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作者： Qi, Nan Miridakis, Nikolaos I. Xiao, Ming Tsiftsis, Theodoros A. Yao, Rugui Jin, Shi Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Institute of Physical Internet School of Intelligent Systems Science & Engineering Jinan University Zhuhai Campus Zhuhai519070 China Department of Informatics and Computer Engineering University of West Attica Aegaleo12243 Greece School of Electrical Engineering of KTH Royal Institute of Technology Stockholm11428 Sweden School of Electronics and Information Northwestern Polytechnical University Xi'an710072 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China

To boost energy saving for the general delay-tolerant IoT networks, a two-stage, and single-relay queueing communication scheme is investigated. Concretely, a traffic-aware $N$-threshold and gated-service policy are applied at the relay. As two fundamental and significant performance metrics, the mean waiting time and long-term expected power consumption are explicitly derived and related with the queueing and service parameters, such as packet arrival rate, service threshold and channel statistics. Besides, we take into account the electrical circuit energy consumptions when the relay server and access point (AP) are in different modes and energy costs for mode transitions, whereby the power consumption model is more practical. The expected power minimization problem under the mean waiting time constraint is formulated. Tight closed-form bounds are adopted to obtain tractable analytical formulae with less computational complexity. The optimal energy-saving service threshold that can flexibly adjust to packet arrival rate is determined. In addition, numerical results reveal that: 1) sacrificing the mean waiting time not necessarily facilitates power savings;2) a higher arrival rate leads to a greater optimal service threshold;and 3) our policy performs better than the current state-of-the-art. © 2020 IEEE.

关键词： Energy conservation

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Learning data-adaptive non-parametric kernels

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2020年第1期21卷 8590-8628页

作者： Fanghui Liu Xiaolin Huang Chen Gong Jie Yang Li Li Department of Electrical Engineering ESAT-STADIUS KU Leuven Belgium Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China and Department of Computing Hong Kong Polytechnic University Hong Kong SAR China Department of Automation BNRist Tsinghua University China

In this paper, we propose a data-adaptive non-parametric kernel learning framework in margin based kernel methods. In model formulation, given an initial kernel matrix, a data-adaptive matrix with two constraints is imposed in an entry-wise scheme. Learning this data-adaptive matrix in a formulation-free strategy enlarges the margin between classes and thus improves the model flexibility. The introduced two constraints are imposed either exactly (on small data sets) or approximately (on large data sets) in our model, which provides a controllable trade-off between model flexibility and complexity with theoretical demonstration. In algorithm optimization, the objective function of our learning framework is proven to be gradient-Lipschitz continuous. Thereby, kernel and classifier/regressor learning can be efficiently optimized in a unified framework via Nesterov's acceleration. For the scalability issue, we study a decomposition-based approach to our model in the large sample case. The effectiveness of this approximation is illustrated by both empirical studies and theoretical guarantees. Experimental results on various classification and regression benchmark data sets demonstrate that our non-parametric kernel learning framework achieves good performance when compared with other representative kernel learning based algorithms.

关键词： support vector machines non-parametric kernel learning gradient-Lipschitz continuous

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Robust lane marking detection algorithm using drivable area segmentation and extended SLT

Robust lane marking detection algorithm using drivable area ...

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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Ozgunalp, Umar Liu, Ming Fan, Rui Cheng, Shanshan Sun, Yuxiang Zuo, Weixun Zhu, Yilong Xue, Bohuan Zheng, Linwei Liang, Qing Cyprus International University Electrical and Electronics Engineering North Cyprus Turkey Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hangzhou ATG Intelligent Technology Co. Ltd. Hangzhou China Guolu Gaoke Engineering Technology Institute Co. Ltd. Beijing China Robotics Institute Hong Kong University of Science and Technology Robotics and Multiperception Laboratory Hong Kong

