Model-Based Systems Engineering Approach to Design a Human Settlement to Better Serve Displaced People

The challenge of transitioning from temporary humanitarian settlements to more sustainable human settlements is due to a significant increase in the number of forcibly displaced people over recent decades, difficulties in providing social services that meet the required standards, and the prolongation of emergencies. Despite this challenging context, short-term considerations continue to guide their planning and management rather than more integrated, longer-term perspectives, thus preventing viable, sustainable development. Over the years, the design of humanitarian settlements has not been adapted to local contexts and perspectives, nor to the dynamics of urbanization and population growth and data. In addition, the current approach to temporary settlement harms the environment and can strain limited resources. Inefficient land use and ad hoc development models have compounded difficulties and generated new challenges. As a result, living conditions in settlements have deteriorated over the last few decades and continue to pose new challenges. The stakes are such that major shortcomings have emerged along the way, leading to disruption, budget overruns in a context marked by a steady decline in funding. However, some attempts have been made to shift towards more sustainable approaches, but these have mainly focused on vague, sector-oriented themes, failing to consider systematic and integration views. This study is a contribution in addressing these shortcomings by designing a model-driving solution, emphasizing an integrated system conceptualized as a system of systems. This paper proposes a new methodology for designing an integrated and sustainable human settlement model, based on Model-Based Systems Engineering and a Systems Modeling Language to provide valuable insights toward sustainable solutions for displaced populations aligning with the United Nations 2030 agenda for sustainable development.

Formal management-specifying approach for model-based safety assessment

In the field of model-based system assessment,mathematical models are used to interpret the system behaviors.However,the industrial systems in this intelligent era will be more manageable.Various management operations will be dynamically set,and the system will be no longer static as it is initially designed.Thus,the static model generated by the traditional model-based safety assessment(MBSA)approach cannot be used to accurately assess the dependability.There mainly exists three problems.Complex:huge and complex behaviors make the modeling to be trivial manual;Dynamic:though there are thousands of states and transitions,the previous model must be resubmitted to assess whenever new management arrives;Unreusable:as for different systems,the model must be resubmitted by reconsidering both the management and the system itself at the same time though the management is the same.Motivated by solving the above problems,this research studies a formal management specifying approach with the advantages of agility modeling,dynamic modeling,and specification design that can be re-suable.Finally,three typical managements are specified in a series-parallel system as a demonstration to show the potential.

Use of models in large-area forest surveys： comparing model-assisted, model-based and hybrid estimation

This paper focuses on the use of models for increasing the precision of estimators in large-area forest surveys. It is motivated by the increasing availability of remotely sensed data, which facilitates the development of models predicting the variables of interest in forest surveys. We present, review and compare three different estimation frameworks where models play a core role： model-assisted, model-based, and hybrid estimation. The first two are well known, whereas the third has only recently been introduced in forest surveys. Hybrid inference mixes design- based and model-based inference, since it relies on a probability sample of auxiliary data and a model predicting the target variable from the auxiliary data.We review studies on large-area forest surveys based on model-assisted, model- based, and hybrid estimation, and discuss advantages and disadvantages of the approaches. We conclude that no general recommendations can be made about whether model-assisted, model-based, or hybrid estimation should be preferred. The choice depends on the objective of the survey and the possibilities to acquire appropriate field and remotely sensed data. We also conclude that modelling approaches can only be successfully applied for estimating target variables such as growing stock volume or biomass, which are adequately related to commonly available remotely sensed data, and thus purely field based surveys remain important for several important forest parameters.

Model-Based Degree Estimation of Unbalance and Misalignment in Flexible Coupling-rotor System

The condition of rotor system must be assessed in order to develop condition-based maintenance for rotating machinery. It is determined by multiple variables such as unbalance degree, misalignment degree, the amount of bending deformation of the shaft, occurrence of shaft crack of rotor system and so on. The estimation of the degrees of unbalance and misalignment in flexible coupling-rotor system is discussed. The model-based approach is employed to solve this problem. The models of the equivalent external loads for unbalance and misalignment are derived and analyzed. Then, the degrees of unbalance and misalignment are estimated by analyzing the components of the equivalent external loads of which the frequencies are equal to the 1 and 2 times running frequency respectively. The equivalent external loads are calculated according to the dynamic equation of the original rotor system and the differences between the dynamical responses in normal case and the vibrations when the degree of unbalance or misalignment or both changes. The denoise method based on bandpass filter is used to decrease the effect of noise on the estimation accuracy. The numerical examples are given to show that the proposed approach can estimate the degrees of unbalance and misalignment of the flexible coupling-rotor system accurately.

