Performance In Real Environment Research Articles

Longevous ionogels with high strength, conductivity, adhesion and thermoplasticity

The longevity of flexible electronics substantially influences their viability for practical use. At present, the service life of pliable devices utilizing conductive gels is frequently compromised due to factors inclusive but not limited to solvent loss. Taking cues from the steadfast structure of human tissues, we hereby delineate a solvent-assisted strategy to fabricate ionogels, predicated on a supramolecular network of hydrogen bonds. Upon subjecting these ionogels to realistic operational environments, it has been empirically ascertained that they maintain a steadfast mechanical and electrical consistency for a minimum duration of 22 months. In terms of dynamic stretchability, they effortlessly endure one million stretch cycles, even when faced with a notch. The induction of a supramolecular structure within the ionogel not only confers exceptional mechanical attributes but also simultaneously enhances conductivity, a feat paralleled by few recent explorations. Furthermore, these ionogels boast of superior anti-freezing and adhesive characteristics, including a toughness extending up to 17.96 MJ/m3 at an ultra-low temperature of −50 °C, and an adhesive capacity permitting movement and bearing of items weighing over 10,000 times its own weight, the lap-shear strength can reach 30.2 kPa. Intriguingly, these gels demonstrate thermoplastic behavior, which facilitates their easy reshaping into diverse forms. Such compelling attributes amplify the potential of supramolecular ionogels, positioning them as formidable contenders for flexible electronics in stringent and challenging scenarios.

(Energy Technology Division Research Award) New Materials vs. Reaction Engineering in Anion Exchange Membrane Fuel Cells

In recent years, the desire to design, create and/or discover advanced functional nanomaterials has changed the way that faculty members and graduate students approach engineering research. Though materials can (and likely will) make a significant impact on most technologies, the truth is that in most disciplines, materials with highly complex structures remain on the fringes of realistic deployment. In some cases, even when so-called “advanced” materials are discovered there are significant difficulties in transitioning them from ex-situ tests to the real reacting environment. Therefore, we must be careful to understand that material chemistry and structure are only two variables in an extremely complex system and often times there are other fundamental (e.g. electronic mobility, thermodynamic barriers, diffusivity) and engineering (porosity, concentration, temperature) properties that drive behavior in such environments. Additionally, there are systems where materials are being sought without a clear understanding of what is truly limiting performance and durability or the properties that are required of a real engineered system to operate effectively.Anion exchange membrane fuel cells (AEMFCs) are an example system where the literature is littered with exotic materials that claim to improve activity and stability (presumed as performance and durability in real devices). However, it will be shown that for several years, such new materials had limited success (if any) in advancing performance and durability. This talk will summarize ~five years of work in AEMFCs where the performance and durability of AEMFCs were improved significantly without any new materials – just using traditional chemical engineering principles for reactor engineering. Only later – once device operation was well understood and controlled – were new materials helpful. On the operational side, the main discussion point will be the transport of water. On the materials side, new membranes and catalysts will be discussed. The talk will end with perspectives on transitioning AEMFCs to more realistic operating environments, including the mitigation of effects from CO2.

Drag-Mitigating Dynamic Flight Path Design and Sensitivity Analysis for an Ultra-Long Tether Underwater Kite

