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arXiv 2020年

作者： Yamada, Jun Lee, Youngwoon Salhotra, Gautam Pertsch, Karl Pflueger, Max Sukhatme, Gaurav S. Lim, Joseph J. Englert, Peter Cognitive Learning for Vision and Robotics Lab United States Robotic Embedded Systems Laboratory Department of Computer Science University of Southern California United States

Deep reinforcement learning (RL) agents are able to learn contact-rich manipulation tasks by maximizing a reward signal, but require large amounts of experience, especially in environments with many obstacles that complicate exploration. In contrast, motion planners use explicit models of the agent and environment to plan collision-free paths to faraway goals, but suffer from inaccurate models in tasks that require contacts with the environment. To combine the benefits of both approaches, we propose motion planner augmented RL (MoPA-RL) which augments the action space of an RL agent with the long-horizon planning capabilities of motion planners. Based on the magnitude of the action, our approach smoothly transitions between directly executing the action and invoking a motion planner. We evaluate our approach on various simulated manipulation tasks and compare it to alternative action spaces in terms of learning efficiency and safety. The experiments demonstrate that MoPA-RL increases learning efficiency, leads to a faster exploration, and results in safer policies that avoid collisions with the environment. Videos and code are available at https://***/mopa-rl. Copyright © 2020, The Authors. All rights reserved.

关键词： Motion planning

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Predicting Grinding Mill Power Consumption in Mining: A Comparative Study

Predicting Grinding Mill Power Consumption in Mining: A Comp...

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International Conference on Information Science and Technology (ICIST)

作者： Chaimae Loudari Moha Cherkaoui Rachid Bennani Imad El Harraki Omar Fares Mohamed El Adnani El Hassan Abdelwahed Intissar Benzakour François Bourzeix Karim Baina dept. LMAID Laboratory National School of Mines of Rabat (ENSMR) Rabat Morocco dept. Digitalization and Microelectronic Smart Devices Moroccan Foundation for Advanced Science Innovation and Research (MAScIR) Rabat Morocco dept. EIA Reminex Research Center MANAGEM Group Casablanca Morocco dept. LISI Laboratory University of Cadi Ayyad (UCA) Marrakech Morocco dept. Embedded Systems and Artificial Intelligence Department Moroccan Foundation for Advanced Science Innovation and Research (MAScIR) Rabat Morocco dept. Al-Qualsadi Research & Development Team National Higher School for Computer Science and System analysis (ENSIAS) Rabat Morocco

The mining industry deals with complexity and high energy consumption in mineral production. Artificial intelligence technology has been integrated to manage and optimize energy usage in mining equipment. A special focus is given to grinding mills, which are essential but difficult to model due to their energy-intensive nature. Data-driven machine learning approaches have emerged as effective solutions, enabling energy consumption prediction and optimization. These methods offer valuable insights to improve the overall energy efficiency of grinding mills in mining. In this article, a comparative study is conducted to investigate the use of machine learning models for predicting the power consumption of grinding mills. The study starts by providing a detailed description of the dataset used and the predictive models employed in the research. Notably, the results of power prediction for grinding mills are compared, with the Random Forest model emerging as the top performer, boasting an impressive R2 value of 0.94. The article underscores the significance of this predictive capability in helping mining experts make informed decisions to optimize energy consumption in grinding mills. Finally, the article concludes by offering insightful recommendations and identifying promising avenues for future research in this field. Overall, this study sheds light on the potential of machine learning to enhance energy efficiency in the mining industry.

