Systems and Soft Computing最新文献

{"title":"Optimizing capacitor size and placement in radial distribution networks for maximum efficiency","authors":"R. Arunjothi,&nbsp;K.P. Meena","doi":"10.1016/j.sasc.2024.200111","DOIUrl":"https://doi.org/10.1016/j.sasc.2024.200111","url":null,"abstract":"<div><p>As distribution systems continue to expand, they face challenges such as increased system losses and inadequate voltage regulation. To address these issues, shunt capacitors are being deployed in distribution networks. These capacitors offer reactive power compensation, enhance power factor, improve voltage profiles, promote system stability, and significantly reduce losses. However, determining the appropriate capacitor sizes and their optimal placements requires careful consideration of both technical and economic factors. The nonlinear nature of optimal capacitor placement and sizing, leveraging optimization techniques becomes crucial in identifying the best locations and values for capacitors. This paper demonstrates the effective utilization of Particle Swarm Optimization (PSO) and Real Coded Genetic Algorithm (RCGA) optimization techniques for capacitor placement and selection. The optimization techniques are applied to a 33-bus IEEE standard radial distribution system, to reduce the real power loss and to improve the voltage profile considering both constant and variable loads. Both PSO and RCGA algorithms identify suitable locations for the placement of capacitors for reactive power compensation within the distribution system. By optimizing the objective function associated with capacitor placement costs and maximizing annual cost savings, the PSO and RCGA techniques yield promising results. After implementing the optimal capacitor placements at the identified candidate nodes, a significant reduction in losses within the radial distribution system is observed. Moreover, the cost savings achieved through optimal placement and sizing are substantial.</p></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"6 ","pages":"Article 200111"},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772941924000401/pdfft?md5=2f73c2c4e142069744e1849b8f093be8&pid=1-s2.0-S2772941924000401-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141541006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantum maximum power point tracking (QMPPT) for optimal solar energy extraction","authors":"Habib Feraoun ,&nbsp;Mehdi Fazilat ,&nbsp;Reda Dermouche ,&nbsp;Said Bentouba ,&nbsp;Mohamed Tadjine ,&nbsp;Nadjet Zioui","doi":"10.1016/j.sasc.2024.200118","DOIUrl":"10.1016/j.sasc.2024.200118","url":null,"abstract":"<div><p>Solar energy is key to achieving a more environmentally responsible future. One way to exploit it is to use semiconductor technology through solar panels to generate clean, sustainable, and controllable energy. However, the use of such solutions must be optimised by methods such as maximum power point tracking (MPPT) to extract the maximum available solar energy. Although MPPT algorithms have been widely used and improved, the use of newer approaches, such as quantum computing, appears to hold the promise of achieving new performance levels, particularly for real-time MPPT implementation. The goal of this work is to develop and test a quantum algorithm for the photovoltaic (PV) energy MPPT problem using quantum particle swarm optimisation. The performance of the classic and quantum MPPT algorithms was evaluated under three main operating conditions: normal, high-temperature, and partial shading conditions. This represents a variety of environmental scenarios that can affect the efficiency of solar power generation. According to the study's results, the classical algorithm recorded 0.15% more power than the quantum algorithm in normal operating conditions, and the quantum algorithm generated 3.33% more power in higher temperature tests and 0.89% more power in the partial shading test. Moreover, the quantum algorithm recorded lower duty cycles for the three tests. While the classical algorithm may have a slight edge in power output under normal operation conditions, the quantum algorithm indicates superior performance in challenging conditions and consistently reveals more promising overall efficiency.</p></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"6 ","pages":"Article 200118"},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772941924000474/pdfft?md5=a289399cd0a5826ec78afa78057d3150&pid=1-s2.0-S2772941924000474-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141622803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a multi-level feature fusion model for basketball player trajectory tracking","authors":"Tao Wang","doi":"10.1016/j.sasc.2024.200119","DOIUrl":"https://doi.org/10.1016/j.sasc.2024.200119","url":null,"abstract":"<div><p>To solve the problems of low matching degree, long tracking time, and low accuracy of multi-target tracking in the process of athlete motion trajectory tracking using deep learning technology, a new athlete motion trajectory tracking model was proposed in this study. The study first optimized the current object detection algorithm in basketball, utilized a hybrid attention mechanism to extract object features, and improved the non-maximum suppression strategy. Then, a hybrid branch network was introduced to improve the residual network and a new