Initial Calibration Research Articles

Multi-protocol updating for seamless key negotiation in quantum metropolitan networks

Quantum key distribution (QKD) can provide long-term security for numerous users. Currently, quantum networks are still in the early stages of small-scale deployment, most of which can only support a single QKD protocol (QKDP). However, with the advancement of various QKDPs, a single-protocol quantum network is no longer sufficient to meet the demands of multiple users, prompting the emergence of multi-protocol quantum networks. Nevertheless, the transition from a single-protocol to a multi-protocol quantum network still faces many challenging issues, such as key negotiation interruptions due to device initialization and channel calibration during protocol updating. To address the quantum key negotiation interruption problem, we propose a seamless key negotiation oriented multi-protocol updating algorithm in this work, which can fulfill the protocol updating requests of different users in quantum metropolitan networks. Furthermore, to better improve network performance while meeting diverse user demands, we propose four heuristic algorithms for optimal QKDP recommendation, focusing on their applications for multi-protocol updating in different types of typical networks. We perform the simulations with different QKDP recommendation algorithms to analyze the impact of the cache time of the key cache area on the key negotiation interruption probability and the time resource utilization. Simulation results demonstrate that the proposed algorithm can reduce the key negotiation interruption probability by 77.7% while increasing the time resource utilization by 15.3% compared to no key cache area.

Current Measurement Gain Compensation Using High-Frequency Signal Injection in Dual Three-Phase PMSM Systems

To online compensate the scaling errors in current measurement caused by incorrect calibration, temperature, aging, and noise, this paper proposes a new solution based on high-frequency (HF) carrier voltage injection in dual three phase permanent magnet synchronous machines. The relationship between the measured HF currents and scaling errors is derived. It shows that the amplitudes of the HF currents are proportional to the scaling errors, based on which three HF current control loops are subsequently designed to eliminate the measured HF currents and compensate the scaling errors. The designed HF controller are independent from the main current and torque regulators, and thus, it can be used for initial scaling gain calibration or periodically used during normal machine operation. Different from general three phase machine systems, the generated HF current components only exist in z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> subspace, and thus, no extra torque ripples will be produced. Experimental results validate the proposed control algorithm.

Implementation and observability analysis of visual-inertial-wheel odometry with robust initialization and online extrinsic calibration

Combining camera, IMU and wheel encoder is a wise choice for car positioning because of the low cost and complementarity of the sensors. We propose a novel extended visual-inertial odometry algorithm based on sliding window tightly fusing data from the above three sensors. Firstly we propose an IMU-odometer pre-integration approach utilizing complete IMU measurements and wheel encoder readings, to make scale estimation more accurate in subsequent 4-degrees of freedom (DoF) optimization. Secondly we develop an original initialization module where encoder readings are fully utilized to refine gravity direction and provide an initial value for camera pose in real scale. Thirdly, we design a computationally efficient online extrinsic calibration method by fixing the linearization point for the rotational component of IMU-odometer extrinsic parameters, which is deployed depending on the convergence of accelerometer bias. Fourthly, we give an observability analysis of our optimization based approach under more general assumption. Extensive experiments are performed on two sets of data in various scenes, bringing the state-of-the-art visual odometry and visual-inertial odometry algorithms into comparison. Experimental results prove the overwhelmingly better performance of our proposed approach on the above two sets of data, as well as the robustness of our initialization module and the improvement resulted from online extrinsic calibration.

Forecasting weekly dengue incidence in Sri Lanka: Modified Autoregressive Integrated Moving Average modeling approach.

