Search

There is a relative lack of research on the development of creepage discharge in aged oil-paper insulation subjected to AC-DC combined voltage in converter transformers. In this study, based on the physical and chemical properties of oil-paper, we propose a sphere-plate electrode model to study the creepage discharge parameters and characteristic spectra of aged oil-paper subjected to AC-DC combined voltage. The results show that the creepage discharge inception voltage of aged oil-paper is related to the AC voltage component of the AC-DC combined voltage. However, when the DC voltage component is 60 kV, the flashover phenomenon was found to occur in one of the severely aged samples before the AC voltage was applied. Despite being subjected to the same DC voltage, the flashover phenomenon did not occur in three other samples aged to different degrees. Large-amplitude discharges were found to appear earlier in the severely aged samples. At the same creepage discharge stage, as the aging time of the oil-paper increased, the number of large-amplitude discharges was found to rapidly increase, and the time interval between adjacent discharges was found to gradually decrease. At each stage of the creepage discharge, the small-amplitude discharge has a “hump” shaped distribution. The larger the DC voltage component, the shorter the endurance time of aged oil-paper, and the more severe the damage to the oil-paper. The DC voltage component was found to have an accelerating effect on the development of creepage discharge in aged oil-paper insulation.

This paper presents a broadband, simple, re-useable and low-cost approach for noninvasive sweat monitoring using a passive microwave circuit and a cellulose filter paper as a superstrate. The proposed sensor is composed of filter paper with the ability to absorb the sample liquid under test (LUT) placed on top of a coplanar waveguide (CPW) transmission line. Various samples of sodium chloride (NaCl) solutions with concentrations in the range of 0.01-2 mol/L and models of artificial sweat are used to test the proposed sensor. The difference in transmission coefficient (S21) between dry and wet states is used to determine the concentrations of tested solutions in the band of 1-6 GHz. The sensor detects concentrations as low as 0.01 mol/L (0.58 g/L) and quantities as low as 137 μL with a maximum sensitivity of 46.7 dB/g/L. The proposed sensor presents a simple approach to sample and characterize liquids with enhanced sensitivity and consistent performance using microwave signals.

Insulated tubular busbar is a new type of current-carrying equipment with excellent performance. However, the research on this equipment is insufficient because of the short application and the lack of technical digestion, which has resulted in many accidents. In this paper, the full-scale model of epoxy-impregnated-paper insulated tubular busbar with bubble defects was designed and produced, its electrical breakdown process was investigated by withstand voltage test. In the experiment, the failure mode of breakdown caused by gas expansion was found for the first time. The research showed that the electrical-mechanical breakdown process could be divided into two stages: gas increase stage and breakdown threshold stage. The research also obtained the characteristics and the change criterions of the two stages. The gas in the bubble defects generated from epoxy decomposition which caused by partial discharge. When the gas pressure exceeds the critical fracture toughness of the bubble defects wall, the unstable crack extension occurs, therefore, improving the ability about resistance to partial discharge of epoxy resin or improving the fracture toughness of epoxy resin by modifying would enhance the breakdown performance of insulated tubular busbar. The result also has reference significance for other epoxy-impregnated-paper insulated electrical equipment.

This paper presents the design and analysis of a paper-based chipless RFID sensor for wetness content measurement in plant leaves. The sensor is designed using a passive microwave resonator to measure the ambient physical parameter. The resonator is designed with a rectangular loop inductively coupled with an interdigital capacitor. The experimental analysis of the designed sensor is illustrated here to prove its efficacy. The resonator provides adequate backscattered signal for wireless measurement in the microwave frequency band. When interrogated with a couple of UWB antennas and a VNA, it yields accurate leaf wetness response of the plant. The commercial competence of this sensing device is validated through its experimental analysis on three different types of leaf samples. This sensor is suitable for plant health monitoring in any small-scale gardening applications such as nurseries and greenhouses.

Oil-immersed insulation paper (OIP) bushing is one of the most important equipment in power transmission and distribution systems. Moisture is one of the most serious factors that affect the insulation status of OIP bushing. In this paper, the OIP bushing was assumed to be in a stable and simplicity condition, and then the transient moisture distribution of capacitor core and its effects on the electric field were studied. First, by dissecting a 110 kV OIP bushing, the specific sizes for modelling were obtained. Afterwards, a moisture diffusion model for OIP bushing was established based on COMSOL Multiphysics software, and it was also verified by moisture diffusion experiment. Then, the moisture distribution for different conditions were obtained and analyzed. Finally, a model for the electric field of OIP bushing was established, and then the effects of moisture on the electric field were studied.

Transformer insulation paper is a key indicator for transformer remaining operational life. Paper decomposition is evaluated using the degree of polymerization (DP) which calls for samples of insulation paper from operating transformers. Since collecting such paper samples is extremely difficult, other indicators have been used to indirectly reveal the DP value of insulation paper. This includes dissolved gases in transformer oil such as carbon oxides and hydrocarbon gases, furan compounds, methanol, ethanol, and moisture along with some oil characteristics such as interfacial tension. However, for the same oil sample, these individual parameters lead to different DP values. This is attributed to the lack of accuracy of the established mathematical and artificial intelligence models correlating DP with each of the above mentioned individual parameters. This paper presents a self-learning method to estimate the DP value of transformer insulation paper based on multiple transformer oil aging parameters. The proposed method comprises data processing, fuzzy c-means and linear regression. Results reveal that estimating the DP value based on multiple aging parameters is more accurate than estimating it using one single parameter as per the current practice. The proposed method not only helps to understand the correlation between multiple oil aging parameters and the DP value of paper insulation, but also promotes the establishment of more accurate life assessment models for power transformers based on these oil aging parameters.

Methanol is regarded as an available indicator to analyze the aging condition of cellulose insulation. However, the diffusion mechanism of methanol molecules in the paper-oil insulation system is still unclear, since it cannot be explored by using only experiments. Given this issue, the molecular dynamics simulations were performed to investigate the diffusion mechanism of methanol molecules. In the current work, the mean square displacement of methanol during the diffusion process was calculated and analyzed, as well as its trajectory tracking. Then, the research on interaction energies and hydrogen bonds in paper/oil system were conducted. Finally, the effect of temperature on the diffusion of methanol molecules was also studied. Findings revealed that the diffusion of methanol in cellulose insulation is faster than that in insulating oil, besides, the presence of hydrogen bonds affects the diffusion rate of methanol molecules in cellulose insulation and insulating oil. Thus, the present findings could support the investigation of the diffusion mechanisms of methanol molecules, by which the aging condition evaluation of cellulose insulation could be also promoted.

