Transmission Failure Research Articles

A 4 × 4 Active Antenna Array with Adjustable Beam Steering.

An adjustable 4×4 antenna array with electrical beam steering and polarization control is presented. Here, adjustability means the ability to correct the beam steering angle post-calibration. The objective is to improve the steering accuracy which is critical in point-to-point communication as inaccuracy will cause transmission failure due to a missed target. The accuracy is enhanced by adjusting the beam steering angle in beamforming calculations. To execute this, the system is calibrated by measuring several unit cells of a partial 4×4 array structure at different voltage bias points and calculating an average model of the phase shift profile. This reduces the phase error from variations between components and robust beam steering is achieved. This technique is utilized in far-field measurements, and fairly accurate initial beam steering angles are achieved at 3 GHz. The accuracy is further improved by over or under steering the desired angle in the beamforming calculations to finally achieve the steering angle of interest with an accuracy of 2∘. Overall, the main beam is incrementally steered from 0∘ to 45∘ with the gain ranging from 4.7 dB to 2.8 dB. The polarization control is also demonstrated in horizontal and vertical directions for a linearly polarized wave.

Gasotransmitter modulation of hypoglossal motoneuron activity.

Obstructive sleep apnea (OSA) is characterized by sporadic collapse of the upper airway leading to periodic disruptions in breathing. Upper airway patency is governed by genioglossal nerve activity that originates from the hypoglossal motor nucleus. Mice with targeted deletion of the gene Hmox2, encoding the carbon monoxide (CO) producing enzyme, heme oxygenase-2 (HO-2), exhibit OSA, yet the contribution of central HO-2 dysregulation to the phenomenon is unknown. Using the rhythmic brainstem slice preparation that contains the preBötzinger complex (preBötC) and the hypoglossal nucleus, we tested the hypothesis that central HO-2 dysregulation weakens hypoglossal motoneuron output. Disrupting HO-2 activity increased the occurrence of subnetwork activity from the preBötC, which was associated with an increased irregularity of rhythmogenesis. These phenomena were also associated with the intermittent inability of the preBötC rhythm to drive output from the hypoglossal nucleus (i.e. transmission failures), and a reduction in the input-output relationship between the preBötC and the motor nucleus. HO-2 dysregulation reduced excitatory synaptic currents and intrinsic excitability in inspiratory hypoglossal neurons. Inhibiting activity of the CO-regulated H2S producing enzyme, cystathionine-γ-lyase (CSE), reduced transmission failures in HO-2 null brainstem slices, which also normalized excitatory synaptic currents and intrinsic excitability of hypoglossal motoneurons. These findings demonstrate a hitherto uncharacterized modulation of hypoglossal activity through mutual interaction of HO-2/CO and CSE/H2S, and support the potential importance of centrally derived gasotransmitter activity in regulating upper airway control.

Machine visual technology based steel corrosion evaluation and analysis of power transformation lines: A Zhejiang power grid case study

It can be seen that the corrosion failure of transmission and transformation equipment has increasingly restricted the safe operation of the Zhejiang power grid bottleneck problem. If effective anticorrosion measures are not taken promptly, transmission and transformation equipment serving in various sophisticated atmospheric environments will suffer serious corrosion damage in a relatively short period, which endangers the safe usage of transmission and transformation equipment and the security of grid operation. In this article, through the establishment of transmission and transformation steel components corrosion fracture mechanics model, a standard corrosion spectrum grading software based on DeepLabV3+ image segmentation technology is developed to determine the quantitative assessment method of corrosion damage and assess the corrosion status with safety degree of transmission and transformation equipment. According to the assessment results, the operation and maintenance units are guided to adopt differentiated corrosion maintenance and replacement strategies, so as to reduce corrosion safety hazards and reduce safety accidents and economic losses caused by corrosion, which is of great significance for the safe operation of power grids.

