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Novel characterization and tuning methods for integrating processes

International Journal of Information technology (Singapore) 2024年第3期16卷 1387-1395页

作者： Jorge, Cárdenas-Cabrera Jorge, Henríquez-Novoa Wendell, Martínez-Rodríguez Edalatpanah, S.A. Shariq, Aziz Butt Naz, Sumera Javier, Jimènez-Cabas Gabriel, Piñeres-Espitia Department of Computer Science and Electronics Universidad de La Costa Calle 58 55-66 Atlántico Barranquilla 080002 Colombia Department of Applied Mathematics Ayandegan Institute of Higher Education Ferdowsi St RVF6+JJ9 Mazandaran Tonekabon *** Iran Deparment of Computer Science University of South Asia 47-Tufail Road Punjab Lahore 110021 Pakistan Department of Mathematics Division of Science and Technology University of Education Lahore Pakistan

The manuscript presents new methodologies for process characterization and control tuning for integrated systems. Integrative systems are widespread in the industrial environment. Some control systems for level, pressure, concentration, and temperature are examples. Although many papers have been published on the subject, no standard has been established, so this remains an open problem. The proposed characterization method consists of deriving the response of the integrating system and applying characterization techniques for self-regulated processes. The proposed tuning method is based on deducing tuning equations for a PD control from the λ tuning rules. The performance of the proposed methods is validated through simulation and experimental tests. © 2024, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Integrating process Process characterization/identification λ tuning

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Physics-Constrained Adversarial Training for Neural Networks in Stochastic Power Grids

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1121-1131页

作者： Li, Wenting Deka, Deepjyoti Wang, Ren Paternina, Mario R. Arrieta LANL Applied Mathematics and Plasmas Physics Division Los AlamosNM87544 United States Illinois Institute of Technology Department of Electrical and Computer Engineering ChicagoIL60616 United States National Autonomous University of Mexico Department of Electrical Engineering Mexico City04510 Mexico

The growth in distributed energy resources (DERs) is an important step toward solving the global climate crisis. However, many DERs, such as wind and solar power, are random and intermittent, causing the data in power grids to be perturbed with high uncertainty. Such perturbations degrade the performance of data-driven algorithms introduced in power grids for sensing and control. Existing approaches can strengthen machine learning models against well-designed malicious attacks. However, such physics-agnostic efforts fail to ensure the robustness of natural perturbations in power grids, that occur due to varying loads and changes in control inputs. This article proposes a novel physics-constrained adversarial training method for robustifying neural networks for the crucial problem of locating edge-faults in power grids. Our approach relies on first deriving analytical physical laws that are satisfied by state perturbations in realistic grids and then using the descriptive sets during adversarial training. Compared to state-of-the-art methods, our perturbation-robust neural networks have higher robust accuracy as well as training efficiency. Also, we demonstrate that the proposed approach achieves a tighter upper bound of robust risks than traditional efforts. The numerical experimental results in the IEEE 68-bus benchmark system validate our adversarial training in two scenarios when loads and control inputs vary randomly. The proposed method shows superior performance in accuracy and efficiency for different perturbed datasets. In addition, we testify the influence of different inputs on the robustness. © 2023 IEEE.

关键词： Neural networks

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A Trichotomy for Transductive Online Learning 37

A Trichotomy for Transductive Online Learning

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Hanneke, Steve Moran, Shay Shafer, Jonathan Department of Computer Science Purdue University United States Faculty of Mathematics Faculty of Computer Science Faculty of Data and Decision Sciences Technion - Israel Institute of Technology Israel Computer Science Division UC Berkeley United States

ISBN: (纸本)9781713899921

We present new upper and lower bounds on the number of learner mistakes in the 'transductive' online learning setting of Ben-David, Kushilevitz and Mansour (1997). This setting is similar to standard online learning, except that the adversary fixes a sequence of instances x1, ..., xn to be labeled at the start of the game, and this sequence is known to the learner. Qualitatively, we prove a trichotomy, stating that the minimal number of mistakes made by the learner as n grows can take only one of precisely three possible values: n, Θ (log(n)), or Θ(1). Furthermore, this behavior is determined by a combination of the VC dimension and the Littlestone dimension. Quantitatively, we show a variety of bounds relating the number of mistakes to well-known combinatorial dimensions. In particular, we improve the known lower bound on the constant in the Θ(1) case from Ω (Equation presented)(plog(d) ) to Ω(log(d)) where d is the Littlestone dimension. Finally, we extend our results to cover multiclass classification and the agnostic setting. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： E-learning

