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Iberian Conference on Information systems and Technologies (CISTI)

作者： Valéria Farinazzo Martins Paulo N. M. Sampaio Antônio José A. Cordeiro Bruno Ferreira Viana Mackenzie Prebisterian University Computing and Systems Graduate Program Salvador University (UNIFACS) Computational Modelling and Industrial Technology Senai - Cimatec Management and Technology Graduate Professional Program (GESTEC) Bahia State Estácio University (Estácio FIB)

Education professionals are always searching for new ways of promoting learning, either by modifying the pedagogical project of courses or by the adoption of new learning strategies. In particular, this paper presents the results of an effort in order to bridge the knowledge acquired by students within classroom in face-to-face classes with technical skills required in real life situations. The contribution presented in this paper is the result of an assignment applied within an undergraduate Data Network Infrastructure Design course in a Brazilian university using an adapted Problem-Based Learning methodology (PBL), as a motivational tool. This paper also presents and discusses the resulting data related to the learning assessment.

关键词： Problem-solving Education Computer networks Electronic mail Laboratories Bridges Tools

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Vibration-based classification of road damages: gyroscope data and a simple neural network model

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IOP Conference Series: Earth and Environmental Science 2018年第1期195卷

作者： Fergyanto E. Gunawan Herriyandi Benfano Soewito Tuga Mauritsius Nico Surantha Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta Indonesia 11480

A system for automatic detection of road damages is essential for logistics management. At the core of the system lies an algorithm for classification of damages. This work intends to establish such an algorithm. In the current proposal, the algorithm receives data of the probe vehicle rate of rotations, extracts statistics descriptive from the data to produce a feature vector, and finally, feeds the vector to a simple-three-layer neural network to classify the damages. The types of damages are limited to four cases: normal, pothole, speed bump, and expansion joint. For the development, a probe vehicle is used to produce 400 empirical data involving those four damage cases. By using Monte Carlo approach, the established neural network model is evaluated. The results show that the approach is able to classify the cases with 85% accuracy. The study also finds that the rates of pitch and roll to be the determining factors.

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Diversity-Guided Generalized Extremal Optimization for Transformer Design Problem

Diversity-Guided Generalized Extremal Optimization for Trans...

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IEEE Symposium Series on Computational Intelligence

作者： Leandro dos S. Coelho Viviana C. Mariani Rafael B. Grebogi Emerson H. de Vasconcelos Segundo Mauricio V. Ferreira da Luz Jean V. Leite Roberto Z. Freire Industrial and Systems Engineering Graduate Program Electrical Engineering Federal University of Parana - UFPR Curitiba Brazil Mechanical Engineering Graduate Program Electrical Engineering Federal University of Parana - UFPR Curitiba Brazil Mechanical Engineering Graduate Program Federal Institute of Santa Catarina - IFSC Lages Brazil Mechanical Engineering Graduate Program Pontifical Catholic University of Parana - PUCPR Curitiba Brazil GRUCAD-EEL-CTC Federal University of Santa Catarina - UFSC Florianópolis Brazil Federal University of Santa Catarina - UFSC Florianópolis Brazil Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana - PUCPR Curitiba Brazil

Market competitiveness and pressure for rational use of resources led manufacturers to look for optimized electromagnetic devices. The large number of variables involving the design of this technology, as well as the compromise between performance and efficiency, have provide the opportunity to adopt optimization techniques in their design stage. Recently, a wide variety of both traditional and nontraditional mathematical algorithms have been introduced to solve challenging and practical optimization problems related to electromagnetic equipment. Evolutionary metaheuristics are general-purpose optimization algorithms inspired by natural self-organized systems and by biological evolution. An optimization method, called Extremal Optimization (EO), which is based on a simplified model of natural selection, was developed to show the emergence of self-organized criticality based on Bak-Sneppen model in ecosystems. Recently, a generalized version of EO method, called Generalized Extremal Optimization (GEO), was proposed. This technique can be applied to a broad class of design optimization problems. In order to enhance and to improve the search performance and efficiency of GEO, a novel Diversity-guided GEO (DGEO) metaheuristic algorithm, with focus in global continuous optimization, is proposed and investigated in this paper considering the design stage of an electromagnetic device. The analysis was carried for a single-phase transformer and revealed that DGEO provided considerable better performance in convergence speed and solution quality than standard GEO.