Accurate modeling and simulation of electric field transients in HVDC cables is an important support to optimize insulation system design and to evaluate the influence of voltage transients and steady-state conditions on accelerated ageing mechanisms and insulation reliability. Traditionally, field models considering time-independent permittivity and conductivity are used, but this approach neglects polarization mechanisms and charge trapping-detrapping phenomena. This article includes polarization dynamics in the field model and shows that its impact on transient electric field simulations in HVDC paper-insulated cables can be significant. A method is presented to infer the model parameters from experimental polarization and depolarization current measurements.

Over the decades, the immense growth has been reported in research publications due to continuous developments in science. To date, various approaches have been proposed that find similarity between research papers by applying different similarity measures collectively or individually based on the content of research papers. However, the contemporary schemes are not conceptualized enough to find related research papers in a coherent manner. This paper is aimed at finding related research papers by proposing a comprehensive and conceptualized model via building ontology named COReS: Content-based Ontology for Research Paper Similarity. The ontology is built by finding the explicit relationships (i.e., super-type sub-type, disjointedness, and overlapping) between state-of-the-art similarity techniques. This paper presents the applications of the COReS model in the form of a case study followed by an experiment. The case study uses InText citation-based and vector space-based similarity measures and relationships between these measures as defined in COReS. The experiment focuses on the computation of comprehensive similarity and other content-based similarity measures and rankings of research papers according to these measures. The obtained Spearman correlation coefficient results between ranks of research papers for different similarity measures and user study-based measure, justify the application of COReS for the computation of document similarity. The COReS is in the process of evaluation for ontological errors. In the future, COReS will be enriched to provide more knowledge to improve the process of comprehensive research paper similarity computation.

We present a novel implementation of a Rock-Paper-Scissors (RPS) game interaction with a social robot. The framework is tailored to be computationally lightweight, as well as entertaining and visually appealing through collaboration with designers and animators. The fundamental gesture recognition pipeline employs a Leap motion device and two separate machine learning architectures to evaluate kinematic hand data on-the-fly. The first architecture is used to recognize and segment human motion activity in order to initialize the RPS play, and the second architecture is used to classify hand gestures into rock, paper or scissors. The employed tabletop robot interacts in the RPS play through unique animated gestural movements and vocalizations designed by animators which communicate the robot's choices as well as cognitive reflection on winning, losing and draw states. Performance of both learning architectures is carefully evaluated with respect to accuracy, reliability and run time performance under different feature and classifier types. Moreover, we assess our system during an interactive RPS play between robot and human. Experimental results show that the proposed system is robust to user variations and play style in real environment conditions. As such, it offers a powerful application for the subsequent exploration of social human-machine interaction.

In this paper, we present a new scheme for implementing virtual keyboards, which uses only two to four motion-recognition rings per hand and a two-dimensional keyboard template (e.g., an A4 size paper with printed key positions). It has the benefit of portability, customizability, and low-cost when compared with existing approaches. Essentially, we have shown that wearing two wireless IoT rings on the middle phalanges of two fingers of each hand, users can input the alphabetic characters into a computing device by typing on a flat paper on a desk, and potentially in mid-air. We have demonstrated that two rings are sufficient in capturing the gestures and motions of all fingers in a typing hand for keystrokes recognition. A single wireless IoT ring, which weighs 7.8 grams, consists of a Bluetooth low energy (BLE) unit, a micro inertial measurement unit (mIMU), and a cell battery. The 3-axes attitude angles and the Z-axis acceleration of each ring are adopted as the features for keystroke recognition. The overall keystroke recognition accuracy rate can reach as high as 94.8% when two IoT rings are worn by a user on each hand; this accuracy rate increases to 98.6%, when four rings are worn on each typing hand.

This study explores the method of applying surface enhanced Raman scattering (SERS) in diagnosing the aging stage of oil-paper insulation equipment in the power system, which provides access to the on-line mornitoring of electrical equipment. Oil-paper samples in different aging stages were obtained by accelerated aging experiments. Effective Raman signals of insulating oil were collected by using the self-made hexagonal sliver nano-plates SERS substrates and the self-assembled Raman detection platform. For the SERS data of oil-paper insulation samples, kernel entropy component analysis (KECA) was applied to extract the Raman spectral features and support vector machine (SVM) was used to recognize the the aging stage of oil-paper insulation. The results demonstrate that the KECA-SVM method exhibits a good diagnostic capacity and the recognition accuracy of the propsed method reached 81.43 % (57/70). In summary, a new spectral method is proposed to diagnose the aging stage of oil-paper insulation, which lays a foundation for the actual diagnosis of the aging condition in running oil-paper insulation equipment by Raman spectroscopy.

This paper presents a thorough review of transmitarray devices particularly aiming antenna beamsteering, gathering some of the most relevant solutions published by the scientific community in the field. First, the background for realizing 1-D and 2-D antenna beamsteering with a transmitarray is introduced. Subsequently, several examples of unit-cells for transmitarray implementation and complete transmitarray designs presented in the literature are outlined. Each solution is analyzed in detail, identifying the nature of its layout, e.g., based on microstrip patches, frequency selective surfaces (FSS), or metamaterials (MMs), and the method employed to enable electronic reconfigurability, e.g., p-i-n diodes, varactor diodes, or microelectromechanical systems (MEMS). In addition, some models with the capability of controlling the wavefront polarization modes are also included herein since these are the base of hybrid transmitarrays, i.e., transmitarray with both electronic beamsteering and polarization control. Finally, all the models are compared against each other in order to highlight their benefits and limitations, summarizing their main characteristics, such as the frequency of operation and bandwidth, insertion loss, physical dimensions, and maximum beamsteering range, when available.

Frequency domain spectroscopy (FDS) technique is widely applied in the condition assessment of the oil-paper system in power transformers. However, the synergistic effect generated by moisture and temperature on the FDS data cannot be analyzed by the existing model since the single independent variable (moisture or temperature) is considered in the construction of the model. To quantify such the synergistic effect, a novel method that utilized for normalizing (or standardizing) the FDS curve is reported based on the theory of the power series and fitting analysis. The present findings reveal that the reported method is capable of predicting the dielectric loss (tan $\delta$ ) curve under diverse test conditions, in which the average error is less than 7%. The synergistic effect can be also explored by using the extracted feature parameters. The potential application is then proved to make up for the measurement errors during the FDS test, the findings are expected to promote the moisture analysis of the transformer insulation.

Recently, it has been shown that channel feedback helps to enhance the physical layer security (PLS) of communication systems. The current use of feedback focuses on generating secret keys protecting the transmitted messages, and this feedback scheme is proved to be optimal for some physically degraded channel models. In this paper, we revisit the model of the state-dependent wiretap channel with the noncausal state at the transmitter, which plays an important role in the PLS of the wireless communication systems. We propose a new feedback scheme for this model and show that the proposed new scheme performs better than the already existing secret key-based feedback scheme. The results of this paper are further explained via a Gaussian example.

