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International Journal of Control and Automation 2016年第1期9卷 33-46页

作者： Long, Zuqiang Xu, Yuebing Li, Long Dept. of Physics and Electronics Information Science Hengyang Normal University Hengyang China College of Electrical and Information Engineering Hunan University Changsha China Dept. of Mathematics and Computational Science Hengyang Normal University Hengyang China

Maintaining phosphoric slurry within the optimal temperature range is of great significance in improving the quality of the phosphoric acid product. Due to the lack of integral parts and the limited number of expert rules, general fuzzy controllers are commonly incapable of achieving accurate output. This paper proposed a novel fuzzy controller based on variable universes of discourse (VUD) to improve the accuracy of the temperature of phosphoric slurry. By tuning the motorized motor in the vacuum pump, the controller can obtain an appropriate vacuum degree in flash evaporators. With the online operation of contraction-expansion factors, fuzzy rules can be reproduced online, which is equivalent to the densification of fuzzy sets on universes of discourse. Thus, the output accuracy of VUD fuzzy controllers can be enhanced significantly. The experimental results show that VUD fuzzy controllers can tune the temperature of phosphoric slurry to within the optimal range and have a much better effect than general fuzzy controllers. © 2016 SERSC.

关键词： Fuzzy control

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Combating Cyber Attacks by Malware Classification Using Deep Neural Networks

Combating Cyber Attacks by Malware Classification Using Deep...

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2024 International Conference on Intelligent and Innovative Practices in engineering and Management, IIPEM 2024

作者： Bennet, Deepthi Tabitha Samantha Bennet, Preethi Anitha Gracy, J. Sharmila, P. Thiagarajar College of Engineering Dept. of Applied Mathematics and Computational Science Madurai India

ISBN: (纸本)9798350390049

The detection and classification of malware pose significant challenges in cybersecurity due to the ever-evolving nature of malicious threats. In this study, we propose a novel approach for classifying malware using deep neural networks (DNNs). Our dataset comprises of 25 distinct classes of malware samples. After developing and testing 4 DNNs, including ResNet50, InceptionResNetV2, VGG16, DenseNet169, a robust and efficient model capable of accurately identifying and categorizing malware instances is built using DenseNet169. The process involves feature extraction from byteplot *** proposed malware detection model uses DenseNet169 as it has shown very high accuracy (95%), precision, recall and F1-score for the *** research contributes to advancing the state-of-the-art in malware classification, providing insights into the efficacy of deep neural networks for combating sophisticated cyber threats in modern computing environments. © 2024 IEEE.

关键词： Cyber attacks

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An Architecture for Dynamic Load Balancing in Cloud Environment 2

An Architecture for Dynamic Load Balancing in Cloud Environm...

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Lokesh, Gudivada Baseer, K.K. Dept. of Computer Science and Engineering Tirupati India Department of Data Science Tirupati India

ISBN: (纸本)9798350347579

Clouds are highly customizable infrastructures that offer a platform as a service and let customers subscribe on a pay-as-you-go basis to their requirements. The straightforward service-oriented cloud computing model is gaining popularity around the world. The number of people using the Cloud is constantly growing. Clouds use modern data centers to manage a massive number of users. The reliability of the Cloud depends on load balancing. Balancing virtual machine loads lowers energy consumption and task rejections by optimizing resource utilization. One can increase performance while using fewer resources using load balancing, resource management, quality of service, etc. The difficulty of overloading and underloading virtual machines in cloud computing can be lessened by load balancing in the *** research study thoroughly examined the load-balancing algorithms found in the literature. First, the traditional approaches are analyzed before moving on to more recent work on load balancing with heterogeneous techniques. Along with the tools available for the current investigation, various metrics are used to evaluate the load-balancing algorithms. The proposed article will primarily serve to assist in the development of new algorithms in the future. © 2023 IEEE.

关键词： Energy utilization

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Sequential inference for latent force models

Sequential inference for latent force models

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作者： Hartikainen, Jouni Särkkä, Simo Dept. of Biomedical Engineering and Computational Science Aalto University Finland

Latent force models (LFMs) are hybrid models combining mechanistic principles with non-parametric components. In this article, we shall show how LFMs can be equivalently formulated and solved using the state variable approach. We shall also show how the Gaussian process prior used in LFMs can be equivalently formulated as a linear statespace model driven by a white noise process and how inference on the resulting model can be efficiently implemented using Kalman filter and smoother. Then we shall show how the recently proposed switching LFM can be reformulated using the state variable approach, and how we can construct a probabilistic model for the switches by formulating a similar switching LFM as a switching linear dynamic system (SLDS). We illustrate the performance of the proposed methodology in simulated scenarios and apply it to inferring the switching points in GPS data collected from car movement data in urban environment.

