检索结果-内蒙古大学图书馆

Doctor-Patient Combined Matching Problem and Its Solving algorithms

IEEE ACCESS 2019年 7卷 177723-177733页

作者： Yu, Jing Xing, Lining Tan, Xu Ren, Teng Li, Zhenping Natl Univ Def Technol Coll Syst Engn Changsha 400073 Peoples R China Shenzhen Inst Informat Technol Sch Software Engn Shenzhen 518172 Peoples R China Cent South Univ Forestry & Technol Sch Transportat & Logist Changsha 410018 Peoples R China Beijing Wuzi Univ Sch Informat Beijing 101149 Peoples R China

"Difficult to see a doctor" and prominent contradictions between doctors and patients in large public hospitals have become an urgent research topic in China. In this paper, we propose a multi-patient treatment mode (MTM) to improve medical efficiency and patient satisfaction. Based on the MTM, a problem named the doctor-patient combined matching problem (DPCMP) is proposed and can be described by a two-stage process: (1) The patients are grouped according to similar disease symptoms. (2) How to capture an optimal matching scheme form the grouped patients? Therefore, to solve the aforementioned problem, a mathematical model of the DPCMP is constructed, and several improved ant colony optimization algorithms are designed. Finally, certain examples verify the effectiveness and good performances of the proposed methods.

关键词： Doctor-patient contradictions patient satisfaction multi-patient treatment mode doctor-patient combined matching problem medical efficiency ant colony optimization algorithm local search operator

来源：评论

学校读者我要写书评

暂无评论

Research on satellite laser ranging observation task scheduling method

引用

MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第4期35卷 046002-046002页

作者： Lv, Zequn An, Ning Fan, Cunbo Liu, Chengzhi Li, Zhenwei Gao, Jian Wen, Guanyu Dong, Xue Chinese Acad Sci Changchun Observ Natl Astron Observ Changchun 130117 Peoples R China Univ Chinese Acad Sci Shenyang 100049 Peoples R China Chinese Acad Sci Changchun Branch Changchun 130117 Peoples R China

Satellite laser ranging (SLR) is a technology with the highest precision of single measurement of satellite radial distance, which is developing rapidly in the direction of long-distance, high-precision and automation. SLR autonomous observation task scheduling is an important step in realizing station automation, which needs to satisfy the principles of satellite tracking priority and maximization of observation revenue at the same time. In order to improve the automation and intelligence level of SLR system, based on the framework of ant colony optimization (ACO) algorithm, this paper combines the dynamic optimization characteristics of ACO algorithm and the local optimization characteristics of greedy algorithm, introduces the maximum-minimum ant mechanism, and puts forward a scheduling scheme for SLR observation task based on greedy ant colony algorithm (GACA). The results show that compared to the current scheduling methods applied in practice. The results show that compared with the current scheduling method applied in practice, the number of observation satellites obtained from the GACA algorithm-based observation task planning for the SLR system has been improved by 37.4%, the total arc segment of satellite observation with higher priority has been extended by 36.47%, and the total observation gain has been increased by 42.39% in the same period of time. It effectively solves the problems of low efficiency, easy to miss stars and less stars in the observation process in manual scheduling, and provides a simple, practical, efficient and convenient observation task planning scheme for the establishment of an unmanned SLR system.

关键词： satellite laser ranging observation task scheduling ant colony optimization algorithm automated ranging system

来源：评论

学校读者我要写书评

暂无评论

Cluster-based data transmission scheme in wireless sensor networks using black hole and ant colony algorithms

引用

INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2021年第9期34卷 e4768-e4768页

作者： Sefati, Seyedsalar Abdi, Mehrdad Ghaffari, Ali Islamic Azad Univ Tabriz Branch Dept Comp Engn Tabriz Iran

