The enlightenment of artificial intelligence large-scale model on the research of intelligent eye diagnosis in traditional Chinese medicine

Eye diagnosis is a method for inspecting systemic diseases and syndromes by observing the eyes.With the development of intelligent diagnosis in traditional Chinese medicine(TCM);artificial intelligence(AI)can improve the accuracy and efficiency of eye diagnosis.However;the research on intelligent eye diagnosis still faces many challenges;including the lack of standardized and precisely labeled data;multi-modal information analysis;and artificial in-telligence models for syndrome differentiation.The widespread application of AI models in medicine provides new insights and opportunities for the research of eye diagnosis intelli-gence.This study elaborates on the three key technologies of AI models in the intelligent ap-plication of TCM eye diagnosis;and explores the implications for the research of eye diagno-sis intelligence.First;a database concerning eye diagnosis was established based on self-su-pervised learning so as to solve the issues related to the lack of standardized and precisely la-beled data.Next;the cross-modal understanding and generation of deep neural network models to address the problem of lacking multi-modal information analysis.Last;the build-ing of data-driven models for eye diagnosis to tackle the issue of the absence of syndrome dif-ferentiation models.In summary;research on intelligent eye diagnosis has great potential to be applied the surge of AI model applications.

Use of generated artificial road profiles in road roughness evaluation

In the evaluation of road roughness and its effects on vehicles response in terms of ride quality, loads induced on pavement, drivers＇ comfort, etc., it is very common to generate road profles based on the equation provided by ISO 8608 standard, according to which it is possible to group road surface profiles into eight different classes. However, real profiles are significantly different from the artificial ones because of the non-stationary fea- ture of the first ones and the not full capability of the ISO 8608 equation to correctly describe the frequency content of real road profiles. In this paper, the international roughness index, the frequency-weighted vertical acceleration awz according to ISO 2631, and the dynamic load index are applied both on artificial and real profiles, highlighting the different results obtained. The analysis carried out in this work has highlighted some limitation of the ISO 8608 approach in the description of performance and conditions of real pavement profiles. Furthermore, the different sensitivity of the various indices to the fitted power spectral density parameters is shown, which should be taken into account when performing analysis using artificial profiles.

Localization of Mobile Robot Aided for Large-Scale Construction Based on Optimized Artificial Landmark Map in Ongoing Scene

The effectiveness of mobile robot aided for architectural construction depends strongly on its accurate localization ability.Localization of mobile robot is increasingly important for the printing of buildings in the construction scene.Although many available studies on the localization have been conducted,only a few studies have addressed the more challenging problem of localization for mobile robot in large-scale ongoing and featureless scenes.To realize the accurate localization of mobile robot in designated stations,we build an artificial landmark map and propose a novel nonlinear optimization algorithm based on graphs to reduce the uncertainty of the whole map.Then,the performances of localization for mobile robot based on the original and optimized map are compared and evaluated.Finally,experimental results show that the average absolute localization errors that adopted the proposed algorithm is reduced by about 21%compared to that of the original map.

The role of hardness and roughness on the wear of different CoCrMo counterfaces on UHMWPE for artificial joints

Wear tests were carried out to study the effect of the hardness and roughness with various counterface materials on UHMWPE wear behaviour. The materials used as counterfaces were based on varieties of CoCrMo: 1) forged (hand-polished) CoCrMo, 2) forged (mass-finished) CoCrMo, and 3) cast (mass-finished) CoCrMo. Additionally, two coatings were proposed: 1) a CoCrMo coating applied to the forged CoCrMo alloy by means of physical vapour deposition (PVD), and 2) a ZrO2 coating applied to the forged CoCrMo alloy by means of plasma-assisted chemical vapour deposition (PACVD). The reciprocating pin-on-flat (RPOF) device for pin-on-disk wear testing was used for this study. The worn surfaces were observed using optical, atomic force and scanning electron microscopes.

Yarn Quality Prediction and Diagnosis Based on Rough Set and Knowledge-Based Artificial Neural Network

In the spinning process, some key process parameters( i. e.,raw material index inputs) have very strong relationship with the quality of finished products. The abnormal changes of these process parameters could result in various categories of faulty products. In this paper, a hybrid learning-based model was developed for on-line intelligent monitoring and diagnosis of the spinning process. In the proposed model, a knowledge-based artificial neural network( KBANN) was developed for monitoring the spinning process and recognizing faulty quality categories of yarn. In addition,a rough set( RS)-based rule extraction approach named RSRule was developed to discover the causal relationship between textile parameters and yarn quality. These extracted rules were applied in diagnosis of the spinning process, provided guidelines on improving yarn quality,and were used to construct KBANN. Experiments show that the proposed model significantly improve the learning efficiency, and its prediction precision is improved by about 5. 4% compared with the BP neural network model.

