Rough set based decision rule generation to find behavioural patterns of customers

Rough sets help in finding significant attributes of large data sets and generating decision rules for classifying new instances. Though multiple regression analysis, discriminant analysis, log-it analysis and several other techniques can be used for predicting results, they consider insignificant information also for processing which may lead to false positives and false negatives. In this study, we proposed rough set based decision rule generation framework to find reduct and to generate decision rules for predicting the Decision class. We conducted experiments over data of Portuguese Banking institution. From the proposed method, the dimensionality of data is reduced and decision rules are generated which predicts deposit nature of customers by 90% accuracy.     

高层结构方案实例库系统及其在结构智能选型中应用

从结构方案设计的角度,建立了工程实例信息的统一表征模型,收集获取了国内外1008个高层结构工程实例信息。在此基础上,利用关系数据库技术建造了高层结构实例库及其管理系统,实现了实例数据录入、修改、删除、查询、统计和知识发现等功能。最后结合高层结构智能选型的实际,给出了基于实例库的定量型与模糊型结构选型规则的挖掘过程与方法,同时还构建了定量属性的模糊区间划分及其模糊化方法,分析探讨了基于Apriori算法的关联分析方法在工程智能设计等方面的可能应用。上述工作为挖掘与充分利用已有工程实例中的知识信息,提高结构设计效率、质量与智能化水平等提供了新的途径、方法与手段等的支持。     

粗糙集与决策树在城轨头型意象设计的应用

目的 为挖掘大众对产品造型意象认知的机理,提出了一种将粗糙集理论和决策树技术相结合的产品造型意象设计方法。方法 首先,在感性工学理论的基础上,获取产品造型设计要素和感性评价数据,并运用粗糙集理论来构建知识表达系统;其次,对决策表属性进行约减,保留贡献值较大的属性,删除冗余属性;最后,通过AAID算法计算得到各个设计要素的重要度排序,在此基础上构建决策树,并通过读树提取相关设计规则。结果 以地铁列车头型设计为例进行研究,得到了影响感性意象“前卫的-过时的”核心设计要素,通过决策树提取到9条设计规则,结果验证了该方法的适用性和有效性。结论 通过对粗糙集理论和决策树技术的探究和应用,提出了地铁列车头型设计思路。研究结论可为其他城市轨道交通工具的产品造型意象设计提供参考依据,丰富轨道交通工具领域的产品创新设计方法。     

An intelligent data base system for composite material selection in structural design

This paper describes the development of a prototype intelligent data base system for composite material selection in structural design. This intelligent system integrates the expert system with the data base system to provide decision making support systems that exhibit some forms of intelligence. The overall architecture of this system is illustrated. The present capabilities of this system are discussed and demonstrated with an example problem.     

A principled approach to the design of healthcare systems: Autonomy vs. governance

In this paper, we look at decision support for post-operative breast cancer care. Our main concerns are to support autonomy of decision making whilst maintaining the governance and reliability of the decision-making process. We describe the context of our work in the wider medical setting. We then present a set of decision support tools based on the situation calculus as a means of maintaining the integrity of rule bases underlying the decision-making system. The decision support system, Neptune, allows for the authoring, maintenance and delivery of decisions in a self-governing framework. Finally we discuss the implications of our work.     

A concept for inferring ‘frontier research’ in grant proposals

This paper discusses a concept for inferring attributes of ‘frontier research’ in peer-reviewed research proposals under the popular scheme of the European Research Council (ERC). The concept serves two purposes: firstly to conceptualize, define and operationalize in scientometric terms attributes of frontier research; and secondly to build and compare outcomes of a statistical model with the review decision in order to obtain further insight and reflect upon the influence of frontier research in the peer-review process. To this end, indicators across scientific disciplines and in accord with the strategic definition of frontier research by the ERC are elaborated, exploiting textual proposal information and other scientometric data of grant applicants. Subsequently, a suitable model is formulated to measure ex-post the influence of attributes of frontier research on the decision probability of a proposal to be accepted. We present first empirical data as proof of concept for inferring frontier research in grant proposals. Ultimately the concept is aiming at advancing the methodology to deliver signals for monitoring the effectiveness of peer-review processes.     

