检索结果-内蒙古大学图书馆

Design of Safety Zone and Optimal Risk Identification of Undesired Events During Loading and Unloading of LNG Terminal Using TSA-GEO: a Hybrid Strategy

引用

PROCESS INTEGRATION AND OPTIMIZATION FOR SUSTAINABILITY 2022年第3期6卷 791-807页

作者： James, Stephy Renjith, V. R. Cochin Univ Sci & Technol CUSAT Sch Engn Div Safety & Fire Engn Kochi Kerala India

Liquefied natural gas (LNG) consumption is continuously rising in both developed and developing countries. Due to relatively cheap pricing and a large gas supply globally, most energy experts believe that major increases in LNG consumption are projected in the next decades. Many novel installations have been designed and are now implemented all over the globe in order to expand the usage of this resource. LNG is more prone to leakage when loading and unloading, thus putting the surrounding areas at greater risk of damage. Therefore, it is important to continue to improve current risk assessment strategies for dealing with safety. The objective of this work is to improve security by detecting failure patterns and minimizing problems. In this paper proposed a scheme to hybrid technique is combination of golden eagle optimizer (GEO) with tunicate swarm algorithm (TSA) and adaptive neuro fuzzy inference system (ANFIS) technique. This hybrid method is used because GEO-TSA detects leaks more accurately compared to other methods. The suggested hybrid technique-based risk assessment model may provide a fresh viewpoint on identifying leaks, hazards, and dangers, as well as assessing the development of LNG accidents from cause to effect. Furthermore, the suggested hybrid approach improves protection by detecting failure patterns and reducing issues. Finally, ANFIS is used as a reliable method for identifying risk, determining SIL rates and designing the safety zone. Predicting risk in overwork requires data on previous problems but not all data are available as all data vary from place to place. So the purpose of this job is to predict the risks from scratch without accurate data and not only that but also to anticipate the risks. This will reduce the risk of loss of life and property during periods of danger. In the proposed method is the implementation MATLAB/Simulink platform. The proposed method is comparable to methods such as particle swarm optimization (PSO) with fuzzy

关键词： Liquid natural gas SIL Risk factor Golden eagle optimizer tunicate swarm algorithm ANFIS

来源：评论

学校读者我要写书评

暂无评论

Adaptive channel equalisation using different hybrid metaheuristic algorithms in digital communication

引用

INTERNATIONAL JOURNAL OF AUTONOMOUS AND ADAPTIVE COMMUNICATIONS SYSTEMS 2024年第4期17卷 287-317页

作者： Shwetha, N. Priyatham, Manoj Gangadhar, N. Dr Ambedkar Inst Technol Dept ECE Bengaluru Karnataka India APS Coll Engn Dept ECE Bengaluru Karnataka India Dr Ambedkar Inst Technol Dept Mech Engn Bengaluru Karnataka India

An adaptive channel equalisation concept is used to reduce the effects of inter-symbol interference (ISI) in digital communication. The equalisation process is considered an optimisation issue to minimise the mean square error (MSE) between the transmitted signal and the output of the equaliser. Therefore, metaheuristic algorithms are widely adopted to enhance the function of adaptive channel equalisers. In this paper, a bio-inspired emperor penguin optimisation (EPO) algorithm is hybridised with five different algorithms to optimise the finite impulse response (FIR) channel for reducing the effects of ISI. The main role of these algorithms is to optimise the weights or coefficients of the equaliser to reduce the effect of ISI. Finally, the performance of each algorithm in channel equalisation is assessed, and it is observed that EPO incorporated with both manta ray foraging and tunicate swarm algorithm has obtained relatively better equalisation results than other hybrid optimisation algorithms.

关键词： adaptive channel equalisation FIR filter MSE mean square error inter symbol interference digital communication TSA tunicate swarm algorithm EPO emperor penguin optimisation

来源：评论

学校读者我要写书评

暂无评论

Oppositional tunicate fuzzy C-means algorithm and logistic regression for intrusion detection on cloud

引用

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第4期34卷

作者： Kanimozhi, P. Victoire, T. Aruldoss Albert IFET Coll Engn Dept Comp Sci & Engn Villupuram 605108 Tamil Nadu India Anna Univ Dept Elect & Elect Engn Reg Campus Coimbatore Tamil Nadu India

