Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Implementation of Genetic Algorithm for Parameter Tuning of PID Controller in Three Phase Induction Motor Speed Control

Induction motor at low speeds has a tough speed setting sets the width of the range. This study tested by giving the load motor disorders to describe the condition. The method used for vector control system so that the resulting performance is good at setting the motor speed and torque. This method is used in setting the Proportional Integral Derivative (PID) Tuning parameter settings based on Genetic Algorithm (GA) to provide a dynamic response to changes in speed and load torque on the motor, so we get smoothness at any speed change and braking and maximum torque motors. Optimization function is required to obtain a new PID parameter values each input value changes or load disturbances, in terms of the initial determination of these parameters using Ziegler-Nichols method based on frequency response. Tests were performed at a speed of approximately 1800 rpm value rise time of about 10 seconds after generation added, at a rate of 1800 rpm rise time value of the average remains around 9 seconds, but slightly reduced the oscillations in the response, and the speed of approximately 1700 rpm rise time value of the average is 9 seconds. The test results show that GA-based PID controller has a good response in approximately 0.85% overshoot at the motor speed change and brakin

A proposal for modeling intersections in traffic systems by using adaptive fuzzy Petri nets.

A medida que se avanza en el desarrollo de la ciudad, aumenta el número de vehículos, accidentes y congestión proporcionalmente. Un sistema de tráfico vehicular se comporta como un sistema a eventos discretos; y debido a las variaciones que influyen en la congestión, su modelo y control se convierten en una tarea compleja. Las Redes de Petri (Petri Nets) son una de las herramientas poderosas para el modelamiento de sistemas de eventos discretos de manera gráfica y matemática. En algunos sistemas existe poca información, datos inexactos y/o cambios permanentes en el modelo del sistema. Esto ha llevado a las técnicas de modelado a trascender a técnicas de adaptación y representación del conocimiento humano mediaste sistemas computacionales bio-inspirados, como las Redes Neuronales (Neural Networks) y la Lógica Fuzzy (Fuzzy Logic). Dichas técnicas son estructuradas en este trabajo como el modelado aproximado mediante el aprendizaje de un sistema concurrente discreto, bajo las redes de Petri Difusas para la representación del conocimiento mediante reglas de inferencia y las Adaptativas para la reacción ante un entorno caótico como un sistema de tráfico vehicular. Abstract It can be observed that the number of vehicles, accidents and congestion increase proportionally with the development of a city. A vehicular traffic system behaves like a discrete event system, and due to variations that affect the level of congestion, modeling and controlling this system becomes a complex task. Petri Nets are one of the most powerful tools for modeling graphically and mathematically. Some systems are characterized by little information, inaccurate data and / or permanent changes with regard to the model of the system, which makes modeling and control difficult. This has led to modeling techniques that apply adaptation techniques and human knowledge representation through bio-inspired computing systems such as Neural Networks and Fuzzy Logic. These techniques will be harnessed in this work in terms of an approximated model for learning in a discrete concurrent system by using Fuzzy Petri Nets to represent knowledge through the application of inference and adaptive rules in a chaotic environment, like it can be found in a traffic system

Interoperability of Traffic Infrastructure Planning and Geospatial Information Systems