Dengue poses a significant and multifaceted public health challenge in Sri Lanka, encompassing both preventive and curative aspects. Accurate dengue incidence forecasting is pivotal for effective surveillance and disease control. To address this, we developed an Autoregressive Integrated Moving Average (ARIMA) model tailored for predicting weekly dengue cases in the Colombo district. The modeling process drew on comprehensive weekly dengue fever data from the Weekly Epidemiological Reports (WER), spanning January 2015 to August 2020. Following rigorous model selection, the ARIMA (2,1,0) model, augmented with an autoregressive component (AR) of order 16, emerged as the best-fitted model. It underwent initial calibration and fine-tuning using data from January 2015 to August 2020, and was validated against independent 2000 data. Selection criteria included parameter significance, the Akaike Information Criterion (AIC), and Schwarz Bayesian Information Criterion (SBIC). Importantly, the residuals of the ARIMA model conformed to the assumptions of randomness, constant variance, and normality affirming its suitability. The forecasts closely matched observed dengue incidence, offering a valuable tool for public health decision-makers. However, an increased percentage error was noted in late 2020, likely attributed to factors including potential underreporting due to COVID-19-related disruptions amid rising dengue cases. This research contributes to the critical task of managing dengue outbreaks and underscores the dynamic challenges posed by external influences on disease surveillance.

Design and Characterization of a Powered Wheelchair Autonomous Guidance System.

The current technological revolution driven by advances in machine learning has motivated a wide range of applications aiming to improve our quality of life. Representative of such applications are autonomous and semiautonomous Powered Wheelchairs (PWs), where the focus is on providing a degree of autonomy to the wheelchair user as a matter of guidance and interaction with the environment. Based on these perspectives, the focus of the current research has been on the design of lightweight systems that provide the necessary accuracy in the navigation system while enabling an embedded implementation. This motivated us to develop a real-time measurement methodology that relies on a monocular RGB camera to detect the caregiver's feet based on a deep learning method, followed by the distance measurement of the caregiver from the PW. An important contribution of this article is the metrological characterization of the proposed methodology in comparison with measurements made with dedicated depth cameras. Our results show that despite shifting from 3D imaging to 2D imaging, we can still obtain comparable metrological performances in distance estimation as compared with Light Detection and Ranging (LiDAR) or even improved compared with stereo cameras. In particular, we obtained comparable instrument classes with LiDAR and stereo cameras, with measurement uncertainties within a magnitude of 10 cm. This is further complemented by the significant reduction in data volume and object detection complexity, thus facilitating its deployment, primarily due to the reduced complexity of initial calibration, positioning, and deployment compared with three-dimensional segmentation algorithms.

A Fast Optimizing Method of Clutch–Clutch Shift Calibration Parameters

Aiming at the drawbacks of the heavy workload and lengthy calibration cycle of transmission control unit (TCU) development, a fast optimizing and test method of clutch–clutch shift calibration parameters is developed based on the “V” development process. A controlled-object simulation model of a six-speed automatic transmission (6AT) is established, and the shift calibration parameters and shift quality evaluation system are studied. Therefore, a model-in-the-loop (MIL) calibration platform for key calibration parameters and evaluation indicators in the shift process is constructed. By setting the optimization target of evaluation indicators, the initial calibration parameters’ ranges are reduced. Finally, the TCU is flashed with the optimized calibration parameters, and a hardware-in-the-loop (HIL) test system is built to verify the shift quality. The same shift quality verification test is carried out in the vehicle. The test results show that the application of the MIL simulation platform can enable the batch simulation analysis of eight key shift calibration parameters, gradually refine the initial rough interval, and ensure shift quality. The shift calibration parameters are cross-validated in multiple development links, and the dependence on vehicle calibration is greatly reduced. The development cycle is effectively shortened to less than 6 months.

Line-based iterative geometric calibration method for a tomosynthesis system.

