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Cilj rada je projektirati laboratorijski postav žiroskopa. Za izradu postolja korišteni su dijelovi isprintani 3D printerom prethodno dizajnirani u programu Autodesk Fusion 360. Nakon konstrukcije žiroskopa, potrebno ga je eksperimantalno provjeriti. Žiroskopski disk i postolje pokreću dva motora. Kut zakreta postolja mjeri enkoder osovinom spojen na donji dio motora. Kut otklona diska očitava se pomoću enkodera spojenog na njegovu osovinu. Poremećaj koji djeluje na sustav je kut zakreta cijelog laboratorijskog postava. Cilj je kompenzirati otklon diska pri skokovitoj promjeni referentne vrijednosti kuta zakreta postava. Odziv kuta otklona diska treba biti aperiodski. Za regulaciju sustava korišten je regulator po varijablama stanja. The goal of project is to design gyroscope laboratory process. 3D printed materials are used to construct gyroscope support that are designed in program Autodesk Fusion 360. After construction, gyroscope should be experimentally verified. Two motors drive gyroscope disc and its support plate. The support plate angle is measured by encoder under the motor that is coupled on its shaft. Inclination of disk is measured by encoder that is coupled on its shaft. Disturbance in process is the angle of laboratory process rotation . Goal is to compensate disc inclination on step change of referent support plate angle. Inclination angle response should be aperiodic. State regulator is used for regulation of a process.

The quality of the air in underground mines is a challenging issue due to many factors, such as technological processes related to the work of miners (blasting, air conditioning, and ventilation), gas release by the rock mass and geometry of mine corridors. However, to allow miners to start their work, it is crucial to determine the quality of the air. One of the most critical parameters of the air quality is the carbon monoxide (CO) concentration. Thus, in this paper, we analyze the time series describing CO concentration. Firstly, the signal segmentation is proposed, then segmented data (daily patterns) is visualized and statistically analyzed. The method for blasting moment localization, with no prior knowledge, has been presented. It has been found that daily patterns differ and CO concentration values reach a safe level at a different time after blasting. The waiting time to achieve the safe level after blasting moment (with a certain probability) has been calculated based on the historical data. The knowledge about the nature of the CO variability and sources of a high CO concentration can be helpful in creating forecasting models, as well as while planning mining activities.

During surgery for brain tumor, expert surgeons consider maximum brain tumor removal and minimum postoperative neuroglial complications. At this brain tumor resection, the surgeon resects based on his knowledge and experience, the surgical process, work contents, and duration of surgery vary by cases. It is difficult for the young surgeons and surgical staffs to understand the surgical process involved. Visualization of the surgical process is an effective tool for aiding the understanding among the surgical staff. This paper presents a surgical identification system using intra-operative information and machine learning. We extract surgical process features using navigation system's log, MR images and microscope video. Then, the surgical processes are identified using Hierarchical Hidden Markov Model. The method has been evaluated using past log data (navigation system's log, MR images and microscope video). The accuracy of the identified surgical processes was 84% in 12 processes. This result indicated that the surgical process identification error is a few minutes, high revel accuracy. In addition, this result is a possibility to support understanding of surgical processes for young surgeons and surgical staffs.

