Detection Delay Time Research Articles

A method for detecting faults in integrated navigation system based on improved SPRT adaptive filtering

GNSS measurement noise faults can easily cause a drop or even divergence in the filter accuracy of integrated navigation system. Therefore, real-time fault detection and processing are necessary. With the aim of the defects of residual Chi-square test and sequential probability ratio test (SPRT) in detecting measurement noise faults, an adaptive filtering algorithm based on improved SPRT detection is proposed. On the one hand, the influence of the current time innovation on the statistics is strengthened by the method of fading weighting. On the other hand, the measurement update equation of the adaptive filter is constructed by calculating the weight factor of the statistics in real-time, which improves the accuracy and robustness of the state estimation. Simulation experiments show that the proposed algorithm is more sensitive and non-missing compared to the residual Chi-square test in the case of large value abrupt faults in the measurement noise. The proposed algorithm reduces the fault detection delay time by about 65.5% compared to SPRT in the case of slow change faults in measurement noise. In the case of slow change faults with small values, the proposed algorithm reduces the missing detection rate by 40% compared to the residual Chi-square test. In addition, the proposed algorithm is compared with the robust Kalman filter based on the residual Chi-square and Sage-Husa adaptive filtering. The results show that the proposed algorithm has higher navigation accuracy when the system has slow change faults.

Availability Evaluation of Warm Standby System with Fault Detection Delay and General Repair Times

This study analyzed the availability of a warm standby system that works with fault detection delay and general repair times. The time-to-detection delay is also considered as exponentially distributed. The detection state is used to detect the faults in the failed unit. The steady state availability of the system is obtained by using supplementary variable technique. Three types of repair time distributions are compared to find the best one.

Detecting stochastic multiresonance in neural networks via statistical complexity measure

This paper employs statistical complexity measure (SCM) to investigate the occurrence of stochastic multiresonance (SMR) induced by noise and time delay in small-world neural networks coupled with FitzHugh-Nagumo (FHN) neurons. Our findings reveal that SCM exhibits four local maxima at four optimal noise levels, providing evidence for the occurrence of quadruple stochastic resonances. When time delay τ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ au$$\\end{document} is taken into account in the information transmission, under moderate noise levels, SCM shows several local maxima when τ=nTe\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ au = nT_{e}$$\\end{document} with n\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$n$$\\end{document} being a positive integer and Te\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$T_{e}$$\\end{document} being the period of subthreshold signal. This indicates the appearance of delay-induced SMR at the multiples of the period of subthreshold signal. Intriguingly, at low noise levels, a strong coherence between time delay and neuronal firing dynamics emerges at τ=nTe-2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ au = nT_{e} - 2$$\\end{document}, as confirmed by a series of SCM maxima at these time delays. Furthermore, the study demonstrates that by adjusting the degrees and sizes of small-world networks, as well as the coupling strength, it is possible to optimize the strength of delay-induced SMR, thus maximizing the detection capability of subthreshold signal. The research results may provide us with an effective approach for understanding the role of time delay in signal detection and information transmission.

Use of FreeStyle Libre for continuous glucose monitoring in adult horses.

To evaluate the feasibility of using the FreeStyle Libre (a continuous glucose monitoring system [CGMS]) for instantaneous continuous monitoring of interstitial glucose in adult horses and examine the applicability and accuracy of this system in horses submitted to combined glucose-insulin test (CGIT). Laboratory measurements and continuous glucose monitoring system (CGMS) readings were analyzed using a 2 × 2 factorial statistical model with repeated measures over time. This analysis assessed the effects of the test (factor 1), group (factor 2), and their interactions (test × group, test × time, and group × time). Pearson's correlation analysis was applied to blood glucose values. Mean comparisons were conducted using the t-test, and agreement between techniques was assessed via the Bland-Altman method, with a 95% confidence interval. Field study on private horse farms in association with a veterinary school. Ten healthy stallions were assigned to one of two groups based on their body condition scores (BCS). Group 1 (G1, n= 5) consisted of nonobese horses with a BCS of 5 or 6, while Group 2 (G2, n= 5) consisted of obese horses with a BCS of 7 or higher. A CGMS sensor was attached to the dorsolateral aspect of the proximal one third of each horse's neck. Laboratory blood glucose measurements and CGMS interstitial glucose readings were compared at different time points for up to 7days after sensor fixation. Obese horses were also submitted to CGIT on Day 4. A comparative analysis of glucose measurements obtained in G1 and G2 horses using the CGMS and enzymatic methods revealed significant group×time interactions (P<0.001) and time effects (P<0.001). No interactions were detected between group (P=0.45), test (P=0.62), group and test (P=0.28), or time and test (P=0.92). In G1 and G2, tests were significantly correlated (r=0.84 and P=0.00) at all time points (T0-T5). Agreement between the glucose values obtained using different methods was excellent despite a small time delay in CGMS detection of rapid changes in blood glucose. It was concluded that the CGMS can be used for indirect assessment of glycemic status (ie, based on interstitial glucose measurements) in nonobese and obese adult horses submitted to CGIT.