A next generation tomosynthesis (NGT) system, capable of two-dimensional source motion, detector motion in the perpendicular direction, and magnification tomosynthesis, was constructed to investigate different acquisition geometries. Existing position-based geometric calibration methods proved ineffective when applied to the NGTgeometries. A line-based iterative calibration method is developed to perform accurate geometric calibration for the NGTsystem. The proposed method calculates the system geometry through virtual line segments created by pairs of fiducials within a calibration phantom, by minimizing the error between the line equationscomputed from the true and estimated fiducial projection pairs. It further attempts to correct the 3D fiducial locations based on the initial geometric calibration. The method's performance was assessed via simulation and experimental setups with four distinct NGT geometries: X, T, XZ, and TZ. The X geometry resembles a conventional DBT acquisition along the chest wall. The T geometry forms a "T"-shaped source path in mediolateral (ML) and posteroanterior (PA) directions. A descending detector motion is added to both X and T geometries to form the XZ and TZ geometries, respectively. Simulation studies were conducted to assess the robustness of the method to geometric perturbations and inaccuracies in fiducial locations. Experimental studies were performed to assess the impact of phantom magnification and the performance of the proposed method for various geometries, compared to the traditional position-based method. Star patterns were evaluated for both qualitative and quantitative analyses; the Fourier spectral distortions (FSDs) graphs and the contrast transfer function (CTF) were extracted. The limit of spatial resolution (LSR) was measured at 5% modulation of theCTF. The proposed method presented is highly robust to geometric perturbation and fiducial inaccuracies. After the line-based iterative method, the mean distance between the true and estimated fiducial projections was [X, T, XZ, TZ]: [0.01, 0.01, 0.02, 0.01] mm. The impact of phantom magnification was observed; a contact-mode acquisition of a calibration phantom successfully provided an accurate geometry for 1.85× magnification images of a star pattern, with the X geometry. The FSD graphs for the contact-mode T geometry acquisition presented evidence of super-resolution, with the LSR of [0°-quadrant: 8.57, 90°-quadrant: 8.47] lp/mm. Finally, a contact-mode XZ geometry acquisition and a 1.50× magnification TZ geometry acquisition were reconstructed with three calibration methods-position-based, line-based, and iterative line-based. As more advanced methods are applied, the CTF becomes more isotropic, the FSD graphs demonstrate less spectral leakage as super-resolution is achieved, and the degree of blurring artifacts reducessignificantly. This study introduces a robust calibration method tailored to the unique requirements of advanced tomosynthesis systems. By employing virtual line segments and iterative techniques, we ensure accurate geometric calibration while mitigating the limitations posed by the complex acquisition geometries of the NGT system. Our method's ability to handle various NGT configurations and its tolerance to fiducial misalignment make it a superior choice compared to traditional calibrationtechniques.

Enhancing Camera Calibration for Traffic Surveillance with an Integrated Approach of Genetic Algorithm and Particle Swarm Optimization.

Recent advancements in sensor technologies, coupled with signal processing and machine learning, have enabled real-time traffic control systems to effectively adapt to changing traffic conditions. Cameras, as sensors, offer a cost-effective means to determine the number, location, type, and speed of vehicles, aiding decision-making at traffic intersections. However, the effective use of cameras for traffic surveillance requires proper calibration. This paper proposes a new optimization-based method for camera calibration. In this approach, initial calibration parameters are established using the Direct Linear Transformation (DLT) method. Then, optimization algorithms are applied to further refine the calibration parameters for the correction of nonlinear lens distortions. A significant enhancement in the optimization process is achieved through the integration of the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) into a combined Integrated GA and PSO (IGAPSO) technique. The effectiveness of this method is demonstrated through the calibration of eleven roadside cameras at three different intersections. The experimental results show that when compared to the baseline DLT method, the vehicle localization error is reduced by 22.30% with GA, 22.31% with PSO, and 25.51% with IGAPSO.

Post-process updating of model parameters to approximate thermal errors of machine tools operating in different configurations

Machine tool (MT) thermal errors induced by external and internal heat sources (e.g. changing environment, friction and power losses in MT components) are an important element in machined workpiece inaccuracies. In the past few decades, indirect software compensation techniques have been used to address thermal errors on account of their economic and ecological aspects. Many slightly different approaches are described in the literature. If thermal error models are properly identified based on experiments with sufficiently varying input parameters, they usually work within similar calibration and verification conditions. As the sensory equipment of machines increases, models can be adapted to regions with higher thermo-mechanical system nonlinearity and inhomogeneity through the introduction of deformation feedback from direct measurements into model structures. However, adaptive functionalities require discrete interruptions of the MT's work cycle, which threaten the integrity of the machined surface and complicate the model structure and thus also implementation and industrial deployment possibilities. In this research, a novel approach of automatic post-process adaptation suitable for repetitive manufacturing, which takes advantage of transfer functions (TF), is proposed for thermal error reduction. The method respects basic heat transfer mechanisms in the MT structure by combining linear parametric models, requires a minimum of additional gauges, and feedback from direct measurements is obtained only during technological pauses in the production process. Post-process adaptation is solved retrospectively by updating model parameters using the heuristic method of genetic algorithms (GA). First, the approach was tuned on a case study of a heavy-duty milling machine with a horizontal headstock for a configuration with an exchangeable spindle head and a configuration with a change in the position of the headstock in the workspace, both of which were different from the initial model's calibration range. Subsequently, the developed approach was applied to repeated production on a medium-sized milling centre. Another goal of the paper is to emphasize the need for quality input information for modelling efforts and the industrial applicability of scientific results.

