Intelligent Decision Support Based on Integration of Fuzzy Clustering and Multi objective Optimization Problem for Non Player Character in Serious Game

Nowadays, decision support plays an important role in decision-making, errors in decisionmaking is able to lose the competition. Decision-making is very complicated especially when the problem is in multiobjective problem. To learn decision making through play a game is an interesting thing. Player plays a game but actually, he or she learns about how to make a decision. In this research, the objective is to make Non-Player Character (NPC) for serious game for electrical power production. This NPC is developed with 2 stages, the first stage is multiobjective optimization problem that uses NSGA2 method. This stage results some optimal solutions. The second stage is clustering that uses FCM method and FLVQ method to decrease number of solutions. In this stage, we compare these methods

Heuristiken zur multikriteriellen Komposition von Diensten in dienstbasierten Informationssystemen

Service-orientierte Architekturen unterstützen die Bereitstellung von Anwendungsfunktionalität durch Dienstkomposition. Dabei werden nicht-funktionale Attribute betrachtet, um zwischen funk-tional gleichwertigen Diensten zu unterscheiden. Wir untersuchen die Auswahl von Diensten aus einer Menge von Dienstkandidaten für den Fall einer sequentiellen Komposition, so dass die Kosten des komponierten Dienstes eine vorgegebene Schranke nicht überschreiten und gleichzeitig die Ausführungszeit minimiert und die Verfügbarkeit des komponierten Dienstes maximiert wird. Da dieses Problem NP-schwer ist, wird ein genetischer Algorithmus zur Ermittlung der Menge von Pareto-optimalen Lösungen vorgeschlagen, der mit problemspezifischen Heuristiken kombiniert wird. Die Ergebnisse numerischer Experimente mit zufällig erzeugten Probleminstanzen zeigen die Leistungsfähigkeit des Ansatzes

A novel approach for traffic accidents sanitary resource allocation based on multi-objective genetic algorithms

[EN] The development of communication technologies integrated in vehicles allows creating new protocols and applications to improve assistance in traffic accidents. Combining this technology with intelligent systems will permit to automate most of the decisions needed to generate the appropriate sanitary resource sets, thereby reducing the time from the occurrence of the accident to the stabilization and hospitalization of the injured passengers. However, generating the optimal allocation of sanitary resources is not an easy task, since there are several objectives that are mutually exclusive, such as assistance improvement, cost reduction, and balanced resource usage. In this paper, we propose a novel approach for the sanitary resources allocation in traffic accidents. Our approach is based on the use of multiobjective genetic algorithms, and it is able to generate a list of optimal solutions accounting for the most representative factors. The inputs to our model are: (i) the accident notification, which is obtained through vehicular communication systems, and (ii) the severity estimation for the accident, achieved through data mining. We evaluate our approach under a set of vehicular scenarios, and the results show that a memetic version of the NSGA-II algorithm was the most effective method at locating the optimal resource set, while maintaining enough variability in the solutions to allow applying different resource allocation policies. 2012 Elsevier Ltd. All rights reserved.This work was partially supported by the Ministerio de Ciencia e Innovacion, Spain, under Grant TIN2011-27543-C03-01, and by the Diputacion General de Aragon, under Grant "subvenciones destinadas a la formacion y contratacion de personal investigador".Fogue, M.; Garrido, P.; Martínez, FJ.; Cano Escribá, JC.; Tavares De Araujo Cesariny Calafate, CM.; Manzoni, P. (2013). A novel approach for traffic accidents sanitary resource allocation based on multi-objective genetic algorithms. Expert Systems with Applications. 40(1):323-336. doi:10.1016/j.eswa.2012.07.056S32333640

