Digital Twin framework for automated fault source detection and prediction for comfort performance evaluation of existing non-residential Norwegian buildings

Numerous buildings fall short of expectations regarding occupant satisfaction, sustainability, or energy efficiency. In this paper, the performance of buildings in terms of occupant comfort is evaluated using a probabilistic model based on Bayesian networks (BNs). The BN model is founded on an in-depth anal- ysis of satisfaction survey responses and a thorough study of building performance parameters. This study also presents a user-friendly visualization compatible with BIM to simplify data collecting in two case studies from Norway with data from 2019 to 2022. This paper proposes a novel Digital Twin approach for incorporating building information modeling (BIM) with real-time sensor data, occupants’ feedback, a probabilistic model of occupants’ comfort, and HVAC faults detection and prediction that may affect occupants’ comfort. New methods for using BIM as a visualization platform, as well as a pre- dictive maintenance method to detect and anticipate problems in the HVAC system, are also presented. These methods will help decision-makers improve the occupants’ comfort conditions in buildings. However, due to the intricate interaction between numerous equipment and the absence of data integra- tion among FM systems, CMMS, BMS, and BIM data are integrated in this paper into a framework utilizing ontology graphs to generalize the Digital Twin framework so it can be applied to many buildings. The results of this study can aid decision-makers in the facility management sector by offering insight into the aspects that influence occupant comfort, speeding up the process of identifying equipment malfunc- tions, and pointing toward possible solutions.Digital Twin framework for automated fault source detection and prediction for comfort performance evaluation of existing non-residential Norwegian buildingspublishedVersionPaid open acces

Poenotenje medaspektnih povezav v metodi FRAM

With a growing complexity of socio-technical systems and event outcomes that cannot be understood in terms of causality, traditional accident modelling approaches are no longer adequate to analyse accidents in such systems. Thus, in recent years novel systemic approaches have been developed. Functional Resonance Analysis Method (FRAM) is a means to understand how seemingly small performance variations of functions in a complex socio-technical system coincide and mutually affect each other in unexpected ways resulting in the functional resonance. A FRAM model consists of essential system functions, each characterised by six aspects. The functional resonance is defined based on couplings among aspects. Currently, the method provides only a general classification of couplings: Matter, Energy or Information (MEI). Such classification prevents an analytical view on the complex structure of relations in observed socio-technical systems and permits the construction of non-uniform models. This thesis, thus, seeks to unify FRAM models by developing a classification scheme of inter-functional couplings. The proposed MEDI classification helps to maximise compatibility, safety and quality of FRAM models in general. It represents one of the necessary steps towards the method automatisation.Ob vse večji zapletenosti socio-tehničnih sistemov in izidov dogodkov, ki jih ni mogoče razumeti z vidika vzročnosti, tradicionalne metode za modeliranje nesreč v takšnih sistemih ne ustrezajo več. V zadnjih letih so bili zato razviti novi analitični pristopi. Metoda FRAM ali metoda analize funkcijske resonance je metodologija, ki omogoča razumevanje, kako na videz majhne variacije delovanja funkcij v zapletenem socio-tehničnem sistemu sovpadajo in medsebojno vplivajo na nepričakovane načine, ki povzročijo funkcijsko resonanco. Model FRAM sestavljajo ključne sistemske funkcije, opisane s šestimi aspekti. Funkcijska resonanca je opredeljena na podlagi povezav med funkcijami. Trenutno metoda ponuja le splošno klasifikacijo povezav: Materija, Energija ali Informacija (MEI). Takšna klasifikacija onemogoča analitični pogled na zapleteno strukturo relacij v opazovanem sistemu in dopušča gradnjo nepoenotenih modelov. Cilj pričujočega dela je poenotiti modele FRAM z razvojem klasifikacijske sheme medaspektnih povezav. Predlagana nova klasifikacija medaspektnih povezav MEDI pripomore k večji združljivosti, varnosti in kakovosti modelov FRAM na splošno, ter predstavlja enega od potrebnih korakov k avtomatizaciji metode

The Design and Implementation of a Prototype Geographic Information System Using a Novel Architecture Based on PS-Algol

This thesis is concerned with the design and implementation of a novel architecture for a geographic information system based on the use of a new database language called PS-algol, in conjunction with a hybrid database structure. The main aspects discussed within the context of this thesis are:- i) the definition of a database; ii) the components and functions of a database management system; iii) the features of PS-algol; iv) the new system architecture; v) the use of operational management system; vi) data entry as carried out by the system; vii) the facility for the cartographic representation of features; viii) data retrieval and its potential use; and ix) the generation of hard-copy output The thesis also includes a review of existing geographical information systems against which the novelty of the new approach can be judged

Strategies of anomalies detection in bridges and tunnels as a tool for structural health management. New approaches and AI support.

