This letter proposes a Ku-band voltage-controlled oscillator (VCO) equipped with low-dropout (LDO) based amplitude control, fabricated in the 0.18μm CMOS process. The LDO regulator combines with the voltage distribution network to generate a stable supply of 1.8V and an internal bias voltage range of 0.64−0.72V to the VCO core. The VCO demonstrates an 11.6% tuning range from 15.69GHz to 17.62GHz. The measured phase noise of 17.62GHz carrier frequency is −111.2 dBc/Hz at 1MHz offset frequency while dissipating 3.5mA from 1.8V voltage regulator output, yielding a Figure-of-Merit of 189.4. The whole chip size is 0.6 mm2, including testing pads.
This article presents a high-efficiency circularly polarized slotted waveguide leaky wave antenna at the W band. A linearly polarized beam-steering antenna is designed by creating an array of longitudinal slots on the WR-10 waveguide and to achieve circular polarization, a single-layer polarization converter has been used. A new technique has been proposed to enhance the overall efficiency of the antenna. The wall thickness of the waveguide containing the radiating slots is reduced to increase the overall radiation efficiency of the provided antenna. The gain of the prototype antenna is above 20 dBi in the intended operating band from 92 GHz to 98 GHz. Furthermore, the antenna provides an average half-power beamwidth (HPBW) of 3.2° in the elevation plane and a cross-polarization below -18 dB in the main beam direction. Attributed to robust structure, reliable fabrication, and simple antenna configuration as well as its superior performance, the proposed structure is a strong candidate for various W band applications.
It is shown experimentally a new digital optical decoding scheme based on the transmission or polarized light at p polarization planes using a K-Nearest Neighbor (KNN) algorithm through a single-mode optical fibre at 633 nm. The optical power signal is sent at p polarization planes which constitute p classes required for signal bit recognition. Results show that it is possible to recognize 32 polarizations planes, 5 bits, using 4 features corresponding to the measurement of optical power at 4 different angles at the photodetector side with an average assertiveness of 99.1%.
When acting as the shared energy storage and participating in electricity market services, the schedulable capacity that shared electric vehicle (shared EV) will provide to the grid needs in future time needs to be predicted accurately. This research first proposes a method to construct a schedulable capacity dataset for shared EVs based on publicly available shared vehicle rental service data. Secondly, a schedulable capacity evaluation model based on model-agnostic meta-learning, convolutional neural network, long short-term neural network and attention mechanism (MAML-CNN-LSTM-Attention) is proposed. Through the model, the aggregated schedulable capacity of shared EVs in different functional communities for the coming 60 minutes is predicted. Model uses MAML to fine-tune the meta-prediction network through multi-task training to quickly adapt to feature changes caused by different travel habits of different functional communities; CNN-LSTM is used to learn spatial features of schedulable capacity and efficiently extract high-dimensional temporal features from historical sequences; Attention mechanism is used to further improve model prediction accuracy. Simulations show that the model proposed in this paper outperforms other existing models and can reliably predict the schedulable capacity for different date types and functional areas, providing useful decision aids for shared EV operators to participate in market services.
Large penetration of renewable energy sources into the power grid has increased the complexity of power system operation and greatly reduced the ability of the system to withstand large disturbances. The frequent occurrence of voltage instability problems in the power grid has brought new challenges to the assessment of transient stability analysis of the power system. To achieve fast and accurate assessment of transient voltages, a transfer learning-based transient voltage stability assessment with small samples is proposed, introducing a domain transfer learning approach, embedding a cooperative attention mechanism in the residual network during the feature extraction stage to capture long-range correlations between features, and using adversarial approaches to reduce the differences between samples from different data sets, using the source domain to guide the target domain for network training to improve model's evaluation capability when the number of samples is insufficient, enhance the generalisation performance of the network, and effectively improve the performance of real-time power system transient voltage stability evaluation in the absence of sufficient historical data. Testing on an improved New England 39-node system validates the superiority of this method in transient voltage stability assessment and provides a new approach to practical field transient voltage stability assessment.
