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In the current industry world, the industrial robot has emerged as a critical device to make the manufacturing process more efficient through automation. However, abnormal operation of industrial robots caused by sensor failures may interrupt the entire manufacturing process, thereby increasing production costs. In this paper, we first propose a domain-specific framework consisting of offline training and online inference to effectively detect anomalies in the scenario of industrial robotic sensors. In a nutshell, the framework can be identified in three folds: (1) the offline training obtaining historical data from the database to train the time series and anomaly detection models; (2) the online inference deploying the offline trained models for online anomaly detection in real-time; (3) the incremental learning updating the online model for a new type of anomalies. We then propose an improved Generative Adversarial Networks (GANs) named MSGAN with the adaptive update strategy mechanism based on WGAN-GP to generate fake anomaly samples, improving anomaly detection accuracy. Specifically, the Wasserstein distance with the gradient penalty is introduced to improve training stability and sample quality. Moreover, adapting the generator’s complexity in robotic sensors, an adaptive update strategy based on the loss change ratio is adopted to speed up training convergence. Extensive experiments based on real robotic sensor datasets demonstrate the effectiveness of the proposed framework. Moreover, the results demonstrate that the detection accuracy can be improved by the synthetic samples based on the proposed MSGAN algorithm.

Graphics Processing Units (GPUs) are now a key component of many devices and systems, including those in the cloud and data centers, thus are also subject to side-channel attacks. Existing side-channel attacks on GPUs typically leak information from graphics libraries like OpenGL and CUDA, which require creating contentions within the GPU resource space and are being mitigated with software patches. This paper evaluates potential side channels exposed at a lower-level interface between GPUs and CPUs, namely the graphics interrupts. These signals could indicate unique signatures of GPU workload, allowing a spy process to infer the behavior of other processes. We demonstrate the practicality and generality of such side-channel exploitation with a variety of assumed attack scenarios. Simulations on both Nvidia and Intel graphics adapters showed that our attack could achieve high accuracy, while in-depth studies were also presented to explore the low-level rationale behind such effectiveness. On top of that, we further propose a practical mitigation scheme which protects GPU workloads against the graphics-interrupt-based side-channel attack by piggybacking mask payloads on them to generate interfering graphics interrupt ``noises’'. Experiments show that our mitigation technique effectively prohibited spy processes from inferring user behaviors via analyzing runtime patterns of graphics interrupt with only trivial overhead.

Accurate prediction of wax deposition is of vital interest in digitalized systems to avoid many issues that interrupt the flow assurance during production of hydrocarbon fluids. The present investigation aims at establishing rigorous intelligent schemes for predicting wax deposition under extensive production conditions. To do so, multilayer perceptron (MLP) optimized with Levenberg-Marquardt algorithm (MLP-LMA) and Bayesian Regularization algorithm (MLP-BR) were taught using 88 experimental measurements. These latter were described by some independent variables, namely temperature (in K), specific gravity, and compositions of C1–C3, C4–C7, C8–C15, C16–C22, C23–C29 and C30+.The obtained results showed that MLP-LMA achieved the best performance with an overall root mean square error of 0.2198 and a coefficient of determination (R2) of 0.9971. The performance comparison revealed that MLP-LMA outperforms the prior approaches in the literature.

Hypervisor intervention in the virtual I/O event path is a main performance bottleneck for I/O virtualization because of the incurred costly VM exits. The shortcomings of prior software solutions against virtual interrupt delivery, a major source of VM exits, promoted the emergence of the hardware-based Posted-Interrupt (PI) technology. PI can provide non-exit interrupt delivery without compromising any virtualization benefit. However, it only acts on the half of the event path, i.e., the interrupt path, while guests' I/O requests may also trigger a large amount of VM exits. Additionally, PI may still suffer a severe latency from the vCPU scheduling while delivering interrupts. Aiming at an optimal event path, we propose ES2 to simultaneously improve bidirectional I/O event delivery between guests and their devices. On the basis of PI, ES2 introduces hybrid I/O handling scheme for efficient I/O request delivery and intelligent interrupt redirection for enhanced I/O responsiveness. It does not require any modification to guest OS. We demonstrate that ES2 greatly reduces I/O-related VM exits with the exit handling time (EHT) below 2.5% for TCP streams and 0.1% for UDP streams, increases guest throughput by 1.9× for Memcached and 1.6× for Nginx, and keeps guest latency at a low level.