Artificial-intelligent-powered safety and efficiency improvement for controlling and scheduling in integrated railway systems

The multi-mode integrated railway system,anchored by the high-speed railway,caters to the diverse travel requirements both within and between cities,offering safe,comfortable,punctual,and eco-friendly transportation services.With the expansion of the railway networks,enhancing the efficiency and safety of the comprehensive system has become a crucial issue in the advanced development of railway transportation.In light of the prevailing application of artificial intelligence technologies within railway systems,this study leverages large model technology characterized by robust learning capabilities,efficient associative abilities,and linkage analysis to propose an Artificial-intelligent(AI)-powered railway control and dispatching system.This system is elaborately designed with four core functions,including global optimum unattended dispatching,synergetic transportation in multiple modes,high-speed automatic control,and precise maintenance decision and execution.The deployment pathway and essential tasks of the system are further delineated,alongside the challenges and obstacles encountered.The AI-powered system promises a significant enhancement in the operational efficiency and safety of the composite railway system,ensuring a more effective alignment between transportation services and passenger demands.

Intrusion detection based on rough set and artificial immune

In order to increase intrusion detection rate and decrease false positive detection rate,a novel intrusion detection algorithm based on rough set and artificial immune( RSAI-IDA) is proposed.Using artificial immune in intrusion detection,anomaly actions are detected adaptively,and with rough set,effective antibodies can be obtained. A scheme,in which antibodies are partly generated randomly and others are from the artificial immune algorithm,is applied to ensure the antibodies diversity. Finally,simulations of RSAI-IDA and comparisons with other algorithms are given. The experimental results illustrate that the novel algorithm achieves more effective performances on anomaly intrusion detection,where the algorithm's time complexity decreases,the true positive detection rate increases,and the false positive detection rate is decreased.

Call for Papers Journal of Electronic Science and Technology Announcing a Special Issue on Artificial Intelligence with Rough Sets

Submission Deadline： 10 December 2010Since the introduction of rough sets in 1982 by Professor Zdzislaw Pawlak, we have witnessed great advances in both theory and applications. In order to promote development of rough sets, we are preparing a special issue on ＂Artificial Intelligence with Rough Sets＂ published by JEST （International）, Journal of Electronic Science and Technology, which is a refereed international journal focusing on IT area. The aim of this special issue is to present the current state of the research in this area, oriented towards both theoretical and applications aspects of rough sets.

Neural Network Modeling and Prediction of Surface Roughness in Machining Aluminum Alloys

Artificial neural network is a powerful technique of computational intelligence and has been applied in a variety of fields such as engineering and computer science. This paper deals with the neural network modeling and prediction of surface roughness in machining aluminum alloys using data collected from both force and vibration sensors. Two neural network models, including a Multi-Layer Perceptron (MLP) model and a Radial Basis Function (RBF) model, were developed in the present study. Each model includes eight inputs and five outputs. The eight inputs include the cutting speed, the ratio of the feed rate to the tool-edge radius, cutting forces in three directions, and cutting vibrations in three directions. The five outputs are five surface roughness parameters. Described in detail is how training and test data were generated from real-world machining experiments that covered a wide range of cutting conditions. The results show that the MLP model provides significantly higher accuracy of prediction for surface roughness than does the RBF model.

Algorithm for Extracting All Relative-Attribute Reducts in Decision System of Large-Scale Records

A new algorithm based on rough core was proposed to extract all relative-attribute reducts in decision information systems of large-scale records. In the algorithm, the rough core of the decision-making information system is first calculated. Then, an approach based on a top-down strategy is adopted to select the non-core condition attributes and generate candidate relative-attribute reducts. Finally, the set of all relative-attribute reducts is obtained by pruning the candidate relative-attribute reducts. Experimental results show that the proposed algorithm is superior to the other methods such as the algorithm without computing core, the exhaustive method and the discernibility matrix method in extracting all relative-attribute reducts for large-scale data sets.