Abstract This paper presents a computational study of an underwater kite operating in environments requiring tethers exceeding a kilometer in length, referred to herein as ultralong tether (ULT) applications. Leveraging a detailed dynamic model of the kite and tether, we study the relationship between path shape and tether drag at varying tether lengths in order to develop meaningful insights as to the operation of systems that require ultralong tethers in order to reach viable flow resources. An initial study of a ULT kite application operating in a uniform flow field is presented, demonstrating that with an appropriately designed flight path, the kite is able to suppress the motion of the majority of the tether in order to achieve an order of magnitude greater power output than can be achieved with a straight tether, which is the typical assumption used in a performance estimation. Tether drag mitigation is characterized through the use of a novel metric termed effective tether length, which characterizes the total length of tether engaged in drag production. It is shown that a high-performance path shape can reduce the effect of tether drag by over 50%. This performance is shown to be comparable to the multi-airborne wind energy system (MAWES) proposed by Leuthold et al. (2017, “Induction in Optimal Control of Multiple-Kite Airborne Wind Energy Systems,” IFAC-PapersOnLine, 50(1), pp. 153–158; 2018, “Operational Regions of a Multi-Kite Awe System,” 2018 European Control Conference (ECC), IEEE, Limassol, Cyprus, June 13–15, pp. 52–57), which suppresses tether motion through mechanical design rather than merely though careful path selection and control. This initial study in a uniform flow field is followed by two sensitivity studies: one that assesses performance in realistic environments where the flow magnitude is a function of altitude above the seabed and a second that assesses the impact of tether drag reduction techniques. It will be shown that by careful selection of path shape, site, and tether design, a single kite in a ULT marine application can achieve performance rivaling that of the MAWES without the extra required mechanical complexity.

OMS-SLAM: dynamic scene visual SLAM based on object detection with multiple geometric feature constraints and statistical threshold segmentation

Abstract SLAM technology is crucial to robot navigation. Despite the good performance of traditional SLAM algorithms in static environments, dynamic objects typically exist in realistic operating environments. These objects can lead to misassociated features, which in turn considerably impact the system’s localization accuracy and robustness. To better address this challenge, we have proposed the OMS-SLAM. In OMS-SLAM, we adopted the YOLOv8 target detection network to extract object information from environment and designed a dynamic probability propagation model that is coupled with target detection and multiple geometric constrains to determine the dynamic objects in the environment. For the identified dynamic objects, we have designed a foreground image segmentation algorithm based on depth image histogram statistics to extract the object contours and eliminate the feature points within these contours. We then use the GMS (Grid-based Motion Statistics) matching pair as the filtering strategy to enhance the quality of the feature points and use the enhanced feature points for tracking. This combined method can accurately identify dynamic objects and extract related feature points, significantly reducing its interference and consequently enhancing the system's robustness and localization accuracy. We also built static dense point cloud maps to support advanced tasks of robots. Finally, through testing on the high-speed dataset of TUM RGB-D, it was found that the root mean square error of the Absolute Trajectory Error (ATE) in this study decreased by an average of 97.10%, compared to ORB-SLAM2. Moreover, tests in real-world scenarios also confirmed the effectiveness of the OMS-SLAM algorithm in dynamic environments.

A computational search for the optimal microelectronic heat sink using ANSYS Icepak

The efficient thermal management of microelectronic devices is crucial for their reliability and performance. Heat sinks, particularly those with inline and staggered fin arrangements, are vital in achieving this goal. This study leverages the advancements in computational fluid dynamics (CFD) software to explore the optimal heat sink design for microelectronic devices. While prior research has extensively investigated heat sink design, this work offers novelty by employing ANSYS Icepak software for simulations. This approach enables a comprehensive thermal performance and pressure drop analysis for two distinct fin cross-sections. The study investigates heat sink assemblies housed within a confined space, simulating a realistic operating environment for microelectronic devices. The heat sink is designed to cool a single heat source (chip) mounted on a substrate board. In this paper, two types of fin sections, circular and conical, are studied for two types of fin configurations, inline (IA) and staggered (SA), which are analysed with 36 fins in each case. Each fin is constructed from aluminium and has specified dimensions. Air serves as the cooling fluid within the cabinet, dissipating heat generated by the chip. When comparing the performance of circular pin and cone fins at the same power and mass flow rate, the results indicated that the maximum temperature for circular pin fins is 0.46% of the maximum temperature for cone fins, and they have a higher pressure drop, especially for staggered arrangements. Moreover, the maximum temperature for staggered arrangements is lower than that for inline configurations by a factor of up to 1.17% and 2.035% for circular fins and cone pin fins, respectively. This article focuses on a deep understanding of the design of microelectronic cooling systems. It aims to continuously improve pressure and thermal efficiency by maintaining the minimum limits of pressure drop within the confined space and obtaining the optimal configuration for efficient heat dissipation.