The ReaxFF molecular dynamics simulation and Monte Carlo method were adopted to analyze the pyrolysis process of cellulose in insulating papers from the perspective of microscopic atom. Molecular dynamics failed to continuously describe the motion behavior of an atom. According to this principle, the system can only calculate the atomic state on the node and then move the atom by a time step to continue calculating the atom. This paper would put forward the optimal step setting method of cellulose thermal decomposition in insulating paper: setting one step every other 0.1 fs. Specifically, for small molecules with a simple structure, such as H2O, the step size was set to 0.4 fs or less, while the step size of macromolecules with complex structures (CH2O2) should be set to 0.2 fs or less. In addition, the relationship between the step size and the temperature to which the system was heated was given as well in this paper. In previous literatures, empirical values were used to set the step size. This study would not only provide a theoretical basis for the study on the bond formation and fracture process of cellulose pyrolysis products, but also offer the data and guidance for related fields in the future, thus rendering an efficient simulation process.

In general, considering standard production, as well as coconut oil production, in oil consumption industries is an important factor. Oil color is an important element, as it is an important factor for consumers or buyers in selecting coconut oil. In the process of producing coconut oil, the cold-pressed method has been chosen to maintain the essential quality of coconut oil. The quality of the coconut oil is inspected from the production process by means of light passing through the coconut oil. Then, the production staff compares the turbidity of coconut oil with the master sample. The turbidity of coconut oil in every production must be compared with a master sample to maintain standards control. According to previous studies, there are many methods for determining coconut oil turbidity. One method that has been utilized is determining turbidity from light passing through the medium in which the transmitted light can be absorbed through the turbidity of the variable medium. This process is applied together with image processing to determine the coconut oil turbidity. In this research, we propose a method for measuring coconut oil turbidity by the Moving Average Median of Hue (MAMoH), which is better in detecting the coconut oil turbidity than the Median of Gray Scale (MoGS) method, Median of Hue (MoH) method, and Random Position Median of Hue (RPMoH) method. In terms of the percentage accuracy of the efficiency test; the MAMoH method has 99 percent accuracy, while the MoGS method is not applicable, the MoH method has 88.04 percent accuracy, and the RPMoH method has 85.91 percent accuracy. Thus, the MAMoH method is considered an appropriate method for measuring coconut oil turbidity.

Abandoned mineral management has become an important global issue, and the collection, recycling, and disposal of mineral processing tailings (MPT) is critical in both mining engineering and environmental protection. In this study, flocculation sedimentation experiments and slurry rheological tests were carried out with unclassified tailings, and the flocculation sedimentation behaviour of the tailings under different conditions was investigated. The experimental results showed the followings: (1) Under close sedimentation effect conditions, different flocculants showed different effects on the fluidity of the slurry underflow, which was used as an important basis for optimization of the flocculation effect. (2) A tailings solid mass concentration of 40% and a flocculant dosage of 25 g/t were obtained as preferable sedimentation conditions. (3) Based on a microparticle agglomeration characteristics tests at the initial stage of flocculation, a flocculation concentration kinetic model was established that conformed to the fractal characteristics of the particles, which improved the applicability and accuracy of the concentration kinetic equation and the characteristic variables. In this study, an objective method for determining the parameters of full tailings flocculation sedimentation was realized. The results of an industrial trial operation also verified the reliability of the preferred parameters, which provided an effective basis for refined control of an efficient tailings recycling process.

Power inversion algorithms (PI) are widely used by ant-jamming receivers in harsh environment, but it also induces pseudo-range measurement error which is fatal to high precision receivers. In this paper, it proposes a more accurate method based on the S Curve Bias(SCB) to evaluate the pseudo-range bias which can actually affect the positioning results, as it excludes a fixed time delay contained in the directly differing method. When using this method to assess the bias, it is found that pseudo-range errors can be influenced by many factors including the type of interference. Simulation as well as experiment based on a 4-element array antenna are designed and carried out. The simulation has shown that both SOP-PI and STAP-PI will bring bias to pseudo-range measurement. Specifically, the maximum bias caused by SOP-PI is 1m while the bias caused by STAP-PI is up to 10m. Moreover, comparing to the method which directly differs the pseudo-range of output signal and the reference signal,the method proposed in this paper has better performance. Furthermore, the experimental results verify the effectiveness of the proposed evaluating method.

Fifth-Generation (5G) mobile cellular networks provide a promising platform for new, innovative and diverse IoT applications, such as ultra-reliable and low latency communication, real-time and dynamic data processing, intensive computation, and massive device connectivity. End-to-End (E2E) network slicing candidates present a promising approach to resource allocation and distribution that permit operators to flexibly provide scalable virtualized and dedicated logical networks over common physical infrastructure. Though network slicing promises the provision of services on demand, many of its use cases, such as self-driving cars and Google's Stadia, would require the integration of a Multi-Access Edge Computing (MEC) platform in 5G networks. Edge Computing is envisioned as one of the key drivers for 5G and Sixth-Generation (6G) mobile cellular networks, but its role in network slicing remains to be fully explored. We investigate MEC and network slicing for the provision of 5G service focused use cases. Recently, changes to the cloud-native 5G core are a focus with MEC use cases providing network scalability, elasticity, flexibility, and automation. A cloud-native microservices architecture, along with its potential use cases for 5G network slicing, is envisioned. This paper also elaborates on the recent advances made in enabling E2E network slicing, its enabling technologies, solutions, and current standardization efforts. Finally, this paper identifies open research issues and challenges and provides possible solutions and recommendations.

Nowadays, 5G is in its initial phase of commercialization. The 5G network will revolutionize the existing wireless network with its enhanced capabilities and novel features. 5G New Radio (5G NR), referred to as the global standardization of 5G, is presently under the $3^{\mathrm {rd}}$ Generation Partnership Project (3GPP) and can be operable over the wide range of frequency bands from less than 6GHz to mmWave (100GHz). 3GPP mainly focuses on the three major use cases of 5G NR that are comprised of Ultra-Reliable and Low Latency Communication (uRLLC), Massive Machine Type Communication (mMTC), Enhanced Mobile Broadband (eMBB). For meeting the targets of 5G NR, multiple features like scalable numerology, flexible spectrum, forward compatibility, and ultra-lean design are added as compared to the LTE systems. This paper presents a brief overview of the added features and key performance indicators of 5G NR. The issues related to the adaptation of higher modulation schemes and inter-RAT handover synchronization are well addressed in this paper. With the consideration of these challenges, a next-generation wireless communication architecture is proposed. The architecture acts as the platform for migration towards beyond 5G/6G networks. Along with this, various technologies and applications of 6G networks are also overviewed in this paper. 6G network will incorporate Artificial intelligence (AI) based services, edge computing, quantum computing, optical wireless communication, hybrid access, and tactile services. For enabling these diverse services, a virtualized network slicing based architecture of 6G is proposed. Various ongoing projects on 6G and its technologies are also listed in this paper.

