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Intelligent monitoring and economic benefits of electric vehicle thermal energy cycle based on artificial intelligence algorithms

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THERMAL SCIENCE AND ENGINEERING PROGRESS 2025年 59卷

作者： Li, Jiangnan Yan, Wen Changji Univ Sch Econ & Management Changji 831100 Xinjiang Peoples R China

With the increasingly severe global energy crisis and the continuous improvement of environmental protection awareness, electric vehicles, as an important alternative to traditional fuel vehicles, can not only reduce the dependence on fossil fuels, but also significantly reduce exhaust emissions, which is of great significance for improving environmental quality. The thermal energy management of electric vehicles involves many aspects such as battery, motor and interior environment. A heat energy cycle intelligent monitoring system based on artificial intelligence algorithm was developed to collect the temperature and heat flow data of electric vehicles in real time through sensors. The AI algorithm model is then designed and trained to predict and optimize the thermal energy cycle process. Then the simulation software is used to build the thermal energy cycle model of electric vehicles, and the algorithm model is tested and adjusted through simulation experiments. Finally, the optimized algorithm is applied to the actual electric vehicle, and the field test and data analysis are carried out to verify the actual effect of the system. The experimental results show that the system can accurately monitor the thermal energy state of electric vehicles and optimize the thermal energy management strategy in real time by artificial intelligence algorithm. Compared with the traditional thermal management method, the new system significantly improves the efficiency of the thermal energy cycle and reduces the energy loss, providing a new solution for the thermal energy management of electric vehicles.

关键词： artificial intelligence algorithm Electric vehicles Heat energy cycle Intelligent monitoring Economic benefit

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Interactive Design of Dynamic Visual Communication Driven by artificial intelligence algorithms

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Procedia Computer Science 2025年 261卷 682-690页

作者： Shen Liu Li Ding School of Culture and Media Eurasia University Xian 710065 Shaanxi China

Dynamic visual communication design usually needs to process multimodal information. How to effectively integrate these multi-dimensional features and establish stable associations between different modalities remains a technical challenge. To this end, this article explores the application of artificial intelligence algorithms, especially spatial attention and channel attention mechanisms, in dynamic image and video generation to achieve more accurate semantic consistency between text and images. This article introduces a spatial attention mechanism, which dynamically focuses on the spatial area related to the text description by calculating the similarity between text and image features; at the same time, the channel attention mechanism further optimizes the match with the text description by adjusting the channel weights in the image feature map. In addition, the study introduces user interaction design, and users can influence the generation target of the generator through real-time feedback and adjustment, making it more personalized and in line with needs. The optimization process of the generator and the discriminator further improves the realism and quality of dynamic image and video generation through GAN (Generative Adversarial Network). Experiments show that the proposed method can effectively enhance the semantic consistency in the image generation process and provide new technical support for dynamic visual communication design.

关键词： Dynamic visual communication design artificial intelligence algorithm spatial attention mechanism channel attention mechanism generative adversarial network

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An artificial intelligence-based strategy for multi-objective optimization of diesel engine fueled with ammonia-diesel-hydrogen blended fuel

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ENERGY 2025年 318卷

作者： Zhang, Zhiqing Hu, Jingyi Wang, Yuguo Pan, Mingzhang Lu, Kai Ye, Yanshuai Yin, Zibin Guangxi Univ Sci & Technol Sch Mech & Automot Engn Liuzhou 545006 Peoples R China Quanzhou Normal Univ Coll Transportat & Nav Quanzhou 362000 Peoples R China Guangxi Univ Coll Mech Engn Nanning Peoples R China Jimei Univ Sch Marine Engn Xiamen 361021 Peoples R China

Ammonia-diesel blended fuel is a promising alternative fuel combination. However, ammonia has issues such as low laminar flame velocity and weak reaction activity. As an efficient and low-carbon fuel, hydrogen enhances the thermal efficiency of the combustion process when introduced. This study establishes a three-dimensional simulation model to explore the effects of hydrogen energy substitution rate, fuel injection timing, and intake pressure on the combustion and emission characteristics of the blended fuel. The results indicate that the hydrogen energy substitution rate can significantly improve fuel combustion efficiency. Although nitrogen oxide (NOx) emissions increase, unburned ammonia emissions are reduced due to the hydrogen addition. In addition, an extreme learning machine (ELM) is used to predict the output variables. Subsequently, the multi-objective particle swarm optimization (MOPSO) algorithm is coupled with ELM to solve the dual objective problem. The research results show that the lowest unburned ammonia and NOx emissions generated are 213.467 ppm and 597.43 ppm, corresponding to a hydrogen energy substitution rate of 8 %, fuel injection timing of-9.215 degrees CA, and intake pressure of 0.228 MPa. The paper provides a theoretical basis for further optimizing the combustion process of internal combustion engines and reducing emissions.