Transfer learning-based data anomaly detection for structural health monitoring

The structural health monitoring (SHM) data of civil infrastructure are inevitably contaminated due to sensor faults, environmental noise interference, and data transmission failures. Anomalous data severely disturb the subsequent structural modal identification, damage identification, and condition assessment. Therefore, it is critical to detect and clean SHM data before data analysis. This paper proposes a novel approach for data anomaly detection based on transfer learning, that makes full use of the similarity of the anomalous patterns across different bridges and shares the knowledge incorporated in a deep neural network to achieve high-accuracy data anomaly identification for bridge groups. In the proposed approach, first, a multivariate database for a source bridge is built by plotting and labeling the raw sequential data. Then, a convolutional neural network (CNN) for data anomaly classification is designed and trained with the database in different conditions. The original CNN with the highest accuracy is transferred to a new bridge with enhancement training using a small part of the target bridge data. To validate the performance of the proposed method, the multivariate SHM data for two real long-span bridges are employed, including the acceleration, strain, displacement, humidity, and temperature data. The results demonstrate that transfer learning leads to a better classification capacity for the case of scarce labeled training data compared with the original network.

Интеллектуальная система управления сельскохозяйственными роботами: формирование структуры

Digital technologies introduced into agriculture are aimed at improving the efficiency of agricultural production. Agricultural robots carrying out technological operations, monitoring the field, crops, or crop yields may reduce their productivity, quality and safety of operation due to the geographical distance from the cloud storage, low bandwidth and network unreliability, significant delays and failures in data transmission. To increase the performance of units with unmanned robotic technical means for agricultural purposes, to ensure the high quality of their technological operations, as well as their safe operation, an intelligent control system using edge computing technologies based on Edge Computing is proposed. During the research, the authors used methods of complex structural-dynamic analysis and an expert-analytical method of information processing. As a result of the research, a structural and functional diagram of an intelligent control system for agricultural units with robotic technical means has been proposed to ensure centralized control of the technological process. Agricultural machines adjust the working tools and their operation process using built-in autonomous control systems that transmit data to the control system to generate commands. The operation of the working tools is adjusted in case of serious failures in the unit operation. The implementation of the Edge Computing concept in digital agriculture will reduce the amount of transmitted information and the load on the data transmission network, without reducing the technological process quality in case of failures in the operation of farm machines and embedded systems.

Modelling a Learning-Based Dynamic Tree Routing Model for Wireless Mesh Access Networks

Link asymmetry in wireless mesh access networks (WMAN) of Mobile ad-hoc Networks (MANETs) is due mesh routers’ transmission range. It is depicted as significant research challenges that pose during the design of network protocol in wireless networks. Based on the extensive review, it is noted that the substantial link percentage is symmetric, i.e., many links are unidirectional. It is identified that the synchronous acknowledgement reliability is higher than the asynchronous message. Therefore, the process of establishing bidirectional link quality through asynchronous beacons underrates the link reliability of asymmetric links. It paves the way to exploit an investigation on asymmetric links to enhance network functions through link estimation. Here, a novel Learning-based Dynamic Tree routing (LDTR) model is proposed to improve network performance and delay. For the evaluation of delay measures, asymmetric link, interference, probability of transmission failure is evaluated. The proportion of energy consumed is used for monitoring energy conditions based on the total energy capacity. This learning model is a productive way for resolving the routing issues over the network model during uncertainty. The asymmetric path is chosen to achieve exploitation and exploration iteratively. The learning-based Dynamic Tree routing model is utilized to resolve the multi-objective routing problem. Here, the simulation is done with MATLAB 2020a simulation environment and path with energy-efficiency and lesser E2E delay is evaluated and compared with existing approaches like the Dyna-Q-network model (DQN), asymmetric MAC model (AMAC), and cooperative asymmetric MAC model (CAMAC) model. The simulation outcomes demonstrate that the anticipated LDTR model attains superior network performance compared to others. The average energy consumption is 250 J, packet energy consumption is 6.5 J, PRR is 50 bits/sec, 95% PDR, average delay percentage is 20%.

