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Controlling energy aware clustering and multihop routing protocol for IoT assisted wireless sensor networks

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第21期34卷 e7106-e7106页

作者： Subramani, Neelakandan Perumal, Santhosh Kumar Kallimani, Jagadish Shivappa Ulaganathan, Sakthi Bhargava, Sanjay Meckanizi, Sangeetha RMK Engn Coll Dept CSE Chennai Tamil Nadu India SRM Inst Sci & Technol Dept IT Ramapuram India MS Ramaiah Inst Technol Dept Comp Sci & Engn Bangalore Karnataka India Saveetha Sch Engn Dept Comp Sci & Engn Chennai Tamil Nadu India Poornima Coll Engn Comp Sci & Engn Jaipur Rajasthan India Panimalar Engn Coll Dept CSE Chennai Tamil Nadu India

Because of recent breakthroughs in information technology, the Internet of Things (IoT) is becoming increasingly popular in a variety of application areas. Wireless sensor networks (WSN) are a critical component of IoT systems, and they consist of a collection of affordable and compact sensors that are utilized for data collecting. WSNs are used in a variety of IoT applications, such as surveillance, detection, and tracking systems, to sense the surroundings and transmit the information to the user's device. Smart gadgets, on the other hand, are limited in terms of resources, such as electricity, bandwidth, memory, and computation. A fundamental issue in the IoT-based WSN is to achieve energy efficiency while also extending the network's lifetime, which is one of the limits that must be overcome. As a result, energy-efficient clustering and routing algorithms are frequently employed in the IoT system. As a result of this inspiration, the authors of this research describe an Energy Aware Clustering and Multihop Routing Protocol with mobile sink (EACMRP-MS) technique for IoT supported WSN. The EACMRP-MS technique's purpose is to efficiently reduce the energy consumption of IoT sensor nodes, consequently increasing the network efficiency of the IoT system. The suggested EACMRP-MS technique initially relies on the tunicate swarm algorithm (TSA) for cluster head (CH) selection and cluster assembly, as well as the TSA. Furthermore, the type-II fuzzy logic (T2FL) technique is used for the optimal selection of multi-hop routes, with multiple input parameters being used to achieve this. Finally, a mobile sink with route adjustment scheme is presented to further increase the energy efficiency of the IoT system. This scheme allows for the adjustment of routes based on the trajectory of the mobile sink, which further improves the energy efficiency of the system. Using a detailed experimental analysis and simulation findings, it was discovered that the EACMRP-MS technique outper

关键词： clustering energy efficiency Internet of Things mobile sink routing tunicate swarm algorithm wireless sensor networks

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Short-term commercial load forecasting based on peak-valley features with the TSA-ELM model

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ENERGY SCIENCE & ENGINEERING 2022年第8期10卷 2622-2636页

作者： Zhou, Mengran Zhu, Ziwei Hu, Feng Bian, Kai Lai, Wenhao Hu, Tianyu Anhui Univ Sci & Technol Sch Elect & Informat Engn Huainan 232000 Anhui Peoples R China

Commercial buildings are consuming an increasing amount of energy, and accurate load demand forecasting is critical for the reliable operation of power systems and the efficient use of resources. Therefore, in this paper, a short-term commercial load forecasting model based on tunicate swarm algorithm (TSA) combined with an extreme learning machine (ELM) under peak-valley features is proposed as a research case for a shopping mall in Romania. This paper's overall structure is divided into two steps. In the first step, the 24-h day is divided into six periods by analyzing the daily load characteristics of the training set, and the peak and valley loads are obtained. The ELM optimized by TSA (TSA-ELM) algorithm is then used to forecast the peak and valley values of the test set one day ahead. In the second step, the actual load, peak, and valley for the previous week of historical load are chosen using the maximum information coefficient (MIC). Following that, the MIC >= 0.8 features are added to the TSA-ELM to achieve short-term commercial electricity load forecasting. The results show that the PV (Peak & Valley)-TSA-ELM model proposed in this paper has higher prediction accuracy compared with other models. Taking ELM as an example, compared with the traditional ELM, the mean absolute error, root mean square error, and mean absolute percentage error of PV-TSA-ELM are reduced by 20.59%, 20.13%, and 19.19% on average in the three commercial data sets. The proposed model is validated with an industrial data set, and ideal results are obtained, which verifies the effectiveness and superiority of the proposed method.