With the large-scale grid connection of power electronic power sources, the power system gradually exhibits the characteristics of ‘low inertia’, and the indexes of frequency characteristics are getting closer to the safety critical value, which seriously affects the frequency safety of the system operation. To quantitatively analyze the minimum inertia requirement of the power electronic power system under the condition of multi-resource participation in frequency regulation (FR) when it is disturbed by active power, based on the improved frequency response model of the multi-machine system, this paper proposes a minimum inertia estimation method of the power system considering the frequency response characteristics. The theoretical inertia of each FR unit is represented in the form of rotor kinetic energy, and the calculated inertia of the power system is quantified based on the Rate of Change of Frequency (RoCoF). The sliding window technique is used to select the data set with the smallest variance and obtain the final calculated inertia of the system. The proposed estimation method takes the initial RoCoF, the maximum frequency deviation, and the steady-state frequency deviation as the frequency change constraint indicators, and adds the minimum inertia improvement measures to reduce the demand for the minimum inertia. Finally, the PSD-BPA software is used to verify the accuracy of the proposed model. And based on the improved frequency response model of the multi-machine system, MATLAB/Simulink is used to verify the proposed minimum inertia estimation method.
This paper considers the variability in the impact of multi-dimensional meteorological information on power load in different regions. To improve the accuracy of load forecasting in the spatial dimension, the method of spatio-temporal fusion (SF) of multi-dimensional meteorological information is proposed. The Copula theory is applied to analyze the nonlinear coupling of meteorological information such as wind speed, rainfall, temperature, and sunshine intensity from multiple meteorological stations with the power load and to achieve spatio-temporal fusion. In the time dimension, the core parameters of the variational mode decomposition (VMD) are improved by the marine predators algorithm (MPA). The weighted permutation entropy (WPE) is used to construct the MPA-VMD fitness function for the adaptive decomposition of the load sequence. In addition, the input sets of the LSTM model and MPA-LSSVM model are constructed by combining each component of the time dimension and each meteorological information of spatial dimension to obtain the prediction results of each component. The prediction model corresponding to each component is selected according to the evaluation index, and reconstructed to obtain the overall prediction results. The analysis results show that the proposed forecasting method is better than the traditional forecasting method and effectively improves the accuracy of power load forecasting.
As the applications using augmented reality have been widespread, the depth information on mobile devices has been important. For example, it can be used for the natural interaction with the environment in augmented reality and the bokeh effects during image processing. However, it is difficult to get depth information on general mobile devices without any depth sensors. As one of the methods for the depth information without any special sensors, stereo vision through recent multiple cameras can be used to generate the disparity map, which can be finally used to generate the depth map. However, the cameras have heterogeneous characteristics each vendor, which makes it challenging to utilize the cameras. In this research, we propose a method for the disparity map to use the cameras that most of the recent devices internally include. With our method, the devices don’t have to include depth sensors such as ToF (Time Of Flight) and SL (Structured Light) and could get the depth information at the raw image from the camera within 275.64 msec (3.63 FPS) on commodity devices. As far as we know, the proposed method is the first research that can generate the disparity map from captured figures by commercial mobile devices.