Federated Deep Reinforcement Learning-Based Spectrum Access Algorithm With Warranty Contract in Intelligent Transportation Systems

Cognitive radio (CR) provides an effective solution to meet the huge bandwidth requirements in intelligent transportation systems (ITS), which enables secondary users (SUs) to access the idle spectrum of the primary users (PUs). However, the high mobility of users and real-time service requirements result in the additional transmission collisions and interference, which degrades the spectrum access rate and the quality of service (QoS) of users in ITS. This paper proposes a spectrum access algorithm (Feilin) based on federated deep reinforcement learning (FDRL) to improve spectrum access rate, which maximizes the QoS reward function with considering the hybrid benefits of delay, transmission power and utility of SUs. To guarantee the utility of SUs, the warranty contract is designed for SUs to obtain compensation for data transmission failure, which promotes SUs to compete for more spectrum resources. To meet the real-time requirements and improve QoS in ITS, a spectrum access model called FDQN-W is proposed based on federated deep Q-network (DQN), which adopts the asynchronous federated weighted learning algorithm (AFWLA) to share and update the weights of DQN in multiple agents to decrease time cost and accelerate the convergence. Detailed simulation results show that, in the multiuser scenario, compared with the existing methods, the proposed algorithm Feilin increases the spectrum access success rate by 15.1%, and reduces the collision rate with SUs and the collision rate with PUs by 46.4% and 6.8%, respectively.

Cascading Failure Analysis of Cyber–Physical Power Systems Considering Routing Strategy

In this brief, we develop a novel model to study the cascading failure in cyber-physical power systems. We use a discrete packet traffic model that follows a specific routing strategy to describe the dynamic data transmission in the information network. Moreover, the dynamic load flow model that takes into account the power-frequency characteristics of loads and generators is applied to describe the dynamic flow process of the power network. Our proposed model allows to consider the impacts of data packet transmission failures and voltage-related failures in the cascading process. Furthermore, we analyze the effects of routing strategy and information network topology on the severity of cascading failure. Simulation results verify the applicability of the proposed model and reveal the way in which routing strategy affects the cascading failure of cyber-coupled systems. In addition, we show that when the main hub in the information network is used as a dispatching center, a spreadout degree distribution of the information network reduces the severity of cascading failure in the cyber-coupled system.

Resource Allocation Based on Digital Twin-Enabled Federated Learning Framework in Heterogeneous Cellular Network

Federated learning (FL) allows user devices (UDs) to upload local model parameters to participate in a global model training, which protects UDs' data privacy. Nevertheless, FL still faces challenges such as core network congestion, UDs' limited resources and less efficient mapping between devices and cyber systems. Therefore, in this article, we integrate the digital twin (DT) and the mobile edge computing (MEC) technologies into a hierarchical FL framework in the heterogeneous cellular network scenario. When the UDs are not in the service range of the small base stations (SBSs), the framework allows macro base stations to assist UDs' local computation, thus reducing the transmission delay. It also protects the user privacy and allows more users to join in the training in order to improve the FL accuracy. In addition, we propose a deep reinforcement learning-based scheme to solve the joint optimization problem of dynamic UDs-stations association and resource allocation, thereby minimizing the energy consumption within a limited time delay. Simulation results show that our proposed scheme not only effectively reduces the task transmission failure rate and energy consumption compared with the baseline scheme, but also saves the communication cost through the DT network.

Renewable-based microgrids’ energy management using smart deep learning techniques: Realistic digital twin case

In this research study, a novel demand response program (DRP) has been proposed for renewable-based microgrids (MGs) which takes into account the high penetration of tidal units and solar energy as important, prevalent renewable resources in the power systems. To this end, multi-objective problem (MOP) structure is a promising solution to reduce the whole operating costs of the scheduling problem as well as decrease the high risk of failure in electrical power transmission because of component increasing failure rates and extended repair time. The complexities and nonlinearity of the problem necessitate an innovative heuristic solution which is derived from the Grey Wolf optimization algorithm to help for resolving the problem without making any assumptions or compromising precision. This paper also proposes the dynamic 3-phase correction (DPC) formulation to boost the layout convergence ability by increasing the global search features. Through such a modification, the diversity of the members in the algorithm population increases which would result in low computational burden and low possibility of trapping in local optima. Moreover, it is necessary to have a clear and accurate estimation of the output power of renewable sources in the system. Considering that solar irradiance is hard to anticipate, this paper develops a deep learning layout that uses generative adversarial networks (GAN) to forecast the hourly power generation of the tidal and solar agents. GAN model consists of two competing networks which help to enhance the training process by increasing the accuracy of distinguishing real data from fake data. At the end, IEEE standard test system is used to evaluate the efficiency and effectiveness of the suggested multi-layer problem. The simulation results display that the suggested deep model outperforms other well-known algorithms in smart microgrids.

