Reliable Acquisition Research Articles

Wheel-rail force inversion via transfer learning-based residual LSTM neural network with temporal pattern attention mechanism

As urbanization progresses, metropolitan transit vehicles are encountering a growing frequency of curved pathways, which presents challenges pertaining to both the safety of the vehicles and the comfort of the passengers. There is no doubt that reliable acquisition of wheel-rail force is critical, since it has great significance for the safety and stability of vehicle operation. However, conventional wheel-rail force measurement methods are costly and difficult to use on in-service vehicles. A data-driven approach to inverting the wheel-rail force will overcome the above problems. In this work, a transfer learning-based residual long short-term memory neural network with temporal pattern attention mechanism (TPA-ResLSTM) is proposed to realize real-time monitoring of wheel-rail force, even in scenarios where the dataset is deficient in sufficient features. Initially, a learnable wheel-rail force inversion neural network model is developed based on the physical relationship that exists between the wheel-rail force and acceleration. Subsequently, a dynamic model for a B-type metro vehicle is utilized to simulate various scenarios, serving as a virtual source to provide data for the neural network. Afterward, the performance of the model is synthetically validated by the ablation study and field experimental data. Finally, the deep learning model is further improved by the transfer learning network, whose performance is comprehensively evaluated using limited data. The results show that the inversion model still has remarkable accuracy, in which the coefficient of determination is more than 0.9, under the case of limited training data. The proposed methodology diminishes the data requirements for the network while facilitating real-time monitoring and feedback regarding wheel-rail forces, thereby enhancing the realism of operational safety assessments for trains.

DPTracer: Integrating Log-Driven Accountability into Data Provision Networks

Emerging applications such as blockchain, autonomous vehicles, healthcare, federated learning, self-consistent large language models (LLMs), and multi-agent LLMs increasingly rely on the reliable acquisition and provision of data from external sources. Multi-component networks, which supply data to the applications, are defined as data provision networks (DPNs) and prioritize accuracy and reliability over delivery efficiency. However, the effectiveness of the security mechanisms of DPNs, such as self-correction, is limited without a fine-grained log of node activities. This paper presents DPTracer: a novel logging system designed for DPNs that uses tamper-evident logging to address the challenges of maintaining a reliable log in an untrusted environment of DPNs. By integrating logging and validation into the data provisioning process, DPTracer ensures comprehensive logs and continuous auditing. Our system uses Process Tree as a data structure to store log records and generate proofs. This structure permits validating node activities and reconstructing historical data provision processes, which are crucial for self-correction and verifying data sufficiency before results are finalized. We evaluate the overheads introduced by DPTracer regarding computation, memory, storage, and communication. The results demonstrate that DPTracer incurs reasonable overheads, making it practical for real-world applications. Despite these overheads, DPTracer enhances security by protecting DPNs from post-process and in-process tampering.

Research on online passive electrochemical impedance spectroscopy and its outlook in battery management

Current lithium-ion battery (LIB) management technique relying solely on the limited time-domain measurements appears to reach its limit, and incorporating new sensing information, particularly the impedance of LIB, offers a promising path for improvement. Using the non-stationary random driving profile of electric vehicle (EV), a method to extract online passive electrochemical impedance spectroscopy (OPEIS) is proposed and validated, with relevant factors influencing its efficacy also investigated. The particularity of actual driving profile is revealed both theoretically and experimentally, and beyond expectation, the highly differentiated driving profiles yield a similar spectral pattern, which facilitates the acquisition of OPEIS. Continuous OPEIS characterized by the distribution of relaxation time (DRT) ranging from 0.2 Hz to 3 kHz are analyzed in detail under different battery conditions. Compared with offline reference EIS, most of the measurement errors are <3%. Based on the acquired spectral information and OPEIS, a safety-relevant detector and an internal temperature estimator for LIB are presented, and they can both realize a near instant electrochemical sensing up to 1 Hz. As the underlying opportunities of OPEIS are outlined, challenges in its reliable acquisition and engineering implementation are evaluated as well. By comprehensively discussing the realization of OPEIS, research in this paper is expected to provide valuable references for a more effective battery management.