Experts' Knowledge Fusion in Model-Based Diagnosis Based on Bayes Networks

In previous researches on a model-based diagnostic system, the components are assumed mutually independent. Howerver , the assumption is not always the case because the information about whether a component is faulty or not usually influences our knowledge about other components. Some experts may draw such a conclusion that 'if component m 1 is faulty, then component m 2 may be faulty too'. How can we use this experts' knowledge to aid the diagnosis? Based on Kohlas's probabilistic assumption-based reasoning method, we use Bayes networks to solve this problem. We calculate the posterior fault probability of the components in the observation state. The result is reasonable and reflects the effectiveness of the experts' knowledge.

Feedback Scheduling of Model-based Networked Control Systems with Flexible Workload

In this paper,a novel control structure called feedback scheduling of model-based networked control systems is proposed to cope with a flexible network load and resource constraints.The state update time is adjusted according to the real-time network congestion situation.State observer is used under the situation where the state of the controlled plant could not be acquired.The stability criterion of the proposed structure is proved with time-varying state update time.On the basis of the stability of the novel system structure,the compromise between the control performance and the network utilization is realized by using feedback scheduler. Examples are provided to show the advantage of the proposed control structure.

Optimized model-based control of main mine ventilation air flows with minimized energy consumption

In early 2018,the Boliden Garpenberg operation implemented an optimized control strategy as an addition to the existing ventilation on demand system.The purpose of the strategy is to further minimize energy use for main and booster fans,whilst also fulfilling airflow setpoints without violating constraints such as min/max differential pressure over fans and interaction of air between areas in mines.Using air flow measurements and a dynamical model of the ventilation system,a mine-wide coordination control of fans can be carried out.The numerical model is data driven and derived from historical operational data or step changes experiments.This makes both initial deployment and lifetime model maintenance,as the mine evolves,a comparably easy operation.The control has been proven to operate in a stable manner over long periods without having to re-calibrate the model.Results prove a 40%decrease in energy use for the fans involved and a greater controllability of air flow.Moreover,a 15%decrease of the total air flow into the mine will give additional proportional heating savings during winter periods.All in all,the multivariable controller shows a correlation between production in the mine and the ventilation system performance superior to all of its predecessors.

On the model-based networked control for singularly perturbed systems

In this paper, the control of a two-time-scale plant, where the sensor is connected to a linear controller/ actuator via a network is addressed. The slow and fast systems of singularly perturbed systems are used to produce an estimate of the plant state behavior between transmission times, by which one can reduce the usage of the network. The approximate solutions of the whole systems are derived and it is shown that the whole systems via the network control are generally asymptotically stable as long as their slow and fast systems are both stable. These results are also extended to the case of network delay.

Comparison of Analyses of Genetic Structure among Chinese Indigenous Chicken Breeds using Distance-based and Model-based Methods

The Nei's improved genetic distance(DA)and gene flow(Nm)were measured using sixteen microsatellite markers.Dendograms based on DA genetic distance using the neighbor-joining(NJ)method and STRUCTURE program were constructed to analyze the genetic structure and relationship among 10 Chinese indigenous chicken breeds.The results showed that dendograms of DA genetic distance using the NJ method divided the 10 chicken breeds into two main clusters;one consisted of breeds of low weight body(CHA,TTB,XIA,GUS and BAI),the other contained heavier breeds(LAN,DAG,YOU,XIS and LUY).In the lighter breeds,TIB and CHA clustered together,as did XIA and GUS.In the heavier breeds,XIS and LUY was clustered together in one branch,but LAN,DAG and YOU clustered in independent branches.The results were consistent with Nm estimates among the 10 indigenous chicken breeds.The STRUCTURE program properly inferred the presence of genetic structure despite not pre-defining the origin of individuals.The genetic cluster inferred by STRUCTURE was basically the same as that from the DA distance clustering method.An advantage of the STRUCTURE program was its ability to identify the migrants and admixed individuals in the 10 chicken populations;this could not be achieved by use of the DA distance clustering method.

