Performance Evaluation of Solar-Blind Gas-Filled Sensors to Detect Electrical Discharges for Low-Pressure Aircraft Applications

Unpressurized aircraft circuits facilitate the initiation of electrical discharges in wiring systems, with consequent damage to related insulation materials and safety hazards, that can and have already caused severe incidents and accidents. Specific sensors and solutions must be developed to detect these types of faults at a very incipient stage, before further damage occurs. Electrical discharges in air generate the corona effect, which is characterized by emissions of bluish light, which are found in the ultraviolet (UV) and visible spectra. However, due to sunlight interference, the corona effect is very difficult to detect at the very initial stage, so the use of solar-blind sensors can be a possible solution. This work analyzes the feasibility of using inexpensive non-invasive solar-blind sensors in a range of pressures compatible with aircraft environments to detect the electrical discharges at a very incipient stage. Their behavior and sensitivity compared with other alternatives, i.e., an antenna sensor and a CMOS imaging sensor, is also assessed. Experimental results presented in this paper show that the analyzed solar-blind sensors can be applied for the on-line detection of electrical discharges in unpressurized aircraft environments at the very initial stage, thus facilitating and enabling the application of predictive maintenance strategies. They also offer the possibility to be combined with existing electrical protections to expand their capabilities and improve their sensitivity to detect very early discharges, thus allowing the timely identification of their occurrence.

A Comprehensive Study on Safety of New Energy Vehicles

New energy vehicles (NEVs) have become a fundamental part of transportation system. Performance of an NEV is hugely determined by batteries, motors, and embedded electric control units. In this paper, a comprehensive study that covers all these key components is presented. Mechanisms and characterizations of failures are given in detail. On top of these, algorithms for fault diagnosis are established based on big data of real-world NEVs with joint considerations of design flaws, usage behaviors, and environmental conditions. In this way, multiple types of faults can be detected ahead of time to avoid accident. Proposed methods have been verified by real-world operational data, indicating effectiveness while providing insights for NEV design optimization.

Practical Multiple Persistent Faults Analysis

We focus on the multiple persistent faults analysis in this paper to fill existing gaps in its application in a variety of scenarios. Our major contributions are twofold. First, we propose a novel technique to apply persistent fault apply in the multiple persistent faults setting that decreases the number of survived keys and the required data. We demonstrate that by utilizing 1509 and 1448 ciphertexts, the number of survived keys after performing persistent fault analysis on AES in the presence of eight and sixteen faults can be reduced to only 29 candidates, whereas the best known attacks need 2008 and 1643 ciphertexts, respectively, with a time complexity of 250. Second, we develop generalized frameworks for retrieving the key in the ciphertext-only model. Our methods for both performing persistent fault attacks and key-recovery processes are highly flexible and provide a general trade-off between the number of required ciphertexts and the time complexity. To break AES with 16 persistent faults in the Sbox, our experiments show that the number of required ciphertexts can be decreased to 477 while the attack is still practical with respect to the time complexity. To confirm the accuracy of our methods, we performed several simulations as well as experimental validations on the ARM Cortex-M4 microcontroller with electromagnetic fault injection on AES and LED, which are two well-known block ciphers to validate the types of faults and the distribution of the number of faults in practice.

Strength & Conduct of Reinforced Concrete Corner Joint under Negative Moment Effect

The aim of our study is to reveal the effect of steel reinforcement details,tensile steel reinforcement ratio, compressed reinforcing steel ratio,reinforcing steel size, corner joint shape on the strength of reinforcedconcrete Fc' and delve into it for the most accurate details and concreteconnections about the behavior and resistance of the corner joint ofreinforced concrete, Depending on the available studies and sources inaddition to our study, we concluded that each of these effects had a clearrole in the behavior and resistance of the corner joint of reinforced concreteunder the influence of the negative moment and yield stress. A studyof the types of faults that can be reinforced angle joints obtains detailsand conditions of crushing that are almost identical for all types of steelreinforcement details and the basic requirements for the acceptable behaviorof reinforced concrete joints in the installations and the efficiency of thejoint and this may help us to prepare for disasters, whether natural or other,as happens with tremors The floor and failure that may occur due to wrongdesigns or old buildings and the possibility of using those connections totreat those joints and sections in reinforced or unarmed concrete facilitiesto preserve the safety of humans and buildings from sudden disasters andreduce and reduce risks, as well as qualitative control over the productionof concrete connections and sections free from defects to the extreme.

Cloud-Based Fault Prediction Using IoT in Office Automation for Improvisation of Health of Employees

The novel paradigm of Internet of Things (IoT) is gaining recognition in the numerous scenarios promoting the pervasive presence of smart things around us through its application in various areas of society, which includes transportation, healthcare, industries, and agriculture. One more such application is in the smart office to monitor the health of devices via machine learning (ML) that makes the equipment more efficient by allowing real-time monitoring of their health. It guarantees indoor comfort as per the user’s satisfaction as it emphasizes on fault prediction in real-life devices. Early identification of various types of faults in IoT devices is the key requirement in smart offices. IoT devices are becoming ubiquitous and provide an assistant to supervise an office that is regulated by ML and data received from sensors is stored in cloud. A recommender system facilitates the selection of an appropriate solution for faults in IoT-enabled devices to mitigate faults. The architecture proposed in this paper is used to monitor each and every office appliance connected via IoT technology using ML technique, and recommender system is used to recommend solutions for fault patterns without much human intervention. The ultrasonic motion sensor is used to fetch the information of employee availability in cubicles and data is sent to the cloud through the WiFi module. ATmega8 is used to control electrical appliances in the office environment. The significance of this work is to forecast the faults in IoT appliances which will have an impact on life and reliability of IoT appliances. The main objective is to design a prototype of a smart office using IoT that can control and automate workplace devices and forecast whether the device needs repairing or replacing, thus reducing the overall burden on the employee and helping out in increasing physical as well as mental health of the person.

