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IEEE International Conference on Consumer Electronics - Berlin (ICCE-Berlin)

作者： Bruna M. J. C. Vilar Fabiano De M Silva Saulo O. D. Luiz J. J. Silva Hyggo Almeida Angelo Perkusich Post-Graduate Program in Electrical Engineering - PPgEE COPELE Embedded Systems and Pervasive Computing Laboratory Universidade Federal de Campina Grande Campina Grande PB BR Center of Electrical Engineering and Informatics Federal University of Campina Grande Campina Grande Brazil Embedded Systems and Pervasive Computing Laboratory Federal University of Campina Grande

Network interfaces are present in a variety of battery powered computer systems, such as notebooks, ultrabooks, tablets and mobile phones. Considering that the battery life is still a constraint, there are opportunities for power management by reducing the power consumption of idle network interfaces. In this work, a dynamic timeout power policy for network interfaces is presented, incorporating workload estimation and a problem optimization at runtime, thus providing power savings and suitable performance.

关键词： Heuristic algorithms Network interfaces Power demand Optimization Mobile handsets Internet Stochastic processes

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Goal-oriented inference of environment from redundant observations

arXiv

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arXiv 2023年

作者： Takahashi, Kazuki Fukai, Tomoki Sakai, Yutaka Takekawa, Takashi Informatics Program Graduate School of Engineering Kogakuin University of Technology and Engineering Japan Neural Coding and Brain Computing Unit Okinawa Institute of Science and Technology Japan Brain Science Institute Tamagawa University Japan

The agent learns to organize decision behavior to achieve a behavioral goal, such as reward maximization, and reinforcement learning is often used for this optimization. Learning an optimal behavioral strategy is difficult under the uncertainty that events necessary for learning are only partially observable, called as Partially Observable Markov Decision Process (POMDP). However, the real-world environment also gives many events irrelevant to reward delivery and an optimal behavioral strategy. The conventional methods in POMDP, which attempt to infer transition rules among the entire observations, including irrelevant states, are ineffective in such an environment. Supposing Redundantly Observable Markov Decision Process (ROMDP), here we propose a method for goal-oriented reinforcement learning to efficiently learn state transition rules among reward-related"core states" from redundant observations. Starting with a small number of initial core states, our model gradually adds new core states to the transition diagram until it achieves an optimal behavioral strategy consistent with the Bellman equation. We demonstrate that the resultant inference model outperforms the conventional method for POMDP. We emphasize that our model only containing the core states has high explainability. Furthermore, the proposed method suits online learning as it suppresses memory consumption and improves learning speed. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Training of an Artificial Neural Network with Backpropagation algorithm using notification oriented paradigm

Training of an Artificial Neural Network with Backpropagatio...

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Latin America Congress on Computational Intelligence (LA-CCI)

作者： Fenando Schütz João A. Fabro Carlos R. E. Lima Adriano F. Ronszcka Paulo C. Stadzisz Jean M. Simão Graduate School in Electrical Engineering and Industrial Informatics (CPGEI) Federal University of Technology - Paraná (UTFPR) - Av. Sete de Setembro 3165. Curitiba-PR Brazil. 80-230-901 Graduate School of Applied Computing (PPGCA) Federal University of Technology - Parana (UTFPR) Curitiba PR Brazil Graduate School in Electrical Engineering and Industrial Informatics (CPGEI)

When implementing Artificial Neural Networks with imperative programing languages, the resulting programs are usually highly coupled. This problem usually hampers distribution over multiple processors, especially when the ANN executes on general-purpose processors. An emerging technique called Notification Oriented Paradigm (NOP) facilitates the development of distributed and decoupled systems, and minimizes the amount of processing calculations, while avoiding structural and temporal redundancies in the logical-causal evaluation. These advantages seem to be relevant to parallel systems, especially in the case of ANNs. In this sense, this paper presents the development of a Multi-Layer Perceptron using Backpropagation training algorithm, based on the concepts of NOP in its implementation. The overall performance of NOP implementation was not so good in comparison with the Imperative Paradigm (IP) implementation, because the current materialization of NOP (framework) is still based on a single thread implementation. Even though, this implementation proved to be plausible and decoupled, as well as parallelizable according the inherent parallelism of ANNs, conceptually intrinsic on the NOP.

关键词： Artificial neural networks Training Neurons Computer architecture Hardware program processors

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Valley notch filter in a graphene strain superlattice: a Green’s function and machine learning approach

arXiv

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arXiv 2019年

作者： Torres, V. Silva, P. De, E.A.T. Souza Silva, L.A. Bahamon, D.A. Instituto de Física Universidade Federal Fluminense Niterói Av. Litorânea sn RJ24210-340 Brazil MackGraphe Mackenzie Presbyterian University Rua da Consolação 930 SP01302-907 Brazil School of Computing and Informatics Graduate Program in Electrical Engineering and Computing

The valley transport properties of a superlattice of out-of-plane Gaussians deformations are calculated using a Green’s function and a Machine Learning approach. Our results show that periodicity significantly improves the valley filter capabilities of a single Gaussian deformation, these manifest themselves in the conductance as a sequence by valley filter plateaus. We establish that the physical effect behind the observed valley notch filter is the coupling between counter-propagating transverse modes;the complex relationship between the design parameters of the superlattice and the valley filter effect make difficult to estimate in advance the valley filter potentialities of a given superlattice. With this in mind, we show that a Deep Neural Network can be trained to predict valley transmission with a precision similar to the Green’s function but with much less computational effort. Copyright © 2019, The Authors. All rights reserved.