Using Wargaming to Model Cyber Defense Decision-Making: Observation-Based Research in Locked Shields

Defensive Cyber Operations (DCO) in complex environments, such as cyber wargames, require in-depth cybersecurity knowledge and the ability to make quick decisions. In a typical DCO, execution rarely follows a pre-planned path because of extensive adversary influence, challenging an already complex decision-making environment. Decision-making models have been extensively studied from perspectives of military operations and business management, but they are not sufficiently researched in the context of cyber. This paper responds to this need by examining the decision-making models of DCO leaders in a live-fire wargame environment. This study was conducted by observing leaders of cyber operations during the world's largest live-fire cyber exercise, NATO Locked Shield 2023. In this exercise, the blue teams plan and execute their defensive cyber operation in a realistic operational environment, while the red team conducts attacks against the defended environment. The large-scale, wargaming-style environment of Locked Shield is one of the best environments for modelling DCO decision-making models; in this exercise, the DCO is broad and multi-faceted, a perspective which cannot be achieved in a typical capture-the-flag competition or a single security incident. DCO leaders must be able to manage two distinct decision-making processes with different sets of required skills to be successful in the mission. While the primary process relates to the execution and evolution of the pre-designed plan with traditional operational leadership skills, the secondary process deals with unplanned and deliberately caused cyber-related events that require a deep understanding of cybersecurity. In this respect, the main contribution of this research is the constructed decision-making model of the DCO leader. This model is based on observations collected and presented in the context of multiple well-known decision-making frameworks. This model can be further used to train future DCO leaders and assess artificial intelligence's usability to support and automate decision-making in such operations.

Learning Newsvendor Problems With Intertemporal Dependence and Moderate Non-stationarities

This work provides performance guarantees for solving data-driven contextual newsvendor problems when the contextual data contains intertemporal dependence and non-stationarities. While machine learning tools have observed increasing use in data-driven inventory management problems, most of the existing work assumes that the contextual data are independent and identically distributed (often referred to as i.i.d.). However, such assumptions are often violated in real operational environments where the contextual data are sequentially generated with intertemporal correlations and possible non-stationarities. By accommodating these naturally arising operational environments, our work adopts comparatively more realistic assumptions and develops out-of-sample performance bounds for learning data-driven contextual newsvendor problems.

Comparison and Evaluation of Learning Capabilities of Deep Learning Methods for Predicting Ship Motions

The development of intelligent ship control systems in real-world conditions relies heavily on the accurate identification and prediction of ship seakeeping and maneuvering trajectories. In this study, we comprehensively evaluate a selection of deep learning methods to assess their learning capabilities in terms of idealizing ship motion behavior in realistic operational environments. To recover real conditions, we utilize historical Automatic Identification System (AIS) data and a time domain 6 Degree of Freedom (6- DoF) grounding dynamics model to generate ship motion sequences for a Ro-Ro passenger ship operating in the Gulf of Finland. Via a rigorous evaluation process, we validate the performance of these methods using extensive data streams. The analysis includes the identification and estimation of uncertainties between two ports. The paper demonstrates the proficiency of the selected deep learning methods in capturing ship maneuvering features, their potential use in the design of ship control and intelligent decision support systems.

Covalent Organic Frameworks: Cutting-Edge Materials for Carbon Dioxide Capture and Water Harvesting from Air.

The implacable rise of carbon dioxide (CO2 ) concentration in the atmosphere and acute water stress are one of the central challenges of our time. Present-day chemistry is strongly inclined towards more sustainable solutions. Covalent organic frameworks (COFs), attributable to their structural designability with atomic precision, functionalizable chemical environment and robust extended architectures, have demonstrated promising performances in CO2 trapping and water harvesting from air. In this Review, we discuss the major developments in this field as well as sketch out the opportunities and shortcomings that remain over large-scale COF synthesis, device engineering, and long-term performance in real environments.

