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Toward Precise Long-Term Rockburst Forecasting: A Fusion of SVM and Cutting-Edge Meta-heuristic algorithms

NATURAL RESOURCES RESEARCH 2024年第5期33卷 2037-2062页

作者： Armaghani, Danial Jahed Yang, Peixi He, Xuzhen Pradhan, Biswajeet Zhou, Jian Sheng, Daichao Cent South Univ Sch Resources & Safety Engn Changsha 410083 Peoples R China Univ Technol Sydney Sch Civil & Environm Engn Ultimo NSW 2007 Australia Univ Technol Sydney Fac Engn & Informat Technol Ctr Adv Modelling & Geospatial Informat Syst Sch Civil & Environm Engn Ultimo NSW Australia

Rockburst is one of the most hazardous geological disasters in underground engineering due to its complex causes and destructive nature. To address this, there is an imperative for methodologies that can predict rockbursts quickly and effectively to mitigate preemptively the risks and damages. In this study, 259 rockburst instances were analyzed, employing six rockburst feature parameters: maximum tangential stress (sigma theta), uniaxial compressive strength of rock (sigma c), uniaxial tensile strength of rock (sigma t), stress coefficient (sigma theta/sigma t), rock brittleness coefficient (sigma c/sigma t), and elastic energy index (Wet) as inputs. By integrating three novel meta-heuristic algorithms-dingo optimization algorithm (DOA), osprey optimization algorithm (OOA), and rime-ice optimization algorithm (RIME)-with support vector machine (SVM), hybrid models for long-term rockburst trend prediction were constructed. Performance evaluations through fivefold cross-validation revealed that for the no rockbursts, DOA-SVM (Pop = 200) demonstrated superior predictive performance, achieving an accuracy of 0.9808, precision of 0.9231, recall of 1, and an F1-score of 0.96. For moderate rockbursts, OOA-SVM (Pop = 100) emerged as the most effective, registering an accuracy of 0.9808, precision of 0.9545, recall of 1, and an F1-score of 0.9767. For light and severe rockbursts, DOA-SVM, OOA-SVM, and RIME-SVM showcased comparable predictive outcomes. However, these hybrid models outperformed traditional SVM models optimized with conventional algorithms in terms of accuracy across all rockburst hazard levels. Moreover, the hybrid models underwent additional validation with a new dataset of 20 rockburst instances collected globally, confirming their robust efficacy and exceptional generalization capabilities. An ensuing analysis using local interpretable model-agnostic explanations (LIME) on the six key feature parameters revealed a significant positive correlation between s

关键词： Dingo optimization algorithm osprey optimization algorithm Rime-ice optimization algorithm LIME Long-term rockburst

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Radial Bias Function Neural Network and Packet Chaining Reservation Protocol for Congestion Prediction and Avoidance in MANET under TCP Traffic Patterns

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OPTOELECTRONICS INSTRUMENTATION AND DATA PROCESSING 2024年第5期60卷 644-658页

作者： Mahajan, Sanjeev Kaur, Jagdeep Sant Baba Bhag Singh Univ Dept Comp Sci & Engn Jalandhar 144030 Punjab India

The effectiveness of Transmission Control Protocol (TCP) connections is significantly impacted by congestion control (CC). Congestion occurs when the network cannot manage the level of traffic that arises when a large number of packets arrive at once. If congestion is anticipated, packet loss can be prevented by taking appropriate measures to lower the rate of packet production at the source. Many existing protocols are developed to control congestion on the other hand, the packet delivery ratio will not sufficiently be high and it lengthens the delay, which affects the performance of TCP. In order to improve TCP performance in MANET, the Packet Chaining Reservation Protocol (PCRP) using radial basis functional neural network (RBFNN) was developed. The initial message is transmitted by the transmitter, then waits for a reply. If the acknowledgement is received, the congestion prediction algorithm is executed to determine whether there is congestion or not on the data transmission line. Based on a few characteristics, RBFNN is used to estimate congestion in the transport layer. The best size of the cwnd is chosen using the osprey optimization algorithm (OOA), which is used for efficient data transfer. If the cwnd value above the threshold value signifies congestion avoidance phases are carried out in accordance with PCRP. This process is repeated until every message has been transmitted. The simulation analysis shows the proposed protocol has 89% packet delivery ratio, 2.5Mbps of throughput and 15 sec delay. Thus, the proposed approach is the better choice for enhancing the performance of TCP under MANET.

