Developing effective platforms for economic energy management is considered a pivotal issue in the field of Electric Vehicles (EVs). To implement a cost-effective Energy Management Platform (EMP), developers must overcome two major challenges. The first challenge lies in the environmental dynamic nature such as EV location, energy price fluctuations, storage levels, and parking availability at charging stations. This causes most traditional one-shot optimizations to fail. The second challenge pertains to the lack of regulation in EV energy exchanges. To address these challenges, we propose a cost-aware two-stage EMP based on blockchain and deep reinforcement learning (DRL), namely TEMP. Specifically, TEMP first develops a sharding-based blockchain energy management framework, which guarantees trust, security, privacy, traceability, and accountability without the need for intermediaries. Then, considering the complex and high-dimensional environment, TEMP devises a two-stage cooperative scheduling scheme by combining ant colony optimization (ACO) with proximal policy optimization (PPO) to enhance learning effectiveness. Evaluations show that TEMP outperforms the two state-of-the-art baselines by 12.3% and 4.4% in terms of long-term profits while reducing costs by 6.7% and 2.8%, respectively. Moreover, energy transaction efficiency can be ensured when the EV number of blockchain networks is gradually increased.

There are problems in classification tasks where noisy data situations can affect the probability density fitting, as well as adaptive problems with current Bayesian classifiers constructed with symmetric kernel density estimation. In this paper, we will propose a plain Bayesian classifier improved based on weighted beta kernel density estimation. The bandwidth selection is also derived for beta kernel estimation. Compared with the classifier based on symmetric kernel density estimator, the beta kernel can be more adaptive to data changes and can solve certain boundary influence problems. Meanwhile, for the presence of noisy data in the data, a weighted kernel density estimation is combined with the beta kernel to weaken the influence of some of noisy data in fitting the data distribution. The experimental results show that our proposed weighted beta kernel density estimation plain Bayesian classifier method has obvious effect and improvement in fitting probability density distribution and data classification for tightly supported data.

Feature selection (FS) is a significant dimensionality reduction technique, which can effectively remove redundant features. Metaheuristic algorithms have been widely employed in FS, and have obtained satisfactory performance, among them, grey wolf optimizer (GWO) has received widespread attention. However, the GWO and its variants suffer from limited adaptability, poor diversity, and low accuracy when faced with high-dimensional data. The hybrid rice optimization (HRO) algorithm is an emerging metaheuristic algorithm derived from the hybrid heterosis and breeding mechanism in nature. It possesses a robust capacity to identify and converge towards optimal solutions. Therefore, a novel approach based on multi-strategy collaborative GWO combined with the HRO algorithm (HRO-GWO) for FS is proposed in this paper. The HRO-GWO algorithm is enhanced by four innovative strategies including dynamical regulation strategy and three search strategies. First, to improve the adaptability of GWO, the dynamical regulation strategy is devised for parameter optimization of GWO. Then, a multi-strategy co-evolution model inspired by HRO is designed, which utilizes neighborhood search, dual-crossover, and selfing techniques to bolster population diversity. Finally, the study develops a hybrid filter-wrapper framework incorporating chi-square and the HRO-GWO algorithm to efficiently select pertinent and informative feature subsets, enhancing the classification performance while conserving time. The performance of HRO-GWO has been rigorously assessed across benchmark functions and the effectiveness of the proposed framework has been evaluated on small-sample high-dimensional biomedical datasets. Our experimental findings demonstrate that the approach on the basis of HRO-GWO outperforms state-of-the-art methods.

Laser soldering is a crucial soldering technique in the realm of electronic assembly. The temperature of the solder joint is intimately connected with the quality of the solder. This paper introduces an adjustable power upper limit variable-structure Proportional-Integral-Derivative (PID) intelligent control method for regulating the temperature of the solder joint during laser soldering. Distinct laser power limits are employed for workpieces with varying heat capacities. The solder joint temperature is monitored through an infrared thermometer, which enables closed-loop temperature control via a variable-structure PID algorithm. Residual neural network (ResNet) models are utilized to predict key soldering process parameters. This method has been executed and validated on a practical testing platform. Compared to other laser soldering control techniques, the proposed method demonstrates a low overshoot, rapid dynamic response, and swift adjustment capabilities, effectively enhancing the soldering quality and production efficiency.

Real-time measurement of solder joint temperature and precise adjustment of the output power of the semiconductor laser are crucial to ensure high-quality laser soldering. To prevent solder joint scorching, virtual soldering, and false soldering caused by measurement errors or slow measurement speed, a high-precision infrared temperature measurement device was designed in this study. The principle of infrared temperature measurement is introduced first, followed by a detailed explanation of the main signal processing method used Butterworth filter and the photoelectric conversion circuit design method. Finally, experimental analysis is presented to verify the performance of the device. The results demonstrate that the infrared temperature measurement device designed in this study is suitable for non-contact measurement of laser soft soldering solder joint temperature with an error range of only 2%. This indicates the effectiveness of the device in enabling real-time temperature measurement and precise power control during laser soldering processes.