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Variable petri nets for mobility

arXiv

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arXiv 2021年

作者： Ding, Zhijun Yang, Ru Cui, Puwen Zhou, MengChu Jiang, Changjun Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China Department of Computer Science and Technology Tongji University Shanghai201804 China Institute of Systems Engineering Macau University of Science and Technology Macau999078 China Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

Mobile computing systems, service-based systems and some other systems with mobile interacting components have recently received much attention. However, because of their characteristics such as mobility and disconnection, it is difficult to model and analyze them by using a structure-fixed model. This work proposes a new Petri net model called Variable Petri Net (VPN) for modeling and analyzing these systems. The definition, firing rule, and related analysis technology of VPN are introduced in detail. In a VPN, the possible interaction interfaces are abstracted as a new kind of places called virtual places, and the occurrences of (dis)connections are described by new functions, which makes it appropriate to describe the component collaboration in systems and realize the scalability and pluggability of systems. Moreover, to overcome the shortcoming that markings cannot reflect link capability of a system, VPNs add a constraint function along with a marking to represent a complete system configuration. Several examples are used to demonstrate the newly proposed model and method. Copyright © 2021, The Authors. All rights reserved.

关键词： embedded systems

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PRIPRO:Solution for user profile control and management based on data privacy

PRIPRO:Solution for user profile control and management base...

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Iberian Conference on Information systems and Technologies (CISTI)

作者： Jonas Cesconetto Luís A. Silva R. Q. Valderi Leithardt María N. Cáceres Nuno M. Garcia Laboratory of Embedded and Distributed Systems University of Vale do Itajaí Brazil Department of Computer Science University of Salamanca Spain Departamento de Informática Universidade da Beira Interior Portugal

ISBN: (数字)9789895465903

ISBN: (纸本)9781728167244

Intelligent environments work collaboratively, bringing more comfort to human beings. The intelligence of these environments comes from technological advances in sensors and communication. IoT is the model developed that allows a wide and intelligent communication between devices. Hardware reduction of IoT devices results in vulnerabilities. Thus, there are numerous concerns regarding the security of user information, since mobile devices are easily trackable over the Internet. Care must be taken regarding the information in user profiles. Mobile devices are protected by a permission-based mechanism, which limits third-party applications from accessing sensitive device resources. In this context, this work aims to present a proposal for materialization of application for the evolution of user profiles in intelligent environments. Having as parameters the parameters presented in the proposed taxonomy. The proposed solution is the development of two applications, one for Android devices, responsible for allowing or blocking some features of the device. And another in Cloud, responsible for imposing the parameters and privacy criteria, formalizing the profile control module (PRIPRO - PRIvacy PROfiles).

关键词： Androids Humanoid robots Internet Privacy Smart phones Hardware Information systems

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Multi-granularity ensemble sample selection and label correction for classification with noisy labels

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Applied Soft Computing 2025年 180卷

作者： Kecan Cai Hongyun Zhang Witold Pedrycz Duoqian Miao Chaofan Chen Department of Computer Science and Technology Tongji University Shanghai 201804 PR China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 201804 PR China Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6G 2R3 Canada Systems Research Institute Polish Academy of Sciences 00-901 Warsaw Poland Istinye University Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer/Istanbul Turkiye

Sample selection is crucial in classification tasks with noisy labels, yet most existing sample selection methods rely on a single criterion. These approaches often face challenges, including low purity of selected clean samples, and underfitting due to an insufficient number of selected clean training samples. To address these challenges, this paper proposes GNet-SSLC, a novel multi-granularity network framework that integrates multiple criteria ensemble sample selection (SS) and multiple views label correction (LC). In the SS phase, this paper proposes a metric learning-based dual k-Nearest Neighbor (k-NN) sample selection method. This method first uses corrected soft labels from the initial k-NN round to guide the selection of clean samples in the subsequent k-NN round. To further enhance selection accuracy, we combine this dual k-NN approach with a small loss sample selection technique through a voting mechanism. This multiple criteria ensemble method addresses the issues of low purity and instability inherent in single criterion approaches. In the LC phase, this paper designs a multiple views label correction framework that generates high-quality pseudo-labels for selected noisy samples. A key innovation of the framework is the design of a regularized contrastive learning loss, which optimizes the semi-supervised learning process by leveraging multiple views of training samples. The additional inclusion of training samples with high-quality pseudo-labels can effectively mitigate underfitting caused by a limited number of clean training samples. Experimental results on both synthetic and real-world noisy datasets indicate that GNet-SSLC enhances the purity and stability of the selected clean samples, and significantly improves classification performance. The enhancement is particularly notable with high noise rate dataset, such as CIFAR-100 dataset with 80% noise rate, achieving a 19.3% increase in classification accuracy compared to the baseline method.