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Multi-Criteria Decision-Making for Energy Management in Smart Homes Using Hybridized Neuro-Fuzzy Approach

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Distributed Generation and Alternative Energy Journal 2024年第1期39卷 83-109页

作者： Anbazhagu, U.V. Koti, Manjula Sanjay Muthukumaran, V. Geetha, V. Munrathnam, Meram Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kattankulathur TN Chennai India Department of MCA Dayananda Sagar Academy of Technology and Management Bangalore560082 India Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur Campus 603 203 India Department of Computer Science School of Applied Sciences REVA University Bangalore India Department of Mathematics Rajiv Gandhi University of Knowledge Technologies R.k.Valley Idupulapaya Andhra Pradesh Kadapa516330 India

The necessity for smart energy oversight solutions has arisen in response to the rising popularity of energy-efficient home automation and other energy-saving technologies. Optimizing smart home energy use using multi-criteria decision-making (MCDM) is a proven methodology. However, the procedure for making decisions and MCDM’s capacity to handle various criteria are typically limiting factors. Hybrid methods, which integrate multiple decision-making approaches like Fuzzy Logic (FL) and Modular Neural Networks (MNN), could potentially be able to circumvent these restrictions and boost energy management systems’ efficacy and precision. This investigation presents a hybrid Neuro-Fuzzy (H-NF) method for MCDM in regulating energy for smart homes by combining FL with an MNN. The suggested approach would optimize energy use in smart homes by considering several parameters, notably cost, ease of use, and environmental effects. In addition, this study aims to examine how the H-NF model fares in comparison to other methods of making important decisions in terms of several performance metrics. The suggested hybridized approach has the potential to deliver more precise and effective decision-making processes for energy management in smart homes, allowing users to optimize their energy consumption while preserving comfort and lowering environmental impact. © 2024 Taylor and Francis Inc.. All rights reserved.

关键词： Decision making

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An Optimization Approach for Convolutional Neural Network Using Non-Dominated Sorted Genetic Algorithm-Ⅱ

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computers, Materials & Continua 2023年第3期74卷 5641-5661页

作者： Afia Zafar Muhammad Aamir Nazri Mohd Nawi Ali Arshad Saman Riaz Abdulrahman Alruban Ashit Kumar Dutta Badr Almutairi Sultan Almotairi Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaMalaysia School of Electronics Computing and MathematicsUniversity of DerbyUK Department of Computer Science National University of TechnologyIslamabadPakistan Department of Information Technology College of Computer and Information SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Kingdom of Saudi Arabia Department of Information Technology College of Computer Sciences and Information Technology CollegeMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Natural and Applied Sciences Faculty of Community CollegeMajmaah UniversityMajmaah11952Saudi Arabia Department of Information Systems Faculty of Computer and Information SciencesIslamic University of MadinahMadinah42351Saudi Arabia

In computer vision,convolutional neural networks have a wide range of *** representmost of today’s data,so it’s important to know how to handle these large amounts of data *** neural networks have been shown to solve image processing problems ***,when designing the network structure for a particular problem,you need to adjust the hyperparameters for higher *** technique is time consuming and requires a lot of work and domain *** a convolutional neural network architecture is a classic NP-hard optimization *** the other hand,different datasets require different combinations of models or hyperparameters,which can be time consuming and *** approaches have been proposed to overcome this problem,such as grid search limited to low-dimensional space and queuing by random *** address this issue,we propose an evolutionary algorithm-based approach that dynamically enhances the structure of Convolution Neural Networks(CNNs)using optimized *** study proposes a method using Non-dominated sorted genetic algorithms(NSGA)to improve the hyperparameters of the CNN *** addition,different types and parameter ranges of existing genetic algorithms are *** study was conducted with various state-of-the-art methodologies and *** have shown that our proposed approach is superior to previous methods in terms of classification accuracy,and the results are published in modern computing literature.

