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Modeling and Enhancement of General Dynamics F-16 Fighting Falcon Missile Launching System Based on Petri Nets

Modeling and Enhancement of General Dynamics F-16 Fighting F...

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international conference on applied System Innovation (ICASI)

作者： Yen-Liang Pan Wei-Hsiang Liao Zi-Yang Wu Jia-Cing Jhang Fu-Rong Gu Mathematics and Physics Division General Education Center R.O.C. Air Force Academy. Department of Applied Foreign Language R.O.C. Air Force Academy. No. Sisou 1 Jieshou W. Rd. Gangshan Dist. Kaohsiung City Taiwan The 6th Composite Wing of ROCAF. No. Shengli 330 Rd. Pingtung Country Pingtung City Taiwan

this paper focuses on the use of Petri nets (PNs) to model a General Dynamics F-16 Fighting Falcon (F-16) missile launching system (MLS). the advances in digital computers and system-integration techniques have made the digitization of fight control computer systems possible. the fourth generation of fighters has used electronic digital signals to launch missiles. the most significant advantage of the digital MLS lies in its ability to cast without depending on the mechanical connecting rod. In other words, digital MLS will not break down due to mechanical failure. As a result, fighter pilots can better focus on their missions. A stable MLS is essential for launching missiles smoothly. therefore, how to design one stable and easy-to-use MLS is important. In this paper, the authors tried to model and develop a stable MLS using the much easier-to-use Petri nets. One real-world digital MLS is used to verify the model. the experimental results using HPsim reveal that the designed model is correct and practical. Most importantly, the proposed model is much easier to use than the existing literature.

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Automatic Domain Extension and Optimization Based on Set-theory 17th

Automatic Domain Extension and Optimization Based on Set-T...

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17th international conference on Informatics in Control, Automation and Robotics, ICINCO 2020

作者： Kast, Bernd Dietrich, Vincent Albrecht, Sebastian von Wichert, Georg Feiten, Wendelin Zhang, Jianwei Siemens AG Corporate Technology Otto-Hahn-Ring 6 Munich81739 Germany Faculty of Mathematics Informatics and Natural Sciences University of Hamburg Vogt-Kölln-Str. 30 Hamburg22527 Germany

ISBN: (纸本)9783030924416

Autonomous machines promise more flexibility and robustness changes in their environment compared to manually programmed solutions in industrial applications. However, the autonomous planning of actions involves discrete as well as continuous properties, which results in a np-hard planning problem. Especially for multiple machines and long planning horizons the design of domains requires a lot of fine tuning and thus manual effort. Modularization and reuse of existing domain knowledge with formalized models is one solution to this issue. However, the models of different projects tend to be misaligned, in particular when several parties contributed to the project, which deteriorates the performance. In this paper, we present two domain optimization and extension algorithms, which adapt the models to facilitate planning. the first algorithm handles inconsistent units, or even misaligned pieces of sub-information. It automatically generates conversions and allows to call operations with a wider range of input types. the second algorithm aligns models from different sources with varying modeling views. After this reformulation, we can compose models more efficiently to a larger domain. For both optimizations, we rely on the formal set-based models that we also use in our previously presented hierarchical planning algorithm. Our hierarchical approach allows an almost linear scalability with the length of the plan. However, it comes with non-optimality effects due to the imposed intermediate goals that depend on the quality of the model. the optimization algorithms of this paper allow to adapt and extend the model so that valid shortcuts reduce these suboptimalities. We conduct experiments on a task and motion assembly problem, demonstrating scalability for up to 62 parts and plans with over 1000 steps, which either result in discrete state or high-level position changes, with planning times of less than 15 min. Our experiments also include the successful plan execution o

关键词： Scalability

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Predicting Parkinson's disease based on transformed speech signal by DWT with two methods for features selection: GA-AdaBoost and BPSO

Predicting Parkinson's disease based on transformed speech s...

