Technologies最新文献

Editorial for the Special Issue “Data Science and Big Data in Biology, Physical Science and Engineering” 为 "生物、物理科学和工程学中的数据科学和大数据 "特刊撰写编辑文章

Technologies Pub Date : 2024-01-08 DOI: 10.3390/technologies12010008

Mohammed Mahmoud

引用次数: 0

Extended-Window Algorithms for Model Prediction Applied to Hybrid Power Systems 应用于混合动力系统的模型预测扩展窗口算法

Technologies Pub Date : 2024-01-05 DOI: 10.3390/technologies12010006

Fu-Cheng Wang, Hsiao-Tzu Huang

{"title":"Extended-Window Algorithms for Model Prediction Applied to Hybrid Power Systems","authors":"Fu-Cheng Wang, Hsiao-Tzu Huang","doi":"10.3390/technologies12010006","DOIUrl":"https://doi.org/10.3390/technologies12010006","url":null,"abstract":"This paper proposes extended-window algorithms for model prediction and applies them to optimize hybrid power systems. We consider a hybrid power system comprising solar panels, batteries, a fuel cell, and a chemical hydrogen generation system. The proposed algorithms enable the periodic updating of prediction models and corresponding changes in system parts and power management based on the accumulated data. We first develop a hybrid power model to evaluate system responses under different conditions. We then build prediction models using five artificial intelligence algorithms. Among them, the light gradient boosting machine and extreme gradient boosting methods achieve the highest accuracies for predicting solar radiation and load responses, respectively. Therefore, we apply these two models to forecast solar and load responses. Third, we introduce extended-window algorithms and investigate the effects of window sizes and replacement costs on system performance. The results show that the optimal window size is one week, and the system cost is 13.57% lower than the cost of the system that does not use the extended-window algorithms. The proposed method also tends to make fewer component replacements when the replacement cost increases. Finally, we design experiments to demonstrate the feasibility and effectiveness of systems using extended-window model prediction.","PeriodicalId":101448,"journal":{"name":"Technologies","volume":"45 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139382316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards a Bidirectional Mexican Sign Language–Spanish Translation System: A Deep Learning Approach 墨西哥手语-西班牙语双向翻译系统：深度学习方法

Technologies Pub Date : 2024-01-05 DOI: 10.3390/technologies12010007

Jaime-Rodrigo González-Rodríguez, Diana-Margarita Córdova-Esparza, Juan R. Terven, J. Romero-González

{"title":"Towards a Bidirectional Mexican Sign Language–Spanish Translation System: A Deep Learning Approach","authors":"Jaime-Rodrigo González-Rodríguez, Diana-Margarita Córdova-Esparza, Juan R. Terven, J. Romero-González","doi":"10.3390/technologies12010007","DOIUrl":"https://doi.org/10.3390/technologies12010007","url":null,"abstract":"People with hearing disabilities often face communication barriers when interacting with hearing individuals. To address this issue, this paper proposes a bidirectional Sign Language Translation System that aims to bridge the communication gap. Deep learning models such as recurrent neural networks (RNN), bidirectional RNN (BRNN), LSTM, GRU, and Transformers are compared to find the most accurate model for sign language recognition and translation. Keypoint detection using MediaPipe is employed to track and understand sign language gestures. The system features a user-friendly graphical interface with modes for translating between Mexican Sign Language (MSL) and Spanish in both directions. Users can input signs or text and obtain corresponding translations. Performance evaluation demonstrates high accuracy, with the BRNN model achieving 98.8% accuracy. The research emphasizes the importance of hand features in sign language recognition. Future developments could focus on enhancing accessibility and expanding the system to support other sign languages. This Sign Language Translation System offers a promising solution to improve communication accessibility and foster inclusivity for individuals with hearing disabilities.","PeriodicalId":101448,"journal":{"name":"Technologies","volume":"38 43","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139382496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Revisiting Probabilistic Latent Semantic Analysis: Extensions, Challenges and Insights 重新审视概率潜语义分析：扩展、挑战和启示