Model-based predictive controller design for a class of nonlinear networked systems with communication delays and data loss

This paper discusses the model-based predictive controller design of networked nonlinear systems with communica- tion delay and data loss. Based on the analysis of the closed-loop networked predictive control systems, the model-based networked predictive control strategy can compensate for communication delay and data loss in an active way. The designed model-based predictive controller can also guarantee the stability of the closed-loop networked system. The simulation re- suits demonstrate the feasibility and efficacy of the proposed model-based predictive controller design scheme.

A Model-Based Unmatched Disturbance Rejection Control Approach for Speed Regulation of a Converter-Driven DC Motor Using Output-Feedback

The speed regulation problem with only speed measurement is investigated in this paper for a permanent magnet direct current(DC)motor driven by a buck converter.By lumping all unknown matched/unmatched disturbances and uncertainties together,the traditional active disturbance rejection control(ADRC)approach provides an intuitive solution for the problem under consideration.However,for such a higher-order disturbed system,the increase of poles for the extended state observer(ESO)therein will lead to drastically growth of observer gains,which causes severe noise amplification.This paper aims to propose a new model-based disturbance rejection controller for the converter-driven DC motor system using output-feedback.Instead of estimating lumped disturbances directly,a new observer is constructed to estimate the desired steady state of control signal as well as errors between the real states and their desired steady-state responses.Thereafter,a controller with only speed measurement is proposed by utilizing the estimates.The performance of the proposed method is tested through experiments on dSPACE.It is further shown via numerical calculations and experimental results that the poles of the observer within the proposed control approach can be largely increased without significantly increasing magnitude of the observer gains.

An EFSM-Based Test Data Generation Approach in Model-Based Testing

Testing is an integral part of software development.Current fastpaced system developments have rendered traditional testing techniques obsolete.Therefore,automated testing techniques are needed to adapt to such system developments speed.Model-based testing(MBT)is a technique that uses system models to generate and execute test cases automatically.It was identified that the test data generation(TDG)in many existing model-based test case generation(MB-TCG)approaches were still manual.An automatic and effective TDG can further reduce testing cost while detecting more faults.This study proposes an automated TDG approach in MB-TCG using the extended finite state machine model(EFSM).The proposed approach integrates MBT with combinatorial testing.The information available in an EFSM model and the boundary value analysis strategy are used to automate the domain input classifications which were done manually by the existing approach.The results showed that the proposed approach was able to detect 6.62 percent more faults than the conventionalMB-TCG but at the same time generated 43 more tests.The proposed approach effectively detects faults,but a further treatment to the generated tests such as test case prioritization should be done to increase the effectiveness and efficiency of testing.

Model-Based Split-Range Algorithm for the Temperature Control of a Batch Reactor

In the manufacturing processes of high value-added products in the pharmaceutical, fine chemical polymer and food industry, insufficient control might produce off-grade products. This can cause significant financial losses, or in the pharmaceutical industry, it can result in an unusable batch. In these industries, batch reactors are commonly used, the control of which is essentially a problem of temperature control. In the industry, an increasing number of heating-cooling systems utilising three different temperature levels can be found, which are advantageous from an economic point of view. However, it makes the control more complicated. This paper presents a split-range designing technique using the model of the controlled system with the aim to design a split-range algorithm more specific to the actual sys- tem. The algorithm described provides high control performance when using it with classical PID-based cascade temperature control of jacketed batch reactors;however, it can be used with or as part of other types of controllers, for ex- ample, model-based temperature controllers. The algorithm can be used in the case of systems where only two as well as where three temperature levels are used for temperature control. Besides the switching between the modes of opera- tion and calculating the value of the manipulated variable, one of the most important functions of the split-range algo- rithm is to keep the sign of the gain of the controlled system unchanged. However, with a more system-specific split-range solution, not only can the sign of the gain be kept unchanged, but the gain can also be constant or less de- pendent on the state of the system. Using this solution, the design of the PID controller becomes simpler and can be implemented in existing systems without serious changes.

Local Polynomial Regression Estimator of the Finite Population Total under Stratified Random Sampling: A Model-Based Approach

In this paper, auxiliary information is used to determine an estimator of finite population total using nonparametric regression under stratified random sampling. To achieve this, a model-based approach is adopted by making use of the local polynomial regression estimation to predict the nonsampled values of the survey variable y. The performance of the proposed estimator is investigated against some design-based and model-based regression estimators. The simulation experiments show that the resulting estimator exhibits good properties. Generally, good confidence intervals are seen for the nonparametric regression estimators, and use of the proposed estimator leads to relatively smaller values of RE compared to other estimators.