It has been argued that when a single-line-to-ground fault is applied at the inverter on the alternating current side, the voltage will drop. Likewise, when a fault is applied on a LLG fault at the inverter, the currents of the alter-nating current system of the rectifier will experience various disturbances. In this study, we focus on a converter station on the rectifier side of a HVDC system. Three fault cases, namely, D4D5 open-circuit fault, D1D2 open-circuit fault, and D3D4D6D2 open-circuit fault and their behavior on the alternating cur-rent system of the rectifier, AC system of the inverter as well as the direct current link with detailed simu-lations were carried out. An HVDC mono-polar system was modeled using the MATLAB/Simulink software environment. It was discovered that the D4D5 and D1D2 open-circuit faults and the AC system of the rectifier side would both have the same increase in AC voltages, whereas the behavior of the AC currents of both faults would remain normal with no effect. It was also shown that during the D3D4D6D2 open-circuit fault, the alternating current volt-ages and currents at the inverter side would experience zeros in a short time and then rise with a false sinusoidal waveform
The concept of a smart city is intertwined with a kind of great data management, and through which, reaching optimal management. The data transaction and the issue of communication between different equipment are all defined on the IoT platform. The concept of IoT is a general keyword referring to the widespread communication between equipment. Nevertheless, the idea that metropolitan management can be implemented with the targeted data processing faces challenges. On the other hand, multicarrier energy management, in which various types of energy generating structures like microgrids, smart grid, energy hub systems, transportation sector, etc. converge together in an optimal energy management program, is of great difficulty. Considering these challenges, the processes of encryption, dispatching data, decryption with the algorithm of the energy management plan should be intertwined and all solved together as a general problem. The current paper has presented a modified IoT architecture along with a multi-carrier energy management application through integrating two distributed and integrated management approaches. In the presented model, each energy-generating agent in the first layer initially controls its own internal supply and demand rate implementing distributed management, and then in the second layer, each agent participates in a macro-competitive market to achieve the optimal work point. Thus, in the presented model, the data blockchain building protocol based on The Advanced Encryption Standard (AES) in IOT platform is synchronized with the cloud-computing center and an integrated energy and data security management program is run, having the nature of the competitive market according to the block building time by each agent. According to the results, in this approach, while achieving the desired answer, the volume of data transaction and consequently, the time to gain the final consensus may be considerably reduced.
As a key component of lithium-ion batteries (LIBs), separator plays a crucial role in the performance and safety of LIBs. In this paper, a cellulose based porous membrane modified by nano CaCO3 is prepared conveniently by electrospinning. The membrane exhibits rich fibrous porous networks and uniform distribution of nanoparticles. Strengthed by CaCO3, the tensile strength of the cellulose porous membrane elevates from 4.7 ± 0.4 MPa to 7.7 ± 0.7 MPa. Besides, the modified membranes possess improved thermal stability and can maintain their original size after treatment at 150 °C and 180 °C. Also, the electrolyte uptake of cellulose/CaCO3 membrane is 73% higher than that of the pure cellulose membrane. Thus, the ionic conductivity of membrane achieves 1.08 mS cm-1 and the electrochemical window is about 4.8V, which meets the practical requirements of LIBs. Significantly, LiFePO4/Li battery this membrane can run for 230 cycles with a capacity retention of 97.4% and a discharge capacity of 149.0 mAh g-1, demonstrating the huge potential for high safety and next-generation LIBs.
Based on the data of indexed papers from Inspec database, scientometrics and statistics-related analysis methods are used to study the overall development trend of international cooperation in advanced manufacturing, the characteristics and evolution of major countries in four dimensions of cooperation scale, cooperation intensity, cooperation network and cooperation network from 2011 to 2020, and this article focuses on the changing trend of China and the differences existing with other countries. The study shows that cooperation in advanced manufacturing has an upward trend, with China and the US having a clear lead in cooperation scale and intensity. China leads the most in the aspect of international cooperation in advanced manufacturing. And the U.S. is at the heart of a network of cooperation across the field.
Mid-infrared absorbers based on metamaterials are investigated intensively for gas detection applications, owing to its ability to achieve excellent light absorption by manipulating electromagnetic waves. Herein, we present a metamaterial absorber for the ultra-compact optical gas sensor. This absorber has 99.9% absorption at 6.2 μm. The full width at half-maximum of the peak was 720 nm. As a sensor, the absorber had an average linear spectral sensitivity of 876 nm/RIU and a detection limit of 1.141×10-5 RIU, when the RI of the gas medium varied between 1 and 1.05. The designed absorber would have great potential application in guiding the design of optical gas sensors.