In this paper, we study learning-assisted multi-user scheduling for the wireless downlink. There have been many scheduling algorithms developed that optimize for a plethora of performance metrics; however a systematic approach across diverse performance metrics and deployment scenarios is still lacking. We address this by developing a meta-scheduler-given a diverse collection of schedulers, we develop a learning-based overlay algorithm (meta-scheduler) that selects that “best” scheduler from amongst these for each deployment scenario. More formally, we develop a multi-armed bandit (MAB) framework for meta-scheduling that assigns and adapts a score for each scheduler to maximize reward (e.g., mean delay, timely throughput etc.). The meta-scheduler is based on a variant of the Upper Confidence Bound algorithm (UCB), but adapted to interrupt the queuing dynamics at the base-station so as to filter out schedulers that might render the system unstable. We show that the algorithm has a poly-logarithmic regret in the expected reward with respect to a genie that chooses the optimal scheduler for each scenario. Finally through simulation, we show that the meta-scheduler learns the choice of the scheduler to best adapt to the deployment scenario (e.g. load conditions, performance metrics).

Interrupt-driven programs are widely deployed in safety-critical embedded systems to perform hardware and resource dependent data operation tasks. The frequent use of interrupts in these systems can cause race conditions to occur due to interactions between application tasks and interrupt handlers (or two interrupt handlers). Numerous program analysis and testing techniques have been proposed to detect races in multithreaded programs. Little work, however, has addressed race condition problems related to hardware interrupts. In this paper, we present SDRacer, an automated framework that can detect, validate and repair race conditions in interrupt-driven embedded software. It uses a combination of static analysis and symbolic execution to generate input data for exercising the potential races. It then employs virtual platforms to dynamically validate these races by forcing the interrupts to occur at the potential racing points. Finally, it provides repair candidates to eliminate the detected races. We evaluate SDRacer on nine real-world embedded programs written in C language. The results show that SDRacer can precisely detect and successfully fix race conditions.

Solid-state dc circuit breaker (SSCB) plays a very important role in the dc system protection against fast rising overload and short-circuit faults. Among various SSCB topologies, z-source dc circuit breakers (ZSCBs) have a great ability to interrupt the fault at a much faster rate, without even using a fault sensing circuit. However, ZSCBs suffer from various issues such as: 1) high starting current in the main thyristor (SCR), 2) unwanted power flow in the load during commissioning and reclosing of ZSCBs, and 3) negative current flow through the load at starting/reclosing of the ZSCB. These problems arise due to the fact that the z-source series capacitor stays charged even after the fault is interrupted or cleared. Unwanted power flow, negative current circulation in the load needs special attention especially in unidirectional loads and also in electromechanical systems. Hence, this article proposes a modified ZSCB to address the aforementioned problems. The proposed topology employs power semiconductor devices such as IGBTs to discharge the capacitor before the next interruption cycle. Initially, a detailed analysis, with simulation and experimental validation showing the issues with the existing ZSCB is presented. Later, a detailed analysis of the proposed modified ZSCB is presented, followed by an extensive simulation and experimental validation on a laboratory scale 120-V/3.5-A (400 W) prototype.

This chapter introduces the concept of an interrupt (IRQ) as a way to efficiently deal with asynchronous external events that the CPU must handle [1–3]. The details of the MSP430 interrupt system are then presented and an example of using the port interrupts on the LaunchPad™ board is examined.

