检索结果-内蒙古大学图书馆

Based on Short Motion Paths and artificial intelligence method for Chinese Chess Game

JOURNAL OF ROBOTICS NETWORKING AND artificial LIFE 2017年第2期4卷 154-157页

作者： Hung, Chien-Ming Guo, Jr-Hung Su, Kuo-Lan Army Acad ROC Dept Power Mech Engn Taoyuan 32092 Taiwan Natl Yunlin Univ Sci & Technol Dept Elect Engn 123 Sec 3Univ Rd Touliu 64002 Yunlin Taiwan

The article develops the decision rules to win each set of the Chinese chess game using evaluation algorithm and artificial intelligence method, and uses the mobile robot to be instead of the chess, and presents the movement scenarios using the shortest motion paths for mobile robots. Player can play the Chinese chess game according to the game rules with the supervised computer. The supervised computer decides the optimal motion path to win the set using artificial intelligence method, and controls mobile robots according to the programmed motion paths of the assigned chesses moving on the platform via wireless RF interface. We uses enhance A* searching algorithm to solve the shortest path problem of the assigned chess, and solve the collision problems of the motion paths for two mobile robots moving on the platform simultaneously. We implement a famous set to be called "wild horses run in farm" using the proposed method. First we use simulation method to display the motion paths of the assigned chesses for the player and the supervised computer. Then the supervised computer implements the simulation results on the chessboard platform using mobile robots. Mobile robots move on the chessboard platform according to the programmed motion paths and is guided to move on the centre line of the corridor, and avoid the obstacles (chesses), and detect the cross point of the platform using three reflective IR modules.

关键词： Evaluation algorithm artificial intelligence method wireless RF interface enhance A* searching algorithm

来源：评论

学校读者我要写书评

暂无评论

A comprehensive review of slope stability analysis based on artificial intelligence methods

引用

EXPERT SYSTEMS WITH APPLICATIONS 2024年 239卷

作者： Gao, Wei Ge, Shuangshuang Hohai Univ Coll Civil & Transportat Engn Key Lab Minist Educ Geomech & Embankment Engn 1 Xikang Rd Nanjing 210098 Peoples R China

For preventing landslide disasters caused by the slope collapse, it is crucial to research on the investigation of slope stability. For the very complex influence factors on the slope stability, nowadays, the studies on slope stability by using the artificial intelligence methods have become the hot topic, and there have been numerous related works in this field. In this study, according to the essential difference, the studies in this field are divided into two main types (computation of slope stability and evaluation of slope stability), from which, the numerous previous studies have been reviewed comprehensively. For the studies on slope stability computation, according to the used artificial intelligence methods, the studies in this field are reviewed from four aspects: studies by quasi-physical intelligence methods, studies by simulated evolutionary methods, studies by swarm intelligence methods, and ones by hybrid intelligence methods. And from the used artificial intelligence methods too, the studies on slope stability evaluation are also reviewed from four aspects: studies by artificial neural network methods, studies by vector machine methods, ones using hybrid intelligence methods, and ones using other intelligence methods. Moreover, the merits and demerits of those studies have been comprehensively analyzed, and their state-of-the-art research advancement has also been summarized. At last, the possible research directions of slope stability investigation based on artificial intelligence methods are also suggested.

关键词： Landslide Slope stability artificial intelligence method Evaluation Research advancement

来源：评论

学校读者我要写书评

暂无评论

Research on Intelligent Detection method of Weak Sensing Signal Based on artificial intelligence 1

引用

3rd European-Alliance-for-Innovation (EAI) International Conference on Advanced Hybrid Information Processing (ADHIP)

作者： Jia, Shuang-cheng Yang, Feng-ping Alibaba Network Technol Co Ltd Beijing Peoples R China

ISBN: (数字)9783030364052

ISBN: (纸本)9783030364052;9783030364045

In order to improve the detection and recognition ability of weak sensing signal, an intelligent detection algorithm of weak sensing signal based on artificial intelligence algorithm is proposed. The weak sensing signal model is constructed, the weak sensing signal is separated and processed adaptively, the scale and delay of the weak sensing signal are estimated adaptively, and the high resolution spectral features are extracted. The extracted spectral feature is studied adaptively and detected intelligently by artificial intelligence algorithm, and the spectral peak search of weak sensing signal is realized. The spectral feature component method is used to realize the interference suppression of weak sensing signals, thereby improving the detection of the method. The simulation results show that the algorithm has high accuracy and anti-interference ability, and improves the detection and recognition ability of weak sensing signal.

