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smart computing models of California bearing ratio, unconfined compressive strength, and resistance value of activated ash-modified soft clay soil with adaptive neuro-fuzzy inference system and ensemble random forest regression techniques

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2021年第3期4卷 207-225页

作者： Iqbal, Mudassir Onyelowe, Kennedy C. Jalal, Fazal E. Shanghai Jiao Tong Univ Sch Naval Architecture Ocean & Civil Engn Shanghai Key Lab Digital Maintenance Bldg & Infra Shanghai Peoples R China Michael Okpara Univ Agric Dept Civil Engn Umudike Nigeria Kampala Int Univ Dept Civil & Mech Engn Kampala Uganda Shanghai Jiao Tong Univ Dept Civil Engn Shanghai Peoples R China

Sugeno or Takagi-Sugeno-Kang (TSK) type fuzzy inference system ANFIS proposed by Jang and ensemble random forest (ERF) regression, an extension of bootstrap aggregation of decision trees, has been employed to forecast the triple targets of strength properties of a hydrated-lime activated rice husk ash stabilized soft clay soil. This was necessitated to deal with the incessant failure being recorded on flexible pavements around the world and the efforts being made to tackle the situation in a more smart and sustainable approach. The independent variables of this model protocol were the HARHA-hydratedlime-activated rice husk ash, w(L)-liquid limit, w(P)-plastic limit, I-P-plasticity index, w(OMC)-optimum moisture content, A(C)-clay activity, delta(max)-maximum dry density, while CBR-California bearing ratio, UCS28-unconfined compressive strength at 28 days curing and R-resistance value were estimated and employed as the targets (dependent variables). The natural clayey expansive soil used for this research work was investigated through preliminary experiments and classified as A-7-6 group according to AASHTO. It exhibits a very high plasticity index with high clay content, hence needed modification to be rendered as a foundation material. The soil was treated with varying percentages of HARHA, and the effect on the consistency limits, compaction, CBR, UCS, and R-value was studied. These observed values gave rise to 61 datasets. The observed datasets were deployed on the learning capacity of ANFIS and ERF regression to proposed models for the targets. The outcome of the results showed that both the models presented a close correlation between the parameters used in the model execution. Evaluation of the models was performed using a variety of statistical errors, Kendall and Spearman's rank correlations. The results of ERF regression outclasses ANFIS model yielding a 100% coefficient of determination (R) for the triple targets. The performance evaluation and validation

关键词： smart computing Ensemble random forest (ERF) regression and adaptive neuro-fuzzy inference system (ANFIS) Unconfined compressive strength (UCS) California bearing ratio (CBR) and resistance value (R) Soft clay soil (SCS) Hydrated-lime-activated rice husk ash (HARHA) and HARHA-stabilized soft clay soil (HSSCS)

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smart computing Mechanism for Noise Detection and Elimination in ECG Signal

Smart Computing Mechanism for Noise Detection and Eliminatio...

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IEEE International Conference on Big Data and smart computing (BigComp)

作者： Ho, Te-Wei Lin, Fong-Ci Lin, Ching-Miao Lai, Feipei Natl Taiwan Univ Grad Inst Biomed Elect & Bioinformat Taipei Taiwan Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei Taiwan Natl Taiwan Univ Dept Elect Engn Taipei Taiwan

ISBN: (纸本)9781509030156

The cardiovascular disease is one of the most common causes of death around the world. The analysis of electrocardiograms (ECGs) is an important tool in early diagnosis of arrhythmias. However, sometime the measurement data would be corrupted by noises which may cause by the wrong equipment operation, poor contact of the electrode, or even the breath of the users. These noises would make cardiologists or automatic detection system hard to make a correct diagnosis. Therefore, the noise detection and elimination of ECG data become an important issue. In this study, we proposed a detection and elimination mechanism for the five types of noise. Besides, if the segment does not have any important information and cannot be repaired, we will eliminate it and combine the remaining usable segments into a pure signal for ECG enhancement. The experimental results showed that the noise recognition classifier yielded 0.956 area under the ROC curve, 84.4% accuracy, 97.8% sensitivity, and 80.5% specificity, respectively. The accuracy of disease detection system also could be improved by using the combination of usable segments. Hence, we believed this smart computing mechanism could address ECG enhancement and interpret a contaminated ECG signal more accurately.

关键词： Electrocardiogram Signal processing smart computing

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smart computing Applications Using BLE and Mobile Intercloud Technologies 44

Smart Computing Applications Using BLE and Mobile Intercloud...

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44th Annual IEEE-Computer-Society International Conference on Computers, Software, and Applications (COMPSAC)

作者： Ho, Yik Him Sartayeva, Yerkezhan Chiu, Chak Pang Chan, Henry C. B. Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

ISBN: (纸本)9781728173030

With the advent of mobile computing and mobile cloud computing, many smart computing applications can be developed. In this paper, we present three smart computing applications using Bluetooth Low Energy (BLE) and mobile Intercloud technologies. The first application is a smart roll call application using BLE, enabling teachers to easily take attendance while at the same time ensuring privacy. The second application is an ad hoc positioning application with a new BLE positioning paradigm. In this case, a user can determine the positions of other users through an ad hoc network Experiments were conducted to evaluate the accuracy of this positioning method. The third application is a mobile Intercloud application, which allows mobile terminals to transfer data through their associated clouds (i.e., using an Intercloud protocol for data transfer). Experimental results are presented to evaluate performance.