RISC-V ISA is a popular open and royalty-free ISA that enables the development of low-cost Systems-on-Chip (SoCs). Ballast is a novel multicore RISC-V-based SoC developed by SoC Hub in collaboration with different companies for Internet of Things (IoT) applications. It includes multiple subsystems that feature three RISC-V cores. Interrupts allow processor power saving and fast interaction with external pe- ripherals. Having proper software support for interrupts enables easy and accurate configuration. Additionally, safety is a concern, and having a language that guar- antees safe operation, such as Rust, will minimize catastrophic bugs. This thesis outlines the process of implementing interrupt software support in the Ballast SoC using the Rust programming language, as well as test techniques for ensuring proper interrupts operation. Software support was implemented as part of Hardware Abstraction Layer (HAL), and two use cases were developed to test the implemented software and validate interrupts on the Ballast SoC development board. Interrupts were successfully brought up in Ballast SoC and were proven to func- tion correctly using the test methodologies introduced in this thesis. Moreover, the developed software support can easily be utilized on other SoCs that use similar cores (such as the new SoC Hub chip Headsail) by including it as a dependency crate using Cargo tool.

In logic locking, the finite-state machine (FSM) embedded in a sequential circuit is often chosen to be obfuscated. Such an obfuscation scheme using a class of nongroup additive cellular automata (CA) called $D1 * CA$ and $D1 * CA_{\mathrm{ dual}}$ to obfuscate each state transition of an FSM has been proposed previously. Since $D1 * CA$ and $D1 * CA_{\mathrm{ dual}}$ provide high testability even in the absence of scan-based design-for-testability techniques, they conceal the sequential elements, thus thwarting several existing scan-chain attacks. In this article, we introduce a novel attack to extract the secret key used to obfuscate each state transition of the FSM, by utilizing the information leaked by the leftmost CA cell, which is obtained via an oracle query to the obfuscated circuit. The proposed attack has two variants: 1) when the combinational circuit of the Interrupt Logic is not logic encrypted and 2) when the Interrupt Logic is logic encrypted with a $k$ -bit key. The first attack variant has a complexity of $\mathcal {O}(n \cdot m)$ , where $n$ denotes the number of transitions in the FSM, and $m$ denotes the maximum transition cycle length of the underlying $D1 * CA$ . The second attack variant has a complexity of $\mathcal {O}(n\cdot q_{\mathrm{ max}})$ , where $q_{\mathrm{ max}}$ denotes the maximum number of oracle queries (equal to the number of primary inputs involved in that state transition), followed by a SAT-attack to extract the $k$ -bit key of the corresponding Interrupt Logic . The experimental evaluation of the attack on CA-based obfuscated benchmark circuits establishes the effectiveness of our proposed attack.

There is an increasing tendency to integrate several High-temperature superconducting equipment into a network. Along with it is the increasing demand for cryogenic power switches that could work in the liquid nitrogen (LN2) to reduce temperature transfer areas. However, none of the current AC arc extinguishing mediums could work reliably in the LN2. As the insulation and cooling medium of the HTS equipment, LN2 can be a potential arc extinguishing medium due to its strong cooling effect. However, the AC interruption performance of LN2 is unknown up to now. The objective of this paper is to obtain the AC interruption performance of the LN2. A free-burning type and a magnetic blowout type LN2 switch are tested, respectively. Current ranging from 1 kA to 6.3 kA is conducted. Heat loss of the arcing process is also calculated. Experimental results show that a 2700 A AC is successfully interrupted by the free burning type switch at a 10 mm gap. The increase of the contact gap leads to slight growth in current interruption ability. Meanwhile, the magnetic blowout type can interrupt an AC higher than 6300 A. Based on the high interrupting properties of LN2, a potential application in novel environmentally friendly switchgear is proposed. The LN2 switch in the switchgear can switch the closed-loop current that could be typically more than 50% of the continuous current. Meanwhile, the heat loss generated during the arcing process will not add an extra burden to the cooling system of the switchgear.