This paper deals with investigating the closed-loop stability boundary of the highly flexible aircraft with structural flexibility and input saturation constraint. A method is presented to analyze the stable region based on the system features of the open-loop instability. In this paper, a dynamic model of highly flexible aircraft is made and the dynamic characteristics are analyzed and the long period mode and stability are investigated in terms of the trimmed model. Considering the saturation constraint of elevator deflection, the closed-loop stability boundary of the system under saturation constraint is discussed by combining the open-loop instability features of highly flexible aircraft. The analysis indicates that the boundary is related to the left eigenvector consistent with the amplitude constraint and the unstable poles of the control signals. Based on the long period instability of the aircraft, the formula of closed-loop stability boundary is analytically obtained. The convergence region of highly flexible aircraft is verified in terms of the LQR controller. The influence of structural flexibility and saturation constraint amplitude of elevator on the stability boundary of highly flexible aircraft is analyzed based on state constraints, which is also compared with the rigid aircraft. The simulation results show that the closed-loop stability of the system is restricted by the open-loop characteristic of the system and the control input saturation constraint.

Though various applications such as flash memory, cache, storage systems, and even indexing for enterprise big data search, adopt hot data identification schemes, relatively little research has been expended into holistically examining alternative strategies. Rather, researchers tend to classify hot data simplistically by considering one or more frequency metrics, thereby disregarding recency, which is also an important consideration. In practice, different workloads mandate different treatment to achieve effective hot data decisions. This paper proposes a dynamic hot data identification scheme that adopts a workload stack distance approximation. Stack distance is a good recency measure, but it traditionally requires high computational complexity as well as additional space. Since stack distance calculation efficiency is a core component for our dynamic feature design, this paper additionally proposes a stack distance approximation algorithm that significantly reduces both computation and space requirements. To our knowledge, the proposed scheme is the first dynamic hot data identification scheme which judiciously assigns more weight to either recency or frequency based on workload characteristics. Our experiments with diverse realistic workloads demonstrate that our stack distance approximation achieves excellent accuracy (up to a 0.1% error rate) and our dynamic scheme improves performance by as much as 49.8%.

Machine reading comprehension is a very challenging task, which aims to determine the answer span based on the given context and question. The newly developed pre-training language model has achieved a series of successes in various natural language understanding tasks with its powerful contextual representation ability. However, these pre-training language models generally lack the downstream processing structure for specific tasks, which limits further performance improvement. In order to solve this problem and deepen the model’s understanding of the question and context, this paper proposes S&I Reader. On the basis of the pre-training model, skimming, intensive reading, and gated mechanism modules are added to simulate the behavior of humans reading text and filtering information. Based on the idea of granular computing, a multi-granularity module for computing context granularity and sequence granularity is added to the model to simulate the behavior of human beings to understand the text from words to sentences, from parts to the whole. Compared with the previous machine reading comprehension model, our model structure is novel. The skimming module and multi-granularity module proposed in this paper are used to solve the problem that the previous model ignores the key information of the text and cannot understand the text with multi granularity. Experiments show that the model proposed in this paper is effective for both Chinese and English datasets. It can better understand the question and context and give a more accurate answer. The performance has made new progress on the basis of the baseline model.

This paper tackles the problem of detecting incongruities between headlines and body text, where a news headline is irrelevant or even in opposition to the information in its body. Our model, called the graph-based hierarchical dual encoder (GHDE), utilizes a graph neural network to efficiently learn the content similarity between news headlines and long body paragraphs. This paper also releases a million-item-scale dataset of incongruity labels that can be used for training. The experimental results show that the proposed graph-based neural network model outperforms previous state-of-the-art models by a substantial margin (5.3%) on the area under the receiver operating characteristic (AUROC) curve. Real-world experiments on recent news articles confirm that the trained model successfully detects headline incongruities. We discuss the implications of these findings for combating infodemics and news fatigue.

A malicious attack may endanger human life or pollute environment on a cyber-physical system (CPS). However, successfully attacking a CPS needs not only the knowledge of information technology (IT) but also the domain knowledge of the system’s operation technology (OT). Therefore, it is critical to identify the vulnerabilities of a CPS. This paper proposes a systematic method for the security verification of a CPS, focusing on OT by using model checking with UPPAAL, so as to enhance cyber security. In our security analysis, we considered unsafe situations to be the result of a potentially effective security attack. Thus, we suggested a systematic method to generate security constraints based on the safety constraints (or safety checks) of the CPS and then enhance these security constraints by security verification using model checking with UPPAAL. UPPAAL’s simulation tool can perform a detailed search for each state in various possible model combinations and can explore human-computer interactions more deeply. The contributions of our method are as follows: First, a systematic method is proposed to generate security constraints based on the overall safety requirements at the OT level. Second, the security constraints thus generated can be used for run-time monitoring to identify the possible security attacks when they are violated. Third, this paper proposes to augment normal system modeling with a suggested Attack Module to simulate the potential OT attacks. Finally, the verification results may be used in the following twofold directions: to identify the vulnerabilities for possible design improvements and to suggest the further additions of security constraints.

Bike sharing is an important tool to reduce congestion and pollution in urban areas. Electrically Power Assisted Bicycles (EPAC’s) make cycling possible also for sedentary people. Standard EPAC’s are difficultly integrable into a free-floating sharing system because the battery pack requires frequent recharging. This paper studies the challenges, opportunities and solutions of implementing a free-floating bike sharing system based on electric bicycles. The analysis revolves around the charge sustaining paradigm. The idea of charge sustaining leverages the metabolic efficiency gaps to reduce the overall physical effort required without determining a net discharge of the battery. Already validated in private bicycles, the idea needs to be modified and adapted to the challenges of a shared fleet. The paper analyzes two approaches to the fleet level energy management and assistance control of a fleet of charge sustaining bicycles. Specifically, we compare a fixed price approach against a flexible pricing approach where the user can select the cost based on the pedaling effort they are willing to exercise. A simulation framework (calibrated on data collected during a large trial in Milan, Italy) assesses the operational costs and revenues of the two approaches quantifying how they depend on the design and environmental parameters. We provide and validate a lower bound in terms of usage rate that guarantees economic sustainability, additionally showing that a flexible pricing strategy can lower this bound and grant more degrees of freedom to the users.

