absorption

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