关键词： BLE smart computing mobile Intercloud

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Multi-policy-aware MapReduce resource allocation and scheduling for smart computing cluster

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JOURNAL OF SYSTEMS ARCHITECTURE 2017年 80卷 17-29页

作者： Yang, Jian Lu, Zhihui Wang, Nini Wu, Jie Hung, Patrick C. K. Fudan Univ Sch Comp Sci Shanghai Peoples R China Univ Ontario Inst Technol Fac Business & IT Oshawa ON Canada

When a user submit a MapReduce job in the smart computing cluster, we first need to allocate cluster resource for the job. It is widely concerned that how to save time and resource costs to provide users with computing capacity and services. Here, we propose a multi-policy-aware Resources Allocation Algorithm that can allocate the appropriate amount of resources to the job to meet the execution deadline in private cloud and an extension in public cloud. Then when job running in the allocated cluster, in order to guarantee the performance of smart computing framework, we further propose a multi-policy-aware resource scheduling optimization model under YARN. If users use default policy, considering the difference of the heterogeneity of jobs and the resource request of tasks, we propose a global task dynamic resource scheduling algorithm-LRD algorithm, based on data locality, resource demand and task dependency. Further, if users set the deadline policy for jobs, we propose a DA (deadline-aware) scheduling algorithm based on performance prediction model. It can optimize the overall execution time of Hadoop jobs and improve the resource utilization of the entire cluster. Finally, we conduct different experiments to evaluate and verify the proposed models and algorithms in this paper. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Big data Hadoop MapReduce Yarn Resource allocation Resource scheduling smart computing

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InSTechAH: Cost-effectively autoscaling smart computing hadoop cluster in private cloud

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JOURNAL OF SYSTEMS ARCHITECTURE 2017年 80卷 1-16页

作者： Lu, Zhihui Wang, Xueying Wu, Jie Hung, Patrick C. K. Fudan Univ Sch Comp Sci Shanghai Peoples R China Univ Ontario Inst Technol Fac Business & IT Oshawa ON Canada

Report shows that in many cloud data centers, computing resources are not used efficiently and thereby cause resource waste. In many cloud nodes, the utilization rate of CPU is only 20%. A large-scale multi layer smart computing system (such as Deep Learning) require computing of a large amount of labeled data. To improve cost-effectiveness of resources in cloud data centers, running big data-based smart computing applications to utilize residual computing resource capacity is a feasible solution. However, performance loss brought by resource competition and interference between host application and smart computing application is the main challenge for us. In this paper, we design, implement and evaluate the InSTechAH: A big data-oriented cost-effective cluster autoscaling and scheduling scheme in private cloud, which improves the resource utilization as well as to maintain required quality of services by autoscaling and scheduling background smart computing analytics tasks. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Autoscaling Cost effectiveness Big data Hadoop smart computing Private cloud Openstack Cloud datacenter

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smart computing Optimization of the Bactericidal Potential of ZnO Nanoparticles: A Support Vector Machine Approach 5

Smart Computing Optimization of the Bactericidal Potential o...

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5th IEEE International Conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Narang, Kashish Kumar, Jayant Saxena, Shreya Chaudhary, Meena Narender Chandwani, Gunjan Manav Rachna University Computer Science Department Faridabad India

ISBN: (纸本)9798350383522

ZnO nanoparticles are tiny particles of zinc oxide, typically ranging from 1 to 100 nanometers, exhibiting unique physical and chemical properties suitable for applications in various fields, including optoelectronics, catalysis, and antibacterial coatings. The optimization of ZnO nanoparticle synthesis faces challenges in precisely controlling particle size, morphology, and dispersity, hindering the fine-tuning required for specific applications. The analysis of ZnO nanoparticle optimization involves scrutinizing parameters like precursor concentration and reaction conditions, aiming to finely control particle size and morphology for tailored applications. SVM (support vector machine) in machine learning can be applied to model the relationship between synthesis parameters and desired antibacterial properties of ZnO nanoparticles. The future scope of optimizing ZnO nanoparticles for antibacterial properties lies in further tailoring synthesis parameters to enhance both efficacy and biocompatibility. Advanced research may focus on fine-tuning surface modifications and doping strategies to improve antibacterial activity while minimizing potential cytotoxic effects, paving the way for the development of advanced antibacterial agents with applications in healthcare, water treatment, and other fields. © 2024 IEEE.

关键词： Antibacterial Properties Machine Learning ZnO (zinc oxide) Nanoparticles Neuromorphic Systems Photocatalytic activity smart computing Support Vector Machine (SVM)

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smart computing Volume 18 Issue 10