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international conference on mathematics and Computers in Sciences and in Industry (MCSI)

作者： Zayrit Soumaya Nsiri Benayad Belhoussine Drissi Taoufiq Ammoumou Abdelkrim Laboratory Industrial Engineering Information Processing and Logistics (GITIL). Faculty of Science Ain Chok. University Hassan II - Casablanca Casablanca Morocco Research Center STIS M2CS National School of Arts and Crafts of Rabat (ENSAM) Mohammed V University in Rabat Morocco Casablanca Morocco

In the medical domain, the cruelest phase is the diagnosis phase. Regarding the phase of diagnosis of the disease as Socrates said “understanding the questions is the half of the answer”, which means when we can make an accurate diagnosis that will be considered as reaching half of the healing process. this article proposes an accurate system for Parkinson's disease detection. Hence, we will interpret the efficacity of the wrapper methods Genetic Algorithm (GA), Binary Particle Swarm Optimization (BPSO), and machine learning algorithms in the diagnosis of Parkinson's disease the second most common neurologic disease after Alzheimer's disease. Based on the speech signal processing of patients uttering vowels by using the discrete wavelet transform DWT. then extract the features matrix that contains Linear Predictive Coding (LPC), Zero-Crossing Rate (ZCR), Mel Frequency Cepstral Coefficient (MFCC), and wavelet Shannon entropy. Furthermore, a hybrid method applied the AdaBoost, KNN classifier, and the genetic algorithm to reduce the features and enhance the accuracy of detection. First, the genetic algorithm occurs in the selection of the features and the creation of a new population then applied a combination of KNN classifier and AdaBoost to validate the accuracy of the proposed system. A second time we use the BPSO to select the features. the best accuracy obtained was 100% by both of the models.

关键词： Machine learning algorithms Parkinson's disease Feature extraction discrete wavelet transforms Particle swarm optimization Mel frequency cepstral coefficient Statistics

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Human Activity Recognition with IMU and Vital Signs Feature Fusion 28th

Human Activity Recognition with IMU and Vital Signs Feature ...

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28th international conference on MultiMedia Modeling (MMM)

作者： Xefteris, Vasileios-Rafail Tsanousa, Athina Mavropoulos, thanassis Meditskos, Georgios Vrochidis, Stefanos Kompatsiaris, Ioannis Informat Technol Inst Ctr Res & Technol Hellas 6th Km Charilaou Thermi Thessaloniki 57001 Greece Aristotle Univ Thessaloniki Sch Informat Thessaloniki Greece

ISBN: (纸本)9783030983581;9783030983574

Combining data from different sources into an integrated view is a recent trend taking advantage of the Internet of things (IoT) evolution over the last years. the fusion of different modalities has applications in various fields, including healthcare and security systems. Human activity recognition (HAR) is among the most common applications of a healthcare or eldercare system. Inertial measurement unit (IMU) wearable sensors, like accelerometers and gyroscopes, are often utilized for HAR applications. In this paper, we investigate the performance of wearable IMU sensors along with vital signs sensors for HAR. A massive feature extraction, including both time and frequency domain features and transitional features for the vital signs, along with a feature selection method were performed. the classification algorithms and different early and late fusion methods were applied to a public dataset. Experimental results revealed that both IMU and vital signs achieve reasonable HAR accuracy and Fl-score among all the classes. Feature selection significantly reduced the number of features from both IMU and vital signs features while also improved the classification accuracy. the rest of the early and late level fusion methods also performed better than each modality alone, reaching an accuracy level of up to 95.32%.

关键词： Human activity recognition Wearable sensors Vital signals Sensor fusion Feature selection

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One-Sided discrete Terrain Guarding and Chordal Graphs 7th

One-Sided Discrete Terrain Guarding and Chordal Graphs

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7th international conference on algorithms and discrete applied mathematics, CALDAM 2021

作者： Prahlad Narasimhan, Kasthurirangan National Institute of Science Education and Research HBNI Bhubaneswar India

the Terrain Guarding60;problem, a variant of the famous Art Gallery60;problem, has garnered significant attention over the last two decades in Computational Geometry from the viewpoint of complexity and approxim... 详细信息

