Journal of Computational Methods in Sciences and Engineering - Volume 24, issue 3

Authors: Wu, Xingping | Yuan, Qiheng | Zhou, Chunlei | Chen, Xiang | Xuan, Donghai | Song, Jinwei

Article Type: Research Article

Abstract: In the field of electric carbon, the mapping relationship between carbon emission flow calculation and power flow calculation was studied by combining techniques such as current trajectory tracking, carbon flow trajectory analysis, power system flow calculation methods, and electric network analysis theory. By delving into the mechanism between these two factors, a better understanding of the correlation between them can be achieved. In addition, by using time series data, graph attention neural networks (GNN), distributed computing technology, and spatiotemporal computing engines, carbon emission fluctuations can be decomposed and a high-frequency “energy-electricity-carbon” integrated dynamic emission factor can be achieved. Through the …spatiotemporal distribution patterns of this dynamic factor in multiple dimensions, the carbon emissions from key industries in cities can be accurately calculated. In this paper, the LSTM-GAT model is used as the core to construct a key carbon emission prediction model for cities. The study focuses on the power plant, chemical industry, steel, transportation industry, and construction industry, which are high energy-consuming industries with an annual electricity consumption of more than 100 million kWh in a major city of China. By analyzing the entire life cycle from power generation to electricity consumption and conducting current flow analysis, monthly, weekly, and daily carbon emission calculations were performed. Additionally, other factors such as the industrial development index, GDP, coverage area of power generation enterprises, regional population, size, and type of power-consuming units were included in the comprehensive calculation to build a measurement system. By conducting experiments and analyzing historical data, we have found that the LSTM-GAT model outperforms the single models of GCN, GAT, LSTM, GRU, and RNN in terms of lower error values and higher accuracy. The LSTM-GAT model is better suited for predicting carbon emissions and related indicators with an accuracy rate of 89.5%. Our predictions show that the carbon emissions will exhibit a slow growth trend in the future, while the carbon emission intensity will decrease. This information can provide a scientific basis for government decision-making. Show more

Keywords: Electric carbon, dynamic emission factor, LSTM-GAT, predict carbon emissions

DOI: 10.3233/JCM-247139

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1405-1421, 2024

Authors: Wang, Chong

Article Type: Research Article

Abstract: The TFAHP-FAHP algorithm model constructed in this article can preferably avoid subjective assumptions caused by human factor. The new energy vehicle power battery is a core component for improving the performance of electric vehicles, and constitutes one of the most valuable parts of the invention patent for new energy vehicles. Patent pledge financing is a brand new form with great potential. However, as a result of the imperfect system of the patent pledge financing, banks and other financial institutions are exposed to an increased risk in the process of the patent pledge financing, which seriously limits the development of the …patent pledge financing for power battery. In view of the above issues, this paper uses the SPSS reliability and validity tests in order to develop a relatively complete and accurate index system. TFAHP algorithm model and fuzzy comprehensive are combined to determine the final correction coefficient. In this study, the income method is used to determine the pledge valuation value of a new energy vehicle power battery patent portfolio, and the AHP algorithm method is used to determine the weight of the value of the invention patent portfolio within the patent portfolio. As a result, in this paper, the pledge value of the patent portfolio is multiplied by a proportion of the invention patent portfolio’s value, and then multiplied by a correction coefficient to obtain the final new energy vehicle power battery invention patent portfolio pledge value. Using this reference value, technology-based energy companies can pledge financing for new energy vehicle power battery invention patent portfolios. Show more

Keywords: TFAHP algorithm, FAHP algorithm, invention patent portfolio for new energy power battery, SPSS reliability and validity test algorithm

DOI: 10.3233/JCM-247154

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1423-1439, 2024

Authors: Chen, Mingming | Bao, Jin | Li, Zhixin | Li, Jun

Article Type: Research Article

Abstract: Wireless charging technology provides a convenient and safe method for electric vehicles. To meet the needs of commercial operations, it is essential to accurately measure the active power at the transmitter end of the wireless charging system. However, due to the high-frequency and high-voltage, measuring the voltage at the transmitting coil is challenging. In response to this issue, this paper focuses on the bilateral LCC compensation network and analyzes the harmonic components of the current and voltage at transmitting coil. It derives the voltage relationship between compensation capacitor and the transmitting coil and indirectly measure voltage at transmitting coil, finally …proposing an active power calculation method for the transmitting coil. An experimental prototype of a wireless charging system utilizing bilateral LCC compensation is built. Simulations and experiments verify that the active power consumed by the transmitting coil is mainly the fundamental component, and the impact of the sampling frequencies and power levels on the measurement accuracy is discussed. Show more

Keywords: Wireless charging system, bilateral LCC compensation network, current characteristics, active power measurement

