In knowledge graph representation learning, link prediction is among the most popular and influential tasks. Its surge in popularity has resulted in a panoply of orthogonal embedding-based methods projecting entities and relations into low-dimensional continuous vectors. To further enrich the research space, the community witnessed a prolific development of evaluation benchmarks with a variety of structures and domains. Therefore, researchers and practitioners face an unprecedented challenge in effectively identifying the best solution to their needs. To this end, we propose the most comprehensive and up-to-date study to systematically assess the effectiveness and efficiency of embedding models for knowledge graph completion. We compare 13 models on six datasets with different sizes, domains, and relational properties, covering translational, semantic matching, and neural network-based encoders. A fine-grained evaluation is conducted to compare each technique head-to-head in terms of standard metrics, training and evaluation times, memory consumption, carbon footprint, and space geometry. Our results demonstrate the high dependence between performance and graph types, identifying the best options for each scenario. Among all the encoding strategies, the new generation of translational models emerges as the most promising, bringing out the best and most consistent results across all the datasets and evaluation criteria.

/pdf/comprehensive-analysis-of-knowledge-graph-embedding-glhflpth.pdf

Comprehensive Analysis of Knowledge Graph Embedding Techniques Benchmarked on Link Prediction

Photovoltaic grid-connected power generation systems are easily affected by external factors, and their anti-interference performance is poor. For example, changes in illumination and fluctuations in the power grid affect the operation ability of the system. Linear active disturbance rejection control (LADRC) can extract the “summation disturbance” information from the system and eliminate the disturbance at the fastest speed by controlling the signal before it affects the final output of the system. In this paper, an improved linear ADRC based on the principle of deviation control is proposed, and the voltage outer loop is controlled by an improved LADRC. This improved LADRC takes the deviation between each state variable and its observed value as the regulation basis for each state variable of the linear extended state observer (LESO). Based on the analysis of the bode diagram in the frequency domain, it can be concluded that, compared with the unimproved LADRC, the new LADRC has better disturbance rejection performance. The simulation results show that the control performance of the new, improved LADRC is better than that of the unimproved LADRC under different operating conditions, and it has better stability performance and anti-disturbance performance.

https://www.mdpi.com/1996-1073/13/15/3790/pdf

Linear Active Disturbance Rejection Control of Grid-Connected Photovoltaic Inverter Based on Deviation Control Principle

Wireless sensor networks (WSNs) have become widely popular and are extensively used for various sensor communication applications due to their flexibility and cost effectiveness, especially for applications where localization is a main challenge. Furthermore, the Dv-hop algorithm is a range-free localization algorithm commonly used in WSNs. Despite its simplicity and low hardware requirements, it does suffer from limitations in terms of localization accuracy. In this article, we develop an accurate Deep Learning (DL)-based range-free localization for WSN applications in the Internet of things (IoT). To improve the localization performance, we exploit a deep neural network (DNN) to correct the estimated distance between the unknown nodes (i.e., position-unaware) and the anchor nodes (i.e., position-aware) without burdening the IoT cost. DL needs large training data to yield accurate results, and the DNN is no stranger. The efficacy of machine learning, including DNNs, hinges on access to substantial training data for optimal performance. However, to address this challenge, we propose a solution through the implementation of a Data Augmentation Strategy (DAS). This strategy involves the strategic creation of multiple virtual anchors around the existing real anchors. Consequently, this process generates more training data and significantly increases data size. We prove that DAS can provide the DNNs with sufficient training data, and ultimately making it more feasible for WSNs and the IoT to fully benefit from low-cost DNN-aided localization. The simulation results indicate that the accuracy of the proposed (Dv-hop with DNN correction) surpasses that of Dv-hop.