关键词： commercial electricity demand extreme learning machine peak and valley loads short-term load forecasting tunicate swarm algorithm

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Improved geographic opportunistic routing protocol for void hole elimination in underwater IoTs: Parameter tuning by TSA optimization

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2024年第3期37卷 e5659-e5659页

作者： Simon, Judy Kapileswar, Nellore Kumar, Polasi Phani Elaveini, Mathiyalakendran Aarthi SRM Inst Sci & Technol Dept Elect & Commun Engn Chennai India

Underwater IoT is incredibly helpful in monitoring a variety of jobs, from instrument monitoring to climate recording, from pollution management to natural catastrophe forecasting. Nevertheless, there exist various issues that have an impact on a network's efficiency such as the formation of void holes, excessive EC, and low PDR. As a result, the IGOR protocol is suggested in this study to increase PDR by reducing the percentage of void hole occurrence. The developed routing protocols' scalability is also examined. Here, the parameter optimization for the EC minimization and PDR maximization is performed by a meta-heuristic optimization algorithm referred to as TSA. In order to verify that the suggested protocol is EC-optimal by calculating the viable areas. In addition, suggested protocols are evaluated against contemporaries' benchmark routing protocols. The outcomes of the simulations clearly demonstrate that the suggested routing protocols obtained greater PDR than the current techniques. Additionally, there exists a reduction in the ratio of void hole incidence. Comparative research reveals that suggested routing protocols outperformed benchmark routing protocols by 80-81% in PDR. Further, the suggested routing procedures reduced the frequency of void holes by around 30%. Conclusion: This study recommends the IGOR technique to boost PDR by decreasing the frequency of empty holes. Here, a meta-heuristic optimization technique known as TSA optimizes the parameters for the EC and PDR maximization. Viable areas are also estimated to confirm that the chosen methodology is EC ***

关键词： energy consumption minimization improved geographic opportunistic routing protocol packet delivery ratio maximization tunicate swarm algorithm underwater internet of things void hole elimination

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An intelligent risk assessment on prediction of COVID-19 pandemic using DNN and TSA: An empirical case study in Thailand

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 253卷

作者： Kengpol, Athakorn Klunngien, Jakkarin King Mongkuts Univ Technol North Bangkok Fac Engn Adv Ind Engn Management Syst Res Ctr Dept Ind Engn Bangkok Thailand Kasetsart Univ Fac Engn Sriracha Dept Ind Engn Ind & Prod Management Res Unit Sriracha Thailand

The WHO has declared that the COVID-19 pandemic is a severe health crisis. Currently, variants of concern are delta and omicron, including sub-lineages of the omicron which are XBB and BQ.1 variants. Decision planning with situation awareness is important during the COVID-19 pandemic, especially demand planning for medical supplies following pandemic probability or severity via pandemic risk assessment. Therefore, this research proposes an intelligent risk assessment on the prediction of the COVID-19 pandemic using deep learning with deep neural network (DNN) and tunicate swarm algorithm (TSA). The results show the model can accurately predict the distance and elapsed time of the next COVID-19 case based upon the previous case and evaluate the associated risks. The contribution of this research, as prediction model is based upon a DNN, it has the ability to learn and by implementing the TSA, it can improve theoretically the performance of the DNN for more precise prediction and faster convergence to the optimal solution. The prediction results are practically expanded to analyze risk assessment using probability and the data envelopment analysis (DEA). The benefit of this research is that the proposed methodology demonstrates the prediction results using risks assessment based upon intelligent risk assessment charts. The Government or those involved can use the proposed methodology to achieve a better decision-making and management to control the COVID-19 pandemic in terms of supplying the medical supplies into pandemic areas.

关键词： Risk assessment Prediction of COVID-19 pandemic Deep neural network tunicate swarm algorithm Data envelopment analysis

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Estimating the mechanical properties of high-performance concrete via automated and ensembled machine learning methods

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MATERIALS TODAY COMMUNICATIONS 2023年 37卷

作者： Liu, Xiaohua Zhang, Yu Lei, Song Yang, Shasha Xijing Univ Shaanxi Key Lab Safety & Durabil Concrete Struct Xian 710123 Shaanxi Peoples R China Xian Fengdong Real Estate Co Ltd Xian 710116 Shaanxi Peoples R China Yanan Univ Sch Petr Engn & Environm Engn Yanan 716000 Shaanxi Peoples R China

Environmentally friendly concrete is needed to reduce increased concrete consumption's environmental and climate impacts. One of the approaches to achieve this goal is using fly ash (FA) and silica fume (SF) in concrete mixes. The mentioned admixtures can enhance the workability and mechanical properties of concrete. Therefore, testing to predict the strength of concrete can be time-consuming and expensive. Thus, operating artificial intelligence methods, especially machine learning (ML), can simplify and speed up the procedure. Therefore, this investigation proposes to explore the possibility of using the ML models to predict the compressive strength (CS) and Slump (SL) of High-performance concrete (HPC). For this task, experimental results of HPC samples are used to develop the ML models, including Light Gradient Boosting Machine (LGBM) and Decision Tree (DT). In addition, some meta-heuristic algorithms have been used to improve the accuracy of presented models, which contain the Zebra Optimization algorithm and tunicate swarm algorithm. Extensive calculational tests were performed to assess the performance of the deemed models by using results achieved through experimental tests by statistical indicators. The LGZO model stands out from the other proposed models, exhibiting superior performance with impressively low RMSE values of 1.859 and 5.234 for CS and SL, respectively. These results prove the model's exceptional accuracy and robust predictive capability. Research findings suggest that utilizing predictive techniques instead of conducting costly and time-consuming tests could reduce costs and save time.

