Lucian Cristian Iacob, Roland Tóth, Maarten Schoukens
The challenge of finding exact and finite-dimensional Koopman embeddings of nonlinear systems has been largely circumvented by employing data-driven techniques to learn models of different complexities (e.g., linear, bilinear, input affine). Although these models may provide good accuracy, selecting the model structure and dimension is still ad-hoc and it is difficult to quantify the error that is introduced. In contrast to the general trend of data-driven learning, in this paper, we develop a systematic technique for nonlinear systems that produces a finite-dimensional and exact embedding. If the nonlinear system is represented as a network of series and parallel linear and nonlinear (polynomial) blocks, one can derive an associated Koopman model that has constant state and output matrices and the input influence is polynomial. Furthermore, if the linear blocks do not have feedthrough, the Koopman representation simplifies to a bilinear model.
Collecting traffic data is crucial for transportation systems and urban planning, and is often more desirable through easy-to-deploy but power-constrained devices, due to the unavailability or high cost of power and network infrastructure. The limited power means an inevitable trade-off between data collection duration and accuracy/resolution. We introduce a novel learning-based framework that strategically decides observation timings for battery-powered devices and reconstructs the full data stream from sparsely sampled observations, resulting in minimal performance loss and a significantly prolonged system lifetime. Our framework comprises a predictor, a controller, and an estimator. The predictor utilizes historical data to forecast future trends within a fixed time horizon. The controller uses the forecasts to determine the next optimal timing for data collection. Finally, the estimator reconstructs the complete data profile from the sampled observations. We evaluate the performance of the proposed method on PeMS data by an RNN (Recurrent Neural Network) predictor and estimator, and a DRQN (Deep Recurrent Q-Network) controller, and compare it against the baseline that uses Kalman filter and uniform sampling. The results indicate that our method outperforms the baseline, primarily due to the inclusion of more representative data points in the profile, resulting in an overall 10\% improvement in estimation accuracy. Source code will be publicly available.
Linear observed systems on manifolds are a special class of nonlinear systems whose state spaces are smooth manifolds but possess properties similar to linear systems. Such properties can be characterized by preintegration and exact linearization with Jacobians independent of the linearization point. Non-biased IMU dynamics in navigation can be constructed into linear observed settings, leading to invariant filters with guaranteed behaviors such as local convergence and consistency. In this letter, we establish linear observed property for systems evolving on a smooth manifold through the connection structure endowed upon this space. Our key findings are the existence of linear observed systems on manifolds poses constraints on the curvature of the state space, beyond requiring the dynamics to be compatible with some connection-preserving transformations. Specifically, the flat connection case reproduces the characterization of linear observed systems on Lie groups, showing our theory is a true generalization.
This letter investigates dynamical optimal transport of underactuated linear systems over an infinite time horizon. In our previous work, we proposed to integrate model predictive control and the celebrated Sinkhorn algorithm to perform efficient dynamical transport of agents. However, the proposed method requires the invertibility of input matrices, which severely limits its applicability. To resolve this issue, we extend the method to (possibly underactuated) controllable linear systems. In addition, we ensure the convergence properties of the method for general controllable linear systems. The effectiveness of the proposed method is demonstrated by a numerical example.
Access to safe drinking water is one of the basic human rights and is essential for a healthy life. The present study, in drinking water in Salem district analyzed the concentration and health risks of various pollutants. From bore Wells, tube wells and Water samples were collected by hand pumps. Improper disposal of sewage and solid waste, excessive use of agrochemicals and poor condition of pipe network and transport Drinking water is a major source of pollution. Contamination of water with coli form bacteria can cause gastroenteritis, diarrhoea, dysentery and viral hepatitis They said that it is a major source of water-borne diseases. To reduce health risks, using drinking water from contaminated sources immediate cessation is necessary. Agricultural chemicals that cause water pollution Avoid overuse. The present study examines factors influencing the selection of SCM suppliers Aims to analyze and decide. For decision-making and evaluation system using the Neutrosophic Model (DEMATEL). To improve DEMATEL performance and to achieve competitive advantage considered a proactive approach. This study uses neutrosophic set theory, Mark each value using a new scale. A case study implementing the proposed method is presented. Interviewing experts in Neutrosophic Demodel data collection study this research is designed for management, procurement and production. In terms of drinking water quality, R+C Omalur ranked first and Sankari ranked lowest. Ri-C Sankari ranked first and Omalur ranked lowest in terms of drinking water quality.
We present SSD, Software for Systems with Delays, a de novo MATLAB package for the analysis and model reduction of retarded time delay systems (RTDS). Underneath, our delay system object bridges RTDS representation and Linear Fractional Transformation (LFT) representation of MATLAB. This allows seamless use of many available visualizations of MATLAB. In addition, we implemented a set of key functionalities such as H2 norm and system gramian computations, balanced realization and reduction by direct integral definitions and utilizing sparse computation. As a theoretical contribution, we extend the frequency-limited balanced reduction to delay systems first time, propose a computational algorithm and give its implementation. We collected two sets of benchmark problems on H2 norm computation and model reduction. SSD is publicly available in GitHub at https://github.com/gumussoysuat/ssd. Our reproducible paper and two benchmark collections are shared as executable notebooks.