ISBN: (纸本)9783030678982

the Terrain Guarding problem, a variant of the famous Art Gallery problem, has garnered significant attention over the last two decades in Computational Geometry from the viewpoint of complexity and approximability. Both the continuous and discrete versions of the problem were shown to be NP-Hard in [14] and to admit a PTAS [8, 15]. the biggest unsolved question regarding this problem is if it is fixed-parameter tractable with respect to the size of the guard set. In this paper, we present two theorems that establish a relationship between a restricted case of the Annotated Terrain Guarding problem and the Clique Cover problem in chordal graphs. these theorems were proved in [11] for a special class of terrains called orthogonal terrains and were used to present a FPT algorithm with respect to the parameter that we require for discrete Orthogonal Terrain Guarding in [2]. We hope that the results obtained in this paper can, in future work, be used to produce such an algorithm for discrete Terrain Guarding. © 2021, Springer Nature Switzerland AG.

关键词： Computational geometry

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Latent Class Cluster for Clustering Villages Based on Socio-economic Indicators in 2018 6

Latent Class Cluster for Clustering Villages Based on Socio-...

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6th international conference on mathematics: Pure, applied and Computation, ICOMPAC 2020

作者： Riyanto, A. Kuswanto, H. Prastyo, D.D. Department of Statistics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Jl. Arif Rahman Hakim Surabaya60111 Indonesia BPS - Statistics Indonesia Jl. Dr. Sutomo 6-8 Jakarta10710 Indonesia

Latent class analysis (LCA) is a statistical method used to classify units into unobserved (latent) variable classes, so-called clusters. In this article, we introduce LCA to demonstrate its use for village groupings. We applied LCA on data processed from the census conducted by BPS-Statistics Indonesia, namely the village potential (Podes) data 2018. the Podes data is used to extract the socio-economic characteristics of a village. the empirical results of the LCA application show that the villages in the Banyumas Regency are grouped into three classes/clusters. © Published under licence by IOP Publishing Ltd.

关键词： Rural areas

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Coalgebraic Approach to Studying discrete Systems with Output: the General and Distributed Cases 16th

Coalgebraic Approach to Studying Discrete Systems with Outpu...

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16th international conference on Information and Communication Technologies in Education, Research, and Industrial Applications, ICTERI 2020

作者： Zholtkevych, Grygoriy Labzhaniia, Maksym School of Mathematics and Computer Science V.N. Karazin Kharkiv National University 4 Svobody Square Kharkiv61022 Ukraine

ISBN: (纸本)9783030775919

Abstract discrete systems with output are widely used in theoretical Computer Science. they are also used as design patterns in Software Engineering. these are reasons to the issue the challenge to develop a theoretical framework for specifying and analysis behavioural constraints for systems of such a kind. In the paper, authors propose to use the theory of coalgebras to answer the challenge. the paper introduces the needed preliminaries, demonstrates the general approach, establishes principal results in the general case. In particular, authors show that safeness is naturally formulated in coalgebraic and category-theoretic terms. Further, this general approach is applied to study causality constraints in distributed systems. Authors prove the existence of a universal language for specifying causality constraints as clock constraints. Also, the practically important class of constraints has been studied. Authors show rigorously that this class is not sufficient for specifying all clock constraints. the paper demonstrates how to specify constraints belonging to the class using Diophantine equations. the constraint verifying algorithms for the class are discussed too. It is shown the complexity of verifying a linear Diophantine constraint. © 2021, Springer Nature Switzerland AG.

关键词： Software engineering

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Wavelet-Based Microbiome Correlations of Host Traits 22

Wavelet-Based Microbiome Correlations of Host Traits

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Proceedings of the 2022 6th international conference on Computational Biology and Bioinformatics

作者： Adeethyia Shankar Stephanie Chang Yongzhong Zhao Xiaodi Wang Tong Liu Department of Mathematics Western Connecticut State University USA Frontage Laboratories USA Department of Mathematical Sciences Tsinghua University China