Cloud computing is the most popular emerging computational model in information technology. The data communication security requirement is increased day by day with the development of cloud computing. Virtualization facilitates cloud computing architecture, which raises a few security concerns. In the previous year, many new research approaches were introduced, resulting in more sophisticated and time-consuming results, as well as less flexibility, a lower attack detection rate, and so on. In this article, we proposed a logistic regression-based oppositional tunicate fuzzy C-mean (LR-OTSFCM) methodology for the detection of cloud intrusion. The major contribution of this article is attack detection from the cloud environment. The proposed work is classified into four stages, namely, preprocessing, partitioning, cluster handling, and intrusion detection stages. In the first stage, we use the text data as the input;then, it is preprocessed. Second, the feature selections are performed via a logistic regression model. The combination of the oppositional tunicate swarm algorithm (OPTSA) and fuzzy C-mean clustering (FCM) model is to perform data clustering. Third, the cluster handling stage is handled by cluster expansion and integration. Fourth, the clustering result builds a profile of normal and abnormal behavior. The local optimum avoidance ability of OPTSA is tested using four benchmark problems, namely, composition functions, unimodal, multimodal, and fixed dimensions of multimodal functions with 20 benchmark functions. Ultimately, the proposed LR-OTSFCM method is compared with state-of-art approaches such as ANN, LR-HID, ML-IDS, and En-ABC. The proposed scheme effectiveness is carried out by using four datasets such as, namely, synthetic, NIB, CICID 2017, and CIDD datasets. The proposed method demonstrated a superior attack detection rate.

关键词： cloud computing fuzzy C-mean clustering intrusion detection tunicate swarm algorithm

来源：评论

学校读者我要写书评

暂无评论

Central tunicate swarm NFOPID-Based Load Frequency Control of the Egyptian Power System Considering New Uncontrolled Wind and Photovoltaic Farms

引用

ENERGIES 2021年第12期14卷 3604页

作者： Fayek, Hady H. Kotsampopoulos, Panos Heliopolis Univ Fac Engn Electromech Engn Dept Cairo 11785 Egypt Natl Tech Univ Athens Sch Elect & Comp Engn Athens 15780 Greece

This paper presents load frequency control of the 2021 Egyptian power system, which consists of multi-source electrical power generation, namely, a gas and steam combined cycle, and hydro, wind and photovoltaic power stations. The simulation model includes five generating units considering physical constraints such as generation rate constraints (GRC) and the speed governor dead band. It is assumed that a centralized controller is located at the national control center to regulate the frequency of the grid. Four controllers are applied in this research: PID, fractional-order PID (FOPID), non-linear PID (NPID) and non-linear fractional-order PID (NFOPID), to control the system frequency. The design of each controller is conducted based on the novel tunicate swarm algorithm at each operating condition. The novel method is compared to other widely used optimization techniques. The results show that the tunicate swarm NFOPID controller leads the Egyptian power system to a better performance than the other control schemes. This research also presents a comparison between four methods to self-tune the NFOPID controller at each operating condition.

关键词： tunicate swarm algorithm NFOPID controller centralized control load frequency control Egyptian power system neural network and self-tuning

来源：评论

学校读者我要写书评

暂无评论

Stochastic Bayesian approach and CTSA based rainfall prediction in Indian states

引用

MODELING EARTH SYSTEMS AND ENVIRONMENT 2024年第3期10卷 3219-3228页

作者： Lathika, P. Singh, D. Sheeba Noorul Islam Ctr Higher Educ Dept Math Thuckalay Tamil Nadu India

Recently the advancement of technology provided numerous ways for predicting the variations of weather from a specific location. In the agricultural field, the success and failure of crop harvesting mainly depend on the amount of rainfall. However, if excess rainfall flows it obtains a challenging role and is not able to predict the accurate rate of rainfall. Elsewhere the early rainfall prediction helped to balance the economic conditions in an agriculture-dominated country like India. Although there have been significant advancements in weather and climate adaptation in recent decades, traditional methods for rainfall prediction remain computationally expensive and complex due to high uncertainty and variability in weather patterns. So to perform an accurate as well as early rainfall prediction the stochastic Bayesian method is proposed that helped to predict the rainfall employed in Indian states such as Uttar Pradesh, Assam, Jharkhand, Tamil Nadu, Andhra Pradesh, etc. Also, the exploration stage is enhanced by tunicate swarm optimization (TSA). The outcomes demonstrate that the scalable stochastic Bayesian approach method was more useful than the existing methods and the accuracy of the rainfall prediction is enhanced by utilizing the crossover-based tunicate swarm algorithm (CTSA). The proposed model is compared in times of MER (%), RMSE (mm), MAPE (%), and MAE (mm). The presented stochastic Bayesian with CTSA achieves 16.23 MER, 4.45 MAPE, 17.96 RMSE, and 13.78 MAE. According to the outcomes, the CTSA algorithm has lower training loss and it proves that the suggested method stochastic Bayesian with CTSA predicts rainfall efficiently.