Given a dynamical system, we prove that the shortest distance between two $n$-orbits scales like $n$ to a power even when the system has slow mixing properties, thus building and improving on results of Barros, Liao and the first author. We also extend these results to flows. Finally, we give an example for which the shortest distance between two orbits has no scaling limit.
Matthew Deakin, Phil C. Taylor, Janusz Bialek
et al.
Distribution systems will require new cost-effective solutions to provide network capacity and increased flexibility to accommodate Low Carbon Technologies. To address this need, we propose the Hybrid Multi-Terminal Soft Open Point (Hybrid MT-SOP) to efficiently provide distribution system interconnection capacity. Each leg of the Hybrid MT-SOP has an AC/DC converter connected in series with a bank of AC switches (Feeder Selector Switches) to allow the converter to connect to any of the feeders at a node. Asymmetric converter sizing is shown to increase feasible power transfers by up to 50% in the three-terminal case, whilst a conic mixed-integer program is formulated to optimally select the device configuration and power transfers. A case study shows the Hybrid MT-SOP increasing utilization of the converters by more than one third, with a 13% increase in system loss reduction as compared to an equally-sized MT-SOP.
A. Smirnov, Kantemir Tsabolov, Liliya Ineshina
et al.
This article discusses the topic of sewage sludge digestion. Anaerobic digestion of sewage sludge makes it possible to obtain biogas, which can later be used to generate heat or electricity. this approach to resource use is recognized worldwide as more environmentally friendly. The article discusses the experience of European countries in the production and use of biogas. In the Russian Federation, the situation is complicated by the fact that wastewater contains a small amount of organic matter, which is a product for biogas production. Therefore, methods have been proposed for increasing the content of organic matter in sediments, for example, by means of separate waste collection and disposal of organic waste through grinders into the sewerage system. Or, the amount of organic matter in the sewage sludge can be increased by adding manure from animal farms. The stages of sediment fermentation are considered. The topic of rationality and payback of the use of biogas is raised. Projects already working in different countries are being considered. Possible volumes of gas production and methods of air purification during sludge treatment are considered.
Eman A. Al-Imara, Rand L. Al-Jaryan, Sabrean F Jawad
et al.
As a consequence of natural pollution, water and sewage are polluted in many nations across the globe. Especially in poorer countries, sewage treatment and disposal practices are often substandard. Throughout many limited-income nations, the poor economic condition and absence of resources assistance severely hamper the planning and application of novel water and sewage systems. This has resulted in a rise in the number of bio-contaminants in the environment. The objective of this study is to use electrocoagulation as a low-cost method to remove or lower the amount of bio contaminant in sewage. By transmitting a voltage between the two conductors, disinfectants are generated in place. Sewerage samples were obtained at the Kerbala wastewater treatment plant, which is situated south of Kerbala, Iraq. In this work, steel plates were utilized to create coagulants. Furthermore, the effect of many factors on the performance of the electrolysis device was studied, namely spacing among electrodes and current density. The analysis indicates after 40 min of irradiation employing electrodes spaced 5mm apart and a current intensity of 2 mA/cm2, the E. coli bacteria as a biocontrol agent were killed. Furthermore, the results demonstrated that an initial pH value of 6.0 is appropriate for bio-contaminants removal utilizing electrocoagulation.
Reetam Sen Biswas, Anamitra Pal, Trevor Werho
et al.
Ensuring reliable operation of large power systems subjected to multiple outages is a challenging task because of the combinatorial nature of the problem. Traditional approaches for security assessment are often limited by their scope and/or speed, resulting in missing of critical contingencies that could lead to cascading failures. This paper proposes a two-component methodology to enhance power system security. The first component combines an efficient algorithm to detect cut-set saturation (called the feasibility test (FT) algorithm) with real-time contingency analysis (RTCA) to create an integrated corrective action (iCA), whose goal is to secure the system against cut-set saturation as well as critical branch overloads. The second component only employs the results of the FT to create a relaxed corrective action (rCA) to secure the system against post-contingency cut-set saturation. The first component is more comprehensive, but the latter is computationally more efficient. The effectiveness of the two components is evaluated based upon the number of cascade triggering contingencies alleviated, and the computation time. The results obtained by analyzing different case-studies on the IEEE 118-bus and 2000-bus synthetic Texas systems indicate that the proposed two-component methodology successfully enhances the scope and speed of power system security assessment during multiple outages.
