Chapter 15 - Considerations when selecting an OTDR

Array processing is an integral part of automatic seismic event detection pipelines for measuring apparent velocity and backazimuth of seismic arrivals. Both quantities are usually measured under the plane-wave assumption, and are essential to classify the phase type and to determine the direction toward the event epicenter. However, structural inhomogeneities can lead to deviations from the plane-wave model, which must be taken into account for phase classification and back-azimuth estimation. We suggest a combined classification and regression neural network, which we call ArrayNet, to determine the phase type and backazimuth directly from the arrival-time differences between all combinations of stations of a given seismic array without assuming a plane-wave model. ArrayNet is trained using regional P- and S-wave arrivals of over 30,000 seismic events from reviewed regional bulletins in northern Europe from the past three decades. ArrayNet models are generated and trained for each of the ARCES, FINES, and SPITS seismic arrays. We observe excellent performance for the seismic phase classification (up to 99% accuracy), and the derived back-azimuth residuals are significantly improved in comparison with traditional array processing results using the plane-wave assumption. The SPITS array in Svalbard exhibits particular issues when it comes to array processing in the form of high apparent seismic velocities and a multitude of frost quake signals inside the array, and we show how our new approach better handles these obstacles. Furthermore, we demonstrate the performance of ArrayNet on 20 months of continuous phase detections from the ARCES array and investigate the results for a selection of regional seismic events of interest. Our results demonstrate that automatic event detection at seismic arrays can be further enhanced using a machine learning approach that takes advantage of the unique array data recorded at these stations.

Ship-cranes are applied in a wide range of offshore operations. During loading the operator has to adapt to the current sea state while normally controlling the crane via a joystick based interface. Increasing the level of automation reduces the operator's responsibilities e.g. by compensating load deflections via secondary control loops. In addition, operational support can be provided by means of an enhanced human-machine interface (HMI) further simplifying the handling task. A two-sided operating concept is proposed incorporating an intuitive HMI as well as a subsequent control loop at crane level. The HMI is implemented by means of a lightweight robot (LWR) allowing the operator to control the crane in Cartesian space while also providing haptic feedback. Hereby, the user is assisted in finding suitable reference trajectories. The control topology is tested in a robot-based laboratory setup. Performance of the HMI is discussed on the basis of two test scenarios illustrating the operating concept. It is shown that a loss of control on the operator's part is effectively avoided through haptic feedback. Moreover, an oscillating load is successfully stabilized by the combination of HMI and secondary control loop.

