Threshold Update Research Articles

Anomaly Detection Algorithm for Urban Infrastructure Construction Equipment based on Multidimensional Time Series

Safety is the foundation of urban sustainable development. The urban construction and operation process involves a large amount of multidimensional time series data. By detecting anomalies in these multidimensional time subsequences (MTSs), decision support can be provided for early warning of urban construction and operation risks. Considering the complexity of urban infrastructure, there is an urgent need for fast and accurate anomaly detection. This paper proposes a real-time anomaly detection algorithm based on improved distance measurement (RADIM). RADIM retains the relationships between dimensions in multidimensional subsequences, using an Extended Frobenius Norm with Local Weights (EFN_lw) and a Euclidean distance based on multidimensional data (ED_mv) to measure the similarity of MTSs. Moreover, a threshold update mechanism based on First-order Mean Difference (TMFD) is designed to detect real-time anomalies by assessing deviations. This method has been applied to tunnel construction. According to comparative experiments, RADIM exhibits better adaptability, real-time performance, and accuracy in risk warning of tunnel boring machines and construction status.

Remaining useful life prediction of rolling bearings based on performance evaluation and multifractional generalized Cauchy model with adaptive drift

The proposed Remaining Useful Life (RUL) prediction framework utilizes several advanced techniques to accurately estimate the remaining service life of rolling bearings. The framework includes early failure assessment, adaptive failure threshold (FT) determination, and a multifractional generalized Cauchy model (MfGC). The early failure assessment is enabled by establishing early FTs and health indicator (HI) curves generated by the Mahalanobis distance cumulative sum (MD-CUSUM) technique. The proposed dynamic fault threshold update method uses the BOX-COX transformation and Chebyshev inequality to determine confidence intervals for evaluating the fault threshold time. The multifractional nature of the MfGC process is characterized by independent, time-varying Hurst indices and fractional dimensions, and the long-range dependence (LRD) characteristics and stochasticity of the process are explained by the diffusion terms generated from the MfGC differential time series. The MfGC model with adaptive drift is constructed for various degenerate trajectories, and a method for estimating the model’s parameters is proposed. The effectiveness of the proposed RUL prediction method is demonstrated using the XJTU-SY bearing dataset.

PDTM: A Provenance-Driven dynamic Trust Management model for IoVs

Internet of Vehicles (IoVs) play an essential role in traffic safety and travel efficiency. However, there is still risk where malicious vehicles without validation or evaluation, to tamper with messages and pollute network with compromised information. Upon fruitful computing resource in 6G, this paper proposes a Provenance-Driven dynamic Trust Management (PDTM) model for IoVs. PDTM integrates data provenance into the trust evaluation of vehicles and messages. Different from literature that only evaluates trust through vehicle, the high reliability of Road Side Unit (RSU) is concerned herein. Specifically, the recommendation trust is calculated by RSU, to eliminate false recommendations of malicious vehicles. Considering the dynamic and real-time nature of trust, this paper proposes a global-based threshold update mechanism. The mechanism can dynamically adjust the threshold, according to the trust value of vehicles. Simulation experiments verify the validity of PDTM in the detection rate of malicious vehicles and delivery rate of authentic messages.

Visualization investigation on stability of shield tunnel face with transparent soil, considering different longitudinal inclination angles

Due to design optimization and geological conditions, shield tunnels often work with different longitudinal inclination angles. In this case, it is very important to understand and master the failure mode of the tunnel face. In this study, an adaptive image update method is introduced to optimize the threshold update method in PIV processing and improve the continuity and smoothness of post-processing images. The failure mechanism of tunnel face in clay stratum under different longitudinal inclination angles δ and the ratio of buried depth to diameter C/D is studied. The results show that the failure shape of the shield tunnel face shows an ‘ellipsoid’ mode under different longitudinal inclination angles δ and C/D conditions. The deformation process of cohesive soil has different development stages, and the failure modes in different areas vary. The deformation zone is mainly divided into subsidence areas and uplift areas. With the increase of the longitudinal inclination angles, from δ = −20°, 0° to 20°, the instability area, the maximum displacement, and the vertical pressure all changes, and the 3D reconstruction or numerical simulation also obtains the same failure mode. Compared with other cases, the tunnel has a higher collapse risk with shallow buried and upward inclination angles. Long-term upward inclined excavation in clay stratum should be avoided in engineering practices.

An Automatic Segmentation Method for Lung Tumor Based on Improved Region Growing Algorithm.

