Published: Oct 25, 2023
Converted to Gold OA:
DOI: 10.4018/IJITSA.332411
Volume 17
Tianlong Wang, Chaoyang Wang, Zhiqiang Liu, Shuai Ma, Huibo Yan
During the deep peak shaving period, the boiler needs to operate at a lower load, so higher requirements were set for the boiler's stable combustion. In order to better evaluate and compare the...
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During the deep peak shaving period, the boiler needs to operate at a lower load, so higher requirements were set for the boiler's stable combustion. In order to better evaluate and compare the stable ignition capacity of bluff-body burners and slotted bluff-body burners, guidance and calculation support for the design of boiler deep peak shaving were needed. This study adopted the reflux heating chain ignition analysis method to study the differences in the steady combustion mechanism between the bluff-body burner and the slotted bluff-body burner. A thermal combustion experiment was conducted in employing the single angle coal powder combustion furnace. The experimental results were compared against the theoretical analysis results. In addition, a study was conducted on the application of slotted bluff-body burners in the deep peak shaving of a 330MW unit power plant boiler. The results showed that as long as the small slot flow can catch fire, the mixed temperature of the reflux fluid of the slotted bluff-body burner will be higher than that of the bluff body burner. This will enhance its steady combustion ability. The combustion test results were found to be consistent with the analysis results, indicating that when the slotted bluff-body burner is used on the 330MW unit, the boiler combustion is in good condition. In this case, it has a stable combustion capacity of 66MW (20% economic continuous rating) without oil injection at low loads. This study revealed the advantages of the slotted bluff-body burner in terms of its steady combustion mechanism compared to traditional bluff-body burners. It verified the feasibility for boiler deep peak shaving in practical applications. This is of great significance for improving the flexibility of coal-fired power generation units, enhancing the flexibility of the power system, and promoting carbon reduction.
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Wang, Tianlong, et al. "Experiment Study and Industrial Application of Slotted Bluff-Body Burner Applied to Deep Peak Regulation." IJITSA vol.17, no.1 2024: pp.1-15. http://doi.org/10.4018/IJITSA.332411
APA
Wang, T., Wang, C., Liu, Z., Ma, S., & Yan, H. (2024). Experiment Study and Industrial Application of Slotted Bluff-Body Burner Applied to Deep Peak Regulation. International Journal of Information Technologies and Systems Approach (IJITSA), 17(1), 1-15. http://doi.org/10.4018/IJITSA.332411
Chicago
Wang, Tianlong, et al. "Experiment Study and Industrial Application of Slotted Bluff-Body Burner Applied to Deep Peak Regulation," International Journal of Information Technologies and Systems Approach (IJITSA) 17, no.1: 1-15. http://doi.org/10.4018/IJITSA.332411
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Published: Oct 25, 2023
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DOI: 10.4018/IJITSA.332798
Volume 17
Xudong Cao, Chenchen Chen, Lejia Zhang, Li Pan
To improve transportation efficiency, this paper analyzes the factors of transportation structure from the two levels of transportation—input and system output. An epsilon-based measure model of...
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To improve transportation efficiency, this paper analyzes the factors of transportation structure from the two levels of transportation—input and system output. An epsilon-based measure model of non-expected output is introduced, and the environmental benefits of transportation are considered. This model is used to analyze the regional transportation efficiency of 30 provinces and cities in China. Tobit regression and geographically weighted regression are applied to analyze the causes and spatial variation of differences in the efficiency of the transportation structure, and corresponding structural adjustment strategies are proposed. The results show that the regression coefficients of the share of secondary industry output in GDP, population density, and social fixed asset investment exert the most significant effects on transportation structure efficiency. The spatial distribution of sub-variable coefficients shows that spatial heterogeneity exists in the degree of influence of various socio-economic factors on the transportation structure efficiency in different regions.
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Cao, Xudong, et al. "Estimation and Convergence Analysis of Traffic Structure Efficiency Based on an Undesirable Epsilon-Based Measure Model." IJITSA vol.17, no.1 2024: pp.1-25. http://doi.org/10.4018/IJITSA.332798
APA
Cao, X., Chen, C., Zhang, L., & Pan, L. (2024). Estimation and Convergence Analysis of Traffic Structure Efficiency Based on an Undesirable Epsilon-Based Measure Model. International Journal of Information Technologies and Systems Approach (IJITSA), 17(1), 1-25. http://doi.org/10.4018/IJITSA.332798
Chicago
Cao, Xudong, et al. "Estimation and Convergence Analysis of Traffic Structure Efficiency Based on an Undesirable Epsilon-Based Measure Model," International Journal of Information Technologies and Systems Approach (IJITSA) 17, no.1: 1-25. http://doi.org/10.4018/IJITSA.332798
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Published: Jan 12, 2024
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DOI: 10.4018/IJITSA.335940
Volume 17
Shengfeng Xie, Jingwei Li
To address issues related to the insufficient representation of text semantic information and the lack of deep fusion between internal modal information and intermodal information in current...