In this thesis a detailed study on the battery technologies used in aircrafts in the last five decades is done. A general background of the battery system is studied and the performance of the batteries based on energy densities and low temperature capabilities is evaluated for the deployment in the more electric aircraft (MEA). Evolution of MEA with its power system architecture and load profile is analysed to understand the requirements of the battery system. The electrical load profile of the MEA “Aircraft X” is designed and modeled. Different types of electrical loads such as primary/conventional loads, the environmental control system (ECS) load, anti-icing (wing ice protection) loads, various electrical actuators (aileron, spoilers, rudder) loads and signal communication (RADAR etc.) loads are designed and modeled. Furthermore, starting profile of engine starter generator (ESG), transient stability profile of military standard (MIL-STD-704F) and emergency load profile of the MEA “Aircraft X” are also studied and analysed. Thus, all these electrical loads are combined and an algorithm is successfully obtained to separate the constant and transient loads. An effective battery system is designed for supplying the transient loads by considering the weight saving and cost analysis factors between Li-ion and Ni-Cd batteries. Based on the analysis, Li-ion battery with Lithium ferrous phosphate (LFP) chemistry is designed and proposed for the MEA “Aircraft X”. The designed LFP based battery system is also modeled using the modified shepherd equation wherein the voltage polarization term is added to have a lower complexity and more proximity with the battery model block in MATLAB®. After the design and modeling of the proposed Li-ion battery system, the battery charger (phase shifted high power bidirectional dc-dc “PSHPBD” converter) is designed and modeled. The design of the components of PSHPBD converter is done keeping in view the requirement of maximum power transfer in the MEA environment. Zero voltage switching (ZVS) operation in wide range is also considered for design of the PSHPBD converter for MEA operating parameters. The PSHPBD converter is modeled using the Fourier series based harmonic analysis wherein an optimal harmonic number (h=7) is chosen by considering the proximity and complexity between the modeled converter and switch model. The battery energy storage system “BESS” (consisting of the battery system and the PSHPBD converter) is successfully integrated with the power distribution 270V dc bus architecture of the MEA and tested under different dynamic conditions using three different control techniques. Firstly, a direct estimation based technique is proposed for determining the phase shift of the PSHPBD converter (used as a charger for the batteries). Secondly, a current mode control for the single phase shift control technique for the PSHPBD converter is proposed. The proposed control technique has an inherent capability of current limit. Thirdly, a peak current controller is proposed which provides a limit on the peak current and provides a fast transient response. The advantages of using the proposed peak current control in avoiding the saturation of the transformer core is also reported in the work. In integration, finally the study and analysis of zero current switching (ZCS) inverter for integration of BESS to ESG set in MEA “Aircraft X” is discussed. Furthermore, a control method which can improve the life of the BESS in MEA is discussed in which three different modes of control based on the load estimation are given. Depending upon the load on the BESS, the corresponding control structure is implemented/activated leading to reduction in RMS current flow in the charger circuit and average current flow in the battery system thereby reducing the losses and temperature rise in the BESS, which eventually leads to an increase in the life of the BESS. Finally, a multi-functional battery charger (MFBC) is proposed for integration of BESS to the power distribution network in MEA. A novel control method is devised for suitable transfer and sharing of power between different buses. Based on the placement and applicability of the MFBC, many new dc power distribution architectures for MEA are proposed wherein energy storage system can be integrated into aircraft network leading towards fault tolerant operation and weight optimization in the MEA.

This paper proposes a system for an automatic detection of indoor scene events with interactive inquiry based on speech dialog and gesture recognition. The system detects the events that various objects are brought in or taken out by image recognition. The user of the system inquires the stored events in the past by pointing the objects or space and using speech dialog. Since automatic event detection may fail in complicated indoor scene, the system can use interactive inquiry to correct such failures.

Sleep apnea hypopnea syndrome (SAHS) is a common sleep disorder that can significantly decrease the quality of life. Apnea hypopnea index, the number of apnea and hypopnea events per hour of sleep, is defined for the severity of SAHS. An automatic and accurate detection of apnea and hypopnea events can overcome the limitations of manual diagnosis of SAHS. This study explored the design of a novel automated algorithm to detect apnea and hypopnea events. From polysomnography records of the Sleep Heart Health Study, the airflow and pulse oximetry signals of 30 subjects were extracted. According to the updated American Academy of Sleep Medicine scoring manual, apnea and hypopnea events were scored by an experienced sleep physiologist. The peak signal excursion was precisely determined from the airflow envelope. An apnea event was detected by the precise determination of its pre-event baseline. A hypopnea event was detected when both the airflow reduction and oxygen desaturation were satisfied. Accordingly, the automated algorithm detected 5122 events (2215 apneas and 2907 hypopneas), against the manual scoring of 5021 events (2235 apneas and 2786 hypopneas). Strong correlations between scoring and detection of apnea, hypopnea, and combined events were achieved. The overall agreement between the scoring and detection of apnea, hypopnea, and combined events were respectively 99.1%, 95.7%, and 98.0%. This automatic algorithm is applicable to any portable sleep monitoring device for the accurate detection of apnea and hypopnea events.