In medical image processing, accurate segmentation of lung tumors is very important. Computer-aided accurate segmentation can effectively assist doctors in surgery planning and treatment decisions. Although the accurate segmentation results of lung tumors can provide a reliable basis for clinical treatment, the key to obtaining accurate segmentation results is how to improve the segmentation performance of the algorithm. We propose an automatic segmentation method for lung tumors based on an improved region growing algorithm, which uses the prior information on lung tumors to achieve an automatic selection of the initial seed point. The proposed method includes a seed point expansion mechanism and an automatic threshold update mechanism and takes the combination of multiple segmentation results as the final segmentation result. In the lung image database consortium (LIDC-IDRI) dataset, we designed 10 experiments to test the proposed method and compare it with 4 popular segmentation methods. The experimental results show that the average dice coefficient obtained by the proposed method is 0.936 ± 0.027, and the average Jaccard distance is 0.114 ± 0.049. The average dice coefficient obtained by the proposed method is 0.107, 0.053, 0.040, and 0.156, higher than that of the other four methods, respectively. This study proves that the proposed method can automatically segment lung tumors in CT slices and has suitable segmentation performance.

Dual-Condition event-Triggered distributed adaptive neural control for multi-Agent systems with nonlinear fault

This paper studies the cooperative adaptive dual-condition event-triggered tracking control problem for the uncertain nonlinear nonstrict feedback multi-agent systems with nonlinear faults and unknown disturbances. Under the framework of backstepping technology, a new threshold update method is designed for the state event-triggered mechanism. At the same time, we develop a novel distributed dual-condition event-triggered strategy that combined the fixed threshold triggered mechanism acted on the controller with the new event-triggered mechanism, which can better reduce the waste of communication bandwidth. To deal with the algebraic loop problem caused by the non-affine nonlinear fault, the Butterworth low-pass filter is introduced. At the same time, the unknown function problems are solved by the neural network technology. All signals of the system are semiglobally uniformly ultimately bounded and the tracking performance is achieved, which proved by the Lyapunov stability theorem. Finally, the results of the simulation test the efficiency of the proposed control scheme.

An Online Entropy-Based DDoS Flooding Attack Detection System With Dynamic Threshold

Distributed denial of service attacks are cyber-attacks that target the availability of servers. As a result, legitimate users no longer have access to the service. This can have a negative impact on an organization, such as lack of reputation and economic losses. Therefore, it is important to design defense mechanisms against these attacks. There are systems for detecting distributed denial of service attacks in the literature, which still have various shortcomings. Some of these systems detect the presence of attack traffic without identifying the attack packets or flows. Others use static thresholds and therefore cannot adapt to changes in legitimate traffic. In this paper, we propose an online system that aims to detect flooding attacks in a short timeframe and a client–server environment. The proposed detection system consists of five modules, namely features extraction and connections construction, suspicious activity detection, attack connections detection, alert generation and threshold update. The suspicious activity detection module calculates the normalized Shannon entropy by considering the source Internet Protocol address as a random variable. Suspicious activity is detected when the computed entropy is below a threshold. The threshold calculation is based on Chebyshev’s theorem. We propose a dynamic threshold algorithm to track changes in legitimate traffic. We evaluate the proposed system through simulations and using a publicly available dataset. Compared to other similar works, the proposed detection system has a better performance in terms of detection rate, false positive rate, precision and overall accuracy.

Event-based consensus control for multi-agent systems against joint sensor and actuator attacks

The paper focuses on the observer-based consensus control issue for a class of discrete-time multi-agent systems suffering from the joint actuator and sensor attacks. An event-triggered communication rule with the adaptive threshold update is introduced to reduce the communication burden. A PI-type controller with a finite-time window integral loop is constructed to achieve bounded consensus in the mean-square sense (BCMS). With the help of common properties of Laplace matrices, the closed-loop multi-agent system is converted into an easy-to-analyze pattern. In light of such a pattern, a sufficient condition is derived to realize the desired consensus by the stochastic analysis. Furthermore, the expected gains of the controller and the observer are determined by resorting to matrix inequalities in combination with the cone complementarity linearization algorithm. Finally, a numerical example and a simulation of the platooning vehicle are proposed to illustrate the effectiveness of the proposed control scheme.