Building Information Modelling (BIM) as a Model-based design facilitates to investigate multiple solutions in the infrastructure planning process. The most important reason for implementing model-based design is to help designers and to increase communication between different design parties. It decentralizes and coordinates team collaboration and facilitates faster and lossless project data exchange and management across extended teams and external partners in project lifecycle. Infrastructure are fundamental facilities, services, and installations needed for the functioning of a community or society, such as transportation, roads, communication systems, water and power networks, as well as power plants. Geospatial Information Systems (GIS) as the digital representation of the world are systems for maintaining, managing, modelling, analyzing, and visualizing of the world data including infrastructure. High level infrastructure suits mostly facilitate to analyze the infrastructure design based on the international or user defined standards. Called regulation1-based design, this minimizes errors, reduces costly design conflicts, increases time savings and provides consistent project quality, yet mostly in standalone solutions. Tasks of infrastructure usually require both model based and regulation based design packages. Infrastructure tasks deal with cross-domain information. However, the corresponding data is split in several domain models. Besides infrastructure projects demand a lot of decision makings on governmental as well as on private level considering different data models. Therefore lossless flow of project data as well as documents like regulations across project team, stakeholders, governmental and private level is highly important. Yet infrastructure projects have largely been absent from product modelling discourses for a long time. Thus, as will be explained in chapter 2 interoperability is needed in infrastructure processes. Multimodel (MM) is one of the interoperability methods which enable heterogeneous data models from various domains get bundled together into a container keeping their original format. Existing interoperability methods including existing MM solutions can’t satisfactorily fulfill the typical demands of infrastructure information processes like dynamic data resources and a huge amount of inter model relations. Therefore chapter 3 concept of infrastructure information modelling investigates a method for loose and rule based coupling of exchangeable heterogeneous information spaces. This hypothesis is an extension for the existing MM to a rule-based Multimodel named extended Multimodel (eMM) with semantic rules – instead of static links. The semantic rules will be used to describe relations between data elements of various models dynamically in a link-database. Most of the confusion about geospatial data models arises from their diversity. In some of these data models spatial IDs are the basic identities of entities and in some other data models there are no IDs. That is why in the geospatial data, data structure is more important than data models. There are always spatial indexes that enable accessing to the geodata. The most important unification of data models involved in infrastructure projects is the spatiality. Explained in chapter 4 the method of infrastructure information modelling for interoperation in spatial domains generate interlinks through spatial identity of entities. Match finding through spatial links enables any kind of data models sharing spatial property get interlinked. Through such spatial links each entity receives the spatial information from other data models which is related to the target entity due to sharing equivalent spatial index. This information will be the virtual properties for the object. The thesis uses Nearest Neighborhood algorithm for spatial match finding and performs filtering and refining approaches. For the abstraction of the spatial matching results hierarchical filtering techniques are used for refining the virtual properties. These approaches focus on two main application areas which are product model and Level of Detail (LoD). For the eMM suggested in this thesis a rule based interoperability method between arbitrary data models of spatial domain has been developed. The implementation of this method enables transaction of data in spatial domains run loss less. The system architecture and the implementation which has been applied on the case study of this thesis namely infrastructure and geospatial data models are described in chapter 5. Achieving afore mentioned aims results in reducing the whole project lifecycle costs, increasing reliability of the comprehensive fundamental information, and consequently in independent, cost-effective, aesthetically pleasing, and environmentally sensitive infrastructure design.:ABSTRACT 4 KEYWORDS 7 TABLE OF CONTENT 8 LIST OF FIGURES 9 LIST OF TABLES 11 LIST OF ABBREVIATION 12 INTRODUCTION 13 1.1. A GENERAL VIEW 14 1.2. PROBLEM STATEMENT 15 1.3. OBJECTIVES 17 1.4. APPROACH 18 1.5. STRUCTURE OF THESIS 18 INTEROPERABILITY IN INFRASTRUCTURE ENGINEERING 20 2.1. STATE OF INTEROPERABILITY 21 2.1.1. Interoperability of GIS and BIM 23 2.1.2. Interoperability of GIS and Infrastructure 25 2.2. MAIN CHALLENGES AND RELATED WORK 27 2.3. INFRASTRUCTURE MODELING IN GEOSPATIAL CONTEXT 29 2.3.1. LamdXML: Infrastructure Data Standards 32 2.3.2. CityGML: Geospatial Data Standards 33 2.3.3. LandXML and CityGML 36 2.4. INTEROPERABILITY AND MULTIMODEL TECHNOLOGY 39 2.5. LIMITATIONS OF EXISTING APPROACHES 41 INFRASTRUCTURE INFORMATION MODELLING 44 3.1. MULTI MODEL FOR GEOSPATIAL AND INFRASTRUCTURE DATA MODELS 45 3.2. LINKING APPROACH, QUERYING AND FILTERING 48 3.2.1. Virtual Properties via Link Model 49 3.3. MULTI MODEL AS AN INTERDISCIPLINARY METHOD 52 3.4. USING LEVEL OF DETAIL (LOD) FOR FILTERING 53 SPATIAL MODELLING AND PROCESSING 58 4.1. SPATIAL IDENTIFIERS 59 4.1.1. Spatial Indexes 60 4.1.2. Tree-Based Spatial Indexes 61 4.2. NEAREST NEIGHBORHOOD AS A BASIC LINK METHOD 63 4.3. HIERARCHICAL FILTERING 70 4.4. OTHER FUNCTIONAL LINK METHODS 75 4.5. ADVANCES AND LIMITATIONS OF FUNCTIONAL LINK METHODS 76 IMPLEMENTATION OF THE PROPOSED IIM METHOD 77 5.1. IMPLEMENTATION 78 5.2. CASE STUDY 83 CONCLUSION 89 6.1. SUMMERY 90 6.2. DISCUSSION OF RESULTS 92 6.3. FUTURE WORK 93 BIBLIOGRAPHY 94 7.1. BOOKS AND PAPERS 95 7.2. WEBSITES 10