关键词： Landforms

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A Unified Framework for Human Centered Design of a Substance Use, Abuse, and Recovery Support System

A Unified Framework for Human Centered Design of a Substance...

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AHFE Virtual Conference on Human Factors and Ergonomics in Healthcare and Medical Devices, and the International Conference on Human Factors in Aging and Special Needs, 2020

作者： schooley, Benjamin Feldman, Sue Tipper, Bradley Health Information Technology Program College of Engineering and Computing University of South Carolina 550 Assembly Street ColumbiaSC29208 United States Graduate Programs in Health Informatics Department of Health Services Administration School of Health Professions University of Alabama - Birmingham 1716 9th Ave South SHPB #590K BirminghamAL35294 United States

ISBN: (纸本)9783030508371

This research explores the analysis, design, and testing of a software application to support opioid peer specialists working to assist substance users, abusers, those in recovery, and their families. The paper presents a framework integrating best practice approaches from Design Science Research, Community Based Participatory Research, and human centered and user experience design principles for the purpose of: 1) guiding community based, goal-directed software design, and 2) building and evaluating a substance use, abuse, and recovery software application. Focus group findings from 33 participants suggest that using the framework enables alignment between human centered system requirements;personal, family, and community-based program goal achievement;and local/state population health directives. End user feedback indicates that the software application design addresses specific human interaction needs for the complex high-stakes context of opioid use, abuse, and recovery. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Application programs

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Improvement of convergence characteristic in nonlinear transient eddy-current analyses using the error correction of time integration based on the time-periodic FEM and the EEC method

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IEEJ Transactions on Power and Energy 2009年第6期129卷 7+791-798页

作者： Takahashi, Yasuhito Tokumasu, Tadashi Fujita, Masafumi Wakao, Shinji Iwashita, Takeshi Kanazawa, Masanori Graduate School of Informatics Kyoto University Yoshida-Honmachi Sakyo-ku Kyoto 606-8501 Japan Toshiba Corporation Power Systems Company 2-4 Suehiro-cho Tsurumi-ku Yokohama 230-0045 Japan Dept. of Electrical Engineering and Bioscience Waseda University 3-4-1 Ohkubo Shinjuku-ku Tokyo 169-8555 Japan Academic Center for Computing Media Studies Kyoto University Yoshida-Honmachi Sakyo-ku Kyoto 606-8501 Japan

This paper proposes novel techniques for the improvement of the convergence characteristic of step-by-step time integrations in nonlinear transient eddy-current analyses. The proposed methods, which are based on the time-periodic finite-element method and the explicit error correction method, can extract poorly-converged error components corresponding to large time constants of an analyzed system. The correction of the extracted error components accelerates the convergence of transient calculation efficiently. Furthermore, we extend the performance of the proposed methods in nonlinear problems. Some numerical results that verify the effectiveness of the proposed methods are also presented. © 2009 The Institute of electrical Engineers of Japan.

关键词： Nonlinear analysis

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Using equivalence classes for testing programs for safety instrumented systems

Using equivalence classes for testing programs for safety in...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Kézia de Vasconcelos Oliveira Angelo Perkusich Kyller Costa Gorgônio Leandro Dias da Silva Aldenor Falcão Martins Center of Electrical Engineering and Informatics Federal University of Campina Grande Campina Grande Paraiba Brazil Computing Institute Federal University of Alagoas Maceio Alagoas Brazil Graduate Program in Electrical Engineering Federal University of Campina Grande Campina Grande Paraiba Brazil

The reliability on Safety Instrumented Systems (SIS) is critical for the safe operation of many industrial applications. In particular, SIS play an important role in oil and gas processing plants. SIS are responsible not only for the continuous operation of the plant, it also keeps the plant in a safe state, avoiding damages to the environment and minimizing risks to employees. Therefore, the correct behavior of such systems is an important goal to achieve when building industrial plants. Verification and testing of SIS programs is a very hard task to accomplish. This happens mainly for two reasons. First, testing the real system is very expensive and sometimes it may take a huge amount of time, weeks or even months. Second, those systems deal with a huge number of variables. It is not always possible for a human tester to check if all of them are correct when performing tests. Providing an automatic and formal testing approach for such systems is an important contribution for the development of such systems. In this work we introduce a new method for generating test cases for SIS programs running on programable Logic Controller (PLC). As we did on previous work, ISA 5.2 diagrams are used as specification of the systems, but now we are using a hardware-in-the-loop technique, and the target artifact is a software running on a PLC. To avoid the execution of redundant tests, we introduced a new test case generation algorithm that is based on equivalence classes. Finally, we discussed a study case in which our method is used to detect error, that were introduced on purpose, on a simple system.