关键词： artificial intelligence method Wireless sensor network Weak sensing signal Intelligent detection

来源：评论

学校读者我要写书评

暂无评论

Exploring the deep neural network model's potential to estimate abnormal audit fees

引用

MANAGEMENT DECISION 2022年第12期60卷 3304-3323页

作者： Choi, Seung Uk Lee, Kun Chang Na, Hyung Jong Kyung Hee Univ Sch Management Seoul South Korea Sungkyunkwan Univ SKK Business Sch SAIHST Samsung Adv Inst Hlth Sci & Technol Seoul South Korea Semyung Univ Sch Global Business Jecheon South Korea

Purpose The paper aims to estimate abnormal audit fees more precisely than the traditional audit fee model by applying an artificial intelligence (AI) method. Design/methodology/approach The AI technique employed in this paper is the deep neural network (DNN) model, which has been successfully applied to a wide variety of decision-making tasks. The authors examine the ability of the classic ordinary least squares (OLS) and the DNN models to describe the effects of abnormal audit fees on accounting quality based on recent research that demonstrates a systematic link between accruals-based earnings management and abnormal audit fees. Thus, the authors seek to imply that their new method provides a more precise estimate of abnormal audit fees. Findings The findings indicate that abnormal audit fees projected using the DNN model are substantially more accurate than those estimated using the classic OLS model in terms of their association with earnings management. Specifically, when abnormal audit fees predicted using the DNN rather than the OLS are incorporated in the accruals-based earnings management model, the adjusted R(2)s are larger. Additionally, the DNN-estimated coefficient of abnormal audit fees is more favorably associated to earnings management than the classic OLS-estimated coefficient. Additionally, the authors demonstrate that the DNN outperforms OLS in terms of explanatory power in a negative discretionary accruals subsample and a Big 4 auditor subsample. Finally, abnormal audit fees projected using the DNN method provide a better explanation for audit hours than those estimated using the OLS model. Originality/value This is the first approach that utilized a machine learning technology to estimate abnormal audit fees. Because more precise measurement yields more credible research results, the findings of this study imply a significant advancement in calculating unusually higher audit fees.

关键词： Abnormal audit fees Earnings management artificial intelligence method Deep neural network

来源：评论

学校读者我要写书评

暂无评论

Silicon Carbide Surface Quality Prediction Based on artificial intelligence methods on Multi-sensor Fusion Detection Test Platform

引用

MACHINING SCIENCE AND TECHNOLOGY 2019年第1期23卷 131-152页

作者： Zhang, Yawei Li, Beizhi Yang, Jianguo Liu, Xiao Zhou, Jinqiang Donghua Univ Coll Mech Engn Shanghai 201620 Peoples R China Shanghai Aerosp Equipments Manufacturer Shanghai Peoples R China

On the basis of the grinding process experiments for SiC ceramic workpiece, grinding process parameters are measured on the multi-sensor fusion detection test platform and experimental results are analyzed. Lempel-Ziv complexity (LZC) is introduced to reflect the integrated grinding process stability due to kinds of factors such as vibration from grinding machine parts and noise from experimental platform. The greater the LZC is, the fewer period factors are in the grinding process, which reflect the nonlinear correlations in the grinding process impacting on grinding process. A method is given based on LZC for analyzing grinding process stability. Under consideration of experimental results, a predictive model for surface quality is given by the Kernel Principle Component Analysis and Modified Extreme learning machine method (KPCA-MELM), and grinding process parameters can be optimized too. KPCA-MELM predictive model overcomes disadvantages of MELM predictive model of the randomness of weight omega and threshold value b by introducing improved genetic algorithm, which makes the roughness predictive error more accurate with the maximum error of 4.803%.

关键词： artificial intelligence method grinding process prediction model silicon carbide surface qualify the multi-sensor fusion detection test platform

来源：评论

学校读者我要写书评

暂无评论

Progress of Seepage Law and Development Technologies for Shale Condensate Gas Reservoirs

引用

ENERGIES 2023年第5期16卷 2446页

作者： Liu, Wenchao Yang, Yuejie Qiao, Chengcheng Liu, Chen Lian, Boyu Yuan, Qingwang Univ Sci & Technol Beijing Sch Civil & Resource Engn Beijing 100083 Peoples R China Texas Tech Univ Bob L Herd Dept Petr Engn 2500 Broadway Lubbock TX 79409 USA

