Network In Terms Research Articles

Adaptive overcurrent protection scheme coordination in presence of distributed generation using radial basis neural network

The operational performance of conventional overcurrent protection relay coordination connected to a distribution network is adversely affected by the penetration of distributed generators (DG) at different buses in the network. To address this problem, this paper proposes a novel adaptive protection coordination scheme using a radial basis function neural network (RBFNN), which automatically adjusts the overcurrent relay settings, i.e., time setting multiplier (TSM) and plug setting multiplier (PSM) based on the penetration of DGs. Short circuit currents and voltages measured at different buses are acquired using the remote terminal units (RTU) connected to different buses within the terminal network. Communication between the various remotes and local end station RTUs is through hybrid communication systems of fiber optic and power line communication system modules. The new adaptive overcurrent protection scheme is applied to the IEEE 33-bus distribution network with and without DGs, for single and multi-DG penetration using both the ETAP and MATLAB software. The simulation results show the proposed scheme significantly improves the protection coordination.

Trust evaluation model of power terminal based on equipment portrait

As the number of power terminals continues to increase and their usage becomes more widespread, the security of power systems is under great threat. In response to the lack of effective trust evaluation methods for terminals, we propose a trust evaluation model based on equipment portraits for power terminals. First, we propose an exception evaluation method based on the network flow order and evaluate anomalous terminals by monitoring the external characteristics of network traffic. Second, we propose an exception evaluation method based on syntax and semantics. The key fields of each message are extracted, and the frequency of keywords in the message is statistically analyzed to obtain the keyword frequency and time-slot threshold for evaluating the status of the terminal. Thus, by combining the network flow order, syntax, and semantic analysis, an equipment portrait can be constructed to guarantee security of the power network terminals. We then propose a trust evaluation method based on an equipment portrait to calculate the trust values in real time. Finally, the experimental results of terminal anomaly detection show that the proposed model has a higher detection rate and lower false detection rate, as well as a higher real-time performance, which is more suitable for power terminals.

Cholinergic centro-cingulate network in Parkinson disease and normal aging.

Decreased cholinergic binding within the recently identified centro-cingulate brain network robustly has been shown to robustly correlate with the severity of cognitive impairment in Parkinson disease (PD). This network with key hubs within the cingulum, operculum and peri-central cortical regions also correlates with elements of parkinsonian motor impairments, including postural instability and gait difficulties, such as falls or freezing. MRI neuroimaging studies have shown that the anterior midcingulate cortex is a key node for cognitive aspects of movement generation, i.e., intentional motor control. Recent evidence also suggests a novel aspect of organization of primary motor cortex, describing "effector" regions for fine movement control intercalated with interlinked "inter-effector" regions devoted to whole-body control. A distinguishing feature of inter-effector regions is tight linkage to the cingular and opercular regions. Such inter-effector regions have been proposed to be part of a greater somato-cognitive action network necessary for integration of goals and movement. Recent evidence also points to vulnerabilities of cholinergic nerve terminals in the centro-cingulate network in older non-PD adults. These features of normal aging underscore that cortical cholinergic terminal losses in age-associated neurodegenerative disorders are likely not exclusively the result of disease-specific etiologies but also related to otherwise normal aging. Practical implications of this overlap are that addressing disease-specific and general aging etiologies involved in neurodegeneration, may be of benefit in age-associated neurodegenerative disorders where significant cholinergic systems degeneration is present.

Intelligent optical transceiver technology based on federated learning traffic prediction

With the development of optical network, modern optical network needs better performance. Because the traditional optical transceiver technology has a delay according to the flow switching transmission configuration, the delay optical network service still adopts the original configuration transmission, so a certain degree of frequency spectrum resources waste and high blocking rate will be caused. The above situation can be improved if the transmission configuration can be deployed in advance based on the predicted traffic. Federated learning is a scheme of distributed training model, which can train the traffic prediction model in distributed way under the premise of ensuring the privacy of client data, which is very suitable for the traffic prediction of optical network terminals. This paper proposes an intelligent optical transceiver technology based on federal learning traffic prediction, applies the federal learning on the traffic prediction of optical communication network terminal, distributed training traffic prediction model, and deploy the optical transceiver early transmission configuration such as modulation format and baud rate parameters, thus to weaken the delay of optical transceiver technology, reduce the network blocking rate and improve the transmission performance of optical network.