L'abstract è presente nell'allegato / the abstract is in the attachmen

Diagnosis and Prognosis of Occupational disorders based on Machine Learn- ing Techniques applied to Occupational Profiles

Work-related disorders have a global influence on people’s well-being and quality of life and are a financial burden for organizations because they reduce productivity, increase absenteeism, and promote early retirement. Work-related musculoskeletal disorders, in particular, represent a significant fraction of the total in all occupational contexts. In automotive and industrial settings where workers are exposed to work-related muscu- loskeletal disorders risk factors, occupational physicians are responsible for monitoring workers’ health protection profiles. Occupational technicians report in the Occupational Health Protection Profiles database to understand which exposure to occupational work- related musculoskeletal disorder risk factors should be ensured for a given worker. Occu- pational Health Protection Profiles databases describe the occupational physician states, and which exposure the physicians considers necessary to ensure the worker’s health protection in terms of their functional work ability. The application of Human-Centered explainable artificial intelligence can support the decision making to go from worker’s Functional Work Ability to explanations by integrating explainability into medical (re- striction) and supporting in two decision contexts: prognosis and diagnosis of individual, work related and organizational risk condition. Although previous machine learning ap- proaches provided good predictions, their application in an actual occupational setting is limited because their predictions are difficult to interpret and hence, not actionable. In this thesis, injured body parts in which the ability changed in a worker’s functional work ability status are targeted. On the one hand, artificial intelligence algorithms can help technical teams, occupational physicians, and ergonomists determine a worker’s workplace risk via the diagnosis and prognosis of body part(s) injuries; on the other hand, these approaches can help prevent work-related musculoskeletal disorders by identifying which processes are lacking in working condition improvement and which workplaces have a better match between the remaining functional work abilities. A sample of 2025 for the prognosis part (from the years of 2019 to 2020) and 7857 for the prognosis part of Occupational Health Protection Profiles based on Functional Work Ability textual re- ports in the Portuguese language in automotive industry factory. Machine learning-based Natural Language Processing methods were implemented to extract standardized infor- mation. The prognosis and diagnosis of Occupational Health Protection Profiles factors were developed in reliable Human-Centered explainable artificial intelligence system to promote a trustworthy Human-Centered explainable artificial intelligence system (enti- tled Industrial microErgo application). The most suitable regression models to predict the next medical appointment for the injured body regions were the models based on CatBoost regression, with R square and an RMSLE of 0.84 and 1.23 weeks, respectively. In parallel, CatBoost’s best regression model for most body parts is the prediction of the next injured body parts based on these two errors. This information can help tech- nical industrial teams understand potential risk factors for Occupational Health Protec- tion Profiles and identify warning signs of the early stages of musculoskeletal disorders.Os transtornos relacionados ao trabalho têm influência global no bem-estar e na quali- dade de vida das pessoas e são um ônus financeiro para as organizações, pois reduzem a produtividade, aumentam o absenteísmo e promovem a aposentadoria precoce. Os distúr- bios osteomusculares relacionados ao trabalho, em particular, representam uma fração significativa do total em todos os contextos ocupacionais. Em ambientes automotivos e industriais onde os trabalhadores estão expostos a fatores de risco de distúrbios osteomus- culares relacionados ao trabalho, os médicos do trabalho são responsáveis por monitorar os perfis de proteção à saúde dos trabalhadores. Os técnicos do trabalho reportam-se à base de dados dos Perfis de Proteção da Saúde Ocupacional para compreender quais os fatores de risco de exposição a perturbações músculo-esqueléticas relacionadas com o tra- balho que devem ser assegurados para um determinado trabalhador. As bases de dados de Perfis de Proteção à Saúde Ocupacional descrevem os estados do médico do trabalho e quais exposições os médicos consideram necessária para garantir a proteção da saúde do trabalhador em termos de sua capacidade funcional para o trabalho. A aplicação da inteligência artificial explicável centrada no ser humano pode apoiar a tomada de decisão para ir da capacidade funcional de trabalho do trabalhador às explicações, integrando a explicabilidade à médica (restrição) e apoiando em dois contextos de decisão: prognóstico e diagnóstico da condição de risco individual, relacionado ao trabalho e organizacional . Embora as abordagens anteriores de aprendizado de máquina tenham fornecido boas pre- visões, sua aplicação em um ambiente ocupacional real é limitada porque suas previsões são difíceis de interpretar e portanto, não acionável. Nesta tese, as partes do corpo lesiona- das nas quais a habilidade mudou no estado de capacidade funcional para o trabalho do trabalhador são visadas. Por um lado, os algoritmos de inteligência artificial podem aju- dar as equipes técnicas, médicos do trabalho e ergonomistas a determinar o risco no local de trabalho de um trabalhador por meio do diagnóstico e prognóstico de lesões em partes do corpo; por outro lado, essas abordagens podem ajudar a prevenir distúrbios muscu- loesqueléticos relacionados ao trabalho, identificando quais processos estão faltando na melhoria das condições de trabalho e quais locais de trabalho têm uma melhor correspon- dência entre as habilidades funcionais restantes do trabalho. Para esta tese, foi utilizada uma base de dados com Perfis de Proteção à Saúde Ocupacional, que se baseiam em relató- rios textuais de Aptidão para o Trabalho em língua portuguesa, de uma fábrica da indús- tria automóvel (Auto Europa). Uma amostra de 2025 ficheiros foi utilizada para a parte de prognóstico (de 2019 a 2020) e uma amostra de 7857 ficheiros foi utilizada para a parte de diagnóstico. . Aprendizado de máquina- métodos baseados em Processamento de Lingua- gem Natural foram implementados para extrair informações padronizadas. O prognóstico e diagnóstico dos fatores de Perfis de Proteção à Saúde Ocupacional foram desenvolvidos em um sistema confiável de inteligência artificial explicável centrado no ser humano (inti- tulado Industrial microErgo application). Os modelos de regressão mais adequados para prever a próxima consulta médica para as regiões do corpo lesionadas foram os modelos baseados na regressão CatBoost, com R quadrado e RMSLE de 0,84 e 1,23 semanas, res- pectivamente. Em paralelo, a previsão das próximas partes do corpo lesionadas com base nesses dois erros relatados pelo CatBoost como o melhor modelo de regressão para a mai- oria das partes do corpo. Essas informações podem ajudar as equipes técnicas industriais a entender os possíveis fatores de risco para os Perfis de Proteção à Saúde Ocupacio- nal e identificar sinais de alerta dos estágios iniciais de distúrbios musculoesqueléticos