DOI: 10.3233/JCM-247164

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1441-1456, 2024

Authors: Qiu, Lingling

Article Type: Research Article

Abstract: In order to enhance the performance of intelligent image recognition, this study optimizes the image recognition model through lightweight convolutional neural networks (CNNs) and cloud computing technology. The study begins by introducing the relevant theories and models of edge computing (EC) and lightweight CNNs models. Next, this study focuses on optimizing traditional image recognition models. Finally, the effectiveness and reliability of the proposed model are experimentally validated. The experimental results indicate that, when recognizing 1000 images, the average recognition times per image on cloud servers and edge servers are 13.33 ms and 50.11 ms, respectively. Despite the faster speed of …cloud servers, the performance of edge servers can be improved by stacking servers. When the number of edge servers reaches 4, their recognition speed surpasses that of the cloud server model. Additionally, comparing the latency and processing time between EC and cloud computing architectures, it is observed that, with an increase in the number of processed images, the average processing time per image in the EC architecture remains relatively stable and consistent. In contrast, the average processing time gradually increases in the cloud computing architecture. This indicates a significant impact of the number of images on the processing rate of the cloud computing architecture. Therefore, as the time gap in processing between cloud computing and EC increases, the advantages of the EC architecture become more apparent. This study’s significance lies in advancing the development of deep learning technology and providing possibilities for its widespread practical application. The contribution of this study lies in promoting the development of EC and lightweight neural network models, offering valuable references and guidance for practical applications in related fields. Show more

Keywords: Edge computing, lightweight CNNs, image recognition, cloud computing technology, edge server, Elastic Compute Service

DOI: 10.3233/JCM-247187

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1457-1471, 2024

Authors: Wu, Yongyi | Jiang, Jingfeng | Wen, Zhendan

Article Type: Research Article

Abstract: This work addresses issues such as inadequate teaching methods, a lack of teaching resources, and low proactiveness in current accounting education. It introduces a novel teaching approach termed “Just-in-Time Teaching (JITT) cloud teaching,” which integrates “real-time teaching” with the Internet of Things-based “cloud teaching model” specifically for accounting education. First, the current status of accounting education in secondary vocational schools is investigated through a questionnaire survey. Subsequently, adjustments are made to the traditional teaching model, considering the limitations in teaching media creation channels and challenges in teaching activities. The teaching content of accounting education is designed in terms of mind …maps, curriculum type, and problem design. The findings indicate: (1) Almost half of the surveyed teachers have heard of but never used the JITT cloud teaching, and the proportion is the largest. Some teachers have used but disapprove of JITT cloud teaching. (2) The proportion of students using website learning resources is 43.81%, while the proportion using mobile learning applications is 38.34%. (3) There is a significant difference between the traditional teaching mode and the JITT teaching mode in terms of “classroom teaching” and “sense of responsibility”. The average values under the JITT teaching mode have significantly improved compared to the traditional one. (4) The experimental group has a higher proportion of students scoring 90–100, which is 58%, significantly higher than the control group. The above research results indicate that there are still many possibilities for the practical application of the JITT teaching method in the future Moreover, applying the JITT cloud teaching model contributes to enhancing teaching quality and supports students’ learning. Show more

Keywords: JITT cloud teaching, accounting teaching, artificial intelligence, implementation and evaluation, Internet of Things

DOI: 10.3233/JCM-247189

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1473-1493, 2024

Authors: Hu, Guo | Chen, Hao | Li, Xuan | Chelli, Karim | Alzyoud, Adel Ali Yassin

Article Type: Research Article

Abstract: AIMS: In order to implement the relevant laws, regulations and technical policies of national urban planning and environmental protection, and meet people’s living and human health, since the 21st century, various countries have adopted various methods to improve the urban ecological environment in varying degrees. The influence of the environment on people is first through the human visual response to the brain, so that the human brain can respond quickly and affect their human health. However, human modernization has brought many serious adverse factors to mankind. We must attach great importance to it. METHODS: Using the …methods of field investigation, mathematical analysis and observation, and using artistic mathematical methods from the perspective of visual communication, the effects of these factors were analyzed. RESULTS: The analysis results show that more than 80% of people believe that the stronger the artistry of urban environment, the better human health and the longer life span. Only 10% of people are indifferent to the artistry of urban environment. 10% thought it was OK to have a place to live. CONCLUSION: 85% of people require that the urban environment should first have artistic beauty. Human beings must consider the artistry of the urban environment as much as possible in the process of urban modernization. The stronger the artistry of the environment, the more beautiful the modern environment will be. With a beautiful environment, human beings will live longer and longer. Effective measures must be taken to avoid excessive confusion in the construction and layout of cities. Show more

Keywords: Mathematical analysis, urban lighting, architecture, artistry, visual communication