Using Irradiation-Acquisition Interleaved Time-Integrated Imaging to Assess Delayed Luminescence and the Noise-Laden Residual Spontaneous Photon Emission of Yeast

Delayed luminescence from organisms informs oxidative stress that may be modulable by external stimulations. In the absence of external stress causing delayed luminescence, organisms may produce spontaneous ultraweak photon emission due to the residual oxygen demand. To better understand the oxidative state of an organism, it is desirable to acquire the delayed luminescence to reach the phase wherein the ultraweak photon emission resides. This, however, is challenging due to the significant difference in the order of magnitude of the photon counts between the two types of photon emission. Conventional time-gated measurement requires a high dynamic range to assess the noise-level photon emission, whereas simple long exposure can miss the kinetics of luminescence. There may be a compromise to be made between robustly acquiring the decay kinetics of the delayed luminescence and reliably acquiring the noise-laden spontaneous photon emission. We demonstrate an irradiation-acquisition interleaved time-integrated imaging approach that may enable the reliable acquisition of slow-decay delayed luminescence down to the level of ultraweak photon emission. Repetitive irradiation was interleaved with a gradually increased time of acquisition to assess the integrated time course of the post-irradiation luminescence. Such instrument configuration performed on yeast facilitated the use of time differentiation to assess the delayed luminescence down to the noise-level ultraweak photon emission at the expense of the total time of acquisition.

Bad Data Repair for New Energy Stations in Power System Based on Multi-Model Parallel Integration Approach

The accurate and reliable acquisition of measurement information is very important for the stable operation of power systems, especially the operation status information of new energy stations. With the increasing proportion of new energy stations in power systems, the quality issues of data from these stations, caused by communication congestion, interference, and network attacks, become more pronounced. In this paper, to deal with the issue of low accuracy and poor performance of bad data restoration in new energy stations, a novel deep learning approach by combining the modified long short-term memory (LSTM) neural network and Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is proposed. The proposed method can be implemented in a parallel ensemble way. First, the normal data set acquired from multiple sections of new energy stations is utilized to train the modified LSTM and WGAN-GP model. Secondly, according to the data characteristics and rules captured by each model, the two models are systematically integrated and the bad data repair model pool is constructed. Subsequently, the results of model repair are screened and merged twice by the parallel integration framework to obtain the final repair result. Finally, the extensive experiments are carried out to verify the proposed method. The simulative results of energy stations in a real provincial power grid demonstrate that the proposed method can effectively repair bad data, thereby enhancing the data quality of new energy stations.

Development of an Automated Experimental System for Thermomechanical and Electrical Characterization of NiTi Shape Memory Alloys

BackgroundComprehensive datasets quantifying the coupled thermo-mechanical and electrical properties of shape memory alloys (SMAs) are lacking, as are standardized techniques for robust characterization. This hampers accurate modeling and design of SMA-based components. Objective: This work develops an automated experimental system to enable simultaneous measurement of stress-strain-temperature behavior and electrical resistivity evolution in NiTi SMA wires under controlled stress conditions. Methods: Customized test frames apply precise mechanical stresses while allowing for in situ electrical measurements and infrared imaging during complete thermal cycling protocols. Specialized instrumentation including a Keithley 2002 multimeter, Agilent E3631A programmable power supply, and FLIR A615 thermal camera are integrated with LabVIEW-based software routines for complete automation of the characterization process. Rigorous metrology principles are implemented throughout the measurement procedure to improve accuracy, repeatability, and consistency compared to prior manual techniques. Results: Extensive datasets are generated which reveal pronounced stress-dependencies in key SMA material parameters including transformation temperatures, recoverable strain, and electrical resistivity. A 3D regression model describes the comprehensive relationship between resistivity, temperature, and applied stress across the entire characterization domain. Conclusions: The automated measurement framework and methodology establishes a foundation for high-fidelity, reliable acquisition of coupled SMA property data. This will enable more accurate modeling and design of components and systems incorporating SMA actuation or sensing functions.

Multi-Channel Soft Dry Electrodes for Electrocardiography Acquisition in the Ear Region.