关键词： Metaheuristic optimization Transformer design Generalized extremal optimization Power-grid Power conversion

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Designing Lead-Lag PSS Employing Backtracking Search Algorithm to Improve Power System Damping

Designing Lead-Lag PSS Employing Backtracking Search Algorit...

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IEEE GCC Conference & Exhibition

作者： Md Shafiullah Md Juel Rana L. S. Coelho M. A. Abido Ahmad Al-Subhi Department of Electrical Engineering King Fahd University of Petroleum & Minerals Dhahran KSA Industrial and Systems Engineering Graduate Program Federal University of Parana Curitiba PR Brazil

ISBN: (纸本)9781538627570

The power system is a complex network having highly nonlinear dynamics. Being electrically interconnected power systems are vulnerable to instability due to the disturbances in any one portion of the system. Even low-frequency oscillations (LFOs) originated from small disturbances in any part of the network may cause the outage of the whole system. Power System Stabilizer (PSS) with optimal parameter settings is an efficient tool for the suppression of these oscillations. In this paper, a novel approach is proposed to tune lead-lag controller based PSS for a single machine infinite bus (SMIB) network by using backtracking search algorithm (BSA) to suppress LFOs through improvement of power system damping. The efficacy of BSA tuned PSS has been investigated by comparing the simulation results with fixed gain conventional PSS. In addition, the damping ratio and eigenvalues of the same network have been compared with genetic algorithm (GA) based tuned PSS.

关键词： Power system stability Sociology Statistics Damping Oscillators Lead Eigenvalues and eigenfunctions

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Resolution of one-dimensional bin packing problems using augmented neural networks and minimum bin slack

Resolution of one-dimensional bin packing problems using aug...

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作者： De Almeida, Ricardo Steiner, Maria Teresinha Arns Industrial and Systems Engineering Post-Graduate Program Pontifical Catholic University of Paraná - PUCPR Curitiba Brazil

The objective of this work is to compare the augmented neural network (AugNN) metaheuristic to minimum bin slack (MBS) heuristic to solve combinatorial optimisation problems, specifically, in this case, the one-dimensional bin packing problem (1D-BPP), a class of cutting and packing problems (CPP). CPP are easily found among various industry sectors and its proper treatment can improve the use of stocks in cutting problems or optimise physical space in packing problems. In order to optimise AugNN parameters, a design of experiment (DOE) was applied in order to guide a statistical analysis of different configurations of AugNN. The tests, developed in many benchmark problems found in the literature, showed that MBS heuristic was, in general superior, both in terms of the solution quality, which is about 70% better, and computational time, which is about 90% less. Copyright © 2016 Inderscience Enterprises Ltd.

关键词： Design of experiments

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B-Spline Neural Network and Chaotic Harmony Search Applied to Yo-yo Motion System Identification

B-Spline Neural Network and Chaotic Harmony Search Applied t...

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IEEE Symposium Series on Computational Intelligence

作者： Rafael B. Grebogi Roberto Z. Freire Viviana C. Mariani Leandro dos S. Coelho Mechanical Engineering Graduate Program Federal Institute of Santa Catarina Brazil Industrial and Systems Engineering Graduate Program PUCPR Curitiba Brazil Mechanical Engineering Graduate Program UFPR Curitiba Brazil Industrial and Systems Engineering Graduate Program UFPR Curitiba Brazil

In an attempt to increase the accuracy in modelling nonlinear systems, a wide variety of mathematical models were developed, such as the Volterra series, Wiener models, bilinear models, Hammerstein-Wiener models, among others. Recently, artificial neural networks (ANNs) have generated considerable interest as alternative nonlinear system identification technique and regression tool. Unlike other models, ANNs are easy to adapt and to apply to different engineering problems. One example is the B-spline neural network (BSNN), which consists in a variation of the basis function artificial neural network, which is usually trained by gradient-based methods. However, gradient-based methods have the characteristic of frequently fall into local minimum during learning procedures. To overcome this problem found in conventional learning methods, Harmony Search (HS), a stochastic metaheuristic approach, can be adopted. This optimization technique was originally conceptualized using musical improvisation process of searching for a perfect state of harmony, and can be considered to adjust the control parameters of BSNNs. In the same way, this paper proposes uses a Chaotic Harmony Search (CHS) approach, based on Chaotic Logistic Map, to train BSNNs to reproduce the dynamic of a nonlinear yo-yo system. Numerical results indicate that the proposed CHS approach is effective in building a reasonable BSNN model for nonlinear identification.