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To address issues related to the insufficient representation of text semantic information and the lack of deep fusion between internal modal information and intermodal information in current multimodal sentiment analysis (MSA) methods, a new method integrating multi-layer attention interaction and multi-feature enhancement (AM-MF) is proposed. First, multimodal feature extraction (MFE) is performed based on RoBERTa, ResNet, and ViT models for text, audio, and video information, and high-level features of the three modalities are obtained through self-attention mechanisms. Then, a cross modal attention (CMA) interaction module is constructed based on transformer, achieving feature fusion between different modalities. Finally, the use of a soft attention mechanism for the deep fusion of internal and intermodal information effectively achieves multimodal sentiment classification. The experimental results CH-SIMS and CMU-MOSEI datasets show that the classification results of proposed MSA method are significantly superior to other advanced comparative methods.
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Xie, Shengfeng, and Jingwei Li. "A Multimodal Sentiment Analysis Method Integrating Multi-Layer Attention Interaction and Multi-Feature Enhancement." IJITSA vol.17, no.1 2024: pp.1-20. http://doi.org/10.4018/IJITSA.335940
APA
Xie, S. & Li, J. (2024). A Multimodal Sentiment Analysis Method Integrating Multi-Layer Attention Interaction and Multi-Feature Enhancement. International Journal of Information Technologies and Systems Approach (IJITSA), 17(1), 1-20. http://doi.org/10.4018/IJITSA.335940
Chicago
Xie, Shengfeng, and Jingwei Li. "A Multimodal Sentiment Analysis Method Integrating Multi-Layer Attention Interaction and Multi-Feature Enhancement," International Journal of Information Technologies and Systems Approach (IJITSA) 17, no.1: 1-20. http://doi.org/10.4018/IJITSA.335940
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Published: Jan 10, 2024
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DOI: 10.4018/IJITSA.335941
Volume 17
Xiaoyuan Wang, Hongfei Wang, Jianping Wang, Jiajia Wang
In this study, the authors perform slant correction on fixed-format dial images using the Hough transform, followed by their “scaling” using binary wavelets to achieve coarse segmentation for...
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In this study, the authors perform slant correction on fixed-format dial images using the Hough transform, followed by their “scaling” using binary wavelets to achieve coarse segmentation for characters or numbers. Simultaneously, they propose a binary threshold iteration method that accurately determines the position of each character or number even in the presence of adhesion or fragmentation, enabling precise segmentation. They employ their proposed approach to segment digits and characters displayed on 98 fixed-format dials. Experimental results demonstrate a recognition rate of 98.5% for both letters and numbers, highlighting significant practical value and real-world implications.
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Wang, Xiaoyuan, et al. "Research on Machine Instrument Panel Digit Character Segmentation." IJITSA vol.17, no.1 2024: pp.1-24. http://doi.org/10.4018/IJITSA.335941
APA
Wang, X., Wang, H., Wang, J., & Wang, J. (2024). Research on Machine Instrument Panel Digit Character Segmentation. International Journal of Information Technologies and Systems Approach (IJITSA), 17(1), 1-24. http://doi.org/10.4018/IJITSA.335941
Chicago
Wang, Xiaoyuan, et al. "Research on Machine Instrument Panel Digit Character Segmentation," International Journal of Information Technologies and Systems Approach (IJITSA) 17, no.1: 1-24. http://doi.org/10.4018/IJITSA.335941
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Published: Jan 17, 2024
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DOI: 10.4018/IJITSA.336475
Volume 17
Jiao Hao, Zongbao Zhang, Yihan Ping
The stability and reliability of the power system are of utmost significance in upholding the smooth functioning of modern society. Fault diagnosis and prediction represent pivotal factors in the...
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The stability and reliability of the power system are of utmost significance in upholding the smooth functioning of modern society. Fault diagnosis and prediction represent pivotal factors in the operation and maintenance of the power system. This article presents an approach employing graph neural network (GNN) to enhance the precision and efficiency of power system fault diagnosis and prediction. The system's efficacy lies in its ability to capture the intricate interconnections and dynamic variations within the power system by conceptualizing it as a graph structure and harnessing the capabilities of GNN. In this study, the authors introduce a substitution for the pooling layer with a convolution operation. A central role is played by the global average pooling layer, connecting the convolution layer and the fully connected layer. The fully connected layer carries out nonlinear computations, ultimately providing the classification at the top-level output layer. In experiments and tests, we verified the performance of the system.
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Hao, Jiao, et al. "Power System Fault Diagnosis and Prediction System Based on Graph Neural Network." IJITSA vol.17, no.1 2024: pp.1-14. http://doi.org/10.4018/IJITSA.336475
APA
Hao, J., Zhang, Z., & Ping, Y. (2024). Power System Fault Diagnosis and Prediction System Based on Graph Neural Network. International Journal of Information Technologies and Systems Approach (IJITSA), 17(1), 1-14. http://doi.org/10.4018/IJITSA.336475
Chicago
Hao, Jiao, Zongbao Zhang, and Yihan Ping. "Power System Fault Diagnosis and Prediction System Based on Graph Neural Network," International Journal of Information Technologies and Systems Approach (IJITSA) 17, no.1: 1-14. http://doi.org/10.4018/IJITSA.336475
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Published: Jan 31, 2024
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DOI: 10.4018/IJITSA.336844
Volume 17
Qinmei Wang
This integration enables the system to collect and monitor information from remote sources efficiently. During the course of this research, a novel predictive PID approach was developed, splitting...
