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Contemporary cyber–physical systems have evolved into highly autonomous and distributed entities, enabled by cutting-edge control frameworks and advanced communication networks. This transformation has empowered these systems to efficiently accommodate inverter-based resources, harnessing the grid's dynamic potential. However, the increased integration of cyber and communication infrastructure has exposed these systems to vulnerabilities stemming from both cyber and physical disturbances. Power system errors in the physical layer of microgrids and malicious attacks on the communication layer pose substantial threats to grid stability and load supply. In response to these challenges, this paper presents an innovative unsupervised learning method for cyber–physical anomaly identification. Leveraging an autoencoder-based neural network, this approach addresses the intricate interplay between the physical and cyber aspects of power system failures and false data injection within the communication network. The method's efficacy is evaluated on an islanded inverter-based microgrid, representing a realistic and vulnerable testbed. The autoencoder error is introduced as a key performance metric, providing a robust and practical measure of the proposed detection technique's effectiveness. Simulation results demonstrate the promising utility of this method, positioning it as a valuable tool for enhancing the resilience of contemporary cyber–physical systems against cyber–physical anomalies. • Advanced control and network integration boosts autonomy in cyber–physical systems. • Systems efficiently host inverter resources, yet face cyber and physical threats. • Paper presents unsupervised learning for anomaly detection in microgrids. [ABSTRACT FROM AUTHOR]

Automated Machine Learning (AutoML) is a subdomain of machine learning that seeks to expand the usability of traditional machine learning methods to non-expert users by automating various tasks which normally require manual configuration. Prior benchmarking studies on AutoML systems—whose aim is to compare and evaluate their capabilities—have mostly focused on tabular or structured data. In this study, we evaluate AutoML systems on the task of object detection by curating three commonly used object detection datasets (Open Images V7, Microsoft COCO 2017, and Pascal VOC2012) in order to benchmark three different AutoML frameworks—namely, Google's Vertex AI, NVIDIA's TAO, and AutoGluon. We reduced the datasets to only include images with a single object instance in order to understand the effect of class imbalance, as well as dataset and object size. We used the metrics of the average precision (AP) and mean average precision (mAP). Solely in terms of accuracy, our results indicate AutoGluon as the best-performing framework, with a mAP of 0.8901, 0.8972, and 0.8644 for the Pascal VOC2012, COCO 2017, and Open Images V7 datasets, respectively. NVIDIA TAO achieved a mAP of 0.8254, 0.8165, and 0.7754 for those same datasets, while Google's VertexAI scored 0.855, 0.793, and 0.761. We found the dataset size had an inverse relationship to mAP across all the frameworks, and there was no relationship between class size or imbalance and accuracy. Furthermore, we discuss each framework's relative benefits and drawbacks from the standpoint of ease of use. This study also points out the issues found as we examined the labels of a subset of each dataset. Labeling errors in the datasets appear to have a substantial negative effect on accuracy that is not resolved by larger datasets. Overall, this study provides a platform for future development and research on this nascent field of machine learning. [ABSTRACT FROM AUTHOR]

In this paper, we consider wireless telerobotic systems and protocol development for low-delay wireless communication. Telerobotics can be defined as the control of robot arms from a remote location. In a telerobotic system, there is a robot arm to be controlled that is identified as the 'slave arm', and a remote operator at a distant location using a robotic manipulator that is called the 'master arm'. In the control system measurements, actuator delays do degrade the system's performance. Therefore, communication delays between the master and slave arms, and their minimization, are of extreme importance in telerobotics. In this paper, we first develop a new wireless communication protocol, the lightweight wireless protocol (LWP), designed on top of the 802.11 media access control layer. This low-delay wireless LWP protocol is implemented on an embedded system (AirDrop-LWP) without an operating system and its associated overhead. Finally, 2 AirDrop-LWP-embedded systems running this low-delay wireless LWP protocol are used to build a telerobotic system with a Mitsubishi RV-2AJ industrial robot. The LWP protocol is also tested on a robot car controlled by an AirDrop-LWP card as a slave arm and a standard PC as a master arm. The key features of the LWP are a reduced packet size, simple protocol stack, predictive compression of operator movements, and prediction of lost data packets. The LWP protocol is compared with the user datagram protocol and significant performance improvements are observed: a reduced delay of up to 50% and an additional 20% lower delay via compression. Variation in the packet delay times is also an important parameter for the wireless control system. As the standard deviation of the packet delay times increase, and becomes less and less predictable, the resulting telerobotic system will be more and more difficult to operate. We measured the standard deviation of packet delays and observed that it increases with the packet size, and this increase is faster than the increase in mean packet delay. [ABSTRACT FROM AUTHOR]