关键词： Nonlinear identification B-spline neural network Harmony search Optimization

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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Adaptive decision making via entropy minimization

arXiv

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arXiv 2018年

作者： Allahverdyan, Armen E. Galstyan, Aram Abbas, Ali E. Struzik, Zbigniew R. Yerevan Physics Institute Alikhanian Brothers Street 2 Yerevan375036 Armenia USC Information Sciences Institute 4676 Admiralty Way Marina del ReyCA90292 United States Industrial and Systems Engineering University of Southern California Los Angeles United States Advanced Center for Computing and Communication RIKEN Institute 2-1 Hirosawa Wako-shi351-0198 Japan Graduate School of Education University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan

An agent choosing between various actions tends to take the one with the lowest cost. But this choice is arguably too rigid (not adaptive) to be useful in complex situations, e.g., where exploration-exploitation trade-off is relevant in creative task solving or when stated preferences differ from revealed ones. Here we study an agent who is willing to sacrifice a fixed amount of expected utility for adaptation. How can/ought our agent choose an optimal (in a technical sense) mixed action? We explore consequences of making this choice via entropy minimization, which is argued to be a specific example of risk-aversion. This recovers the ǫ-greedy probabilities known in reinforcement learning. We show that the entropy minimization leads to rudimentary forms of intelligent behavior: (i) the agent assigns a non-negligible probability to costly events;but (ii) chooses with a sizable probability the action related to less cost (lesser of two evils) when confronted with two actions with comparable costs;(iii) the agent is subject to effects similar to cognitive dissonance and frustration. Neither of these features are shown by entropy maximization. Copyright © 2018, The Authors. All rights reserved.

关键词： Entropy

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Author Correction: Effect of de novo transcriptome assembly on transcript quantification

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Scientific reports 2019年第1期9卷 18041页

作者： Ping-Han Hsieh Yen-Jen Oyang Chien-Yu Chen Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei 10617 Taiwan. Department of Computer Science and Information Engineering National Taiwan University Taipei 10617 Taiwan. Department of Bio-Industrial Mechatronics Engineering National Taiwan University Taipei 10617 Taiwan. chienyuchen@ntu.edu.tw. Genome and Systems Biology Program National Taiwan University and Academia sinica Taipei 10617 Taiwan. chienyuchen@ntu.edu.tw.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Highly accurate model for prediction of lung nodule malignancy with ct scans

arXiv

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arXiv 2018年

作者： Causey, Jason Zhang, Junyu Ma, Shiqian Jiang, Bo Qualls, Jake Politte, David G. Prior, Fred Zhang, Shuzhong Huang, Xiuzhen Department of Computer Science Arkansas State University JonesboroAR72467 United States Department of Industrial and Systems Engineering University of Minnesota MinneapolisMN55455 United States Department of Systems Engineering and Engineering Management Chinese University of Hong Kong Shatin N.T. Hong Kong Research Center for Management Science and Data Analytics School of Information Management and Engineering Shanghai University of Finance and Economics Shanghai200433 China Mallinckrodt Institute of Radiology Washington University St. LouisMO63110 United States Department of Biomedical Informatics University of Arkansas for Medical Sciences Little RockAR72205 United States UALR/UAMS Joint Graduate Program in Bioinformatics Little RockAR72204 United States

Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort. All nodules were identified and classified by four experienced thoracic radiologists who participated in the LIDC project. NoduleX achieves high accuracy for nodule malignancy classification, with an AUC of ~0.99. This is commensurate with the analysis of the dataset by experienced radiologists. Our approach, NoduleX, provides an effective framework for highly accurate nodule malignancy prediction with the model trained on a large patient population. Our results are replicable with software available at http://***/NoduleX. Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting

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