Use Of Detection Devices Research Articles

A Review of Smart Grid Anomaly Detection Approaches Pertaining to Artificial Intelligence

The size of power grids and a complex technological infrastructure with higher levels of automation, connectivity, and remote access make it necessary to be able to detect anomalies of various kinds using optimal and intelligent methods. This paper is a review of studies related to the detection of anomalies in smart grids using AI. Digital repositories were explored considering publications between the years 2011 and 2023. Iterative searches were carried out to consider studies with different approaches, propose experiments, and help identify the most applied methods. Seven objects of study related to anomalies in SG were identified: attacks on data integrity, unusual measurements and consumptions, intrusions, network infrastructure, electrical data, identification of cyber-attacks, and use of detection devices. The issues relating to cybersecurity prove to be widely studied, especially to prevent intrusions, fraud, data falsification, and uncontrolled changes in the network model. There is a clear trend towards the conformation of anomaly detection frameworks or hybrid solutions. Machine learning, regression, decision trees, deep learning, support vector machines, and neural networks are widely used. Other proposals are presented in novel forms, such as federated learning, hyperdimensional computing, and graph-based methods. More solutions are needed that do not depend on a lot of data or knowledge of the network model. The use of AI to solve SG problems is generating an evolution towards what could be called next-generation smart grids. At the end of this document is a list of acronyms and terminology.

Identification and assessment of occupational safety risks in case of failure to capture an orphan source of ionizing radiation

Occupational safety and health belong to fundamental human rights. This fully applies to the protection of employees and other persons, which operate or are located in facilities for the collection of scrap metal. The founder of the landfill or the employer has an irreplaceable place in the field of protection of persons against the effects of ionizing radiation. Based on the identification and assessment of occupational safety risks in the event of failure to ensure timely detection of an orphan source of ionizing radiation, the operator must assess all risk factors with emphasis on radiation. At the same time, it must take specific regime measures, which would minimize the impact on people and the environment. The article deals with the causes of failure the system of protection against the effects of ionizing radiation. The assessment of these causes is performed using the fault tree analysis method with the application of Boolean equations. The result of solving the problem is the calculation of the failure of the regime protection system in the devices, intended for the collection of scrap metal, caused by human error as a result of intentional or negligent actions of the operator and insufficient training in the areas of control of measuring devices, principles of correct use of detection devices, insufficient visual identification of potentially dangerous objects. The contribution of this paper is in the design of appropriate regime measures eliminating the consequences of non-capturing a source of ionizing radiation.

Detection devices in entanglement-based optical state preparation

We study the use of detection devices in entanglement-based state preparation. In particular we consider optical detection devices such as single-photon sensitivity detectors, single-photon resolution detectors and detector cascades (with an emphasis on the performance of realistic detectors). We develop an extensive theory for the use of these devices. In entanglement-based state preparation we perform measurements on subsystems, and we therefore need precise bounds on the distinguishability of these measurements (this is fundamentally different from, e.g., tomography, where an ensemble of identical states is used to determine probability distributions, etc.). To this end, we introduce the confidence of preparation, which may also be used to quantify the performance of detection devices in entanglement-based preparation. We give a general expression for detector cascades of arbitrary size for the detection up to two photons. We show that, contrary to the general belief, cascading does not give a practical advantage over detectors with single-photon resolution in entanglement-based state preparation.