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This integration enables the system to collect and monitor information from remote sources efficiently. During the course of this research, a novel predictive PID approach was developed, splitting the control architecture into two tiers. The upper tier utilizes the extreme learning machine (ELM) as an intelligent predictive model, while the lower tier integrates an enhanced single-neuron adaptive predictive PID control algorithm, combining the strengths of ELM and PID control. The research findings suggest that the AI algorithm-based instrument automatic monitoring and control system holds significant promise. This technology has the potential to enhance production efficiency, reduce energy consumption, improve environmental monitoring, and provide superior safety and quality control.
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DOI: 10.4018/IJITSA.337388
Volume 17
Wenzhen Mai, Mohamud Saeed Ambashe, Chukwuka Christian Ohueri
Chinese financial institutions (CFIs) are increasingly embracing artificial intelligence (AI) for their financial decision-making driven by AI's capacity to mitigate risks and enhance efficiency and...
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Chinese financial institutions (CFIs) are increasingly embracing artificial intelligence (AI) for their financial decision-making driven by AI's capacity to mitigate risks and enhance efficiency and accuracy. However, there remain ethical challenges related to the integration of AI in financial decision-making. This study develops the AI ethics best practices model (AB-PraM) to mitigate ethical concerns and enhance the application of AI in financial decision-making. By employing a quantitative methodology, this research collected questionnaire data from 320 financial experts in CFIs. Structural equation modelling (SEM) was adopted to identify AI ethics best practices for the implementation of the AB-PraM. The findings of this research will mitigate AI ethics challenges in CFIs and provide a practical framework for transparent and accountable decision-making in alignment with ethical standards and regulations.
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Mai, Wenzhen, et al. "Artificial Intelligence Ethics Best Practices Model for Financial Decision-Making in Chinese Financial Institutions." IJITSA vol.17, no.1 2024: pp.1-18. http://doi.org/10.4018/IJITSA.337388
APA
Mai, W., Ambashe, M. S., & Ohueri, C. C. (2024). Artificial Intelligence Ethics Best Practices Model for Financial Decision-Making in Chinese Financial Institutions. International Journal of Information Technologies and Systems Approach (IJITSA), 17(1), 1-18. http://doi.org/10.4018/IJITSA.337388
Chicago
Mai, Wenzhen, Mohamud Saeed Ambashe, and Chukwuka Christian Ohueri. "Artificial Intelligence Ethics Best Practices Model for Financial Decision-Making in Chinese Financial Institutions," International Journal of Information Technologies and Systems Approach (IJITSA) 17, no.1: 1-18. http://doi.org/10.4018/IJITSA.337388
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Published: Feb 7, 2024
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DOI: 10.4018/IJITSA.337797
Volume 17
Xiaojun Li, PeiDong He, WenQi Shen, KeLi Liu, ShuYu Deng, LI Xiao
In order to solve the problems that most models are complex, time-consuming, and have difficulty in identifying image errors, an image identification and error correction method of test report based...
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In order to solve the problems that most models are complex, time-consuming, and have difficulty in identifying image errors, an image identification and error correction method of test report based on deep reinforcement learning and the internet of things platform in the smart lab was proposed. Firstly, a smart lab architecture was designed based on the internet of things platform, achieving efficient operation of the laboratory through cloud edge collaboration. Then, the depth separable convolution improved convolutional neural network is used to extract image features, and the features are input into bidirectional recurrent neural networks (BiLSTM) for analysis to complete image recognition. Finally, the ICNN-BiLSTM model is used as the agent of reinforcement learning, and image error correction is completed by identifying the distance between the image and the key points of the reference image. Based on the Python platform, the proposed method was experimentally demonstrated, and the results showed that its average error correction accuracy reached 96.75%, with a processing time of 15.37s.
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Li, Xiaojun, et al. "Image Identification and Error Correction Method for Test Report Based on Deep Reinforcement Learning and IoT Platform in Smart Laboratory." IJITSA vol.17, no.1 2024: pp.1-18. http://doi.org/10.4018/IJITSA.337797
APA
Li, X., He, P., Shen, W., Liu, K., Deng, S., & Xiao, L. (2024). Image Identification and Error Correction Method for Test Report Based on Deep Reinforcement Learning and IoT Platform in Smart Laboratory. International Journal of Information Technologies and Systems Approach (IJITSA), 17(1), 1-18. http://doi.org/10.4018/IJITSA.337797
Chicago
Li, Xiaojun, et al. "Image Identification and Error Correction Method for Test Report Based on Deep Reinforcement Learning and IoT Platform in Smart Laboratory," International Journal of Information Technologies and Systems Approach (IJITSA) 17, no.1: 1-18. http://doi.org/10.4018/IJITSA.337797
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Published: Feb 19, 2024
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DOI: 10.4018/IJITSA.338910
Volume 17
Junhua Xu
Traditional methods often fall short in modeling the nonlinear, seasonally variable nature of urban water demand. This proposed solution is an integrated ARIMA-LSTM deep learning model, combining...