With the continuous development of conventional oil and gas resources, the strategic transformation of energy structure is imminent. Shale condensate gas reservoir has high development value because of its abundant reserves. However, due to the multi-scale flow of shale gas, adsorption and desorption, the strong stress sensitivity of matrix and fractures, the abnormal condensation phase transition mechanism, high-speed non-Darcy seepage in artificial fractures, and heterogeneity of reservoir and multiphase flows, the multi-scale nonlinear seepage mechanisms are extremely complicated in shale condensate gas reservoirs. A certain theoretical basis for the engineering development can be provided by mastering the percolation law of shale condensate gas reservoirs, such as improvement of productivity prediction and recovery efficiency. The productivity evaluation method of shale condensate gas wells based on empirical method is simple in calculation but poor in reliability. The characteristic curve analysis method has strong reliability but a great dependence on the selection of the seepage model. The artificial intelligence method can deal with complex data and has a high prediction accuracy. Establishing an efficient shale condensate gas reservoir development simulation technology and accurately predicting the production performance of production wells will help to rationally formulate a stable and high-yield mining scheme, so as to obtain better economic benefits.

关键词： shale condensate gas seepage law productivity prediction empirical method characteristic curve analysis artificial intelligence method well productivity

来源：评论

学校读者我要写书评

暂无评论

Simulation of a Bubble-Column Reactor by Three-Dimensional CFD: Multidimension- and Function-Adaptive Network-Based Fuzzy Inference System

引用

INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2020年第2期22卷 477-490页

作者： Tian, Erlin Babanezhad, Meisam Rezakazemi, Mashallah Shirazian, Saeed Zhengzhou Univ Light Ind Coll Comp & Commun Engn Zhengzhou 450002 Peoples R China Islamic Azad Univ Fac Mech Engn Dept Energy South Tehran Branch Tehran Iran Shahrood Univ Technol Fac Chem & Mat Engn Shahrood Iran Ton Duc Thang Univ Dept Management Sci & Technol Dev Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Appl Sci Ho Chi Minh City Vietnam

Recently, novel approaches have been developed for simulating bubbly flow as well as distributed and constant phase evolution by means of a two-phase reactor. Among these approaches, the Eulerian-Eulerian method and soft computing approaches can be mentioned. Since complex numerical methods (for example, multidimensional Eulerian-Eulerian method) require several runs for fluid conditions optimization, a method which can decrease these runs can be very useful and practical. This method is provided by joining computational fluid dynamic (CFD) to the adaptive neuro-fuzzy inference system (ANFIS). In this technique, valuable information is provided for a careful analysis of fluid conditions. Also, it can facilitate a vast amount of data categorization in synthetic neural network nodes, which eliminates the need for a complex nonstructured CFD mesh. Moreover, a neural geometry can be provided, in which no limitation of mesh numbers in the fluid domain would exist. The key CFD parameters in the scale-up of the reactorstaken into consideration in the current research are gas and liquid circulations. These factors are applied as output factors for prediction tool in various dimensions in the ANFIS method. The results obtained in this study show appropriate conformity concerning ANFIS and CFD results depending on multiple dimensions. In this study, the grouping of CFD and multifunction the ANFIS method delivers the nondiscrete domain in different dimensions and presents an intelligent instrument for the local prediction of multiphase flow. The result shows that three inputs, which represent the dimension of the reactor, and learning stage of the ANFIS method provide a better understanding of flow characteristics in the two-phase reactor, while the two-dimensional ANFIS method even with multistructured functions cannot predict well the multiphase flow in the reactor.

关键词： Multidimensional machine learning ANFIS method artificial intelligence method Bubble-column reactor CFD

来源：评论

学校读者我要写书评

暂无评论

Research on hierarchical control and optimisation learning method of multi-energy microgrid considering multi-agent game

引用

IET SMART GRID 2020年第4期3卷 479-489页

作者： Liu, Hong Li, Jifeng Ge, Shaoyun Tianjin Univ Key Lab Smart Grid Minist Educ Tianjin 300072 Peoples R China

Due to the depletion of traditional fossil energy, to improve energy efficiency and build a cost-effective integrated energy system has become an inevitable choice. Aiming at the problems that the traditional centralised scheduling method is difficult to reflect the multi-dimensional interests of different agents in the multi-energy microgrid system, and the application of artificial intelligence technology in integrated energy scheduling still needs further exploration, this manuscript proposed a hierarchical control optimisation learning method with consideration of multi-agent game. Firstly, the multi-energy microgrid was taken as the research object, the microgrid system architecture was analysed, and the multi-agent partition in the system was pursued based on different economic interests. Secondly, for the technical aspects involved in the integrated energy regulation and management, the management layers of the multi-energy microgrid were divided, and the functions of different management layers were analysed. Based on this, the regulation functions were realised by considering the Nash Q-learning and the artificial intelligence method of Petri-net. Finally, the learning and decision-making ability of the method through practical cases were analysed, and the effectiveness and applicability of the proposed method were explained. This study explores the application of artificial intelligence technology in energy Internet energy management.

