On the Design of a Decision Support System for Robotic Equipment Adoption in Construction Processes

The construction sector is one of the major global economies and is characterised by low productivity and high inefficiencies, but could highly benefit from the introduction of robotic equipment in terms of productivity, safety, and quality. As the development and the availability of robotic solutions for the construction sector increases, the evaluation of their potential benefits compared to conventional processes that are currently adopted on construction sites becomes compelling. To this end, we exploit Bayesian decision theory and apply an axiomatic design guideline for the development of a decision-theoretic expert system that: (i) evaluates the utility of available alternatives based on evidence; (ii) accounts for uncertainty; and (iii) exploits both expert knowledge and preferences of the users. The development process is illustrated by means of exemplary use case scenarios that compare manual and robotic processes. A use case scenario that compares manual and robotic marking and spraying is chosen for describing the development process in detail. Findings show how decision making in equipment selection can be supported by means of dedicated systems for decision support, developed in collaboration with domain experts.

Battery Charging in Collision Models with Bayesian Risk Strategies

We constructed a collision model where measurements in the system, together with a Bayesian decision rule, are used to classify the incoming ancillas as having either high or low ergotropy (maximum extractable work). The former are allowed to leave, while the latter are redirected for further processing, aimed at increasing their ergotropy further. The ancillas play the role of a quantum battery, and the collision model, therefore, implements a Maxwell demon. To make the process autonomous and with a well-defined limit cycle, the information collected by the demon is reset after each collision by means of a cold heat bath.

Comparative numerical analysis of Bayesian decision rule and probabilistic neural network for pattern recognition

At present, neural networks are increasingly used to solve many problems instead of traditional methods for solving them. This involves comparing the neural network and the traditional method for specific tasks. In this paper, computer modeling of the Bayesian decision rule and the probabilistic neural network is carried out in order to compare their operational characteristics for recognizing Gaussian patterns. Recognition of four and six images (classes) with the number of features from 1 to 6 was simulated in cases where the images are well and poorly separated. The sizes of the training and test samples are chosen quiet big: 500 implementations for each image. Such characteristics as training time of the decision rule, recognition time on the test sample, recognition reliability on the test sample, recognition reliability on the training sample were analyzed. In framework of these conditions it was found that the recognition reliability on the test sample in the case of well separated patterns and with any number of the instances is close to 100 percent for both decision rules. The neural network loses 0,1–16 percent to Bayesian decision rule in the recognition reliability on the test sample for poorly separated patterns. The training time of the neural network exceeds the training time of the Bayesian decision rule in 4–5 times and the recognition time – in 4–6 times. As a result, there are no obvious advantages of the probabilistic neural network over the Bayesian decision rule in the problem of Gaussian pattern recognition. The existing generalization of the Bayesian decision rule described in the article is an alternative to the neural network for the case of non-Gaussian patterns.

Rough Set Theory-Based Multi-Class Decision Making Framework for Cost Effective Treatment of COVID-19 Suspected Patients

Rough set theory approximates a concept by the three regions, namely positive, negative and boundary regions. The three regions enable us to derive three types of decisions, namely acceptance, rejection and deferment. The deferment decision gives us the flexibility to further examine suspicious objects and reduce misclassification. The main objective of this paper is to provide a cost effective treatment of a patient suspect to COVID-19 positive by using multiclass three-way decision making with the help of Rough set theory. The cost-based analysis of three-way decisions brings the theory closer to real-world applications where costs play an indispensable role. In our approach, we extend the three-way decision to three-way multiclass decision, offering a new framework of multiple classes. Different types of misclassification errors are treated separately based on the notation of loss function from Bayesian decision theory. In our cost sensitive classification approach, the cost caused by a different kind of error are not assumed to be equal. Finally, a numerical example for a cost effective treatment of a patient with COVID-19 disease is considered to demonstrate the practicability and efficacy of the developed idea in real-life applications.