Improved ViBe algorithm based on adaptive threshold and dynamic update factor

Improved ViBe algorithm based on adaptive threshold and dynamic update factor

A Buffer Management Algorithm Based on Dynamic Marking Threshold to Restrain MicroBurst in Data Center Network

The data center has become the infrastructure of most Internet services, and its network carries different types of business flow, such as query, data backup, control information, etc. At the same time, the throughput-sensitive large flows occupy a lot of bandwidth, resulting in the small flow’s longer completion time, finally affecting the performance of the applications. Recent proposals consider only dynamically adjusting the ECN threshold or reversing the ECN packet priority. This paper combines these two improvements and presents the HDCQ method for coordinating data center queuing, separating large and small flows, and scheduling in order to ensure flow completion time. It uses the ECN mechanism to design load-adaptive marking threshold update algorithms for small flows to prevent micro-bursts from occurring. At the same time, packets marked with ECN or ACK are raised in priority, prompting these packets to be fed back to the sender as soon as possible, effectively reducing the TCP control loop delay. Extensive experimental analysis on the network simulator (NS-2) shows that the HDCQ algorithm has better performance in the face of micro-burst traffic, reducing the average flow completion time by up to 24% compared with the PIAS.

An Efficient Method for Mining Rare Association Rules: A Case Study on Air Pollution

Most pattern mining techniques almost singularly focus on identifying frequent patterns and very less attention has been paid to the generation of rare patterns. However, in several domains, recognizing less frequent but strongly related patterns have greater advantage over the former ones. Identification of compelling and meaningful rare associations among such patterns may proved to be significant for air quality management that has become an indispensable task in today’s world. The rare correlations between air pollutants and other parameters may aid in restricting the air pollution to a manageable level. To this end, efficient and competent rare pattern mining techniques are needed that can generate the complete set of rare patterns, further identifying significant rare association rules among them. Moreover, a notable issue with databases is their continuous update over time due to the addition of new records. The users requirement or behavior may change with the incremental update of databases that makes it difficult to determine a suitable support threshold for the extraction of interesting rare association rules. This paper, presents an efficient rare pattern mining technique to capture the complete set of rare patterns from a real environmental dataset. The proposed approach does not restart the entire mining process upon threshold update and generates the complete set of rare association rules in a single database scan. It can effectively perform incremental mining and also provides flexibility to the user to regulate the value of support threshold for generating the rare patterns. Significant rare association rules representing correlations between air pollutants and other environmental parameters are further extracted from the generated rare patterns to identify the substantial causes of air pollution. Performance analysis shows that the proposed method is more efficient than existing rare pattern mining approaches in providing significant directions to the domain experts for air pollution monitoring.

A 1.2nW Analog Electrocardiogram Processor Achieving a 99.63% QRS Complex Detection Sensitivity.

An energy-efficient electrocardiogram (ECG) processor for real-time QRS detection is presented. The proposed algorithm is based on the Pan-Tompkins algorithm and it is implemented in the analog domain leveraging ultra-low power analog electronics biased in subthreshold. Operational transconductance amplifiers with ∼100 mV linear range are used in almost all of the processing blocks, while squaring is performed on current signals. Additionally, instead of adaptive thresholding, a fixed-level thresholding is performed, thereby eliminating the need for additional blocks such as memory and threshold update. The processor is designed in 65 nm TSMC CMOS technology and has a footprint of 0.078 mm2. When supplied by a 1 V supply, the processor consumes 1.2 nW. Using the recordings in the MIT-BIH database, the processor achieves an average QRS detection sensitivity of 99.63% and positive predictivity of 99.47%.

A 760-nW, 180-nm CMOS Fully Analog Voice Activity Detection System for Domestic Environment

This article presents a fully analog, signal-to-noise-ratio-based voice-activity detection (VAD) circuit. The circuit is based on an energy-efficient analog implementation with continuous-time nonlinear operation and fully passive switched-capacitor signal processing. The overall chain is composed of a programmable-gain amplifier, a squarer, an integrator, an SC-based signal averaging circuit, and a periodic threshold update circuit for adaptability. This implementation allows the minimization of both power consumption and chip area. The VAD circuit prototype has been fabricated in a 180-nm CMOS technology and occupies an area of 0.14 mm2. The device achieves 99.5% classification accuracy in a domestic environment in the presence of loud ambient noise, consuming 760 nW from a 1.2-V supply.