In-ear acquisition of physiological signals, such as electromyography (EMG), electrooculography (EOG), electroencephalography (EEG), and electrocardiography (ECG), is a promising approach to mobile health (mHealth) due to its non-invasive and user-friendly nature. By providing a convenient and comfortable means of physiological signal monitoring, in-ear signal acquisition could potentially increase patient compliance and engagement with mHealth applications. The development of reliable and comfortable soft dry in-ear electrode systems could, therefore, have significant implications for both mHealth and human-machine interface (HMI) applications. This research evaluates the quality of the ECG signal obtained with soft dry electrodes inserted in the ear canal. An earplug with six soft dry electrodes distributed around its perimeter was designed for this study, allowing for the analysis of the signal coming from each electrode independently with respect to a common reference placed at different positions on the body of the participants. An analysis of the signals in comparison with a reference signal measured on the upper right chest (RA) and lower left chest (LL) was performed. The results show three typical behaviors for the in-ear electrodes. Some electrodes have a high correlation with the reference signal directly after inserting the earplug, other electrodes need a settling time of typically 1-3 min, and finally, others never have a high correlation. The SoftPulseTM electrodes used in this research have been proven to be perfectly capable of measuring physiological signals, paving the way for their use in mHealth or HMI applications. The use of multiple electrodes distributed in the ear canal has the advantage of allowing a more reliable acquisition by intelligently selecting the signal acquisition locations or allowing a better spatial resolution for certain applications by processing these signals independently.

Echocardiography to Assess Cardiac Structure and Function in Genetic Cardiomyopathies.

Rodents are the most common experimental models used in cardiovascular research including studies of genetic cardiomyopathies. Genetic cardiomyopathies are characterized by changes in cardiac structure and function. Echocardiography allows for relatively inexpensive, non-invasive, reliable, and reproducible assessment of these changes. However, the fast heart and small size present unique challenges for investigators. To ensure accuracy and reproducibility of these measurements, investigators need to be familiar with standard practices in the field, normal values, and potential pitfalls. The goal of this chapter is to describe steps needed for reliable acquisition and analysis of echocardiography in rodent models. Additionally, we discuss some common pitfalls and challenges.

Prospective motion correction for cervical spinal cord MRS.

To develop prospective motion correction for single-voxel MRS in the human cervical spinal cord. A motion MR navigator was implemented using reduced field-of-view 2D-selective RF excitation together with EPI readout. A short-echo semi-LASER sequence (TE = 30 ms) was updated to incorporate this real-time image-based motion navigator, as well as real-time shim and frequency navigators. Five healthy participants were studied at 3 T with a 64-channel head-neck receive coil. Single-voxel MRS data were measured in a voxel located at the C3-5 vertebrae level. The motion navigator was used to correct for translations in the X-Y plane and was validated by assessing spectral quality with and without prospective correction in the presence of subject motion. Without prospective correction, motion resulted in severe lipid contamination in the MR spectra. With prospective correction, the quality of spinal cord MR spectra in the presence of motion was similar to that obtained in the absence of motion, with comparable spectral signal-to-noise ratio and linewidth and no significant lipid contamination. Prospective motion and B0 correction allow acquisition of good-quality MR spectra in the human cervical spinal cord in the presence of motion. This new technique should facilitate reliable acquisition of spinal cord MR spectra in both research and clinical settings.

Acquisition of Precision and Reliability of Modalities for Facial Reconstruction and Aesthetic Surgery: A Systematic Review.

The foundation of reconstructive and cosmetic surgery is a confluence of advanced technologies, plethora of procedures, inventive modifications, and planned strategies. In surgical planning, the most crucial steps for treating a patient are evaluating the facial morphometry and recognizing the deviations from the baseline values of facial parameters. Various imaging and non-imaging modalities and sub-modalities contribute to diagnosis, treatment planning, and follow-up care. These techniques are an important milestone of pre-, peri-, and postoperative care in facial reconstruction. The current research aims to comprehensively explain imaging and non-imaging technologies encompassing both innovative and traditional approaches in facial reconstruction. PubMed, Scopus, and Web of Science were searched from 1990 to 2022, and systematic review was conducted in accordance with the PRISMA recommendations. Undoubtedly, various factors impact the selection of facial analysis acquisition approaches and their prospective. The surgical team must understand such modalities' potential for diagnosis and treatment. The evolution of three-dimensional imaging has been fueled because of the need for devices with high speed, small size, and several functions. Automation with more efficiency and precision is the way of the future for three-dimensional imaging. Stereophotogrammetry can clearly quantify the field of facial analysis. All the publications under consideration came to the same conclusion: Canfield's Vectra three-dimensional imaging devices can provide accurate, repeatable stereophotogrammetric pictures. Although a few minor mistakes were recorded, most examined devices are deemed reliable and accurate tools for Plastic surgeons.