Grain-128AEAD is a lightweight authenticated encryption stream cipher and one of the finalists in the National Institute of Standards and Technology (NIST) Lightweight Cryptography (LWC) project. This paper provides an independent third-party analysis of Grain-128AEAD against fault attacks. We investigate the application of three differential fault attack models on Grain-128AEAD. All these attacks can recover the initial state of Grain-128AEAD. First, we demonstrate an attack using a bit-flipping fault that requires access to 27.80 faulty outputs to recover the initial state. Then, we demonstrate an attack with a more relaxed assumption of a random fault with a probabilistic approach. Our probabilistic random fault attack requires access to 211.60 faulty outputs and 210.45 fault injections to recover the initial state with a success rate over 99%. Both of the above two attacks are based on precise control on the fault target. Finally, we apply a random fault attack with a deterministic approach (can conclusively determine the random fault value) and using different precision controls. For the precise control, we use existing approaches that have been applied to other ciphers, such as Tiaoxin-346. We also propose a technique for less stringent precision models, such as moderate control and no control, which are more practical than the precise control. Our result indicates that the deterministic random fault attack with a precise control requires an average of 27.64 fault injections and a data complexity of 28.80. The deterministic random fault attack with moderate control requires a weak assumption on the fault injection and hence, is the best attack presented in this paper; and is expected to require about 29.39 fault injections with a data complexity of about 212.98. All the attacks discussed in this paper are verified experimentally.

This paper aims to provide an in-depth study of the detection of historical alarm subsequences, which are frequently used as an initial step for alarm flood analysis methods. Therefore, state-of-the-art approaches are comprehensively examined, evaluated, and compared. To overcome the limitations of these methods, a novel approach is presented, which uses outlier detection in time distances between alarm events (activation and return to normal) and an alarm coactivation constraint. The effectiveness and performance of the examined methods are illustrated by means of an openly accessible dataset, which is introduced in this paper. It is based on the “Tennessee-Eastman-Process”, a benchmark in process automation. The intent is to provide a suitable dataset for the development and evaluation of alarm management methods in complex industrial processes using both quantitative and qualitative information from different sources. It is shown that the integration of supplementary information is beneficial for the overall performance and robustness of the detection method proposed here. This method allows for a more accurate detection of coherent historical abnormal situations, including phases with active root-cause disturbances and the normalization phases that follow their termination. Furthermore, the proposed method has the advantage that the detection results are less influenced by the alarm count, the propagation velocity, the duration of the situation, and the time distance between two causally independent situations in comparison to state-of-the-art approaches.

The aim of this paper is to design, analyze and optimize the “Multi-Stack Slotless Axial Flux Switching Permanent Magnet Machine”. In the design process, mathematical models are implemented, and the Finite Element Method (FEM) is performed to analyze the machine performances. The paper aims to minimize the occurrence of cogging torque and torque ripple in the multi-stack slotless stator AFPM machine. As a consequence, it reduces the vibrations in the machine and increases its life span. Multi-stack slotless stator AFPM machine with a right-angled trapezoid-shaped PM is proposed and comparison is done with conventional shape AFPM machine. In order to examine the performance of multi-stack slotless stator AFPM machine Finite Element Analysis (FEA) is used. To further enhance the characteristics of the designed machine with the proposed right-angled PM shape, optimization is done by considering inner and outer pole pitch as the design variables. In optimization process, krigging method assigned with Latin Hyper-cube Sampling and a genetic algorithm (GA) is performed due to suitability with non-linear data. Then, finite element analysis by JMAG-Designer is performed to verify the results. It is determined that optimized model has achieved 65% reduction in torque ripples as compared with the conventional design. Hence, this work attempts to optimize the performance of the AFPM machine.

This paper reveals the fundamental reason for the asymmetrical issue in magnetic-geared machines (MGMs) based on flux modulation theory. The analysis indicates that the magnetic circuits of three-phase windings in MGMs are inherently asymmetrical. This asymmetrical issue is even more severe in conventional consequent-pole MGMs (CP-MGMs) because of the more distorted magnetic field. Hence, to address the asymmetrical issue, a novel structure featuring enhanced harmonic elimination capability is proposed in this paper. Consequently, torque density can also be improved. In particular, the proposed CP-MGM employs modular structure and axially embedded permanent magnets (PMs) to achieve symmetrical back electromotive force (EMF) waveforms and improved torque capability, respectively. To further improve the electromagnetic performance of the proposed CP-MGM, the PM arc ratio and flux modulator width ratio are analytically designed, which provides a general design guideline for CP-MGMs. To illustrate the merits of the proposed CP-MGM, a few other MGMs are included for a fair comparison based on finite element analysis (FEA). Results show the proposed CP-MGM can achieve more symmetrical back EMF waveforms and lower torque ripple, as well as lower PM consumption and higher torque density, as compared with its MGM counterparts. Published version Schaeffler Group Research Grant M4062765.040.601001

An intrusion detection system (IDS) is an important protection instrument for detecting complex network attacks. Various machine learning (ML) or deep learning (DL) algorithms have been proposed for implementing anomaly-based IDS (AIDS). Our review of the AIDS literature identifies some issues in related work, including the randomness of the selected algorithms, parameters, and testing criteria, the application of old datasets, or shallow analyses and validation of the results. This paper comprehensively reviews previous studies on AIDS by using a set of criteria with different datasets and types of attacks to set benchmarking outcomes that can reveal the suitable AIDS algorithms, parameters, and testing criteria. Specifically, this paper applies 10 popular supervised and unsupervised ML algorithms for identifying effective and efficient ML-AIDS of networks and computers. These supervised ML algorithms include the artificial neural network (ANN), decision tree (DT), k-nearest neighbor (k-NN), naive Bayes (NB), random forest (RF), support vector machine (SVM), and convolutional neural network (CNN) algorithms, whereas the unsupervised ML algorithms include the expectation-maximization (EM), k-means, and self-organizing maps (SOM) algorithms. Several models of these algorithms are introduced, and the turning and training parameters of each algorithm are examined to achieve an optimal classifier evaluation. Unlike previous studies, this study evaluates the performance of AIDS by measuring the true positive and negative rates, accuracy, precision, recall, and F-Score of 31 ML-AIDS models. The training and testing time for ML-AIDS models are also considered in measuring their performance efficiency given that time complexity is an important factor in AIDSs. The ML-AIDS models are tested by using a recent and highly unbalanced multiclass CICIDS2017 dataset that involves real-world network attacks. In general, the k-NN-AIDS, DT-AIDS, and NB-AIDS models obtain the best results and show a greater capability in detecting web attacks compared with other models that demonstrate irregular and inferior results.

In the past few years, game randomizers have become increasingly popular. In general, a game randomizer takes some aspect of a game that is usually static and shuffles it somehow. In particular, in this paper we will discuss the type of randomizer that shuffles the locations of items in a game where certain key items are needed to traverse the game world and access some of these locations. Examples of these types of games include series such as The Legend of Zelda and Metroid. In order to accomplish this shuffling in such a way that the player is able to reach the end of the game, some novel algorithms in graph theory must be utilized, where the game world and its item locations are represented as a graph and each edge on the graph has some rule for which items are required to traverse it. In this paper, we define these algorithms formally and evaluate them with different metrics that can guide a developer’s decision about which algorithm works best for their game.