ISBN: (纸本)9781450397636

the gut microbiome is composed of a plethora of microorganisms, and these microbes contribute to overall human health. It has been shown that dysbiosis of the microbiome is associated with certain diseases, including colorectal cancer and diabetes, yet the role of the microbiome is still little-known. Here, we aim to develop a novel wavelet-based framework to dissect the microbiome correlations of host traits. Due to the clinical nature of the biological dataset, we utilize the discrete wavelet transform (DWT)—enabling us to impute sparse matrices and decompose the data into different frequency components. We further carry out regressions of host traits with the microbiome relative abundances followed by computing correlations between the regression-predicted trait values. Moreover, we visualize these microbiome correlations of host traits with heat maps and build microbiome correlations of host traits network. As a result, our results revealed that microbiome correlations of host traits are prevalent. Our wavelet-based microbiome correlations of host traits analytic framework aims to lay the foundation for further causality analysis of the complex interplays between the microbiome and host traits.

关键词： Imputation Microbiome Regression discrete Wavelet Transform Sparse Matrix Host Trait Correlation Network

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6th international conference on Computer Science, applied mathematics and Applications, ICCSAMA 2019

6th International Conference on Computer Science, Applied Ma...

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6th international conference on Computer Science, applied mathematics and Applications, ICCSAMA 2019

ISBN: (纸本)9783030383633

the proceedings contain 37 papers. the special focus in this conference is on international conference on Computer Science, applied mathematics and Applications . the topics include: Assessment of the Water Area in the Lowland Region of the Mekong River Using MODIS EVI Time Series;palmprint Recognition Using Discriminant Local Line Directional Representation;a New Solution Method for a Mean-Risk Mixed Integer Nonlinear Program in Transportation Network Protection;speech Assessment Based on Entropy and Similarity Measures;deep Clustering with Spherical Distance in Latent Space;a Channel-Pruned and Weight-Binarized Convolutional Neural Network for Keyword Spotting;Fusing of Deep Learning, Transfer Learning and GAN for Breast Cancer Histopathological Image Classification;Attentive biLSTMs for Understanding Students’ Learning Experiences;computing Residual Diffusivity by Adaptive Basis Learning via Super-Resolution Deep Neural Networks;an Intensive Empirical Study of Machine Learning algorithms for Predicting Vietnamese Stock Prices;improvement of Production Layout in the Furniture Industry in Indonesia with the Concept of Group Technology;reinforcement Learning in Stock Trading;a Survey on Forecasting Models for Preventing Terrorism;a Novel Approach for Travel Time Optimization in Single-Track Railway Networks;on the Design of a Privacy Preserving Collaborative Platform for Cybersecurity;secure and Robust Watermarking Scheme in Frequency Domain Using Chaotic Logistic Map Encoding;an Improvement of Applying Multi-objective Optimization Algorithm into Higher Order Mutation Testing;belief Merging for Possibilistic Belief Bases;discrete Time Sliding Mode Control of Milling Chatter;efficient Processing of Recursive Joins on Large-Scale Datasets in Spark;DCA with Successive DC Decomposition for Convex Piecewise-Linear Fitting;preface.

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A Brief Review on Existing Techniques for Detecting Digital Image Forgery 6

A Brief Review on Existing Techniques for Detecting Digital ...

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6th international conference on Image Information Processing, ICIIP 2021

作者： Rani, Rajneesh Kumar, Akshay Rai, Amrita Nit Jalandhar Department of C.S.E Punjab India

ISBN: (纸本)9781665433617

Law enforcement in the 21st century works on the evidence present in digital images or videos. Digital image processing is, thus, being heavily applied in the field of law enforcement, especially when it comes to detecting whether digital evidence related to legal matters has been tampered with or not. Due to the easy availability of various software, it is effortless for any law offender to have evidence such as digital images or videos transformed for their cause. Hence, two types of tampering detection techniques are used to maintain the integrity of digital evidence, namely Active and Passive. the active methods require that some kind of pre-embedded data be present in the image, using which detection can be performed while the passive techniques are applicable without any such condition. the differences, working, and classifications of these techniques are elaborately discussed here. © 2021 IEEE.

关键词： discrete cosine transforms

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