Development and Assessment of Internet of Things-Driven Smart Home Security and Automation with Voice Commands

With the rapid rise of digitalization in the global economy, home security systems have become increasingly important for personal comfort and property protection. The collaboration between humans, the Internet of Things (IoT), and smart homes can be highly efficient. Interaction considers convenience, efficiency, security, responsiveness, and automation. This study aims to develop and assess IoT-based home security systems utilizing passive infrared (PIR) sensors to improve user interface, security, and automation controls using voice commands and buttons across different communication protocols. The proposed system incorporates controls for lighting and intrusion monitoring, as well as assessing both the functionality of voice commands and the precision of intruder detection via the PIR sensors. Intelligent light control and PIR intruder detection with a variable delay time for response detection are unified into the research methodology. The test outcomes examine the average effective response time in-depth, revealing performance distinctions among wireless fidelity (Wi-Fi) and fourth- and fifth-generation mobile connections. The outcomes illustrate the reliability of voice-activated light control via Google Assistant, with response accuracy rates of 83 percent for Thai voice commands and 91.50 percent for English voice commands. Moreover, the Blynk mobile application provided exceptional precision regarding operating light-button commands. The PIR motion detectors have a one hundred percent detection accuracy, and a 2.5 s delay is advised for PIR detection. Extended PIR detection delays result in prolonged system response times. This study examines the intricacies of response times across various environmental conditions, considering different degrees of mobile communication quality. This study ultimately advances the field by developing an IoT system prepared for efficient integration into everyday life, holding the potential to provide improved convenience, time-saving effectiveness, cost-efficiency, and enhanced home security protocols.

Layer-wise multi-defect detection for laser powder bed fusion using deep learning algorithm with visual explanation

In Laser Powder Bed Fusion (LPBF), it is a major challenge to obtain detailed spatial information on different powder bed defects in real-time and simultaneously. Deep Learning (DL) algorithms under the field of Machine Learning (ML) have promoted the intelligent development of the powder bed defect detection method. However, they still need to be further evaluated in terms of detection accuracy and time delay, training data overhead, and model robustness under complex environments. Also, the DL model usually treated as a black-box demands further explanation. Herein, three advanced DL models are constructed using bounding boxes to locate multi-defects quickly and accurately for the powder spreading of LPBF. High detection accuracy is achieved with limited training samples through both data augmentation aiming at expanding image samples and model-based Transfer Learning (TL) used for transferring from the source domain to the target domain by reusing trained models. Further, the data augmentation method is employed to generate low-resolution images with interference to test the robustness of the detection model in harsh environments. Besides, visual feature maps and saliency maps were generated with the Detector Randomized Input Sampling for Explanation (D-RISE) method to help understand the validity of the defect detection process of the DL model. Overall, this work shows that the proposed multi-defect detection algorithms can provide comprehensive information on powder bed defects accurately and quickly.

Experimental Study on Distribution Characteristics and Leakage Detection of Hydrogen Release from Hydrogen Supply System of Fuel Cell Truck

The hydrogen supply system of a fuel cell truck is in a semi-enclosed space where hydrogen is easy to accumulate if a hydrogen leak occurs. The acquisition of hydrogen dispersion behavior data is essential to support the detection of hydrogen release. The purpose of this article is to present the characteristics of hydrogen concentration distribution and delay time of hydrogen leakage detection under different leakage parameters. The experiments have been performed in a hydrogen storage cabin with six hydrogen sensors arranged on the roof to measure hydrogen concentration. During the tests, hydrogen was released into the test cabin through standard leaks. Two different release rates (80 NL/min and 450 NL/min), three different release positions, and six release directions are investigated to analyze the effects on the distribution of hydrogen concentration and leakage detection delay time. This article presents both the experimental facility and results. The experimental results can help optimize the placement of hydrogen sensors and the design of a hydrogen leakage detection system.