Artificial intelligence ecosystem for computational psychiatry:Ideas to practice

Computational psychiatry is an emerging field that not only explores the biological basis of mental illness but also considers the diagnoses and identifies the underlying mechanisms.One of the key strengths of computational psychiatry is that it may identify patterns in large datasets that are not easily identifiable.This may help researchers develop more effective treatments and interventions for mental health problems.This paper is a narrative review that reviews the literature and produces an artificial intelligence ecosystem for computational psychiatry.The artificial intelligence ecosystem for computational psychiatry includes data acquisition,preparation,modeling,application,and evaluation.This approach allows researchers to integrate data from a variety of sources,such as brain imaging,genetics,and behavioral experiments,to obtain a more complete understanding of mental health conditions.Through the process of data preprocessing,training,and testing,the data that are required for model building can be prepared.By using machine learning,neural networks,artificial intelligence,and other methods,researchers have been able to develop diagnostic tools that can accurately identify mental health conditions based on a patient’s symptoms and other factors.Despite the continuous development and breakthrough of computational psychiatry,it has not yet influenced routine clinical practice and still faces many challenges,such as data availability and quality,biological risks,equity,and data protection.As we move progress in this field,it is vital to ensure that computational psychiatry remains accessible and inclusive so that all researchers may contribute to this significant and exciting field.

A comprehensive review of rectangular duct solar air heaters featuring artificial roughness

Solar air heaters(SAHs)are widely used solar thermal systems with applications in diverse sectors.However,its effectiveness is re-strained by low convective heat transfer(HT)coefficients at the absorber plate,leading to inefficient HT,and the elevated temperature of the absorber plate causes significant heat losses,reducing thermal efficiency.This study addresses these challenges by introducing ribs or roughness on the absorber plate creating turbulence in the airflow,resulting in significant improvements.The research inves-tigates various rib configurations,the influence of rib parameters,performance methods,and arrangements to evaluate their HT and friction characteristics.Among these rib configurations,a comparative analysis is done on various factors such as the Nusselt number ratio,thermal enhancement factor,friction factor ratio,and thermal efficiency to optimize distinct roughness parameters and rib ar-rangement patterns.This study also provides valuable recommendations from existing literature,offering insights into the effective design,prospects,and implementation of SAH systems.

基于Rough Set理论的油层识别方法

本文简介了智能信息处理中新出现的ＲｏｕｇｈＳｅｔ（ＲＳ）理论，讨论了ＲＳ理论进行决策分析的方法，提出了将连续属性离散化的最优化思想，并探讨了ＲＳ理论用于测井数据判别油水层的问题。判别结果表明：本方法具有速度快、易实现、可优选属性等特点，且判别符合率优于手工方法与ＢＰ网络方法，具有实用价值。

一种基于Rough集的启发式人工选择算法

在传统二进制编码遗传算法(GA)的基础上,提出一种基于Rough集的启发式人工选择算子和人工选择算法。利用粗糙集对遗传算法的历史数据进行分析,发现重要基因位,获得重要模式信息,并以此为启发式信息,选择优秀模式进行人工育种,从而对复杂优化问题进行有效求解。采用该算法对典型测试函数进行了验证,算例结果表明,人工选择算法加速了常规遗传算法进化速度,提高了收敛效率。

基于CFD的直角六边形人工粗糙度元素太阳能空气集热器的数值模拟

带有光滑吸热板的太阳能空气集热器传热效率很低,在吸收板上使用人工粗糙度元素能中断边界层,提高系统的传热效率。文章在矩形人工粗糙度元素的基础上进行改进,使用ANSYS FLUENT对带有直角六边形粗糙度的太阳能空气集热器进行二维模拟;在特定雷诺数范围内,讨论了人工粗糙度元素间距对传热效率和流动特性的影响,并对其热工水力性能(THPP)进行评价;使用有限体积法求解控制方程,RNG k-ε湍流模型求解湍动能和湍动能耗散率的输运方程。结果表明,与光滑吸热板相比,加入直角六边形人工粗糙度元素后显著提高了系统的传热效果。当雷诺数Re=4000、间距p=10 mm时,太阳能空气集热器的传热性能和压力损失保持最佳比例,系统最大的热工水力系数THPP=1.76,是相同几何参数下矩形人工粗糙度元素的1.6倍。

Characteristics Prediction Method of Electro-hydraulic Servo Valve Based on Rough Set and Adaptive Neuro-fuzzy Inference System