Optical fiber sensing is a novel sensing technology with the advantages of anti-electromagnetic interference, anti-corrosion and high sensitivity suiting for many special applications. In this paper, a Michelson optical fiber voice monitoring system is introduced and a novel sound extraction algorithm is proposed to improve the noise stability of it. The voice demodulated by a traditional algorithm based on $3\times 3$ fiber coupler has unstable background noise intensity with the change of the working point of the interferometer. The unstable noise comes from the shot noise of photodetectors. The sound extraction method proposed in this paper uses the relation between shot noise and light intensity to smooth the unstable noise and does not affect the quality of voice monitoring. Voice monitoring systems using this algorithm have stable monitoring quality without any working point control technology. The fluctuation range of noise intensity in 300~3500 Hz decrease from 10 dB to less than 2 dB compared with the traditional demodulation algorithm. Such a method and system can be widely used in many special voice monitoring application fields such as prison, underground utility tunnel.

With the rapid development of remote-sensing imaging technology, remote-sensing images have become increasingly diverse, and people are paying more attention to ocean remote-sensing research. Because ocean remote-sensing data are complex, and the ocean environment is diverse, results will differ, even if the same target is detected at different times in the same scene. To obtain more semantic features and better pixel-level prediction capabilities, this paper proposes a pixel-level ocean remote-sensing image algorithm (GLPO-Net) that combines local and global features. First, texture features, color features, and spatial relationship features are extracted. Second, the algorithm constructs a multiscale local cross-attention mechanism strategy to obtain feature weight information in different directions to fully mine the local features of ocean remote-sensing images. Concurrently, an algorithm constructs a multiscale global cross-attention mechanism strategy to obtain global features. Then, the fusion of global features and local features is described in each submodule to obtain more representative deep features. Finally, small-sample ocean remote-sensing is described via image pixel-level prediction. The algorithm proposed in this paper has been tested with three public ocean remote-sensing datasets. The experimental results show that the proposed GLPO-Net algorithm can learn features from small samples of ocean remote-sensing images. Compared to the prediction results of other remote-sensing image algorithms, GLPO-Net exhibits better prediction capabilities.

The proposed approach uses blockchain-based technology to strengthen the data security of wireless sensor networks (WSNs). This paper integrates blockchain-based technology with data transfer to establish an extremely secure WSNs structure. The present wireless network is built on the architecture of the Internet of Things (IoT) and employs a blockchain-based method to make the reliability of data transmission strong. In this proposed research, many small-area wireless sensor networks establish the entire WSNs structure, and every small-area wireless sensor network has a primary data collection node called a “mobile database.” The “mobile database” node of this study uses embedded microcontrollers with an operating system, such as Raspberry Pi and Arduino Yun. This block contains the sensor data collected by itself and the hash value of the previous block. Then the hash value of its own block, which is also part of the hash calculation of the next block, was calculated through the mining calculation program. Any block in the proposed method includes the encrypted hash-value of the previous block, the current timestamp, and the transaction data. In our research content, the transaction data is represented as wireless network sensing data. Basically, the system employs the hash function for calculation using the Merkel-tree algorithm. Such programming makes the block with blockchain-based technology difficult to tamper with content. This study approach revises the blockchain-based transaction ledger to become a sensor data record. Therefore, the proposed system gathers and analyzes sensor data for more reliability in the wireless sensing network structure. Furthermore, the innovative system with blockchain-based technology can treat a private cloud-end. This paper also carries on to visualize the uploaded sensing data by the sensors and draws corresponding charts based on big data analysis. The wireless network architecture proposed in this paper is built on embedded devices, making it easy for the system to build a web server. Using Python or JavaScript programming language in the web environment is relatively more convenient for data visualization and data analysis. Finally, this study uses traditional methods and innovative methods to compare data transmission. When the system uses innovative methods with blockchain-based technology, it is almost impossible for any operator to tamper with the data transmitted by the sensor.

The simple rules that govern the interactions between the different components of a complex system often lead to interesting behaviours that are unexpected. The experiments described in this paper involved creating a variation of a traditional Braitenberg Vehicle, by placing sensors on ten different possible locations on a simulated vehicle, and incorporating an obstacle avoidance behaviour using a subsumption-like architecture, which resulted in different unusual and unexpected behaviours being produced. The vehicle was allowed to explore a simulated environment which contained a single bright light in the centre with walls on the border. By using a novel combination of the Prediction by Partial Matching compression algorithm and ${k}$ -means clustering, interesting emergent behaviours were effectively discovered within a search space of over 10,000 simulations produced from a simple interaction of light and proximity sensors on a vehicle and a single light source. The clustering algorithm discovered five distinct behaviours: circling and spiralling behaviours; interesting behaviours creating intricate rose petal-like structures; behaviours that create simple rose petal-like structures; behaviours with large movements and low complexity; and behaviours with less movement. The novel algorithm demonstrated in this paper has useful potential in the science of complex systems and modelling to help expedite the systematic exploration of a substantial search space of simulations in order to discover interesting behaviours.

Near-infrared spectroscopy (NIR) has become one of the most important methods for counterfeit drugs identification for its low cost, non-destructive, and on-site detection. However, it is often invalid for unknown samples beyond the scope of modeling samples, as well as it is not efficient in conditions of insufficient samples (insufficient number of samples within the class), unbalanced samples (large difference in the number of samples between classes), and sensitivity of identification results (different tolerance for different errors in application scenarios). To solve these problems, this paper proposes a general method for on-site identification of counterfeit drugs based on Siamese-network modeling with near-infrared spectroscopy, which especially constructs the train set and test set, learning the general knowledge of spectral differences to identify the different drugs by a costumed one-dimensional convolution neural network (1D-CNN), and finally answered the question of whether the on-site two spectra are pointed to the same drug. Based on experimental modeling samples of 1314 spectra, which are involved 9 drugs produced by 25 manufacturers, this paper has constructed and fully trained its model. Then, not known at the time of modeling, 4 drugs produced by 9 manufacturers are used for testing in the on-site application, and the accuracy rate amounts to 97.3%. For generalizing consideration, randomly divided into training and testing categories, the 32015 spectra of 135 drugs produced by 391 manufacturers in the spectral library are handled by the same processing. The generalization model is equally applicable, and the accuracy is above 97%. Compared with traditional binary classification identification methods such as SVM, PLS-DA, Auto-encoding, and one class (OC) threshold identification algorithms such as SVM-OC, SIMCA, conformity test (CT), the proposed method has the best identification ability for unknown samples in modeling.

