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Multi-objective optimization of smart grid operations via preventive maintenance scheduling using time-dependent unavailability
(Elsevier, 2025-08-15) Krpelík, Daniel; Vrtal, Matěj; Briš, Radim; Praks, Pavel; Fujdiak, Radek; Toman, Petr
This paper presents a method for multi-criteria optimization of system operations using transient operation models. Real systems often combine long-living components with rapid repairs, creating challenges for numerical integration due to fine discretization requirements. These challenges significantly increase the computational cost when evaluating renewal processes with recurrent terms of quadratic complexity in mission time. To address this, we derive new mathematical formulas for evaluating unavailability and operational costs of components under periodic, age-based preventive restoration. The key innovation is a decomposition of the renewal equation: repair-related terms are approximated analytically, eliminating the need for fine discretization throughout the process. A special formula is introduced for components with uniformly distributed repair times and mean time to repair much shorter than mean time to failure, applicable to many real-world systems. The accuracy of the proposed approach is validated against Monte Carlo simulations, showing significant reduction in computational effort. This efficiency enables repeated evaluations in optimization tasks, demonstrated on a real-world case involving interconnected energy and communication infrastructure in the Czech Republic. A multi-objective NSGA-II algorithm is employed to optimize the preventive replacement policy, minimizing both system maintenance cost and expected downtime. We also explore systems with components of non-zero initial age. Results show that relying solely on asymptotic approximations may lead to suboptimal strategies, potentially worsening performance. However, allowing preventive renewal of selected components at time zero enables identification of superior solutions.
Research on Passive Assessment of Parkinson’s Disease Utilising Speech Biomarkers
(Springer Nature, 2023-06-11) Kováč, Daniel; Mekyska, Jiří; Brabenec, Luboš; Košťálová, Milena; Rektorová, Irena
Speech disorders, collectively referred to as hypokinetic dysarthria (HD), are early biomarkers of Parkinson’s disease (PD). To assess all dimensions of HD, patients could perform several speech tasks using a smartphone outside a clinic. This paper aims to adapt the parametrization process to running speech so that a patient is not required to interact actively with the device, and features can be extracted directly from phone calls. The method utilizes a voice activity detector followed by a voicing detection. The algorithm was tested on a database of 126 recordings (86 patients with PD and 40 healthy controls) of monologue mixed with noise with different signal-to-noise ratios (SNR) to simulate the real environment conditions. Pearson correlation coefficients show a strong linear relationship between speech features and patients’ scores assessing HD and other motor/non-motor symptoms – p-value < 0.01 for the normalized amplitude quotient (NAQ) with Test 3F Dysarthric Profile (DX index) and Unified Parkinson’s Disease Rating Scale (part III) in 20 dB SNR conditions, p-value < 0.01 for the jitter and shimmer with the Mini Mental State Exam (10 dB SNR). A model based on the Extreme Gradient Boosting algorithm predicts the DX index with a 10.83% estimated error rate (EER) and the Addenbrooke’s Cognitive Examination-Revise (ACE-R) score with 13.38% EER. The introduced algorithm can potentially be used in mHealth applications for passive monitoring and assessment of PD patients.
Stress and Emotion Open Access Data: A Review on Datasets, Modalities, Methods, Challenges, and Future Research Perspectives
(Springer Nature, 2025-06-18) Ometov, Aleksandr; Mezina, Anzhelika; Lin, Hsiao-Chun; Arponen, Otso; Burget, Radim; Nurmi, Jari
Remote continuous patient monitoring is an essential feature of eHealth systems, offering opportunities for personalized care. Among its emerging applications, emotion and stress recognition hold significant promise, but face major challenges due to the subjective nature of emotions and the complexity of collecting and interpreting related data. This paper presents a review of open access multimodal datasets used in emotion and stress detection. It focuses on dataset characteristics, acquisition methods, and classification challenges, with attention to physiological signals captured by wearable devices, as well as advanced processing methods of these data. The findings show notable advances in data collection and algorithm development, but limitations remain, e.g., variability in real-world conditions, individual differences in emotional responses, and difficulties in objectively validating emotional states. The inclusion of self-reported and contextual data can enhance model performance, yet lacks consistency and reliability. Further barriers include privacy concerns, annotation of long-term data, and ensuring robustness in uncontrolled environments. By analyzing the current landscape and highlighting key gaps, this study contributes a foundation for future work in emotion recognition. Progress in the field will require privacy-preserving data strategies and interdisciplinary collaboration to develop reliable, scalable systems. These advances can enable broader adoption of emotion-aware technologies in eHealth and beyond.
Fractional-Order Equivalent Circuit Representation of Sucrose Solutions Electrical Impedance Measured in Bipolar Configuration
(IEEE, 2025-03-30) Duckworth, Colin; Šotner, Roman; Jeřábek, Jan; Freeborn, Todd
In this work, the alteration in electrical impedance of a liquid (unsweetened FuzeTea) with varying increases in sucrose concentrations (up to 785 mM) was investigated. The aim was to further understand how changes in liquid solutions with adulterants (in this case sucrose) change the electrical impedance as a potential method for food quality monitoring. The electrical impedance was measured from 100 Hz to 1 MHz using gold and platinum electrodes, then numerical optimization was applied to collected datasets to estimate the model parameters of a fractional-order equivalent circuit model to best represent the data. This model demonstrated less than 2% impedance magnitude and less than 6% impedance phase deviation compared to the experimental data. Overall, the model parameter representing the liquid resistance showed large increases for increasing concentrations of sucrose, suggesting that it may be a potential marker for assessing sucrose adulterants in liquid solutions.
Differentiator circuits with scalable and electronically adjustable time constant and their application in phase shift evaluation
(Elsevier, 2025-05-03) Šotner, Roman; Polák, Ladislav; Petržela, Jiří; Semenov, Dmitrii; Langhammer, Lukáš; Jaikla, Winai
Two novel scalable and electronically adjustable differentiator designs are presented in this paper. These designs are based on special variable gain amplifiers extending well-known concept of standard single operational amplifier-based differentiators. The key novelty lies in their scalability, which allows for an enhanced time constant value by adjusting the ratio of resistors. Simultaneously, the special form of gain control using a DC voltage offers wide electronic tunability. The solution performs high input and low output impedance, both independent of frequency. Experimental testing demonstrated time constant adjustments in two configurations: from 64 ns to 4.5 mu s (a ratio of maximal and minimal value 70) and from 8.7 mu s to 183 mu s (a ratio of 21). As an application example, the proposed differentiator is utilized in the design of a readout system for an absolute phase shift difference to pulse width ratio converter, suitable for monitoring a very slow phenomenon such biosignals.

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