Effectiveness of Data Augmentation for Localization in WSNs Using Deep Learning for the Internet of Things

In this study, machine learning (ML) algorithms were employed to predict the pseudo-1st-order reaction rate constants for the sonochemical degradation of aqueous organic pollutants under various conditions. A total of 618 sets of data, including ultrasonic, solution, and pollutant characteristics, were collected from 89 previous studies. Considering the difference between the electrical power (P 

Machine learning model to predict rate constants for sonochemical degradation of organic pollutants

Grid-connected inverter, as one of the core components of power system, plays an important role in renewable energy generation. Many control methods for inverters are widely studied. Among them, finite control set model predictive control (FCS MPC) has many advantages, such as fast transient response, simple implementation and convenience to consider nonlinear and constraints. However, FCS MPC also has some shortcomings, one of which is that the performance of predictive algorithm is easily affected by the system model. The performance of the traditional FCS MPC algorithm is susceptible to model errors and parameter variations. To solve this problem, an adaptive FCS MPC algorithm is designed in this paper. The single-phase inverter with LCL filter is selected as the control plant. Since the use of sensors will increase the cost of system, how to reduce the number of sensors is also researched. The traditional FCS MPC and proposed adaptive FCS MPC algorithm with fewer sensors are simulated respectively. The effectiveness of proposed method is confirmed by the results.

Adaptive Finite Control Set Model Predictive Control Scheme for Single-phase Inverters with LCL Filter

To improve the performance of data-driven reaction prediction models, we propose an intelligent strategy for predicting reaction products using available data and increasing the sample size using fake data augmentation. In this research, fake data sets were created and augmented with raw data for constructing virtual training models. Fake reaction datasets were created by replacing some functional groups, i.e., in the data analysis strategy, the fake data as compounds with modified functional groups to increase the amount of data for reaction prediction. This approach was tested on five different reactions, and the results show improvements over other relevant techniques with increased model predictivity. Furthermore, we evaluated this method in different models, confirming the generality of virtual data augmentation. In summary, virtual data augmentation can be used as an effective measure to solve the problem of insufficient data and significantly improve the performance of reaction prediction. 

/pdf/virtual-data-augmentation-method-for-reaction-prediction-1ys50jha.pdf

Virtual data augmentation method for reaction prediction

A novel control strategy that is based on iterative calculation of structural parameters is proposed for grid-connected inverter in this paper. The proposed strategy has a good dynamic performance, which makes it particularly suitable for the application of PV grid-connected generation. First, a second-order discretization mathematical model of grid-connected inverter control is established in the dq frame. The corresponding relation between the control signal and the output current is deduced in formulas. Then, the values of structural parameters in the formulas can be obtained through iterative calculation, which can further reduce the amount of calculation. After several iteration cycles, the structural parameters are approximately equal to their actual values and the inverter can be controlled as an open-loop system with its dynamic performance optimized. At last, simulation and experiments are performed. The results show that the static performance of the proposed strategy is as good as that of the classical ones, but its dynamic performance is improved significantly.

A Novel Control Strategy for Grid-Connected Inverter Based on Iterative Calculation of Structural Parameters

To improve the performance of data-driven reaction prediction models, a new data augmentation method for augmenting data volumes is presented that aims to add fake data in training dataset. This method is only pay attention to small-scale reactions, and manually generates fake data which is chemical and credible molecules by replacing functional groups in reaction sites. And we call this method as virtual data augmentation. Additionally, the transformer model is introduced to explore the effectiveness of virtual data augmentation method in the task of reaction prediction based on small data sets. We apply our method to five classic coupling reactions, the results show that the overall performance of the transformer-baseline model and transformer-transfer learning model combined with virtual data augmentation method is obviously improved, compared to raw datasets. Especially for Suzuki reaction, combining transfer learning strategy and virtual data augmentation method, reaches top-1 accuracy of 97.8%. To sum up, virtual data augmentation can be used as a measure to face up the problem of insufficient data and significantly improves the performance of reaction prediction.

pdf/virtual-data-augmentation-method-for-reaction-prediction-in-b1jqm42g.pdf

Virtual data augmentation method for reaction prediction in small dataset scenario

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Virtual data augmentation method for reaction prediction

A Novel Control Strategy for Grid-Connected Inverter Based on Iterative Calculation of Structural Parameters

Virtual data augmentation method for reaction prediction in small dataset scenario