TSP is one of the most famous problems in graph theory, as well as one of the typical NP-hard problems in combinatorial optimization. Its applications range from how to plan the most reasonable and efficient road traffic to how to better set up nodes in the Internet environment to facilitate information flow, among others. Reinforcement learning has been widely regarded as an effective tool for solving combinatorial optimization problems. This paper attempts to solve the TSP problem using different reinforcement learning algorithms and evaluated the performance of three RL algorithms (Q-learning, Sarsa, and Double Q-Learning) under different reward functions, ε-greedy decay strategies, and running times. The results show that the Double Q-Learning algorithm is the best algorithm, as it could produce results closest to the optimal solutions, and by analyzing the results, better reward strategies and epsilon-greedy decay strategies are obtained.
Today’s healthcare system relies on MRI (medical resonance imaging) for early diagnosis and treatment planning. For open MRI systems to achieve resolutions of about a hundred microns, a high voltage is required, as well as a specialized power supply. NP0 (Negative-Positive-Zero) ceramic is selected for the fabrication of adjustable capacitors. Specifically, it stands for which is a classification based on the temperature coefficient of capacitance (TCC) of the ceramic material used in the capacitor. NP0 capacitors have a TCC of 0 ±30 ppm/°C, which means that their capacitance value does not change significantly with temperature and frequency. They are known for their stability and low losses, making them ideal for applications that require high accuracy and reliability, such as timing circuits for RF applications. In this paper, MgTiO-CaTiO ceramic is used to make an adjustable capacitor with desired properties for MRI systems. To enhance the dielectric properties of MgTiO3 ceramics,CaTiO3 was added in varying concentrations. After pressing and sintering, the resulting samples were tested using a vector network analyzer in the frequency range of 10 MHz to 130 MHz.The adjustable capacitor fabricated using high co-fired NP0 ceramic may have been used for MRI applications such as tuning circuits and matching networks, where precise capacitance values and low loss are critical. MRI systems with resonance frequencies of 128 MHz require trimmers with ceramic cores.
5G network slicing is a promising solution to prioritize time-critical protection communication in wireless networks. However, recent trends indicate that a 5G slice could encompass all smart grid applications lacking the necessary granularity. At the same time, while substation communication standards recommend prioritization of protection communication traffic to improve reliability, these recommendations are only for wired connections. Therefore, this paper investigates traffic shaping and uplink (UL) bitrate adaptation of video stream based on existing commercial solutions as methodologies for prioritizing the protection communication in a 5G slice. These methodologies are validated in an experimental setup combining controller-hardware-in-the-loop (CHIL) simulation with a quality of service (QoS) measurement system. The system under test consists of commercial 5G networks, commercial intelligent electronic devices (IEDs), and merging units to validate the methodologies on three smart grid applications: fault location, line differential, and intertrip protection. The results show improvement in protection communication when traffic shaping and UL bitrate adaptation are applied. Traffic shaping even improves prioritization with a wired connection.
Compressed sensing (CS) techniques have enabled efficient acquisition and recovery of sparse high-dimensional data via succinct low-dimensional projections, which usually consist of an encoder and a decoder. Unlike conventional CS techniques with the encoding-decoding architecture, the uncertainty autoencoder (UAE) can sample from the learned input data distribution without an explicit likelihood function, hence avoids potential uninformative latent representations. However, existing works on UAE mainly focus on the encoders and maximize the lower bound of the mutual information between input and measurements, rather than the decoders, which brings the shortcoming that the two may not cope well. In this letter, we propose a novel training scheme for UAE that blurs the measurements to learn the encoder and decoder simultaneously. Experimental results show that the proposed method improves the reconstruction performances when applied to UAE.
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.
The continued quest for finding a low-power and high-performance hardware algorithm for signed number multiplication led to designing a simple and novel radix-8 signed number multiplier with 3-bit grouping and partial product reduction performed using magnitudes of the multiplicand and the multiplier. The pre-computation stage constitutes magnitude calculation and non-trivial computations required to generate partial products. A new partial product reduction strategy is deployed in the design to improve the speed with low cost. 8 X 8, 16 X 16, 32 X 32, and 64 X 64 designs are presented for the proposed architectures. Performance results include area, power, delay, and power-delay-product of synthesized and post-layout designs using 32 nm CMOS technology with 1.05 V supply voltage.
The IOT management platform is used to handle and transmit data from many types of power system terminal devices. The current IOT management platform has a low data processing efficiency and a high mistake rate when it comes to finding anomalous data. Furthermore, the effective selection and optimum decision of the convolutional neural network’s structural parameters has a significant impact on prediction performance. Based on this, the paper proposes a decision algorithm for locating anomalous data in an IOT integrated management platform using a convolutional neural network (CNN) and a global optimization decision of key structural parameters of a convolutional neural network using an improved particle swarm optimization (APSO) algorithm. First, an index model is created to determine if the data retrieved from the IOT management platform is anomalous or not. Second, the structure of the convolutional neural network-based decision method for finding anomalous data is examined. Following that, an enhanced particle swarm optimization technique is developed to optimize the structural parameters of the convolutional neural network, and an APSO-CNN with improved performance for anomalous data localization is generated. Finally, the established algorithm’s correctness, feasibility, and efficacy were evaluated using the Adam optimizer. The results reveal that the established APSO-CNN-based decision algorithm for anomaly data localization offers considerable benefits in terms of accuracy and running time, with extremely interesting application potential.
