Implementasi Adaptive Neural Fuzzy Inference System (ANFIS) Untuk Menguji Kualitas Printed Circuit Board (PCB)

Pengujian kualitas menggunakan teknik pengolahan citra dan kecerdasan tiruan banyak diterapkan dalam berbagai industri, misalnya industri tekstil, perakitan kendaraan, makanan, minuman, perakitan elektronik, dan lain – lain. Pengujian model ini sering disebut dengan istilah Automated Visual Inspection System (AVIS) atau dalam bahasa Indonesia Sistem Inspeksi Visual Otomatis (SIVO). Penelitian ini mengacu pada model sistem inspeksi, di mana objek pengujiannya adalah keping Printed Circuit Board (PCB). Banyak penelitian tentang pengujian PCB yang sudah dilakukan, tetapi masih banyak yang belum memberikan hasil yang optimum, diantaranya waktu akses yang masih lambat, keakuratan data masih rendah, dan tingkat kesalahan yang masih tinggi. Berdasarkan hasil penelitian dan pengujian yang sudah dilakukan, model ANFIS sangat layak dijadikan sebagai model inferensi kecerdasan buatan dalam sistem yang berbasis inspeksi otomatis khususnya menguji kualitas keping PCB, karena terbukti model ANFIS dengan model hybrid trapesium mf memiliki tingkat kesalahan yang sangat kecil yaitu 4.0186e-007 dan untuk tingkat akurasi pengujian datanya mencapai 99%.&nbsp

Properties, Learning Algorithms, and Applications of Chain Graphs and Bayesian Hypergraphs

Probabilistic graphical models (PGMs) use graphs, either undirected, directed, or mixed, to represent possible dependencies among the variables of a multivariate probability distri- bution. PGMs, such as Bayesian networks and Markov networks, are now widely accepted as a powerful and mature framework for reasoning and decision making under uncertainty in knowledge-based systems. With the increase of their popularity, the range of graphical models being investigated and used has also expanded. Several types of graphs with dif- ferent conditional independence interpretations - also known as Markov properties - have been proposed and used in graphical models. The graphical structure of a Bayesian network has the form of a directed acyclic graph (DAG), which has the advantage of supporting an interpretation of the graph in terms of cause-effect relationships. However, a limitation is that only asymmetric relationships, such as cause and effect relationships, can be modeled between variables in a DAG. Chain graphs, which admit both directed and undirected edges, can be used to overcome this limitation. Today there exist three main different interpretations of chain graphs in the lit- erature. These are the Lauritzen-Wermuth-Frydenberg, the Andersson-Madigan-Perlman, and the multivariate regression interpretations. In this thesis, we study these interpreta- tions based on their separation criteria and the intuition behind their edges. Since structure learning is a critical component in constructing an intelligent system based on a chain graph model, we propose new feasible and efficient structure learning algorithms to learn chain graphs from data under the faithfulness assumption. The proliferation of different PGMs that allow factorizations of different kinds leads us to consider a more general graphical structure in this thesis, namely directed acyclic hypergraphs. Directed acyclic hypergraphs are the graphical structure of a new proba- bilistic graphical model that we call Bayesian hypergraphs. Since there are many more hypergraphs than DAGs, undirected graphs, chain graphs, and, indeed, other graph-based networks, Bayesian hypergraphs can model much finer factorizations and thus are more computationally efficient. Bayesian hypergraphs also allow a modeler to represent causal patterns of interaction such as Noisy-OR graphically (without additional annotations). We introduce global, local and pairwise Markov properties of Bayesian hypergraphs and prove under which conditions they are equivalent. We also extend the causal interpretation of LWF chain graphs to Bayesian hypergraphs and provide corresponding formulas and a graphical criterion for intervention. The framework of graphical models, which provides algorithms for discovering and analyzing structure in complex distributions to describe them succinctly and extract un- structured information, allows them to be constructed and utilized effectively. Two of the most important applications of graphical models are causal inference and information ex- traction. To address these abilities of graphical models, we conduct a causal analysis, comparing the performance behavior of highly-configurable systems across environmen- tal conditions (changing workload, hardware, and software versions), to explore when and how causal knowledge can be commonly exploited for performance analysis

PhysioSim – A Full Hard- And Software Physiological Simulation Environment Applying A Hybrid Approach Based On Hierarchical Modeling Using Algebraic And Differential Systems and Dynamic Bayesian Networks

A system for physiological modeling and simulation is presented. The architecture is considering hardware and software support for real-time physiological simulators, which are very important for medical education and risk management. In contrary to other modeling methods, in this work the focus is to provide maximal modeling flexibility and extensibility. This is provided on the one hand by a hierarchical modeling notation in XML and on other hand by extending current methods by dynamic stochastic system modeling. Dynamic Bayesian Networks as well as deterministic system modeling by systems of algebraic and differential equations lead towards a sophisticated environment for medical simulation. Specific simulations of haemodynamics and physiological based pharmacokinetics and pharmacodynamics are performed by the proposed methods, demonstrating the applicability of the approaches. In contrary to physiological modeling and analysis tools, for an educational simulator, the models have to be computed in real-time, which requires extensive design of the hardware and software architecture. For this purpose generic and extensible frameworks have been suggested and realized. All the components together lead to a novel physiological simulator environment, including a dummy, which emulates ECG, SaO2 and IBP vital signals in addition to software signal simulation. The modeling approaches with DBN are furthermore analyzed in the domains of psychological and physiological reasoning, which should be integrated into a common basis for medical consideration. Furthermore the system is used to show new concepts for dependable medical data monitoring, which are strongly related to physiological and psychological simulations

Building a Bayesian Network Model of Heart Disease

) Jayanta K. Ghosh and Marco Valtorta Department of Computer Science and Engineering University of South Carolina Columbia, South Carolina USA 29208 fghosh,[email protected] Obesity ECG Diet Chol/HDL Atheriosclerosis HeartDisease AdvMedicine MedicineTaken ModExercise SerumSelenium RapidHeart SmokeAlcohol FamilyHistory BloodPressure MioInfarction AnginaPectoris SerumTrig SerumLDL Figure 1: Bayesian Network Structure Bayesian networks [2] represent a promising technique for clinical decision support and provide powerful capabilities for representing uncertain knowledge, including a flexible representation of probability distributions that allows one to specify dependence and independence of variables in a natural way through the network topology. Because dependencies are expressed qualitatively as links between nodes, one can structure the domain knowledge qualitatively before any numeric probabilities need to be assigned. We report here on a Bayesian network model of ..