Virtual Environment Model Generation for CPS Goal Verification using Imitation Learning

Cyber-Physical Systems (CPS) continuously interact with their physical environments through embedded software controllers that observe the environments and determine actions. Field Operational Tests (FOT) are essential to verify to what extent the CPS under analysis can achieve certain CPS goals, such as satisfying the safety and performance requirements, while interacting with the real operational environment. However, performing many FOTs to obtain statistically significant verification results is challenging due to its high cost and risk in practice. Simulation-based verification can be an alternative to address the challenge, but it still requires an accurate virtual environment model that can replace the real environment interacting with the CPS in a closed loop. In this article, we propose ENVI (ENVironment Imitation), a novel approach to automatically generate an accurate virtual environment model, enabling efficient and accurate simulation-based CPS goal verification in practice.To do this, we first formally define the problem of the virtual environment model generation and solve it by leveraging Imitation Learning (IL), which has been actively studied in machine learning to learn complex behaviors from expert demonstrations. The key idea behind the model generation is to leverage IL for training a model that imitates the interactions between the CPS controller and its real environment as recorded in (possibly very small) FOT logs. We then statistically verify the goal achievement of the CPS by simulating it with the generated model. We empirically evaluate ENVI by applying it to the verification of two popular autonomous driving assistant systems. The results show that ENVI can reduce the cost of CPS goal verification while maintaining its accuracy by generating accurate environment models from only a few FOT logs. The use of IL in virtual environment model generation opens new research directions, further discussed at the end of the article.

The rapid chloride migration test as a method to determine the chloride penetration resistance of concrete in marine environment

This paper analyses the use of the rapid chloride migration test (RCMT) in assessing the long-term chloride penetration resistance of concrete in real marine environment. Based on extensive experimental data, RCMT results were related to the diffusion and penetration properties of a wide range of normal weight and lightweight aggregate concretes, 5-year exposed under real submerged (XS2) and wetting and drying (XS3) conditions. Most usual structural concrete with different water/binder, types of aggregates and binders were covered. Concretes were ranked in terms of their chloride durability performance in real environment and a new classification was proposed to evaluate the durability of concrete to chloride attack. Classes were established based on service life prediction analysis. Overall, it is shown that the RCMT classification can distinguish the resistance to the chloride penetration of different concretes under distinct exposure environments, allowing for an expeditious method to evaluate the concrete quality.

Intrusion Detection of Industrial Internet-of-Things Based on Reconstructed Graph Neural Networks

Industrial Internet-of-Things (IIoT) are highly vulnerable to cyber-attacks due to their open deployment in unattended environments. Intrusion detection is an efficient solution to improve security. However, because the labeled samples are difficult to obtain, and the sample categories are imbalanced in real applications, it is difficult to obtain a reliable model. In this paper, a general framework for intrusion detection is proposed based on graph neural network technologies. In detail, a network embedding feature representation is proposed to deal with the high dimensional, redundant but categories imbalanced and rare labeled data in IIoT. To avoid the influence caused by the inaccurate network structure, a network constructor with refinement regularization is designed to amend it. At last, the network embedding representation weights and network constructor are trained together. The high accuracy and robust properties of the proposed method were verified by conducting intrusion detection tasks based on public datasets. Compared with several state-of-art algorithms, the proposed framework outperforms these methods in many evaluation metrics. In addition, a hard-in-the-loop platform is designed to test the performance in real environments. The results show that the method can not only identify different attacks but also distinguish between cyber-attacks and physical failures.

Using embedded prosthesis sensors for clinical gait analyses in people with lower limb amputation: A feasibility study

BackgroundBiomechanical gait analyses are typically performed in laboratory settings, and are associated with limitations due to space, marker placement, and tasks that are not representative of the real-world usage of lower limb prostheses. Therefore, the purpose of this study was to investigate the possibility of accurately measuring gait parameters using embedded sensors in a microprocessor-controlled knee joint. MethodsTen participants were recruited for this study and equipped with a Genium X3 prosthetic knee joint. They performed level walking, stair/ramp descent, and ascent. During these tasks, kinematics and kinetics (sagittal knee and thigh segment angle, and knee moment) were recorded using an optical motion capture system and force plates (gold standard), as well as the prosthesis-embedded sensors. Root mean square errors, relative errors, correlation coefficients, and discrete outcome variables of clinical relevance were calculated and compared between the gold standard and the embedded sensors. FindingsThe average root mean square errors were found to be 0.6°, 5.3°, and 0.08 Nm/kg, for the knee angle, thigh angle, and knee moment, respectively. The average relative errors were 0.75% for the knee angle, 11.67% for the thigh angle, and 9.66%, for the knee moment. The discrete outcome variables showed small but significant differences between the two measurement systems for a number of tasks (higher differences only at the thigh). InterpretationThe findings highlight the potential of prosthesis-embedded sensors to accurately measure gait parameters across a wide range of tasks. This paves the way for assessing prosthesis performance in realistic environments outside the lab.