ISBN: (纸本)9781728163215

In this paper, a robust lane detection algorithm is proposed, where the vertical road profile of the road is estimated using dynamic programming from the v-disparity map and, based on the estimated profile, the road area is segmented. Since the lane markings are on the road area and any feature point above the ground will be a noise source for the lane detection, a mask is created for the road area to remove some of the noise for lane detection. The estimated mask is multiplied by the lane feature map in a bird's eye view (BEV). The lane feature points are extracted by using an extended version of symmetrical local threshold (SLT), which not only considers dark light dark transition (DLD) of the lane markings, like (SLT), but also considers parallelism on the lane marking borders. The segmentation then uses only the feature points that are on the road area. A maximum of two linear lane markings are detected using an efficient 1D Hough transform. Then, the detected linear lane markings are used to create a region of interest (ROI) for parabolic lane detection. Finally, based on the estimated region of interest, parabolic lane models are fitted using robust fitting. Due to the robust lane feature extraction and road area segmentation, the proposed algorithm robustly detects lane markings and achieves lane marking detection with an accuracy of 91% when tested on a sequence from the KITTI dataset. © 2019 IEEE.

关键词： Hough transforms

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Reviewing continual learning from the perspective of human-level intelligence

arXiv

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arXiv 2021年

作者： Chang, Yifan Li, Wenbo Peng, Jian Tang, Bo Kang, Yu Lei, Yinjie Gui, Yuanmiao Zhu, Qing Liu, Yu Li, Haifeng The State Key Laboratory of Fire Science Department of Automation and Institute of Advanced Technology University of Science and Technology of China Hefei China Institute of Intelligent Machines Chinese Academy of Sciences Hefei230031 China The Department of Electrical and Computer Engineering Mississippi State University United States College of Electronics and Information Engineering Sichuan University Chengdu China Faculty of Geosciences and Environmental Engineering The Southwest Jiaotong University Chengdu China Institute of Remote Sensing and Geographic Information System Peking Universit Beijing China School of Geosciences and Info-Physics Central South University South Lushan Road Changsha410083 China

Humans continual learning (CL) ability is closely related to Stability Versus Plasticity Dilemma that describes how humans achieve ongoing learning capacity and preservation for learned information. The notion of CL has always been present in artificial intelligence (AI) since its births. This paper proposes a comprehensive review of CL. Different from previous reviews that mainly focus on the catastrophic forgetting phenomenon in CL, this paper surveys CL from a more macroscopic perspective based on the Stability Versus Plasticity mechanism. Analogous to biological counterpart, "smart" AI agents are supposed to i) remember previously learned information (information retrospection);ii) infer on new information continuously (information prospection:);iii) transfer useful information (information transfer), to achieve high-level CL. According to the taxonomy, evaluation metrics, algorithms, applications as well as some open issues are then introduced. Our main contributions concern i) rechecking CL from the level of artificial general intelligence;ii) providing a detailed and extensive overview on CL topics;iii) presenting some novel ideas on the potential development of CL. © 2021, CC BY.

关键词： Petroleum reservoir evaluation

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A Malware Identification and Detection Method Using Mixture Correntropy-Based Deep Neural Network 3rd

A Malware Identification and Detection Method Using Mixture ...

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3rd International Conference on Cyberspace Data and Intelligence, Cyber DI 2019, and the International Conference on Cyber-Living, Cyber-Syndrome, and Cyber-Health, CyberLife 2019

作者： Luo, Xiong Li, Jianyuan Wang, Weiping Gao, Yang Zhao, Wenbing School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083 China Beijing Intelligent Logistics System Collaborative Innovation Center Beijing101149 China Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA15261 United States China Information Technology Security Evaluation Center Beijing100085 China Department of Electrical Engineering and Computer Science Cleveland State University ClevelandOH44115 United States