New Developments in Detector and Bund Wall Standards to Mitigate the Risk of Hazardous Liquid Leaks

Ensuring early leak detection and implementing effective secondary containment systems are critical for preventing the dispersion of hazardous liquids and minimizing the casualties of a chemical accident. This study reviews the standards for leak detectors and their placement in various regions, as well as the key parameters involved in bund design such as bund capacity, bund wall height, and separation distance from the bund wall. In this study, Computational Fluid Dynamics (CFD) simulations are conducted considering storage tanks with heights (H) of 1.75 m, 3.5 m, and 7.0 m. The detection times using a level transmitter (LT) and a leak sensor are compared at eight monitoring points. Exclusively employing the leak sensor led to a significant detection time delay of up to 16 times, ranging from 0.5 s to 8.1 s depending on the placement of the leak sensors. However, the average rate of liquid level change was 3.0 mm/s, which demonstrates that the LT consistently detects leaks faster than the leak sensor at all monitoring points. Hence, the integration of an LT alongside a leak detector offers a valuable approach to expedite leak detection, regardless of the direction or location of the leak. Furthermore, the optimized separation distance between the storage tank and the bund wall is suggested. The analytical solution and numerical solution for a separation distance matches up to 95% for H=1.75 m. The separation distance increases as both the tank height and the height of the leak source increase. The relationship between the separation distance and the tank height exhibits a square root dependence on the liquid level from the leak point. The proposed detection method and optimized separation distance hold the potential to facilitate a revision of liquid storage and handling standards grounded in robust scientific and quantitative evidence.

Adversarial Deep Learning based Dampster–Shafer data fusion model for intelligent transportation system

Intelligent Transportation Systems (ITS) have revolutionized transportation by incorporating advanced technologies for efficient and safe mobility. However, these systems face challenges ensuring security and resilience against adversarial attacks. This research addresses these challenges and introduces a novel Dampster–Shafer data fusion-based Adversarial Deep Learning (DS-ADL) Model for ITS in fog cloud environments. Our proposed model focuses on three levels of adversarial attacks: original image level, feature level, and decision level. Adversarial examples are generated at each level to evaluate the system’s vulnerability comprehensively. To enhance the system’s capabilities, we leverage the power of several vital components. Firstly, we employ Dempster–Shafer-based Multimodal Sensor Fusion, enabling the fusion of information from multiple sensors for improved scene understanding. This fusion approach enhances the system’s perception and decision-making abilities. For feature extraction and classification, we utilize ResNet 101, a deep learning architecture known for its effectiveness in computer vision tasks. We introduced a novel Monomodal Multidimensional Gaussian Model (MMGM-DD) based Adversarial Detection approach to detect adversarial examples. This detection mechanism enhances the system’s ability to identify and mitigate adversarial attacks in real-time. Additionally, we incorporate the Defensive Distillation method for adversarial training, which trains the model to be robust against attacks by exposing it to adversarial examples during the training process. To evaluate the performance of our proposed model, we utilize two datasets: Google Speech Command version 0.01 and the German Traffic Sign Recognition Benchmark (GTSRB). Evaluation metrics include latency delay and computation time (fog–cloud), accuracy, MSE, loss, and F-score for attack detection and defense. The results and discussions demonstrate the effectiveness of our Dampster–Shafer data fusion-based Adversarial Deep Learning Model in enhancing the robustness and security of ITS in fog–cloud environments. The model’s ability to detect and defend against adversarial attacks while maintaining low-latency fog–cloud operations highlights its potential for real-world deployment in ITS.

Performance Evaluation of VANET using Directional Location Aided Routing (D-LAR) Protocol with Sleep Scheduling Algorithm

Neighboring radio networks are those that do not instantly supply communications infrastructure after deployment, have radio transceivers with restricted transmission range, and are unaware of possible neighbors. Vehicle-to-vehicle communication is an effective means to convey information through an asymmetric vehicle ad hoc network, and it can provide high security while also reducing energy usage. A new protocol is known as energy efficiency, the D-LAR (Directed Location Assist) protocol, is used to determine a node's energy efficiency. To determine the sleep and active states of nodes, propose a sleep scheduling technique. It can also provide a more accurate location of a moving vehicle than existing location support protocols. In comparison to other current systems, the simulation results suggest that the proposed D-LAR technique has the advantages of decreasing the collision attack detection delay time and enhancing security.