Synthesis characteristics of the electro-hydraulic servo valve are key factors to determine eligibility of the hydraulic production. Testing all synthesis characteristics of the electro-hydraulic servo valve after assembling leads to high repair rate and reject rate, so accurate prediction for the synthesis characteristics in the industrial production is particular important in decreasing the repair rate and the reject rate of the product. However, the research in forecasting synthesis characteristics of the electro-hydraulic servo valve is rare. In this work, a hybrid prediction method was proposed based on rough set（RS） and adaptive neuro-fuzzy inference system（ANFIS） in order to predict synthesis characteristics of electro-hydraulic servo valve. Since the geometric factors affecting the synthesis characteristics of the electro-hydraulic servo valve are from workers＇ experience, the inputs of the prediction method are uncertain. RS-based attributes reduction was used as the preprocessor, and then the exact geometric factors affecting the synthesis characteristics of the electro-hydraulic servo valve were obtained. On the basis of the exact geometric factors, ANFIS was used to build the final prediction model. A typical electro-hydraulic servo valve production was used to demonstrate the proposed prediction method. The prediction results showed that the proposed prediction method was more applicable than the artificial neural networks（ANN） in predicting the synthesis characteristics of electro-hydraulic servo valve, and the proposed prediction method was a powerful tool to predict synthesis characteristics of the electro-hydraulic servo valve. Moreover, with the use of the advantages of RS and ANFIS, the highly effective forecasting framework in this study can also be applied to other problems involving synthesis characteristics forecasting.

A survey on rough set theory and its applications

After probability theory, fuzzy set theory and evidence theory, rough set theory is a new mathematical tool for dealing with vague, imprecise, inconsistent and uncertain knowledge. In recent years, the research and applications on rough set theory have attracted more and more researchers＇ attention. And it is one of the hot issues in the artificial intelligence field. In this paper, the basic concepts, operations and characteristics on the rough set theory are introduced firstly, and then the extensions of rough set model, the situation of their applications, some application software and the key problems in applied research for the rough set theory are presented.

Neural network fault diagnosis method optimization with rough set and genetic algorithms

Aiming at the disadvantages of BP model in artificial neural networks applied to intelligent fault diagnosis, neural network fault diagnosis optimization method with rough sets and genetic algorithms are presented. The neural network nodes of the input layer can be calculated and simplified through rough sets theory; The neural network nodes of the middle layer are designed through genetic algorithms training; the neural network bottom-up weights and bias are obtained finally through the combination of genetic algorithms and BP algorithms. The analysis in this paper illustrates that the optimization method can improve the performance of the neural network fault diagnosis method greatly.

Guest Editorial: Rough Sets and Data Mining

A rough set,first described by Polish computer scientist Zdzis?aw Pawlak,is a formal approximation of a crisp set,and it is now known as a new mathematical tool to process vague concepts.They are used for machine learning,knowledge discovery,feature selection,etc.,and are applied to artificial intelligence,medical informatics,civil engineering,Kansei engineering,decision science,business administration,and so on.Especially,research on data mining using rough sets is widely spreading,and the obtained association rules are applied to the characterisation of data and decision support.

Large-Scale Model Meets Federated Learning:A Hierarchical Hybrid Distributed Training Mechanism for Intelligent Intersection Large-Scale Model

The large-scale model(LSM)can handle large-scale data and complex problems,effectively improving the intelligence level of urban intersections.However,the traffic conditions at intersections are becoming increasingly complex,so the intelligent intersection LSMs(I2LSMs)also need to be continuously learned and updated.The traditional cloud-based training method incurs a significant amount of computational and storage overhead,and there is a risk of data leakage.The combination of edge artificial intelligence and federated learning provides an efficient and highly privacy protected computing mode.Therefore,we propose a hierarchical hybrid distributed training mechanism for I2LSM.Firstly,relying on the intelligent intersection system for cloud-network-terminal integration,we constructed an I2LSM hierarchical hybrid distributed training architecture.Then,we propose a hierarchical hybrid federated learning(H2Fed)algorithm that combines the advantages of centralized federated learning and decentralized federated learning.Further,we propose an adaptive compressed sensing algorithm to reduce the communication overhead.Finally,we analyze the convergence of the H2Fed algorithm.Experimental results show that the H2Fed algorithm reduces the communication overhead by 21.6%while ensuring the accuracy of the model.