Multi-objective reliability optimization design of high-speed heavy-duty gears based on APCK-SORA model

For high-speed heavy-duty gears in operation is prone to high tooth surface temperature rise and thus produce tooth surface gluing leading to transmission failure and other adverse effects, but in the gear optimization design and little consideration of thermal transmission errors and thermal resonance and other factors, while the conventional multi-objective optimization design methods are difficult to achieve the optimum of each objective. Based on this, the paper proposes a gear multi-objective reliability optimisation design method based on the APCK-SORA model. The PC-Kriging model and the adaptive k-means clustering method are combined to construct an adaptive reliability analysis method (APCK for short), which is then integrated with the SORA optimisation algorithm. The objective function is the lightweight of gear pair, the maximum overlap degree and the maximum anti-glue strength; the basic parameters of the gear and the sensitivity parameters affecting the thermal deformation and thermal resonance of the gear are used as design variables; the amount of thermal deformation and thermal resonance, as well as the contact strength of the tooth face and the bending strength of the tooth root are used as constraints; the optimisation results show that: the mass of the gear is reduced by 0.13kg, the degree of overlap is increased by 0.016 and the coefficient of safety against galling Compared with other methods, the proposed method is more efficient than the other methods in meeting the multi-objective reliability design requirements of lightweighting, ensuring smoothness and anti-galling capability of high-speed heavy-duty gears.

The Coupled Thermal-Structural Resonance Reliability Sensitivity Analysis of Gear-Rotor System with Random Parameters

The resonance of the gear-rotor system will produce a large number of responses that do not exceed the threshold value, resulting in structural fatigue failure and transmission failure, affecting its life and reliability. It is particularly critical to consider the temperature rise under high-speed and heavy-load conditions. Therefore, the research will take the main drive gear-rotor system of a certain type of aeroengine accessory gearbox as the research object, consider the influence of the temperature field on the natural frequency of the gear-rotor system, and take the difference between the natural frequency of the gear-rotor system and the excitation frequency (gear meshing frequency) as the performance function. The PC-Kriging and adaptive design of experimental strategies are applied to the thermal-structural coupling parametric model to analyze the resonance reliability and sensitivity of the gear-rotor system. For complex mechanical mechanisms, the method has better accuracy than other surrogate models and greatly saves the time of finite element simulation in reliability analysis. The results show that the natural frequency of a gear rotor decreases with an increase in temperature, and the natural frequency of different orders varies with the change in temperature. The influence of the sensitivity of different random parameters on the resonance reliability of the gear-rotor system is obtained. Reliability research on resonance failure of high-speed and heavy-load aviation gear-rotor systems considering random parameters under a temperature rise field has important practical engineering application value and scientific research significance.

Quantum‐Error‐Rejection for Hyperentanglement Transmission without Calibrated Reference Frames

AbstractIn quantum communication, the channel noise and the misalignment of the reference frames between the communication parties will lead to the failure of quantum state transmission. Here an alignment‐free spatial‐polarization hyperentanglement transmission scheme is provided for hyperentangled photons. In this scheme, before the spatial‐polarization hyperentanglement is transmitted through the fiber channel, it is first encoded as a time‐bin entanglement with the same polarization. After the photons pass through the noise channel, the polarization errors caused by reference frames misalignment and channel noise can be corrected by time‐bin entanglement. In principle, by implementing this scheme, the communication parties can share the original hyperentangled state, and the success probability can approach unity. The scheme is robust to random channel noise and reference frames misalignment, and the decoherence effect caused by the misalignment of the reference frames between the communication parties can be completely suppressed by implementing this scheme.

Genetic etiological analysis of auditory neuropathy spectrum disorder by next-generation sequencing.