ISBN: (纸本)9789811519215

With the rapid development of CPS technology, the identification and detection of malware has become a matter of concern in the industrial application of CPS. Currently, advanced machine learning methods such as deep learning are popular in the research of malware identification and detection, and some progress has been made so far. However, there are also some problems. For example, considering the existing noise or outliers in the datasets of malware, some methods are not robust enough. Therefore, the accuracy of classification of malware still needs to be improved. Aiming at it, we propose a novel method thought the combination of correntropy and deep neural network (DNN). In our proposed method for malware identification and detection, given the success of mixture correntropy as an effective similarity measure in addressing complex dataset with noise, it is therefore incorporated into a popular DNN, i.e., convolutional neural network (CNN), to reconstruct its loss function, with the purpose of further detecting the features of outliers. We present the detailed design process of our proposed method. Furthermore, the proposed method is tested both on a popular benchmark dataset and a real-world malware classification dataset, to verify its learning performance. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Mixtures

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A Framework for the Integration of Coarse Sensing Information and Environmental Constraints*

A Framework for the Integration of Coarse Sensing Informatio...

引用

IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Rui Li Yingbai Hu Yanjun Cao Mengyao Li School of Automation Chongqing University Chongqing China Informatics 6 Technical University of Munich Munich Germany Department of Computer and Software Engineering École Polytechnique de Montréal Boul Édouard-Montpetit Québec Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9781728160757

ISBN: (纸本)9781728160764

A series of previous work has found that the environmental constraint (EC), which is the natural result of the contact of the robot and the interacting objects, is immensely helpful for the realization of high-precision robotic tasks. However, due to the existence of multifarious errors, such as mechanical error, modeling error and sensing error, there would be discrepancy between the actual constraints and the ideal models. In such case, it is hard to realize manipulation with EC-based strategies. Inspired by human, a preliminary framework which aims at the integration of the coarse sensing information and the environmental constraints is proposed for robotic manipulation. By mapping the sensing information into the new space, where the environmental constraint can be formally described, the region that integrates the sensing information and the environmental constraint is constructed and the conditions to achieve high-precision manipulation are derived. Based on the conditions, a motion planning strategy is proposed to achieve the required task. The effectiveness of this strategy is verified by case studies.

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Finger Movement Regression with Myoelectric Signal and Deep Neural Network

Finger Movement Regression with Myoelectric Signal and Deep ...

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International Conference on computer Science, Information Technology, and electrical engineering (ICOMITEE)

作者： Khairul Anam Dwiretno Istiyadi Swasono Aris Zainul Muttaqin Faruq Sandi Hanggara Department of Electrical Engineering University of Jember Jember Indonesia Department of Computer Engineering University of Jember Jember Indonesia Department of Mechanical Engineering University of Jember Jember Indonesia Intelligent System and Robotics Lab University of Jember Jember Indonesia

Research on electromyographic (EMG) signals is intensively carried out to help disabled people to control prosthetic hands. Neural Networks have been widely used in research on the classification of finger movements using EMG. The study of a classification system generally still works on a limited number of movements, even though the human body, especially fingers, has a nearly unlimited combination of movements to help do daily activities. To overcome this, a proportional control system is needed. In its recent development, research on myoelectric control using EMG devices is still in a laboratory environment. Hence, the results obtained in a clinical setting are often different. However, along with technological developments, the emergence of affordable and wearable commercial EMG devices such as Myo Armband, has encouraged this study to develop control systems of prosthetic fingers using regression. One of many options available is neural networks that have been widely used in various fields. By estimating each joint with a different neural network, the result shows the predicted is fitted to the actual angle with R 2 as high as 99%.

