Abnormal Conditions Research Articles

Terahertz Sensing with Extraordinary Optical Transmission Hole Arrays

Purpose: The purpose of this paper is to substantiate the enhanced performance of hyperbolic anisotropic (HA) meta-surfaces at anomalous extraordinary optical transmission (EOT) resonances. These resonances exhibit distinct transmission peaks highly sensitive to environmental changes, making them particularly valuable for sensing applications. By demonstrating improved transmission efficiency and sensitivity, this study aims to contribute to developing advanced sensing technologies that leverage the unique properties of HA meta-surfaces in detecting minute variations in their surroundings. Methodology: To verify the enhanced sensing performance of anomalous EOT resonances, this study employs a well-established experimental method involving depositing a thin analyte layer of varying thicknesses onto the meta-surface. The study aims to determine which resonance condition provides optimal sensitivity by comparing the performance between regular and anomalous EOT resonances. It explores the improved performance of HA meta-surfaces at anomalous EOT resonances, which occur under certain non-standard conditions, offering potentially superior sensing capabilities compared to regular EOT. Findings: The results indicate that when the analyte is applied to the non-patterned side of the metasurface, the anomalous EOT resonance achieves superior sensing performance. This enhanced sensitivity can be attributed to the unique interaction dynamics between the incident THz waves and the meta-surface structure under abnormal conditions. Unique Contribution to Theory, Policy and Practice: This phenomenon is beneficial in the terahertz (THz) range, making HA meta-surfaces ideal for thin-film label-free sensing applications. The EOT resonance occurs due to the interaction between incident light and the periodic structure of the holes, leading to enhanced light transmission at specific wavelengths. The findings highlight the potential of using anomalous EOT resonances in HA meta-surfaces to develop highly sensitive and efficient sensors for detecting changes in thin films, paving the way for advanced sensing technologies in various scientific and industrial applications.

KENDALI BERBASIS WEB PADA ANOMALI NEUTRAL GROUND RESISTOR (NGR)

A real-time monitoring and control system for Neutral Grounding Resistor (NGR) has been developed utilizing Internet of Things (IoT) technology. The system employs the PZEM-004T sensor for measuring current and voltage, and the NodeMCU ESP8266 for data processing and communication. Measurement data is displayed in real-time on an LCD screen and a website, with early warning alerts provided through a buzzer/alarm if abnormal conditions are detected. This system addresses the limitations of traditional monitoring methods, which are typically visual and periodic, by enabling continuous monitoring. Integration with a circuit breaker adds an additional layer of protection by disconnecting the power supply when necessary. Testing results demonstrate the effectiveness of the device in measuring voltages ranging from 5V to 220V and current, with a reliance on internet connectivity to ensure measurement accuracy. This system offers an efficient and proactive solution for maintaining the safety and reliability of electrical equipment at substations

RANCANG BANGUN MODEL MONITORING UNDERGROUND TANK SPBU DENGAN MENGGUNAKAN SENSOR ULTRASONIK BERBASIS INTERNET OF THINGS (IOT)

The management of underground tanks at gas stations is a crucial aspect to ensure the availability and safety of fuel. However, conventional methods for monitoring fuel levels in tanks are often ineffective and prone to human error. To address this issue, this research designs and develops a model for an underground tank monitoring system using ultrasonic sensors Internet of Things (IoT) integrated technology. The ultrasonic sensors are used to measure the fuel level in the tank in real-time, and this data is then transmitted to an IoT platform for analysis and monitoring through a user interface that can be accessed remotely. This system is expected to enhance operational efficiency and safety in the management of underground tanks at gas stations and provide early warnings in the event of abnormal conditions, such as leaks or excessively low fuel levels. The implementation results demonstrate that this system can provide accurate and reliable information, enabling better decision-making in fuel stock management

A voltage sensorless technique for a shunt active power filter adopting band pass filter under abnormal supply conditions

Shunt active power filters (SAPFs) are effective in addressing power quality issues such as harmonics and voltage unbalance. During abnormal grid conditions, there will be a substantial variation in both the actual and reactive power of the system. Various SAPF control approaches were employed to address these issues. This paper presents an improved bandpass filter technique that serves multiple purposes: extracting the reference current harmonic for SAPF, providing reactive current for power factor correction, and functioning as a backup synchronization method in the event of a loss of bus voltage measurement. Furthermore, it should be minimally affected by the unbalance in supply voltage. The case study model and the proposed reference technique have been executed and validated using the MATLAB software environment.