(136) Development and Evaluation of a Novel Penile Blood Oxygen Sensor for Assessing Tissue Oxygenation

Abstract Introduction Penile blood oxygen concentration is a relatively unexplored clinical parameter that can provide valuable insights into various disease processes. Despite the significant role of oxygenation in maintaining penile tissue health and function, there is a paucity of research focusing specifically on monitoring penile blood oxygen levels. This knowledge gap hinders our understanding of the physiological and pathophysiological mechanisms underlying penile disorders and limits the development of targeted therapeutic interventions. Objective The aim of this project was to develop and test a novel noninvasive penile blood oxygen sensor that can accurately assess the oxygen concentration in the penis, enabling a deeper exploration of its clinical significance and potential implications in disease management. Methods We developed a novel prototype of an oxygen sensor designed specifically for penile blood-oxygen assessment. Ethical approval from the Institutional Review Board (IRB) was obtained prior to conducting the study. The trial participants consisted exclusively of patients scheduled to undergo penile duplex ultrasonography at our center. The study population was categorized into three distinct groups: the first group comprised patients diagnosed with Peyronie's Disease (PD) but without clinical erectile dysfunction (ED), serving as our control group; the second group consisted of patients clinically diagnosed with ED, who utilized the sensor with a band and an adhesive film; and the third group included patients who used the sensor without the adhesive film. Results At the time of submission, preliminary data analysis was performed for multiple patients with ED. The analysis of sensor data revealed a distinctive pattern during the initial calibration period, wherein the penile partial pressure of oxygen (pO2) exhibited a gradual decline, likely due to equilibration of the sensor with the penile oxygen concentration. Following the injection, a sharp increase in penile oxygen concentration was observed. Approximately 3-5 minutes following erection, the data from multiple patients showed a discernible downward trend towards the baseline level in penile pO2 levels, and changes in penile oxygenation could be detected as a result of penile manipulation both prior to and after injection. An examination of a patient’s penile changes in pO2 is summarized in Figure 1. Conclusions The observed temporal sequence of changes in penile oxygenation offers valuable insights into the dynamic physiological responses that occur during distinct stages of the erectile process. These findings contribute to our understanding of the intricate mechanisms underlying penile vascular physiology and their roles in erectile function. An analysis of the full patient cohort is currently ongoing. Disclosure Yes, this is sponsored by industry/sponsor: PETROS. Clarification: Industry funding only - investigator initiated and executed study. Any of the authors act as a consultant, employee or shareholder of an industry for: Disclosures: Consultant for AbbVie, Marius, Tolmar, Endo, Petros, Boston Scientific, Coloplast Investor: Sprout.

Hybrid algorithm for initial phase calibration of optical phased array.

The synthesis of laser coherence and the accuracy of beam scanning, which are based on an optical phased array (OPA), are severely constrained by phase noise. This limitation hampers their applications in various fields. Currently, the most widely utilized calibration method is adaptive optics, which can effectively mitigate phase noise and enhance the quality of the output beam. However, because of the multiple array elements of the OPA and the large optimization range for each element, the adaptive optimization method experiences slow convergence and a high risk of falling into local optima. We propose a narrowing search range algorithm that can quickly reduce phase noise by narrowing the search range of the optimal value. After initial optimization, the SPGD algorithm was used. This study was verified through simulations and experiments utilizing the OPA of various array elements. These findings indicate that the hybrid algorithm expedites the calibration process, requires simple experimental equipment, and can be broadly utilized.

Transfer learning promotes acquisition of individual BCI skills.