Measurement Optimization for Orientation Tracking Based on No Motion No Integration Technique

The main goal of this article is to fully explore the capability of the “No Motion No Integration” (NMNI) technique for the optimization of the Euler angles in orientation tracking. The gyroscope is the critical component in the inertial measurement unit for angle detection in Industry 4.0. An upgraded NMNI model is introduced to remove the drift of the gyroscope significantly for the advanced measurement approach for roll, pitch, and yaw. A model of threshold update is implemented into the NMNI algorithm to compensate for the increased offset of temperature. This preprocessing method is applied to the Madgwick filter and Mahony filter to acquire the optimal performance. The experiments were carried out by using a low-cost platform equipped with microelectromechanical system sensors. A pan-tilt unit with high accurate positioning was used to move the sensors and obtain a reference angle during both static and dynamic experiments. A substantial improvement was clearly demonstrated after the optimization process. The measurements of the Euler angles have minimized noise and tracks around the reference points properly. The results show strong competition from both fusion filters where the fused Mahony accomplishes more stable less variation in roll and pitch, but the fused Madgwick shows more precision in heading/yaw estimation.

Classroom education effect evaluation model based on MFO intelligent optimization algorithm

In order to improve the evaluation effect of classroom education, this paper proposes the MFO intelligent optimization algorithm based on the idea of machine learning, and builds the classroom education effect evaluation model based on the MFO intelligent optimization algorithm. Moreover, this paper uses a logarithmic spiral to simulate the path of the moth to the flame and invert the pending parameters in the mathematical model, and adds vertical and horizontal algorithms and chaos operators on this basis. The crisscross algorithm allows different moth individuals and the same moth to perform cross calculations with different computing dimensions to increase the diversity of moth populations, so that moths in the search space can traverse the entire search space as much as possible to find a better solution. Moreover, in view of the problems of BP neural network such as low fitting accuracy, this paper applies the CCMFO algorithm to improve the BP neural network to form the CCMFO-BP algorithm, and improves the weight and threshold update process of the BP neural network to make the network operation more efficient and accurate. Finally, this paper designs experiments to analyze the performance of the model constructed in this paper. The research results show that the model constructed in this paper meets the expected requirements.

CODES: Efficient Incremental Semi-Supervised Classification Over Drifting and Evolving Social Streams

Classification over data streams is a crucial task of explosive social stream mining and computing. Efficient learning techniques provide high-quality services in the aspect of content distribution and event browsing. Due to the concept drift and concept evolution in data streams, the classification performance degrades drastically over time. Many existing methods utilize supervised and unsupervised learning strategies. However, supervised strategies require labeled emerging records to update the classifiers, which is unfeasible to work in the practical social stream applications. Although unsupervised strategies are popularly applied to detect concept evolution, it takes tremendous run-time computation cost to run online clustering. To this end, in this paper, we address these major challenges of social stream classification by proposing an efficient incremental semi-supervised classification method named CODES (Classification Over Drifting and Evolving Stream). The proposed CODES method consists of an efficient incremental semi-supervised learning module and a dynamic novelty threshold update module. Thus, in the drifting and evolving social streams, CODES is able to provide: 1) semi-supervised learning ability to eliminate dependency on the labels of emerging records; 2) fast incremental learning with real-time update ability to tackle concept drift; 3) efficient novel class detection ability to tackle concept evolution. Extensive experiments are conducted on several real-world datasets. The results indicate a higher performance than several state-of-the-art methods. CODES achieves efficient learning performance over drifting and evolving social streams, which improves practical significance in the real-world social stream applications.