关键词： Non-dominated sorted genetic algorithm convolutional neural network hyper-parameter optimization

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Multi-attribute Group Decision-making Based on Hesitant Bipolar-valued Fuzzy Information and Social Network

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computer Systems science & Engineering 2023年第3期44卷 1939-1950页

作者： Dhanalakshmi R Sovan Samanta Arun Kumar Sivaraman Jeong Gon Lee Balasundaram A Sanamdikar Sanjay Tanaji Priya Ravindran Department of Computer Science and Engineering KCG College of TechnologyChennai600097Tamil NaduIndia Department of Mathematics Tamralipta MahavidyalayaWest Bengal721636India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127Tamil NaduIndia Division of Applied Mathematics Wonkwang UniversityIksan-SiJeonbuk54538Korea School of Computer Science and Engineering Center for Cyber Physical SystemsVellore Institute of TechnologyChennai600127Tamil NaduIndia Department of Instrumentation Engineering PDEAs College of EngineeringManjari412307PuneIndia Engineering and Research Services HCL TechnologiesChennai600119India

Fuzzy sets have undergone several expansions and generalisations in the literature,including Atanasov’s intuitionistic fuzzy sets,type 2 fuzzy sets,and fuzzy multisets,to name a *** can be regarded as fuzzy multisets from a formal standpoint;nevertheless,their interpretation differs from the two other approaches to fuzzy multisets that are currently *** fuzzy sets(HFS)are very useful if consultants have hesitation in dealing with group decision-making problems between several possible ***,these possible memberships can be not only crisp values in[0,1],but also interval values during a practical evaluation *** bipolar valued fuzzy set(HBVFS)is a generalization of *** paper aims to introduce a general framework of multi-attribute group decision-making using social *** propose two types of decision-making processes:Type-1 decision-making process and Type-2 decision-making *** the Type-1 decision-making process,the experts’original opinion is proces for thefinal ranking of *** Type-2 decision making processs,there are two major aspects we ***,consistency tests and checking of consensus models are given for detecting that the judgments are logically ***,the framework demands(partial)decision-makers to review their ***,the coherence and consensus of several HBVFSs are established forfinal ranking of *** proposed framework is clarified by an example of software packages selection of a university.

关键词： Group decision-making aggregation operators hesitant bipolar-valued fuzzy set

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Use of Polygram Substitution in Spotting the Swindler in Secret Sharing 11th

Use of Polygram Substitution in Spotting the Swindler in Sec...

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11th International Conference on Intelligent Computing and Applications, ICRTC 2023

作者： Yadav, Poonam Satheesh Kumar, S. Muthukumaran, V. Sandeep Kumar, Mathivanan Radhika, E.K. Department of Computer Science School of Applied Sciences Reva University Bangalore India Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India School of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida203201 India Department of Computer Science and Applications Sindhi College Bangalore560024 India

ISBN: (纸本)9789819717231

In the polygram substitution, one set of characters is replaced by another set based on algorithm. The model is proposed for calculating and estimating secure online secret shares, where dynamic modification in any of the shares will be identified if number of the shares are more than specified threshold value. The security is based on the intractability of factoring as it uses Lagrange polynomial function. This is further enhanced by a graphical masking and unmasking technique;it includes two phases: encryption of the individual secrets and sharing of the data. The generation of the shares is done by masking using Polygram substitution, and the shares are regenerated and unmasking of the qualified shares is done to find original secret. Masking is done on original secret, then shares are generated. Each encrypted share contains information which is different than original secret. Masking by Polygram substitution gives additional protection of secret data on the Internet. Moreover, we achieve efficient recovery of the original secret data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Polynomials

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Performance and power modeling and prediction using MuMMI and 10 machine learning methods

Performance and power modeling and prediction using MuMMI an...

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作者： Wu, Xingfu Taylor, Valerie Lan, Zhiling Mathematics & Computer Science Division Argonne National Laboratory University of Chicago LemontIL United States Department of Computer Science Illinois Institute of Technology ChicagoIL United States

Energy-efficient scientific applications require insight into how high performance computing system features impact the applications' power and performance. This insight can result from the development of performance and power models. In this article, we use the modeling and prediction tool MuMMI (Multiple Metrics Modeling Infrastructure) and 10 machine learning methods to model and predict performance and power consumption and compare their prediction error rates. We use an algorithm-based fault-tolerant linear algebra code and a multilevel checkpointing fault-tolerant heat distribution code to conduct our modeling and prediction study on the Cray XC40 Theta and IBM BG/Q Mira at Argonne National Laboratory and the Intel Haswell cluster Shepard at Sandia National Laboratories. Our experimental results show that the prediction error rates in performance and power using MuMMI are less than 10% for most cases. By utilizing the models for runtime, node power, CPU power, and memory power, we identify the most significant performance counters for potential application optimizations, and we predict theoretical outcomes of the optimizations. Based on two collected datasets, we analyze and compare the prediction accuracy in performance and power consumption using MuMMI and 10 machine learning methods. © 2022 John Wiley & Sons, Ltd.