关键词： Rainfall prediction Bayesian approach tunicate swarm algorithm Crossover optimization Mean absolute error

来源：评论

学校读者我要写书评

暂无评论

Sentiment Data Analysis for Detecting Social Sense after COVID-19 using Hybrid Optimization Method

引用

SN Computer Science 2023年第5期4卷 1-16页

作者： Seth, Rakhi Sharaff, Aakanksha Department of Computer Science and Engineering National Institute of Technology Raipur Chhattisgarh Raipur India

Sentiment analysis is the most effective way to understand opinions presented in digital media. Social sense is the concept of understanding every user's emotions connected through an online social media platform. This emotion helps to understand the mental health of people. Every stage of extracting social impact contains several problems with several methods associated with it, like lexicon-based techniques, which were proposed, developed, and evaluated but sometimes had poor accuracy. Machine Learning (ML) is useful to recognize patterns, in various aspects of sentiment analysis and it will best suited for this traditional algorithm where the large dataset is chunked into the smaller dataset to train the model. NLP methods do not account for different domains when modeling sentiment information. In a classification dilemma, reducing the feature set size diminishes the algorithm's time demand while improving the method's accuracy to obtain the optimal features. Scalability refers to handling large amounts of data and performing several computations like time and cost efficiently. This paper's perspective is to overcome the shortcomings of each system, and this article proposes a hybrid approach to sentiment analysis. Data has been collected from Twitter via the Twitter API. This research investigates the effects of negations, URLs, usernames, punctuation, repeated character normalization, and hashtags on sentiment classification output using an n-gram representation model, which is a kind of probabilistic language model and also integrates a cross-domain sentiment-aware learning model that can collect both sentiment awareness and domain validity of a word simultaneously to solve the problem of words from various domains. To fix the scalability problem, the article offers the novel tunicate swarm algorithm (TSA), which increases scalability and reduces computation time. Meanwhile, this paper custom a hybrid Harris Hawks Optimization (HHO) algorithm based on simulat

关键词： ANN classification Butterfly optimization algorithm Feature selection N-gram representation ROC curve Sentiment analysis Simulated annealing tunicate swarm algorithm Twitter Word embedding

来源：评论

学校读者我要写书评

暂无评论

Multi-objective Grasshopper Optimisation algorithm applied to Design DNA Sequences 15

Multi-objective Grasshopper Optimisation Algorithm applied t...

引用

15th International Conference on Advanced Computational Intelligence (ICACI)

作者： Guo, Huaiyu Feng, Zhiyu Wang, Xiao Zhejiang Normal Univ Sch Comp Sci & Math Jinhua 321000 Zhejiang Peoples R China Zhejiang Normal Univ Xingzhi Coll Jinhua 321004 Zhejiang Peoples R China

ISBN: (纸本)9798350321456

High stable and high reliable DNA sequences are requisite in DNA computing search field. DNA sequences can be quantified in an array of thermodynamical and logical constraints which play an important role in process of hybridization. Since designing of DNA sequences set regraded as a multi objective optimization problem due to these constraints, epsilon constraint introduced in this paper which is a thought of reduce object dimensions. These constraints can be classified in single squence constraints and multiple sequences constraints, two meta heuristic algorithms tunicate swarm algorithm and grasshopper optimisation algorithm used in different steps according to their features and applicable scenarios. Result of this progress shows that epsilon constraints collaborate with other algorithms may produce advanced DNA sequences set compare to previous work.

关键词： DNA sequence design DNA computing tunicate swarm algorithm grasshopper optimisation algorithm epsilon constraint

来源：评论

学校读者我要写书评

暂无评论

Assessment of tunnel blasting-induced overbreak: A novel metaheuristic-based random forest approach

引用

TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY 2023年第1期133卷

作者： He, Biao Armaghani, Danial Jahed Lai, Sai Hin Univ Malaya Fac Engn Dept Civil Engn Kuala Lumpur 50603 Malaysia Univ Technol Sydney Sch Civil & Environm Engn Sydney NSW 2007 Australia