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A survey on different human-machine interactions used for controlling an electric wheelchair

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Procedia computer Science 2019年 159卷 398-407页

作者： Agnès Ghorbel Nader Ben Amor Mohamed Jallouli University of Sfax Ecole Nationale d'Ingénieurs de Sfax (ENIS) Computer and Embedded Systems Laboratory Sfax Tunisia

Nowadays, enhanced mobility is a major challenge in the public healthcare sector, especially for the elderly and disabled persons living alone. Therefore, the demand for assistance systems as smart wheelchairs, especially for patients who are not able to walk normally because of some injury, has considerably increased within the healthcare industry. The power of modern computer systems has created new opportunities for human-machine interaction researchers to expand the capabilities of conventional wheelchairs by introducing specialized interfaces to enable the user to emit a command perceptible and understandable by a machine. In this paper, we present a survey on different human-machine interactions used for controlling an electric wheelchair and we demonstrate the effectiveness of our interface based on human facial expressions.

关键词： Healthcare Smart wheelchair Smart HMIs

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Accelerating Binary-Matrix Multiplication on FPGA

Accelerating Binary-Matrix Multiplication on FPGA

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IEEE International SOC Conference

作者： Debjyoti Bhattacharjee Anupam Chattopadhyay Ricardo Jack Liwongan Hardware and Embedded Systems Laboratory School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (数字)9781728134833

ISBN: (纸本)9781728134840

Matrix multiplication is required for a wide variety of applications, including data mining, linear algebra, graph transformations, etc. Most of the existing works to accelerate matrix multiplication have focused on matrices with floating point elements. In this work, we propose for the first time an FPGA based accelerator architecture for binary matrix multiplication. It consists of processing elements laid out in regular tiled manner. The communication structure used is a torus. We undertook detailed experimental study of the proposed architecture. The architecture shows excellent scalability with increase in number of processing elements, with minimal drop in operating frequency. The proposed system achieves maximum throughput of 1120 Gops for 4 x 4 network size with 2048 x 2048 matrix size. The performance achieved by the system is considerably higher than existing works of floating point multiplication on FPGAs, due to optimized PE design for binary matrix multiplication.

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Unified Framework for Oculomotor Nerve Reconstruction: Tractography-Based Anatomical Assessment

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Journal of Neuroimaging 2025年第3期35卷 e70052页

作者： Jiahao Huang Qingrun Zeng Ye Wu Jiawei Zhang Mengjun Li Lei Xie Mingchu Li Yuanjing Feng Institute of Information Processing and Automation College of Information Engineering Zhejiang University of Technology Hangzhou China Zhejiang Provincial United Key Laboratory of Embedded Systems Hangzhou China School of Computer Science and Technology Nanjing University of Science and Technology Nanjing China Department of Radiology Second Xiangya Hospital Central South University Changsha China Department of Neurosurgery Capital Medical University Xuanwu Hospital Beijing China

Background and Purpose The oculomotor nerve (OCN) innervates the eye muscles and can be affected by inflammatory, compressive, and pathological conditions. Diffusion MRI (dMRI) tractography shows the potential ability to describe the trajectory of the OCN. However, reconstruction of the OCN in the cavernous sinus is still challenging due to the complex tissue environment at the skull base. Methods In this study, we integrated anatomical knowledge to propose a unified framework for OCN tractography, using 45 dMRI datasets from the Human Connectome Project subjects aged 22–36 years and data from four neurosurgical patients aged 41–53 years with visual behavior disorders. We first employed automatically labeled direct and indirect anatomical landmarks as reference locations for individualized tractography. Next, we compared five widely used cranial nerve reconstruction algorithms to assess the most suitable method for OCN reconstruction. Finally, we tested the combination of the unified framework and the optimized tractography method in tumor patients. Results We found that unscented Kalman filter (UKF)-2T and probabilistic tractography outperformed other methods in OCN fiber tractography, owing to their “step-by-step” fiber direction computation and multidirectional consideration, respectively. In neurosurgical patients, UKF-2T effectively reconstructed OCN fibers around lesions. Conclusion Our study provides valuable insights for researchers and clinicians in the diagnosis and treatment of OCN-related diseases and neurosurgeries.