Multi-objective evolutionary algorithm for land-use management problem

Due to increasing population, and human activities on land to meet various demands, land uses are being continuously changed without a clear and logical planning with any attention to their long term environmental impacts. Thus affecting the natural balance of the environment, in the form of global warming, soil degradation, loss of biodiversity, air and water pollution, and so on. Hence, it has become urgent need to manage land uses scientifically to safeguard the environment from being further destroyed. Owing to the difficulty of deploying field experiments for direct assessment, mechanistic models are needed to be developed for improving the understanding of the overall impact from various land uses. However, very little work has been done so far in this area. Hence, NSGA-II-LUM, a spatial-GIS based multi-objective evolutionary algorithm, has been developed for three objective functions: maximization of economic return, maximization of carbon sequestration and minimization of soil erosion, where the latter two are burning topics to today's researchers as the remedies to global warming and soil degradation. The success of NSGA-II-LUM has been presented through its application to a Mediterranean landscape from Southern Portugal

Towards generating diverse topologies of path tracing compliant mechanisms using a local search based multi-objective genetic algorithm procedure

Abstract — A new bi-objective optimization problem is for-mulated for generating the diverse topologies of compliant mechanisms tracing a user-defined path. Motivation behind the present study is to generate the compliant mechanisms which perform the same task of tracing a prescribed trajectory near minimum-weight solution. Therefore, the constraint are imposed at each precision point representing a prescribed path for accomplishing the tracing task. An additional constraint on stress is also included for the feasible designs. The study starts with a single objective analysis of minimum-weight of compliant mechanism and the obtained topology is referred as the reference design. Thereafter, a bi-objective optimization problem is solved by considering the objectives as minimization of weight of structure and maximization of diversity of structure with respect to the reference design. Here, the diversity is evaluated by finding the dissimilarity in the bit value at each gene position of the binary strings of the reference design and a structure evolved from the GA population. A local search based multi-objective genetic algorithm (MOGA) optimization procedure is used in which the NSGA-II is used as a global search and optimization algorithm. A parallel computing is employed in the study for evaluating non-linear geometric FE analysis and also for the NSGA-II operations. After the NSGA-II run, a few solutions are selected from the non-dominated front and the local search is applied on them. With the help of a given optimization procedure, compliant mechanism designs tracing curvilinear and straight line trajectories are evolved and presented in the study. In both examples, compliant mechanisms are designed to have any arbitrary support and loading regions. I

Novel measurement based load modeling and demand side control methods for fault induced delayed voltage recovery mitigation

The continuous increase in electric energy demand and limitations in the reinforcement of generation and transmission systems, have progressively led to a greater utilization of power systems and transmission lines. As a result, system conditions may arise where voltage collapse phenomena have a high probability to occur, either due to the accidents in the system structure, or to load becoming particularly heavy. Recently, Workshop on Residential Air Conditioner (A/C) Stalling of Department of Energy (DOE) reported that fault-induced delayed voltage recovery (FIDVR) is now a national issue since residential A/C penetration across U.S. is at an all time high and growing rapidly. The unique characteristics of air conditioner load could cause short-term voltage instability, fast voltage collapse, and delayed voltage recovery. In order to study and mitigate FIDVR problem, a systematic load modeling methodology utilizing novel parameter identification technique and an online demand side control scheme based on load shedding strategy are developed in this dissertation. As load characteristics change from traditional incandescent light bulbs to power electronics-based loads, and as the characteristics of motors change with the emergence of high-efficiency, low-inertia motor loads, it is critical to understand and model load responses to ensure stable operations of the power system during different contingencies. Developing better load models, therefore, has been an important issue for power system analysis and control. It is necessary to take advantage of the state-of-the-art techniques for load modeling and develop a systematic approach to establish accurate, aggregate load models for bulk power system stability studies. In this dissertation, a systematic methodology is provided to derive aggregate load models at the high voltage level (transmission system level) using measurement-based approach. A novel parameter identification technique via hybrid learning is also developed for deriving load model parameters accurately and efficiently. According to NERC\u27s definition, FIDVR is defined as the phenomenon whereby system voltage remains at significantly reduced levels for several seconds after a fault in transmission, subtransmission, or distribution has been cleared. Various studies have shown that FIDVR usually occurs in the areas dominated by induction motors with constant torque. These motors can stall in response to sustained low voltage and draw excessive reactive power from the power grid. Since no under voltage or stall protection is equipped with A/Cs, they can only be tripped by thermal protection which takes 3 to 20 seconds. Severe FIDVR event could lead to fast voltage collapse. In this dissertation, a novel online demand side control method utilizing motor kinetic energy is developed for disconnecting stalling motors at the transmission level to mitigate FIDVR and fast voltage collapse