关键词： High-performance concrete compressive strength and slump Light gradient boosting machine Decision tree Zebra optimization algorithm tunicate swarm algorithm

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Implementation of combined hydrogen aqua electrolyser-fuel cell and redox-flow-battery under restructured situation of AGC employing TSA optimized PDN(FOPI) controller

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JOURNAL OF TAIBAH UNIVERSITY FOR SCIENCE 2024年第1期18卷

作者： Saha, Arindita Dash, Puja Chiranjeevi, Tirumalasetty Babu, Naladi Ram Regent Educ & Res Fdn Grp Inst Dept EE Kolkata India Power Res & Dev Consultants Pvt Ltd Bengaluru India Rajkiya Engn Coll Dept EE Sonbhadra India AdityaEngineering Coll Dept EEE Surampalem 533437 AP India

This work addresses the automatic-generation-control of multiple-area and sources under a restructured-situation. Sources within area-1 represent geothermal power plant, thermal, and gas, and area-2 sources represent thermal, hydro, and wind. An original endeavour is brought about to execute a controller with an admixture of proportional-derivative with filter (PDN) (integer-order) besides fractional-order proportional-integral (FOPI). Examination manifests excellence of PDN(FOPI) over integer order controllers likely integral, proportional-integral, proportional-integral-derivative-filter from perspective concerning depleted status of peak aberrations, extent-of-oscillations, and duration of settlement. To attain the controller's attributes bioinspired meta-heuristic tunicate swarm algorithm is exercised. The occurrence of renewable sources makes arrangements meaningfully improved related to base thermal-gas-hydro arrangement. The action of hydrogen aqua electrolyzer-fuel cell and redox flow battery is examined using a PDN(FOPI) controller, providing noteworthy outcome in dynamic performance. The analysis is conducted under all the schemes of restructured situations.

关键词： Automatic generation control fuel cell hydrogen aqua electrolyzer PDN(FOPI) controller redox flow battery tunicate swarm algorithm

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Power system stability enhancement through optimal PSS design

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2024年 9卷

作者： Islam, Md. Rashidul Azam, Md. Samiul Hossen, Md. Saber Islam, Mohammad Saiful Worku, Muhammed Y. Shahriar, Mohammad Shoaib Shafiullah, Md. Department of Electrical and Electronic Engineering International Islamic University Chittagong Chittagong 4318 Bangladesh Interdisciplinary Research Center for Renewable Energy and Power Systems King Fahd University of Petroleum & Minerals Dhahran 31261 Saudi Arabia Department of Electrical Engineering University of Hafr Al-Batin Hafr Al Batin 31991 Saudi Arabia Control & Instrumentation Engineering Department King Fahd University of Petroleum & Minerals Dhahran 31261 Saudi Arabia Interdisciplinary Research Center for Sustainable Energy Systems (IRC-SES) King Fahd University of Petroleum & Minerals Dhahran 31261 Saudi Arabia

Low-frequency oscillations (LFO) are generated in interconnected electric networks because of the weak tie lines between the parts of the networks. Such oscillations have been a significant challenge for engineers that may lead to system instability if appropriate measures are not taken. This article proposes two new metaheuristic algorithms, the jellyfish search algorithm (JSA) and the tunicate swarm algorithm (TSA), to design the power system stabilizers (PSS) for single machine infinite bus (SMIB) and multimachine power system (MPS) networks. The proposed method uses the JSA and TSA to dampen out the LFOs by modifying the vital parameters of lead-lag type conventional PSS (CPSS). A damping ratio-based objective function improves both models' system damping. These methods are tested on an SMIB system and two distinct MPS networks exposed to the three-phase fault under various loading conditions. The effectiveness of the suggested approach has been studied by comparing the system eigenvalues and minimum damping ratio (MDR) produced from JSA and TSA-tuned PSS and the CPSS. In addition, time domain simulation results are compared, demonstrating that the new approach outperforms the standard techniques, such as particle swarm optimization (PSO) and backtracking search algorithm (BSA). In the SMIB system, MDR produced by JSA and TSA-based PSS is 2.55 times higher than that of CPSS. Similarly, in comparison to PSO and BSA-based PSS, JSA, and TSA-tuned PSS offer >1.4 times greater MDR in the Two Area Four Machine network and up to 8.4 times larger MDR for the IEEE-39 Bus network. Furthermore, the efficacy of the suggested JSA and TSA approaches' prediction capacity is validated by satisfying the values of statistical performance metrics. © 2024 The Author(s)

关键词： Damping ratio Dynamic stability Jellyfish search algorithm Low frequency oscillation Power system stabilizers tunicate swarm algorithm

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