关键词： diffusion magnetic resonance imaging Human Connectome Project neurosurgery oculomotor nerve tractography

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Intelligent Condition Monitoring for Rotating Machinery Using Compressively-Sampled Data and Sub-space Learning Techniques

Intelligent Condition Monitoring for Rotating Machinery Usin...

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作者： Ahmed, H.O.A. Nandi, A.K. Department of Electronic and Computer Engineering Brunel University London UxbridgeUB8 3PH United Kingdom The Key Laboratory of Embedded Systems and Service Computing College of Electronic and Information Engineering Tongji University Shanghai China

Rotating machines are widely used in industry. Unforeseen machine failures affect production schedules, product quality, and production costs. Therefore, condition monitoring of rotating machine can play an important role in machine availability. There is a growing number of methods for Machine Condition Monitoring (MCM). Yet, the performance of these methods is limited by the massive amounts of data need to be collected for MCM. This work proposes a computational method which can greatly reduce the high dimensional vibration dataset to a set of compressively-sampled measurements using Compressive Sampling (CS). Then, to learn fewer features from these compressively-sampled measurements we propose an effective multi-step feature learning algorithm that combines the advantages of Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Canonical Correlation Analysis (CCA). Finally, with these learned features, we use multi-class Support Vector Machine (SVM) to classify machine health conditions. Experiments on a roller element bearing fault classification task based on vibration signals are used to evaluate the efficiency of the proposed method. The most obvious finding to emerge from this study is that we are able to achieve high classification accuracy even from highly reduced vibration signal measurements. Moreover, the efficiency of our proposed method outperforms some recently published results. The proposed method offers better accuracy and has lower costs in time and storage requirements. © 2019, Springer Nature Switzerland AG.

关键词： Compressed sensing

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Profi-load: An FPGA-based solution for generating network load in profinet communication

arXiv

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arXiv 2019年

作者： Khaliq, Ahmad Saha, Sangeet Bhatt, Bina Gu, Dongbing McDonald-Maier, Klaus Embedded and Intelligent Systems Laboratory Computer Science and Electronic Engineering Department University of Essex Colchester United Kingdom

Industrial automation has received a considerable attention in the last few years with the rise of Internet of Things (IoT). Specifically, industrial communication network technology such as Profinet has proved to be a major game changer for such automation. However, industrial automation devices often have to exhibit robustness to dynamically changing network conditions and thus, demand a rigorous testing environment to avoid any safety-critical failures. Hence, in this paper, we have proposed an FPGA-based novel framework called "Profi-Load" to generate Profinet traffic with specific intensities for a specified duration of time. The proposed Profi-Load intends to facilitate the performance testing of the industrial automated devices under various network conditions. By using the advantage of inherent hardware parallelism and re-configurable features of FPGA, Profi-Load is able to generate Profinet traffic efficiently. Moreover, it can be reconfigured on the fly as per the specific requirements. We have developed our proposed Profi-Load framework by employing the Xilinx-based "NetJury" device which belongs to Zynq-7000 FPGA family. A series of experiments have been conducted to evaluate the effectiveness of Profi-Load and it has been observed that Profi-Load is able to generate precise load at a constant rate for stringent timing requirements. Furthermore, a suitable Human Machine Interface (HMI) has also been developed for quick access to our framework. The HMI at the client side can directly communicate with the NetJury device and parameters such as, required load amount, number of packet(s) to be sent or desired time duration can be selected using the HMI. Copyright © 2019, The Authors. All rights reserved.

关键词： Field programmable gate arrays (FPGA)

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