检索结果-内蒙古大学图书馆

Autonomous plume Near-Source search assisted by intermittent visible plume information using finite state Machine and YOLOv3-tiny

引用

EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期228卷

作者： Ma, Shengshan Yuan, Jie Guo, Zhenyu Wu, Qiong Xinjiang Univ Sch Elect Engn Urumqi Peoples R China

Since a plume's visual characteristics have not been fully considered and utilized, the traditional plume nearsource search methods lose a large amount of potentially available information, resulting in the low efficiency and reliability of near-source search. This paper proposes a novel plume near-source search approach assisted by the intermittent visible plume information using finite state machine and YOLOv3-tiny for a robot. A finite state machine is used to realize the autonomous behavioral decision-making of the robot according to the obtained visible plume information, olfactory information, and lidar information. In addition, the YOLOv3-tiny network is used to detect the intermittent visible plume information obtained by a depth camera to capture the color and texture of the plume. In order to improve the search reliability of the robot in the olfactory tracking behavior, a whale optimization algorithm is utilized to enable the robot to simulate the hunting behavior and the social mechanism of the whale group and conduct olfactory tracking of the plume. The experimental results show that this proposed plume near-source search approach achieves the success rates of 91.67% and 83.33% in windless and windy environments respectively, and the average total running time is 113.8 s and 159.6 s in the experimental environment with obstacles, respectively.

关键词： Intermittent Visible Plume Information YOLOv3-tiny Finite State Machine whale optimization algorithm Near-Source Search

来源：评论

学校读者我要写书评

暂无评论

An efficient multi-threshold image segmentation for skin cancer using boosting whale optimizer

引用

COMPUTERS IN BIOLOGY AND MEDICINE 2022年第Pt A期151卷 106227-106227页

作者： Zhu, Wei Liu, Lei Kuang, Fangjun Li, Lingzhi Xu, Suling Liang, Yingqi Cent South Univ Sch Resources & Safety Engn Changsha 410083 Peoples R China Sichuan Univ Coll Comp Sci Chengdu 610065 Sichuan Peoples R China Wenzhou Business Coll Sch Informat Engn Wenzhou 325035 Peoples R China Ningbo Univ Affiliated Hosp Med Sch Dept Dermatol Ningbo 315020 Zhejiang Peoples R China Wenzhou Med Univ Wenzhou 325035 Zhejiang Peoples R China

Due to the terrible manifestations of skin cancer, it seriously disturbs the quality of life status and health of patients, so we needs treatment plans to detect it early and avoid it causing more harm to patients. Medical disease image threshold segmentation technique can well extract the region of interest and effectively assist in disease recognition. Moreover, in multi-threshold image segmentation, the selection of the threshold set de-termines the image segmentation quality. Among the common threshold selection methods, the selection based on metaheuristic algorithm has the advantages of simplicity, easy implementation and avoidable local optimi-zation. However, different algorithms have different performances for different medical disease images. For example, the whale optimization algorithm (WOA) does not give a satisfactory performance for thresholding skin cancer images. We propose an improved WOA (LCWOA) in which the Levy operator and chaotic random mutation strategy are introduced to enhance the ability of the algorithm to jump out of the local optimum and to explore the search space. Comparing with different existing WOA variants on the CEC2014 function set, our proposed and improved algorithm improves the efficiency of the search. Experimental results show that our method outperforms the extant WOA variants in terms of optimization performances, improving the convergence accuracy and velocity. The method is also applied to solve the threshold selection in the skin cancer image segmentation problem, and LCWOA also gives excellent performance in obtaining optimal segmentation results.

关键词： Image segmentation Skin cancer whale optimization algorithm Levy operator Chaotic random mutation strategy Kapur ?s entropy

来源：评论

学校读者我要写书评

暂无评论

A conformable fractional-order grey Bernoulli model with optimized parameters and its application in forecasting Chongqing's energy consumption

引用

EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartB期255卷

作者： Wu, Wenqing Ma, Xin Zeng, Bo Zhang, Yuanyuan Civil Aviat Flight Univ China Sch Sci Guanghan 618307 Peoples R China Southwest Univ Sci & Technol Sch Math & Phys Mianyang 621010 Peoples R China Chongqing Technol & Business Univ Coll Business Planning Chongqing 400067 Peoples R China