Learning to Train CNNs on Faulty ReRAM-based Manycore Accelerators

The growing popularity of convolutional neural networks (CNNs) has led to the search for efficient computational platforms to accelerate CNN training. Resistive random-access memory (ReRAM)-based manycore architectures offer a promising alternative to commonly used GPU-based platforms for training CNNs. However, due to the immature fabrication process and limited write endurance, ReRAMs suffer from different types of faults. This makes training of CNNs challenging as weights are misrepresented when they are mapped to faulty ReRAM cells. This results in unstable training, leading to unacceptably low accuracy for the trained model. Due to the distributed nature of the mapping of the individual bits of a weight to different ReRAM cells, faulty weights often lead to exploding gradients. This in turn introduces a positive feedback in the training loop, resulting in extremely large and unstable weights. In this paper, we propose a lightweight and reliable CNN training methodology using weight clipping to prevent this phenomenon and enable training even in the presence of many faults. Weight clipping prevents large weights from destabilizing CNN training and provides the backpropagation algorithm with the opportunity to compensate for the weights mapped to faulty cells. The proposed methodology achieves near-GPU accuracy without introducing significant area or performance overheads. Experimental evaluation indicates that weight clipping enables the successful training of CNNs in the presence of faults, while also reducing training time by 4 X on average compared to a conventional GPU platform. Moreover, we also demonstrate that weight clipping outperforms a recently proposed error correction code (ECC)-based method when training is carried out using faulty ReRAMs.

Mechanical Switch Based Adaptive Fault Ride-through Strategy for Power Quality Improvement Device

Cascaded H-bridge power quality improving device (PQID) has garnered extensive attention for its flexible electric energy conversion and fast voltage response. However, the failure rate of PQID is relatively high due to the use of large numbers of power electronic devices. This paper proposes a mechanical-switch based adaptive fault ride-through strategy for improving the operational stability and power supply reliability of PQID. According to the features of the topology and working principle of PQID, this paper summarized the types of internal faults and analyzed the characteristics of different types of faults. Based on the shortcomings of existing mechanical switches as a bypass method, corresponding adaptive fault ride-through strategies are proposed for different types of faults, and a comprehensive simulation test has been carried out. The results show that the proposed strategy can adaptively ride through unit faults and effectively improve the output waveform quality during the ride through time.

A Torsional Vibration Measurement Method for Rotating Blades Based on Blade Tip Timing

During the working process of the turbine, some types of faults can cause changes in the vibration characteristics of the blades. The real-time vibration monitoring of the blades is of great significance to the stable operation and state-based maintenance. Torsional vibration is a kind of blade vibration modes and results in failures such as cracks easily. Thus, it is important to measure it due to the harmfulness of torsional vibration. Firstly, the principle of blade tip timing (BTT) is introduced, and the models of the blade are built to analyze the characteristics of torsional vibration. Then, the compressed sensing theory is introduced, and its related parameters are determined according to the measurement requirements. Next, based on the condition that the blade rigidity axis is not bent and bent, respectively, the layout method of sensors is proposed and the numerical simulation of the measurement process is performed. The results of the above two types of numerical simulation verify the proposed measurement method. Finally, by analyzing the influencing factors of measurement uncertainty, the optimization method of sensors’ layout is further proposed. This study can provide important theoretical guidance for the measurement of blade torsional vibration.

REDUCING OVERHEAD OF SELF-STABILIZING BYZANTINE AGREEMENT PROTOCOLS FOR BLOCKCHAIN USING HTTP/3 PROTOCOL: A PERSPECTIVE VIEW

Today, there is a tendency to reduce the dependence on local computation in favor of cloud computing. However, this inadvertently increases the reliance upon distributed fault-tolerant systems. In a condition that forced to work together, these systems often need to reach an agreement on some state or task, and possibly even in the presence of some misbehaving Byzantine nodes. Although non-trivial, Byzantine Agreement (BA) protocols now exist that are resilient to these types of faults. However, there is still a risk for inconsistencies in the application state in practice, even if a BA protocol is used. A single transient fault may put a node into an illegal state, creating a need for new self-stabilizing BA protocols to recover from illegal states. As self-stabilization often comes with a cost, primarily in the form of communication overhead, a potential lowering of latency - the cost of each message - could significantly impact how fast the protocol behaves overall. Thereby, there is a need for new network protocols such as QUIC, which, among other things, aims to reduce latency. In this paper, we survey current state-of-the-art agreement protocols. Based on previous work, some researchers try to implement pseudocode like QUIC protocol for Ethereum blockchain to have a secure network, resulting in slightly slower performance than the IP-based blockchain. We focus on consensus in the context of blockchain as it has prompted the development and usage of new open-source BA solutions that are related to proof of stake. We also discuss extensions to some of these protocols, specifically the possibility of achieving self-stabilization and the potential integration of the QUIC protocol, such as PoS and PBFT. Finally, further challenges faced in the field and how they might be overcome are discussed.