In this paper, we study optimal in vitro realization of pulsatile coronary artery flows using newly proposed closed-loop feedback algorithms with a clearly defined mathematical performance objective, and we compare several flow control device technologies in terms of their ability to generate pulsatile flow signals as accurately as possible. In the literature, there are several published results for in vitro pulsatile flow realization systems. However, most of the proposed systems operate in an open-loop setting and use a single flow control device, and their performance is assessed mostly by graphical means without a clearly defined mathematical performance objective. Furthermore, some authors take the average of the generated pulsatile flow and compare it with the desired flow signal for performance analysis. What makes this work different from other published results in this area are: 1) the use of closed-loop feedback control rather than an open-loop approach; 2) the use of a clearly defined mathematical performance objective rather than a visual comparison of the generated and desired flow graphs; 3) newly proposed control algorithms with demonstrated performance improvements as compared to the published ones; 4) the use of multiple flow control devices, their performance comparisons, and optimal technology selection; and 5) not taking the average of the generated flow signal over several cycles for performance assessment, but instead using the nonaveraged actual flow signal and comparing it with the desired one. In pulsatile flow realization, what is important is the L1 distance between the desired and delayed versions of the generated flows in the steady state regime, and the defined mathematical performance objective is completely based on this point. Furthermore, there are 3 different flow control devices that are comparatively analyzed: the pneumatic valve, the servo valve, and an AC inverter driving a centrifugal pump. Finally, there are several new closed-loop control algorithms proposed in this paper: P-Σ, P-Σ predictive, P- Σ look ahead, and model-based nonlinear feed-forward, feed-forward predictive, and feed-forward look-ahead control. Each algorithm's performance is compared with that of PID performance as a benchmark test to demonstrate performance improvements. By proper selection of the flow control device, and optimal selection of the control algorithm and its parameters, we were able to achieve up to 75% reduction in error. [ABSTRACT FROM AUTHOR]

This paper presents a robust telerobotic system that consists of a real-time vision-based operator hand tracking system (client) and a slave robot (server) which are interconnected through a LAN. The tracking system: 1) monitors the operator hand motion and 2) determines its position and orientation which are, used to control the slave robot. Two digital cameras are used to monitor a four-ball-based feature frame that is held by the operator hand. To determine the three-dimensional (3-D) position a tracking algorithm based on uncalibrated cameras with weak perspective projection model is used. This allows finding 3-D differential position and orientation of the operator hand. The features of the proposed system are: 1) a metric for color matching to discriminate the balls from their background; 2) a uniform and spiral search approach to speed up the detection; 3) tracking in the presence of partial occlusion; 4) consolidate detection by using shape and geometric matching; and 5) dynamic update of the reference colors. The operator can see the effects of the previous motion which enables making the necessary corrections through repetitive operator hand-eye interactions. Evaluation shows that the static and dynamic errors of the tracking algorithm are 0.1 % and 0.6 % for a centered workspace of 20³ in³ that is 40-60 in away from the cameras Running the tracking algorithm on two PCs in parallel allowed: 1) a parallel image grabbing delay of 60 ms; 2) a stereo matching delay of 50 ms; and 3) a global refresh rate of 9 Hz. [ABSTRACT FROM AUTHOR]

Presents a study that investigated the tracking properties of linear shift-invariant feedback control systems. Examination of the tracking performance problem pertaining to a vector version of the step signal; Analysis of the tracking performance for real sinusoids and unit ramp signals; Limitations of tracking performance in time-delay systems.

Storage and management of energy is becoming a more and more important problem every day, especially for electric and hybrid vehicle applications. Li-ion battery is one of the most important technological alternatives for high capacity energy storage and related industrial applications. State of Health (SoH) of Li-ion batteries plays a critical role in their deployment from economic, safety, and availability aspects. Most, if not all, of the studies related to SoH estimation focus on the measurement of a new parameter/physical phenomena related to SoH, or development of new statistical/computational methods using several parameters. This paper presents a new approach for SoH estimation for Li-ion battery systems with multiple battery cells: The main idea is a new circuit topology which enables separation of battery cells into two groups, main and test batteries, whenever a SoH related measurement is to be conducted. All battery cells will be connected to the main battery during the normal mode of operation. When a measurement is needed for SoH estimation, some of the cells will be separated from the main battery, and SoH estimation related measurements will be performed on these units. Compared to classical SoH measurement methods which deal with whole battery system, the proposed method estimates the SoH of the system by separating a small but representative set of cells. While SoH measurements are conducted on these isolated cells, remaining cells in the main battery continue to function in normal mode, albeit in slightly reduced performance levels. Preliminary experimental results are quite promising, and validate the feasibility of the proposed approach. Technical details of the proposed circuit architecture are also summarized in the paper. [ABSTRACT FROM AUTHOR]