Submitted by Natasha Valladão (natashasilvaa4@gmail.com) on 2021-02-02T01:21:51Z No. of bitstreams: 1 CharlesRajivCavalcanteDoLago-min.pdf: 1451572 bytes, checksum: 9725d066dfcef422e904aa42dc5c775d (MD5) Made available in DSpace on 2021-02-02T01:21:51Z (GMT). No. of bitstreams: 1 CharlesRajivCavalcanteDoLago-min.pdf: 1451572 bytes, checksum: 9725d066dfcef422e904aa42dc5c775d (MD5) Previous issue date: 2019-02 O controle de processos tem, em sua representação matemática tradicional, a descrição de processos por meio de equações diferenciais ordinárias, EDOs. Uma alternativa mais geral de representar os processos dinâmicos a serem simulados e controlados é através de sua modelagem por meio de equações algébricodiferenciais, EADs. O controle de processos é classicamente representado por controladores PIDs (Proporcional, Integral e Derivativo), tendo como camada superior o controle preditivo baseado em modelo matemático, ou apenas controle preditivo, MPC (Model Predictive Control). Há uma crescente atividade acadêmica no desenvolvimento do controle ótimo, uma abordagem de controle também baseada em modelo, na qual são utilizados algoritmos de otimização. O presente trabalho utiliza uma técnica de simulação computacional de sistemas de EADs, chamada inicialização direta, que consiste em inicializar o modelo a partir de uma condição estacionária e utilizar uma função de regularização para realizar a transição de uma condição à outra. Essa técnica foi utilizada para identi cação e controle de quatro processos descritos por EADs de índice superior. Os processos são descritos por EADs de índice 2 sendo o modelo benchmark de condensador utilizado por Pantelides, dois modelos de reatores, um isotérmico e outro não isotérmico, e um modelo de ash reativo adimensional. Os processos descritos por modelos foram identi cados para funções de transferência e controlados com um controlador MPC do tipo GPC (Generalized Predictive Control). Ficou evidenciado que a redução de índice pode levar a comportamentos incoerentes e os modelos identi cados lineares, baseados em função de transferência, representaram adequadamente o comportamento local de modelos EAD de ordem superior. Process control has in its traditional mathematical representation the process description by means of ordinary di erential equations, ODEs. A more general alternative for representing the dynamic process to be simulated and controlled is through its discription in di erential-algebraic equations, DAEs. The process control is classically represented by PID controllers (Proportional, Integral and Derivative) and having in the upper layer the model predictive control strategies, MPC. It presenting nowadays a growing research activity on the development of optimal control, a control technique also based on a model, in which optimization techniques are used. The present work employs a computational simulation technique for DAE systems, known as direct initialization, which consists of initializing the model from a stationary condition and using a regularization function to carry out the transition from one condition to the other. This approach was used to identify and control four processes described by high-index DAEs. The processes are described by index 2 DAEs being the benchmark model of a condenser used by Pantelides, two models of reactors, one isothermal and other non-isothermal, and a non-dimensional model of reactive ash drum. The processes described by models were identi ed to transfer functions and controlled with MPC controller of the GPC (Generalized Predictive Control) type. It was evidenced that the index reduction can lead to incoherent behaviors and the identi ed linear models, based on transfer function, adequately represented the local behavior of high-index DAE models.

Leader election is a classical problem in distributed system applications. There are many leader election algorithms, but we focus here on Bully Algorithm (BA). The main drawback of BA algorithm is the high number of messages passing. In BA algorithm, the message passing has order O (\(n^2\)) that increases heavy traffic on the network. In this paper, an Adaptive BA (ABA) is proposed to reduce the number of messages and make the leader election operation more flexible and safer. The proposed algorithm is based on the Highest Process Identification (HPI) and the Next HPI (NHPI) to facilitate the leader election operation. Moreover, the repetition of the leader election is stopped when the candidate coordinator fails. Our analytical equations show that the ABA algorithm is more efficient rather than BA algorithm, in both, the number of message passing and the latency, and the message passing complexity decreased to O(n).

The paper describes the use of a recently introduced relay shifting method for estimation parameters of a second order time delayed model. The aim is to obtain a process model for setting PID control parameters. For this purpose, an algorithm is designed for estimation of model parameters from two frequency response points obtained from a single relay feedback test without any assumptions about a model transfer function. The relay shifting method is slightly modified here by using an integrator to receive the frequency response points in positions more suitable for model fitting. This modification enables to better estimate the static gain even under constant load disturbance. The proposed solution is demonstrated on simulated examples.

Features of identification of technological process in real time are considered. It is shown that the use of standard statistical methods in conditions of insufficiency and inaccuracy of measuring information is limited. Special methods of processing the measurement results are given. An algorithm for operational evaluation and prediction of product parameter values based on measurement results, taking into account identification features, is proposed.

