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Penetration of DC networks is rapidly increasing in modern power systems. As transient behavior of DC grids is different from that of AC ones, AC grid protection schemes might not be applicable to protect DC networks. Exact fault location is vital for protection of DC networks to reduce repair cost and achieve fast power recovery. This paper proposes a novel topology independent multi-terminal DC scheme to identify fault section and location for DC transmission systems. Voltage and current signals measured at the terminals are analyzed by the least squares error (LSE) algorithm. Then, the proposed scheme uses low frequency components of the signals to locate DC faults offline. Unlike some other methods, it does not require sophisticated sensors and hardware to capture very high frequency content of the signals. Hence, the proposed scheme can be implemented in practice without additional measuring infrastructure. The performance of the proposed scheme is evaluated under various fault conditions for different topologies of DC networks in both overhead lines and cables. Numerous simulations carried out demonstrate that the proposed scheme is quite accurate and provides excellent performance under different fault resistances, sections, and locations.

The identification protocol is a type of zero-knowledge proof. One party (the prover) needs to prove his identity to another party (the verifier) without revealing the secret key to the verifier. One can apply the Fiat–Shamir transformation to convert an identification scheme into a signature scheme which can be used for achieving security purposes and cryptographic purposes, especially for authentication. In this paper, we recall an identification protocol, namely the RankID scheme, and show that the scheme is incorrect and insecure. Then, we proposed a more natural approach to construct the rank version of the AGS identification protocol and show that our construction overcomes the security flaws in the RankID scheme. Our proposal achieves better results when comparing the public key size, secret key size, and signature size with the existing identification schemes, such as Rank RVDC and Rank CVE schemes. Our proposal also achieves 90%, 50%, and 96% reduction for the signature size, secret key size, and public key size when compared to the Rank CVE signature scheme.

The exponential growth of the Internet of Things (IoTs) has led to an increasing demand for intelligent IoT devices (IoTDs), requiring innovative network capacity expansion. Recently, several research has been conducted on the identification of hidden network resources for network capacity expansion. However, the spatial resource identification scheme through the omnidirectional antenna has limitations in terms of frequency efficiency compared to the scheme with the directional antenna. In this article, we propose a directional spatial-resource identification technique for device-to-device (D2D) communication. To find the optimal identification parameters, we design the objective function and apply a reinforcement learning. The training data used for reinforcement learning are collected in each report phase, and $Q$ -learning is applied to find the optimal beam set. Furthermore, based on the obtained frequency information, we propose a contention-based D2D communication scheme. The proposed contention-based D2D communication scheme can efficiently solve the deafness problem occurring in a directional D2D communication. Finally, we perform a simulation using optimum network performance (OPNET) to measure the performance and evaluate the effectiveness of the proposed technique. The simulation results show that the proposed schemes realize a better performance than the existing schemes proposed in previous works in terms of energy efficiency, frequency efficiency, aggregate network throughput, and deafness duration.

This research involves a 4-DOF ship maneuvering modeling with full-scale trial data. In order to avert the inversion of the multi-innovation matrix in the traditional multi-innovation least squares algorithm, a new novel is proposed based on the recognition concept new multi-innovation least squares algorithm to identify the innovation of the stochastic gradient hyperbolic tangent nonlinearity. A lot of work and efforts have been made to ensure the consistency and final convergence. Combined with relevant data and statistical indicators, the author derives a more effective hyperbolic tangent nonlinear innovation identification scheme to identify ship maneuvering motion. Compared with the previous results, this design scheme has significant computational advantages, with higher accuracy, faster identification speed, higher computational efficiency, and requiring fewer parameters. At the same time, the example is given to illustrate the effectiveness of the algorithm, especially for identification applications with full-scale trial data.

This article proposes a precise identification scheme of an interior permanent-magnet synchronous motor (IPMSM) flux linkage through experimental tests considering motor nonlinearities, such as the magnetic saturation and spatial harmonics. In the proposed scheme, the flux linkage is identified based on the voltage equation of IPMSM. The fundamental and harmonic electromotive force in the rotor reference frame is obtained in real time by applying a discrete Fourier transform with the phase compensation of the time delay due to digital control and pulsewidth modulation. The effect of the inverter nonlinearity is compensated using an experimentally obtained lookup table. Based on the identified flux-linkage map, a flux-based IPMSM model that accurately simulates motor behavior can be constructed. The effectiveness of the proposed flux-linkage identification scheme is verified through the comparison of simulation and experimental results.