Chongqing is the only province and city with net energy input in southwest China, and its energy security has been in a tight balance for a long time. To propose scientific and reasonable energy policies for Chongqing under the "dual carbon"goal, this paper studies Chongqing's total energy consumption, coal consumption, natural gas consumption, and oil consumption by a conformable fractional -order grey Bernoulli model with optimized parameters. The new model is studied with its linearized form, the conformable fractional -order accumulation operation, the grey modelling technique, the least squares method, and the whale optimization algorithm. According to historical data, several forecasting models are built. From calculation results, the mean absolute validation percentage errors of the new model are smaller than those of other models in the total energy consumption, coal consumption and natural gas consumption, with values of 1.31%, 1.43%, and 2.41%, respectively. In the oil consumption, the MAPE valid is 3.85%, which is the second smallest among these models. The results illustrate that the new model can provide competitive results. Further, the values in the next five years are calculated.

关键词： Grey Bernoulli model Optimized parameters Linearized form whale optimization algorithm Energy consumption of Chongqing

来源：评论

学校读者我要写书评

暂无评论

A hybrid model for VNF deployment capable of responding to online requests at network edge

引用

COMPUTER NETWORKS 2024年 247卷

作者： Zahedi, Seyed Reza Jamali, Shahram Islamic Azad Univ Dept Comp Engn Khalkhal Branch Khalkal Iran Univ Mohaghegh Ardabili Dept Comp Engn Ardebil Iran

Network function virtualization (NFV) is a new network architecture that replaces dedicated hardware appliances with software instances and run them via software virtualization on general-purpose servers at edge clouds. As edge clouds are resource-constrained, optimal placement of virtual network functions (VNFs) is a challenging task. This problem has proven to be NP-hard, and thus, metaheuristic algorithms have been widely used to solve it. However, these techniques suffer from high computational complexity due to their iterative, time-consuming process and cannot be performed in real-time for online requests. This paper proposes a hybrid model based on heuristic algorithms and the whale optimization algorithm (WOA) to optimally assign VNFs to physical servers at edge clouds. In this method, a multi-criteria heuristic model is used for online VNF deployment, while WOA is performed offline to tune the hyperparameters of the heuristic model. Simulation results demonstrate the efficiency of the proposed method against existing techniques in terms of power consumption and acceptance rate.

关键词： Virtual network functions Edge computing Placement Real-time requests whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

A hybrid RBF neural network based model for day-ahead prediction of photovoltaic plant power output

引用

FRONTIERS IN ENERGY RESEARCH 2024年 11卷

作者： Zhang, Qipei Tang, Ningkai Lu, Jixiang Wang, Wei Wu, Lin Kuang, Wenteng NARI Technol Co Ltd NARI Res Inst Nanjing Jiangsu Peoples R China

Renewable energy resources like solar power contribute greatly to decreasing emissions of carbon dioxide and substituting generators fueled by fossil fuels. Due to the unpredictable and intermittent nature of solar power production as a result of solar radiance and other weather conditions, it is very difficult to integrate solar power into conventional power systems operation economically in a reliable manner, which would emphasize demand for accurate prediction techniques. The study proposes and applies a revised radial basis function neural network (RBFNN) scheme to predict the short-term power output of photovoltaic plant in a day-ahead prediction manner. In the proposed method, the linear as well as non-linear variables in the RBFNN scheme are efficiently trained using the whale optimization algorithm to speed the convergence of prediction results. A nonlinear benchmark function has also been used to validate the suggested scheme, which was also used in predicting the power output of solar energy for a well-designed experiment. A comparison study case generating different outcomes shows that the suggested approach could provide a higher level of prediction precision than other methods in similar scenarios, which suggests the proposed method can be used as a more suitable tool to deal such solar energy forecasting issues.

关键词： photovoltaic power plant solar energy forecasting radial basis function neural network whale optimization algorithm low carbon

来源：评论

学校读者我要写书评

暂无评论

Strengthening energy transition through effective urban electric demand prediction: A multidisciplinary exploration of AI, policy, and administrative strategies

引用

SUSTAINABLE CITIES AND SOCIETY 2024年 107卷

作者： Chang, Qingyu Wu, Jiang Zhang, Yang Hunan Int Econ Univ Business Sch Changsha 410205 Peoples R China Changsha Univ Sch Econ & Management Changsha 410022 Peoples R China Univ Technol & Business Collaborat Innovat Ctr Anticorruput Changsha 410205 Peoples R China