Since sensor nodes use limited battery power, sensor nodes power is considered to be a challenge and fundamental issue in wireless networks. Wireless sensor networks (WSNs) have recently used clustering-based methods and different routing protocols in clusters which are full of sensor nodes. In this way, cluster heads (CHs), which are regarded as the selected nodes among other nodes within a given cluster, accumulate the information transmitted from their own cluster and send it to the sink. In this method, they try to control power consumption in a balanced way. In clustering method and routing among network nodes, network lifetime is enhanced which leads to achieving the best efficiency and productivity. In this paper, the optimized black hole algorithm is employed to select an optimal CH from sensor nodes. The CH selection is optimized by the free buffer of nodes, residual energy, and distance. The path between source CH and sink is identified by using ant colony optimization (ACO) algorithm. The combination of black hole algorithm and ant colony algorithm, with respect to clustering and routing, leads to the optimization of the proposed method in terms of operational criteria such as power consumption and eventually lifetime enhancement. The results obtained from simulating the proposed method in Matlab environment indicated that it has better results in comparison with other methods. The outputs of the obtained results from the proposed method indicated that it outperformed the other four comparative methods in terms of packet delivery rate and the number of transmitted packets to the CH and to the sink.

关键词： ant colony optimization algorithm black hole algorithm clustering routing wireless sensor network (WSNs)

来源：评论

学校读者我要写书评

暂无评论

Blockchain for consortium: A practical paradigm in agricultural supply chain system

引用

EXPERT SYSTEMS WITH APPLICATIONS 2021年 184卷 115425-115425页

作者： Kyzy, Indra Eluubek Song, Huaming Vajdi, Ahmadreza Wang, Yongli Zhou, Junlong Nanjing Univ Sci & Technol Sch Econ & Management Nanjing 210094 Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China

Trading aspect of agricultural supply chain system is sophisticated since it consists of many stages and involves various entities/agencies. Recently, blockchain technology could prove its effectiveness to solve some of the concerns in agricultural supply chain systems. Nevertheless, maximizing profit for producers (in our study farmers) is another important concern that can be addressed by consortium establishment which blockchain technology is the best solution for this purpose due to the following reasons. First, since all the nodes in the blockchain keep the verified and synchronized version of the chain, each node can verify the transactions' transparency. Second, blockchain technology is temper-proof that means no one can change the history of the transactions. These two main features of blockchain technology can provide a suitable ground to construct a consortium among the producers. However, there are other specific requirements that a successful consortium in agricultural supply chain system should address them that motivate us to a new design of blockchain technology. More precisely, in our design we consider the problems of trustability, scalability, and share amount assignment. For trustability, we utilize cyber physical system to ensure the quantity and quality of the products. Scalability is being addressed by adopting the concept of public service platform and proposing a new consensus algorithm. And finally share amount assignment is being solved by our improved version of ant colony optimization algorithm. Experimental results and analysis prove the effectiveness and accuracy of our proposed design for blockchain technology.

关键词： Blockchain ant colony optimization algorithm Agricultural supply chain system Cyber physical system

来源：评论

学校读者我要写书评

暂无评论

Route Planning of Helicopters Spraying Operations in Multiple Forest Areas

引用

FORESTS 2021年第12期12卷 1658页

作者： Fang, Shuping Ru, Yu Liu, Yangyang Hu, Chenming Chen, Xuyang Liu, Bin Nanjing Forestry Univ Coll Mech & Elect Engn Nanjing 210037 Jiangsu Peoples R China Anhui Sci & Technol Univ Coll Mech Engn Chuzhou 233100 Peoples R China Anhui Agr Univ Sch Engn Hefei 230036 Anhui Peoples R China

It is of great value to research the problem of forest pest and disease control. Currently, helicopters play an important role in dealing with this problem. However, the spraying route planning still depends on the pilot's driving experience, which leads to low efficiency and less accurate coverage. For this reason, this paper attempts to use intelligent algorithms to plan the pesticide spraying route for helicopters. When the helicopter is conducting spraying operations in multiple forest areas, the routes are divided into two parts: pesticide spraying routes for individual forest areas and dispatch routes between multiple forest areas. First, the shorter spraying route with fewer turnarounds for individual forest areas was determined. Then a two-layer intelligent algorithm, a combination of a genetic algorithm (GA) and ant colony optimization algorithm (ACO), was designed to determine the dispatch route between multiple forest areas, which is referred to as GAACO-GA. The performance was evaluated in self-created multiple forest areas and compared with other two-layer intelligent algorithms. The results show that the GAACO-GA algorithm found the shortest dispatch route (5032.75 m), which was 5.60%, 5.45%, 6.54%, and 4.07% shorter than that of GA-GA algorithm, simulated annealing-GA (SA-GA) algorithm, ACO-GA algorithm, and particle swarm optimization-GA (PSO-GA) algorithm, respectively. A spraying experiment with a helicopter was conducted near Pigzui Mountain, Huai'an City Jiangsu Province, China. It was found that the flight path obtained from the proposed algorithm was 5.43% shorter than that derived from a manual planning method. The dispatch route length was reduced by 16.93%, the number of turnarounds was reduced by 11 times, and the redundant coverage was reduced by 17.87%. Moreover, helicopter fuel consumption and pesticide consumption decreased by 10.56% and 5.43%, respectively. The proposed algorithm can shorten the application route, reduce the number of