Industrial process plants are increasingly becoming complex structures with high level of automation. Nonetheless, the final plant productivity and the overall equipment efficiency does not solely depend on an optimized engineering design/installation practice, but also on human operators supervision. In parallel, along with the classic demand to minimize costs and time-to-market during the design phases, issues concerning human safety and failure prevention play a crucial role, one of the highest target being the avoidance of dangerous process states. Within this context, Simulation-Based-Training (SBT) allows plant operators to learn how to command complex automated machineries within a secure virtual environment. Similar to its usage in medical, aerospace, naval and military fields, SBT for manufacturing systems can be employed in order to involve the user within a realistic scenario, thus providing an effective, lifelike, interactive training experience under the supervision of experienced personnel. In addition, also according to previous literature, industry-driven SBT may be effectively envisaged as a natural extension of the plant life-cycle simulation practice, comprising Design Simulation & Optimization, Virtual Commissioning, Operator Training, up to Plant Maintenance. In this context, since the overall system behavior depends both on manufacturing process dynamics and Control Logics, the main challenge for an effective SBT is related with the development of a real-time environment where control system responsiveness is fully reproduced. Owing to this consideration, this paper reports a successful industrial case study, concerning a novel SBT workbench used for steel plants operator training, discussing both the virtual prototyping phase and the development of a real-time simulation architecture. In particular, a hybrid process simulation is employed, where a virtual process model is coupled with physical PLC and Human–Machine Interface, thus achieving an accurate reproduction of the real plant/operator interaction.

The EU funded Iliad project builds on the assets resulting from two decades of investments in policies and infrastructures for the blue economy aiming to establish an interoperable, data-intensive, and cost-effective Digital Twin of the Ocean (DTO). Iliad DTO combines ocean observations, state-of-the-art forecast models, citizen science, AI and advanced computing infrastructures (cloud computing, HPC, Internet of Things, Big Data, social networking, and more) to create high-resolution, multi-variable and multi-dimensional near real-time virtual representations of the ocean. Continuously integrating data from numerous in-situ sensors and satellites, Iliad DTO can provide a platform for researchers, policy makers, the industry, and citizens, to monitor the impact of climate change and human activities on ocean health and productivity and make data driven management decisions for a sustainable blue economy. Several Digital Twin (DT) pilots will be undertaken in key thematic areas such as offshore wind energy, wave and tidal energy, biodiversity assessments, fisheries and aquaculture, marine pollution and more. The current work presents the Oil Spill Response Digital Twin pilot developed in the frame of Iliad. The DT focuses on Cretan Sea and aims to provide early detection of marine oil spills and operational forecasting of spill trajectories to support immediate response to pollution events, minimizing thus the impact on marine ecosystems, coastal communities and the economy and reducing the time for environmental recovery. A multi-model approach is followed for predicting the fate and transport of oil spills, employing MEDSLIK-II and OpenDrift particle tracking models, coupled to operational, high-resolution numerical weather (WRF), hydrodynamic (NEMO) and sea state (WAVEWATCH III) models for Cretan Sea. Real-time observations (current speed/direction, sea water temperature, wave height) from novel, low-cost on-line sensors are integrated in the DT and assimilated into the operational metocean forecasting chain for validation purposes and for improving models’ forecasting skills. A near-real time automatic oil spill detection system, from Sentinel-1 SAR images, is developed, which allows early detection of marine oil spills and triggers the oil spill forecasting system, producing accurate short-term forecasting of spills’ trajectories and fate. Automatic detection and classification of oil spill events employs a trained FCOS which performs initial object detection, fine-tuned with a dataset of +1000 SAR images, including 4 different classes (oil spill, look-alike, ship, land). Image pre-processing and oil spill mask delineation is performed using SNAPpy-based (Sentinel Application Platform Python toolbox) adaptive thresholding algorithms. The oil spill detection and forecasting system is tested by reconstructing the Ulysse-Virginia oil spill, which occurred off the coast of Corsica on October 7th, 2018. Sentinel-1 images are used to detect and delineate the coverage of the spill, to initiate Medslik-II and OpenDrift models for simulating the oil spill fate and transport. Oil spill predictions are produced using different metocean forcings, highlighting the importance of high-resolution metocean data in accurate forecasting of oil spill trajectories. Quantitative metrics are used to evaluate the ability of the oil spill models to reproduce the satellite oil spill observations.    Acknowledgement: This research has received funding from the European Union’s H2020 RIA programme under GA No 101037643.