Review of Model-Based Testing Approaches in Production Automation and Adjacent Domains—Current Challenges and Research Gaps

As production automation systems have been and are becoming more and more complex, the task of quality assurance is increasingly challenging. Model-based testing is a research field addressing this challenge and many approaches have been suggested for different applications. The goal of this paper is to review these approaches regarding their suitability for the domain of production automation in order to identify current trends and research gaps. The different approaches are classified and clustered according to their main focus which is either testing and test case generation from some form of model automatons, test case generation from models used within the development process of production automation systems, test case generation from fault models or test case selection and regression testing.

A Method for the Application of Numerical Simulations during Firefighting Operations Using Pre-Simulated, Model-Based Fire Scenarios

The spread of fire and smoke during a fire incident plays a crucial role in rescuing people from the burning building. So it is important for the decision makers (the head of rescue staff) to get a prediction about the spread of fire inside the building through computational techniques like numerical fire simulations. But these techniques require advanced mathematical knowledge and are very time consuming. This paper presents a new method which employs a set of pre-simulated and model-based scenarios to find the closest one to the real fire and present its results to the decision makers. For this purpose, we shift the performance consuming numerical fire simulations into a former phase by integration of these simulations into the planning process of the building. This is realized by enhancing the methods of Building Information Modeling (BIM). To provide the fire simulation results during a real case, our new concept includes a scenario database where all simulated fire scenarios will be collected. In a real case, a special search algorithm will go through this database to find the closest pre-simulated fire scenario to the real fire on the basis of reported information from the burning building.

Model-based Analysis of Ventilation Inhomogeneity in Respiratory Mechanics

Individualized models of respiratory mechanics help to reduce potential harmful effects of mechanical ventilation by supporting the evaluation of patient-specific lung protective ventilation strategies. Assessing ventilation inhomogeneities might be an important aspect in optimizing ventilator settings. The aim of this studyis to capture and analyze ventilation inhomogeneity by a mathematical model using clinical data. The results show that the lung physiology of mechanically ventilated patients without lung condition can be described by an inhomogeneity model revealing two alveolar compartments with median time constants of 0.4 and 3.9 s. Thus, the IHM in combination with specific ventilation maneuver might be suitable to capture lung physiology for model-based optimization of ventilator settings but requires additional image-based investigations to further support the validity of the model.

Model-based design method of two-axis four-actuator fast steering mirror system

This work was focused on the model-based design method of two-axis four-actuator(TAFA) fast steering mirror system(FSM), in order to improve the design efficiency. The structure and operation principle commonality of normal TAFA FSM were investigated. Based on the structure and the commonality, the conditions of single-axis idea, high-frequency resonance and coupling were modeled gradually. Combining these models, a holonomic system model was established to reflect and predict the performance of TAFA FSM. A model-based design method was proposed based on the holonomic system model. The design flow and design concept of the method were described. In accordance with the method, a TAFA FSM was designed. Simulations and experiments of the FSM were done, and the results of them were compared. The compared results indicate that the holonomic system model can well reflect and predict the performance of TAFA FSM. The bandwidth of TAFA FSM is more than 250 Hz; adjust time is less than 15 ms;overshoot is less than 8%; position accuracy is better than 10 μrad; the FSM prototype can satisfy the requirements.

Approach to Model-Based Development of Context-Aware Application

In order to address challenges posed by advances in mobile computing, ubiquitous devices software engineering, wireless and sensor technologies in many applications running, this paper provides an approach that simplifies the design and the implementation of context-aware Interactive systems called CAISDA (Context-Aware Interactive System Development Approach). This approach is based on the analysis and synthesis of context-aware frameworks proposed in literature.

Analytical Solution for Model-Based Dynamic Power Factor Measurement in AC Resistance Spot Welding

On the basis of welding transformer circuit model, a new measuring method was proposed. This method measures the peak angle of the welding current, and then calculates the dynamic power factor in each half-wave. An artificial neural network is trained and used to generate simulation data for the analytical solution, i.e. a high-order binary polynomial, which can be easily adopted to calculate the power factor online. The tailored sensing and computing system ensures that the method possesses a real-time computational capacity and satisfying accuracy. A DSP-based resistance spot welding monitoring system was developed to perform ANN computation. The experimental results suggest that this measuring method is feasible.