복잡한 동특성을 갖는 다상 반응기의 설계를 위한 계산 효율적인 모사 및 최적화 전략

학위논문(박사)--서울대학교 대학원 :공과대학 화학생물공학부,2020. 2. 이종민.본 박사학위논문에서는 멀티 스케일 모델링, 실험 결과를 이용한 모델 보정법, 최적화 순으로 진행되는 산업용 화학 반응기의 설계 전략을 제시한다. 반응기는 화학 공정에서 제일 중요한 단위이지만, 그 설계에 있어서는 최신 수치적 기법들보다는 여전히 간단한 모델이나 실험 및 경험 규칙에 의존하고 있는 현실이다. 산업 규모의 반응기는 물리, 화학적으로 몹시 복잡하고, 관련 변수 간의 스케일이 크게 차이나는 경우가 많아 수학적 모델링 및 수치적 해법을 구하기가 어렵다. 모델을 만들더라도 부정확하거나 시뮬레이션 시간이 너무 긴 문제가 있어 최적화 알고리즘에 적용하기가 힘들다. 반응기 내 현상의 복잡성과 스케일 차이 문제는 멀티 스케일 모델링을 통해 접근할 수 있다. 전산유체역학 기반 구획 모델(CFD-based compartmental model)을 이용하면, 불균일한 혼합 패턴을 보이는 대형 반응기에서도 긴 시간 동안의 동적 모사가 가능하다. 이 모델은 큰 반응기를 완벽하게 균일한 작은 구획들의 네트워크로 간주하고, 각 구획을 반응 속도식들과 CFD 결과로부터 가져온 유동 정보가 포함된 질량 및 에너지 균형 방정식으로 표현한다. 기체, 액체, 고체 3상이 상호작용하며 복잡한 유동을 보이는 수성 광물 탄산화 반응기를 이 방법을 사용해 모델링하였다. 이 때 모델은 미분 대수 방정식(DAE)의 형태를 띠며, 메커니즘 상 모든 반응들(기-액 간 물질 전달 반응, 고체 용해 반응, 이온 간 반응, 앙금 침전 반응)과 유체 역학, 반응열, 열역학적 변화 및 운전 상의 이벤트 발생을 모두 고려할 수 있다. 모델을 이용해 이산화탄소 제거 효율, pH 및 온도 변화를 예측하여 실제 운전 데이터와 비교한 결과, 파라미터를 통한 보정이 전혀 없이도 7 % 이내의 오차를 보여주었다. 모델의 부정확성 문제는 모델링 후 실험 결과를 이용한 모델 보정으로 극복 할 수 있다. 본 논문에서는 광물 탄산화 반응기 모델을 베이지안 보정(Bayesian calibration)을 통해 강화하는 방법을 제시한다. 먼저 모델 중 불확실한 부분에 8개의 파라미터를 도입한 후, 베이지안 파라미터 추정법(Bayesian parameter estimation) 및 실험실 규모에서의 실험 결과들을 이용하여 파라미터들의 사후 확률 분포를 추정하였다. 얻어진 파라미터의 확률 분포들은 모델 및 실험의 불완전성으로 인해 나타나는 파라미터의 불확실성 및 다중 봉우리 특성을 반영하고 있다. 이를 이용하여 실험 결과를 잘 따라가는 확률론적 모델 예측치(stochastic model response)를 얻을 수 있었다. 16개의 실험 데이터셋 및 테스트셋의 피팅 에러(fitting error)는 결정론적인 최적화 알고리즘(deterministic optimization)을 사용할 때보다 비슷하거나 낮은 것으로 측정되었다. 수학적 최적화에 쓰이기에 너무 긴 시뮬레이션 시간 문제는 베이지안 최적화 알고리즘을 적용하여 해결할 수 있다. 화학 반응기 설계 최적화를 위해 본 논문에서는 다중 목적 베이지안 최적화(Multi-objective Bayesian Optimization, MBO)를 사용해 시뮬레이션 횟수를 최소화 하는 CFD 기반 최적 설계 방법을 제안하였다. 여섯 가지 설계 변수를 가지는 기-액 교반 탱크 반응기에서 전력 소비를 최소화하고 가스 분율(gas holdup)를 극대화하기 위해 이 방법을 이용한 결과, 단 100 회의 시뮬레이션 만으로 최적 파레토 커브(Pareto curve)를 얻을 수 있었다. 제안된 최적 설계안들은 문헌에 보고된 기존 반응기들과 비교해 뛰어난 성능을 보여주었다. . 본 논문을 통해 제안된 CFD 기반 구획 모델링법, 베이지안 모델 보정법 및 베이지안 최적화 방법은 복잡한 물리적 및 화학적 특징을 갖는 산업 규모의 화학 반응기에 적용될 수 있을 것으로 기대된다.This thesis presents a design strategy for industrial-scale chemical reactors which consists of multi-scale modeling, post-modeling calibration, and optimization. Although the reactor design problem is a primary step in the development of most chemical processes, it has been relied on simple models, experiments and rules of thumbs rather than taking advantage of recent numerical techniques. It is because industrial-size reactors show high complexity and scale differences both physically and chemically, which makes it difficult to be mathematically modeled. Even after the model is constructed, it suffers from inaccuracies and heavy simulation time to be applied in optimization algorithms. The complexity and scale difference problem in modeling can be solved by introducing multi-scale modeling approaches. Computational fluid dynamics (CFD)-based compartmental model makes it possible to simulate hours of dynamics in large size reactors which show inhomogeneous mixing patterns. It regards the big reactor as a network of small zones in which perfect mixing can be assumed and