Development and Application of Decision Support Model for the Performance Optimization of Office Buildings Based on Grasshopper

With the expansion of the office building area,the energy consumption of office buildings is growing.High⁃performance building design contributes to energy saving and the development of green buildings.However,there is a lack of high⁃performance building tools and the workflow is often time⁃consuming.The building performance simulation,multiple objective optimizations,and the decision support model are the new approaches of high⁃performance building design.This paper proposes a newly developed decision support model,a high⁃performance building decision model named HPBuildingDSM,which integrates the building performance simulation,building performance multiple objective optimizations,building performance sampling,and parameter sensitivity analysis to design high⁃performance office buildings.In this research,the HPBuildingDSM was operated to search for the desirable office building design results with low⁃energy and high⁃quality daylighting performances.The simulated results had better daylighting performance and lower energy consumption,whose UDI100-2000 was 37.94%and annual energy consumption performance was 76.28 kWh/(m2·a),indicating a better building performance than the optimized results in the previous case study.

The Manufacturing Equipment Support Model Based on the Manufacturing Equipment Role

This work aims to investigate the manufacturing equipment support model for the purpose of improving the efficiency and quality of manufacturing. First, the concept of manufacturing capacity is defined, and the re- lationship between practical and expected manufacturing capacity is described. Then the concept of role is intro- duced and the manufacturing equipment role is defined in detail. Based on the analysis of manufacturing capacity and manufacturing equipment role, the three-stage manufacturing equipment support model is proposed. With this model, the manufacturing task can be decomposed into several manufacturing equipment roles, and the ex- pended manufacturing capacity involved in the manufacturing equipment role can be matched with the practical manufacturing capacity of the enterprise. The measures are discussed depending on different matching degrees.

A Decision Support Model for Predicting Avoidable Re-Hospitalization of Breast Cancer Patients in Kenyatta National Hospital

This study aimed to develop a clinical Decision Support Model (DSM) which is software that provides physicians and other healthcare stakeholders with patient-specific assessments and recommendation in aiding clinical decision-making while discharging Breast cancer patient since the diagnostics and discharge problem is often overwhelming for a clinician to process at the point of care or in urgent situations. The model incorporates Breast cancer patient-specific data that are well-structured having been attained from a prestudy’s administered questionnaires and current evidence-based guidelines. Obtained dataset of the prestudy’s questionnaires is processed via data mining techniques to generate an optimal clinical decision tree classifier model which serves physicians in enhancing their decision-making process while discharging a breast cancer patient on basic cognitive processes involved in medical thinking hence new, better-formed, and superior outcomes. The model also improves the quality of assessments by constructing predictive discharging models from code attributes enabling timely detection of deterioration in the quality of health of a breast cancer patient upon discharge. The outcome of implementing this study is a decision support model that bridges the gap occasioned by less informed clinical Breast cancer discharge that is based merely on experts’ opinions which is insufficiently reinforced for better treatment outcomes. The reinforced discharge decision for better treatment outcomes is through timely deployment of the decision support model to work hand in hand with the expertise in deriving an integrative discharge decision and has been an agreed strategy to eliminate the foreseeable deteriorating quality of health for a discharged breast cancer patients and surging rates of mortality blamed on mistrusted discharge decisions. In this paper, we will discuss breast cancer clinical knowledge, data mining techniques, the classifying model accuracy, and the Python web-based decision support model that predicts avoidable re-hospitalization of a breast cancer patient through an informed clinical discharging support model.

Support Modeler在管道三维设计中的应用

通过若干项目的运用,我公司对Support Modeler的使用积累了丰富的工程经验。本文对Support Modeler的组成和建模过程做了详细介绍。通过与传统模式管道支吊架设计比较,突出了Support Modeler在三维设计中的智能化、信息化、高效性和正确性。最后展望了Support Modeler在未来管道三维设计中的美好前景。

A novel machine learning-assisted clinical diagnosis support model for early identification of pancreatic injuries in patients with blunt abdominal trauma:a cross-national study