关键词： Transmission Control Protocol congestion prediction congestion avoidance MANET osprey optimization algorithm

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Efficient collision control and auction-based resource allocation mechanism in dense LoRaWAN network via TCP using DRL technique

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International Journal of Information Technology (Singapore) 2024年第7期16卷 4039-4057页

作者： Parween, Sultana Hussain, Syed Zeeshan Department of Computer Science and Engineering Jamia Hamdard New Delhi India Department of Computer Science Jamia Millia Islamia New Delhi 110025 India

The Internet of Things (IoT) is basically about communication, which permits devices and circumstances to gather, share, and act on data. A transmission method called LoRa (Long Range) allows for the long-range transmission of data at a low delivery rate. The problems and the limitations of the IoT conception with a focus on LoRa technology are proposed in this study. This paper presents an analysis of the challenges and constraints associated with the Internet of Things (IoT) concept, with a specific emphasis on LoRa technology. A LoRaWAN (Long Range Wide Area Network) network consists of battery-operated devices that enable communication in both directions and is a form of Low Power Wide Area Network (LPWAN). However, various challenges present in IoT data transmission between sensor nodes due to its transmission rate that leads to high network collision. To overcome such challenges, Transmission Control Protocol (TCP) is used which is a transport layer protocol for efficient data transmission. However, existing work performs collision detection, collision avoidance, and resource allocation with limited parameters, which leads to inefficient collision avoidance. To leverage the existing issues, the proposed work adopts proper collision control and efficient resource allocation in the LoRaWAN network via TCP. In our proposed work, Multi-hop clustering is performed for LoRaWAN nodes and gateways using the Mini Batch K Means Clustering (MBKMC) algorithm to reduce the imbalance load among networks and computational complexities. Collision detection is performed using the Improved Gradient Boosting Decision Tree (IMPGBDT) algorithm. After that, collision avoidance is performed using the osprey optimization algorithm (OOA) based on channel transmission signal strength. The channel is allocated to the demanded resource request using the Deep Q Network (DQN) algorithm. Here, resource allocation is performed based on the Profit Maximization Multi Round Auction (PMMRA) algo

关键词： Deep Q network Improved gradient boosting decision tree LoRaWAN Mini batch K means clustering osprey optimization algorithm PMMRA TCP

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Optimize the solid waste collection route in construction using the POA algorithm

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Asian Journal of Civil Engineering 2024年第3期25卷 2947-2962页

作者： Pham, Vu Hong Son Tran, Thiet Van Faculty of Civil Engineering Ho Chi Minh City University of Technology (HCMUT) 268 Ly Thuong Kiet Street District 10 Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Linh Trung Ward Thu Duc City Ho Chi Minh City Viet Nam

This study provides an in-depth exploration of the urban municipal solid waste (MSW) collection issue in the urban context, where there is a continuous rise in individual waste production levels and vulnerability to climate change, particularly in developing countries. The research focuses on a specialized vehicle routing model to optimize the solid waste transportation system within the city, aiding civil engineers and urban planners in strategically locating waste collection station positions for efficient waste collection routes. To enhance the model’s efficiency, a novel combined method is proposed, integrating with the Pelican optimization algorithm (POA). Experimental evaluations, conducted using real-world data, unequivocally demonstrate that the proposed combined method outperforms alternative approaches, including the current manual MSW collection process in the city. This research makes a significant contribution to the fields of waste management and urban sustainability, with the ultimate goal of mitigating the social, economic, and environmental consequences associated with urban solid waste collection. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Heuristics osprey optimization algorithm Project management Routing problem Solid waste collection