关键词： Thumb Electromyography Biological neural networks Feature extraction Muscles

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GardnerNet: An Interpretable Deep Learning Model for Quantitative Human Blastocyst Evaluation - A Retrospective Model Development and Validation Study

SSRN

引用

SSRN 2024年

作者： Liu, Hang Chen, Longbin Shan, Guanqiao Sun, Chen Liao, Hongqing Lu, Changfu Zhang, Shuoping Dong, Shaonan Xu, Xinxin Yan, Qiuyun Gong, Fei Zhang, Zhuoran Dai, Changsheng Chen, Wenyuan Song, Haocong Chen, Lei Sun, Haixiang Wang, Shanshan Lin, Ge Sun, Yu Gu, Yifan Department of Mechanical and Industrial Engineering University of Toronto 5 King’s College Road TorontoONM5S 3G8 Canada Institute of Reproductive and Stem Cell Engineering School of Basic Medical Science Central South University 172 Tongzipo Road Hunan Province Changsha China Hengyang Nanhua-Xinghui Reproductive Health Hospital 30 Jiefang Road Hunan Province Hengyang China The second Affiliated Hospital Hengyang Medical School University of South China 30 Jiefang Road Hunan Province Hengyang City China Reproductive & Genetic Hospital of CITIC-Xiangya 567 Tongzipo Road Hunan Province Changsha China NHC Key Laboratory of Human Stem Cell and Reproductive Engineering School of Basic Medical Science Central South University 172 Tongzipo Road Hunan Province Changsha China Clinical Research Center for Reproduction and Genetics in Hunan Province Reproductive & Genetic Hospital of CITIC-Xiangya 567 Tongzipo Road Hunan Province Changsha China School of Science and Engineering The Chinese University of Hong Kong-Shenzhen 2001 Longxiang Boulevard Longgang District Shenzhen China Institute of Robotics and Intelligent Systems Dalian University of Technolgy 2 Linggong Road Liaoning Province Dalian China Department of Computer Science University of Toronto 40 St George Street TorontoONM5S 2E4 Canada Center for Reproductive Medicine and Obstetrics and Gynecology Nanjing Drum Tower Hospital Nanjing University Medical School 321 Zhongshan Road Jiangsu Province Nanjing China National Engineering and Research Center of Human Stem Cell 8 Luyun Road Jiangsu Province Changsha China Department of Electrical and Computer Engineering 10 King’s College Road TorontoONM5S 3G8 Canada Institute of Biomedical Engineering University of Toronto 164 College Street TorontoONM5S 3G9 Canada

Background: Selecting the blastocyst with the highest potential for live birth from a cohort is crucial for the success of in vitro fertilization (IVF). While the Gardner and Schoolcraft morphological grading system, commonly known as the Gardner grading system, is widely used by embryologists worldwide, blastocysts having similar or the same inner cell mass (ICM) and trophectoderm (TE) grades cause challenges for embryologists in decision-making. Furthermore, human assessment is subjective and inconsistent in predicting which blastocysts have higher potential to result in live birth. This study aims to develop an interpretable, quantitative method aligned with the Gardner grading system, providing continuous scores that correlate with live birth outcomes, thereby assisting embryologists in improving blastocyst selection. Methods: In this retrospective study, we developed GardnerNet, an interpretable deep learning model that outputs expansion degree grade and continuous scores quantifying a blastocyst's ICM morphology and TE morphology, based on the Gardner grading system. The continuous ICM and TE scores were calculated by weighting each base grade's predicted probability and summing the predicted probabilities. To represent each blastocyst's overall potential for live birth, we combined the ICM and TE scores using their odds ratios (ORs) for live birth. We further assessed the correlation between the ICM score, TE score, and the OR-combined score (adjusted for expansion degree) with live birth rates by applying GardnerNet to blastocysts with known live birth outcomes. To test its generalizability, we also applied GardnerNet to an external IVF institution, accounting for variations in imaging conditions, live birth rates, and embryologists' experience levels. Findings: GardnerNet was developed using data from 2760 blastocysts with majority-voted grades for expansion degree, ICM, and TE. The model achieved mean area under the receiver operating characteristic curve

关键词： Risk assessment

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High Conductance Margin for Efficient Neuromorphic Computing Enabled by Stacking Nonvolatile van der Waals Transistors