Subject training is crucial for acquiring brain-computer interface (BCI) control. Typically, this requires collecting user-specific calibration data due to high inter-subject neural variability that limits the usability of generic decoders. However, calibration is cumbersome and may produce inadequate data for building decoders, especially with naïve subjects. Here, we show that a decoder trained on the data of a single expert is readily transferrable to inexperienced users via domain adaptation techniques allowing calibration-free BCI training. We introduce two real-time frameworks, (i) Generic Recentering (GR) through unsupervised adaptation and (ii) Personally Assisted Recentering (PAR) that extends GR by employing supervised recalibration of the decoder parameters. We evaluated our frameworks on 18 healthy naïve subjects over five online sessions, who operated a customary synchronous bar task with continuous feedback and a more challenging car racing game with asynchronous control and discrete feedback. We show that along with improved task-oriented BCI performance in both tasks, our frameworks promoted subjects' ability to acquire individual BCI skills, as the initial neurophysiological control features of an expert subject evolved and became subject specific. Furthermore, those features were task-specific and were learned in parallel as participants practiced the two tasks in every session. Contrary to previous findings implying that supervised methods lead to improved online BCI control, we observed that longitudinal training coupled with unsupervised domain matching (GR) achieved similar performance to supervised recalibration (PAR). Therefore, our presented frameworks facilitate calibration-free BCIs and have immediate implications for broader populations-such as patients with neurological pathologies-who might struggle to provide suitable initial calibration data.

The Design and Control of a Biomimetic Binocular Cooperative Perception System Inspired by the Eye Gaze Mechanism.

Research on systems that imitate the gaze function of human eyes is valuable for the development of humanoid eye intelligent perception. However, the existing systems have some limitations, including the redundancy of servo motors, a lack of camera position adjustment components, and the absence of interest-point-driven binocular cooperative motion-control strategies. In response to these challenges, a novel biomimetic binocular cooperative perception system (BBCPS) was designed and its control was realized. Inspired by the gaze mechanism of human eyes, we designed a simple and flexible biomimetic binocular cooperative perception device (BBCPD). Based on a dynamic analysis, the BBCPD was assembled according to the principle of symmetrical distribution around the center. This enhances braking performance and reduces operating energy consumption, as evidenced by the simulation results. Moreover, we crafted an initial position calibration technique that allows for the calibration and adjustment of the camera pose and servo motor zero-position, to ensure that the state of the BBCPD matches the subsequent control method. Following this, a control method for the BBCPS was developed, combining interest point detection with a motion-control strategy. Specifically, we propose a binocular interest-point extraction method based on frequency-tuned and template-matching algorithms for perceiving interest points. To move an interest point to a principal point, we present a binocular cooperative motion-control strategy. The rotation angles of servo motors were calculated based on the pixel difference between the principal point and the interest point, and PID-controlled servo motors were driven in parallel. Finally, real experiments validated the control performance of the BBCPS, demonstrating that the gaze error was less than three pixels.

Ability of a 24-h ambulatory cuffless blood pressure monitoring device to track blood pressure changes in clinical practice.

There is an increasing number of cuffless blood pressure (BP) measurement (BPM) devices. Despite promising results when comparing single measurements, the ability of these devices to track changes in BP levels over 24 h related to an initial calibration BP (CalibBP) is unknown. Our aim was to analyse this ability in a cuffless device using pulse transit time. We prospectively enrolled 166 participants for simultaneously performed cuffless (Somnotouch-NIBP) and cuff-based (Spacelabs 90217A/IEM Mobil-O-graph) 24 h BPM. As CalibBP for the cuffless device, first cuff-based BP was used. As surrogate for changes in BP levels after the CalibBP, we used the difference between the CalibBP and mean 24 h, awake and asleep BP measured by the two devices. In addition, we analysed the relationship between the difference of the CalibBP and the cuff-based BPM versus the difference between the cuff-based and the cuffless BPM devices. Mean(SD) difference between the CalibBP and mean 24hBP by the cuff-based or cuffless BP device were 7.4 (13.2) versus 1.8 (8.3) mmHg for systolic ( P < 0.0001) and 6.6 (6.8) versus 1.6 (5.8) mmHg for diastolic ( P < 0.0001). A near linear relationship was seen among the difference between the CalibBP and the cuff-based BPM values and the difference between the cuff-based and cuffless BPM device. Our data indicate a lower ability of the cuffless BPM device to track changes of BP levels after CalibBP. In addition, cuffless device accuracy was associated with the changes in BP levels after the initial CalibBP - the larger the BP level change, the larger the difference between the devices. https://www.clinicaltrials.gov ; Unique identifier: NCT03054688; NCT03975582.