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An Optimized Design Modeling of PV Integrated SEPIC Based Single-Phase System

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2024年第2期31卷 518-524页

作者： Sudhanthirarajan, Ravikumar Subaramaniyan, Venkatanarayanan St Michael Coll Engn Dept Elect & Elect Engn Sivagangai Tamilnadu India KLN Coll Engn Dept Elect & Elect Engn Sivagangai India

Power converters are widely used in renewable energy conversion systems such as photovoltaics. Their main role is to adapt the output voltage to a desired voltage *** study proposes a highly efficient Single-Ended Primary Inductor Converter (SEPIC) to enhance the utilization of PV arrays by ensuring a continuous flow of current. To minimize DC voltage ripples, a Proportional-Integral (PI) controller is presented to continuously monitor the High power extracted from the PV array. The PI controller parameters are optimized using the osprey optimization algorithm (OOA), which mimics the hunting behavior of ospreys in nature. The OOA draws inspiration from the hunting strategy of ospreys when they hunt fish in the seas. The osprey first detects the position of its prey and then captures it, carrying it to a suitable location to consume it. Similarly, the OOA iteratively optimizes the PI controller parameters to advance the SEPIC converter and achieve a DC-link voltage for steady-state at the converter's output. Then it is fed into a voltage source inverter (VSI), which connects the system to a single-phase utility grid. A fuzzy logic tuned PI controller is employed to synchronize the inverter's output with the grid. The performance is evaluated by analyzing the harmonic content in the grid current. To validate the proposed approach, a Simulink model is developed and compared against a hardware prototype. The proposed results achieved an effectiveness of performance and efficient power conversion in the system.

关键词： osprey optimization algorithm proportional integral controller PV system single ended primary inductor converter voltage source inverter

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Secured osprey-based energy efficient routing and congestion control in WSN

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2024年 44卷

作者： Bhavadharini, R. M. Surendiran, B. Vellore Inst Technol Sch Comp Sci & Engn Chennai 600127 Tamil Nadu India Natl Inst Technol Puducherry Karaikal 609609 Puducherry India

An adaptive metaheuristic optimization-based QoS-aware, Energy-balancing, Secure Routing Protocol (AQoSESRP) is proposed in this article. The network is modelled as a biconcentric hexagon (BiCon-HexA), and the clusters are formed within the BiCon-HexA network. The BiCon-HexA is divided into six sectors to support effective data aggregation, and then clusters are formed within all sectors. The optimal cluster head (CH) selection mechanism is modelled by an Adaptive Hunter-Prey optimization (AdapH-PO) algorithm considering QoS parameters. Data aggregation is then done securely with an enhanced encryption approach. Here, upgraded elliptic curve cryptography (UEllip-CC) is used to encode data in CH. This UEllip-CC approach provides security improvements in data transmission. Furthermore, in this study, CHs are combined in the multi-hop routing of data packets to reduce the power consumption problems of wireless sensor networks (WSN). To determine the optimal route for data transmission, an energy-balanced multi-path routing algorithm called improved convolutional osprey network (ICON) is presented. Nevertheless, the data transmission nodes can be overloaded in the data routing phase. Here, the congestion problem can be solved by applying an improved version of the Random Early Detection (RED) congestion control model to discard the data packets more noticeably. The simulation of AQoS-ESRP is done with Matlab, and the performance is evaluated using different metrics. When compared to existing systems, the simulation results clearly indicate a significantly higher throughput and delay. Thus, the AQoS ESRP model is employed to maximize the overall data transfer in the WSN.