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Vefrifikacija prognoza oborine WRF modelom nad Indijom tijekom monsuna 2010.: CRA metoda

The WRF model forecast during monsoon season 2010 has been verified with daily observed gridded rainfall analysis with 0.5° spatial resolution. First- ly, the conventional neighborhood technique has been deployed to calculate common scores like mean error and root mean square error. Along with, widely used two categorical skill scores have been computed for seven different rainfall thresholds. The scores only found the general nature of the model performance and depicted the degradation of forecast accuracy exceeding moderate rainfall category of 7.5 mm. The object oriented Contiguous Rain Area method also has been considered for the verification of rainfall forecasts to gather more informa- tion about model performance. The method similarly has endorsed that the performance of the model degrades along with the increase in rainfall amount. But at the same time, the decomposition of mean square error has pointed out that the maximum error occurred due the shifting of rain object or event in the forecast compared to observation. The volume error contributes less as compared to pattern error in 24 hour forecasts irrespective of rainfall thresholds. But in 48 hour forecasts, their values are comparable and change along with rainfall threshold. During whole monsoon season, all contiguous rain areas in model forecasts have been searched over observed rainfall analyses applying best-fit criteria. For contiguous rain areas below 50 mm more than 70 percent match was found.Prognoza oborine dobivena modelom WRF za monsunsku sezonu 2010. verificirana je korištenjem analize dnevne opažene oborine u mreži prostorne rezolucije od 0,5°. Određeni su jednostavni, standardni pokazatelji poput srednje pogreške i srednje kvadratne pogreške, a također i dva uobičajena kategorička pokazatelja uspješnosti koji su izračunati za sedam različitih pragova oborine. Ti pokazatelji su omogućili općenitu procjenu uspješnosti modela te su ukazali na smanjenu pouzdanost za kategorije oborine veće od 7,5 mm. kako bi se detaljnije procijenila uspješnost modela, verifikacija prognoze oborine je također napravljena pomoću objektno orijentirane metode bliskih oborinskih područja CRA (Contiguous Rain Area). Ova metoda je također ukazala na smanjenje uspješnosti modela s povećanjem količine oborine. Međutim, dekompozicija srednje kvadratne pogreške je ukazala da najveću pogrešku uzrokuje pomak prognoziranog oborinskog područja ili događaja u odnosu na izmjerene vrijednosti. Za 24-satne prognoze volumna pogreška doprinosi manje u usporedbi s prostornom pogreškom, neovisno o pragovima oborine. Međutim, za 48-satne prognoze iznosi volumne i prostorne pogreške su usporedivi te rastu s pragom oborine. Susjedna oborinska područja za prognoziranu oborinu su određena obzirom na izmjerenu oborinu primjenom kriterija nabolje podudarnosti. Postupak je proveden za cijelu monsunsku sezonu. Za područja s količinom oborine manjom od 50 mm podudaranje je veće od 70%