引用

Physical Review Applied 2021年第4期16卷 044049-044049页

作者： Liping Xu (徐丽萍) Hao Xiong (熊浩) Zhichao Fu (付志超) Menghan Deng (邓梦晗) Shuiyuan Wang (王水源) Jinzhong Zhang (张金中) Liyan Shang (商丽燕) Kai Jiang (姜凯) Yawei Li (李亚巍) Liangqing Zhu (朱亮清) Liang He Zhigao Hu (胡志高) Junhao Chu (褚君浩) Technical Center for Multifunctional Magneto-Optical Spectroscopy (Shanghai) Engineering Research Center of Nanophotonics & Advanced Instrument (Ministry of Education) Department of Materials School of Physics and Electronic Science East China Normal University Shanghai 200241 China Center for Advanced Electronic Materials and Devices School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai 200240 China School of Computer Science and Technology East China Normal University Shanghai 200062 China ASIC & System State Key Laboratory School of Microelectronics Fudan University Shanghai 200433 China Collaborative Innovation Center of Extreme Optics Shanxi University Taiyuan Shanxi 030006 China Shanghai Institute of Intelligent Electronics & Systems Fudan University Shanghai 200433 China

High-performance artificial synaptic devices are key building blocks for developing efficient neuromorphic computing systems. However, the nonlinear and asymmetric weight update of existing devices has restricted their practical applications. Herein, floating gate nonvolatile memory (FG NVM) devices based on two-dimensional (2D) HfS2/h-BN/FG-graphene heterostructures have been designed and investigated as multifunctional NVM and artificial optoelectronic synapses. Benefiting from the FG architecture, the HfS2-based NVM device exhibits competitive performances, such as a high on:off ratio (>105), large memory window (approximately 100 V), excellent charge retention ability (>104s), and robust durability (>103 cycles). Notably, the artificial optoelectronic synapses based on HfS2 FG NVM show an impressive large conductance margin and good linearity, owing to the ultrahigh photoresponsivity and photogain of HfS2. The energy consumption of per spike in our artificial synapse is as low as 0.2 pJ. Therefore, a high recognition accuracy up to 91.5% of the artificial neural network on the basis of our HfS2-based optoelectronic synapse at the system level has been achieved, which is superior to other reported 2D artificial optoelectronic synapses. This work paves the way forward for all 2D material-based memory for developing efficient optogenetics-inspired neuromorphic computing in brain-inspired intelligent systems.

关键词： electrical conductivity Optoelectronics Synapses 2-dimensional systems Artificial neural networks Transistors Transition metal dichalcogenides Lithography

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On diffusive non-reciprocity and thermal diode

arXiv

引用

arXiv 2019年

作者： Li, Ying Li, Jiaxin Qiu, Cheng-Wei Department of Electrical and Computer Engineering National University of Singapore Kent Ridge17583 Singapore State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China

Wave propagation and diffusion in linear materials preserve local reciprocity in terms of a symmetric Green's function. For wave propagations, people usually focus on the signals entering and leaving a system, and do not need detailed information about the fields inside it. In such cases, the global reciprocity of the scattering off a system through several ports is more important, which is defined as the symmetric transmission between the scattering channels. When a two-port system supports non-reciprocal (electromagnetic, acoustic) wave propagation, it is a (optical, phonon) diode directly following the definition. However, to date no clear definition or discussion has been made on the global reciprocity of diffusive processes through a system. It thus remains a question whether diffusive non-reciprocity is related with diode for diffusive fields, say, a thermal diode. Here, we provide theoretical analysis based on local reciprocity and define the global reciprocity of diffusion through a two-port system. We further prove the equivalence between a non-reciprocal heat transfer system and a thermal diode, and discuss some methods to realize it. Our results set a general background for future studies on symmetric and asymmetric diffusive processes. Copyright © 2019, The Authors. All rights reserved.

关键词： Acoustic wave propagation

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