关键词： Wireless Sensor Networks Bi-Concentric Hexagons Hunter-Prey optimization Upgraded Elliptic Curve Cryptography osprey optimization algorithm Congestion control Data transmission and security

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Enhanced multi-constraint dung beetle optimization-kernel extreme learning machine for lithium-ion battery state of health estimation with adaptive enhancement ability

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ENERGY 2024年 307卷

作者： Mo, Daijiang Wang, Shunli Fan, Yongcun Takyi-Aninakwa, Paul Zhang, Mengyun Wang, Yangtao Fernandez, Carlos Southwest Univ Sci & Technol Sch Informat Engn Mianyang 621010 Peoples R China Inner Mongolia Univ Technol Coll Elect Power Hohhot 010080 Peoples R China Robert Gordon Univ Sch Pharm & Life Sci Aberdeen AB10 7GJ Scotland

Accurately estimating the state of health (SOH) of lithium batteries is a critical and challenging task in battery management systems. Data-driven models are widely used for SOH estimation but still suffer from the difficulty of balancing speed, accuracy, and adaptability. Therefore, this study constructs the dung beetle optimization algorithm to optimize the kernel extreme learning machine model. This paper addresses the issues of long iteration time and mismatches in kernel function mapping in data-driven models. To improve the model's generality, an adaptive learning kernel function is designed to complement the polynomial kernel function and form a joint function. This joint function is then introduced into a single implicit-layer extreme learning machine, which achieves fast speed and strong adaptive capability. To enhance the algorithmic parameter search capability, the optimal Latin hypercube idea, and the osprey algorithm's global exploration strategy are introduced, which effectively improves the algorithm's global search capability. Additionally, it successfully regulated the positional update through the design of the logarithmic weighting factor, which improved the local search and convergence capabilities of the algorithm. The experiment validates the effectiveness and rationality of the proposed model for advancing battery management system applications.

关键词： State of health estimation Lithium-ion battery Dung beetle optimization algorithm Kernel extreme learning machine osprey optimization algorithm

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Low carbon and environmental preservation of residential buildings: MOESOM

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FRONTIERS IN ENERGY RESEARCH 2023年 11卷

作者： Liang, Nan Yu, Mengxuan Anhui Polytech Univ Sch Architecture & Civil Engn Wuhu Peoples R China

Introduction: The crucial transition toward carbon neutrality is developing and adopting low-carbon buildings and communities to achieve the recycling and reuse of resources and to minimize the damage to the natural environment by humans. Energy saving for residential buildings is essential for enhancing cost-effectiveness and redundant energy drain. Considering the increasing attention to energy conservation and the accessibility of sustainable energy sources, common energy-saving solutions expose inherent inadequacies limiting their effectiveness. The ineffectual use of traditional energy sources can result in waste, greater operating costs, and excessive energy consumption in residential ***: Hence, a Multi-Objective Energy-Saving optimization Method (MOESOM) has been proposed to optimize energy use and conservation in residential buildings in southern Anhui, China. The proposed approach examines lower operational costs and carbon emissions by using green energy sources and encouraging effective energy consumption habits. The suggested Multi-Objective Energy-Saving optimization Method technique offers insight into energy saving by utilizing green energy sources and confining energy uses. The multi-objective turns around energy saving and resource usage for decreasing operational costs and averting carbon emissions. Thus, the suggested technique is verified utilizing the osprey optimization algorithm (OOA);the detailed goal is recognized utilizing the multiple objectives described. Based on the progress of low-carbon emissions and energy saving, the number of iterations for augmenting osprey agents is identified. This agent-based optimization is executed if the novel augmented agent fulfills any of the trailing progression. The emission control level and energy-saving factor are assessed considering the variance between new and old agent progression. This encourages the various objectives to be fulfilled under similar criteria balancing their outcom

关键词： energy-saving low-carbon emission multi-objective osprey optimization algorithm residential buildings green energy resources resource utilization agent-based optimization

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Qualitative and quantitative detection of microplastics in soil based on LIF technology combined with OOA-ELM/SPA-PLS

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MICROCHEMICAL JOURNAL 2024年 201卷

作者： Yan, Pengcheng Li, Guodong Wang, Wenchang Zhao, Yuting Wang, Jingbao Wen, Ziming Anhui Univ Sci & Technol Sch Elect & Informat Engn Huainan 232001 Peoples R China Anhui Univ Sci & Technol State Key Lab Min Response & Disaster Prevent & Co Huainan 232001 Peoples R China Anhui Univ Sci & Technol Collaborat Innovat Ctr Mine Intelligent Equipment Huainan 232001 Peoples R China