DOI: 10.3233/JCM-247156

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1495-1505, 2024

Authors: Cang, He | Feng, Dan

Article Type: Research Article

Abstract: In order to improve the security and performance of the oral English instant translation model, this paper optimizes the instant translation model through the Internet of Things (IoT) security technology and deep learning technology. In this paper, the real-time translation model based on deep learning and IoT technology is analyzed in detail to show the application of these two technologies in the real-time translation model, and the related information security issues are discussed. Meanwhile, this paper proposes a method combining deep learning network and IoT technology to further improve the security of instant translation model. The experimental results show that …under the optimized model, the parameter upload time is 60 seconds, the aggregation calculation time is 6.5 seconds, and the authentication time is 7.5 seconds. Moreover, the average recognition accuracy of the optimized model reaches 93.1%, and it is superior to the traditional machine translation method in accuracy and real-time, which has wide practical value and application prospects. Therefore, the research has certain reference significance for improving the security of the English corpus oral instant translation model. Show more

Keywords: Information security, Internet of Things technology, deep learning, oral instant translation model, English corpus

DOI: 10.3233/JCM-247183

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1507-1522, 2024

Authors: Liang, Xiaolong | Pan, Derun | Yu, Jiayi

Article Type: Research Article

Abstract: This study aims to overcome the impact of complex environmental backgrounds on the recognition of wildlife in monitoring images, thereby exploring the role of a deep learning-based intelligent wildlife recognition system in biodiversity conservation. The automatic identification of wildlife images is conducted based on convolutional neural networks (CNNs). Target detection technology, based on regression algorithms, is initially employed to extract Regions of Interest (ROI) containing wildlife from images. The wildlife regions in monitoring images are detected, segmented, and converted into ROI images. A dual-channel network model based on Visual Geometry Group 16 (VGG16) is implemented to extract features from sample …images. Finally, these features are input into a classifier to achieve wildlife recognition. The proposed optimized model demonstrates superior recognition performance for five wildlife species, caribou, lynx, mule deer, badger, and antelope, compared to the dual-channel network model based on VGG16. The optimized model achieves a Mean Average Precision (MAP) of 0.714, with a maximum difference of 0.145 compared to the other three network structures, affirming its effectiveness in enhancing the accuracy of automatic wildlife recognition. The model effectively addresses the issue of low recognition accuracy caused by the complexity of background information in monitoring images, achieving high-precision recognition and holding significant implications for the implementation of biodiversity conservation laws. Show more

Keywords: Deep learning, convolutional neural networks, intelligent identification of images, wild animals, dual-channel network, biodiversity conservation law

DOI: 10.3233/JCM-247185

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1523-1538, 2024

Authors: Ni, Junhong | Hu, Xiaorui

Article Type: Research Article

Abstract: In order to solve the problems of monthly electricity generation forecasting being limited by the lack of actual data source, and the large errors caused by the influence of various factors such as weather and holidays, and the limitations of the applicable scenarios of the existing research results, a monthly electricity generation forecasting model based on similar month screening and Seasonal and Trend decomposition using Loess (STL) was proposed in this paper. The complementary advantages of Multiple Linear Regression (MLR) and Improved Random Forest Regression (RFR) are utilized to achieve the monthly electricity generation prediction in the province. This prediction …model does not require a large number of data to obtain a better prediction accuracy, and breaks through the limitations of the existing monthly electricity prediction model that are only suitable for a certain industry or a certain region. Experiments performed on an actual electric power generation series validate the efficiency of the proposed model. Show more

Keywords: Monthly electricity generation forecast, STL decomposition, similar month screening, multiple linear regression, random forest

DOI: 10.3233/JCM-247141

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1539-1556, 2024

Authors: Yin, Li | Chen, Yijun

Article Type: Research Article

Abstract: The application of computer information management system (IMS for short here) in university management faces problems such as incomplete system software and complex system design. Applying clustering algorithms (CA for short here) to computer student IMS can help optimize the system’s overall effectiveness. This article constructed a computer student IMS based on computer technology and applied it to the management of college students. This article also combined CA to conduct relevant effectiveness tests on the system, in order to optimize the overall effectiveness of the system. Under the algorithm in this article, the average connection speed for each user accessing …the system was 9.17 Mbps. The average reaction time was 0.34 seconds, the average security level was 92.47%, and the highest memory usage rate of the system was 34.27%; Under the decision tree algorithm, the average connection speed of each user accessing the system was 8.82 Mbps, and the average reaction time reached 0.64 s. The average security level was 88.41%, and the highest memory usage rate was 42.58%. Under the artificial neural network algorithm, the average connection speed of the system was 8.47 Mbps, the average response time was 0.86 s, and the highest memory usage rate was 45.97%. Analyzing the data reveals that the algorithm introduced in this paper significantly enhances system connection speed and reduces reaction time. This improvement not only enhances security measures but also minimizes memory usage, effectively optimizing the overall efficiency of the system. Show more

Keywords: Computer information management system, university management work, student information management, system safety, clustering algorithm

DOI: 10.3233/JCM-247177

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1557-1569, 2024