Schema redescription in cellular automata: Revisiting emergence in complex systems

The data deluge is changing the operating environment of many sensing systems from data-poor to data-rich-so data-rich that we are in jeopardy of being overwhelmed. Managing and exploiting the data deluge require a reinvention of sensor system design and signal processing theory. The potential pay-offs are huge, as the resulting sensor systems will enable radically new information technologies and powerful new tools for scientific discovery.

UNFIS: A Novel Neuro-Fuzzy Inference System with Unstructured fuzzy rules

Leaf area index (LAI) is one of the key biophysical indicators for characterizing the growth and status of vegetation and is also used in modeling earth system processes. Machine learning algorithms (MLAs) such as random forest regression (RFR), artificial neural network regression (ANNR) and support vector regression (SVR) based on satellite data have been widely used for the estimation of LAI. However, the selection of input variables has a great impact on the estimation performance of MLAs. In this study, we aimed to improve the LAI inversion model of Inner Mongolia grassland based on MLAs incorporating empirical knowledge. Firstly, we used the ANNR, SVR and RFR approaches, respectively, to rank the input variables including vegetation indices, climate factors, soil factors and topography factors and found that Normalized Difference Phenology Index (NDPI) contributed the most to LAI estimation. Secondly, we selected four sets of input variables, namely, all variables—A, model selected variables—B, overlapping variables—C and self-defined variables—D, respectively. Subsequently, we built twelve LAI estimation models (RFR-A, RFR-B, RFR-C, etc.) based on three MLAs and four sets of input variables. The evaluation of them showed the RFR produced higher prediction accuracy, followed by ANNR and SVR. Furthermore, the RFR-D presented the highest accuracy in predicting LAI (R2 = 0.55, RMSE = 0.37 m2/m2, MAE = 0.29 m2/m2). Finally, we compared our results with MODIS LAI and GEOV2 LAI products and found that all of them showed a similar spatial distribution of grassland LAI in the four sub-regions covering all grassland types, but our model exhibited larger LAI values in the desert steppe and smaller LAI values in the others. These findings demonstrated that MLAs incorporating empirical knowledge could improve the accuracy of modelling LAI and further study is necessary to reduce the uncertainty in LAI mapping in grassland.

Unconventional modelling of complex system via cellular automata and differential evolution

Hydrate formation temperature (T) is an important parameter for any industrial process that deals with natural gas hydrates. In this study, the Group Method of Data Handling (GMDH) approach is used to predict hydrate formation T in natural gas binary mixtures. A comprehensive database containing 728 data samples is compiled from 46 published experimental works. To find the best combination of input variables, different sets of input variables were assessed. A total of seven models were developed using different sets of input variables. Compared to the correlations proposed in the literature, the developed models in this study performed better and the model developed based on input Set #7 was the most accurate: RMSE values of 1.6381 and 1.5499 for the training and testing datasets, respectively. All models also were evaluated using a blind dataset-that was not included in testing or training-to check model applicability to wider data. Similarly, all GMDH models performed excellently for the external dataset, where the developed model based on input Set #5 showed the best performance: RMSE values of 1.3482. The findings of this study contribute to our understanding of hydrate formation conditions in natural gas binary mixtures. As pure and binary mixtures of natural gas main constituents are studied, the results can be especially useful for purification and energy transport applications.

In systems development, it is necessary to think thoroughly about any emergent behaviors that might occur and create strategies to prevent the undesirable emergent behavior from occurring or at least mitigate them. Often dealing with that at later development stages is costly. Emergent behaviors are the main concern, particularly in the development of complex systems. However, identifying and predicting them is challenging in complex systems as it involves lots of components and interactions. Prior to making wise design decisions, developers need to elucidate and understand the emergent behaviors. For this reason, a conceptual model that highlights the presence of emergent behavior at an early stage is essential in the systems development. The conceptual model is useful in achieving higher coverage as it is correlated with the probability of identifying more emergent behaviors. Therefore, this paper goes through the existing work focusing on effort in modeling emergent behavior and presents perspectives on developing a conceptual model that is able to inform the presence of emergent behavior.

For the purpose of describing change in an economy's structure, and addressing issues of transformation, the notion of a time-dependent macroeconomic potential function is introduced. It penalizes deviations from equilibrium (entrepreneurial error) and induces moves toward equilibrium. Thus, from the concept of a potential function is derived the concept of short-term and long-term change forces. We focus here on the long-term structural changes of an economy as distinct from short-term cyclical variations, and we represent economic transformations as phase-transitions between monostable and ambiguous bistable states of the economy. One important feature of the potential function approach is that the parameter of the potential can be determined from empirical data. In particular, the parameters can be regressed for input variables. Hence, a relationship has been established between the structural change force and a set of input variables, some of which are controlled in part by either public or private sector agents. The method has been applied to West German and United States industry data for 1950–1980.