On the basis of consideration of dissociation, hydration, association, and ligand exchange, the assignment of absorption bands in the electronic spectra of aqueous solutions of the Na4[UO2(O2)CO3)2] complex has been performed. It has been demonstrated that the absorption in the range 190–400 nm is caused by the oxygen atoms of the O22- and CO32- groups and hydration water molecules of dissociated and neutral complex species Na3[UO2(O2)(CO3)2]–, Na2[UO2(O2)(CO3)2]2–, and Na4[UO2(O2)(CO3)2].

The task of real time identification of pulse energy conversion system state is investigated in this work. Algorithm of identification of the pulse energy conversion system state is described and experimentally tested. The designed algorithm provides determination of the main process period of the pulse energy conversion system.

The article presents the results of analysis, the use of continuous flow of logic at the stage of production planning and control of the company producing furniture. The concept of continuous flow tends to regulate the flow of materials in a manner that provides the shortest flow path without unnecessary activities (Muda is a Japanese word meaning waste), a constant takt and defined throughput at constant resource requirements for the so-called transfer of material through the whole process. In the study Glenday’d sieve method was used to identify the correct area, which requires the value stream mapping, and areas called excessive complexity, which do not provide added value. The use of Glenday’s sieve method made it possible to identify areas in which it must be improve production capacity.

Additive Manufacturing (AM) is a novel manufacturing method where a component is fabricated by accumulating material layer-by-layer therefore facilitating customized near-net-shape components while maximizing design freedom. A metal AM technology suited for large-scale component fabrication that has been emerging in recent years and is commonly referred to as Wire and Arc AM (WAAM) uses Gas Metal Arc Welding (GMAW) technology to deposit material and large-scale robotic serial manipulator systems (reach >1 m cubed). One of the requirements for applying GMAW welding technology to AM is to identify and optimize deposition parameters in order to achieve a desired deposition quality measured in terms of geometrical, mechanical and metallurgical consistency. In this work, deposition process parameters qualitatively and quantitatively influencing the geometrical, mechanical and metallurgical consistency are identified and statistically validated. A deposition parameter combination is found that optimizes the quality of single-track wall benchmark components made from low-carbon steel. Moreover, correlations are found between accumulated heat during fabrication and the geometrical variations of the benchmark components due to bead slumping. Additionally, correlations between microstructure variations and geometrical variations are found. Finally, based on the presented analyses, in-situ temperature monitoring methods are proposed in order to achieve optimal component quality.

Background A significant portion of each country's budget is assigned to its health care system annually. Given the current economic conditions, saving costs is a key issue. Therefore, any situation that leads to lower efficiency and higher costs should be remedied. With much of the hospitals' budgets spent on operating rooms, it is important to use strategies to increase their efficiency and quality. The purpose of this study is to design a mechanism to reduce the cancellation of elective surgical procedures, which incurs considerable costs. Methods To this end, the data gathered from an educational hospital in Tehran were evaluated. The reasons for the surgical cancellations were determined through data analysis and process identification. Subsequently, considering the contents of the current literature and consultations with the hospital staff, suggestions were made to reduce surgical cancellations. Finally, using a solution allocation model, the best solution in terms of costs, efficiency, and implementation time was assigned by using the Fuzzy Extended Analytical Hierarchy Process to each of the reasons and surgeries. Results By solving the allocation model using the GAMs software, the solutions were assigned to specific reasons and surgeries. This solved model assigns the best solutions to cancellation reasons regarding solutions cost, efficiency and implementation time. Conclusions The cancellation of the six investigated surgeries alone imposes on the hospital approximately 1165,653,450 rials annually, so it is true to assume that, in general, reducing the cancellation of surgery can reduce hospital costs considerably.