Influence Minimization With Conformity-Aware Competitions

Negative information (such as rumors that prevail) may produce tremendous damage in society incredibly quickly. One practical way to control negative information is to add positive information into the spread mechanism, which enjoys both effectiveness and cost-economic counterbalance. This article presents a novel conformity-aware competitive independent cascade model to simulate the spread process of two items (e.g., truth and rumor). Two advanced algorithms named the rumor-aware betweenness first (RBF) and conformity-aware demand degree (CDD) are proposed to solve the corresponding influence minimization problem. Extensive experiments conducted on seven synthetic/real-world networks have demonstrated that first, the proposed CDD or RBF algorithm outperforms other state-of-the-art methods in terms of rumor containment and efficiency in running speed; second, the time delay of rumor detection is entitled to be concerned about and costly measures are necessary if rumors have diffused chronically; and third, authoritative truth information plays a critical role in competing with rumors, relying on truth seeds will generate negative feedback when their credibility ratio is less than a specific threshold.

Digital Twin-based Anomaly Detection with Curriculum Learning in Cyber-physical Systems

Anomaly detection is critical to ensure the security of cyber-physical systems (CPS) . However, due to the increasing complexity of attacks and CPS themselves, anomaly detection in CPS is becoming more and more challenging. In our previous work, we proposed a digital twin-based anomaly detection method, called ATTAIN, which takes advantage of both historical and real-time data of CPS. However, such data vary significantly in terms of difficulty. Therefore, similar to human learning processes, deep learning models (e.g., ATTAIN) can benefit from an easy-to-difficult curriculum. To this end, in this paper, we present a novel approach, named digitaL twin-based Anomaly deTecTion wIth Curriculum lEarning ( LATTICE ) , which extends ATTAIN by introducing curriculum learning to optimize its learning paradigm. LATTICE attributes each sample with a difficulty score, before being fed into a training scheduler. The training scheduler samples batches of training data based on these difficulty scores such that learning from easy to difficult data can be performed. To evaluate LATTICE, we use five publicly available datasets collected from five real-world CPS testbeds. We compare LATTICE with ATTAIN and two other state-of-the-art anomaly detectors. Evaluation results show that LATTICE outperforms the three baselines and ATTAIN by 0.906%-2.367% in terms of the F1 score. LATTICE also, on average, reduces the training time of ATTAIN by 4.2% on the five datasets and is on par with the baselines in terms of detection delay time.

Benchmarking Change Detector Algorithms from Different Concept Drift Perspectives

The stream mining paradigm has become increasingly popular due to the vast number of algorithms and methodologies it provides to address the current challenges of Internet of Things (IoT) and modern machine learning systems. Change detection algorithms, which focus on identifying drifts in the data distribution during the operation of a machine learning solution, are a crucial aspect of this paradigm. However, selecting the best change detection method for different types of concept drift can be challenging. This work aimed to provide a benchmark for four drift detection algorithms (EDDM, DDM, HDDMW, and HDDMA) for abrupt, gradual, and incremental drift types. To shed light on the capacity and possible trade-offs involved in selecting a concept drift algorithm, we compare their detection capability, detection time, and detection delay. The experiments were carried out using synthetic datasets, where various attributes, such as stream size, the amount of drifts, and drift duration can be controlled and manipulated on our generator of synthetic stream. Our results show that HDDMW provides the best trade-off among all performance indicators, demonstrating superior consistency in detecting abrupt drifts, but has suboptimal time consumption and a limited ability to detect incremental drifts. However, it outperforms other algorithms in detection delay for both abrupt and gradual drifts with an efficient detection performance and detection time performance.

Robust early detection of hunting on railway vehicles: single-sensor based methods and critical comparisons

ABSTRACT The problem of robust and early detection of railway hunting is considered and two single-sensor ‘dynamics-based’ methods employing adaptive Recursive AutoRegressive (RAR) modelling of the car-body lateral random vibration signal are postulated. The first is based on a Degree-of-Stochasticity (DS) measure of the vibration signal and the second on the minimum underlying Damping Ratio (DR). Fully automated tuning for both methods is achieved via Bayesian optimization. The performance of the methods is systematically assessed via high numbers of Monte Carlo simulations under various Scenarios employing a high-fidelity SIMPACK-based vehicle model and three performance criteria: the True Positive Rate (TPR), the False Positive Rate (FPR), and the Detection Delay Time (DDT) with respect to conventional hunting initiation. The results reveal very good performance and robustness to suspension faults and worn track conditions for both methods, with the DR-based exhibiting an edge. Its performance and achievable robustness are also shown to be clearly superior to those of three alternative methods.