面向应急救援的可穿戴智能装备设计研究

目的 基于对可穿戴智能装备基本特征和应急救援工作中“人与环境”整体需求的分析,阐述了可穿戴智能装备设计中存在的问题和发展趋势。方法 运用KJ法建立应急救援中可穿戴智能装备功能需求指标,得出目标层1个、一级指标4个和二级指标21个;运用AHP层次分析法将各功能需求指标进行综合评价,得出各指标权重值;通过对传感器、运动控制、新型加工制造、新材料等关键技术的研究,探讨了信息处理与决策、运动控制、新材料应用等问题。结果 确立了功能为基础、安全为保障、强调人机交互体验、关注生理舒适性、提升科技美学的设计原则;明确了系统设计的方法和外观设计策略;构建了包括运行原理、信息采集及反馈、智能运动在内的系统设计模型。结论 为设计过程提供了基础数据和理论依据。从而实现面向应急救援的可穿戴智能装备的优化创新设计。     

基于知识的板料冲裁工艺与模具设计支持技术

为了提高板料冲裁工艺与模具设计的质量和效率,实现知识的重用和共享,对板料冲裁工艺与三维模具设计的智能化、参数化设计技术进行了系统地研究,提出了以数据库、框架、规则、面向对象和数学模型等混合方法表示工艺设计知识,以几何模型/数据库混合表示法描述结构设计知识;提出了面向工程的动态设计方法,将实际设计知识记录到动态知识库中,成为半标准化的知识,支持系统的智能设计.根据提出的方法,基于Ⅰ-DEAS软件平台,利用宏命令与API接口函数相结合的方法,建立了冲裁工艺与三维模具设计支持系统,具体设计实例表明所建立的系统大大提高了设计的质量和效率.     

反坦克智能雷扫描探测方法研究

主动探测目标技术是反坦克智能雷设计的一个关键性技术.在描述毫米波/红外信息融合原理的基础上,本文构建了毫米波和红外传感器技术在智能雷主动探测目标系统中的应用框架.根据反坦克智能雷攻击坦克时的运动特性,建立了智能雷扫描探测轨迹模型和扫描探测捕获准则,并应用蒙特卡洛方法计算分析了区域系数对智能雷扫描捕获概率的影响.结果表明:设计的反坦克智能雷目标探测系统,建立的扫描探测轨迹模型和扫描探测捕获准则基本符合工程实际情况.     

面向独居青年的智能烹饪器具设计研究

目的 独居青年日益增多,他们倾向于通过简单的膳食或外卖等方式快速用餐。为了改变独居青年当前不健康的饮食习惯,设计一款集便捷性、智能化及多功能性于一体的智能烹饪器具,以简化烹饪流程,推动健康的饮食方式。方法 首先,通过市场调研和用户访谈收集用户对智能烹饪器具的需求,并利用KJ法对需求进行分类整理;其次,运用KANO问卷分析需求属性,以更好地理解用户需求特征;随后,引入AHP理论构建层次结构模型,对不同属性的需求进行权重计算,以确定各需求的相对重要性;最后,根据需求排序进行方案构思,设计出符合用户需求的合理解决方案。结论 设计实践结果表明,该方法有助于明确用户需求属性及准确获取优先排序,为智能烹饪器具的方案设计提供有效思路,从而增加设计方案的合理性。     

工资智能决策支持系统SIDSS的设计与实现

根据工资政策的特点,在此提出一种三库结构的智能工资决策支持系统,即规则库、方法库和数据库。与四库结构相比,它具有独立性较好,易于实现的特点。本文介绍工资智能决策支持系统SIDSS系统结构和主要功能,讨论了事件处理器、规则处理器及方法库与数据库等关键技术。     