The tire lateral force control is crucial to vehicle lateral stability. Vehicle side slip and out of control can be prevented effectively by observing accurately the lateral force. Thus, a novel hyperbolic tangent sliding mode observation algorithm (NTSMO) is proposed. The algorithm adopts the longitudinal tire force error as feedback considering vehicle parameter uncertainties and without a complex tire model. First, the on-line verification of the algorithm was carried out by dSPACE to using the experimental data of the real vehicle linear acceleration and deceleration conditions, and comparison of experimental output with different observation algorithms. Furtherly, the simulation under emergency obstacle avoidance conditions and the double-line shifting conditions were conducted to verify the accuracy of the algorithm respectively. Simulation results show that the percentage errors between the tire lateral forces from the proposed NTSMO and the actual data are less than 5.35%, and the prediction accuracy of the NTSMO by 38.78% is higher than that of the sliding mode observation(SMO), which indicates that the NTSMO is superior to the SMO.
The assembly process of flareless pipe joints is very important for the sealing performance of hydraulic pipeline system. Based on the theory of contact mechanics, a theoretical model of the assembly process of pipe joints is established to simulate the extrusion molding process of flareless pipe joints. It is found that tightening torque is an important factor affecting the sealing performance of pipe joints. By comparing the changes of the contact stress between the sleeve and the pipe joint under different tightening tortures, combined with the mechanical transfer and deformation results of the contact surface, the results show that the fitting situation of the sleeve and the pipe is good when the expansion pressure is 180Mpa, and the sealing performance of the pipe joint is good when the tightening tortures are between 15N·m and 18N·m.
In order to locate the mobile robots in three-dimensional indoor environment, mostly global navigation satellite system-denied space, a monocular visual space positioning algorithm based on deep neural network is proposed. First, we employ the lightweight YOLOv5 algorithm for target detection, and the LibTorch deep learning framework is used for model deployment to improve the inference speed. Moreover, a multi-layer perceptron (MLP) neural network with four inputs and two outputs is constructed, which regress the coordinates of the robot in the field coordinate system to complete the target localization, and this method is compared with the mathematical model solving algorithm to reflect the accuracy and superiority of positioning algorithm based on deep neural network. The proposed positioning and tracking system has been successfully applied to ICRA robot competition, and results show that the positioning error estimated by our method is within 10cm whilst having good real-time performance.
The health monitoring for disease diagnosis and prognosis in a desired smart medical structure is realized by interpreting the health data. The advances in sensor technologies and biomedical data acquisition tools have led to the new era of big data, where different sensors collect massive medical data every day. This special issue explores the latest development in emerging technologies of biomedical engineering, including big medical data, artificial intelligence, cloud/fog computing, federated learning, ubiquitous computing and communication, internet of things, wireless technologies, and, security and privacy. The biological wearable sensors can enhance the decision-making and early disease diagnosis processes by intelligently investigating and collecting large amounts of biomedical data (i.e., big health data). Hence, there is a need for scalable advanced learning, and intelligent algorithms that lead to reliable and interoperable solutions to make effective decisions in emergency medicine technologies. The optimization algorithms can be used in order to acquire the sensor data from multiple sources for fast and accurate health monitoring.
The existing prefabricated wireline construction technology is difficult to accurately measure the real-time length of the deployed wires, resulting in too large construction errors and cannot be popularized and applied. To this end, a length measuring device that can accurately measure the length of the wire during the construction of the prefabricated wire is developed. In terms of the hardware of the length measuring equipment, based on the construction characteristics of the prefabricated wiring and the characteristics of the wire, the photoelectric encoder and the STC8G series single chip microcomputer are used to complete the collection and processing of the wire length data, and the mature and stable LoRa technology is used to complete the wireless transmission of the data. In terms of software program of length measurement equipment, the measurement of wire length data, transmission and interaction of control command signals are realized by optimizing length measurement scheme, interface development of terminal equipment and information interaction program design. Finally, according to the actual working conditions of the prefabricated wiring construction, the mechanical structure of the length measuring equipment is designed, and the assembly is completed. The developed length measuring equipment has been successfully applied to practical projects. According to the wire length measuring equipment, the deviation between the observation sag and the design sag after the wire length is measured by the wire length measuring equipment is 2.0%, which meets the design requirement of ±2.5%.