This research endeavors to strengthen energy transition in urban management through the enhanced accuracy of electric load demand forecasting within a multi-faceted approach considering legal and policy decision making associate with the proposed problem. Firstly, a prediction model is devised utilizing an evolving Peephole LSTM (PLSTM). This sophisticated architecture incorporates peephole connections, allowing for enhanced information flow within the network. Subsequently, a hybrid evolutionary algorithm is introduced for fine-tuning the parameters of the peephole LSTM (PLSTM). This hybrid approach amalgamates the whale optimization algorithm (WOA) and cultural algorithm (CA), harnessing their complementary strengths for optimal parameter configuration. The study extends beyond model development to address the practical implications of real-time electric load forecasting. A comprehensive stability analysis is conducted on datasets sourced from an urban area, offering insights into the model's reliability and performance under dynamic conditions. This tripartite investigation not only contributes to the advancement of predictive modeling in electric load demand forecasting but also underscores the significance of robust parameter optimization and stability assessments for real-world applicability in urban energy management. The simulation results on the practical dataset clearly show the high robustness and capabilities of the proposed model. By combining cutting-edge model architecture, hybrid evolutionary optimization, and thorough stability assessments, this study not only contributes to the forefront of predictive modeling in electric load forecasting but also emphasizes the critical importance of parameter optimization and stability evaluations for the seamless integration of such models into the dynamic landscape of urban energy management.

关键词： Electric load forecasting Peephole LSTM Hybrid evolutionary algorithm whale optimization algorithm Cultural algorithm Energy law and policy analysis

来源：评论

学校读者我要写书评

暂无评论

Joint Task Offloading and Resource Management in NOMA-Based MEC Systems: A Swarm Intelligence Approach

引用

IEEE ACCESS 2020年 8卷 190463-190474页

作者： Pham, Huong-Giang T. Quoc-Viet Pham Pham, Anh T. Nguyen, Chuyen T. Hanoi Univ Sci & Technol Sch Elect & Telecommun Hanoi 100000 Vietnam Pusan Natl Univ Res Inst Comp Informat & Commun Busan 46241 South Korea Univ Aizu Comp Commun Lab Aizu Wakamatsu Fukushima 9658580 Japan

In this paper, we study the issue of computation offloading in non-orthogonal multiple access (NOMA)-based multi-access edge computing (MEC) systems. A joint optimization problem of offloading decision, subchannel assignment, transmit power, and computing resource allocation is investigated to improve system performance in terms of both completion time and energy consumption. The formulated problem is a mixed-integer non-linear programming one, it is therefore hard to solve. To make the problem tractable, we first decompose the problem into subproblems of computing resource allocation (CRA), transmit power control (TPC), and subchannel assignment (SA). Then, we address the CRA subproblem by a convex optimization technique. For the remaining two subproblems TPC and SA, we propose to use a gradient-free swarm intelligence approach, namely whale optimization algorithm, to provide a very general but efficient solution. Computer simulations are performed to show the convergence of the proposed algorithm, also its better performance in comparison with conventional schemes.

关键词： Task analysis optimization Particle swarm optimization NOMA Servers Resource management Energy consumption Computation offloading swarm intelligence multi-access edge computing non-orthogonal multiple access resource management whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

A hybrid method of cooling load forecasting for large commercial building based on extreme learning machine

引用

ENERGY 2022年第PartC期238卷 122073-122073页

作者： Gao, Zhikun Yu, Junqi Zhao, Anjun Hu, Qun Yang, Siyuan Xian Univ Architecture & Technol Xian Shaanxi Peoples R China

Air conditioning system is extensively used in large commercial buildings. The fast and accurate building cooling load forecasting is the basis for improving the operation efficiency of the air conditioning system, which is conducive to implement the effective management of the air conditioning system. Therefore, a hybrid prediction model based on random forest-improvement parallel whale optimizing-extreme learning machine neural network (RF-IPWOA-ELM) is proposed to predict the cooling load of large commercial buildings. First, the influence of different parameters on the cooling load is analyzed, and the random forest (RF) method is used to extract the parameters with high degree of influence as the input variables of prediction model. Then, the extreme learning machine (ELM) optimized by the improved parallel whale optimization algorithm (IPWOA) is established to predict. Finally, a simulation experiment is carried out using measured data of two large commercial buildings in north of China. The experimental results show that the root mean square error (RMSE) and mean average percentage error (MAPE) of RFIPWOA-ELM predicting the cooling load for these two buildings are 2.8735, 0.2% and 4.7721, 0.45%, respectively. Compared with the other prediction model, the RMSE and MAPE of this model are reduced by 66.17%-90.62%, 81.48%-95.79% and 71.91%-84.40%, 74.14%-86.15%, respectively, which has higher prediction accuracy. Simultaneously, for different prediction models, RF-IPWOA-ELM has a shorter prediction time, which presents superiority in time complexity. And when there are few training samples, RF-IPWOA-ELM can still effectively predict the cooling load in different months, indicating that it possesses strong generalization ability. Therefore, the proposed hybrid model can be used as a reliable tool for cooling load prediction in the energy conservation of air conditioning system and energy management. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Large commercial building cooling load Random forest whale optimization algorithm Extreme learning machine Prediction accuracy