工业设计决策问题研究

目的 对工业设计过程的决策问题进行分析。方法 通过分析工业设计决策的特点,建立了工业设计决策的语义模型,综述了其研究现状与相关设计决策方法,指出了工业设计决策面临的问题与挑战,给出了未来可能的研究方向。结果 工业设计决策的模糊性、感性与理性并存、多阶段性、非线性与动态性等特点,决定了设计决策将面临网络化、动态性、多模态、冲突性、不确定性等挑战,使得未来研究将从多源数据融合、多阶段联合决策、共识驱动决策、智能决策及借助更多的数学理论和方法等多方面,推动工业设计决策的科学化、客观化与智能化发展。结论 工业设计决策是创新设计过程的重要组成部分之一,梳理工业设计过程的决策问题,将对工业设计方法论体系的完善起到补充与推动作用,减少工业设计过程的无效迭代。     

Scalable Skin Lesion Multi-Classification Recognition System

Skin lesion recognition is an important challenge in the medical field. In this paper, we have implemented an intelligent classification system based on convolutional neural network. First of all, this system can classify whether the input image is a dermascopic image with an accuracy of 99%. And then diagnose the dermoscopic image and the non-skin mirror image separately. Due to the limitation of the data, we can only realize the recognition of vitiligo by non-skin mirror. We propose a vitiligo recognition based on the probability average of three structurally identical CNN models. The method is more efficient and robust than the traditional RGB color space-based image recognition method. For the dermoscopic classification model, we were able to classify 7 skin lesions, use weighted optimization to overcome the unbalanced data set, and greatly improve the sensitivity of the model by means of model fusion. The optimization and expansion of the system depend on the increase of database.     

Intelligent dispatching for flexible manufacturing

A decision rule for real-time dispatching of parts, each of which may have alternative processing possibilities, has been developed and tested in a simulated flexible manufacturing system. A part, upon completion of an operation, is not routed to a specific machine, but is, in effect, sent to a general queue. Thus, a machine has a global option for choosing parts which in turn may be processed on alternative machines. For effective use of the system's routeing flexibility under these circumstances, the machine needs an intelligent part-selection strategy (rather than shallow heuristics represented by the conventional dispatching rules) that takes into account the current state and trends of the system. The proposed intelligent reasoning procedure has been found to achieve better shop performance than some of the popular dispatching rules, the improved performance being due to the ability to respond to changing circumstances.     

An explanatory analysis of driver injury severity in rear-end crashes using a decision table/Naïve Bayes (DTNB) hybrid classifier

Rear-end crashes are a major type of traffic crashes in the U.S. Of practical necessity is a comprehensive examination of its mechanism that results in injuries and fatalities. Decision table (DT) and Naïve Bayes (NB) methods have both been used widely but separately for solving classification problems in multiple areas except for traffic safety research. Based on a two-year rear-end crash dataset, this paper applies a decision table/Naïve Bayes (DTNB) hybrid classifier to select the deterministic attributes and predict driver injury outcomes in rear-end crashes. The test results show that the hybrid classifier performs reasonably well, which was indicated by several performance evaluation measurements, such as accuracy, F-measure, ROC, and AUC. Fifteen significant attributes were found to be significant in predicting driver injury severities, including weather, lighting conditions, road geometry characteristics, driver behavior information, etc. The extracted decision rules demonstrate that heavy vehicle involvement, a comfortable traffic environment, inferior lighting conditions, two-lane rural roadways, vehicle disabled damage, and two-vehicle crashes would increase the likelihood of drivers sustaining fatal injuries. The research limitations on data size, data structure, and result presentation are also summarized. The applied methodology and estimation results provide insights for developing effective countermeasures to alleviate rear-end crash injury severities and improve traffic system safety performance.     

A Multi‐attribute Reliability Allocation Method Considering Uncertain Preferences

In reliability allocation, certain reliability values are assigned to subsystems and components to achieve the required system reliability. One big challenge in solving such reliability‐based design problems is how to handle the uncertain preferences of a decision maker on multiple attributes of interest. In this paper, we propose a new ordered weighted averaging (OWA) method based on an analytic hierarchy process to address the decision maker's uncertain preferences in reliability allocation. In the proposed OWA operator, a bi‐objective mathematical programming model considering both maximal entropy and minimal variance is transformed into a single‐objective mathematical programming model using an ideal‐point method. The maximum entropy minimal variance OWA operator takes full advantage of available information and avoids overestimating the decision maker's preferences. A detailed computational procedure is presented to facilitate the implementation of the proposed method in practice. An illustrative example about the powertrain of fuel cell vehicles is provided to demonstrate the effectiveness of this method in handling multiple attributes with uncertain preferences in reliability allocation. Copyright © 2015 John Wiley & Sons, Ltd.     