关键词： Ammonia-diesel blended fuel Hydrogen Inject strategy artificial intelligence algorithm Multi-objective optimization

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An artificial intelligence-enabled electrocardiogram algorithm for the prediction of left atrial low-voltage areas in persistent atrial fibrillation

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JOURNAL OF CARDIOVASCULAR ELECTROPHYSIOLOGY 2024年第9期35卷 1849-1858页

作者： Tao, Yirao Zhang, Deyun Tan, Chen Wang, Yanjiang Shi, Liang Chi, Hongjie Geng, Shijia Ma, Zhimin Hong, Shenda Liu, Xing Peng Capital Med Univ Beijing Chaoyang Hosp Dept Cardiol Beijing 100020 Peoples R China Capital Med Univ Beijing Chaoyang Hosp Heart Ctr Beijing Peoples R China Capital Med Univ Beijing Chaoyang Hosp Beijing Key Lab Hypertens Beijing Peoples R China HeartVoice Med Technol Hefei Peoples R China HeartRhythm HeartVoice Joint Lab Beijing Peoples R China Hebei Yanda Hosp Dept Cardiol Hebei Hebei Peoples R China Heart Rhythm Cardiovasc Hosp Dept Cardiol Dezhou 251100 Peoples R China Peking Univ Natl Inst Hlth Data Sci Beijing 100191 Peoples R China Peking Univ Hlth Sci Ctr Inst Med Technol Beijing Peoples R China

ObjectivesWe aimed to construct an artificial intelligence-enabled electrocardiogram (ECG) algorithm that can accurately predict the presence of left atrial low-voltage areas (LVAs) in patients with persistent atrial *** study included 587 patients with persistent atrial fibrillation who underwent catheter ablation procedures between March 2012 and December 2023 and 942 scanned images of 12-lead ECGs obtained before the ablation procedures were performed. artificial intelligence-based algorithms were used to construct models for predicting the presence of LVAs. The DR-FLASH and APPLE clinical scores for LVA prediction were calculated. We used a receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis to evaluate model *** data obtained from the participants were split into training (n = 469), validation (n = 58), and test sets (n = 60). LVAs were detected in 53.7% of all participants. Using ECG alone, the deep learning algorithm achieved an area under the ROC curve (AUROC) of 0.752, outperforming both the DR-FLASH score (AUROC = 0.610) and the APPLE score (AUROC = 0.510). The random forest classification model, which integrated a probabilistic deep learning model and clinical features, showed a maximum AUROC of 0.759. Moreover, the ECG-based deep learning algorithm for predicting extensive LVAs achieved an AUROC of 0.775, with a sensitivity of 0.816 and a specificity of 0.896. The random forest classification model for predicting extensive LVAs achieved an AUROC of 0.897, with a sensitivity of 0.862, and a specificity of *** deep learning model based exclusively on ECG data and the machine learning model that combined a probabilistic deep learning model and clinical features both predicted the presence of LVAs with a higher degree of accuracy than the DR-FLASH and the APPLE risk scores.

关键词： artificial intelligence algorithm deep learning model electrocardiogram low-voltage area persistent atrial fibrillation

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Application of artificial intelligence algorithms in image processing

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2019年第May期61卷 42-49页

作者： Xin Zhang Wang Dahu Jilin Business & Technol Coll Engn Inst Changchun Jilin Peoples R China Henan Polytech Univ Sch Elect Engn & Automat Jiaozuo Henan Peoples R China

As the main media of human communication and understanding of the world, image is one of the important information sources of human intelligence activities. With the development of the times, the demand for image processing technology is increasing day by day. The rapid development of computer technology also provides a platform for the application of image processing. In order to achieve better image processing effect, this paper focuses on the application of artificial intelligence algorithm in image processing. Image segmentation is a technology that decomposes images into regions with different characteristics and extracts useful targets. It can be regarded as a combinatorial optimization problem. It is completely feasible to apply artificial intelligence algorithm to optimization problems. Firstly, this paper introduces the ant colony algorithm in artificial intelligence algorithm, elaborates the basic principle and mathematical model of the ant colony algorithm. Secondly, in order to improve the ability of global search of ant colony algorithm, this paper introduces the crowding degree function of fish into the ant colony algorithm. Finally, the improved ant colony algorithm is used in image segmentation to improve the effect of image segmentation. The simulation results show that it is feasible to use ant colony algorithm in image segmentation. And the optimization improvement of ant colony algorithm is effective. The improved ant colony algorithm applied in image segmentation can significantly improve the segmentation performance. (C) 2019 Published by Elsevier Inc.