Wood is one of Indonesia’s very rich natural resources abundant because the number reaches around 4,000 species. The process of identifying wood species currently it is still done manually in a relatively long time by observing types of fibers, vessels, rays, and other structures directly because there is not a much automatic application of identification of wood species is made. This is an obstacle for experts anatomy of wood because it must check wood species accurately and quickly. Therefore that, the field of Computer Vision is the right solution to develop the process Identification of wood species automatically. In this research program will be made application of Computer Vision to identify wood species with using the Daubechies Wavelet (DW) and Local Binary Pattern (LBP) methods for The extraction of the wood pattern is then classified Support Vector Machine (SVM) method. Results obtained in this study is able to identify the microscopic image of wood as a species of wood with average SVM accuracy is 85%.

Process reengineering (PR) in manufacturing organizations is a big challenge, as shown by the high rate of failure. This research investigated different approaches to process reengineering to identify limitations and propose a new strategy to increase the success rate. The proposed methodology integrates data as a procedure for process identification (PI) and mapping and incorporates process verification to analyze the changes made in a specific process. The study identifies interdependency within the manufacturing process (MP) and proposes a generic process reengineering approach that uses simulation and analysis of production line data as a method for understanding the changes required to optimize the process. The paper discusses the methodology implementation technique as well as process identification and the process mapping technique using simulation tools. It provides an improved data-driven process reengineering framework that incorporates process verification. Based on the proposed model, the study investigates a production line process using the WITNESS Horizon 21 simulation package and analyse the efficiency of data-driven process reengineering and process verification in terms of implementing changes.

When very detailed simulation of geometrically complex objects is needed, finite element models are often the best choice due to their description of spatial and temporal behavior of the modelled object. The drawback of finite element models is their large number of parameters. This makes it difficult to match a model to a real measured object by process identification methods. In this paper this problem is addressed by new software that is compatible to ANSYS as the probably most known finite element software.

This paper considers the problem of estimating the frequency response of an unknown plant from impulse or step response measurements, possibly with non-ideal step signals. The proposed method is direct no model identification of the plant is needed and it can be applied using a single set of data generated by the plant, with no need for specific experiments nor iterations. The frequency response of the plant, at a desired frequency, is estimated by a virtual experiment generating a filtered version of the data set. Numerical simulations show the effectiveness of the method.

Spatial spread of infectious diseases among populations via the mobility of humans is highly stochastic and heterogeneous. Accurate forecast/mining of the spread process is often hard to be achieved by using statistical or mechanical models. Here we propose a new reverse problem, which aims to identify the stochastically spatial spread process itself from observable information regarding the arrival history of infectious cases in each subpopulation. We solved the problem by developing an efficient optimization algorithm based on dynamical programming, which comprises three procedures: i, anatomizing the whole spread process among all subpopulations into disjoint componential patches; ii, inferring the most probable invasion pathways underlying each patch via maximum likelihood estimation; iii, recovering the whole process by assembling the invasion pathways in each patch iteratively, without burdens in parameter calibrations and computer simulations. Based on the entropy theory, we introduced an identifiability measure to assess the difficulty level that an invasion pathway can be identified. Results on both artificial and empirical metapopulation networks show the robust performance in identifying actual invasion pathways driving pandemic spread., 14pages, 8 figures; Accepted by IEEE Transactions on Cybernetics

This paper considers parameter estimation for linear parameter varying (LPV) systems with randomly missing output data. The multi-model LPV model is adopted and the identification problem is formulated under the scheme of the generalized expectation–maximization (GEM) algorithm. In order to deal with the missing output data, the local models are firstly taken to have the finite impulse response (FIR) model structure. To alleviate potential overparameterization problem, a prior on FIR model coefficients is imposed and the GEM algorithm is modified to derive the maximum a posterior (MAP) estimates of the multi-mode LPV FIR model parameters. Since the FIR model is not suitable for general control applications, a multi-mode LPV output error (OE) model is then identified by applying the GEM algorithm to the same identification data set with parameters initialized based on the estimated FIR models. One simulation example and two experiments are presented to demonstrate the efficiency of the proposed method.

This paper investigates the application of an optimization method, which is the genetic algorithm, in order to identify the parameters of an activated sludge process for waste water treatment. The studied model is a hybrid reduced-order one. The genetic algorithm tries to find out the optimal parameters that ensure a minimal mean square error between the measured and simulation outputs. Simulation results show the competence of the proposed approach to seize the true model's parameter values.