关键词： route planning helicopter two-layer intelligent algorithm genetic algorithm ant colony optimization algorithm multiple forest areas spraying operations

来源：评论

学校读者我要写书评

暂无评论

A method for indoor Wi-Fi location based on improved back propagation neural network

引用

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2019年第4期27卷 2511-2525页

作者： Chen, Jinghui Dong, Chen He, Guorong Zhang, Xiaoyu Fuzhou Univ Coll Math & Comp Sci Key Lab Spatial Data Min & Informat Sharing Minist Educ Fuzhou Fujian Peoples R China Fujian Prov Key Lab Network Comp & Intelligent In Fuzhou Fujian Peoples R China Fujian Prov Key Lab Informat Secur Network Syst Fuzhou Fujian Peoples R China

In order to achieve high precision on indoor location, a Wi-Fi indoor location method based on improved back propagation (BP) neural network is proposed. The classical BP neural network is optimized in real time by the ant colony optimization algorithm. Meanwhile, the momentum term is introduced to construct an improved four-layer BP neural network model. The model uses the Wi-Fi signal feature as the input of the BP neural network and succeeds in the area classification under multiple Wi-Fi signal features. The experimental results demonstrate that the improved BP neural network can increase the classification accuracy of the classifier effectively, and achieve a high-precision indoor area location. Furthermore, it performs better practical results while ensuring the time complexity. The advantages of this method are high practicability, low cost, high prediction classification accuracy, and robust stability, which can achieve the efficient classification of the short-range area.

关键词： Indoor Wi-Fi location back propagation neural network ant colony optimization algorithm momentum term

来源：评论

学校读者我要写书评

暂无评论

A Two-stage Stochastic Programming for AGV scheduling with random tasks and battery swapping in automated container terminals

引用

TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2023年第1期174卷

作者： Li, Linman Li, Yuqing Liu, Ran Zhou, Yaoming Pan, Ershun Shanghai Jiao Tong Univ Dept Ind Engn & Management Shanghai 200240 Peoples R China

Automated guided vehicle (AGV) is one of the main equipment for horizontal transportation in automated container terminals, and the optimization of AGV scheduling has become increasingly important. Existing scheduling systems tend to make decisions based on deterministic conditions, ignoring the dynamic changes and uncertainties of the terminal environment, such as the arrival of random tasks during AGV operations. In addition, the battery swapping process is neglected in most AGV scheduling studies, yet it is crucial to ensure the operation of AGVs. In this paper, we construct a two-stage stochastic programming model for the joint scheduling problem of battery swapping and task operation with random tasks. A double-threshold constraint for battery swapping decision-making is adopted. The results show that the double-threshold strategy is better for AGV utilization than the single-threshold one. Upon the solution method, a simulationbased ant colony optimization algorithm is proposed. Sample average approximation is used to calculate the expected cost, and two local search procedures are introduced to improve the quality of the solutions. In the cases of multiple instances and several random task samples with different arrival rates, our method was compared with three practical policies under a deterministic model. Computational results show that the scheduling scheme considering random tasks in advance is more robust and stable.

关键词： Two-stage stochastic programming AGV scheduling Random tasks Battery swapping ant colony optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Prediction of the bond strength of FRP-to-concrete under direct tension by ACO-based ANFIS approach

引用

COMPOSITE STRUCTURES 2022年 282卷

作者： Pei, Zuan Wei, Yufeng Chengdu Univ Technol State Key Lab Geohazard Prevent & Geoenvironm Chengdu Peoples R China