A Biometric Identification for Multi-Modal Biomedical Signals in Geriatric Care

With the acceleration of global population aging, the elderly have an increasing demand for home care and nursing institutions, and the significance of health prevention and management of the elderly has become increasingly prominent. In this context, we propose a biometric recognition method for multi-modal biomedical signals. This article focuses on three key signals that can be picked up by wearable devices: ECG, PPG, and breath (RESP). The RESP signal is introduced into the existing two-mode signal identification for multi-mode identification. Firstly, the features of the signal in the time–frequency domain are extracted. To represent deep features in a low-dimensional feature space and expedite authentication tasks, PCA and LDA are employed for dimensionality reduction. MCCA is used for feature fusion, and SVM is used for identification. The accuracy and performance of the system were evaluated using both public data sets and self-collected data sets, with an accuracy of more than 99.5%. The experimental data fully show that this method significantly improves the accuracy of identity recognition. In the future, combined with the signal monitoring function of wearable devices, it can quickly identify individual elderly people with abnormal conditions, provide safer and more efficient medical services for the elderly, and relieve the pressure on medical resources.

A Quick Look at the Atmospheric Circulation Leading to Extreme Weather Phenomena on a Continental Scale

The study delves into the primary large-scale atmospheric features contributing to extreme weather events across Europe during early September 2023. The period was examined using a dataset composed by the European Centre for Medium-Range Weather Forecasts (ECMWF) analysis and satellite imagery. In early September 2023, an omega blocking pattern led to the development of a low-pressure system over the Iberian Peninsula producing heavy precipitation and flooding over Spain and acting as a mechanism for a mineral dust outbreak. A second low-pressure system developed over Greece. Extreme precipitation was recorded across Greece, Turkey, and Bulgaria as the system gradually shifted southward over the Mediterranean. The system earned the name “Storm Daniel” as it acquired subtropical characteristics. It caused floods over Libya and its associated circulation favoured the transport of mineral dust over Northern Egypt as it moved eastward. Meanwhile, the high-pressure blocking system associated with the omega pattern induced heatwave temperatures in countries further north. This period was compared with the large-scale circulation observed in mid-September 2020, when severe weather also affected the Mediterranean region. However, the weather systems were not directly connected by the large-scale circulation, as shown in September 2023. Although mesoscale conditions are relevant to formation and intensification of some atmospheric phenomena, the establishment of an omega blocking pattern in early September 2023 showed how large-scale atmospheric dynamics can produce abnormal weather conditions on a continental scale over several days.

Real-time monitoring and protection strategies for dense granular flow spallation target in Accelerator-Driven System

An innovative concept of a high-power, gravity-driven, dense granular spallation target has been integrated into the prototype design of the China initiative Accelerator-Driven System (CiADS), which is envisioned as a promising nuclear demonstration facility aimed at converting long-lived fission products and minor actinides into short-lived isotopes. This spallation target system, bridging the proton beam accelerator and the subcritical reactor, plays a crucial role in ensuring safety control and facilitating dynamic operational strategies. In this study, we have developed a system design for an online detector array, incorporating several neutron fission chambers and thermocouples installed in the gap between the spallation target and the subcritical reactor to monitor operations. Furthermore, we have devised real-time monitoring and protection methods to tackle the challenges of measuring beam position and the blockage condition associated with granular flow, considering the distribution of neutron flux and heat deposition under abnormal irradiation conditions. Monte Carlo simulations have demonstrated the applicability and feasibility of the control system for the dense granular spallation target in the accelerator-driven system. The numerical results confirm that the proposed control system meets the requirements for stable measurement with acceptable accuracy.

Feasibility of Monitoring Heart and Respiratory Rates Using Nonwearable Devices and Consistency of the Measured Parameters: Pilot Feasibility Study.