A quick elimination of short-circuit faults in a short time can significantly promote the transient stability of an AC power grid. This paper proposes controlled fast vacuum breaking (CFVB) of a short-circuit current within a short-arcing time. The CFVB technology performs the following: determining a lower-limit opening speed of 3.5 m/s for a 40.5 kV fast vacuum circuit breaker (FVCB), quickly predicting the fault current-zero within 4.0 ± 0.4 ms to command the FVCB to open at a selected current phase-angle, and optimizing an electromagnetic repulsion actuator with an opening time of 2.5 ± 0.05 ms to achieve an accurate opening operation. The CFVB technology was deployed in a prototype of single-phase multiple-break 363 kV FVCB to validate its feasibility. The validation involved test duties of T100s(b) and T100a following standards IEC 62271-100 2017 and GB/T 1984–2014. Test results demonstrated that by adopting the CFVB technology, the 363 kV FVCB can successfully interrupt a fault current of 80 kA rms within the first half-wave fault current with a short-arcing time of 2.5 ms. Further application of the CFVB technology could help promote the transient stability of power distribution and transmission systems.

Perceptual gaps can be caused by objects in the foreground temporarily occluding objects in the background or by eyeblinks, which briefly but frequently interrupt visual information. Resolving visual motion across perceptual gaps is particularly challenging, as object position changes during the gap. We examine how visual motion is maintained and updated through externally driven (occlusion) and internally driven (eyeblinks) perceptual gaps. Focusing on both phenomenology and potential mechanisms such as suppression, extrapolation, and integration, we present a framework for how perceptual gaps are resolved over space and time. We finish by highlighting critical questions and directions for future work.

In this paper, we studied resource allocation in a dynamic multiplexing scenario of two 5G services, namely, enhanced mobile broadband (eMBB) and ultrareliable low-latency communication (URLLC). Considering that eMBB services focus on high data rates while URLLC is very strict in terms of latency and reliability, we formulate a joint eMBB/URLLC resource allocation problem. To solve the formulated problem, in particular, we propose a joint resource allocation algorithm that can satisfy both eMBB user rate and URLLC interrupt probability requirements. We propose an RB allocation scheme that takes into account the needs of eMBB and URLLC users. The program consists of two phases as follows: (1) establish an RB allocation for eMBB users and (2) assign the minislot for URLLC users and calculate the transmission power of URLLC users that ensures the reliability constraint. We also realize and verify the algorithm through the simulation.

This article discusses one of the ways to generate a common cryptographic key using synchronized artificial neural networks. This option is based on a combined method of forming a cryptographic key [1]. The proposed combined formation consists of two stages: the formation of partially coinciding binary sequences using synchronized artificial neural networks and the elimination of mismatched bits by open comparison of the parities of bit pairs. The purpose of this article is to increase the cryptographic strength of this method in relation to a cryptanalyst. In this regard, it is proposed to prematurely interrupt the synchronization process at the first stage of the combined method and make changes to the resulting binary sequence by randomly inverting a certain number of bits. To confirm the quality of this method, possible attacks are considered and the scale of enumeration of possible values is illustrated. The results obtained showed that the combined method of forming a cryptographic key with a secret modification of the synchronization results of artificial neural networks, proposed in this article, provides its high cryptographic strength, commensurate with the cryptographic strength of modern symmetric encryption algorithms, with a relatively simple implementation.

Cyber security is most widely seen in many domains. From various domains these attacks plays the major role in damaging the servers and making websites unavailable. Many challenges are facing with the various cyber attacks.Internet of Things (IoT) is most widely used to define as a pervasive network of a (broad) range of connected smart nodes that offer diverse digital services, including the collection of environmental and user data. Detection of cyber attacks is difficult task and this may cause the loss of data packets and may interrupt the server. In this paper, the Enhancement is developed to handle the attack packets effectively. This is the window based application i,e based on the window size the data is processed. The Enhancement is called as an Amplified and Forward for Bi- Directional traffic from Attacks. This is very significant model to detect the several types of attacks that occur in IoT. Results show the performance of proposed system.