Stock Prediction Based on Technical Indicators Using Deep Learning Model

Stock market trends forecast is one of the most current topics and a significant research challenge due to its dynamic and unstable nature. The stock data is usually non-stationary, and attributes are non-correlative to each other. Several traditional Stock Technical Indicators (STIs) may incorrectly predict the stock market trends. To study the stock market characteristics using STIs and make efficient trading decisions, a robust model is built. This paper aims to build up an Evolutionary Deep Learning Model (EDLM) to identify stock trends’ prices by using STIs. The proposed model has implemented the Deep Learning (DL) model to establish the concept of Correlation-Tensor. The analysis of the dataset of three most popular banking organizations obtained from the live stock market based on the National Stock exchange (NSE) – India, a Long Short Term Memory (LSTM) is used. The datasets encompassed the trading days from the 17 of Nov 2008 to the 15 of Nov 2018. This work also conducted exhaustive experiments to study the correlation of various STIs with stock price trends. The model built with an EDLM has shown significant improvements over two benchmark ML models and a deep learning one. The proposed model aids investors in making profitable investment decisions as it presents trend-based forecasting and has achieved a prediction accuracy of 63.59%, 56.25%, and 57.95% on the datasets of HDFC, Yes Bank, and SBI, respectively. Results indicate that the proposed EDLA with a combination of STIs can often provide improved results than the other state-of-the-art algorithms.     

SMA迟滞系统的PID控制及参数优化研究

形状记忆合金(shape memory alloy,SMA)是一类新型功能材料,作为电热驱动器,广泛应用于工程领域。但由于材料内在的非线性性质和迟滞效应使其在控制系统的设计和热力学建模上较为困难,不利于简单控制系统和实时控制的实现。基于Preisach理论给出SMA驱动器系统的温度-位移迟滞模型,分析系统的热传导过程,将传统的比例-积分-微分PID(proportional-integral-differential,PID)策略应用于控制器的设计,并引入蚁群和粒子群两类智能算法优化控制器参数,讨论优化过程和控制效果;与传统PID参数整定方法相比,系统的阶跃响应和正弦信号跟踪性能取得比较理想的效果,且智能算法调节控制器参数的实时性更强,克服非线性和迟滞效应的影响。     

"岩石模型"虚拟仪器库中的自动拼搭系统

在分析“岩石模型”大型虚拟仪器库库中资源的基础上提出了一种可在该仪器库中实现虚拟仪器自动化拼搭的系统结构并给出了系统的工作流程。对此自动拼搭系统进行了方案设计,并对其决策控制环节、现有资源的分类管理模式和方法以及关系数据库和规则集的建立进行了研究。虚拟仪器的自动拼搭能极大程度地降低虚拟仪器的开发要求,并能对虚拟仪器的推广起到一定的促进作用。     

Leveraging the nugget parameter for efficient Gaussian process modeling

Gaussian process (GP) metamodels have been widely used as surrogates for computer simulations or physical experiments. The heart of GP modeling lies in optimizing the log‐likelihood function with respect to the hyperparameters to fit the model to a set of observations. The complexity of the log‐likelihood function, computational expense, and numerical instabilities challenge this process. These issues limit the applicability of GP models more when the size of the training data set and/or problem dimensionality increase. To address these issues, we develop a novel approach for fitting GP models that significantly improves computational expense and prediction accuracy. Our approach leverages the smoothing effect of the nugget parameter on the log‐likelihood profile to track the evolution of the optimal hyperparameter estimates as the nugget parameter is adaptively varied. The new approach is implemented in the R package GPM and compared to a popular GP modeling R package ( GPfit) for a set of benchmark problems. The effectiveness of the approach is also demonstrated using an engineering problem to learn the constitutive law of a hyperelastic composite where the required level of accuracy in estimating the response gradient necessitates a large training data set.