Unmanned Aircraft Systems (UAS) research increasingly requires high-performance, multi-agent simulation environments that integrate realistic dynamics with immersive visualization. This paper introduces a distributed co-simulation framework that seamlessly combines the dynamic modeling capabilities of Hopsan with the advanced rendering and interaction tools of Unreal Engine 5.3. Communication between the two platforms is achieved through a lightweight, User Datagram Protocol (UDP) based plugin, which supports bi-directional real-time data exchange and is complemented by a USB Raw Input plugin to integrate human-in-the-loop joystick control. The proposed framework was validated across progressively complex scenarios. First, a single F‑16 aircraft data model was imported from Hopsan, encompassing waypoint-guided dynamics, atmospheric effects, actuation, and geoid-based altitude calibration. Its state reinforced by Hopsan was visualized in Unreal in real-time. Second, the platform was extended to support two independent F‑16 agents, each communicating via dedicated UDP ports, thereby demonstrating modular, scalable, multi-agent operation. Third, we introduced a Human Machine Interface (HMI) scenario, where one aircraft was piloted manually via joystick input, while the second autonomously followed the same waypoint sequence. This validated the framework’s capacity to handle human interaction in a multi-agent context. Results evidence synchronized simulation at real-time performance, accurate environmental interactions (terrain, wind, collision), and reliable human-in-the-loop control. The framework’s architecture promotes modularity, scalability, and deployment flexibility across multiple machines. Future enhancements will explore tighter coupling with Unreal’s environmental physics, adoption of fluid dynamics, and scaling to larger agent ensembles. By integrating open-source dynamical modeling with high-fidelity graphical simulation, this platform offers a robust foundation for UAS mission planning, operator training, and AI-driven control validation.

The trend in next-generation switched-mode power supplies will lead to modular, scalable solutions which deliver power efficiently over a wide range of operation. This paper details a new approach to introduce more advanced control features like phase-alignment and frequency synchronization into such scalable solutions. While these methods have been incorporated into multi-phase converters in the past, they all require the distribution of information among the individual converters. In distributed solutions, dedicated communication signals have been used to share this information. An advantage of the proposed method is that it does not require such communication signals between the individual power supplies and is therefore fully scalable and cost effective. Perturbances generated by the switching actions of the individual converters on the common input/output voltage are used by each converter to harvest information about the switching actions of its counterparts. An algorithm is proposed to align the individual phases and synchronize the switching frequencies based on this information. This allows a reduction of input/output capacitor ripple currents, similar to techniques used in multi-phase designs. Experimental results for an FPGA prototype implementation are presented.

A novel automatic data collection and categorizing (ADCC) technique is proposed, and it captures data based on the content of the acoustic signal. An important problem in automatic acoustic event detection system is to find the start and finish point indexing of acoustic event signal in the recorded signal. Firstly, a pre-profiling-based acoustic signal capturing technique is proposed. In the non-stationary environments (low signal-to-noise ratio), conventional approaches for start and finish indexing detection often fail. It is observed that the accuracy of the automatic event detection technique also degrades. To overcome this problem, secondly, feature extraction is done using Mel Filter Cepstral Coefficient (MFCC). A combined approach for classification is employed using K-means with 1-Nearest Neighbor (1NN). It is expected that ADCC approach integrated with classifier is computationally economical.