This paper attempts to establish a quantitative relationship between output indicators such as automated-guided vehicle (AGV) utilization and input variables like part arrival rate, processing times at machining centers, and various decision rules employed at AGV and Resources in a dynamic dedicated flexible manufacturing system (FMS). Dummy data are generated and the system is simulated to produce the output indicator for a given set of input variables and decision rules. Design and controlling of a FMS require much investment. So it is essential to make best decisions for selection of a FMS option. The proposed Principle Component Analysis is performed on a dataset that contains requirement of FMS alternatives.KeywordsSimulationResourcesAGVFMSPrincipal component analysis

Research in wireless sensor networks has accelerated rapidly in recent years. The promise of ubiquitous control of the physical environment opens the way for new applications that will redefine the way we live and work. Due to the small size and low cost of sensor devices, visionaries promise smart systems enabled by deployment of massive numbers of sensors working in concert. To date, most of the research effort has concentrated on forming ad hoc networks under centralized control, which is not scalable to massive deployments. This thesis proposes an alternative approach based on models inspired by biological systems and reports significant results based on this new approach. This perspective views sensor devices as autonomous organisms in a community interacting as part of an ecosystem rather than as nodes in a computing network. The networks that result from this design make local decisions based on local information in order for the network to achieve global goals, thus we must engineer for emergent behavior in wireless sensor networks. First we implemented a simulator based on cellular automata to be used in algorithm development and assessment. Then we developed efficient algorithms to exploit emergent behavior for finding the average of distributed values, synchronizing distributed clocks, and conducting distributed binary voting. These algorithms are shown to be convergent and efficient by analysis and simulation. Finally, an extension of this perspective is used and demonstrated to provide significant progress on the noise abatement problem for jet aircraft. Using local information and actions, optimal impedance values for an acoustic liner are determined in situ providing the basis for an adaptive noise abatement system that provides superior noise reduction compared with current technology and previous research efforts.

An effective and efficient planning of an urban growth and land use changes and its impact on the environment requires information about growth trends and patterns amongst other important information. Over the years, many urban growth models have been developed and used in the developed countries for forecasting growth patterns. In the developing countries however, there exist a very few studies showing the application of these models and their performances. In this study two models such as cellular automata (CA) and the SLEUTH models are applied in a geographical information system (GIS) to simulate and predict the urban growth and land use change for the City of Sana’a (Yemen) for the period 2004–2020. GIS based maps were generated for the urban growth pattern of the city which was further analyzed using geo-statistical techniques. During the models calibration process, a total of 35 years of time series dataset such as historical topographical maps, aerial photographs and satellite imageries was used to identify the parameters that influenced the urban growth. The validation result showed an overall accuracy of 99.6 %; with the producer’s accuracy of 83.3 % and the user’s accuracy 83.6 %. The SLEUTH model used the best fit growth rule parameters during the calibration to forecasting future urban growth pattern and generated various probability maps in which the individual grid cells are urbanized assuming unique “urban growth signatures”. The models generated future urban growth pattern and land use changes from the period 2004–2020. Both models proved effective in forecasting growth pattern that will be useful in planning and decision making. In comparison, the CA model growth pattern showed high density development, in which growth edges were filled and clusters were merged together to form a compact built-up area wherein less agricultural lands were included. On the contrary, the SLEUTH model growth pattern showed more urban sprawl and low-density development that included substantial areas of agricultural lands.

Agent-based models (ABM) and their simulations have been used to study complex systems with interacting entities and to model multi-agent systems. Simulations are used to explore the dynamic consequences of these models. In many cases, the behaviours that are of interest are emergent ones that arise as a result of interactions between agents rather than the actions of any individual agent. In this paper, we propose a formalism for describing emergent behaviours at any level of abstraction based on the idea that event types can be defined that characterise sets of behavioural 'motifs'. This provides the basis for a method for studying the associations between multi-level behaviours in simulations. There are two categories of hypotheses that we seek to address with respect to an ABM and its simulations: - Hypotheses concerned with associations between emergent behaviours defined at various levels of abstraction. - Hypotheses concerned with the links between parameter sensitivity / initial conditions and emergent behaviours e.g. the ABM is sensitive to a parameter x because x predisposes the system or part of the system to exhibit a particular (emergent) behaviour.