关键词： Switching

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Molecular dynamics simulations of crystal rearrangement at grain boundaries and the vicinity

Molecular dynamics simulations of crystal rearrangement at g...

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作者： Uehara, Takuya Wakabayashi, Naoki Ohno, Nobutada Dept. Computational Science and Engineering Nagoya University Furo-cho Chikusa-ku Nagoya Aichi 464-8603 Japan Graduate School of Engineering Nagoya University Japan Dept. Computational Science and Engineering Nagoya University Japan

Molecular dynamics simulations of rearrangement of atoms around the grain boundaries are carried out. The purpose of this study is to estimate the stability of grain boundaries and to observe their motion based on the atomistic considerations, so that the results are able to be applied for multi-scale scheme such as the phase field model. First, the grain-boundary energy is calculated as a function of misorientation angle, which revealed that several specific angles exhibit cusp-like low values, for both symmetric and non-symmetric grain boundaries. Secondly, changes in morphology of a square grain embedded in a bulk single crystal are simulated. Grains surrounded by specific grain boundaries having low energy are stable and do not incorporate rotation or migration, while unstable grains change their shape or orientation. A circular-grain model is also applied for the simulations, and similar tendency is obtained. Finally, a simulation at a high temperature for the model which consist of a grain stable at low temperature is carried out, which reveals that grain contraction occurs resulting in disappearance of the grain.

关键词： Crystal atomic structure

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Automatic Identification of Teff (Eragrostis Tef) Flour Adulteration Using Machine Learning 10

Automatic Identification of Teff (Eragrostis Tef) Flour Adul...

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10th International Conference on Systems and Informatics, ICSAI 2024

作者： Bochera, Zekiyos Abayneh Gizaw, Solomon Clement, Onime Addis Ababa University Dept. of Computational Data Science Addis Ababa Ethiopia Addis Ababa University Dept. of Computer Science Addis Ababa Ethiopia Trieste Italy

ISBN: (纸本)9798331510138

Food adulteration, the act of mixing foodstuffs with lower-quality foodstuffs or other food and non-food items, has become a global issue, particularly in developing countries like Ethiopia. This is a significant problem as it can lead to nutritional deficiencies and in some cases, illnesses, diseases or even death. The identification of adulterated food typically requires physio-chemical laboratory tests that vary depending on the type and level of adulterant. Teff flour, a daily staple food in Ethiopia, is often adulterated with wood flour and gypsum powder. In this research work, we utilized computer vision techniques to extract textural and color features from images of both adulterated and pure Teff flour. We then trained a Support Vector Machine (SVM) based machine learning model to identify adulterated Teff flour and predict the level of adulteration. The presented results show varying levels of prediction accuracy depending on the type of Teff flour, the level of adulteration. The developed model was also found to provide good results with images sizes ranging from 128 × 128 to 2048 × 2048 pixels. The results show overall model accuracy was above 90% when the adulteration level of red Teff flour is more than 25% and above 84% when the adulteration level of white wood flour was above 25%. © 2024 IEEE.

关键词： Food chemistry

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TCCL: Discovering Better Communication Paths for PCIe GPU Clusters 24

TCCL: Discovering Better Communication Paths for PCIe GPU Cl...

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29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2024

作者： Kim, Heehoon Ryu, Junyeol Lee, Jaejin Dept. of Computer Science and Engineering Seoul National University Seoul Korea Republic of Dept. of Data Science Dept. of Computer Science and Engineering Seoul National University Seoul Korea Republic of

ISBN: (纸本)9798400703867

Exploiting parallelism to train deep learning models requires GPUs to cooperate through collective communication primitives. While systems like DGX, equipped with proprietary interconnects, have been extensively studied, the systems where GPUs mainly communicate through PCIe have received limited attention. This paper introduces TCCL, a collective communication library designed explicitly for such systems. TCCL has three components: a profiler for multitransfer performance measurement, a pathfinder to discover optimal communication paths, and a modified runtime of NCCL to utilize the identified paths. The focus is on ring-based collective communication algorithms that apply to popular communication operations in deep learning, such as AllReduce and AllGather. The evaluation results of TCCL on three different PCIe-dependent GPU clusters show that TCCL outperforms (up to ×2.07) the state-of-the-art communication libraries, NCCL and MSCCL. We also evaluate TCCL with DL training workloads with various combinations of parallelism types. © 2024 Copyright held by the owner/author(s).

