BSAS: A Blockchain-Based Trustworthy and Privacy-Preserving Speed Advisory System

Indoor mobile positioning is widely used in shopping malls, energy saving in buildings, medical care and emergency rescue. Range-free algorithms typically use the connectivity of a wireless sensor network system to estimate the location of target nodes, typically through Dv-hop, the prime method, ap-proximate point-in-triangulation test, or APIT for short. -free algorithms can be obtained by changing the distribution density of reference nodes or by changing the blind node communication radius to obtain higher accuracy. Due to constraints such as complex indoor environments, current indoor positioning methods are often less accurate and more expensive. To address these issues, this paper improves on the traditional center-of-mass algorithm by using a six-point center-of-mass algorithm for indoor positioning to further improve the accuracy of the indoor positioning algorithm. To this end, the following work is accomplished in this paper: 1. introduced the algorithms currently used for indoor positioning and their shortcomings, laying the theoretical foundation for the improved approach proposed in the following paper. 2. introduced the basic working principle of the center-of-mass algorithm and proposed an improved system using the six-point center-of-mass algorithm. 3. established the best possible model through experiments, then input test data and evaluated it based on expert judgement. The experimental results show that the model proposed in this study has excellent accuracy in indoor wireless positioning.

Section: Figure 2 Position Relation Between Reference Nodes and Blind...mentioning

confidence: 99%

“…First find the intersection of the line AB and the circle A. Combine (5) and (8) and solve the equations to obtain the solution as in equations (10) and (11).…”

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confidence: 99%

Research on Indoor Wireless Positioning Method based on Center-of-mass Algorithm

Wang

2023

“…Fault detection of electric power metering and detection equipment involves analyzing the state of key components during operation to determine whether there are any abnormalities, and diagnosing the type, location, and cause of the fault when it occurs [3]. Researchers have proposed various diagnostic techniques in equipment fault diagnosis, among which analysis and diagnosis of signals from key components of the equipment is a research focus [4]. By installing signal sensors on the surfaces of equipment components, real-time monitoring and acquisition of signals and other measurement values from various components during operation can be achieved, thereby obtaining the operating status data of the equipment.…”

Section: Introductionmentioning

confidence: 99%

Fault Status Monitoring of Power Metering and Detection Equipment Based on BiLSTM

Yao,

Zhao,

Zhang

et al. 2023

The power metering and detection equipment is a device used to measure the power consumption of users and energy loss of power lines. It is of great significance to timely identify and handle operational anomalies of electric power metering and detection equipment to ensure stable and reliable operation of the metering device. In response to the problems of low accuracy and poor robustness in existing fault diagnosis methods for electric power metering and detection equipment, this paper proposes an intelligent fault diagnosis method based on deep learning. The paper combines the characteristics of convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) in extracting local and global features, and combines them with self-attention (SA) mechanism to propose a hybrid network model SA-CNN-BiLSTM for monitoring the status of electric power metering and detection equipment. This model first extracts local information through the CNN network. At the same time, BiLSTM can extract global features, and then the feature fusion layer combines these two different features. Then, the self-attention mechanism weight adjustment layer is used to calculate and adjust the weights of the fused feature vectors. Finally, the fused feature vectors are input into the fully connected layer, and fault classification predictions are obtained through a softmax classifier. System experiments have demonstrated the correctness and effectiveness of this method.

“…One of the key factors in optimizing the performance of ESVSMs is the accurate prediction of their energy efficiency. Understanding the efficiency of energy storage systems is vital for effective energy management, as it directly impacts the overall economic viability and environmental benefits of integrating renewable energy sources [4]. By predicting energy efficiency, grid operators and energy system planners can make informed decisions regarding the optimal scheduling and control strategies of ESVSMs, thereby enhancing grid stability and reducing operational costs.…”

Section: Introductionmentioning

confidence: 99%

Energy Efficiency Prediction of Energy Storage Virtual Synchronous Machine Based on Long Short-Term Memory Network

Li,

Liu,

Lin

et al. 2023

The integration of renewable energy sources in the power grid has led to an increased demand for energy storage systems to manage the intermittency and variability of these sources. Among various energy storage technologies, Energy Storage Virtual Synchronous Machines (ESVSMs) have emerged as a promising solution for enhancing grid stability and energy efficiency. However, optimizing the performance of ESVSMs requires accurate energy efficiency predictions. This paper proposes a energy efficiency prediction network (EEPNet) to achieve energy efficiency prediction of ESVSMs. Firstly, considering the peculiarity of storing virtual synchronous machine efficiency fluctuations, this paper improves the traditional Inception structure. This allows for the extraction of information at different scales and its fusion to obtain a better representation. Secondly, this paper introduces residual connections in the structure. This not only avoids the difficulties in model training caused by deep networks but also helps to integrate the original information with the extracted information. Thirdly, building upon the improved Inception structure, this work combines long short-term memory (LSTM) and attention mechanisms to construct a complete network architecture, further enhancing the performance of the entire model. Finally, the effectiveness and validity of the model are validated through comprehensive experiments.