CTG-Net: an efficient cascaded framework driven by terminal guidance mechanism for dilated pancreatic duct segmentation

Pancreatic duct dilation indicates a high risk of various pancreatic diseases. Segmentation for dilated pancreatic duct (DPD) on computed tomography (CT) image shows the potential to assist the early diagnosis, surgical planning and prognosis. Because of the DPD’s tiny size, slender tubular structure and the surrounding distractions, most current researches on DPD segmentation achieve low accuracy and always have segmentation errors on the terminal DPD regions. To address these problems, we propose a cascaded terminal guidance network to efficiently improve the DPD segmentation performance. Firstly, a basic cascaded segmentation architecture is established to get the pancreas and coarse DPD segmentation, a DPD graph structure is build on the coarse DPD segmentation to locate the terminal DPD regions. Then, a terminal anatomy attention module is introduced for jointly learning the local intensity from the CT images, feature cues from the coarse DPD segmentation and global anatomy information from the designed pancreas anatomy-aware maps. Finally, a terminal distraction attention module which explicitly learns the distribution of the terminal distraction regions is proposed to reduce the false positive and false negative predictions. We also propose a new metric called tDice to measure the terminal segmentation accuracy for targets with tubular structures and two other metrics for segmentation error evaluation. We collect our dilated pancreatic duct segmentation dataset with 150 CT scans from patients with five types of pancreatic tumors. Experimental results on our dataset show that our proposed approach boosts DPD segmentation accuracy by nearly 20% compared with the existing results, and achieves more than 9% improvement for the terminal segmentation accuracy compared with the state-of-the-art methods.

Feasibility analysis of 2-dimensional permutation vector optical code division multiple access passive optical network

This paper analyzes the feasibility of a 2-dimensional (2D) permutation vector (PV) optical code division multiple access (OCDMA) passive optical network (PON) in terms of transmission capacity, network reliability, power budget, and the number of subscribers. The proposed PON is built with a novel 2D PV-OCDMA code having ideal auto- and cross-correlation properties. Furthermore, a ring-based topology at the feeder level and a tree-based topology at the distribution level are utilized to support ubiquitous computing. The proposed architecture also employs multiple remote nodes at the feeder level to facilitate adequate power budget and cardinality for high-capacity applications. Simulation results indicate that the proposed PON elevates system transmission capacity by eliminating phase-induced intensity noise and multiple access interference. Furthermore, reliability analysis using mathematical formalism demonstrates that the proposed architecture can provide a connection availability of ≈0.099993 (5 nines), which is significantly higher than the conventional tree-based topology. Power budget analysis also determines that the proposed PON can support well above 320 subscribers at adequate receiver sensitivity and optical network terminals (ONTs) per remote node.

Morphological signs of neurogenic inflammation in rat epicardial adipose tissue during aging

The aim of this work is to study the neuroinflammatory process in the epicardial adipose tissue (EAT) of the aorticpulmonary region of the rat heart during aging. The work was performed on Wistar rats aged 18-23 months (n=18). The adipose tissue of the pericardial region of rats was the object of study. Neuroimmunohistochemical markers (9.5 protein, tyrosine hydroxylase, synaptophysin) were used to study EAT innervation. The method of staining with toluidine blue and azure-eosin was used to study inflammation processes. Using histological and immunohistochemical methods, involutive and neurodegenerative changes in parasympathetic and sympathetic postganglionic nerve fibers were revealed in the white and brown adipose tissue of the heart of old rats (18-23 months old). A number of pathological changes were revealed in EAT: fibrosis, dystrophic changes and lysis of white adipocytes, disintegration of varicose axons of the terminal nerve plexus, as well as the formation of focal monocyticlymphoid infiltrates. It has been shown that the axons of the terminal nervous network are in close relationship with immune and inflammatory cells (lymphocytes, plasmablasts, monocyte-macrophage elements, mast cells). The noted morphological features indicate a neurogenic type of immune inflammation in the EAT of the heart. The identified changes may be the cause of chronic disorders of lipid metabolism during aging, leading to the development of pathological processes in the tissues of the main vessels of the aortic-pulmonary region of the heart, and also serve as prognostic indicators for clinical practice.

Organization and morphology of calcitonin gene-related peptide-immunoreactive axons in the whole mouse stomach.