A semi-autonomous robot control based on bone layer transition detection for a safe pedicle tapping.

Automatic robotic platforms for robot-aided spinal surgery are mostly employed for drilling the pedicle screw path and do not adapt the tool rotational speed depending on the variation of the bone density. This feature is highly desirable in control strategies for robot-aided pedicle tapping, which may result in a poor quality thread if the surgical tool speed is not adequately tuned depending on the bone density to be threaded. Therefore, the objective of this paper is to propose a novel semi-autonomous control for robot-aided pedicle tapping that is able to (i) identify the bone layer transition, (ii) adapt the tool velocity depending on the detected bone layer density and (iii) stop the tool tip before propulsion of the bone boundaries. The proposed semi-autonomous control for pedicle tapping consists of: (i) a hybrid position/force control loop that allows the surgeon to move the surgical tool along a pre-planned axis and (ii) a velocity control loop that allows him/her to finely tune the tool rotational speed by modulating the tool-bone interaction force along the same axis. The velocity control loop integrates also a bone layer transition detection algorithm that dynamically limits the tool velocity depending on the bone layer density. The approach was tested on the Kuka LWR4+ provided with an actuated surgical tapper which was used to tap a wood specimen simulating the bone layer density characteristics and bovine bones. A normalized maximum time delay in the bone layer transition detection of 0.25 was achieved by the experiments. A success rate of [Formula: see text] was achieved for all the tested tool velocities. The proposed control achieved a maximum steady-state error of 0.4 rpm. The study demonstrated high capability of the proposed approach to i) promptly detect transition among the specimen layers and ii) adapt the tool velocities depending on the detected layers.

Undetected hypertension in primary care – a public health iceberg?

Abstract Background Hypertension is largely asymptomatic and contributes to considerable lifetime cardiovascular morbidity and mortality, costing the NHS £2.1 billion annually. The national prevalence of hypertension is 13.7 % and lack of a national screening programme, despite meeting aspects of the Wilson Junger criteria, adds to delays in detection and treatment. Earlier detection could mitigate future cardiovascular risk. We wanted to understand the potential of detection of prehypertension in primary care to see if this fits the Wilson and Junger criteria for a screening programme. Methods GP records of adult patients n = 2178 with a known diagnosis of hypertension on the hypertension register from a practice population of 10,000 patients (prevalence is 22%.) were analysed for the prevalence of prehypertension systolic 120-139 mm hg and diastolic bp of 80-89. The average length of prehypertension, the time delay in detection and treatment were assessed, alongside the prevalence of clinical and therapeutic inertia. Results A retrospective analysis of a sample size of 1809 patients out of 2178 patients (83.1%) with known hypertension across 3 primary care sites over 20 years was undertaken. Of these 1809 patients, we found that 1095 patients (60.5%) were prehypertensive prior to being diagnosed. The mean time interval between detection of prehypertension to a formal hypertension diagnosis was 10.6 years, with a standard deviation of 7.89 years with no variation with age or sex. However, 588 patients (32.5%) did not have readings within the prehypertensive ranges prior to diagnosis and were opportunistically detected. 51 patients (2.82%) never had readings recorded within the prehypertensive range. Conclusions Prehypertension predates hypertension by an average of 10.6 years. Offering annual screening nationally to patients of risk groups e.g., those with a family history, obesity, and alcohol excess, would enable earlier detection, treatment, and considerable cost saving. Key messages • Prehypertension predates hypertension; therefore, hypertension meets the Wilson Junger criteria for earlier detection by a screening programme, which is lacking in the UK. • Offering a national scheme to screen for hypertension to those at a higher risk, can only be considered a benefit to the public and should be implemented.

A model of software fault detection and correction processes considering heterogeneous faults

AbstractNumerous software reliability growth models (SRGMs) are proposed in the last four decades to track the reliability growth of software during the testing phase. In many existing models, it is assumed that the faults in the software are of the same type. However, this assumption may not be realistic. Software contains different faults and the detection/correction of different faults can require different efforts. In this paper, we propose a more general software reliability model considering heterogeneous faults, where the parameters for the detection time and correction delay of different faults are assumed to be faults specific and allowed to follow arbitrary distributions. Considering different parameters makes the model more flexible and adaptable to different practical software development situations. Some specific models are derived under different assumptions on the distributions of fault detection time, fault correction delay, and the corresponding parameters. Both MLE and LSE are proposed for parameter estimation. Illustrative examples with real data set are studied to compare the proposed models with homogeneous faults model, and the results have shown advantages of our models. The optimal software release policy based on the proposed model is also studied.