Underwater Image Enhancement Using MIRNet

In recent years, enhancement of underwater images is a challenging task, which is gaining priority since the human eye cannot perceive images under water. The significant details underwater are not clearly captured using the conventional image acquisition techniques, and also they are expensive. Hence, the quality of the image processing algorithms can be enhanced in the absence of costly and reliable acquisition techniques. Traditional algorithms have certain limitations in the case of these images with varying degrees of fuzziness and color deviation. In the proposed model, the authors used a deep learning model for underwater image enhancement. First, the original image is pre-processed by the white balance algorithm for colour correction and the contrast of the image is improved using the contrast enhancement technique. Next, the pre-processed image is given to the MIRNet for enhancement. MIRNet is a deep learning framework that can be used to enhance the low-light level images. The enhanced image quality is measured using peak signal-to-noise ratio (PSNR), root mean square error (RMSE), and structural similarity index (SSIM) parameters.

Heterostructure Engineering of Solution-Processable Semiconductors for Wearable Optoelectronics

Wearable and implantable optoelectronics, including light-emitting diodes (LEDs), photovoltaics (PVs), and photodetectors (PDs), have recently gained great interest for their potential applications in wearable technology, the Internet of things (IoT), and personalized healthcare. The development of optoelectronics with superior mechanical deformability (i.e., stretchability) is highly desired, as such devices can be worn seamlessly on the body, enabling noninvasive, continuous, and accurate real-time health monitoring using light. However, conventional optoelectronics build on inorganic semiconductors with high rigidity and brittleness. The lack of mechanical deformability impedes the reliable acquisition of biosignals during the motion of the human body, hindering the biomedical application of optoelectronics. Innovative design for intrinsically stretchable optoelectronics is thus urgently needed. This Spotlight identifies strategies and advances in intrinsically stretchable optoelectronics. We focus on heterostructure engineering, which can effectively impart softness in solution-processable optoelectronic semiconductors, including organic semiconductors (OSCs), colloidal quantum dots (CQDs), and metal halide perovskites (MHPs). Lastly, we propose a roadmap for future stretchable optoelectronics research toward its practical application in healthcare, renewable energy, soft robotics, and beyond.

A research of the respiratory status detection method measured by abdominal rise and fall

Respiratory activity is the most important basic life activity of human body, respiratory state detection is of great practical significance. Based on the characteristic of high correlation between the change of tidal volume and the change of abdominal displacement, a method of detecting respiratory status through abdominal displacement data is proposed. The method uses a gas pressure sensor to collect the tidal volume in the steady state of the subject once, which is used as the baseline data. The abdominal displacement data of the subject in the three breathing states of slow breathing, steady breathing and rapid breathing were collected with an acceleration sensor. The warp path distance between the lung and abdominal data in the three different states was calculated, this warp path distance together with the period extracted from the abdominal data is used as a two-dimensional feature and input to the support vector machine classifier. The experiments show that the accuracy of the classification results reaches 90.23%. The method only needs to measure the lung data once in smooth breathing state, and the subsequent continuous detection is achieved by measuring the displacement of the abdomen only. This method has the advantages of stable and reliable acquisition results, low implementation cost and simplified wearing method, and has high practicality.