The problem of outliers in industrial processes databases is outlined. The outlier detection data preprocessing procedure is examined. The different ways of outlier detection are compared. В докладе изложена проблема наличия выбросов в базах данных производственных процессов. Рассмотрена процедура обработки данных, направленная на поиск выбросов. Произведено сравнение различных методов поиска выбросов.

For many decades, process models have built the basis for economically successful participation in the market. Companies are still faced with the task of identifying, defining and visualizing their processes, especially in today’s era of digitization. In this era and due to more and more complex inter-organizational processes across corporate boundaries, the question arises as whether techniques and approaches for elicitation of processes in the context of business process management have changed or if the established techniques are still appropriate. Here, digitization could have significant potential to automate the elicitation of processes. To address this issue we have conducted a systematic literature review and identified the theoretical requirements for the elicitation of processes. Then, based on an interview study with experienced consultants, we compared the theoretic results with the current applied techniques in today’s practice. Selected results are presented and discussed in this paper.

Item does not contain fulltext Employing nurses as opinion leaders to implement guidelines may be a promising implementation activity. Until now, insight into necessary competencies of nurse opinion leaders is lacking. We studied and supported aspiring nurse opinion leaders, using a training program based on social influence and implementation theory. Twenty-one competencies were identified, of which the most important were cooperating, communicating, delegating, giving feedback, networking, and information processing. Understanding and addressing these competencies may support the implementation of evidence-based guidelines.

This paper presents a software tool developed for aiding the identification process of dynamic systems. This first version of the tool allows the user to generate excitation signals that are suitable for each kind of plant and also to automatically treat the raw process data in order to identify different types of models, which may include multiple-input and multiple-output (MIMO) and nonlinear behaviors. The potential of the tool is illustrated by performing the identification process of an industrial test stand for the energetic performance evaluation of refrigerant compressors. The results compare linear and nonlinear models for the process around an operating point and show that the proposed tool provides good results even for this nontrivial MIMO process, which presents dead times and other nonlinearities.

Model Predictive Control (MPC) Relevant Identification (MRI) methods are a good option for identification, if there is model structure mismatch. Herein a new MRI method, named Enhanced Multistep Prediction Error Method (EMPEM), is proposed. EMPEM combines the best characteristics of others MRI methods in a single algorithm. It was developed to identify either closed-loop or open-loop systems; its convergence and stability make it perform better than the other presented methods. To show the advantages of EMPEM, a comparison is made against two other methods (one MRI and one PEM). The statistical analysis indicates that in the cases studied, the performance and the robustness of the new method is equal or better than the other ones.

In this work, a novel relay feedback experiment method is proposed for system identification by connecting a fractional order integrator in front of a basic relay component. With this approach, a linear time invariant process model can be estimated at an arbitrary phase angle in the third or fourth quadrant on the Nyquist curve. Another contribution of this paper is to present a pseudo frequency response concept to generalize the describing function method so as to make it compatible with the frequency response operations. The proposed setup is illustrated through mathematical derivation using the proposed concept. Simulations are provided to verify the effectiveness.

The paper describes a mathematical model of PHA biopolymer production process by Pseudomonas putida KT2442 where the octanoic acid is used as a substrate. The process is modeled using mass balances for fed-batch cultivation. Proper fitting to experimental data is obtained by identification of the model parameters. The model exhibits good agreement with experiments and its possible application for control is considered in the paper.

[1] A correlation between solar wind velocity (VSW) and energetic electron fluxes (EEF) at the geosynchronous orbit was first identified more than 30 years ago. However, recent studies have shown that the relation between VSW and EEF is considerably more complex than was previously suggested. The application of process identification technique to the evolution of electron fluxes in the range 1.8 − 3.5 MeV has also revealed peculiarities in the relation between VSW and EEF at the geosynchronous orbit. It has been revealed that for a constant solar wind density, EEF increase with VSW until a saturation velocity is reached. Beyond the saturation velocity, an increase in VSW is statistically not accompanied with EEF enhancement. The present study is devoted to the investigation of saturation velocity and its dependency upon solar wind density using the reverse arrangement test. In general, the results indicate that saturation velocity increases as solar wind density decreases. This implies that solar wind density plays an important role in defining the relationship between VSW and EEF at the geosynchronous orbit.