As Japan is the world's fastest-aging society with a declining population, it is challenging to secure human resources for care providers. Therefore, the Japanese government is promoting digital transformation and the use of nursing care equipment, including nonwearable devices that monitor heart and respiratory rates. However, the feasibility of monitoring heart and respiratory rates with nonwearable devices and the consistency of the rates measured have not been reported. In this study, we focused on a sheet-type nonwearable device (Safety Sheep Sensor) introduced in many nursing homes. We evaluated the feasibility of monitoring heart rate (HR) and respiratory rate (RR) continuously using nonwearable devices and the consistency of the HR and RR measured. A sheet-type nonwearable device that measured HR and RR every minute through body vibrations was placed under the mattress of each participant. The participants in study 1 were healthy individuals aged 20-60 years (n=21), while those in study 2 were older adults living in multidwelling houses and required nursing care (n=20). The HR was measured using standard methods by the nurse and using the wearable device (Silmee Bar-type Lite sensor), and RR was measured by the nurse. The primary outcome was the mean difference in HR and RR between nonwearable devices and standard methods. The mean difference in HR was -0.32 (SD 3.12) in study 1 and 0.04 (SD: 3.98) in study 2; both the differences were within the predefined accepted discrepancies (<5 beats/min). The mean difference in RR was -0.98 (SD 3.01) in study 1 and -0.49 (SD 2.40) in study 2; both the differences were within the predefined accepted discrepancies (3 breaths/min). HR and RR measurements obtained using the nonwearable devices and the standard method were similar. Continuous monitoring of vital signs using nonwearable devices can aid in the early detection of abnormal conditions in older people.

Basic study of structural health monitoring for layered structures subjected to vibration from a semi-active dynamic vibration absorbers

Vibration suppression of structures and structural health monitoring (SHM) are important technologies for realizing a sustainable society, and as stated in SDGs No. 11 "Sustainable cities and communities,"their importance is widely known. Semi-active dynamic vibration absorbers (SADVA) are used in vibration suppression technology, in addition to passive and active dynamic vibration absorbers. Basically, the physical properties of SADVA, which are passive elements such as the spring constant and viscous damping coefficient, can be changed externally. These dynamic vibration absorbers generally operate only when an earthquake occurs. Here, to achieve SDG No. 12 "Responsible consumption, production," we verify whether it is possible to use some of the SADVA mentioned above for SHM. The vibration data of structures is sometimes used in SHM technology. Specifically, an abnormality is diagnosed by measuring the frequency response with a sensor installed in a structure and focusing on the change in the frequency response between normal and abnormal conditions. In this study, we propose a method to identify the location and level of abnormalities in layered structures using a part of SADVA as a shaker.

Data Poisoning Attack against Neural Network-Based On-Device Learning Anomaly Detector by Physical Attacks on Sensors.

In this paper, we introduce a security approach for on-device learning Edge AIs designed to detect abnormal conditions in factory machines. Since Edge AIs are easily accessible by an attacker physically, there are security risks due to physical attacks. In particular, there is a concern that the attacker may tamper with the training data of the on-device learning Edge AIs to degrade the task accuracy. Few risk assessments have been reported. It is important to understand these security risks before considering countermeasures. In this paper, we demonstrate a data poisoning attack against an on-device learning Edge AI. Our attack target is an on-device learning anomaly detection system. The system adopts MEMS accelerometers to measure the vibration of factory machines and detect anomalies. The anomaly detector also adopts a concept drift detection algorithm and multiple models to accommodate multiple normal patterns. For the attack, we used a method in which measurements are tampered with by exposing the MEMS accelerometer to acoustic waves of a specific frequency. The acceleration data falsified by this method were trained on an anomaly detector, and the result was that the abnormal state could not be detected.

A Diagnostic Study Using STARD Platform on Liquid-Based Cytology in Cervical Smear and Its Positivity Rate among Females with Abnormal Vaginal Conditions Attending a Tertiary Care Center

Abstract Background: Liquid-based cytology (LBC) is a newer method of preparing cervical cell samples. This technique involves collecting cells in a liquid fixative and preparing and evaluating them. Aim: This study aims to investigate cervical smears prepared using the Ezi-Prep LBC method and analyze the positivity rate for cervical cancer and assess the diagnostic accuracy of LBC in detecting cervical abnormalities among females with abnormal vaginal conditions attending a tertiary care center. Materials and Methods: The present diagnostic study was performed in a tertiary care hospital over a period of 1 year (April 1, 2021–November 30, 2021). A total of 230 women, aged 21–70 years, presented at the gynecology outpatient department for either routine cervical screening or with complaints of vaginal bleeding, discharge, or pelvic pain. Cervical smears were prepared using the Ezi-Prep method and stained with ULTRA-PAP stain. Results: LBC smears are monolayer smears and are without nuclear overlapping with spread over a diameter of 16 mm. Minimum cellular overlapping and crowding were observed in LBC smears. The mean age of the participants was 35.5 ± 5.1 years. The smears were studied by using nine morphological parameters. Conclusion: The majority of the smears were inflammatory smears. A total of 16 cases and three cases were found to be bacterial vaginosis and candidiasis, respectively. In an 8-month study, HSIL was found in two cases, whereas LSIL was found in only one case. The positivity rate was observed to be 0.9% in HSIL cases and 0.4% in LSIL cases, 0.9% ASC-US case, and 0.4% invasive carcinoma case.