athlete identity recognition model was proposed. Finally, a new trajectory tracking model was designed by combining the object detection model and the athlete identity recognition model. The research results indicated that in the object detection experiment, the detection time of the proposed object detection algorithm was always below 0.4 s, and its average accuracy reached up to 0.63. In trajectory tracking testing, the final built tracking model had a multi-target tracking accuracy of up to 0.98, and its tracking overlap rate was as low as 0.02. This study has the following two contributions. Firstly, a new model of athlete trajectory tracking is proposed, which improves the accuracy and efficiency of multi-target tracking by optimizing object detection algorithm and introducing hybrid branch network. Second, the model has excellent performance in both object detection and track tracking, which can not only provide a new solution for athletes' motion trajectory tracking, but also significantly improve the effect of motion tracking.</p></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"6 ","pages":"Article 200119"},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772941924000486/pdfft?md5=e3ea1a5ddad1763f0cf4e482d1ae93e8&pid=1-s2.0-S2772941924000486-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141605952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hybrid machine learning model for classifying gene mutations in cancer using LSTM, BiLSTM, CNN, GRU, and GloVe","authors":"Sanad Aburass ,&nbsp;Osama Dorgham ,&nbsp;Jamil Al Shaqsi","doi":"10.1016/j.sasc.2024.200110","DOIUrl":"https://doi.org/10.1016/j.sasc.2024.200110","url":null,"abstract":"<div><p>In our study, we introduce a novel hybrid ensemble model that synergistically combines LSTM, BiLSTM, CNN, GRU, and GloVe embeddings for the classification of gene mutations in cancer. This model was rigorously tested using Kaggle's Personalized Medicine: Redefining Cancer Treatment dataset, demonstrating exceptional performance across all evaluation metrics. Notably, our approach achieved a training accuracy of 80.6 %, precision of 81.6 %, recall of 80.6 %, and an F1 score of 83.1 %, alongside a significantly reduced Mean Squared Error (MSE) of 2.596. These results surpass those of advanced transformer models and their ensembles, showcasing our model's superior capability in handling the complexities of gene mutation classification. The accuracy and efficiency of gene mutation classification are paramount in the era of precision medicine, where tailored treatment plans based on individual genetic profiles can dramatically improve patient outcomes and save lives. Our model's remarkable performance highlights its potential in enhancing the precision of cancer diagnoses and treatments, thereby contributing significantly to the advancement of personalized healthcare.</p></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"6 ","pages":"Article 200110"},"PeriodicalIF":0.0,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772941924000395/pdfft?md5=8ae5f40d03651d91a8bf2e25ac0d2e45&pid=1-s2.0-S2772941924000395-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141487476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The application of deep learning-based technique detection model in table tennis teaching and learning","authors":"Shunshui He","doi":"10.1016/j.sasc.2024.200116","DOIUrl":"https://doi.org/10.1016/j.sasc.2024.200116","url":null,"abstract":"<div><p>With the development of computer technology, the teaching methods of table tennis have ushered in a new technological revolution. To solve the problem of traditional teaching methods overly focusing on athlete limbs and athlete force movements, this study uses an improved deep learning algorithm technology detection model to analyze the trajectory of table tennis and provide targeted tactical training for athletes. The results showed that the success rate and accuracy score of the model were 95 % and 96 %, respectively, with a calculation time of only 21.75 ms, indicating high analytical accuracy and computational efficiency. Meanwhile, the winning rate of the training strategy under this method can reach over 65 %, effectively improving the winning rate of athletes. This proves that the proposed technology detection model has good algorithm performance and data analysis ability, and can provide data support for table tennis training and teaching work.</p></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"6 ","pages":"Article 200116"},"PeriodicalIF":0.0,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772941924000450/pdfft?md5=8cce984bd2a0aaca98ffb72830733291&pid=1-s2.0-S2772941924000450-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141487475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Innovative design of wood texture images for indoor furniture based on variable space","authors":"Chuan Xue,&nbsp;Ling Jin","doi":"10.1016/j.sasc.2024.200114","DOIUrl":"https://doi.org/10.1016/j.sasc.2024.200114","url":null,"abstract":"<div><p>In the design of furniture wood texture images, image restoration is a key issue. This study proposes a Bregmanized operator splitting optimization algorithm based on variable space. This study combines variable spatial morphology to process texture images and effectively extract image features using different operators, thereby