关键词： Image processing artificial intelligence algorithm Image segmentation Ant colony algorithm

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Application of genetic programming in electrokinetic treatment of soil and COPR: Focus on Cr(VI) fractionation

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JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING 2025年第2期13卷

作者： Yu, Qiu Zheng, Yi Jiang, Zhao Yu, Beichen Xihua Univ Sch Emergency Management Chengdu 610039 Peoples R China China Univ Min & Technol Key Lab Gas & Fire Control Coal Mines Xuzhou 221116 Peoples R China China Univ Min & Technol State Key Lab Coal Mine Disaster Prevent & Control Xuzhou 221116 Peoples R China

Eliminating highly hazardous hexavalent chromium (Cr(VI)) from solid waste is momentous for alleviating Cr (VI) toxicity to the environment. Cost-effective electrokinetic remediation technology was used for the Cr(VI) removal from contaminated soil and chromite ore processing residue (COPR). While the technology's principle was straightforward, the associated physicochemical processes and pollutant characteristics were highly intricate, and it is hard to build the quantitative relationship between the treatment effect and various influencing factors. Thus, this study innovatively applied artificial intelligence algorithm to solid waste disposal, established a deep learning prediction model based on genetic programming (GP) for Cr(VI) evolution in water-soluble, weakly adsorbed, and insoluble form, and specifically analyzed how the algorithm function enabled the model's self-adaptive optimization. Results displayed that the generated prediction models were differentially distributed depending on the pollution medium and Cr(VI) fractionation. Current and anolyte pH were dominant in Cr(VI) elimination, and catholyte pH played a greater role in COPR than in soil. The predicted values was close to the experimental values, the prediction accuracies were all higher than 95 %, and could reveal the evolution patterns of Cr(VI) in different forms. For weakly adsorbed and insoluble Cr(VI) prediction in COPR, the error level was within 4 % and the data frequency within the error level of 4 % reached 90 % for water-soluble Cr(VI), suggesting that GP accurately predicted the majority of the Cr(VI) concentration data, which was an important guideline for optimizing electrokinetic remediation schemes.

关键词： Electrokinetic remediation Cr(VI) removal artificial intelligence algorithm Deep learning Self-adaptive optimization

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Combined ultra-short-term photovoltaic power prediction based on CEEMDAN decomposition and RIME optimized AM-TCN-BiLSTM

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ENERGY 2025年 318卷

作者： Zhou, Daixuan Liu, Yujin Wang, Xu Wang, Fuxing Jia, Yan Inner Mongolia Univ Technol Sch Energy & Power Engn Hohhot 010051 Peoples R China Inner Mongolia Univ Technol Sch Elect Power Hohhot 010080 Peoples R China

Photovoltaic power prediction is crucial to the stable operation of the power system. In order to further improve the accuracy of photovoltaic power prediction, a Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Rime-ice (RIME) optimization algorithm and optimization Attention Mechanism (AM)-Time Convolutional Network (TCN)-Bidirectional Long Short-Term Memory Neural Network (BiLSTM) combined ultra-short-term photovoltaic power prediction model is proposed. First, the original power sequence is decomposed using CEEMDAN to obtain smoother data;then, for the inherent intermittency, variability, and stochasticity of PV power generation, a combined AM-TCN-BiLSTM prediction model is constructed to extract features and learn the PV power, and the RIME simulates the growth and crossover behaviors of the mistletoe-particle populations with powerful global optimization functions. The hyperparameters of the prediction model are optimized by the RIME algorithm, and the optimized hyperparameter prediction model is used to predict each subsequence obtained from the decomposition. Finally, the prediction results of each subsequence are integrated and reconstructed to obtain the final PV power prediction value. The simulation verification shows that the model can effectively improve the prediction accuracy compared with the comparison algorithm. In the experimental results, the MAE for the first and second predictive steps were recorded as 4.3116 and 5.0342, respectively. The RMSE values for these steps were 6.7357 and 8.5834, respectively. Additionally, the R2 showed a significant improvement, reaching 0.9879 for the first step and 0.9803 for the second step. These outcomes validate the effectiveness of the model proposed in this paper.