关键词： Petri nets energy conservation multi-agent systems decision making distributed power generation game theory learning (artificial intelligence) Internet optimisation scheduling artificial intelligence multidimensional interests multienergy microgrid system artificial intelligence technology integrated energy scheduling hierarchical control optimisation multiagent game microgrid system architecture multiagent partition integrated energy regulation artificial intelligence method energy Internet energy management optimisation learning method traditional fossil energy energy efficiency cost-effective integrated energy system traditional centralised scheduling method

来源：评论

学校读者我要写书评

暂无评论

AI in arcing-HIF detection: a brief review

引用

IET SMART GRID 2020年第4期3卷 435-444页

作者： Hao, Bai Elect Power Res Inst China Southern Power Grid Power Distribut Technol Dept Guangzhou Peoples R China

In the past few decades, the arcing-high-impedance fault (arcing-HIF) detection problems have become an important issue in the effectively grounded distribution network. Many solutions have been proposed to address this problem. The most attractive way is artificial intelligence (AI) method. The paper gives a comprehensive review of arcing-HIF detection in distribution network-based AI. First, characteristics and models of arcing-HIF are analysed, the arcing-HIF database construction method is also explained;this part is a foundation work for arcing-HIF detection. Next, arcing-HIF detection methods based AI are summarised in details including data acquisition, feature extraction and classifier selection. Then, a set of criteria are proposed to evaluate the reliability of arcing-HIF detection algorithm. Finally, the future trends and challenges to arcing-HIF detection are also fully accounted. This review can be a valuable guide for researchers who are interested in arcing-HIF detection-based AI.

关键词： artificial intelligence power distribution faults fault diagnosis arcs (electric) power distribution reliability power engineering computing arcing-high-impedance fault detection problems arcing-HIF database construction method arcing-HIF detection-based AI algorithm distribution network-based AI algorithm grounded distribution network artificial intelligence method data acquisition feature extraction classifier selection reliability

来源：评论

学校读者我要写书评

暂无评论

Deep reinforcement learning-based beam Hopping algorithm in multibeam satellite systems

引用

IET COMMUNICATIONS 2019年第16期13卷 2485-2491页

作者： Hu, Xin Liu, Shuaijun Wang, Yipeng Xu, Lexi Zhang, Yuchen Wang, Cheng Wang, Weidong Beijing Univ Posts & Telecommun Sch Elect Engn Beijing 100876 Peoples R China Chinese Acad Sci Inst Software Beijing 100190 Peoples R China China United Network Commun Corp Network Technol Res Inst Beijing Peoples R China

Beam hopping (BH) is the key technology to improve the system throughput and decrease the transmission delay in multibeam satellite systems. The objective of this study is to find a policy to maximise the expected long-term resource utilisation. The BH illumination plan (BHIP) optimisation problem aimed at minimising the transmission delay is formulated and modelled as a partially observable Markov decision process. To tackle the issue of unknown dynamics and prohibitive computation, an artificial intelligence method named deep reinforcement learning (DRL) is first proposed to solve the BHIP problem in multibeam satellite systems. The proposed DRL-BHIP algorithm considers a series of realistic conditions, including the traffic demands in spatial distribution and temporal variation, ModCod constraints, antenna radiation pattern and inter-beam interference. The state reformulation concept is adopted to characterise the traffic spatial and temporal features. Simulation results show that the proposed DRL-BHIP algorithm can decrease the transmission delay and improve the system throughput compared with existing algorithms.

关键词： Markov processes learning (artificial intelligence) optimisation antenna radiation patterns satellite antennas telecommunication computing telecommunication traffic radiofrequency interference multibeam satellite systems transmission delay long-term resource utilisation BH illumination plan optimisation problem partially observable Markov decision process BHIP problem DRL-BHIP algorithm antenna radiation pattern deep reinforcement learning-based beam Hopping algorithm interbeam interference ModCod constraints traffic spatial feature traffic temporal feature artificial intelligence method

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：