solves mass and energy balance equations with kinetics and flow information adopted from CFD hydrodynamics model at each zone. An aqueous mineral carbonation reactor with complex gas–liquid–solid interacting flow patterns was modeled using this method. The model considers the gas-liquid mass transfer, solid dissolution, ionic reactions, precipitations, hydrodynamics, heat generation and thermodynamic changes by the reaction and discrete operational events in the form of differential algebraic equations (DAEs). The total CO2 removal efficiency, pH, and temperature changes were predicted and compared to real operation data. The errors were within 7 % without any post-adjustment. The inaccuracy problem of model can be overcome by post-modeling approach, such as the calibration with experiments. The model for aqueous mineral carbonation reactor was intensified via Bayesian calibration. Eight parameters were intrduced in the uncertain parts of the rigorous reactor model. Then the calibration was performed by estimating the parameter posterior distribution using Bayesian parameter estimation framework and lab-scale experiments. The developed Bayesian parameter estimation framework involves surrogate models, Markov chain Monte Carlo (MCMC) with tempering, global optimization, and various analysis tools. The obtained parameter distributions reflected the uncertain or multimodal natures of the parameters due to the incompleteness of the model and the experiments. They were used to earn stochastic model responses which show good fits with the experimental results. The fitting errors of all the 16 datasets and the unseen test set were measured to be comparable or lower than when deterministic optimization methods are used. The heavy simulation time problem for mathematical optimization can be resolved by applying Bayesian optimizaion algorithm. CFD based optimal design tool for chemical reactors, in which multi-objective Bayesian optimization (MBO) is utilized to reduce the number of required CFD runs, is proposed. The developed optimizer was applied to minimize the power consumption and maximize the gas holdup in a gas-sparged stirred tank reactor, which has six design variables. The saturated Pareto front was obtained after only 100 iterations. The resulting Pareto front consists of many near-optimal designs with significantly enhanced performances compared to conventional reactors reported in the literature. It is anticipated that the suggested CFD-based compartmental modeling, post-modeling Bayesian calibration, and Bayesian optimization methods can be applied in general industrial-scale chemical reactors with complex physical and chemical features.1. Introduction 1 1.1. Industrial-scale chemical reactor design 1 1.2. Role of mathematical models in reactor design 2 1.3. Intensification of reactor models through calibration 5 1.3.1. Bayesian parameter estimation 6 1.4. Optimization of the reactor models 7 1.4.1. Bayesian optimization 9 1.5. Aqueous mineral carbonation process : case study subject 10 1.6. Outline of the thesis 12 2. Multi-scale modeling of industrial-scale aqueous mineral carbonation reactor for long-time dynamic simulation 14 2.1. Objective 14 2.2. Experimental setup 15 2.3. Mathematical models 19 2.3.1. Reactor model 19 2.3.2. CFD model 28 2.3.3. Numerical setting 30 2.4. Results and discussions 32 2.4.1. CFD-based compartmental model for industrial-scale reactor. 