关键词： Automata Testing Safety Valves Input variables Computational modeling Instruments

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Dynamic programming Algorithm using Furniture Industry 0/1 Knapsack Problem 5

Dynamic Programming Algorithm using Furniture Industry 0/1 K...

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5th International Conference on Cybernetics and Intelligent Systems, ICORIS 2023

作者： Anditta, Mietha Amartya, Natashya Warnars, Laurens Spits Warnars, Harco Leslie Hendric Spits Ramadhan, Arief Siswanto, Teddy Mantoro, Teddy Noordin, Nurulhuda Bina Nusantara University School of Computer Science Computer Science Departement Jakarta11530 Indonesia Bina Nusantara University School of Information Systems Information Systems Departement Jakarta11530 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Departement Jakarta11530 Indonesia Telkom University Pjj Informatika Bandung40257 Indonesia Trisakti University Information Systems Department Jakarta11440 Indonesia Sampoerna University Faculty of Engineering and Technology Jakarta12760 Indonesia Universiti Teknologi Mara School of Computing Sciences College of Computing Informatics and Mathematics Selangor Malaysia

ISBN: (纸本)9798350369489

Dynamic programming is a fundamental algorithm that can be found in our daily lives easily. One of the dynamic programming algorithm implementations consists of solving the 0/1 knapsack problem. A 0/1 knapsack problem can be seen from industrial production cost. It is prevalent that a production cost has to be as efficient as possible, but the expectation is to get the proceeds of the products higher. Thus, the dynamic programming algorithm can be implemented to solve the diverse knapsack problem, one of which is the 0/1 knapsack problem, which would be the main focus of this paper. The implementation was implemented using C language. This paper was created as an early implementation algorithm using a Dynamic program algorithm applied to an Automatic Identification System (AIS) dataset. © 2023 IEEE.

关键词： Dynamic programming

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Multi-task Learning Graph Neural Networks for Cancer Prognosis Prediction with Genomic Data

Multi-task Learning Graph Neural Networks for Cancer Prognos...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Tsung-Wei Lin Sofia Ormazabal Arriagada Che Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taiwan International Graduate Program AIoT NTU Research Center for Information Technology Innovation Academia Sinica Department of Electrical Engineering NTU Center for Advanced Computing and Imaging in Biomedicine NTU Smart Medicine and Health Informatics Program NTU

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Providing robust prognosis predictions for cancers with limited data samples remains a challenge for precision oncology. In this study, we propose a novel approach that combines multi-task learning (MTL) and graph neural networks (GNNs) to address this issue. By representing gene-gene interactions as a graph network, our approach leverages multi-task learning to effectively capture the relationships of genes relevant to the oncogenesis and progression of breast, lung, and colon cancer. We demonstrate that our approach improves the cancer prognosis prediction for cancers with fewer samples, such as colon adenocarcinoma, by leveraging the shared gene-gene interactions across different cancer types, obtaining increases in the area under the precision-recall curve (AUPRC) of 24%. Our work contributes to the field of smart healthcare by demonstrating the potential of MTL and GNNs for enhancing cancer prognosis prediction, even with limited data samples.

关键词： Lungs Genomics Medical services Colorectal cancer Breast Multitasking Oncology Graph neural networks Prognostics and health management Cancer

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Computer-aided instruction for introductory linear circuit analysis

Computer-aided instruction for introductory linear circuit a...

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Frontiers in Education (FIE) Conference

作者： B. J. Skromme P. J. Rayes C. D. Whitlatch Q. Wang A. Barrus J. M. Quick R. K. Atkinson T. S. Frank School of Electrical Computer and Energy Engrg. School of Computing Informatics and Decision Systems Engrg. Arizona State University Tempe AZ USA Engineering Program South Mountain Community College Phoenix AZ USA

A step-based tutoring system for linear circuit analysis is being developed with the capabilities to automatically generate circuit problems with specified characteristics, including randomly generated topologies and element values. The system further generates fully-worked, error-free solutions using the methods typically taught in such classes, and accepts a rich variety of student input such as equations, matrix equations, numerical and multiple-choice answers, re-drawn circuit diagrams, and sketches of waveforms. A randomized, controlled study was conducted using paid student volunteers to compare the effectiveness of two of our tutorials in comparison to working conventional textbook-based problems. The average learning gain was only 3/100 points for the textbook users, but 29/100 points, about 10 times higher, for the tutorial users. The effect size on the post-test scores was 1.21 pooled standard deviations (Cohen d-value) and was statistically significant. A motivational survey administered to these students yielded a 0.53 point higher rating for the software than for the textbook (on a 1-5 scale). The system is being used in Spring 2013 by over 340 students in EEE 202 at Arizona State and two community colleges. About 99% of these students rated the system as “very helpful” or “somewhat helpful”.

关键词： Equations Tutorials Software Materials Educational institutions Linear circuits Topology

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