Synthetic Data -- what, why and how?

This explainer document aims to provide an overview of the current state of the rapidly expanding work on synthetic data technologies, with a particular focus on privacy. The article is intended for a non-technical audience, though some formal definitions have been given to provide clarity to specialists. This article is intended to enable the reader to quickly become familiar with the notion of synthetic data, as well as understand some of the subtle intricacies that come with it. We do believe that synthetic data is a very useful tool, and our hope is that this report highlights that, while drawing attention to nuances that can easily be overlooked in its deployment.Comment: Commissioned by the Royal Society. 57 pages 2 figure

Knowledge Modelling and Learning through Cognitive Networks

One of the most promising developments in modelling knowledge is cognitive network science, which aims to investigate cognitive phenomena driven by the networked, associative organization of knowledge. For example, investigating the structure of semantic memory via semantic networks has illuminated how memory recall patterns influence phenomena such as creativity, memory search, learning, and more generally, knowledge acquisition, exploration, and exploitation. In parallel, neural network models for artificial intelligence (AI) are also becoming more widespread as inferential models for understanding which features drive language-related phenomena such as meaning reconstruction, stance detection, and emotional profiling. Whereas cognitive networks map explicitly which entities engage in associative relationships, neural networks perform an implicit mapping of correlations in cognitive data as weights, obtained after training over labelled data and whose interpretation is not immediately evident to the experimenter. This book aims to bring together quantitative, innovative research that focuses on modelling knowledge through cognitive and neural networks to gain insight into mechanisms driving cognitive processes related to knowledge structuring, exploration, and learning. The book comprises a variety of publication types, including reviews and theoretical papers, empirical research, computational modelling, and big data analysis. All papers here share a commonality: they demonstrate how the application of network science and AI can extend and broaden cognitive science in ways that traditional approaches cannot

A Survey of Learning-based Automated Program Repair

Automated program repair (APR) aims to fix software bugs automatically and plays a crucial role in software development and maintenance. With the recent advances in deep learning (DL), an increasing number of APR techniques have been proposed to leverage neural networks to learn bug-fixing patterns from massive open-source code repositories. Such learning-based techniques usually treat APR as a neural machine translation (NMT) task, where buggy code snippets (i.e., source language) are translated into fixed code snippets (i.e., target language) automatically. Benefiting from the powerful capability of DL to learn hidden relationships from previous bug-fixing datasets, learning-based APR techniques have achieved remarkable performance. In this paper, we provide a systematic survey to summarize the current state-of-the-art research in the learning-based APR community. We illustrate the general workflow of learning-based APR techniques and detail the crucial components, including fault localization, patch generation, patch ranking, patch validation, and patch correctness phases. We then discuss the widely-adopted datasets and evaluation metrics and outline existing empirical studies. We discuss several critical aspects of learning-based APR techniques, such as repair domains, industrial deployment, and the open science issue. We highlight several practical guidelines on applying DL techniques for future APR studies, such as exploring explainable patch generation and utilizing code features. Overall, our paper can help researchers gain a comprehensive understanding about the achievements of the existing learning-based APR techniques and promote the practical application of these techniques. Our artifacts are publicly available at \url{https://github.com/QuanjunZhang/AwesomeLearningAPR}

SPATIAL TRANSFORMATION PATTERN DUE TO COMMERCIAL ACTIVITY IN KAMPONG HOUSE

ABSTRACT Kampung houses are houses in kampung area of the city. Kampung House oftenly transformed into others use as urban dynamics. One of the transfomation is related to the commercial activities addition by the house owner. It make house with full private space become into mixused house with more public spaces or completely changed into full public commercial building. This study investigate the spatial transformation pattern of the kampung houses due to their commercial activities addition. Site observations, interviews and questionnaires were performed to study the spatial transformation. This study found that in kampung houses, the spatial transformation pattern was depend on type of commercial activities and owner perceptions, and there are several steps of the spatial transformation related the commercial activity addition. Keywords: spatial transformation pattern; commercial activity; owner perception, kampung house; adaptabilit