A new model Gm(s), defined by the four measurable parameters ultimate gain, ultimate frequency, amplitude of ultimate oscillation and dead-time, is obtained from tangent to the Nyquist curve at the critical point. This model represents the generalization of the Ziegler-Nichols process dynamics characterization method, for a large class of stable, integrating and unstable processes Gp(s). In the present paper, by applying time and amplitude scaling technique to the model Gm(s), a new tuning formulae are developed for the PI controller. These tuning formulae guarantee almost optimal performance/robustness tradeoff for a large class of processes with integral action.

This paper proposes a spreadsheet method for continuous-time model identification. More precisely, the method is based on the use of the Excel Solver program. First, implementation issues are highlighted. Afterwards, the identification method is illustrated through several examples, including a Monte Carlo simulation analysis to demonstrate the robustness of the tool against noise. The method is also applied to construct the model of two laboratory pilot-scale processes. The results show that process identification can be carried out satisfactorily using the Solver tool.

This study addresses the problem of controlling by simulation the magnitude of the maximal catalyst temperature, or hot spot, in a four catalyst beds SO2 converter by manipulating the reaction mixture volumetric flow rate. The control of the maximal catalyst temperature is carried out in order to avoid the occurrence of a hot spot inside the catalyst mass and to keep high catalyst efficiency. Command algorithm used is the generalised predictive control (GPC) with off line process identification. The performance and robustness of the GPC controller are evaluated for the case of a kinetic complex and reversible exothermic reaction. The results obtained by numerical simulation show the possibility of the regulation of the hot spot temperature below a pre-specified value despite the occurrence of strong perturbations.

Changes in human capital management, new requirements regarding knowledge and skills of employees compel higher education institutions to redefine their learning programmes. This requires evaluation of the didactic process realization, which should be oriented on competences. In the article authors presents an approach to competence modeling. New tools and collaboration mechanisms are proposed, which allow defining the structure of competence, analyzing the level of competence development, and assessing the competence process realization in relation “expected benefit-required expense”.

The use of multi-parameter analysis of acoustic emission signals allows identifying destructive processes not only in prestressed concrete elements but also in classical concrete and reinforced concrete members. The analysis is not based on single descriptors but on destructive processes, which may be used to evaluate structural integrity of the elements. Therefore the key question is the minimum number of parameters needed to develop a database of reference signals. To determine the effect of particular descriptors on the correctness of the database being created, the authors applied dendograms and three statistical criteria, Wilks' Lambda, Rij and Tou criteria.

This study presents a new method to identify parameters representing cutting process and transfer function of flexible mechanical structures mounted on a traveling stage by utilizing only internal information of computerized numerical control (CNC) system. Disturbance force input to CNC is estimated by disturbance observer and cutting force is estimated based on cutting force model. Analyzing influence of the estimated cutting force on the disturbance force, parameters used in the assumed models of cutting process and structural dynamics are identified in quasi-real-time. Least square method (LSM) is utilized for the parameter identification. Face turning experiment using an ultra-precision machine tool was conducted to verify feasibility of the proposed method. Experimental results clarified that the cutting force coefficient and the modal parameters representing the dynamic characteristics of the force transfer function can be identified accurately by the proposed method.

Mathematical modeling is essentially required for understanding the dynamics of a process. Process Identification is a technique used for deriving the mathematical model of a process. There are numerous identification methods, which can be classified as two categories viz. on-line methods and off-line methods. In this paper various model identification techniques have been applied for modeling a liquid system. An experiment is conducted on laboratory setup and by using this experimental data, process transfer function is determined and the results were discussed for drawing conclusions.