GIS Modeling of the Prominent Geohazards in Arkansas

The State of Arkansas is prone to numerous geohazards. This thesis is a twofold study of prominent geohazards in Arkansas: the first fold includes a novel triggerless approach for mass wasting susceptibility modeling applied to the Boston Mountains in NW Arkansas, and the second fold is a GIS-based regression modeling of the extreme weather patterns at the state level. Each study fold is presented in this thesis as a separate chapter embracing a published peer-reviewed paper. In the first paper, I have used the analytical hierarchy process to assign preliminary statistical weights to the most cogent variables influencing mass wasting in the central Boston Mountains. These most significant variables are then incorporated in Fuzzy modeling of mass wasting susceptibility within the 1200 km2 study area. For comparison and accuracy assessment, a second model has been established using a conventional weighted overlay (WO) approach. Results indicate that the developed novel approach is superior, with approximately 83% accuracy, to the traditional WO approach that has a marginal success of about 28% accuracy. Road related mass wasting events recorded by the Arkansas Department of Transportation have been used to validate both models. In the second paper, I have conducted a systematically gridded analysis of severe weather events, including tornadoes, derechos, and hail, during 1955-2015. The study examines and statistically determines the most significant explanatory variables contributing to the spatial patterns of severe weather events between 1955 and 2015, consequently it identifies severity indices for the entire state. These weather-related hazards and their associated risk will always abide; therefore, the best defense is employ geospatial technologies to plan for hazard mitigation. The mass wasting model developed in this study contributes pivotal information for identifying zones of high risk along roadways in NW Arkansas, which definitely can be adapted to avoid disastrous road failures. In addition, the weather-related severity indices determined at the state level can profoundly benefit state and federal agencies focused on increasing the availability of public and private storm shelters in previously under-represented zones of high risk. This undoubtedly will save lives from unavoidable catastrophic events across the entire state

Vefrifikacija prognoza oborine WRF modelom nad Indijom tijekom monsuna 2010.: CRA metoda

The WRF model forecast during monsoon season 2010 has been verified with daily observed gridded rainfall analysis with 0.5° spatial resolution. First- ly, the conventional neighborhood technique has been deployed to calculate common scores like mean error and root mean square error. Along with, widely used two categorical skill scores have been computed for seven different rainfall thresholds. The scores only found the general nature of the model performance and depicted the degradation of forecast accuracy exceeding moderate rainfall category of 7.5 mm. The object oriented Contiguous Rain Area method also has been considered for the verification of rainfall forecasts to gather more informa- tion about model performance. The method similarly has endorsed that the performance of the model degrades along with the increase in rainfall amount. But at the same time, the decomposition of mean square error has pointed out that the maximum error occurred due the shifting of rain object or event in the forecast compared to observation. The volume error contributes less as compared to pattern error in 24 hour forecasts irrespective of rainfall thresholds. But in 48 hour forecasts, their values are comparable and change along with rainfall threshold. During whole monsoon season, all contiguous rain areas in model forecasts have been searched over observed rainfall analyses applying best-fit criteria. For contiguous rain areas below 50 mm more than 70 percent match was found.Prognoza oborine dobivena modelom WRF za monsunsku sezonu 2010. verificirana je korištenjem analize dnevne opažene oborine u mreži prostorne rezolucije od 0,5°. Određeni su jednostavni, standardni pokazatelji poput srednje pogreške i srednje kvadratne pogreške, a također i dva uobičajena kategorička pokazatelja uspješnosti koji su izračunati za sedam različitih pragova oborine. Ti pokazatelji su omogućili općenitu procjenu uspješnosti modela te su ukazali na smanjenu pouzdanost za kategorije oborine veće od 7,5 mm. kako bi se detaljnije procijenila uspješnost modela, verifikacija prognoze oborine je također napravljena pomoću objektno orijentirane metode bliskih oborinskih područja CRA (Contiguous Rain Area). Ova metoda je također ukazala na smanjenje uspješnosti modela s povećanjem količine oborine. Međutim, dekompozicija srednje kvadratne pogreške je ukazala da najveću pogrešku uzrokuje pomak prognoziranog oborinskog područja ili događaja u odnosu na izmjerene vrijednosti. Za 24-satne prognoze volumna pogreška doprinosi manje u usporedbi s prostornom pogreškom, neovisno o pragovima oborine. Međutim, za 48-satne prognoze iznosi volumne i prostorne pogreške su usporedivi te rastu s pragom oborine. Susjedna oborinska područja za prognoziranu oborinu su određena obzirom na izmjerenu oborinu primjenom kriterija nabolje podudarnosti. Postupak je proveden za cijelu monsunsku sezonu. Za područja s količinom oborine manjom od 50 mm podudaranje je veće od 70%