A multi-layer constrained spectral k-embedded clustering methodology approach for intelligent partitioning of power grid to enhance resiliency in transmission networks

The intentional controlled islanding by intelligent partitioning of the power grid is considered as essential to protect the grid from cascading events, faults, and High Impact Low Probability (HILP) events. To enhance the resilience, stability, and security of the power grid, the proposed model in this paper intentionally divides the affected power network into islands. This paper presents an intelligent partitioning approach to create an islanding solution through multilayer graphs using spectral clustering. The controlled islanding algorithm uses a multi-criteria objective function that considers the correlation coefficients among the frequency of the buses and minimal disturbances in the real and reactive power. The proposed control technique is implemented in two phases. The first phase employs correlation coefficients between frequency of the buses and modularity clustering to identify clusters of coherent buses. During the second stage, all nodes are categorised into groups using Multi-level constrained Spectral Clustering (ML-CSC) to determine the solution for Intentional controlled Islanding that satisfies bus coherency with the minimum level of disruptions to real and reactive power flows across the boundaries. The proposed algorithm for resolving the generator coherency issue and an intelligent islanding solution is demonstrated by simulation experiments conducted on an IEEE 39-bus transmission test system developed in DIGSILENT Powerfactory version 2023. The MATLAB version R2023a is used to construct the ML-CSC control method. The results demonstrated that the proposed ML-CSC algorithm substantially impacts the functioning of the power system, enabling the formation of intelligent islanding during abnormal conditions. Also, the results clearly show that instead of using single-layer spectral clustering, the multi-layer spectral clustering yields a better intentional islanding solution with minimum power flow mismatch which enhances the transient stability of the islands.

Wavelet-based arcing signal source localization algorithm using a compact multi-square microstrip antenna

Ensuring power system safety involves effective arc fault detection and localization. Existing devices struggle to differentiate between normal and abnormal conditions, especially in confined spaces, posing precision challenges. Strategically placing antennas around the arc helps detect electromagnetic radiation, even in limited areas, enabling valuable data collection for real-time monitoring. To address these challenges, this paper proposes integrating experimental work using a compact multi-square microstrip antenna and signal processing techniques. The study compares the effectiveness of two signal processing approaches: Discrete Wavelet Transform (DWT), and Continuous Wavelet Transform (CWT). These techniques separate genuine arc signals from background noise by identifying unique characteristics and isolating the dominant frequency. The Time of Arrival (ToA) is measured and used in Least Square and Gauss-Jordan Elimination methods to calculate the arc source location. The outcomes illustrate the precision of the proposed model in detecting and pinpointing arc source signals, with error margins ranging from 0.0615 to 0.0713 m for the CWT technique, 0.0688 to 0.0789 m for the DWT technique, and 0.0799 to 0.0844 m from the actual signal captured. These results hold promise for enhancing the integration of experimental approaches in assessing arcing conditions, thereby addressing challenges in insulation systems.

Artificial intelligence-based power market price prediction in smart renewable energy systems: Combining prophet and transformer models

With the increasing integration of smart renewable energy systems and power electronic converters, electricity market price prediction is particularly important. It is not only crucial for the interests of power suppliers and market regulators but also plays a key role in ensuring the reliable and flexible operation of the power system, particularly during extreme weather events or abnormal conditions. This study develops a hybrid time series forecasting model that combines Prophet and Transformer, which takes advantage of deep learning to provide a new solution for electricity market price forecasting. By introducing the Stacking optimization strategy, this study improves the accuracy and stability of electricity market price sequence prediction. In addition, the study tries to integrate traditional time series forecasting methods (such as the Prophet model) with deep learning models (such as the Transformer model), aiming to make full use of their respective advantages to achieve more accurate and stable predictions. Through experimental evaluation on four electricity market data sets, this study finds that the hybrid forecast model exhibits significant performance improvements in enhancing the accuracy and stability of electricity market price predictions. This method not only provides a more accurate tool for power market price prediction, but also provides solid technical support for the efficient operation and sustainable development of smart renewable energy systems. Experimental results also show that the combination of deep learning models with traditional time series methods and the introduction of Stacking strategies is crucial to improving the performance of power market price prediction, and also helps us better understand and design smart renewable energy systems, price and energy management strategies, thereby providing an effective method for achieving efficient and reliable power and energy transmission.