Highlights: [•] Novel statistical features are proposed for the design of statistical mode sensors. [•] Proposed statistical features are first and second order moments. [•] Features are compared in terms of precision error, non-linearity, and hysteresis. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we consider optimal sensor design problem and compare power-meter and CCD camera based techniques for strain measurements using Modal Power Distribution (MPD). MPD is a sensitive and low-cost fiber optic sensing technique which uses spatial intensity modulation in two dimensional setting. In a power-meter based fiber optic sensor, light intensity is measured at one or more points, and selection of these points is usually very critical in sensor design. Single point based sensors are not usually very successful, because of the time varying feature of a laser source output intensity. To the best of authors’ knowledge, most of the power-meter based MPD sensors utilize a single photo-detector, and there is only one known work where two photo-detectors are used with no optimization on measurement locations. In this work, we both consider optimal measurement location selection problem, and compare of power-meter and CCD camera based sensor measurements. We also show that there is an almost one-to-one relationship between CCD based and power-meter based fiber optic sensor measurements, which allows estimation of power-meter based measurements using CCD camera images. In our experimental setup, power-meter measurements and CCD camera images were recorded for different strain values. Power-meter measurements were estimated from CCD camera images with an average error of less than 4% by utilizing image processing techniques and non-linear least squares techniques. [Copyright &y& Elsevier]

Autonomous vehicles (AVs) rely on various types of sensor technologies to perceive the environment and to make logical decisions based on the gathered information similar to humans. Under ideal operating conditions, the perception systems (sensors onboard AVs) provide enough information to enable autonomous transportation and mobility. In practice, there are still several challenges that can impede the AV sensors' operability and, in turn, degrade their performance under more realistic conditions that actually occur in the physical world. This paper specifically addresses the effects of different weather conditions (precipitation, fog, lightning, etc.) on the perception systems of AVs. In this work, the most common types of AV sensors and communication modules are included, namely: RADAR, LiDAR, ultrasonic, camera, and global navigation satellite system (GNSS). A comprehensive overview of their physical fundamentals, electromagnetic spectrum, and principle of operation is used to quantify the effects of various weather conditions on the performance of the selected AV sensors. This quantification will lead to several advantages in the simulation world by creating more realistic scenarios and by properly fusing responses from AV sensors in any object identification model used in AVs in the physical world. Moreover, it will assist in selecting the appropriate fading or attenuation models to be used in any X-in-the-loop (XIL, e.g., hardware-in-the-loop, software-in-the-loop, etc.) type of experiments to test and validate the manner AVs perceive the surrounding environment under certain conditions. [ABSTRACT FROM AUTHOR]

In today''s world, research on cardiovascular diseases has increased due to the social losses. Diagnosis and treatment of cardiovascular disorders are necessary to understand the biological and mechanical causes of the diseases. In vitro and in vivo experiments are performed to understand the mechanism of the disease. In in vitro experiments physiological blood flow signals are used to simulate the blood flow inside human body. In this study novel closed-loop flow control system has been developed with control technique and flow control devices to simulate the physiological waveform, unlike studies conducted to date. In vitro experimental system consists of centrifugal pump, pump driver card, solenoid valve, valve driver card, data acquisition card (DAQ), flow meters, in vitro model and sample fluid piping system. The control algorithm is written and run using Matlab/Simulink AC pump and solenoid valves were used to simulate the pulsatile flow in femoral and coronary arteries that they have the high and low flow rates of 25 and 3ml/s, respectively. In the human body blood flow velocity and geometry of the measurement location can be recorded and visualized by MR imaging techniques. Images of blood flow signal can be transferred to experimental system by providing equivalence of dynamic simulation. For realization of blood flow, Fourier coefficients were obtained from MR blood flow image using image processing techniques and they were used to create flow velocity profile. Using Fourier coefficients femoral and coronary artery blood flow have been realized on the experimental system. Error performance (EP) criterion was developed during the studies to compare human blood flow signal to in vitro experimental blood flow signal. This study is a pioneer in terms of using close-loop control, two flow control devices and EP criteria in in vitro experimental systems. The algorithm developed in this research will be applied to different regions of cardiovascular system for future studies to better understand the cardiovascular diseases and related treatment. [Copyright &y& Elsevier]