Background:The recognition of pancreatic injury in blunt abdominal trauma is often severely delayed in clinical practice.The aim of this study was to develop a machine learning model to support clinical diagnosis for early detection of abdominal trauma.Methods:We retrospectively analyzed of a large intensive care unit database(Medical Information Mart for Intensive Care[MIMIC]-IV)for model development and internal validation of the model,and performed outer validation based on a cross-national data set.Logistic regres-sion was used to develop three models(PI-12,PI-12-2,and PI-24).Univariate and multivariate analyses were used to determine variables in each model.The primary outcome was early detection of a pancreatic injury of any grade in patients with blunt abdominal trauma in the first 24 hours after hospitalization.Results:The incidence of pancreatic injuries was 5.56%(n=18)and 6.06%(n=6)in the development(n=324)and internal validation(n=99)cohorts,respectively.Internal validation cohort showed good discrimination with an area under the receiver operator characteristic curve(AUC)value of 0.84(95%confidence interval[CI]:0.71–0.96)for PI-24.PI-24 had the best AUC,specificity,and positive predictive value(PPV)of all models,and thus it was chosen as the final model to support clinical diagnosis.PI-24 performed well in the outer validation cohort with an AUC value of 0.82(95%CI:0.65–0.98),specificity of 0.97(95%CI:0.91–1.00),and PPV of 0.67(95%CI:0.00–1.00).Conclusion:A novel machine learning-based model was developed to support clinical diagnosis to detect pancreatic injuries in patients with blunt abdominal trauma at an early stage.

Winkler Support Model and Nonlinear Boundary Conditions Applied to 3D Elastic Contact Problem Using the Boundary Element Method

This work presents a numerical methodology for modeling the Winkler supports and nonlinear conditions by proposing new boundary conditions. For the boundary conditions of Winkler support model, the surface tractions and the displacements normal to the surface of the solid are unknown, but their relationship is known by means of the ballast coefficient, whereas for nonlinear boundary conditions, the displacements normal to the boundary of the solid are zero in the positive direction but are allowed in the negative direction. In those zones, detachments of nodes might appear, leading to a nonlinearity, because the number of nodes that remain fixed or of the detached ones (under tensile tractions) is unknown. The proposed methodology is applied to the 3D elastic receding contact problem using the boundary element method. The surface t r actions and the displacements of the common int erface bet ween the two solids in contac t under the influence of different supports are calculated as well as the boundary zone of the solid where the new boundary conditions are applied. The problem is solved by a double-iterative met hod, so in the final solut ion, t here are no t r act ions or pene trations between the two solids or at the boundary of the solid where the nonlinear boundary conditions are Simula ted. The effectiveness of the proposed method is verified by examples.

Small-time scale network traffic prediction based on a local support vector machine regression model

In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the reconstructed phase space, the local support vector machine prediction method is used to predict the traffic measurement data, and the BIC-based neighbouring point selection method is used to choose the number of the nearest neighbouring points for the local support vector machine regression model. The experimental results show that the local support vector machine prediction method whose neighbouring points are optimized can effectively predict the small-time scale traffic measurement data and can reproduce the statistical features of real traffic measurements.

FAULT DIAGNOSIS APPROACH BASED ON HIDDEN MARKOV MODEL AND SUPPORT VECTOR MACHINE

Aiming at solving the problems of machine-learning in fault diagnosis, a diagnosis approach is proposed based on hidden Markov model （HMM） and support vector machine （SVM）. HMM usually describes intra-class measure well and is good at dealing with continuous dynamic signals. SVM expresses inter-class difference effectively and has perfect classify ability. This approach is built on the merit of HMM and SVM. Then, the experiment is made in the transmission system of a helicopter. With the features extracted from vibration signals in gearbox, this HMM-SVM based diagnostic approach is trained and used to monitor and diagnose the gearbox＇s faults. The result shows that this method is better than HMM-based and SVM-based diagnosing methods in higher diagnostic accuracy with small training samples.

SOFT SENSING MODEL BASED ON SUPPORT VECTOR MACHINE AND ITS APPLICATION

Soft sensor is widely used in industrial process control. It plays animportant role to improve the quality of product and assure safety in production. The core of softsensor is to construct soft sensing model. A new soft sensing modeling method based on supportvector machine (SVM) is proposed. SVM is a new machine learning method based on statistical learningtheory and is powerful for the problem characterized by small sample, nonlinearity, high dimensionand local minima. The proposed methods are applied to the estimation of frozen point of light dieseloil in distillation column. The estimated outputs of soft sensing model based on SVM match the realvalues of frozen point and follow varying trend of frozen point very well. Experiment results showthat SVM provides a new effective method for soft sensing modeling and has promising application inindustrial process applications.