关键词： Rime algorithm Attention mechanism artificial intelligence algorithm Power prediction

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Research on logistics distribution path analysis based on artificial intelligence algorithms

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INTERNATIONAL JOURNAL OF BIOMETRICS 2020年第1期12卷 100-108页

作者： Yao, Cuiping Shanghai Sipo Vocat & Tech Coll 1408 Chengnan Rd Shanghai 230019 Peoples R China

Logistics distribution path optimisation model design is the key to ensure the smooth flow of logistics distribution path network, the logistics distribution path optimisation is designed to improve the efficiency of logistics distribution, a logistics distribution path optimisation model is proposed based on artificial intelligence algorithm. A logistics distribution path search model based on rough set theory is established. Ant colony search method is used to design the artificial intelligence algorithm of logistics distribution path optimisation. Adaptive weighting method is used to extract and schedule the information of logistics distribution path, and the shortest path optimisation method is used to optimise the route planning of logistics distribution, which can reduce the path overhead and time cost of logistics distribution. The efficiency of logistics distribution is improved. The simulation results show that this method is used to construct the logistics distribution path model, which reduces the time cost and the road cost of the logistics distribution, and improves the throughput of the logistics distribution significantly.

关键词： artificial intelligence algorithm logistics distribution ant colony search shortest path optimisation

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Path planning for mobile robot based on improved ant colony Q-learning algorithm

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INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM 2025年第4期19卷 3069-3087页

作者： Cui, Mengru He, Maowei Chen, Hanning Liu, Kunpeng Hu, Yabao Zheng, Chen Wang, Xuliang Tiangong Univ Sch Artificial Intelligence Tianjin 300387 Peoples R China Tiangong Univ Sch Comp Sci & Technol Tianjin 300387 Peoples R China Tiangong Univ Sch Control Sci & Engn Tianjin 300387 Peoples R China Tianjin Univ Sci & Technol Sch Comp Sci & Technol Tianjin 300387 Peoples R China Minist Educ Engn Res Ctr Integrat & Applicat Digital Learning Beijing 100039 Peoples R China Open Univ China Beijing 100039 Peoples R China

With the development of artificial intelligence technology, mobile robots have become a popular research direction. As one of the basic technologies for robot navigation, path planning occupies an important position in the field of robot research. The traditional ant colony algorithm (ACO) is one of the most widely used methods in solving path planning. However, ACO still has some disadvantages, such as low search efficiency and easily falling into the local optimum. To address these shortcomings of ACO, the learning strategy of the Q-learning algorithm is introduced to improve the convergence speed and global optimization of ACO. Therefore, an improved ant colony Q-learning algorithm (IQLACO) is proposed. In IQLACO, firstly, the turn times is introduced into heuristic information that improves the smoothness of planned paths. An angular guidance factor is introduced into the state transfer probability, which improves the search efficiency of ants. An adaptive parameter pseudo-random search strategy is introduced, which improves the global search ability of ACO. Secondly, in order to improve the convergence ability of ACO, a new pheromone update rule is proposed. Then, the Q-learning algorithm is used for pre-training pheromones to provide some direction for ants. Finally, the three indicators of optimal path length, convergence speed, and turn times are analyzed. To demonstrate the performance of IQLACO, IQLACO is compared with three algorithms. The experimental results show that the three indicators are considered comprehensively, and IQLACO has a more obvious advantage than the other three algorithms in finding the optimal paths. Both 'Std' and 'Mean' are smaller than the other three algorithms, which proves the stability of IQLACO. In real life, the autonomous navigation ability of robots is improved in complex environments by optimum path planning algorithms, which enable them to complete their tasks accurately.

关键词： artificial intelligence algorithm Path planning Mobile robot Ant colony Q-learning algorithm

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Construction of Psychological Crisis Prevention Mechanism Based on artificial intelligence algorithms

Construction of Psychological Crisis Prevention Mechanism Ba...

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作者： Liu, Hui Beijing Information Technology College Beijing China

artificial intelligence technology has become increasingly mature and has been applied in large quantities, which also provides a new idea for the construction of a psychological crisis prevention mechanism. Establishing a more objective and more efficient psychological crisis prevention mechanism is to use artificial intelligence algorithms to collect psychological information in multiple ways and conduct psychological health evaluations in multiple dimensions. In this case, psychological health prevention will realize the transformation from static to dynamics, from subjective evaluation to big data algorithm evaluation, so as to better prevent psychological crisis prevention. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： artificial intelligence algorithm Association rules Psychological crisis prevention

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