Nociceptive afferent axons innervate the stomach and send signals to the brain and spinal cord. Peripheral nociceptive afferents can be detected with a variety of markers (e.g., substance P [SP] and calcitonin gene-related peptide [CGRP]). We recently examined the topographical organization and morphology of SP-immunoreactive (SP-IR) axons in the whole mouse stomach muscular layer. However, the distribution and morphological structure of CGRP-IR axons remain unclear. We used immunohistochemistry labeling and applied a combination of imaging techniques, including confocal and Zeiss Imager M2 microscopy, Neurolucida 360 tracing, and integration of axon tracing data into a 3D stomach scaffold to characterize CGRP-IR axons and terminals in the whole mouse stomach muscular layers. We found that: (1) CGRP-IR axons formed extensive terminal networks in both ventral and dorsal stomachs. (2) CGRP-IR axons densely innervated the blood vessels. (3) CGRP-IR axons ran in parallel with the longitudinal and circular muscles. Some axons ran at angles through the muscular layers. (4) They also formed varicose terminal contacts with individual myenteric ganglion neurons. (5) CGRP-IR occurred in DiI-labeled gastric-projecting neurons in the dorsal root and vagal nodose ganglia, indicating CGRP-IR axons were visceral afferent axons. (6) CGRP-IR axons did not colocalize with tyrosine hydroxylase or vesicular acetylcholine transporter axons in the stomach, indicating CGRP-IR axons were not visceral efferent axons. (7) CGRP-IR axons were traced and integrated into a 3D stomach scaffold. For the first time, we provided a topographical distribution map of CGRP-IR axon innervation of the whole stomach muscular layers at the cellular/axonal/varicosity scale.

An Attentive Consensus Platform for Collaborative Reinforcement Learning Agents

This work integrates concurrent team learning into a deep reinforcement learning model for collaborative multiagent problems to reach a common goal. RL trains each agent to decide its best actions, and then the joint decision is made to accomplish the assigned team task based on the collaborative platform. Each agent learns individually and shares the observed feature with others. The collaborative platform makes the decision based on these features provided by agents. The collaborative platform consists of two attention mechanisms—soft and self-attention. The self-attention module is applied to distinguish local environmental features sensed by an individual agent for concurrent learning. The platform integrates collective information locally from agents through a soft attention interface. A termination network is introduced to the collaborative model to determine the number of elapsed sequences in collecting sufficient information to support the joint decision instead of a fixed number of iterations. Experiments on limit-sighted agent deployment for static image classifications, video game playing, and aerial image object detection of multiple airborne sensors have evaluated the performance of the proposed model. The experimental results show that the proposed model provides a more efficient collaborative mechanism for multiagent systems than the state-of-the-art methods.

Design and Implementation of Ambiently Powered Internet of Things-That-Think With Asynchronous Inference

This work offers design and implementation of in-network inference, using message passing among ambiently powered wireless sensor network (WSN) terminals. The stochastic nature of ambient energy harvesting dictates intermittent operation of each WSN terminal and as such, the message passing inference algorithms should be robust to asynchronous operation. It is shown, perhaps for the first time in the literature (to the best of our knowledge), a proof of concept, where a WSN harvests energy from the environment and processes itself the collected information in a distributed manner, by converting the (network) inference task to a probabilistic, in-network message passing problem, often at the expense of increased total delay. Examples from Gaussian belief propagation and Average Consensus are provided, along with the derivation of a statistical convergence metric for the latter case. A k-means method is offered that maps the elements of the calculated vector to the different WSN terminals and overall execution delay (in number of iterations) is quantified. Interestingly, it is shown that there are divergent instances of the in-network message passing algorithms that become convergent, under asynchronous operation. Ambient solar energy harvesting availability is also studied, controlling the probability of successful (or not) message passing. Hopefully, this work will spark further interest for asynchronous message passing algorithms and technologies that enable in-network inference, towards ambiently-powered, batteryless Internet-of-Things-That-Think.

Behavioral Modeling Method of Macromodels for Interconnected Systems With Frequency Characteristics and Nonlinear Termination Networks

This study presents a behavioral macromodeling method for electronic or electrical interconnected systems, when there are frequency-characterized interconnects loaded by nonlinear terminations in systems. Analog behavioral modeling (ABM) was leveraged to capture frequency-dependent characteristics of multiconductor interconnects and properties of nonlinear terminations. Black-box macromodels were developed from the external mathematical behavior of parts, and real systems could be simulated in the SPICE circuit solver. Also, several critical points and constraints concerning ABM models were analyzed and proposed.From the validation results, the relative errors of proposed models are all less than 3%, and the execution time reduces by a factor over 10 concerning the existing interconnect model, which implies the proposed interconnect model can significantly improve the simulation efficiency, and the analysis accuracy can be guaranteed using proposed models of interconnect systems.

