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.

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[1]. MRI systems with resonance frequencies of 128 MHz require trimmers with ceramic cores.

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.

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.

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.

Automatic Violence Detection and Classification (AVDC) with deep learning has garnered significant attention in computer vision research. This paper presents a novel approach to combining a custom Deep Convolutional Neural Network (DCNN) with a Gated Recurrent Unit (GRU) in developing a new AVDC model called BrutNet. Specifically, we develop a time-distributed DCNN (TD-DCNN) to generate a compact 2D representation with 512 spatial features per frame from a set of equally-spaced frames of dimension 160×90 in short video segments. Further to leverage the temporal information, a GRU layer is utilised, generating a condensed 1D vector that enables binary classification of violent or non-violent content through multiple dense layers. Overfitting is addressed by incorporating dropout layers with a rate of 0.5, while the hidden and output layers employ rectified linear unit (ReLU) and sigmoid activations, respectively. The model is trained on the NVIDIA Tesla K80 GPU through Google Colab, demonstrating superior performance compared to existing models across various video datasets, including hockey fights, movie fights, AVD, and RWF-2000. Notably, our model stands out by requiring only 3.416 million parameters and achieving impressive test accuracies of 97.62%, 100%, 97.22%, and 86.43% on the respective datasets. Thus, BrutNet exhibits the potential to emerge as a highly efficient and robust AVDC model in support of greater public safety, content moderation and censorship, computer-aided investigations, and law enforcement.

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.

Grease in the normal operation of the RV reducer has a role that can not be ignored, for the variable working conditions of the RV reducer, the performance of the lubricant changes directly affect its reliable operation. Therefore, the study of the rheological properties of the grease has become the focus of the study of RV reducer performance.In this paper, SK-1A grease is taken as the research object, and its rheological characteristics under wide temperature range working conditions (-20℃~40℃) are investigated through rheological experiments, to analyze the potential influence of SK-1A grease on the performance of RV reducer.In addition, to better study the rheological properties of grease under different working conditions, the Elman neural network model was used to predict the rheological properties of grease based on the rheological experiments of grease, and the results were compared with those of the BP neural network and the RBF neural network.The prediction accuracy of the Elman neural network model was assessed using Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) in a cross-validation approach.The results show that the viscoelastic properties of SK-1A grease generally show a decreasing trend as the temperature rises in a wide temperature range, and the degree of entanglement of soap fibers decreases more obviously, but its fluidity is more stable.The results of the three neural network prediction models show that the Elman neural network model used for the prediction of grease rheological properties shows high prediction accuracy, and the model can provide a valuable theoretical reference for the accurate prediction of the rheological properties of grease affected by complex multifactor.

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

Secure and reliable electricity supply is a prerequisite for the development of smart cities, and the trustworthy and efficient transmission of electrical data is the foundation for the safe and stable operation of the power grid. This paper introduces a real-time data transmission blockchain technique based on parallel proof of work algorithm. The new block generation progress of proposed blockchain is divided into five subroutines: hash pointer computation, real-time data pudding, signature value iteration, interruption, block header assembly. The real-time data pudding and signature value iteration are parallel processed, which brings the effect of decreasing energy loss of blockchain system, and upgrades the speed of new block generation and the bandwidth of data storing on blockchain. Computer simulation shows the proposed strategy can be effectively applied in real-time electrical data transmission application, raising the data transmission reliability with no harm to real-time data transfer function. This strategy provides a solution to guarantee data transmission safety in the digital conversion of power grid.

Abstract: Path planning is a crucial component for ensuring the safety and efficiency of flight missions, especially for fighter aircraft. To enhance the combat effectiveness of fighter aircraft, it is important to consider how to avoid danger sources and terrain obstacles, reduce fuel consumption, and utilize the aircraft’s own performance to accomplish the mission objectives. In the modern battlefield environment, the shortest path is not the only criterion for planning, but also other factors such as the threat level to the aircraft, fuel consumption, mission completion time, and minimum turning radius. In this paper, we propose a multi-constraint path planning method for fighter aircraft that incorporates these factors into an improved particle swarm algorithm. We transform the constraints of three-dimensional terrain, threat source, fuel consumption, and mission time into an aggregated fitness function. We construct a limit curvature matrix to evaluate the feasibility of the generated path. We also introduce an adaptive adjustment strategy based on the activation function for the parameters in the particle swarm algorithm. The weights of each constraint are determined according to the actual demand. The experiment results show that our method can efficiently plan the optimal path that satisfies the requirements. Compared with other improved particle swarm algorithms, our method has higher optimal search efficiency and better convergence effect. We also provide optimal values for important parameters such as mission energy consumption, mission time, flight speed and others to support the overall mission planning. Our method has certain practical application value.

Increasing Internet of Things (IoT) deployments present a growing surface over which villainous actors can carry out attacks. This disturbing revelation is amplified by the fact that a majority of IoT devices use weak or no encryption at all. Specific Emitter Identification (SEI) is an approach intended to address this IoT security weakness. This work provides the first Deep Learning (DL) driven SEI approach that upsamples the signals after collection to improve performance while simultaneously reducing the hardware requirements of the IoT devices that collect them. DL-driven upsampling results in superior SEI performance versus two traditional upsampling approaches and a convolutional neural network only approach.

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.

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.

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.

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.

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 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.