European Journal of Electrical Engineering and Computer Science

Estimating Battery Life in Electric Vehicles using Deeper Long Short-Term Memory (DLSTM) Algorithm

2024-11-20T18:04:10+01:00

The paper estimates electric vehicle battery life using a deep, long-term memory algorithm (DLSTM). This algorithm employs a Forget Gate with a sigmoid function to retain or discard information from previous states. The Input Gate, also using a sigmoid function, determines new information to add, while a tanh function creates a new vector for updating the cell state. The Cell State Update combines inputs from the forget and input gates. The Output Gate uses a sigmoid function to select which part of the cell state to output. This AI algorithm analyses voltage, current, and temperature during charging to predict the lithium-ion battery’s state.

A Data-Driven Model for Classification of Traditional Chinese Medicine Materials

2024-11-15T17:56:33+01:00

This paper proposes a data-driven classification model for traditional Chinese medicinal herbs based on mid-infrared spectral data. Addressing the limitations of traditional identification methods when the herbs’ appearance is damaged or incomplete, this study employs machine learning techniques to achieve accurate classification of medicinal herb types through spectral data preprocessing and feature extraction. Firstly, the Savitzky-Golay convolution smoothing method and Standard Normal Variate (SNV) transformation were used for denoising the spectral data. Then, Principal Component Analysis (PCA) was employed to reduce the dimensionality of the high-dimensional spectral data and extract the key features. Finally, the Gaussian Mixture Model (GMM) was applied to cluster the reduced data, categorizing the medicinal herbs into six classes. The results show that this method produces the accurate and stable classification. The constructed model is not only applicable to the classification and origin identification of medicinal herbs but also provides an important reference value for the classification and origin identification of other plant species.

Impact of Ad Blockers on Computer Power Consumption while Web Browsing: A Comparative Analysis

2024-10-16T17:43:30+02:00

This study explores the impact of various ad blockers on power consumption during web browsing, focusing on different types of online content. By analyzing power use across ten popular websites, the study assesses the performance of five widely utilized ad blockers: AdBlock, AdBlock Plus, uBlock, uBlock Origin, and uBlock Origin Lite. Power consumption was measured under controlled conditions, comparing scenarios with and without ad blockers to gain insight into their efficiency. The findings indicate substantial differences in power savings, with some ad blockers significantly reducing power usage, particularly on media-heavy sites, while others unexpectedly increased consumption under certain conditions. The study underscores the potential of ad blockers to enhance power efficiency in digital environments, highlighting the importance of optimizing ad-blocking techniques to reduce the environmental impact of online activities. Through comprehensive analysis and comparison, this research offers insights into selecting effective ad blockers to minimize power consumption, promoting more sustainable web browsing practices.

A Comparative Analysis of Power Consumption While Using Open-Source and Proprietary Media Players

2024-09-19T17:32:57+02:00

This study investigates the power consumption of various media player software applications, comparing open-source and proprietary options. The experiment measured the average power consumption of CPU, GPU, and memory usage of media players such as Kodi, MPC, MPV, SMP, VLC, Windows Media Player, ACG, ALLPlayer, GOM, KMPlayer, LAPlayer, POTPlayer, and RealPlayer while playing 4K video. The results revealed that proprietary media players generally consume less power compared to their open-source counterparts. Statistical analysis, including descriptive statistics and independent samples t-tests, confirmed these findings. Long-term power consumption projections indicated substantial energy savings with more efficient media players. These findings underscore the importance of considering energy efficiency in software selection for sustainable computing.

Implementation of Machine Learning in the Categorization of Improvement Areas for Kaizen Submissions

2024-08-19T01:24:46+02:00

This paper explores the synergy between Kaizen methodologies and Machine Learning, with the goal of improving the efficiency of categorizing areas of improvement. The implementation of Machine Learning algorithms, utilizing the ML.NET tool, has proven to be highly advantageous. These algorithms automate the categorization of Kaizen proposals based on structured data and predefined labels. This automation significantly reduces manual workload, expedites the classification process, and ultimately accelerates decision-making within the realm of continuous improvement. Furthermore, the application of machine learning has improved the categorization accuracy, reduced the risk of human errors, and ensured a more precise assignment to the relevant categories. This, in turn, enhances the quality of the collected information, facilitating well-informed decision-making. Additionally, the automated categorization has streamlined the process for users submitting Kaizen proposals, eliminating the need for manual category selection. This reduces cognitive load and promotes active engagement in the continuous improvement process. Furthermore, the system has introduced a category-based incentive structure, where users accrue points corresponding to the category assigned to their Kaizen proposals. These points can be exchanged for items provided by the company, acting as an additional incentive for active employee participation in the continuous improvement process. In conclusion, the integration of Machine Learning with the ML.NET tool has become a valuable tool for optimizing the management of improvement proposals, resulting in a marked enhancement in efficiency, accuracy, and user engagement in the categorization process.