The development of mega constellations inevitably brings various problems for the development of routing techniques. Most of the existing work considers end-to-end delay and load balancing problems, while the analysis of routing strategies in case of link performance degradation is neglected, and an optimization approach applicable to mega satellite networks is not developed. In this letter, we propose a robust routing strategy based on deep reinforcement learning (RRS-DRL) that regards the Age of Information (AoI) of packets as an optimization target, and ensures the effectiveness of message transmission throughout the network. Extensive simulation results show that our proposed RRS-DRL algorithm obtains a lower average AoI across the network and better utilization of the resources than the traditional shortest path algorithm, significantly increasing the robustness of the constellation.
The tracking and data relay satellite (TDRS) in the geosynchronous orbit (GEO) is capable of providing ranging and communication services for user spacecraft in low Earth orbit (LEO) through the inter-satellite link (ISL). The time delay of TDRS’ ISL transponder in orbit differs from that before launch and changes due to complex space environment, which contributes non-negligible errors to user spacecraft’s orbit determination based on the four-way ranging data. We construct a calibration system and propose an efficient on-orbit calibration method for time delay of ISL transponder in TDRS. Experimental results verify the effectiveness of the proposed method and the RMS of calibration residuals is less than 0.5 meters.
In this paper, a 2-bit digital reflection-type phase shifter working at 120 GHz is presented. It uses a compact coupled-lines coupler with low insertion loss and high isolation over a wide bandwidth. The loads are made by a microstrip-line loaded by three PIN diodes whose states are tuned ON/OFF to obtain 90°, 180° and 270° phase shift relative to the reference (0°). Measurement results show RMS phase and amplitude error equal to 10.3° and 1.2 dB, respectively. The maximum insertion loss is equal to 8.6 dB, leading to a figure of merit of 31°/dB. As shown by simulation, by flipping PIN diodes and use negative voltage for biasing, the maximum insertion loss could be reduced to 3.6 dB (figure of merit of 75°/dB) along with a great improvement in RMS phase and amplitude errors, thus showing the potential of the proposed architecture.
Chiral Iridium complexes are very important for the preparation of circularly polarized light emitting diodes (CP-OLEDs). Three novel Iridium(III) complexes, Ir(dnfppy)2(Cl/pyrrole), Ir(dfppy)2(dpp) and Ir(tfmqz)2(sdpp) have been synthesized and characterized, respectively. These Iridium(III) complexes emitted deep-blue, blue and red photoluminescence with high quantum yields, for ((Ir(dnfppy)2(Cl/Pyrrole): λmax=447 nm, F=62.4%; Ir(dfppy)2(dpp): λmax=467 nm, F= 25%; Ir(tfmqz)2(sdpp): λmax=609 nm, F=73.7%). Compared with Ir(dnfppy)2(Cl/pyrrole), HOMO energy level of Ir(dfppy)2(dpp) and Ir(tfmqz)2(sdpp) was calculated to be −5.71/-5.73 eV, and LUMO energy level increased to be -2.75/-3.36 eV, respectively. CD spectra of two pairs of the enantiomers for Ir(dfppy)2(dpp) or Ir(tfmqz)2(sdpp) displayed symmetry with opposite polarization between 300 and 600 nm. The maximum external quantum efficiency (EQEmax) of OLEDs based on Ir(tfmpqz)2(sddp) was 13.8%, showing a relatively low efficiency roll-off with the EQE of 10.7% at 5000 cd/m2.
Recently, Wind Turbines (WTs) and Electric Vehicles (EVs) have been integrated into the demand side of many countries. WTs and EVs have uncertainties in electrical energy generation and consumption, respectively. Additionally, Thermal Units (TUs) suffer from random failures. As always, secure power system operation is the main goal of an independent system operator, therefore, these uncertainties should be considered. This paper proposes a two-stage reliability-based model for the economic dispatch of TUs and WTs in the presence of a demand-side response program. At the first stage, the well-being analysis is performed to determine the power generation and spinning reserve of the TUs regarding the timely power generation of WTs. At the second stage, the adoption of the responsive load consumption with the various conditions of the generation system in the power pool market is established using the cost of expected energy not served criterion. This optimization problem is solved at two stages using the genetic algorithm. To validate the proposed model, numerical studies have been applied to the generation part of an IEEE test power system including eleven TUs, one WT, and one thousand EVs.