32 2.4.2. Design and simulation of higher-scale reactors 42 2.5. Conclusions 47 3. Model intensification of aqueous mineral carbonation kinetics via Bayesian calibration 50 3.1. Objective 50 3.2. Experimental methods 51 3.2.1. Solution and gas preparation 51 3.2.2. Laboratory-scale mineral carbonation process 53 3.3. Mathematical models 56 3.3.1. Kinetics of aqueous mineral carbonation process 56 3.3.2. Differential algebraic equation (DAE) model for the reactor 65 3.3.3. Discrete events for simulation procedure 71 3.3.4. Numerical setting 72 3.4. Bayesian parameter estimation 72 3.4.1. Problem formulation 73 3.4.2. Bayesian posterior inference 76 3.4.3. Sampling 81 3.5. Results and discussions 82 3.5.1. Stochastic output response 82 3.5.2. Quality of parameter estimtates 86 3.5.3. Assessment of parameter uncertainties 91 3.5.4. Kinetics study with the proposed model parameters 99 3.6. Conclusions 103 4. Multi-objective optimization of chemical reactor design using computational fluid dynamics 106 4.1. Objective 106 4.2. Problem Formulation 107 4.3. Optimization scheme 113 4.3.1. Multi-objective optimization algorithm 113 4.3.2. CFD-MBO optimizer 120 4.4. CFD modeling 125 4.4.1. Tank specifications 125 4.4.2. Governing equations 125 4.4.3. Simulation methods 127 4.5. Results and discussion 128 4.5.1. CFD model validation 128 4.5.2. Optimization results 130 4.5.3. Analysis of optimal designs 139 4.6. Conclusions 144 5. Concluding Remarks 146 Bibliography 149 Abstract in Korean (국문초록) 163Docto

Método multiobjetivo de aprendizaje para razonamiento inductivo difuso

It has been recognized in various studies that the variations in the granularity (number of classes per variable) and the membership functions have a significant effect in the behaviour of the fuzzy systems. The FIR methodology is not an exception. The efficiency of the qualitative model identification and fuzzy forecast processes of FIR is very influenced by the fuzzification parameters of the system variables (i.e. number of classes and shape of the membership functions). To resolve this problematic we have been presented in previous works hybrid methodologies called Genetic Fuzzy Systems (GFSs) that try to learn in a joint way or by separated those parameters. These methods have used monoobjetive functions for the evaluation of the chromosomes. In this investigation another method of automatic learning is presented. This new method permits to obtain at the same time the fuzzification parameters of the FIR methodology but using Multiobjective Genetic Algorithms. Its main components are described and the results obtained on an environmental application are presented.Postprint (published version