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Traditional methods often fall short in modeling the nonlinear, seasonally variable nature of urban water demand. This proposed solution is an integrated ARIMA-LSTM deep learning model, combining ARIMA's proficiency in linear trend and seasonal modeling with LSTM's strength in capturing nonlinear time dependencies. In these experiments, the authors trained and evaluated using daily water demand data from 2015 to 2020, with its performance validated for the year 2021. The ARIMA-LSTM model demonstrates promising results, outperforming individual models in terms of accuracy. In validation, it achieves a high coefficient of determination (R2) of 0.98 and a significantly low root mean square error (RMSE) of 2.94. These metrics indicate an excellent fit to the data and a high level of precision in its predictions. The significance of this research lies in its potential to advance the field of urban water demand forecasting, ultimately contributing to better water resource management and sustainability in urban areas.
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DOI: 10.4018/IJITSA.339003
Volume 17
Jian Wu, Jianhui Zhang, Li Pan
Bitcoin is a digital currency system built on the foundation of fairness. However, some malicious miners, driven by their own interests, employ unfair tactics such as selfish mining to compete...
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Bitcoin is a digital currency system built on the foundation of fairness. However, some malicious miners, driven by their own interests, employ unfair tactics such as selfish mining to compete, which disregard the legitimate miners' investments in computational power and energy consumption. In order to assess the efficiency of the Bitcoin system in real-time and promptly detect malicious miners in the network, this paper proposes a data collection framework called BitTrace, which addresses the issues of low efficiency, lack of timeliness, and data loss in traditional data collection frameworks. BitTrace enables real-time collection and analysis of the blockchain formation process, storing it as structured data. Furthermore, the paper discusses factors that influence the efficiency of data collection and proposes a topological control scheme based on the DPC algorithm to enhance the integrity and efficiency of data collection. Researchers can explore various research areas and applications, such as selfish mining detection and legitimate mining strategy research.
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Wu, Jian, et al. "BitTrace: A Data-Driven Framework for Traceability of Blockchain Forming in Bitcoin System." IJITSA vol.17, no.1 2024: pp.1-21. http://doi.org/10.4018/IJITSA.339003
APA
Wu, J., Zhang, J., & Pan, L. (2024). BitTrace: A Data-Driven Framework for Traceability of Blockchain Forming in Bitcoin System. International Journal of Information Technologies and Systems Approach (IJITSA), 17(1), 1-21. http://doi.org/10.4018/IJITSA.339003
Chicago
Wu, Jian, Jianhui Zhang, and Li Pan. "BitTrace: A Data-Driven Framework for Traceability of Blockchain Forming in Bitcoin System," International Journal of Information Technologies and Systems Approach (IJITSA) 17, no.1: 1-21. http://doi.org/10.4018/IJITSA.339003
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Published: Mar 19, 2024
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DOI: 10.4018/IJITSA.340774
Volume 17
Zhou Li, Hanan Aljuaid
Existing models still exhibit a deficiency in capturing more detailed contextual information when processing architectural images. This paper introduces a model for architectural image segmentation...
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Existing models still exhibit a deficiency in capturing more detailed contextual information when processing architectural images. This paper introduces a model for architectural image segmentation and retrieval based on an image segmentation network. Primarily, spatial attention is incorporated into the U-Net segmentation network to enhance the extraction of image features. Subsequently, a dual-path attention mechanism is integrated into the U-Net backbone network, facilitating the seamless integration of information across different spaces and scales. Experimental results showcase the superior performance of the proposed model on the test set, with average dice coefficient, accuracy, and recall reaching 94.67%, 95.61%, and 97.88%, respectively, outperforming comparative models. The proposed model can enhance the U-Net network's capability to identify targets within feature maps. The amalgamation of image segmentation networks and attention mechanisms in artificial intelligence technology enables precise segmentation and retrieval of architectural images.
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Li, Zhou, and Hanan Aljuaid. "Integrated Design of Building Environment Based on Image Segmentation and Retrieval Technology." IJITSA vol.17, no.1 2024: pp.1-14. http://doi.org/10.4018/IJITSA.340774
APA
Li, Z. & Aljuaid, H. (2024). Integrated Design of Building Environment Based on Image Segmentation and Retrieval Technology. International Journal of Information Technologies and Systems Approach (IJITSA), 17(1), 1-14. http://doi.org/10.4018/IJITSA.340774
Chicago
Li, Zhou, and Hanan Aljuaid. "Integrated Design of Building Environment Based on Image Segmentation and Retrieval Technology," International Journal of Information Technologies and Systems Approach (IJITSA) 17, no.1: 1-14. http://doi.org/10.4018/IJITSA.340774
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Published: Apr 2, 2024
Converted to Gold OA:
DOI: 10.4018/IJITSA.341789
Volume 17
Runmin Guan, Huan Chen, Jian Shang, Li Pan
Aimed at the problem of large errors in traditional power cable temperature measurement methods, a method based on edge computing and radio frequency identification (RFID) is proposed. Firstly, a...