关键词： Forecasting

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SHA-AES based on Chaining Block Code and Galois Counter Mode Encryptions for the Privacy of Smart Farming Systems

SHA-AES based on Chaining Block Code and Galois Counter Mode...

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2023 International Conference on science, Engineering and Business for Sustainable Development Goals, SEB-SDG 2023

作者： Alfa, Abraham Ayegba Aro, Taye Oladele Adunni, Olukiran Oyenike Iliya, Danjuma Zakwoi Awe, Oluwayomi Owonipa, Ayodeji Confluence University of Science and Technology Department of Computer Science Osara Nigeria Ladoke Akintola University of Technology Department of Computer Science Ogbomoso Nigeria Niger State College of Education Department of Computer Science Minna Nigeria University of Lagos Department of Computer Science Lagos Nigeria University of Stirling Division of Computer Science and Mathematics United Kingdom

ISBN: (纸本)9798350324785

In smart farming, IoT plays a dominant role in diverse data generation, data transmission, and applications such as irrigation estimation, soil and environmental monitoring, food supply chain and field monitoring. The current architecture of IoT is mainly centralized and single-point controlled;thereby opening up privacy and security lapses. Several approaches have favoured advanced data encryption to preserve privacy during transmission or storage. The debilitating challenges of IoT systems sparingly reported in previous works include: security of physical/logical components;software/hardware interoperability;high energy requirements;low processing/storage capacity, and network architecture/scalability. This paper comparatively studied encryption modes of Chaining Block Code (CBC) and Galois Counter Mode (GCM) based on hybrid algorithm of SHA1-256 and AES246 (known as SHA-AES scheme) towards preserving privacy of IoT data. The outcomes revealed that, the SHA-AES scheme based on CBC was better in terms of speed - encryption (8.34%) and decryption (43.42%);storage consumption - secret key sizes (50.00%) and ciphertext sizes (26.88%) against GCM. This implies that, the GCM approach offers the highest privacy protection but, less effective due to sluggish speed and high memory utilization. Also, strong cryptographic schemes interleaved with CBC and GCM hardening schemes offered improved confusion state, diffusion state, complexity, and performances. Future works could consider modifying the complex processes of GCM approach by minimizing key generation time to support resource-constrained devices. © 2023 IEEE.

关键词： Cryptography

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The Importance of Robust Communication in Large-Scale Agile Development

The Importance of Robust Communication in Large-Scale Agile ...

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2023 International Symposium on Green Technologies and Applications, ISGTA 2023

作者： Butt, Shariq Aziz Naz, Sumera Gabriel, Piñeres-Espitia Patriciac, Paola Ariza-Colpas Piñeres-Melo, Marlon Alberto Department of Computer Science University of South Asia Lahore Pakistan Department of Mathematics Division of Science and Technology University of education Lahore Pakistan Universidad De la Costa CUC Barranquilla Colombia

The agile methodology stands out as a prevalent model for efficient software development, particularly favored for its adaptability and suitability in small-scale projects across various software industries. Nevertheless, its widespread adoption has brought to light certain communication challenges, particularly when applied to large-scale distributed teams. Agile, it appears, may not be the optimal choice for extensive teams engaged in global software development efforts. This study delves into the intricacies of issues faced by teams employing agile in the context of large-scale distributed development, particularly focusing on communication-related challenges and their repercussions. Our approach involved in-depth interviews with diverse developers and teams hailing from various sectors within the software industry. Moreover, we conducted an extensive quantitative analysis, surveying 50 developers representing different distributed teams. The outcomes of our investigation unearthed several communication-related deficiencies that significantly impact the development process. To arrive at these insights, we employed two robust statistical analysis methods: descriptive analysis and regression analysis. The implications of our findings have led us to propose innovative software solutions, bearing distinctive features engineered to mitigate the communication issues often encountered in large-scale software development. These solutions have the potential to enhance the efficiency and effectiveness of agile practices when applied in extensive and globally dispersed development endeavors. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Software design

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