Handwritten mathematical expressions are an essential part of many domains, including education, engineering, and science. The pervasive availability of computationally powerful touch-screen devices, similar to the recent emergence of deep neural networks as high-quality sequence recognition models, result in the widespread adoption of online recognition of handwritten mathematical expressions. Also, a deeper study and improvement of such technologies is necessary to address the current challenges posed by the extensive usage of distance learning, and remote work due to the world pandemic. This paper delineates the state-of-the-art recognition methods along with the user's experience in pen-centric applications for operating with handwritten mathematical expressions. Recognition methods have been categorized into classes, with a description of their merits and limitations. Particular attention is paid to end-to-end approaches based on encoder-decoder architecture and multi-modal input. Evaluation protocols and open benchmark datasets are considered as well as the comparison of the recognition performance, based on open competition results. The use of handwritten math recognition is illustrated by examples of applications for various fields and platforms. A distinctive part of the survey is that we also considered how UI design relies on the use of different recognition approaches, which is aimed at helping potential researchers improve the performance of the introduced approaches toward the best responses in practical applications. Finally, this paper presents the prospective survey of future research directions in handwritten mathematical expression recognition and their applications.

In this paper, the effects of rotor-rotor interaction on the wake structure and thrust generation of a quadrotor unmanned aerial vehicle (UAV) are experimentally investigated in the rotor tip Reynolds number range of 34000 – 54000. The interaction strength is manipulated by varying the number of rotating rotors and the normalized rotor separation distance. A stronger rotor-rotor interaction places the inner tip vortices between rotors closer to each other, forming an upflow region through vortex pairing and intensifying the turbulence intensity between rotors. To comprehensively evaluate the effect of interaction on the wake structure, we propose a modified Landgrebe’s model that accurately describes the wake boundary of UAV, given the number of rotating rotors and the normalized rotor separation distance. The wake analysis based on the model shows that the stronger the rotor-rotor interaction, the less the wake contracts and the closer the vena contracta moves to the rotor-tip path plane. The momentum theory combined with the modified Landgrebe’s model shows that the loss of axial momentum transfer due to the wake inclination is insufficient to account for the thrust loss caused by the rotor-rotor interaction. This paper shows that the shift of the inner tip vortex away from the rotational axis and the corresponding increase of induced axial velocity followed by a decrease in the local effective angle of attack is another important mechanism for the thrust loss.

In the past few decades, although people have conducted in-depth research on community detection in one-mode networks, community detection in bipartite networks has not been extensively researched. In this paper, we propose an improved artificial bee colony algorithm named IABC-BN, which is used to detect the communities in two-mode graphs (i.e. bipartite graphs) with two kinds of vertices in the cluster (i.e. community). Firstly, this paper proposed a novel population initialization process of artificial bee colony (ABC) method for two-mode graph cluster identification. This initialization method can improve the diversity of initial population of ABC and speed up its convergence rate. Secondly, in the employed bee search step of the algorithm, a new combinatorial search equation is proposed. This equation is guided by the global optimal solution and the better neighbour solution of the current solution. By using this combination equation and the increased parameter perturbation frequency, the exploitation ability of the algorithm is further enhanced. Thirdly, in the onlooker bees step, another new combination search equation is also proposed. This equation improves the exploitation level of the algorithm, and an opposition based studying method is employed to promote the exploitation ability of the algorithm. Lastly, in scout bee stage, a probability threshold $\beta $ is introduced to enhance the exploration ability of the algorithm and improve the population diversity of the algorithm. To our knowledge, the IABC-BN method presented in this paper is the first ABC method used to cluster identification in two-mode graphs with two kinds of vertices in the cluster. For verifying the accuracy of the results of the proposed method, a large number of experiments are carried out making use of synthetic bipartite graphs and real bipartite graphs. The test outcomes show that this algorithm is an excellent algorithm for cluster discovery in two-mode graph.

Timing synchronization has a vital role in swarm drones’ network (SDN) or a swarm of unmanned aerial vehicle (UAV) network. Current timing synchronization methods focus on enhancing single-hop skews which remarkably improve timing synchronization precision at this level. The improper clock of the drone system can cause interference, affect spectrum precision and interrupt the operation of the transceiver. In the drones’ network, master drones’ (MD) neighbor drone’s timing synchronization approaches like Reference Broadcast System (RBS) realize a good performance. However, the requirement of one super drone with a large number of broadcasts for RBS makes it unrealistic to use in some situations like SDN network situation. Appropriate study and adjustments are needed to have real timing synchronization by eliminating the clocks drift and enhancing the timing synchronization precision. Therefore, a new self-timing synchronization approach is proposed in this paper where several MD drones can autonomously generate swarm clusters. The cluster head (CH) instigates a timing synchronization procedure starting with intra-Swarm cluster timing synchronization. The intermediate drones (ID) are elected between two swarm clusters to synchronize all drones in line with the inter-swarm cluster timing synchronization approach. The proposed approach is distributed and flexible to achieve high timing synchronization precision. The paper proposes a novel self-timing synchronization approach for in large scale semi-flat SND network architecture. Self-timing synchronization is swarm cluster-based and applicable for a huge number of master drones in SDN. One is the intra-Swarm cluster where the timing synchronization procedure starts with the CH to synchronize all CM. Secondly, in the inter-swarm cluster timing synchronization, two clusters are synchronized via intermediate drone (ID). However, the simulations demonstrated that in many cases all CHs are synchronized by the synchronized CHs from intra-swarm cluster timing synchronizations; this increased the system throughput and synchronization delay to about 75% compared to what we planned to achieve. Moreover, the simulation results also proved that the achieved synchronization precision can be used for position estimation and prediction with high accuracy.

For the on-orbit service spacecraft attitude tracking task with external disturbances, considering the limited capacity of its actuators and high control performance required, an improved performance constraint control algorithm based on command filters is proposed in this paper. For the proposed control algorithm, before the theoretical control signal is transmitted to the actuator, it needs to be filtered by the command filter. This filter can constrain the rate, magnitude, and bandwidth of the signals, smooth the command signals and avoid peak interference. In terms of performance constraints, inspired by the performance constraint curve, this paper uses the determined points to re-design a new performance constraint function, which shows the maximum settling time parameters, allowing designers to pre-defined this limited time of the closed-loop system according to needs. Therefore, the maximum settling time of the actual closed-loop system will be less than the pre-defined maximum settling time. The controller based on the command filter is designed, and on this basis, the corresponding performance-constrained control algorithm with pre-defined maximum settling time guaranteed is proposed. Through the proof and simulation, the rationality and effectiveness of the proposed controller are further demonstrated.