Auditory neuropathy spectrum disease (ANSD) is caused by both environmental and genetic causes and is defined by a failure in peripheral auditory neural transmission but normal outer hair cells function. To date, 13 genes identified as potentially causing ANSD have been documented. To study the etiology of ANSD, we collected 9 probands with ANSD diagnosed in the clinic and performed targeted next-generation sequencing. Nine probands have been identified as ANSD based on the results of the ABR tests and DPOAE/CMs. Genomic DNA extracted from their peripheral blood was examined by next-generation sequencing (NGS) for a gene panel to identify any potential causal variations. For candidate pathogenic genes, we performed co-segregation among all family members of the pedigrees. Subsequently, using a mini-gene assay, we examined the function of a novel splice site mutant of OTOF. We analyzed nine cases of patients with ANSD with normal CMs/DPOAE and abnormal ABR, discovered three novel mutants of the OTOF gene that are known to cause ANSD, and six cases of other gene mutations including TBC1D24, LARS2, TIMM8A, MITF, and WFS1. Our results extend the mutation spectrum of the OTOF gene and indicate that the genetic etiology of ANSD may be related to gene mutations of TBC1D24, LARS2, TIMM8A, MITF, and WFS1.

Apical anchorage and stabilization of subpellicular microtubules by apical polar ring ensures Plasmodium ookinete infection in mosquito

Morphogenesis of many protozoans depends on a polarized establishment of cortical cytoskeleton containing the subpellicular microtubules (SPMTs), which are apically nucleated and anchored by the apical polar ring (APR). In malaria parasite Plasmodium, APR emerges in the host-invading stages, including the ookinete for mosquito infection. So far, the fine structure and molecular components of APR as well as the underlying mechanism of APR-mediated apical positioning of SPMTs are largely unknown. Here, we resolve an unprecedented APR structure composed of a top ring plus approximate 60 radiating spines. We report an APR-localizing and SPMT-binding protein APR2. APR2 disruption impairs ookinete morphogenesis and gliding motility, leading to Plasmodium transmission failure in mosquitoes. The APR2-deficient ookinetes display defective apical anchorage of APR and SPMT due to the impaired integrity of APR. Using protein proximity labeling, we obtain a Plasmodium ookinete APR proteome and validate ten undescribed APR proteins. Among them, APRp2 and APRp4 directly interact with APR2 and also mediate the apical anchorage of SPMTs. This study sheds light on the molecular basis of APR in the organization of Plasmodium ookinete SPMTs.

New Selective Directional Ground Fault Protection Method for Ungrounded Systems

Ground faults are the main cause of electrical failure in transmission and distribution systems. In ungrounded distribution networks, it is difficult to detect where a first ground fault has happened as only capacitive currents are available. Also, it is important to trip only the faulty line in order to ensure the system's reliability. Directional residual overcurrent relays are the standard solution for these systems, but their difficult commissioning and high costs make it not trivial. This paper introduces a new selective and directional protection method to determine the faulty line in ungrounded medium voltage networks with three or more feeders. The method evaluates the residual currents in the feeders coupled to the same busbars in combination with a reference current created by the sum of a combination of residual currents of all feeders. Both simulation and laboratory results validate this new method, which is easy to start up, as it does not need previous configurations for its implementation, and it presents an economical solution to this problem.

Dynamic Multichannel Access Based on Deep Reinforcement Learning in Distributed Wireless Networks

With the emergence of innovative applications in vertical industries such as smart home and industrial automation, machine communication has shown a spurt of development. Different from the traditional human-oriented cellular communication, machine communication is characterized by strong uncertainty and abruptness, large-scale concurrent device connection as well as uneven and unsaturated data traffic. This article investigates the dynamic multiple-devices multiple-channels access for unsaturated traffic with retransmission mechanism, which is aimed at reducing the long-term data packet loss resulting from buffer overflows and transmission failure. The instant channel selection will lead to a non-negligible impact on the future decision, motivating us to model this problem as a Markov decision process. Limited by the unknown environment knowledge, we proposed a dynamic access policy based on deep reinforcement learning algorithm to optimally select the channel for transmission or keep silent for Internet-of-Things devices. Simulation results confirm that our proposed channel access strategy can reduce the collision and the packet loss of network. Furthermore, it can also work well when coexisting with the devices that adopt time division multiple access (TDMA) or ALOHA protocol.