(Invited) Conformations of Exciton Pairs Associated with Spin-Entanglement Transports during Singlet Fissions

Singlet fission (SF) is expected to exceed the Shockley–Queisser theoretical limit of efficiency of organic solar cells. Spin-entanglement in the triplet pair state via one singlet exciton is a promising phenomenon for several energy conversion applications including quantum information science. However, direct observation of the electron spin polarization by transports of entangled spin-states has not been demonstrated. Furthermore, vibronic mechanisms of the dissociation of the triplet excitons are poorly understood. In this study, time-resolved electron paramagnetic resonance has been utilized to observe the transportations of the singlet and quintet characters generating spin-correlated correlated triplet pair (SCTP) by probing the electron spin polarization (ESP) generated in thin films of 6,13-bis(triisopropylsilylethynyl)pentacene.[1] We have clearly demonstrated that the ESP detected in resonance field positions of the individual triplet excitons are dependent on morphology and on detection delay time after laser flash to cause SF. The ESPs were clearly explained by quantum superposition[1,2,3] of singlet-triplet-quintet wavefunctions via picosecond triplet-exciton dissociation as the electron spin polarization transfer from strongly exchange-coupled singlet TT states to the weakly-coupled SCTP via spin-spin dipolar couplings with preserving conformations of the excitons. Although the coherent superposition of the spin eigenstates was not directly detected, the present interpretation of the spin correlation of the separated T+T exciton pair may pave new avenues not only for elucidating the vibronic role on the de-coupling[2,3] between the two excitons but also for scalable quantum information processing using quick T+T dissociation via one-photon excitation. References Matsuda, S.; Oyama, S.; Kobori, Y. Sci. 2020, 11, 2934-2942.Kobori, Y.; Fuki, M.; Nakamura, S.; Hasobe, T. Phys. Chem. B 2020, 124, 9411-9419.Nakamura, S.; Sakai, H.; Nagashima, H.; Fuki, M.; Onishi, K.; Khan, R.; Kobori, Y.; Tkachenko, N. V.; Hasobe, T. Phys. Chem. C 2021, 125, 18287-18296.

Node Replication Attack Detection in Distributed Wireless Sensor Networks

Wireless sensor network (WSN) is an emerging technology used in emergency scenarios. There are a number of possible threats to WSNs because they use unsupervised IP addresses. Securing networks with unattended sensors is a real challenge nowadays. Sensor nodes lack power and storage, making them incompatible with normal security checks. It will be vital to make advancements in sensor network architecture and protocol design. There will be more vulnerability to attack if there is a lack of security. Especially, one key attack is node replication which induces the sensor node to acts as an original node, collecting data from the network and sending it to the attacker. In dynamic WSN, detecting an assault is difficult to find replica nodes. Therefore, this paper proposes a Strategic Security System (SSS) to discover replica nodes in static and dynamic distributed WSNs. It is mainly focused on enhancing detection accuracy, time delay, and communication overhead. The present system includes Single Stage Memory Random Walk with Network Division (SSRWND) and a Random-walk-based approach to detect clone attacks (RAWL). The proposed system has less memory and better detection accuracy.

Partial engine fault detection and control of a Quadrotor considering model uncertainty

This paper presents a trajectory tracking fault-tolerant control strategy inside an autonomous emergency landing architecture to control a quadrotor in case of partial rotor fault. The proposed architecture, which is composed of required hardware and subsystems, aims to ensure a fully autonomous safe landing of the impaired quadrotor to a suitable landing site. The controller strategy, which is tried to be coincident with the proposed emergency landing architecture and the Pixhawk autopilot contains a cascade three-loop structure of adaptive sliding mode and a modified PID algorithm along with a fault detection algorithm. The adaptive sliding mode and the PID algorithms are applied to the fast dynamics of angular velocity rates and the position control of the quadrotor, respectively. A lightweight fault detection algorithm is developed to detect and identify the partial faults of engine using the controller outputs and the filtered angular rates. The simulation results demonstrate that the proposed fault-tolerant controller can control the multi-rotor in partial engine faults with satisfactory tracking performance. The results also demonstrate the effect of fault detection time delay on the overall control performance.