Technologies Pub Date : 2024-01-03 DOI: 10.3390/technologies12010005

Pau Figuera, Pablo García Bringas

{"title":"Revisiting Probabilistic Latent Semantic Analysis: Extensions, Challenges and Insights","authors":"Pau Figuera, Pablo García Bringas","doi":"10.3390/technologies12010005","DOIUrl":"https://doi.org/10.3390/technologies12010005","url":null,"abstract":"This manuscript provides a comprehensive exploration of Probabilistic latent semantic analysis (PLSA), highlighting its strengths, drawbacks, and challenges. The PLSA, originally a tool for information retrieval, provides a probabilistic sense for a table of co-occurrences as a mixture of multinomial distributions spanned over a latent class variable and adjusted with the expectation–maximization algorithm. The distributional assumptions and the iterative nature lead to a rigid model, dividing enthusiasts and detractors. Those drawbacks have led to several reformulations: the extension of the method to normal data distributions and a non-parametric formulation obtained with the help of Non-negative matrix factorization (NMF) techniques. Furthermore, the combination of theoretical studies and programming techniques alleviates the computational problem, thus making the potential of the method explicit: its relation with the Singular value decomposition (SVD), which means that PLSA can be used to satisfactorily support other techniques, such as the construction of Fisher kernels, the probabilistic interpretation of Principal component analysis (PCA), Transfer learning (TL), and the training of neural networks, among others. We also present open questions as a practical and theoretical research window.","PeriodicalId":101448,"journal":{"name":"Technologies","volume":"51 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139451929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Novel Machine Learning-Based Prediction Method for Early Detection and Diagnosis of Congenital Heart Disease Using ECG Signal Processing 基于机器学习的新型预测方法，利用心电图信号处理实现先天性心脏病的早期检测和诊断

Technologies Pub Date : 2024-01-02 DOI: 10.3390/technologies12010004

Prabu Pachiyannan, M. Alsulami, D. Alsadie, Abdul Khader Jilani Saudagar, Mohammed Alkhathami, R. C. Poonia

{"title":"A Novel Machine Learning-Based Prediction Method for Early Detection and Diagnosis of Congenital Heart Disease Using ECG Signal Processing","authors":"Prabu Pachiyannan, M. Alsulami, D. Alsadie, Abdul Khader Jilani Saudagar, Mohammed Alkhathami, R. C. Poonia","doi":"10.3390/technologies12010004","DOIUrl":"https://doi.org/10.3390/technologies12010004","url":null,"abstract":"Congenital heart disease (CHD) represents a multifaceted medical condition that requires early detection and diagnosis for effective management, given its diverse presentations and subtle symptoms that manifest from birth. This research article introduces a groundbreaking healthcare application, the Machine Learning-based Congenital Heart Disease Prediction Method (ML-CHDPM), tailored to address these challenges and expedite the timely identification and classification of CHD in pregnant women. The ML-CHDPM model leverages state-of-the-art machine learning techniques to categorize CHD cases, taking into account pertinent clinical and demographic factors. Trained on a comprehensive dataset, the model captures intricate patterns and relationships, resulting in precise predictions and classifications. The evaluation of the model’s performance encompasses sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve. Remarkably, the findings underscore the ML-CHDPM’s superiority across six pivotal metrics: accuracy, precision, recall, specificity, false positive rate (FPR), and false negative rate (FNR). The method achieves an average accuracy rate of 94.28%, precision of 87.54%, recall rate of 96.25%, specificity rate of 91.74%, FPR of 8.26%, and FNR of 3.75%. These outcomes distinctly demonstrate the ML-CHDPM’s effectiveness in reliably predicting and classifying CHD cases. This research marks a significant stride toward early detection and diagnosis, harnessing advanced machine learning techniques within the realm of ECG signal processing, specifically tailored to pregnant women.","PeriodicalId":101448,"journal":{"name":"Technologies","volume":"129 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139453394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0