A vine-copulas based multi-sensor fusion structural damage monitoring method and its application in dam engineering

As a civil engineer that is susceptible to the mechanism of slow and continuous deterioration, the dam must detect damage according to the static data of the arranged sensors, which is an important issue in structural health monitoring. Due to the characteristics of huge solid structures, damage with a slight degree and small range usually does not cause significant changes in the monitoring data of a single sensor. Therefore, it is difficult to effectively detect local damage by traditional monitoring methods that establish operating warning values for a single sensor. Currently, there have been some approaches to fuse sensor information that consider correlations between multiple sensors as well as the overall operational behavior of the dam. However, their objectives are focused on improving the accuracy of response variable prediction models without addressing the local damage identification aspect. In this study, we apply the Vine-Copula model for the first time in a multi-sensor information fusion dam damage detection method based on static monitoring data. In this way, it improves the sensitivity of local damage identification for huge solid structures. A prediction model is fitted with the measuring data of each sensor to obtain the residual series. Then the joint probability distribution of the multidimensional residuals is acquired by constructing the Vine-Copula model. Finally, the joint cumulative distribution function value is used as an alarm limit to determine whether the structure has an abnormal damage condition. The effectiveness and accuracy of the proposed method were demonstrated in both a numerical arch dam simulating local damage and an actual rockfill dam engineering with cracks at the crest.

Insights into food incompatibilities: Ayurveda and modern perspectives

Classical Ayurveda books extensively discuss several aspects of food, including its diverse origins, qualities in different seasons and locations, and its unique roles in normal and abnormal physiological conditions. Various diseases can be initiated by alterations in dietary patterns and a deficiency in understanding appropriate nutrition. Viruddha Ahara is a concept in Ayurveda that refers to a particular diet and its combination that negatively affects the body’s metabolism of tissues, hinders the creation of Dhatus, and possesses properties that are opposed to Dhatus (known as incompatible food). In recent years, there has been an increase in the occurrence of lifestyle disorders, with poor dietary patterns being a significant contributing factor. Therefore, it is imperative to comprehend the concept of ViruddhaAhara in the modern era. This review encompasses details about Viruddha Ahara and its reported hazards in classical literature along with the contemporary know-how and reported shreds of evidence in food incompatibility.

Diverse Concepts in Definitions of Dilated Cardiomyopathy: Theory and Practice.

Our understanding of dilated cardiomyopathy (DCM) is evolving as new insights into the underlying pathophysiology become available. Professional organizations and clinical experts are improving definitions of DCM, allowing for more accurate treatment recommendations. This review summarized key published literature describing definitions and/or diagnostic criteria for DCM. Embase was searched from database inception to September 19, 2022 for 1) publications reporting definitions of DCM by major professional organizations and related opinion papers, and 2) clinical studies in DCM and heart failure with reduced ejection fraction. Sixty-eight records were included in this review. Definitions of DCM provided by two major professional organizations (American Heart Association (AHA) and European Society of Cardiology (ESC)) agreed on the clinical presentation of DCM; however, they differed in the classification of DCM within the larger context of cardiomyopathy taxonomies. Both organizations agreed that DCM could be clinically defined by the presence of left ventricular dilation and contractile dysfunction in the absence of abnormal loading conditions and severe coronary artery disease. AHA guidelines divided cardiomyopathies into two major groups (primary and secondary) based on predominant organ involvement. DCM was classified as primary cardiomyopathy with mixed (genetic and/or acquired) etiology. Conversely, ESC published a clinically oriented taxonomy in which cardiomyopathies were grouped into specific morphological and functional phenotypes; each was subclassified into familial or non-familial forms. Opinion papers further elaborated on the complex interplay between genetics and environment in the etiology of DCM. Several articles summarized the importance of the new and updated diagnostic tools, such as cardiac magnetic resonance imaging, electrocardiogram, and other biomarkers, in correctly identifying the etiology of DCM. Within clinical studies, most inclusion criteria used standard definitions proposed by leading professional associations (AHA and ESC). Clinical study investigators sometimes used a narrower definition of DCM using additional criteria for the left ventricular ejection fraction threshold value and left ventricular dilatation. Current efforts in cardiology research are focused on a more granular understanding of DCM etiology and the natural history of the disease. Definitions of DCM found in clinical studies mainly rely on published guidelines, with some studies adding idiosyncratic inclusion criteria refining the broad definitions of DCM.