Biocompatible and Long-Term Monitoring Strategies of Wearable, Ingestible and Implantable Biosensors: Reform the Next Generation Healthcare

Sensors enable the detection of physiological indicators and pathological markers to assist in the diagnosis, treatment, and long-term monitoring of diseases, in addition to playing an essential role in the observation and evaluation of physiological activities. The development of modern medical activities cannot be separated from the precise detection, reliable acquisition, and intelligent analysis of human body information. Therefore, sensors have become the core of new-generation health technologies along with the Internet of Things (IoTs) and artificial intelligence (AI). Previous research on the sensing of human information has conferred many superior properties on sensors, of which biocompatibility is one of the most important. Recently, biocompatible biosensors have developed rapidly to provide the possibility for the long-term and in-situ monitoring of physiological information. In this review, we summarize the ideal features and engineering realization strategies of three different types of biocompatible biosensors, including wearable, ingestible, and implantable sensors from the level of sensor designing and application. Additionally, the detection targets of the biosensors are further divided into vital life parameters (e.g., body temperature, heart rate, blood pressure, and respiratory rate), biochemical indicators, as well as physical and physiological parameters based on the clinical needs. In this review, starting from the emerging concept of next-generation diagnostics and healthcare technologies, we discuss how biocompatible sensors revolutionize the state-of-art healthcare system unprecedentedly, as well as the challenges and opportunities faced in the future development of biocompatible health sensors.

A system for in-situ, wave-by-wave measurements of the speed and volume of coastal overtopping

Wave overtopping of sea defences poses a hazard to people and infrastructure. Rising sea levels and limited resources mean accurate prediction tools are needed to deliver cost-effective shoreline management plans. A dearth of in-situ data means that the numerical tools used for flood forecasting and coastal scheme design are based largely on data from idealised flume studies, and the resulting overtopping predictions may have orders of magnitude uncertainty for complicated structures and some environmental conditions. Furthermore, such studies usually only provide data on the total volume of overtopping water, and no data on the speed of the water. Here we present WireWall, an array of capacitance-based sensors which measure the speed and volume of overtopping water on a wave-by-wave basis. We describe the successful validation of WireWall against traditional flume methods and present results from the first trial deployments at a sea wall in the UK. WireWall results are also compared with numerical predictions based on EurOtop guidance. WireWall technology offers an approach for reliable acquisition of the data needed to develop resilient coastal protections schemes.

Adaptive Buffer Capture and Storage for High-Speed Cameras

The output speed of a high-resolution camera with a fast frame rate is high, reaching 800 MB/s or even more. Consequently, the abundant data recorded can easily cause data overflow during transmission and frame loss during storage. To address this problem of data overflow and frame loss while achieving reliable acquisition rates, herein, we design a high-speed video adaptive transmission and storage method based on the camera link transmission protocol. The system uses the camera link transmission protocol and high-speed cameras for data acquisition. During data transmission, memory space is employed as the data buffer pool, an adaptive memory buffer pool is designed using the hardware and acquisition parameters, and a circular read/write buffer, combined with a block device read/write method, is used inside the buffer pool for data transmission. For data storage, input/output completion port asynchronous storage mixed with frame sequence number filtering is employed to write data, and the adaptive buffer of the hard disk is used to achieve efficient data writing. The experimental results reveal that the proposed method can realize efficient and stable writing of data on conventional industrial cameras based on the camera link interface, effectively addressing the problem of frame loss during data storage. Moreover, the stored data can be transferred efficiently with less resource occupation, thereby meeting the application requirements of high-precision vision measurement systems and demonstrating broad application value.

Investigation of Drought Risk Using a Dynamic Optimization Framework in Regional Water Allocation Procedure With Different Streamflow Scenarios

Recently, the water supply process has experienced serious challenges, because, with the intensification of drought, the imbalance between available water resources and demand for water in different sectors has led to increased system risk. Therefore, this study proposed an optimal dynamic framework under different scenarios aimed at improving the drought risk of the water supply system. In this sense, given the negative effects of drought on the water supply procedure, the degree of drought risk is analyzed and improved according to system performance parameters. After examining the developed model by initial data collected from a real case study of Hamoun wetland in Iran, the most sensitive parameters were included reliability and vulnerability, which, in this regard, the highest degree of drought risk is related to the agricultural and industrial sectors due to the acquisition of less reliability and greater vulnerability. In addition, given the final output, adaptation measures such as demand governance scheme and weight scenario analysis have been developed in order to investigate the drought risk of the system in more detail.