Robustly stable model predictive control based on parallel support vector machines with linear kernel

Robustly stable multi-step-ahead model predictive control (MPC) based on parallel support vector machines (SVMs) with linear kernel was proposed. First, an analytical solution of optimal control laws of parallel SVMs based MPC was derived, and then the necessary and sufficient stability condition for MPC closed loop was given according to SVM model, and finally a method of judging the discrepancy between SVM model and the actual plant was presented, and consequently the constraint sets, which can guarantee that the stability condition is still robust for model/plant mismatch within some given bounds, were obtained by applying small-gain theorem. Simulation experiments show the proposed stability condition and robust constraint sets can provide a convenient way of adjusting controller parameters to ensure a closed-loop with larger stable margin.

High-rise building fire pre-warning model based on the support vector regression

Aiming at reducing the deficiency of the traditional fire pre-warning algorithms and the intelligent fire pre-warning algorithms such as artificial neural network,and then to improve the accuracy of fire prewarning for high-rise buildings,a composite fire pre-warning controller is designed according to the characteristic（ nonlinear,less historical data,many influence factors）,also a high-rise building fire pre-warning model is set up based on the support vector regression（ SV R）. Then the wood fire standard history data is applied to make empirical analysis. The research results can provide a reliable decision support framework for high-rise building fire pre-warning.

Support vector machine-based multi-model predictive control

In this paper, a support vector machine-based multi-model predictive control is proposed, in which SVM classification combines well with SVM regression. At first, each working environment is modeled by SVM regression and the support vector machine network-based model predictive control （SVMN-MPC） algorithm corresponding to each environment is developed, and then a multi-class SVM model is established to recognize multiple operating conditions. As for control, the current environment is identified by the multi-class SVM model and then the corresponding SVMN-MPC controller is activated at each sampling instant. The proposed modeling, switching and controller design is demonstrated in simulation results.

Modelling and Optimisation of a Spring-Supported Diaphragm Capacitive MEMS Microphone

Audio applications such as mobile communication and hearing aid devices demand a small size but high performance, stable and low cost microphone to reproduce a high quality sound. Capacitive microphone can be designed to fulfill such requirements with some trade-offs between sensitivity, operating frequency range, and noise level mainly due to the effect of device structure dimensions and viscous damping. Smaller microphone size and air gap will gradually decrease its sensitivity and increase the viscous damping. The aim of this research was to develop a mathematical model of a spring-supported diaphragm capacitive MEMS microphone as well as an approach to optimize a microphone’s performance. Because of the complex shapes in this latest type of diaphragm design trend, analytical modelling has not been previously attempted. A novel diaphragm design is proposed that offers increased mechanical sensitivity of a capacitive microphone by reducing its diaphragm stiffness. A lumped element model of the spring-supported diaphragm microphone is developed to analyze the complex relations between the microphone performance factors and to find the optimum dimensions based on the design requirements. It is shown analytically that the spring dimensions of the spring-supported diaphragm do not have large effects on the microphone performance com pared to the diaphragm and backplate size, diaphragm thickness, and air-gap distance. A 1 mm2 spring-supported diaphragm microphone is designed using several optimized performance parameters to give a –3 dB operating bandwidth of 10.2 kHz, a sensitivity of 4.67 mV/Pa (–46.5 dB ref. 1 V/Pa at 1 kHz using a bias voltage of 3 V), a pull-in voltage of 13 V, and a thermal noise of –22 dBA SPL.

Mathematical model of electric hydraulic and powered support control system at a plough mining face

Given the actual working of a fully mechanized plough at a mining face, we have proposed a formula for running constraints between powered supports and a coal plough under assumed geological conditions of the coal face and, on this basis, established an automatic control model of powered supports for the coal plough face. We introduced the working principle of the powered support control system of the plough at the mining face. We established three advanced characteristics of this control system: response speed, reliability and easy maintenance of the system. As well, we briefly introduced, the principal function of primary and subordinate controllers and the realization of the communication system by a Single Bus. Ten controllers were constructed and tested in our laboratorium. The results show that the control model is practical and meets actual conditions. It provides a theoretical basis for designing a computer control system for a powered support system of a plough at a mining face.

A Structural Equation Model of Residents' Support for Mega Event

The aim of this article is to develop a structural equation model to assess key factors of residents' support for hosting mega event based on previous literature.The model consisted of five latent constructs and eight path hypotheses.A survey was conducted in Shanghai before 2010 World Expo.It was found that the support for mega events is affected directly and/or indirectly by four determinants factors:perceived benefits,perceived costs,personal benefits and community attachment,and support relies heavily on perceived benefits rather than costs.This study contributes to the existing body of knowledge in an attempt to understand local residents' support for a mega event in different economic and cultural settings.