The dissipativity framework is widely used to analyze stability and performance of nonlinear systems. By embedding nonlinear systems in an LPV representation, the convex tools of the LPV framework can be applied to nonlinear systems for convex dissipativity based analysis and controller synthesis. However, as has been shown recently in literature, naive application of these tools to nonlinear systems for analysis and controller synthesis can fail to provide the desired guarantees. Namely, only performance and stability with respect to the origin is guaranteed. In this paper, inspired by the results for continuous-time nonlinear systems, the notion of incremental dissipativity for discrete-time nonlinear systems is proposed, whereby stability and performance analysis is done between trajectories. Furthermore, it is shown how, through the use of the LPV framework, convex conditions can be obtained for incremental dissipativity analysis of discrete-time nonlinear systems. The developed concepts and tools are demonstrated by analyzing incremental dissipativity of a controlled unbalanced disk system.
Pegah Hoseingholi, Ramtin Moeini, Mohammad Reza Zare
Wastewater network is an inseparable part of urban life. Due to importance of this network as one of the urban infrastructure, the failure of this system will lead to stopping service, causing many social, economic and environmental consequences. Hence, assessing the wastewater networks condition and its failure is an important approach for managing it. Generally, failure of system means any condition which is lead to stopping service. In general, artificial intelligence methods are used as a low-cost method to predict failure. In this research, genetic programming (GP) is used to predict the number of blockage (hydraulic failure) in the wastewater network and its results are compared with the results of the artificial neural network (ANN). As a case study, here, a part of Isfahan wastewater network is investigated. The parameters such as age, pipe length, slope and depth as input data and the number of blockage are considered as the output data of the model. In this research, the number of blockage data in the wastewater network at 1394 and 1395 are used, in which the 70% of the data is used for training and 30% for the test. These data are classified in three way leading to three model. In the first model, data are classified based on the slope and in two other models the data are classified according to the cover depth. The results show that all models predicts the number of blockage with good accuracy. In addition the accuracy of the result of GP model is better than the ANN model. For example, for GP model, the values of R2 and RMSE for the second model at the training stage are 0.97 and 0.8 and at the test stage are equal to 0.94 and 0.69, respectively. However these values for ANN model are 0.96 and 0.95 at the training stage and 0.87 and 0.96 at the test stage respectively. These results show the superiority of the GP model in comparison with ANN model in which the results of second proposed model are better. The results of these proposed model can be used for preventive maintenance, prioritization of sewage network repairs and inspections, and finally to prevents the occurrence of suddenly accidents.
Technology, Water supply for domestic and industrial purposes
A framework is presented for handling a potential loss of observability of a dynamical system in a provably-safe way. Inspired by the fragility of data-driven perception systems used by autonomous vehicles, we formulate the problem that arises when a sensing modality fails or is found to be untrustworthy during autonomous operation. We cast this problem as a differential game played between the dynamical system being controlled and the external system factor(s) for which observations are lost. The game is a zero-sum Stackelberg game in which the controlled system (leader) is trying to find a trajectory which maximizes a function representing the safety of the system, and the unobserved factor (follower) is trying to minimize the same function. The set of winning initial configurations of this game for the controlled system represent the set of all states in which safety can be maintained with respect to the external factor, even if observability of that factor is lost. This is the set we refer to as the Eyes-Closed Safety Kernel. In practical use, the policy defined by the winning strategy of the controlled system is only needed to be executed whenever observability of the external system is lost or the system deviates from the Eyes-Closed Safety Kernel due to other, non-safety oriented control schemes. We present a means for solving this game offline, such that the resulting winning strategy can be used for computationally efficient, provably-safe, online control when needed. The solution approach presented is based on representing the game using the solutions of two Hamilton-Jacobi partial differential equations. We illustrate the applicability of our framework by working through a realistic example in which an autonomous car must avoid a dynamic obstacle despite potentially losing observability.
People’s participation is one of the means financing investment through which will develop investment in different sectors such as the supply of public goods and services. In this research, how to participate of people in the proposed project of water transfer from Caspian Sea to Iran central plateau has been studied. The paper evaluates the value of the Semnan citizen’s participation in the implementation of water transfer project from the Caspian Sea to the Central Plateau using the Contingent Valuation Method (CVM) and identifies effective factors on willingness to the pay by applying Logit model in the framework of Maximum Likelihood Method. Data through two-dimensional dual questionnaire includes 384 members have been collected. The results from model estimation show that the firstly, the variables of income, educational level have a positive and significant effect and the number of household members and the proposed price has a negative and significant effect on the willingness to pay of Semnan’s citizens. The secondly, the average of per household willingness to pay, 433077,096 Rials annually and the people’s annual participation value, 30.1 milliard Rials is estimated. Citizen’s willingness to financing costs of project implementation indicates citizens' responsibility in the resolving of regional problems that can be presented as a good model in the performance of national plans and solving of regional problems.
Technology, Water supply for domestic and industrial purposes