FHAP: Fast Handover Authentication Protocol for High-Speed Mobile Terminals in 5G Satellite–Terrestrial-Integrated Networks

The integration of satellite and terrestrial networks presents new opportunities and challenges for High Speed Rail (HSR) communications. Since the HSR runs vary fast, user terminals on the HSR have to perform handover authentication when the HSR passes through different terrestrial base stations or satellite coverage areas. To improve the security and robustness of HSR communication services, a Fast Handover Authentication Protocol (FHAP) for high-speed mobile terminals in the 5G Satellite-Terrestrial integrated Networks (STN) is proposed. In the FHAP, user terminals in the same carriage form a temporary group managed by a relay node. The pre-handover authentication mechanism is employed, in which the terrestrial 5G core network configures the pre-authentication information to multiple access nodes based on the Chinese Remainder Theorem according to the location information of the HSR. Thus, group members and one of the access nodes can achieve fast handover authentication with the pre-configured information. Considering that HSR has a high running speed, when handover authentication fails, user terminals can perform handover authentication with other access nodes, and the probability of handover authentication failure caused by a single access node can be effectively reduced. We use the protocol verification tool Scyther and the Burrows-Abadi-Needham (BAN) logic to prove the security of the FHAP and compare it with other similar protocols in terms of signaling, bandwidth, and computational overhead. The analysis results show that the FHAP satisfies better security properties and has excellent performance.

Deep Learning OFDM Receivers for Improved Power Efficiency and Coverage

In this article, we propose multiple machine learning (ML) based physical-layer receiver solutions for demodulating orthogonal frequency-division multiplexing (OFDM) signals that are subject to high level of nonlinear distortion. Specifically, three novel deep learning based convolutional neural network receivers are devised, containing layers in time- and/or frequency-domains, allowing to demodulate and decode the transmitted bits reliably despite the high error vector magnitude (EVM) in the transmit signal. Applicable training procedures are also described, such that the learned layers in the receiver processing properly generalize over different nonlinear distortion and multipath channel characteristics. Extensive set of numerical results is provided, in the context of 5G NR uplink (UL) incorporating also measured terminal power amplifier (PA) characteristics. The obtained results show that the proposed receiver systems are able to clearly outperform the classical linear minimum mean-squared error (LMMSE) receiver as well as the existing ML receiver approaches, especially when the EVM is high compared to modulation order. This is particularly so when the devised ML receiver is of hybrid nature with layers both in time and frequency. The proposed ML receivers can thus facilitate pushing the terminal PA systems deeper into saturation, and thereon improve the terminal power-efficiency, radiated power and network coverage. Through combining the obtained radio link performance results with link budget calculations, all carried out at the 28 GHz mmWave band, it is shown that the proposed ML receivers can enhance the network coverage in terms of maximum UL link distances by close to 100%, when compared to classical LMMSE receiver based networks.

Typical Fault Characteristics Analysis of Distribution Network

China has built the largest power network in the world, forming a pattern of “nationwide interconnection, sending power from west to east, mutual supply between south and north.” As the user-oriented terminal power network, the safe operation of distribution network is the basis of power system stability. However, the distribution network system is huge, widely distributed and prone to failure. In order to quickly and accurately judge the fault category, improve the efficiency of fault location and reduce the non-fault outage area after the fault occurs in the distribution network system. Four typical faults of distribution network, namely single-phase grounding fault, phase-to-phase short circuit fault, ferromagnetic resonance and magnetizing inrush current, are studied in this paper, their fault characteristics are analyzed, and fault simulation analysis is carried out by MATLAB. Finally, the theoretical analysis results are verified by the actual on-site fault recording data, which proves the scientific and feasibility of the theoretical analysis.

A Regional Road Network Capacity Estimation Model for Mountainous Cities Based on Auxiliary Map

The focus of sustainable urban transportation development lies in realizing the untapped capacity potential of the existing road network and enhancing its operational efficiency without expanding its physical footprint. To quantify the supply capacity of road networks in mountainous cities, this paper converts the problem of solving the capacity of road networks into the problem of solving the minimum cut set in road networks from the perspective of road network capacity, using the idea of the auxiliary diagram method in graph theory. By improving the limitation that the auxiliary map method is only applicable to the single starting point and terminal point network, a regional road network capacity estimation model of a mountain city based on the auxiliary map is constructed. Combined with the actual regional road network, the model results presented in this paper show that the road network capacity calculated by the auxiliary graph method is 30,137 pcu/h. Using the improved traffic distribution simulation method, the network capacity is 38,776 pcu/h. Compared with the traffic distribution simulation method, the regional road network capacity model based on an auxiliary map proposed in this paper is more realistic.