Industrial production processes are increasingly collecting data from machines in operation due to the cost reduction and popularization of sensor technologies. Valuable information is generated by using the right sensors and proper techniques on the machine’s current state in operation. This extraction allows detecting whether the machine is operating in a degraded state and then, if necessary, interrupts its operation before it goes into a broken state. The detection of the abnormal behavior of a machine is relevant since detection at the right time can reduce financial costs due to machine breakdown and production downtime. This work proposes a novel unsupervised method to detect anomalies in industrial machines and interrupt their operation before this machine goes into a state of breakdown. The proposed method receives as input a set of time series data from several sensors. Using a collection of statistical features, it calculates an indicator that characterizes the failure’s severity, issuing an alert to the machine operator if necessary. The method was evaluated in two benchmarks with known univariate data and two proprietary datasets with multivariate data. Conducted experiments revealed the low computational time spent on training and on evaluation. Overall results measured in Area Under the Receiver Operating Characteristic Curve (AUC) in Yahoo’s benchmark were 89.3%; in Numenta, it was 70.3%, and in the two multivariate datasets evaluated, it was 92.4% and 91.2%. These high AUC values reveal the potential of the proposed method applied in predictive maintenance in a large soybean oil production industry.

We study the stochastic resource-constrained project scheduling problem with uncertain resource availability, called SRCPSP-URA, and model it as a sequential decision problem. A new Markov decision process (MDP) model is developed for the SRCPSP-URA. It dynamically and adaptively determines not only which activity to start at a stage, but also which to interrupt and delay when there is not sufficient resource capacity. To tackle the curse-of-dimensionality of an exact solution approach, we devise and implement a rollout-based approximate dynamic programming (ADP) algorithm with priority-rule heuristic as the base policy, for which theoretical sequential improvement property is proved. Computational results show that with moderately more computational time, our ADP algorithm significantly outperforms the priority-rule heuristics for test instances up to 120 activities.

Monte Carlo simulation (MCS) is useful for verifying analytical derivations and studying complex systems on an empirical basis. Although MCS is straightforward and does not require a priori knowledge of the sampled probability measures (PMs), it consumes a tremendous amount of computational resource and suffers from slow simulation procedures for sophisticated systems. To mitigate this drawback of MCS, we make full use of the monotone property and the logarithmic presentation convention of PMs in regard to power related metrics (PRMs) in communications science and propose an easy-to-implement interrupt mechanism to accelerate MCS for estimating PMs. To facilitate the programming on different platforms and provide a solid theoretical foundation, we present a generic implementation framework suited for estimating PMs with certain properties by MCS and analyze the underlying theory of the interrupt mechanism. In particular, we apply the de Moivre-Laplace theorem and analytic continuation to prove the asymptotic consistency under the indirect setup of MCS. We also design a hypothesis test for studying the proposed mechanism on a statistical basis.

In this letter, a novel solid-state circuit breaker (SSCB) based on mixture device (MD-SSCB) is proposed for dc distribution systems. The proposed MD-SSCB can actively interrupt faults without introducing additional thyristor, charging power supply, and charging strategies. Besides, the interruption capability of the MD-SSCB is not affected by the parameters of the outer circuit. As a result, the MD-SSCB has advantages of compact structure, economic design, high efficiency, and high reliability. Moreover, the MD-SSCB can turn off its thyristors without reverse voltage stress, which eliminates the severe reverse overvoltage problem in the fault-interrupting process, and hence further enhances the reliability of the breaker. Detailed operation principle and design guidelines of the MD-SSCB are discussed through mathematical modeling. The feasibility of the proposed breaker is validated by experimental results.

Using drones for cellular coverage enhancement is a recent technology that has shown a great potential in various practical scenarios. However, one of the main challenges that limits the performance of drone-enabled wireless networks is the limited flight time. In particular, due to the limited on-board battery size, the drone needs to frequently interrupt its operation and fly back to a charging station to recharge/replace its battery. In addition, the charging station might be responsible to recharge multiple drones. Given that the charging station has limited capacity, it can only serve a finite number of drones simultaneously. Hence, in order to accurately capture the influence of the battery limitation on the performance, it is required to analyze the dynamics of the time spent by the drones at the charging stations. In this paper, we use tools from queuing theory and stochastic geometry to study the influence of each of the charging stations limited capacity and spatial density on the performance of a drone-enabled wireless network.