Risto Ritala

Practical systems thinking

System is a dynamic and complex whole, interacting as a structured functional unit. Systems thinking provides tools for understanding a such system structure and its dynamic behavior. Practical systems thinking course teaches first year bachelor students basics about systems and how open problem can be formulated to system task.

Wireless Measurement of RFID IC Impedance

Accurate knowledge of the input impedance of a radio-frequency identification (RFID) integrated circuit (IC) at its wake-up power is valuable as it enables the design of a performance-optimized tag for a specific IC. However, since the IC impedance is power dependent, few methods exist to measure it without advanced equipment. We propose and demonstrate a wireless method, based on electromagnetic simulation and threshold power measurement, applicable to fully assembled RFID tags, to determine the mounted ICs input impedance in the absorbing state, including any parasitics arising from the packaging and the antenna-IC connection. The proposed method can be extended to measure the ICs input impedance in the modulating state as well.

Bayesian network model of overall print quality: Construction and structural optimisation

Prediction of overall visual quality based on instrumental measurements is a challenging task. Despite the several proposed models and methods, there exists a gap between the instrumental measurements of print and human visual assessment of natural images. In this work, a computational model for representing and quantifying the overall visual quality of prints is proposed. The computed overall quality should correspond to the human visual quality perception when viewing the printed images. The proposed model is a Bayesian network which connects the objective instrumental measurements to the subjective opinion distribution of human observers. This relationship can be used to score printed images, and additionally, to computationally study the connections of the attributes. A novel graphical learning approach using an iterative evolve-estimate-simulate loop learning the quality model based on psychometric data and instrumental measurements is suggested. The network structure is optimised by applying evolutionary computation (evolve). The estimation of the Bayesian network parameters is within the evolutionary loop. In this loop, the maximum likelihood approach is used (estimate). The stochastic learning process is guided by priors devised from the psychometric subjective experiments (performance through simulation). The model reveals and represents the explanatory factors between its elements providing insight to the psychophysical phenomenon of how observers perceive visual quality and which measurable entities affect the quality perception. By using true data, the design choices are demonstrated. It is also shown that the best-performing network establishes a clear and intuitively correct structure between the objective measurements and psychometric data.

Broke management optimization in design of paper production systems

Abstract This paper presents an optimization strategy for a broke system design and operation in papermaking process. A multiobjective stochastic optimization model is presented featuring (i) a stochastic two-state Markov process based submodel for the broke tower, (ii) an operational submodel for the optimization of the broke dosage, and (iii) a multiobjective design problem. An efficient optimization strategy is also proposed involving a quadratic optimization step for the operational subproblem and an effective multiobjective design optimization step.

Framework for modeling visual printed image quality from the paper perspective

Due to the rise in performance of digital printing, image-based applications are gaining popularity. This creates needs for specifying the quality potential of printers and materials in more detail than before. Both production and end-use standpoints are relevant. This paper gives an overview of an on-going study which has the goal of determining a framework model for the visual quality potential of paper in color image printing. The approach is top-down and it is founded on the concept of a layered network model. The model and its subjective, objective and instrumental measurement layers are discussed. Some preliminary findings are presented. These are based on data from samples obtained by printing natural image contents and simple test fields on a wide range of paper grades by ink-jet in a color managed process. Color profiles were paper specific. Visual mean opinion score data by human observers could be accounted for by two or three dimensions. In the first place these are related to brightness and color brightness. Image content has a marked effect on the dimensions. This underlines the challenges in designing the test images.

Bioprocesses and other production processes with multi-stability for method testing and analysis

Abstract This paper describes a simulator of a simple but stochastic and bistable bioprocess. Our emphasis is to generate test data for our novel analysis methods of transient and nonstationary systems. We show through simulations how the stochastic effects cause transitions between the stable fixed points of the deterministic model. We illustrate modeling the data with non-normal distributions and demonstrate how transients are analyzed through dynamic simulation of probability density functions.

Planning for robotic exploration based on forward simulation

Abstract We address the problem of controlling a mobile robot to explore a partially known environment. The robot’s objective is the maximization of the amount of information collected about the environment. We formulate the problem as a partially observable Markov decision process (POMDP) with an information-theoretic objective function, and solve it applying forward simulation algorithms with an open-loop approximation. We present a new sample-based approximation for mutual information useful in mobile robotics. The approximation can be seamlessly integrated with forward simulation planning algorithms. We investigate the usefulness of POMDP based planning for exploration, and to alleviate some of its weaknesses propose a combination with frontier based exploration. Experimental results in simulated and real environments show that, depending on the environment, applying POMDP based planning for exploration can improve performance over frontier exploration.

Mutual Information and Multidimensional Scaling as Means to Reconstruct Network Topology

Complex networked systems, such as mobile telecommunication networks, may have disturbance modes in which a large number of network nodes interact coherently. We are developing an appropriate statistical model to analyse stochastic disturbances in such networked systems. We present studies on a simple statistical state model based on Ising model known from statistical physics. We discuss how the network topology can be reconstructed from data, a crucial step in analysis of coherent systems. In particular, we apply multidimensional scaling (MDS) with statistical significance of mutual information (SSMI) as similarity measure to reveal the logical topology. We apply our method both to synthetic and real data, and show that MDS provides useful information about the topology when both the interactions between network nodes and the direct loading of nodes are relevant for the node state; that is when the net work can neither be described as a single state system nor as a system consisting of independent elements

Production-line wide dynamic Bayesian network model for quality management in papermaking

Abstract The quality parameters of paper are managed with rather independent decisions made by many process operators through the production line. Improving one quality parameter typically deteriorates another, and hence incoherent decisions tend to lead to suboptimal overall quality. Vast amount of laboratory measurements data support these operator decisions, yet how this information is utilized in practice, is not well known and appears to vary from production line to production line and operator to operator. We aim at coherent quality management of a paper production line through both optimizing the operator actions and scheduling the measurements of quality management optimally. We have chosen a Bayesian network formalism to integrate qualitative human knowledge and the measurement data about quality. We present an application with a Bayesian network as a model within stochastic dynamic programming. We demonstrate our modeling approach in a realistic case study, yet not in full-scale production-line wide quality management case.

Statistical Model Describing Networked Systems Phenomena

We are analysing stochastic disturbances in networked systems with the aim of finding an appropriate and parsimonious statistical model for them. We have studied a simple statistical state model based on Ising model known from statistical physics. Our previous studies have derived an identification method for the model. We seek the ways to combine the statistical state model to real networked systems through mutual information between node states, the statistical significance of which is used as an absolute dependency measure of network nodes. In this paper we examine how the networked systems phenomena, such as coherence and hysteresis, appear in this statistical state model. In particular, our interest lies in the statistical dependence between the nodes when coherence and hysteresis occurs.