Cylinder Pressure Based Method for In-Cycle Pilot Misfire Detection

<div class="section abstract"><div class="htmlview paragraph">For the reduction of emissions and combustion noise in an internal combustion diesel engine, multiple injections are normally used. A pilot injection reduces the ignition delay of the main injection and hence the combustion noise. However, normal variations of the operating conditions, component tolerances, and aging may result in the lack of combustion i.e. pilot misfire. The result is a lower indicated thermal efficiency, higher emissions, and louder combustion noise. Closed-loop combustion control techniques aim to monitor in real-time these variations and act accordingly to counteract their effect. To ensure the in-cycle controllability of the main injection, the misfire diagnosis must be performed before the start of the main injection. This paper focuses on the development and evaluation of in-cycle algorithms for the pilot misfire detection.</div><div class="htmlview paragraph">Based on in-cylinder pressure measurements, different approaches to the design of the detectors are compared. For non-adaptive methods, a constant threshold, direct misfire probability, and posterior misfire probability detectors are investigated. For adaptive methods, an adaptive threshold update is suggested, an adaptation of the predictive stochastic models and a sensor fusion of them is proposed to increase the detection performance.</div><div class="htmlview paragraph">A Scania D13 engine is used to perform the experiments under different operating conditions. The effectiveness of the algorithms is tested for different engine speeds, rail pressures, injection durations, starts of injection, EGR levels, and fuels. The results show that the observability of in-cycle pilot misfire depends on the operating conditions, and its detection can be performed successfully before the start of the main injection. With a maximum in-cycle pilot misfire observability of 98.5%, a maximum successful detection ratio of about 96% can be reached with the proposed in-cycle pilot misfire detectors. The algorithms are therefore suitable for in-cycle closed-loop combustion control feedback. By including cycle-to-cycle adaptation, the detection performance and robustness are improved significantly. The limitations are directly related to the signal-to-noise ratio of each operating condition.</div></div>

Efficient DDoS attacks mitigation for stateful forwarding in Internet of Things

Stateful forwarding plane is fully considered as a novel forwarding paradigm, which is proven to be beneficial to delivery efficiency and resilient to certain types of attacks. However, this fresh attempt also introduces “varietal” Denial-of-Service attack due to complicated forwarding state operations, which may cause long-term memory exhaustion of forwarding nodes, especially for resource-limited IoT nodes. This new distributed exhaustion attack is extremely hidden and there is currently no effective defense against it. In this paper, we first establish a game model to analyze the attack benefit between attacker and defender. To further make the defender obtain more utility, it is significative to make the defender manage expired state-entries during stateful forwarding. To this end, we propose an enhanced distributed low-rate attack mitigating (eDLAM) mechanism. Particularly, eDLAM maintains a lightweight malicious request table (MRT), which is very small, to offload burden of practical forwarding state table. When a packet request is matched in MRT, it will be marked and dropped directly without any impact on forwarding state table. Based on this, eDLAM adopts an optimal threshold update method for MRT to achieve a maximum defender utility. We evaluate the eDLAM performance in terms of false negatives rate (FNR) and false positives rate (FPR). Extensive experimental results show that eDLAM can reduce FNR by 10.5% and FPR by 44% on average compared with state-of-the-art mechanisms.

Optimal Threshold Design for Quanta Image Sensor

Quanta Image Sensor (QIS) is a binary imaging device envisioned to be the next generation image sensor after CCD and CMOS. Equipped with a massive number of single photon detectors, the sensor has a threshold $q$ above which the number of arriving photons will trigger a binary response "1", or "0" otherwise. Existing methods in the device literature typically assume that $q=1$ uniformly. We argue that a spatially varying threshold can significantly improve the signal-to-noise ratio of the reconstructed image. In this paper, we present an optimal threshold design framework. We make two contributions. First, we derive a set of oracle results to theoretically inform the maximally achievable performance. We show that the oracle threshold should match exactly with the underlying pixel intensity. Second, we show that around the oracle threshold there exists a set of thresholds that give asymptotically unbiased reconstructions. The asymptotic unbiasedness has a phase transition behavior which allows us to develop a practical threshold update scheme using a bisection method. Experimentally, the new threshold design method achieves better rate of convergence than existing methods.

Designing Low-Complexity Heavy-Traffic Delay-Optimal Load Balancing Schemes

In this paper, we establish a unified analytical framework for designing load balancing algorithms that can simultaneously achieve low latency, low complexity, and low communication overhead. We first propose a general class \Pi of load balancing policies and prove that they are throughput optimal and heavy-traffic delay optimal. This class \Pi includes popular policies such as join-shortest-queue (JSQ) and power-of- d as special cases, but not the recently proposed join-idle-queue (JIQ) policy. In fact, we show that JIQ is not heavy-traffic delay optimal even for homogeneous servers. By exploiting the flexibility offered by the class \Pi , we design a new load balancing policy called join-below-threshold (JBT-d), in which the arrival jobs are preferably assigned to queues that are no greater than a threshold, and the threshold is updated infrequently. JBT-d has several benefits: (i) JBT-d belongs to the class \Pi and hence is throughput optimal and heavy-traffic delay optimal. (ii) JBT-d has zero dispatching delay, like JIQ and other pull-based policies, and low message overhead due to infrequent threshold update. (iii) Extensive simulations show that JBT-d has excellent delay performance, comparable to the JSQ policy in various system settings.