To meet the demands on a comfortable screening, or even diagnostic, equipment without interfering with the sleep, this study develops a level IV portable system, equipped with two tri-axial accelerometers (TAA) measuring the thoracic and abdominal respiratory efforts, and one oximeter measuring the oxygen saturation (SpO2), to identify obstructive sleep apnea (OSA), central sleep apnea (CSA), and hypopnea (HYP) events. The prototype integrates all the hardware and software for physiological information extraction. In addition, an automatic event detection algorithm is proposed to reduce the labor-intensive work on scoring the events. Based on 63 subjects, with 80% data for training and 20% for validation, the classification accuracy of the apnea hypopnea-index (AHI) is 84.13%. The results indicate that the proposed algorithm has great potential to classify the severity of patients in clinical examinations for both the screening and the homecare purposes.

Automatic detection of EEG artefacts arising from head movements using EEG and gyroscope signals

Annotation of foot-contact and foot-off events is the initial step in post-processing for most quantitative gait analysis workflows. If clean force plate strikes are present, the events can be automatically detected. Otherwise, annotation of gait events is performed manually, since reliable automatic tools are not available. Automatic annotation methods have been proposed for normal gait, but are usually based on heuristics of the coordinates and velocities of motion capture markers placed on the feet. These heuristics do not generalize to pathological gait due to greater variability in kinematics and anatomy of patients, as well as the presence of assistive devices. In this paper, we use a data-driven approach to predict foot-contact and foot-off events from kinematic and marker time series in children with normal and pathological gait. Through analysis of 9092 gait cycle measurements we build a predictive model using Long Short-Term Memory (LSTM) artificial neural networks. The best-performing model identifies foot-contact and foot-off events with an average error of 10 and 13 milliseconds respectively, outperforming popular heuristic-based approaches. We conclude that the accuracy of our approach is sufficient for most clinical and research applications in the pediatric population. Moreover, the LSTM architecture enables real-time predictions, enabling applications for real-time control of active assistive devices, orthoses, or prostheses. We provide the model, usage examples, and the training code in an open-source package.

To meet the requirement of the battery charging characteristics and electric vehicle interoperability, wide power range and wide voltage range are important for the wireless electric vehicle (EV) charging system. Hence, ensuring zero voltage switching (ZVS) and high efficiency over the wide operating range is a hot issue but not well resolved. To address the current state of research, an efficiency-oriented control strategy is proposed for LCC-S compensated Wireless Power Transfer (WPT) system to achieve minimum ZVS current over a wide voltage and power regulation range. Based on the accurate harmonic-considered time-domain model, a universal design method for system parameters under the worst ZVS current conditions within wide operation range is proposed. Then, the universal explicit expression at the unit voltage gain point is given. Moreover, based on the simplified fitting method of piecewise bi-quadratic interpolation (PBQI), minimum ZVS current and minimum non-active current can be ensured over the full output voltage and output power range. A 3.3 kW prototype has been built to verify that the converter can realize ZVS within the whole voltage range, the measured peak efficiency is about 95.2%.

The data of a wafer with defects can provide engineers with very important information and clues to improve the yield rate and quality in manufacturing. This paper presents a microscope automatic positioning and wafer detection system with human-machine interface based on image processing and fuzzy inference algorithms. In the proposed system, a XY table is used to move the position of each die on 6 inch or 8 inch wafers. Then, a high-resolution CCD and one set of two-axis optical linear encoder are used to accurately measure the position on the wafer. Finally, the developed human-machine interface is used to display the current position of an actual wafer in order to complete automatic positioning, and a wafer map database can be created. In the process of defect detection, CCD is used for image processing, and during preprocessing, it is required to filter noise, acquire the defect characteristics, define the defective template, and then take the characteristic points of the defective template as the reference input for fuzzy inference. A high-accuracy optical automatic positioning and wafer defect detection system is thus constructed. This study focused on automatic detection of spots, scratches, and bruises, and attempted to reduce the time to detect defective die and improve the accuracy of determining the defects of semiconductor devices.