来源：评论

学校读者我要写书评

暂无评论

Advanced modeling based on machine learning for evaluation of drug nanoparticle preparation via green technology: Theoretical assessment of solubility variations

引用

CASE STUDIES IN THERMAL ENGINEERING 2023年 45卷

作者： Begum, M. Yasmin King Khalid Univ KKU Coll Pharm Dept Pharmaceut Abha 62529 Saudi Arabia

Variability of drug solubility in supercritical solvent was investigated theoretically in this study using multiple machine learning models. The method of supercritical preparation can be used to manufacture solid-dosage drugs with enhanced solubility which can increase drug bioavailability and efficacy. In this study, we applied bagging regression to three core models, namely SVM, KNN, and linear regression (LR) to predict the solubility and density of Hyoscine drug based on Temperature and Pressure parameters as inputs. The WOA optimization algorithm was used to tune hyperparameters, and the performance of these models was compared with r2, RMSE, MAE, and AARD% metrics. The results revealed that the BAG-KNN model outperformed the others in predicting solubility, with an r2 score of 0.989 and a low RMSE of 0.041. Also, the BAG-LR model had the highest AARD% value of 7.50974, indicating that it has higher average relative deviation from the actual density values. Overall, the bagging approach proved to be effective in improving the accuracy of the core models, and the WOA optimization algorithm was successful in finetuning their hyperparameters. These findings can have significant implications for the accurate prediction of chemical properties in various fields, such as drug discovery and material science.

关键词： Nanomedicine Modeling Pharmaceutical manufacture Drug efficacy Machine learning whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Novel Mathematical Design of Triple-Tuned Filters for Harmonics Distortion Mitigation

引用

ENERGIES 2023年第1期16卷 39页

作者： Maher, Mohamed Abdel Aleem, Shady H. E. Ibrahim, Ahmed M. M. El-Shahat, Adel Cairo Univ Fac Engn Elect Power & Machines Dept Cairo 12613 Egypt Sci Valley Acad Valley High Inst Engn & Technol Dept Elect Engn Qalyubia 44971 Egypt Purdue Univ Sch Engn Technol Energy Technol Program W Lafayette IN 47907 USA

The design of AC filters must meet the criteria of harmonic distortion mitigation and reactive power support in various operating modes. The stringent reactive power-sharing requirements currently lead to sophisticated filter schemes with high component ratings. In this regard, triple-tuned filters (TTFs) have good potential in harmonic mitigation of a broad range of harmonics. In the literature, the TTF design has been presented using a parametric method, assuming that the TTF is equivalent to a three-arm single-tuned filter (TASTF). However, no direct methods of designing it or finding its optimal parameters have been provided. This paper presents novel mathematical designs of TTFs. Three different design methods are considered-the direct triple-tuned filter (DTTF) design method, as a TASTF, and a method based on the equivalence between the two design methods called the equivalence hypothesis method to design the triple-tuned filter (EHF). The parameters of the three proposed design methods are optimized based on the minimization of a proposed multi-objective function using a recent metaheuristic algorithm called artificial rabbits optimization (ARO) to mitigate harmonics, improve power quality, and minimize power losses in an exemplary system presented in IEEE STD-519. Further, the system's performance has been compared to the system optimized by the ant lion optimizer (ALO) and whale optimization algorithm (WOA) to validate the effectiveness of the proposed design. Simulation results emphasized harmonics mitigation in the system, the system losses reduction, and power quality improvement with lower reactive power filter ratings than conventional single and double-tuned filters.

关键词： triple-tuned filter passive filter power quality harmonics optimization artificial rabbits optimization ant lion optimizer whale optimization algorithm

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：