Seismic resilience evaluation and retrofitting strategy for substation system

Electrical substation systems have always had an obvious vulnerability during earthquakes. To find an efficient retrofitting strategy at a substation’s functional network level, this paper proposes a seismic resilience evaluation framework that combines seismic simulation and function recovery. According to the structural connectivity and redundancy of substation seismic behavior, a directed acyclic graph was built. After careful consideration of the load transmission capacity of substation equipment and important coefficients of the output terminal, a structural and electrical dual-function network model was established using the Simulink module. The model was divided into four analytical steps for hierarchical simulation. Post-earthquake iterative analysis was carried out according to equipment repair efficiency. The post-earthquake repair sequence was found, and the steps to functional recovery were obtained. To quantify the seismic retrofitting efficiency of equipment, a set of seismic analytical methods for retrofitting was developed using a typical 220 kV substation. Seismic resilience analysis using the Monte Carlo method led to new optimal retrofitting strategies under different resource limitation conditions. These new strategies are presented in this paper.

Design of long-distance multichannel system based on passive optical fiber audio transmission system

To meet sound transmission requirements in complex work environments, this paper constructs a passive audio pickup sensor based on an extrinsic Fabry-Perot interferometer (EFPI) and develops a one-way four-channel passive audio parallel transmission system based on a passive bidirectional audio over fiber(PB-AOF) transmission system and wavelength division multiplexing technology. The system realizes optical fiber audio transmission over a distance of 20 km. The experimental results show that the signal-to-noise ratio (SNR) is greater than 64 dB, and the highest sensitivity of the four channels in the proposed system is 1.06 V/Pa. Thus, the proposed long-distance terminal passive audio communication network is ideally suited for applications in certain fields.

Rusatom Overseas JSC, Moscow, Russian Federation

The article made a comparative analysis of scientific research carried out in recent years in the field of management of the development of the transport complex in Russia. A grouping of scientific works by different authors in the areas was carried out, as well as proposals for the further development of scientific research were given. Based on the completed research, an attempt was made to build the structure of the research carried out by scientists into a single system to form a global concept for managing the development of inland water transport of the Russian Federation. It was noted that the greatest interest for scientists in river transport is the areas of cost optimization and efficiency improvement, improvement of state regulation mechanisms and public-private partnership, integrated development of the transport industry and the formation of a support network of combined cargo terminals. The least developed issues are indicated at the moment, namely: improvement of regulatory support and regulation of port activities, tariff policy and taxation for the development of water transport hubs, the use of public-private partnership, management of sustainable development of the transport industry in general and enterprises in particular, building an effective internal control system at inland water transport enterprises.

A Resilient Shipping Industry in the Wave of the Digital Economy during the Post Pandemic Era: Based on Maersk's Approach

The purpose of this essay is to focus on how Maersk made choices about its career development and to analyze and explain it using a set of "addition and subtraction" principles. Two distinct aspects of e-commerce are highlighted: "simplification" and "supplementation". Faced with cyclical shipping demand, the impact of the COVID-19 pandemic, and inherent industry shortcomings, Maersk entered the shipping e-commerce market by developing a series of platform products for online booking services. This helped simplify and connect the supply chain through subtraction. In addition, Maersk built a terminal delivery network to support B2B and B2C electronic fulfillment with powerful and flexible e-commerce technology. This promotes the integration of technology and consumption modes, extends the supply chain through addition, and complies with the digital economy. In the pursuit of green development and decarbonization in the shipping industry, Maersk has made subtractions by controlling carbon emissions and making efforts in ship and fuel development and manufacturing. This essay also considers the future management implications and direction of Maersk and the shipping industry it represents.

User scheduling and slicing resource allocation in industrial Internet of Things

Heterogeneous base station deployment enables to provide high capacity and wide area coverage. Network slicing makes it possible to allocate wireless resource for heterogeneous services on demand. These two promising technologies contribute to the unprecedented service in 5G. We establish a multiservice heterogeneous network model, which aims to raise the transmission rate under the delay constraints for active control terminals, and optimize the energy efficiency for passive network terminals. A policy-gradient-based deep reinforcement learning algorithm is proposed to make decisions on user association and power control in the continuous action space. Simulation results indicate the good convergence of the algorithm, and higher reward is obtained compared with other baselines.