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Aimed at the problem of large errors in traditional power cable temperature measurement methods, a method based on edge computing and radio frequency identification (RFID) is proposed. Firstly, a RFID electronic label design scheme was constructed utilizing the radio frequency signal between alternating electromagnetic fields to achieve the communication and information identification between two devices. Then, the design of power cable edge intelligent terminal is analyzed from the aspects of hardware and software. On this basis, the early warning criterion formula of power cable temperature fault state is abstracted by using edge computing algorithm. Based on edge computing and RFID, the corresponding temperature measurement method is proposed. Finally, the proposed power cable temperature measurement method is compared with other three methods through simulation experiments. The results indicate that the temperature measurement error of the proposed method is the minimum under different currents and in cross-sectional areas. Compared to the other three methods, the maximum improvement is 5.15% and 7.92%, while the minimum improvement is 1.05% and 1.45%, which outperforms the comparable algorithms in terms of performance.
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Guan, Runmin, et al. "Temperature Measurement Method and Simulation of Power Cable Based on Edge Computing and RFID." IJITSA vol.17, no.1 2024: pp.1-20. http://doi.org/10.4018/IJITSA.341789
APA
Guan, R., Chen, H., Shang, J., & Pan, L. (2024). Temperature Measurement Method and Simulation of Power Cable Based on Edge Computing and RFID. International Journal of Information Technologies and Systems Approach (IJITSA), 17(1), 1-20. http://doi.org/10.4018/IJITSA.341789
Chicago
Guan, Runmin, et al. "Temperature Measurement Method and Simulation of Power Cable Based on Edge Computing and RFID," International Journal of Information Technologies and Systems Approach (IJITSA) 17, no.1: 1-20. http://doi.org/10.4018/IJITSA.341789
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Published: Apr 9, 2024
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DOI: 10.4018/IJITSA.342084
Volume 17
Fahong Yu, Meijia Chen, Xiaoyun Xia, Dongping Zhu, Qiang Peng, Kuibiao Deng
Multi-depot vehicle routing problem with time windows (MDVRPTW) is a valuable practical issue in urban logistics. However, heuristic methods may fail to generate high-quality solutions for massive...
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Multi-depot vehicle routing problem with time windows (MDVRPTW) is a valuable practical issue in urban logistics. However, heuristic methods may fail to generate high-quality solutions for massive problems instantly. Thus, this article presents a novel reinforcement learning algorithm integrated with a multi-head attention mechanism and a local search strategy to solve the problem efficiently. The routing optimization was regarded as a vehicle tour generation process and an encoder-decoder was used to generate routes for vehicles departing from different depots iteratively. A multi-head attention strategy was employed for mining complex spatiotemporal correlations within time windows in the encoder. Then, a decoder with multi-agent was designed to generate solutions by optimizing reward and observing transition state. Meanwhile, a local search strategy was employed to improve the quality of solutions. The experiments results demonstrate that the proposed method can significantly outperform traditional methods in effectiveness and robustness.
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Yu, Fahong, et al. "Logistics Distribution Route Optimization With Time Windows Based on Multi-Agent Deep Reinforcement Learning." IJITSA vol.17, no.1 2024: pp.1-23. http://doi.org/10.4018/IJITSA.342084
APA
Yu, F., Chen, M., Xia, X., Zhu, D., Peng, Q., & Deng, K. (2024). Logistics Distribution Route Optimization With Time Windows Based on Multi-Agent Deep Reinforcement Learning. International Journal of Information Technologies and Systems Approach (IJITSA), 17(1), 1-23. http://doi.org/10.4018/IJITSA.342084
Chicago
Yu, Fahong, et al. "Logistics Distribution Route Optimization With Time Windows Based on Multi-Agent Deep Reinforcement Learning," International Journal of Information Technologies and Systems Approach (IJITSA) 17, no.1: 1-23. http://doi.org/10.4018/IJITSA.342084
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Published: Apr 17, 2024
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DOI: 10.4018/IJITSA.342130
Volume 17
Chenguang Li, Jie Luo, Xinyu Wang, Guihuang Jiang
The presence of structure heterogeneity in regional innovation networks reflects the complexity and diversity of knowledge diffusion and collaborative R& D relationships. This article introduces a...
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The presence of structure heterogeneity in regional innovation networks reflects the complexity and diversity of knowledge diffusion and collaborative R& D relationships. This article introduces a network model based on the multiple systems generating functions mathematical algorithm to analyze the resilience of interacting networks under different link patterns. The percolation threshold is illustrated at two different levels: the subcritical and supercritical states. The algorithm is then tested on both simulated networks and real-world networks. The results of the simulation study highlight the crucial role of linking between sub-networks and emphasize the effectiveness of a moderate degree protection strategy.
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Li, Chenguang, et al. "The Influence of Structure Heterogeneity on Resilience in Regional Innovation Networks." IJITSA vol.17, no.1 2024: pp.1-14. http://doi.org/10.4018/IJITSA.342130
APA
Li, C., Luo, J., Wang, X., & Jiang, G. (2024). The Influence of Structure Heterogeneity on Resilience in Regional Innovation Networks. International Journal of Information Technologies and Systems Approach (IJITSA), 17(1), 1-14. http://doi.org/10.4018/IJITSA.342130
Chicago
Li, Chenguang, et al. "The Influence of Structure Heterogeneity on Resilience in Regional Innovation Networks," International Journal of Information Technologies and Systems Approach (IJITSA) 17, no.1: 1-14. http://doi.org/10.4018/IJITSA.342130
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Published: Apr 26, 2024
Converted to Gold OA:
DOI: 10.4018/IJITSA.342613
Volume 17
Liyang Chu, Haifeng Guo, Qingshi Meng
Aiming at the problem that the environmental planning path of intelligent logistics vehicles on urban roads and remote mountainous areas cannot fit the actual driving scene well. This study creates...
