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.

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.

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.

Liben Building, constructed in 1822 AD, is located in Xinnan Village, Hukeng Town, Yongding District, Longyan City, Fujian Province, China. This square earthen building covers an area of about 2,100 square meters. In 1931 AD, the building was sacked and burned down by bandits during a war, leaving behind only the remnants of the walls of the main building. In the meta-universe environment, firstly, this study adopted blockchain DAO technology to conduct a study on the digital collection, storage, processing, display and dissemination of Liben Building, revealing the problems with digital regeneration of cultural heritage. Then, the questionnaire with 20 questions was designed, and 158 valid completed copies of the questionnaire were collected. Combining influence relationship with sample clustering analysis methods, this paper explored the findings of a study of the historical sites under blockchain digital heritage preservation and protection in the metaverse.

Fault detection is crucial in smart grid control and monitoring operations. The use of smart meters leads to appearance of a large amount of digital data whose conventional and chronological techniques are not efficient enough for processing and decision-making. In this paper, a novel data analysis model based on deep learning and neuro-fuzzy algorithm is proposed for detection and classification of faults in a smart grid. First, the Long Short Term Memory (LSTM) based deep learning model is applied for training the data samples extracted from the smart meters. Then, the Adaptive Neuro Fuzzy Inference System (ANFIS) is implemented for fault detection and classification from the trained data. With this intelligent method proposed, single-phase, two-phase and three-phase faults can be identified using a restricted amount of data. To verify the effectiveness of our methodology, an intelligent model of the IEEE 13-node network is used. The results indicate that the combined ANFIS-LSTM deep learning model outperforms existing machine learning methods in the literature in terms of accuracy for fault detection and classification.

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.

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.

As self-driving cars perform more tasks, new challenges arise. One of these challenging tasks is autonomous driving decision-making due to the uncertainty of the vehicle’s complex environment. This paper provides an overview of decision-making technology and trajectory control for autonomous vehicles. The main common goal in decision-making is to consider uncertainties, unpredictable situations, and driving tasks to propose a global and robust solution adapted to each situation. The main concern is safety. Decision-making falls into three categories. The first is the traditional approach, which often consists of building a rule system and deriving optimal operations. The advantages of such an approach are well known for being easy to understand and applicable to small problems. The second category of decision-making is based on a probabilistic process and, due to its efficiency, has several applications in this area. The third category is learning-based approaches. Once a decision has been made, manipulate the steering angle or accelerator/brake pedals to perform the appropriate action. Two approaches are existing to designing autonomous driving controllers. Either based on imitating human drivers that includes approaches based on the use of driver models such as AI, or the use of approach-based models

In this paper, Short-term predicting of load and spinning reserve is first performed using a combination of ANFIS and meta-heuristic algorithms including Differential Evolution (DE), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The ANFIS-PSO combination is selected as the best ANFIS combination in load and spinning reserve prediction with a lower error criterion than other methods. As a DL method, LSTM network can provide good accuracy for load and spinning reserve forecasting. In the optimal ANFIS-PSO method, the average error value is low, but the error variance is high, on the contrary, in the LSTM method, the average error value is high, and the error variance is low. Therefore, we use the combination of ANFIS-PSO and LSTM to reduce the average error and error variance to an acceptable level. The weighted average method is as follows: the accuracy of each Method is obtained in the training step, then the predicted value for each data in the test step is calculated in each Method, then they are multiplied, and after that added together, finally will be divided to the total accuracy of two methods. The results obtained from the weighted average Method show the success of the proposed Method.

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.

This paper highlights the impact of curved and flat vehicular plastic parts on the radiation characteristics of two dual-band antennas for C-V2X applications. The radiation patterns of the antennas are measured in SATIMO near field measurement system and are compared during the following setups: (a) antennas alone in the near field system, without the presence of a plastic part; (b) antennas mounted on the inside curved surface of a driver’s side mirror cover; (c) antennas mounted on the outside curved surface of the driver’s side mirror cover; (d) antennas mounted on a flat trunk lid; (e) antennas mounted on a curved plastic retrieved from the A-pillar of a vehicle. Comparison among the antennas radiation pattern measurements during these different setups, results in the conclusion that the inside surface of the side mirror cover is the most suitable position to mount the presented dual-band antennas. The curvature of the inside surface at the point where the antenna was mounted is less steep than the placement point at the outside surface, allowing the antenna to keep its polarization axis mostly unaffected. Moreover, the curve of the inside surface makes the antenna radiation more directional, creating an increase in the antenna gain. The side mirror cover, compared to trunk lid, is further from the ground protecting the antenna radiation from additional reflections.

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.

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.

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 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 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.

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.

In this study, an optimisation model is developed for two-stage energy management of a residential building to minimise energy cost under monthly power-based tariffs for peak demand and time variable electricity prices. The expected peak demand is determined in the first stage, and then the energy management system minimises the energy cost in the second stage. The optimisation problem of the second stage is solved in a rolling time window for the real-time operation of the flexible energy sources in the building. The optimal charging and discharging of the battery energy system, the charging of the electric vehicle battery, the operation of the heating system and the optimal start times of washing machines and dishwashers are determined close to real-time. The proposed approach allows the user to determine the expected peak during the month ahead and try to keep the peak demand in daily operation below that value using a close to real-time energy management system. The performance of the two-stage approach for demand-side management of a residential building has been validated by a realistic case study.