Summary Traditional approaches for housing price prediction fall short of accuracy, as it is difficult to identify a set of variables and account for their weightings when conducting forecasting. This study aims to explore an effective and efficient mathematical model for the housing price forecasting, so as to help developers, purchasers and financial institutes to obtain more reasonable property pricing through better decision‐making in the context of the fluctuant property market in Hong Kong. It began with a review of the macro and micro factors that affect the housing price and an entropy‐based rating and weighting model was presented with the aim of providing objectives and reasonable weighting to these variables. Then based on the key variables, the predictive ability of artificial neural networks (ANNs) was examined. In the empirical study, data were quantified and scaled with reasonable assumptions. Various networks were designed to examine the performance of ANN towards different parameters. Different sample sizes and different sets of input variables, together with different net structures and net types were undertaken to test the accuracy of ANN. From the comparison results of the R squared, as well as the mean absolute errors, the authors found that ANN performs well in forecasting with smaller sample size and standard net type. The overall results of this research demonstrated that the integration of Entropy and ANN can serve desirable function in the housing price forecasting progress.

Modern society developed on the base of technology improvement. Progress in technologies based on computer, communication and automatic controls engineering all are depends on Integrated Circuit(IC). In the first half of 1990s, the electronic industry experienced an explosion in the demand for personal computers, cellular telephones, and high speed data communication devices. Vying for market share, vendors built products increasingly greater functionality, higher performance; lower cost, lower power consumption, and smaller in dimensions. To do these, vendors created highly integrated, complex systems with fewer IC devices and less printed-circuit-board (PCB) area. Minimizations of combinational logic functions before implementation in hardware have many advantages. A reduced number of gates considerably decreases the cost of the hardware, reduces the heat generated by the chip and most importantly increases the speed. Boolean functions may be simplified by algebraic means by applying the basic laws and theorems. However, this procedure is awkward because it lacks specific rules to predict each succeeding step in the minimization process for multi-input variables. The simplification by K- mapping has many disadvantages of redundant grouping and non-unique solution. But no method is effective for the simplification of Boolean functions, if it involves more than six variables. A few algorithm and modified technique are found in literature. This paper proposes an algorithm for reduction from multi input into two input variables.

wave are propagated in blood flow from heart to arteries. Formation of cellular automata (CA) rules for Allan-Cahn equation has been done using ultradiscretization. CA rules 250 and 254 have been obtained from Allen-Cahn equation. An emergent behaviour from CA rule 250 have been captured that exhibits travelling waves. CA modelling framework helps in analysing global behaviour of the blood flow in arteries with simple set of rules.

The Radio-Magnetotelluric (RMT) method is a geophysical near-surface imaging technique with a broad range of possible applications. In 2020, the GFZ Potsdam has acquired a newly developed horizontal magnetic dipole transmitter that allows the application of the RMT method even in regions with an insufficient coverage of radio transmitters which normally serve as source signal. First controlled-source RMT measurements were conducted at three different locations in Chile in 2020. Further measurements were recently conducted in Ireland.  As we are able to store the raw time series, we have full control over the subsequent data processing. The processing tools at GFZ include the modular processing suite EMERALD, which was originally designed for MT processing, but has recently been adapted to process RMT data. One main difference is that in RMT the transmitter data is considered as signal, while in natural source MT this would be regarded as electromagnetic noise that needs to be removed using automated robust statistical approaches. However, processing the entire time series in an automated manner has a large drawback: The different emitted frequencies are transmitted in a sweep implying that only a smaller fraction of the time series contains the required signal for a particular target frequency and leading to an unfavourable signal-to-noise ratio. Since it is technically impossible to have the same time base for the data logger and the transmitter with an accuracy of a few nanoseconds, an automated detection scheme is required to find time segments that contain the transmitter signal. Usually, several Gigabytes of raw time series are collected during field measurements, making manual editing and supervision of the time series virtually impossible. However, a careful selection of appropriate time segments is essential for the success of the data processing. To address the challenge, machine learning algorithms have a high potential to solve both problems. Initial experience was gained with a recurrent neural network approach in order to identify suitable time segments (Patzer & Weckmann, EMTF 2021 – conference contribution and personal communication). However, many questions remained open, e.g. if other machine learning algorithms can result in better performances, which machine learning algorithms are in principle suitable for the characteristics and properties of RMT time series and which parameters should be used as input variables (features) for the algorithms. A large number of machine learning algorithms exist, which can be divided into different groups according to their operating principle and their activity fields. We will test unsupervised methods, especially for clustering the data, to identify a set of suitable input variables. Subsequently, we will use these features to train supervised algorithms as logistic regression, support vector machine and different kinds of neural networks to find the best performing algorithm. We will mainly use the RMT data from Chile within the training process. Furthermore, we will test if the trained algorithm is applicable to other new data sets measured at different locations (e.g. Ireland) and/or with different equipment.