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Aiming at the problem that the environmental planning path of intelligent logistics vehicles on urban roads and remote mountainous areas cannot fit the actual driving scene well. This study creates the algorithm model that combines an ant colony algorithm with a dynamic window algorithm and a Bessel smoothing strategy. Compared to the traditional colony algorithm with the same parameters, this fusion algorithm makes the path smoother by 72.2% when used on an urban highway. It also follows the right-hand rule for right-turn intersections. When the vehicle's height is determined in a mountain environment, this fusion algorithm reduces the driving's mean square deviation of height by 81.5% and shortens the path distance by 38.7%. The fusion algorithm can plan the target path of intelligent logistics vehicles and has the characteristics of scenarios available, multiple factors coordinated, and driving safety. It has provided certain research value and ideas for the digital transformation of the logistics industry.
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Chu, Liyang, et al. "Intelligent Logistics Vehicle Path Planning Using Fused Optimization Ant Colony Algorithm With Grid." IJITSA vol.17, no.1 2024: pp.1-20. http://doi.org/10.4018/IJITSA.342613
APA
Chu, L., Guo, H., & Meng, Q. (2024). Intelligent Logistics Vehicle Path Planning Using Fused Optimization Ant Colony Algorithm With Grid. International Journal of Information Technologies and Systems Approach (IJITSA), 17(1), 1-20. http://doi.org/10.4018/IJITSA.342613
Chicago
Chu, Liyang, Haifeng Guo, and Qingshi Meng. "Intelligent Logistics Vehicle Path Planning Using Fused Optimization Ant Colony Algorithm With Grid," International Journal of Information Technologies and Systems Approach (IJITSA) 17, no.1: 1-20. http://doi.org/10.4018/IJITSA.342613
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Published: May 2, 2024
Converted to Gold OA:
DOI: 10.4018/IJITSA.342855
Volume 17
Qinjian Zhang, Chuanchuan Zeng
The construction of subway projects involves tight engineering cycles, multiple technical challenges, and complex coordination among various stakeholders. Due to the influence of uncertain factors...
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The construction of subway projects involves tight engineering cycles, multiple technical challenges, and complex coordination among various stakeholders. Due to the influence of uncertain factors during the construction process, the investment in subway project construction exhibits non-linear changes over time. Investment decision-making is the process through which the investment entity determines its investment activities. For typical investment entities, project investment decision-making primarily entails analyzing and evaluating proposed engineering projects based on investigation, analysis, and argumentation, ultimately deciding whether to invest. With the widespread application of information technology (IT) across various fields, decision support systems (DSS) have emerged to enhance the decision-making capabilities of enterprise management. This article designs an intelligent subway project investment DSS, leveraging data mining (DM) technology to integrate DSS with a data warehouse (DW).
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Zhang, Qinjian, and Chuanchuan Zeng. "Design and Implementation of an Intelligent Metro Project Investment Decision Support System." IJITSA vol.17, no.1 2024: pp.1-15. http://doi.org/10.4018/IJITSA.342855
APA
Zhang, Q. & Zeng, C. (2024). Design and Implementation of an Intelligent Metro Project Investment Decision Support System. International Journal of Information Technologies and Systems Approach (IJITSA), 17(1), 1-15. http://doi.org/10.4018/IJITSA.342855
Chicago
Zhang, Qinjian, and Chuanchuan Zeng. "Design and Implementation of an Intelligent Metro Project Investment Decision Support System," International Journal of Information Technologies and Systems Approach (IJITSA) 17, no.1: 1-15. http://doi.org/10.4018/IJITSA.342855
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Published: May 2, 2024
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DOI: 10.4018/IJITSA.343047
Volume 17
Xiaoyuan Wang, Hongfei Wang, Jianping Wang, Maoyu Zhao, Hui Chen
This study aims to address the issues of image noise and distortion in machine dial recognition via initial denoising. A wavelet local threshold denoising method that combines high-frequency wavelet...
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This study aims to address the issues of image noise and distortion in machine dial recognition via initial denoising. A wavelet local threshold denoising method that combines high-frequency wavelet coefficients with wavelet decomposition coefficients in various directions is proposed. This method shows good results on 102 images of car dashboards, aircraft instrument panels, spacecraft displays, and dial instruments on robots. Although a few denoised images exhibit distortion due to intense lighting or heavy contamination, the denoising accuracy for the remaining images is 98.04%, demonstrating substantial practical value. Future research will concentrate on addressing complex image noise and structures.