A hearing aid “test drive”: Using virtual acoustics to accurately demonstrate hearing aid performance in realistic environments

A common challenge for audiologists when fitting hearing aids is that quiet clinics are unlike the noisy, reverberant places where patients report difficulty. This prevents direct user feedback about hearing aid performance. Virtual acoustics can allow a patient to experience a hearing aid in such environments, without leaving the clinic. A virtual reality (VR) audio-visual demonstration has been created which “test drives” hearing aid features in real-world scenarios, dynamically rendered over custom wired hearing aids and headphones. The scenes were created from 360° photographs and acoustic measurements made in real rooms. A room acoustic model was used for the simulation, tuned to match the measured data. Audio was rendered using principal component-base amplitude panning (PCBAP), which can provide dynamic VR audio with broadband magnitude and phase accuracy. This talk will provide comparisons between PCBAP and higher-order Ambisonics (HOA) for rendering hearing aid beamformers in anechoic and reverberant environments. Results show that PCBAP can render a beamformer’s directivity broadband with 2 to 3 dB error (95%) using only 36 filters in both anechoic and reverberant conditions. Even seventh-order HOA (64 filters) does not achieve similar performance. The talk will also include videos of the demonstration, highlighting viability for clinical applications.

Markov-modulated model for landing flow dynamics: An ordinal analysis validation.

Air transportation is a complex system characterized by a plethora of interactions at multiple temporal and spatial scales; as a consequence, even simple dynamics like sequencing aircraft for landing can lead to the appearance of emergent behaviors, which are both difficult to control and detrimental to operational efficiency. We propose a model, based on a modulated Markov jitter, to represent ordinal pattern properties of real landing operations in European airports. The parameters of the model are tuned by minimizing the distance between the probability distributions of ordinal patterns generated by the real and synthetic sequences, as estimated by the Permutation Jensen-Shannon Distance. We show that the correlation between consecutive hours in the landing flow changes between airports and that it can be interpreted as a metric of efficiency. We further compare the dynamics pre and post COVID-19, showing how this has changed beyond what can be attributed to a simple reduction of traffic. We finally draw some operational conclusions and discuss the applicability of these findings in a real operational environment.

AI Testing Framework for Next-G O-RAN Networks: Requirements, Design, and Research Opportunities

Openness and intelligence are two enabling features to be introduced in next generation wireless networks, for example, Beyond 5G and 6G, to support service heterogeneity, open hardware, optimal resource utilization, and on-demand service deployment. The open radio access network (O-RAN) is a promising RAN architecture to achieve both openness and intelligence through virtualized network elements and well-defined interfaces. While deploying artificial intelligence (AI) models is becoming easier in O-RAN, one significant challenge that has been long neglected is the comprehensive testing of their performance in realistic environments. This article presents a general automated, distributed and AI-enabled testing framework to test AI models deployed in O-RAN in terms of their decision-making perfor-mance, vulnerability and security. This framework adopts a master-actor architecture to manage a number of end devices for distributed testing. More importantly, it leverages AI to automatically and intelligently explore the decision space of AI models in O-RAN. Both software simulation testing and software-defined radio hardware testing are supported, enabling rapid proof of concept research and experimental research on wireless research platforms.