Traditional methods for detecting soil microplastics include chemical digestion, density separation, staining, etc. These methods often only provide qualitative analysis of microplastics and are difficult to accurately quantify. To address this issue, laser induced fluorescence (LIF) technology is introduced, which can achieve high sensitivity, non-destructive, and rapid detection, and improve the accuracy and efficiency of qualitative and quantitative analysis of soil microplastics. Therefore, based on LIF technology, a qualitative prediction model for optimizing Extreme Learning Machine (ELM) using osprey optimization algorithm (OOA) and a quantitative prediction model combining Continuous Projection algorithm (SPA) with Partial Least Squares algorithm (PLS) were established. Qualitative analysis used eight types of microplastics, with AS, PA66, ABS, PS, POM, PBT, PET, and PVC as experimental samples. Initially, spectral data is obtained using LIF technology and the raw spectral data is pre-processed using polynomial smoothing algorithm (SG) and moving average (MA) methods. Subsequently, factor analysis (FA) and local linear embedding (LLE) techniques are applied to dimensionality reduction and then input into the ELM model. At the same time, the ELM parameter is optimized by using OOA algorithm. In the quantitative analysis, PA66 powder and soil are mixed in different proportions (10:0, 8:2, 5:5, 2:8, 0:10), SG and MA pre-processing are performed on the spectral data obtained by LIF technology, and SPA is used to select the spectral characteristic wavelength for the original data and the pre-processed spectral data, and then a linear regression model is established combined with PLS. In order to facilitate the comparison of data, the experimental results are reserved to 5 decimal places, the experimental results indicate that LIF technology is faster than traditional methods in extracting raw data. The convergence of the OOA-optimized ELM model is better than tha

关键词： Laser induced fluorescence technique Qualitative and quantitative detection Extreme learning machine osprey optimization algorithm Partial least squares Successive projections algorithm

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GSOOA-1DDRSN: Network traffic anomaly detection based on deep residual shrinkage networks

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HELIYON 2024年第11期10卷 e32087页

作者： Zuo, Fengqin Zhang, Damin Li, Lun He, Qing Deng, Jiaxin Guizhou Univ Coll Big Data & Informat Engn Guiyang 550025 Peoples R China

One of the critical technologies to ensure cyberspace security is network traffic anomaly detection, which detects malicious attacks by analyzing and identifying network traffic behavior. The rapid development of the network has led to explosive growth in network traffic, which seriously impacts the user's information security. Researchers have delved into intrusion detection as an active defense technology to address this challenge. However, traditional machine learning methods struggle to capture complex threats and attack patterns when dealing with large-scale network data. In contrast, deep learning methods have the advantages of automatically extracting features from network traffic data and strong generalization capabilities. Aiming to enhance the ability of network anomaly traffic detection, this paper proposes a network traffic anomaly detection based on Deep Residual Shrinkage Network (DRSN), namely "GSOOA-1DDRSN". This method uses an improved osprey optimization algorithm to select the most relevant and essential features in network traffic, reducing the features' dimensionality. For better detection performance of network traffic anomalies, a one-dimensional deep residual shrinkage network (1DDRSN) is designed as a classifier. Validation is performed using the NSL-KDD and UNSWNB15 datasets and compared with other methods. The experimental results show that GSOOA1DDRSN has improved multi-classification accuracy, precision, recall, and F1 Score by approximately 2 % and 3 %, respectively, compared to the 1DDRSN model on two datasets. Additionally, it reduces the time computation costs by 20 % and 30 % on these datasets. Furthermore, compared to other models, GSOOA-1DDRSN offers superior classification accuracy and effectively reduces the number of features.

关键词： Cyberspace security Network traffic anomaly detection Deep residual shrinkage network osprey optimization algorithm Dimensionality reduction

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