achieving image restoration. The results of comparing the proposed algorithm with other image processing algorithms showed that the research algorithm achieved a peak signal-to-noise ratio of 29.86 and a structural similarity index of 0.87 in image denoising, respectively, and had a good denoising effect. In terms of image deblurring, the research algorithm had the lowest root mean square error values on the France and Boat datasets, with values of 8.98 and 8.82, respectively, indicating that the image processed by the algorithm had a high similarity with the real image. In terms of image resolution reconstruction, the peak signal-to-noise ratio and root mean square error values of the research algorithm reached 29.74 and 12.67, respectively, indicating that the reconstructed image had the best fit with the original image and the smallest error. In summary, the proposed algorithm has shown good performance in image processing and can be effectively applied in fields such as image denoising, deblurring, and resolution reconstruction. It provides effective methods and technical support for innovative design of wood texture images in indoor furniture.</p></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"6 ","pages":"Article 200114"},"PeriodicalIF":0.0,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772941924000437/pdfft?md5=3a9821790c5bff1dabd0ac63c8fc06f4&pid=1-s2.0-S2772941924000437-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141487477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A pose generation model for animated characters based on DCNN and PFNN","authors":"Boli Wang","doi":"10.1016/j.sasc.2024.200115","DOIUrl":"https://doi.org/10.1016/j.sasc.2024.200115","url":null,"abstract":"<div><p>In the current field of animation and gaming, the action collection cost for 3D animated character generation is high, and the accuracy of action recognition is poor. Therefore, to reduce the cost of generating 3D animated characters and improve the similarity between animated characters and real humans, a 3D action recognition and animated character generation model based on ResNet and phase function neural network is proposed. The experiment outcomes denote that the raised model begins to converge at 50 iterations, with a minimum loss value of 0.13. The convergence speed and loss value are better than other models. In human pose classification, the raised algorithm has the highest accuracy of 99.46 % and an average accuracy of 99.13 %. The highest classification precision and average precision are 97.79 % and 97.33 %, respectively. In terms of human pose orientation classification, the average accuracy and precision of the raised algorithm are 98.09 % and 97.41 %, respectively, which are also higher than other models. In addition, the mean per joint position error of the proposed algorithm is the highest at 80.1 mm and the lowest at 79.3 mm, respectively. The average recognition time for each image is only 46.8 ms, which is lower than other algorithms. In addition, the average update times of the algorithm and the Unreal Engine are 39.28 ms and 27.52 ms, respectively, and both run at different frame rates. The above results indicate that the proposed 3D human pose recognition and animated character generation model based on ResNet and phase function neural network can not only improve the accuracy of pose recognition, but also improve recognition speed, effectively reducing the cost of 3D animated character generation. The animation character generation method includes data collection and the application after data collection, which shows the various roles that deep learning technology can play in the field of computer graphics animation, and also provides excellent solutions for other computer graphics problems.</p></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"6 ","pages":"Article 200115"},"PeriodicalIF":0.0,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772941924000449/pdfft?md5=454fee9bb27864e863d48b37856ed255&pid=1-s2.0-S2772941924000449-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141487474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Telugu language hate speech detection using deep learning transformer models: Corpus generation and evaluation","authors":"Namit Khanduja ,&nbsp;Nishant Kumar ,&nbsp;Arun Chauhan","doi":"10.1016/j.sasc.2024.200112","DOIUrl":"https://doi.org/10.1016/j.sasc.2024.200112","url":null,"abstract":"<div><p>In today's digital era, social media has become a new tool for communication and sharing information, with the availability of high-speed internet it tends to reach the masses much faster. Lack of regulations and ethics have made advancement in the proliferation of abusive language and hate speech has become a growing concern on social media platforms in the form of posts, replies, and comments towards individuals, groups, religions, and communities. However, the process of classification of hate speech manually on online platforms is cumbersome and impractical due to the excessive amount of data being generated. Therefore, it is crucial to automatically filter online content to identify and eliminate hate speech from social media. Widely spoken resource-rich languages like English have driven the research and achieved the desired result due to the accessibility of large corpora, annotated datasets, and tools. Resource-constrained languages are not able to achieve the benefits of advancement due to a lack of data corpus and annotated datasets. India has diverse languages that change with demographics and languages that have limited data availability and semantic differences. Telugu is one of the low-resource Dravidian languages spoken in the southern part of India.