Aging prediction in single based propellants using hybrid strategy of machine learning and genetic algorithm

This study proposes novel approach that combines optimization and machine learning to predict the aging of single-base propellants in alignment with the high-performance liquid chromatography (HPLC). To obtain aging and reduce HPLC experimentation, the proposed algorithm provides an efficient procedure. A hybrid strategy of genetic algorithm and ML models including support vector machine, ensemble trees, Gaussian process, and regression tree is combined to optimize SBPs aging. The correlation map provides an interdependence table of aging with initial composition, caliber, and environmental factors. The coefficient of determination and RMSE will determine predictive capabilities of ML models. ET-GA is an optimum-performing ML model with a 0.89 coefficient of determination. Partial dependence plots give an overview of the impact on aging by several variables with maximum impact by temperature, humidity, and diphenylamine. ET-GA shows 95% agreement with experimental data. This results in an economically viable interface that reduces experimentation and provides real-time aging prediction.

User Training With Error Augmentation for sEMG-Based Gesture Classification.

We designed and tested a system for real-time control of a user interface by extracting surface electromyographic (sEMG) activity from eight electrodes in a wristband configuration. sEMG data were streamed into a machine-learning algorithm that classified hand gestures in real-time. After an initial model calibration, participants were presented with one of three types of feedback during a human-learning stage: veridical feedback, in which predicted probabilities from the gesture classification algorithm were displayed without alteration; modified feedback, in which we applied a hidden augmentation of error to these probabilities; and no feedback. User performance was then evaluated in a series of minigames, in which subjects were required to use eight gestures to manipulate their game avatar to complete a task. Experimental results indicated that relative to the baseline, the modified feedback condition led to significantly improved accuracy. Class separation also improved, though this trend was not significant. These findings suggest that real-time feedback in a gamified user interface with manipulation of feedback may enable intuitive, rapid, and accurate task acquisition for sEMG-based gesture recognition applications.

Surface Water Wave Detector for Floating Devices with Capacitive Sensor

Water wave monitoring is essential in collecting marine parameters for oceanography studies and early warning systems on security and safety, such as drowning detection, weather detection, and gas leakage from underwater pipeline detection, because these activities create different water wave patterns that can be further analyzed. The current wave detection methods, such as underwater pressure and resistive sensors, have lower durability as they require direct contact with the water. Monocular camera wave detection can detect the line where water waves propagate. However, a static platform is required to perform monitoring operations. This research aims to develop a continuous capacitive sensor system that a buoy can implement for contactless water surface wave detection and to develop a water wave direction detection algorithm by Principal Component Analysis (PCA) calculation. Capacitive sensors arranged in a circular shape and a compass module are implemented inside the prototype buoy. Each capacitive sensor detects the real-time wave height change by changing the capacitance value. The capacitance values from all the capacitive sensors and the North of the compass sensor are sent to the embedded server for further computations. Processes carried out in the embedded server are initial calibration, centroid calculation, PCA calculations for water wave detection, and data visualization on the webpage. The prototype buoy with a capacitive sensor system and compass sensor developed can detect the four positions tested in the experiment with a mean square error of 38.42° and a mean absolute error of 5.85°.