A-star algorithm based on admissible searching for strategically placing PMU considering redundancy and cost/benefit analysis

This research examines an admissible search algorithm-dependent A-star strategy and takes into account redundancy and cost/benefit analysis under normal operating conditions. The goal is to allocate a phasor measurement unit (PMU) for maximal observability of the interconnected power network. To determine the fewest number of PMU required to make the connected power network totally observable using redundancy analysis, the A-star approach is utilized. The redundancy analysis of the power network is carried out in order to determine the appropriate PMU placement, which results in the acquisition of total power network observability and reliability. To put the suggested technique through its paces, it has been tested on IEEE-standard test systems such as IEEE-14 bus, IEEE-30 bus, New England-39 bus, IEEE-57 bus, and IEEE-118 bus. The results obtained using the suggested methodology are compared to those obtained through standard literature research. The experimental findings of the suggested method revealed the resilience and accuracy of the A-star algorithm as well as its effectiveness in achieving maximum observability of the connected power network.

Condition Assessment of the Cable Trench Based on an Intelligent Inspection Robot

Accurate evaluation of the cable trench condition is the key to realizing its intelligent operation and maintenance, and the intelligent cable trench inspection robot is a reliable information acquisition method for the condition assessment of cable trench. According to task requirements and site’s environment, an intelligent inspection robot is designed. The crawler-type motion mechanism is determined based on the demand for sizes and obstacle-surmounting. Cartographer SLAM technology is used to map the underground cable trench environment, and a path planning method combined with the improved A∗ algorithm and dynamic window method (DWA) is presented. Multiple sensors are equipped on this robot, and various kinds of information are obtained. Based on the information obtained, a condition assessment method of an underground cable trench is proposed. An extension neural network model is constructed to assess air quality. Channel capacity and ground flatness are calculated to evaluate the internal structure of the cable trench. The water depth and water surface area are comprehensively used to evaluate the hazard of water accumulation. The evaluation results are used to realize the linkage control of the cable trench’s operation and maintenance. A field test shows that the intelligent inspection robot can reliably obtain seven kinds of information from the underground cable trench, and the proposed condition evaluation method can assess the condition of the cable trench and provide four kinds of suggestions for potential hazards.

Drone-Borne Ground-Penetrating Radar for Snow Cover Mapping

Ground-penetrating radar (GPR) is one of the most commonly used instruments to map the Snow Water Equivalent (SWE) in mountainous regions. However, some areas may be difficult or dangerous to access; besides, some surveys can be quite time-consuming. We test a new system to fulfill the need to speed up the acquisition process for the analysis of the SWE and to access remote or dangerous areas. A GPR antenna (900 MHz) is mounted on a drone prototype designed to carry heavy instruments, fly safely at high altitudes, and avoid interference of the GPR signal. A survey of two test sites of the Alpine region during winter 2020–2021 is presented, to check the prototype performance for mapping the snow thickness at the catchment scale. We process the data according to a standard flow-chart of radar processing and we pick both the travel times of the air–snow interface and the snow–ground interface to compute the travel time difference and to estimate the snow depth. The calibration of the radar snow depth is performed by comparing the radar travel times with snow depth measurements at preselected stations. The main results show fairly good reliability and performance in terms of data quality, accuracy, and spatial resolution in snow depth monitoring. We tested the device in the condition of low snow density (&lt;200 kg/m3) and this limits the detectability of the air–snow interface. This is mainly caused by low values of the electrical permittivity of the dry soft snow, providing a weak reflectivity of the snow surface. To overcome this critical aspect, we use the data of the rangefinder to properly detect the travel time of the snow–air interface. This sensor is already installed in our prototype and in most commercial drones for flight purposes. Based on our experience with the prototype, various improvement strategies and limitations of drone-borne GPR acquisition are discussed. In conclusion, the drone technology is found to be ready to support GPR-based snow depth mapping applications at high altitudes, provided that the operators acquire adequate knowledge of the devices, in order to effectively build, tune, use and maintain a reliable acquisition system.