The mouse hematopoietic system has served as a paradigm for analysis of developmental fate decisions in tissue homeostasis and regeneration. However, multiple immunophenotypic definitions of, and sometimes divergent nomenclatures used to classify, murine multipotent progenitors (MPPs) have emerged in the field over time. This has created significant confusion and inconsistency in the hematology field. To facilitate easier comparison of murine MPP phenotypes between research laboratories, a working group of four International Society for Experimental Hematology (ISEH) members with extensive experience studying the functional activities associated with different MPP phenotypic definitions reviewed the current state of the field with the goal of developing a position statement toward a simplified and unified immunophenotypic definition of MPP populations. In November of 2020, this position statement was presented as a webinar to the ISEH community for discussion and feedback. Hence, the Simplified MPP Identification Scheme presented here is the result of curation of existing literature, consultation with leaders in the field, and crowdsourcing from the wider experimental hematology community. Adoption of a unified definition and nomenclature, while still leaving room for individual investigator customization, will benefit scientists at all levels trying to compare these populations between experimental settings.

This study explores a nonparametric identification scheme for a ship maneuvering mathematical model. To overcome the difficulty in setting support vector regression (SVR) hyperparameters, a modified grey wolf optimizer algorithm is proposed. The algorithm introduces a nonlinear convergence factor and an adaptive position update strategy to enhance the search ability, which contributes toward identifying optimal hyperparameters. Using these optimal hyperparameters, SVR can predict the state variables pertaining to ship motion with high precision. The prediction of the motion state variables of the vessel YUKUN is considered as an illustrative example to verify the algorithm’s generalization ability and robustness. The prediction results indicate that, compared with the SVR based on the firefly algorithm and the particle swarm optimization, the proposed scheme offers the advantages of robustness, fewer iterations, and smaller prediction errors.

International audience; This paper proposes an efficient methodology to monitor the formation of cracks in concrete after non-destructive ultrasonic testing of a structure. The objective is to be able to automatically detect the initiation of cracks early enough, i.e. well before they are visible on the concrete surface, in order to implement adequate maintenance actions on civil engineering structures. The key element of this original approach is the wavelet-based multiresolution analysis of the ultrasonic signal received from a sample or a specimen of the studied material subjected to several types of solicitation. This analysis is finally coupled to an automatic identification scheme of the types of cracks based on artificial neural networks (ANNs), and in particular deep learning by convolutional neural networks (CNNs); a technology today at the cutting edge of machine learning, in particular for all applications of pattern recognition. Wavelet-based multiresolution analysis does not add any value in detecting fractures in concrete visible by optical inspection. However, the results of its implementation coupled with different CNN architectures show cracks in concrete can be identified at an early stage with a very high accuracy, i.e. around 98%, and a loss function of less than 0.1, regardless of the implemented learning architecture.

Modern communication systems tend to use different channel codes to find a balance between communication speed and quality. Accordingly, the blind identification techniques of encoders have drawn much research interest. In this letter, we propose a blind identification scheme for the Low-Density Parity Check (LDPC) coded communication systems based on a known candidate set. The proposed scheme can adaptively adjust the length of the received sequence to approach an arbitrary probability of missed detection and false alarm for codes in the candidate set, where the latter probability has received little attention by the existing methods. On this basis, the proposed scheme can determine whether there are unknown codes in the received vector, which is useful in practical applications. The performance of the proposed scheme is investigated using system simulations.

