Iskanje po katalogu digitalne knjižnice

Opis: Sensitive data requires careful consideration and adherence to best practices to ensure its confidentiality, integrity, and availability. Essential steps in handling sensitive data are identification and classification of sensitive data, implementation of data access control, encryption of sensitive data, secure storage and transmission, implementation of data breach response plan, backup and monitoring usage of data, complying with regulation, and disposing of data securely. Misconduct in handling sensitive data can compromise data confidentiality, integrity, and availability. These include data breaches (unauthorised access or disclosure, theft, insider threats, falsification, fabrication, imputation, and amputation of data), failure to comply with data protection regulations, inadequate data security practices, improper retention and disposal of data, and failure to report data breaches and incidents. In this presentation Milan Ojsteršek presents how to manage sensitive data, desensitise it, and which are the most common breaches in handling sensitive data incidents. This presentation was given at the 4th ENAI Academic Integrity Summer School 2024, 16th – 21th September 2024, University of Konstanz, Germany.
Ključne besede: open science, metadata, research data management, sensitive data, academic integrity, data management ethics, research misconduct, licensing of open data, FAIR, Slovenian open access infrastructure
Objavljeno v DKUM: 18.04.2025; Ogledov: 0; Prenosov: 2
Celotno besedilo (16,66 MB)

Opis: The purpose of this article is to present the use of a previously validated wearable sensor device, Armbeep, in a real-life application, to enhance a tennis player’s training by monitoring and analysis of the time, physiological, movement, and tennis-specific workload and recovery indicators, based on fused sensor data acquired by the wearable sensor—a miniature wearable sensor device, designed to be worn on a wrist, that can detect and record movement and biometric information, where the basic signal processing is performed directly on the device, while the more complex signal analysis is performed in the cloud. The inertial measurements and pulse-rate detection of the wearable device were validated previously, showing acceptability for monitoring workload and recovery during tennis practice and matches. This study is one of the first attempts to monitor the daily workload and recovery of tennis players under real conditions. Based on these data, we can instruct the coach and the player to adjust the daily workload. This optimizes the level of an athlete’s training load, increases the effectiveness of training, enables an individual approach, and reduces the possibility of overuse or injuries. This study is a practical example of the use of modern technology in the return of injured athletes to normal training and competition. This information will help tennis coaches and players to objectify their workloads during training and competitions, as this is usually only an intuitive assessment.
Ključne besede: tennis, training, data-based coaching, shot recognition, wearable device, workload, recovery
Objavljeno v DKUM: 31.03.2025; Ogledov: 0; Prenosov: 3
Celotno besedilo (4,62 MB)
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Opis: Model-driven engineering is a well-established software development methodology that uses models to develop applications where the end-users with visual elements model abstractions from a specific domain. These models are based on domain-specific modeling language (DSML), which is particular to the problem domain. During DSML use, new ideas emerge and DSMLs evolve. However, reports on DSML evolution are rare. This study presents a new DSML called RT-Sequencer that evolved from our DSML Sequencer to support, in addition to the Data Acquisition domain, also a new domain—Real-Time Control (RTC) systems. The process of defining models with a new language RT-Sequencer has changed in a way that new end-users were introduced—advanced endusers, which use general-purpose language (GPL) and advanced programming concepts to define modeling environments for the RT-Sequencer end-users. More specifically, an industrial experience with the RT-Sequencer is presented, where DSML was opened for extension so that a GPL code could be inserted into the model to create new visual blocks for the end-user, and the possibility to adapt and optimize the execution code for a particular task. Our experience shows the specific case of DSML evolution supporting another problem domain, and the implementation effort needed to extend domain-specific modeling language with GPL support.
Ključne besede: model-driven engineering, domain-specific modeling languages, measurement systems, Real-Time Control systems, data acquisition, language evolution, experience report
Objavljeno v DKUM: 27.03.2025; Ogledov: 0; Prenosov: 2
Celotno besedilo (1,70 MB)
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Opis: The traditional approach in public transport planning was to collect travel demand data for a more extended period and compose timetables to serve this demand. There are two significant identifiable issues. In the rural areas and off-peak hours, public transport operators provide much more capacities than needed. On the other hand, more capacities than scheduled are needed on certain lines at certain departures on some sporadically occurring occasions. The problem is how to react to short-term changes (daily) triggered by exceptional circumstances and events and midterm changes (weekly, monthly basis) in travel demand. We can trigger changes in travel demand chiefly by introducing a desirable (almost for free) tariff system applied to specific populations. No long-term travel response data exists for this kind of intervention, but an immediate response in public transport supply is needed. In Slovenia, public transport for free for the whole population over 65 years was introduced. With the modern ticketing system, which was designed to be as simple as possible for users (that means "check-in only" at the moment of boarding), the research task was to analyze the travel behavior of the retired population, faced with a new attractive option to travel, based on data of purchased tickets and their afterward validation, for better mid-and long-term planning. Our study finds that ITS technology (in this case, e-ticketing system) can satisfactorily solve the discussed planning and management task.
Ključne besede: fare-free public transport, smart card data collecting, population mobility, travel demand
Objavljeno v DKUM: 13.03.2025; Ogledov: 0; Prenosov: 1
Celotno besedilo (2,11 MB)
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Opis: Direct verification of the geometric accuracy of machined parts cannot be performed simultaneously with active machining operations, as it usually requires subsequent inspection with measuring devices such as coordinate measuring machines (CMMs) or optical 3D scanners. This sequential approach increases production time and costs. In this study, we propose a novel indirect measurement method that utilizes motor current data from the controller of a Computer Numerical Control (CNC) machine in combination with machine learning algorithms to predict the geometric accuracy of machined parts in real-time. Different machine learning algorithms, such as Random Forest (RF), k-nearest neighbors (k-NN), and Decision Trees (DT), were used for predictive modeling. Feature extraction was performed using Tsfresh and ROCKET, which allowed us to capture the patterns in the motor current data corresponding to the geometric features of the machined parts. Our predictive models were trained and validated on a dataset that included motor current readings and corresponding geometric measurements of a mounting rail later used in an engine block. The results showed that the proposed approach enabled the prediction of three geometric features of the mounting rail with an accuracy (MAPE) below 0.61% during the learning phase and 0.64% during the testing phase. These results suggest that our method could reduce the need for post-machining inspections and measurements, thereby reducing production time and costs while maintaining required quality standards
Ključne besede: smart production machines, data-driven manufacturing, machine learning algorithms, CNC controller data, geometrical accuracy
Objavljeno v DKUM: 10.03.2025; Ogledov: 0; Prenosov: 6
Celotno besedilo (4,44 MB)
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