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MLA
Wang, Xiaoyuan, et al. "Research on Removing Image Noise and Distortion in Machine Dial Recognition." IJITSA vol.17, no.1 2024: pp.1-20. http://doi.org/10.4018/IJITSA.343047
APA
Wang, X., Wang, H., Wang, J., Zhao, M., & Chen, H. (2024). Research on Removing Image Noise and Distortion in Machine Dial Recognition. International Journal of Information Technologies and Systems Approach (IJITSA), 17(1), 1-20. http://doi.org/10.4018/IJITSA.343047
Chicago
Wang, Xiaoyuan, et al. "Research on Removing Image Noise and Distortion in Machine Dial Recognition," International Journal of Information Technologies and Systems Approach (IJITSA) 17, no.1: 1-20. http://doi.org/10.4018/IJITSA.343047
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Published: May 10, 2024
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DOI: 10.4018/IJITSA.343317
Volume 17
Qiankun Li, Juan Li, Yao Li, Feng Jiu, Yunxia Chu
A malicious traffic sample adaptive enhancement device based on Deep Convolutional Generative Adversarial Network (DCGAN) is designed to address the issue of imbalanced network traffic data...
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A malicious traffic sample adaptive enhancement device based on Deep Convolutional Generative Adversarial Network (DCGAN) is designed to address the issue of imbalanced network traffic data distribution, aiming to enhance the accuracy and efficiency of anomaly detection. By leveraging generative adversarial network technology, this device can generate samples similar to real malicious traffic to balance the training dataset. It utilizes the generator and discriminator of the Deep Convolutional Generative Adversarial Network (DCGAN), combined with the residual network (ResNet) in the CNN model, to enhance the quality of generated samples. The device can switch states to adapt to various network environments and has been experimentally validated for its effectiveness and feasibility.Moreover, employing an adaptive device, the samples of malicious traffic are adjusted. Experimental analysis demonstrates that the device significantly enhances the accuracy of anomaly traffic detection, improves robustness, and provides robust support for network security protection.
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Li, Qiankun, et al. "An Adaptive Enhancement Method of Malicious Traffic Samples Based on DCGAN-ResNet System." IJITSA vol.17, no.1 2024: pp.1-17. http://doi.org/10.4018/IJITSA.343317
APA
Li, Q., Li, J., Li, Y., Jiu, F., & Chu, Y. (2024). An Adaptive Enhancement Method of Malicious Traffic Samples Based on DCGAN-ResNet System. International Journal of Information Technologies and Systems Approach (IJITSA), 17(1), 1-17. http://doi.org/10.4018/IJITSA.343317
Chicago
Li, Qiankun, et al. "An Adaptive Enhancement Method of Malicious Traffic Samples Based on DCGAN-ResNet System," International Journal of Information Technologies and Systems Approach (IJITSA) 17, no.1: 1-17. http://doi.org/10.4018/IJITSA.343317
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Published: May 17, 2024
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DOI: 10.4018/IJITSA.343632
Volume 17
Zhengqing LU, Jiajie Zhou, ChaoWei Wang, Zhihong Zhou, Guoliang Shi, Ying Yin
In the context of rapid urbanization, the challenge of effective garbage disposal has become increasingly significant. Traditional methods for addressing illegal littering by pedestrians are not...
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In the context of rapid urbanization, the challenge of effective garbage disposal has become increasingly significant. Traditional methods for addressing illegal littering by pedestrians are not only inefficient but also resource-intensive, demanding considerable manpower and materials. This study introduces a deep learning-based approach for detecting improper garbage disposal behavior. Leveraging advanced deep learning technologies, this approach focuses on object detection, tracking, and human posture analysis to identify and alert against illegal dumping activities captured in video footage, specifically targeting incidents outside designated times or areas. The primary aim is to facilitate prompt detection and mitigate associated health risks. The system uses three deep learning models, YOLOv5, DeepSORT and MobilePose. YOLOv5 is used to identify the human body and garbage bag, DeepSORT is used to track the two, and MobilePose identifies the key points of the human body for posture estimation. The tracking algorithm is used to determine whether to throw garbage.
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LU, Zhengqing, et al. "Delivery Garbage Behavior Detection Based on Deep Learning." IJITSA vol.17, no.1 2024: pp.1-15. http://doi.org/10.4018/IJITSA.343632
APA
LU, Z., Zhou, J., Wang, C., Zhou, Z., Shi, G., & Yin, Y. (2024). Delivery Garbage Behavior Detection Based on Deep Learning. International Journal of Information Technologies and Systems Approach (IJITSA), 17(1), 1-15. http://doi.org/10.4018/IJITSA.343632
Chicago
LU, Zhengqing, et al. "Delivery Garbage Behavior Detection Based on Deep Learning," International Journal of Information Technologies and Systems Approach (IJITSA) 17, no.1: 1-15. http://doi.org/10.4018/IJITSA.343632
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Published: May 22, 2024
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DOI: 10.4018/IJITSA.344019
Volume 17
Weilan Fang, Zhengqing LU, ChaoWei Wang, Zhihong Zhou, Guoliang Shi, Ying Yin
In the realm of computer vision, recognizing pedestrian attributes is a crucial task, with the objective of deducing the identity characteristics of individuals on foot. This differs from...
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In the realm of computer vision, recognizing pedestrian attributes is a crucial task, with the objective of deducing the identity characteristics of individuals on foot. This differs from conventional pedestrian detection methods in that it not only identifies the presence of pedestrians but also examines their attributes such as gender, age, and attire by scrutinizing their visual features. Not only can recognition identify the existence of pedestrians, but it can also delve into the analysis of pedestrian attributes. This paper proposes, explores, and implements a pedestrian attribute recognition algorithm grounded in deep convolutional neural networks. First, several datasets containing large-scale pedestrian images and their corresponding data are used for the recognition of pedestrian attributes. containing large-scale pedestrian images and their corresponding attribute labels are utilized. Through data pre-processing and enhancement techniques, the noise and inconsistency of pedestrian images are reduced. techniques, the noise and inconsistency of the data are.