Surface Water Wave Detector for Floating Devices with Capacitive Sensor

Water wave monitoring is essential in collecting marine parameters for oceanography studies and early warning systems on security and safety, such as drowning detection, weather detection, and gas leakage from underwater pipeline detection, because these activities create different water wave patterns that can be further analyzed. The current wave detection methods, such as underwater pressure and resistive sensors, have lower durability as they require direct contact with the water. Monocular camera wave detection can detect the line where water waves propagate. However, a static platform is required to perform monitoring operations. This research aims to develop a continuous capacitive sensor system that a buoy can implement for contactless water surface wave detection and to develop a water wave direction detection algorithm by Principal Component Analysis (PCA) calculation. Capacitive sensors arranged in a circular shape and a compass module are implemented inside the prototype buoy. Each capacitive sensor detects the real-time wave height change by changing the capacitance value. The capacitance values from all the capacitive sensors and the North of the compass sensor are sent to the embedded server for further computations. Processes carried out in the embedded server are initial calibration, centroid calculation, PCA calculations for water wave detection, and data visualization on the webpage. The prototype buoy with a capacitive sensor system and compass sensor developed can detect the four positions tested in the experiment with a mean square error of 38.42° and a mean absolute error of 5.85°.

Drift Characteristics of Sea-Bird Dissolved Oxygen Optode Sensors

Abstract The Sea-Bird 63 dissolved oxygen optode sensors used on various oceanographic platforms are known to drift over time. Corrections for drift are necessary for accurate dissolved oxygen measurements on the time scale of months to years. Here, drift on 14 Sea-Bird 63 dissolved oxygen optode sensors deployed on Spray underwater gliders over 5 years is described. The gliders with oxygen sensors were deployed regularly for 100-day missions as part of the California Underwater Glider Network (CUGN). A laboratory two-point calibration was performed on the oxygen sensor before and after glider deployment. Sensor drift during 100-day deployments was larger than during 100-day storage periods. Sensor behavior is modeled with a gain that asymptotically approaches 1.090 ± 0.005 with an e-folding time scale of 3.70 ± 0.361 years. At zero oxygen concentration, the sensor consistently reads around 3 μmol kg−1; a negative offset term is used in addition to the gain to correct the sensor oxygen. The correction procedure removes the error due to long time drift, one of the major sources of error, with an uncertainty of 0.5% (0.9% including outliers) or 0.5 μmol kg−1 depending on concentration, which improves the accuracy of the Sea-Bird 63 although uncertainty from other sources of error including the initial factory calibration and the sensor response time remain. Suggested procedures for implementing a two-point calibration procedure in the laboratory are discussed. Calibrations must be considered starting 6 months after initial factory calibration to keep error from sensor time drift under 1%. Significance Statement Dissolved oxygen measurements are widely used in oceanography. The optode sensors used to measure dissolved oxygen are known to drift over time. Here, the characteristics of drift for the oxygen optode sensor from Sea-Bird Scientific are described using a two-point calibration at zero and full saturation. The calibration procedure can be applied to oxygen optode sensors deployed on a variety of platforms when it is impractical to complete a multipoint calibration.

Design and implementation of an IoT-based Automatic Water Level Recorder (AWLR) as a tool to mitigate the risk of tidal flood disasters in the coastal area of Jakarta

Jakarta’s land continues to sink, and rising sea levels pose a high risk of tidal flooding in Jakarta Bay. From 2022 until May 2023, three tidal floods in Jakarta have been recorded due to high-intensity rainfall, causing the sea level to rise above normal. This threat is of public concern due to its material and immaterial losses. To address this issue, a real-time monitoring system that can provide accurate information on sea level heights on the coast of Jakarta needs to be developed. The research aims to develop a web-based Automatic Water Level Recorder (AWLR) system tool to support flood disaster mitigation in Jakarta’s coastal areas. This system integrates several sensors to measure sea level heights, temperature, humidity, and data transmission through a telemetry system. Based on running tests conducted from December 2022 to February 2023, the AWLR recording had been validated with BMKG’s Tide Gauge data installed in the same waters. The results show the data recording performance with high accuracy (98%). The successful performance of the sensors in this device is supported by monitoring devices involving temperature and humidity sensors. The initial calibration stage also shows significant accuracy test results with an error range of 2 -5%. The measurement accuracy of the pressure, temperature, and humidity sensors are 98%, 97%, and 95%, respectively, so the data obtained is reliable. This IoT-based AWLR data recording can be an input for coastal management decision-making, especially for monitoring coastal disasters.