</p><p>In this paper, we present a monolingual Telugu corpus consisting of tweets posted on Twitter annotated with hate and non-hate labels and experiments to provide a comparison of state-of-the-art fine-tuned deep learning models (mBERT, DistilBERT, IndicBERT, NLLB, Muril, RNN+LSTM, XLM-RoBERTa, and Indic-Bart). Through transfer learning and hyperparameter tuning, the models are compared for their effectiveness in classifying hate speech in Telugu text. The fine-tuned mBERT model outperformed all other fine-tuned models achieving an accuracy of 98.2. The authors also propose a deployment model for social media accounts.</p></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"6 ","pages":"Article 200112"},"PeriodicalIF":0.0,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772941924000413/pdfft?md5=75eb56e1d4134dd28aab80ba7539f1c8&pid=1-s2.0-S2772941924000413-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141487479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The design of advertising text keyword recommendation for internet search engines","authors":"Fang Wang,&nbsp;Liuying Yu","doi":"10.1016/j.sasc.2024.200109","DOIUrl":"10.1016/j.sasc.2024.200109","url":null,"abstract":"<div><p>As the growth of internet technology, human life is full of various advertisements. It is possible for individuals to obtain the advertising information they require, whether in an online or offline context. A research proposal is presented with the objective of enhancing the precision of online advertising recommendations. The proposal is based on the design of internet search engine advertising text keyword recommendation models, which integrate entity naming recognition models to facilitate tasks such as text classification and feature extraction. A recommendation algorithm based on content similarity is used to achieve keyword recommendation. Under the similarity calculation method of continuous bag-of-words model, when K is 100, the model weighted precision of the feature extraction method based on graph sorting and inverse text frequency index is 0.88, the weighted recall is 0.76, and the weighted F1-score is 0.82. In offline simulation testing, 85 % of the keyword recommendation model's recommendation time is less than 1 s, 99 % of the recommendation time is less than 2 s, and the recommendation cost can be significantly reduced by 75 %. In practical applications, the recommendation efficiency of this method can reach 96.3 %, and the recommendation precision can reach 95.8 %. The recommended satisfaction rate can reach 99.5 %. The results demonstrate that this method can provide highly accurate keyword recommendations and reduce the cost of advertising placement. Furthermore, it has been recognized and praised by users.</p></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"6 ","pages":"Article 200109"},"PeriodicalIF":0.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772941924000383/pdfft?md5=29f04862d9bf98b993a8cc22d6caf146&pid=1-s2.0-S2772941924000383-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141415745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-spectral remote sensing image fusion method based on gradient moment matching","authors":"Haiying Fan ,&nbsp;Gonghuai Wei","doi":"10.1016/j.sasc.2024.200108","DOIUrl":"10.1016/j.sasc.2024.200108","url":null,"abstract":"<div><p>Image fusion is a popular research direction in the field of computer vision. Traditional image fusion algorithms can achieve good results in fusing grayscale images, but it is difficult to achieve ideal results in processing multi-spectral images. To address the shortcomings of multi-spectral image fusion, this study proposes a low computational complexity and low latency multi-spectral image fusion model by utilizing a multi-step degree moment matching algorithm and a generative adversarial network for fusion. Through experiments, it was found that the F1 score of the GAN-MMN model on the TinyPerson dataset was 89.79 %, with an average recall rate of 89.76 %. The GAN-MMN performance was higher than that of the control model. Meanwhile, the GAN-MMN model also exhibited superior performance in high-frequency feature extraction and time delay compared to the control model. According to the experimental results, the proposed multi-spectral remote sensing image fusion model had a high feature extraction effect, and its recall rate and F1 score were better than the control model, so the research model had a certain progressiveness. The proposal of this model gives a new approach for the processing of multi-spectral remote sensing images, effectively promoting the development of the computer vision industry.</p></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"6 ","pages":"Article 200108"},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772941924000371/pdfft?md5=73f7885802860ee996ced14f62fd1080&pid=1-s2.0-S2772941924000371-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141274727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}