This paper discusses a novel fault location approach using single ended measurement. The natural dissipation of the circuit parameters are considered for fault location. A relationship between the damped natural frequency of oscillation of the transmission line current and fault location is established in this paper. The hybrid dc circuit breaker (dcCB) interrupts the fault current and the line current attenuates under the absence of any driving voltage source. The line capacitance discharges into the fault at a specific frequency of oscillation and rate of attenuation. Utilizing this information, the fault location in a multi-terminal direct current (MTdc) network can be predicted. A three terminal radial model of a MTdc is used for performance evaluation of the proposed method using Power System Computer Aided Design (PSCAD)/Electromagnetic Transients including dc (EMTdc)., 8 pages, 12 Figures, 6 Tables

As the final stage of power system restoration, the critical task of load restoration is to restore the remaining load as quickly as possible. With the continuous increase of the temperature-controlled load and the proportion of electric vehicle load in the urban power grid, the complexity of the load side in the restoration process gradually increases. Therefore, based on the existing grid environment, this paper considers the sudden increase in load recovery caused by cold load pick-up and the auxiliary effect of electric vehicle discharge on load recovery during the load recovery process. From the perspective of economy, safety, and speed, this paper establishes a multi-objective function that includes the amount of load, improved weighted power flow entropy, and the number of recovered lines. The multi-objective evolutionary algorithm based on decomposition is used to optimize the constructed multi-objective load recovery model. Through the IEEE30 node system, it is verified that the method proposed in this paper can effectively establish a fast and safe load recovery plan that meets the actual grid environment.

Industry 4.0 is a revolution in manufacturing by introducing disruptive technologies such as Internet of Things (IoT) and cloud-computing into the heart of the factory. The resulting increased automation and the improved production synergy between stocks, supply chains and customer demands, come along with the threats and attacks from the Internet. Despite extensive literature on the cybersecurity topic, many actors in manufacturing factories are just realizing the impact of cybersecurity in the preservation of their business. This paper introduces step-by-step the concepts and practical aspects of an Industry 4.0 manufacturing factory that are related to cybersecurity. Based on a subdivision of a typical factory into several generic perimeters, we present the vulnerabilities and threats regarding the network and devices usually found in each perimeter. Therefore, it is more efficient to present the recent proposals of the literature regarding cybersecurity guidelines and solutions in Industry 4.0. Instead of spreading a lot of references regarding every aspect of cybersecurity, we focused on a limited number of papers among the recent references. However, for each paper, we provide the details about the purpose of the proposal, the methodology adopted, the technical solution developed and its evaluation by the authors. These solutions range from classical cybersecurity countermeasures to innovative ones, such as those based on honeypots and digital twins. In order to deliver a review also useful to non scientists, we present our guidelines along with those of some organizations involved in cybersecurity harmonization and standardization in the world.

In this paper, a modified COMSOL Multiphysics model with artificial stability is adopted, and the boundary condition developed in the previous paper are applied to the calculation of the photoionization rate. The main processes involved in air discharge were simulated by 39 plasma chemical reactions composed of 12 kinds of particles. The characteristics of low-temperature sub-atmospheric pressure air discharge under unipolar square wave pulse voltage with 13kV applied amplitude, 25 kHz-50 kHz frequency and a duty cycle of 50%-75% are discussed. The results show that: When the duty cycle increases, the average density of electron, N2+, N4+, O2+, O4+, and N2O2+ show an increasing trend, and the average electron temperature changes little; When the frequency increases, the average electron density, the average density of N4+ and O4+ show a downward trend, the average density of N2+, O2+, N2O2+ change little; The average electron temperature shows an increasing trend, but the duration of high electron temperature becomes shorter. When discussing a single period, it is found that the potential difference between the electrode and the plasma determines the direction of the electric field in the space. For the photoionization reaction, even if its influence on the negative streamer is not important, it still needs to be considered in the simulation. The effect of temperature on discharge is also taken as the research emphasis, it includes the following aspects: Firstly, at low temperature, the collision reaction and attachment reaction rates are increased, while the recombination reaction rate is almost unaffected; Secondly, low temperature results in the decrease of secondary electron emission coefficient, which indirectly increases the electric field gradient and the peak value of electric field; Finally, at low temperature, the mobility of carriers, including electrons, positive and negative ions, is increased, but the streamer velocity is decreased.

In order to overcome the short battery life and poor long-distance maneuverability of Unmanned Aerial Vehicle (UAV) in power inspection operations, some provincial and municipal power companies promote a new mode of “UAV and operating vehicle (OV)” collaborative power inspection. This paper investigates into the actual problems in the cooperative inspection operations of the OV and multi-UAVs, and proposes a model of task allocation for the cooperative inspections of the OV and multi-UAVs, which includes four sub-problems: the division of a single operation circle, the location of parking spots, multi-UAVs task allocation, and the path planning of OV. The proposed model is solved by using improved K-means algorithm and genetic algorithm, and case analysis is carried out. The calculation results show that the model of task allocation for cooperative inspections proposed in this paper is practical and feasible, which can provide theoretical guidance for actual power inspections.

This paper proposes a linear compensator algorithm to improve the performance of an absolute multipolar magnetic encoder (AMPME). An AMPME is an absolute magnetic rotary encoder that uses a multipolar magnet (MPM) to increase the resolution. The resolution can be dramatically increased in proportion to the number of poles in the MPM. However, various hardware problems that occur during the encoder manufacturing process degrade the AMPME performance. Also, harmonic components occur in the raw data due to various problems, such as the resistance error of the analog circuit, magnetic field overlap between the magnets, position error between the sensor and magnet, and pole-pitch difference. In particular, during the magnetization process of the MPM, the pole-pitch difference becomes a problem when the sizes of each pole are not uniform. This problem causes harmonic components that reduce the absolute position accuracy. To solve these problems, this paper proposes a linear compensation method. The proposed linear compensator consists of two parts. The first part is the enhanced ratiometric linearization for phase calculation and calibration. The second is the phase compensator for removing the phase difference via the pole-pitch difference of the MPM. The linear compensator improves various parameters by precomputing the offset, amplitude, and phase corrections. After compensating for sinusoidal signals, the linear compensator applies appropriate parameters at the appropriate times. This method is faster, easier to set up, and more accurate than the conventional method. Furthermore, this method is experimentally verified against the existing harmonic rejection method. Experimental results are provided to verify the effectiveness of the proposed method.

This paper introduces the application of a newly developed heuristic nature-inspired optimization technique, viz, tuned Marine Predator Algorithm (MPA), to solve the optimal power flow (OPF) problem of multi-regional systems. The paper proposes MPA parameters' tuning to enhance the algorithm performance. The paper takes into account the variability of different types of renewable energy resources (RERs) and loads. Two modeling approaches are presented: holistic (multi-regions are modeled as one large network) and inter-bounded (modeling the regional interfaces). The MPA is applied to the IEEE-48 bus connected system, and the results are compared with another well-established heuristic algorithm, namely the Genetic Algorithm (GA). The results demonstrate the validation, applicability and effectiveness of using the MPA for solving multi-region OPF problem considering renewable energy sources and load variability.