RF-Badge: Vital Sign-Based Authentication via RFID Tag Array on Badges

Nowadays, authentication systems are usually required to provide continuous, contactless, and non-intrusive services. In this paper, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RF-Badge</i> , a vital sign-based authentication scheme on human subjects to meet the above requirements by using RFID technology. We consider two biometric features with individual diversity to characterize the vital sign of users, including the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">movement effect</i> from respiration and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reflection effect</i> from organs, especially the heart. To derive the movement effect from respiration, we build a phase-based geometric model to restore the fine-grained badge moving trace as the feature. To derive the reflection effect from human internal organs, we extract the reflection signal from the original signal and generate the spectrum as the feature. Besides, to deal with the feature deviation in different physical conditions of users, we propose a multi-condition network (MCNet) to further guarantee the generalization of RF-Badge. We implement a prototype system and evaluate the performance in real environments. The experiment results show that our system achieves the average false positive rate (FPR) of 3.9 percent and false negative rate (FNR) of 3.3 percent for continuous authentication within four signal cycles.

Hardware-in-the-Loop Testing for Protective Relays Using Real Time Digital Simulator (RTDS)

With the increasing size and complexity of power systems, it is crucial to have an effective protection system in place to ensure its reliability. One of the important components of the protection system are relays. It is important for a relay to operate dependably and securely so that any fault can be cleared in time to minimize damages to the power network. However, it is important to test a relay in a realistic environment before commissioning it to the network. Testing a relay in the actual network can be expensive with limited fault scenarios. Hence, Hardware-in-the-Loop (HIL) testing is an efficient method to perform closed-loop testing of a relay since numerous fault cases can be simulated to provide a realistic operating environment for the relay under test. This paper sheds light on the HIL testing done for protective relays using a sample distribution system using RTDS. Two SEL-351 relays have been used in this experiment, and proper settings for the relays are calculated for coordination. The paper also describes the procedure of configuring the relay and other RTDS components crucial for interfacing of the relay with RTDS. After test setup, a pre-fault, fault, and post-fault analysis was done for the system. The results obtained from these analyses are cross-checked with the event history of the two SEL-351 relays, obtained with AcSELerator Quickset software. This paper provides thorough information for researchers to replicate the presented study or to develop new HIL experiments. It can also help in developing a fundamental understanding of the HIL testing setup that can be further applied to a more complex power system.

Experimental monitoring of autonomous curtain walling facade module

This paper introduces an innovative concept for active wall-curtain façade modules aiming at high level of energy autonomy. The façade module consists of two opaque panels and one transparent panel integrating building integrated photovoltaics supported by façade integrated battery Peltier air-conditioning unit, active shading, and LED lighting. The developed demo-protype was deployed at testing facility and subjected to long-term experimental monitoring to assess the energy performance in real environment. The complex monitoring system captured key power and thermal fluxes, temperatures, mass flows and other operational states of the active wall-curtain façade modules. These measurements were assessed in form of monthly overview depicting total electricity consumption, on-site energy production, heating and cooling delivery as well as self-sufficiency and self-consumption indicators.

Improved LSTM-Based Time-Series Anomaly Detection in Rail Transit Operation Environments

Anomaly detection is crucial to the reliability and safety of rail transit systems. The rapid development of Internet of Things (IoT) and cloud technologies together with recent advances in machine learning offered various cloud-based data-driven approaches to automatic anomaly detection. However, the challenges introduced by the different types of equipment in rail transit systems with highly diverse data distributions and the lack of labeled anomaly data have not been sufficiently addressed. In this article, we attempt to cope with such challenges by proposing an improved long short term memory (LSTM)-based time-series anomaly detection scheme. The key elements of the proposed scheme include an improved LSTM model that may achieve more accurate time-series prediction for various rail transit devices and a method for determining an appropriate error threshold for detecting anomalies based on the prediction errors. In order to further enhance anomaly detection performance, we also propose a pruning algorithm for reducing the number of false anomalies. Our method does not rely on scarce anomaly labels but dynamically determines a threshold of prediction errors to identify anomalies; therefore, it overcomes the challenge of the extremely uneven distribution of rail transit data. We conducted extensive experiments in a real metro operation environment for performance evaluation. The experiment results prove the effectiveness of the proposed scheme and show a superior performance of the scheme compared to existing anomaly detection methods.