关键词： Graphics processing unit

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TPredictor: A micro-blog based system for teenagers' stress prediction 19

TPredictor: A micro-blog based system for teenagers' stress ...

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19th International Conference on Extending database Technology, EDBT 2016

作者： Huang, Jing Li, Qi Feng, Zhuonan Li, Yiping Feng, Ling Dept. of Computer Science and Technology Centre for Computational Mental Healthcare Research Institute of Data Science Tsinghua University Beijing100084 China

ISBN: (纸本)9783893180707

Too much stress is easy to do harm to the physical and psychological health of teenagers, because most teenagers neither have the ability to cope with the stress by themselves nor like seeking adults' help initiatively. Social media has demonstrated its feasibility in detecting teenagers' stress with the micro-blog having become a popular channel for teenagers' self-expression. In this demonstration, we present a system called tPredictor, which can predict teenagers' future stress based on the social media micro-blog. Two questions are to be resolved: (1) what will the stress level of the teenager(s) be in the next time unit? (2) how will the stress level of the teenager(s) change (increase, decrease, remain unchanged) in the next time unit? tPredictor tackles the above prediction questions, taking into account the influence of future stressful events on teenagers' emotion. Considering the similarity of stock price movement and the stress level change, we define the stress candlestick charts and the stress reversal signals for stress change trend prediction. tPredictor can predict the stress of both individuals and a group of teenagers, which provides a platform for teenagers themselves, their parents or some institutions such as schools to know teenagers' future stress for taking measures timely to prevent the serious consequences. © 2016, Copyright is with the authors.

关键词： Forecasting

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Content Based Classification of Short Messages using Recurrent Neural Networks in NLP

Content Based Classification of Short Messages using Recurre...

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2024 International Conference on Artificial Intelligence, Computer, data sciences, and Applications, ACDSA 2024

作者： Bennet, Deepthi Tabitha Bennet, Preethi Samantha Thiagarajan, Priya Sundarakantham, K. Thiagarajar College of Engineering Dept. of Applied Mathematics and Computational Science Madurai India Thiagarajar College of Engineering Dept. of Computer Science and Business Systems Madurai India Thiagarajar College of Engineering Dept. of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350394528

Short message spam poses a significant threat for all mobile phone users, as it can act as an efficient tool for cyberattacks including spreading malware and phishing. Traditional anti-spam measures are only minimally effective against modern spammers. Intelligent analysis of the content to categorise the messages as Spam (unwanted and unsolicited) or Ham (useful messages) is therefore essential to safeguard the user from such attacks. Various artificial intelligence (AI) techniques are proving to be useful in the analysis of the content of such short messages to classify and filter spam. We have trained, validated and tested seven such AI techniques on the SMS spam collection dataset to identify the best model for designing and developing a content based classification system. Recurrent Neural Networks (RNN) have shown the highest performance metrics (Test Accuracy: 99.28%) and hence our proposed system includes a RNN model for classification. A web app of this system has also been deployed where a single SMS can be input and the designed system can classify it as Spam or Ham. The designed system is compared against existing systems and is found to be significantly better. © 2024 IEEE.

关键词： Recurrent neural networks

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Improving the permutation layer of type 1, type 3, source-heavy, and target-heavy generalized feistel structures

Improving the permutation layer of type 1, type 3, source-he...

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作者： Yanagihara, Shingo Iwata, Tetsu Dept. of Computational Science and Engineering Nagoya University Nagoya-shi 464-8603 Japan

The Generalized Feistel Structure (GFS) generally uses the sub-block-wise cyclic shift in the permutation layer, the layer between the two F function layers. For Type 2 GFS, at FSE 2010, Suzaki and Mine-matsu showed that a better diffusion property can be obtained if one uses some other sub-block-wise permutation. In this paper, we consider Type 1, Type 3, Source-Heavy (SH), and Target-Heavy (TH) GFSs, and study if their diffusion properties can be improved by changing the sub-block-wise cyclic shift. For Type 1 GFS and Type 3 GFS, we show that better permutations in terms of diffusion exist. For SH and TH GFSs, we show that the diffusion property does not change even if we change the sub-block-wise cyclic shift. We also experimentally derive optimum permutations in terms of diffusion, and evaluate the security of the resulting schemes against saturation, impossible differential, differential, and linear attacks. Copyright © 2013 The Institute of Electronics, Information and Communication Engineers.

关键词： Diffusion

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