A new method for thermal runaway warning of electric vehicle charging

Abstract With the large-scale development of electric vehicle (EV) in China, the frequent occurrence of EV fire accidents has attracted the attention of insiders to the fault monitoring and early warning during the charging process of EV. According to data from the National Emergency Management Ministry, there were over 3,000 EV fire accidents nationwide in 2023, underscoring the urgency for enhanced fault monitoring and early warning measures. In response to these issues, this article proposes an EV thermal runaway early warning method based on Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU) by combining the two technologies. The method aims to improve EV safety by monitoring battery status for early thermal runaway warning. First, the historical normal charging data of the battery are preprocessed and feature extracted using the TCN, and then features fed into GRU for time-series modeling and forecasting. This combined model not only demonstrates high prediction accuracy and stability but also swiftly responds to abnormal conditions during charging, effectively preventing thermal runaway accidents and ensuring charging safety. Furthermore, this model possesses excellent generalization capabilities and can adapt to different types and specifications of EV battery systems.

Prenatal immune activation in rats and adult exposure to inescapable shocks reveal sex-dependent effects on fear conditioning that might be relevant for schizophrenia

Prenatal infection is considered a relevant factor for neurodevelopmental alterations and psychiatric diseases. Administration of bacterial and viral components during pregnancy in rodents results in maternal immune activation (MIA), leading to schizophrenia-like neurochemical and behavioral changes. Despite some evidence for abnormal fear conditioning in schizophrenia, only a few animal studies have focused on this issue. Therefore, we addressed the impact of the administration of the viral mimetic polyI:C to pregnant Long-Evans rats on the adult offspring response to inescapable shocks (IS) and contextual fear conditioning. In males, polyI:C induced a greater endocrine (plasma ACTH) response to IS and both polyI:C and IS enhanced fear conditioning and generalization to a completely different novel environment (hole-board), with no additive effects, probably due to a ceiling effect. In contrast, a modest impact of polyI:C and a lower impact of IS on contextual fear conditioning and generalization was observed in females. Thus, the present results demonstrate that polyI:C dramatically affected fear response to IS in adult males and support the hypothesis that males are more sensitive than females to this treatment. This model might allow to explore neurobiological mechanisms underlying abnormal responsiveness to fear conditioning and stressors in schizophrenia.

Non-invasive ultrasonic sensing of internal conditions on a partial full-scale spent nuclear fuel canister mock-up

The safe storage of spent nuclear fuel (SNF) in dry cask storage systems (DCSSs) is critical to the nuclear fuel cycle and the future of nuclear energy. A critical component of DCSSs is the SNF canister. The canister is a sealed stainless-steel structure, which is first vacuum dried and then backfilled with helium. The structural deterioration within a canister can be monitored through its internal gas properties. This monitoring serves as the driving force behind the non-invasive ultrasonic sensing approach in this paper. A major challenge in collecting gas-borne signals using ultrasonic sensing is the impedance mismatch between the stainless-steel canister and the helium gas inside. Only a small fraction of the ultrasonic signal makes its way from the transmitter to the receiver through the gas medium. In this paper, experimental studies on a partial full-scale canister mock-up were carried out to capture the gas-borne signals. Damping materials were applied on the outside, and blocking and unblocking tests were conducted to identify the gas-borne signal. The research results showed that the excitation frequency played an important role in maximizing the gas-borne signals. The gas-borne signal was successfully detected at around the theoretical time-of-flight (TOF) at 225 kHz. A high signal-to-noise ratio (SNR) was achieved in the measurements. Next, acoustic impedance matching (AIM) layers were added, and it was found that the gas signal energy was improved by 160.4% compared with that of no AIM layers. Subsequently, the relative humidity (RH) level and temperature of the gas were varied to simulate abnormal internal conditions of the canister. The non-invasive testing system demonstrated reliability and sensitivity in detecting gas temperature and RH variations. Theoretical calculations demonstrated the potential for detecting low-level xenon and air within an actual SNF canister filled with helium. Last, an active noise cancellation (ANC) method, previously developed by the authors, was verified on the canister mock-up for the first time. The results showed that the SNR of the gas signal was improved by 213.6% compared with that of no ANC.