A Wireless Real-Time Continuous Monitoring System for the Internal Movements of Mountain Glaciers Using Sensor Networks

With the escalation of global warming, the shrinkage of mountain glaciers has accelerated globally, the water volume from glaciers has changed, and relative disasters have increased in intensity and frequency (for example, ice avalanches, surging glaciers, and glacial lake outburst floods). However, the wireless monitoring of glacial movements cannot currently achieve omnidirectional, high-precision, real-time results, since there are some technical bottlenecks. Based on wireless networks and sensor application technologies, this study designed a wireless monitoring system for measuring the internal parameters of mountain glaciers, such as temperature, pressure, humidity, and power voltage, and for wirelessly transmitting real-time measurement data. The system consists of two parts, with a glacier internal monitoring unit as one part and a glacier surface base station as the second part. The former wirelessly transmits the monitoring data to the latter, and the latter processes the received data and then uploads the data to a cloud data platform via 4G or satellite signals. The wireless system can avoid cable constraints and transmission failures due to breaking cables. The system can provide more accurate field-monitoring data for simulating glacier movements and further offers an early warning system for glacial disasters.

Recovery of missing field measured wind pressures on a supertall building based on correlation analysis and machine learning

In long-term field measurements and structural health monitoring (SHM), data loss sometimes occurs due to interruption of power supply, sensor failure, and disruption of data transmission, which inevitably affects subsequent data analysis and structural condition assessment. To address this issue, this paper proposes a novel data recovery method based on correlation analysis and machine learning (ML) for recovery of missing wind pressures on cladding of high-rise buildings in field measurements and SHM. The method first utilizes correlation analysis to select appropriate inputs of the ML model, and subsequently to recover the missing wind pressures using the ML model with the selected appropriate inputs. In this study, four ML models (i.e., data recovery models), including deep neural network (DNN), particle swarm optimization-DNN (PSO-DNN), extreme learning machine (ELM), and support vector regression (SVR), are developed based on the proposed data recovery method. The time series of wind pressures with non-Gaussian features, which were collected by a multi-point synchronous monitoring system on a 600-m-high skyscraper during Super Typhoon Mangkhut, are employed to validate the performance of the developed data recovery models. The results show that all the four ML models perform satisfactorily in the recovery of the missing time series of wind pressures, among which the PSO-DNN model performs best and has good recovery accuracy for data loss that occurs in different areas of building cladding. This demonstrates that the proposed data recovery method based on correlation analysis and machine learning is a reliable and effective tool to recover the missing time series of wind pressures on high-rise buildings under severe wind conditions. Notably, this is the first attempt based on field measurements to recover the missing time series of wind pressures with non-Gaussian characteristics on a skyscraper during an extreme typhoon event, which aims to provide a useful means for the recovery of missing monitoring data in field measurements and SHM.

Analyzing Reliability and Maintainability of Crawler Dozer BD155 Transmission Failure Using Markov Method and Total Productive Maintenance: A Novel Case Study for Improvement Productivity

Surface mining is the world’s most costly industry due to its enormous expenses. Reduced production is forcing mining companies to automate their equipment, predominantly heavy earth mining machinery (HEMMs), for example, dump trucks, shovels, and dozers. The backbone of pit mining is the crawler dozer, commonly known as a dozer. Crawler dozers are tracked earth-moving machines with metal blades positioned in front for pushing materials such as rocks, soil, etc. In order to survive the harsh competition, dozers must be durable and adequately maintained. Crawler dozers work under challenging conditions to avoid production delays that result in losses such as breakdowns, transmission failures, and other issues in mining operations. Transmission failures, among other issues with dozers, are one of the hardest to resolve. This study evaluates the reliability, availability, and maintainability (RAM) of a BD155 crawler dozer transmission using failure and repair data and the Markov method. A realistic case study on (BD155) transmission failure and associated subsystems has been performed. Potential approaches and alternatives are also identified to increase dependability and performance. This article also discusses best maintenance practices for minimizing transmission failures and boosting productivity. The availability of the BD155 increases to 71% from 62% using proper planning and maintenance.