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Fang, Weilan, et al. "Research and Implementation of Pedestrian Attribute Recognition Algorithm Based on Deep Learning." IJITSA vol.17, no.1 2024: pp.1-18. http://doi.org/10.4018/IJITSA.344019
APA
Fang, W., LU, Z., Wang, C., Zhou, Z., Shi, G., & Yin, Y. (2024). Research and Implementation of Pedestrian Attribute Recognition Algorithm Based on Deep Learning. International Journal of Information Technologies and Systems Approach (IJITSA), 17(1), 1-18. http://doi.org/10.4018/IJITSA.344019
Chicago
Fang, Weilan, et al. "Research and Implementation of Pedestrian Attribute Recognition Algorithm Based on Deep Learning," International Journal of Information Technologies and Systems Approach (IJITSA) 17, no.1: 1-18. http://doi.org/10.4018/IJITSA.344019
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Published: May 24, 2024
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DOI: 10.4018/IJITSA.344423
Volume 17
John Wang, Jeffrey Hsu, Zhaoqiong Qin
The article provides an in-depth analysis of Nvidia's technological evolution and its profound impact on Machine Learning, Big Data, and Artificial Intelligence (AI) on a global scale. Nvidia has...
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The article provides an in-depth analysis of Nvidia's technological evolution and its profound impact on Machine Learning, Big Data, and Artificial Intelligence (AI) on a global scale. Nvidia has emerged as a trailblazer, reshaping computational capabilities, and establishing itself as a prominent player in a fiercely competitive landscape. The examination meticulously scrutinizes the role of Nvidia's graphics processing unit (GPU) technologies in spearheading a transformative computing revolution, emphasizing the collaborative prowess inherent in Nvidia's developer ecosystem. The analysis extends to the dynamics of GPU innovation, its disruptive influence on the market, and the robust innovation engine ingrained within Nvidia's culture of calculated risk-taking. Internal and external factors contributing to Nvidia's remarkable success, and its consequential industry dominance are thoroughly investigated. Special attention is directed towards Nvidia's strategic development, technological advancements, influence on the industry, global footprint, and anticipated future implications.
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Wang, John, et al. "A Comprehensive Analysis of Nvidia's Technological Innovations, Market Strategies, and Future Prospects." IJITSA vol.17, no.1 2024: pp.1-16. http://doi.org/10.4018/IJITSA.344423
APA
Wang, J., Hsu, J., & Qin, Z. (2024). A Comprehensive Analysis of Nvidia's Technological Innovations, Market Strategies, and Future Prospects. International Journal of Information Technologies and Systems Approach (IJITSA), 17(1), 1-16. http://doi.org/10.4018/IJITSA.344423
Chicago
Wang, John, Jeffrey Hsu, and Zhaoqiong Qin. "A Comprehensive Analysis of Nvidia's Technological Innovations, Market Strategies, and Future Prospects," International Journal of Information Technologies and Systems Approach (IJITSA) 17, no.1: 1-16. http://doi.org/10.4018/IJITSA.344423
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Published: May 31, 2024
Converted to Gold OA:
DOI: 10.4018/IJITSA.346819
Volume 17
Chen Peng, Bilal Alatas
The Chinese ship trading market has undergone remarkable development, transitioning into a global exemplar of ship transactions. Nevertheless, this market continues to confront a series of...
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The Chinese ship trading market has undergone remarkable development, transitioning into a global exemplar of ship transactions. Nevertheless, this market continues to confront a series of challenges, encompassing low transaction rates, delayed transaction values, and market instability. Blockchain, with its secure, transparent, and tamper-resistant technological features, presents a potential solution for the ship trading market. Through its decentralized architecture, blockchain technology can reduce platform operational costs, enhancing market competitiveness. Concurrently, the utilization of asymmetric encryption technology can enhance the security of platform data, effectively addressing privacy concerns. Furthermore, through a sharing mechanism, blockchain can aid in establishing a unified credit evaluation system, thereby increasing market trust to address the absence of a credit evaluation system. Moreover, blockchain technology can be employed to construct a risk identification mechanism, fortifying the platform's regulatory model to address regulatory deficiencies.
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Peng, Chen, and Bilal Alatas. "An Exploratory Study on the Application of Blockchain Technology to the Chinese Ship Auction Market." IJITSA vol.17, no.1 2024: pp.1-18. http://doi.org/10.4018/IJITSA.346819
APA
Peng, C. & Alatas, B. (2024). An Exploratory Study on the Application of Blockchain Technology to the Chinese Ship Auction Market. International Journal of Information Technologies and Systems Approach (IJITSA), 17(1), 1-18. http://doi.org/10.4018/IJITSA.346819
Chicago
Peng, Chen, and Bilal Alatas. "An Exploratory Study on the Application of Blockchain Technology to the Chinese Ship Auction Market," International Journal of Information Technologies and Systems Approach (IJITSA) 17, no.1: 1-18. http://doi.org/10.4018/IJITSA.346819
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