The early fault identification in high-voltage power systems is a substantial aspect not only to minimize equipment failure but also to increase both the reliability and stability in power system. Subsequently, the aim of this paper is to propose the adaptive fault-identification scheme based on multi-resolution analysis technique. The proposed method is dependent on monitoring both voltages and currents from single-ended measuring system. The correlation among the reactive power computation and discrete wavelet transform is used to generate the significant criteria which are used to discriminate between short-circuit currents and energizing heavy loads behaviour. Different transmission network configurations are investigated to assess the dependability, security, and reliability of fault identification relay as well. The correlative protection scheme attains the accurate results under healthy disturbances, and therefore it is superior to other conventional approaches. In addition, a selective study is applied to different mother wavelets to find the best one. The response of the proposed scheme to the compensated transmission line is also verified at a wide range of compensation levels with faults before and after compensated bank. Simulation tests have been handled via ATP-EMTP to investigate the proper practicability and adaptability of the fault-identication relay.

We study hypothesis testing problems with fixed compression mappings and with user-dependent compression mappings to decide whether or not an observation sequence is related to one of the users in a database, which contains compressed versions of previously enrolled users' data. We first provide the optimal characterization of the exponent of the probability of the second type of error for the fixed compression mappings scenario when the number of users in the database grows exponentially. We then establish operational equivalence relations between the Wyner-Ahlswede-Korner network, the single-user hypothesis testing problem, the multi-user hypothesis testing problem with user-dependent compression mappings and the identification systems with user-dependent compression mappings. These equivalence relations imply the strong converse and exponentially strong converse for the multi-user hypothesis testing and the identification systems both with user-dependent compression mappings. Finally they also show how an identification scheme can be turned into a multi-user hypothesis testing scheme with an explicit transfer of rate and error probability conditions and vice versa. QC 20210621

For high-voltage direct current (HVDC) transmission systems that use overhead transmission lines, the lightning disturbance is the main reason for the line protection mal-operation. To avoid the impact of lightning disturbance on the safety and stability of DC line protection, the model of HVDC transmission system including lightning strokes is constructed. On this basis, the complementary ensemble empirical mode decomposition (CEEMD) and Hilbert transform are combined as a time-frequency analysis method to calculate the energy distribution of the signals. Then the low-to-high-frequency energy ratio of the 1-mode voltage signal in a 3 ms data window after the protection startup is used as the protection criterion for identifying lightning disturbance, and that of the current signal in 1 ms is used as the criterion to further distinguish lightning and non-lightning faults. Verified through simulation tests, the scheme appears good adaptability and accuracy in different situations.

Multi-terminal high voltage DC (MT-HVDC) grid has broad application prospects in connecting different energy sources, asynchronous interconnection of power grids, remote load power supply, and other fields. At present, the key technologies that affect the development of MT-HVDC transmission system include swift fault identification and location in the DC line and its rapid isolation. Traditional fault monitoring relies on line communication, which cannot guarantee the rapidity and reliability of protection; moreover, it may even cause device damage. A fault identification scheme based on a single-terminal transient is presented in this paper. This scheme calculates the line inductance by using the rise rate of fault current at the initial stage of the fault, and determines the occurrence of the fault by comparing the observed line inductance with the set value, which lays a foundation for calculating the location of the fault point using distance protection. A simulation model on the PSCAD/EMTDC platform is built; the simulation example verifies that the proposed scheme can identify faults under dissimilar conditions while maintaining a low error level on the premise of no communication lines so as to meet the protection requirements of the MT-HVDC grid.

Wireless Sensor Networks (WSNs) is an emerging technology that pays more attention to the monitoring and acquiring of data in a deployed environment. Sensor networks often perform end to end transmission, so strong requirements should be defined to avoid data loss. The strong requirements may be precisely defined in terms of Quality of Service (QoS). The data transmission resumed, and the routing mechanism performed a vital role to forward the packets. The route path cache requires a crucial responsibility for on-demand routing standards that enhance the Quality of Service. The path identification stage is employed in the on-demand routing standard. The path identification scheme defines the path cache policies that are employed to move packet forwarding in an efficient manner. Therefore, the intention is to design a fresh scheme for path cache revisions. In a traditional scheme, nodes are little aware of path faults and they are engaged in the routing by revising their routing table. While SeDSR, based on the scattered cache substitution scheme, the source node relays the path fault data of the size of 50 bytes to all their adjacencies. Therefore, all the adjacency substitutes the stale path in their cache. The designed scheme enhances the performance employing diverse QoS metrics such as Packet loss, energy utilization, packet delivery and End to End delay.