In this study, a hybrid model integrating the ant colony optimization (ACO) algorithm and fuzzy c-means (FCM) clustering method into the adaptive neuro-fuzzy inference system (ANFIS) was proposed to predict the bond strength between fibre-reinforced polymer (FRP) sheets and concrete surface under direct tension. Eight parameters including the compressive strength of concrete, maximum aggregate size, tensile strength of FRP, thickness of FRP, elastic modulus of FRP, adhesive tensile strength, length of FRP and width of FRP are employed as the inputs, and the bond strength is used as the output variable. A comparison was conducted between some existing empirical models and the proposed hybrid ACO-based ANFIS model. The results confirmed that the developed ACO-based ANFIS model exhibits greater accuracy than the other eleven models, with higher coefficient of determination (R2 = 0.97) and Nash-Sutcliffe efficiency index (NS = 0.97), and lower root mean squared error (RMSE = 1.29 kN), mean absolute error (MAE = 0.81 kN) and mean absolute relative error (MARE = 0.053), while according to the Akaike information criterion (AIC) index, the accuracy of this model lies in its considerable complexity compared to others.

关键词： Fibre-reinforced polymer Adaptive neuro-fuzzy inference system ant colony optimization algorithm Fuzzy c-means clustering method

来源：评论

学校读者我要写书评

暂无评论

An adaptive population control framework for ACO-based community detection

引用

CHAOS SOLITONS & FRACTALS 2020年 138卷 109886-109886页

作者： Wang, Chunyu Zhang, Fan Deng, Yue Gao, Chao Li, Xianghua Wang, Zhen Southwest Univ Coll Comp & Informat Sci Chongqing 400715 Peoples R China Northwestern Polytech Univ Sch Mech Engn Xian 710072 Peoples R China Northwestern Polytech Univ Ctr OPTIMAL Xian 710072 Peoples R China

The community structure is one of the most important features of complex networks and has wide research and application prospects. To find the community structure, many researchers currently focus on natural heuristic methods, where an extraordinary swarm intelligence algorithm (i.e., the ant colony algorithm) is widely adopted to detect the potential community structures. However, the computational cost of such an algorithm is so high that it restricts the property and range of application. In this paper, we present a novel adaptive population control framework for ACO-based community discovery approaches to overcome the mentioned shortcomings. Specifically, this framework dynamically controls the number of ants based on the slope of the modularity and iterations. Such a framework is adopted in two different algorithms and we make corresponding comparison between this one and traditional ACO-based algorithms in six classical real networks and five synthetic datasets. Experiments show that ant colony algorithms with our proposed framework have evidently reduced time complexity and maintained the quality of community structure simultaneously. (c) 2020 Published by Elsevier Ltd.

关键词： Community detections ant colony optimization algorithm Adaptive population control framework

来源：评论

学校读者我要写书评

暂无评论

Towards Efficient Milling of Multi-Cavity Aeronautical Structural Parts Considering ACO-Based Optimal Tool Feed Position and Path

引用

MICROMACHINES 2021年第1期12卷 88-88页

作者： Xin, Yupeng Li, Yuanheng Li, Wenhui Wang, Gangfeng Taiyuan Univ Technol Coll Mech & Vehicle Engn Taiyuan 030024 Peoples R China Taiyuan Univ Technol Coll Aeronaut & Astronaut Taiyuan 030024 Peoples R China Changan Univ Sch Construct Machinery Key Lab Expressway Construct Machinery Shaanxi Pr Xian 710064 Peoples R China

Cavities are typical features in aeronautical structural parts and molds. For high-speed milling of multi-cavity parts, a reasonable processing sequence planning can significantly affect the machining accuracy and efficiency. This paper proposes an improved continuous peripheral milling method for multi-cavity based on ant colony optimization algorithm (ACO). Firstly, by analyzing the mathematical model of cavity corner milling process, the geometric center of the corner is selected as the initial tool feed position. Subsequently, the tool path is globally optimized through ant colony dissemination and pheromone perception for path solution of multi-cavity milling. With the advantages of ant colony parallel search and pheromone positive feedback, the searching efficiency of the global shortest processing path is effectively improved. Finally, the milling programming of an aeronautical structural part is taken as a sample to verify the effectiveness of the proposed methodology. Compared with zigzag milling and genetic algorithm (GA)-based peripheral milling modes in the computer aided manufacturing (CAM) software, the results show that the ACO-based methodology can shorten the milling time of a sample part by more than 13%.

关键词： smart manufacturing complex structural parts processing sequence planning corner milling ant colony 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：