Overbreak is a detrimental phenomenon caused by tunnel blasting, which can lead to increased time and cost in the construction schedule. It is very important to establish a model that can accurately predict the overbreak caused by tunnel blasting. To achieve this goal, the random forest (RF) is an ensemble machine learning model optimised by three metaheuristic algorithms to predicted overbreak, i.e. the grey wolf optimiser (GWO), the whale optimisation algorithm (WOA), and the tunicate swarm algorithm (TSA). The primary roles of GWO, WOA, and TSA are to search for the optimal hyper-parameters of the RF model in the solution space. To create the models above, 523 data samples were taken from a highway tunnel in China. The established database comprised seven predictors or inputs, including the number of holes, hole depth, total charge, advance length, rock mass rating, tunnel cross-sectional area, and powder factor. Three hybrid RF-based models (RF-GWO, RF-WOA, and RF-TSA) were constructed to predict overbreak. Subsequently, the performance levels of the developed hybrid models were evaluated according to four indices: the coefficient of determination, the root mean square error, the variance accounted for, and the A-20 index. The results showed that the TSA optimisation algorithm was better than the other two algorithms (WOA and GWO) at finding the best hyper-parameters for the RF model. Moreover, the results of comparative analysis with the single RF model confirmed that the proposed RF-TSA model is a strong solution with high accuracy for tackling the overbreak issues. The results of this study showed that the developed models can provide more accurate overbreak values compared to the intelligent techniques available in the literature;they can be used in practice and similar projects.

关键词： Tunnel blasting Overbreak random forest Metaheuristic algorithms tunicate swarm algorithm

来源：评论

学校读者我要写书评

暂无评论

Dynamic hyperparameter tuning-based path tracking control for robotic rollers working on earth-rock dam under complex construction conditions

引用

AUTOMATION IN CONSTRUCTION 2022年第0期143卷

作者： Guan, Shiwei Wang, Jiajun Wang, Xiaoling Shi, Mengnan Lin, Weiwei Chen, Wenlong State Key Lab Hydraul Engn Simulat & Safety Tianjin 300072 Peoples R China

Robotic roller technology is important for ensuring the compaction quality and construction efficiency of earth -rock dams, and its key challenge is related to path tracking control. The current easy-to-use and effective pro-portion-integration-differentiation (PID) path tracking controller requires time-consuming and labor-intensive hyperparameter tuning, making its application in complex construction environments with a set of fixed hyperparameters, such as transition, misalignment, and position drift, difficult. This paper describes the TSABFA-PID path tracking controller based on hybrid tunicate swarm algorithm (TSA) and bacterial foraging algorithm (BFA), and modified kinematic model. The proposed controller can dynamically self-tune hyperparameters to adapt to complex construction condition by optimizing the objective function. The proposed controller achieved better results in both simulation environment and on-site tests than state-of-the-art methods. In the future, the deep reinforcement learning and human-computer interaction techniques will be integrated into the proposed controller to improve the compaction performance.

关键词： Path tracking control Robotic roller PID controller tunicate swarm algorithm Bacterial foraging algorithm Earth -rock dam

来源：评论

学校读者我要写书评

暂无评论

Optimized hyperbolic tangent function-based contrast-enhanced mammograms for breast mass detection

引用

EXPERT SYSTEMS WITH APPLICATIONS 2023年第PartB期213卷

作者： Laishram, Romesh Rabidas, Rinku Manipur Inst Technol Manipur 795004 India Assam Univ Silchar 788011 Assam India

Breast cancer is a grave concern among women due to its high mortality rate in women as compared to that in men. Mass, an early symptom of breast cancer, is difficult to detect due to its subtle nature. Mammography, an effective and most preferred screening technique, generally uses low-contrast images where identification of some of the mass lesions from surrounding normal tissues is a difficult task even for skilled radiologists. Therefore, to alleviate the issue, this paper introduces an effective computer-aided detection scheme based on a novel contrast enhancement scheme for mammograms to automate the localization procedure of mass cases. Hyperbolic tangent function, a simple yet effective method, is introduced as an enhancement transfer function whose adjustable parameter is optimized by the tunicate swarm algorithm, a nature-inspired optimization algorithm, via fitness function. To minimize the false positives generated after the detection, the median robust extended local binary pattern, a texture-based descriptor, is introduced, which possesses the ability to capture both micro-and macro-structure information from an image patch. The analysis includes four classifiers: Fisher linear discriminant analysis, K-nearest neighbor, support vector machine, and an ensemble classifier in the feature-based classification for false positive reduction (FPR). The proposed approach of automatic detection of masses when verified using two standard databases, mini-MIAS and Digital Database for Screening Mammography, comprised 68 and 550 mammograms, respectively, achieved sensitivities of 95.3% and 94.1% with 0.36 and 0.40 false positives per image after false positive reduction.

关键词： Breast mass detection Mammogram enhancement Hyperbolic tangent function tunicate swarm algorithm MRELBP

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：