This paper presents a simple method to realize a circular polarizer by inserting rods arranged helically in the cylindrical waveguide. In the proposed polarizer, the rods are installed to excite the electric fields on the perpendicular axis to the input field. In addition, the rod locations create a 90⁰ phase difference between two perpendicular axes to convert the polarization from linear to circular. In the proposed structure, the axial ratio is smaller than 0.4 dB and the phase difference is about 90⁰ at 10 GHz frequency. In addition, the reflection coefficients are better than -14 dB for both E_x and E_y polarizations at the same frequency. Also, this structure is very compact compared to similar structures. Furthermore, this is made only of metal. Therefore, there is no dielectric loss and it has high endurance. The validation results have a good agreement with the simulation.
The clearing price in electricity spot market is an important reference that guides marker participants in making energy purchase. Current electricity price forecasting methods consider the numerical accuracy of the forecast result only, ignoring the need to optimize economic benefits, while higher numerical precision sometimes leads to lower electricity-purchase gain. This paper proposes a price forecasting method that considers both economic benefits and numerical accuracy. A function representing the relationship between the predicted electricity prices and the cost reference for making energy purchase decisions is calculated, and then introduced to the loss function of the prosumers' forecasting model as a revenue-optimizing term. A sequence comparison neural network structure is designed and added to consumers' forecasting model, so that the results of numerical prediction and comparison both contribute to predicting better prices. By co-optimizing numerical precision and electricity-purchase gain, the prediction is more conducive to reducing the cost of purchasing power. Actual electricity market price data are used to verify the feasibility of the proposed forecasting method in improving economic benefits.
This paper is concerned with the formation control problem for a class of large-scale mobile sensor networks. The dynamic of mobile sensors are modeled by class of semilinear parabolic system, which is a class of partial differential equation(PDE) and has rich geometric family. In this model, the communication topology of agents is a chain graph and fixed. Leader feedback laws which designed in a manner to the boundary control of semilinear parabolic system allow the mobile sensors stable deployment onto planar curves. By constructing appropriate Lyapunov functional and using linear matrix inequality, several sufficient criteria are derived ensuring the mobile sensor networks to be globally asymptotically stable at the equilibrium. A simulation example is provided to demonstrate the usefulness of the proposed formation control scheme.
This paper studies chatter stability of composite cutter bar milling system in rotating coordinate frame. Based on the structural dynamic equation and regenerative milling force model of composite cutter bar in rotating coordinate frame, the continuous distributed chatter analysis model of composite cutter bar milling system is established. The stability of milling system with a rotary symmetric dynamic cutter bar is predicted by using the semi-discrete time domain method. Influences including internal damping, external damping, symmetrical and asymmetric laminates on the stability of milling system are analyzed, and the results obtained in rotating and fixed coordinate frame are compared. It is shown that the results are consistent for symmetrical cutter bar either in the rotating coordinate frame or in the fixed coordinate frame. A new chatter instability zone appears at high rotating speeds due to material internal damping of the rotating composite cutter bar.
In this paper, an adaptive sliding mode observer (ASMO) associated with a phase locked loop (PLL) is assessed for the sensor-less control of a rotor-tied doubly-fed induction generator (RDFIG). In the proposed PLL-ASMO estimator, the ASMO utilizes the stator current, the stator voltage and the back electromotive force (EMF) as state variables. The proposed ASMO is used in order to estimate the back-EMF from which the slip position/speed is extracted using a PLL. The design of the ASMO gains is based on the Lyapunov stability criteria to ensure the convergence of the proposed observer in a finite time. Therefore, the main contribution of this paper is to propose a PLL-based ASMO estimator that aims to improve the estimation by reducing the chattering effect. A comparative study between the standard PLL-SMO estimator and the ASMO-PLL estimator is presented. Also, For the first time, an adaptive sliding mode observer is used for the sensor-less control of a RDFIG. The performance of the proposed sensor-less control strategy is validated through simulation and experimental measurements under various operating conditions. Furthermore, the estimator is shown to be robust to machine parameter variation.
Due to its stochastic nature, wind energy imposes unprecedented challenges on the power grid, and a properly scheduled reserve is essential to accommodate wind power’s intermittency and volatility. Many power reserve scheduling studies have considered the uncertainties of the renewable energy integration but few address how different wind speed forecast techniques influence the scheduling of reserves in the congested transmission networks. In this paper, three forecasting techniques: artificial neural network, autoregressive integrated moving average, and probability distribution function-based model are adopted to forecast one day of wind speed at Taylor, TX in 2012. To evaluate the impacts of the forecast techniques on power reserve scheduling, a stochastic reserve optimization model was developed to ensure the delivery of reserve in the event of transmission congestion and ramping constraints. A modified RTS-96 test system was employed and the results claim that different forecast models significantly affect the amount of scheduled up and down reserves in a stochastic reserve optimization problem. The level of operating reserve that is induced by wind is not constant during all hours of the day. Dynamic up and down reserves will be needed with a large scale of wind farm integration.