Support vector regression-based internal model control

This paper proposes a design of internal model control systems for process with delay by using support vector regression(SVR).The proposed system fully uses the excellent nonlinear estimation performance of SVR with the structural risk minimization principle.Closed-system stability and steady error are analyzed for the existence of modeling errors.The simulations show that the proposed control systems have the better control performance than that by neural networks in the cases of the training samples with small size and noises.

System dynamics model of the support-surrounding rock system in fully mechanized mining with large mining height face and its application

Fully mechanized mining with large mining height(FMMLMH)is widely used in thick coal seam mining face for its higher recovery ratio,especially where the thickness is less than 7.0 m.However,because of the great mining height and intense rock pressure,the coal wall rib spalling,roof falling and the instability of support occur more likely in FMMLMH working face,and the above three types of disasters interact with each other with complicated relationships.In order to get the relationship between each two of coal wall,roof,floor and support,and reduce the occurrence probability of the three types of disasters,we established the system dynamics(SD)model of the support-surrounding rock system which is composed of"coal wall-roof-floor-support"(CW-R-F-S)in a FMMLMH working face based on the condition of No.15104 working face in Sijiazhuang coal mine.With the software of Vensim,we also simulated the interaction process between each two factors of roof,floor,coal wall and the support.The results show that the SD model of"CW-R-F-S"system can reveal the complicated and interactive relationship clearly between the support and surrounding rock in the FMMLMH working face.By increasing the advancing speed of working face,the support resistance or the length of support guard,or by decreasing the tipto-face distance,the stability of"CW-R-F-S"system will be higher and the happening probability of the disasters such as coal wall rib spalling,roof falling or the instability of support will be lower.These research findings have been testified in field application in No.15104 working face,which can provide a new approach for researching the interaction relationship of support and surrounding rock.

Spatial Decision Support Applications Based on Three-Dimensional City Models

The basic mathematic models, such as the statistic model, the time\|serial model, the spatial dynamic model etc., and some typical analysis methods based on 3DCM are proposed and discussed. A few typical spatial decision making methods integrating the spatial analysis and the basic mathematical models are also introduced, e.g. visual impact assessment, dispersion of noise immissions, base station plan for wireless communication. In addition, a new idea of expectation of further applications and add\|in\|value service of 3DCM is promoted. As an example, the sunshine analysis is studied and some helpful conclusions are drawn.

Model based decision support system for land use changes and socio-economic assessments

Hydrological models are often linked with other models in cognate sciences to understand the interactions among climate, earth, water, ecosystem, and human society. This paper presents the development and implementation of a decision support system（DSS） that links the outputs of hydrological models with real-time decision making on social-economic assessments and land use management. Discharge and glacier geometry changes were simulated with hydrological model, water availability in semiarid environments. Irrigation and ecological water were simulated by a new commercial software MIKE HYDRO. Groundwater was simulated by MODFLOW. All the outputs of theses hydrological models were taken as inputs into the DSS in three types of links： regression equations, stationary data inputs, or dynamic data inputs as the models running parallel in the simulation periods. The DSS integrates the hydrological data, geographic data, social and economic statistical data, and establishes the relationships with equations, conditional statements and fuzzy logics. The programming is realized in C＋＋. The DSS has four remarkable features：（1） editable land use maps to assist decision-making;（2） conjunctive use of surface and groundwater resources;（3） interactions among water, earth, ecosystem, and humans; and（4） links with hydrological models. The overall goal of the DSS is to combine the outputs of scientific models, knowledge of experts, and perspectives of stakeholders, into a computer-based system, which allows sustainability impact assessment within regional planning; and to understand ecosystem services and integrate them into land and water management.

Nonlinear Model Predictive Control Based on Support Vector Machine with Multi-kernel

非线性的系统和它的特定的鉴定方法的 Multi-kernel-based 支持向量机器(SVM ) 模型结构被建议，它 SVM 是镇静的，线性内核功能与花键内核功能由 SVM 在系列列在后面。在这个模型的帮助下，非线性的模型预兆的控制能被转变到线性模型预兆的控制，并且因而预兆的控制是可能的发源的 multi-step-ahead 的最佳的输入的一个统一分析答案。这个算法不要求联机反复的优化以便对有更少的计算的即时控制合适。pH 中立化过程和 CSTR 反应堆的模拟结果显示出介绍算法的有效性和优点。